Digital Transformation on Manufacturing, Infrastructure & Service: DTMIS 2022 3031327187, 9783031327186
This book contains theoretical, econometric, experimental, and policy-oriented contributions of the DTMIS conference par
329 35 54MB
English Pages 1030 [1031] Year 2023
Preface
Contents
Technologies and Trends of Public Administration and HR in Digital Era
An Assessment Threats to the Economic Security of a Region in the Digital Economy: A Case Study of Public Procurement in Russia
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusions
References
Revision of Modern Education Strategies as the Basis for the Development of Sustainable Smart Cities
1 Introduction
2 Literature Review on Implementation of Smart City Concept in Russia
3 Discussion
4 Conclusion
References
The Impact of Artificial Intelligence on Employee and Employer Risks
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Digital Technologies in the Security of the National Economy Under Constraints: Analysis of Experience and Perspectives for Adaptation
1 Introduction
2 Literature Review
3 Methods
4 Results and Discussion
5 Conclusion
References
Problems of Information Interaction Between Public Authorities and the Population of St. Petersburg in the Context of the Digital Transformation of the Region
1 Introduction
2 Literature Review
3 Results
4 Discussion
5 Conclusion
6 Disclaimer
References
State Support Measures for the Tourism Industry During the Covid-19 Pandemic: Digital Solutions
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
References
Digitalization of Public Administration and Public Trust
1 First Section
2 Research Methodology and Research Hypothesis
3 Digitalization of Public Services and the User Aspect
4 Discussion
5 Conclusion
References
Analysis of the Impact of the Socio-economic Environment on Innovative Digital Development of the North-Western Federal District of the Russian Federation
1 Introduction
2 Analysis of the Impact of the Socio-Economic Environment on Innovative Digital Development of the North-Western Federal District of the Russian Federation
3 Characteristics of Innovative Development of Regions
4 Statistical Analysis of Innovative Development of Regions
5 Discussion
6 Conclusion
References
Improving the UN Methodology of the E-Government Development Index
1 Introduction
2 Materials and Methods
3 Results and Discussion
4 Conclusion
References
Digital Transformation of Personnel Management in Organizations Under the Influence of Big Data Technologies
1 Introduction
2 Materials and Methods
3 Research Methodology
4 Results and Discussion
4.1 Interviews with Experts
4.2 Questionnaire Survey of Employees and Candidates
4.3 Management Recommendations
5 Conclusion
References
E-Government in Russia: Developing and Improving the Quality of Implementation of the e-Government Program
1 Introduction
2 Purpose
3 Results
3.1 Level of E-Government Development in Russia and Globally
3.2 Danish e-government
3.3 The Experience of e-Government in South Korea
3.4 E-Government Services in Estonia
3.5 E-Government Service in the Russian Federation
4 Proposals for Improving e-Government in the Russian Federation
5 Conclusion
References
Economic Efficiency and Social Consequences of Innovations
Implementation Risk of New Distance Learning Technologies
1 Introduction
2 Literature Review
3 Materials and Methods
4 Results
5 Discussion
6 Conclusion
References
Opportunities for Development of Smart Stop Pavilions in Saint Petersburg
1 First Section
2 Materials and Methods
3 Results
4 Conclusion
References
Modern Trends in the Sharing Economy
1 Introduction
2 Research Results
3 Conclusion
References
The Information Environment Cluster Distribution of the Regional Socio-Economic Systems in Transition Economy
1 Introduction
2 Methodology
3 Results
4 Discussion
5 Conclusions
References
Event Study on the Stock Performance: The Case of US Logistics Companies
1 Introduction
2 Materials and Methods
3 Results
4 Discussion and Conclusion
References
Prospective Avenues for Digitalization of Tourism in Russia
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusions
References
Modeling of Medical Technology Life Cycle
1 Introduction
1.1 Forecasting
1.2 Forecasting of Medical Technologies
2 Methods
2.1 General Concept of the Medical Technology Life Cycle
2.2 Methodology of Forecast Modeling. General Characteristics of a Forecast
2.3 Key Elementary Functions for Spline Modeling
3 Results and Discussion
3.1 Generalized Conceptual Scheme of Medical Technology Life Cycle
3.2 Example of a Life Cycle Mathematical Model
4 Conclusion
References
Digital Financial Inclusion in a Decentralised Financial Environment
1 Introduction
2 Methods
3 Results
4 Conclusion
References
The Contribution of Mobile Companies to Sustainable Economic Development in Sub-Saharan Africa
1 Introduction
2 Methods
3 Results and Discussion
4 Conclusions
References
Exploring the Customer’s Acceptance Towards Food E-commerce Sites: Evaluation from Service Quality Perspectives
1 Introduction
2 Materials and Methods
3 Main Results
4 Discussion
5 Conclusions
References
Actual Problems and Analysis of Anti-avoidance Tax Measures in Post-Soviet Countries in the Context of Digitalization of the Economy
1 Introduction
2 Literature Review
3 Methods
3.1 Descriptive Part
3.2 Research Part
4 Results and Discussion
5 Conclusions
References
Development of Modern Financial Technologies at the National and International Levels
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Digitalization and Economic Development of Territories
1 Introduction
2 Methods
3 Results and Discussion
4 Conclusions
References
Managing Efficiency of Innovative Activity of Industrial Enterprises Within Digital Economy
1 Introduction
2 Methods
3 Results
4 Discussion
5 Conclusion
References
Forming a Methodology for Organizing Investment and Financial Reporting in the Activities of Subsidiaries in the Organization of Antitrust Compliance in Uzbekistan
1 Introduction
2 Materials and Methods
3 Results
3.1 Liquidity Ratios
3.2 The Sustainability Level
4 Discussion
5 Conclusion
References
Agent-Based Modeling of Tourist Flow Distribution Based on the Analysis of Tourist Preferences
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Being Smarter in the Pursuit of a Smart City
1 Introduction
2 Learning from Others
3 Potential Next Steps
4 Collaborating Cities
5 Conclusions
References
Economy and Industry 4.0 Development
Prospects for Improving the Benchmarking Activity of Automotive Enterprises in Uzbekistan
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Socioeconomic Mechanisms of Managing Intellectual Capital of the Industrial Ecosystem
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Assessing Global Trends in World Energy: Genesis of the New Energy Transition
1 Introduction
2 Methods
3 Discussion
4 Results
5 Conclusion
References
Developing the Informatization of the Technological Waste Management Process in the Lean Production System of an Enterprise
1 Introduction
2 Literature Review
3 Materials and Methods
4 Results
5 Discussion
6 Conclusion
References
Competencies for Digital Economy: Economic Engineer for Transport Industry
1 Introduction
2 Materials and Methods
2.1 Methodolody
2.2 Identification of Competencies and Skills According to Professional Standards in Russia
2.3 Identification of Competencies and Skills According to Foreign Standard Occupational Classification
3 Results
4 Conclusion and Discussion
References
Development of Tools for Decarbonization of Electricity Consumption in the Russian Federation
1 Introduction
2 Certification of Energy Origin
3 Discussion
References
Sino-Russian Industrial Joint Investment—Taking Oil and Gas Resource Development and Energy Cooperation as an Example
1 Introduction
2 Analysis of Global Oil and Gas Resources
3 Reserves of Oil and Gas Resources in Eastern Russia
4 Status Quo of Sino-Russian Energy Cooperation
5 Sino-Russian Investment-Analysis of the Processing of Oil and Gas Resources in Eastern Russia
6 Countermeasures and Suggestions for China-Russia Energy Cooperation
7 Conclusions
References
Use and Processing of Digital Data in the Era of Industry 4.0
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Specifics of Implementing Digital Technologies in Investment and Construction Projects in China
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Measurement and Evaluation of China’s Regional Innovation Efficiency: Analysis Based on Network Super SBM-Malmquist Model
1 Introduction
2 Literature Review
3 Research Design
3.1 SBM Network DEA Model
3.2 Malmquist-Luenberger Index
3.3 Selection of Indicators and Data Sources
4 Result Analysis
5 Discussion
5.1 Overall Evaluation of Provincial Innovation Efficiency
5.2 Countermeasures and Recommendations
References
The Influence of Announcement and Event Dates of M&A Deals on Return of Company’s Shares in Oil and Gas Industry
1 Introduction
2 Data and Methodology
3 Results
4 Discussion and Conclusion
References
Investments in the Fixed Capital of the Fuel and Energy Complex as a Factor in Growing Innovations and Increasing Digitalization
1 Introduction
2 Materials and Methods
3 Results
4 Conclusion
5 First Section
References
Risk Assessment of Decarbonization Projects in the Context of Digital Transformation of the Oil and Gas Industry
1 Introduction
2 Literature Review
3 Materials and Methods
4 Results
4.1 Principles of the Proposed Methodology
5 Discussion
6 Conclusions
References
Industry 5.0 and Digital Ecosystems: Scientometric Research of Development Trends
1 Introduction
2 Materials and Methods
2.1 Research Methodology
2.2 Theoretical Fundamentals
3 Results
3.1 Analysis of Search Queries «Digital Ecosystems» and «Industry 5.0» in Google Trends Service
3.2 The Research of Search Queries “Digital Ecosystems” and “Industry 5.0” Based on the Results of Issuance on the WoS Platform
3.3 Scientometric Analysis of the Data Array Obtained from the WoS Database on the Search Masks “Digital Ecosystems” and “Industry 5.0” in the VOSviewer Program
4 Discussion
5 Conclusion
References
Assessment of the Digital Production Management Potential Based on Costs Statistical Analysis in Machine Industry
1 Introduction
2 Purpose of the Study
3 Research Method
4 Results
5 Conclusion
References
Indicators and Digital Technologies for Assessing the Condition of Urban Soils
1 Introduction
2 Literature Review
3 Materials and Methods
4 Results and Discussion
5 Conclusion
References
Conceptual Basis of Digital Platform Development for Managing Innovative Investment Projects
1 Introduction
2 Purpose of the Study
3 Materials and Methods
4 Results
5 Conclusion
References
Data Management and Digital Solutions
Development of a Methodology for Integral Assessment of the Effectiveness of Medical Organizations Under Conditions of Changes in the Main Business Processes in the Health Care System
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Emergence of the New Start Up Ecosystem: How Digital Transformation Is Changing Fintech and Payment System in Emerging Markets?
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Stock Market Reaction to the Blockchain-Related Technologies Adoption: An Event Study Analysis
1 Introduction
2 Methodology
2.1 Data
2.2 Event Study
2.3 Regression Analysis
3 Results and Discussion
4 Conclusion
References
Analytics in the Era of Digital Transformation
1 Introduction
2 Materials and Methods
2.1 Stage I: Traditional Analytics
2.2 Stage II: Big Data Analytics
2.3 Stage III: Digital Analytics
3 Results
4 Discussion
5 Conclusion
References
Perspectives for the Implementation and Development of AI in Banking Sphere
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Modern Digital Assets: Trends of the Central Bank Digital Currencies
1 Introduction
2 Key Issues
3 Results
4 Discussion
References
Correlation-Regression Model for Analysis of Overdue Debt and AI-System for Prediction the Finance Risk of Russian Commercial Banks
1 Introduction
2 Materials and Methods
3 Results
3.1 Correlation-Regression Model
3.2 Neural Network Perseptron
4 Discussion
5 Conclusions
References
Forecasting of the Global Market of Software that Uses Artificial Intelligence Algorithms
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Evaluation of Data Visualizations with Bloom’s Six Levels of Understanding
1 Introduction
2 Materials and Methods
2.1 Literature Review
2.2 Methodology and Procedure
2.3 Design of Visualizations
2.4 Hypothesis
3 Results
3.1 Knowing
3.2 Comprehension
3.3 Application
3.4 Analysis
3.5 Synthesis
3.6 Evaluation
4 Discussion
5 Conclusion
References
Development of the Company’s IT Infrastructure in the DAMA-DMBOK Standard Implementation
1 Introduction
2 Materials and Methods
2.1 Methods
2.2 Materials
3 Results
4 Discussion
5 Conclusion
References
Visualization of Business Processes Through Data Comics
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Data Comics for Business Process Visualization
1 Introduction
2 Materials and Methods
2.1 STUDY N° 1: Using Comics to Communicate Legal Content
2.2 STUDY N° 2: Using Comics in the Veterinary Industry
3 Results
3.1 STUDY N° 1: Using Comics to Communicate Legal Content
3.2 STUDY N° 2: Using Comics in the Veterinary Industry
4 Discussion
4.1 Regarding the Third-Party Studies
4.2 Pros and Cons About Data Comics
5 Conclusion
References
Methodology for Prognostic Effectiveness Evaluating of Digital Twins Implementation as an Example of the Railway Traffic Management Task
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Digital Transformation of Business: Industrial Solutions
Digital Transformation of Small Business Development Management in the Region
1 Introduction
2 Materials and Methodology
3 Discussion
4 Results
5 Conclusion
References
Concept of Forming the Digital Strategy for Business Structure Development
1 Introduction
2 Materials and Methods
3 Research Results
4 Discussion
5 Conclusion
References
The Distinctions of the Automated Accounting Information System for Sole Proprietors Trading on Online Marketplaces
1 Introduction
1.1 Relevance of Research
1.2 Literature Review
1.3 Purpose
2 Methods and Materials
2.1 Research Results
3 Discussion
4 Conclusion
References
Study of the Impact of the Digital Transformation of the Economy on SMEs
1 Introduction
2 Methods
2.1 Description of Research Methods
2.2 Description of Research Methods
3 Results and Discussion
4 Conclusions
References
Formalizing the Materiality Assessment for Audit Procedures
1 Introduction
2 Methods
3 Results
4 Conclusions
References
Using Predictive Modeling to Reduce Uncertainty in Managing Industrial Enterprises
1 Introduction
2 Materials and Methods
2.1 Methodology of Predictive Analysis and Enterprise Environment Estimation for Reducing Uncertainty of Management Processes
3 Results: Evaluation of the Method
4 Discussion and Conclusion
References
Modelling as a Basis for the Transformation of Service Enterprises in the Digital Economy
1 Introduction
2 Methods
3 Results and Discussion
4 Conclusions
References
Strategic Diagnostics of Directions Circular Transformation Industrial Complex
1 Introduction
2 Materials and Methods
2.1 Research Methodology
2.2 Theoretical Fundamentals
3 Results
4 Discussion
5 Conclusion
References
Key Trends in the Digital Transformation of Business and Their Impact on the Business Processes
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Using Design Thinking to Build Skills for Working with Agile Methodologies in a Digitalized Environment
1 Introduction
2 Materials and Methods
3 Results
3.1 Companies’ Experience in Applying Design Thinking
3.2 Application in the Educational Process
3.3 Benefits of the Approach
3.4 Challenges of Application
4 Discussion
5 Conclusion
References
Industrial Enterprise Digital Transformation Navigator: Stages and Tools for Strategic Change
1 Introduction
2 Materials and Methods
2.1 Outline of Digital Transformation of an Industrial Enterprise
2.2 Specifics of Strategic Planning and Management in Digital Environment
3 Results
4 Discussion
5 Conclusion
References
Business Digital Maturity Assessment in Strategic Decision Making
1 Introduction
2 Materials and Methods
2.1 Research Methodology
2.2 Theoretical Fundamentals
3 Results
4 Discussion
5 Conclusions
References
Digital Solutions for Multimodality in the China-Europe Route
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Digital Transformation in Russian Transport Companies
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Information Management as a Basis for Change Management in Enterprise Digital Transformation Projects
1 Introduction
2 Materials and Methods
2.1 Information Management as the Most Important Part of the Overall Management System at the Enterprise
2.2 Information Management in Enterprise Architecture as a Platform for Enterprise Digital Transformation
3 Results
3.1 Business Architecture Management and Analysis of Its State
3.2 IT Infrastructure Management, Analysis of Its Condition and Formation of a Development Strategy
4 Discussion
5 Conclusion
References
Analysis of Economic Consequences of Digital Solutions in Logistics on the Example of Russian Railways Holding
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusion
References
Application of Robotic Process Automation Technology for Business Processes in the Field of Finance and Accounting
1 Introduction
2 Materials and Methods
3 Results and Discussion
3.1 Accounts Payable
3.2 Consumer Loan Processing
3.3 Investment and Asset Management
3.4 Insurance Claims
4 Conclusion
References
Digital Transformation and Business Processes Reengineering of the Education Services
1 Introduction
2 Matherials and Methods
3 Results
3.1 Analysis of an Existing Enterprise
3.2 Business Process Reengineering
4 Discussion
5 Conclusion
References
Rental Processes Digitalization in Commercial Real Estate on the Example of the Development Company
1 Introduction
2 Materials and Methods
2.1 Case Study
2.2 Key Business Processes and Problems
3 Results
3.1 Commercial Realty Purchasing
3.2 Commercial Realty Development
3.3 Searching for Tenants
3.4 Maintenance of Rented Premises
4 Conclusion
References
Author Index
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Lecture Notes in Networks and Systems 684
Igor Ilin Mariana Mateeva Petrova Tatiana Kudryavtseva Editors
Digital Transformation on Manufacturing, Infrastructure & Service DTMIS 2022
Lecture Notes in Networks and Systems
684
Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas—UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Türkiye Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).
Igor Ilin · Mariana Mateeva Petrova · Tatiana Kudryavtseva Editors
Digital Transformation on Manufacturing, Infrastructure & Service DTMIS 2022
Editors Igor Ilin Peter the Great St.Petersburg Polytechnic University St. Petersburg, Russia
Mariana Mateeva Petrova St. Cyril and St. Methodius University of Veliko Turnovo Veliko Tarnovo, Bulgaria
Tatiana Kudryavtseva Peter the Great St.Petersburg Polytechnic University St. Petersburg, Russia
ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-32718-6 ISBN 978-3-031-32719-3 (eBook) https://doi.org/10.1007/978-3-031-32719-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Dear colleagues, We are pleased to present the Proceedings of the International Scientific Conference “Digital Transformation on Manufacturing, Infrastructure & Service” (DTMIS 2022), which was held on April 25–26, 2022, in St. Petersburg. The conference is an annual one, and its permanent organizer is Peter the Great St. Petersburg Polytechnic University, one of the leading technical universities in Russia. In fact, 183 applications were submitted to the DTMIS 2022, each of which was carefully considered by at least three reviewers. In this book, you will see the results of the work of the scientific committee members and reviewers: Really high-quality articles were selected and distributed into five main thematic sections: Section 1. Technologies & Trends of Public Administration & HR in Digital Era Section 2. Economic Efficiency & Social Consequences of Innovations Section 3. Economy & Industry 4.0 Development Section 4. Data Management and Digital Solutions Section 5. Digital Transformation of Business: Industrial Solutions We want to thank our keynote speakers, active participants, and listeners, who decorated the conference with their scientific contribution and active presence in all sections and made it a major event worthy of respect! We are grateful to all the committees’ members for the organization of the conference and for providing valuable and profound reviews. This proceedings turned out to be really versatile and voluminous, and we sincerely hope that each reader will find something important and useful for their research and practical work. Enjoy reading! Igor Ilin Mariana Mateeva Petrova Tatiana Kudryavtseva
Contents
Technologies and Trends of Public Administration and HR in Digital Era An Assessment Threats to the Economic Security of a Region in the Digital Economy: A Case Study of Public Procurement in Russia . . . . . . Valentina Kravchenko
3
Revision of Modern Education Strategies as the Basis for the Development of Sustainable Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . Natalia Putinceva, Maria Liubarskaia, and Daria Ipatova
13
The Impact of Artificial Intelligence on Employee and Employer Risks . . . . . . . Anna A. Kurochkina, Olga V. Lukina, Victoriya A. Degtereva, and Tatyana V. Bikezina
27
Digital Technologies in the Security of the National Economy Under Constraints: Analysis of Experience and Perspectives for Adaptation . . . . . . . . . Tatyana Feofilova, Iuliia Alekseeva, Mehdi Imani, and Evgeny Radygin
41
Problems of Information Interaction Between Public Authorities and the Population of St. Petersburg in the Context of the Digital Transformation of the Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aleksandr Volodin, Ekaterina Sokolova, Viktoriya A. Degtereva, and Maxim Ivanov
52
State Support Measures for the Tourism Industry During the Covid-19 Pandemic: Digital Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anna V. Tanina, Larissa V. Tashenova, Dinara G. Mamrayeva, and Evgeny V. Konyshev Digitalization of Public Administration and Public Trust . . . . . . . . . . . . . . . . . . . Elena Vasilieva, Karina Tirabyan, Maria Rubtsova, Anton Barabanov, and Natalia Vyshinskaia Analysis of the Impact of the Socio-economic Environment on Innovative Digital Development of the North-Western Federal District of the Russian Federation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oleg Kichigin, Grigory Kulkaev, Natalia Mozaleva, and Galina Nazarova
66
87
96
viii
Contents
Improving the UN Methodology of the E-Government Development Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marina Ivanova, Grigory Kulkaev, and Anna Tanina Digital Transformation of Personnel Management in Organizations Under the Influence of Big Data Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ekaterina Okrushko, Sergey V. Rasskazov, Albina N. Rasskazova, and Natalia Vasetskaya E-Government in Russia: Developing and Improving the Quality of Implementation of the e-Government Program . . . . . . . . . . . . . . . . . . . . . . . . . K. Nazmetdinova and S. Kalmykova
111
130
140
Economic Efficiency and Social Consequences of Innovations Implementation Risk of New Distance Learning Technologies . . . . . . . . . . . . . . A. Chernova and I. Lyukevich Opportunities for Development of Smart Stop Pavilions in Saint Petersburg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vladislav Seredin, Svetlana Gutman, and Evgenii Seredin Modern Trends in the Sharing Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Igor Lyukevich and Renata Sharipova The Information Environment Cluster Distribution of the Regional Socio-Economic Systems in Transition Economy . . . . . . . . . . . . . . . . . . . . . . . . . Dmitriy Rodionov, Aleksandra Grishacheva, Aleksandra Shmeleva, Polina Chertes, Zhanna Melnikova, Vladimir Markevich, Evgeniy Konnikov, and Darya Kryzhko Event Study on the Stock Performance: The Case of US Logistics Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tatiana Kudryavtseva, Maria Rodionova, and Angi Skhvediani
157
173
188
203
218
Prospective Avenues for Digitalization of Tourism in Russia . . . . . . . . . . . . . . . . Artur Kuchumov, Yana Testina, Svetlana Egorova, and Natalya Kulakova
230
Modeling of Medical Technology Life Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Irina Rudskaya, Dmitrii Alferiev, and Darya Kryzhko
248
Digital Financial Inclusion in a Decentralised Financial Environment . . . . . . . . Svetlana Demidova, Stanislav Svetlichnyy, Chulpan Misbakhova, and Tatyana Miroshnikova
257
Contents
ix
The Contribution of Mobile Companies to Sustainable Economic Development in Sub-Saharan Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liudmila A. Guzikova and Nicolas Francois Somga Bitchoga
265
Exploring the Customer’s Acceptance Towards Food E-commerce Sites: Evaluation from Service Quality Perspectives . . . . . . . . . . . . . . . . . . . . . . . Thuy Dam Luong Hoang, Thi Hong Van Lo, and Tamara Selentyeva
278
Actual Problems and Analysis of Anti-avoidance Tax Measures in Post-Soviet Countries in the Context of Digitalization of the Economy . . . . . Irina Zhuravleva
291
Development of Modern Financial Technologies at the National and International Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Viktoriya Razletovskaia, Igor Stepnov, Iurii Guzov, and Stanislav Svetlichnyy Digitalization and Economic Development of Territories . . . . . . . . . . . . . . . . . . . Sofia Popova, Ekaterina Koroleva, and Marina Efremova Managing Efficiency of Innovative Activity of Industrial Enterprises Within Digital Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ekaterina Burova, Svetlana Suloeva, and Sergei Grishunin Forming a Methodology for Organizing Investment and Financial Reporting in the Activities of Subsidiaries in the Organization of Antitrust Compliance in Uzbekistan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mansur P. Eshov and Dilafruz S. Nasirkhodjaeva Agent-Based Modeling of Tourist Flow Distribution Based on the Analysis of Tourist Preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kirillov Dmitriy, Zhanna Burlutskaya, Aleksei Gintciak, and Daria Zubkova Being Smarter in the Pursuit of a Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roy Woodhead
307
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331
349
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Economy and Industry 4.0 Development Prospects for Improving the Benchmarking Activity of Automotive Enterprises in Uzbekistan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kongiratbay Sharipov and Umida Zaynutdinova
379
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Socioeconomic Mechanisms of Managing Intellectual Capital of the Industrial Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aleksandr Babkin, Natalia Alekseeva, Larissa Tashenova, and Akram Ochilov
390
Assessing Global Trends in World Energy: Genesis of the New Energy Transition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrey Sosnilo and Alexander Gorovoy
398
Developing the Informatization of the Technological Waste Management Process in the Lean Production System of an Enterprise . . . . . . . . . . . . . . . . . . . . Natalia Lytneva and Vasily Krestov
416
Competencies for Digital Economy: Economic Engineer for Transport Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maria Anisimova, Irina Rudskaya, Angi Skhvediani, and Valeriia Arteeva
431
Development of Tools for Decarbonization of Electricity Consumption in the Russian Federation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tatiana Bugaeva, Aleksandra Grishacheva, and Olga Novikova
442
Sino-Russian Industrial Joint Investment—Taking Oil and Gas Resource Development and Energy Cooperation as an Example . . . . . . . . . . . . . . . . . . . . . . YuanYuan Fu
455
Use and Processing of Digital Data in the Era of Industry 4.0 . . . . . . . . . . . . . . . Aleksei Gintciak, Zhanna Burlutskaya, Darya Fedyaevskaya, and Artem Budkin
468
Specifics of Implementing Digital Technologies in Investment and Construction Projects in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhimin Ju and Natalia Solopova
481
Measurement and Evaluation of China’s Regional Innovation Efficiency: Analysis Based on Network Super SBM-Malmquist Model . . . . . . . . . . . . . . . . . Shuquan Li and Marina Ianenko
492
The Influence of Announcement and Event Dates of M&A Deals on Return of Company’s Shares in Oil and Gas Industry . . . . . . . . . . . . . . . . . . . Ekaterina Koroleva, Maria Tikhomirova, and Vladlen Shakhov
504
Investments in the Fixed Capital of the Fuel and Energy Complex as a Factor in Growing Innovations and Increasing Digitalization . . . . . . . . . . . . Olga Nadezhina and Alexandra Geraseva
519
Contents
Risk Assessment of Decarbonization Projects in the Context of Digital Transformation of the Oil and Gas Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vladislav M. Krasilnikov, Alexander A. Iliinsky, and Alexandra A. Saitova Industry 5.0 and Digital Ecosystems: Scientometric Research of Development Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aleksandr Babkin, Larissa Tashenova, Dinara Mamrayeva, and Elena Shkarupeta Assessment of the Digital Production Management Potential Based on Costs Statistical Analysis in Machine Industry . . . . . . . . . . . . . . . . . . . . . . . . . Elena Shkarupeta, Vladimir S. Tikhonov, Anton N. Sunteev, Yulia V. Veis, and Aleksander V. Babkin Indicators and Digital Technologies for Assessing the Condition of Urban Soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gutman Svetlana, Vargasova Maria, and Evseeva Ksenia Conceptual Basis of Digital Platform Development for Managing Innovative Investment Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elena Shkarupeta, Yulia V. Veis, Oksana Yu. Eremicheva, Irina B. Kostyleva, and Vladimir S. Tikhonov
xi
530
544
565
580
593
Data Management and Digital Solutions Development of a Methodology for Integral Assessment of the Effectiveness of Medical Organizations Under Conditions of Changes in the Main Business Processes in the Health Care System . . . . . . . Olga S. Chemeris, Alissa S. Dubgorn, and József Tick Emergence of the New Start Up Ecosystem: How Digital Transformation Is Changing Fintech and Payment System in Emerging Markets? . . . . . . . . . . . . Samrat Ray, Elena V. Korchagina, Andrey E. Druzhinin, Vladislav V. Sokolovskiy, and Pavel M. Kornev Stock Market Reaction to the Blockchain-Related Technologies Adoption: An Event Study Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Varvara Nazarova and Artem Shumeiko Analytics in the Era of Digital Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evgenii S. Artemenko
607
621
639
650
xii
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Perspectives for the Implementation and Development of AI in Banking Sphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ekaterina P. Mochalina, Galina V. Ivankova, Yulia A. Dubolazova, Alexey Davydov, and Vladislav Bolonkin Modern Digital Assets: Trends of the Central Bank Digital Currencies . . . . . . . Kseniia Lakovich, Igor Lyukevich, and Olesya Lakovich Correlation-Regression Model for Analysis of Overdue Debt and AI-System for Prediction the Finance Risk of Russian Commercial Banks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nikolay Lomakin, Anastasia Kulachinskaya, Uranchimeg Tudevdagva, Natalia Bescorovaynaya, Natalya Mogharbel, and Ivan Lomakin
662
673
693
Forecasting of the Global Market of Software that Uses Artificial Intelligence Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Djamilia F. Skripnuk, Kseniia N. Kikkas, and Viktor I. Merkulov
707
Evaluation of Data Visualizations with Bloom’s Six Levels of Understanding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Enrico Pezzella and Ed Overes
722
Development of the Company’s IT Infrastructure in the DAMA-DMBOK Standard Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oksana Iliashenko, Victoria Iliashenko, and Alexandra Shuvalova
732
Visualization of Business Processes Through Data Comics . . . . . . . . . . . . . . . . . Saida Dospan and Anastasia Khrykova
745
Data Comics for Business Process Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . Erick Leonel García Ibañez
759
Methodology for Prognostic Effectiveness Evaluating of Digital Twins Implementation as an Example of the Railway Traffic Management Task . . . . . Andrey V. Timofeev, Aleksander B. Titov, Alexander M. Kolesnikov, and Alexandra K. Antonova
772
Digital Transformation of Business: Industrial Solutions Digital Transformation of Small Business Development Management in the Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Svetlana Baranova and Daria Ostroukhova
793
Contents
xiii
Concept of Forming the Digital Strategy for Business Structure Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Irina Avdeeva and Ilya Mikhalev
801
The Distinctions of the Automated Accounting Information System for Sole Proprietors Trading on Online Marketplaces . . . . . . . . . . . . . . . . . . . . . . Tatiana Nepryakhina
816
Study of the Impact of the Digital Transformation of the Economy on SMEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vadim Karapetov and Andrei Stepanchuk
828
Formalizing the Materiality Assessment for Audit Procedures . . . . . . . . . . . . . . . Yu. Yu. Kochinev, Elena R. Antysheva, and Bokhodir Isroilov
839
Using Predictive Modeling to Reduce Uncertainty in Managing Industrial Enterprises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Irina A. Goryacheva, Olga A. Myzrova, and Larisa O. Serdyukova
847
Modelling as a Basis for the Transformation of Service Enterprises in the Digital Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuri Gusev, Tatyana Polovova, and Alexey Pinsky
859
Strategic Diagnostics of Directions Circular Transformation Industrial Complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ekaterina Kaplyuk and Kristina Rudneva
871
Key Trends in the Digital Transformation of Business and Their Impact on the Business Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Svetlana V. Shirokova, Olga V. Rostova, Anastasiia Prosvirnina, and Anastasia Odainic Using Design Thinking to Build Skills for Working with Agile Methodologies in a Digitalized Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ekaterina A. Kharkina, Olga V. Rostova, Svetlana V. Shirokova, Anna V. Valyukhova, and Anastasiia S. Shmeleva Industrial Enterprise Digital Transformation Navigator: Stages and Tools for Strategic Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vladimir V. Glukhov, Tatiana A. Gileva, Margarita P. Galimova, Dier Karimov, and Ekaterina D. Malevskaia-Malevich Business Digital Maturity Assessment in Strategic Decision Making . . . . . . . . . Aleksandr V. Kozlov, Irina M. Zaychenko, and Darya P. Kolotova
885
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908
921
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Digital Solutions for Multimodality in the China-Europe Route . . . . . . . . . . . . . Igor V. Ilin, Sofia E. Kalyazina, Anastasia I. Levina, and Bulat D. Khusainov
935
Digital Transformation in Russian Transport Companies . . . . . . . . . . . . . . . . . . . Igor V. Ilin, Nina V. Trifonova, and Bulat D. Khusainov
945
Information Management as a Basis for Change Management in Enterprise Digital Transformation Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alexey B. Anisiforov, Arkady A. Evgrafov, Alena S. Ershova, and Dayana M. Gugutishvili Analysis of Economic Consequences of Digital Solutions in Logistics on the Example of Russian Railways Holding . . . . . . . . . . . . . . . . . . . . . . . . . . . . Olga S. Chemeris, Alexandra D. Borremans, and József Tick Application of Robotic Process Automation Technology for Business Processes in the Field of Finance and Accounting . . . . . . . . . . . . . . . . . . . . . . . . . Alena S. Ershova, Dayana M. Gugutishvili, Alexander A. Lepekhin, and Andrea Tick Digital Transformation and Business Processes Reengineering of the Education Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jorge P. Olivos Salazar, Oksana A. Balabneva, and Alexandra D. Borremans
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Rental Processes Digitalization in Commercial Real Estate on the Example of the Development Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1008 Alexander K. Frolov, Konstantin V. Frolov, and Ulyana Yu. Muhina Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1021
Technologies and Trends of Public Administration and HR in Digital Era
An Assessment Threats to the Economic Security of a Region in the Digital Economy: A Case Study of Public Procurement in Russia Valentina Kravchenko(B) Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. To this day the process of digitalization has affected all spheres of national economy. The economic security of the region is not an exception. The purpose of this paper is to check the possibility of interconnection between the digital maturity of the region and its economic security. Taking into account the fact that the available index, interdisciplinary and indicative methodologies for assessing the economic security of regions are a static approach, the author’s method of assessing the likelihood of threats to the economic security of a region was used, which results in a typology of regions according to the likelihood of threats to the economic security of regions in the sphere of public procurement Used method is based on the relationship between indicators of competition and economy. There are four groups of regions in the typology: regions with a low probability of occurrence of threats to economic security in the field of public procurement (group 1), regions that are characterized by an average probability of occurrence of threats to economic security in the field of public procurement (group 2), regions with a high probability of occurrence of threats economic security in the field of public procurement (group 3) and effective regions in the field of public procurement, in which there is no likelihood of threats to economic security (group 4). The results of the typology were compared with the level of digital maturity of the regions of the Russian Federation, which includes five sectors: healthcare, education, public administration, urban development, transport and logistics. These sectors are the main ones for public procurement. Results of comparison of these indicators show the need to add digitalization indicators to the assessment of the likelihood of the spread of threats to the economic security of regions in the field of public procurement in order to possibly prevent them. Keywords: Economic security of the region · Digital transformation · Digital maturity · Public Procurement · Regions of Russia
1 Introduction The economic security of the country is shown by the financial stability [1], by sustainable development and social well-being [2], by factors of digitalization as research and development in the country [3] or by governance efficiency [4]. However in all these © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 3–12, 2023. https://doi.org/10.1007/978-3-031-32719-3_1
4
V. Kravchenko
cases, sustainable development of its territories plays the greatest role in this process [5–13]. Economic security is seen as an endogenous factor in regional development. This is “the ability of the region as a system to achieve its goals with the efficient use of resources and to maintain its inherent attributive characteristics in the presence of internal and external threats and risks” [10, 14, 15]. Living in the XXI century it is impossible not to mention the process of digitalization that changes all shears, economic security of the region is not the exception [16–19]. Moreover, some risks and threats to economic security have been increasing since the process of digitalization. The Global Risks Report by World Economic Forum showed such examples as the growth of the “digital divide”, the ever-increasing level of cybercrime, job cuts due to large-scale digitalization of economic sectors, abuse of the dominant position of digital platforms, information risks. Despite the high importance of ensuring ES, there is a serious lack of scientific and practical materials focusing on it, especially from the point of view of creating innovative potential at the regional level. This problem does not allow the design of tools to influence the ES and develop effective management methods. From the authors’ point of view, a comprehensive assessment should be only the first step before the formation of strategic support mechanisms for ES with a parallel build-up of the territory’s innovative capabilities [20]. The most popular methods for estimation the economic security of a region are the index approach [21] and its modification as fast indicators [22], interdisciplinary and indicative approach, which in their essence represent a similar algorithm of actions. Monitoring of the economic security of the region, proposed by the scientists of the Nizhny Novgorod State Technical University is a collection of information in 10 categories. These include macroeconomic development, industrial security, food security, energy security, budgetary and financial security, personnel security, innovative development, social development, environmental development, foreign economic development, each of which contains 3 indicators. The obtained indicators are compared with threshold values, which are obtained on the basis of international comparison and expert opinions. The real sector of the economy, food security, energy security, financial security, innovation security, social security and foreign economic security are components of system of macroeconomic indicators of the economic security of the region proposed by researchers from the Institute of Economics of the Russian Academy of Sciences [21]. Then, the deviations of the differential and integral indices of economic security are ranked in five zones: the zone of catastrophic threats, the zone of critical threats, the zone of significant threats (hazards), the zone of moderate threats (challenges), the zone of no threats. However, all approaches are static and include a scattered list of indicators [12]. That is why the aim of this paper is to determine the likelihood of threats to the economic security of the region in connection with its digital maturity. For this reason, it is important to take sequential tasks: apply the author’s method for assessing threats to the economic security of the region, upgrade typology of the regions based on the connection between the average number of bidders per purchase [10] and the average price drop and note conditions of digitalization based on the indicator for Russian regions of “digital maturity”.
An Assessment Threats to the Economic Security of a Region
5
Nowadays, there is a lack of a consolidated scientifically-recognized concept for assessing the issues related to the economic security of clusters. Specifically, the problem of methodological support of an integrated assessment of the economic security, taking into account specific features of the cluster management structure based on levels, remains unaddressed [23].
2 Materials and Methods Public procurement is one of the most important element of the system of economic security of a region [10, 24, 25], that is why the research focuses on it. “The developed method involves assessing threats to the economic security of the Russian regions through the framework of public procurement” [10]. It was determined in previous research that “the greatest risks for the system were associated with the connection between competition and budget savings. It was proposed to rank analyzed regions into four groups: ineffective government procurement, effective government procurement, and government procurement that threatens the system of economic security of the region, that is, high competition with low savings and low competition with high savings” [10]. The median number of bidders per purchase and the median price drop are the boundaries for these groups. It is important to mention that the average value has been changed to median value in this upgraded method. The median, unlike the mean value, is resistant to “outliers”, thus it has 50% before and after. Group 1—regions with a low probability of threats to economic security in the field of public procurement. These regions are characterized by low competition and, as a result, the price reduction is less than the median of average price reduction throughout Russia; Group 2— regions that are characterized by an average probability of occurrence of threats to economic security in the field of public procurement. This group includes regions with a high level of competition, but suspiciously low price reductions (less than the median level in Russia); Group 3— regions with a high probability of threats to economic security in the field of public procurement. Characterized by a suspiciously high price reduction with little competition; Group 4— effective regions in the field of public procurement. There is no likelihood of threats to economic security. This group includes regions with a favourable atmosphere for the development of the public procurement system, that is, where a high level of competition contributes to a high price reduction. The median values of Russian regions indicators, average number of bidders in one procurement and average price drop in one procurement, are presented in Table 1.
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V. Kravchenko Table 1. Borders of proposed groups. Median Price Drop, %
Median Number of Bidders, pcs
2016
19,00
2,5
2017
21,55
2,7
2018
20,00
2,6
2019
23,95
2,8
2020
17,60
3
2021
15,85
2,65
3 Results The quantitative distribution of the regions of the Russian Federation in terms of the relationship between competition and the economy from 2016 to 2021 is presented in Table 2. Table 2. Number of regions in the group, 2016–2021. Group 1
Group 2
Group 3
Group 4
2016
29
11
11
32
2017
28
19
15
24
2018
25
18
18
25
2019
29
16
14
27
2020
27
15
16
29
2021
30
13
13
30
The maximum number of regions in the first group with a low level of competition and a low drop in prices was in 2021. These are 30 regions. There are 19 regions in 2017 as the maximum number of second group. There are 18 regions of third group in 2018. Among regions with a high probability of threats to economic security in the field of public procurement are Altai Republic, Altai region, Voronezh region, Irkutsk region, Kabardino-Balkarian Republic, Kursk region, Leningrad region, Lipetsk region, Mari El Republic, Murmansk region, Penza region, Rostov region, Samara region, Sverdlovsk region, Stavropol region, Republic of Tyva (Tuva), Ulyanovsk region, Chuvash Republic. In 2016 it was maximum number of effective regions as 32. It is interesting to mention that the distribution by generalized groups is almost equal in 2021: 34.5% inefficient public procurement (group 1), 34.5% effective public procurement (group 4) and 30% for regions with an average and high probability of spreading threats to economic security (group 2 and 3). The quantity of regions is shown on Fig. 1.
An Assessment Threats to the Economic Security of a Region
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90 80 70 60 50 40 30 20 10 0 2016
2017 Group 1
2018 Group 2
2019 Group 3
2020
2021
Group 4
Fig. 1. Distribution of regions by groups, 2016–2021 (Source: Compiled by the authors)
A characteristic dynamic over the course of the six years is that the number of regions with a competition index above the median and high budget savings has been increasing (4th group in the typology). This trend may indicate the success of the contract system of the Russian Federation. However, in most regions, the possibility of threats to the system of economic security of the region remains (the relationship between economy and competition is typical for the 2nd and 3rd groups). Besides, approximately 30% of all regions are in the 1st group, it means that system of public procurement is inefficient. The reasons are not only the specifics of the regions, but also external and internal violations in the organization of the public procurement procedure. The digitalization of the region can be shown with the rating of regions by the level of digital maturity, which was made by Ministry of Digital Development, Telecommunications and Mass Media of the Russian Federation. It which includes the parameters of healthcare, education, public administration, the development of the urban environment and transport, three main conclusions are obtained. Each industry contributed equally to the final score and was characterized by a different set of indicators. The index for each industry was calculated as an average of the degree of achievement of target values for each indicator. An assessment of the results of the typology of regions in terms of the likelihood of threats to economic security, together with the rating of regions by the level of digital maturity in 2021, (Fig. 2) can show several ideas. Firstly, regions with medium level of digital maturity are most of regions in every group, from 69 to 84%. Secondly, majority of regions with low level of digital maturity (7 regions) are the regions with a low probability of threats to economic security in the field of public procurement (group 1). Thirdly, majority of regions with high level of digital maturity (7 regions) belong to group 4, effective regions in the field of public procurement.
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V. Kravchenko 35 30 25 20 15 10 5 0 Group 1
Group 2
Group 3
Low level of digital maturity
Group 4
Medium level of digital maturity
High level of digital maturity
Fig. 2. Distribution of regions by level of digital maturity in 2021 (Source: Compiled by the authors)
In total, 9 regions are in the category of a high level of digital maturity, while the first and second groups of the developed typology include the Belgorod region and the Republic of Tatarstan, respectively. There are 14 regions in the category of a low level of digital maturity, of which two are from the 4th typology group, the Republic of Crimea and the Sverdlovsk region.
Group 1
Group 2
Low level of digital maturity
50
Medium level of digital maturity
High level of digital maturity
Group 3
35
11
11 0
Group 4
21
14
17
14
14
33
78
Fig. 3. Distribution of regions by the level of digital maturity in 2021, % (Source: Compiled by the authors)
It is important to mention that if we look to the regions through the level of digital maturity it will be clear that most of the regions from 4th group of typology has high
An Assessment Threats to the Economic Security of a Region
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level of digital maturity, while regions from other group prevail in low and medium level (Fig. 3).
4 Discussion Nowadays there is no official methodology for assessing the economic security of the region. In addition, not every one of the methods, which is an index approach, an interdisciplinary approach, or an indicative approach, was applied to each of the 85 regions of the Russian Federation. In this regard, the comparison of the results of applying the author’s method for assessing threats to the economic security of the region in the field of public procurement with other methods is not appropriate. In addition, public procurement is just one element of the economic security system of the region. The study revealed that there are many definitions of the term “cluster”, approaches in the literature highlighted the same cluster characteristics: geographical affiliation, integration of production processes, relationship between enterprises, and benefits for the enterprises in the cluster [26]. Five sectors of digitalization as healthcare, education, public administration, urban development, transport and logistics are the most popular sectors for public procurement. Besides, as the research has shown that the level of digital maturity of the region and the level of economic security of the region are interrelated. However, it is known that digitalization entails both positive and negative changes. For example, digitalization can cause such threats to the economic security of regions as threats of a corruption and criminal nature, rising unemployment, disruption of the financial support of the territory within hotel entities, risks, and threats in the field of information security [18, 27, 28]. On the one hand, there are different methods of assessing the dependence of economic security of the state from the level of digitalization. On the other hand, there is no method that shows the influence of digitalization on the elements of the system of economic security of the region. Considering the above and the results obtained, it is appropriate to assume that for a more accurate assessment of the likelihood of the spread of threats to the economic security of regions and the definition of a region in one or another group, it is necessary to take into account not only activity in the field of public procurement. When characterizing “digital maturity”, one should look more not at quantitative indicators that have already been measured (for example, the number of computers supplied, schools connected to the Internet, and so on), but at the effect of digital transformation in achieving social and economic effects that should positively influence the economic security of the region. Additional introduction of regions and expansion of the scope of the developed typology will be a further direction of research.
5 Conclusions The author’s method for assessing threats to the system of economic security of the region based on the connection of average price drop and average number of bidders was used. Additionally, the indicator of "digital maturity" was used, which includes three components. The first one takes into account the number of specialists who intensively use information and communication technologies. It is about both IT specialists
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(software developers and analysts, multimedia designers, and others), and representatives of other professions (finance, administration, marketing, and so on). The second component is the expenses of organizations for the implementation and use of modern digital solutions. The third indicator characterizes the level of “digital maturity” depending on the achievement of the target value of 2030 in 12 industries. This is about industry, agriculture, construction, energy infrastructure, financial services, healthcare, public administration. Regions of Russian Federation were divided to several categories. Firstly, regions with a low probability of threats to economic security in the field of public procurement for 5 years. At the same time the Republic of Dagestan, Republic of Kalmykia, Republic of North Ossetia – Alania, Chechen Republic and Chukotka are the regions with low level of digital maturity, but Belgorod region is with high. Secondly, there are regions with permanent effectiveness in the field of public procurement, for example, Republic of Crimea with low level of digital maturity, Astrakhan, Vladimir, Perm and Tyumen regions with medium level and St. Petersburg with high level. Thirdly, Kabardino-Balkarian Republic and Republic of Udmurtia are the regions with low level of digital maturity and always on the group with medium or high probability of occurrence of threats to economic security in the field of public procurement. The fourth category includes the unstable regions. These include regions with a gradual improvement in public procurement, such as Ivanovo and Novgorod regions, and one year of a worsening public procurement situation, such as Kemerovo and Rostov region. This research shows that it is important to include in the composition indicators of digitalization and study their impact on the likelihood of spreading threats to the economic security of the region. Acknowledgments. The research was financed as part of the project "Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization" (FSEG-2023–0008).
References 1. Chen, J., Zhu, X., Zhong, M.: Nonlinear effects of financial factors on fluctuations in nonferrous metals prices: a markov-switching VAR analysis. Resour. Policy 61, 489–500 (2019) 2. Kendall, G.E., Ha, N., Rachel, O.: The Association between Income, Wealth, Economic Security Perception, and Health: A Longitudinal Australian Study 28(1), 20–38 (2018). https:// doi.org/10.1080/14461242.2018.1530574, last accessed 04 April 2022 3. Mikhnevych, L., Victor, M., Petur, H., Aleksandra, K.: Conceptual relationships between country image and economic security. Marketing and Management of Innovations (1), 285– 93 (2020). https://essuir.sumdu.edu.ua/handle/123456789/77086, last accessed 17 February 2022 4. Haagh, L.: The political economy of governance capacity and institutional change: the case of basic income security reform in european welfare states. Social Policy and Society 18(2), 243–63 (2019). https://www.cambridge.org/core/journals/social-policy-and-soc iety/article/political-economy-of-governance-capacity-and-institutional-change-the-case-ofbasic-income-security-reform-in-european-welfare-states/F6C8188D0120A9DE6257CACF 77B8D36A, last accessed 04 April 2022
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5. Burak, P.I., Rostanets, V.G., Zvorykina, T.I., Kabalinsky, A.I.: Russian macroregions in the system of territorial groupings and comparative parameters of development of their socioeconomic complexes. Social’naya politika i sociologiya 19(2), 15–26 (2020) 6. Feofilova, T.Yu., Litvinenko, A.N., Grachev, A.V.: The socioeconomic system of a region as a source of threat to the national security of the russian federation. In: Proceedings of the 32nd International Business Information Management Association Conference, pp. 6852–60 (2018) 7. Gryshova, I., Kyzym, M., Hubarieva, I., Khaustova, V., Livinskyi, A., Koroshenko, M.: Assessment of the EU and ukraine economic security and its influence on their sustainable economic development. Sustainability 12, 7692 (2020). https://doi.org/10.3390/su12187692 8. Kahler, M.: Economic Security in an Era of Globalization: Definition and Provision. Pac. Rev. 17(4), 408–502 (2004) 9. Kazakova, N.A., Bolvachev, A.I., Gendon, A.L., Golubeva, G.F.: Monitoring economic security in the region based on indicators of sustainable development. Studies on Russian Economic Development 27(6), 638–48 (2016) 10. Kravchenko, V., Kudryavtseva, T., Kuporov, Y.: A method for assessing threats to the economic security of a region: a case study of public procurement in Russia. Risks 9(1), 1–10 (2021) 11. Stephen, B., Helen, W.: Sustainable procurement in the public sector: an international comparative study. Int. J. Oper. Prod. Manag. 31(4), 452–476 (2011). https://doi.org/10.1108/014 43571111119551 12. Tsvetkov, V.A., Dudin, M.N., Lyasnikov, N.V.: Analytical approaches to estimate economic security of the region. Ekonomika regiona [Economy of Region] 15(1), 1–12 (2019) 13. Uspenskij, M.B., Makarov A.S., Sochnev A.V., Shirokova S.V., Petrov V.D.: Development of a Software Structure for Monitoring the Working Capacity of the Data Storage System for Predicting Failures and Preventing Critical Situations. In: Proceedings of the 33rd International Business Information Management Association Conference, IBIMA 2019: Education Excellence and Innovation Management through Vision 2020, pp. 8508–8514 (2019) 14. Hacker, J.S., et al.: The economic security index: a new measure for research and policy analysis. Review of Income and Wealth 60(1) (2014). https://doi.org/10.1111/roiw.12053 15. Husainova, E.A., Urazbahtina, L.R., Serkina, N.A., Dolonina, E.A., Filina, O.V.: Monitoring tools of regional economic security. E3S Web of Conferences 124, 05009 (2019). https://doi. org/10.1051/e3sconf/201912405009 16. Andruseac, G.: Economic Security – New Approaches In The Context Of Globalization. Socio-Economic Research Bulletin 3(58), 144–150 (2015) 17. Babskova, O., Nadezhina, O., Zaborovskaya, O.: Innovative activities in a region in the conditions of the development of the digital environment. Int. J. Innov. Technol. Explor. Eng. 8(12), 4361–4365 (2019) 18. Maiti, D., Castellacci, F., Melchior, A.: Digitalisation and Development: Issues for India and Beyond. In: Maiti, D., Castellacci, F., Melchior, A. (eds.) Digitalisation and Development, pp. 3–29. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-9996-1_1 19. Rodionov, D.G., Rudskaia, I.A., Gorovoj, A.A., Kudryavtseva, T.J.: Scheme of program cooperation between participants of regional innovation system. Procedia. Soc. Behav. Sci. 207, 824–832 (2015). https://doi.org/10.1016/j.sbspro.2015.10.173 20. Zaytsev, A., Sun, P.K., Elkina, O., Tarasova, T., Dmitriev, N.: Economic security and innovative component of a region: a comprehensive assessment. Sustainable Development and Engineering Economics 2(4) (2021). https://doi.org/10.48554/SDEE.2021.2.4 21. Senchagov, V.K., Mityakov, S.N.: Evaluation of economic crises using short-term indexes and average indexes of economic security of Russia. Stud. Russ. Econ. Dev. 27(2), 148–158 (2016)
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Revision of Modern Education Strategies as the Basis for the Development of Sustainable Smart Cities Natalia Putinceva1 , Maria Liubarskaia2(B) , and Daria Ipatova2 1 Peter the Great St. Petersburg Polytechnic University, Saint-Petersburg, Russia 2 Saint Petersburg State University of Economics, Saint-Petersburg, Russia
[email protected]
Abstract. Implementation of the smart city concept is supposed to solve the problems of cities at the industrial stage of social evolution. The aim of the research is to analyze and systematize the problems of smart cities, and to highlight the obstacles that the implementation of the smart city concept facing under modern conditions. The authors admit that smart cities offer their citizens enormous opportunities for a comfortable life and personal growth. Criticism of the smart city is associated with the increasing use of unverified technologies and shifting responsibility for decisions regarding urban projects from people to artificial intelligence. The failure of smart cities to create conditions for taking into account citizens’ opinions, and growing social inequality, exacerbated by digital inequality, complicates the situation. Inability to ensure the confidentiality of personal data of citizens and companies, and some technological, environmental, and economic restrictions make smart cities not a safe place to live. In recent years, a lot of criticism is coming from the representatives of the world’s leading countries and scientists regarding the fact that IT companies and global corporations have significantly increased their influence in all spheres of society, especially during the pandemic. The theoretical significance of the study is related to highlighting the problems of smart cities not reflected in the antedated research. Based on the results of the analyses and systematization of the problems, the authors elaborated the suggestions on the revision of the basic principles of modern educational strategies. Implementation of these ideas will allow developing sustainable cities with active civil society and to increase the involvement of citizens in the implementation of urban projects. Keywords: Smart city · Sustainability · Education strategy · Active citizen
1 Introduction Trends of urbanization and industrialization had affected almost all countries of the world and concentrated huge crowds of people in the cities. As a result, modern cities face a number of social, economic, environmental, infrastructural, and psychological problems. Contemporary scientists see the opportunity of solving these problems through © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 13–26, 2023. https://doi.org/10.1007/978-3-031-32719-3_2
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the introduction of innovative smart city concepts. Smart technologies imply the usage of artificial intelligence (AI), phone applications, big data, and a large number of various types of ‘sensors’ built into the urban economy. The concept of a smart city originated in world urbanism in the late 1990s and meant primarily the implementation of information technologies in the management of urban infrastructure. It has been significantly advanced over time. Nowadays, the smart city framework presupposes not only embedding of smart technological solutions in different spheres of urban economy, but also the active involvement of citizens and businesses in city management. Smart city concept implementation has the following effects [1]: • Reduction of the negative impact on the environment (20–40%). • Savings of the maintenance of law enforcement and rescue services due to the introduction of video surveillance (up to 20%). • Reduction of waste disposal costs due to separate waste collection (up to 30%). • Savings of energy and maintenance costs with energy-efficient lighting systems and motion sensors (up to 70%). • Resource savings due to smart electricity and gas meters (up to 30%). • Savings of capital and operating costs through the use of energy-saving technologies in construction (up to 30%). • Reduction of travel time (20%). • Reduction of the number of accidents due to the traffic and transit control system (30%). The concepts of smart city development recently have been criticized due to the low involvement of citizens and urban communities in the transformation of cities and the fact that the main beneficiaries of smart cities’ functioning are IT companies and global corporations trying to manipulate people’s behavior in their own interests. The research is aimed at justification of the methods of avoiding the obstacles facing the implementation of the smart city concept under modern conditions. The main research question is how to convert a smart city into the territory where the majority of the residents become beneficiaries. Based on the results of the study of international research works, it could be stated that this approach had not been taken by the experts before, so, accordingly, the prospective options did not receive a proper methodological justification. The solution to this problem is going to be proposed, implying a revision of the principles of modern educational strategies. The presented study was carried out based on the principles of systems analysis, which allows to classify the problems of smart city functioning and development and to formulate a research question that was not considered in the antedated works of the experts. The methodology also involves the techniques and tools of economic and social comparative analysis, as well as logical and content analysis. In the course of the study, a content analysis of academic papers, books, and regulatory documents for the period from January 2005 to June 2021 related to the concept of ‘smart city’ was carried out, and the most cited works on the topic were selected. The next step involves the method of a logical analysis of the smart city concept. The chosen approach allows tracing the evolvement of this concept, which ultimately made it possible to classify the problems of smart cities. At the final phase, documents related to
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the implementation of a smart city concept in Moscow and regulating the transformation of the traditional education system in Russia were analyzed. The theoretical significance of the study lies in the systematization of smart city problems and highlighting the obstacles that the implementation of the smart city concept facing under modern conditions. Based on the theoretical findings, the suggestions were elaborated on how to form an active civil society in cities. Only this type of society is ready to defend its vital interests and preserve the very essence of a person’s right to choose. So, the practical significance is associated with the possibility of improving legislation and state programs for the development of Russian education.
2 Literature Review on Implementation of Smart City Concept in Russia The experience of smart city concept’s implementation in the world allows to highlight the following problems: 1. Social inequality, exacerbated by the digital divide. 2. Failure to create conditions for consideration of citizens’ opinions regarding the formation of the urban development concept and the implementation of municipal projects. 3. Inability to ensure the confidentiality of personal data of citizens and companies. 4. Technological, environmental and economic constraints. Russian experts, analyzing ‘smart transformations’ in the country, conclude that digitalization carried out in Russian non-capital cities is fragmentary (Center for Strategic Research). However, three key circumstances indicate the feasibility of the implementation of the Smart Cities 3.0 concept (HSC - Human Smart City) in Russia [2]. First, there is a discussion among representatives of government agencies and decision-makers about the need to create a ‘humane city’ and about the HSC model. Second, the need for digital development is increasing. Third, the desire of citizens to be involved in the city development decision-making process is growing, and new tools for this purpose are becoming available, including participatory budgeting. In general, the development of smart cities in Russia boils down to the development of individual systems: automation of housing and communal services, smart public transportation, etc. The Russian state program ‘Smart City’, aimed at modernizing 180 cities with a population of 100 000 people, should ensure the systematic implementation of smart city technologies in Russia. The priority of this program is to bring the Russian capital to a new level of development of electronic services. By 2030, Moscow should become a data-driven city. That means that all decisions should be based on automatic processing and analysis of accumulated big data. The Russian capital’s strategy ‘Moscow Smart City - 2030’ [3] takes into account the forecasts of futurologists and representatives of leading technology corporations. All forecasts are adapted to the peculiarities of such a metropolis like Moscow. Futurists and IT specialists think over the smallest details when planning the implementation of the ‘Moscow Smart City - 2030’ strategy. Moscow in 2030 will be a space where a person is considered a valuable resource, where gene therapy technologies have spread, where genetic digital platforms are formed. These
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elements will become sources of data for scientific research, analysis, educational programs of universities, and machine learning. In Moscow 2030, wearable digital medical devices, medical digital devices implanted into the body, and smart clothing will be further developed. With the help of a single digital identifier (digital twin) of the city dweller, the level of accessibility and awareness of the social services provided by the city will increase. Moscow citizens will be able to take an active part in the city life and city management system through involvement in the electronic referendums, voting, discussion of urban problems on digital platforms, and providing feedback on decisions made. In the perspective of Moscow’s development until 2030, financial transactions by citizens will be carried out using biometric parameters. In 2030, 100% of the territory of Moscow will be covered with wireless networks of the latest technologies. It will help to drive a huge amount of information to data storages, including urban ones. As a valuable resource and will be used by the city, enterprises, markets and the residents of the city themselves. Even the apartments of Moscow citizens will be equipped with video analytics and acoustic control systems combined into a single network. Artificial intelligence will make it possible to search for faces and objects both in the entire array of stored data, as well as to find and track their movement throughout the city online and in real time mode. Thus, Moscow’s new development strategy will be based on key modern technologies, among which artificial intelligence stands out for automating decision-making based on data analysis, blockchain technology for paperless contracts, big data for targeted services, etc. At the same time, a critical approach to assessing the use of artificial intelligence for automating decision-making allows concluding the following. The result of using this technology can lead to shifting responsibility from officials to automatic systems and to the devaluation of such a basic attitude as responsibility. Now artificial intelligence will examine digital appeal, digital complaint or proposal of citizens, and not in all cases a person will be involved in the decision-making process. Withal, the Federal Law 123FZ (adopted in order to implement the ‘Moscow Smart City – 2030’ strategy) does not spell out a mechanism for protecting people from errors or premature application of new technologies. Despite the fact that the world has accumulated quite a lot of experience when artificial intelligence gave errors and people suffering. Already at the end of 2018, the dangers had become clear, and AI researchers had warned about them. But negative consequences manifested themselves much earlier than expected. For example, in March 2018, Uber’s AI-driven autonomous car did not see a woman on the road, and it ended in death. The voice recognition system in the UK Immigration Service, designed to expose the intruders, has decided to expel hundreds of people from the country. The digital assistant IBM Watson recommended to people with cancer the wrong and unsafe drugs. The question arises, is it possible in this case to say that a society where a person is not responsible for the actions and decisions made is a progressive society? There is one more important point in the Federal Law 123-FZ, designed to ensure the confidentiality of the collected data. In this law, the term ‘anonymized data’ is used several times for personal data obtained as a result of anonymization. But in fact, some academic researchers confirm the high level of possibility to identify a person by a set of impersonal data. Simultaneously, the problem of the threat of ensuring the
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confidentiality of personal data arises, which can be used to program the behavior of Moscow citizens in the interests of individual users of the smart city systems (primarily global corporations). Also, of interest that the Federal Law 123-FZ presupposes the creation of a certain regulatory body, the Coordination Council of the experimental legal regime, which will have enormous powers, and will make decisions on how to introduce AI technologies and how to control them. The members of the Coordination Council will be chosen by the Moscow Mayor’s Office with the approval of the Government of the Russian Federation, which can also introduce its representatives to this body. At the same time, there is no mechanism for inclusion in this Coordination Council the representatives of the public organizations or political parties. In general terms, the activities of the Coordination Council will be based on the ethical provisions of the ‘Concept for the development of regulation in the field of artificial intelligence and robotics technologies for the period up to 2024’. This Concept stipulates a prohibition on causing harm to a person on the initiative of AI systems and robotics. However, it is almost impossible to prove the malicious intent of the algorithm when the computer decided to harm the random person. The concept proclaims the human control of all AI systems, but with the proviso that, to the extent that this is possible, taking into account the required degree of autonomy of AI systems and other circumstances. All the provisions in the Concept, when formulating the basic ethical standards for the development of artificial intelligence, release the developers of systems with AI from responsibility. If the developer simply did not consider something, then he cannot be charged. As a result, a resident of Moscow in disputes with AI will have to prove his case, even in the event of an algorithm error. This approach to the development of AI and robotics makes Moscow residents extremely vulnerable. According to experts, ethics in AI will only work if it is organically inscribed in the development process, and developers are directly accountable for non-compliance with ethical principles. And of course, the ideology of transhumanism laid down in the project raises a greater number of questions. The ideology of transhumanism involves the use of the achievements of science and technology to improve the mental and physical capabilities of a person, to eliminate those aspects of human existence that are considered undesirable - suffering, disease, aging, and death with the help of various implants. What are the consequences of such elimination of unwanted human traits, and will this new person still have the right to choose? And won’t humanity turn into a community with swarming consciousness when the expression of personal opinion becomes unsafe? The review of the Moscow ‘Smart City – 2030’ document allows concluding that the implementation of this program will lead to all the problems that have already been faced by cities implemented smart city concepts. Based on the results of a review of the smart city development problems, it was concluded that most of the experts do not take into consideration the level and quality of smart city citizens’ education. The hypothesis introduced by the authors of the given study implies that the high-quality education of the citizens is the basis for the successful implementation of the smart city concept in the interests of the majority of its users. Analysis of the main documents governing educational strategies in Russia has shown the following. The Moscow strategy ‘Smart City - 2030’contains the main provisions of the development of the education system, but in rather general phrases. This strategy
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assumes scaling up distance education, an invention of a digital teacher with AI, as well as injecting various types of game reality (virtual, augmented, mixed) in the educational process. In more detail, the prospects for the development of the education system in Russia can be seen in documents such as the ‘Education - 2030’ roadmap, developed by the Russian State Agency for Strategic Initiatives. This map allows seeing the key trends influencing education, trace the relationships between them, and build trajectories based on events, technologies, and formats that are most relevant in the near future [4]. The ‘Education - 2030’ roadmap assumes that game will become the dominant form of learning, professional educators and AI will take the place of teachers, and by 2030 the traditional educational system model is to be dismantled and eliminated [5]. The Russian State Agency for Strategic Initiatives and the Russian management community contributed to the elaboration of ‘Foresight of Russian Education - 2030’. In general terms, this document involves the introduction of implants and genetic profiles, as well as segmentation of the market for the youth educational services. Education will be divided into a premium segment, a mass segment, and a low-budget segment. The premium segment will be traditional education with real teachers, and tutors, and for all other segments education will be in the remote format. The next important document for future of the Russian education is the ‘Global Agenda - 2035’ report. This document was developed the Russian Innovative Center Skolkovo with the support of the Russian State Agency for Strategic Initiatives, the Ministry of Education and Science of the Russian Federation, the Center for Strategic Development ‘North-West, American IT companies Cisco and Intel, and the US government project ‘Harmony’. This document promotes the idea of stratified learning. Mass knowledge and skills will be transferred primarily through automated solutions with fully automated mentor systems. Live training will be relatively more expensive and, as a result, will be of a premium nature. An important part of such in-person learning must be working with values and ultimate meanings. In the new education system, a new form of evaluation of both students and teachers will be implemented. Everyone will be involved in the evaluation process. Thus, the document creates the basis for the introduction of a social rating system (Table 1). An element of the new system will be the presence of various educational trajectories and the widespread use of multi-user online courses. Thus, the process of getting the full-fledged knowledge will be replaced by acquiring competencies and precedents. Training will be carried out as much as possible using artificial intelligence in network groups. According to these documents, online multimedia libraries, multi-user online courses, virtual mentors (AIs), wearable simulators (training through biofeedback), and virtual or wearable simulators will become the main educational tools based on new technologies through which the broadcast of reference experience or practice will be carried out. In the new education system, even sports competitions, hiking, laboratory work, and discussion in a scientific group are not part of the program. All these forms of independent gaining experience (or in a team) are being replaced by distributed group work in social networks, work in virtual environments, and city quests in augmented reality. Thus, modern educational tools minimize in-person contact with teachers and other students. A new and extremely important trend in education will be the opportunity
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Table 1. Summary of the main changes in the Russian education system by 2035 Change
2017
2025
2035
Introducing new elements
The proliferation of multi-user online courses Investing in talent Personal learning style Replacing assessment with recognition of achievement
Concentration of knowledge translation markets Development of virtual tutors and mentoring networks development of “non-systemic education” Development of gaming environments and augmented reality Using neural interfaces in training
Learning through play and teamwork Artificial intelligence as a mentor in cognition Training in neuronet groups
Eliminating traditional elements
Teacher-provider of knowledge and experience SAT and its analogues Semester / Quarter Grades
Graduation Diploma Journals and Citation Standards Intellectual Property Management System Author’s textbook
Comprehensive school Research University Text (book, article) as a source of knowledge
to invest in talents and the students with extraordinary skills. Investments in this asset can bring total value (in terms of expected income) of more than a billion dollars. And the last important point of this document. Table 2 shows the key beneficiaries of the implementation of the new model of the educational system and the main opponents of its implementation. Table 2. Key stakeholders of ‘Future of Russian Education Supporters
Opponents
Undecided
ICT sphere
Organized religions
Employers
Progressive Universities
Academic elite
New World Leaders (China, India)
NGOs (reformers) Regulators Progressive Parents Non-university and young researchers Large businesses (entertainment, medicine, baby products, etc.)
Regulators (internal policy) Teachers and teaching corps Conservative parents
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As seen from Table 2, IT companies and global corporations are the main beneficiaries of the implementation of the new education model. A brief overview of the existing strategies for the Russian educational system allows drawing the following conclusions regarding the prospects of smart city concept implementation in the world as a whole since new educational strategies are offered by IT companies and global corporations to most countries of the world. 1. The new model of education is primarily focused on the introduction of online education using artificial intelligence and games. The new focus of education will increase the involvement of students in virtual and augmented reality more than in real life. This fact arises concern about the readiness of these students in the future to become active citizens interested in solving urgent problems of a smart city and participating in its social life. 2. The new model of the education presupposes the presence of different segments of educational market: premium (implying live communication with other students and teachers) and others (based on online learning in the form of games with AI as a teacher). That means the difference in the content of the education received. Stratification of the educational process will not solve the existing problem of social and digital inequality in smart cities and in society. Such education system will only exacerbate this problem and deepen the inequality. 3. The new model of education in Russia with a predominance of online education does not contribute to the socialization of students. Half of the school time in the traditional educational system was intended for in-person communication of students helping to build relationships with each other. The lack of such an opportunity in the new model of education will lead to the fact that students will not be able to build and improve relationships with each other in the future. In turn, the lack of this skill will lead to the fact that the citizens of a smart city will simply not be able to unite and create an active civil society capable of defending their interests and making mutually beneficial decisions.
3 Discussion Researchers L. Antopoulos, M. Janssen, and V. Virakkody [6], as well as T. Nam and T.A. Pardo [7] note that technology is the basis for the development of Smart Cities 1.0. A number of scientists, including F. Appia, M. Limab, and S. Parutis [8], consider the formation of urban ecosystems, which are designed to provide adequate answers to the global challenges of a modern city, as an important characteristic of smart cities. In Smart Cities 1.0, as a rule, there is no common development strategy shared by all stakeholders [9]. The main feature of Smart Cities 2.0, according to a number of experts, is the integration of management and technological solutions [10, 11]. New management models, such as ‘Government as a platform’, ‘Government 2.0’, can be used as a unifying tool for the participants in the course of the urban environment transformation. Consolidation and analysis of Big Data serve as a basis for the managerial decision-making process. Public Sector Informatization (PSI) is recognized as a valuable resource for the service economy [12]. But at the same time, the critical discourse in relation to the Smart city
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2.0 model is increasing due to the low involvement of citizens and urban communities in the processes of city transformation [13]. In this regard, the need for the concept of Smart Cities 3.0 is being actively discussed with the idea to provide services perfectly match the citizens’ needs and use the models of co-creation, co-design [14]. R. Robinson [15] stated that commercial programs stimulating investments in digital tools and services can create convenience for consumers and profit for companies, but they cannot create sustainable, socially mobile, vibrant, and healthy cities. K. Ratti, E. Townsend, H. Chourabi [16], and some other researchers [17] consider the necessity in the Smart Cities 3.0 models to pay special attention to the development of human capital, the empowerment of citizens. In Russia, a lot of studies are focused on the possibilities of smart city concepts’ implementation in different regions, including assessment of the potential infrastructural changes [18–21]. According to temporary researchers [22], smart cities generate a lot of e-waste and are not always ready to cope up with this situation. Another group of Russian researchers [23, 24] suggested a set of indicators for the evaluation of the development of smart sustainable cities and their elements. M.V. Bolsunovskaya, S.V. Shirokova, and A.V. Loginova [25] contributed to the theoretical justification of the necessary software and hardware systems for the prediction of failures in data storage systems of smart cities. G. Burdakova, A. Byankin, I. Usanov, and L. Pankova [26] participated in the evaluation of the impact of smart technologies on education and formation of entrepreneurial competencies. The criticism of Smart Cities 1.0 and Smart Cities 2.0 is related to the fact that the implementation of innovations did not contribute to solving a number of problems of cities and population. First of all, it should be noted the problems of social inequality, exacerbated by digital inequality. McKinzey research critically evaluates the economic effects of the implementation of individual smart city solutions [27]. According to R. Hollands [11], in practice, the implementation of the smart city concept often led to the creation of fortified high-tech enclaves or districts to attract and retain a creative class of workers. At the same time, a lot of citizens suffer from a high level of poverty, and social problems in cities remain unresolved. For example, the experience of introducing smart city technologies in Seattle contributed to the growth of social inequality. It exacerbated the problems of institutional and structural racism, and the number of cases of racial segregation in the city increased [28]. The next group of problems related to the implementation of the smart city concept is failure to create conditions for taking into consideration the citizens’ opinion about the city development and urban projects execution. According to [29], such cities as Masdar and Songdo were created without taking into account the opinion of citizens, technology played a primary role in this process, and their creators had no idea about rational urban development. As a result, nowadays Songdo is a half-empty city being abandoned by its citizens. E. Townsend writes that the widespread penetration of modern technology into cities must be adjusted to the infrastructure, architecture, surrounding objects, and even our senses. Smart cities must retain the ability to be spontaneous, intuitive insight and communication of the citizens. The work of Canadian researchers [30] provides an in-depth analysis of the problems of creating smart cities without taking into account the views of beneficiaries on the
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example of the ‘Sidewalk Toronto’ project implemented by the company Sidewalk Labs (a subsidiary of Google). Independent experts made the following conclusions based on the results of 2 years of smart city concept implementation. The project contains too many ‘technologies for the sake of technology’, some innovations are inappropriate or unnecessary, their implementation does not take into account the interests of citizens and in fact, technologies are used to collect information in favor of IT companies. An experiment by Sidewalk Labs highlights another problem of smart cities development. It is the problem of confidentiality of the personal data of citizens and companies. At one time, writer and urban planning theorist and founder of the new urbanism movement Jane Jacobs, in her book ‘Death and Life of Great American Cities’ noted that privacy is one of the main advantages of living in cities, as opposed to suburbia [31]. Modern smart cities that generate and transmit in real-time huge amounts of data consist of personal information about citizens, including their location, preferences, and habits, can hardly be called a safe place to live. This kind of information is very valuable for many market participants, primarily large commercial companies in order to impose a certain model of behavior. Shoshanna Zuboff [32] describing the Sidewalk Labs experience, suggests that tech companies today have the ability to move from monitoring data to using that data to model behavior and to manipulate it. In her opinion, cities are no longer a place for freedom of creativity but become a zone for increasing profits by companies, a zone where digital technologies and algorithms of the future are replacing laws, democratic municipal governance. Even smart dumpsters have been spotted taking control of consumers in the UK. The leading countries of the world started to realize the problem of ensuring the safety of data confidentiality. Thus, the President of the European Commission Ursula von der Leyen in her speech at the Davos Economic Forum stated that attention must be paid “to the dark sides of the digital world” [33]. The business model of online platforms threatens not only free and fair competition but also democracy, security and quality of information. This is why this enormous power of large digital companies needs to be contained. The goal of the European Commission is to bring these virtual, but key territories today under the control and thereby break the monopoly of platforms. In December 2020, in order to achieve this goal, the European Commission adopted the Directives ‘On Digital Services’ and ‘On Digital Markets’. The head of the European Commission considers the blocking of the account of US President Donald Trump by Twitter as a flagrant example of a violation of freedom of speech. Russia also has a wary attitude towards the AI technologies that underlie the smart city. In October 2020, Special Representative of the President of the Russian Federation for Digital and Technological Development, spoke out at the Moscow International Forum for Innovative Development ‘Open Technologies’ regarding the revision by the Government of approaches to AI and digitalization in Russia [34]. From Dmitry Peskov’s speech, it follows that artificial intelligence was introduced too early, and other technologies that were supposed to ensure high-quality work of AI are not ready yet. He also noted that the operation of innovative technologies based on AI requires too much energy and money, and the promised ‘explainable AI’ also did not appear. In addition, AI technologies are developing too quickly and are not absorbed by society. People do not understand how technologies develop, and protest moods are brewing in society. Fears
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in society grow much faster than the opportunities that AI can provide. Dmitry Peskov also recalled the examples of Europe and the USA, where some cities have introduced restrictions on face recognition systems as an element of a smart city based on artificial intelligence. In 2020, China began to fight large technology companies, realizing the power that IT companies received during the pandemic [35]. Ant Group (financial and technology subsidiary of Alibaba) was the first to be hit by the Chinese regulatory machine when it was banned from going public. In 2021, the same ban was imposed on the taxi service DiDi, accused of illegal collection and processing of personal data. Moreover, the requirements for all Chinese IT companies have become more stringent. If a service has 1 million active users and processes their personal data, it must obtain special permission to enter foreign exchanges. In June 2021, China passed the Data Security Law, which sets out the rules on collecting, storing, processing, and transmitting data by companies. The aim of the law is to classify data according to the level of its importance for the fulfillment of public interests. The Chinese government also developed a separate Law on the Protection of Personal Information. The fourth block of smart city problems comprises technological, environmental, and economic constraints arising from the implementation of smart technologies. As a rule, under the smart city development such factors, as the capacity of urban infrastructure and data processing centers (DPC) are not taken into account properly. Smart cities generate a lot of e-waste and there are no technologies yet that can provide 100% recycling of billions of used old sensors and devices [36].
4 Conclusion The emergence of smart city concepts was a response to the challenges of big cities. Smart city concepts are transforming, AI technologies started to play an increasing role in this process [37]. More and more criticism can be heard regarding the implemented smart city concepts. In addition, the main complaint is that the beneficiaries of the implementation of smart city projects are a minority of its consumers, represented by IT companies and global corporations. A smart city could be considered sustainable only in case of the deep involvement of the majority of citizens in the process of the development and assessment of the urban projects and city management decisions. A review of the problems of smart cities made it possible to conclude that the experts did not pay necessary attention to the reforms of the education system, which is aimed at stratifying the acquired knowledge and skills, as well as the prevalence of online learning technologies. The education system without rational changes will only contribute to the further aggravation of the existing problems of the smart city. Based on the results of the study, several conclusions and recommendations were elaborated. Namely, in order to make life in smart cities beneficial for the majority of citizens, it is necessary to revise the strategies for the development of educational models. 1. Online education should be developed in a reasonable proportion, and cooperate with traditional ‘in person’ teaching. 2. The role of the educators should be played by professional teachers, not artificial intelligence.
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3. There should be no stratification in the education system, where a minority of students have access to full-fledged knowledge, and the majority of students, as a result of online training, receive only some skills. Of course, this is not a complete list of recommendations for smart cities to become a place of a prosperous life for most of their citizens. But even taking into account recommendations regarding the development of the education system will make it possible to form an active civil society in cities. This society will be ready to defend its vital interests and preserve the very essence of a person with the right to choose. Active citizens are not afraid to think differently from what the swarm demands. Nowadays, deep knowledge, especially in the natural sciences and philosophy has high demand, even more than ever. The wide range of educational tools is now available, and could lead to a new era of enlightenment. Many people would be engaging in personal growth and prove themselves through this knowledge. A sharply increased level of awareness among the people would lead to a transition to a new level of collective thinking. Moreover, the discoveries of new relationships between society and the environment will be real, not imaginary. The trends of social development could be found in the thesis of Russian cosmists, such as Nikolai Fedorov, Konstantin Tsiolkovsky, Alexander Chizhevsky, the French thinker Teilhard de Chardin, and many other outstanding philosophers.
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The Impact of Artificial Intelligence on Employee and Employer Risks Anna A. Kurochkina1 , Olga V. Lukina1(B) , Victoriya A. Degtereva1 , and Tatyana V. Bikezina2 1 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected] 2 Russian State Hydrometeorological University, St. Petersburg, Russia
Abstract. Companies need to adapt to the changing environment quickly to do business effectively. The need to introduce digital technologies is becoming more and more clear to the leaders of both large and small organizations every day. The purpose of the study: to study the risks of workers and employers in the introduction of artificial intelligence. As a methodology, a systematic approach, logical methods were used, namely: analysis and synthesis, comparison and comparison, the method of logical interpretation; observation as a method of empirical investigation. The work studied historical analogies of the concept of artificial intelligence, conducted an analytical review of definitions of artificial intelligence from various scientific sources, systematized a review of the scientific literature on the use of artificial intelligence (AI) in the field of human resources management, modern services based on artificial intelligence technologies were analyzed, the risks of an employee and employer in the introduction of artificial intelligence were studied, the positive and negative consequences of the introduction of artificial intelligence in Russian companies were identified. The current problems associated with changes in the business sector caused by the coronavirus pandemic and the peculiarities of the use of modern digital technologies in a pandemic were also considered. Examples of the use of digital technologies in the HR field in the training and development of personnel are considered. The novelty of the study includes conclusions about the importance of an integration approach to the role of man in the digital age. Artificial Intelligence technologies will allow the company to reduce costs, optimize staffing, rationalize production and work with Big Data. However, the introduction of artificial intelligence into the management of human resources is associated with a number of risks that can cause not only intellectual, but also physical damage to both employers and employees. Keywords: Artificial intelligence · Digital technologies · Employee risk · Employer risk · Introduction of artificial intelligence
1 Introduction The digital economy creates new products, new needs, and the speed and volume of information increases every day. All these processes open up opportunities for opening or developing a business based on new technological solutions and business models that have not previously been applied. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 27–40, 2023. https://doi.org/10.1007/978-3-031-32719-3_3
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The importance of the development of digital technologies is evidenced by numerous studies reflected in the scientific literature [1–4]. Not all digital technologies can influence the economic state of the company in the same way, but this is indeed a rather serious indicator of performance and plays a decisive role in choosing digitalization methods. The ability of a person to think, learn, develop their decisions, it is advisable to act in the interests of the goals and tasks set at all times prompted to create a man-made analogue of his own intelligence. At this stage, significant progress has been made in developing tools that can support the intellectual activities of people. This direction to create this kind of means was called artificial intelligence. The relevance of this problem is due to changes in the world associated with the pandemic of coronavirus infection. Many companies faced with dramatically changed environmental conditions could not quickly take the necessary measures to continue the effective operation of the organization. Companies were forced to transfer employees to a remote format of work, which also affected the efficiency of work: staff turnover increased, labor productivity decreased, the number of marriages several times increased, many processes began to take longer, the costs of transferring to remote work increased, the quality of communication worsened, etc. In everyday and routine work, artificial intelligence allows you to customize and automate any production process. The work of Artificial Intelligence over time becomes more effective through constant training - the more the neural network knows the details and needs, the better it functions. In the modern world artificial Intelligence is present in many branches of our lives: in the global Internet, medicine, business, and even in the transport industry. For example, in the global Internet, Artificial Intelligence technologies are present in the form of advertising selection algorithms and interesting videos, in medicine - Artificial intelligence technologies are used in complex operations on the human brain, in business they are used as assistants (voice assistants, etc.), in transport, Artificial Intelligence can be used as a driver (Tesla cars) or as a way to pay for travel (for example, Face Pay). In almost every modern large-scale production, the main part of the cycle of work is performed by Artificial Intelligence technologies. All this allows you to produce products quickly and efficiently with a minimum level of marriage. It is worth noting that technology is applicable not only to production, but also to work with human resources - the personnel of the organization.
2 Materials and Methods Materials: scientific articles and monographs on the digitalization of business and business processes, online business, the introduction of artificial intelligence technologies; Legislation of the Russian Federation, administrative acts of the Government of the Russian Federation and regulatory documents; Analytical reviews from the Internet. The used methodology was a systemic approach, logical methods, namely: analysis and synthesis, comparison, and a method of logical interpretation; observation as an empirical research method. From the point of view of the system approach, the study of the subject of the study includes a number of aspects: systemic-structural - the study of internal connections
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between elements of the system; system-target - definition of system sub-goals and tasks; system-element - identification of elements that make up this system; system-functional - definition of system functions; system-communication - analysis of external relations of the system with the external environment and other systems; system-historical - study of the emergence of the system, prospects and stages of its development. The use of system analysis in the work as one of the most important methods of the system approach allows you to solve complex, fuzzy tasks. The use of systematic analysis eliminates existing uncertainties in identifying differences in the behavior of workers and employers in enterprises. Introducing elements of artificial intelligence. The synthesis method was used in the study in theoretical generalization of accumulated empirical data. From scattered data obtained from the use of empirical methods, a single picture was compiled giving a holistic idea of the phenomenon being studied. In this aspect, the synthesis acts as a means of detecting causal relationships. The comparison method made it possible to compare the studied phenomenon with the previously known characteristics studied to determine common features or differences between them. This approach made it possible to identify the specific features of the study phenomenon, determine the changes that are taking place and identify the trends in its development. The object of the study is the risks of employees and employers when introducing artificial intelligence mechanisms at enterprises (Fig. 1).
Fig. 1. Method sequence (compiled by authors).
3 Results Artificial intelligence (AI) is not a format or function, it is a process and the ability to think and analyze data. Often, when referring to “artificial intelligence” in conversation, many people imagine intelligent human-like robots that capture the world, but in fact AI is not intended to replace humans. Its main goal is to expand the boundaries of human capabilities and abilities. That is why this technology, that is, artificial intelligence, is a valuable business resource. Now, we exist at the stage of development of society, which involves changing one process mode to another. Now “smart” machines and programs are quickly trained,
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thereby increasing the level of their cognitive abilities, which allows you to replace people in solving an increasingly wide range of problems as routine, but also creative. Humanity is getting used to artificial intellectual surroundings, that is, AI is increasingly present in our lives in the form of various online platforms, “smart gadgets,” voice assistants, cookies, etc. Now many young people feel uncomfortable without various electronic devices. The use of various artificial intelligence systems in sectors of the economy, as well as in the fields of public interaction, is increasing. This growth is facilitated by the “endto-end” nature of technological solutions based on artificial intelligence, the high level of influence of these solutions on performance, as well as the increasing availability of devices for the development of software, robotics products. Analytical reviews of major companies such as McKinsey, Pricewaterhouse Coopers, Gartner and others have recently emphasized that AI development is one of the defining business opportunities. That is, as a result, exactly those entrepreneurs who can effectively use artificial intelligence will be successful. Ideally an enterprise should be based entirely on AI to retain leadership in competition. In turn, artificial intelligence technologies are increasingly being introduced into production, services, and agriculture. For example, large enterprises with the help of artificial intelligence systems spread management with complex programs with AI automate many organizational processes including personnel selection, labor rationing, as well as monitoring the performance of employees’ labor duties [5]. The introduction of artificial intelligence in various processes significantly affects the labor market. New opportunities are emerging, some professions are adapting to new conditions, but many are gradually disappearing altogether. In the modern world artificial intelligence, combined with robotics and durable online technologies, has a huge impact in all spheres of life. Directions that were previously available only to people are now easily realized by smart intelligence. AI also includes areas where knowledge in the humanities is required. Humanities are social sciences that study society, man and his culture, history, philology, pedagogy, sociology, philosophy, linguistics, political science, and many others. Paying attention to the field of medicine, it can be noted that artificial intelligence is actively being introduced in health care. For example, special platforms based on artificial intelligence help accurately make diagnoses for patients, select a therapy technique, or investigate new drugs. In the field of transport production, the replacement of humans with robots under the control of sophisticated software has already been completed. The system under automatic control manages the delivery of parts to the conveyor, the process of assembling models and other operations. A person only monitors the operation of the machine, correcting errors if they are. As a result, the number of jobs in the automotive industry is rapidly declining [6]. A key component of the transformation of the digital business is the ability to analyze information resulting from digitalization of processes and relationships between different systems. Analytical tools allow us to immerse ourselves in data to obtain useful
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information for business: customer behavior, buying habits, trends, etc. Thus, companies can get to know their customers better by reducing costs and increasing the loyalty and value of their product. In the field of working with data arrays related to the activities of entrepreneurs, you can see a similar replacement, where the artificial mind replaced human intelligence. Previously, only specialized analysts were interested in this task. But in the modern world, a significant share of activity is carried out by specialized programs and systems of the BI class (business analytics). Moreover, these systems perform a number of functions better than humans, which are presented in Fig. 2.
Fig. 2. Functions performed by artificial intelligence (compiled by authors).
Artificial intelligence technologies are becoming more complex and constantly developing, as a result they have the opportunity to replace some analysts in various segments. For example, in the field of education, automatic data processing is carried out, thanks to which you can obtain information about students and any statistics and forecasts based on predefined parameters, using business analytical solutions. The disappearance of some professions is the result of the introduction of technologies based on repetitive algorithms and scenarios. For example, professions such as: news journalist; train driver (unmanned locomotives are already used in different cities of the world); taxi driver; employees of courier and warehouse logistics; help desk operators and other professionals. Nowadays most of the specialists need to think about retraining. Those who are now at the stage of choosing a future profession should take into account possible competition with machine intelligence. Many large companies are already actively implementing and using the latest technologies: - Microsoft. As part of the annual state optimization, artificial intelligence took over part of the functions of MSN news service employees. - Watson algorithm. In 90% of cases, he makes a correct diagnosis of cancer in the early stages. The level of accurate diagnoses in this area could not be transshipped for the mark
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of 50%. This can lead to a reduction in the number of diagnostic doctors in oncological departments and not only. - Facebook neural network. She actively competes with specialists who are engaged in the selection of personnel for vacancies in the company. Now artificial intelligence is learning to competently analyze the profiles of all specialists registered on Facebook and conduct extremely accurate personnel selection for various companies. It is expected that in the near future the social network itself will offer the company to hire a new employee with his profile. At the same time, specialists will receive recommendations on employment in a suitable enterprise for them. Automation replaced specialists in the field of analytics, as a result of which this replacement was far advanced in terms of implementing uniformity of activity at the enterprise. This trend is gaining momentum annually. Therefore, we note that workers in this area should think about retraining. Integration of advances in artificial intelligence in industrial robots and technological processes helps to increase production productivity, add new functions to the equipment used, and optimize work [4]. Artificial intelligence technology is being introduced at an uneven rate. In different countries and even in certain regions of the state, implementation can be carried out quickly or not particularly actively. In some places, the usual vacancies will quickly decrease, in other places, on the contrary, people will not feel much change. Much depends on the level of concentration of high-tech industries and branches of large enterprises in a particular region. As a result, it is important to consider technological development before evaluating the chosen profession. As mentioned earlier, due to the pandemic of coronavirus infection, many companies were forced to transfer most employees to remote work. Because of this, many production processes had to be suspended. As a result of illiterate remote work, many companies suffered huge losses. Firstly, they were due to the fact that production was suspended due to lack of workers, and secondly, in such a situation, the use of Artificial Intelligence technologies would be rational to avoid large financial and economic losses. Artificial intelligence technologies would reduce company costs, as well as accelerate certain production processes. For example, a team of researchers from Capgemini concluded that today the most popular area of the application of Artificial Intelligence in production processes is the prediction of the timing of the likely failure of machines/equipment and the provision of recommendations on the optimal terms of maintenance (maintenance by condition). The finances of companies were aimed at ensuring the remote work of employees. Now, artificial intelligence is used not only in the production department, but also in other departments of the company, for example, in the personnel management department. Artificial intelligence is used in various subsystems of personnel management: algorithms based on Artificial Intelligence allow you to study resumes, find suitable candidates for filling vacancies on employee selection sites (hh.ru, superjob, avito, etc.), withdraw candidates from the personnel reserve, identify employees with the highest efficiency, etc. Similar technologies based on Artificial Intelligence are recommended when making a transition to remote work. They will temporarily replace staff, which will reduce the cost of equipping staff with communications equipment; Minimize downtime.
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It should be noted that the digital technologies used in training and personnel development so far in most companies consist only in the creation or use of already ready-made online training systems, including various tests, online lessons, as well as additional information resources. So, for example, for safety training in the US construction industry, a new technology was created that was able to increase the involvement and provision of high-quality visualization of construction sites in virtual environments with a 360-degree panorama. The concept validation platform was designed to evaluate the virtual learning environments produced in terms of hazard identification, risk perception, and presence. The 360 degree panorama uses photography and video shooting as a reality capture technique that creates a realistic environment of types of construction environment in digital format. This method allows you to create means that give the observer “a sense of presence, being there.“ This option of using digital technologies can make the training process not only interesting, but also prepare workers for work in real conditions. It should be emphasized that one of the most unusual teaching methods is the use of VR technology. The NX Mechatronic Concept Designer supports VR collaboration mode and allows you to immerse several people fully in virtual space at once for a detailed study of device designs of any complexity. This option of digitalization of the training process is the ideal for companies engaged in the production, operation and development of the automotive industry, shipbuilding industry, etc. but it must be understood that it will be one of the most expensive methods for commissioning. Compared to it, it is worth citing one of the most affordable automation options, namely, chat bot [7]. The convenience and efficiency of using HR bots is that the HR manager developing a training program for certain purposes can easily implement it in a chat bot, which communicates with an employee at a time convenient for him, and all useful materials will be stored in his smartphone. Therefore, a feature of this digitalization tool can be called the ability to individualize the learning process, which in our time will be especially valuable among template and uniform learning systems. In general, artificial intelligence does not have the ability to completely replace highly qualified people in the next two decades. For specialists of this profile artificial intelligence will remain only an auxiliary tool for a long time. However, it will be able to replace workers who perform secondary tasks of medium or low complexity. And even then, human control is needed. Autonomous artificial intelligence, which does not need help and verification in the future, and not in real time. Having analyzed current sources of information, we can conclude that in the world an integration approach to the role of man in the digital age is becoming more and more popular [8]. The integration approach involves the use of all scientific approaches to optimally solve the problems associated with the introduction of new technologies both in the working process and in the labor market as a whole. This approach considers the human factor, which plays an important role in the era of digitalization. A person’s intelligence, creative and creative abilities, coupled with advanced skills, will be able to provide employment. With the development of technology, in addition to highly skilled labor, professions related to creativity and creative thinking are gaining popularity. Also, service and human orientation are actively developing. In order to be in demand in the era of digitalization, you need to be able to constantly retrain and revise your views,
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otherwise knowledge will quickly become obsolete, and the worker will be replaced by a machine or a more qualified employee. The labor market inevitably changes depending on automation, but professions not only disappear, but vice versa, some specialties are becoming more and more popular in connection with the introduction of artificial intelligence: - architects of automation. Algorithms are created for all necessary processes, that is, scenarios of robot behavior in various conditions. - copywriters that create texts for dialog interfaces and bots. This is rather the modernization of the existing profession. If earlier such specialists wrote scripts for employees of the support service and sales department, now they are working on building effective communications between the robot and the person. - Lawyers for the protection of intellectual property. Analysts of the consulting company Glassdoor Economic Research are convinced that this direction will be in great demand in the near future. The result of a study of vacancies over the past 5 years has been the conclusion that human labor in some areas of activity has lost its relevance as a result of replacing it with artificial intelligence, which can perform many tasks without the help of people. However, at the same time, there is a growing demand for specialists who understand interpersonal communication, who have an idea of interaction with various audiences. They are necessary to teach artificial intelligence flexibility and versatility of communication with people. The difference between new professions is that already in our time, and even more so in the future, employees should have additional digital competencies. People will deal exclusively with what cannot be automated, or there is no way to automate at the moment. Advantages and problematic aspects of professions of the future are given in Table 1. Table 1. Positive and negative aspects of professions in the era of digitalization (compiled by authors) The Merits of Profession in the Age of Digitalization
- digital technologies simplify the work of specialists; - absence of routine tasks in personnel operation; - creative work that allows to unleash the potential of a person;
Problematic aspects of professions in the era of - a wide list of requirements to specialist digitalization competencies; - requirements for digital skills; - requirements for innovation, creativity, which are inherent not all; - the specialist must monitor the automated systems and be able to respond quickly to emergency situations
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Enterprises will have the opportunity to reduce the cost of production processes as a result of the introduction of robots [9]. Economists believe that the problem of unemployment may soon grow due to a lack of jobs. This effect is called the “mismatch of skills and technologies,” that is, the difference between the abilities of man and artificial intelligence. However, most analytical specialists believe that over time, demand for new specialties will grow in the market, where people will be much more effective than artificial intelligence [10, 11]. As a result, people will be able to get a new job, albeit in another specialty. And companies with the help of competent automation will be able to save time, labor and money; Increase revenue while improving production or service delivery. Commercial organizations in the market economy are influenced by competition, so management needs to introduce artificial tools, as well as automate activities to increase profits and reduce costs. When implementing AI, the company has a personnel reserve, that is, free workers can be redirected to other sections of the labor process, and personnel rotation. There is a risk of an invisible effect, the reasons for which may be “sabotage” by employees (for example, fear of dismissal) or errors in the implemented AI, that is, design errors, resource deficiencies, etc. [12, 13]. For example, company managers have introduced artificial intelligence, in turn, employees have a risk of losing their jobs. That is, at this stage, 2–3 economists are enough for the enterprise, instead of 15. At this stage, management needs to explain that AI is a data processing tool that needs to be provided to produce results. That is, it is the employee in his sphere of activity who knows what the applied purpose of processing is, where to get the data, what to enter the data, where the processing rules are taken from, how to interpret the results, as well as which aspects of the work cannot be automated, and which are primarily transferred to AI (Fig. 3).
The employee Opportunities: - Increasing competence - Salary increases
Risks: Loss of workplace
The employer Opportunities: - Profit increase - Improving Speed and
Risks: - Absence of the effect with poor implementation quality - "Sabotage of workers"
Fig. 3. Risks and opportunities of employees and employers in the implementation of artificial intelligence (compiled by authors).
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Specialists of research centers believe that in the near future there will be separate areas where robotization will not be required. These organizations will account for the bulk of jobs during the transition to another specialization. The main change in the usual world will be to robotize processes at the everyday level. Programs will replace vehicle drivers and will be able to control unmanned aerial vehicles that will perform the delivery function. There will be no more jobs like driver and courier. This means that the cost of staff salaries will decrease significantly, and the services or products offered will change in price, and it will also be much easier and cheaper to take a taxi than to buy your own car. Similar solutions will be in demand in everyday processes, speaking of a smart house. Technologies are already being effectively implemented, but they will move to a higher level in the coming future. The systems will optimize the consumption of water and electricity, clean the room thanks to the use of small machines and look behind the safety line. There will also be changes in the public sector. Artificial intelligence will allow you to control tracking systems and predict, avoid undesirable situations. For example, a violator of the law will be able to “catch” a smart video surveillance system by recognizing his face. The same applies to document verification. Advanced algorithms will allow you to process and systematize the issuance of documents, licenses, patents. Lengthy checks on receipt of the required documents will remain in the past.
4 Discussion In 1950 Alan Turing published an article in which he formulated the very concept of artificial intelligence first and tried to describe an experiment answering the question: “Is it possible to build a machine that could have intelligence and replace a thinking person?” This article was called “Computers and Reason” and became one of the most published and discussed works in the field of computer science and cybernetics.” We can say that the science of artificial intelligence appeared in 1943. At that time, neurophysiologist Warren Mack Callock and mathematician Walter Pitts created the first simplified diagram of the neural network, which described a possible mechanism for interaction of human brain neurons. Thus, with the help of this scheme, they laid the foundations of the theory of an artificial neural network. An original systematic review of the scientific literature on the application of artificial intelligence (AI) in the field of human resources management (RM) was conducted in the work of [14]. Using content analysis and structural conceptual analysis, this study elucidates the extent and impact of the application of AI in RM functions, followed by the synthesis of a conceptual map that illustrates how to use. The term “artificial intelligence” was first used at the Dartmouth Conference in 1956, it was then that the scientific discipline “Artificial Intelligence Research” appeared. In 1965, the first Dendral expert system appeared at Stanford University, which represented the joint work of computer specialists and a group of experts in the field of chemistry. In subsequent stages, a large number of studies and the development of software and hardware based on them, which showed elements of artificial intelligence, increased several times. So, in the work of [15] for better support of these processes an artificial intelligence-based
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data mining model is presented that helps companies to identify emerging problems and trends at a higher level of automation than before. In modern conditions, there are many works by various authors that define the concept of “artificial intelligence” in various forms. Some authors define it as “Artificial intelligence is an ensemble of rational logical, formalized rules developed and encoded by man, which organize processes that allow to simulate intellectual structures, produce and reproduce healing actions, and carry out subsequent coding and making instrumental decisions regardless of person” [16]. Other authors define Artificial Intelligence as a fully or partially autonomous selforganizing computer-hardware-software virtual or cyber-physical, including a cybernetic, system endowed with/possessing the ability to think, self-organize, learn, make decisions independently, etc. [17]. Interesting are definitions of artificial intelligence, showing its connection with the digital reproduction of the processes of conscious activity of a person and society as a whole in terms of creative processing and reasoning based on non-trivially formalized information in conditions of time and resource constraints of uncertainty and insufficiency of initial data, creating cybernetic objects capable of independently setting goals and achieving them with a quality of at least an average specialist, capable of replacing existing activities and professions in the future [18]. Other authors note that “artificial intelligence is a subject of computer science, and the technologies created on its basis are information technologies that allow you to make reasonable reasoning and actions using computing systems and other artificial devices [19]. In the work of these authors, a systematic review was first carried out to study the relationship between artificial intelligence and workplace results. Having studied the presented interpretations of various authors, we can say that there is no generally accepted definition of artificial intelligence at the moment. If we generalize the above definitions, artificial intelligence is a property of an intellectual system to perform functions and tasks that are characteristic of intelligent beings. That is, these may be propensity for reasoning, generalization, manifestation of any creative abilities, training based on previous experience, etc. Researchers of artificial intelligence and competitive advantages Krakowski, S., Luger, J., Raisch, S. also conclude in their work that AI-based technologies increasingly replace and complement a person in managerial tasks, such as decision making. They study the impact of such changes on sources of competitive advantage. A number of scientists write about the impact of artificial intelligence on the results of the company’s work in their scientific papers [20–22]. These researchers believe that the goal of implementing artificial intelligence is to optimize procedures and reduce the workload of human resource management (HR), which increases operational efficiency and increases system performance. Many authors, Weixuan Hu and Shukuan Zhao [23], Stephanie Kelley [24], Siliang Tong et al. [25], Manlio Del Giudice et al. [26], consider in their articles the issues of positive and negative impact of artificial intelligence on workers, effective adoption of the principles of artificial intelligence (AI) in their organizations, difficulties in using human resources (HR) in the practice of data science, human adoption of artificial intelligence. In the works of these scientists, a scientific debate is being held about the impact that future artificial intelligence will have on business and society.
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Also, some scientists Sebastian Kot et al. [27] are concerned about the role of artificial intelligence-based human resource management to determine employer reputation.
5 Conclusion Summing up, we can say that competition in the market leads enterprises to understand the need to introduce artificial intelligence, its tools. There are areas of activity when AI cannot be dispensed with, for example, a person will not be able to process unmistakably thousands of documents per day. Artificial intelligence has great potential for innovation in employee workplaces. The risk to the employer when introducing artificial intelligence is determined mainly by the loss of part of the budget with the inefficient use of the introduced tools. We can say that without the introduction of AI and experience in its use, the company may lose its competitiveness. In turn, an employee without digital competencies will be uninterested in future business. Automation and robotization of HR processes is not only possible, but also necessary in the conditions of modern realities, directly related to constant technological progress. So, the field of staff training is most promising in terms of digitalization of processes, since this can affect not only the efficiency of staff, but also the economic condition of the company. This is a contribution to the future of the company, which has a huge choice of means to digitalize basic processes and reduce costs. Due to the actively ongoing process of digitalization, low-skilled jobs that do not require creative thinking will die out in society, and professions in which the mobilization of human cognitive resources, emotional intelligence and other qualities that have not yet been reproduced using technology will belong to people. Thus, it can be noted with confidence that the modern world inevitably changes as a result of automation of all spheres of human activity. People who have lost their jobs as a result of this process have the opportunity to take various retraining courses, thanks to which they will be able to choose another specialization for their future career.
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Digital Technologies in the Security of the National Economy Under Constraints: Analysis of Experience and Perspectives for Adaptation Tatyana Feofilova1(B) , Iuliia Alekseeva2 , Mehdi Imani3 , and Evgeny Radygin4 1 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected] 2 International Career School, St. Petersburg, Russia 3 Imam Khomeini International University, Qazvin, Iran 4 Russian State University of Justice, St. Petersburg, Russia
Abstract. The imposed restrictions actualized the problem areas in the economy of the Russian Federation, new vulnerabilities in the system of economic security were revealed. Restrictions on access to modern technology are particularly dangerous to the country’s level of economic development and possible future development. The purpose of the study is to summarize and systematize the measures and experience of the introduction of innovative, including digital, technologies of economic sectors and the use of digital technologies in the process of ensuring the security of national economies applicable in the context of restrictions. It was found that the Russian Federation was more affected by the country destruction and the resulting disruption of economic ties than by the sanctions imposition on Iran. However, while the Russian economy recovered during the decade, the negative dynamics in the Iranian economy persisted during the period under study. It is concluded that an impact of sanctions on national economies is long-lasting. It is found that Iran is inferior to Russia in the level of digitalization, but this gap is not critical. This allowed us to conclude that Iranian industry is adapting to the restrictions and implementing digital technologies in spite of its. An overview is given of some economy sectors that actively and effectively use digital technology, in particular in the defense industry, aircraft construction, the automotive industry and in the social sphere. It is proposed to use the experience of specific projects to study and implement its in Russian industry and services. Proposals have been formulated aimed at combining the resources and potential of countries under sectional pressure to produce analogues of high-tech products, as well as products made using their own technology. Keywords: Digitalization · Iran · Limitations · Russia · Technology
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 41–51, 2023. https://doi.org/10.1007/978-3-031-32719-3_4
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1 Introduction International instability creates uncertainty for states, regions, companies, and populations. At the same time, vulnerabilities that were previously masked by relatively stable international relations are being exposed [1]. In recent years, Western European countries, under U.S. auspices and supported by several Asian countries, have actively imposed repressive measures and other restrictions that pose threats to the economic security of countries interested in preserving its sovereignty. In this environment, sovereign states have taken various measures to counter the threats posed by the restrictions. Measures are also being taken to identify and address pre-existing vulnerabilities and to repair the damage done [2]. National economic security in the Russian Federation is an integral element of national security. This is evidenced by the National Security Strategy of the Russian Federation (2021), where economic security is positioned as a strategic national priority, which is defined as the direction of activity to ensure the national interest in the sustainable development of the country’s economy on a new technological basis. The new technological basis is largely related to the use of artificial intelligence technology. However, the imposed additional restrictions on the transfer of digital technology in the Russian Federation significantly hinder the effective achievement of goals in the field of sustainable technological development due to the presence of objective as well as systemic problems of countering threats to the security of the national economy. These problems consist of technical and technological dependence of the country on foreign countries.
2 Literature Review In the Islamic Republic of Iran there is no document reflecting the vision and measures of the state to ensure the protection of national interests in the economic sphere similar to the Russian one. However, the issues of innovative development of the economy in Iran are certainly paid attention at the state level [3]. Thus, for the period from 1997 to 2017, the following documents were adopted, which, to varying degrees, determined the influence of the state on the digitalization of the national economy and its protection from the threats of technological threats (listed according to the date of adoption of the document): Act of Maximum Use of Production and Services to Satisfy the Country’s Needs and Enhance them in Exports, 2025 Vision: 20-year Vision Plan, The Law of Registration of Patents, Industrial Designs, and Trademarks, Law for Supporting Knowledge-based Firms and Commercializing Innovations, National Master Plan for Science and Education, National Policy for S&T 2014 and National Policy for a Resilient Economy, Development plans (containing STI-related articles). Taking into account the volume of sanctions imposed on the Russian Federation, the interest in the experience of the Islamic Republic of Iran (Iran), which has been under restrictive political and economic measures for four decades, and the assessment of its applicability, on the one hand, is predictable. On the other hand, there is a scientific and practical significance of assessing the possibility of combining the potential of the Russian Federation and Iran for the wider implementation of digital technologies, including the possible joint development, production and adaptation of existing foreign technologies to meet the challenges
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of ensuring the security of national economies and creating conditions for sustainable socio-economic development of countries under constraints. The issues of ensuring the security of the national economy are considered from different perspectives. The development of effective ways to adapt to changes in the framework of digital innovation technology is of particular importance in the context of constraints. The analysis of the impact of digital innovation technologies on national security is presented in the works of [4, 5] points to the threats and risks to the development of socio-economic systems that are formed through digital transformation; he highlights the main strategic objectives of the digital economy as part of the state’s economic security. K. Fartash, M. Eliasi, A. Gorbani, and A. Sadabadi [1] argue that the effectiveness of digital environment development is determined by the level of involvement of public authorities in innovation policy, which demonstrates the ability of the state to implement a number of economic, social and environmental objectives at the federal level. Zharnitskaya [5] refers to the "unique conditions" of the digital economy, which contribute to the effectiveness of standard security measures of the national economy (in particular, increasing labor productivity, the formation of new demand for products, goods and services, etc.). Ryapenko, Fedorenko and Livkina [4] conclude that the use of digital innovation technologies will improve the country’s competitiveness in the global market. Baig and Szewczyk [6] single out "smart city" as an information digital technology capable of improving the quality of life of the country’s population. Rogozhina [7] considers the digital economy as a necessary condition for economic security. Authors [8] note the dual nature of the digital economy impact on the national economic security of the country but sees the potential in terms of risk management of digital innovation technologies as an opportunity to strengthen the state economic security. The works of [9–12] and others were also studied for a more complete characterization of the issue of the restrictions impact on technological development and state national security. Hufbauer [11] notes that developed countries are relatively immune to restrictions, but in order to maintain national economic security the state should provide measures related to cybersecurity and imports of modern technology. Minakov and Lapina [9] performed an analysis of macroeconomic indicators that characterize the economic security of the state, which is affected by sanctions. The scientists concluded that economic restrictions are not the key factor in the deterioration of national economic security. Korobeinikova and Sadykova [12] single out the low innovative activity of the country’s economy as one of the main threats to the national economy. The authors consider it necessary to develop economic relations with friendly countries, using national currencies for transactions with such states. Kozlyakova [13] refers to the strengthening of contradictions within the country, subject to the impact of sanctions. Authors [10] explores Iran’s experience in the application of advanced technologies in areas that directly affect the country’s national security, under constraints (for example, military digital technologies, which are used in aircraft construction, drone construction, technology in the automotive industry). Authors [14] believe that restrictions form risks in the management of digital security, which, according to the authors, determines the sustainability of economic security of industrial enterprises. Baig, Szewczyk and Kerai [6] refer to the inability to implement the concept of a smart city in the context of a
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lack of technology and innovation, which negatively affects the digital economy and the economy as a whole. Thus, based on the analysis of specialized literature, we can conclude that: - there is no unified understanding of the adaptation of the country’s economy in limitation conditions; - technological backwardness of the country has a negative impact on the its national economy; - ensuring innovation security is one of the key elements in the formation of national economic security. The objects of the study are the national economies of the Russian Federation and the Islamic Republic of Iran, due to the volume of restrictions imposed on international cooperation of economic subjects of the countries. The purpose of the study is to summarize and systematize the measures and experience of the introduction of innovative, including digital, technologies of economic sectors and the use of digital technologies in the process of ensuring the security of national economies applicable in the context of restrictions.
3 Methods We used statistical data obtained from trustworthy sources. In particular, these are data from the World Bank, the Statistical Center of Iran and the Federal State Statistics Service of the Russian Federation. To get an idea of the level of development of digital technology in Iran and Russia, we considered the data of international rankings of countries on the speed and cost of the Internet per month, the number of broadband Internet subscribers, and also used the data of the rating of countries innovation activities. To assess the possibility of using the experience of the Islamic Republic of Iran on the digitalization of the economy sectors in constraint conditions, the data characterizing the amount of spending on R&D in Russia and in Iran were studied. In the course of the study, general scientific methods were used: analysis and synthesis. During the processing of quantitative data, methods of comparison, structural analysis, analysis of dynamics were used.
4 Results and Discussion In 1991, after the formation of the Russian Federation as a sovereign state, GDP had a negative trend until 1998. During the same period, Iran was under sectional pressure, but maintains the growth of its GDP. During the same period, Iran was under sectional pressure, but maintained growth of its GDP. Despite the fact that since 1998, Russia’s GDP has had a positive trend at the level of Iran’s per capita value only reached in 2006 (Fig. 1). Modern innovations are most often digital technologies. For the basic criterion of digitalization of social and economic relations it is reasonable to take the characteristics of the Internet: speed, in terms of quality conditions of digital technologies (a), cost,
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Fig. 1. GDP per capita in Iran and Russia from 1990 to 2020
in terms of accessibility for implementation and use of these technologies (b) and the number of broadband Internet access subscribers, in terms of accessibility of modern technologies for individuals and legal entities (c). We have compared Russia and Iran on the speed and cost of the Internet per month. To understand the level of Russia and Iran in the world a fragment of the ranking of countries on these indicators is presented (Fig. 2). 140 120 100 80 60 40 20 US A Ja pa n
an Sp ai n I K y tal y rg yz st an Ru ss ia
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s Az tri a er ba i ja n Be la ru Be s G r lg iu ea m t ri t a Hu i n ng a G e ry rm an y Gr ee c Ge e or gi a
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Fig. 2. Rating of countries by speed and cost of Internet per month, in USD
Access to broadband Internet access networks is analyzed in the dynamics for the period from 2003 to 2020. To understand the level of Russia and Iran in comparison with other countries of the world, as a fragment with the dynamics of the indicator of a number of states, as the world leaders in terms of GDP, and representatives of less developed economies (Fig. 3). Statista experts estimate that the share of gross domestic spending on the development of the digital economy in Russia’s GDP grew by 0.2% from 2017 to 2020. At the
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Fig. 3. Subscribers to broadband Internet access in 2003–2020 (per 100 people)
same time, organizations’ domestic spending on digital technology increased by 0.2%, and households’ spending on the use of digital technology by 0.4%. The Federal State Statistics Service of the Russian Federation provides similar data (Fig. 4).
Fig. 4. Share of gross domestic spending on the development of the digital economy in Russia’s GDP from 2017 to 2020
We could not find statistical information on the share of gross domestic expenditure on the development of the digital economy in the GDP of Iran. Therefore, we used only data from the IRNA information agency, according to which the share of the digital economy in Iran’s GDP in 2012 was 3.7%, at the beginning of 2021 - 6.5% (Fig. 5). Taking into account that the share of the digital economy in world GDP is about 4%, it is most likely the so-called expanded interpretation of the concept of "digital economy", which includes the entire ICT sector and activities related to the use of digital technology in various industries. Using the method of coefficients, we determined the share of gross
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Fig. 5. Trends in the share of the digital economy in Iran’s GDP
domestic spending on the development of the digital economy in Iran’s GDP in 2020 to be 1.3%, which is respectively less than the level of the Russian Federation both in absolute values and in relative to GDP. Figure 6 shows a fragment of the global innovation index rating, with data for the Russian Federation and Iran, as well as technologically developed states.
Fig. 6. Global innovation index
Statistical data shows a significant impact of sanctions on Iran’s GDP; their weakening from 2006 to 2010 was accompanied by an increase in GDP. While Russia’s GDP per capita without the impact of sanctions grew by 37.7% over the decade, the value of the indicator of the Islamic Republic of Iran declined by 24.6% over the same period. During the analyzed period, the GDP of Iran has a negative trend and in 2020 the level of GDP decreased to the values of 2005. There is also a negative trend in the values of
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consumer price index (for the period from 2011 to 2019 the increase was 424%), inflation rate (for the same period the growth rate increased by 13% in annual terms), unemployment especially among the young population of the country and other indicators that negatively reflect on the level of socio-economic development of Iran. Analysis of data for the period from 1991 to 2006 showed that the destruction of the country (the USSR) affected the Russian Federation to a greater extent than the sanctions imposed on Iran and the effects of the war with Iraq. However, after the adaptation of Russia’s socio-economic system to the new economic conditions, GDP had a generally positive trend, whereas during the same period, especially during the periods of tightening restrictions imposed against Iran. From 2006 to 2010 the sanctions pressure on Iran eased, and during this period the economy grew. However, after a new round of GDP restrictions in 2011. The country’s GDP declines significantly. Iran’s experience confirms experts’ forecasts about their subsequent negative impact on GDP and the level of socio-economic development of the Russian Federation. An analysis of the indicators that characterize the level of digitalization of the Iranian and Russian economies shows that the technological development of the Iranian economy was affected by the restrictions, which caused the country to rank lower than the Russian Federation in the ratings. At the same time, the gap between the level of Russia and Iran in the share of the digital economy is not that significant, taking into account the period during which the country has been subjected to restrictions, which is confirmed by the country’s place in the international ranking on the global innovation index. This fact is due to the fact that the country was able to partially adapt to the sanctions pressure, but not enough for positive socio-economic development. Thus, the analysis of quantitative data allows us to conclude that the experience of Iran in terms of countering threats in the technological sphere cannot be used by the Russian Federation as a certain benchmark of measures to ensure economic security. However, it may be useful for the development of the Russian Federation’s own approach, taking into account the achievements that Iran has. In particular, the application of innovation and technology in industry, healthcare, pharmaceuticals, education and tourism. Industry. Analysis shows that Iran can produce high-tech products under constraints. For example, as of early 2022, Iran produces some of the best unmanned aerial vehicles (Kaman-12, Kaman-22), and the defense industry produces high-level armored vehicles, missiles, and significant successes in the drone industry (Shahed-136). According to H. Vahedi, commander of Iran’s Air Force, the country’s national security is determined by the potential of the defense industry. Science-intensive technologies, scientific collaborations, and cooperation between high-tech companies and the defense sector are actively used for this purpose. Production of aircrafts (Iran-141, Kowsar and Azarakhsh) should also be noted. Given the impossibility of producing electronic components, products and devices in Iran and insufficient competitiveness, for example, of semiconductor enterprises in Russia, it is possible and promising to combine the efforts of countries that are under technological constraints. One of the organizational forms of interaction is attraction to the activities of the Supreme Committee for Science and Technology Cooperation
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between Iran and Russia and its expansion by new members of the committee - representatives of electronic industry of the Republic of Belarus, which successfully produces microcircuits exported to Russia, China, India and other countries. Health and Pharmacy. The coronavirus infection made adjustments and gave impetus to the development of e-health in Iran. Although the first digital health platform appeared in the country in 2018, it gained popularity among Iran’s population in 2020. In 2021, about 150 million times, Iranian residents accessed the platform for online consultations, including: - diagnosis by disease symptoms through the use of artificial intelligence technologies; - obtaining a prescription for medicine; - and obtaining general health advice. In our opinion, Iran’s experience in this area is applicable in Russia, since its implementation will greatly simplify the process of receiving prompt and high-quality medical care and will make medical services more accessible to citizens who live far away from medical institutions. It is of practical interest that citizens living in other countries can also act as users and implementers of services, improving the quality of solutions to health problems. In addition, it should be noted that domestic demand for medical devices is met by domestic production capabilities (90% by 2020). Education. The implementation of the development of e-learning in the country is difficult due to the weak Internet infrastructure in rural areas of Iran (45% of the country). At the same time, in 2020 the digital educational platform “Shad” was developed, which allows attending online classes in case of cancellation of face-to-face classes in educational institutions. Teachers and tutors are also able to post useful information that aims to help and broaden students’ horizons and motivate learning. Tourism. The development of relations between Russia and Iran has intensified the adoption of measures to increase the volume of mutual tourist flows, including the introduction of visa-free regime for tourist groups from January 2023. To develop historical and cultural tourism, it is advisable to develop and implement a digital platform, which in addition to various tourist programs, would contain historical, cultural and other information about Russia, about Iran, as well as the development of relations between the countries. Equally important is the possibility of registering 1) professional guides, which the user can choose before the tourist trip; 2) travel consultants who, taking into account the preferences of the client will make an individual program, which is also advisable to provide as part of the tourist digital platform. Despite the fact that the Iranian government has declared the need to form a digital space with the participation of the country’s young population in all sectors of the economy, the population aged 26–33 years prefers to receive higher education abroad. Since the conditions and quality of life, for example, in the United States, Germany, and Canada attract the young population and contribute to the outflow of potential professional personnel from the country.
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5 Conclusion The study showed that the imposed restrictions have had a negative impact on the socioeconomic development of the Islamic Republic of Iran. Over the decades of sanctions, Iran’s GDP and, consequently, the living standards of the population have declined. The country’s economy has many problems, which are largely due to lack of access to modern technology. Therefore, it is unacceptable for the Russian Federation to use Iran’s experience. At the same time, it has been established that projects and high-tech products are being implemented in Iran, which do not exist in Russia or are of inadequate quality. Given that in the last decade there was an opportunity for technological development and digitalization of the economy, and Iran was under sectional pressure, the analysis of experience in the production of individual products and its adaptation by Russian manufacturers is necessary to organize the process of implementation and improvement of global technologies. And the mutual interest between the countries in the sphere in question is revealed. Thus, Russian companies may be interested in projects related to medicine, pharmaceuticals and defense industry. Representatives of Iranian business structures, in turn, need to exchange technologies for the development of agriculture and food industry. In our opinion, it is reasonable to combine human, financial, material, and technical resources of the countries subjected to illegal restrictions on the part of technologically developed states in order to produce inaccessible or limitedly accessible products. We believe that such unification will contribute to the expansion of scientific and technological cooperation and obtain not only analogues of existing high-tech products, but also the production of products based on their own innovations and advanced technologies. Acknowledgments. The research was financed as part of the project "Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization" (FSEG-2023–0008).
References 1. Fartash, K., Ed’yasi, M., Gorbani, A., Sadabadi, A.: Analiz innovacionnoj politiki i strategii razvitiya Irana. Zhurnal Nacional’nogo issledovatel’skogo universiteta «Vysshaya shkola ekonomiki». Forsajt 15(3), 81–92 (2021) 2. Chechin, O.P.: Cifrovaya transformaciya v koncepcii ekonomicheskoj bezopasnosti. Ekonomicheskie nauki 7, 92–97 (2019) 3. Khan, Z., Lew, K.Y., Marinova, S.: Exploitative and exploratory innovations in emerging economies: The role of realized absorptive capacity and learning intent. Int. Bus. Rev. 28, 499–512 (2019) 4. Ryapenko, A.I., Fedorenko, A.A., Livkina, E.A.: Razvitie cifrovoj ekonomiki v Rossii i nacional’naya bezopasnost’. student, №8, https://cyberleninka.ru/article/n/razvitie-tsifrovoyekonomiki-v-rossii-i-natsionalnaya-bezopasnost 5. Zharnickaya, K.D.: Vliyanie cifrovoj ekonomiki na obespechenie ekonomicheskoj bezopasnosti gosudarstva. Aktual’nye issledovaniya. 11(38), 53–56 (2021). https://apni.ru/article/ 2053-vliyanie-tsifrovoj-ekonomiki-na-obespechenie, last accessed 21 February 2022
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6. Baig, Z.A., et al.: Future challenges for smart cities: Cyber-security and digital forensics. Digit. Investig. 22, 3–13 (2017) 7. Rogozhina, N.V.: Digital economy as a factor of strategic development and economic security. Young Scientist 41(331), 258–260 (2020). https://moluch.ru/archive/331/74005 8. Sergeev, D.M., Kuznetsov, E.V., Smirnov, A.A.: Analysis of the Russian derivatives market: dynamics and development features in 2016–2018. In: Proceedings of the 33th International Business Information Management Association Conference, IBIMA 2019 (2019). https://ibima.org/accepted-paper/analysis-of-the-russian-derivatives-marketdynamics-and-development-features-in-2016-2018/ 9. Minakov, A., Lapina, S.: Ensuring state economic security in the context of sanctions of western countries. Bulletin of Economic Security 2 (2021). https://cyberleninka.ru/article/n/obe spechenie-ekonomicheskoy-bezopasnosti-gosudarstva-v-usloviyah-sanktsiy-zapadnyh-stran 10. Feofilova, T., Alekseeva, I., Radygin, E., Imani, M.: Innovation in the Iranian economy: Risks and threats to Iran’s national economic security. In: Proceedings of the International Scientific Conference - Digital Transformation on Manufacturing, Infrastructure and Service (DTMIS ‘20), Article 57, pp. 1–7. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3446434.3446511 11. Hufbauer, G.C., Jung, E.: What’s new in economic sanctions?. European Economic Review 130 (2020). https://doi.org/10.1016/j.euroecorev.2020.103572 12. Korobejnikova, E.V., Sadykova, L.M.: Vliyanie sankcij na ekonomicheskuyu bezopasnost’ Rossii. ANI: ekonomika i upravlenie 4(29) (2019). https://cyberleninka.ru/article/n/vliyaniesanktsiy-na-ekonomicheskuyu-bezopasnost-rossii 13. Kozlyakova, D.N.: Sankcii kak problema ekonomicheskoj bezopasnosti Rossii. Nauka bez granic 7(35) (2019). https://cyberleninka.ru/article/n/sanktsii-kak-problema-ekonomich eskoy-bezopasnosti-rossii 14. Schinagl, St., Shahim, A., Khapova, S.: Paradoxical tensions in the implementation of digital security governance: toward an ambidextrous approach to governing digital security. Computers & Security 122 (2022). https://doi.org/10.1016/j.cose.2022.102903
Problems of Information Interaction Between Public Authorities and the Population of St. Petersburg in the Context of the Digital Transformation of the Region Aleksandr Volodin(B) , Ekaterina Sokolova, Viktoriya A. Degtereva, and Maxim Ivanov Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. The purpose of this work is to analyze the information interaction between citizens and public authorities of St. Petersburg and to model the process of this interaction. The ontological models of information interaction are built in the work: 1. Interaction between public authorities of St. Petersburg; 2. The interaction of the “black box” model between the request of a resident and the position of the authority on his issue; 3 Interaction of a state civil servant of the executive authorities of St. Petersburg with the information systems required to resolve the request of a city resident and communication channels with them. As a result of the work, the authors come to the following conclusion: 1. The lack of electronic interaction with public authorities leads to a serious decrease in the effectiveness of interaction. 2. The existing systems of electronic interaction, despite the fact that they are high-quality and effective, are of a framework nature, and the absence of such systems at the institutional level is a serious shortcoming of the authorities. 3. The indirect losses of the lack of a unified system of work with citizens’ requests on the activities of government bodies only for the executive bodies of state power of St. Petersburg are estimated by the authors at more than 500 million rubles a year in 2020 prices. These results can become a rationale for further research and development aimed at improving public administration in St. Petersburg and in Russia as a whole, based on improving the efficiency of interaction between authorities, authorities with citizens, as well as intra-organizational interaction in authorities. Keywords: Public administration of the region · Information development of state institutions · Digital interaction · Electronic government
1 Introduction Today, commercial structures that have a rigid hierarchical model and do not produce changes often exist due to the accumulated inertia of the economic scale. Small and medium-sized companies with highly qualified production that manage in this way are going bankrupt. At the same time, in the era of the 4th industrial revolution and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 52–65, 2023. https://doi.org/10.1007/978-3-031-32719-3_5
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widespread digitalization, many organizations are switching to working on the Internet, and quite often use services related to work with employees. Often, only enterprises and organizations remain in the “analog” form, for which manual labor is primary and the economic losses from the coordination of employees in manual mode are not significant enough. At the same time, the availability of electronic services and corporate portals, leading to an increase in the efficiency of work processes, is an established fact for medium and large companies in Russia that work with highly qualified specialists at the management level. These corporate tools include both generalized services (Atlassian, Oracle WebCenter Suite, MS SharePoint, IBM WebSphera, 1C-Bitrix, Joomla!, Jboss, Plone, Drupal, Jive and many campaigns make their own developments), and in the absence of common tools, they are presented as separate collaboration services (using Telegram and WhatsApp instant messengers to solve work issues, uploading documents to Google documents for collaboration, and other interaction formats). At the same time, the issues of electronic-digital interaction of employees today are quite actively introduced into the work of civil servants. In particular, this issue is quite acute for the regional level, since new information services are created both at the federal and at the regional level, a lot of systems “from above” come to the implementation of specific civil service units, while at this level all of them are often a factor in increasing the working load, which causes a decrease in the efficiency of the work of the authority as a whole, which is the relevance of this work. The purpose of this work is ontological modeling of the process of interaction between the public authorities of St. Petersburg among themselves and with citizens. But then in the introduction it is not necessary to write about the interaction between the authorities and between civil servants within the authorities. To achieve this goal, the following tasks are solved: 1. Description of the existing system of organization of public administration in St. Petersburg. 2. Presentation of the system of interaction between information environments of public authorities in St. Petersburg. 3. Formalization of the ontological model of interaction between a citizen and public authorities of St. Petersburg on the principle of “as is”. 4. Calculation of the economic effect of the current information infrastructure of the executive authorities of St. Petersburg. For the purposes of this work, the public authorities of St. Petersburg, which are the subject of consideration, mean: 1. State authorities of the constituent entity of the Russian Federation (St. Petersburg). 2. Local self-government bodies located within the boundaries of the city of St. Petersburg. 3. Territorial subdivisions of federal authorities located in St. Petersburg.
2 Literature Review The issues of economic development of regions and states in the scientific literature are raised quite often [1–3]. Many works are devoted to the search for additional ways to increase economic efficiency [4], in particular, among the reasons that can ensure the
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growth of the regional economy, highlight the development of human potential [5, 6] and improvement of the housing stock [7], but these solutions do not lead to a de-crease in the problems arising in the digital development of society, but on the contrary, they take into account their proportional growth. In addition, in the analysis of economics, emphasis is often placed on neural networks [8], but not on the applicability of these tools for the state apparatus; ex ante and ex post approaches [9] are also used. Economic modeling is currently a very common phenomenon that helps to effectively predict the future development of a region or the processes of leveling its problems [10, 11]. At the same time, the developed economic models can be used for environmental assessments [12]. However, it should be noted that today a large number of models have been created to improve the socio-economic situation of precisely Russian regions according to the innovative scenario of their development [13–15], but these are precisely the development models that often neglect the solution of existing problems. In the literature, it is the social development of citizens that is singled out separately to create balanced demand in the regional economic system, which is noted in the works [16, 17]. The issues of basing the economy on a digital basis are common for business work, but they are very often forgotten in the public sector, and often the issue of digitalization of the state apparatus fades into the background in practical work. At the same time, many works have been devoted to the issues of rooting digitalization in society, for example, Bataev and Plotnikova, raising the positive aspects and effectiveness of digital banking, which consider the issues of the inaccessibility of this tool for middle-aged and elderly people, as well as economic risks, which they bear when trying to use these services, which bring young people a large number of eco-nomic preferences [18]. However, taking into account the digital component in eco-nomic assessments comes down to the use of digitalization at the level of enterprises of various kinds. So, [1], for example, use digitalization to assess the capitalization of companies, and [19] - to assess the automotive industry in Russia. In the scientific community, there are also often works on the development of various kinds of rating systems, in particular, on a global scale, these are the works [20], as well as a number of works with a similar methodology. For applied tasks. These are the works of such researchers [21] and [22]. At the same time, there are not many works affecting the country-wide character of the study of socio-economic development, which is why this article is of sufficient relevance and significance for the international community in general and for understanding the location of Russia in particular. The system of public authorities of St. Petersburg is regulated by the Decree of the Governor of St. Petersburg dated May 31, 2012 No. 36-pg “On the structure of the executive bodies of state power of St. Petersburg” [23] and consists of a standard scheme of separation of powers: 1. Legislative branch - the Legislative Assembly of St. Petersburg. 2. Judicial branch - Charter and justices of the peace of St. Petersburg. 3. Executive branch - The highest official of St. Petersburg - the Governor of St. Petersburg and the Government of St. Petersburg, to which they are subordinate: 3.1. Committees, departments, inspections and services. 3.2. Administration of the Governor of St. Petersburg.
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3.3. Administrations of districts of St. Petersburg [23]. The system of public authorities of St. Petersburg is formed by: 1. The highest and only legislative body of state power is the Legislative Assembly of St. Petersburg. 2. The Government of St. Petersburg is the highest executive body of state power of St. Petersburg, headed by the highest official of St. Petersburg - the Governor of St. Petersburg, and other executive bodies of state power of St. Petersburg headed by the Government of St. Petersburg, which make up the system of executive bodies of state power St. Petersburg - the Administration of St. Petersburg; 3. Judicial authorities of St. Petersburg - the Statutory Court of St. Petersburg, justices of the peace of St. Petersburg. First of all, the developed project of the Unified Platform for Interagency Interaction (hereinafter referred to as the UIMI) concerns the executive authorities of St. Petersburg - these are 8455 staff positions [24] organizing and supporting the activities of more than 5.35 million residents of St. Petersburg [25]. In addition, group 1 includes 50 deputies of the legislative assembly with at least 3 assistants and the apparatus of this authority, as well as 211 justices of the peace [26] with apparatuses and the statutory court of St. Petersburg. The listed structures also have a staff of employees. The second major subject of interaction between public authorities are 111 intracity municipalities, each of which has a staff of municipal employees, according to the information of the Committee for Territorial Development of St. Petersburg, 2078 people. The activities of local governments in St. Petersburg are regulated by the Federal Law of October 6, 2003 No. 131-FZ (as amended on November 9, 2020) “On the General Principles of Organizing Local Self-Government in the Russian Federation” and the Law of St. Petersburg of September 23, 2009 No. 420- 79 “On the organization of local self-government in St. Petersburg”. The third major subject of interaction is the territorial subdivisions of federal authorities. In total, there are about 75 different ministries and departments in the Russian Federation, while about 50 of them have one or another territorial representation [27] with which, to one degree or another, the executive bodies of state power of a constituent entity of the Russian Federation interact. In this work, 1 group of subjects of interaction is considered as the main one, and with the help of this project, 2 other groups are introduced into its system for ensuring the life of the city, fulfilling their own specific goals. Metric indicators of the effectiveness of the interaction between the executive bodies of state power (hereinafter referred to as the IOGV) of St. Petersburg with citizens are determined by the Decree of the Government of St. Petersburg dated January 19, 2018 No. 4 “On approval of the Procedure for assessing the activities of the executive bodies of state power of St. Petersburg” and are expressed in the following metrics: 1. Key performance indicators calculated quarterly. 1.1. The share of applications for the provision of public services for which the established deadline for recording information about the decision taken in electronic form was observed.
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1.2. The share of public services for which an administrative regulation has been approved, out of the total number of services included in the Register of State and Municipal Services (Functions) of St. Petersburg with the status “Provided”. 1.3. The coefficient of executive discipline on instructions that are under centralized control. 1.4. The coefficient of executive discipline for working with appeals from citizens and legal entities put under control by the Office for Working with Appeals from Citizens of the Administration of the Governor of St. Petersburg (hereinafter - UROG AG). 1.5. Coefficient of performance discipline for working out messages from citizens coming to the portal “Our St. Petersburg”, for which the IOGV is the Controller. 1.6. Coefficient of executive discipline for working out messages from citizens coming to the portal “Our St. Petersburg”, for which the IOGV is the Coordinator. 1.7. Place in the ranking of the OGV in terms of performance in the implementation of personnel policy for the reporting period (quarter). 2. Key performance indicators of the work of the IOGV, calculated annually. 2.1. The degree of achievement of the target indicator “The share of applications for the provision of public services accepted in electronic form, out of the total number of applications for the provision of public services (according to the subsystem “Statistics” of the MAIS EGU)”. 2.2. Place in the IOGV rating based on the results of a comprehensive assessment of the quality of financial management of the main managers of budgetary funds in St. Petersburg. 2.3. A place in the rating of the State Institution of State Competence for assessing the effectiveness of customers in the procurement of goods, works, services to meet the needs of St. Petersburg for the reporting year. 2.4. Compliance with the deadlines for providing information to the program complex “Property of St. Petersburg” of the State Institutions of State of the Republic of Georgia and their subordinate organizations. 2.5. Place in the ranking of the OGV in terms of performance in the implementation of personnel policy based on the results for the year. Executive power in St. Petersburg is exercised by the Government of St. Petersburg, headed by the Governor, and other executive bodies of state power of St. Petersburg, which make up the system of executive bodies of state power of the city - the Administration of St. Petersburg. The city government is located in the building of the Smolny Institute. The administration of St. Petersburg consists of various committees, services and inspections, subordinate to the members of the Government of St. Petersburg. Heads of district administrations of St. Petersburg are directly subordinate to the governor. In total, there are 18 districts in St. Petersburg. Local self-government in St. Petersburg is carried out on the basis of the federal law "On the general principles of the organization of local self-government in the Russian Federation" [28] and the law of St. Petersburg "On the organization of local self-government in St. Petersburg" [29]. There are 111 intracity municipalities in St. Petersburg. The structure of local governments in St. Petersburg is: 1. Representative body of an intracity municipality;
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2. Head of an intracity municipality; 3. Local administration (executive and administrative body); 4. Control and accounting body of an intracity municipality; 5. Other bodies of local self-government and elected officials of local selfgovernment. The territory of St. Petersburg is divided into 111 intra-city municipalities (intra-city territories of a city of federal significance), among which are: 1. 81 municipal districts, 2. 9 cities (Zelenogorsk, Kolpino, Krasnoye Selo, Kronstadt, Lomonosov, Pavlovsk, Peterhof, Pushkin, Sestroretsk), 3. 21 villages (Aleksandrovskaya, Beloostrov, Komarovo, Levashovo, Lisiy Nos, Metallostroy, Molodyozhnoye, Pargolovo, Pesochny, Petro-Slavyanka, Ponton, Repino, Sapperny, Serovo, Smolyachkovo, Solnechnoye, Strelna, Tyarlevo, Ust-Izhora, Ushkovo, Shushary). Intra-city municipal formations (intra-city territories of the city of federal significance) of St. Petersburg are territorial units formed within the boundaries of administrative-territorial units - districts of St. Petersburg. Each of the above-designated structures at the same time has its own corporate information systems for organizing work. At the same time, systems for core activities of specific departments are extremely common. At the same time, in the executive authorities, these systems are often developed by each committee separately to meet their own needs, as a result of which a rather extensive list of information environments descends to the level of district administrations, which is one step lower (Information environments - state information systems, software and hardware and communication networks that ensure the provision of services and the implementation of functions in electronic form by state authorities and local self-government of the city of St. Petersburg, when interacting with each other, as well as with citizens and legal entities). In a general simplified ontological form, the process of interaction between these information environments and the actors located in them is shown in Fig. 1. When considering this ontology, one should pay attention to the following number of factors. 1. The only and operating in all executive authorities of the subject is the unified electronic document management system (ESEDD), which provides information interaction with other public authorities and processing of incoming documentation, and also has the functions of messaging and issuing local regulatory legal acts. At the same time, it should be noted that in a number of public authorities this system is limited to the work of a certain department, which subsequently sends all incoming documentation in printed form for consideration by departmental affiliation, and the rest of the functionality is not used at all, while all interaction with “external” organizations and citizens partially takes place in paper form, with duplication in the ESEDD. 2. Managerial interaction in this system on budgetary institutions of various kinds subordinate to public authorities also occurs heterogeneously. Institutions that have this system in their possession receive tasks for the implementation of their powers both through it and through letters and protocols in paper form, depending on the desire and
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Fig. 1. Model of interaction between public authorities and their information environments (studies by authors).
capabilities of a particular leader, while tasks can reach this manager both electronically, and in paper or even oral form, which does not contribute to the implementation of their effective accounting and control. 3. Electronic interaction at the official level between public authorities is not established in principle, all elements of interaction at the official level, coming out of one or another information environment, presented in Fig. 1, come out only in paper form. That is, when sending letters, departments do not have the opportunity to use electronic document management. At the same time, it should be noted that theses 2 and 3 do not apply to certain public authorities and service organizations (budgetary institutions) insofar as they relate to the operation of certain information environments. In particular, the most indicative example of the Our St. Petersburg portal, shown in the diagram of Fig. 1, where information management interaction in terms of the work of city services in the field of improvement covers not only the relevant authorities, but also organizations serving the housing and communal services sector, housing agencies and intra-city municipalities, with a common center of coordination at the level of city leadership and a vertical of execution, which includes local governments, although this causes a number of difficulties associated with the need for one executive authority to be subordinate to others located at the same hierarchical level and not having implementation of such managerial influences of legal grounds. At the same time, the problem of the need to embed local authorities in the hierarchy of executive bodies of state power is very acute, which contradicts the European Charter of Local Self-Government, but is conditioned by operational necessity [28, 29]. 4. In terms of election commissions that are state authorities, it should be noted that this vertical includes the St. Petersburg Election Commission, while there are no subordinate and subordinate territorial election commissions.
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5. In this model, a number of specialized information environments are omitted in order to most relevantly demonstrate the existing interaction of systems. Some of them will be discussed further.
3 Results The analysis of information interaction in Fig. 1 made it possible to show the principle of operation of the public authorities of St. Petersburg, which in its general form is a “black box” model. The “black box” model is a system in which only input and output values are available to an external observer, and the structure and internal processes are not known. As a result of the analysis of the information environments of public authorities, the abstract model of interaction between a resident of St. Petersburg with them according to the “black box” principle can be expressed as shown in Fig. 2.
Fig. 2. An abstract model of an information system for interaction between public authorities in St. Petersburg and residents of the city (studies by authors).
Thus, a resident who is a conditional “client” (unlike simply a recipient of public services in this case, the concept of “client” also includes the desire to influence the development and worldview of the “company”) of public authorities has 10 digital channels of interaction with public authorities, some of which may overlap. As a result of consideration of the information entered by the “client” into this model of the “black box”, at the output we have the position of the authority on the issue of the citizen. It should be noted that this position in some cases may be irrelevant, since the redirection of citizens’ appeals often occurs in accordance with the Federal Law of May 2, 2006 No. Body and that public civil servant (hereinafter referred to as the SGS) to whom it was received [30]. From this moment on, the competence of a particular employee plays
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a rather significant role, since the correctness of this redirection depends on it, otherwise the appeal goes to the body, which, according to the law, cannot send the appeal again, and therefore is forced to answer questions on which it does not have sufficient competence. This situation causes a repeated appeal, or additional requests already to the relevant authorities, which often lead to an additional load on the system as a whole. Considering that a fairly extensive list of this interaction is kept in paper form, one can judge the inefficiency of the rather inert work of the system with residents, which leads both to excessive terms for obtaining relevant answers, and to the accumulation of social tension and slowness in making managerial decisions that determine the life of a multimillion cities. In addition, information systems are considered by the authors in the application for a specific civil servant of St. Petersburg (hereinafter referred to as GGS) in an attempt to open the “black box” of information environments. Figure 3 shows an ontological scheme of the interaction of one particular actor, which determines both the speed of issuing answers and their quality in the application to the systems of electronic-digital interaction, which he has to resort to when exercising certain powers. The lines of interaction are built depending on the strength of the connection between the civil servant and the service, or the channel of interaction with the citizen.
Fig. 3. Ontological scheme of interaction between the SGS of St. Petersburg with a resident and other departments using information services (studies by authors)
As can be seen from this diagram, only channels 1, 2, and 5 interact “directly” with a specific GHS. In many ways, these are acute social local problems associated mainly with urban life, which require a speedy solution. For them, the fastest and least formalized channels for responding to the population’s request are created. At the same time, the alignment of the work of this system was not articulated with the acceleration
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of the work of existing information environments and their unification, as a result of which a paradoxical situation arises in which the response time for the incident system is limited to one working day, half of which will be a search in the regional geographic information system (RGIS), caused by its insufficient resource provision, after which it switches to the telephone directory system, sends a request through a separate mail service and receives a response from the final contractor with a response through a third-party instant messaging messenger, or a response to requests service. As a result, the implementation of management processes significantly loses both in the speed and efficiency of decision-making, and in the speed of adaptation of new employees, which significantly makes the system more personalistic and less unified, since the employee’s work efficiency begins to depend almost exclusively on his experience and the time he spent when interacting with these systems. At the same time, strict institutions for the implementation of state functions are not built, which is a systemic mistake in the work of management, which needs to be corrected by unifying the multitude of services with which the SGS of St. Petersburg has to interact when making managerial decisions. When describing management problems in the public sector, one should also resort to economic estimates of production losses. Since it is not possible to calculate the direct economic effect of the lack of a unified information system for supporting the activities of the SHS of St. Petersburg, the authors will resort to an indirect assessment of economic losses. The average time for concentration when performing a task for an office worker is 25 min. On average, every hour there is a distraction from the process of performing a certain task. Thus, on average, for an 8-h working day, an employee loses 3 h and 20 min of effective working time associated with the above problems [31]. According to the Pareto principle, we assume that the real time losses from the need to switch processes are 20%. Thus, daily losses are on average equal to 40 min (8.33% of the working day) of the time of a civil servant of St. Petersburg for distraction to various interaction services and switching between them. The average salary of the SGS SPb is 64,774 rubles as of June 2021 [32]. The number of employees in the executive authorities of St. Petersburg, as noted above, is 8455 employees (Personnel Portal of the Administration of St. Petersburg, n.d.). Indirect loss of disorganization 8.33% in time equivalent. Thus, when multiplying these indicators, according to rather modest estimates, we can conclude that losses are approximately 45.64 million rubles per month, or 547.66 million rubles per year, which is a significant part of the budget costs of St. Petersburg from 9,697.48 million rubles, which are planned for payments to personnel of public authorities in 2022 in St. Petersburg [33]. It should be noted that this conclusion concerns only an indirect assessment of the efficiency of the public administration system in terms of communication losses. At the same time, the optimization of the SGS staff with an increase in salaries could, on the contrary, significantly improve the quality of the services provided, making the work of public authorities more understandable, and the answers more competent and faster. Which, in turn, could significantly affect the quality of life of the inhabitants of the region.
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4 Discussion It should be noted that the economic results concern only an indirect assessment of the efficiency of the public administration system in terms of communication losses. At the same time, the optimization of the SGS staff with an increase in salaries could, on the contrary, significantly improve the quality of the services provided, making the work of public authorities more understandable, and the answers more competent and faster. Which, in turn, could significantly affect the quality of life of the inhabitants of the region. Approbation of the method in the framework of public procurement in Russian regions revealed that the non-standard ratio of competition indicators to economy indicators entails a threat to the economic security of the region [34]. There is a problem of differentiation of the digital development of regional economic systems and the plans and programmes for the strategic development of territories have to be adjusted so that the consequences of the current situation can be mitigated and new points of economic growth can be found [35]. Unlike the authors who wrote works on similar topics, the results obtained in this article allow not only to characterize the advantages of adopting a full-fledged electronic interaction system, but also to demonstrate the practical effect of indirect budget savings. Usually, the authors draw theoretical conclusions and substantiate the benefits of using systems of this kind [36] or the calculation of the economic effect is made to justify the direct commercial benefits of electronic document management systems [37]. At the same time, in this work, an analysis is made for public management information systems, which are the most significant for the socio-economic development of the region.
5 Conclusion Thus, based on the results of the research, the following conclusions can be drawn: 1. The problem of paper workflow, considered more than once in many works of various scientists, has been fundamentally solved for a long time only at the local level of regional authorities and partially - in the interaction of federal authorities with their territorial divisions. Fundamentally, the lack of a unified system of electronic interaction leads to excessive labor costs, paper consumption and inhibition of sustainable socioeconomic development, which can be easily avoided. 2. The fundamental problem is the lack of building strict institutions for the implementation of state functions and the provision of public services, which is a systemic mistake in the work of management that needs to be corrected by unifying the many services that a civil servant of St. Petersburg has to interact with when making managerial decisions. 3. Indirect losses only in the executive authorities of St. Petersburg from temporary losses during the work of employees are estimated by the authors at 547.66 million rubles a year, which could be spent both on the implementation of a unified unified system of electronic interaction and on social projects of the city. As a result, the authors come to the conclusion that it is necessary to build strict formal institutions and mechanisms for electronic interaction, which today are present in a point form to perform only narrowly focused tasks.
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6 Disclaimer The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program ‘Priority 2030’ (Agreement 075–15-2021–1333 dated 30.09.2021).
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State Support Measures for the Tourism Industry During the Covid-19 Pandemic: Digital Solutions Anna V. Tanina1(B) , Larissa V. Tashenova2 , Dinara G. Mamrayeva2 , and Evgeny V. Konyshev3 1 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected] 2 Karaganda Buketov University, Karaganda, Kazakhstan 3 Perm State National Research University, Perm, Russia
Abstract. The COVID-19 pandemic has had a significant impact on the development of the tourism industry around the world. Restrictive measures to contain the pandemic have had a negative impact on tourism mobility and tourism business. Under these conditions, the importance of government measures to support the tourism sector has increased. One of the consequences of the pandemic was the acceleration of digitalization processes in the economy and public life. The relevance of this research is due to the need for a regional analysis of using the digital technologies in the implementation of state support measures for the tourism industry in the border regions of Russia and Kazakhstan. The purpose of the research is to identify and systematize measures of state support for the tourism sector of the Russian Federation and the Republic of Kazakhstan, including those of a digital nature. The following tasks are defined: bibliometric analysis of publications in the Web of Science within the framework of the scientific problem under consideration with the definition of a search formula; analysis of state support measures in Russia and Kazakhstan, highlighting the digital component; description of a number of digital platforms that are important for the promotion of tourist areas. For solving the set goal and objectives, the method of analysis, the method of synthesis, the method of scientific description, bibliographic analysis, and the method of graphical data interpretation were used. An analysis of the distribution of types of state support by types and recipients is given, regional features of measures to support tourism organizations during the COVID-19 pandemic are identified using the example of the regions of Russia and Kazakhstan. The results obtained can be used to further improving the system of state support measures for the two countries, giving priority to digital solutions. Keywords: Tourism · Support measures · Covid-19 pandemic · Digital solutions
1 Introduction Currently, issues related to the research of the features of using the digital technologies are becoming more relevant, as they relate to aspects of the formation and development of digital economies in a number of countries and regions of the world. It should be © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 66–86, 2023. https://doi.org/10.1007/978-3-031-32719-3_6
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noted that there is an expansion of the areas of application of information and telecommunication tools, which can significantly reduce the time for processing and performing both individual operations and their groups. In the last decade, we have observed that digitalization has affected not only the activities of the enterprises and industries, but has also become an integral component of ensuring the sustainable development of territories, including those that are tourist destinations [1–3]; they are also actively reflected in the activities of state bodies and various structures as part of the implementation of tasks related to improving the efficiency of public administration [4–6], especially during the period of quarantine restrictions caused by the COVID-19 pandemic [7–9]. It is obvious that the pandemic has caused significant damage to several industries, including tourism. The risks of global epidemics, which were not paid enough attention before the onset of coronavirus restrictions, made it difficult to realize the potential of territories [10–13], including tourism and recreation [14], and suspended the processes of creating and offering new, competitive tourism products. Over the past 2–3 years, according to a study conducted by the authors, the number of scientific articles in the sci-tech databases that reveal the impact of the pandemic on the development of the entire tourism sector has increased [15–21]. Thus, using the search formula ““tourism” and “COVID-19”” (All Fields) only in the Web of Science database (Clarivate Analytics) 2642 publications were found, of which: articles - 2339, abstracts, published in conference proceedings, - 70, sections in books - 3. At the same time, for the period 2020–2021, there is an increase in works by more than 296% (2020 - 613 units; 2021 - 1813). It is important to note that separate articles are devoted to the research of foreign experience in maintaining the tourism business [22, 23], bibliometric and scientometric analysis of industry research in the period under review [24], as well as assessing the prospects for tourism development and determining the magnitude of tourist flows in the post-pandemic period [25–29]. Undoubtedly, in the context of a pandemic, the role of using various digital solutions is increasing, and therefore, a separate pool of publications is allocated in the scientific community devoted to determining the role of digital services to ensure the sustainability of tourist areas [30–32], as well as reflecting the specifics of the use of smart technologies for understanding the conceptual foundations and the subsequent formation of tourism ecosystems [33]. A separate group of scientific papers reflects the features of the use of smart tools in tourism-providing industries: excursion services [34] and accommodations [35]. Some researchers pay special attention to studying the attitude of urban residents to new technologies before the COVID-19 pandemic and during quarantine restrictions [36]. In general, the role of digital representation of information about the possibilities of obtaining state support in modern economic realities increases many times, which once again emphasizes the relevance of scientific issues considered in this scientific article. The purpose of the research is to identify and systematize measures of state support for the tourism sector of the Russian Federation and the Republic of Kazakhstan, including those of a digital nature. The following tasks are defined: bibliometric analysis
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of publications in the Web of Science within the framework of the scientific problem under consideration with the definition of a search formula; analysis of the state support measures in Russia and Kazakhstan, highlighting the digital component; description of several digital platforms that are important for the promotion of tourist areas.
2 Materials and Methods The methodology is based on achieving the goal and objectives of the research. It includes general scientific methods, including: the method of analysis, which made it possible to obtain a detailed understanding of the objects and phenomena studied in the article, in particular, the impact of restrictive measures on tourism over the past 2 years; the synthesis method by which the methods of state support for the tourism sector were systematized during the COVID-19 pandemic; the method of scientific description of the totality of the studied information, which made it possible to characterize the groups of support measures from the standpoint of their implementation and digital capabilities, as well as special methods presented by bibliographic analysis, on the basis of which an array of publications (2642: articles - 2339, conference proceedings - 70, book’s chapters - 3) on the research topic was analyzed for the entire period of their appearance in the scientometric database Web of Knowledge (Clarivate Analytics) using the search formula “tourism” and “COVID-19” (search mode -“All Fields”; Web of Science Core Collection is defined as a source; among publications -“All”), as well as a graphical interpretation method that made it possible to visualize the structures of existing software products created to support the tourism business during the period of validity restrictive measures, as well as the distribution of state support measures by types and recipients in the Russian Federation. The main stages of the research are shown in Fig. 1.
Fig. 1. The main steps of the research.
The theoretical basis of the research was the laws, regulations, decrees of sectoral ministries, State programs and Concepts for the development of tourism, Programs for the development of territories, etc., in force on the territory of the Russian Federation and the Republic of Kazakhstan, materials from the open press and the Internet, as well as articles and monographs from the scientometric databases Web of Science (Clarivate Analytics), Scopus and RSCI.
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3 Results The Russian Federation has developed a complete regulatory framework for the activities of organizations, including in the service sector, during the period of coronavirus restrictions. In addition to legal federal acts that establish certain restrictions on the activities of enterprises in various industries, it should be noted the Methodological recommendations of Rospotrebnadzor. These recommendations include the section “Defining a set of measures, as well as indicators that are the basis for the phased lifting of restrictive measures in the context of the epidemic spread of COVID-19”, and recommendations for organizations in individual industries. These recommendations apply to the following activities of organizations in the field of tourism: theaters and concert organizations, museums, museum-reserves and palace and park museums, children’s recreation and rehabilitation, recreation areas of water bodies, water parks, shipping companies operating in the field of passenger transportation inland waterway transport, air, river and road transport, hotels, summer recreation facilities, health resorts, trade enterprises, public catering establishments, sports organizations. It should be noted that to improve information support for activities during the COVID-19 epidemic, a special “Stop COVID-19” portal has been created. This site can be accessed, among other things, by links from the websites of regional administrations and get information about the features of the legal regulation of the activities of organizations in the region during the COVID-19 pandemic. The “Stop COVID-19” website contains a link to the “Plan for overcoming the consequences of the coronavirus epidemic in the Russian Federation”, leading to the website of the Government of the Russian Federation (http://government.ru/support_m easures/). The section “Measures of the Government of the Russian Federation to combat coronavirus infection and support the economy” includes the following blocks: general measures, health, social support, taxes, finances, tourism, transport. In addition, there is a link to the decree defining the list of the most affected industries. This list includes 12 areas of activity, tourism directly includes “activities of travel agencies and other organizations providing services in the field of tourism” and “hotel business”. The areas of activity of the tourist infrastructure can also include transport activities, the sphere of culture, leisure and entertainment, physical culture and recreation activities and sports, catering, activities for organizing conferences and exhibitions, retail trade in non-food products. 9 of the 12 areas of activity identified by the Government belong to tourism to one degree or another, which underlines the significance of the impact of coronavirus restrictions on tourism. However, among the backbone enterprises (as of December 03, 2020, 1392 units were allocated), distributed among the Federal executive authorities, only 10 under the jurisdiction of Rostourism (0,72% of the total) are classified as tourism enterprises. All of them belong to the hotel companies. The digital service http://government.ru/support_measures/wizard/ seems to be useful, allowing to find out what support measures can get during the period of coronavirus restrictions. Each of the three groups (legal entities, individuals and individual entrepreneurs) has its own support options depending on the selected parameters. For example, 44 types of support are available for a small or medium-sized enterprise in the tourism sector. It is
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worth noting that among the 44 results, irrelevant support measures are included: some of them are completed (22 out of 44 or 50%), some are not suitable for the type of activity (for example, support for folk arts and crafts enterprises, a simplified procedure for labeling medicines, preferential loans for agricultural exporters). As a result, according to the selected parameters (SMEs in the field of tourism, not related to the backbone), 10–12 measures can be attributed to real support measures. A few more measures can be applied by tour operators. When choosing an individual entrepreneur in the field of tourism instead of a legal entity, the list of support measures does not change; for individual entrepreneurs, events related to tour operators are shown, which proves the formality of the approach when choosing support measures. Another way to select suitable support measures is choosing process from three areas: support for the population, business or systemic support measures. For ease of orientation, all support measures are separated using colored indicators related to one of the types of support (general measures, health, social support, taxes, finance, tourism, transport). Based on this information, it is possible to estimate the distribution of support measures by type (Fig. 2). transport tourism finance taxes social support health general… 0
10 20 systemac measures
30 business
40 populaon
50
Fig. 2. Distribution of types of support by types and recipients.
Most support measures are provided to businesses (75 types of support), most of them are financial (53,33%) or tax (21,33%) measures. 50 types of support are offered to the population, mainly social support (64%). Systemic measures contain 23 types, most (60,87%) relate to health or general measures (30,43%). The study was conducted based on data in the border regions of the Russian Federation and Kazakhstan. Let us characterize the specifics of tourism development in the regions of the Russian Federation bordering Kazakhstan. The tourism development level of the regions had an assessment according to the results of entering the “National Tourist Rating - 2021” (http://russia-rating.ru). According to the rating, five regions got on the list of twenty most developed tourist areas of Russia (Altai Krai, Samara Oblast, Novosibirsk Oblast, Chelyabinsk Oblast, Tyumen Oblast). Saratov Oblast, Volgograd Oblast, Omsk Oblast, Astrakhan Oblast, Orenburg
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Oblast, Altai Republic are the regions with an average level of tourism development. Kurgan Oblast is in the third group - outsiders. Altai Krai belongs to regions with a prominent level of tourism development. According to the results for 2021, Altai Krai takes seventh place in the tourist attractiveness rating. Altai Krai specializes in health resorts and ski tourism (Belokurikha), thus demonstrating an elevated level of loading of organizations throughout the year. Active tourism, rural tourism, and ecological tourism have a prominent level of development. The special economic zone “Turquoise Katun” promotes tourism potential realization in the region. “Siberian Coin” gambling zone implementation may become a unique advantage for Altai Krai. The consumers are residents and tourists from neighboring Novosibirsk Oblast and Kemerovo Oblast. Altai Territory is one of the few where the resort fee exists. Since May 2018, regional authorities have collected 115 million rubles, which went to the area improvement. In Belokurikha, since the beginning of 2022, the fee will increase to fifty rubles per person per day. The Altai Krai belongs to priority tourist territories at the federal level. It takes part in the national project “Tourism and hospitality industry”. Thus, in 2022 the region will receive more than five hundred million rubles for tourist infrastructure development. According to the results for the year 2021, investments in the Altai Krai tourist infrastructure will exceed 1,5 million rubles. Samara Oblast is in the top ten regions of Russia for tourist attractiveness and tourism development level in 2021. The tourist flow increased by 30% and amounted to 1,7 million people, comparing 2020. Expenditures of tourists increased by 41.5% and amounted to 6.85 billion rubles. Samara Oblast has prominent visiting by tourists from the Orenburg Oblast, Moscow, Ulyanovsk Oblast, Tatarstan, Bashkortostan, Saratov, and Nizhny Novgorod Oblasts, St. Petersburg, and Tyumen Oblast. Samara Oblast specializes in cruise and ecological tourism (Samarskaya Luka), offering fishing and yachting (Volzhskoye More) (Volga Sea)). Event tourism development has high prospects. For example, more than five hundred thousand people have visited the festival “Volgafest” and the international arts festival “Shostakovich XX century”. In the future, there is a plan to create tourist and recreational clusters with a twenty billion rubles turnover in the Samara Oblast. Some people already work on the filling and promotion “samara. Travel” service. In addition, the project “Poekhali!” (Let us go!) implements successfully with the participation of media representatives and bloggers, aimed at promoting domestic tourism among residents of the Samara Oblast and guests from other regions. Novosibirsk Oblast takes 13th place in the rating. In late December 2021, the region adopted a state program “Development of Tourism in the Novosibirsk region” until 2030, which will create conditions for the development of clusters (Ob Parks, Lakes), environmental, health, rural (rural eco-park “Khomutina”), business and scientific tourism development. Also, the region has approved the Concept of creative industries development, under which there is a plan to create territories of preservation and development of traditions and lifestyles. By 2025 there is a plan to increase the tourist flow to 1.5 million people.
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The Chelyabinsk Oblast takes 14th place in the ranking. The basis of tourism specialization in the region is ski tourism (Sunny Valley, Zavyalikha, Adzhigardak, Egoza, Eurasia). In addition, there are plans to create a resort “Ural Sapphire” with 14,5 billion rubles of investments. Among promising types of tourism is eco-tourism, the development of which requires “green” infrastructure on the territory of the national parks “Taganai”, “Zyuratkul” and “Zigalga” and Lake Turgoyak. Chelyabinsk Oblast authorities pay special attention to the project implementation on the unique cultural site “Arkaim” territory. They also set a task to develop industrial tourism (Chelyabinsk, Magnitogorsk, Zlatoust, etc.). In 2021, the region opened the first center of competencies for industrial tourism in the country. Also, the regional authorities plan a network of tourist routes along rivers (the Ai River), the shores of lakes (Yelovoye, Chebarkyul, and Kisegach), and forests in the suburbs for residents’ recreation. The Tyumen Oblast also belongs to the regions with an elevated level of tourism development and high tourist attractiveness (17th place in the rating). Tourist dominants are Tobolsk, balneological and thermal spas, and ethnographic tours. The region’s tourist attractiveness has increased significantly due to the Remezov airport opening in 2021 in Tobolsk and charter flights from Moscow with the tour operator TUI Russia. Tourism development follows the Tyumen Oblast state program “Development of domestic and incoming tourism” till 2025. There is a prediction that tourist flow will increase to 4,1 million people by the end of its realization. Saratov Oblast belongs to regions with an average level of tourism development (25th place in the rating) but with good dynamics of growth of tourism industry indexes. As proof, 735 thousand people visited Saratov Oblast, and tourist flow was 182% of the level of 2020, according to the Regional Tourism Committee data on November 1, 2021. There are 253 hotels, twenty health resort organizations, and tourists bases in the region. Among the traditional tourist destinations is a cruise on the Volga. The popularity of ecological tourism, rafting on the rivers, and walks in the woods is growing. A landmark event in 2021 was the large-scale site opening on Yuri Gagarin’s landing in the Engels district of the Saratov Oblast on the 60th anniversary day of the first-person flight in space. Volgograd Oblast ranked 30th according to the results of 2021. Traditionally, tourists visit Volgograd Oblast for cultural, informative, and patriotic purposes. Tourism development profitable areas of tourism development are children’s and medical tourism. Volgograd Oblast has created the first Children’s Tourist Board. Also, from 2020 to 2022, 3000 foreigners had treatment in the region. Omsk Oblast (32nd place in the national ranking of tourist attractiveness in 2021) specializes in health, educational, and sports tourism. The main tourist dominant of the region is the regional center. Omsk has a prominent tourist infrastructure, including historical sites, museums, theaters, monuments, parks, squares, sports centers, malls, hotels, transport organizations, etc. five hundred thousand people visited Omsk Oblast, and the tourist establishments’ income amounted to about 2.5 billion rubles, according to the results of 2019.
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Cruise tourism development has primary importance for the Astrakhan Oblast, including the Caspian Sea, which reflects in the Concept of cruise tourism development in the Russian Federation until 2024. Besides, there is an agreement on cooperation with “Turizm. RF” corporation. Also, the regional authorities have approved the Astrakhan Oblast program of social and economic development till 2026, where there is event, pilgrimage, ecological, and sports tourism development mechanisms. Regional authorities plan to develop balneological tourism based on Lake Baskunchak in the Akhtuba district. In general, though the region takes 37th place in the tourist attractiveness rating of Russian regions, it receives about 1,5 million tourists annually. Orenburg Oblast takes 38th place. 1.2 million people have visited the region primarily to visit the salty lakes, according to preliminary estimates. Moreover, in April 2021, people opened the Victor Chernomyrdin Museum, the largest in the South Urals, in the Cherny Otrog village. The region has signed an agreement with Tourism.RF corporation to develop tourism comprehensively. The anchor investment project of the Orenburg Oblast is a tourist and recreational cluster “Salty Lakes”. A project of tourist-recreational cluster “Atmosphere. Steppe”, new tourist routes (Horizons of Discovery), improvement of the Orenburg historical part, the former aviation school restoration, where Yuri Gagarin studied, as well as the project on the Przhevalsky horse’s reintroduction in “Orenburg” nature reserve. The Altai Republic is 64th in the ranking and specializes in rafting on rivers, equestrian, rural and ethnographic tourism. A tourist industry attraction is an all-season resort “Manzherok”, which plans to increase tourist flow to 1,2 million people by 2024. Kurgan Oblast is one of the regions with a low level of tourism development (75th place among eighty-five subjects of the Russian Federation). The Kurgan Oblast plans to develop industrial tourism (Kurgan Engineering Plant, Kurganpribor, Veles) and recreational tourism on salt lakes. Consider the regional support measures in the constituent entities of the Russian Federation, using the example of cross-border regions with the Republic of Kazakhstan. An analysis of legal acts, official websites of regional authorities showed the absence of measures to support the tourism industry in general. The most support measures relate to small and medium-sized enterprises. But the majority of tourism organizations can take advantage of these measures because they belong to small and medium-sized businesses. In general, all measures to support tourism organizations during the pandemic can be divided into several types: informational (including digital), administrative, investment, property, financial and tax (Table 1). Among the specific regional measures to support the tourism sector, it is worth noting the experience of the following regions of the Russian Federation: subsidies from the regional budget for the payment of property tax to legal entities in the field of tourism in the Samara region; organization of interaction with the Federal Tourism Agency, the Federal Tax Service for the Orenburg region, Rospotrebnadzor for the Orenburg region to provide legal advice to tourism entities; development of a leaflet “Measures to support the tourist industry of the Orenburg region in the context of the spread of coronavirus infection”;
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provision of subsidies to support entrepreneurial initiatives aimed at developing tourism and supporting infrastructure in the Kurgan region: to support entrepreneurial initiatives aimed at developing tourism and supporting infrastructure - 2,000.0 thousand rubles; reimbursement of part of the costs associated with the transportation of organized groups of children in order to provide them with tourist excursion services on the territory of the Kurgan region – 234,6 thousand rubles; moratorium on rent for residents of the business incubator of the State Budgetary Institution of the Republic of Altai “Center for the Development of Tourism and Entrepreneurship of the Republic of Altai”; development of a product for providing guarantees for industries most affected by the coronavirus infection by the Fund for the Development of Small and Medium Enterprises in the Novosibirsk Region; a special product “Urgent” for SMEs taking measures to counter and (or) affected by the spread of a new coronavirus infection (COVID-19), employed in the most affected areas, to provide targeted support (Altai Republic). Table 1. Measures to support tourism organizations during the pandemic COVID-19 in the regions of the Russian Federation (on the example of cross-border regions with the Republic of Kazakhstan) Support measures
Regions 1
INFORMATIONAL (including DIGITAL)
Opening of hotlines
2
3
4
5
+
Development of a memo on measures to support the tourism industry in the context of the spread of coronavirus infection
+
Organization of interaction with the Federal Tourism Agency, the Federal Tax Service for the region, Rospotrebnadzor for the region to provide legal advice to tourism entities
+
Monitoring the organization of obtaining state support and support for entrepreneurs by municipalities of the region
+
6
7
8
9
10
11
+
+
+
12
(continued)
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Table 1. (continued) Support measures
Regions 1
2
3
4
5
6
Establishment of a headquarters for work with enterprises and organizations with problems of paying utility bills and settlements with personnel
+
Development of recommendations for credit institutions, government customers and employers
+
Launch of training modules for entrepreneurs online
+
Monitoring wage arrears
+
7
Monitoring information from employers on the dismissal and underemployment of employees, the formation of labor market forecasts
+
Creation of an emergency situational center for informing and advising small and medium-sized businesses on obtaining financial and non-financial support at the regional and federal levels
+
9
10
11
Specialized consulting on business diversification, crisis planning
+
Organization of distance learning and consulting for entrepreneurs
+
Preparation of documents on force majeure circumstances
+
Recommendations to local governments and credit institutions to take measures to reduce the tax and credit burden
+
Automatic renewal of licenses and permits
+
+
+ +
Free certification of products by the “My Business” center Introduction of a moratorium on inspections
12
+
Conducting a regional information campaign to create a favorable image of entrepreneurship and stimulate interest in doing business
ADMINISTRATIVE
8
+
+
+
+
+
+
+
(continued)
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Support measures
Regions 1
2
Moratorium on bankruptcy due to debt to the budget
+
Reducing the timeframe for assessing the regulatory impact of draft regulatory legal acts
+
3
4
5
6
+
+
7
8
Drawing up a list of the backbone enterprises Selection of the list of activities subject to a drop in demand
+
State guarantees of the Government of the region for obtaining a loan
+
Development of a draft law on investment tax credit
+
Labor Market Relief Program
+
11
12
+
Establishment of the Headquarters for Sustainable Economic Development
+
Application to the federal budget for the provision of funds for the provision of guarantees and preferential microloans Moratorium on the termination of state support and termination of agreements
+
Reducing the size of the scale criteria to meet the requirements of the regional project
+
+
Increase in the authorized capital of a microcredit organization
+
+
+ +
Development of a guarantee product for industries most affected by the coronavirus infection by the Small and Medium Enterprise Development Fund PROPERTY
10
+
Reducing the requirements for the average wage
INVESTMENT
9
Simplification of the procedure for obtaining collateral
+
Postponement of rent payments
+
+
+
+
+
+
+
+
+
(continued)
Considering the indicators of tourism development in Kazakhstan, it is necessary to note the low share of gross value added created directly by tourism in the country’s GDP – 1,2% in 2019, while, if analyzed in dynamics, this indicator grows annually by only 0,1%. This suggests that currently tourism in Kazakhstan remains a low-income
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Table 1. (continued) Support measures
Regions 1
2
Three months rent exemption
+
Providing SMEs with a grace period under contracts for the sale of property
+
3
4
Concessional lending and microcredit
+
+
8
9
10
11
+
Postponement of principal payment
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+
+
+ +
Subsidies to legal entities in the employment of workers in the direction of the employment service of the region
+
Simplification of requirements for borrowers
+
+
+
Refinancing of existing liabilities
+
+
+
+
+
Subsidizing interest rates on loans Development of new support programs for the Guarantee Fund Reducing the size of the commission for all categories of borrowers of the regional guaranteed organization
+ +
Information about the presence of delays in current payments is not transferred to the Credit Bureau Settlement of accounts payable to all levels of the budget system
12
+
+
Liberalization of conditions for granting guarantees
Subsidies from the regional budget for the payment of property tax to legal entities in the field of tourism
7
+
Individual repayment schedules + for microloans, interest payments
Elimination of the restructuring fee and penalties
6
+
Reduced rental rate for business incubator residents FINANCIAL
5
+ + + +
(continued)
industry due to the dominance of the raw material sector of the economy. Despite the fact that Kazakhstan has a significant tourist and recreational potential, the country has all the prerequisites for the development of rural, ecological, mountainous, sports and other
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A. V. Tanina et al. Table 1. (continued)
Support measures
Regions 1
2
3
4
5
6
7
Providing subsidies to support entrepreneurial initiatives aimed at developing tourism and supporting infrastructure
+
Reimbursement of part of the costs associated with the transportation of organized groups of children to provide them with tourist excursion services in the region
+
8
9
11
12
+
Special product “Urgent” for SMEs that apply measures to counteract and (or) have suffered from the spread of a new coronavirus infection (COVID-19), employed in the most affected areas, to provide targeted support
TAX
10
Interest-free loans to pay salaries
+
Credit holidays or a reduction in the amount of payment under a loan agreement for individual entrepreneurs with a sharp drop in income
+
Reduction of the tax rate for organizations applying the simplified taxation system: – before 1% in 2020
+
+
– 3% in 2021
+
+
+
+
+
+
+
– 2% in 2020 + +
– 4% in 2022
+
Extension of deadlines for paying taxes under special tax regimes
+
Individual tax payment schedules + Exemption from corporate property tax
+ +
Reducing the cost of a patent +
+
+
+
+
+
+
Tax incentives for land tax
+
Minimum transport tax rate
+
Reducing the K2 coefficient for the UIIT
+
+ +
+
+
+
(continued)
types of tourism. More than 100 thousand people work in this industry, and innovative programs for the development of tourism in the country are developed annually.
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Table 1. (continued) Support measures
Regions 1
2
3
4
5
6
7
8
9
10
11
12
Introduction in the region of a special tax regime “Tax on the self-employed”
+
Extension of the term for payment of insurance premiums and reduction of tariffs on insurance premiums from 30% to 15% subject to payment of wages
+
Moratorium on tax sanctions and audits and extension of reporting deadlines for the Federal Tax Service
+
No penalties for arrears on taxes and insurance premiums
Note*: Compiled based on service data smarteka.com 1 - Astrakhan region; 2 - Volgograd region; 3 – Saratov region; 4 - Samara region; 5 - Orenburg region; 6 - Chelyabinsk region; 7 - Kurgan region; 8 - Tyumen region; 9 - Omsk region; 10 Novosibirsk region; 11 - Altai region; 12 - Altai Republic
The pandemic caused by COVID-19 also had a negative impact on the development of the tourism industry in Kazakhstan: due to quarantine, as well as other restrictive measures, the tourist flow decreased, there was a sharp drop in the income of representatives of the tourism industry, many travel agencies ceased their activities, some refocused on domestic tourism. In the pandemic year of 2020, the number of tourists visiting Kazakhstan, according to official statistics, decreased by almost 4 times and amounted to about 2 million people. For comparison, in 2019 this figure was 8,5 million people. Of course, the flow of outbound tourists also decreased by 3 times compared to 2019 (in 2020 – 2,8 million, in 2019 – 10,7 million people). Considering the regions of Kazakhstan bordering the Russian Federation, it can be seen that the largest reduction in the flow of incoming tourists was observed in 2020 in the Atyrau region: the number of foreign tourists served in 2020 decreased by 4,5 times compared to the previous year. In general, in all regions in the pandemic year 2020, a high negative increase in the number of tourists served in the locations of the analyzed regions of the Republic of Kazakhstan was recorded (Fig. 3). Positive dynamics in the number of non-resident visitors in the locations of the regions of Kazakhstan cross-border with the Russian Federation can be observed in 2021, except for Atyrau and East Kazakhstan regions, where the decline in tourist flow continues. The quarantine caused by the COVID-19 pandemic practically stopped the activities of service enterprises in Kazakhstan, in particular, the volume of services by accommodation facilities decreased by 45%, the number of visitors served decreased by more than 43%, hotel occupancy dropped to 17% against 24% in 2019 year. In the tourism industry of Kazakhstan, according to national statistics, about 118 thousand people work. Already in 2020, this indicator decreased by 4,000 employees compared to the previous
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Fig. 3. Dynamics of the number of non-resident visitors in the locations of regions of Kazakhstan cross-border with the Russian Federation for the period from 2019 to 2021.
year. It was noticeable in the hotel business: the reduction was more than 12%. At the same time, the number of employees employed in tourism companies increased by 400 people. The reason is that many tourist companies have shifted to domestic tourism. The state is taking several measures to support and gradually restore the tourism industry from the devastating effects of the pandemic. During the period of quarantine restrictions, the Government of Kazakhstan, together with the “Atameken” National Chamber of Entrepreneurs, introduced anti-crisis measures. According to the report of the Ministry of Culture and Sports, due to the state of emergency, entrepreneurs in the tourism sector were exempted from paying certain taxes (on property, as well as from the wage fund - until the end of 2020), bank loans were deferred until October 1, 2020, the rate on loans was unified. In addition, changes were made to the existing state programs to support entrepreneurship in terms of expanding measures to finance working capital, unifying the interest rate to 6% per annum and increasing the amount of a loan with a state guarantee against it. As part of the Business Roadmap program, sectoral restrictions for industry entities were lifted, previously soft loans could only be issued to the hotel business, and now to tour operators with travel agents. Also, in order to support the personnel of travel companies, within 2 months the state paid social benefits in the amount of the minimum wage (42,500 tenge or 85 US dollars). On April 30, 2021, the President of Kazakhstan signed the Law “On Amendments and Additions to Certain Legislative Acts of the Republic of Kazakhstan on Tourism Activities”, which establishes new approaches to the regulation of tourism activities. Now the following additional state measures to stimulate the development of the tourism industry have been legally approved: reimbursement of costs of private business in the construction of tourist facilities and roadside service facilities - up to 10% of the investment amount; reimbursement of 25% of the cost of purchasing ski equipment and tourist class vehicles;
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subsidizing the costs of tour operators for each foreign tourist in the amount of 15,000 tenge (about 30 US dollars); reimbursement of 100% of the carriage fee for children on domestic air routes; subsidizing the cost of maintaining sanitary facilities in the amount of 83,300 tenge (about 170 US dollars). To restore the tourism industry in the country, namely the flow of visitors, according to Kazakh National Tourism, it is planned to launch 57 investment projects worth over 455 billion tenge (911,8 million US dollars). This will create new jobs, build and restore accommodation facilities. At the same time, work is underway to implement the project and attract investors to develop tourist destinations that are on the list of the most popular in the country. Also, to support the tourism industry, the Resource Center “Qazakhstan Travel and Tourism Council” was created, designed to accompany innovative programs in the field of national tourism to provide services to entrepreneurs in supporting tourism projects, training in project management. One of the main digital solutions created to support the country’s tourism was the creation of a single digital tourism ecosystem, the components of which are shown in Fig. 4.
Fig. 4. Digital tourism ecosystem of Kazakhstan.
The presented ecosystem can be conditionally divided into 2 groups: the first is the digital solutions for business, including: 1. the “Commercial Platform” module is a marketplace with suggestions for routes, accommodations, tourist attractions, car camping and car rentals; the offer from the leading tourist companies of the country is concentrated on it, with the possibility of booking a tourist product; 2. the “Tourism Online” module includes an interactive map of tourist routes and attractions; projects implemented in the regions (for example, promotion of urban tourism, agritourism, caravanning, tours along the Great Silk Road, etc.); TourForum (where tourists can exchange opinions about any objects, tourist destinations, as well as simply post useful information, for example, route threads, information about hiking trails, etc.); webinars - educational, informative and entertaining videos; information on current state support measures; 3. the “Guides” module allows to undergo training and obtain a license to carry out activities in the field of tour guides, as well as enter the official register of guides;
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4. the “Tourstat” module is an information system for the collection and processing of data in the field of tourism; 5. the “Investments” module - information is provided on priority areas of investment in the tourism sector, as well as on investment opportunities and state support measures for domestic and foreign investors; 6. the information system “eQonaq” is a platform for collecting and recording the tourist flow and migration control, which allows all accommodation facilities to send a notification of the arrival of a guest in real time; 7. the “Subsidization” module is a digital tool that allows the tour operator, by submitting an electronic application, to receive remuneration (cost compensation) for attracting each foreign tourist to Kazakhstan (the amount of payments for 1 person is 15,000 tenge / about 30 US dollars), as well as to carry out booking a free air flight to the tourist destinations of the country for two children accompanied by 1 adult (“KIDS GO FREE” program); 8. the “MICE” module, which provides detailed information about the possibilities of holding major events in the regions of Kazakhstan. The second is the solutions for tourists, which, in addition to the above modules “Guides”, “Commercial Platform” and the information system “eQonaq”, contains the following components: the national tourism portal, which contains information on natural and cultural heritage sites, national cuisine, key attractions, as well as documents required by a tourist on a trip, exchange rates, routes, etc.; the “Golden Horde” module - dedicated to the 750th anniversary of the Golden Horde; it contains a calendar of events, an interactive map, as well as information about guides who can accompany visits to places with an interesting excursion about key milestones in the development of the Golden Horde; the “Photobank” module is the Kazakhstani photo stock with photos of objects, sights, nature and landscapes of the country. Undoubtedly, the creation of a single ecosystem containing various modules and solving a wide range of tasks has become an effective solution for improving operational activities, unifying several mandatory procedures and simplifying them in the tourism sector during the COVID-19 pandemic.
4 Discussion The number of publications on the Covid-19 pandemic effects on the tourism sector is growing. Though, there are no studies on the effectiveness of measures to support tourism at the national level in different countries. There is a point that research on the federal support measures’ impact on tourism in various directions (financial support, tax burden reduction, administrative methods, and others) would improve the effectiveness of these measures. The lack of statistical data does not allow conducting these studies in the Russian Federation and Kazakhstan. The study of tourism support measures at the regional level showed a variety of supporting forms. The almost complete absence of necessary data on the regional support measures’ impact on the activities of tourism organizations and scarce data on the tourism industry performance does not allow for conducting a full-fledged study.
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Issues related to the research of measures of state support for tourism during the COVID-19 pandemic, especially from the standpoint of their consideration in the framework of digitalization, are certainly relevant. At the same time, it is necessary to highlight a number of limitations when conducting studies of this kind: firstly, this is the choice of approach when studying this scientific area, on which the classification of state support measures will subsequently depend, as well as determining the specifics of their application in relation to the tourism market; secondly, the scale of the object under consideration (country, region, individual tourist destination), which will predetermine the set of considered indicators and target indicators as resultant ones, depending on the measures implemented by the state to support tourism; thirdly, this is the determination of the degree of impact of the pandemic on individual territories, on which the development of supportive and corrective measures, including digital ones, will depend. In general, these limitations can be overcome with a comprehensive consideration of the issue, as well as through a thorough study of digital tools, the introduction and use of which can provide real assistance in overcoming the crisis in the tourism services market. Further research by the authors will be aimed at assessing the sustainability of the tourist and recreational complex of the regions of Russia and Kazakhstan during the COVID-19 pandemic. The introduction of digital solutions in the tourism support field has an immersive impact. Firstly, this is increasing the information awareness of tourism organizations. Secondly, maintaining a proper level of social distance during the pandemic, and thirdly accelerating the receipt of support through digital services use. Before the enviro-19 pandemic, the primary areas of support for tourism were systemic measures following strategic documents in the tourism development field. Outbound tourism field restrictions led to an increase in the domestic tourist flow. One of the most effective forms of stimulating demand for domestic tourism was the tourist cashback program in the Russian Federation, implemented in a digital format on a specially created website. The implementation of this program made it possible to reduce the drop in tourist flow and support solvent demand in tourism organizations. The tourist cashback program has brought an elevated effect on the regions with the developed mechanism of tourist potential realization. “Pandemic” support measures have strengthened the effectiveness of regional acts to develop tourism. At the federal level, the Stop “COVID-19” portal has been created in Russia, which makes it possible to obtain the necessary information about existing support measures in digital format. However, the relevance and convenience of finding information are not perfect; when sorting support measures in the tourism sector, results that are irrelevant to the query are shown. Nevertheless, this portal is known, links to it are posted on the official websites of regional authorities. In the considered regions of the Russian Federation, comprehensive plans to support entrepreneurship during the pandemic have been developed and are being implemented. Their advantages are the availability of various support formats from informational to financial, considering regional specifics and the implementation of support in stages, depending on the current situation. In all regions, measures were taken to reduce the tax burden (first, to reduce the rates under the simplified tax system and the K2 coefficient
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for UTII), the provision of preferential micro-credits and loans, and the deferral of rental payments. In several regions, it is worth noting the development and implementation of special programs and loans. Several regions have added to the list of industries most affected by the consequences of COVID-19. But information about existing support measures in digital form is difficult to find. There is no such information in a structured and complete form on the official websites of administrations. The Kazakhstani tourism business has fully felt the negative consequences of the COVID-19 pandemic. The Government of Kazakhstan has developed and urgently put into effect a set of organizational, economic, social and administrative measures. As a digital solution aimed at supporting the tourism industry, it should be noted the creation of a single digital tourism ecosystem, including digital solutions for business, as well as solutions for tourists. These digital solutions have improved the operational activities of travel companies, as well as unified and improved organizational procedures during the COVID-19 pandemic. In general, the legislative innovations and measures adopted in Kazakhstan are aimed at the speedy restoration of the tourism business in Kazakhstan, which is in crisis due to the global unfavorable epidemiological situation. Acknowledgments. The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program ’Priority 2030’ (Agreement 075–15-2021–1333 dated 30.09.2021).
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Digitalization of Public Administration and Public Trust Elena Vasilieva1 , Karina Tirabyan1 , Maria Rubtsova2 , Anton Barabanov3(B) , and Natalia Vyshinskaia3 1 North-West Institute of Management, RANEPA, St. Petersburg, Russia 2 St. Petersburg State University, St. Petersburg, Russia 3 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected], [email protected]
Abstract. This paper presents the results of a study aimed at determining whether there is a relationship between the digitalization of public services and the level of public trust, as well as assessing the potential of this relationship. The methodological basis of the study was the theory of trust by N. Luhmann, who distinguished three definitions of this concept: familiarity, confidence, and trust. It was determined that in the digital environment the relations between actors are more often concentrated in the sphere of “confidence”, that is based on the actors’ belief that their expectations will not be disappointed in the absence of alternatives, while in modern Russia, trust in the political sphere is more often considered just in the meaning of authority, recognition, and approval, that is, in the meaning of “familiarity”. The main hypothesis of the study was the statement that the digitalization of public administration the system of public service delivery, leads to an increase in the level of public trust. It was found that the level of digitalization of public services increased steadily during 2012–2021, while the level of trust in public authorities increased only in the early years and showed a steady downward trend after 2015 when the pace of digitalization accelerated. Digitalization of public administration relies on the desire to reduce communication risks by exclusion the subjectivity of public decision-making, but confidence is declining as the availability of information about the unreliability of technology destroys confidence in the assurance of the outcome and thus the expectation of successful interaction. This may suggest that forcing citizens to engage digitally with public authorities, reduces their trustworthiness. Keywords: digital government transformation · society · trust · public services in digital form · public authority
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 87–95, 2023. https://doi.org/10.1007/978-3-031-32719-3_7
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1 First Section Digitalization of public administration in the Russian Federation began in 2012 after the adoption of several regulatory legal acts within the framework of the Open Government concept. The main goal of introducing new information technologies into the process of public decision-making was the following: - creating a system of active interaction between public authorities and public associations; - more active involvement of citizens in the governance process; - creating a system of public evaluation of the effectiveness of public authorities; - ensuring wider access of citizens to the system of public services and increasing their effectiveness [1]. It was expected that eventually citizens would be able to convey their needs to the state more effectively; eliminating direct interaction between civil servants and consumers of state services would increase objectivity of decisions, as well as reduce bureaucracy and minimize corruption risks, which, ultimately, would increase public trust and consent [2]. The practice of digitalization of the interaction between citizens and the state, developed in subsequent years, was deemed successful. Consequently, in 2019, the federal project “Digital Public Administration” of the national program “Digital Economy of the Russian Federation” [3] was approved, and on July 21, 2020, Presidential Decree No. 474 “On National Development Goals of the Russian Federation until 2030” was signed. The Decree established the increase in the share of mass socially significant services, available in electronic form, up to 95% by 2030 as one of the key indicators of the “Digital Transformation” national goal [4]. However, the impact of digitalization on public trust has not been assessed, the claim that increased digitalization contributes to greater citizen satisfaction with public administration is seen as an axiom [5]. Theoretical comprehension of the impact of the digital technology adoption on public administration began in the 1990s, when the concept of “e-government” emerged. In the first period, there was an optimistic view of the prospects of transferring the process of managerial decision-making and organization of interaction between public authorities and citizens into the digital environment [6]. The notion of e-government included an emphasis on citizen engagement, consultation, and informed participation in order to develop a policy that would better meet the needs of citizens and, therefore, would increase support and trust in government and its policies [7]. Later, as the emerging security risks were recognized, doubts began to appear in the literature regarding the exclusively positive impact of digitalization on public administration [8], but to date, optimism about this issue has prevailed in the works on “co-creation” [9–14]. Meanwhile, N. Luhmann raised the question of ambiguity of the term “trust” as early as in the 1970s [15]. When analyzing this concept in English, he proposed three terms to distinguish different ways of organizing the interaction of actors: - Familiarity: relationships are defined by known rules and roles, are based on an “inescapable fact of life” and symbols that represent the differences between the familiar and the unfamiliar within the customary world; - Confidence: relationships are based on the belief of actors that their expectations will not be disappointed in the absence of alternatives;
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- Trust: relationships require prior interaction between subjects and imply a situation of risk. In the conditions of the digital environment, trust is more often concentrated in the sphere of “confidence”, since such interaction, on the one hand, does not involve an interpersonal aspect (a service consumer does not get into a personal relationship with its provider) and, on the other hand, is not yet institutionalized enough to cause unconditional acceptance by the subjects. It should be noted that in modern Russian, trust in the political sphere is more often considered in the meaning of authority, recognition, and approval [16], i.e., in the meaning of “familiarity”. Thus, converting public services into a digital form may question trust in the state as a social institution. In the Russian Federation, this problem acquires an additional aspect based on a specific interpretation of the concept of “public service”. The legislation defines this concept as “activity to implement the functions of… an executive authority… Which is carried out at the request of applicants within the limits established by regulatory legal acts of the Russian Federation and regulatory legal acts of the constituent entities of the Russian Federation of the powers of bodies providing public services”, i. e. it is organizational and permissive activity of public authorities [17]. Due to this, digitalization of public services is analyzed in the Russian scientific literature: the main research issues are the need to study international experience in providing public services [18] and to assess effectiveness of the “single window” technology used in Russia in the format of multifunctional centers (MFC) [19]. We consider the Unified Portal of Public Services (gosuslugi.ru) as a practical embodiment of the open government concept; the Portal is accessible to any Internet user and is organized in such a way as to provide a convenient search for information on state or municipal services. All services placed on the Unified Portal are linked to a specific region of the Russian Federation: the place where a service is received determines both the availability of the service itself and the conditions of its provision [20]. State and municipal services are classified by various features (by department, by life situation, by user category, by popularity) and presented in the form of a catalog. However, the literature analyzing the Russian experience in the implementation of public services in digital form, despite the generally positive attitude, notes the lack of effectiveness, determined by the lack of highly qualified staff, the increased time required to provide services, inaccessibility of MFC offices from the places of residence of citizens, etc. [21]; as well as the fact that electronic public services are designed and provided not from the perspective of web users, but from the perspective of service managers, which significantly complicates the process of entrepreneurs’ perception of the information they need [22]. Nevertheless, the authors believe that despite the insufficient level of effectiveness of public services in electronic form, further digitalization of this sphere of public administration should inevitably lead to an increase in the level of public trust, but the rationale for this confidence is not presented in the analyzed sources. The purpose of this paper is to analyze the relationship between digitalization of public services and the level of public trust, as well as to evaluate the potential of this relationship.
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2 Research Methodology and Research Hypothesis Secondary comparative analysis was used as the main method of research. The study design included the following steps: 1. on the basis of the aggregate data of the Federal State Statistics Service of the Russian Federation (Rosstat) by the indicator “Share of citizens using the mechanism of receiving state and municipal services in electronic form” determined within the framework of federal statistical observation form No. 1-IT “Sample survey questionnaire on the use of information technologies and information and telecommunication networks”, the schedule of accelerating the digitalization of public services in the Russian Federation is determined [23]; 2. on the basis of the analysis of the list of public services presented on the Unified Portal of Public Services, the type of the services offered is determined by the criterion: a) voluntariness of application; b) obligation under the law; 3. Based on the secondary analysis of the data from the surveys conducted by the Russian Public Opinion Research Center (VTsIOM) in the period of 2012–2021, the graph of changes in the level of trust in the Government of the Russian Federation on the Confidence criterion is determined. The main hypothesis of the study was the statement that digitalization of public administration, particularly the system of providing public services, leads to an increase in the level of public trust. As the main indicators of the digitalization of the public service system, the following ones were analyzed: - dynamics of the increase in the digitalization coefficient - calculated as the share of citizens who received public services in electronic form in the total number of citizens who applied for public services (Rosstat data); - level of trust in the Government of the Russian Federation according to the VCIOM methodology - the question “In general, do you approve or disapprove of the activities of the Government of the Russian Federation?”; - ratio of the digitalization coefficient and the level of trust in the Government of the Russian Federation.
3 Digitalization of Public Services and the User Aspect The analysis of the catalog of public services provided in electronic form on the Unified Portal of Public Services shows that the majority of services offered (33.6%) are of permissive nature, i.e., these are the services related to issuing licenses, permits, and to confirmation of the rights of citizens and legal entities to perform certain activities or actions (see Table 1). 23.5% of the public services provided in electronic form are of application nature. These include services that are not provided in the absence of a request from a citizen or organization, such as the provision of a sick leave, registration of recalculation of pensions or maternity capital, obtaining an expert report, which is not mandatory, etc. It can be assumed that these are the services that play a key role in the formation of a positive attitude of a citizen to the digital form, as in this case the citizen is the initiator of interaction and considers it as service experience. Obligatory are 21% of
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the offered services, which implies obtaining the necessary documents, without which it is impossible to carry out certain activities (for example, obtaining a citizen’s passport, paying fines, registering the birth or death of a citizen, etc.). Thus, we can assume that the use of several digitalized public services by citizens is forced: most appeals are not initiated by the applicant, but they are prescribed by regulations or instructions of public authorities. It should be noted that some services of a mandatory and permissive nature are provided exclusively in electronic form and on a single platform, hereby citizens and organizations find themselves in a situation where there is no alternative, which excludes the formation of trust at the level of their past experience (Familiarity) or personal interaction (Trust). Table 1. List of public services provided in electronic form on the Unified Portal of Public Services by category of life situations.
Passport
Amount
%
Application
Permissive
Obligatory
Help & information
8
2.0
2
0
4
2
Job
10
2.5
0
6
0
4
Entertainment
10
2.5
0
7
1
2
Education
11
2.7
7
3
0
1
Taxes and finance
13
3.2
1
3
1
8
Safety
14
3.4
1
4
5
4
Healthcare
15
3.7
4
8
1
2
Transport
18
4.4
1
9
7
1
Environment
24
5.9
2
14
3
5
Family and children
25
6.1
17
0
3
5
Communication
28
6.9
7
17
0
4
Commerce
38
9.3
8
0
22
8
Pensions
41
10.0
28
4
5
4
Business
41
10.0
7
7
20
7
Certificates
112
27.5
11
55
14
32
Total:
408
100%
96 (23.5%)
137 (33.6%)
86 (21.1%)
89 (21.8%)
The digitalization of public services has been growing steadily throughout the entire period since the introduction of certain principles of the “e-government” concept into the practice of public administration in the Russian Federation, and, as of 2020, it was 80%, which means that the interaction between citizens and the state was actively moving to the digital environment. Moreover, the digitalization gained pace after 2015.
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The situation is different with the level of trust (in the meaning of approval and recognition) in the activities of the Government of the Russian Federation. In the first years of introducing digital technologies into the system of public services, the public trust was growing, but after 2015, this trend reversed, even though the dynamics of digitalization of the public administration had intensified (see Fig. 1). However, the slowdown in the pace of the digitalization in the last two years was accompanied by an increase in the level of the public trust. 50
100
40
80
30
60
20
40
10 0 -10
20 2012
2013
2014
2015
2016
Доверие
2017
2018
2019
2020
2021
0
Цифровизация
Fig. 1. Correlation of the digitalization coefficient and the level of trust in the Government of the Russian Federation.
The Pearson correlation coefficient is -0.614 which indicates that there is a stable negative relationship between the level of digitalization and the level of trust.
4 Discussion Thus, the presented results of the study state that a direct positive relationship between the digitalization of the public administration and the level of the public trust, accepted as an axiom in several scientific schools, may be subject to revision. The use of electronic government implies the emergence of new public institutions. Among them is the institution of e-democracy, which is a form of organization for the socio-political activity of citizens, which, using information and communication technologies, provides a qualitatively new level of interaction of citizens with each other, with public authorities and local self-government, with public organizations, and commercial structures. However, the level of trust within such an environment is not determined by traditional factors of habitualness or political charisma, it starts to significantly depend on the level of risk, the assessment of which in the digital environment is still under intense discussion [24–26]. N. Luhmann noted that the situation with the lack of Confidence and the need for Trust could lead to a vicious circle, which would not allow the political system to respond actively in situations of uncertainty or risk [15]. Digitalization of the public administration, as it is currently being implemented, relies precisely on the desire to reduce communication risks by reducing subjectivity of public decision-making. However, Confidence
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is reduced, as the information about unreliability of technologies destroys confidence in the certainty of the result and, consequently, expectations of the interaction success. Thus, the increasing digitalization of public services in some cases may lead to a decrease in the level of trust in the state as a social institution.
5 Conclusion It can be noted that the main hypothesis is not fully confirmed: the statement that digitalization of public services leads to an increase in public trust in public authorities is currently debatable. Moreover, we can see that the increase in the pace of digitalization, accompanied by an increase in compulsory nature, is accompanied by a certain decrease in the level of trust. We believe that one of the reasons for this is a kind of “negative expectations” effect: most technical solutions are imperfect and publications about technological failures and information leaks form a generally negative attitude towards the use of technological innovations in public administration. For example, if from 2011 to 2015, the information about leaks of users’ personal data from the Unified Portal appeared quite rarely (316 pages according to Google), and from 2015 to 2018, it doubled (690 pages), and in 2019–2020, the number of reports increased to 1430, and in 2021, it reached more than 1500 reports. Clearly, the increase in negative information increases the subjective sense of risk, thereby decreasing the level of trust in both the technology itself and in solutions that leave no other alternative. So, the results of the study show that when citizens are forcefully involved in a digital format of interaction with the public authorities, there may be a decrease in the level of approval of these practices. Undoubtedly, this assumption needs further research using other research methods, of which qualitative ones, such as focused interviews or narrative interviews, are a priority. Acknowledgments. The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program ’Priority 2030’ (Agreement 075-15-2021-1333 dated 30.09.2021).
References 1. Degtereva, V., Ivanov, M., Barabanov, A.: Issues of building a digital economy in modern Russia. In: Liargovas, P., Kakouris, A. (eds.) Proceedings of the european conference on innovation and entrepreneurship 2019, ECIE 1(2), pp. 246–254. Kalamata, Greece (2019) 2. Constitution of the Russian Federation, http://archive.government.ru/eng/gov/base/54.html, last accessed 08 September 2021 3. Federal project «Digital Public Administration», https://joinup.ec.europa.eu/sites/def ault/files/inline-files/Digital_Public_Administration_Factsheets_Slovenia_vFINAL.pdf, last accessed 08 September 2021 4. on National Development Goals of the Russian Federation until 2030, Pub. L. No. 474, https:// www.fao.org/faolex/results/details/en/c/LEX-FAOC202096/, last accessed 08 September 2021
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Analysis of the Impact of the Socio-economic Environment on Innovative Digital Development of the North-Western Federal District of the Russian Federation Oleg Kichigin, Grigory Kulkaev(B) , Natalia Mozaleva, and Galina Nazarova Peter the Great Saint-Petersburg Polytechnic University, Saint-Petersburg, Russia [email protected]
Abstract. This article raises the issue of innovative digital development of the regions of the North-Western Federal District of the Russian Federation. The relevance of the topic is due to the global trend in the development of innovative activities, as well as the actualization of issues in the field of information technology to increase the efficiency of production and management processes taking place in the regions. The paper examines the literature in the field of regional innovation and digital development, based on this, a scientific gap is identified in the form of insufficient assessment of the socio-economic environment for the further development of highly scientific technologies. During the study, the analysis of project management documents at the federal level and strategic planning documents at the regional level in the field of innovative digital development of territories was carried out. Based on this analysis, the structure, or orientation, of innovation and digitalization of the studied regions is determined – the social bias. Further in the article, calculations are carried out that allow us to draw conclusions about the relationship between the state of the socio-economic environment and the pace of innovative digital development. In addition, the factors hindering effective innovative digital development in each region were identified. Based on all the results of the study, general recommendations were developed for the regions of the North-Western Federal District of Russia, allowing to solve obvious problems that slow down the pace of innovative and digital development by increasing the attractiveness of the regions and their further economic development. The authors consider the increase in the values of socio-economic indicators, that is, the improvement of the environment, as the most important factor of innovative digital development. Keywords: Innovative Development · Regional Economy · Statistical Analysis · Strategic Analysis
1 Introduction The XXI century is characterized by a constant acceleration of the processes of renewal and modernization and digitalization of fixed production assets, management processes, social infrastructure facilities, etc. Important points of such transformations are increasing efficiency, increasing the quality of final goods, works and services, that is, the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 96–110, 2023. https://doi.org/10.1007/978-3-031-32719-3_8
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introduction of innovative activities in socio-economic processes. Of course, in the Russian Federation, an important role in innovative digital development is played by the activities of the authorities at the federal level [1]. As part of their functioning, state programs and national projects are being implemented, strategies for socio-economic and innovative development are being formed. However, a decisive role in innovative development is played by the activities of regional authorities, which, using the distinctive individual characteristics of the regions, form their own trajectory for the development of territories. The North-West district plays a significant role in the development of the entire state due to the concentration of industrial production, mineral deposits, higher educational institutions, IT-infrastructure, tourism industry facilities, as well as due to the successful geopolitical location, etc. The purpose of this article is to analyze the impact of the socio-economic environment on the innovative digital development of the regions of the North-Western Federal District. Among the tasks are: 1. Conducting a literary review of the importance and factors of innovative digital development, as well as identifying the problem in the field under researching. 2. Conducting analysis of project management documents in the field of innovation and development of the IT industry at the federal level. 3. Description of the main characteristics of innovative development of the regions of the North-West district. 4. Conducting correlation analysis to identify indicators affecting innovative development. 5. Constructing a regression equation and identifying a standard regression error. 6. Identification of problem areas of regions that impede innovative development. 7. Development of recommendations aimed at accelerating the pace of innovative and IT development of the studied regions. Research methods: comparative, strategic, statistical, correlation-regression analysis. The relevance of the topic is due to the global trend in the development of innovative activities as well as the actualization of issues in the field of information technology to improve the efficiency of the production and management processes taking place in the regions. The scientific novelty of the researching is in supplementing the theoretical and methodological base of innovative and digital regional development by considering the socio-economic situation of the territories.
2 Analysis of the Impact of the Socio-Economic Environment on Innovative Digital Development of the North-Western Federal District of the Russian Federation In the context of modern development, the need for scientific and technological progress as an important component of digital transformation is increasing, which contributes to the development of both society as a whole and individual territories and determines the strategic directions of development.
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Issues of innovative development of regions are considered in the works [1–7] and others. As noted in the work of G.S. Migunova, innovative development in Russia is a necessary component for the transition from a raw material economy to an innovative one. Innovations are based on the research, development, and implementation of modified processes. Introduction of innovative technologies should correspond to the specifics and requirements of a region. The authors propose to use several socio-economic indicators to analyze the regions to determine its level of development, based on which to develop a system of measures for the introduction of innovations [8]. This view is also shared by E.O. Mirgorodskaya [9], E. Lapinova [10], since the realization of the innovative potential of the region directly depends on the readiness of the modified system, the socio-economic environment to changes and improvements. For sustainable innovative development, it is necessary to monitor indicators, which allows you to make informed decisions and introduce innovations uninterruptedly. I. Rudenko notes that for innovative development it is important not only to determine the socio-economic conditions that can hinder innovative development, but also to determine the availability of the region’s capabilities to realize its innovative potential [11]. The formation of effective mechanisms for innovative development within the framework of the local economic system, according to R. Polyakov, is determined by many factors, including the competitiveness of the system, the number of necessary resources, the level of innovative activity, etc. R. Polyakov also notes that an effective mechanism should realize the full potential of the system [12]. D. Trzmielak notes that innovations have an impact on relations between various stakeholder groups in the region, including companies that make a significant contribution to the development of the region. On the other hand, the local environment is also a key resource for generating innovation processes. The author notes that the economic growth of regions, based on innovation and urban development, can be achieved using regional development strategies [13]. Modern innovative development is impossible without digital transformation of the system and the use of digital technologies. Digital transformation of state and regional management is aimed at increasing efficiency and optimizing the functions performed [14]. Digital government is a system consisting of government, non–governmental organizations, businesses, associations of citizens and individuals who support the production and availability of data and services through interaction with the government [15]. Digital transformation promotes civic participation in the formation of political interests and decision-making. Despite the significant work done, which increased the degree of interaction between the state and society using special online platforms and the development of a mechanism for providing state and municipal services in electronic form, the digitalization of public administration in Russia is still at an early stage. A. Balashov notes the need to invest in human capital to optimize the process of digital transformation of public administration [16]. O.V. Belyakova defines problems that hinder the effectiveness of this process such as lack of qualified personnel and low level of financing, inadequate technological standards, insufficient regulatory norms and unsatisfactory level of information security [17].
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Thus, the digital transformation is also influenced by socio-economic indicators that can restrain digitalization. A review of the literature showed that the issue of the relationship between the socio-economic environment and innovative digital development of regions remains poorly studied. Therefore, the authors propose to solve the problem posed, to realize the innovative potential of the region, it is necessary to determine the priority areas of its innovative development. To do this, it is proposed to analyze the strategies of scientific, technical and innovative development of the region, as well as to analyze the socio-economic indicators on which the formation of the region’s development strategy can be based. The formation of the optimal direction of strategic development should be based on an objective analysis of the socio-economic indicators of the regions. These circumstances indicate the need to improve the theoretical and methodological base of innovative development of regions in modern conditions of digital transformation. Statistical methods of data analysis allow to analyze development trends and predict further changes in the process or object. In addition, statistical tools make it possible to reasonably design development strategies considering many influencing factors. It is proposed to compile a list of socio-economic and innovative indicators for the characteristics of the regions, based on which to conduct a comprehensive analysis of the impact of the social and economic environment on innovative activity in the region for the purpose of further strategic planning. Factors are variables, hidden and not directly measured, in one way or another related to the observed indicators of objects - manifestations of these factors. The meaning of factor analysis is to search for quantities that are not known to the researcher, through which relationships between variables are expressed. Factor analysis is a set of models and methods for identifying, constructing and analyzing internal factors based on information about their «external» expression. Factor analysis is designed to identify hypothetical factors to explain the correlation matrix of the measured variables [18]. The goal of statistical analysis can be presented as research of the relationship between the resulting indicators of the region and those affecting the result. With the help of factor analysis, it is possible to determine the relationship between parameters, reveal the basics of phenomena and understand why phenomena are related to each other. Regression analysis allows to model the observed data and explore their properties and features. Multifactorial regression facilitates the analysis of the relationship between the resulting dependent variable and the independent influencing variables. Factor analysis provides a simple model of the relationship of phenomena at a higher causal level, which is very important for both theoretical and practical research.
3 Characteristics of Innovative Development of Regions Issues of innovative and digital development are increasingly contained in the strategies for socio-economic development. If this phenomenon in the early 2000s was local in nature, today the needs to increase the innovative activity and IT industry of the regions are discussed at the federal level. Innovation in Russian legislation is understood as
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«a new or significantly improved product (service) or process introduced, a new sales method or a new organizational method in business practice, workplace organization or in external relations» [19]. To date, in Russia, as part of stimulating the innovative activity of the regions, there is a separate direction of state programs - innovative development and modernization of the economy. Within the framework of this direction, it is planned to diversify the Russian economy through the introduction of the results of research activities and high digital technologies. The creation of an innovative infrastructure, scientific and technical clusters (technopolises), the improvement of all spheres of industry, especial IT, the development of human capital, integration within the framework of the Eurasian Union should ensure the achievement of this goal. Among the state programs in this area, it is worth noting «Economic Development and Innovative Economy», «Scientific and Technological Development of the Russian Federation» and «Information Society», as programs directly aimed at realizing the innovative potential of the country [20]. The state program «Economic Development and Innovative Economy» is designed to adapt the Russian economy to the global trend of introducing digital innovative technologies in both production and management. During the implementation of the program, it was proposed to increase the investment attractiveness of the state, intensify entrepreneurial activity, increase the overall competitiveness of Russia on the world stage, as well as reduce the share of the energy industry in the country’s economy. Within the framework of this project, there were 12 subprograms aimed at modernizing the Russian economy. The amount of budget financing of this program from 2013 to 2024 is 2.2 trillion rubles. The next important state program is «Scientific and Technological Development of the Russian Federation». Within the framework of this plan, greater emphasis is placed on the development of science and its interaction with the processes of production of goods, works and services. Among the objectives of the program are such important points as the creation and ensuring the effective functioning of scientific and technological centers (technopolises), the qualitative growth of scientific personnel, IT-specialists and their adaptation to the innovative economy, the creation of the necessary infrastructure for high-tech activities and the formation of a communication system along the cycle of science-technology-innovation-economy. The volume of budget allocations in the period 2019–2030 within the framework of this project is 10.6 trillion rubles. If we talk about the state program «Information Society», then this project is aimed at transforming public life through the implementation of some of the foundations of the «smart city», e-government and, in general, the digitalization of general household processes through the introduction of innovative technologies. The objectives of the program are to increase the degree of digitalization of state and municipal administration, ensure equal access of citizens to the media environment, ensure the information security of the country and others. The total amount of funding for the program from 2011 to 2024 is 2 trillion rubles. Despite the success of the realization of these programs, from January 1, 2022, their implementation at the federal level was stopped. Now the state plans to allocate subsidies to the regions based on the results of competitive selection to achieve the relevant goals.
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In addition to these state programs at the federal level, the national project «Digital Economy» also operates as part of innovative digital development. The national project has 10 initiatives affecting the digitalization of all spheres from the legal regulation of informatization to the introduction of artificial intelligence in decision-making processes. The objectives of the project are the withdrawal of social infrastructure facilities to the information environment, full automation of the process of providing state and municipal services, increasing the degree of openness of information on the activities of public authorities, the formation of a regulatory framework consider the issues of informatization and the use of advanced high technologies, etc. To achieve the tasks set, 1.8 trillion rubles were allocated from the state budget. The goals and objectives outlined at the federal level are actively used to develop strategies for the socio-economic and innovative and digital development of the regions of Russia. Further, it is proposed to conduct a brief description of the innovative activities of the regions of the North-Western Federal District, based on the strategy of socioeconomic development and the information provided by the relevant state authorities. Republic of Karelia (based on Strategy of socio-economic development of the region). In terms of the degree of innovative activity, the Republic of Karelia is in the middle positions. The main organization in this field is Petrozavodsk State University (PetrSU). On its basis, an innovation cluster was formed, which includes 34 enterprises. Innovations in the region have 3 directions: the implementation of the new concept of «genuine well-being», agroindustry and ecology. Within the framework of the concept of «genuine well-being», it is planned to introduce social innovative products, that is, such innovations that contributed to improving the quality of life of the population, increasing the comfort of the urban environment, simplifying the processes of interaction between business and government bodies, and minimizing the response time of government departments to various problems due to the functioning of high digital technologies. As part of the direction of development of agroindustry, it is planned to introduce innovations into the main production assets of the sphere, which would increase the efficiency of production itself and the quality of the final product. The direction of innovative development of Karelia in ecology implies the use of high technologies for the processing of solid waste, monitoring certain environmental indicators in the region due to the operation of big data analysis systems, as well as the introduction of innovations for the spread of waste-free production. Komi Republic (based on Strategy of socio-economic development of the region). To date, despite the frequent updating of the topic in the documents of strategic planning of the Republic of Komi, there is no information in open data on the results of innovative activities of the region. Guided by the latest edition of the Strategy for socio-economic development of the Komi Republic for the period up to 2035, only development priorities can be identified. For example, the tasks here include the growth of investment attractiveness, diversification of production through innovative goods, works and services, digital transformation of fixed production assets, creation and maintenance of innovation clusters and others. Arkhangelsk region (based on Strategy of socio-economic development of the region). In the early 2010s, the Arkhangelsk region occupied the lowest positions in the ratings, which to varying degrees assessed the level of innovation. Today, the
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Arkhangelsk Region has shown significant success in the development and activation of innovative processes on its territory. Within the framework of the strategic planning documents of the region, there are many projects that do not directly affect innovative development but imply implementation through digitalization. The most striking of these projects is «Innovative Medical Technologies». This initiative involves the introduction of high technologies for health examination and monitoring of its condition. A key indicator of the project is a significant reduction in mortality in the region. In addition to this project, it is possible to single out initiatives aimed at using the potential of young scientists, modernizing workplaces, developing the Arctic zone, informatization of social infrastructure facilities etc. Of the actual shortcomings of innovative development in the region, the lack of technopolises, a small share of the use of innovations by commercial organizations and the outflow of specialists employed in the scientific field are highlighted. Vologda region (based on Strategy of socio-economic development of the region). The positions of the Vologda region in the field of innovative development have low indicators. In the region, the main advantages of the functioning of the sphere are interaction with large innovation funds (for example, RUSNANO) as part of the region digitalization, many organizations using innovative technologies (84 organizations), distinctive successes in the field of innovative medicine, the implementation of regional support for scientific, technical and IT activities. Of the shortcomings, we can single out the lack of science-business connection, a small share of participation of higher education institutions in the process of introducing innovations, insufficient scientific infrastructure. A distinctive drawback that regional authorities distinguish is the lack of a single information platform or environment in the field of scientific and scientific-technological progress. Despite the significant lag of the region in this industry, by 2030 it is planned to develop the intellectual property market, popularize the achievements of the region’s innovative activities not only within its border, but also throughout the country, create an environment for effective communication of scientific organizations with business representatives, attract researchers and IT-specialists to the region, as well as increase the share of the region’s participation in the overall digitalization of the state economy. Kaliningrad region (based on Strategy of socio-economic development of the region). The Kaliningrad region is also characterized by a low level of innovation activity. The main obstacles in this direction of development are the insensitivity of the population and organizations to innovations and digital technologies, high competition in the market due to the location of the region among Eastern European countries, the lack of ITspecialists for normal development. The priority direction in the framework of the growth of innovative activity is the modernization of the main production assets of the region with high technologies, which should lead to an increase in the quality of the final product and the creation of new innovative workplaces. Leningrad region (based on Strategy of socio-economic development of the region). The Leningrad region is an example of one of the most diverse regions in its development. The main achievements to date are the successful clustering of scientific organizations of the region, the creation of scientific and technical centers (Sosnovoborsky and Gatchina), the introduction of high digital technologies in the field of health and ecology, a large amount of investment in the innovative economy and the modernization of workplaces.
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Among the shortcomings in the region, there is a low involvement of industrial enterprises in scientific clusters and an underdeveloped innovation and IT-infrastructure. In the near future, it is planned to implement a number of major projects in the region, for example, the construction of an innovative high-tech multifunctional medical complex with a nuclear medicine and radiation therapy unit, it is also planned to diffusion innovations in society, that is, to increase the loyalty of the region’s population to new technologies, to implement, together with foreign investors, major innovative projects in the field of improvement of territories and the transport network and region digitalization. Murmansk region (based on Strategy of socio-economic development of the region). Innovations in the Murmansk region are mainly related to industrial nature. Thus, the results of innovative activity in the form of modernization of fixed assets of production in the oil and gas, mining, and fishing spheres are actively used in the region. The need for constant modernization of the means of production is justified by difficult climatic conditions: the territory of the region is characterized by the presence of glaciers, as well as low air temperatures, because of which there is a need to create such equipment that would ensure the uninterrupted operation of production. Until 2035, it is planned to popularize the processes of introducing innovations in the Murmansk region by attracting educational and scientific organizations to further create scientific and technological clusters that will allow the region to reach a new level of innovative development. Novgorod region (based on Strategy of socio-economic development of the region). The analysis of the region’s innovative activities showed the competitiveness of the Novgorod region not only among the regions of the North-Western Federal District, but also among all regions of Russia. Today, as part of the implementation of the innovation and digital policy, the Center for Innovations in the Social Sphere of the Novgorod Region has been created. The Center forms the Cluster of Social Economy and Innovations, aimed at solving such problems as improving the environment, increasing the comfort of social infrastructure facilities, improving the urban environment through the implementation of major projects in the field of digital innovation, etc. The main existing problems of the region in the field of innovation are low demand for high technologies, insufficient IT-infrastructure development, as well as the lack of a regional regulatory framework in the field of innovative and digital development. In the future, in the social sphere, it is planned to create an innovative tourist complex and build innovative medical facilities, in industrial terms it is planned to introduce high technologies in mechanical engineering and metalworking, these industries make up about 20% of the gross regional product, so their modernization and improvement should lead to an improvement in the economic situation of the region. Pskov region (based on Strategy of socio-economic development of the region). By their target aims, the state authorities of the Pskov region determine innovative development because of digital transformation. Today, the Center for Social Innovations of the Pskov Region operates in the region, which is mainly aimed at introducing digital technologies into the life of the younger generation. The Pskov region positions itself as a region maximally adapted for innovation. Despite such a confident position, the main threats to the development of innovations in the region are the rapid pace of development of neighboring large cities, the lack of highly qualified IT-specialists in the field and the digital lag of the region. By 2035, it is planned to implement programs of innovative and
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digital education of the adult population in the region, increasing the share of investments in innovative production, activating the work of Pskov State University within the framework of the new technopolis, as well as applying the experience of introducing innovations in Moscow and St. Petersburg. Saint Petersburg (based on Strategy of socio-economic development of the region). St. Petersburg is one of the largest innovative cities, as 10% of the results of Russia innovative activity are concentrated in this city. Of the strengths of the development of the high-tech sphere, a high concentration of educational institutions, the effective functioning of scientific and technological clusters, the formation of a favorable ecosystem of innovative activity through the development of innovation infrastructure are distinguished. Of the shortcomings, it is worth highlighting the low degree of susceptibility of the population of St. Petersburg to innovation and the low growth rate of investment in the development of new technologies and the development of science and digital area. Despite this, St. Petersburg remains one of the largest centers for the development of science and technology. A large share in the structure of the results of innovation is occupied by the production of composite materials. Composite materials are called such materials, which were created by combining two or more components, completely different in properties, in order to increase the energy efficiency of the final product. The city uses such materials for the construction of energy-efficient housing, social and transport infrastructure facilities, as well as for the purpose of landscaping. In addition, measures within the framework of the smart city concept are being actively implemented in the region, characterized by the introduction of digital technologies into the life of the city. Thus, by having characterized the innovative activity of each region, it can be concluded that the innovative activity of the North-Western Federal District of the Russian Federation has a social orientation. This can be observed from the most innovative goods, works and services of each region, as well as from the scope of application: health, tourism, landscaping, science, education, etc. [21].
4 Statistical Analysis of Innovative Development of Regions The accuracy and reliability of the results of a statistical study directly depend on the quality of the initial data. To avoid a large error in the results, it is proposed to refer to a single proven source, as well as analyze data for a sufficiently long period to reduce the impact of single values of indicators on the final result. The authors compiled a list of indicators characterizing the socio-economic development of the regions of the North-Western Federal District: 1. Per capita cash income (per month), rubles (x1) 2. Employment rate, % (x2) 3. Graduation of bachelors, specialists, masters by population, person (x3) 4. Gross regional product per capita (for 2019), rubles (x4) 5. Investments in fixed assets per capita, rubles (x5) 6. Degree of depreciation of fixed assets, % (x6) 7. Net financial result (profit minus loss) of activities, organizations, mln. Rub. (x7)
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8. Indices of industrial production, % (x8). Production index is a relative indicator characterizing the change in the scale of production in the periods compared. The resulting indicator characterizing the level of innovative development of the region was the indicator «Volume of innovative goods, works, services, million rubles» (y). To verify the significance of the selected indicators for innovative development, it is proposed to conduct a correlation analysis for the North-Western Federal District in general from 2015–2020 for 8 selected indicators. The results of the correlation analysis are presented in Table 1. Table 1. Correlation analysis for the North-Western Federal District. Index
Correlation with Y
X1
0,956
X2
-0,872
X3
-0,853
X4
0,908
X5
0,736
X6
0,974
X7
0,833
X8
-0,430
Indicators with high significance for the resulting factor have correlation coefficient values of more than 0.7. Consequently, the indicator «Indices of industrial production» should be excluded from the model to reduce the error of further research. The low influence of industrial production indices is explained by the social orientation of the innovation activity of the regions, based on the description of strategies for the socioeconomic development of the regions. The remaining highlighted indicators have a high value of the correlation coefficient, therefore, are significant for the model. To form the basis for the creation of strategic directions for the development of regions, it is proposed to build a multifactor regression, which allows for an analysis between the resulting variable and the influencing indicators. It is proposed to conduct a regression analysis with the identified significant indicators for the regions of the NorthWestern Federal District for 2020. For this purpose, a statistical database was formed based on official data (Regions of Russia, 2021), including 10 regions for 8 development indicators for 2020, presented in Table 2. Based on the collected data, a regression analysis was carried out and a regression equation was constructed: Y = 4,22∗ x1 + −15980∗ x2 + 1160107∗ x3 + 0,16∗ x4 − 0,09∗ x5 − 4119∗ x6 + 0,2∗ x7 + 881368
(1)
Next, based on the resulting regression equation, you can get the calculated value of Y and compare it with the actual value, which is a standard regression error (Fig. 1).
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Region
Y
X1
X2
X3
X4
X5
X6
X7
Republic of Karelia
7 060,50
32583
53,5
0,0031
522245,3
93 133
53,8
51 164
Komi Republic
8 885,10
36677
57
0,0037
749219,3
171 900
55,1
13 785
Arkhangelsk Oblast (with Nenets Autonomous Okrug)
53 202,00
36779
53,7
0,0031
697648,2
175 641
54,8
191
Vologda Oblast
16 518,70
29522
56,2
0,0031
544379,2
176 144
55
387 605
Kaliningrad Oblast
6 820,90
29518
60
0,0032
530036,9
96 038
38,5
256 262
Leningradskaya Oblast
16 358,90
33149
58,9
0,0005
661328,6
243 132
47,7
148 729
Murmansk Oblast
112 798,50
46355
61,5
0,0015
1072337,1
259 283
42,7
223 069
Novgorod Oblast 4 570,40
26268
54,4
0,0027
471333,3
71 062
52,9
42 587
Pskov region
2 017,60
26436
53,2
0,0034
325659,6
60 883
51
7 466
Saint Petersburg
448 024,90
49207
65,7
0,0109
971158
144 305
41,5
1 898 954
If the calculated and original values of the resulting indicator coincide, it can be concluded that in this region the socio-economic environment contributes to innovative development. If the actual and calculated values do not coincide, therefore, the socioeconomic situation of the region may hinder the introduction of innovations in the region.
5 Discussion Based on these calculations, it can be concluded that in the Republic of Komi, the Arkhangelsk Region, the Murmansk Region and St. Petersburg, a favorable socioeconomic environment has been formed that contributes to the introduction and development of innovations, especial digital, in the regions. Use of high digital technologies, the high innovative potential of scientific centers, the availability of appropriate fixed production assets, a significant amount of investment in the industry, etc. Indeed, analyzing such regions as the Arkhangelsk region, the Murmansk region and St. Petersburg, one can observe the successful implementation of the results of innovative digital activity due to an effective model of interaction between the subjects.
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Fig. 1. Standard regression error. Source: studies by authors.
The results of the analysis of the Komi Republic showed a slightly different picture. In the region, a favorable socio-economic environment has been formed for the development of innovative potential, but a commensurate result has not yet been observed. In this case, it can be concluded that, despite the favorable conditions for the development of high technologies, there is still a factor in the region that slows down this development. This includes insufficient measures of state support for innovative and IT enterprises, irrational planning of the development of the region., insufficient regulatory and legal regulation of the sphere and others. In other regions of the North-Western Federal District, the socio-economic environment may hinder innovative development and prevent the region from fully realizing its innovative potential. Thus, in the Republic of Karelia, the development of innovations is hampered by insufficient investment in the sphere and the presence of only one scientific center, the Vologda region - an insufficient level of infrastructure support and a small number of educational organizations, the Kaliningrad region - a lack of scientific personnel, IT-specialists and low susceptibility of the population to innovations and digital technologies, the Leningrad region - an underdeveloped infrastructure and a low level of investment attractiveness, the Novgorod region - the lack of highly qualified specialists in the field of innovative and digital development, the Pskov region - the digital lag of the region and a high degree of competition between other regions. In addition to these problems of innovation development in these regions, in these regions, attention should be paid to issues of socio-economic development, which impede the introduction of high digital technologies. In the research to assess the socio-economic situation, such indicators as per capita income, employment level, gross regional product per capita and others were taken. Figure 1 showed that for the development of innovative activity, the regions should be characterized by favorable conditions for this development. Thus, in the context of the widespread actualization of trends in innovative digital
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development, the lagging regions of the North-Western Federal District need to reconsider the priorities of innovative development and, first, direct efforts to the formation of innovative development trends, favorable socio-economic environment due to the growth of the values of the main socio-economic indicators. The research question posed by the authors of the relationship of the socio-economic environment with the innovative development of the region has shown a strong mutual dependence and inseparable influence on each other. This confirms the conclusions made by E.O. Mirgorodskaya (2021) [9], E. Lapinova (2016) [10], I. Filimonova (2018) [24]. In research on the problems of innovative development, the issue of the impact of innovations on the socio-economic environment is often touched upon. The question of the reverse influence of the socio-economic environment on innovative development is poorly understood, which is also confirmed in several studies [25–27].
6 Conclusion During the researching, the following results were achieved: 1. The theoretical basis of innovative development was formed and a research gap was identified in the form of the lack of interrelation of indicators of socio-economic development and the introduction of innovations. 2. Analysis of project management documents at the federal level made it possible to identify the main state priorities in the field of innovative development, as well as to identify the importance of highly scientific technologies in the Russian economy. 3. The description of the innovative development of each region contributed to the formation of the general orientation of innovations of the North-Western Federal District, namely the social orientation. Also, the structural analysis of innovations and infrastructure made it possible to identify strong tools of public administration to achieve the goal within the framework of innovative development. 4. Correlation analysis confirmed the social orientation of innovations in the regions, showing a strong connection of socio-economic indicators with the resulting indicator of the innovation sphere. 5. Regression analysis and calculation of its standard error made it possible to display the description of the strategies of the regions in the form of a graph, highlighting the regions that have achieved distinctive success due to the formation of a favorable socio-economic environment. 6. Based on the calculations and analysis, regions with slowed rates of innovative development were identified and individual factors that impede these regions in accelerating the pace of development of this industry were identified. 7. General recommendations were developed to the regions of the Federal District on the formation of favorable sustainable conditions for the development of innovative activities. Thus, the tasks of the study were fulfilled, and the goal of the work was achieved. As a general conclusion, in the conditions of lack of external investment in innovative and IT projects due to their social orientation in the district, it is worth paying attention to increasing the level of attractiveness of the regions by improving the quality of life of the population, improving territories, forming cluster complexes, branding territories,
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etc. These measures will attract not only new investments, but also attract employees of the scientific, technological sphere and IT-specialists, to intensify the work of regional educational institutions with their subsequent inclusion in innovation clusters, as well as to increase the loyalty of the population to the use of innovative digital technologies. The results of this researching can be used by the authors for further analysis and synthesis of innovative activities of the regions, region digitalization, as well as the creation of individual recommendations for the formation of regional strategies for socioeconomic and innovative and digital development. Acknowledgments. The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program ’Priority 2030’ (Agreement 075-5-2021-1333 dated 30.09.2021).
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12. Polyakov, R., Gegechkori, O., Nikitina, N.: Scientific and Innovative Environment of Spatially Localized Economic Systems. Presented at the April 1 (2020). https://doi.org/10.15405/epsbs. 2020.04.64 13. Trzmielak, D.: Innovation in the development of regions and cities-comparable analysis. Przedsiebiorczosc i Zarzadzanie. Entrepreneurship and Management XIX (3), 47–62 (2018) 14. Degtereva, V., Ivanov, M., Barabanov, A.: Issues of building a digital economy in modern Russia. In: Proceedings of the European conference on innovation and entrepreneurship, ECIE, pp. 246–253. European Publisher, Crete, Greece (2019) 15. Ivanova, M., Degtereva, V., Lukin, G.: Evaluation of digital transformation of government. In: Proceedings of the 2019 International SPBPU Scientific Conference on Innovations in Digital Economy, pp. 1–8. ACM, New York, NY, USA (2019). https://doi.org/10.1145/337 2177.3373330 16. Balashov, A., Barabanov, A., Degtereva, V., Ivanov, M.: Prospects for digital transformation of public administration in Russia. In: Proceedings of the 2nd International Scientific Conference on Innovations in Digital Economy: SPBPU IDE-2020, pp. 1–7. ACM, New York, NY, USA (2020). https://doi.org/10.1145/3444465.3444506 17. Belyakova, O.: Digital Transformation Of Public Administration: Achievements And Problems, 1st edn. European Publisher, Crete, Greece (2022) 18. Sarstedt, M., Mooi, E.: Regression Analysis. A Concise Guide to Market Research, pp. 193-233. Springer-Verlag, Berlin Heidelberg (2014). https://doi.org/10.1007/978-3-64253965-7_7 19. Federal Law dated 23.08.1996 (as amended on 02.07.2021) «On Science and State Scientific and Technical Policy», http://www.consultant.ru/document/cons_doc_LAW_11507/, last accessed 08 September 2021 20. Information portal of state programs of the Russian Federation, https://www.rfbr.ru/rffi/eng, last accessed 08 September 2021 21. Tanina, A., Mudrova, E.: Social tourism in leningrad region: challenges and opportunities. In: ICTR 3rd International Conference on Tourism Research ICTR 2020 (2022) 22. Merzlikina, G., Babkin, A., Pshenichnikov, I.: Innovative potential of the region: formation and development strategy. Bulletin of the astrakhan state technical university. Series: economics 3, 99–109 (2015) 23. Regions of Russia. Socio-economic indicators (2021) 24. Filimonova, I., Eder, L., Komarova, A., Provornaya, I., Nemov, V.: Resource regions of russia: socio-economic indicators and innovative development. In: IOP Conference Series: Earth and Environmental Science. Institute of Physics Publishing (2018). https://doi.org/10.1088/17551315/206/1/012020 25. Geraskina, I., Kopyrin, A.: Balanced innovative development of socio-economic systems in the context of digitalization. In: IV International Scientific and Practical Conference, pp. 1–5. ACM, New York, NY, USA (2021) 26. Yulenkova, I.B.: Factors in innovative development of a region. REGIONOLOGY 27, 661– 677 (2019) 27. Godina, O., Maksimenko, L., Ushvitsky, L., Slavnetskova, L., Denshchik, M.: The structure of the mechanism of strategic management of innovational development of socio-economic system. Part of the Lecture Notes in Networks and Systems book series 57, 1094–1103 (2018)
Improving the UN Methodology of the E-Government Development Index Marina Ivanova, Grigory Kulkaev(B) , and Anna Tanina Peter the Great Saint-Petersburg Polytechnic University, St. Petersburg, Russia {ivanova.mv,kulkaev_g}@spbstu.ru
Abstract. Governments are implementing e-government reforms in an attempt to reap the benefits of digital transformation - a higher quality of governance and interaction with society. Therefore it is important to be able to evaluate the digital transformation both in a separate country and in intercountry comparison. One of the key tools here is the United Nations e-Government Development Index (EGDI). Nevertheless, a number of researchers point out the shortcomings of the EGDI methodology. The purpose of the study is to analyze the methodology of the EGDI index according to modern approaches to digital government, as well as to put forward proposals for improving the methodology. The authors analyze the theoretical background of the digital transformation measuring; evaluate the list of indicators included in the EGDI index in comparison to scientific recommendations for such assessment. With the help of correlation-regression analysis, it was found that the main group of indicators that affect the country’s place in the ranking is a set of human potential indicators. The establishment of its own weight coefficient for each of the EGDI sub-indices is substantiated, with a decrease in the weight of the human potential coefficient. In order to establish the correspondence of the results of the assessment, according to the EGDI methodology, to the level of development of e-government in a number of countries selected for the study, an expert assessment was carried out using a system of alternative indicators. Based on the results of the expert assessment, it was concluded that the composition of indicators in the ranking can affect the final place of the country, and that the list of indicators used in the EGDI methodology can be supplemented with indicators from other areas of assessment. Keywords: Digital government · assessment · digital transformation · e-government · e-government development index
1 Introduction The introduction of e-government technologies in the mid-1990s offered a significant number of benefits for both governments and citizens. The primary goals of introducing digital technologies were to increase efficiency, reduce budget expenditures and increase the transparency of services [1]. At the same time, with the development of digitalization processes and the expansion of digital reforms in the world, there was, firstly, a transition from the concept of e-government to the concept of digital government, and secondly, the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 111–129, 2023. https://doi.org/10.1007/978-3-031-32719-3_9
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goals of its implementation have undergone significant changes [2, 3]. The World Bank points out that the ultimate goal of government digitalization is a radical transformation of the relationship between the state, citizens and society, as well as between departments within the official bodies. The essence of the digitalization of public administration lies not in a simple reorganization of governance (or separate processes), but in changing the values of the government and increasing its transparency, developing citizen participation. The European Council emphasizes in its recommendations to countries on digital government reforms that “the focus should not be on information and communication technologies (ICTs) themselves, but on their use in combination with organizational changes and new skills to improve public services, democratic processes and public policy [4]. The expansion of the use of digital technologies in the field of public administration allows increasing transparency. It creates new channels for citizens to participate in political life, which, ultimately, allows achieving a higher quality of public administration [3, 5–8]. The governments of many countries are trying to achieve higher quality of governance and to improve interaction with society through digital reforms [9, 10]. In this regard, it seems important to be able to assess the digital transformation both in a single country and when comparing countries with each other. One of the key tools for assessing the development of e-government is the E-Government Development Index (EGDI) developed by the UN. The index is widely used both in scientific research on the problems of digital transformation and in the practice of digital reforms [9, 11, 12]. However, a number of studies point to the shortcomings of the EGDI methodology for assessing government digital change [13]. The purpose of this paper is to analyze the methodology of the EGDI index in view of modern approaches to digital government, as well as to make proposals for improving the methodology. The objectives of the study are determined by its purpose and include: - studying the theoretical foundations of evaluating the digital transformation of the government, - analysis of the EGDI methodology, in particular, determining the closeness of the relationship between the index indicators, analysis of the regression of the most related elements, - carrying out alternative calculations of the rating of selected countries using other assessment indicators and the hierarchy analysis method, - comparative analysis of the original EGDI rating and the obtained alternative rating of countries, - recommendations for improving the methodology of the UN. The research methods are: correlation analysis, expert analysis, comparison method, Saaty hierarchy analysis method, regression analysis, cognitive maps.
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2 Materials and Methods The study is based on the rating of countries by the level of e-government development [14]. The study encompasses 5 countries (United States of America, Germany, Russian Federation, Kazakhstan and China) with different positions in the ranking, as well as with different approaches to digital reforms. To analyze the methodology of the EGDI index, the authors used correlationregression analysis, in particular, the assessment of the closeness of the relationship between the indices, also the study of the regression of the most related elements. Correlation analysis was carried out using the data analysis package in MS Excel software. An expert assessment was carried out in order to establish the conformity of the assessment by the EGDI methodology with the level of development of e-government in the studied countries. For the expert assessment, the authors chose alternative indicators for assessing digital transformation, justified by summarizing the results of research in this area [15–17]. The selected indicators include all seven identified areas of assessment. To conduct an expert assessment, the authors used methods of paired comparisons and hierarchy analysis. The experts were employees of higher education institutions - researchers in the field of public administration from the Russian Federation, Kazakhstan, the USA and Estonia. Based on the expert assessment, a comparative analysis of the original EGDI rating and the obtained alternative rating of countries by the development of e-government was carried out in order to identify discrepancies between the results of the expert assessment and the EGDI rating, which may indicate gaps in the methodology for calculating the UN rating.
3 Results and Discussion Features of the digital transformation of public administration and its assessment. By the beginning of the 2000s, the first definitions of "electronic government" were formed. Means and Schneider [18] use the following definition: e-government is “the relationship between authorities, their clients (business, other authorities, citizens) and their suppliers (the same) using electronic means”. The World Bank defines e-government as “the use by government agencies of information technology tools, such as Wide Area Networks (WANs), the Internet, and mobile computing, that have the ability to transform relations with citizens, businesses, and other arms of government” (World Bank, 2001). It is important to notice that already at that time there was understanding that digital technologies were restructuring the relations of the state with society and within the official bodies. The large-scale digital transformation of all spheres of society has led to rethinking the implementation of digital technologies in the activities of government. For example, in research and scientific literature, the concepts of e-government and digital government have been divided. The OECD [19] defines e-government as the “use by the governments of information and communication technologies (ICTs), and particularly the Internet, as a tool to achieve better government”, and digital government as “the use of digital technologies, as an integrated part of governments’ modernisation strategies, to create public
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value”. Digital government is, in fact, an ecosystem that includes government agencies, non-governmental organizations, business structures, citizens and their associations and supports the production of and access to data, services and content through interactions with the government. The development of civic participation and cooperation in the digital environment is due to the fact that ICTs develop not only the channels for obtaining public services, but also the possibilities for their active design, change, participation in decision-making and expression of political priorities. A number of researchers come to the conclusion that digitalization gives rise to radically new concepts of public administration [20]. Special attention is paid by researchers to the definition of the very concept of "digital transformation". Although definitions may differ, most authors agree that digital transformation involves the use of ICT to create fundamentally new opportunities for citizens, businesses, governments, to significantly increase the social value of public services [21–24]. Mergel et al. in their study argue that the concepts of digital transformation, digitalization and simple digitization of government services should be separated [25]. Each of them means, in fact, different approaches to digital government and its implementation. “Digitization” - the transition from analog to digital services and to digital channels for their delivery; digitalization - involves the restructuring of the processes themselves, and not the simple digitization of existing ones; digital transformation causes organizational, cultural and relationship changes. Modern public administration is gradually ceasing to be guided in its activities by its own forecast of the needs of society and business (the so-called citizen-centric approach), and is moving to the so-called citizen-driven approach, when citizens and businesses themselves determine their needs and implement them in cooperation with the state [19]. Thus, the key characteristics of modern ideas about digital government are as follows [19]: - governance is based on the needs of consumers of public services; - proactive approach to service delivery and decision making; - the data-based policy; - the principle of “digital by default” is implemented for public services; - the government as a platform for interaction (i.e. a unified infrastructure of public services is being formed with common payment systems, databases, etc.) [26, 27]. At the same time, modern research indicates that neither platform solutions nor data-based approaches can be considered as the result of the digitalization of public administration, but are a stage of transformation. As the result of this transformation, full digitalization and the transition to “smart government” should occur [20, 28, 29]. T. Janowski suggests that digital transformation is not a one-time project with clear start and end dates, but an ongoing process that includes comprehensive changes that will appear in its development [2]. For the success of digital transformation, it is important to have a well-designed reform strategy, part of which should be indicators of achieving transformation goals and evaluating its process. At the same time, there is no generally accepted unified methodology of the digital transformation assessment. It is important to understand that
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if digital transformation itself is a changing and ongoing process, then the assessment systems used to analyze it must also change. In addition, as mentioned above, the very idea of digital government has been changing. For example, in the early stages of e-government implementation, the proportion of government services provided online may be considered as an important thing to evaluate. At more advanced stages of digital transformation, the indicators and effects of implementation change: if genuine digitalization occurs and services on the portal are replaced by those provided automatically, then the estimated result may be a reduction in the number of requested public services [28]. One of the key tools for assessing e-government development is the UN EGovernment Development Index (EGDI). EDGI reflects global trends and is a decisionmaking tool for "building better governance, developing services and more equal involvement of citizens in governance" [12]. EGDI is a composite measure of three different e-government characteristics, which are evaluated by three sub-indices: 1. Online service index, OSI. OSI evaluates the coverage and quality of Internet services and, through them, the level of digital presence of the state, its readiness to provide services and interact with consumers in digital form. OSI assesses which of the four stages of online accessibility the state is in (informational stage, interaction stage, two-way interaction stage, transactional stage). The sub-index consists of indicators such as initial information services, integrated e-services, advanced services and interaction-based services, measured as percentages. 2. Telecommunication index, TII. TII assesses telecommunications connectivity, the level of development of the existing ICT infrastructure that citizens need to use egovernment services. This sub-index includes indicators of the number of Internet users, the number of mobile phone users, wired Internet subscribers and fixed broadband users (all per 100 inhabitants, in units). 3. Human capital index (HCI). HCI assesses the ability of the population to master ICT; the ability of citizens to use e-government services. The following indicators are assessed: the overall enrollment rate in primary, secondary and tertiary education, measured as a percentage; the level of literacy of the population; duration of education (expected and actual), measured in years. Thus, EGDI evaluates not only electronic services per se, but also considers how the population is able and willing to use them. Each of the sub-indices is formed on the basis of the normalized values of the indicators included in its composition. The normalization procedure is designed in such a way that the maximum possible value of the subindex (and, consequently, the integral index) is equal to 1, and the minimum possible value is zero. The calculation is based on the arithmetic mean, first for each sub-index, and then for the three components together. The final value of the EGDI index is calculated as the arithmetic mean of its three sub-indices. The EGDI index and its individual sub-indices are used both in research and in digital reforms in various countries [9, 13]. For example, the Russian Federation indicates its place in the EGDI ranking as one of the target indicators in the Information Society program.
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Researchers use the EGDI index to assess the impact of e-government development on the level of corruption [30], on the shadow economy [31], to assess the relationship between national culture and the development of the digital state. At the same time, researchers study the problems of this index. In particular, Whitmore argues about the need to change the methodology for calculating the index and include factor analysis [32]. Other researchers propose an improved EGDI methodology adjusted for a country’s gross domestic product and point out that the index should measure how successful a country is in reforms relative to its capacity, and not just in absolute terms [13]. An analysis of the indicators included in the EGDI sub-indices also shows that, for all their breadth of coverage and a comprehensive approach, in general they are based on an assessment of the formation of digital government, and not on the quality of its functioning. Also, from the authors’ point of view, the index does not allow any reflection of the relative importance of individual indicators for the overall assessment. For example, the OSI sub-index includes an assessment of the number of government services in an online format. At the same time, it can be assumed that, for example, the transfer of the submission of tax returns to a fully electronic form is more important in terms of assessing the level of digitalization of the government than, say, the registration of land transactions (both in terms of user coverage and in terms of the impact on the release of employees’ time). It is also possible to raise the question of how appropriate it is to use the arithmetic mean in calculations, since, firstly, the arithmetic mean gives errors in rankings, and secondly, the units of measurement of indicators are different (years, percentages, pieces). Researchers also offer their own systems for assessing the development of egovernment, which, to one degree or another, allow taking into account the requirements of the current stage of digital transformation and allow assessing the effects of the introduction of ICT and the changes taking place with governments. So, the researchers propose a system of seven basic assessments (including a number of individual indicators) [15]: - success of digitalization projects; - price competitiveness of digitalization projects; - relative efficiency of government IT systems; - use, convenience and usefulness of state digital platforms; - data security; - completion of the most important digitalization projects on time; - the use of data in the formation of policies and strategies, organizational design and service delivery. The researchers recommend the following list of key indicators for digital transformation programs [17]: -time to achieve results - how quickly new demand is met through new digital services; -level of acceptance of digital channels for interaction with the government by citizens/businesses;
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-to what extent the transition to a digital channel for the provision of services allows closing alternative traditional channels; -a decrease in the number of citizens who need help to carry out digital interaction with the government, as well as an increase in the digital independence of people who previously needed such help; -saving of public finances; -time saving of citizens/businesses; -user rating of digital services of the state in comparison with digital services of the private sector; -transactions accuracy; -efficiency of policy implementation; -reduction in the number of cases of fraud, errors and corruption; -share of requests processed fully automatically and immediately; -the share of resources of the state body that can be used to perform proactive rather than routine procedures; -percentage of fully digital interactions. By studying these and other scientific and theoretical proposals, it is possible to form the main assessment areas that are important in evaluating digital transformation: -Assessment of e-government maturity stage. -Coverage assessment. -Assessment of readiness and acceptance by society. -Infrastructure assessment. -Assessment of the operating environment. -Evaluation of changes in intragovernmental processes. -Impact assessment. Study of EGDI indicators. The countries selected for the study (the United States of America, Germany, the Russian Federation, Kazakhstan and China) have different values of the basic indicators in the UN rating of countries by the level of e-government development in 2020 (see Table 1). Using correlation-regression analysis, the authors have assessed the closeness of the relationship between individual EGDI indicators and the rating of the countries under study. The results of the analysis are presented in Table 2. Table 2 shows the degree of interconnection between the indicators of the EGDI Index. The higher the modulo value, the higher the degree of dependence. The minus sign means an inverse relationship, in other words, the greater the value of one indicator, the lower the value of the other, and vice versa. The degree of closeness of the interconnection is limited from -1 to 1. For regression analysis purposes the authors selected the indicators that have a greater influence on the place of countries in the rating (value greater than 0.7, strong correlation). These indicators include: the percentage of Internet use (-0.78), the enrollment rate - (0.7), the expected training time (-0.75), the average training time (-0.82). Next, the authors conducted regression analysis for each of the selected indicators (Figs. 1, 2, 3 and 4).
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Indicator/Country
US
Germany
China
Kazakhstan
Russia
Place in the rating (y1)
9
25
45
29
36
Number of telephone sets per 100 people (x1)
120
120
115,53
120
120
Internet usage percentage (x2)
87,27
89,74
54,3
78,9
80,86
Number of broadband wireless and fixed line users (x3)
33,8
41,11
28,54
13,44
22
Number of connected subscriber devices per 100 people (x4)
120
82,56
93,46
77,57
87,88
Literacy rate (x5)
99
99
96,84
99,8
99,73
Enrollment rate (x6)
98,38
96,22
80,27
99,15
96,71
Expected training time (x7)
16,3
17,1
13,9
15,44
15,5
Average training time (x8)
13,75
14,15
7,9
11,8
12
Initial information services (stage 1) (x9)
100
81,48
96,3
96,3
87,04
Unified e-services (stage 2) (x10)
100
71,43
100
100
95,24
Extended e-services (stage 3) (x11)
100
54,55
90,91
27,27
72,73
Services provided on the basis of electronic interaction (stage 4) (x12)
100
75,58
96,51
88,37
82,71
Each of the figures shows the dependence of the place in the rating of countries on the selected indicators. The trend line in each graph shows the average dependency value based on the dependency ratios for different countries. Further, to visualize the dependence, the authors built a cognitive map. For simplicity, the image indicates only the links with a coefficient value above 0.7, which, based on the correlation analysis performed earlier, is characterized as strong. The cognitive map of the components of the EGDI Index is shown in Fig. 5. Thus, based on the results obtained, we can conclude that the main group of indicators that affect the country’s rating is a set of human potential indicators (HCI sub-index). It is also worth noting that other indicators do not have such a strong influence on the rating of states, however, they have some dependence on the degree of development of human capital. In this case, such indicators as the expected and average time of training, as well as the enrollment rate, have a great influence. Thus, based on the results obtained, we can conclude that the main group of indicators that affect the country’s rating is a set of human potential indicators (HCI sub-index). It is also worth noting that other indicators do not have such a strong influence on the rating of states, however, they have some dependence on the degree of development of human capital. In this case, such indicators as the expected and average time of training, as well as the enrollment rate, have a great influence.
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Table 2. Results of correlation analysis of the e-Government Development Index. y1 y1
x1
x2
x3
x4
x5
x6
x7
x8
x9
1,00 -0,67 -0,78 -0,35 -0,59 -0,46 -0,70 -0,75 -0,82 -0,20
x10
x11
x12
0,11 -0,12 -0,17
x1
-0,67 1,00
0,95
-0,04 -0,04 0,95
0,99
0,82
0,91
-0,30 -0,30 -0,42 -0,44
x2
-0,78 0,95
1,00
0,27
0,10
0,80
0,91
0,95
0,99
-0,37 -0,49 -0,26 -0,45
x3
-0,35 -0,04 0,27
1,00
0,39
-0,34 -0,13 0,51
0,37
-0,37 -0,67 0,47
-0,15
x4
-0,59 -0,04 0,10
0,39
1,00
-0,22 -0,01 0,07
0,16
0,56
0,73
x5
-0,46 0,95
0,80
-0,34 -0,22 1,00
0,95
0,61
0,73
-0,25 -0,13 -0,56 -0,45
x6
-0,70 0,99
0,91
-0,13 -0,01 0,95
1,00
0,77
0,87
-0,16 -0,18 -0,44 -0,34
x7
-0,75 0,82
0,95
0,51
0,07
0,61
0,77
1,00
0,97
-0,49 -0,69 -0,22 -0,51
x8
-0,82 0,91
0,99
0,37
0,16
0,73
0,87
0,97
1,00
-0,36 -0,52 -0,20 -0,41
x9
-0,20 -0,30 -0,37 -0,37 0,56
-0,25 -0,16 -0,49 -0,36 1,00
0,87 0,33
0,95
x10
0,11 -0,30 -0,49 -0,67 0,36
-0,13 -0,18 -0,69 -0,52 0,87
1,00 0,27
0,81
0,84
-0,56 -0,44 -0,22 -0,20 0,33
0,27 1,00
0,60
x12 -0,17 -0,44 -0,45 -0,15 0,73
-0,45 -0,34 -0,51 -0,41 0,95
0,81 0,60
1,00
x11 -0,12 -0,42 -0,26 0,47
0,36 0,84
Fig. 1. The Percentage of Internet Use Regression (authors’ calculations).
Fig. 2. The Enrollment Rate Regression (authors’ calculations).
It can also be concluded that if we consider the indicators in the context of the main areas of assessment (identified above), then the rating of a country is most influenced by indicators of the operating environment.
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Fig. 3. The Expected Training Time Regression (authors’ calculations).
Fig. 4. The Average Training Time Regression (authors’ calculations).
Services provided on the basis of electronic interaction
Expected training time
Literacy rate 0,73
0,77
0,97
0,73 0,75
0,84
Extended e-services 0,87
Average training time 0,99
0,82
0,81
0,95
Unified e-services
Enrollment rate
Place in the rating
Initial information services
0,87
0,70 0,91
0,82 0,95
Number of connected subscriber devices per 100 people
0,78 0,95 0,91
Number of broadband wireless and fixed line users
0,95 0,99
0,80
Internet usage percentage
0,95
Number of telephone sets per 100 people
Fig. 5. Cognitive Map of EGDI Index Components (compiled by the authors).
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At the same time, more specific digital transformation indicators related to the OSI (online public services) and TII (ICT infrastructure) sub-indices have a lesser impact, which, according to the authors, is not a strong point of the index calculation methodology. It is possible to put forward a hypothesis about the need to add weight coefficients for sub-indices to the methodology. To achieve this goal, the paper proposes to conduct an expert assessment of the development of e-government in the countries under study with an assessment of countries by alternative indicators and compare the obtained alternative rating of countries with the EGDI rating 2020. Expert assessment of e-government development in the studied countries. The purpose of the peer review is to form a rating of the studied countries according to the level of e-government development. In doing so, the authors used the evaluation indicators that differ from those used in the EGDI methodology. The choice of indicators was based on the need for the most comprehensive coverage of all the above areas of assessment, formed on the basis of generalization of research. Thus, the experts evaluated the countries according to the following indicators: • Achieved stage of e-government development, • The share of the population and organizations using digital technologies to interact with the government, • Loyalty of the population to the use of e-government portals, • Provision of the population with modern communication devices with access to the Internet, • The level of digital literacy of the population, • The degree of simplification of management processes, • The share of electronic document workflow in total. At the first stage of the assessment, the hierarchy analysis method was used - pairwise comparison of alternatives for each of the criteria and pairwise comparison of the criteria in terms of importance for the goal. The assessment is made by pairwise comparison according to the following scale: • 1 – equal, indifferent. • 3 (1/3) - slightly better (worse). • 5 (1/5) – better (worse). • 7 (1/7) – much better (worse). • 9 (1/9) – fundamentally better (worse). • 2 (1/2), 4 (1/4), 6 (1/6), 8 (1/8) - intermediate values. The results of the expert assessment of the weight of each indicator in the final level of e-government development are shown in Table 3. The results of normalizing the previous matrix and assessing the weights of indicators are shown in Table 4. It can be seen from Table 4 that, according to experts, the criteria for the degree of simplification of management processes, the loyalty of the population to the use of elements of e-government, and the share of electronic document workflow have the greatest weight. This is consistent with what was noted in a number of works [3, 16, 17] - the need to shift the focus from the availability of e-government technologies to the effect of its implementation.
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Table 3. The results of expert assessment of the weight of each indicator in the final level of e-government development. E-government development level
Achieved stage of e-government development
The share of the population and organizations using digital technologies to interact with the government
Loyalty of the population to the use of e-government portals
Provision of the population with modern communication devices with access to the Internet
The level of digital literacy of the population
The degree of simplification of management processes
The share of electronic document workflow in the total
Achieved stage of e-government development
1
1/3
3
5
4
1/4
1/2
The share of the population and organizations using digital technologies to interact with the government
3
1
1/2
5
1
1/2
1/2
Loyalty of the population to the use of e-government portals
1/3
2
1
4
3
1/2
2
Provision of the population with modern communication devices with access to the Internet
1/5
1/5
1/4
1
2
1/3
1/2
The level of digital literacy of the population
1/4
1
1/3
1/2
1
1/5
1
The degree of simplification of management processes
4
2
2
3
5
1
4
The share of electronic document workflow in the total
2
2
1/2
2
1
1/4
1,00
At the next stage the experts, using the same method, pairwise compared the countries selected for analysis according to the e-government development indicators set for assessment. After that, the obtained values were normalized. In Tables 5 and 6 the results of the assessment for the indicator “Achieved stage of development of e-government” are given as an example. Similarly, the experts carried out a pairwise comparison of states by other studied indicators. To assess the overall weight of countries in the overall ranking, the resulting matrices were multiplied. The results are presented in Table 7.
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Table 4. Normalized matrix of indicator weights. E-government development level
Achieved stage of e-government development
The share of the population and organizations using digital technologies to interact with the government
Loyalty of the population to the use of e-government portals
Provision of the population with modern communication devices with access to the Internet
The level of digital literacy of the population
The degree of simplification of management processes
The share of electronic document workflow in the total
Weight
Achieved stage of e-government development
0,06
0,02
0,21
0,21
0,22
0,08
0,06
12,4%
The share of the population and organizations using digital technologies to interact with the government
0,18
0,07
0,03
0,21
0,05
0,18
0,06
11,4%
Loyalty of the population to the use of e-government portals
0,16
0,19
0,08
0,16
0,14
0,18
0,17
15,2%
Provision of the population with modern communication devices with access to the Internet
0,13
0,12
0,03
0,05
0,08
0,10
0,06
8%
The level of digital literacy of the population
0,02
0,07
0,03
0,03
0,05
0,06
0,12
5,6%
The degree of simplification of management processes
0,27
0,30
0,40
0,20
0,30
0,32
0,42
31,7%
The share of electronic document workflow in the total
0,18
0,22
0,21
0,14
0,15
0,08
0,11
15,7%
Thus, according to the final calculations and the results of the expert assessment, the USA became the leader among the studied countries in the development of e-government (29.2%). When interpreting the results of the hierarchy analysis method, it is also necessary to take into account the consistency of the experts’ answers for each matrix of paired comparisons. The consistency score is calculated as the ratio of the consistency index to the average random value of this index. The matrix will be considered consistent
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Table 5. The results of the expert assessment of countries by the stage of e-government development achieved. Achieved stage of e-government development
US
Germany
China
Kazakhstan
Russia
US
1
2
7
4
4
Germany
1/2
1
7
4
4
China
1/7
1/7
1
1/3
1/5
Kazakhstan
1/4
1/4
3
1
1
Russia
1/4
1/4
5
1
1
Table 6. Normalized matrix of the expert assessment of countries by the stage of e-government development achieved. Achieved stage of e-government US development
Germany China Kazakhstan Russia Weight
US
0,42 0,54
0,30
0,36
0,37
40%
Germany
0,28 0,27
0,30
0,36
0,37
31,8%
China
0,07 0,04
0,04
0,03
0,02
4%
Kazakhstan
0,11 0,07
0,13
0,09
0,12
10,6%
Russia
0,12 0,08
0,22
0,16
0,11
13,7%
Table 7. Summarized weight of countries in the overall ranking. Country
Summarized weight
US
29,2%
Germany
25,6%
China
6,5%
Kazakhstan
14,5%
Russia
23,3%
(which means experts have taken no actions contradictory to their own opinion during assessment) if the value of the consistency index is not higher than 0.1. For Table 3, the value of the consistency ratio is 0.09; for Table 5 - 0.09. For other matrices, the values ranged from 0.06 to 0.1. Thus, all consistency relations are in an acceptable scope, which means that the obtained values of the consistency index emphasize the correctness of the chosen alternative. Comparison of the results of the expert evaluation with the results of the EGDI method is shown in Table 8.
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Table 8 shows that the results of the comparative positions of countries have changed slightly. Russia moved up one position while Kazakhstan moved down. The expert assessment of the level of development of e-government in the studied countries according to alternative indicators, in many respects, coincided with the EGDI rating. Table 8. Comparison of the results of expert evaluation with the results of the EGDI method. Country
Place in the overall rating (EGDI methodology)
Place in the rating among studied countries (EGDI methodology)
Place in the rating among studied countries (hierarchy analysis method)
US
9
1
1
Germany
25
2
2
China
45
5
5
Kazakhstan
36
4
3
Russia
29
3
4
At the same time, it is important to emphasize that the countries under study have a fairly large gap in positions in the world ranking, so they could not significantly shift with regard to each other in the resulting ranking. However, it is worth noting that the total weight of the studied states does not differ much. However, the authors consider it possible to conclude, based on the results of the analysis, that the composition of indicators in the ranking can affect the final place of the country, which is confirmed by the examples of Russia and Kazakhstan. In the EGDI rating the two countries had a noticeable gap in favor of Kazakhstan, while according to the results of the expert assessment based on alternative indicators Russia moved up in the rating. The analysis of expert opinions regarding the importance of indicators for assessing the overall level of e-government development revealed that the most important of them were the degree of simplification of management processes, the loyalty of the population (its readiness to use new technologies), and the share of electronic document workflow. These indicators relate to such areas of assessment as readiness and acceptance by society; impact and intragovernmental changes. At the same time, these indicators are missing in the EGDI methodology. It seems obvious that conducting an expert assessment using the pairwise comparison for the entire range of indicators, including those used in the EGDI methodology, would show clearer preferences of experts. However, even at this stage it can be concluded that the list of indicators used in the EGDI methodology can be broadened. In particular, the indicators from areas of assessment that are not currently included in the EGDI can be added. Also, based on the correlation-regression analysis, it was revealed that in the EGDI methodology, the HCI sub-index (human development indicators) has the greatest impact on the final rating. These indicators can be attributed to the assessment area - assessment
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of the operational environment and, from the point of view of the authors, despite the importance of this block of indicators, it cannot be the leader in the assessment. To solve this problem, it is proposed to establish its own weighting coefficient for each of the EGDI sub-indices, and in order to equalize the contribution of each of the three sub-indices, the HCI weight should be reduced. The development of proposals for specific weights of sub-indices is the direction of future research by the authors.
4 Conclusion An important part of a successful digital transformation is the ability to reasonably and adequately assess the process of change, including for the purposes of comparing countries with each other. One of the key tools for this is the E-Government Development Index (EGDI) developed by the UN. The index is widely used both in research in the field of digital transformation and in digital reforms. It is important to understand that if digital transformation itself is a changing and ongoing process [2], then the assessment systems used to analyze it must also change. This study analyzed the methodology for evaluating the e-government development of the UN (EGDI) in the context of the question - does it correspond to modern approaches to the concept of “digital government”? The analysis of the indicators included in the three EGDI sub-indices shows that, in general, they are based on an assessment of the maturity of the digital government, and not on the quality of its functioning. Also, from the authors’ point of view, the index does not reflect the relative importance of individual indicators in the overall assessment. In terms of methodology, the appropriateness of using the arithmetic mean in calculations also raises a question, since, firstly, the arithmetic mean gives a large error when building ratings, and secondly, the units of measurement of indicators are different. A review of related studies highlights seven key e-government assessment areas that are recommended for use in reform and progress assessments. These are e-government maturity stage assessment, coverage assessment, public readiness and acceptance assessment, infrastructure assessment, operational environment assessment, intra-government process change assessment, and impact assessment. The EGDI methodology contains indicators that do not cover all of these areas, which can be recognized as a gap. An expert assessment was carried out in order to assess the conformity of the results of the assessment according to the EGDI methodology with the level of development of e-government in the countries. The authors selected 5 countries, as well as alternative indicators for assessing digital transformation, including all seven identified areas of assessment, which contributes to greater coverage of various manifestations of the digital transformation process by the measuring indicators. The expert assessment of e-government development in the studied countries according to alternative indicators largely coincided with the EGDI rating. However, the authors consider it possible to conclude, based on the results of the analysis, that the composition of indicators in the ranking can influence the final place of the country. The analysis of expert opinions regarding the importance of indicators for assessing the overall level of e-government development revealed that the most important of them
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were the degree of simplification of management processes, the loyalty of the population (that is, its readiness to use new technologies), and the share of electronic document workflow. These indicators relate to such areas of assessment as readiness and acceptance by society; impact and intragovernmental changes. These indicators are missing in the UN methodology. It can be concluded that the list of indicators used in the EGDI methodology can be broadened. In particular, the indicators from areas of assessment that are not currently included in the EGDI can be added. An in-depth correlation and regression analysis of the indicators of the methodology made it possible to find out that the main group of indicators that affect the final rating of the country is the set of human potential indicators (HCI sub-index). The remaining indicators do not have such a strong influence on the rating of states, however, they have some dependence on the degree of development of human capital. The degree of development of human potential can be attributed to the indicators of the digital transformation environment, i.e. to the indicators that do not directly assess the changes taking place, but assess the environment in which these changes occur. At the same time, more specific digital transformation indicators related to the OSI (online public services) and TII (ICT infrastructure) sub-indices have a lesser impact, which, according to the authors, is not a strong point of the index calculation methodology. To solve this problem, it is proposed to establish its own weighting coefficient for each of the EGDI sub-indices, and in order to equalize the contribution of each of the three sub-indices, the HCI weight should be reduced. Acknowledgements. The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program ’Priority 2030’ (Agreement 075–15-2021–1333 dated 30.09.2021).
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Digital Transformation of Personnel Management in Organizations Under the Influence of Big Data Technologies Ekaterina Okrushko1 , Sergey V. Rasskazov1 , Albina N. Rasskazova2(B) , and Natalia Vasetskaya3 1 St. Petersburg State University, St. Petersburg, Russia 2 North-Western State Medical University Named After I.I. Mechnikov, St. Petersburg, Russia
[email protected] 3 Peter the Great Saint-Petersburg Polytechnic University, St. Petersburg, Russia
Abstract. The research issue in the field of Big Data has been and remains its application in specific areas. The purpose of the research is to empirically study the changes that occur in the personnel management system of an organization under the influence of Big Data technology. The research methodology is based on the system and structural/functional analysis, the idea of personnel management as a social institution, as well as the properties of Big Data. The empirical base was formed by the materials of interviews on the topic of the research with five leading specialists in personnel management of large and medium-sized organizations, as well as a questionnaire survey of employees and candidates (82 people). Data processing was performed with the use of descriptive statistics and regression methods. Key findings: HR digital transformation of the organization under the influence of Big Data is considered within the framework of sociology of organizations and personnel management; the views on the problem of the use of Big Data of both the interacting parties in personnel management system are specified; management recommendations are provided on the use of Big Data in HRM. The areas of further research include, in particular, the development of methodology of the use of unstructured data in HR with due account for ethical constraints, as well as the specification of management decisions in relation to the various functions of “digital” personnel management. Keywords: Big Data Technology · Human Resources Management · Unstructured Data
1 Introduction Suddenly and “unexpectedly”, the whole world started talking about “digital transformation”. Everyone has heard the names of technologies which have become buzzwords, such as Big Data, artificial intelligence, or blockchain. However, almost all of them, only under other names, have long been known in science and technology. What has happened leading to this hype which we have now? © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 130–139, 2023. https://doi.org/10.1007/978-3-031-32719-3_10
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One explanation is that there has been a transformation of quantity into quality. The advances in the computerization of society have made it possible to accumulate and continue to generate vast amounts of diverse information and have made its use in everyday life economically feasible. To take advantage of these benefits, a digital transformation of “people, data, and processes” is required. In Russia, the digital transformation turned practical with the adoption of the program “Digital Economy of the Russian Federation” on July 28, 2017. It lists Big Data among the nine key technologies. The Personnel and Education direction is among five basic ways of the digital economy development. At the level of an individual company, its “people are at the center of digital transformation”. The People ecosystem is one of the four critical organization ecosystem layers [1]. In many ways, this is the area of competence of human resources management (HRM). There is, therefore, no doubt that research in the field of digital transformation, Big Data and HRM is relevant. The subject of our publication is at the intersection of these. The purpose of the work is to empirically study the changes that occur in the human resources management system of an organization under the influence of Big Data technology.
2 Materials and Methods According to one definition, digital transformation is “the process of integrating digital technology into all aspects of business, requiring fundamental changes in technology, culture, operations, and value delivery”. Together with the structural changes and financial aspects, they form “dimensions” of the corporate digital transformation strategy [2]. It gives priority to the practical application of Big Data, artificial intelligence, Distributed Ledger Technology and a number of other technologies [3]. It is currently difficult to clearly define what is meant by digital HR transformation. In a narrow sense, it may include, for instance, online interviews, the use of computer analysis systems, the organization of network interactions between employees and online personnel training [4]. The HR functions subject to digitalization are systematized in [5]. The use of Big Data is one of the trends of digital transformation of HR departments. A publication of the National Institute of Standards and Technology of the United States lists 23 definitions given by various authors. Rather conventionally, they can be classified into 3 groups, covering (1) the description of the properties of Big Data (very large volume, velocity, variety, etc., often referred to as VVV), (2) hardware and software for their collection, processing and storage, and (3) applications of Data Science to the society or an individual company. The first group of definitions gives an ontological idea of the subject. The second group reflects the “engineering” view regardless of the problem area of its application. Finally, in the third group, Big Data is understood as Big Data Analytics. Here “big data is the broad name given to challenges and opportunities we have as data about every aspect of our lives becomes available. It’s not just about data thought; it also includes the people, processes, and analysis that turn data into meaning” (J. Ferguson) [6]. Our position pertains to the third point of view.
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Big Data types are subdivided into structured data and unstructured data. An example of the first type is the traditional relational data model. The second type of data is microtexts with comments, social relationship data, and other options. Automation of HR decision making with the use of Big Data technology concerns such tasks as managing the profile of success in hiring, solving the issue of staff turnover and building a digital footprint of an employee [7]. The exclusive experience of leading companies confirms the possibility of using Big Data in HRM [8]. Specialist surveys, for example, [9] show the state of digital transformation in companies from various countries, the experience gained and the emerging problems. As a rule, cases relate to the achievements of leaders. They provide valuable information, but the opportunities that the leaders have are often very different from those of other market participants. It is no coincidence that there is an opinion that “the digital transformation hype is not reflected by reality”. Therefore, it is important to know the views of the “median” organizations. We must also take into account the fact that “although technology might be a prime element in many public issues, nontechnical factors take precedence in technology-policy decisions” (Kranzberg’s Fourth Law). We summarize the points above in the form of the following hypothesis: H1: The CEO resolution to introduce Big Data technologies in HRM is not an unconditionally predetermined organizational goal and depends on a set of external and internal factors specific to a particular organization. The publications on digital transformation focus on the description of the technical aspects of new technologies, their possible applications, as well as on their direct impact on organizational development. Meanwhile, “technology’s interaction with the social ecology is such that technical developments frequently have environmental, social, and human consequences that go far beyond the immediate purposes of the technical devices and practices themselves, and the technology can have quite different results when introduced into different contexts or under different circumstances” [10]. Thus, “The Civil Rights Principles for the Era of Big Data” state that it is “vitally important that these [new] technologies be designed and used in ways that respect the values of equal opportunity and equal justice”. They are mainly fixed in the General Data Protection Regulation (GDPR), and, to a lesser extent, in the Russian law “On Personal Data”. The building blocks of one of the Big Data classifications include Data, Information, Knowledge, Understanding, and Wisdom [3]. By positioning “data” at the bottom and the other blocks above it, one on the other, we produce the generalized structure of Big Data. The future and “human” estimates are taken into account at the top, in the fifth category of wisdom of the final result. Among other components, it involves ethics and morals. Theoretical analysis with the use of theories of ethics to Big Data revealed that “the collection and use of Big Data has little to recommend it from an ethical perspective” [11]. This explains the negative attitude of many people to the use of Big Data related to them. However, Big Data has already shown high potential for both income generation and their potential to bring improvement to everyday life. Balancing between economic benefits and ethical questions, it is necessary to look for ways to mitigate the contradictions between them. Ethics and morality are closely related to trust between Big Data stakeholders. It is a factor of crucial importance, as “when it breaks down… there are
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serious repercussions on both sides”. To establish and reproduce trusting relations, it is necessary to anticipate threats to trust. This is all the more relevant because “any data about people inevitably raises privacy issues”, in which “abuse or sloppiness could do untold damage to the emerging field [Big Data application]” [12]. As applied to the subject of our work, the above is generalized in the form of a hypothesis: H2: the ethical component is the main factor that determines the attitude of employees/candidates to the use of Big Data concerning them.
3 Research Methodology Our publication focuses on empirical research, so we will not go into the details of the underlying theories. The theoretical basis is the sociology of management and some areas of general sociology. In general, personnel management is understood as a social institution. Organizations are considered with the use of system and structural-functional analysis methodology. The content of theories can be found, for example, in textbook [13]. Let us consider in more detail the methodology of the empirical part. It discovers the views of both HR professionals and employees/applicants. The first group was interviewed, and the second gave answers to the questionnaire. During the interview, the following research questions were asked: 1. The characteristics of HR data in the modern Russian organization. 2. The concepts that personnel professionals have of Big Data technology and Big Data Analytics. 3. Opportunities that Big Data analytics can provide for human resources management. This question included: – in which functional HR subsystems it takes place; – direction of HR management transformation. 4. Competencies of HR employees which are important when using HR analytics of Big Data. 5. Difficulties and barriers in the implementation of HR analytics with the use of Big Data. The survey was conducted to study the opinion of respondents about the use of Big Data about them by the organization. The main research questions included in the questionnaire were the following: the ethical feasibility of the collection and use of Big Data by employers; the changes in trust between an employee/candidate and an employer. The study consists of three main stages: development of the array of tools, data collection and processing. The interview is semi-structured. The questions for the questionnaire were developed on the basis of the understanding of Big Data as HR analytics. For interviews five leading specialists from different Russian companies were selected. They agreed to give interviews on condition of anonymity. The questionnaire was published on Google Forms online platform and was available for a month.
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The invitation to participate, with a link to the questions, was posted in the social network VKontakte, as well as distributed via email for further distribution; 82 people responded. Sampling type: haphazard; snowball technique was used. This is nonprobability sampling. The initial processing of questionnaires included checking for “bad” data and excluding it when it was detected. Deductor Studio Academic software was used. All data were suitable for further processing. After the responses were arranged as a table, a descriptive analysis was performed, and regression weights were calculated.
4 Results and Discussion 4.1 Interviews with Experts The experts interviewed included the following: a leading specialist of the HR department of a large University (E1); the head of the HR department of a large University (E2); HR Director of a medium-sized commercial IT company (E3); a chief HR manager of a large international company (E4); an HR manager of a medium-sized logistics company (E5). HR data is characterized by sufficiently large volumes, diversity, and veracity requirement. These are the most important features of Big Data. The main body of the information about personnel is processed by means of relational databases. Some experts expressed professional interest in the personal information of the employee, which could be useful in the formation of career strategies and the construction of internal communications. The use of personal information is difficult because of its unclear boundaries and the ethical aspect. In general, experts highly appreciate the potential of Big Data technology in HR management. They perceive it, first of all, as analytical methods. Not all specialists view the accumulated HR data as Big Data. In which functional HR subsystems? Experts pointed to the possibility of using Big Data technology in the recruitment, selection and evaluation of candidates, adaptation and training of personnel, their motivation, development and loyalty enhancement. Automation and analytics make it possible to reduce the amount of routine work, as well as to obtain new information about employees and their interactions, to evaluate the efficiency of HR solutions. Direction of HR transformation? Modern HRM remains largely subjective. Experts recognize the feasibility of broad implementation of objective information based on Big Data. At the same time, it is important for HR specialists not to lose their professional “skills when delegating things to the algorithm and no longer doing these things ourselves” (a phrase from the interview with A. Khachuyan). Many human resources professionals lack an understanding of analytical approaches, which does not let them interact with data in a meaningful way. However, not all HR specialists have to be math and programming experts. It is important to follow the principle of Ockham’s Razor: “Don’t multiply entities beyond necessity”. To perform analytics based on Big Data in a large organization, it is advisable to create a special position for it, or, based on practical considerations, to outsource this work.
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Speaking about the barriers in the implementation of Big Data technology in HRM, experts highlight the high costs of its introduction, the lack of competencies, the fear of the significant changes which are required, the low demand for the implementation of Big Data technologies in the organization, as well as some other aspects. The important factors are the personal interest of HR department managers in obtaining an objective picture, as well as the lack of understanding by the company’s employees of the need to collect more detailed information about them. In general, the introduction of Big Data technologies in HRM is not a panacea and depends on many objective and subjective factors which exist in a particular organization. As can be seen, the opinions and arguments of the interviewed experts testify in favor of hypothesis H1. 4.2 Questionnaire Survey of Employees and Candidates The summary statistics of the socio-demographic section of the questionnaire is as follows: – 82 people participated in the survey, including 49 women (59.8%) and 33 men (40.2%); – the majority of participants were aged between 18 and 24 (64.6%); – the majority of respondents have work experience not exceeding 3 years (64%), with only 26% who had more than 5 years’ experience. The questionnaire is aimed at identifying the attitude of employees and candidates to the use of Big Data information about them obtained from various sources. It is classified into internal (derived within the organization) and external (e.g., from social networks) information. The emphasis was made on external information. The presentation is based on the following scheme: the wording of the question in the questionnaire, the distribution of responses to it and their brief description. In the distribution of responses, the percentages may not total 100 due to rounding. Question 1. “Do you think it is important for the employer to know a broad range of information about you to treat you individually on the current job?”. Distribution of answers to question 1: “absolutely unimportant”: 9%, “not important”: 16%, “does not matter”: 34%, “important”: 29%, “extremely important”: 12%. As can be seen, within this sample, about a third of respondents gave neutral answers to the question. Let us notice the fact that the number of “supporters” is almost twice as high as the number of “pessimists”. Question 2. “You know that your manager has a variety of internal information about you (team relationships, qualifications, performance, number of projects, incidents at work, etc.). How does this knowledge affect your trust in your manager?” (see Fig. 1). Question 3. “You know that your manager has a variety of external information about you (social networks, job search platforms, loan history, etc.). How does this knowledge affect your trust in your manager?” (see Fig. 1). From Fig. 1, it is seen that almost half of the respondent’s state that their trust in their manager “will not change”. It is interesting to compare the side columns, which indicates the multidirectional impact of different data types. About half of the respondents believe
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Fig. 1. Distribution of answers to questions 2 and 3.
that the use of internal information about themselves increases the level of their trust in the employer, while the use of external reduces the trust. Question 4. “You were asked to include links to your profiles in social networks in the contact details section of your resume. How do you feel about the fact that the employer will collect and process a broad range of information about you from social networks to make a hiring decision about you?”. Question 5. “Do you think it is important for the employer to know a broad range of information about you from the Internet to make a hiring decision in your respect?”. Question 6. “Is it ethical for an employer to collect and use data concerning you (social networks, loan history, forums) to make a hiring decision in your respect?”. The distribution of responses received: – Almost ¾ of the respondents have an indifferent or negative attitude to the collection of information from social networks by the employer, some of them expressing an extremely negative view, and about the same number of the participants believe that this information is neutral or not important for making an employment decision. – The widespread opinion about the importance of taking into account the ethical aspect when using data from social networks, loan histories, or online forums, was confirmed. Almost half of the respondents consider the use of such data at the recruitment stage to be unethical. For the purpose of a combined valuation of the importance of the factors influencing the attitude of the candidates to the collecting of Big Data concerning them by their employer, a regression model was developed. Its structure and weights are shown in Fig. 2 (see Fig. 2). Explanatory variables are as follows: Trust_ext (uses data series of individual answers to question 3), Importance (question 1), Ethic (question 6). Response variable is denoted as Relat_to_BD (question 4). The regression weights for Trust_ext, Importance, Ethic are significantly different from zero at the 0.001 level. The diagram shows their standardized estimates. Coefficient of determination has a value of 0.58. As can be seen, the attitude of respondents to the use of Big Data is largely determined by the importance of information for the individualization of the labor process, its impact on the trust in the manager, as well as the ethical aspect. Thus, hypothesis H2 was partially confirmed in the sense that in addition to ethics it is necessary to take into account the
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Trust_ext err ,34 ,34 ,58
Importance
-,36
-,38
,38
Relat_to_BD
-,28
Ethic
Fig. 2. Structure and weights of the regression model.
influence of other variables. There is a positive relationship between the first two factors and the output variable, and a negative relationship between the third factor and the response. To complete this point, we would like to note the following. Since nonprobability sampling was used, it is not entirely correct to extrapolate the above numerical values (%) beyond the sample. However, the data provide an indication of the current state and trends in the area under consideration. 4.3 Management Recommendations Recommendations for digital transformation of personnel management include the following provisions: – to develop a strategy for the digital transformation of HRM and align it with the provisions of a similar document for the organization as a whole; – to specify the HR information which is valuable for the company, outline its place in the HR strategy of the organization and determine whether to use Big Data Analytics; – to assess the appropriateness of the assignment of an employee of the HR department for managing digital transformation. The provisions on the Chief Digital Officer from the project of the Ministry of Economic Development of the Russian Federation [14] can be used as the basis for the list of duties, tasks, functions and powers. An alternative to the above can be outsourcing of HR tasks with the use of Big Data; – to organize teamwork involving HR employees and IT specialists in the field of Big Data aimed at the development and implementation of analytical tools taking into account the specifics of the organization [15–17]. – to complement and develop the competences of HR department employees in the field of Big Data and in Data Science in general. These include knowledge of standard methods and analytical tools useful for HRM [16, 10] the ability to use and adapt to the current requirements the template software which implement that, as well as information security skills required for operations with data; – in HR management to prevent possible conflicts associated with the use of Big Data concerning employees or candidates, it is necessary to take into account the importance of information for the individualization of the labor process [18–19], its impact on the trust in the manager, as well as the ethical aspect.
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5 Conclusion The main results obtained in this research are as follows: – HR digital transformation of the organization under the influence of Big Data is considered within the framework of a holistic approach of sociology of organizations and HR management; – the views on the problem of the use of Big Data by both parties to the HR management system interaction (HR specialists and employees) are outlined, which contributes to its better understanding and adoption of better management decisions; – management recommendations are provided on the use of Big Data in HRM. The areas for further research include, in particular, the development of methods for using unstructured data in HRM with due account for ethical constraints and their impact on the processes in the organization, as well as the specification of management decisions in the context of the digital transformation of HR management functions. The areas for further research include, in particular, the development of methods for using unstructured data in HRM, with due account for ethical constraints and their impact on the processes in the organization [20, 21], as well as the specification of management decisions in the context of the digital transformation of HR management functions in terms of value creation [22]. Acknowledgments. The work was performed under the Russian Science Foundation grant 1918-00210 «Political ontology of digitalization: Study of institutional bases for digital forms of governability».
References 1. Geissbauer, R., Lübben, E., Schrauf, S., Pillsbury, S.: Global digital operations study 2018 Digital champions (2018) 2. Hess, T., Benlian, A., Matt, C., Wiesböck, F.: How German media companies defined their digital transformation strategies. MIS Q. Exec 15, 103–119 (2016) 3. Yao, Y., Sullivan, T., Yan, F., Gong, J., Li, L.: Balancing data for generalizable machine learning to predict glass-forming ability of ternary alloys. Scr. Mater 209, 114–366 (2022) 4. Wang, C., Zuo, M., An, X.: Differential influences of perceived organizational factors on younger employees’ participation in offline and online intergenerational knowledge transfer. Int. J. Inf. Manag 37, 650–663 (2017) 5. Smirnova, A.M., Zaychenko, I.M., Bagaeva, I.V.: Formation of requirements for human resources in the conditions of digital transformation of business. In: Proceedings of the International Conference on Digital Technologies in Logistics and Infrastructure (ICDTLI 2019) (2019) 6. Maghsoodi, A.I., Riahi, D., Herrera-Viedma, E., Zavadskas, E.: An integrated parallel big data decision support tool using the W-CLUS-MCDA: a multi-scenario personnel assessment. Knowledge-based Syst 195, 105749 (2020) 7. Kempeneer, S.: A big data state of mind: epistemological challenges to accountability and transparency in data-driven regulation. Gov. Inf. Q. 38, 101–578 (2021) 8. Scholz, T.M.: Big Data and Human Resource Management. Edward Elgar Publishing. pp. 69– 89 (2019)
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9. Schultz, C.: The Future of HR. In: Beyond Human Resources - Research Paths Towards a New Understanding of Workforce Management Within Organizations, IntechOpen (2021) 10. Tashenova, L., Babkin, A., Mamrayeva, D., Babkin, I.: Method for evaluating the digital potential of a backbone innovative active industrial cluster. Int. J Technol. 11(8), 1499 (2020) 11. Herschel, R., Miori, V.M.: Ethics & Big Data. Technol. Soc 49, 31–36 (2017) 12. Editoria. A matter of trust. Nature. A matter of trust 449(7163), 637–638 (2007) 13. Tarando, E.E., Borisov, A.F., Chelenkova, I.Y., Pruel, N.A., Sinyutin, M.V.: Corporate governance: mechanisms for control and alignment of interests of participants of corporate relations in the transitive economy. Mediterr. J. Soc. Sci. 6, 118–129 (2015) 14. Saarikko, T., Westergren, U.H., Blomquist, T.: Digital transformation: five recommendations for the digitally conscious firm. Bus. Horiz. 63, 825–839 (2020) 15. Barykin, S.Y., Kapustina, I.V., Kirillova, T.V., Yadykin, V.K., Konnikov, Y.A.: Economics of digital ecosystems. J. Open Innov. Technol. Mark. Complex 6, 1–16 (2020) 16. Malevskaia-Malevich, E., Demidenko, D., Yalymov, S.: Risk-based assessment of the efficiency level of the digital environment and its key institutions. In: ACM International Conference Proceeding Series (2020) 17. Skhvediani, A., Sosnovskikh, S., Rudskaia, I., Kudryavtseva, T.: Identification and comparative analysis of the skills structure of the data analyst profession in Russia. J. Educ. Bus. 295–304 (2021) Published online: 16 Jun 18. Rodionov, D.G., Konnikov, E.A., Nasrutdinov, M.N.: A transformation of the approach to evaluating a region’s investment attractiveness as a consequence of the covid-19 pandemic. Economies. 9, 1–20 (2021) 19. Tereshko, E., Rudskaya, I.: Digitalization of the construction complex of the region as a factor of development RIS. IOP Conf. Ser.: Mater. Sci. Eng. 940(1), 012022 (2020) 20. Barthel, P., Fuchs, C., Birner, B., Hess, T.: Embedding digital innovations in organizations: a typology for digital innovation units. WI2020 Zentrale Tracks 780–795 (2020) 21. Balducci, B., Marinova, D.: Unstructured data in marketing. J. Acad. Mark. Sci. 46(4), 557– 590 (2018) 22. Mansour, O., Ghazawneh, A.: Value creation in digital service platforms. In: Proceedings 28th Australasian Conference Information System ACIS 2017 (2017)
E-Government in Russia: Developing and Improving the Quality of Implementation of the e-Government Program K. Nazmetdinova(B) and S. Kalmykova Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. In today’s world, the level of digital integration plays an important role in the development of the state. One aspect of digital integration is the implementation of digital government. The concept of digital government emerged relatively recently, and the beginning of implementation in many countries is at the end of the 20th and beginning of the 21st century. Now this direction of development is significant as an indicator of digital development of the country. The creation and implementation of the digital ecosystem of government allows optimizing the process of interaction between public authorities and the population, as well as improving the efficiency of public bodies through the use of digital technologies. The purpose of the study was to formulate proposals to improve the efficiency in the development of state digital ecosystem of the Russian Federation, based on the positive experience of the leading countries in the world rating of e-Government program implementation. The research methods used in this article are theoretical research methods: analysis of statistical data and documents within the scope of the research topic. A comparative analysis of the implementation of e-Government program in Russia and the top three countries in the ranking of countries for the development and implementation of digital government was also carried out. This was followed by identifying the reasons for the success of the leading countries in terms of the effectiveness of government e-ecosystem development. As part of the study, measures were proposed to improve the efficiency of the Russian state’s digital ecosystem development. These measures included: solving the problems hindering the development of e-Government in Russia, as well as borrowing the successful experience of the leading countries in the digital government rating. Keywords: E-government · E-portal · Digital Government Ecosystem · Digital Services · Information Infrastructure · Information and Communication Technology · Public Services · Digital transformation
1 Introduction In today’s world, many familiar processes are going digital. The development of information technology and its integration is becoming a key factor in economic growth and scientific and technological development. The introduction and continuous improvement © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 140–154, 2023. https://doi.org/10.1007/978-3-031-32719-3_11
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of information technology has an impact on all spheres of society, from domestic use to the optimization of production processes and increasing the speed and efficiency of public services to the population [1, 2]. The introduction of information technologies has a mainly positive impact on business, scientific, governmental, and other activities, and leads to an improvement in the quality of life of people. The problem of the low level of development of the information ecosystem of the state in a number of countries is quite acute. Since the state can be perceived as a representative of the residents of its country, it reflects the level of societal development, including information development. Therefore, it is the state that should be the main initiator in terms of introduction and application of modern ICT, setting an example for the commercial and household sectors. The concept of “e-government” can be interpreted in different ways: as a new form of organization of activities of public authorities; as informatization of public administration; as a new form of organization of activities of public authorities, providing, through extensive use of information and communication technologies, a qualitatively new level of efficiency and convenience for organizations and citizens to obtain public services and information about the results of public authorities’ activities, and so on [3]. The author of this article gives the following definition: e-Government is a single digital platform or a set of them in the information and telecommunication network “Internet”, which is managed by public authorities in order to simplify the provision of government services to individuals and legal entities, as well as to optimize the performance of official activities and interaction of public authorities. The development of e-Government services opens up great opportunities for optimizing the work of the authorities: reducing the time for carrying out various paperwork operations by introducing electronic document management; obtaining quick access to necessary information and prompt data exchange through corporate information systems, and much more. In addition, the convenient electronic service of public services increases the trust and loyalty of citizens, as it saves a lot of time in bureaucratic procedures. In today’s world, programs for the formation and development of e-Government are being formed and implemented in most countries. This sphere is actively expanding and modernizing. The development of e-government, as a relatively new and rapidly developing sphere, requires a comprehensive approach: equipping state structures with modern equipment and introduction of advanced technologies, improving the system of public services in digital format, integrating portals of all state and municipal authorities into a single electronic resource, transfer of paperwork into digital format, and improving digital literacy of the population.
2 Purpose At the beginning of the 21st century, the importance of digitalization of the state was recognized and enshrined in many documents. At the international level, the World Summit on the Information Society was held in the form of a two-stage summit in accordance with United Nations General Assembly resolution 56/183. The first phase was held in 2003 in Geneva and the second in 2005 in Tunis. Thus, Russia adopted the Tunis Commitment and the “Tunis Agenda for the Information Society”. The program
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mentioned, in Article 48, noted “the increased use of ICTs by governments to deliver services to citizens, followed by a call for countries which have not implemented this in their territories to develop national programs and strategies for e-government” [4]. Following the Tunis Summit, a Digital Solidarity Fund was established, headquartered in Geneva. The purpose of this fund was to narrow the global digital divide. However, the fund was already dissolved in 2009 because it proved to be unsustainable, as such a goal requires huge funding.
3 Results 3.1 Level of E-Government Development in Russia and Globally The implementation of the digital government agenda is uneven, with most European countries doing quite well, as well as advanced Asian countries such as South Korea and Japan, while much of Africa is mostly lagging. This is evidenced by the UN study, in a regular report presented in tabular, graphical and textual form [5]. To assess the level of e-Government development, the United Nations regularly conducts research that results in a ranked list of UN member states with the corresponding values of the indicator. “The Global e-Government Index is a composite indicator that assesses the readiness and capacity of national government structures to use information and communication technologies to deliver public services to citizens. The study is conducted to examine the development of both the institution of e-Government itself and innovative trends in the socio-political systems of states” [6]. There are three main components to the assessment: • Extent of coverage and quality of Internet services. • Level of ICT infrastructure development. • Human capital. According to the United Nations study, many European countries are quite successful in implementing e-Government development on their territory. The data in Table 1, compiled by the authors on the basis of UN statistics [7], shows the results of the UN study over three years at eight-year intervals, where Estimate shows the absolute values of the e-government development index and Rank shows the ranking of UN member states by the value of the indicator under study: To explain Russia’s lagging behind the leaders of the ranking in question by assessing the values of the parameters based on which the EGDI index is calculated. Such parameters include: scope and quality of online services (Online Service Index, OSI), status of the development of telecommunication infrastructure (Telecommunication Infrastructure Index, TII), inherent human capital (Human Capital Index, HCI). Telecommunication Infrastructure Index. This indicator assesses the level of development of telecommunication infrastructure. To analyze this indicator, we need to look at its components separately. The first component is Mobile-cellular subscriptions (per 100). As of 2020, Russia has shown high result, surpassing the leaders of the EGDI rating with a value of 163.59. At the same time, South Korea had 137.54, Denmark 123.34 and Estonia 145.13 (URL:
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Table 1. E-Government Development Index. 2020
2012 Rank of 193
Estimate
2004
Country
Estimate
Rank of 193
Estimate
Rank of 193
Denmark
0,9758
1
0,8889
4
0,9047
2
Republic of Korea
0,956
2
0,9283
1
0,8575
5
Estonia
0,9473
3
0,7987
20
0,7029
20
Finland
0,9452
4
0,8505
9
0,8239
9
Sweden
0,9365
6
0,8599
7
0,8741
4
Great Britain 0,9358
7
0,896
3
0,8852
3
USA
0,9297
9
0,8687
5
0,9132
1
Netherlands
0,9228
10
0,9125
2
0,8026
11
Russian Federation
0,8244
36
0,7345
27
0,5017
52
https://data.worldbank.org/indicator/IT.CEL.SETS.P2). This characterizes positively the user activity and the availability of mobile services in Russia. The second component is Fixed-telephone subscriptions (per 100). This indicator refers to telephone lines connecting a customer’s terminal equipment. According to the data for 2020, the index values are: South Korea (48,27), Estonia (24,47), Russia (18,97), Denmark (17,39) [8]. Based on the data obtained, we can conclude that South Korea is the absolute leader among the studied countries, having surpassed the nearest leader, Estonia, by almost twice the number of main fixed telephone lines per 100 inhabitants. The third component is Wireless broadband subscriptions (per 100 inhabitants). The values of this indicator by country are as follows: Estonia (157.60), Denmark (138), South Korea (114.90), Russia (97.40) [9]. The indicator values suggest that the sum of satellite broadband subscriptions, terrestrial fixed wireless broadband subscriptions and active mobile broadband subscriptions on the public Internet in Russia are much lower than in other countries studied. The fourth component, fixed (wired)-broadband subscriptions (per 100) had the following values: Denmark (44.4), South Korea (43.55), Estonia (31.33) and Russia (23.21) [10]. This suggests that fixed subscriptions to high-speed access to the public Internet are lower in Russia than in the leading countries of the EGDI rating. The fifth component is Individuals using the Internet (% population). According to the world statistics for 2020, the values of this indicator in the countries studied were: Denmark (96.55), South Korea (96.51), Estonia (89.06), Russia (84.99) [11]. The index shows that Russia has slightly fewer active Internet users than the other countries surveyed. Government Online Service Index evaluates the scope and quality of government online services. The index value can range from 0 to 1, where 1 is the maximum possible positive result. The highest scores were recorded in South Korea (0.98) and Estonia
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(0.77). The values for Russia and Denmark were 0.71 and 0.66 respectively. Thus, it can be concluded [12]. Thus, it is possible to conclude that the Russian government’s capability to provide services to its citizens electronically is quite low. The Human Capital Index (HCI) is based on the recognition that education is a fundamental pillar in supporting human capital, measuring adult literacy, average years of education, etc. The indicator ranges in values from 0 to 1, where 1 is the best result. According to the research results, the values of the indicator for 2020 were: South Korea (0.80), Denmark (0.76), Estonia (0.76), Russia (0.68) [13]. Thus, the value of this indicator in the current study, Russia is slightly lower than in the other surveyed countries. From the start of the study, in 2003, to 2008, the US has been at the top of the rankings. This fact is explained by the fact that the United States was one of the first countries that began to form an e-Government system [14]. Already in 1993, the United States created the President’s website, and “later the new trends were picked up in Western Europe: in 1995, the European Commission established the Information Society Forum and in 1998, the program “Electronic Europe” was adopted” [15]. The early formation of e-Government explains the fact that most of the top 10 leaders in e-Government development are from the European Union [16]. However, the 2010 ranking moved the lead from the US to South Korea, the only Asian country to rank in the top 10 for e- government development since 2004. One of the first strategic documents of modern Russia in the direction of building an information society was the 1998 Concept of State Information Policy of the Russian Federation. The main strategic goal of this concept was defined as the construction of a democratic information society and the country’s entry into the global information community. In the next phase, The federal target program “Electronic Russia” was developed, which operated in the Russian Federation from 2002 to 2010. The main objective of this program was to improve the quality of interaction between the state and society, as well as to optimize interdepartmental interaction using information and telecommunication technologies. However, disagreements between the Ministry of Digital Development, Communications and Mass Media and the Ministry of Economic Development arose during implementation, as the latter believed that the “Electronic Russia” program should be an instrument of administrative reform, while the Ministry of Digital Development did not support this position, although it was not negative. In addition to this there were problems of lack of funding. Eventually the websites of the agencies, the state information and analytical system of control and accounting bodies and some other information resources were created. However, the goal of increasing the efficiency of interaction with citizens using ICT was not achieved as few interactive services allowing users to receive public services faster and easier were developed. In the next stage the Concept of e-government”, which was approved in 2008. This “Concept defines the main priorities, directions and stages of formation of e-Government in the Russian Federation for the period until 2010. In the first stage, in 2008, involved the development and approval of the main documents. Thus, among the developed documents there were: RF Government Decree N 358 of 6 May 2008 “On approval of the regulation on keeping registers of small and medium-sized enterprises - recipients of support and on the requirements to the technological, program, linguistic, legal and
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organizational means ensuring the use of the above registers”, RF Government Decree N 622-r of 4 May 2008 “On approval of the concept of an interdepartmental integrated automated system of federal executive bodies controlling state border crossing points”. “On approval of the concept of creating an interdepartmental integrated automated system of federal executive bodies exercising control at checkpoints across the state border of the Russian Federation”. Regional documents, such as the Decree of the Moscow City Government dated August 5, 2008 “On the City Target Program “Electronic Moscow (2009–2011)”, were also developed. In the second phase, from 2009 to 2010, practical implementation took place. Thus, in December 2009, the Gosuslugi portal, an interactive service for providing services to the population, developed by the Russian IT company NVision Group, was officially launched. Since 2014, the Government Decree “On Approval of the State Program of the Russian Federation ‘Information Society’” has been in force in Russia. This program establishes a number of objectives as part of the digital transformation until 2030. These goals include: improving the accessibility and quality of public services; increasing the share of households with Internet access to 97%, while according to official statistics, as of 2019, the share is 76.9% [17]; achieving 100% “digital maturity” in key economic and social sectors, such as healthcare and public administration, and others. 3.2 Danish e-government “Denmark, like the most other OCED members, conceives e-Government not as a goal in itself but as a means to achieve policy ends. E-Government is regarded as the most effective tool - i.e., “a necessity and not an option” - to reach efficiency goals within the public sector” [18]. E-Government is seen as a tool to improve the competitiveness of the economy, solve problems in the social sphere to improve the quality of life of citizens. Among the European Union countries, Denmark was one of the first countries to launch an e-Government programme on its territory, in 2001. This resulted in the creation of the e-portal borger.dk. Now “A high degree of internet penetration has furthered the digital transformation of the Danish society:94% have internet at home, 89% use the internet daily, and over a 12-month period 88% of citizens interact digitally with public authorities” [19]. Human capital in Denmark ranks 7th among EU countries in the availability of digital competences among the population. A total of 71% of Danes are noted to have digital skills, of which almost 50% have skills above the basics. The percentage of ICT professionals reached 4.4% in 2018, a higher proportion of the workforce than in the EU (3.7%). The “Digital Skills for All” project is now one of six strategic directions in Denmark’s human capital growth plan [20]. Researchers note the high degree of trust citizens have in their country’s government and its electronic service. According to the OCED survey, the results for 2020 show that 71.6% of the Danish citizens aged 15 and over surveyed expressed confidence in their government. This is quite high, as the leader of this ranking, Switzerland, had a trust level of 84.6%. In the same study, the level of trust in the government of Russian citizens left 47.8%, which means that a large proportion of those surveyed expressed distrust in the government [21]. According to a study by the Economist Intelligence Unit, the
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country, the country with the highest degree of readiness for the information society is Denmark. This European state is characterized by the highest level of development of the ICT sector, which is due to the strong support of information technology by the state and the rapid penetration of advanced digital technologies, especially broadband access to the Internet [22]. It is important to consider the standard of living of citizens and assess their ability to purchase gadgets, access the internet and use electronic portals. Table 2, compiled by the authors on the basis of global statistics from “The world bank” [23], presents data reflecting the standard of living of citizens in some countries: Table 2. Population by GNI per capita for 2021. Country
GNI per capita (US$)
Rank
Denmark
63 950
10
Finland
50 010
18
Germany
48 580
19
Great Britain
42 220
27
Republic of Korea
33 790
30
Estonia
23 260
41
Russia
69
3.3 The Experience of e-Government in South Korea The peculiarity of the historical formation of e-Government in the Republic of Korea is that in the first stage, “Government 1.0”, automation was aimed at computerizing the administrative system, digitizing the main work processes of the public sector. The process of transferring interaction with citizens online was launched only in the second phase, Government 2.0. Already by the end of 2007, the end of the second phase, all processes of interaction of citizens and businesses with the government were digitized. A key result of the ongoing reforms was the possibility of two-way communication between government and citizens via the internet and the automated provision of limited information on applicants’ requests electronically [24]. It follows that the South Korean government has found it more promising and efficient to start implementing e-Government projects by modernizing government processes before shifting the focus to remote interaction and online service delivery to citizens. South Korea has now moved into the implementation phase of Government 3.0, or Smart Government, the goal of which is to move towards personalization through the provision of unique, personalized services and the introduction of innovative G2C and G2B services. The goal is expected to be achieved through the application of ICT and Big Data.
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Another important feature of the Korean e-Government is the On-Nara workflow management system to which all work computers of Korean civil servants are connected. On-Nara is a unified electronic document management system for preparing, approving and sending documents as well as interacting with citizens [25]. This system greatly facilitates and optimizes interaction between government agencies as well as with citizens and businesses. 3.4 E-Government Services in Estonia One of the most successful countries in terms of implementing e-Government has been Estonia. To date, the country is not at the top of the list in terms of the most important technological and economic indicators, with Estonia in third place in the ranking of eGovernment development countries. For example, GDP per capita at purchasing power parity for the year 2020 was 35215.36 U.S. dollars, while the indicator of the leader, Luxembourg, was 112226.91 U.S. dollars [26]. The fact that at the beginning of the 21st century the number of Internet users per capita was already higher than in many other EU countries has played an important role. This assertion can be supported by a table compiled by the authors based on data from “The world bank” [27], showing the proportion of the population using Internet technology at different points in time (Table 3): Table 3. Individuals using the Internet (% of population). 1995
2000
2005
2010
2015
2020
Denmark
0,1
1990
3,83
39,17
82,74
88,72
96,33
96,55
Republic of Korea
0,023
0,82
44,7
73,5
83,7
89,896
96,51
Great Britain
0,87
1,895
26,822
70
85
92
94,82
Sweden
0,58
5,1
45,7
84,8
90
90,6
94,5
Finland
0,4
13,9
37,2
74,5
86,9
86,4
92,2
Germany
0,13
1,8
31,7
68,7
82
87,6
89,8
Estonia
0
2,78
28,58
61,45
74,1
88,41
89,06
Poland
0
0,65
7,3
38,8
62,3
67,9
86,8
Russia
0
0,15
1,98
15,23
43
70,1
85
Italy
0,018
0,5
35
53,7
58,1
76,1
23,1
“Estonia already adopted a new Telecommunications Act (2000) in February 2000, which removed all market access restrictions and state controls by 1 January 2001” [28]. Such measures by the government led to open competition in the telecommunications market, which in turn led to the availability of services to the public. The number of Internet users grew, as did the number of qualified specialists in information and communication technology, since “as early as the 1960s, Estonia began investing in its Institute of Cybernetics” [29].
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Today, Estonia ranks third in the ranking of countries for the development of eGovernment [30]. About 99% of public services in Estonia are available remotely. The state has made great efforts to combat digital illiteracy. Today, the government organizes courses on the use of digital technology for senior citizens; makes it compulsory to have school subjects that promote knowledge of information technology for the younger generation; and promotes and encourages the use of modern technology in business. The development of e-Government and the improvement of the internet portal has made it possible to create the E-residency program. Estonia became the first country where any foreign citizen can become an e-resident. This opportunity involves the creation of an electronic digital identity for an alien, which allows, while in another country, secure access to the electronic services of the Estonian state. The E-resident can establish a business association in Estonia, manage and develop it, and conduct transactions in an online bank while in another country [23]. Such an innovation opens up great opportunities for attracting additional capital to the country and, as a consequence, growth of the national economy, attracting attention and interest in the country, leading to an increase in investment activity as well as contributing to the process of globalization and the development of global commercial relations. Today, more and more countries are implementing the E-residency program: Portugal, Japan, USA, Singapore and others have joined the list. The growing interest in the program is indicative of its effectiveness and positive impact on the country’s economy. 3.5 E-Government Service in the Russian Federation The Russian Federation now has a unified state electronic portal “Gosuslugi” where every citizen can set up an account and, by confirming the validity and ownership of the data being registered (through identification at a commercial bank, for example), carry out various operations to interact with the government. Such as: application for foreign passport, payment of state duty, making an appointment at the office and so on. There is also a chat-bot on the portal that helps users to find out their questions. According to Article 2021 of the Ministry of Digital Development, Communications and Mass Media of the Russian Federation, “In 2020, the number of registered users on the State Service portal exceeded 78 million citizens. This is almost 2/3 of all citizens over the age of 14”, “the average daily audience of the portal exceeded 4 million users per day. The number of appeals to the portal last year exceeded 1.5 billion” [31]. This is a rather high level of e-Government portal use, which characterizes Russian e-services from the positive side [32]. However, there are still a huge number of problems with the Russian e-government portal. These problems include: Cyber-attacks on the public services portal. With the development of electronic services and technology comes the problem of increasing crime in the information space. Hackers pay particular attention to portals from which it is possible to extract sensitive information about users and then use it for illegal purposes. For example, in November 2021, an attack was recorded on a portal with a capacity of over 680 gigabits per second. This led to difficulties in logging into the portal. A chatbot was also attacked. Such cases are not isolated. This suggests that despite the high level of security of the Russian e-government portal, hackers manage to find new ways of hacking [33].
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Low level of digital literacy of the population. The most common way to steal accounts on “Gosuslugi” is through phishing emails. This suggests that many users “themselves” hand over information to fraudsters due to their digital illiteracy. According to the think tank’s 2021 study, only 27% of Russians have a high level of digital literacy. There is no doubt that the impact of the coronavirus pandemic has had a major impact on improving digital literacy among Russian citizens. The proportion of Russians with a basic level of digital literacy has risen from 66% in 2020 to 70%, but the proportion of citizens with a high level has remained unchanged from 2020. All the above problems lead to the threat of intruders gaining access to sensitive data of portal users: passport data of the user, SNILS, TIN, electronic digital signature and others. And this, in turn, can lead to various transactions by fraudsters from the user’s account, such as donation of real estate, theft of bank account funds, and even lead to problems with the law for an innocent citizen. “An imperfect legal and regulatory framework for the provision of public services electronically, consisting in the fact that there are many norms in federal sectoral legislation that effectively make digital transformation impossible, such as the requirement to issue only a paper output of a service” [34]; The psychological barrier of the older generation leads to a low demand for eGovernment services [35]. People in the older age group rarely have a sufficient level of digital literacy to freely use the e-Government portal, nor can they overcome the psychological barrier that many people have with regard to digital technology, which is expensive and difficult to use. Devices have become much more affordable and easier to use, but pensioners still prefer to receive public services in a face-to-face format without the use of modern gadgets. That is why, despite the fact that the state provides free training courses for pensioners, not everyone decides to take advantage of this opportunity. The uneven development of the national information space. This problem is caused by a number of factors: different levels of wealth of the regions, population density, high costs of building digital infrastructure, etc. Thus, in 2017, the Central Federal District and the North-West Federal District were the leaders with digital literacy index values of 6.41 and 5.95, respectively. Less developed regions showed much lower results, for example, in the Far Eastern Federal District the value of the mentioned index was 4.17 in 2017 [36]. The level of equipment with digital devices also has a strong influence on the demand for e-Government services: it is higher in large metropolitan areas than in the periphery due to a higher standard of living and developed urban infrastructure. The one-stop-shop principle has not been fully implemented. The lack of a single portal into which the electronic services of all state bodies are integrated leads to difficulties for users in finding the right site.
4 Proposals for Improving e-Government in the Russian Federation Based on this analysis, the following options can be proposed to solve the identified problems of e-Government project implementation in the Russian Federation: Paying more attention to improving e-Government in order to digitalize and increase the efficiency of civil servants. First and foremost, the authorities need to be better equipped with modern technology. This can also be done by developing and introducing
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unified programmes to simplify the interaction of government agencies: electronic document management programmes, user-friendly and informative databases with a reliable security system, and so on [37]. In order to improve the usability of the e-Government portal, the services of other public authorities need to be integrated into a single information platform. In doing so, the interface needs to be carefully designed to be intuitive, as integration can make it much more difficult to use as additional sections and windows are added to the website page. The problem of insufficient digital literacy of the population can be solved by drawing the attention of the state to the importance of this issue: introduction of lessons for high school classes on how to use the services of State Services; wider use of incentives for users of digital government services: discounts, cash rebates, gifts. To overcome the psychological barrier of pensioners, the state can organize call centers with volunteers for counselling and building communication with e-government [38]. It is also necessary to conduct advertising and campaigning in the media actively used by pensioners: television, newspapers. Strengthening the protection of the e-portal against hacking, reducing the risk of leakage of users’ personal data will lead to a higher level of trust on the part of the population. This can be achieved by applying various methods to protect user data, for example, storing personal information in encrypted form on a secure server, with only the last three digits of the document number displayed on the account page. Also, to carry out transactions from the personal account, it is necessary to request additional personal identification information: password, code word, code from SMS [39]. The uneven development of the national information space can be minimized by building infrastructure in small settlements, but this type of activity requires large financial outlays. The creation and development of new, alternative technologies for accessing the Internet can significantly reduce the cost of use but require large financial investment in the development process. Unfortunately, this problem is unlikely to be solved on its own, as it is a consequence of the complex problem of uneven regional development inherent in Russia. The availability and quality of the Internet and the minimal difference in technological and information development between Estonia and Denmark have greatly influenced the success of the governmental e-portals [40]. The introduction of e-residency into the e-Government structure of the Russian Federation will have a positive impact on the country’s economy and will enable foreign entrepreneurs to conduct commercial activities in Russia. Funds received for the benefit of the state from the activities of e-residency users in the form of taxes and other deductions will help recoup the costs of implementation of the project and can subsequently be used to implement other proposals for the development of e-government in the Russian Federation. Based on the results of studies by other authors who have conducted similar research on this topic, it is possible to say that the conclusions are similar to those given in this article. The basic conclusions about the need to develop and improve the e-government infrastructure can be traced in most of the studies devoted to e-government, and the main conclusions concerning the directions of development are largely the same [41]. Thus, the conclusion on the need to expand the possibilities of using the e-ecosystem of the
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national government not only for its residents, but also for foreign users coincides with the study on the development of e-government in Slovakia [42]. The need to introduce modern technologies into the e-government system, including systems of electronic document management, reporting, etc. is also formulated in many studies [43]. At the same time, researchers from different countries point out the similar to the Russian problems, which hinder the successful development of e-government in their countries. For example, one of the biggest challenges for e-government development in Rwanda is that most ICT infrastructure and e-services are concentrated in the capital city, while in the rural areas, where the majority of the population lives, the infrastructure is almost non-existent [44]. In Russia, there is a similar problem of concentration of high-tech infrastructure in large centers: the Central Federal District and the North-West Federal District.
5 Conclusion The study substantiated that the leadership of the world ranking in terms of e-Government development belongs to countries with a high level of digitalization, user activity and digital literacy of the population. The key role in the development of such important aspects is played by government policies focused on the development and implementation of modern technologies. The article explains the reason for the Russian e-ecosystem’s relatively low position in the global ranking by lagging behind in many important aspects-components that make up the EGDI 2020 index. Such components included: Fixed (wired)-broadband subscriptions (per 100 inhabitants), Wireless broadband subscriptions (per 100 inhabitants), Human Capital Index etc. Based on these indices, the study has identified challenges to the successful implementation of e-Government and proposed solutions. It also suggested some measures suitable for borrowing from the experience of foreign countries, which have successfully formed and improved the digital ecosystem of the state. At the same time, we should take into account the fact that it is necessary to develop the digital ecosystem of the Russian state, based on the successful experience of foreign countries, relying primarily on the national territorial and cultural peculiarities of Russia [45]. Recently, in the formation of Russian development programmes, more and more attention has been paid to digitalization, the development and implementation of modern technologies, viewing them as one of the main benchmarks for development. For example, in February 2022, Russia launched a long-term programme to improve digital literacy of the country’s residents. This suggests that Russia is gradually moving towards digitalization, recognizing that technology development is key to the success of a modern state, both in its relations with the citizens of its country and with the global community. Nevertheless, this study has its limitations. In this paper, a theoretical analysis has been carried out to examine the implementation of e-government in Russia and the leading countries in the world ranking of e-government. However, in order to conduct a more in-depth analysis and identify more examples of positive experience that can be applied to improve the Russian digital ecosystem, further research may look at the
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experience of e-government programme implementation in other countries that showed good results in the reporting year as well as in the previous and subsequent study periods. Such countries include the UK, the USA, Scandinavian countries and others [45].
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Economic Efficiency and Social Consequences of Innovations
Implementation Risk of New Distance Learning Technologies A. Chernova and I. Lyukevich(B) Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. Since the beginning of the pandemic, universities have been involved in developing distance learning systems. At the same time, the risks in the implementation of new technologies have become the most important issue, since the implementation risk is difficult to assess. Implementation risk assessment cannot guarantee the successful application of technologies, but it can significantly increase the chances of their effective implementation. This paper discusses the management of the implementation risk of educational technologies and possible mechanisms for its reduction. The purpose of the work is to study the features of implementation risk using the example of distance learning technologies, assess the implementation risk of the proposed block-chain system and develop measures to reduce it. Theoretical relevance is conditioned by the systematization of a new distance learning block-chain technology, which implies performance assessment to increase the confidence of students and teachers in distance learning. Practical value is provided by the approbation of the methods of “expert assessments” and “decision tree”. Keywords: Implementation risk · distance learning · assessment of implementation risk · distance learning technologies
1 Introduction Today, each activity involves various digital technologies. Organizations in all fields of activity (education, finance, healthcare, industry, and so on) are constantly introducing the latest technologies, trying to improve and secure their activities, automate processes, and reduce possible costs [1]. The introduction of new technology is a rather complicated and expensive process, so special attention must be paid to risks. Implementation risks are the most important issue, since the implementation risk is dangerous for the enterprise and is difficult to assess. Thus, the relevance of this work is due to the need to assess the risk of implementation. Underestimation of risk can lead to substantial losses, and even bankruptcy of the organization. Implementation risk assessment cannot guarantee the successful application of technologies, but it can significantly increase the chances of their effective implementation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 157–172, 2023. https://doi.org/10.1007/978-3-031-32719-3_12
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Many projects being implemented are based on Internet websites, consequently new risks (viruses, hacker attacks, cyber terrorism, and so on) appear that impede the implementation of technologies and its evaluation. Therefore, this work will pay attention to the management of implementation risk and possible mechanisms for its reduction. Since the beginning of the pandemic, universities and schools have switched to distance learning, and although now the incidence of a virus has sharply decreased and learning takes place in the usual full-time format, universities continue to develop distance learning systems. The introduction of block-chain technology in the education industry is currently at an early stage. Only a small number of educational institutions have introduced this technology. Most of them are using block-chain to evaluate and share their academic certificates and learning goals. However, researchers in the education sector are confident that block-chain technology can revolutionize the field of education. Block-chain technology distributes records of digital events in a decentralized format in which third parties do not control the information and the transactions associated with it. The main advantages of the block-chain are transparency, authenticity and smart contracts executed by peers, which makes it possible to ensure the reliability and security of online learning. Block-chain also allows us to collaboratively manage records in the academic education system in a decentralized format. Due to intensive development of online learning, it was decided to investigate the main problems of distance learning, and to apply modern approaches to assessing implementation risk using the example of a block-chain system for assessing student progress on a distance learning platform. The object of study in this work is a block-chain technology to ensure increased confidence in online learning based on an improved algorithm for assessing academic performance. The subject of study is the implementation risk of distance learning technologies.
2 Literature Review Any economic activity is characterized by the risk as a combination of the probability and consequences of the adverse events that cause losses to business entities. Currently, much attention is paid to risks, as in the context of digitalization organizations are implementing various digital technologies, automating many processes. As a result, enterprises are forced to manage risks, that is, to monitor and calculate the likelihood of their occurrence, and to think over methods for minimizing risks and eliminating them. One of the most important risks for an enterprise in any field of activity is implementation risk. Implementation risk is defined as the risk of a potential error in the process of developing or launching a project, product or technology. Implementation risk is a combination of individual risks: informational, investment, financial, commercial and professional [2]. Vladimirova A.S. believes that implementation risk belongs to the group of functional risks, which also includes technological, strategic and operational risks [3]. In this classification, the risk of implementation is an independent, separate element. Sultanov I.A. believes that the risk of implementation is a combination of individual risks,
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namely: financial, resource, property and investment ones [4]. Balabanova I.T. states that implementation risk includes commercial risks, risks of direct financial losses, reduced profitability, as well as the risk of lost profits [5]. One of the possible ways to influence the implementation risk in the risk management system is to reduce it. Risk reduction implies either a reduction of possible damage, or a decrease in the likelihood of a negative event. Most often, risk reduction is provided by some organizational or technical measures. Classification by risk reduction mechanisms includes four components: localization, dispersion, avoidance and compensation [6]. The method of avoidance involves avoiding risk, for example, of the implementation of the project, if there is even the slightest possibility of failure. This approach could completely minimize all kinds of risks, but in this case, the development of the enterprise will remain at the same level, and many investment decisions will simply be missed. One of the avoidance mechanisms, in addition to the complete rejection of any solution, is insurance. Thus, the responsibility for possible damage is transferred to the insurance company. However, there are also disadvantages here: it is impossible to insure against all possible risks, and it is also necessary to make insurance premiums, even if the insured event does not occur. Localization applies only to easily identifiable risks. The bottom line is to transfer a risky project to separate units, where they are then tightly controlled. The mechanisms of this method include the creation of subsidiaries that will deal only with projects, while the main activities of the company will remain safe. Another mechanism is the transfer of tasks for the implementation of a project to third-party companies. The dispersion (dissipation) method allows us to distribute risks between business partners: joint-stock companies, concerns and other organizations are created. The reduction mechanism is diversification, that is, expanding the scope of activities, the list of suppliers, and so on, due to which it will be possible to compensate for the damage from the unsuccessful implementation of the project. The last method is compensation. This method is the most difficult, but it allows for a deep analysis, has the largest number of mechanisms, unlike other methods, and is most often used in practice. To reduce the implementation risk, one can single out a strategic planning mechanism, which consists in developing a set of compensatory measures based on threat analysis. It is worth noting that quite a lot of different block-chain systems for distance learning have already been developed. Thus, in 2021, Liang and Zhao compiled a mechanism for assessing the general condition of students based on the block-chain system [7], and Sastri and Banik proposed a block-chain methodology that creates a flexible and secure data transfer by interacting with current educational information. However, these and other studies are mainly aimed at providing access control and certification for online learning platforms, while the learning and assessment processes themselves have not yet been considered [8].
3 Materials and Methods The proposal of a new distance learning technology is based on the ethereal block-chain (Ethereum). It is a block-chain platform that has the Ether (ETH) currency as well as the Solidity programming language. All in all, it is a decentralized public ledger for
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verifying and recording transactions on the block-chain network. Proof-of-Work (PoW) consensus protocol is used. Confidential security is based on the IECCA algorithm. Implementation risk assessment was carried out by a statistical method - a decision tree in the aggregate and an analytical method - by expert assessments. A matrix of paired alternatives has been compiled, where the expert identifies the most significant and least significant criteria.
4 Results The main problem of distance learning is the distrust of users. In order to increase the confidence of students and teachers in distance learning, it is necessary to change the algorithms of the learning management system (hereinafter - LMS), thereby increasing data security, as well as the transparency of systems and assessment. To solve the problem of distrust in distance learning, a block-chain is proposed using an advanced cryptography algorithm based on elliptic curves to assess student performance on an e-learning platform. Online education is based on a software platform focused on meeting the educational needs of users. These systems provide the automation of learning processes, including teacher evaluation activities. LMS provides the opportunity to manage all types of training (electronic, full-time and part-time); testing knowledge and skills; analysis of learning and evaluation of results; providing content and programs; archive of educational materials. As a rule, distance learning is divided into three main blocks that are responsible for different functions such as (1) managing the learning process, (2) interaction between users, and (3) developing learning materials. The control unit (1) has the following functionality: definition of competencies, automated compilation of educational material, organization of LMS and account management, activity recording, technical support, formation of learning outcomes, comparative analysis of learning. The interaction block (2) of the participants includes the main means of organization: video and audio communication, messenger, wiki, personal account, blog, specialized forums and much more. Development (3) refers to the development of educational content. This block solves a large number of tasks with the help of tools in the form of tests, verification tasks and multimedia courses. In this case, much depends on quality software. To build a unified information environment, it is necessary to integrate various systems: personnel management, performance evaluation, regulation of user knowledge and skills. If you correctly integrate these components, then it is possible to build a communication system between training participants. Online learning is implemented using various distance learning technologies, which are a set of methods that ensure the conduct of the learning process at a distance based on the use of modern information and telecommunication technologies. The main principle of distance learning technologies is independent learning. The main distance learning technologies are complex case technologies, computer network technologies and distance technologies using television networks and satellite data channels.
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Complex case technologies are based on independent study of materials provided to the student in the form of a case. This technology includes lectures, seminars, masterclasses, often organized as a game. Classes are aimed at increasing the activity of students, their interest in learning, and facilitate intensive interaction of students with each other and with teachers. Each case is a complete program and methodological complex, where all materials are interconnected. Basically, such training is conducted face-to-face, however, case studies can be conducted using, for example, Microsoft Teams, which allows us to make video calls, chat in real time with classmates and teachers. Computer network technologies are characterized by the use of computer training programs, electronic textbooks and other electronic materials, which are available for students on the Internet or the local network of an educational institution. Such technologies are implemented with the help of LMS, as well as Information and Library Complex. Students have both conventional and electronic access to any educational and methodological materials of the university. Via LMS, students have the opportunity to communicate with a teacher and classmates on forums, to do various tasks and projects, to study text, graphic and video materials compiled and recorded personally by university teachers. Remote technologies using television networks and satellite data transmission channels are implemented according to the principle of modules, which involves dividing the discipline into blocks, each of which ends up with a control task. Electronic testing systems are used to monitor the quality of knowledge acquisition by students. Among the stages of monitoring, one can distinguish: operational lecture testing, individual computer testing or training, control testing by modules, as well as examination testing. An excellent example of this technology is Coursera and IVET, where each course has modules, each with a control test and a practical problem. However, distance learning systems and distance learning technologies are not always reliable. The main problems of distance learning can be divided into two groups: technical and organizational. Despite the fact that the development of online learning began three years ago, and it would seem that students and teachers should have mastered all the facilities of distance learning, currently the universities staff and students yet face difficulties. The most important are technical problems, since the solution of these problems depends solely on the university. Thus, among the technical problems, the following ones can be distinguished: • Failure of the authentication system. A personal login and password are used to enter various distance learning platforms. From time to time, users fail to enter the LMS and other sites. These failures are usually eliminated quite quickly, within a couple of hours, a maximum of a day. However, it is the most frequent problem. • Lack of backups. This problem is relevant due to frequent system failures. Existing distance learning systems do not create backup copies of data, which can result in the loss of files.
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• Technical failures. Quite often, users encounter failures of websites, as well as communication tools, such as corporate mail and MS Teams, which interferes with the effective learning of students and the work of teachers. Organizational problems, caused by the human factor are no less important. Among the organizational problems, the main ones are the following [9]: • Maintenance of MOOCs. Since distance learning is used in all forms of education, the teachers of the online education department constantly create new courses and upgrade existing ones. Teachers are forced to monitor courses across multiple platforms. All this creates a huge burden on the staff, and planned work is shifted, or even not carried out at all. Also, the assessment of students’ works may be delayed, or carried out inattentively due to the heavy workload on teachers. • Lack of necessary technical equipment. Teachers and students sometimes do not have computers equipped with necessary software, well-functioning audio and video equipment, and a stable Internet connection. This problem results in learning time losses during the class, and creates difficulties for students willing to work online, and teachers who have to make up for the time spent on troubleshooting sound or video quality. • Low motivation of students and teachers. In the process of distance learning, the performance of each student is not constantly controlled. It is quite difficult for many students to motivate themselves to study independently, to work during online classes, and to complete assignments on time. However, the motivation of teachers is also declining: many are accustomed to working face-to-face and getting feedback from the students, having a dialogue with them, while in online classes there is often no contact between the teacher and students. • Lack of confidence in distance learning. This problem is perhaps the most serious. Many current students, potential students, and teachers are not interested in online learning, as they consider it ineffective. On the part of students, there is a fear of unfair and biased assessment, too simple or too complex knowledge testing, lack of communication with teachers, long waiting time for checking students’ work or answering their questions. On the part of teachers, one can note the difficulty in assessment, since this is a rather lengthy process, and, in addition, it is necessary to evaluate the work of full-time students. It can also be noted that both parties are afraid of leaking their data. The big volume of online learning materials makes evaluating online learning difficult and can lead to data losses. Therefore, a block-chain system was proposed using a new encryption algorithm to assess student performance on an online learning platform (Fig. 1). The main tasks to be solved by the proposed system are the following: • integration of block-chain technology into the distance learning platform; • increase of confidence in the online learning system using a new encryption algorithm; • secure automatic assessment of student performance on a distance learning platform using smart contracts [10]. A schematic representation of the proposed system is shown in Fig. 1.
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Fig. 1. LMS block-chain using a new encryption algorithm to assess student performance on an online learning platform.
The system allows us to create three different types of “players” that interact with each other using smart contracts: teachers; students of any form of education; users who access or verify data, such as employers and institutions of further education. A. Input data. At this stage, the data set used is determined - the number of students and courses. B. Pre-processing using normalization. Since the input data has not been processed, there may be duplicates or missing data. To eliminate redundant, duplicate or missing data, we process it. Since the data set for the education system is quite large, the sample size should be minimized. At this stage, the dataset can be normalized. At the initial stage of the normalization process, a z-score is obtained, which is expressed by Eq. (1). Z = [(Y − α)/ω]
(1)
Here α is the mean of the dataset and ω is the standard deviation and Z is determined by Eq. (2). Z=
Y −Y D
(2)
Y is the sample mean and D is the standard deviation of the model. The random sample should follow the pattern shown in Eq. (3). Z = β0 + β1 Yj + εj
(3)
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εj denotes errors that rely on ω2 . After that, the errors should not depend on each other, as shown below. √ y yj ∼ ω (4) 2 y −ω−1 y denotes a random variable. After that, the standard deviation is used to standardize the movements of the variables. The following expression is used to calculate moment scale deviations. M =
λm ∅m
(5)
Here m denotes the scale of the moments. λm = E(Y − α)M Here Y is a random variable and E is the expected value. ∅m = ( E(Y − α)M )2 ym =
m Y
(6)
(7) (8)
ym denotes the dispersion coefficient. The feature scaling procedure will be stopped by setting all values to 0 or 1. Unison based normalization approach is the name of this procedure. The normalized equation will be expressed as. Y =
(y − ymin ) (ymax − ymin )
(9)
After normalization, the dataset can be manipulated and the volume and variability of the data can be kept constant. This step is designed to minimize or eliminate data latency. Further, the normalized data can be used as input data for sub-sequent stages. C. Data offload. Since the data in the online learning platform is huge, data offloading is necessary. Changing the information component in most previous upload methods could result in incorrect data. This problem can be solved within the framework of information storage technology by planning the data to be used in the near future. In response, after sending the data, the server sends a list of data identifiers whose data values will be shared later, and then shares the data values in the list of identifiers. The ID is started and determines when the information will be received at the end of the data transfer. D. Evaluation Smart Contract (SC). Smart contracts can be compared to written processes in relational database maintenance systems, as they are a compilation of transactional scripts. A transaction on the block-chain will provoke certain parts of the smart contract code, and this is the only way for peers to change the status of the block-chain. Preprogrammed scripts can run on block-chain peers and offer updates to the block-chain.
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If the network reaches consensus, the changes are accepted. Each approved change is permanent. Upon completion of the course, the student has the option of submitting a certification request to the education department. The Department of Education, in turn, will review the student’s course credits. If the student has achieved the required number of credits to complete the course, a digital certificate with detailed information is generated, which is digitally signed first by the education department, then by the student. The double-signed digital certificate is registered on the block-chain as a new block of digital certificates. In the course of the work, two types of assessment were considered: automated and manual. The manual type, i.e. assessment by the grader itself, is the traditional method. The bottom line is to use the personal experience of the lecturer to evaluate the work of students. This format allows us to use any standard assessment methodology. The only difference is that the points are entered digitally and stored in the block-chain system. Automated assessment presents computer-generated tests and provides results and feedback to the teacher. This format saves time and efforts and provides online feedback. This method of assessment is becoming more common, especially in the field of computer science and programming. The interaction of the two transactions that complete the evaluation of smart contracts is shown in Fig. 2. Each time a submitted file is modified with a passing score, links to the file must be included in the certificate as an acceptance for both the AddSubmission process and the SubmitResult process. The AddSubmission transaction is used by the student to record the evaluation on the block-chain. The block-chain, in turn, guarantees the confidentiality of the sent files. 1. Automatic marking. The AddSubmission transaction allows you to quickly submit the results of an automatic assessment. Test files for automated assessment are stored on the block-chain and are ready to be sent along with files submitted by students to an appropriate external automated assessment service, such as a standalone web application. 2. Assessment by the teacher. The teacher uses the SubmitResult transaction to update the evaluation results of submitted files on the block-chain. The assessment can be made by the teacher, the application or smart contract. The evaluation formula is predetermined and stored in each node of the block-chain. For example, teachers can use the grade description grid to submit grades. Smart contracts produce final scores based on predefined rules. Teachers can then use comments to make changes to the final grade, making grade moderation more transparent. 3. Credential generation. Evaluation transactions in the block-chain ensure the reliability and immutability of the record of student progress. Platform credentials are invariably linked to past transactions, which can be verified by anyone if the student agrees. Teachers can demonstrate and authorize the certificate on the block-chain using the optional SignCertificate transaction. It is used, for example, to award academic degrees in educational institutions, and it is also the last stage in a comprehensive automated review process before a student receives a course certificate.
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Fig. 2. Chart representing transactions (blue arrows) for evaluation. (Color figure online)
IECCA is a public key cyber security technique based on elliptic curves in finite domains. An elliptic curve encryption algorithm is a discrete algebra encryption method that replaces the multiplicative group of a finite set with an elliptic curve grouping. With a key length of 1/10, it provides the same level of security as the RSA encryption scheme. To interact, the client develops a public key, distributes it among clients, and then generates a private key in an encryption system using the elliptic curve algorithm. First, the message is transmitted by encrypting it with a secret key, after that it is received and decrypted. To ensure security, sensitive data that should be hidden is encrypted before transmission. To do this, the IECCA algorithm is used. The private key is generated using the chosen prime number and the universal point. The generation of the private key varies based on a prime number. The proposed algorithm differs from the existing ECC in order to further reduce the size of the generated keys and, as a result, minimize the computational overhead. The following steps demonstrate the IECCA algorithm: 1. Encryption algorithm. Input: prime number, x, y, personal data. Output: index values for encrypted points. • Select the appropriate curve Ep(x,y) and determine all points of Ep(x,y). Each point on the curve corresponds to an alphabet, a number, and a unique character.
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• Assign index values to alphabet, numbers, and unique characters to de-sign an index table. • Choose a global point P with a higher order m in Ep(x, y). • Sender and recipient choose a private key. • Evaluate the public key of senders and recipients. • The private keys of the sender and recipient are evaluated. • Receive private data and match it to the equivalent points created in step 2. • Points created for personal data are used as input for encryption. • Using the universal point P, an arbitrary integer, and the recipient’s public key evaluate the encrypted points. • Encrypted points and their equivalent characters are matched against the index value assigned in step 3. • The resulting index value is provided as input to the evaluation. 2. Decryption algorithm. Input: index values, prime number, x, y. Output: Private message. • The index values generated from the data encryption algorithm are passed as input to the decryption. • The recipient must use the same Ep(x,y) curve type and use the same index table in the sender’s aspect. • The reached index value is compared to the equivalent characters and equivalent IECCA cipher points expressed in the index table. • For the decryption procedure, it is necessary to subtract the product of the recipient’s private key, an arbitrary number, and a universal point from the encrypted points. • IECCA points are mapped to equivalent characters in an index table, which is plain text information. The proposed block-chain based on the cryptography algorithm uses smart contracts on the public block-chain to correspond to educational grades and individualized curriculum. It demonstrates how this new technology can improve many key learning experiences, including assessment, curriculum customization, and student privacy. By increasing transparency and credibility, while maintaining controls on the security of student data, it will be possible to increase the credibility of educational procedures and certifications. Now let us conduct a risk assessment of the implementation of the proposed blockchain system. The methods used to analyze the decision tree and expert assessments were discussed in the second chapter. Five experts were involved in the peer review. Expert 1 is a senior engineer in the security sector of Sberbank of Russia, Expert 2 is product manager of bork.ru, Expert 3 is deputy head of production at IC Eurodom, Expert 4 is an employee of the regional center of competence in the field online learning, Expert 5 is a graduate of SPbPU with a major of “Information systems and technologies”. The experts were asked to rank the risks that are most likely to affect the implementation of the system. (1) High energy demand. (2) Lack of flexibility (difficulty in making changes to the block-chain protocol). (3) Expensive implementation of the block-chain
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system. (4) Insufficient knowledge of the internal block-chain among IT department specialists. (5) Deviation from the protocol and algorithm of the block-chain system at the development stage, entailing a disruption in the functioning of the technology and, as a result, a security threat. (6) Incorrectly coded updates to the system, which may result in loss or corruption of information. The risk ranking process by the expert group is presented in Table 1. Table 1. Pairwise Comparison Matrix. Expert
Risks 1
2
3
4
5
6
1
5
6
2
3
4
1
2
4
6
2
5
1
3
3
2
4
6
3
5
1
4
2
6
5
3
4
1
5
5
6
2
3
1
4
The definition of the average value of the scores for each of the risks is displayed in Table 2. Table 2. Average scores. Expert
Risks 1
2
3
4
5
6
Sum of ranks
18
28
17
17
15
10
Mean
3.6
5.6
3.4
3.4
3
2
The calculation of the total deviation of the estimates of each of the experts for all risks is presented in Table 3. Next, we calculate the total deviation of the opinions of all experts for all criteria as follows (Table 4). α =
5,6 + 6,8 + 9,2 + 6 + 7,6 6
(10)
As a result, we obtain a matrix: |0.27; 0.93; 3.33; 0.13; 1.73|. It can be seen that expert 3 gives the largest deviation, so, this expert can be excluded. Next, we conduct a survey of the remaining four experts. Their opinions, in general, are similar. This stage involves the identification and assessment of risks according to the degree of significance, where 1 is a high degree, 2 is medium, and 3 is low. The results are presented in Table 5.
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Table 3. Calculation of the total deviation. Expert
Risks
Total deviation
1
2
3
4
5
6
1
1.4
0.4
1.4
0.4
1
1
5.6
2
0.4
0.4
1.4
1.6
2
1
6.8
3
1.6
1.6
2.6
0.4
2
1
9.2
4
1.6
0.4
1.6
0.4
1
1
6
5
1.4
0.4
1.4
0.4
2
2
7.6
Table 4. Calculation of partial deviation modules. Experts
1
2
3
4
5
Total Deviation of Grades
5.87
Total mean deviation
5.6
6.8
9.2
6
7.6
Partial deviation modulus
0.27
0.93
3.33
0.13
1.73
Table 5. Risk ranking by experts. Risks
Experts
Ranking
1
2
4
5
1
3
3
3
3
3
2
2
2
1
2
1.75
3
2
3
2
2
2.25
4
3
1
3
3
2.5
5
1
2
2
1
1.5
6
1
1
1
1
1
Thus, according to experts, the most significant was the risk of incorrectly coded updates for the system, which can result in the loss of information. This is explained by the fact that the most important is the safety and security of data. Compared to the risks associated with information and security, the issue of energy consumption is less significant.
5 Discussion Since the block-chain system will be able to increase user confidence in distance learning, the number of students on a fee basis will increase, thereby increasing the income of the university. Based on the study by Harvard University [11, 12], increase in the level of
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user loyalty to distance learning raises the profitability of the university by an average of 8%. This will be our rate of return. The cost of implementing a block-chain system is on average 2,130,600 rubles. It is necessary to decide whether the proposed block-chain system is feasible. If the system performs well, the university will receive a profit in the amount of 8% × 2,130,600 thousand rubles = 170,448 rubles. • In case of unsuccessful implementation, the university will lose the cost of test implementation of the technology. Referring to expert assessments, we can state 40% chance that the system will have a positive effect on increasing user confidence in online learning. Therefore, it is possible to conduct a test implementation. The cost of the test implementation is on average 213,060 thousand rubles. Based on the opinions of experts, the chances that the test version will be successfully implemented are 75% [13]. In this case, the efficiency of the implemented system is 85%. If the test run fails, there is only a 20% chance that the implementation of the technology will be successful at all. Thus, the decision tree for assessing the implementation risk of a block-chain system for assessing student progress on a distance learning platform is shown in Fig. 3.
Fig. 3. Evaluation of implementation risk using the decision tree method (thousand rubles).
Let us consider the expected value estimate (EMV), that is, the maximum of the sums of estimates of payoffs multiplied by the probability of realizing payoffs for all possible options. Let us use the formula. EMV = P × I ,
(11)
P is the probability of an outcome; I is the impact of the outcome, thousand rubles.
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Initially, we calculate the expected cost estimate for the F-node: EMV (F) = 0.4 × 170 448 + 0.6 × (−213 060) = −59 656.8 rubles. EMV (G) = 0. So, in this node, two development options are possible: either “Successful implementation of the system” (probability 40%), which will bring an income of 170,448 rubles, or “Unsuccessful implementation of the system” (probability 60%), which will bring a loss in the amount of a test run - 213,060 rubles. In the fourth node, we choose between the decision “Implement the system” (estimation EMV(F) = −59,656.8 rubles) and the decision “Do not implement the system” (EMV(G) = 0). EMV(4) = max {EMV(F), EMV(G)} = max {−59, 656.8;0} = 0 = EMV(G). Therefore, the decision to implement the system without a test run is no longer considered. According to the formula, we similarly calculate the estimate: EMV(B) = 0.85 × 170 448 + 0.15 × (−213, 060) = 112, 921.8 rubles. EMV(C) = 0. So, in the second node, the decision “Do not implement the system” is not considered. Similarly: EMV(D) = 0.2 × 170,448 + 0.8 × (-213,060) = −136,358.4 rubles EMV(E) = 0. EMV(3) = max{EMV(D), EMV(E)} = max{−136,358.4; 0} = 0 = EMV(E). Therefore, in the third node we do not consider the decision “Implement the system”. Further according to the formula: EMV(A) = 0.75 × 112,921.8 + 0.25 × 0 = RUB 84,691.35 EMV(1) = max{EMV(A), EMV(4)} = max{84691.35; 0} = 84,691.35. So, in the first node, we do not consider the decision “Do not conduct a test run”. As a result, the expected value of the best solution is the income of 84,691.35 rubles. To achieve this result, it is necessary to conduct a test run of the system. If the test is successful, the block-chain system can be implemented. In case of failure, it is advisable to postpone the implementation. Based on the work done, we propose minimizing the risks of implementation. First of all, advanced training for understanding the internal structure and operation of the block-chain could reduce the cost of the system implementation. Equally important are the steps to move from an existing database to a block-chain. During development and implementation, there may be problems with access, so it is reasonable to inform in advance about possible failures, and to create a temporary platform for tasks fulfillment. It is also extremely important to monitor risk management. The sooner the threat of any risk is detected, the more likely it is to be eliminated at the stage when the risk will not affect the processes. Finally, it is necessary to constantly monitor the work of all departments involved in the development and implementation of technology.
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6 Conclusion The method of expert assessments made it possible to identify the most significant risks among those possible during the implementation of the system. The most significant was the risk of incorrect coding, which can lead to loss of information. Experts consider the risk of high energy demand to be the least important. The results of the evaluation by the decision tree method, in turn, made it possible to draw a conclusion about the feasibility of introducing the block-chain system, and to assess the implementation process in financial terms. It can be concluded that the proposed technology would be worth introducing, but the assessment showed that in order to develop the effective system, it is necessary to involve experienced developers to avoid possible losses and risks. Acknowledgments. The research was financed as part of the project “Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization” (FSEG-2023-0008).
References 1. Babkin, A., Alekseeva, N., Tashenova, L., Karimov, D.: Study and assessment of the structural capital of an innovation industrial cluster. Sustain. Dev. Eng. Econ. 2(4), 50–62 (2022) 2. Mikhailova, N.: Methods for assessing and analyzing risks. Research of young scientists: materials of the XVI International Scientific Conference, Kazan, pp. 9–11 (2021) 3. Vladimirova, A.: Strategy for managing credit risks of a commercial bank. Bank. Technol. 6, 30–35 (2017) 4. Sultanov, I.A.: Specifics of economic risks in commercial activity. Management of risks, http://projectimo.ru/upravlenie-riskami/ehkonomicheskie-riski.html. Accessed 10 Sept 2021 5. Balabanov, I.: Risk Management: Textbook. Finance and Statistics, Moscow (1996) 6. Korolkova, E.: Risk management: project risk management. TSTU, Tambov (2013) 7. Liang, X., Zhao, Q.: On the design of a blockchain-based student quality assessment system. In: Proceedings of the 2020 International Conference on High-Performance Big Data and Intelligent Systems (HPBD&IS), pp. 1–7. IEEE, Shenzhen, China (2020) 8. Sastry, J., Banik, B.: A novel blockchain framework for digital learning. Technology 12, 15 (2021) 9. Teachbase: a platform for organizing distance learning. Distance learning system. https://tea chbase.ru/learning/obuchenie/sistema-distancionnogo-obucheniya-obshij-obzor/. Accessed 07 Sept 2021 10. Alshahrani, M.: Implementation of a blockchain system using improved elliptic curve cryptography algorithm for the performance assessment of the students in the e-learning platform. Appl. Sci. 12(1), 74 (2022) 11. Distance learning technologies in the educational process. Network platform project. http:// www.ntmm.ru/proekt-setevaya-ploshchadka/. Accessed 07 Sept 2021 12. Information technologies in scientific activity. Tula state Lev Tolstoy Pedagogical University. https://tsput.ru/res/informat/aosit/Lection3.htm#_Toc117301365. Accessed 08 Sept 2021 13. National Academies of Sciences, Engineering, and Medicine: Guide for the Process of Managing Risk on Rapid Renewal Projects, p. 361. The National Academies Press, Washington (2012)
Opportunities for Development of Smart Stop Pavilions in Saint Petersburg Vladislav Seredin(B) , Svetlana Gutman, and Evgenii Seredin Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia [email protected]
Abstract. In this era of the unstable political and economic conditions caused by various events in recent years, ranging from the coronavirus pandemic to various sanctions, as well as the ubiquitous trend toward ecological “green” economic development, the uncertainty about the global cost of energy resources has increased again. As a consequence, the use of public transport instead of private transport has become increasingly popular. The essential element of public transport is a stopping point. In this paper presents a study on the possibility of refitting old and installing new stop pavilions in St. Petersburg at existing and prospective stopping points. Using the analytic hierarchy process and criterion comparison, and taking into consideration the opinion of transportation experts, a model for the distribution of “smart stop pavilions” across all stopping points was developed. The following criteria were used to evaluate and select the suitable type of “smart pavilion”: the number of routes passing through the stopping point; the passenger flow at entrance; the number of trips and the number of tourist facilities. As for the pavilions themselves, they differed in length (from 4 to 8 m) and digital equipment (from type A1 - the most digitized pavilion; to type A5 - installation of only a signpost). As a result of the calculations, we were able to determine the appropriate “smart” pavilion for each stopping point of surface urban transport in Saint Petersburg, taking into consideration the statistical data collected for the particular point according to four criteria. Keywords: Smart Transport · Smart Stops · Public Transport
1 First Section In today’s world, due to global warming caused, among other things, by the growing influence of the anthropogenic factor, concern for ecology has come to the forefront. Today, it is also manifested in the attempts to build the world economy on the principles of sustainable development, which includes not only the social and economic blocks, but especially the environmental one [1]. Besides, in the conditions of the global trend towards digitalization, as well as taking into consideration Russia’s course towards economic digitalization, such an issue as digitalization of the transport complex in general and ground urban passenger transport (GUPT) in particular is also becoming very relevant. Studies in the field of information development of the transport complex are called “Smart Transport”. The concept of “Smart Transport” is quite broad, as the transport itself is a complex system which includes many components. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 173–187, 2023. https://doi.org/10.1007/978-3-031-32719-3_13
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In the academic environment, a lot of various works are devoted to the topic of “Smart Transport”, which consider the transport from almost all explicit and non-explicit aspects. Conventionally, some works can be divided into the following thematic blocks: “Transport Safety” [2]; “Stops” [3–7]; “Evaluation of effectiveness/readiness to introduce new technologies” [8–11]; “Transport Infrastructure” [12–14]; “Smart Mobility” (MaaS) [15]; [16]; “Smart City” [17–19]; “Transport Sustainability” [20–22]; “Digitalization of Transport” [23–25]; “Ecology” [26–28]. Thus, the above examples of works confirm the relevance and the current interest in the “Smart Transport” in the academic environment. Considering these works for the goal and methodology of research, it can be noted that all of them in one way or another are devoted to the analysis of the current situation and the construction of predictive models for the future state of the transport system and the objects under study. As for the used methodology, a wide range of different research methods is applied: qualitative (literature review, historical review, comparison, synthesis, interviewing, and surveys) and quantitative (statistical analysis, modeling, programming, etc.), as well as various combinations of these methods. As for this study, we will focus on such an interesting and important area of the “Smart Transport” development as “Smart Stops”. Stops and stop pavilions are one of the key elements of the system of public passenger transport (including ground transport). Specifically, in our case, we will focus on the GUPT of St. Petersburg, namely, the network of public transport stops, because this topic is as relevant as ever in the light of the ongoing transport reform in St. Petersburg - the New Transport Service Model (NTSM). As stated by the city authorities, the main goal of the reform is transition to a fundamentally new unified standard of quality of transport services. Within the framework of this reform, it is planned to optimize the route network of public transport (removing unreasonable routes and introducing new ones); replacing obsolete and noncompliant vehicles; introducing new quality standards for transport services; creating new stopping points, as well as replacing and installing new stop pavilions. Within the framework of the reform, it is planned to install/replace about 200 new pavilions, however, in the presented study, it is proposed to re-equip/replace/install/improve all 7037 stopping points in St. Petersburg. This number includes both existing and new stopping points planned to be introduced as part of the transport reform. An important difference between this study and the works that exist in the academic environment is that it proposes a new way of determining a suitable “smart” stop pavilion for a particular stopping point according to some characteristics corresponding to the selected (together with transportation experts) criteria in combination with the analytic hierarchy process to determine the importance and priority of some criteria over others and their subsequent pairwise comparison in conditions of large data sets and different degrees of digitalization of the proposed pavilions. Therefore, the main goal of the presented work is to create a mechanism for determining a suitable “smart” stop pavilion with satisfactory (according to several criteria) characteristics (with allowance for the degree of potential digitalization and the length of the stop pavilion) for a particular stopping point.
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2 Materials and Methods Qualitative and quantitative methods were used to achieve the goal of this study. Qualitative methods include interviews and surveys of transportation experts. As for quantitative methods, such analysis tools include methods of collecting and processing statistical information, comparative criterion analysis, as well as the analytic hierarchy process, developed and described by T. Saaty. The analytic hierarchy process (AHP) was developed by Thomas Saaty in the 1970s. The essence of the method consists in modeling multi-criteria decision-making tasks by a specific person, who performs activities on developing, identifying, and selecting the most suitable, according to certain criteria (parameters), alternative to the further activity of the analyzed object. The main idea of the method is structurization of the problem of choosing an alternative by constructing a multi-level hierarchy, which combines all necessary components of the problem: goal, sub-goal, criteria, strategic alternatives, results, influencing forces, and comparison of components using procedures specially designed for this purpose. As a result, it is possible to obtain numerical estimates of the intensity of the mutual influence of specific elements in the hierarchy. Thus, based on the obtained results, the priority ranking of the alternatives under consideration in relation to the main goal is assessed. [29–31]. The actual procedure for determining the appropriate types of pavilions for the number of stopping points under consideration will be carried out in several stages: 1. Identification and selection of evaluation criteria necessary to classify types of pavilions together with an expert group consisting of transportation specialists. 2. Selection of four priority criteria from the whole pool of proposed ones. 3. Finding the necessary intervals corresponding, according to the authors and experts, to each type of stop pavilions in the best way (in terms of rational use of GUPT at the corresponding stopping point). 4. By the means of the analytic hierarchy process, determining the appropriate “weights” for each pavilion type (A1–A5), as well as its length (4, 6, 8 m) for every stopping point in accordance with the priority of the criteria. 5. According to the structured statistical information collected for each analyzed stopping point in the context of the four evaluation criteria used, the number of “matches”, corresponding to each of the four data criteria, with a particular type of stopping pavilions, depending on the selected evaluation intervals, is determined. 6. Calculating a suitable pavilion type for each stopping point according to the number of “matches” of data from each of the four criteria with a suitable pavilion type, taking into consideration the “weights” of each pavilion type (each strategic alternative) by its equipment (digitalization) and length. 7. Calculating the required number of pavilions of each type in accordance with the results of the calculations. As an example of “smart” pavilions, which are going to be considered in this paper, we will use the solutions offered by the company OOO “FAVOR-GARANT”, namely: Equipment of the A1 type stopping point (Fig. 1): 1. Built-in information display.
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A0 size information stand. Name of the stopping point. Ticket activation machine. Security cameras. Free Wi-Fi. USB charger, “Citizen to Police” button. Built-in LED lighting. Advertising space.
Fig. 1. A1 stop pavilion.
Equipment of the A2 type stopping point (Fig. 2): 1. 2. 3. 4. 5.
Built-in information display. A0 size information stand. Name of the stopping point. Built-in LED lighting. Space for placing advertisements or social information.
Fig. 2. A2 stop pavilion.
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Equipment of the A3 type stopping point (Fig. 3): 1. A0 size information stand. 2. Name of the stopping point. 3. Space for placing advertisements or social information.
Fig. 3. A3 stop pavilion.
Equipment of the A4 type stopping point (Fig. 4): 1. Electronic information display.
Fig. 4. A4 stop pavilion.
A5 type stopping point (Fig. 5): 1. A0 size information stand. In addition to pavilion equipment, there is also a variation in the length of the pavilion (this refers to the stopping points of the A1–A3 types): the length of the pavilion varies from 4 to 8 m (4 m, 6 m, 8 m).
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Fig. 5. A5 stop pavilion’s information stand.
Thus, a combination of the use of the AHP and comparative criterion analysis will make it possible to form a relevant and reasonable list of stopping points with the appropriate types of “smart” pavilions. The advantages of the AHP include the fact that this type of analysis is versatile enough to allow for both qualitative and quantitative characteristics of the considered indicators, as well as to make a mathematically justified choice of one or another strategic alternative. Besides, the AHP considers the opinions of different experts by determining their priorities; allows demonstrating both the nature of interaction of factors with one another and their mutual influence on the evaluation and choice of strategic alternatives under consideration; makes it possible to avoid or significantly reduce the number of contradictions, since it minimizes subjectivity of opinions, and data collection is carried out using pairwise comparisons; allows the possibility to use it together with other evaluation methods for formalized tasks. As for the disadvantages of the AHP, we can note the fact that this method allows only the ranking of alternatives, and it is difficult or practically impossible to check the reliability of the raw data [31]. However, this study uses fairly reliable primary data (relevant for 2020), as they were previously collected by transportation specialists of St. Petersburg.
3 Results At stages 1–3 of the study, consultation work with transportation experts was conducted to determine the necessary criteria for evaluating stopping points to determine the necessary type of stop pavilion. In particular, 9 criteria were initially identified but, due to their mutual inconsistency, only 4 main and representative criteria were left, and their respective values were determined (Table 1). The number of routes passing through the stopping point (C1). Values vary from 1 to 22 routes. Passenger flow at entrance on all routes passing through the stopping point (C2). Values vary from 100 to 8448 people.
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The number of trips made on all routes passing through the stopping point (C3). Values vary from 100 to 2034 trips. The number of tourist facilities located in the immediate vicinity of the stopping point (C4). Values vary from 0 to 24 units. Table 1. Criteria for evaluating stopping points. Criterion
Equipment (digitalization)
Type of stopping point
A1
Number of routes (C1)
A2
A3
Length of the stop
A4
A5
4m
6m
8m
22
15
10
3
1
3
10
22
Passenger flow at entrance (C2)
8448
4000
1000
500
100
500
2000
8448
Number of trips (C3)
2034
1050
500
300
100
150
500
2334
24
12
5
0
0
0
0
0
Number of tourist facilities (C7)
After defining the criteria and their corresponding ranges of data values, the analytic hierarchy process was used to prioritize and calculate the “weights” of importance of each criterion. Four matrices (of size m × n) for the four above criteria in the context of pavilion types (according to their “digitalization”) and three matrices in the context of the pavilion length (when determining the pavilion length, the criterion C7 - the number of tourist attractions was not considered, because, according to experts, the number of tourist attractions has no direct influence on the pavilion length) were compiled. A pairwise comparison of the criteria based on a nine-point scale was carried out beforehand. Such a system of comparisons allows us to obtain a certain result, which can be represented in the form of an inverse of a symmetric matrix. An element of such a matrix as C1 (i, j), for example, is the intensity of the manifestation of the element in hierarchy i relative to the element of hierarchy j, which is evaluated on an intensity scale (from 1 to 9) [29–31]. The scale scores have the following meaning: 1 - equal importance; 3 - moderate superiority; 5 - considerable superiority; 7 - strong superiority; 9 - very strong superiority; 2, 4, 6, 8 - intermediate values (4 is placed between moderate and considerable superiority). The formed matrices are presented in Tables 2, 3, 4, 5, 6, 7 and 8. Next, the following calculations were undertaken: the values of the elements of the columns for each matrix were summed; the matrices were normalized by dividing the elements of each column for each matrix by the sums of the elements of the columns for each matrix; the average values of the elements in the rows were calculated; the resulting values provided the corresponding weights for each alternative (the type of innovativeness of the stopping pavilion A1–A5);
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SP type
A1
A2
A3
A4
A5
A1
1
3
5
7
9
A2
0.33
1
2
5
7
A3
0.2
0.5
1
3
4
A4
0.14
0.2
0.33
1
2
A5
0.11
0.14
0.25
0.5
1
Table 3. Matrix - Criterion C2 (passenger flow at entrance) for SP types. SP type
A1
A2
A3
A4
A5
A1
1
3
5
7
9
A2
0.33
1
3
5
7
A3
0.20
0.33
1
3
5
A4
0.14
0.33
0.33
1
2
A5
0.11
0.14
0.20
0.5
1
Table 4. Matrix - Criterion C3 (number of trips) for SP types. SP type
A1
A2
A3
A4
A5
A1
1
3
5
7
9
A2
0.33
1
2
4
6
A3
0.20
0.5
1
3
5
A4
0.14
0.25
0.33
1
2
A5
0.11
0.17
0.2
0.5
1
Table 5. Matrix - Criterion C7 (number of tourist facilities) for SP types. SP type
A1
A2
A3
A4
A5
A1
1.00
3
5
7
9
A2
0.33
1
3
5
7
A3
0.20
0.33
1
2
4
A4
0.14
0.2
0.5
1
2
A5
0.11
0.14
0.25
0.5
1
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Table 6. Matrix - Criterion C1 (number of routes) for Pavilion length. Pavilion length
C14
C16
C18
C14
1
0.33
0.14
C16
3
1
0.50
C18
7
2
1
Table 7. Matrix - Criterion C2 (passenger flow at entrance) for Pavilion length. Pavilion length
C24
C26
C28
C24
1
0.25
0.11
C26
4
1
0.25
C28
9
4
1
Table 8. Matrix - Criterion C3 (number of trips) for Pavilion length. Pavilion length
C34
C36
C38
C34
1
0.25
0.13
C36
4
1
0.25
C38
8
4
1
the resulting matrices were inconsistent, since the columns of the matrices are not the same, so the matrices were checked for consistency (each element of the original matrices was multiplied by their corresponding weight coefficients). The next step was to check the consistency of local priorities by calculating such characteristics as: λ_1max, consistency index (CI), random consistency index (RI), and consistency ratio (CR). λmax − n n−1
(1)
1.98 ∗ (n − 2) n
(2)
CI RI
(3)
CI = RI =
CR =
It should be noted that the scores in the matrices are consistent if CR ≤ 0.1. If, however, the index values do not fall within the stated range, a revision of the estimates in the matrices is required. In our calculations, the resulting values satisfy this condition, which means that all the matrices are consistent (Table 9). Since the first criterion (C1 - number of routes passing through the stopping point), according to transportation experts, under current conditions is more important and
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Consistency score
Value
CR (C1)
0.043
CR (C2)
0.086
CR (C3)
0.045
CR (C4)
0.045
CR (C1, pavilion length)
0.003
CR (C2, pavilion length)
0.047
CR (C3, pavilion length)
0.067
of higher priority in comparison with other criteria, it is necessary to make two more matrices: a matrix that allows for this condition, as well as the priority of C2 (passenger flow at entrance) in relation to criteria C3 (number of trips) and C4 (number of tourist facilities) in the context of the choice of pavilion types and a matrix that allows for the priority of pavilion lengths according to the experts (in terms of social and economic feasibility, as well as the physical possibility of installing a stop pavilion of certain size), Tables 10, 11. Table 10. Matrix of criteria priorities in relation to each other. Criterion
C1
C2
C3
C4
C1
1
2
3
4
C2
0.5
1
1
2
C3
0.33
1
1
2
C4
0.25
0.5
0.5
1
Table 11. Priority matrix of stop pavilion lengths in relation to each other. Pavilion length
4m
6m
8m
4m
1
0.5
4
6m
2
1
4
8m
0.25
0.25
1
After performing all similar operations with the priority matrix of the criteria, as well as after checking the consistency of the scores in the matrices, we obtain the final “weights” for each type of stopping point equipment, namely (Tables 12, 13 and 14): The last step in the study was the distribution of types of pavilions (A1–A5) for each stopping point (existing and prospective) in St. Petersburg. In total, there are 7037 such
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Table 12. Values of consistency scores. Consistency score
Value
CR (criterion priority)
0.007
CR (pavilion length priority)
0.026
Table 13. Priority values of types of stop pavilions in the context of their “digitalization”. Stop pavilion type
Score (weight)
A1
0.513
A2
0.248
A3
0.137
A4
0.063
A5
0.039
Table 14. Priority values of the length of stop pavilions. Stop pavilion size
Score (weight)
4m
0.076
6m
0.246
8m
0.678
stopping points. The distribution was carried out by counting the number of matches of values for each analyzed criterion C1, C2, C3, C7) in the context of the type of SP and its length with their respective intervals of the statistical values collected earlier for each stopping point. In other words, the type of a suitable pavilion was initially determined in accordance with the ranges of values corresponding to a particular stopping point established earlier. Since different types of “smart” pavilions could be suitable for one stopping point according to different criteria (there could be contradictions due to the equal number of suggested types of pavilions of different digitalization and length), in order to avoid such errors, the obtained number of options for each criterion of each stopping point was adjusted with the “weights” found by the analytic hierarchy process (evaluation with correction for the significance of each type of “smart” pavilion and its preferable length): the number of times each “matched” pavilion type was multiplied by the previously defined “weights” (significance and priority of pavilion types in relation to each other) of stop pavilions (A1–A5) and their lengths (4 m, 6 m, and 8 m) corresponding to these types. The results are presented in Table 15. Due to the algorithm proposed in this paper for determining the appropriate type of “Smart” stop pavilion, it became possible to determine and select the appropriate type of stop pavilion for each of the 7037 stopping points in St. Petersburg quickly enough and
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Table 15. Estimated number of distributed types of stop pavilions by stopping points in St. Petersburg. Type of pavilion and its length A1 4m
Number, pcs. 144 4
6m
4
8m
136
A2
1024
4m
21
6m
61
8m
942
A3
3156
4m
401
6m
2527
8m
228
A4
625
A5
2088
Total
7037
without much labor and financial expenses. The use of the analytic hierarchy process as an objective way to determine the priority of each criterion (which were previously determined and agreed with transportation experts) in relation to each other indicates that the obtained results can be considered representative, because, other things being equal, they allow us to reasonably approach the interpretation of the reasons for the installation of a particular type of “smart” pavilion at a particular stopping point. The uniqueness of this study also lies in the fact that the used methodology and selected criteria can be applied to the network of passenger stops of any city in the Russian Federation and other world cities, because it is simple, logical, and unified in terms of criteria. Anyway, even taking into consideration the sufficiently elaborated mechanism, the presented study is not devoid of shortcomings, which will have to be considered in the following works on the given subject. In particular, in determining the dimensions of pavilions, only three of the four criteria were included, namely: the number of passing routes (C1), the passenger flow at entrance (C2) and the number of trips made (C3). The very possibility to install a pavilion in a certain place and its potential operability were not taken into consideration: the availability of a stopping pocket for vehicle entry, the availability of space to install the pavilion (distance from the roadway to buildings located in close proximity), availability of communications (electrical cable), compliance with traffic rules and standards, availability of permits and rights to install the pavilion. If there is already a stop pavilion at the analyzed stopping point, it is necessary to check who owns it and whether there is a right to replace it with a new one (in St. Petersburg,
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some stop pavilions belong to the Committee on Improvement, and some belong to the Committee on Press). Among other things, we should not forget about PR decisions of the city management that can be taken in order to advertise and promote St. Petersburg as an innovative and sustainable city with environmentally friendly public transport, which can lead to the installation of pavilions in the central and most popular places of the city. All these factors, which directly and indirectly affect the installation and maintenance of stop pavilions, should be taken into account when compiling a specific address list of stopping points before the actual purchase and installation of both the pavilions and the necessary equipment so that these pavilions become “smart” and can work in a unified urban passenger transport system. It is also worth mentioning that apart from installing the equipment required for digitalization, it is important to integrate the whole stop complex into a unified transport information system of St. Petersburg, which has not been completely finished and presented by now. The existing information systems are fragmentary and do not cover the entire range of tasks related to GUPT, which leads to the fact that the functionality of the smart pavilion will not be fully implemented.
4 Conclusion As a result of this research, the main goal of creating a mechanism to determine a smart stop pavilion suitable for a particular stopping point with characteristics that satisfy several criteria (with allowance for the degree of its potential digitalization and length) has been fully achieved: a method combining the ranking of each stopping point data by criteria, their comparison and the analytic hierarchy process has been proposed. Moreover, to find the most relevant and objective criteria, several transportation experts were interviewed, which also made it possible to consider our method of selecting pavilions for stops reasonable and confirmed by expert evaluation. As a result of the calculations, we were able to determine the appropriate smart pavilion for each stopping point (7037) of ground urban transport in St. Petersburg, allowing for the statistical data collected on a given point in the context of the four criteria. In further work in this area, it is necessary to take into consideration those issues and features that were not initially considered, but which are very important in terms of subsequent installation of pavilions and their daily operation. It is noteworthy that refinement and development of the created algorithm will make it possible to calculate all the necessary financial costs for the purchase, installation, and maintenance of stop pavilions for a given number of years for each stopping point individually, as well as for the entire development program as a whole. The obtained results can form the basis not only for further research on this issue, but also serve as a guide for the city authorities to develop programs for installation/equipping of new and existing stop pavilions in the ongoing policy of improving the quality of public transport service in St. Petersburg, including its safety, accessibility, and convenience, as well as increasing its level of “digitalization”. Acknowledgments. This research was funded by the Russian Science Foundation under project No. 23-28-01206, https://rscf.ru/project/23-28-01206/.
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Modern Trends in the Sharing Economy Igor Lyukevich(B) and Renata Sharipova Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. The achievements of scientific and technological progress have become a catalyst for development of IT that is penetrating all spheres of human life. With the growth of digital technology, people have globally reconsidered possessing, using and exchanging goods. An idea of temporary use of wealth has become wide-spread and brought about a new economic model – the sharing economy. The sharing economy is a challenging and developing model. This paper is aimed at learning about the modern trends of the sharing economy and evaluating its efficiency (using an example of a crowdfunding project). The theoretical significance of the work lies in a comparative characteristic of the sharing economy models. The practical significance is expressed in suggesting recommendations for a successful crowd campaign and approaches to evaluating the results of a crowdfunding project. Keywords: Sharing economy · crowdfunding · crowdfunding platforms · effectiveness of a crowd-funding project
1 Introduction To date, the achievements of scientific and technological progress have become a catalyst for development of Internet Technology that is penetrating all spheres of human life. With the growth of digital technologies, people have been changing their mind about the ownership, use and exchange of goods. The idea of temporary use of goods has become widespread. Current beliefs have resulted in a new economic model – the sharing economy. The sharing economy is a promising and progressing model. According to PwC, the revenue of companies in the sharing economy was $ 15 billion in 2015, and is expected to increase by 22 times by 2025. One of the directions of the shared economy is crowdfunding – a new tool for financing projects that connects the initiators of ideas and their sponsors on specialized Internet sites. In 2020, the share of the crowdfunding market was more than 7 billion rubles, 60% of which accounted for crowdfunding and crowd investing. The research topic is relevant because crowdfunding can act as an alternative source of financing for companies that are just starting their activities and cannot attract the monetary resources they need through lending, public financing or venture capital. Crowdfunding is a relatively new financing instrument that is still gaining popularity on the market and has not been scrutinized yet [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 188–202, 2023. https://doi.org/10.1007/978-3-031-32719-3_14
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The object of the study is crowdfunding as a tool for attracting funds that is used in the shared economy. Thus, our work analyzes the directions of the sharing economy by industry: the sharing of physical goods, content, and, in particular, finance. We evaluated the effectiveness of a crowdfunding project and analyzed the largest international and national crowdfunding platforms. A comparative analysis was carried out to study an investment project implemented at the expense of credit and by means of crowdfunding.
2 Research Results The term “sharing economy” was coined in 2008 by law professor Lawrence Lessig from Stanford University. What’s Mine Is Yours: The Rise of Collaborative Consumer (2010: How the Sharing Economy is Changing Our Lives) by economists Rachel Botsman and Ru Rogers is considered one of the first scientific papers studying the modern concept of consumption in the sharing economy, and its drivers of growth. Some researchers of the sharing economy believe that this model is one of the most significant trends in the modern world economy and a phenomenon that can lead to a change in the economic paradigm [2]. In total, the sharing economy can be described as a business model in which one party can benefit from its underutilized assets (both material and non-material ones), and the other party can get the desired result without taking possession of a tool for this. At the same time, both sides of the relationship obtain their own benefits, which pushes them to use this economic model. The discussion of the term “sharing economy” continues to this day, but in 2015, the following definition was added to the Oxford Dictionary: “An economic system in which assets or services are shared between private individuals, either free or for a fee, typically by means of the internet”. Two main and special features of the sharing economy come from this definition, helping to characterize its inherent business models: 1. Services or assets are distributed among individuals for temporary use at a cost; 2. Transactions are conducted via the Internet. Increasing the efficiency of a resource is the main advantage of this model. This idea is appealing to both the consumer and the owner of the resource, which is why new sharing services are offered all the time. The basic principle of the sharing economy is: an unused resource is a lost resource. The common features of business models in the sharing economy are: 1. Automated online services, processes that do not require special maintenance. 2. A community of service users is at the center of the model. 3. Crowdfunding rating systems in which ratings and user reviews are processed and affect the rating of service providers. 4. Payment for the result. 5. Information sharing, user content and feedback. 6. Market prices due to low self-cost. The sharing economy is a hybrid model of economic exchange, which has the features of traditional market relations, with two participants exchanging the right of ownership
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of a service or product for cash or donations, and the transaction costs are not re-imbursed [3]. These goods and services can be either transferred from consumer to consumer or coordinated by companies and online services. In this regard, there is a classification of the services of the sharing economy from participating subjects: 1. Startups. Startups bring most of the innovation to the sharing economy. Some startups are worth more than a billion dollars, including WeWork, Airbnb, Uber. 2. Dominant companies. Traditional companies can also act as service providers in the sharing economy. For example, IKEA allows customers to sell used furniture on its website. 3. B2C. Despite the fact that the sharing economy mainly focuses on the exchange of services and goods between users, access to these resources often involves companies offering additional services to users. Lack of trust is one of the main causes of this phenomenon, for example, the customer’s concern about the damage to a commonly used item, which can be regulated by an intermediary, such as an insurance agency. 4. C2C. In the model of the sharing economy, the line between consumers and producers is blurred, because in the C2C scenario, the supplier is a consumer. Considering models in the sharing economy, we see that economic relations follow the B2C, C2C, B2B models and P2P model, which is relatively new for the economy. In terms of the sharing economy, Russia is gradually shifting towards the B2B model, with global corporations introducing it into their business. B2B sharing is gaining popularity in Western countries (especially in the field of office sharing, sharing of construction equipment and materials – for example, waste from one industry can be used in the production chain of another industry). Players in the sharing economy expect this trend to develop in Russia. According to the purpose of transactions, four shared consumption models can be distinguished (Table 1). The exchange of benefits in different forms have accompanied humankind throughout its existence, because it is essential for its physical survival. Why is it that the sharing economy began to develop only in the early 21st century? Researchers suggest many answers to this question. The fashion for environmentally friendly behavior and growing recognition of our responsibility to the future generation have largely popularized the idea of shared consumption. Conscious consumption of an individual in the shared economy is replacing emotionally-driven overconsumption as a rational consuming behavior of an individual. The idea of sustainable development is gradually penetrating the activities of our daily living (Fig. 1). Thus, sharing economy platforms allow us to tackle problems in the following areas of Russia’s strategic development [4]: 1. Environment. Reducing atmospheric air pollution due to short-term car rental services. 2. Housing and urban environment. Thanks to carsharing, bike-sharing, self-service, etc., the comfort of the urban environment increases and air pollution goes down, which results in the urban environment quality index improving by 30%.
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Table 1. Shared consumption models. Model name
Model description
Examples of companies Industries
Rent
Short-term centralized rent of things and rent of small commercial premises from firms
BlaBlaCar, WHOOSH, Delimobile, Velobike, Regus
Carsharing, bikesharing, scootersharing, coworking
Share
The owner benefits from Rentmania, Next2u.ru, a possession, which is not Airbnb used to its full capacity, while the provider sells his free time and skills, often unrelated to his main work. The customer rejects the need for personal ownership of things or the need for using standard services
To rent personal property or provide irrevocable access, to provide services at will
Pool
Joined project financing and cost sharing. Users of services or products can satisfy their needs at the lowest possible price than in the case of conventional purchases of goods. They can jointly finance the development of new products they are interested in. Not only the provider of services or goods can recoup the costs, but also see the demand for the potential supply in real life
Boomstarter, Planeta.ru Crowdfunding, crowdlending, crowd investing
Donate
The owners of the property clear out the premises of their apartment or garage, partially compensating for the initial cost of the acquired possession
Avito, Yula
Selling, donating or exchanging things that the owner no longer needs
3. Support for small and medium business, as well as individual entrepreneurial initiatives. Developing a pool of service providers. Co-working and office sharing creates favorable conditions for self-employment and entrepreneurship.
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Fig. 1. Sustainable development concept.
4. Digital economy. Crowdfunding and search for raw materials to accelerate digital technology, and create a unified system of project funding. 5. Safe and high-quality roads. Joint assessment of federal roads congestion. Physical Wealth Sharing. Despite the trends, the new model of the sharing economy has not been replaced by the old one [5]. According to a study conducted by the international consulting company PwC, users of sharing platforms believe that this makes the world more environmentally friendly, and their lives more rational and efficient. However, savings is still the decisive factor, with 86% of respondents believing that sharing platforms make life more affordable, and 81% thinking that it is cheaper to rent a thing than to own it. Thus, in 2020, the volume of transactions on the most popular sharing economy platforms in Russia was about 1.07 trillion rubles (see Fig. 2). Compared to 2019, the volume of transactions increased by about 39%. The global crisis caused by the COVID-19 pandemic resulted in the growth of the rental market (+85%), C2C sales (+48%), the rental market for scooters and bicycles (+35%) and the P2P part-time earnings market (+31%). Market players expected that carsharing would also demonstrate double-digit growth rates as it is an epidemiologically safer alternative to public transport. But the segment showed only a slight increase (+9%) due to the restrictions imposed by regional authorities. Further, the dynamics of the markets by sector was considered. In 2020, the Russian C2C trading market grew by 48%, slowing a bit compared to the growth in 2019 (+53%).
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Transacons by Sector Renng things Croudfunding Individual mobility House sharing Car pooling Car sharing Р2Р services С2С sales 0
100
200
300
400
2020
2019
500
600
700
800
900
Fig. 2. Transactions by sector.
However, given the dynamics of other segments of the sharing economy and the global economy as a whole, C2C trade is still a growth driver. Figure 3 shows the dynamics of the C2C sales market.
Dynamics of the C2C sales market 1000
838
800 566
600
200
370
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400
281 116
90
177
0 2017
2018
Number of transacons (mln)
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Fig. 3. Dynamics of the C2C sales market for 2017–2020.
The next sector considered was the P2P market sector. The Russian P2P market grew by 31% in 2020. Due to the pandemic, the demand and supply for courier services soared. Many users have tried their hand at performing virtual tasks. At the same time, many services were suspended during the lockdown, which smoothed the growth of the segment. In March 2020, a law was passed to introduce the professional income tax (self-employment tax) to all regions of the Russian Federation. In the fall of 2020, the
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number of officially registered self-employed people exceeded 1 million. We estimate that they take up 7–10% in the sharing economy. Figure 4 shows the dynamics of the P2P services market.
Dynamics of the P2P Services Market 200 150 100 50 0 2017
2018
Number of transacons (mln)
2019
2020
Transacon Volume (billion rubles)
Fig. 4. Dynamics of the P2P services market for 2017–2020.
In 2020, the carsharing market grew by a modest 9% (compared to >50% per year in 2018–2019). The number of trips did not increase. The operators’ receipts increased due to a longer average trip, as well as higher tariffs. Figure 5 shows the dynamics of the carsharing market.
Dynamics of the Carsharing Market 100 80 60 40 20 0 2017
2018
Number of transacons (mln)
2019
2020
Transacon Volume (billion rubles)
Fig. 5. Dynamics of the carsharing market for 2017–2020.
In 2020, the short-term accommodation rental market sank by 48%. During the pandemic, the demand for infrequent premises in which daily contact between the host and guests occurs in huge spaces fell especially sharply. The share of houses on the market, on the contrary, grew. Figure 6 shows the dynamics of the short-term accommodation rental market.
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Short-Term Accommodation Rental Market 20 15 10 5 0 2017
2018
Number of transacons (mln)
2019
2020
Transacon Volume (billion rubles)
Fig. 6. Short-Term Accommodation Rental Market for 2017–2020.
Content Sharing. In fact, we can refer the process that has become routine for us to the sharing economy. Namely: the distribution of postings, texts, videos and audio recordings in order to make a large audience learn about the content. Content can be shared by marketers: for example, publish links to articles on the company’s social media. Or it can be done by users: for example, they can share an interesting and useful article using a special “Share” button on the website or in the posting. When we see or hear “social media”, we immediately think of websites or phone apps like F, I, T, Snapchat, etc. These channels, which allow people to interact and share content quickly in real time, have grown rapidly over the past decade, with 3.2 billion active users daily, representing about 42% of the world’s population. Next, let us consider the quantity of published content (postings, repostings, comments) in open access on social media in Russia (Fig. 7). The basic research terms are: An author is a user who has written at least 1 public message. A message is any open (public) posting – in a status, on the wall, in groups, comments, etc. Messages in personal correspondence or in the “friends only” mode were not considered. In October 2020, the number of active authors who wrote over 1.2 billion public messages (postings, repostings and comments) on social media in Russia was 64 million. Finance Sharing. Today the most urgent problem faced by novice developers is project financing. Traditional forms of capital raising, such as credit, are helpful, but can be problematic for startups and the self-employed who may not be able to take out a loan on good terms. The problem of capital raising has given momentum to new models of project financing, one of which is crowdfunding. However, today crowdfunding is not only a tool for financing innovative projects. It is used virtually in all spheres of activity: from providing aid during natural disasters to creating free software. Crowdfunding is the raising of financial resources from a large number of people aimed at selling a product or service, helping those in need, holding events, supporting individuals and legal entities, etc. It is believed that the term crowdfunding appeared almost simultaneously with the
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Study of Active Audience on Social Media 7.7 22
Youtube
5.3 29
TikT T
0.7
F
1.6
Odnokl
6.6
32.5 56.2 108.6
28.7
I
265.2
42.8
VK 0
100
496.2 200
Authors per month (million)
300
400
500
600
Messages per month (million)
Fig. 7. Study of active audience on social media in Russia for 2020.
term crowdsourcing in 2006, being coined by Jeff Howe. However, the phenomenon of crowdfunding is much older. An example can be the building of the Statue of Liberty in New York City, which was financed by the entire nation [6]. Let us analyze various types of attracting investment and describe the strengths and weaknesses of each of them (Table 2). A comparative analysis of the investing sources allows us to conclude that crowdfunding is, indeed, a new alternative method of financing that can be used to attract the required financial resources with minimal costs. Today, raising funds for a project is an urgent problem for novice entrepreneurs. Classic options for raising capital, in the form of venture financing, a loan or grant, can solve this problem, but all these methods are either too complicated, or entail a high risk of refusal to provide funds and a high interest rate in case of a loan. All this has preconditioned gradual development of alternative ways for financing projects, one of them being crowdfunding.
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The mechanism of crowdfunding is effective and simple. On a specialized Internet site, the authors publish information about their projects, indicating the amount of money needed to start the project. The decision to invest in a project is made directly by donor investors, who, if interested, can contribute funds directly through the platform [7]. There are two financial models of crowdfunding platforms: “All-Or-Nothing” – if the authors of the project failed to raise funds in full, the invested money is returned to investors. “Keep-It-All” – if the project does not collect the declared amount during the specified period, then all the collected funds remain with the author of the project. If the project demonstrates successful results, then the crowdfunding platform allows the resources to be used for further development of the project. Table 2. Strengths and Weakness of Funding Sources. Funding Sources
Advantages
Disadvantages
Self-funding
– availability; – a reliable way of financing; – financial independence of the enterprise; – relatively high return on investment due to the absence of interest payments
– limited volumes; – implementation of small investment projects; – diversion of funds from the company’s economic turnover; – difficulty in financing startups
Credit financing
– big size of possible loans; – external control over the effective use of borrowed funds
– difficult to raise and register for small businesses; – collateral requirements; – high cost of funds raised; – lower financial stability of the enterprise; – increased risk of bankruptcy in case of late repayment of loans
Equity financing
– unlimited period for attracting resources; – freedom to use the resources obtained; – no collateral
– equity financing is only possible for joint-stock companies; – strict requirements for the emitter; – high production costs; – expenses for payment of dividends on stocks and coupons on bonds; – significant time costs
State financing
– state support; – grants, subsidies; – relatively low interest rates on state guarantees
– high requirements for selected financing companies; – multi-year selection; – dependence on the size of the budget; – red tape (continued)
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Funding Sources
Advantages
Disadvantages
Leasing
– relative ease of obtaining – sometimes high rates of VAT and financing despite having import customs duties when insignificant financial resources; equipment is imported to Russia; – convenient schedule of lease – limited rights to the rented object payments; – the rented property is the collateral; – the possibility of using the accelerated depreciation mechanism to reduce property tax and income tax payments; – accompanied by financing the asset during the service life of the asset
Venture financing – providing the necessary capital to – transferring a stake in the startups; company management to a – when developing a company, venture investor; venture investors do not claim – “a double trust dilemma”, return on the invested capital or expressed in a combination of the payment of interest; entrepreneur’s and investor’s – in addition to the capital, a assets based on the trust of the start-up company is provided with two parties to each other; assistance in managing the – possible inconsistency in the company to increase its goals of the entrepreneur and the profitability; venture investor – consulting, expert, and organizational support from the investor Crowdfunding
– funds are raised from the public; – a relatively inexpensive way of financing; – crowd campaign is a marketing tool; – investors can be attracted during the PR campaign
– limited amount of funding; – open information about the project can result in stealing ideas; – payment of taxes and commissions; – the risk of fraud on the part of crowd platforms and project authors
After the market saw this new way of investing, more and more new platforms working on this principle began to appear. Let us consider the most popular international platforms (Table 3). Today there are quite a number of crowdfunding platforms in Russia (Table 4). Crowdfunding platforms known today have been developed for a long time, with many studies proving this. Most of them have been operating for more than ten years, which
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Table 3. Major International Crowdfunding Platforms. IndieGoGo
Kickstarter
Year of foundation
2008
2009
Country
USA
USA
Annual budget of the platform, billion USD
0.8
1
Number of successful projects
79 200
120 881
Percentage of project success, %
18
40
Means of fund raising
Keep-It-All
All-Or-Nothing
Main directions of fund raising
Technology, business projects, social and media projects
Games, technology, design
Commission, %
4, if the whole amount is raised; 9 in other cases
5
indicates their popularity. It should be noted that the market for crowdfunding platforms continues to change [8]. Table 4. Major Russian Crowdfunding Platforms. Planeta.ru
Boomstarter
Year of foundation
2011
2012
Country
Russia
Russia
Annual budget of the platform, billion USD
0.24
0.12
Number of successful projects
660
1965
Percentage of project success, %
35
20
Means of fund raising
All-Or-Nothing, Keep-It-All
All-Or-Nothing
Main directions of fund raising
Creative and charity projects
Sports, theatres, art, videos, photos and films; society, fashion, publications, technologies, food, health, music and choreography, design, events, and business
Commission, %
5–15
5
Now crowdfunding is continuously developing, gaining popularity and attracting new players such as project organizers and sponsors. Financing of crowdfunding
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projects is accompanied by some activities aimed at raising funds on specialized Internet platforms [9]. After analyzing various sources and definitions of crowdfunding as a new method of financing, a general model of crowdfunding was built to reveal its principles (Fig. 8).
Founders Investors (backers) • Project inializaon • Implementaon of interesng, useful, socially significant projects Organized platform Technical assistance
Placement of financial resources on predetermined conditions
Compliance
Fig. 8. General Crowdfunding Model.
Crowdfunding means that the investor and the founder interact using a crowd-funding platform, and thus funds can be raised for the projects displayed, which can be seen from the model presented above. The functions shown in the figure have all the subjects of the financing process [10]. Here are the stages for deciding on the effectiveness and developing a strategy for a crowd project. 1. Substantiate a business idea, present it in calculations and draw up a business plan. These are the initial steps in conducting any campaign to raise funds. 2. Choose the source of financing. Traditional methods of attracting investment include credit financing, equity financing, venture financing, government financing, leasing. When choosing a financing instrument, the costs and possible risks accompanying capital raising have to be considered. In practice it is common to use mixed financing, involving several instruments. When choosing a source of financing, the cost of capital has to be taken into account. 3. Choose a crowdfunding platform. At this stage, the experience in implementing similar crowdfunding projects is studied, the platform is chosen based on the project category. 4. Register the crowd project on the platform. Evaluate the cost of fees. The cost of fees is calculated based on the production cost of goods and may include the cost of delivery, or the cost of delivery may be transferred to the sponsor. 5. Determine the financial goal of the crowdfunding project. Assess the effectiveness of the crowdfunding project. Launch the business project for which the funds were raised. Fulfill your fee obligations to the sponsors.
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3 Conclusion At the age of the digital economy in Russia, crowdfunding is a key element. Due to its advantages significant amounts of financial resources can be attracted to finance innovative projects. The analysis of crowd platforms showed that the size of the Russian crowd technology market is much smaller than that of foreign countries. This is caused by the lack of information support for crowdfunding, low financial literacy of the population and, as a result, a distrust of this innovative tool. The goal that we achieved in this study was investigating crowdfunding as an element of the sharing economy and learning about practical application of the knowledge gained in the evaluation of a crowd project. That way, the sharing economy combines the ideas of temporary possession of wealth to save natural resources. It is reflected in the circular economy model, where all elements of the environment are reused, and the concept of waste is absent. The sharing economy is realized in industries such as transportation (carsharing, carpooling), rental of premises (office sharing, coworking, temporary rental of premises), C2C sales, rental of things, P2P services (freelancing), crowdfunding (crowdfunding based on donations, crowdlending, crowd investing), etc. Crowdfunding is a financial instrument that acts as a source of funding for innovative and socially significant projects, being a test site for authors of ideas. Compared to other ways of attracting investment, in crowdfunding funds are obtained from a wide audience, and there is no need for many supporting documents. Crowdfunding is inexpensive, and in case of failure does not bring a lot of losses to the project organizer. On the other hand, crowdfunding projects have high transparency, which can result in stealing of ideas. Summing up, we can say that there are various ways to raise funds for investment projects. Some methods have gained a good reputation and are in great demand, with a loan being an example. However, we should not ignore new ways to raise funds. It can be noted that no other type of fundraising entails so little costs. Hence, we can conclude that, given the current development of crowdfunding, in future it will become one of the main forms of capital raising for investment projects. Acknowledgments. The research was financed as part of the project “Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization” (FSEG-2023-0008).
References 1. Sukhareva, M.A.: From the concept of post-industrial society to the concept of knowledge economy and digital economy: critical analysis of the terminological field. Public Adm. Electron. Bull. 68, 445–464 (2018) 2. Botsman, R.: Defining the Sharing Economy: What is Collaborative Consumption and What Isn’t FastCompany (2015). Fast Company. https://www.fastcompany.com/3046119/definingthe-sharing-economy-what-is-collaborative-consumption-and-what-isnt. Accessed 21 Nov 2021
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3. Allen, D., Berg, C.: The sharing economy: how overregulation could destroy an economic revolution. Institute of Public Affairs, Melbourne (2014), 40 p. https://darcyallendotnet.files.wor dpress.com/2017/07/thesharingeconomy-how-over-regulation-could-destroy.pdf. Accessed 25 Dec 2021 4. Evans, P.C., Gawer, A.: The rise of the platform enterprise: a global survey. The Emerging Platform Economy Series (1) (2016). https://www.thecge.net/app/uploads/2016/01/PDFWEBPlatformSurvey_01_12.pdf. Accessed 10 Jan 2022 5. Howe, J.: Crowdsourcing. Why the Power of the Crowd is Driving the Future of Business. Alpina Publisher (2012). 288 p. 6. Akst, R.: The Anatomy of Crowdfunding or the Phenomenon of ICO. Publishing Solutions (2017). 70 p. 7. Shumeiko, E.V., Nekrasova, T.P.: The economic evaluation of crowdfunding as a method for attracting investments. Sci. Tech. Bull. St. Petersburg State Polytech. Univ. Ser.: Econ. Sci.: Sci. Publ. 10(5) (2017) 8. Gubnitsyn, A., Streltsov, K.: The Sharing Economy in Russia 2019. https://tiarcenter.com/ wp-content/uploads/2020/03/RAEC_Sharingeconomy-in-Russia-2019_March-2020.pdf. Accessed 10 Jan 2022 9. Galievsky, D.: Important Tips from Indiegogo. Indemand (2019). http://indemand.ru/archives/ 7-important-advices-from-indiegogo/ 10. Makarova, E.N.: Assessing the effectiveness of a crowdfunding campaign. Probl. Account. Financ.: Collect. Artic. 28, 35–38 (2017)
The Information Environment Cluster Distribution of the Regional Socio-Economic Systems in Transition Economy Dmitriy Rodionov , Aleksandra Grishacheva, Aleksandra Shmeleva, Polina Chertes, Zhanna Melnikova, Vladimir Markevich, Evgeniy Konnikov , and Darya Kryzhko(B) Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia [email protected]
Abstract. The global trend of digitalization of social interaction has become the root cause of the global transformation of both the information environment in general and the patterns of information consumption and generation. Such transformation has led to a single digital information space emergence of the “Internet”. The hypothesis of this study is the assumption that all regional socioeconomic systems are different, and there is one unique thing characterizing all of them – the information environment. The purpose of this study is to identify the unique content specifics of each region’s information environment in order to find individual thematic clusters, which may have exceptional geographical, social, economic etc. specifics, to analyze the presence of each individual cluster in the general news agenda of the regions to assess the distribution of certain topics in each region. The specificity of this research is manifested in a unique methodology built on the basis of the Python programming language. Using this tool, information units were collected from the regions’ information communities (news hubs). By means of an automatic algorithm, the information received from the communities (tokens) was divided into seven thematic clusters, which are described in detail. Keywords: region · socio-economic system · regional socio-economic system · information · information environment · information background · social networks
1 Introduction At this time, any country can be perceived as a complex, branched system. Since there is an inextricable link between the whole and its elements, this direct change in the welfare of one of the regions will affect not only the country in general, but also each of its subjects. Therefore, such an extensive system requires a thorough study in order to determine the specifics of each region. Some researches mentioned that socio-economics indicators should be classified into groups. In particular, the parameters can be distributed as follows. First of all, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 203–217, 2023. https://doi.org/10.1007/978-3-031-32719-3_15
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demographic (social) indicators were taken into account for the study—the area of the territory, the population. Next were the indicators that characterize the monetary income of the population (socio-economic) – the average per capita monetary income, consumer spending per capita, monthly average nominal salary for employees of organizations. The last group included the parameters of economic development (economic) - GRP, investments in fixed assets, fixed assets in the economy, the average annual number of employees. Using various groups of parameters can help fully consider the development and effectiveness of each subject [1, 2, 13, 15, 19, 26]. According to the results of the ANOVA, it can be noted that the value of the F-critical is almost 10 times less than the calculated one, therefore, there is a variation of the characteristic by region [2]. So, it becomes obvious that the subjects are indeed different. Since today the development of the information environment makes it possible to simplify communication within society through the Internet, there is a chance to identify key regional issues through the analysis of social networks that act as a “free” space on the Internet. People’s problems may be the primary source of socio-economic indicators. Additionally, using the analysis of social networks, it is available to assess whether the population of the region understands their problems. Moreover, well-timed identification of the problems and their elimination contribute to improving the quality of socioeconomic parameters, and consequently, the efficiency of the economy of the subject in general. Thus, the hypothesis of this study can be formulated as follows: the initial problem, interpreted by the population, is the primary source of changes in the indicators of the regional socio-economic system condition. We can identify and quantify this problem by analyzing the information generated by the population in digital social media. It becomes clear that the only thing that exists in all regional systems and that is unique for all of them is the information environment, communications. Based on the above, it is necessary to study the specifics of regional socio-economic systems by analyzing their information background. As each region has an information environment, and it is certainly formed by the news background and reflected in it, it opens up the opportunity to consider current problems and topical issues of a socio-economic nature for certain groups of regions. The objectives of the research are: 1. Identification of the unique content specifics of the information environment of each regional subject in country with transition economics in order to identify individual thematic clusters, which, in turn, may have exceptional geographical, social, economic, political, environmental and other specifics; 2. Analysis of the presence of each individual cluster in the general news agenda of the country subjects to assess the distribution of certain topics in each region. We propose to consider some articles to determine the methodology of this article and other hypotheses and conclusions they came to. First of all, it is necessary to define the concepts of cluster and clustering, as there are different interpretations. The cluster can be defined as a socio-economic and institutional system. In the articles [3, 4, 6, 10, 25] the authors explore the concept of cluster as an institutional theory, and also identify specific institutions that are inherent in the cluster. For example, the institute of cooperation, communications, egalitarianism, etc.
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The cluster as the socio-economic system is analyzed in some articles [7, 14, 20, 24]. The authors propose a unique classification of clusters according to economic, organizational and institutional characteristics. According to the article [11], the clustering process is considered as “a set of measures that are carried out by state and public institutions to unite enterprises (economic entities) into clusters to establish network cooperation between them. Based on this, the main stages of the development of cluster policy in countries with transition economics were identified and their main features were determined: 1. 2. 3. 4. 5.
Soviet period (up to 1991); The period of regional cluster initiatives (1991–2006); The period of clustering the innovation sphere (2006–2007); The period of state cluster initiatives (2007–2014); Period of activation of regional cluster initiatives (2014 - present time).
The last period is the most relevant for the conducted research. The main features of the fifth stage are parameters that determine the development of cluster initiatives, which were facilitated by the activities of regional authorities. In addition, when analyzing the sectoral structure of clusters, the priority sectors are high- and medium-tech industries, such as microelectronics, pharmaceuticals, medical industry and ICT. According to [18], the concept of cluster initiative is a kind of metaphor implying the initial stage of the formation of territorial clusters. It is also necessary to take into account the specifics of the regions. Many authors propose different ways of dividing regions into peculiar groups or clusters according to certain criteria. Since the differentiation of regions has increased recently and, as a result, the increasing inequality of the quality of life of the population living in different countries, Konkov [8] suggests distributing regions according to the prevailing type of economy: – resource-extracting and export-oriented regions with low added value of the created final product; – regions with a developed manufacturing and service industries that create significant added value. In the article [10] it was proposed to use several groups of factors: factors of national and international influence on the regional economy; intraregional factors of economic development; factors of species development of the regional industrial complex; factors of territorial development of the regional industrial complex; factors of scientific and innovative development of the regional economy. On their basis, the problems of the production complex of the Orenburg region were identified. So, the method of clustering parameters into groups can be applied to identify the problems of a region and establish its development. Currently, one of the main sources of information is the media and social networks, so more and more researchers pay attention in their works to the information background and its components. The concept and basic properties of the information background are described in the work of sociologist Erstein [5]. From the author’s point of view, the information background should be considered as the most popular data characteristic of the information environment.
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As mentioned earlier, the frequency of researchers’ turning to the media is increasing today, as it is possible to analyze raw data from primary sources. In the work [12] the author pays special attention to the consideration of various methods of clustering the news stream of the media, which can include statistical, incremental and online algorithms. In addition, many authors today turn specifically to social media, because information within social networks allows clustering according to various parameters, and then apply the obtained results to analyze problems at the regional and state levels. In many studied articles, the clustering process is reduced to the construction of graphs, with the help of which common features are determined. For example, social networks can be considered as a subject of clustering. In the article [16], the group of authors built graphs and determined the parameters of the density and saturation of users in social media depending on its size. Regarding the problems of social networks, it can be seen that people publish various messages on the Twitter network, based on social group, for which they belong to. Accordingly, some common features of each group of people are highlighted. Then with the help of these features further clustering is carried out [9]. In the work [23], the authors are engaged in clustering people into groups, analyzing text information units. Possible tokens that are collected through an application connected to the Twitter social network are considered. The researchers conclude that the k-means algorithm and the spectral clustering algorithm used in the work give almost equal results for the textual similarity of people based on social networks. If we consider the specifics of clustering social networks, then we can divide the communities in this network into certain triads, or groups of three. With regard to this, communities can be clustered into 4 different types: – Closed: all three know each other. An example of this would be family relationships in which everyone knows each other. – Open: one person knows two other people, but the other two do not know each other. There is a person who knows the person at work and another person at home, but the person at work knows nothing about the person at home. – Linked couple: one person knows another person in the triad but does not know the third person. In this situation, two people who know something about each other meet someone new - someone who potentially wants to be part of the group. – Unrelated: the triad forms a group, but no one in the group knows each other. An example of this would be a convention or seminar. People at these events form a group but may not know anything about each other. However, since they have similar interests, they can use clustering to understand the behavior of their group. Based on this distribution of users by groups, the social network LinkedIn invites its users to connect to thematic communities to increase the mass of users, so that it is easier to disseminate information and share content [17]. In addition, [22] conducted research on the clustering of users’ media content in relation to their published images on social networks. Thirteen groups of images were selected: family, selfies, cars, sports, etc. In this case, the idea of clustering users was the possibility of evaluating the most frequently published content to show them the corresponding targeted advertising.
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Accordingly, each work devoted to the creation of clusters or the clustering process has its own specifics, which explains the structure of the behavior of clusters in the socioeconomic environment, as well as their interaction with each other. Various processes can be considered, ranging from technology enterprises to social media. Each study helps to better perceive the environment in which the objects are located, as their common characteristics are highlighted.
2 Methodology The current study analyzes the information environment of regional systems, the primary source of which is the social network VKontakte (the most frequently used networking site in Russian Federation). Therefore, the resulting array of information for consideration has a number of features due to the very specifics of social network users. It should be noted that the VKontakte audience covers almost the entire population of Russia today, because this social network attracts users with simplicity, convenience, and it is intuitively clear to understand how to use it. At the same time there is no doubt that age groups are heterogeneously represented in the social network. The distribution of the social network users number by age can be characterized as normal. The users core of VKontakte is the category of persons of the age 25–34, and there is a trend of gradual maturation of the audience. At the same time, 70% of users are 18–44 years old. This age group is the most active in publishing and discussing posts, while older categories of people are less inclined to publicly discuss any issues via the Internet. Even geographically the site faces practically no borders, allowing you to collect a dataframe for analyzing all regions of the country. The methodology of this study can be divided into 4 logical stages, each is based on the Python programming language, and therefore, it differs in automation. The stages are presented sequentially in Fig. 1. Python programming language is used due to the possibility of differentiating a number of research tasks and, in particular, methodology, as well as working with a large amount of data that is necessary in solving these problems [21]. The above-mentioned Python functionality allows aggregating the solution of problems in a single program, and in addition, optimizing the process of data collection and analysis using specialized tool libraries. The given methodology in the software environment can be divided into three conditional blocks implemented sequentially. The first block includes the first two stages of the above methodology and reflects the process of searching for primary information, its aggregation, as well as the formation of a primary data array, the second – the third stage and involves the formation of intermediate information, including the allocation of meaningful content units, and the allocation of thematic clusters, the third block – the fourth stage and includes the RSES clustering. Each block of the applied solution of the methodology is presented below. The stage 1 of block 1 presents the search for initial information describing the information environment of each subject of the Russian Federation, which can be reflected both in official statistical collections of the Russian Federation or its region, and in unofficial ones, however, characterizing the information environment of the RSES. In this research it was decided to use the second option, namely the social network Vkontakte.
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The Russian-speaking audience’s breadth of coverage gives freedom to analyze more than 90% of the RSES, which also allows us to cluster all regions of Russia. One of the consequences of the popularity of Vkontakte was the introduction of various information sources into it – news information hubs that can be presented by both the local administration and the people, as well as reflect representative information for analysis.
Fig. 1. RSES clustering methodology
The objects of the study are directly the regions of Russia, the sources of information are official groups with information provided by the region’s administration, groups with a significant number of subscribers and unofficial news hubs – “podclyxano” (“overheard” - groups where subscribers share their opinions on various issues); total of 237 sources. Thus, Vkontakte provides up-to-date, objective data generated beyond the context of the study that corresponds to the features of BigData. As part of stage 2, the received data arrays are collected into a single dataframe, including text, chronological and topographic arrays. The algorithm for primary information searching, its aggregation, as well as dataframe formation involves sequential extraction and storage of the
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group records ID, the record’s publication date, the text of the record, and the array of geographical belonging to the RSES. According to the results of this algorithm application, 34909 information units, text records, were obtained from all information hubs of the Russian Federation regions. It is worth noting that the specifics of the data obtained are reflected in the fact that the data is presented in the form of a calculated average value for years from 2017 to 2021. Furthermore, the Chechen Republic region was not included in the process of clustering. To cluster the regions, it is initially necessary to allocate elementary information units that describe the content component of the content (tokens). The allocation of tokens array allows identifying content-themed clusters and directly clustering the Russian Federation regions by identifying the presence of each cluster in the general information background of a particular RSES. In block 2 at stage 3 of the methodology, intermediate information is being allocated. The texts of posts are being tokenized (they are being cleared of punctuation marks, registers, extra spaces, and words with a low content component are also being excluded). In addition, the received tokens are being lemmatized – they are being converted to a dictionary form. After that, an intermediate data array is being formed, which is being submitted for clustering. The k-means algorithm is used to select the number of clusters, and the clustering quality is evaluated using silhouette estimates (the silhouette measure is an integral characteristic of the connectivity and separation of data clusters. It helps to estimate how each data point fits into its cluster. The silhouette score is a metric that measures the similarity degree of a data point with its own cluster compared to other clusters.). Finally, an array is created in which the tokens are divided into thematic clusters which is made for the mental processing of clusters. As a result, tokens were collected, which were subsequently formed into 7 semantic groups - clusters. Block 3 (stage 4 of the methodology) was implemented for clustering of the RSES. Within the framework of this block the resulting array with tokens by cluster and by region was reformed into a single array reflecting the presence share of each thematic cluster in a certain RSES.
3 Results Based on the research methodology there were initially obtained thematic clusters by means of an automated algorithm. An array of elementary units that describe the semantic component of information content (tokens) was processed into 7 resulting thematic clusters. Then they were named and sequentially described. Cluster 1 includes such tokens as “biznec” (business), “zdpavooxpanenie” (healthcare), “konomika” (economy), “yclygi” (services), “infpactpyktypa” (infrastructure), “financipovanie” (financing), “ppedppinimatelctvo” (entrepreneurship). It refers to socio-economic topics, describing the field of business, entrepreneurship and health. Cluster 1 also includes government decisions, among them municipal governments, on issues of the socio-economic standard of living improvement - the development of various national projects, the introduction of gratuities and allowances, financing enterprises, etc. The topic of health concerns coronavirus infection and vaccination. Cluster 2 is characterized by the tokens “ygolovnoe” (criminal), “cyd” (court), “vozbydeno” (initiated), “ppopal” (disappeared), “iwem” (looking
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for), “bezdomnye” (homeless), “pepekpectke” (crossroads), “vpezalc” (crashed), “avapi” (accident), “pexexody” (pedestrians). It describes the problem of crime. The tokens are partially devoted to the search of missing people, problem of homeless animals and pets’ loss, and car accidents. In cluster 3 there are tokens “dopony” (road), “tpancpopt” (transport), “avtomobil” (car), “izolci” (isolation), “macoqnogo” (mask), “pandemie” (pandemic); “poap” (fire), “cvalki” (landfills), “pavodki” (floods). It brings together topics of health, transport and ecology. In turn, the environmental problem is not limited only to vehicles, but also includes cases of natural disasters, etc. At the same time, the topic of automobile traffic touches upon the health sector, which is associated with coronavirus infection nowadays. Cluster 4 includes the semantic units “ppazdnik” (holiday), “kyltypa” (culture), “vyctavka” (exhibition), “myze” (museum), “ickycctvo” (art), “vetepany” (veterans), “napodny” (folk), “pobeda” (victory), “pokoleni” (generations), “tpadici” (traditions). This is dedicated to activities of popularizing cultural and historical heritage, moral traditions, national holidays and political education. Cluster 5 describes the everyday problems of the population, their day-to-day reflections on life and on relationships. This is clearly expressed by the tokens “covety” (advice), “pogoda” (weather), “cvadby” (weddings), “bpak” (marriage), “podpyga” (girlfriend), “byvxi” (ex), “filmy” (films), “otnoxeni” (relationships). There are tokens of emotional color, therefore, people increasingly feel comfortable due to expressing themselves on social networks, sharing personal problems and achievements. Cluster 6 is characterized by the tokens “golocovani” (voting), “mapafon” (marathon), “volontepy” (volunteers), “mediafopym” (media forum), “ctydenqectva” (students), “izbipatelnogo” (electoral), “ppoekt” (project), “pazvitie” (development). It refers to the topic of social movements - youth and volunteer movements, forums and conferences, elections. Thus, the cluster combines news about various events and projects aimed at developing programs to improve the comprehensive development of youth and the population as a whole. Cluster 7 consists of semantic units like “tvopqecki” (creative), “cpoptcmen” (athlete), “kybok” (cup), “doctoppimeqatelnoct” (attraction), “qempion” (champion), “kapnaval” (carnival). It is devoted to the theme of sports and entertainment events: competitions and championships, exhibitions and tournaments, festivals and concerts, trips to the museum and other cultural and educational aspects of life. For better perception of the specifics of the distribution of clusters in RSES, a map of Russia with corresponding clusters, which were assigned with its own colors. This map is shown in Fig. 2. The formed clusters will now be described in more detail. Cluster 1, which can be designated as describing economic and business issues, as well as cluster 5, characterized by household topics of posts, are covered in the most active way in half of the regions of the Russian Federation (43 out of 84 regions), of which discussion of everyday aspects of life (Cluster 1) prevails in 28 regions.
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Fig. 2. Regional Distribution Visualization Map
Cluster 2, described by crime tokens, also prevails in the news agenda of a large number of regions. Here we can note that the Republic of Buryatia and the Trans-Baikal Territory are leaders in the total average annual number of criminal crimes. Areas such as the Krasnoyarsk Territory, the Yamal-Nenets Autonomous Area and the Orenburg Region are characterized as depressed regions, so they are one of the leaders in committing particularly serious crimes. The central regions can be characterized as areas with increased business activity, so these regions are centers of the fight against bribery and other malfeasances. We also note that the Krasnoyarsk Territory, the Yamal-Nenets Autonomous Area, the Orenburg Region, the center are regions with high protest activity, which is also actively covered in the Internet space. Cluster 3, describing the topic of the road transport system, is clearly represented in the Republic of Sakha (Yakutia). In this territory there is an increased seasonality and high cost of transport, which are acute problems for the local population. The main reason for this is the underdeveloped road system and the complexity of the transport schemes of the region. Cluster 4, which has a cultural and ideological agenda, is represented by such regions as Republic of Tatarstan, Republic of Tyva, Karachaev-Circassian and Chechen Republics, Republic of Ingushetia. Tatarstan is the cultural center of the Volga region, this area contributes to the ethnocultural development of the local population, promoting ideas of the cultural heritage. Tyva is characterized by active social movements, many socially oriented organizations of this region began to emphasize the revival of national traditions of Tyvans. Ingushetia is a region with a unique cultural and historical heritage, so the problem of its preservation is a strategic priority for the socio-political activities of residents, local projects are aimed at extensive audience involvement there. Karachay-Cherkessia and Chechnya are also oriented to the issue of mainstreaming the problems of preserving and developing the ethnocultural component of society (especially in developing image concepts of the North Caucasus).
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It is also necessary to note the fact that all the listed regions have low indicators of the average age of the population. Cluster 6, which describes the subject matter of social movements, cannot be interpreted logically and significantly from a geographical point of view. Cluster 7, which is dedicated to the theme of sports and entertainment events, is represented by the Chukotka autonomous area and the Murmansk region, which are areas with harsh climatic and natural conditions, and local residents here are actively involved in the discussions on sports issues (these entertainments help them to stay healthy).
4 Discussion Consider how the problems are divided by the frequency of prevalence in the regions. The data is presented in Fig. 3.
Fig. 3. The most frequent problems in the regions identified in the information environment
A third of the RSES of the Russian Federation most often mention issues related to everyday life in the information environment, a little more than a fifth of the regions more often cover the topic of crime, 18% of the RSES are most interested in business, entrepreneurship and the economy as a whole, 10% - in sports, 7% of the regions cover transport and environmental issues, 6% - social movements in their various forms, and 5% of the RSES pay more attention to culture and education. Thus, in general, it can be noted that there is no uniform specificity of the geographical distribution of the regions of the Russian Federation, so the main reasons for the differentiation of regions by identified clusters are not geographical. Therefore, the reasons for the distribution are concentrated within other features of regional socio-economic systems. It is of interest to compare the data obtained with social surveys on the Internet. These surveys are devoted to the level of satisfaction of the population in the regions of Russia, as well as the main problems that concern citizens living in a particular area in the country.
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In contrast to the specifics of the study, which considered the prevailing problems for each individual RSES, the statistics in the surveys reflect the overall satisfaction/dissatisfaction of the Russian population with certain issues, which does not allow us to fully compare the results but makes it possible to assess whether the selected thematic clusters correspond with the official surveys for the country. Thus, the website of the RBC business portal has compiled a rating of problems in Russian regions, formed according to statistics by the federal state statistics service. This rating includes 17 socio-economic problems that concern the population for 2019. Particularly acute issues are “the state of roads, road safety” (62.9%), “remoteness of places of recreation and leisure” (38.4%), “poor organization of housing and communal services” (37.2%), “environmental pollution” (36.9%), etc. (Fig. 4).
Fig. 4. Rating of problems in the Russian Federation 2019 (presented in RBC, source Rosstat).
To determine the main topics or groups, the number of presented problems can be narrowed by combining them by semantic load or by sectors of people’s social life. Thus, there are 7 thematic groups of problems: recreation, cultural education and education; transport and road traffic; crime; health care; ecology; housing and communal services; others. A detailed division is presented in Table 1. After comparing the obtained results and the survey data, it can be noticed there are a number of similar issues, such as crime, culture and road accidents, which indicates the acute significance of these problems and the importance of their solution and management. After comparing the obtained results and the data on the issues, it is noticeable that there are a number of similar issues, such as crime, culture and road accidents. However, there are more problems intersecting point-by-point than can be identified by the initial analysis of thematic clusters. Let’s look at them in more detail.
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Theme
Determinates
Value
Recreation, cultural education, and education
The remoteness of recreation and leisure places
38.4%
The remoteness of physical education and sports facilities
34.5%
The remoteness of cultural institutions The remoteness of retail outlets
30.1% 11.6%
Problems with state and municipal preschool and school education services
10.7%
Road condition & safety
62.9%
Poor public transport organization
24.5%
The spread of alcoholism
32.0%
Vandalism
19.4%
Transport and road traffic Crime
High crime rate Drug distribution
8.3% 17.8%
Health care
Problems with state and municipal medical 26.8% services The remoteness of pharmacies
20.2%
Ecology
Pollution
36.9%
Disorganization, poor landscaping
32.7%
Housing and communal services
Poor housing and communal services organization
37.2%
Other
Other problems
18.0%
Recreation, cultural education and education prevail in cluster 4, as examples of tokens can be given “ickycctva” (arts), “myze” (museums), “tpadicii” (traditions), “teatp” (theater), “nayk” (sciences”), “tvopqeckie” (creative), “xkolnye” (school), it is also possible to note the presence of this issue in cluster 6 “pamtniki” (monuments), “ictopi” (history), “pedagogi” (teachers), “odapennyx” (gifted), “intellektyalno” (intellectual), “patpiotiqeckix” (patriotic), “ctydenqeckoe” (student), “volontepy” (volunteers). The problems of transport and road traffic are related to cluster 3. Examples of tokens: “napyxeni” (violations), “avapii” (accidents), “mapxpyt” (route), “xtpafy” (fines), “pdd” (traffic regulations), “avtodopogi” (highways), “bezopacnogo” (safe), “paccaipam” (passengers). In addition, the environmental problem identified in the results of citizen surveys can also be attributed to cluster 3. Here, examples of tokens are: “zagpznwix” (polluting), “kologiqecki” (ecological), “ppipodno” (natural), “mycopa” (garbage), “poapov” (fires), “ybopke” (cleaning).
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The problem of crime identified from the surveys is similar to cluster 2. Such tokens as: “cledctvi” (investigations), “kolonii” (colonies), “viny” (guilt), “ppectyplenie” (crime), “cydom” (cour), “zloymyxlennik” (attacker) can be attributed here. The problems related to healthcare are clearly evident in cluster 1. As examples, the following tokens can be distinguished: “medicincko” (medical), “leqeni” (treatment), “bolnica” (hospital, “ppepapatov” (drugs), “medyqpedenix” (medical institutions). Also, housing and communal services as a problem identified in the surveys can be traced in cluster 1. So, examples of tokens on this topic are: “il” (housing), “kx” (housing and communal services), “pemonta” (repair), “vodocnabeni” (water supply). Therefore, the analysis indicates the acute significance of these problems and the importance of their solution and management, as well as the high accuracy of the clusters identified in this study, since they sufficiently cover the main issues causing unrest among the population.
5 Conclusions The primary hypothesis of this study was confirmed, that is, the prevailing problems were identified and quantitatively described by analyzing the information generated by the population in digital social media. In addition, the study showed that an automated algorithm built based on the Python programming language is one of the most effective ways to conduct such work. The study revealed the unique content specifics of the information environment of each subject and identified seven thematic clusters with exceptional features. During the analysis of each separate cluster presence in the general news, it helps to assess the distribution of certain topics in each region. An important conclusion was made that there is no single specificity of the geographical distribution regions in our example; therefore, the main reasons for the regions differentiation by identified clusters are not geographical. Consequently, the reasons for the distribution are concentrated within the social, economic, political and other characteristics of regional socio-economic systems. By achieving the goals of the study, a number of objectives were solved: The regions were clustered on various topics. For this purpose, a unique methodology was developed based on the Python programming language. The use of this algorithm made it possible to collect publications from the information communities of the regions. The social network VKontakte was used as a source of data collection; The reasons for clustering of various subjects, as well as the prevalence of certain problems in each of the regions, are determined. This study will allow to timely identifying the most problematic issues of the regions in countries with transition economies and the sources that prevent the successful development. The information environment and its analysis will allow us to qualitatively improve the state of not only each individual subject, but also the country since it is a kind of bridge between a person and the state (it is a link between a person and the state). Social
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media, which represent part of the information background, both reflect the state of the economy and the wellbeing of the population, and set the vector of its development, because they can be used by regional administrative offices in order to increase the level of satisfaction of citizens in socio-economic issues. Acknowledgements. This research was funded by the Russian Science Foundation. Project No. 20-78-10123.
References 1. Asheim B.T.: Smart specialisation, innovation policy and regional innovation systems: what about new path development in less innovative regions?. Innov.: Eur. J. Soc. Sci. Res. 1(32), 8–25 (2019) 2. Chistov, S.Yu.: Formation of a system of indicators of socio-economic development of regions of the Russian Federation. Vestnik Tambov. Univ. (Bull. Tambov Univ.) 6, 32–36 (2011). (in Russian) 3. Claver-Cortés, E., et al. Explanatory factors of entrepreneurship in food and beverage clusters in Spain. Sustainability 14(12), 5625 (2020) 4. Damayanthi, S., Gooneratne, T.N., Jayakody, J.: Logics, complexities and paradoxical tensions: management controls in a clustered firm. Account. Audit. Account. J. (2020) 5. Ershtein, L.B.: Influence of the information background and useful information as the main properties of the information environment on the development of society. Vestnik NSUEM (Bull. NSUEM). 4, 183–191 (2016) 6. Gebhardt, C.: What governance for regional innovation clusters? Meta-organisational analysis of the German innovation cluster “physics for food”. Designing and Managing Clusters for Sustainability. Routledge (2022) 7. Kleiner, G.: Socio-economic ecosystems in the light of system paradigm, pp. 4–12 (2018) 8. Konkov, A.T.: Inequality of regions as a problem of development in Russia and China. SahSU Int. Jurn.: “Nauka, Obraz., Obsh.” (SakhSU Internet J.: “Sci. Educ. Soc.”) (2012) 9. Konnikov, E., et al.: Analyzing natural digital information in the context of market research. Information 10(12), 387 (2021) 10. Korabeynikov, I.N., Shchasteva, L.M., Tokareva, Y., Koretskaya, I.M.: Factors affecting the effective functioning of the regional production complex. Vestnik OSU (Bull. OSU) 4, 53–59 (2009) 11. Kreidenko, T.F., Rodionova, I.A., Bogachev, I.I.: Clustering in Russia: dynamics and regional specifics of development. Zhurn. Belaruss. Gos. Univer. Geograf. Geolog. (J. Belarus. State Univ. Geogr. Geol.) 1, 62–70 (2017) 12. Kutukov, D.S.: Application of clustering methods for news flow processing. In: Technical Sciences: Problems and Prospects, pp. 77–83 (2011) 13. Kuzmenko, O.V., et al.: Why do regions differ in vulnerability to COVID-19? Spatial nonlinear modeling of social and economic patterns (2020) 14. Lozhachevska, O., et al.: Management of logistics and marketing behavior of innovation clusters in territorial communities in the context of digitalization of society and the online market. Laplage em Revista 3(7), 315–323 (2021) 15. Miłek, D.: Spatial differentiation in the social and economic development level in Poland. Equilib. Q. J. Econ. Econ. Policy 3(13), 487–507 (2018) 16. Mishra, N., Schreiber, R., Stanton, I., Tarjan, R.E.: Clustering social networks. In: Bonato, A., Chung, F.R.K. (eds.) WAW 2007. LNCS, vol. 4863, pp. 56–67. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77004-6_5
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Event Study on the Stock Performance: The Case of US Logistics Companies Tatiana Kudryavtseva , Maria Rodionova(B)
, and Angi Skhvediani
Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. The article relates to the development of companies’ comparative analysis. It investigates “green” and “non-green” companies’ stock performance in terms of news shocks on the stock market. This study provides event study analysis as a method for the evaluation of stock performance in times of high uncertainty due to many news shocks. It is relevant for both companies listed on stock markets and investors. The analysis in this study is carried out on the basis of the event study methodology in STATA using daily data on stock prices of US logistics companies from 2007 to 2020. The results show a significant impact of company’s commitment to ESG policy on increasing of stocks resistance to news shocks on the US stock market. The most volatile moments on the US stock market are analyzed: the Great Recession and the COVID-19 pandemic. The current analysis confirms that green logistics stocks are less volatile and more sustainable during crises and shocks on the stock market. Keywords: Event Study · Stock Performance · ESG Risk
1 Introduction Stock market research is one of the most complex issues in terms of economic theory, as it is often reduced to the analysis of stochastic processes that have a random nature. In addition, these studies are closely related to the field of behavioral economics, namely behavioral finance. In fact, any stock market research is reduced to the systematization of human behavior in various information flows. Taking into account constantly developing technologies and accelerating information flows, for effective investment, a person now needs to know not only financial theory, but also have an understanding of rapidly changing trends in various areas of the economy, as well as be aware of the general patterns of other market participants’ behavior. In recent years, the trend of sustainable development has been firmly entrenched in the news agenda, which raises many questions for all parties to this process. On the one hand, the management of companies based on ESG principles (responsible attitude to the environment, high social responsibility, high quality of corporate governance) should lead to positive effects for most stakeholders. On the other hand, managing a company based on ESG principles leads to increased costs for companies, while the return on such costs is difficult to calculate. Also, an important aspect is the positioning of the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 218–229, 2023. https://doi.org/10.1007/978-3-031-32719-3_16
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company as stable for external stakeholders, including investors in the stock market. In this regard, many authors consider this issue in terms of behavioral economics, analyzing the loyalty of investors to “green” companies [1–11]. Thus, the relevance of this study is to analyze the profitability of investments in stocks of “green” logistics companies, in comparison with “non-green” ones, on the stock market during the period of news shocks. The transport and logistics sector was chosen due to the fact that it creates a great burden on the environment due to the large amount of CO2 emissions into the atmosphere resulting from the operating activities of companies. Moreover, Zhou D. and Zhou R., 2020 highlights, that a good ESG performance in company can serve as a hedge during crisis (it was observed COVID-19 pandemic) [12]. The study considers the MSCI index disclosed in Bloomberg database to measure different corporate governance, environmental and social performance of A-share listed companies. The results of the study indicate excellent ESG performance of single listed companies is reducing stock price volatility under the impact of crisis and stabilizing stock price. In contrast to companies with poor ESG, difference in difference method shows that excellent ESG performance companies have lower volatility and more stable stock prices during economic recession via COVID-19. However, the study, Engelhardt N. et al., 2021, concluded that sample of publicly-listed European firms from 16 different European countries, show negative but unsignificant effect of ESG score on stock return volatility during COVID-19 crisis [13]. Whereas Xiong J. X. in 2021 found that the “green” stocks with low ESG risk have higher returns and less volatile during the COVID19 crisis, than stocks with high ESG risk ratings [7]. Also, the study, Albuquerque et al., 2020, uses differential difference method to measure the mitigation effect of ESG input on corporate downside risk during the COVID-19 crisis, considering the results it is concluded that the downside risk of companies with excellent ESG performance was significantly lower than that of companies with poor ESG performance [14]. Ouchen A. in 2021 confirmed that ESG portfolio “MSCI USA ESG Select” is less turbulent during crisis than benchmark of the market, index “S&P 500” [15]. For the testing such issue as an influence of news shocks on the stock market many researchers obtain information from the event study analysis. Alam M. N. et al., investigating the impact of the lockdown period caused by the COVID-19 to the stock market of India, use market model event study methodology. For the aim of research, they examine 31 companies listed on Bombay Stock Exchange are selected at random [16]. Another research of Alam M. M. et al. provides the event study analysis with 10-day event window of the COVID-19 announcement and its influence on the stocks of different sectors in Australia (from Australian Securities Exchange). Considering the transportation sector, they obtain decreasing of performance such stocks on the market [17].
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This research is a continuation of a study published earlier by the same authors. In the previous stage of the research we concluded, that green logistics stocks are less volatile, and hence less risky, and more profitable when compared with non-green logistics stocks. Third, the logistics companies, divided into green and non-green groups, were on average more profitable and riskier than the S&P 500. Fourth, the Great Recession (2007–2009) and the COVID-19 pandemic (2020) had the greatest impact in terms of the US stock market on stock volatility [18].
2 Materials and Methods In this study, the methodology of event analysis is used, implemented by the Stata software. As part of the study, 2 groups of shares of logistics companies listed on the US stock market were analyzed: “green” and “non-green”. The division into groups was carried out on the basis of the “ESG risk” and “exposure level” indicator: stocks of companies with an ESG risk of less than 25 and with a low level of environmental impact were classified as “green” stocks, while those with an ESG risk of more than 25 points and with an average or high exposure level were classified as “non–green” stocks. The data were reviewed from 2007 to 2020 for 32 logistics companies. Data on indicators for grouping stocks were taken from the Wharton research data services database [19], daily stock price data from yahoo.finance [20]. Therefore, 16 companies with tickers were assigned to the group of “green” stocks: JBHT, EXPD, R, ARCB, WERN, SAIA, CAR, MRTN, KNX, UPS, ODFL, XPO, HTLD, CHRW, LSTR, FDX. The ESG risk range of this group is from 14 to 21. The group of “non-green” stocks includes 16 companies with stock tickers: HUBG, UNP, ULH, RLGT, AAWW, GOGL, EGLE, AAL, JBLU, UAL, HA, LUV, SKYW, ALGT, AIR, USAK. The ESG risk range of the “non-green” group is from 25 to 43. An event study measures the valuation effects of events, such as a macroeconomic or microeconomic, by examining the response of the stock price around the announcement of the event. Return event studies quantify an event’s economic impact in so-called abnormal returns. Abnormal returns are calculated by deducting the returns that would have been realized if the analyzed event would not have taken place (normal returns) from the actual returns of the stocks. While the actual returns can be empirically observed, the normal returns need to be estimated. For this, the event study methodology makes use of expected return models. The market model, or single index model (SIM) – the special case of CAPM, is the most frequently used expected return model. It builds on the actual returns of a reference market and the correlation of the firm’s stock with the reference market. Equation (1) describes the model formally. The abnormal return on a distinct day within the event window represents the difference between the actual stock return on that day and the normal return, which is predicted based on two inputs; the typical relationship between the firm’s stock and its reference index (expressed by the α and β parameters), and the actual reference market’s return. ARi,t = Ri,t − (αi + βi Rm,t ), where ARi,t – The abnormal return on a distinct day within the event window; Ri,t – the actual stock return on the day within the event window;
(1)
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Rm,t – the actual reference market’s return. To measure the total impact of an event over a particular period (termed the event window), one can add up individual abnormal returns to create a cumulative abnormal return. It becomes necessary to operate a time-series aggregation of the ARs, obtaining the CARs as described by Eq. (2). CAR(t1 ,t2 ) =
t2 t=t1
ARi,t
(2)
If instead the object of interest is the impact on a pool of firms, a cross-section aggregation becomes necessary, and AAR calculation can be performed using (3). AARt =
1 N ARi,t , i=1 N
(3)
where N – the securities’ population. In a sample event study that holds multiple observations of individual event types, one can further calculate cumulative average abnormal returns (CAARs), which represent the mean values of identical events, when the focus is on the average effect over multiple days. It is necessary to perform both of the aggregations just described and compute the CAARs by summing over time the AARs, as shown in (4). The presented CAARs represent the average stock market responses (in percent) to news. CAAR =
t2 t=t1
AARt
(4)
3 Results Event analysis begins with determining the event day and the event window. The article examines the following most volatile periods in the US stock market during the period under review [18]: 1. The Great Recession of 2007–2009 (06.03.2009). 2. Introduction of lockdown in the USA due to the COVID-19 pandemic (19.03.2020).
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The first table with the results of event study analysis is considered the Great Recession (2007–2009), see Table 1. One of the most volatile periods for the stock markets. As this period no exact event for the beginning of financial crisis, but many factors of it, it was decided to consider as the event date – March 6, 2009 – the bottom of the Great Recession, and to investigate how stocks surge, also comparing “non-green” and “green” groups of stocks. The periods of investigations are: – 30 days before the event date; – 30 days after the event date; – from the 30th day before the event date till the 30th day after the event date. As is seen from the table above, it can be concluded following statements: 1. There are no significant values at the pre-event period for the CARs and CAAR of the groups, but significant losses of “non-green” companies are higher. However, the EGLE stock has significant abnormal return about 50% (48,42%). 2. For the results of the post-event period, it can be highlighted that, for both groups, CARs and CAAR are significantly positive. That is, at the period of 30 days after the bottom values on the US stock market, it is seen stock surge. And for the “non-green” stocks, the surge is higher for the sum of abnormal returns (CAR) as well as for the sum of average abnormal returns (CAAR). The CAR of “non-green” stocks is higher by 4%–26,33%, in comparison with the “green” group – 22,11%. The CAAR of “non-green stocks” is higher by 5%–28,58%, in comparison with the “green” group – 23,30%. 3. The significant results for the overall observed period are the CAAR of the “nongreen” group equals to 16,62%, the CAAR of the “green” group – 21,46%. Therefore, stock surge of the “non-green” stocks is higher after the achieving of the lowest stock values due to the Great Recession, but losses also are higher before the event. That is why, when the overall observed period is investigated, it shows values of the abnormal returns higher for the “green” group of stocks. The CAARs’ values are depicted more detailed in Fig. 1. As can be seen from the figure below, “green” stocks have their bottom earlier than the observed event date, it was about 10 days before the 6th of March. Nevertheless, the lowest value of loss is not so much as for the “non-green” stocks. The following event is connected with COVID-19 pandemic. It is analyzed influence of the lockdowns in the USA, that began on March 19, 2020, on the US logistics stocks abnormal returns. The explored periods of the event study analysis are following: – 30 days before the event date; – 30 days after the event date; – from the 30th day before the event date till the 30th day after the event date.
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Table 1. Event study analysis of Great Recession (06.03.2009) Ticker
CAAR(−30,0)
CAAR(0,30)
CAAR(−30,30)
HUBG
3,37%
1,76%
7,27%
UNP
3,16%
5,90%
10,55%
ULH
25,66%
11,86%
32,03%
RLGT
5,57%
75,07%
97,46%
AAWW
−14,02%
53,77%***
36,04%
GOGL
3,84%
−9,65%
−7,76%
EGLE
48,42%*
10,14%
41,88%
AAL
−78,10%**
33,16%
−54,34%
JBLU
−47,68%**
42,09%**
−9,64%
UAL
−62,35%
19,75%
−49,89%
HA
−50,22%**
61,56%***
0,06%
“Non-green” stocks
LUV
−28,17%**
18,90%
−11,55%
SKYW
−37,64%**
25,13%
−21,13%
ALGT
−2,62%
25,01%
23,89%
AIR
−26,44%*
−2,62%
−27,39%
USAK
5,69%
−4,48%
4,56%
Non-green Ptf CARs
−11,71%
26,33%**
11,71%
CAAR of non-green group
−8,96%
28,58%***
16,62%*
JBHT
8,37%
8,16%
19,65%
EXPD
4,26%
4,07%
8,78%
R
−32,52%***
−0,07%
−27,72%
ARCB
−12,05%
3,91%
−6,85%
WERN
2,24%
−2,40%
0,80%
SAIA
12,80%
19,03%
29,79%
CAR
−22,39%
198,75%***
167,94%***
MRTN
8,50%
−3,88%
4,75%
KNX
11,95%
−1,05%
13,81%
UPS
−1,89%
13,79%**
13,47%
ODFL
5,05%
1,78%
11,98%
XPO
−35,01%**
8,17%
−23,91%
“Green” stocks
(continued)
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T. Kudryavtseva et al. Table 1. (continued)
Ticker
CAAR(−30,0)
CAAR(0,30)
CAAR(−30,30)
HTLD
5,38%
−0,35%
7,24%
CHRW
−2,52%
−0,16%
−1,57%
LSTR
−1,42%
−1,70%
−1,43%
FDX
−25,29%***
13,10%
−3,80%
Green Ptf CARs
−4,30%
22,11%**
19,43%
CAAR of green group
−3,42%
23,30%***
21,46%***
The asterisks depict the intervals of the p-value (*** p < 0.01, ** p < 0.05, * p < 0.1)
Fig. 1. CAARs of Great Recession
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The obtained results of the event study about the implementing of lockdown in the USA are provided below, in Table 2. Table 2. Event study analysis of COVID-19 (19.03.2020) Ticker
CAAR(−30,0)
CAAR(0,30)
CAAR(−30,30)
HUBG
18,36%
−18,48%
−5,84%
UNP
−8,16%
12,16%*
−1,15%
ULH
7,16%
−10,12%
3,02%
RLGT
−20,17%
19,35%
−13,47%
AAWW
53,28%***
27,83%*
70,20%***
GOGL
8,65%
−3,74%
5,88%
EGLE
22,27%
−20,74%
−19,80%
AAL
−42,31%**
−26,31%
−56,13%*
JBLU
−56,28%***
5,75%
−49,14%**
UAL
−73,45%***
5,85%
−66,35%**
HA
−78,73%***
23,18%
−51,24%**
LUV
−27,77%***
−31,76%***
−46,85%***
SKYW
−55,55%***
77,56%***
−21,06%
ALGT
−49,36%***
−10,19%
−56,78%***
AIR
−61,69%***
57,49%***
−40,03%**
USAK
−2,83%
52,87%***
23,94%
Non-green Ptf CARs
−19,67%***
13,69%*
−14,70%
CAAR of non-green group
−17,71%***
15,38%***
− 11,10%*
JBHT
1,14%
−0,06%
2,27%
EXPD
8,65%
−2,04%
11,63%
R
−9,86%
−6,71%
−8,23%
ARCB
28,29%*
−21,69%
13,76%
WERN
18,02%**
3,13%
21,58%*
SAIA
14,06%
−4,45%
17,17%
CAR
−55,81%**
45,86%*
−33,53%
MRTN
18,65%*
2,57%
26,88%*
KNX
11,48%
−3,70%
15,91%
UPS
20,18%***
−21,69%***
1,92%
ODFL
10,23%
6,67%
21,61%
XPO
−54,67%***
27,24%*
−20,29%
“Non-green” stocks
“Green” stocks
(continued)
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T. Kudryavtseva et al. Table 2. (continued)
Ticker
CAAR(−30,0)
CAAR(0,30)
CAAR(−30,30)
HTLD
17,36%**
2,32%
16,48%
CHRW
15,11%*
−6,90%
9,86%
LSTR
6,29%
−7,57%
4,59%
FDX
8,81%
0,71%
−2,02%
Green Ptf CARs
5,17%
2,86%
9,44%
The asterisks depict the intervals of the p-value (*** p < 0.01, ** p < 0.05, * p < 0.1)
As is seen from the table above, it can be concluded following statements: 1. Mostly, significant negative results of abnormal stock returns are provided by the “non-green” group. 8 “non-green” stocks have quite high losses in the pre-event period, when the decision about lockdowns was discussed all around the world. In comparison, only 2 “green” stocks have significantly negative ARs in the pre-event period, but 6 stocks with significantly positive ARs, in contrast to 1 “non-green” significantly positive stock’s AR (AAWW). Considering the post-event period, her, vice versa 5 significant positive “non-green” stock’s ARs, in comparison with 2 significant positive “green” stock’s ARs. While there is 1 stock in each of the group have significant loss in the post-event period. The whole observed period shows 7 “non-green” stock’s ARs with significantly negative values and 1 with positive one (AAWW), in contrast, there are 2 “green” stocks with significantly positive ARs. 2. Comparing the group indicators, it can be emphasized, that the “green” CAARs of all periods show positive values (5,72%, 3,36% and 10,47%), whereas the “non-green” CAARs in the pre-event and the whole periods show negative values, −17,71% and − 11,10%, respectively. But the post-event period shows the increase of the “non-green” CAAR by 15,38%, that is 12% higher, than “green” one (3,36%). The results of the event study of the COVID-19 also show that “non-green” stocks more volatile due to such shock. More detailed observation of the CAARs’ results can be found below, see Fig. 2. As is seen, the lowest value of the “non-green” CAAR is exactly obtained on the event day, in contrast the lowest value of the “green” CAAR is at the pre-event period (it is about 10 days before the event day). Therefore, the event day on the “green” CAAR graph is located on the stock surge till 13% of CAAR value, while the “nongreen” is at −27% point. At the post-event period “non-green” CAARs are located in the range from 0 to −20%, whereas “green” CAARs are placed at the positive range from 3% to 13%. However, the pre-event period shows similar values of CAARs, but abnormal returns of “non-green” logistics companies’ stocks have declined much more than “green” stocks (the lowest values are −27% and −5%, respectively). That means that certain event has more negative impact on “non-green” logistics stocks than on “green” one, it has confirmed also faster recovery of “green” average abnormal return after the implementing of the lockdown system in the USA. Also, it can be mentioned that this event is mainly observed at the last time in the research of ESG factors impact on resilience of the stocks to crisis periods, as the event
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provoke the great declining of the world economy and, in particular, the US stock market. And, of course, it also directly had impact on logistics industry. According to the event study analysis, it was obtained that “green” logistics stocks was more sustainable to COVID-19 crisis than its peers – “non-green” logistics stocks. That again confirms higher resilience of “green” stocks during the crisis, in contrast to “non-green” ones.
Fig. 2. CAARs of COVID-19
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4 Discussion and Conclusion As is suggested by the previous research of Zhou D. and Zhou R. in 2020, it can be highlighted, that a good ESG performance in company can serve as a hedge during crisis (it was observed COVID-19 pandemic). In contrast to companies with poor ESG, difference in difference method shows that excellent ESG performance companies have lower volatility and more stable stock prices during economic recession via COVID-19 [12]. However, the study, Engelhardt N. et al., 2021, shows negative but unsignificant effect of ESG score on stock return volatility during COVID-19 crisis. Whereas Xiong J. X. in 2021 found that the “green” stocks with low ESG risk have higher returns and less volatile during the COVID-19 crisis, than stocks with high ESG risk ratings [13]. Ouchen A. in 2021 confirmed that ESG portfolio “MSCI USA ESG Select” is less turbulent during crisis than benchmark of the market, index “S&P 500” [15]. The current research confirms the obtained results in previous stage, where green and non-green logistics portfolios were optimized [18]. All observed green portfolios obtain higher risk, return and effectiveness, indicated by Sharpe ratio, in comparison with the non-green portfolios. Also, the historical data analysis shows the less volatility in “green” stocks log returns. Therefore, the current event study analysis of the Great Recession (06.03.2009) and COVID-19 (19.03.2020) also confirms the resistance of green stocks to the market volatility. The results show the significant positive impact of the company’s commitment to ESG policy on increasing the stability of logistics companies’ stocks during the Great Recession and the COVID-19 pandemic. It means, that the green stocks are more profitable. This fact can be caused by behavioural finance effects that influence on company’s image, but it requires more complex research of this certain issue. Acknowledgements. The research was financed as part of the project “Development of a methodology for instrumental base formation for analysis and modeling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization” (FSEG-2023-0008).
References 1. Serafeim, G., Amel-Zadeh, A.: Why and how investors use ESG information: evidence from a global survey. Finan. Anal. J. 74, 87–103 (2018) 2. Sustainable Investing: Resilience Amid Uncertainty. BlackRock: Official Website. https:// www.blackrock.com/institutions/en-us/solutions/sustainable-investing/. Accessed 11 Jan 2021 3. Landi, G., Sciarelli, M.: Towards a more ethical market: the impact of ESG rating on corporate financial performance. Soc. Respons. J. 15, 11–27 (2018) 4. Eccles, R.G., Ioannou, I., Serafeim, G.: The impact of corporate sustainability on organizational processes and performance. Manag. Sci. 60, 2835–2857 (2014) 5. Cao, J., Liang, H., Zhan, X.: Peer effects of corporate social responsibility. Manag. Sci. 65, 5487–5503 (2019) 6. Gillan, S.L., Koch, A., Starks, L.T.: Firms and social responsibility: a review of ESG and CSR research in corporate finance. J. Corp. Finan. 66, 1–16 (2021)
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7. Xiong, J.X.: The impact of ESG risk on stocks. J. Impact ESG Invest. 2, 7–18 (2021) 8. Guenster, N., Bauer, R., Derwall, J., Koedijk, K.: The economic value of corporate ecoefficiency. Eur. Finan. Manag. 17, 679–704 (2011) 9. Ashwin Kumar, N.C., Smith, C., Badis, L., Wang, N., Ambrosy, P., Tavares, R.: ESG factors and risk-adjusted performance: a new quantitative model. J. Sustain. Finan. Invest. 6(4), 292–300 (2016) 10. Shakil, M.H.: Environmental, social and governance performance and stock price volatility: a moderating role of firm size. J. Public Aff. 22(3), e2574 (2020) 11. Sabbaghi, O.: The impact of news on the volatility of ESG firms. Glob. Finan. J. 51, 100570 (2020) 12. Zhou, D., Zhou, R.: ESG performance and stock price volatility in public health crisis: evidence from covid-19 pandemic. Int. J. Environ. Res. Public Health 19(1), 202 (2021) 13. Engelhardt, N., Ekkenga, J., Posch, P.: ESG ratings and stock performance during the COVID19 crisis. Sustainability 13(13), 7133 (2021) 14. Albuquerque, R., Koskinen, Y., Yang, S., Zhang, C.: Resiliency of environmental and social stocks: an analysis of the exogenous COVID-19 market crash. Rev. Corp. Finan. Stud. 9(3), 593–621 (2020) 15. Ouchen, A.: Is the ESG portfolio less turbulent than a market benchmark portfolio? Risk Manag. 24(1), 1–33 (2022) 16. Alam, M.N., Alam, M.S., Chavali, K.: Stock market response during COVID-19 lockdown period in India: an event study. J. Asian Finan. Econ. Bus. 7(7), 131–137 (2020) 17. Alam, M.M., Wei, H., Wahid, A.N.: COVID-19 outbreak and sectoral performance of the Australian stock market: an event study analysis. Aust. Econ. Pap. 60(3), 482–495 (2021) 18. Rodionova, M., Skhvediani, A., Kudryavtseva, T.: ESG as a booster for logistics stock returns—Evidence from the US stock market. Sustainability 14(19), 12356 (2022) 19. Wharton Research Data Services. https://wrds-www.wharton.upenn.edu/. Accessed 3 Mar 2022 20. Yahoo! Finance. https://finance.yahoo.com/. Accessed 10 Mar 2022
Prospective Avenues for Digitalization of Tourism in Russia Artur Kuchumov1(B) , Yana Testina2 , Svetlana Egorova3 , and Natalya Kulakova3 1 Saint-Petersburg State University of Economics, Saint Petersburg, Russia
[email protected] 2 Saint-Petersburg State University, Saint Petersburg, Russia 3 Pskov State University, Pskov, Russia
Abstract. One of the pronounced trends in the modern societal development is digitalization in every sphere of activity: over 6.62 billion people use the Internet for communication, business, and entertainment, including travel. Tourism has suffered greatly during the pandemic, with many countries not receiving their planned revenues due to epidemiological restrictions; however, now the industry is recovering, among other things through digitalization. The assessed experience of Bulgaria, Germany, and China showed that digitalization of tourism occurs in each country, although with different speed and reliance on different indicators. The study analyzed methods for assessing the mutual impact of digitalization processes and the tourism industry development. As the most optimal, The Digital Economy and Society Index (DESI) was adapted for the public domain data from Russian statistical reporting. Analysis of the resulting digitalization index was conducted in ten regions of Russia with different levels of tourist development. The correlation between the calculated Regional Digitalization Index and the number of tourists accommodated in hotels showed a direct strong relationship, made possible predicting the development of tourism through digital processes, drafting strategic plans for the development of the industry taking into account modern digital tools, and calculating effective measures of state support and financing. Keywords: Tourism · Digitalization · Digitalization Index
1 Introduction Currently, digitalization of various spheres of public life is a very common topic. It is changing the process of producing goods, the ways and mechanisms of doing business and providing services, the whole world around a person, as well as their habits and behaviors. Today, people use large numbers of gadgets that act as entry points for shopping and receiving services. In the context of digitalization, businesses acquire new ways of interacting with their customers through their personal devices and the Internet. The impact of digitalization can be visualized in Fig. 1, which, according to the Digital 2023 Global Overview Report, produced in partnership with Meltwater and We Are Social [1] reflects the positive dynamics of the number of Internet users for 2018?2023, as well as the trend of increasing the share of involvement of the world?s population in the network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 230–247, 2023. https://doi.org/10.1007/978-3-031-32719-3_17
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According to the Fig. 1, we can note that there is a systematic increase in the number of Internet users, with a simultaneous increase in the share of the world?s population using the network. Between the pre-pandemic 2018 and 2022, this share increased by 7.6% or 823 million people. In addition to increasing the involvement of the population in the network, we can note a tendency to reduce the time spent on the Internet, which in 2022 amounted to an average of 06:37 in hours and minutes. This indicates the people?s desire to spend their time on the network more consciously, and also the fact that it is no longer necessary to be online as much as during the Covid-19 pandemic. In our opinion, there is a rethinking of the essence of the Internet for users from a tool for entertainment towards a physiological need and a need for safety, that is, downwards according to the Maslow?s theory of human motivation [2]. About 1/4 of life is spent on the Web, so there is an inevitable involvement and restructuring of the surrounding reality in the digital space. The number of people who made online purchases in 2022 increased by 315 million compared to the previous year [1]. Digitalization processes inevitably influence all areas of activity, including the tourism industry most affected by the Covid-19. During the pandemic, countries were economically weakened by the cancellation of bookings and a reduction in tourist activity [3]. Governments and tourism enterprises were forced to spend additional resources to reduce the risks of impact of global epidemiological hazards on travel [4?7]. One of the ways [8] to overcome these difficulties was digitalization, which provided huge opportunities for businesses, governments and public organizations [9, 10] interacting through a digital product with a complex architecture [11, 12]. All stakeholders are forced to quickly introduce innovative IT-technologies in order to adapt to a rapidly changing world [13, 14], which should lead to lower costs in the long term [15]. In turn, cost reduction leads to lower prices for the tourist products, and, consequently, to an increase in the consumer demand and travel dynamics [16?18]. Therefore, digitalization of the tourism industry leads to simulation of its development, reducing the impact of external negative factors. Digitalization provides the industry with many opportunities [19]. Nevertheless, this process manifests itself differently in different countries, with different levels of impact
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on the tourist product and different degrees of digitalization of tourist destinations [15]. For example, in Bulgaria, it expresses in using booking platforms, assigning ratings, using social networks and collecting data from customers [19]. In Germany, the most important processes of digitalization for tourism enterprises are [18]: customization of the tourist offer, presence in social networks, sharing economy, increasing sales through brand building, attracting and retaining customers online [20] regardless of geographical and time constraints [21]. In China, digitalization of all processes is rapidly developing [22, 23], including the growing number of consumers who search for travel information on social networks such as Xiaohongshu, Kuaishou, Weibo, and Bilibili, chose online travel agencies such as Trip.com, Qunar, and Meituan, or prefer digital contactless payment [24]. In Hangzhou, a five-star Flyzoo Hotel [25] has opened, in which all procedures, from check-in to room service, are performed by robots equipped with biometric facial recognition technologies and the ability to interact through recorded voice messages, and the rooms are equipped with smart systems. Some researchers [26] believe that the main indicator of the digitalization of tourism is the ability to quickly find information during the journey (and not in the preparation stage) using smartphones thanks to a combination of easy access to information, mobility, and local tracking, so it is important to increase the number of Wi-Fi access points in tourist infrastructure facilities. In India [27], the processes of digitalization, including in the tourism industry, are not yet sufficiently developed, due to the high cost of processes, protests from citizens associated with fears of job loss, as well as theft of personal data, and depreciation of the digital infrastructure. Currently, the digitalization of tourism consists in the presence in social networks, however, there is a problem of fake reviews. In the Russian Federation, digitalization is one of the most promising areas for the development of all sectors of the national economy. According to the assignment of President V.V. Putin, all spheres of activity should be digitized, and Big Data and artificial intelligence should be introduced throughout by 2030 [28, 29]. According to the Decree of the President of the Russian Federation ?On National Development Goals of the Russian Federation for the Period up to 2030? [30], one of the goals of the country?s breakthrough development to improve socio-demographic indicators is digital transformation. These indicators include: • achieving ?digital maturity? of key sectors of the economy and the social sphere, including health and education, as well as public administration; • increasing the share of mass socially significant services available digitally to 95%; • increasing the share of households provided with broadband access to Internet to 97%; • increasing investments in domestic solutions in the field of information technology fourfold compared to the indicator of 2019. In 2022, all the target values of the indicators were exceeded [31]. In the field of digitalization, it is important for Russia to accelerate [32] and ensure technologically independent development of the IT industry, provide support for accredited IT companies [31, 33, 34], introduce the Fast Payment System [35] and Digital Ruble [36, 37], increase the share of digital public services, as well as online trade [38], distance learning and much more. Such a rapid development of digital technologies in Russia
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[39] has affected digitalization of the tourism industry. Further we will consider the dynamics of the development of the tourism industry, its connection with the processes of digitalization, and rank Russian regions by the degree of digitalization of tourism.
2 Materials and Methods One of the methods for assessing digitalization of territories is presented in the Order of the Ministry of Construction of Russia dated December 31, 2019 No. 924/pr ?On Approval of the Methodology for Assessing the Progress and Effectiveness of the Digital Transformation of the Urban Economy in the Russian Federation (IQ of Cities)? [40]. It describes 47 indicators that allow to assess the level of digitalization of cities (IQcities index) in 10 categories, including tourism and services, smart transport, as well as economic conditions and investment climate. The disadvantage of this theory is that it addresses digitalization of specific cities, as well as the binary nature of most indicators [41]. Marchesani F. and Masciarelli F. in ?Exploring the Existing Relationship between Digital Implementation, Communication Services and Tourism Inflow in the ?Smart? (R)evolution in Cities? [42] conducted a study using regression models, which analyzed the relationship between the introduction of digital innovations in the life of individual cities on dynamics of the tourist flow. As a result, they identified a direct relationship between the ?Digital Implementation Index?, variables focused on the reason for visiting (art, theater, museums, sports), the quality of the city?s website and mobile application and the influx of tourists. Pasha M., Yasirandi R. and Oktaria D. in their paper ?Measuring and Analyzing Ereadiness at Tourist Places in Alamendah Village in Facing Tourism Digitization Using the Technology Readiness Index (TRI) Method? [43] describe the assessment of digitalization of the tourist area through the Technology Readiness Index, which includes the following criteria: optimism, innovation, discomfort and uncertainty of the managers of the tourist destination. This methodology is microeconomic in nature due to its application in a village and cannot be used to assess the digitalization of the industry. Evaluation methodology Digital Economy Country Assessment (DECA) for Russia [44] includes non-digital factors (public policy, leadership and institutions, human capital, business environment, R&D and innovation, information security and trust) and digital foundations (telecommunications infrastructure, data centers, digital platforms, etc.) of digital economy development. Separately, the level of maturity of the digital sector of the economy (ICT sector and the content and media sector), the direction of using digital technologies in the public sector, in business and citizens was assessed. This technique is too extensive and cannot be transferred only to the tourism industry. However, for the formation of a strategy for the development of the digital society in general, this methodology is a qualitative tool for working out a wide range of factors and assessing their impact. To evaluate the effectiveness of the process of digitalization of tourism, it is necessary to develop a system of criteria for assessing the mutual impact of regional digitalization and tourism. We should take into account that in each region there may be a different number of destinations, which, accordingly, will contribute to the overall level of digitalization of the region. Nevertheless, in our opinion, it is necessary to propose a way to
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assess the region on the basis of public domain data, which would ensure an independent assessment of the territory. For the purposes of this study, it is possible to use The Digital Economy and Society Index (DESI) [45, 46], adapted for application of data reports by statistical agencies of the Russian Federation. DESI includes the following elements: 1. Connectivity: broadband infrastructure deployment and its quality; 2. Digital skills: the skills needed to take advantage of the opportunities offered by the digital society; 3. Public use of the Internet: the variety of activities performed by citizens on the Internet; 4. Digital integration in business: digitization of business and development of the online sales channels; 5. Digital public services: digitization of public services directing the vector to the government. DESIX = Connectivityx · 0.25 + Digital Skillsx · 0.25 + Public Use of the Internetx · 0.15+ Digital Integration in Businessx · 0.2 + Digital Public Servicesx · 0.15
(1) where X is the name of the country [46]. Further, we adapt this index for application in the analysis of the digitalization of Russian regions and the impact on the dynamics of the tourist flow.
3 Results The period beginning in 2020 was marked by serious shocks in the tourism industry in the Russian Federation: pandemic restrictions and sanctions? impacts led to a change in the dynamics of tourist flows. Further, we will consider their values (Fig. 2). The Fig. 2 shows a trend towards a recovery in tourist traffic across all types of tourism after the pandemic. There is a pronounced recovery in domestic tourism, which in 2022 surpassed the data of 2019. The diagram presents data on the domestic tourist flow based on the reporting of collective accommodation facilities for Russian tourists. In 2022, data on collective accommodation facilities (CAF), excluding sanatorium and resort organizations, in terms of the number of accommodated Russian travelers amounted to more than 65 million people, and the total tourist flow, taking into account the additional assessment, according to Rosstat [47], is 141 million people. (The methodology for assessing the tourist flow is used only from 2022, so data for other years are not available.) It should be noted that the total tourist flow increases more than twofold. This is explained by the fact that the data of the form 1-CAF ?Information on the activities of the collective accommodation facility?, used for analysis, are not submitted by small businesses, and they also do not take into account citizens of the Russian Federation who rent housing in the private sector, using Internet booking platforms and sharing services, as well as staying with relatives and friends. Based on the data on inbound tourism, we should note that its amount in 2022 increased by about 10% compared to 2021, however, this is 67% less than during the pre-Covid 2019. The dynamics of the inbound tourist flow is influenced by the formation
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Fig. 2. Dynamics of inbound, outbound and domestic tourist flow, thousand people. [Compiled by the author on the basis of data from the portal [47].
of a negative image of Russia and Russian citizens by foreign governments [48, 49], the inability to pay with Visa and Mastercard cards [50], the limited air and rail traffic [51, 52], pandemic restrictions [53] and more. To qualitatively improve the Russian tourist product and boost its attractiveness in the conditions of restrictions, it is necessary to form standards for the sustainable development of the tourism industry, taking into account modern trends in the field of digitalization. In the ?Tourism Development Strategy in the Russian Federation until 2035? [54], the ?Introduction of Digital Technologies in the Field of Tourism? is singled out as a tool to increase the competitiveness of the Russian tourist product. It is assumed that this will lead to lower costs and increased awareness of market participants through digital services and platforms. The goals in this area include: • creation of a tourist marketplace to promote the Russian tourist product; • introduction and development of multilingual tourist assistance services; • development and implementation of a digital guest card for tourists and a similar mobile application in the cities and Russian regions; • creation of a rating of tourist services and objects; • application of VR and AR tourism, online tours, etc. to cities and objects of display; • development of an open data system in the field of tourism; • introduction and development of big data technologies and artificial intelligence; • development of services for online construction of a tourist route with the possibility of buying tickets and booking hotels; • creation of a digital platform for the involvement of self-employed persons in tourist activities (guides, instructors, tour guides);
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• development of multimedia applications for display objects, audio and video guide services capable of integrating with GPS navigation and using QR codes for the formation of requests. To increase economic efficiency, prompt response to challenges and synchronization of resources in the context of state regulation of the tourism industry in the difficult conditions faced by the industry, in October 2022 it was decided to abolish the Federal Agency for Tourism (Rosturizm) and transfer its powers to the Ministry of Economic Development. It is planned that this will have a positive impact on the implementation of the national project ?Tourism and Hospitality Industry? [55, 56]. We would like to note that the curator of this national project [57] is the Deputy Prime Minister of the Russian Federation Dmitry Chernyshenko, as well as the National Program ?Digital Economy of the Russian Federation? [58], which indicates great digital prospects for the tourism industry. The most promising projects for the digitalization of the tourism industry include the following. 1. Digital visa, a single-entry digital analogue of a visa that allows citizens of certain countries to enter the territory of Russia through an approved list of checkpoints for up to 16 days with a guest or business visit, as a tourist, as well as to participate in scientific, cultural, socio-political, economic, sports events and carry out appropriate connections and contacts [59]. At the beginning of 2023, digital visas are not issued, however, they are promised to be launched again soon [60]. 2. Digital voucher, which will allow the state information system ?Unified Information System of Digital Vouchers? to perform real-time monitoring of the activities of tour operator companies, monitoring the tourist market [61]. Until 01.03.2023, the system operates in test mode, then it will be mandatory for registration of outbound tours, and from 01.09.2023 mandatory for domestic and inbound tours. 3. Application of distributed ledger technology (blockchain) in tourism resolves the need to reduce the number of intermediaries in the creation and sale of a tourist product [62], as well as the safety of data, user information and transaction security. This is why blockchain technologies are now quite popular, despite the problems that periodically arise in the field of cryptocurrency [63]. Promising areas of their application in the tourism industry can be the introduction of distributed ledger technologies in the activities of the Global Distribution Systems (GDS) [64], intermediary activities [65, 66], luggage tracking [67] and others. However, many projects in this area have failed due to the low level of awareness and financial literacy in the society, on the one hand, and the lack of financial competence of the market participants themselves, on the other. It is necessary to increase the digital literacy of the population, as well as to form digital competencies among workers in the tourism industry. 4. Digital payments, which we define for the purposes of this study as non-cash payments using digital systems. Currently, the Russian Federation has a national payment system MIR, created to maintain national financial security [68]. Contactless payment services such as Apple Pay and Samsung Pay are not operational, however, on devices with the Android operating system it is possible to transfer payments using the MIR Pay service. Figure 3 shows the dynamics of the use of bank cards and digital money [69].
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Fig. 3. Dynamics of the number of transactions via bank cards issued in Russia and via digital money [Compiled by the author on the basis of [69].
According to the Fig. 3, the dynamics of the use of digital payments has a positive trend. The forecast for 2023, constructed on the basis of the trend line, suggests with a fairly high probability the continuation of the increase in the number of operations using non-cash digital payments. Cryptocurrency on the territory of the Russian Federation is not permitted as a means of payment. It is important to further develop contactless payment systems, both through smartphones and with the help of other devices (rings, charms), although this direction has not received proper development. To this end, it is necessary to increase the number of POS-terminals, payment options using QR codes, embedded payment systems through digital ecosystems, for example, Yandex Pay [70]. 5. The next direction of digital processes in the tourism industry is the use of artificial intelligence [71], which, aided by the machine learning technologies, will be able to create a typical product for mass tourism. Currently, we are not talking about individual routes, since this digitalization tool has just begun to be used in the tourism industry. An example is Trip Planner [72], which can create 5 routes for $1. However, the route compiled through the city of St. Petersburg (Russia) directed prospective tourists to the Hermitage Museum at 8 A.M., while the museum is still closed at this time. However, in general, the developed tourist program was quite rich and diverse. Many companies in the tourism and hospitality industry use AI to create chatbots and voice assistants. Currently, AI cannot yet solve complex problems [71] or formulate a high-quality response to an individual request on par with a person. However, the use of AI can optimize the cost of line staff in a hotel, make a basic version of the tour. Tourism is, first of all, an impression, and the use of AI can create a WOW-effect. 6. Cloud technologies and digitalization of office work received a significant impetus during the COVID-19 pandemic. Many companies operated remotely for a fairly
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long period of time, which led to the need to purchase cloud software. Travel agency specialists use cloud GIS technologies, CRM systems, document management and accounting systems, ERP systems, systems for communication with customers and within the company, website builders and much more. The most popular products are: Microsoft Office 365, Google Maps, Bitrix 24, Microsoft Dynamics CRM, 1C: Cloud, which includes a set of products, Cloud Master-tour, Bnovo, and many others. The advantages of cloud services are obvious: reducing the cost of buying software, increasing productivity due to the ability to work in the office and at home, increasing communication, energy saving, etc. However, there are also disadvantages, for example, Microsoft suspends new sales in Russia [73], which led to the impossibility of using their service, and the need to switch to alternative software. In addition, cloud services are not reliable and can be subjected to a hacker attack [74], as a result of which user information may be leaked [75]. Russian public administration uses a portal Kontur.Gosoblako [76] for operative solution of work tasks and employees? communication within the company. It is a unified accounting and management system developed in Russia on the basis of free software. This portal includes several solutions for the tourism and hospitality industry: Kontur.Travel, an online service for the arrangement of business trips, Kontur.Hotel, a Cloud service for hotels, hostels, inns, guest houses, sanatoriums, and apartments. It allows to manage the number of rooms, receive booking information from the hotel website and booking sites, etc. One of the elements of cloud technologies in tourism is the activity of online travel agencies (OTA). In 2022, a number of international services in the OTA segment ceased to operate in Russia: Booking, Airbnb, RoomGuru, Skyscanner, which served as an impetus for the development of Russian platforms: Yandex.Travel, Ostrovok, Sutochno.ru, OneTwoTrip, bronevik.com. Currently, in Russia, the largest number of bookings occurs through Ostrovok.ru. 7. Unmanned technologies find their application in tourism. Currently, they are still in their embryonic state, but in the future, it is planned to increase the number of the devices used in the industry by orders of magnitude. Unmanned aerial vehicles are used to map the terrain, plot routes, search for lost tourists, deliver medicines, create videos and photos, protect reserves and sanctuaries, surf. In the future, passenger and cargo drones will be created that will deliver tourists to hard-to-reach areas [77]. Drones (robots) are used to deliver food to rooms of hotel guests or towels to the beach. Driverless cars and taxis are being actively developed by digital giants: Waymo (Google), Tesla, as well as other car manufacturers. In Russia, unmanned taxis have already been tested on the streets of cities since 2017. Then Yandex launched a selfdriving taxi in Innopolis. In addition, Yandex taxis are trained to drive in other cities: Moscow, Tel Aviv, and Ann Arbor [78]. Unmanned technologies need to be closely linked to the Internet of Things, which will allow, on the basis of crowdsourcing mechanisms, to link devices to scale their effect, an example of which can be light shows of drones that can replace fireworks. 8. Big Data takes part in all the processes described above, allowing them to be optimized by handling the storage, monitoring and processing of large amounts of information.
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When digitalizing a tourist destination, such processes include: data on the number, frequency and duration of tourist visits, on the amount of money spent by a tourist when visiting a destination, their income level, travel triggers, tourist preferences [15]. In St. Petersburg (Russia) there is a project of the City Committee for Tourism Development, an analytical center under the government of Russia and Rosstat, called Tourbarometer, which studies data provided by OTA, mobile operators, carriers, etc. As a result, these Big Data are drawn up in a report that allowed us to identify trends in tourism, adapt tour operators to new consumer requirements and form forecasts for the next period. For example, according to the Tourbarometer, St. Petersburg has become a popular destination for family tourism, and the portrait of the ?average? tourist has also become younger [79]. The avenues of development of digitalization in tourism, presented above, are constantly supplemented by new trends and tendencies (sharing economy, ESG, VR, AR and XR reality, etc.); taking into account geopolitical, economic, technological and social processes, there is a constant change in the leaders of the impact on the industry. At the same time, the situation is highly uneven in different countries of the world, associated with both the difference in development of the tourism industry, the size of the tourist flows, and technological development. To promote digitalization in the tourism industry, it is necessary to train advisory personnel in educational institutions of secondary and higher vocational education. In 2022, there were 209 higher educational institutions in Russia, teaching the majors 43.03.02 ?Tourism? and 93 teaching 43.03.03 ?Hotel Business?, while the major 09.03.03 ?Applied Informatics? is taught in 350 universities, 09.03.01 ?Informatics and Computer Science? in 258, 09.03.02 ?Information Systems and Technologies? in 215, 01.03.02 ?Applied Mathematics and Informatics? in 146, and 02.03.03 ?Mathematical Support and Administration of Information Systems? in 53 [80]. If we consider secondary vocational educational institutions, then the major 43.02.10t ?Tourism? is taught in 190 colleges and technical schools, and the major 09.02.07 ?Informatics and Computer Engineering? in 659 [81]. These data indicate the prevalence of both digital education in Russia and education in the tourism and hospitality industry, which will contribute to the further development of these areas, including in symbiosis. Let us consider further the indicators making it possible to analyze the digitalization index in the regions of Russia and assess the mutual influence of these factors on the tourism industry [82]. We will perform the calculations on the basis of data from ten subjects of the Russian Federation (Moscow Region, Moscow City, St. Petersburg City, Republic of Kalmykia, Krasnodar Territory, Republic of Ingushetia, Republic of Tatarstan (Tatarstan), Sverdlovsk Region, Altai Territory, Chukotka Autonomous District). A sample of regions was taken on the basis of the number of tourists accommodated in collective accommodation facilities, directions of different popularity were selected). We will list the indicators that are a symbiosis of DESI and indicators used to assess digitalization in Russia: 1. Connectivity: broadband infrastructure deployment and quality: Share of population using the Internet;
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Share of organizations using broadband Internet access in the total number of organizations. Digital skills: skills needed to take advantage of the opportunities offered by the digital society: Share of adults possessing information and communication technology skills; Share of young people possessing information and communication technology skills. Public use of the Internet: the variety of activities performed by citizens on the Internet: Share of Internet sales in total retail turnover; Share of households with broadband Internet access. Integration of business technologies: digitization of business and development of the online sales channel: Share of organizations that use personal computers; Share of products of high-tech and knowledge-intensive industries in the gross domestic product; Share of museums with a website on the Internet in the total number of museums in the Russian Federation; Share of theatres with a website on the Internet in the total number of theatres in the Russian Federation. Digital public services: digitization of public services directing the vector to the government: Share of the population that used the Internet to receive state and municipal services.
Table 1 shows the data of the official statistical bodies of the Russian Federation, accessible in the public domain.
Share of organizations using broadband Internet access in the total number of organizations
72.4
67.7
75.4
73.9
76
71.8
75.2
81.4
78.7
71.1
Region of the Russian Federation
Moscow Region
Moscow City
Saint Petersburg City
Republic of Kalmykia
Krasnodar Territory
Republic of Ingushetia
Republic of Tatarstan
Sverdlovsk Region
Altai Territory
Chukotka Autonomous District
87.1
77.4
87.4
85.5
79.5
79.2
76.6
85
76
82
Share of organizations that use personal computers, by region
-
3.8
7.8
5.3
0.3
4.3
0.8
8.8
9.2
8.8
Share of online sales
96.6
70
78.1
72.1
72.2
71.9
89.7
93.3
87.7
77.5
Share of adults possessing information and communication technology skills, %
100
91.2
94.5
93.1
99.1
86.9
95.2
99.7
98
98.9
Share of young people possessing information and communication technology skills, %
9.9
19.9
22.2
19.7
23.1
16.4
15.7
32.1
24.2
21.3
Share of products of high-tech and knowledge-intensive industries in the GDP
92
79.9
78.5
83.9
76.7
86.5
92.6
87.3
94.4
86
Share of households with broadband Internet access
49.7
55.3
52.6
85.6
56.8
82.9
60.5
53.9
96.8
89.8
Share of the population that used the Internet to receive state and municipal services
93.4
81.7
87.1
91.1
80.8
91.5
89.2
92.1
96.2
92.5
Share of population using the Internet
Table 1. Data for the calculation of the Regional Digitalization Index of the Russian Federation.
55.6
84.5
85.4
73.6
50
98.4
33.3
93.9
96.9
94.2
Share of museums with a website on the Internet in the total number of museums
0
100
100
95.2
100
100
66.7
100
97.9
100
Share of theatres with a website on the Internet in the total number of theatres
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In Table 2, we will analyze the relationship between the Regional Digitalization Index based on the DESIx index and indicators of the tourism industry. Table 2. Analysis of the mutual influence of the Regional Digitalization Index and tourism. Region of the Russian Federation
Number of tourists accommodated in hotels
Regional Digitalization Index
Republic of Ingushetia
22769
151.565
Chukotka Autonomous District
30884
142.05
Republic of Kalmykia
41254
148.545
Altai Territory
700412
157.71
Sverdlovsk Region
1507462
164.9
Republic of Tatarstan
2088024
163.835
Saint Petersburg City
4216895
174.845
Moscow Region
4355236
172.515
Moscow City
7690263
176.5
Krasnodar Territory
7969424
166.35
Based on the Table 2, it can be concluded that with an increase in the digitalization index of a region, the number of tourists increases and vice versa. The correlation coefficient is 0.7876, which indicates a strong direct relationship between the indicators. We will visualize their interaction and display it in Fig. 4. 200 150 y = -9E-13x2 + 1E-05x + 148.15 R² = 0.9016
100 50 0 0
2000000
4000000
6000000
8000000
10000000
Fig. 4. Dissipation chart of the Regional Digitalization Index and the number of tourists accommodated in hotels.
According to the Fig. 4, the regression equation and the approximation confidence level, which has a high value of R2 ?=?0.9016, indicate the possibility of compiling reliable forecasts for the development of digitalization of regions and tourism within the framework of a single strategic project.
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4 Discussion The authors? conclusions regarding promising avenues for further digitalization of tourism are confirmed by the dynamics of their research. The tools of digitalization in tourism in 2019 indicated in the article ?Digitalization of a Tourist Destination? have quite significantly developed during the course of four years, largely during the pandemic, which is reflected in this work. Barashok I., Rudenko L. and others in the article ?Digitization: New possibilities for the Tourism industry? [83] note that digitalization creates a singular advantage for the tourism sector in different areas: for tourists, business, destination, industry and government, which is consistent with the levels of the economy. At the same time, the authors confirm the conclusions that the processes of digitalization are positive, stimulating the tourism sector for development. Stryzhak O. in the article ?Tourism and Digital Technologies: Analysis of the Relationship? [84] conducts a study based on the Networked Readiness Index (NRI) as an indicator of digital development and the International tourism indicators estimated by the World Bank revealed that the widespread use of digital tools expands the opportunities to attract customers and partners in any business. Currently, the tourism sector is largely dependent on information technology, the analysis conducted by the researcher demonstrated that this connection is direct and quite strong, which is confirmed by our study.
5 Conclusions Digitalization affects all areas of life. A strong impetus in the development of information technology was received during the COVID-19 period. In turn, the tourism industry suffered from the pandemic and epidemiological restrictions. The digitalization of tourism has become a lifeline that was handed over to the drowning man. Current digital transformations through the creation of digital visas and vouchers, the use of blockchain and digital payments, artificial intelligence and neural networks, big data, as well as cloud technologies, etc. lead to a qualitative improvement in the Russian tourist product, increasing its attractiveness and competitiveness in the global market of tourist services. The process of digitalization of Russian regions occurs at different rates, which depends on various factors: economic, technological, geographical, etc. This paper analyzes the mutual impact of the level of digitalization of Russian regions and the development of tourism. A direct strong relationship was identified, which makes it possible to use this system to track the impact of digital innovations on the development of the tourism industry, assess the effectiveness of dissemination of digital technologies, the responsibility of business and society, and allow the authorities to evaluate the possibilities of applying state support measures and adjust the amount of public funding.
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Modeling of Medical Technology Life Cycle Irina Rudskaya1
, Dmitrii Alferiev2 , and Darya Kryzhko1(B)
1 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected] 2 Vologda Research Center of the Russian Academy of Sciences, Vologda, Russia
Abstract. Forecasting is a powerful tool in the organization of management activities. Business entities with more accurate and reliable forecasts have an impressive competitive advantage over their opposing opponents in the market. Forecasting has special features in various areas of economic and social activity. When making forecasts in health care, increased efficiency and accuracy are required, since this sphere of human life is directly related to the life and death of people. In this study, we set out to refine and model the life cycle of medical technology in order to more effectively predict the consequences of their implementation. This is the purpose of the work. After conducting a major review on the stated topic, the following results were achieved. Firstly, the conceptual scheme of the life cycle of medical technology was supplemented in terms of its legal framework and the consequences associated with the COVID-19 epidemic. Secondly, its mathematical model has been developed, which is a set of interrelated splines from elementary functions. In conclusion, the potential possibilities for improving the created mathematical apparatus by connecting Markov chains to it are discussed. Keywords: Forecasting · Healthcare sector · Life cycle · Medicine · Time series
1 Introduction 1.1 Forecasting Forecasting and foreseeing of the future are strong competitive advantages in business. One may note that it is human intellectual activity this skill is inherent in. Throughout human history people have been striving to master and improve forecasting. In particular, it is the theme of a message of the Indonesian scientist and chief editor of the IJTech journal Dr. M.A. Berawi [1]. Developing computer programs, increasing computing device power and total digitizing of the reality around us have enabled more accurate and reliable prediction of various events that can dramatically affect our lives. So, we can try to accelerate the occurrence of these events or, in case of an impending catastrophe, make efforts to prevent it. It is discussed in detail by the American scientist in the field of machine learning PhD E. Siegel in one of his books [2]. Forecasting is largely associated with time, and mathematicians are actively developing tools that take account of the dynamics of modeled processes and phenomena. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 248–256, 2023. https://doi.org/10.1007/978-3-031-32719-3_18
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This topic is fundamental, quite complex and especially relevant when studying humanitarian systems that are characterized by the extremely unpredictable behavior in case of an incident in them. However, it is typical not only for humanitarian systems, but also, for example, for natural phenomena with various living organisms. All of them can be defined as complex systems. The problem of difficult predictability of such systems behavior in force majeure circumstances is discussed in detail in a book of the Lebanon-born American economist PhD N.N. Taleb, in which he calls such phenomena “black swans” [3]. Forecasting methods for nonstationary time series are described in a major book by Russian scientists, candidates of physical and mathematical sciences, Yu. Orlov and K. Osminin [4]. Using the raw materials market (the Russian electricity market and the international oil and gas market) and the financial market as examples, they show how to apply the methods developed by them. Another contemporary scientist who studies the time series of socio-economic processes is a student of the prominent Russian econometrician S. Ayvazyan [5], the Italian scientist PhD D. Fantazzini who is actively teaching in Russia. His main work is devoted to forecasting the dynamics of cryptocurrencies, implemented using the R programming language [6]. If we speak about the development of the ARMA stationary predictive model, it is necessary to mention the work of Iranian researchers, who supplemented it with a genetic algorithm and some heuristic rules [7]. Another Iranian scientist has implemented a hybrid predictive model that has more accurate estimates. He managed to achieve the final result by combining the concepts of the Fourier series, the Markov chain and the Gray model (GM) [8]. The implementation of forecasting potential demand for consumer goods is described in the work of a group of Indonesian scientists [9] who used the classic ARIMA model, neural networks and their hybrid. Their interesting finding is that the tools of artificial neural networks, not their joint use with another model, are most effective. Nowadays, in forecasting socio-economic processes and phenomena, it is quite widespread to use of the hybrid concept, since in modeling a complex situation the use of classical methods is unsuccessful, whereas hybrids sometimes give a successful applicable result. The core difficulty is to assemble this hybrid model for a particular research object and to some extent, make it universal with the least possible number of modules connected thereto. In this regard, methodology of forecasting has been studied to develop general universals necessary for building a correct forecast. For example, Russian scientists put forward and work out the concept of forming additional features of a finite predictive model based on particular models created from individual parts of the time series under study [10]. When implementing the hybrid, a group of Spanish researchers proposed an algorithm for assessing the importance of the methods used in the forecast model and a method for determining the significance of each of the made forecasts [11]. Not only hybrids, but forecasting systems as well, the ones based on the so-called “soft computing” have become widespread. A joint article by Romanian scientists and a Czech
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researcher analyzes and discusses the advantages of this approach in comparison with classical time series forecasting algorithms [12]. 1.2 Forecasting of Medical Technologies Human life is the fundamental value of the modern society. An important role in life sustaining is played by the healthcare sector and medical technologies functioning in it. Real forecasting of the introduction and spreading speed of medical innovations will make it possible to allocate material and financial resources more effective in order to achieve positive returns from medical innovations as quickly as possible, which in turn will have a direct impact on the people life quality and expectancy. A group of Russian scientists wrote a paper on identifying fundamental trends in medicine, in which they tried to model a medium- and long-term forecast of healthcare sector development in Russia. The important idea of the article necessary to be mentioned is the indicated generalized duration of the life cycle of a medical technology, that is, according to the authors, about 5–7 years [13]. Russian scientists led by the academician, Doctor of Biology, V. Skulachev, in their book clarifies that 5–7 years is not the time of the technology existence, but the period necessary for its introduction to the mass market [14]. Another work of interest is the publication by a group of USA scientists. They predict the spread of telemedicine in agricultural areas, taking as a basis the classic Bass diffusion model that allows making a fairly high-quality and reliable forecast in conditions of partial uncertainty [15]. Telemedicine per se is a modern and extremely relevant direction of the current healthcare sector, due to the development of digital technologies, computing power and means of communication. A relevant article written by a group of Russian scientists assesses the attitude of the Russians to this technology after COVID-19, a previously mentioned “black swan” [16]. Forecasting in a direct manner is applied in medicine itself. A group of Russian scientists have published a major review of the mathematical methods of processing and analyzing time series used in healthcare sector, professional suitability of which has already been confirmed [17]. Development of digital technologies, machine learning algorithms and highprecision images of the insides of the human body have shaped a new direction in medicine, associated with the search and diagnosis not of the disease itself, but of its background and causes. It is called “predictive medicine” and, according to its name, is based on methodology of forecasting [18]. Therefore, people need and are willing to put a bridle on future uncertainty. As can be seen from this review, the forecasting skill does give a real competitive advantage to an economic entity. In this regard, we set a goal of modeling the life cycle of a medical technology in order to visually manage its introduction into public practice and plan further actions to improve healthcare sector mechanisms.
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2 Methods 2.1 General Concept of the Medical Technology Life Cycle In general, the life cycle of a medical technology from its creation to its logical finale can be viewed as a sequence of the following stages: – the idea and its implementation; – technical evaluation of the implemented project; – clinical validation that confirms or refutes the positive impact of the technology on the health of patients, and its safety; – calculation of economic feasibility. Doctor of Medicine O. Rebrova [19] in her work gives a detailed description of the stages of the medical technology life cycle described above in legal and economic aspects. In this regard, we have created (see cl. 3.1.) a more detailed scheme, which is a graph of the development and implementation of a medical project (Fig. 1). 2.2 Methodology of Forecast Modeling. General Characteristics of a Forecast Usually, when making a forecast, the person modeling it faces the following aspects: The foresight horizon. Meaning how far into the future the implemented predictive model allows you to dip. In classical economic theory, it is common to speak of short-term, medium-term and long-term forecasts. However, we supposed that such an approach is remote from reality and in practice, serves only to establish some reference control points in the implemented management plans. When predicting the humanitarian objects dynamics, each time we have to deal with a unique situation and build a unique predictive model. The minimum amount of source data. It is also quite an interesting and ambiguous point in forecasting. In theory, predicting something with a given accuracy first of all requires to determine its distribution, and then, accordingly, to obtain the necessary number of observations. In practice, we have what we have, and in a limited time, we need to make the right management decisions based on these data. These decisions are often very unsuccessful as one may have noted. It happens even if they are backed by competent experts in the relevant sphere. A good example of it is described in the article published in Digital Economy journal by its chief editor, Doctor of Economics A.N. Kozyrev, in which he demonstrates the incompetence of about 1,144 specialists (15 of whom are Nobel Prize winners) regarding their stance on the import duties introduced in 2018 [20]. The number of possible scenarios. In the most general terms, operational management usually models one central forecast line, within which we act in such a manner as to precisely adapt to the planned future. An alternative option for our actions in case of non-fulfillment of the forecast is also considered.
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However, in practice, socio-economic processes and phenomena are so complex that they can follow a route that is qualitatively new and unplanned. To some extent, it has now become easier to control humanitarian objects due to total digitization of human life and development of machine learning algorithms. 2.3 Key Elementary Functions for Spline Modeling In order to construct a function that models the dynamics of the predicted value, the concept of spline modeling can be used. When applying spline modeling, we will divide the total time period of the process under study into certain intervals, which will be characterized by elementary functions (F). Linear function (Formula 1): F(t) = a + bt
(1)
where t is a variable that characterizes time, a and b are parameters. This function can be used to describe any process that is uniformly distributed with a fixed proportion over time. Exponential function (Formula 2): F(t) = et
(2)
where e is the Euler’s number. This function can be used for a process that significantly accelerates or slows down over time. Logarithmic function (Formula 3): F(t) = ln t
(3)
where ln is a natural logarithm. This function can characterize a process that fades or slows down over time. Quadratic function (Formula 4): F(t) = a + bt + ct 2
(4)
where c is a parameter. This function can characterize a process that reaches its highest or lowest value over time, and then returns to its original state. With only the 4 functions described above, it is possible to construct a system of algebraic equations that will visually and intelligibly characterize the dynamic process under study. An example of constructing such a system is presented in cl. 3.2.
3 Results and Discussion 3.1 Generalized Conceptual Scheme of Medical Technology Life Cycle The new one is the “legal registration” stage, which in practice is a rather complicated procedure of preparing the relevant documents. The bureaucratic red tape in many spheres of public life is a stumbling block for implementation of the ideas and can vary greatly depending on the sphere and jurisdiction of the law enforcement entity in whose legal field it is planned to carry out future activities.
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Fig. 1. Conceptual Scheme of Medical Technology Life Cycle
Once the red tape is cut through, production can be launched, but it has to be done at creator’s expenses or from the attracted funds of investors. An “economic analysis” of the current and future situation is carried out simultaneously in order to recover the initial expenses, if possible, and in a more favorable perspective, to make some profit in the future. Unfortunately, this process includes some moral and ethical aspects. Specifically, some medical technologies necessary to save human life, in order to pay off, must be very expensive, and an average patient does not have resources to pay for them. To some extent, it is the problem faced in 2022 by customers of Second Sight, which produced high-tech eye implants. High cost of the technology that the company was implementing on the market, caused Second Sight to go bankrupt leaving patients without further vital servicing [21]. Once the technology has proven effective in treating the claimed pathology, has clear boundaries within its safety, and presents a rational relationship between costs and output, its status from experimental development naturally changes to an established clinical standard. After that, this medical technology can be considered successfully integrated into everyday human life, in which it is fully provided at the expense of the people at whom it is directly aimed. Additionally, we should note the stage of “clinical trials”. It is quite a time-consuming process that usually consists of four stages. In general, according to approximate estimates, as already noted, the full-fledged launch of a medical product to the market before its wide use is about 5–7 years. But practice has proved that this procedure can be significantly accelerated in case of a “black swan” that threatens a large number of people. The most recent example of such unpredicted events was the COVID-19 pandemic, when the development of vaccines and their testing were performed much faster than in usual conditions. Such a critical situation has probably had a significant impact on the healthcare industry as a whole, and the rapidly created positive practices of legal regulation will now be successfully applied in other areas of medicine.
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However, the following point shall be noted. Even if a medical technology has successfully passed I–III phases of clinical trials, i.e. its safety and effectiveness have been confirmed to some extent, there can occur serious incidents resulting in its complete withdrawal from the market. According to an article by a group of Chinese researchers and scientists, negative consequences of drugs already in phase IV are faced by about two million Americans every year. In order to eliminate these problems, billions of US dollars are spent [22]. 3.2 Example of a Life Cycle Mathematical Model When modeling the life cycle of an object, we divide it into some splines (Fig. 2), which can be characterized by the following equations: exponential growth (Formula 5), uniform growth (Formula 6), reaching the peak performance (Formula 7), uniform decline (Formula 8). F(t)1 = et − 1, 0 < t < t1
(5)
F(t)2 = a2 + b2 t, t1 < t < t2
(6)
F(t)3 = a3 + b3 t + c3 t 2 , t2 < t < t4
(7)
F(t)4 = a4 − b2 t, t4 < t < t5
(8)
When the derivatives of the functions presented above (Formulas 5–8) are found, the following system of algebraic equations can be constructed (Formula 9):
Fig. 2. Life Cycle Curve
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⎧ ⎪ et1 − 1 = a2 + b2 t1 ⎪ ⎪ ⎪ t1 ⎪ ⎪ ⎪ e = b2 ⎨ a2 + b2 t2 = a3 + b3 t2 + c3 t22 ⎪ b2 = b3 + 2c3 t2 ⎪ ⎪ ⎪ ⎪ a3 + b3 t4 + c3 t42 = a4 − b2 t4 ⎪ ⎪ ⎩ b3 + 2c3 t4 = −b2
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(9)
With 6 equations, there are 6 unknowns: a2 , a3 , a4 , b2 , b3 , c3 . This means that with the set time limits of the splines, unknown parameters can be successfully found. Combining various functions from cl. 2.2.2 and setting different values of the planned periods allow getting a visual representation of the life cycle of the object under study. These ideas are based on the work by the Russian scientist, Doctor of Economics, L. Mylnikova [23].
4 Conclusion Summing up, the following results can be noted: 1. The general concept of the medical technology life cycle in terms of its legal regulation has been detailed. New features of clinical phases resulted from the global epidemiological situation cause by COVID-19 have been described. 2. A spline model of the life cycle of the object under study has been implemented by combining elementary mathematical functions into a system. This type of modeling is characterized by relative simplicity of the algorithms implemented therein, as well as an intelligible and understandable visualization of the results. The model can be developed by using Markov chains in it. The transition between splines is probabilistic [24]. Acknowledgements. The article has been prepared within the grant of the President of the Russian Federation No. MD-2258.2022.2 “Development of a Digital Model for Assessing the Potential and Directions of Personalized Medicine Development in the Russian Federation Based on Technical Methods of Processing and Analysis of Natural Languages”.
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Digital Financial Inclusion in a Decentralised Financial Environment Svetlana Demidova1
, Stanislav Svetlichnyy2(B) , Chulpan Misbakhova3 and Tatyana Miroshnikova4
,
1 Financial University under the Government of the Russian Federation, Moscow, Russia 2 Moscow Technological Institute, Moscow, Russia
[email protected] 3 Kazan Cooperative Institute (Branch of the Russian University of Cooperation), Kazan, Russia 4 Moscow Metropolitan Governance Yury Luzhkov University, Moscow, Russia
Abstract. The relevance of the work is determined by the rapid digital transformation of the financial system, in which both the sphere of financial corporations and the sphere of public finance are equally involved. Financial technologies and digital currencies are one of the key factors shaping the space of inclusion. Despite the prospects and expectations from the introduction of innovations, there are risks associated with changing the role of financial and public institutions. Theoretical approaches related to the development of inclusiveness of financial relations in the field of commercial and public finance are examined. The role of the institutional environment is reconsidered, taking into account the introduction of a digital national currency and decentralised finance. The inclusiveness of the financial system and financial accessibility, participation in financial management can have a positive impact on the growth of the well-being of the population, provided that actions between financial institutions are coordinated and processes are secured. The institutional environment plays a crucial role in achieving economic growth and inclusiveness of the financial system, financial resilience. It diversifies risks and increases responsibility in decision-making. The problems associated with the overlap between the powers of monetary and credit institutions and those of public financial institutions are critically analysed. Drivers for increasing the inclusiveness of financial services are analysed. The directions for further research are the development of methodological approaches to the structure and functions of the financial system, taking into account the increasing centralisation of institutions and the decentralisation of financial transactions. Keywords: Financial Inclusion · Financial Resilience · Financial Accessibility · Digital Finance · Payment System · Decentralized Finance
1 Introduction Financial inclusion is internationally recognised as one of the priorities for stimulating economic growth by ensuring that people have timely access to financial services at an affordable price. Digital financial inclusion enables the country’s financial system to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 257–264, 2023. https://doi.org/10.1007/978-3-031-32719-3_19
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serve a community from all walks of life, especially the poor or previously financially excluded [1]. The World Bank has identified financial inclusion as a factor in increasing prosperity and overcoming extreme poverty [2]. In post-crisis periods, finance has a special role to play in a sustainable and equitable recovery [3]. The components of digital financial inclusion are: digital platforms; digital devices; retail agents with digital devices. Access to financial services [4]. Through the components of the digital financial environment, access to financial transactions and services, “personalised” financial services are provided; opportunities for action in the management of financial assets are expanded. The definition of digital financial access is considered in different aspects. Financial inclusion is defined as the formal ownership of an account with a financial institution [5]. Financial inclusion is a process of accessibility and usefulness of formal financial services among businesses and users [6]. There is also a characteristic of digital financial inclusion such as the affordability of digital means [7]. Financial services can include a wide range of products and services provided by banks and non-bank financial institutions, but the main products are bank accounts, bank cards, ATM services, loans and other forms of credit. Financial inclusion implies the universal availability of high quality financial services to retail consumers, who in turn actively use these services [8]. The concept of financial inclusion is also seen as the availability of financial services, consumer protection and financial education [9]. Financial accessibility is also defined as access to a basic set of financial services for citizens, small and medium-sized enterprises [10]. There are different levels of financial accessibility: the physical level, the price level, the product range level and the mental level. The following processes of financial inclusion are considered: use, penetration and accessibility. The authors note that financial inclusion should include the aspect of satisfaction and achievement of expected outcomes [12]. The World Bank proposes to assess a person’s financial resilience as the ability to find additional money in case of unforeseen expenses, the benchmark being defined in 30 days [13]. Financial inclusion is examined in conjunction with studies of the impact of the age and gender gaps. This acknowledges the complexity of the financial inclusion category. At the same time, the research practically does not address the issue of the increasing role of state financial institutions, in particular state treasuries, creating payment systems within the public sector, managing the consolidated liquidity of the budgetary system. Although the concept of responsible financial accessibility reflects a coordinated policy of the public and corporate sectors to develop new approaches, it takes into account the interaction of financial institutions to a lesser extent. The result of this interaction is a set of benefits for the population and benefits for the state. This study is conducted in order to show the current state of development of digital financial inclusion and the role of state financial institutions, to consider key approaches, the main risks of financial decentralization.
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2 Methods In the course of the study, the authors used conceptual and theoretical analysis, empirical and statistical methods, and the method of expert assessment. The sources of statistical information were the Global Findex database, reports of the International Monetary Fund, the World Bank, and data of the Bank of Russia, Tresuary. The analysis was conducted for the period 2014–2021.
3 Results Non-inclusion in the operations of the financial system may be due not only to the lack of available infrastructure and limited financial resources but also to personal choices, for example: informal activities; preference for cash; moral reluctance to use new forms and instruments of the financial system. Public participation in the management of the financial sector can be measured by several indicators (Figs. 1 and 2). Data shows the dynamics of population and youth participation in digital financial services in highly developed countries. Of the countries represented, 100% have an account in Finland, Sweden, Germany and the UK, but only in 2021 will this figure reach 100%, and in Norway, on the contrary, it will fall to 99%. Evidently the dynamics of engagement is unstable. For example, in France, Italy and Germany in 2017 the indicators were worse than in 2014, there was a decrease in the level of credit card ownership and savings among young people aged 15–24. In Norway, a decline was observed in 2021. The most uneven indicator is the payment of utilities via mobile phone – from 46% in Finland to 9% in Germany; as well as the rate of credit card ownership among young people – from 54% in the UK to 7% in Finland. Data show the dynamics of population and youth participation in digital financial services in a sample of transition and developing countries. No country has reached a 100% rate of account opening. In 2017, the level of credit card ownership among young people aged 15–24 in Brazil, India, the Russian Federation and South Africa decreased compared to 2014. In 2021, the share of the population making digital payments in Russia (87%), Brazil (77%) and South Africa (81%), the average in highly developed countries is 96%; 39% of the population in Russia paid for utilities using a mobile phone, 26% of the population in Brazil, 15% in South Africa, with an average in highly developed countries of 21% in 2021. It is important to note that the majority of people who received a digital payment also made a digital payment and were more likely to save, store and borrow money. The presence of an account is associated with a certain level of income, which means it increases a person’s financial stability and the likelihood that they will be able to attract external funds. The Global Findex 2021 study shows that 55% of the adult population in developing countries and 79% in developed countries can easily receive money within 30 days. However, the influence of the cultural code of the country’s population, rules of conduct and customs must be taken into account.
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Fig. 1. Indicators of population involvement in digital financial services (ages 15+ and 15–24), developed countries, %. Source: based on Global Findex data.
The indicators discussed above largely reflect the processes of digitalisation of financial services provided by the banking sector and the fintech sector. The public sector needs to be considered separately (Fig. 3). The data show the indicators that can characterise public sector involvement in digital financial services in the group of developed countries and the group of developing countries, compared with the average indicators for OECD countries. Among the developed countries, Norway, Sweden and Estonia are the absolute leaders, with all indicators above the OECD average. In France (9%), Italy (5%) and Japan (4%), the share of public sector employees receiving transfers from a financial institution is lower than the OECD average (12%). In France (15%), Germany (10%), Italy (12%), the United Kingdom (20%) and the United States (22%), the share of the population receiving transfers to the
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Fig. 2. Indicators of population involvement in digital financial services (ages 15+ and 15–24), developing countries, %. Source: based on Global Findex data.
Fig. 3. Indicators of involvement in digital financial services in the public sector, developed developing countries, %. Source: based on Global Findex data.
account of a financial institution is lower than the OECD average (23%). The indicator ‘government payments received’ is lower than the OECD average (42%) in France (29%), Germany (30%) and Italy (33%). At the same time, Russia has the best indicators among the developing and transitioning countries, almost all of them above the average level of the OECD countries. Exception: wages in the private sector e private sector at the expense of a financial institution (27% vs. 37%), but this is the best indicator in a group of countries. Financial inclusion concerns not only banking services, but also public financial institutions. The World Bank developed the concept of government payments in 2012 [14].
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The issue of creating a payment system of the Russian Ministry of Finance was conceptualised in 2013, and the System of Treasury Payments (STP) was launched in 2021. STP belongs to a special type of payment systems together with the national payment system and the national payment card system. The main differences between the STP and the National Payment System are as follows The operator of the STP is a federal authority (the Russian Ministry of Finance) acting on behalf of a public institution. The STP operator performs the functions of money transfer in the public administration sector. The STP ensures the movement of special (treasury) payments; the control over the operations in the STP is carried out within the framework of budgetary control. Taking into account the purpose of the organisation, the tasks to be solved, the circle of participants, the volume and number of transactions carried out, as well as a number of other factors, STP should be considered as a nationally significant payment system [15]. And the Treasury itself as a quasi-banking organization. This status is also strengthened by the implementation of the policy on managing liquidity at the CEN and centralization of accounting. In our opinion, these features of the architecture of financial institutions provide a contribution to financial inclusion on the part of the public administration sector. The “competition” between the Treasury and the Bank of Russia may also lie in the plane of the introduction of digital money. UPC is actually a system of digital wallets for participants in the public administration sector. The movement of funds within the UPC is actually carried out in the form of a record on accounts, without the real movement of funds that are stored on the CEN. In addition, the UPC controls the movement of funds with the treasury support of government contracts. The digital ruble can also significantly expand and strengthen the mechanism of «coloring» targeted funds. Thus, the state financial institution is able to have a direct impact on financial inclusion. Let’s highlight the drivers of financial inclusion development that lie in the field of digital technologies: – the digital national currency of the ruble with the prospect of offline payments; – digitization of financial instruments, including smart contracts, digital assets, which provide an unprecedented level of communication between participants, products, assets. – cryptocurrencies (decentralized finance), mainly for international settlements under sanctions; – digital financial assets, represent a wide range of financial engineering opportunities; – electronic certificates in the field of public services (quasi-payment document). All of the above tools are able to expand the availability and quality of financial services for the least involved in digital financial processes: residents of remote and difficult to access areas, citizens working in the field of small business, people with disabilities. The risks of expanding financial inclusion lie in the plane of digitalization and decentralization of financial relations can be divided into private, corporate and public risks.
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Private risks – risks for consumers: lack of transparency of service rules, risks of misunderstanding the “language” of digitalization, aggressive marketing, unresolved dispute resolution, identity theft, Internet fraud and others [16]. Corporate risks – loss of income when using decentralized financial assets, including in international payments; unscrupulous counterparties; risks of restricting financial transactions under sanctions. Public risks – violation of the stability of the financial system; risks of delay in legal regulation.
4 Conclusion The state, non-governmental financial institutions and citizens themselves are interested in increasing financial inclusion and involving the population in digital financial services. Digitisation of the financial sector ensures speed of transactions, traceability of payments, competition between financial institutions, and opens up opportunities to increase the financial well-being of citizens. The receipt of digital payments, such as a government payment or transfer, the receipt of a salary, catalyses the other financial process (saving, borrowing). The processes of decentralisation are reflected not only in the settlements in digital payment systems, in the verification of transactions in which banks do not participate, but also in the transformation of the institutional relationships themselves within the financial system. The article contributes to the development of new approaches to financial accessibility and inclusiveness, the interaction of financial institutions. The factor of financial inclusion is trust not only in traditional financial institutions, but also in decentralised participants.
References 1. Tay, L.Y., Tai, H.T., Tan, G.S.: Digital financial inclusion: a gateway to sustainable development. Heliyon 8(6), e09766 (2022). https://doi.org/10.1016/j.heliyon.2022.e09766 2. Povetry and shared prosperity. The world Bank (2022). https://openknowledge.worldbank. org/bitstream/handle/10986/37739/9781464818936.pdf. Accessed 21 Feb 2023 3. Lauer, K., Lyman, T.: Digital Financial Inclusion: Implications for Customers, Regulators, Supervisors, and Standard-Setting Bodies (2015). https://www.cgap.org/sites/default/files/res earches/documents/Brief-Digital-Financial-Inclusion-Feb-2015.pdf. Accessed 21 Feb 2023 4. Bruhn, M., Love, I.: The real impact of improved access to finance: evidence from Mexico. J. Finan. 69(3), 1347–1376 (2014) 5. World Development Report 2022: Finance for an Equitable Recovery. World Bank, Washington, DC (2022) 6. Demirguc-Kunt, A., Klapper, L., Singer, D., Ansar, S.: World Bank Publications; 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution (2018) 7. CGAP: What Is Digital Financial Inclusion and Why Does it Matter? (2015). https://www. Cgap.Org/Blog/What-Digital-Financial-Inclusion-And-Why-Does-It-Matter. Accessed 21 Feb 2023
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8. Global Financial Development Report 2014: Financial Inclusion. https://openknowledge.wor ldbank.org/handle/10986/16238. Accessed 21 Feb 2023 9. Yuzefalchik, I.: Finansovaya vovlechennost naseleniya: teoreticheskiye aspekty i sostoyaniye v Respublike Belarus [Financial involvement of the population: theoretical aspects and state of affairs in the Republic of Belarus]. Banka˘yski Vesnik. SAKAVIK, pp. 33–42 (2019). (in Rus.) 10. Finansovaya dostupnost [Financial accessibility]. Bank of Russia. https://www.cbr.ru/dev elop/development_affor/. Accessed 21 Feb 2023 11. Dahiya, S., Kumar, M.: Linkage between financial inclusion and economic growth: an empirical study of the emerging Indian economy. Vis.-J. Bus. Perspect. 24(2), 184–193 (2020). Special Issue: SI. https://doi.org/10.1177/0972262920923891 12. Demidova, S., Lesnevskaya, N., Lomakin, N.: Development of financial system inclusiveness at the present stage: international and Russian experience. In: ACM International Conference Proceeding Series, p. 3490877 (2021) 13. Asli, D.-K., Klapper L., Singer D., Ansar, S.: The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. World Bank, Washington, DC. License: Creative Commons Attribution CC BY 3.0 IGO (2022). https://doi.org/ 10.1596/978-1-4648-1897-4 14. General Guidelines for the Development of Government Payment Programs: Financial infrastructure series; World Bank, Washington, DC (2012). https://openknowledge.worldbank.org/ handle/10986/22127. License: CC BY 3.0 IGO. Accessed 21 Feb 2023 15. Sitnik A.A.: % Sistema kaznachejskih platezhej v nacional’noj platezhnoj sisteme [The system of treasury payments in the national payment system]. Vestnik universiteta imeni Kutafina (MGYUA), pp. 145–152 (2021). (in Rus.). https://doi.org/10.17803/2311-5998.2021.85.9. 145-15742 16. Majorie, C.-M., Duflos, E.: The Evolving Nature and Scale of Consumer Risks in Digital Finance. CGAP (blog), 19 October 2021. Consultative Group to Assist the Poor, Washington, DC (2021) 17. Author, F.: Article title. Journal2(5), 99–110 (2016) 18. Author, F., Author, S.: Title of a proceedings paper. In: Editor, F., Editor, S. (eds.) CONFERENCE 2016. LNCS, vol. 9999, pp. 1–13. Springer, Heidelberg (2016) 19. Author, F., Author, S., Author, T.: Book Title, 2nd edn. Publisher, Location (1999) 20. Author, F.: Contribution title. In: 9th International Proceedings on Proceedings, pp. 1–2. Publisher, Location (2010) 21. LNCS Homepage. http://www.springer.com/lncs. Accessed 21 Nov 2016
The Contribution of Mobile Companies to Sustainable Economic Development in Sub-Saharan Africa Liudmila A. Guzikova(B) and Nicolas Francois Somga Bitchoga Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia [email protected], [email protected]
Abstract. Sub-Saharan African economy envisages thriving its economy through the customer relationship management. The Sub-Saharan African economy has a challenge to take its economy away from the low-income economy to the middleincome economy to 2035. To attain this result, the Sub-Saharan African economy would scrutinize the contribution of mobile companies to sustainable economic development in Sub-Saharan Africa. The objective of this study is to invite the African countries to create an enabling environment spurring training educational technological digital ecosystem permitting them to provide an economic development sustainability. This study should refer to be as a guidance for junior, senior scientists, private and governmental scientific laboratories, as well as companies and professionals that envisage validating their future scientific research. The study in question has used experiments data collection, data collection methods, econometric methods, such as the inference in two variables by putting an accent on the Fisher-test, and that of Schwarz. Table methods, likelihoods and forecasts have been used in order to find out mathematical results. However, the mathematical analysis have been used and validated through excel and Gretel. The results obtained have been based on the estimate of the technological breakthrough, the analyses of the interrelation between the number of subscribers and the revenue of mobile companies. Thus, the contribution of the mobile companies to the Sub-Saharan African economic development has been one of the fundamental results yielded. The assessment of the performance between the 5G and the non-5G has been made. Keywords: Applied computing · Applied computing law · Economics · Network services · Social and behavioral sciences
1 Introduction The analysis of the contribution of the mobile companies to the Sub-Saharan African economic development can be operated through the diagnosis established on the time series’ number of mobile subscribers involved in the use economic development [1–4]. The mobile economy is considered to be as an economic sector that participate in the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 265–277, 2023. https://doi.org/10.1007/978-3-031-32719-3_20
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customer relationship management [5–9]. Thanks to the fact that mobile and digital companies enable companies to develop their customer relationship management through the digitalization [10–13], investors in all the subject areas can position their goods by pulling buyers and consumers of telephone devices, TV-set, computers, IPhones, internet users, and other types of goods. Mobile enterprises permit communication between businesspeople, banks and their customers or clients, governments and banks, and mobile companies, internet providers, mobile money consumers and providers and people living in the same country and those living abroad [1, 14–16]. The second approach that permits to analyze the mobile companies consists of putting accent on the development of the information technology and communication in connection with the development of the expansion of the private investment in digital infrastructure through the value chain [17–20]. The Value chains permit to train developers in schools, high schools and universities in order to acquire knowledge on how to master the broadband markets, thanks to which digitalization would prosper [21–24]. The development of mobile companies can be realized, in case the private companies put in place sophisticated networks development in order to supply African with an adequate consistent network enabling the better communication to telephone users [25– 28]. To examine the contribution of the mobile companies to the Sub-Saharan African economic development, we have to put accent on the technological breakthrough. In addition, we have to estimate the number of subscribers. Thus, we will assess the interrelation between the number of subscribers and the revenue of the mobile companies. Then, we envisage estimating the mobile companies’ networks. Finally, we will measure the contribution of the mobile companies to the development of the Sub-Saharan African economy. Objective of the study. This study invites the African countries to create an enabling environment spurring training educational technological digital ecosystem, thanks to which the Sub-Saharan African economy would produce digital specialists capable to master the network, develop. In addition, the implement of an enabling environment that constitutes private investors, job creation in order to better African people living conditions. Novelties. In terms of novelties, this study assessed the technological breakthrough, the estimate of the number of subscribers, and the interrelation between the number of subscribers and the revenue of the mobile companies. In addition, the networks of the mobile companies and its contribution to the development of the Sub-Saharan economy have been made. This study should be used by scientific laboratories, governmental, professional and private scientific institutions, universities, senior researchers, and young researchers, companies in order to validate their research fields and in the view of putting an end to their scientific research in the sphere of innovation. Literature and discussions. Kapur Radhika studied the significance of digital technology, whereas Prakash Yerragola, Sagarika Y. scrutinized the challenges and opportunities of digitalization. However, Rauf Salahodjaev and Asongu A.S. put emphasis on the scrutiny of the demand-side money drivers of financial inclusions.
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Rauf Salahodjaev, Asongu A. S. demand-side mobile money drivers of financial inclusion by assessing the mobile money’s economic growth threatholds for innovations. However, Crossan A., McKelvey N., and Curran K. have assessed the mobile technologies impact on economic development in Sub-Saharan Africa. Saima Hussain, Rizwan Raheem A. and Olaniyi E. studied the Smartphone buying behaviors in a framework of brand experience and brand equity by putting accent on the Information and communication technologies and economic development. Kouladoum J.C., Wirajing muhamadu A. Kindzeka, Tii Nchofoung and Asongu, Nchikaodi Nwachukwu and Izegbua studied the digital technologies and financial inclusion and the mobile phones’ institutional quality and entrepreneurship. Asongu S., Odhiambo N.M. have assessed the foreign direct investment through the information technology and total factor productivity dynamics. Whereas Guzikova L.A. and Somga Bitchoga N.F. analyzed the significance of digital technology by not only putting emphasis on the number of people by city as an opportunity to spur investment in African countries by training African people in order to raise the technological level thanks to which Africa would put in place the economic development sustainability. As a differential advantage in comparison with the empirical studies, we have tested and validated our hypothesis by mathematically proving that there are strong, negative, and weak relationships between the number of subscribers and the revenue of mobile companies. However, in terms of innovation, the study in question has made a graphical estimate permitting to evaluate the technological breakthrough on the basis of the from the beginning of the use of mobile technology with the 2G to the use of the 5G. Thanks to this estimate, we have discovered the impact of the 2G and its fall that has been replaced by the 3G, whereas the 4G replaced the 3 and the 5G replaced the 4G. Having mathematically estimate the distinction between the 5G and the non-5G, we obtained lower mode, confidence intervals permitting to conclude that the lower the indicators, the lower the risks of developing the 5G and the non-5G.
2 Methods The experiment data collection consists of laying emphasis on time series data on the number of subscribers, by means of which the yearly growth of the number of subscribers should be determined [29–32]. The use of tables and observation methods has been put in vigor in this scientific work in order to analyze the distinction between variables. However, the econometric methods such as the inference in the two-variable, leastsquares model, and prediction in the two-variable regression model can permit our analysis to examine econometric questions in the view of validating the hypotheses posed by the study [33–36]. Interval sampling offers the opportunity to provide reasons why the coefficient between variables are positive and negative. In addition, interval sampling permits to distinguish the reasons why companies’ revenue evolve, while the number of subscribers’ growth does not match the expectations of the decision-makers [37–40].
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In addition, the use of econometric tests in this work should permit us to find out the power of the tests following Fisher, Schwarz test [40]. The methodology encompasses the data collected by the scientific international organization such as GSMA. Thanks to these data, the mathematical analyses will be made. The data put emphasis on the mobile revenue, the number of subscribers, the mobile companies’ investment in the networks, and the contribution of mobile companies to the development of the Sub-Saharan African economy [40]. The data in Table 1 serve to compute the results provided in Tables 1, 2, 3, 4, 5, 6, 7 and 8. Table 1. The revenue of mobile Companies (operators) and the number of subscribers [34]. Period of time
Operator revenue (in billions of $)
Number of subscribers
2017
40
420
2018
42
456
2019
45
470
2020
46
500
2021
47
560
2022
48
590
The data provided in Table 2 permit to compute the results yielded in Table 8. Table 2. The Mobile operators’ investment in their networks from 2017 to 2022. Year
5G
Non-5G
2018
0
8
2019
0,12
8
2020
0,9
7,5
2021
1,5
7
2022
2
8
3 Results and Discussion The analysis of the contribution of mobile companies to sustainable economic development in Sub-Saharan Africa can be realized by means of putting emphasis on the technological breakthrough. The technological breakthrough is been depicted in the Fig. 1. The figure above shows that the 2G did not only attain its optimal point in 2016, but also started spiraling downwards in 2017 by equating the 3G the same year (2017).
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7 6 5 4 3 2 1 0
10
20
30 5G
40 4G
50 3G
60
70
80
2G
Fig. 1. The analysis of the technological breakthrough in the sphere of information technology and communication from 2016 to 2022 [34].
It is important to highlight the fact that the fall of the 2G permitted the evolvement of the 3G in 2017. The 3G has been satisfying mobile subscribers in Sub-Saharan Africa until 2019 by realized its optimal performance that enabled the attainment of its climax and turning point. For this, the 4G appeared by attracting a large number of digital subscribers, although the 5G has been launched in Sub-Saharan Africa in the dawn of 2020. To evaluate the mobile market can depend on the number of people going digital in Sub-Saharan Africa. This is why the study in question put accent on the graphical analysis on the basis of the number of subscribers. The number of subscribers is depicted in Fig. 2. The results provided by the figure above shows that the number of subscribers in 2013 grew by 1.05 times in comparison with the past. However, in 2014 in relation with the past the number of subscribers highered by 1.2 times. In 2015, the number of subscribers rose by 1.06 times, whereas, in 2016, the number of subscribers hovered around 1.1 times. In the same vein, in 2017 the number of subscribers provided the score of 1.05 in connection with the past. In 2018, the number of subscribers provided a score of 1.1 in comparison with the past. In 2019, the number of subscribers reach 1.12 time in comparison with the past. In 2020, the number of subscribers provided 1.1 times in relation with the past, whereas in 2021 in comparison with the past the number of subscribers was 1.12 times. In 2022, the number of subscribers amounted to 1.05 times in comparison with the pat.
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11
590
10
560
9
500
8
470
7
456
6
420
5
400
4
370
3
350
2
300
1
287 0
100
200
300
400
500
600
700
Fig. 2. The Number of subscribers in Sub-Saharan Africa from 2021 to 2022 [34].
The discovery of the interrelation between the number of subscribers and the revenue of mobile companies has to be made in the view of understanding the relationship between the indicators by means of the mathematical results provided in terms of coefficients yielded, and the forecasts. Table 3. Mathematical estimate. Statistics – regression Linear regression – R
0,894464
R2
0,800066
Nominal R2
0,733421
Standard error
29,86237
Observations
5
The results computed by the author.
The results provided by the coefficient of determination certifies of the accuracy and the significance of the Fisher test. The results obtained in Table 6 ascertains that the interrelation between the number of subscribers and that of the revenue of companies permit to observe negative and positive coefficients. From this, we think that, if Africa envisage boosting the mobile economy, it should calibrate its revenue to ramp up infrastructure and marketing management methods, thanks to which the number of subscribers would higher by enabling the
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Table 4. Estimate on. Designation
Df
SS
MS
F
Significance F
Regression
1
10705,52
10705,52
12,00492
0,040498
Residues or remainder
3
Total
4
2675,283
891,761
13380,8
The results computed by the author.
Table 5. Mathematical analysis. Designation
Coefficient
SE
t-Stat
P-Significance
Mim. 95%
Max. 95%
Y-Intersection
−509,56
296,05
−1,72
0,18
-1451,67
432,65
40
22,47
3,46
0,04
1,83
6,48
43,11
The results computed by the author.
capacity of earning positive coefficients establishing positive interrelationships between the investment of firms in mobile sphere and the growth of subscribers in numbers. Table 6. Statistical summary providing the forecast. Observation
Forecast 420
Remainders
Standard rounding
Percentile
420
1
434,30
21,70
0,84
10
456
2
501,72
−31,72
−1,23
30
470
3
524,19
−24,19
−0,93
50
500
4
546,66
13,34
0,51
70
560
5
569,13
20,87
0,81
90
590
The results computed by the author.
The forecast shows that number of subscribers would spike from 2018 to 2022. In 2019, the number of subscribers would rise by 1.15 time in connection with the past. However, the number of subscribers would skyrocket by 1.04 time in 2020 in comparison with the past. In fact, the number of subscribers would grow by 1.04 in 2021 in connection with the past. However, the number of subscribers would spike by 1.04 (see Table 7 and Fig. 3). The estimate of mobile companies would permit to evaluate the existing performance between the 5G and the non-5G. The analysis of Table 8 permitted us to conclude that the according to the results provided by this study 5G offers the best results in comparison with the Non-5G on the basis of the lowest median, minimum and maximum of interval.
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8.2 8
8
7.8 7.6 7.4 7.2 7 6.8
0
20
40
60
80
100
Fig. 3. Forecast on the interrelation between the number of subscribers and the revenue of the mobile companies [34].
Table 7. Estimate on mobile operators’ networks. Designation
Variables 5G
Non – 5G
Mean
1,13
7,62
SE
0,40
0,24
Median
1,2
7,75
Mode
#H/D
8
SD
0,81
0,48
Sample variance
0,65
0,23
Kurtosis
−0,82
−1,3
Assymmetry
−0,42
−0,85
Confidence interval
1,88
1
Minimum
0,12
7
Maximum
2
8
Sum
4,52
30,5
Number
4
4
Outlier
2
8
Strict Minimum
0,12
7
The confidence level (95%)
1,3
0,76
The results computed by the author.
With regard to the confidence interval, the Non-5G provide the best score, because the lower the confidence interval, the better the security of investment.
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In order to testify of the contribution of mobile companies to the development of the Sub-Saharan African economy mathematical scrutiny has to be elaborated. Table 8. Mathematical analysis. R
0,999984
R2
0,999968
Nominal R2
0,999905
SE
0,084262
Observation
4
Results computed by the author.
The results provided in Table 9 testifies of the significance of the Fisher - test (Table 10). Table 9. Dispersion analysis. df
SS
MS
F
Significance F
Regression
2
224,7429
112,3714
15826,73
0,005621
Remainder
1
0,0071
0,0071
Total
3
224,75
Results computed by the author. Table 10. The mathematical analysis on the economic contribution of mobile companies to the development of the Sub-Saharan African economy. Coeff
SE
t-Stat.
P-Likelihood
Min. 95%
Max. 95%
Max. 95,0%
Y-Intersection
−166,8
1,56
−106,62
0,006
−186,67
−146,92
−166,8
39
6,7
0,038
177,80
0,003
6,22
7,18
6,7
11
0
0
65535
0,001
0
0
0
The results computed by the author.
The results obtained in Table 11 shows, in the one hand, the negative coefficient, and the positive coefficient, on the other hand. This can consider being as a proof that considerable efforts should be put in place in order to better the economic contribution of mobile companies to the development of the Sub-Saharan African economy as a whole. The results provided in Table 12 certify of the presence of strong and positive coefficients. However, weak coefficient have been discovered. For this, consistent efforts need to be put in place in the view of the betterment of the contribution of mobile companies to the economic development (Table 13).
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Table 11. Mathematical analysis on the economic contribution of mobile in Sub-Saharan Africa. Coefficient
SE
Z
P-Significance 0,0000***
Phi – 1
0,9996
0,0007
1429
Theta – 1
0,999996
0,79
1,26
Indirect contribution
0,601011
0,57
1,044
Results computed by the author. *** Indicates the power of the inferential test.
Table 12. Econometric estimate. Designation
X
SD
Dependent variable
41,00
1,58
Average innovation
0,48
0,50
R2
0,99
-
Direction of R2
0,98
-
23,80
-
Schwartz criterion Akaike criterion
25,36
-
Henna-Quinn test
21,17
-
Results computed by the author.
Table 13. Econometric estimate. Designation
Action part
Min. Part
Module
Frequency
AR – Root1
1,004
0,000
1,0004
0,000
MA – Root1
−1,000
0,000
1,000
0,5000
4 Conclusions The examination of the contribution of mobile companies to the Sub-Saharan African economic development sustainability has been realized thanks to the mathematical model used in the methodology. With regard to the technological breakthrough, the results provided has shown the 2G affected the Sub-Saharan African digital ecosystem, although its life cycle spiraled down as soon as the 3G started influencing the digital market. However, the 4G replaced the 3G thanks to its innovative solutions to the expectations of the digital ecosystem. However, the 5G appeared to be as one of the technological solutions, thanks to which the mobile subscribers would solve their digital problems. This is why it is in the brink of replacing the 4G. Having estimated the number of subscribers, we concluded that the further the time, the higher the number of subscribers.
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The estimate of the interrelation between the number of subscribers and the revenue of the mobile companies ascertained that the results on the one hand constitute strong positive, and on the other hand constitute negative coefficients. The forecast highlighted that the number of subscribers gradually rises from year to year. The Analysis of the 5G and the non-5G shows that the lower the mode, minimum, and maximum, the lower the risks. In the same vein, the results obtained for the 5G have ascertained that the lower the indicators, the higher the confidence interval. The forecast of the contribution of the mobile companies to the Sub-Saharan economic development sustainability has shown negative, weak, and strong positive results. We think that, if mobile companies efficiently need to contribute to the Sub-Saharan African economy, they should work in an efficient an efficient enabling environment procured by decision-makers. In addition, if the Sub-Saharan African economy need to render the mobile sector sustainable, it should calibrate investment spending destined to develop the mobile companies to the development of infrastructures by developing schools, universities, in which theoretical and practical programs should be used for the training of developers that would work according to the need of mobile companies. Acknowledgments. The research was financed as part of the project “Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization” (FSEG-2023-0008).
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Exploring the Customer’s Acceptance Towards Food E-commerce Sites: Evaluation from Service Quality Perspectives Thuy Dam Luong Hoang1 , Thi Hong Van Lo1 , and Tamara Selentyeva2(B) 1 VNU University of Economics and Business, Vietnam National University in
Hanoi, Hanoi, Vietnam 2 Peter the Great Saint Petersburg Polytechnic University St. Petersburg, St. Petersburg,
Russian Federation [email protected]
Abstract. In the beginning of 21st century, with the never-ceasing high-tech enhancement and the speedy Internet development, electronic commerce appeared the most promising revolution of business in the world. Online shopping has offered unimaginable potential and convenience for both sellers and buyers, not only in developed countries but also in developing one like Vietnam. However, online shopping is meeting numerous barriers in term of service quality and customer’s perception. Accordingly, this study aims to investigate the customer technology acceptance in the context of online food shopping in Vietnam through service quality perspectives, namely website quality, assurance, responsiveness, trust, personalization. To obtain research purpose, quantitative method is adopted to analysis data and provide research findings. Usable data is gathered from 179 respondents through online questionnaire survey and then analyzed by SPSS 26 software to explore the correlation among variables. Four main tests are descriptive analysis, reliability analysis, exploratory factor analysis and regression analysis. The findings reveal that all key factors in E-service quality perspectives have significantly positive impact on customer acceptance in Vietnam online food shopping. Among them, personalization and website design are two most significant factors on Vietnamese customers when purchasing food online. This result suggests that personalized strategy implementation and website design improvement can make customers trustful and satisfied, which can become the best business plan for every e-retailer in current digital market. After that, current limitations are pointed out for better future research in this context. Along with it, several practical recommendations can be suggested for better e-commerce’s service quality in Vietnam market. Keywords: Customer’s technology acceptance · Food E-commerce · Service quality perspectives · E-service quality
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 278–290, 2023. https://doi.org/10.1007/978-3-031-32719-3_21
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1 Introduction Internet shopping is a subset of electronic commerce (or e-commerce), since it enables users to purchase products or services directly via the Internet without the need of a middleman [1, 2]. This trend has advanced significantly since the mid-1990s, when Web technologies played a significant role and is likely to accelerate further because of its benefits such as ease of use, expanded options, competitive price, and more access [3]. However, it should be mentioned that the service quality of electronic and traditional commerce is significantly different; e-service applications in e-commerce are more crucial than traditional sales since consumers and merchants cannot interact face to face during the transaction. As a result, e-retailers should focus their efforts on reformulating service aspects to make them more significant in the context of online buying [4]. It is well-known that buying food through social media platforms is a rapidly expanding trend in Vietnam, where users may purchase prepared foods and beverages from online companies that manufacture, market, and sell their own items [5]. This phenomenon is most noticeable in two major cities (Ho Chi Minh City and Hanoi) and has been fueled by Facebook’s quick growth in Vietnam, as well as the rapid proliferation of other social media platforms [5]. However, Vietnam’s e-commerce platforms for food still have several disadvantages. In terms of food quality, the conventional face-toface method gives subjective knowledge of food quality and cleanliness levels among customers [5]. On the contrary, internet food is mostly self-advertised, making it very difficult to check the accuracy of product information such as food origin, food processing, and even trader licensing [6]. In other words, bank system integration into electronic payment is still a work in progress because Vietnamese banks do not standardize transfer fees between branches and do not provide adequate customer support for electronic payment; as a result, the majority of customers lack experience with and trust in online transactions [7]. Thus, online food shopping has overcome numerous barriers such as lengthy loading times, transaction difficulties, payment security, and low-quality food products [8]. Consequently, this study investigates the topic “Exploring the customer’s acceptance towards food E-commerce sites: Evaluation from service quality perspectives” to clarify significant dimensions of service quality that bring critical impacts on customer technology acceptance toward online food shopping sites in Vietnam. Along with it, several practical recommendations can be suggested for better e-commerce quality in Vietnam market.
2 Materials and Methods Sampling method. To ascertain the true state of consumer technology acceptance among food Ecommerce sites in Vietnam, a questionnaire is presented to elicit responses from Vietnamese customers about their attitudes toward technology adoption in food E-commerce sites. As a result, data collecting is conducted using both online and offline surveys, with Vietnamese social media platforms (such as Facebook, Gmail, and Google Drive) playing a significant role in contacting target respondents. The sample is drawn using easy
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sampling techniques and is balanced in terms of age, gender, education level, and income in order to maximize the number of participants for this subject. Sample size. The sample size for quantitative analysis is proportional to the number of variables. Hair et al. (2013) state that the number of observations must be at least five times the number of question items [9]. Because the number of items in this research is 22, the minimum needed sample size is 110 observations. To assure the research’s credibility, 240 surveys are sent, but only 179 are certified. This sample size satisfies the sample size criteria; hence, it is suitable for quantitative analysis [9]. Measurement and methodology. The theoretical framework is derived from the dimension of service quality, and then a questionnaire is constructed using this framework. The final questionnaire consists of 22 questions split into two sections. The first section of the questionnaire covers all elements affecting consumer adoption of technology for food E-commerce sites in Vietnam, while the second section focuses on customer demographic information. To minimize questionnaire weariness and comprehensive mistakes, the first part’s experience statements are entirely favorably written [9]. All the study’s measures are on a 5-point Likert scale. The following procedures are used to analyze valid data: I descriptive statistics, (ii) reliability and validity tests, (iii) exploratory factor analysis, and (iv) multiple regression analysis.
3 Main Results Literature review. E-service quality perspectives. Numerous research conducted over the last two decades have shown that service quality influences consumer behavior, but these results have not been applied to ecommerce until recently [10]. To be more precise, Bhatt [11] defined e-service quality as a mix of website functionality and successful shopping for the purpose of purchasing and delivering items or services. Additionally, Kalia and Paul [12] define e-service quality as the customer’s view of a website’s capacity to react to their wants, concerns, and security in a buying environment. In terms of customer perceptions of the perceived quality of e-services, online shopping is a complicated process that may be subdivided into several sub-processes such as navigation, information seeking, online transactions, and customer interactions [13]. Customers are unlikely to assess each sub-process during a single visit to an online business, but rather see the service [14]. Thus, for relationship marketing in the modern era, e-service quality has been appropriately considered as a trustworthy instrument for achieving durable competitive advantages, implying that e-service quality is a strategic factor in online company. Dimensions of e-service quality perspectives. Numerous research have been conducted over the last few years to determine the underlying aspects of e-service quality that have a direct effect on how users perceive the service quality of a website. Consider the investigations of Parasuraman et al. [15] and Parasuraman et al. [16]. Their study concluded that the fundamental E-SQUAL
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measures four dimensions: efficiency, fulfillment, system availability, and privacy. It is worth noting that the E-SQUAL scale is used as the basis for several additional studies on e-service quality since it is especially well-suited to the online environment. Accordingly, this study provides aspects of e-service quality via site design, assurance, responsiveness, trust, and personalization, based on the updated E-SQUAL scale items and the research on e-service quality. Importantly, these elements are critical utilized in other outstanding research about e-service measurements such as Shankar and Datta [13], Kalia and Paul [12]. Each of them, however, may be characterized in terms of process and outcome, and can also be analyzed in terms of individual quality criteria. As a result, the assessment and importance of these characteristics will vary among e-services and even within features. Indeed, since Vietnam’s e-services are underdeveloped, it has been discovered that discontent and incidents are often caused by errors in order processing or payment methods [4]. Factors affecting consumer acceptance of food technology E-commerce from the e-service quality perspectives. 1. Website Design. Website design refers to the aesthetics of the user interface, which is critical for any online shop [14]. The effect of website design on the performance of e-services has been widely examined. For example, Akgul [17] performs empirical study to measure consumer happiness with online buying and discover that customer satisfaction is impacted by the quality and design of the web site. Additionally, another research reveals that website features are significant indicators of consumer perceptions, happiness, and loyalty for online shops [11]. Kalia et al. [10] also reveals that the most significant criteria in determining why users repeat a website are design aspects such as content, layout, ease of discovering information, navigability, and emotional experience…. Thus, the following theory is advanced: Hypothesis 1 (H1): Web Design significantly influences customer’s technology acceptance towards food E-commerce sites in Vietnam. 2. Assurance. Assurance or dependability refers to a website’s capacity to process orders effectively, deliver on time, and safeguard personal information [3]. The relevance of assurance has been underscored by a general trustworthiness toward the technological service provided by these e-stores, which has also been identified as a critical component of e-service satisfaction [10]. Additionally, Hoang et al. [18] suggested that the assurance dimension has a direct beneficial influence on the quality of electronic banking services and consumer satisfaction. We suggest Hypothesis 2 (H2): Hypothesis 2 (H2): Assurance significantly influences customer’s technology acceptance towards food E-commerce sites in Vietnam. 3. Responsiveness. In the context of online commerce, responsiveness refers to the e-retailers’ accurate and rapid reaction to client concerns. In other words, customers expect online merchants to reply correctly and swiftly to their questions [11]. Kalia and Paul (2021) also conduct a series of studies to determine how customers’ perceptions of e-retailer performance are impacted by waiting time. They point out that online retailers’ customer service departments need sufficient people to connect with and assist their
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consumers through a variety of communication methods, including their website, email, phone, and fax lines [3]. Thus, Hypothesis 3 (H3) is proposed: Hypothesis 3 (H3): Responsiveness significantly influences customer’s technology acceptance towards food E-commerce sites in Vietnam. 4. Trust. Numerous studies have emphasized the significance of online trust when it comes to buyer-seller interactions in an online context [8]. Trust is a critical component of economic engagement in general, but more so in the age of online commerce, with the proliferation of opportunistic online merchants [19]. Additionally, trust is described as a customer’s readiness to take risks in an online purchase as a result of their favorable expectations for future online business behavior [17]. Indeed, customer hazards associated with purchase behavior in digital economy might include product damage, poor seller reputation, cash loss in transaction systems, and even the leakage of personal information [20]. That is, trust shapes attitude and motivates clients to do more online transactions with certain online merchants. The next hypothesis (H4) is: Hypothesis 4 (H4): Trust significantly influences customer’s technology acceptance towards food E-commerce sites in Vietnam. 5. Personalization. The absence of real-time contact often discourages prospective buyers from buying online. Personalization entails tailored attention, personalized messages from online merchants, and the presence of a message space for customer’s inquiries or comments [21]. Parasuraman et al. [16] also referred to personalization as empathy, which may be defined as the degree to which messages are tailored to the individual requirements of clients [16]. In comparison to online purchases of music, movies, travel, fashion, and books, online food buying is more difficult to personalize [22]. Numerous food sellers, on the other hand, are attempting to use big data to identify particular people rather than groups by displaying a limited number of things that are personally relevant to each of the millions of online grocery buyers [7]. Personalization has a huge impact on customers’ adoption of technology when it comes to food E-commerce sites in Vietnam. Hypothesis 5 (H5): Personalization significantly influences customer’s technology acceptance towards food E-commerce sites in Vietnam. Customer acceptance in online shopping. Customer acceptability is often closely related to technological advancements in the online shopping environment [10]. With the use of technology, retail organizations’ customer-based campaigns become more efficient in their development efforts and in identifying opportunities to increase competitiveness and profitability. TAM has been identified as the most appropriate model for examining the drivers of new technology adoption by individual consumers in online purchasing environment [23]. It also plays a significant influence in deciding whether clients accept or are willing to embrace new technologies. In other words, TAM does not capture all aspects of e-commerce explicitly, but rather for a general information system [17]. Additionally, TAM focuses on consumer perceptions such as perceived utility or perceived ease of use but does not differentiate between e-retailer and non-e-retailer [23]. As a result, another paradigm is required to characterize the link between consumer acceptability and the dimension of e-commerce.
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As consequence, customer acceptance is examined in this research from a new angle, namely the perceived service quality of e-commerce sites. The following characteristics contribute to the acceptance of technology by Vietnamese customers in e-commerce sites, namely website quality, assurance, responsiveness, trust, and personalization. The proposed theoretical framework is stated in Fig. 1.
Fig. 1. Theoretical framework (Authors’ creation).
Literature review. E-service quality perspectives. Numerous research conducted over the last two decades have shown that service quality influences consumer behavior, but these results have not been applied to ecommerce until recently [10]. To be more precise, Bhatt [11] defined e-service quality as a mix of website functionality and successful shopping for the purpose of purchasing and delivering items or services. Additionally, Kalia and Paul [12] define e-service quality as the customer’s view of a website’s capacity to react to their wants, concerns, and security in a buying environment. Descriptive analysis. 22 questions were included in the self-administered questionnaire to operationalize the perceived value aspects of e-service quality in the Vietnamese online food market. Five dimensions were used to quantify the items: (i) Website design; (ii) Assurance; (iii) Responsiveness; (iv) Trust; and (v) Personalisation. According to Table 1, descriptive analysis indicated that the mean for most constructs is lower than 4, which means that customers generally do not feel totally satisfied with e-service quality received. However, the construct of website design, especially item WD1 and WD4, has the mean score over 3.7 in the 5-point scale. Thus, in term of correlation web design and purchase intention, respondents tend to agree with all statements about the positive correlation between them. Besides, item PN4 with the mean of 3.69 also shows the compliance of respondents with this statement “I will revisit the online store if I get more individualized attention”. On the other hand, the mean of Trust factor is lower
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than 3, indicated that respondents tend to perceive high level of disagreement, especially lowest meaning item TR1 “I believe that quality of food in online shopping is good” and TR2 “I feel totally safe to provide sensitive information about myself through website”. This result seems to express the attitude of respondents that they do not trust the security of personal information protection and food quality in online shopping activities. Table 1. Summary of Descriptive Statistics of the Variables (Authors’ creation). Item
Minimum
Maximum
Mean
WD1
1
5
3.70
WD2
1
5
3.51
WD3
1
5
3.54
WD4
1
5
3.76
AS1
1
5
3.66
AS2
1
5
3.55
AS3
1
5
3.34
AS4
1
5
2.93
RP1
1
5
3.40
RP2
1
5
3.37
RP3
1
5
3.36
TR1
1
5
2.98
TR2
1
5
2.74
TR3
1
5
3.08
TR4
1
5
2.83
PN1
1
5
3.49
PN2
1
5
3.32
PN3
1
5
3.44
PN4
1
5
3.69
PI1
1
5
3.59
PI2
1
5
3.55
PI3
1
5
3.55
Exploratory factor analysis (EFA). Exploratory factor analysis of independent factors. As indicated in Table 2, the KMO value of 0.901 is deemed to be amazing statistics since it is more than the minimal threshold of 0.5. Additionally, the sig. of Bartlett’s Sphericity test is 0.000. As a consequence, this suggested that the dataset is suitable for factor analysis, the results of which are provided below.
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Table 2. KMO and Bartlett’s Test Result (Authors’ creation). Kaiser-Meyer-Olkin Measure
.901
Barlett’s Test of Sphericity
Approx. Chi-Square
1859.927
df
153
Sig
.000
In this factor analysis, items with a significant impact on Vietnamese customers’ perceptions of online food shopping services were finally summarized into four underlying factors, with each item strongly loaded on a particular factor, satisfying the convergent and discriminant validity requirements [24]. Table 3 illustrates the rotated component matrix used in the last phase of factor analysis. Table 3. Rotated Component Matrix for Final Step (Authors’ creation). Items
1
RP3
.812
RP2
.804
RP1
.782
AS2
.622
AS1
.597
AS3
.538
2
WD1
.868
WD2
.804
WD4
.772
WD3
.729
3
4
PN4
.710
TR4
−.675
PN3
.657
PN1
.623
PN2
.578
TR2
.849
TR1
.735
TR3
.721
As noted, respondents’ data revealed that four dimensions of E-service quality were significant in influencing consumers’ purchase intention, with Responsiveness and
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Assurance merging to form a dimension value that may be referred to as "Reliable Service." For further information, there have been past comparisons and contrasts between these aspects, as shown in research done by Parasuraman et al. [16] and Suhartanto et al. [25]. Responsiveness and assurance may be combined into a single online service quality characteristic called dependable service in the basic E-SERVQUAL. Indeed, the two characteristics seem to be mutually reinforcing, since customers generally do not want to obtain services and goods swiftly but in an unreliable manner, or vice versa [25]. Additionally, despite the fact that item AS2 has a cross loading, it is conceivable to retain this item since the difference between two factor loadings is.10 bigger [26]. Finally, since TR4 is a reversed item, a negative loading simply indicates that the question should be understood in the opposite manner of how it is written; hence, no consideration of its positive or negative correlation is necessary. As a conclusion, a set of four key underlying characteristics is identified as follows: (1) Reliable Service (Responsiveness/Assurance), (2) Website Design, (3) Personalization, and (4) Trust. Exploratory factor analysis of dependent factor. Table 4. The result of factor analysis (Authors’ creation). Component 1 CA1
.897
CA2
.910
CA3
.875
Kaiser-Meyer-Olkin Measure
.735
Sig. of Barlett’s Test of Sphericity
.000
Eigenvalues
2.397
% Variance
79.911
As shown in Table 4, KMO = 0.735, indicating that factor analysis was performed appropriately, and the significance of the Bartlett Test of Sphericity is 0.000 percent (less than 0.05); hence, all items are correlated in the sample population. Similarly, the eigenvalues are 2.397 (more than 1) and the proportion of variance is 79.911, which indicates a need for further investigation. Reliability analysis. Cronbach’s Alpha and item-to-total correlation will be calculated in this section to determine the reliability of study constructs. Cronbach’s Alpha values more than 0.7 are deemed acceptable; however, values greater than 0.8 are preferred [27]. Additionally, the corrected item-total correlation, which indicates the link between answers to individual items and the questionnaire’s overall total score, should be greater than 0.3 to indicate a positive association with the overall total [28]. A reliability test was conducted on these four independent factor items to confirm that they all measured the same construct. In general, the dependable service factor, the web design factor, and the trust factor
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are all very reliable and acceptable for further analysis due to their high item-to-total correlation coefficients (about 0.5–0.8) and strong Cronbach’s alpha (0.874, 0.905 and 0.813 respectively). Regression Analysis. Table 5. Regression Model 1_ Model Summary (Authors’ creation). Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.773a
.598
.577
.57661
The Model Summary is shown in Table 5. As can be seen from the table, the R value is 0.773, indicating a high degree of connection. The modified R2 value reflects the extent to which the independent variables can account for the dependent variable. The corrected R2 value in this model is 0.577. Thus, customer acceptance of online food purchasing in Vietnam may be explained by 57.7 percent of reliable service (assurance and responsiveness), personalization, website design, and trust. Table 6. Regression Model 1_ANOVA (Authors’ creation). Model 1
Sum of Squares
df
Regression
83.683
9
Residual
56.190
169
139.872
178
Total
Mean Square
F
Sig.
9.298
27.966
.000b
.332
The ANOVA table is the next critical table in regression analysis (Table 6). The table indicates that the significance level is less than 0.05. As a result, we may infer that the model used is sufficiently important for predicting the outcome variable. According to Table 7, the results suggest that all four dimensions of service quality perspectives bring positive impact on customer acceptance of E-commerce sites due to p. value < 0.05. Table 7. Regression Model 1 _ Coefficient (Authors’ creation). Model 1
Standardized Coefficients Beta (Constant)
t
Sig.
Collinearity Tolerance
Standardized Coefficients VIF
.706
.481
AS_RP
.186
2.633
.009
.477
2.096
WD
.265
3.907
.000
.517
1.934
PN
.315
4.746
.000
.539
1.855
TR
.149
2.453
.015
.646
1.548
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4 Discussion To begin, it was discovered that website design is a crucial role in determining a customer’s technological acceptance throughout their online purchasing trip. Almost all customers in Vietnam are interested in websites that are simple to navigate, simple to use, and beautiful in design, which is supported by prior researches about service quality and online food shopping [6, 29]. As a consequence, people will recall this website for a longer period of time and return for their next buy. Moreover, reliable service has a little effect on a customer’s technological adoption, which includes Assurance and Responsiveness, and concerned with the website’s technological functionality. In other words, it delivers secured online transactions, proper orders and responsive customer’s demands. Indeed, this research result might be different because it combines assurance and responsiveness; however, other studies also prove the important role of these factors in e-commerce shopping behavior, especially in the COVID-19 pandemic [31] [32]. Additionally, although trust has a little effect on purchase intention with the p-value of 0.149, it is critical for the overall quality of e-services. This is because Vietnam’s e-commerce business is still in its infancy; clients lack comprehensive knowledge and extensive familiarity with the online environment, particularly the elderly or those unfamiliar with the internet or computer services. This finding has been referred in several online shopping researches in Asian countries like Hoang et al. [18], Adrian [22]. Finally, and perhaps most surprisingly, personalization had the greatest impact on consumer acceptance of technology in online buying [25, 31] [32]. On the one hand, Vietnamese consumers may be worried about online privacy and security; on the other hand, they still want more personalized attention from online retailers in order to buy the most appropriate and greatest cuisine. They are gradually becoming used to sharing their information and interacting with e-sellers through online platforms and other communication methods.
5 Conclusions This study was conducted in Vietnam, with the assistance of online food customers. The results present that all service quality’s dimensions, including Website Design, Reliable service, Trust and Personalization, bring important influences on customer technology acceptance towards online food shopping. The primary conclusions of this study are to provide suggestions and to assist online shop managers in Vietnam in better understanding their consumers’ characteristics and successfully improving the online service quality. Nevertheless, this study mostly focuses on several main theories and researches such as Parasuraman et al. [16], Dang et al. [6], Shankar, A., & Datta, B. [13], which might result in a bias viewpoint in service quality’s perspectives. Therefore, future research should utilize different studies about service quality, e-service quality, customer technology acceptance and e-commerce business for a comprehensive result. Moreover, future research also needs to refer the COVID-19 pandemic as a significant factor that can impact on customer technology acceptance in e-commerce sector.
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Acknowledgments. The research was financed as part of the project “Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization” (FSEG-2023-0008).
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Actual Problems and Analysis of Anti-avoidance Tax Measures in Post-Soviet Countries in the Context of Digitalization of the Economy Irina Zhuravleva(B) Financial University under the Government of the Russian Federation, Moscow, Russia [email protected]
Abstract. The relevance of the study is due to the trends in the formation of a multipolar world order with the predominant role of large integration spaces. After the collapse of the Soviet Union in 1991 (in fact, the largest geopolitical association in the world at that time), the republics that were part of it formed their own tax systems and international legislation, however, globalization processes led to imbalances in profits and tax abuses, despite the integration of the post-Soviet states to various commonwealths (CIS, EU, EAEU, etc.). The Organization for Economic Co-operation and Development (OECD) has proposed a BEPS plan to create a harmonious tax environment with over 70 countries around the world. The purpose of the scientific research is to consider the directions for improving the international tax policy of individual countries of the post-Soviet space, but which, on the one hand, have chosen the path of integration into the EU countries, and, on the other hand, the formation of the EAEU, based on a system analysis, hysterotomy and analytical approach, using generalization methods, comparison, deduction, modeling and induction. The findings demonstrated the desire of the countries under study to improve domestic and foreign tax policies, but revealed existing problems the risks of insufficient tax collection, erosion of tax bases, the use of aggressive tax planning, the impact of the political situation on the openness and friendliness of international tax policy, low managerial potential in the tax legal environment, high tax administration costs. The application of the proposals and results of the study is possible within the framework of improving the model tax code developed by the CIS countries in the national tax systems of the countries under consideration, in creating a supranational tax document that contributes to solving problems of tax administration. Keywords: BEPS plan · EAEU · International taxation · Tax · Tax administration
1 Introduction To combat tax abuses, a plan was developed to prevent base erosion and shift profits to low-tax jurisdictions (BEPS plan). Considering the implementation of the BEPS (Base Erosion and Profit Shifting) plan within the framework of the post-Soviet countries of the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 291–306, 2023. https://doi.org/10.1007/978-3-031-32719-3_22
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EAEU and the EU, it is necessary to note the existence of several problematic issues. At the present stage of development of national tax systems, the countries in question apply the practice of the OECD to improve them; they implement the actions of the BEPS plan in their national tax legislation in varying degrees of adaptability, using digital technologies and creating new information spaces. The main problem for the countries considered in the scientific study is the significant financial costs for the introduction and administration of new provisions in the national norms of tax and financial law, digital technologies, information aggregates. Not all countries are ready and can financially and economically afford to implement these BEPS actions (in whole or in part) based on the digital platforms of the world. These problems are common for all states and for interstate relations, and to some extent threaten the strategic, political, financial, and economic interests of countries, therefore it will be relevant to put forward a hypothesis of a nationwide approach and cooperation in this modern direction based on system economic analysis and the creation of a single digital platforms and adaptive budget information technologies [1]. The theoretical significance of the study lies in the development of theoretical and methodological provisions concerning the policy of international taxation and the role of international organizations in this aspect [2]. At present, the “cooperation” of states based on international organizations to develop concepts for the development of some aspects of the policy of international taxation, the application of the provisions of the BEPS plan is more relevant than ever, given the seriousness and scale of the problems facing the governments of the countries under study, including the Russian Federation. The performed scientific work led to the formulation of several the following tasks: – systematically analyze the anti-avoidance measures of the EAEU and EU member states, considering their national tax systems and the implementation of several BEPS plans. – to study Double Taxation Avoidance Agreements (DTAAs) in the countries under study and analyze their practical significance, identify problems. – to conduct a regression analysis of anti-avoidance measures on the example of Russia. The concept of analyzing anti-avoidance tax measures in the context of the countries that were part of the USSR, now choosing the EU or the EAEU, is proposed in the scientific community for the first time, however, it is considered partially according to the format of the publication. Countries not included in these unions, occupying the position of an observer or included in local unsettled associations, are not considered in this article. Russia undoubtedly plays a significant role in the financial and economic structure of the EAEU, and those models and developments in terms of tax regulation and neutralization of existing gaps in international cooperation are taken into account both by the member countries of the EAEU and other Eurasian communities, therefore, in scientific work, the regression the analysis is carried out on the example of the Russian Federation. Currently, most countries from the former USSR do not have confirmed up-to-date statistical data regarding permanent establishments and controlled foreign companies, these data are either not published or not available at all (for example, in several countries there are no CFCs). Therefore, regression analysis for different groups of countries is not possible in this study.
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2 Literature Review Issues of economic interaction between the countries of the Eurasian continent arose both during the existence of the USSR and after its collapse, when the republics took shape with new tax legislation and chose ways for further development, and in modern times with emerging new centers of power that form interested states around themselves and influence economic situation on a local and global scale. So, in Poland, the authors K. Czerewacz-Filipowicz and A. Konopelko come to the conclusion that the EAEU is economically, trade and transport attractive for Europe and other countries, despite a number of political contradictions [3]. Author Karina Ponomareva examines the directions of harmonization in the field of direct taxation in the Eurasian Economic Union (EAEU) and the actions of the EAEU member states to implement the Base Erosion and Profit Shifting Action Plan (BEPS) into national tax legislation [4]. Here, the researcher concludes that the European Union is more harmoniously built, even to the detriment of sovereign rights, and the fear of losing tax sovereignty prevents the EAEU countries from integrating more closely in the tax field. The countries of Central Asia, together with several countries of the former USSR, were studied in the development of the “New Silk Road” by Chinese authors Ma Caichen and Shan Miao [5]. They identified three main tax risks in the development of the Silk Road Economic Belt: differences in the tax system, incompleteness of the tax treaty system, and risks associated with BEPS, but they did not analyze the application of BEPS rules in the EAEU countries. Most of the studies are devoted to certain aspects of the policy of international taxation. Quite a lot of attention is paid to the issues of combating tax evasion by Russian scientists [6–8] such as L.P. Grundel, L.K. Polezharova, A.G. Vankaeva, A.V. Demin, N.S. Mologulov. P.I. Yakovlev considers the peculiarities of international and Russian taxation of foreign organizations acting through a permanent establishment (PE). In turn, O.A. Zykova explores the automatic exchange of tax information as a way of international tax control. I.M. Stepnov, Yu.A. Kovalchuk analyze digital challenges and tax fairness in the context of digitalization of public relations. Thus, the existing projects of economic synergy between Europe and Asia are of interest to researchers in terms of problems, benefits, and a number of unfinished positions in the field of international taxation [9]. Increased attention to the problems of tax integration allows us to speed up the solution of problems and come to the harmonization of economic relations and cooperation in a single field of tax administration. The theoretical and methodological basis of the study is the work of foreign and domestic scientists in the field of application of the provisions of the BEPS plan in the EAEU member countries and problems of international tax administration. The information basis of the study was scientific monographs and articles on the problem under study. The legal basis of the study was the legal documents in the field of international taxation of the EAEU member countries and national tax codes, international treaties, laws of the Russian Federation and post-Soviet countries.
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3 Methods 3.1 Descriptive Part Within the framework of the countries under consideration, the EAEU is the main economic union, but there are states that have joined the EU, for example, the tax systems of Estonia, Latvia and Lithuania are moving closer to the tax system of the EU countries, and the tax systems of Armenia, the Russian Federation, Belarus, Kyrgyzstan and Kazakhstan are being consolidated within the framework of the EAEU. To date, a number of changes are taking place, incl. International taxation, which are aimed at improving the national legislation of countries. The tax systems of the countries under consideration, although they are based on a systemonomic nature, remain imperfect. According to the OECD, countries annually lose between $100 billion and $240 billion in revenue due to tax avoidance by multinational companies (TNCs). As part of its mandate, the OECD has developed a plan to prevent base erosion and profit shifting to low-tax jurisdictions [10]. Analysis of the Central Bank of the Russian Federation (CBR) and the International Monetary Fund (IMF) showed that the Russian Federation is losing up to 1 trillion. Rub. Per year in tax avoidance schemes. According to Janský & Palanský, about 85 billion dollars of corporate profits are withdrawn from the Russian Federation, which amounts to 17 billion dollars of shortfalls in the country’s budget system. Analyzing the tax systems of the EAEU member countries, we note that these countries have formed a unified tax administration system. Harmonization in terms of indirect taxation is gaining momentum based of a single economic and digital space. Figure 1 reflects shortfalls in budget revenues due to international tax evasion in Estonia, Latvia, and Lithuania according to the EU data. Thus, in 2018, Estonia lost 0.12 billion euros due to tax evasion, Latvia - 0.13 billion euros, Lithuania - 0.11 billion euros, respectively.
Fig. 1. Lost taxes due to international tax evasion, billion euros.
To combat tax evasion, states develop measures to prevent them in their tax laws and regulatory documents. For example, countries are implementing general anti-avoidance measures (GAAR). For example, in Art.6 of Council Directive (EU) 2016/1164 of 12.07.2016, which lays down rules against the practice of tax evasion, has the concept of GAAR. In addition to GAAR, there are special anti-avoidance rules (SAAR). The analysis of legal norms made it possible to group these anti-evasion rules in Table 1.
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Table 1. Implementation of anti-avoidance measures in the national legislation of the countries of the former USSR. Country
GAAR
SAAR TCO
CFC
Interest Restrictions
PR
Azerbaijan
–
+
+
–
+
Armenia
–
+
–
–
+
Belarus
+
+
–
+
+
Georgia
–
+
–
–
+
Kazakhstan
–
+
+
+
+
Kyrgyzstan
–
+
–
–
+
Latvia
–
+
+
+
+
Lithuania
+
+
+
+
+
Moldova
–
–
–
–
+
RF
–
+
+
+
+
Tajikistan
–
+
+
–
+
Turkmenistan
+
+
–
–
+
Uzbekistan
+
+
–
+
+
Estonia
+
+
+
+
+
Thus, we can conclude that, despite the constant improvement of national tax legislation in the post-Soviet countries, the following problems still exist: – Absence of GAAR in Azerbaijan, Armenia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Moldova, RF, Tajikistan. – Lack of CFC rules in Armenia, Belarus, Georgia, Kyrgyzstan, Moldova, Turkmenistan, Uzbekistan. According to the European Commission, in 2016, the EU lost 46 billion euros as a result of tax evasion at the international level. Therefore, a common, systemic and systemonomic, unified approach is needed to prevent various options for the use of BEPS. To do this, at the initial stage of solving urgent problems, the OECD developed a BEPS plan, which so far consists of 15 actions. The Inclusive Framework for the BEPS Plan was created by the OECD to ensure that interested countries and jurisdictions, including developing countries, can participate equally in the development of standards on issues related to BEPS. The inclusive structure of the BEPS plan includes Armenia, Belarus, Georgia, Kazakhstan, Latvia, Lithuania, Russia, Ukraine, Estonia. All members of the inclusive BEPS plan structure are committed to meeting the four minimum standards of the BEPS plan, which are: – Harmful tax practices (Action 5); – Prevention of abuses under DTT (Action 6);
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– CbCR (Action 13); – MAP (Action 14). Implementing of the BEPS plan is carried out through implementation in national legislation. Action 5 of the BEPS plan also includes a transparency framework that involves the spontaneous exchange of information on tax rulings. Spontaneous or proactive exchange digitally is the transfer of data, information and documents by the tax authority of one country to the tax authority of another country without prior request, at its own request. Action 6 of the BEPS aims to combat an attempt by an individual or entity to access tax benefits under a DTT between two countries without being a resident of either country. Ratification of the MLI is an important element for the effective implementation of Action 6 of the BEPS plan. Implementation of the BEPS plan is also done through the Preamble, Primary Purpose Test (PPT), and Limitation of Benefits Article (LOB). CbCR is a type of information exchange that aims to provide the tax authorities with a general overview of the activities and tax risks of TNCs. CbCR is used for TNCs with an annual consolidated revenue of more than 750 million euros. According to the BEPS plan published by the OECD, TNCs must report annually for each country in which they do business, on the amount of revenue, profit before tax, income tax, number of employees, tangible assets, retained earnings, etc. Next, we will consider the launches of MAP stages in the countries of the EAEU and the EU. For example, in the Russian Federation there is MAP, but there is limited experience in resolving cases under this procedure and a small list of MAPs. There are a small number of new cases filed each year and 32 pending cases as of December 31, 2019. In general, RF corresponds to half of the elements of Action 14 of the BEPS plan. MAP is in force in Lithuania, but there is limited experience in resolving cases under this procedure and a small list of MAPs. In order to fully comply with all areas of an effective dispute resolution mechanism, Lithuania needs to update and amend a certain number of its DTTs. MLI is a new tool in Russian tax practice. The ratification and signing of the MLI complemented the trends that affect international taxation in the Russian Federation. To date, the MLI covers 71 DIDs. In Latvia, the MLI will not apply to the DTT with North Macedonia and Germany. However, North Macedonia has added a tax treaty with Latvia as part of the MLI. It can be concluded that all those actions that have not been implemented by countries, or implemented, but do not fully show the imperfections, shortcomings, gaps and problems in the systemic functioning of the tax system of states. However, in order to implement these actions, significant technical, informational and digital financial resources are needed, but not all countries have such opportunities to implement the BEPS plan. For example, the tax authorities of the United States of America (USA) spent about $380 million to administer and implement the Foreign Account Tax Reporting Act (FATCA). Countries use two Model Tax Conventions (MTCs) to enter into DTTs: the OECD MTC and the UN MTC. OECD MNEs are mainly used by developed countries, while UN MNEs are used by developing countries.
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DTTs are the source of international law. Currently, more than 3,000 DTTs regulate a significant proportion of cross-border investments, which are based on the OECD MNEs. Figure 2 shows the development of DTT within the countries of the EAEU and the EU. Russia has the largest number of DTTs signed. In Russia, Belarus, there are DTTs that were signed before the collapse of the USSR. For example, the DTT of the Russian Federation and Malaysia was signed in 1987 and came into effect in 1988. Kyrgyzstan has the least DTT among the EAEU countries (29 DTT).
Fig. 2. Development of DTT within the EU EAEU (by date of entry into force), units [11].
It should be noted that in general, there is a significant number of existing DTTs between the EAEU member countries and the EU, and problems in the international taxation of the countries under consideration concern a small number of DTTs and are related to the fact that: – there are no contracts themselves; – signed, but not entered into force; – Tax treaties initialed but not signed.. Increasing transparency is a key element in the fight against tax evasion and tax avoidance. Automatic exchange of information covered 84 million financial accounts for a total of 10 trillion euros in 2019, during which 107 billion euros of additional revenues were revealed worldwide. Developing countries received e30 billion in additional revenue from voluntary disclosure programs and offshore tax investigations. The IMF noted that offshore deposits are down by 8–12% after the conclusion of bilateral agreements on the exchange of information on request. And under the agreement on the automatic exchange of information, offshore deposits are reduced by 25%. Further, as part of solving urgent problems of international tax relations and ongoing digitalization in this direction, we will consider the following types of information exchange: information exchange based on FATCA, information exchange on request (EOIR), information exchange according to the common reporting standard (CRS) and exchange via CbCR.
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The data show that in addition to the Inclusive Framework for BEPS, there is a Global Forum on Transparency and Information Exchange for Tax Purposes, which includes Azerbaijan, Armenia, Belarus, Georgia, Kazakhstan, Latvia, Lithuania, Moldova, Russia, Estonia. The main direction of his work is to increase the transparency of tax systems and establish an effective exchange of information for tax purposes. Almost all countries of the EAEU and the EU exchange information based of FATCA. In 2014, the United States suspended negotiations with the Russian Federation on the FATCA agreement. EOIR is an important tool for the tax authorities to ensure that taxpayers pay their taxes in full and on time. Armenia, Estonia, Georgia, Latvia, Lithuania, Russian Federation, Ukraine have the necessary domestic legal framework for the spontaneous exchange of information. [12] And in Kazakhstan, the necessary domestic legal framework for spontaneous exchange has not yet been created. The Russian Federation takes the exchange of information seriously and therefore supports most international initiatives. The Russian Federation participates in the Tax Administration Forum, in the OECD Committee on Fiscal Issues, in the European Organization of Tax Administrations, etc. The Russian Federation in 2017 updated its tax legislation to implement CbCR. Summarizing the above, the following conclusions can be drawn: – EAEU and EU countries implement BEPS countermeasures (to varying degrees) in their international tax policies; – the network of international tax treaties continues to develop, some DTTs are being revised, but there are still problems with international tax relations between the countries of the former USSR. Therefore, it is necessary to actively sign and implement agreements to further avoid double taxation; – the exchange of information based on digital technologies remains a significant tool in the fight against tax evasion, increasing tax transparency. The exchange of information is not significant and there is a need to continue to increase the volume of data exchange; – there are still open questions: whether developed countries will provide information in full and whether the tax authorities of the countries in question will properly apply the data received. 3.2 Research Part The countries of the EAEU and the EU are implementing rules in their national tax systems that prevent tax evasion, ensuring fair tax competition and transparency. Therefore, it is necessary to consider and evaluate how these implemented rules affect budget revenues. The introduction of the provision on the “Permanent Representation” (PR) in the Tax Code of the Russian Federation gave the budget additional revenues. Thus, Fig. 3 reflects the positive dynamics of income from PR for the period 2017–2019. In 2020, receipts were 12% less than last year. This decline is explained by the pandemic that affected the whole world in 2020–2021.
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Fig. 3. Proceeds from PR in the Russian Federation, billion rubles.
From Fig. 4, we can say that in 2021 the regional budgets received 15% less personal income tax from CFCs than last year. The Boston Consulting Group noted that Russian citizens have assets abroad worth more than $400 billion. The Federal Tax Service said that more than 13 trillion rub. Russians keep on accounts abroad. In 2021, 66% of personal income tax with CFC was paid by residents of Moscow.
Fig. 4. Receipts from CFCs in the Russian Federation, billion rubles.
The International Monetary Fund (IMF) [13] noted that the implementation of thin capitalization and transfer pricing rules negatively affects the investment of TNCs. With an average corporate tax rate of 27%, the thin capitalization rule reduces TNC investment by an average of 20%. Transfer pricing rules have reduced investment in TNCs by more than 11%. Let’s consider the impact of the number of PRs on corporate income tax receipts. This requires a regression analysis. Table 2 presents data on the number of PRs and corporate income tax receipts for each district of the Russian Federation for 2020. To do this, we take the initial data for a regression analysis of the impact of the number of PRs on corporate income tax receipts for 2020 by federal districts of the Russian Federation. Figure 5 shows the relationship between the two inputs (the number of PRs and income tax receipts) and gives an idea of whether there is a linear relationship between X and Y. From the figure, it can be said that there is a direct linear relationship between the two data. Linear dependence can be represented as an equation: y = 1, 54x + 158, 6
(1)
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Table 2. Initial data for regression analysis of the impact of the number of PRs on corporate income tax receipts for 2020 by constituent entities of the Russian Federation. District of the Russian Federation
Number of PRs, units (X)
Receipts from corporate income tax, billion rubles (Y)
Central Federal District
1054
1788,68
219
446,64
North Caucasian Federal District
20
31,35
Southern Federal District
119
185,00
Volga Federal District
147
371,99
Ural federal district
87
472,08
Siberian Federal District
54
351,00
Far Eastern Federal District
84
371,38
Northwestern Federal District
Fig. 5. Scatterplot for regression analysis of the impact of the number of PRs on corporate income tax receipts for 2020.
So, according to the initial data, a regression analysis was made, which is reflected in table 3. Based on this table, the following conclusions can be drawn: – The coefficient of determination is a summary measure of the overall quality of the regression equation. The coefficient of determination varies from 0 to 1, and the closer it is to one, the better the model will be. In this case, the coefficient of determination is 0.95, therefore, the regression model is qualitative; – The significance of the coefficients of the regression equation is checked using the Student’s test. The value of T critical will be equal to 2.45. T-statistics are greater than critical T, therefore the coefficients 158.61 and 1.54 are statistically significant; – Fisher test. Fcritical is 5.99. F > Fcritical, hence the regression equation is statistically significant..
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Table 3. Regression analysis of the impact of the number of PRs on corporate income tax receipts for 2020.
Regression statistics Multiple R
0,974
R-square
0,949
Normalized R-square
0,941
Standard error
131,236
Observations
8 Analysis of variance
Regres-
df
SS
MS
F
Significance F
1
1934929
1934929
112,346
0
6
103338
17223
7
2038266
sion Remainder Total Coefficients
Standard
t-statistic P- Meaning Bottom Top
error Y 158,608
56,605
95% 2,802
0,0310
95%
Bottom Top 95,0%
95,0%
20,101 297,11 20,102 297,115 4
Х
1,541
0,145
10,599
0
1,185
1,897
1,185
1,897
In addition, it is possible to analyze the impact of CFCs on corporate income tax receipts in the Russian Federation. We also use regression analysis for this. Table 4 shows the initial data on the number of CFCs and corporate income tax receipts within each federal district of the Russian Federation. The initial data for the regression analysis of the impact of the number of CFCs on corporate income tax receipts for 2020 is also taken for the federal districts of the Russian Federation. The scatterplot shows whether there is a linear relationship between X and Y. Figure 6 shows that there is a direct linear relationship between X and Y, which is given by the equation: y = 0, 59x + 206, 18
(2)
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Table 4. Initial data for regression analysis of the impact of the number of CFCs on corporate income tax receipts for 2020 by constituent entities of the Russian Federation. District of the Russian Federation
Number of CFCs, units (X)
Receipts from corporate income tax, billion rubles (Y)
Central Federal District
2647
1788,68
589
446,64
North Caucasian Federal District
12
31,35
Southern Federal District
150
185,00
Volga Federal District
151
371,99
Ural federal district
114
472,08
Siberian Federal District
351
351,00
12
371,38
Northwestern Federal District
Far Eastern Federal District
Fig. 6. Scatterplot for regression analysis of the impact of the number of CFCs on corporate income tax receipts for 2020.
After conducting the regression analysis, as tareflected in table 5, we can draw the following conclusions: – the coefficient of determination, reflecting the quality of the model, is 94%, therefore the regression model is qualitative; – Student’s test (testing the significance of the coefficients of the model). T critical is 2.45. T statistics > T critical, therefore, the model coefficients of 0.59 and 206.18 are statistically significant; – Fisher test. Fcritical is 5.99. F > Fcritical, therefore, the model equation is statistically significant. Thus, after conducting a regression analysis and studying the statistical data of countries, we can say that the implemented anti-avoidance measures give a positive result to the dynamics of budget revenues, increase in tax transparency and in fair tax competition.
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Table 5. Regression analysis of the impact of the number of CFCs on corporate income tax receipts for 2020.
Regression statistics Multiple R
0,967
R-square
0,936
Normalized R-square
0,925
Standard error
147,884
Observations
8 Analysis of variance
df
SS
MS
Regression
1
1907049,142 1907049,142
Remainder
6
131217,293
Total
7
2038266,435
F
Significance F
87,201
0
21869,549
Coeffi- Standard er- t-statistic P- Mean- Bottom
Top
Bottom
Top
cients
95%
95,0%
95,0%
ror
ing
У 206,177
61,148
3,372
0,015
Х
0,063
9,338
0
0,588
95%
56,554 355,800 56,554 0,434
0,743
0,434
355,800 0,743
4 Results and Discussion Starting from 2023, it is planned to implement a package of measures (Pillar 1 and Pillar 2) to change the taxation of TNCs. The goal of Pillar 1 and Pillar 2 is to redistribute the excess profits of TNCs to source countries and introduce a global minimum profit tax rate of 15%, respectively. The OECD has estimated the impact of Pillar 1 and Pillar 2. The global net increase in tax revenue is estimated at up to 4% of global corporate income tax revenue, or US$100 billion per year. Income tax revenue gains are broadly similar across high-, middle-, and low-income countries. A significant reduction in profit shifts is expected as a result of the cumulative effect of Pillar 1 and Pillar 2. Pillar 1 will cover about 100 MNCs worldwide and will cover the redistribution of about US$125 billion in taxable profits. Armenia, Belarus, Estonia, Georgia, Kazakhstan, Latvia, Lithuania, Russia have already joined Pillar 1 and Pillar 2. For CbCR, the OECD makes the following recommendations for improvement: – Armenia needs to take measures as soon as possible to implement the domestic legal and administrative framework for CbCR.
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– Georgia does not have bilateral relations for CbCR exchange. Therefore, Georgia needs to have valid agreements of the competent authorities with the countries of the Inclusive Framework; – Kazakhstan is encouraged to take steps to put in place processes to ensure that information exchange is carried out under the terms of reference regarding the structure of information exchange; – it is recommended that Latvia change or clarify that the rule for calculating the annual threshold for consolidated group income is used in a manner consistent with OECD guidance on exchange rate fluctuations in relation to TNCs whose ultimate parent company is not in Latvia; – Estonia, Lithuania meet all the requirements of Action 13 of the BEPS plan and do not give any recommendations for improvement by the OECD. Regarding ways to improve the automatic exchange of information, the OECD makes its recommendations: – Azerbaijan should amend its domestic legal framework to ensure that the approach to identifying controlling persons under the automatic exchange of information standard is consistent with the approach to determining beneficial owners under its internal anti-money laundering procedures. – Estonia should remove supplementary funded pension insurance contracts from the list of excluded accounts, as they do not meet the requirements of the automatic exchange of information standard; – Latvia should include the definition of “managed” in relation to the definition of an investment entity; – Lithuania complies with all the requirements of the standard for automatic information exchange and does not provide any recommendations from the OECD; On January 20, 2022, the OECD released a new version of the TP guide for TNCs and tax administrations. The OECD Guidelines on Transfer Pricing provide guidance on the use of the “arm’s length principle”, which is an international consensus on the assessment for tax purposes of the profits of cross-border transactions between related companies. Improvement within the framework of integration processes is an important issue. The processes of globalization and digitalization in the world are fundamentally changing economic relations. Therefore, the processes of tax administration and collection of taxes should also keep up with global trends. To do this, the OECD every year develops new projects and proposals to improve international taxation and prevent tax avoidance, erosion of tax bases and the withdrawal of profits, elements of aggressive tax planning. It should be noted that the Russian Federation in relation to international tax policy sets a certain vector of direction, and the rest of the countries join it.
5 Conclusions The scientific study examined the tax systems of a number of countries of the EAEU and the EU. It can be noted that, in general, countries are moving in two directions in terms of tax harmonization: some countries (such as Latvia, Lithuania, Estonia) are improving
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tax systems within the EU, other countries (Russia, Armenia, Kyrgyzstan, Kazakhstan, Belarus) - within the framework of EAEU. In addition, tax integration is taking place within the CIS: a Model Tax Code has been developed, information is being exchanged within the CIS member countries, etc. A study was carried out of general and special anti-avoidance measures in the postSoviet countries, and it was concluded that it is necessary to continue the introduction of anti-avoidance measures and improve the already introduced rules. It is also possible to implement targeted anti-avoidance measures, which are more narrowly focused rules than SAAR. A regression analysis of the impact of CFCs and PPs on revenues to the budgets of the Russian Federation showed that there is a direct linear relationship between the introduction of anti-avoidance measures and tax revenues to the country’s budgets. In other words, the more CFCs and PPs, the more tax revenues went to the budgets. However, IMF data showed that anti-avoidance rules could have a negative impact on investment. Therefore, the state in its tax policy, internal and external, should be based not only on fiscal goals, but also on stimulating ones. In addition, an analysis of the DTT between the countries studied was carried out and it can be said that countries should continue to conclude tax agreements, according to a positive trend. After all, the purpose of the DTT is not only the prevention of double taxation, it is aimed at increasing investment in the country, preventing discrimination, etc. It is worth noting that national legislation may contain provisions for the prevention of double taxation. In terms of information exchange for tax purposes, countries also need to continue to implement CRS, CbCR, exchange of information on demand, spontaneous exchange, etc. However, all these measures are associated with significant budget expenditures, with modern digital technologies, and not every country can afford it at the moment. For example, the US tax authorities spent about $380 million to administer and implement FATCA. Thus, the countries of the EAEU and the EU are improving within the framework of international tax policy. However, there are still open questions and problems of information exchange, implementation of the BEPS plan, tax agreements, etc. The main obstacles to improving the tax system are the political situation in the country and significant government spending on tax administration and the introduction of information technology.
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Development of Modern Financial Technologies at the National and International Levels Viktoriya Razletovskaia1 , Igor Stepnov1 , Iurii Guzov2 and Stanislav Svetlichnyy3(B)
,
1 MGIMO University, Moscow, Russia 2 Ningbo University of Technology, Ningbo, China 3 Moscow Technological Institute, Moscow, Russia
[email protected]
Abstract. The development of financial technologies touches on cross-sectoral and interregional problems, national security, and the integrity of the State, indicating that the new solutions are increasingly showing signs of not just a means of settlement, but also have the signs of a new management category of technological content that affects all aspects of life. The importance of this study stems from the need for a holistic strategic vision of the transformation ways of financial technologies, which requires an appropriate research methodology based on the comparative analysis of trends in their development. The article presents an original methodological approach to the study of the development processes of financial technologies and the results of the review of their trends at the global and national level. The intention of the article was to summarise a variety of processes taking place and to develop recommendations on a governmental approach to the evaluation of the situation in order to justify regulatory measures. The article asserts that the idea of financial technologies as a service provided using innovative solutions narrows and simplifies the significance of ongoing processes. The methodology of the conducted study is based on the managerial view of the fintech’s substance, which allows us to move on from considering it a financial revolution to the evolutionary model of its development that goes together with the development of state policies in most countries. The hypothesis was that financial technology, being a synthesis of finance and technology, requires appropriate public regulation methods that complement sectoral management approaches with coordinated cross-sectoral and international development paths that take convergence into account. Keywords: Financial Technology · Neurotechnology · Intersectoral Coordination · Fintech · National Development Models
1 Introduction In recent decades, the strong growth of the economies of countries and the formation of their competitive advantages is ensured to a greater extent by wide digitalization processes [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 307–319, 2023. https://doi.org/10.1007/978-3-031-32719-3_23
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EY estimates [2] that by 2035, financial technology penetration into payment and transfers segment will exceed 96%; into financing segment – 37%; into private capital management and financial advisory – 46%; and operators of fintech insurance services will cover about 10% of all insurance claims. The application of artificial intelligence, neuromarketing and social networking technologies in finance and fintech has led to the emergence of fundamentally new business models that are based on the development of expert systems, knowledge bases, and metamodels of banking architecture [3]. Such a phenomenon can lead to the emergence of super-dominant companies and contains risks of concentration of power. Despite the rapid growth of the financial and technological sector, to date, there are not many scientific publications devoted to the specifics of its essence and causes or its development (functioning as an independent subsector of financial services). The questions of fintech’s essence, history of its emergence, role in the organization of financial relations, whether it is a result and product of scientific and technological progress or an independent actor in the financial market, as well as finding models that allow traditional and innovative financial services coexist are the matter of discussion [4]. There are different models and classifications of fintech. However, the key feature is its connection with innovative technologies regardless of their specifics and differences. At the same time, there is a noticeable activity (extent and speed of regulation, number and speed of adoption of normative acts and regulatory documents) on the international and national levels of legal and administrative regulation of fintech development [5]. In practice, among legal regulators fintech is defined as an integral part of financial services, being a procedure using innovative technologies: European Payment Services Directive [6]. The Russian Central Bank [7] defines fintech in accordance with the international organizations approach, as the provision of financial services using innovative technologies such as big data, artificial intelligence and machine learning, robotization, blockchain cloud technologies, and biometrics. The challenge of researching the processes of fintech development is complicated by scattered and unstructured information in different areas of converged technologies, by the difference of methodological approaches in various analytical reports and by the lack of official statistics and accounting methodology at the level of national regulators, and by the absence of the possibility of constructing time series. The above-mentioned makes it difficult to use general scientific methods of cognition applied in economic science: comparative research, empirical, logical, analysis, interpretation and comparison. Carrying out research in these conditions relying purely on the analysis of data provided by international organizations and consulting companies (KPMG, Deloitte, Global Fintech Index, FSB, GFIN, IMF, World Bank) does not fully represent the occurring processes and correlations so it requires clarification of methodological approaches. Numerous modern studies on certain types of financial technologies used in the management of financial assets [4] essentially represent a methodology of their promotion, description and manifestation in various spheres, a management instruction. At the same time, there are studies [8–13] devoted to the assessment of the impact of fintech on various spheres of human life, calculated on the basis of an interdisciplinary approach; the impact of innovative financial instruments on the interaction models of participants, considering fintech as an independent market participant; the coordination of fintech
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development at the level of integration formations, as well as scenario variants of its development, which deserve unconditional attention and further research. Therefore, the scientific problem that this study attempts to solve is the lack of a holistic strategic vision of transformations in the sphere of financial and intellectual technologies and forms of their state regulation, which has not been supported by appropriate research methods. This scientific problem requires clarification of the research methodology applied to the processes of development of financial and intellectual technologies. The purpose of this study is to systematize such methodology of studying, taking into account the formation and manifestation of new qualitative correlations and processes of their development, based on convergence, to justify the state regulation of their development. The study generalized and systematized various processes taking place in this field and on the basis of clarification of methodology outlined the appearance of national models of development of financial technologies, trends and perspective directions of their development.
2 Materials and Methods The methodological approaches chosen by the authors are based on the theoretical and methodological justification of the facts of financial technology development, based on a managerial view of the content of fintech and an interdisciplinary approach. According to the authors, financial technologies do not simply mediate the process of value movement, but create value themselves through the management of financial information, based on the creation and development of convergent networks with intelligent industries to achieve the goals of organising and securing the links of financial, economic and socio-economic relations, including at inter-industry and international levels. The management of financial technology development processes goes beyond the management of financial innovation, as well as the programme-oriented and sector-specific approach to development based on the product and industry life cycle: the processes of origin, growth and diffusion of innovation, and is based primarily on the genetic component of financial technology of its managerial and technological nature, changing the quality of links, responding to the impact and modelling external environmental conditions. That is, in fact, according to the authors, requires taking into account the evolutionary approach, reflecting the patterns of development of financial innovations and technologies, taking into account the influence of local civilizational features [14–17]. Evolutionary view on formation and diffusion of financial innovations and technologies is confirmed by representatives of the German historical school and American institutionalists [18, 19], as well as modern historians, lawyers, ethnographers and philosophers [20–22]. In this regard, the processes of fintech development should be considered from the point of view of the formation and destruction of financial and economic links, procedures and processes, as well as coordination mechanisms, for all areas of the financial policy of the state, considering the flow and intersectoral nature of their nature: impact on intersectoral and interregional proportions, processes and spheres of development. Michel Callon’s actor-network theory allows the most complete disclosure of the causeand-effect relationships of the evolutionary dynamics of financial technologies, as well
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as the relationship between technology and management subjects of modern research [23]. Therefore, the use of these approaches in relation to financial technologies, their development processes, in addition to the methodology based on a sectoral and targetoriented approach, will make it possible to assess the impact of factors on these processes, on national security and on the balance of the public financial system.
3 Results According to the authors, in the context of a variety of methodological approaches to the study of their development, the lack of structuring and fragmentation of information and data, the absence of official statistics and accounting at the regulatory level, it is expedient to clarify the direction of the research methodology of the processes of fintech development using evolutionary and process-oriented approaches based on the application of advanced analytical tools using comparative analysis techniques for processes of development of convergent technologies. This will allow the identification of development processes that affect national security, as well as patterns and mechanisms of transformation through: analysis of cause-effect relationships and adaptation mechanisms: Identification of the source of the initiative of financial innovations and directions of their impact, the way of building network integration, depending on the level of consideration and decision-making of management subjects, assessment of compliance with the processes of development of institutions and coordination mechanisms. In order to justify and formulate public policies, it is therefore advisable to supplement the traditional methods of statistical modelling, based on retrospective analysis or analysis of the current situation, with new approaches, to rationalise and methodically systematise the following areas: 1. The monitoring and analysis of financial indicators and ratios should be complemented by an analysis of the main processes and procedures influencing their development, taking into account the convergence of technologies, the coordination of resource flows and human interactions: Processes of concentration of resources investment policy, M&A transactions, cluster development, infrastructure exports, accreditation systems and processes (standardisation, certification, accreditation, unification processes), international cooperation at intersectoral level (alliances, coalitions and agreements), the stability and sustainability of the network based on the convergence of indicators and parameters of homogeneity over time, the involvement and influence of actors, the processes of design and distribution and expansion of these networks, the sequence and chronology of key events and the adoption of legislation, and their initiators, leapfrogging and the introduction of tools and measures of regulation of development institutions. 2. Ensuring national security objectives throughout the stages of the process of creation and diffusion of new technologies at intersectoral, interregional and international levels, assessing structural changes, defining regional specialisation. 3. Compliance of institutions, structures, and mechanisms of coordination with the processes of development of financial technologies, the goals of integration - network integration systems, appropriate intersectoral coordination.
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4. Assessment of the potential, rather than the capacity, of the development processes of fintech, considering national security, determining the maneuverability and coordination of processes at the international level and influencing the conditions for development. 5. Development and implementation at the State level of an index reflecting the development of the financial technology industry at the intersectoral and interregional level, evaluation of development initiation activities (capacity and initiative, coordination mechanisms). The assessment of financial technology development processes (dynamics, structure, chronology, stages and geography) was then carried out in accordance with the research methods presented. Foreign experience from open sources, regulatory practices and public authorities was used to analyse the cause-effect relationships of financial technology development, taking into account the chronology of regulations and the sequence of key events and development leaps, the implementation of tools and regulatory measures by development institutions, the analysis of international organisations, the composition of international alliances and coalitions in the fintech sector. Countries are at different stages of the development of financial technologies and have different potential for these processes - this is evidenced by the statistical dynamics of fintech investments, technology composition, infrastructure development and development mechanisms, growth of transnational and global mergers and acquisitions, and the chronological sequence of regulatory and legal regulations and initiatives at the level of national and international organisations, the composition of participants in international coalitions and alliances in the field of fintech. The expansion of digitalisation processes is reflected in the stages of development of modern fintech [10], from the active development of payment systems (credit cards, cheques, ATMs, EMV standard, mobile money M-Pesa, TELCOS), telecommunications companies and mobile solutions, competition between IT financial service providers in the areas of credit to mobile payments and robotic advice. The leader in terms of volume, speed and influence is the US [11]. Meanwhile, the major financial markets in the AsiaPacific region (APAC) entered this stage of competition around 2015–2016. [3]. China became a leader in the region and its current fintech leaders such as CreditEase, Lufax and Ant Financial (AliPay) entered the market around the same time as the US fintech pioneers. Russia entered the fintech market in 2017 [24]. The US accounts for just under half of total global fintech investment - $42.1 billion. Europe is second in terms of fintech investment, with the UK leading the way with $24.5bn, followed by Germany. The Asia-Pacific region ranks third in terms of investment: India with $2 billion and China with $1.3 billion. Experts expect new financial technology players to emerge in Central and North Asia, as well as South America, while the US continues to dominate fundraising [25]. In 2021, there was strong growth in emerging markets, with record growth in Africa in particular. Five African countries and their cities from Somalia, Mogadishu and Hargeisa, were active behind only the United States and the United Kingdom in engaging new cities in fintech development[25]. Indonesia, Malaysia, Myanmar, the Philippines and Thailand have placed particular emphasis on digital financial services as part of their national financial inclusion strategies, including monitoring and evaluation mechanisms [26]. The
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leading indicator of the development of the global market for financial technologies is the level of their penetration in the regions [12]. According to the results of various research projects, the level of penetration of financial technologies in Russia ranges from 40 to 80%. The leaders are the megacities - Moscow, St. Petersburg and Kazan. Russia ranks third in terms of population engagement after China and India [24]. Russian financial institutions, such as banks, are introducing financial technology on their own, buying promising start-ups or forming strategic partnerships (such as Sberbank, VTB, Tinkoff, AK Bars, etc.). They almost duplicate foreign experience and trends of digitalisation of business processes, which was due to deep integration of Russian banks into the world banking system and carefully adapted standards of digital etiquette and methodology of construction of operational business processes in the Figure [27]. A particular surge in the development of fintech can be identified after a pandemic caused by online operations in small cities, based on the cluster approach. Thus, at the present stage, the dynamics of the development of financial technologies at the world level, despite the high rate of investment and its significant volume, is characterised by impetus and differentiation, which is confirmed by the growth and degree of concentration of capital and investment, the development of clusters, periods and geographical structure of their development. The clearly dominant positions in the development of financial technologies and their initiators are the leaders of the previous evolutionary stage of their development - “globalisation” - the USA, England, China and Switzerland, Japan take an active part in regulation at the global level. In Africa, Asia, India, Brazil, Latin America, etc., there has been a strong diffusion and penetration of financial technology. While there are similarities in emerging market market structures, such as high smartphone penetration and limited access to banking services, which contribute to the development of fintech, the increase in the availability of financial services is not necessarily related to an increase in population demand, but rather to non-mandatory processes. There is no direct correlation between financial inclusion and explosive economic growth [28]. Areas of fintech development in high-income countries - USA, UK and China are characterised by product line diversification, wealthtex development, roboeducation [16]. In developing countries, these are traditional payments and lending, usually based on a single super-application. Modern trends in economic development have led to a change in the role, functions and model of investment [17]. Thus, one of the main channels for the development of domestic investment demand is the creation and implementation of ICO websites, cryptocurrency exchanges and fintech lending platforms [21]. In addition, such companies offer advice based on artificial intelligence, neurotechnologies and big data, micro-investment platforms or trading solutions based on social networks [16]. By changing business processes, the industry has led to the emergence and growth of wealthtech asset management at a qualitatively new level. Partnerships are developing between major players in artificial intelligence, neurotechnology and fintech. The most prominent examples of collaboration are ChinaAMC, the 2017 deal between the Chinese Venture Fund and Microsoft, and a similar deal between Bank of China and Tencent. A joint venture between Amazon, Berkshire and JPMorgan was launched in early 2018. Going forward, B2B services such as banking as a service will have an even greater
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impact on investors - not only in payments, but also in areas such as insurtech, wealthtech and regtech [11]. Global investment in wealthtech grew in the first half of 2021, exceeding total investment in 2020. CB Insights has identified 90 wealth management companies with robotic advisors [25]. The largest concentration of these firms is in the US, where the robo-advisor market receives 30% of all wealthtech funding, according to BI Intelligence. The following are also common: – Social trading platforms, based on social networks, allow users to copy the portfolios of the most successful investors in the network. – Micro-investment platforms - applications that present investing as something simple, convenient and affordable. – Robotic advisors - automated services that provide users with recommendations based on the most profitable investment options in the market, profitability goals, the user’s risk aversion profile and other variables such as age and income. According to BI Intelligence, more than 200 robotic consulting firms are registered in the US, compared to just 20 in the UK and 20 in China (as of April 2017). However, the same consulting firm predicts that the Asian market will grow rapidly and reach the size of the US market by 2022. Global practice shows that every M&A deal is driven by clearly defined financial benefits or potential synergies. For example, the average speculative increase in shareholder value is about 7%, while the average long-term increase is 26%. [22]. The asset management industry in Europe, particularly in the UK, remains fragmented, making it difficult for firms to innovate [29]. Nevertheless, there have been consolidation processes among major asset management players across the value chain to provide diversified asset management platforms. In 1H21, there has been an explosion of activity in the blockchain and crypto space. The focus is on the crypto ecosystem - from cryptocurrencies and trading platforms to NFT, alternative asset trading and support structures. In addition, blockchain technology is creating many new asset classes. [16] The financial technology market is evolving, transforming the highly specialised wealth management segment into a global, disruptive industry with record levels of investment and mergers and acquisitions - “from niche status to global industry”. Against this backdrop, cybersecurity will become increasingly important [30]. Serious cyber threats to business identified on the market require the development of cyber insurance. World leaders in this market are the USA and the UK [31]. The next stage in the development of fintech is its use in technology-based blockchain, gaming social networking platforms - the methodical universe (examples: Decentraland with a capitalisation of 4.8 billion dollars, Somnium Space, Meta (Fasebook), Cryptovoxels). Such technologies allow taxing land, property and voting rights, have internal cryptocurrencies, their users own and manage digital assets and can influence the future of the project. The view of the disruptive power of fintech and the threat to financial institutions has changed since 2016, and recent trends in the US and Japan show that established traditional financial institutions are taking the initiative to partner and jointly develop with fintech companies. In other words, the conflict between fintech and the traditional sector is more geographically focused on competition for markets. This argument is supported
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by the analysis of financial market participants: until the 21st century, there were no start-up teams or non-bank structures performing almost identical banking functions as independent factors, and financial technologies themselves were used and developed by a narrow pool of participating banks and institutional investors (e.g. investment funds) [32]. An active and large-scale transition to the processes of convergence of finance and information, and bio- and neuro-technologies, will lead to the adaptation of national models of fintech development. National governments have also taken an active role in the development of the industry. Interdepartmental coordination and cooperation in the development of financial technologies, artificial intelligence and neurotechnology is planned to be adopted in September 2021. The Artificial Intelligence Development Strategy of the National AI Strategy of the United Kingdom and the regulatory system “Functional, Intersectoral Financial Regulations” of Japan, which is also actively engaged in international coordination at the level of integration associations and organisations. State-level strategic documents developed by China, including the agreed financial technology industry development index - Xiangmihu Fintech Index, plans for joint coordinated municipal initiatives to expand 5G technologies and blockchain, national certification programmes, targeted actions of the 7 largest national IT giants - formation of cultural environment and monitoring of financial flows from the point of view of ideology, as well as support for the expansion of Chinese companies in Thailand and other Asian countries, the European market and Africa. In 2020, Lithuania became the centre of embedded finance with social networks, providing an opportunity for the development of British fintech, which indirectly affected other industries and as a result ranked fourth in the ranking of countries - the neural ecosystem. In adapting developing countries to such large-scale development of fintech by transnational corporations and leading countries, one option for nation states is to develop the infrastructure of fintech. A striking example is India. The “Indian Stack” essentially combines “Aadhaar” - a universal digital identifier issued to more than 1.25 billion Indians - with a nationwide unified payment interface (UPI). National governments have applied adaptive strategies in different ways in regulating the development of fintech to the rapid invasion of transnational corporations and the different trajectories of fintech development in global markets. The development of such responses to challenges and risks requires new models of interaction of countries in the sphere of regulation of fintech, the level of international cooperation will play a special role, the choice of its directions in the development of this sphere [33]. There are proactive strategies that nation states themselves develop through public banks and national infrastructure development, or set the rules and conditions for the development of innovations, and then absorb the most successful or integrate through their infrastructure - examples of regulatory sandboxes, ecosystems. This proactive regulation by public authorities stimulates the creation of an integrated national fintech environment and infrastructure [34]. Contributing to this scenario are international initiatives. For example, the UK’s “Open Banking” initiative and the European PSD2 directive have created rules that allow large technology companies and retailers to create entire ecosystems of financial products based on newly available banking data [35]. Mexico
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passed a fintech law in 2018, and last year issued additional rules to encourage innovation. Singapore has introduced innovative products through regulatory sandboxes and has the largest number of international agreements with foreign countries on cooperation on fintech issues with the UK, Japan, Australia and Switzerland [23]. Initially, a laissez-faire approach left some countries with structural problems, social and systemic risks after a series of defaults. The risks of a wait-and-see attitude were felt in Kenya and China. As a result, China and Kenya have now set strict rules for fintech. Singapore, which remains a leader in Islamic finance, has funded innovative labs to develop fintech in Kenya. The country has organised active cooperation with Israel to set up innovative labs with the Tel Aviv FinTech Development Association. In the mid-2010s, Kuala Lumpur became the base for the Association of Southeast Asian Nations (ASEAN) community. Uruguay exports the services of a growing cluster of companies, a fintech focused on international markets, not only in Latin America but throughout the developing world. England, Japan, Singapore and others are initiating the creation of international alliances and coalitions to harmonise international standards in fintech. There is also activity by industry associations at the national level, which indicates increased coordination and harmonisation of actions within the industry and at the international level. At the international level, fintech and blockchain technology issues are regulated by more than ten international organisations: five standardisation committees at the ISO level, WTO, OECD, United Nations and its specialised agencies, FATF, Commonwealth of Nations, GPFI, FinCoNet (Committee for Digital Economy), GFIN, FSB World Bank, IMF, BSCBS, Eurasian Economic Union, UNESCO [8].
4 Discussion Analysis of the activities of international organisations in the field of fintech allows us to note that international coordination in the field of financial and intellectual technology development is just beginning [36]. Preparations for the introduction and dissemination of blockchain technology in all industries are underway [12]. The international stage of standardisation of financial and intellectual technologies is inconsistent, systematic and fragmented, not all countries are involved or actively participating in the process, there is no agreement on mechanisms for intersectoral coordination of the development of fintech, artificial intelligence and neurotechnology [37]. International coordination mechanisms range from bilateral agreements and initiatives (e.g. Fintech Memoranda of Understanding) to multilateral ones coordinated by international bodies and standard setting [36]. Both the decentralised model of coordination, based on common national rules and criteria and recommendations for national decision-making, and the centralised collective model of coordination, where countries delegate certain powers to a supranational body - an international organisation. Global regulatory processes in these areas are complicated by infrastructure and market competition. The need to harmonise financial regulation raises issues of national economic security. Digitalisation, artificial intelligence, virtual reality and other technological changes have led to both new opportunities and increased uncertainty in the economy and in legislative power [38]. In the international financial sphere, a specific soft-law influence mechanism has emerged
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[39], which provides formally non-binding recommendations in audits and assessments of the implementation of standards carried out by these institutions, as well as reciprocal horizontal reviews; provides incentives to implement international financial standards in national legal systems; and identifies adverse consequences of non-implementation [40]. The rules contained in the recommendations of international intergovernmental and non-governmental institutions, which do not have the force of international law, have a significant potential to influence the legal systems of states and integration entities [41].
5 Conclusion Fintech, which embeds its algorithms in traditional sector processes, is growing with related fields of artificial intelligence, biotechnology and neurotechnology, expanding the range of digital financial services offered outside the interfaces of banks, pre-financing providers, insurance and investment. Given the managerial content of fintech, the study of its development involves supplementing traditional methodological approaches with comparative analysis using advanced analytical tools, an evolutionary approach and methods of process-oriented analysis. The assessment of trends in the development of financial technologies based on this methodology shows that the processes of convergence of financial, information and bio-neurotechnologies determine their overall vector and national models of fintech development. This is confirmed by the similarities in the processes and timing of events, the convergence of leaders, the dynamics and concentration of investments, most of which are made by transnational corporations, the geography of mergers and acquisitions, the diversification of product lines based on artificial intelligence and neuro- and biotechnologies [42]. Financial technology development processes are usually initiated at the level of transnational corporations or nation-states and their regional development clusters, which determine regional development trends at the level of the Asian region, the African region, the Middle East region, Europe and the US, or developed and developing countries. Despite regional affiliation, financial technology development processes are transnational. National models of financial technology development are characterised by both active creative approaches and adaptive strategies to change, and there are considerations that could lead to an increase in national security threats due to the rapid invasion of transnational corporations. There are countries that are initiating these processes and countries that are exploiting the potential of financial technologies. The US, China, the UK and Japan are pursuing a consistent policy of developing financial technologies: creating a resource base and infrastructure, forming and regulating intersectoral clusters, stimulating demand, scaling up, monitoring and tracking, internal initiative and active regulation, active participation in international organisations and harmonising standards. The rest of the countries, despite rapid diffusion, often bypassing the technological development stages (human resources training, R&D, etc.), simply take full advantage of the capacity of the implementation infrastructure (provision of Internet, smartphones, etc., which characterise fintech penetration) and attract either by acquiring ready-made
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technological solutions or by developing joint solutions with developers from other countries. The main directions are participation in interregional and intersectoral clusters of fintech development, active regulation at the national level, creating demand among the population by digitising banking and public services. Most countries are still using traditional sector-based management of fintech development, while the international regulatory model and the national models of leading financial technology countries are focused on process-based fintech regulation - primarily as technology, conductive infrastructure and scaling. The intersectoral nature of fintech development predetermines the transition to wealth-generating technologies using artificial intelligence, biotechnology and neurotechnology, the increasing influence of which exacerbates the problem of ensuring human rights and ethical issues [43]. For developing countries, there are risks: fintech is changing business processes and it is necessary to assess the benefits and threats of emerging technologies in terms of industry balance sheets and regional development [29]. The aim of the authors’ future research could be to propose a national model for the development of financial technologies and to choose strategic scenarios that meet the modern needs of society.
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Digitalization and Economic Development of Territories Sofia Popova(B) , Ekaterina Koroleva, and Marina Efremova Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. A new economic system, the digital one, is being actively formed at the present stage in the global information society. It implies the digitalization of economic processes and the penetration of innovative technologies into many spheres of human activity. As a result, such changes entail new issues for the competitive advantages of companies and concepts of their management and functioning. The study focuses on studying the relationship between digitalization factors and the economic development of Russian regions. Building on the regression models, we examine the influence of the digitalization of economic processes on gross regional product based on the dataset in the period from 2017 to 2019 years. The results revealed that the share of households with a personal computer, the share of the population that used the Internet to receive state and municipal services, the amount of information transmitted from/to subscribers of the reporting operator’s network have a positive effect on the economy of Russian regions. Nevertheless, we don’t reveal a link between the proportion of households with a telephone, the proportion of households with Internet access, the proportion of the population using the Internet to order goods or services and regional development. It can be explained by early stages of digitalization of economic processes in Russia. Thus, the results expand the existing literature in part of accessing the digitalization of economic processes and the positive economic growth of territories. Keywords: Economic development · Digitalization · Gross regional product · Regions · Regional innovation system
1 Introduction The digitalization of the economy is mainly the widespread application of innovative and digital technologies into the economy [1, 2]. Digital technologies (for example, machine learning, artificial intelligence, robotics et al.) are transforming the existing socio-economic relations [3, 4]. The main advantages of the application of innovative and digital technologies are reducing costs of sourcing, transportation, and production, releasing great potential for cost-effectiveness [5]. Nevertheless, it does not always lead to increasing the gross regional product due to the following. The new challenges for organizing socio-economic relations also require additional costs, connected with protecting innovative technologies, and ensuring the confidentiality and security of information [6, 7]. Thus, the link between digital factors and the economic development of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 320–330, 2023. https://doi.org/10.1007/978-3-031-32719-3_24
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the territories is not obvious. To the knowledge of authors, there are few studies [8–11], that focused on analyzing the influence of digital factors on the economic development of territories. Most of the research is descriptive. We fulfil the revealed research gap from positions of regression analysis. The link between digital factors and the economic development of territories is analyzed in the case of Russia. In 2017, the Russian Government approved the Program “Digital Economy of the Russian Federation”, the goals of which are the creation of an ecosystem of the digital economy. In 2018 it was reformatted into the National Program “Digital Economy of the Russian Federation”, which includes six main directions of development - regulation; information Infrastructure; personnel; information security; digital Management. According to the Digital McKinsey report of the global expert group on digital technologies, Russia in 2017 is in first place in Europe and sixth in the world in terms of the number of Internet users [12]. According to the data presented at the World Economic Forum, in terms of readiness for the digital economy, Russia ranks 41st, far behind countries such as Finland, Singapore, the United States, and others. In terms of the effectiveness of the use of digital technologies - 38th place, also lagging the leading countries [13]. The main components of the digital economy for Russia today are consumption/electronic commerce, investment in development, public administration, and export-import activities [14–19]. The above makes Russia an interesting case for study. In the study’s framework we test the following hypothesis: the conditions of digitalization have a positive effect on the economic development of Russian regions. Thus, the authors of the study are faced with the task of identifying a set of indicators that determine the level of digitalization of the region, presenting a test model, and using econometric analysis to identify indicators that contribute to the effective development of the region. The results confirmed the hypothesis of the study. Such factors as the share of households with a personal computer in the total number of households; the share of the population that used the Internet to receive state and municipal services in the total population; the amount of information transmitted from/to subscribers of the reporting operator’s network when accessing Internet networks, petabytes per person have a positive impact on the economy of Russian regions. The results contribute to the existing studies from the positions of evidence of the link between digital factors and the economic development of territories using quantitative methods. The paper is structured as follows. Section 2 presents the data and methodology. We analyze and discuss our results in Sect. 3. Finally, Sect. 4 includes the main conclusions.
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2 Methods The empirical basis of the study is official state statistics. The period of the study is identified based on the country’s features. Russia determined the path of digitalization, starting in 2017. Thus, the state statistics evaluating the information society was published beginning in 2017. It is also necessary to highlight the other limitation of the study - continuity and incompatibility of data, due to the emergence of new indicators of digitalization of regions [20]. Based on the limitations, the authors of the research developed a number of requirements for initial information. First, the indicator of digitalization should have dynamic from 2017 to 2019. Moreover, the indicator should contain numerical data for 95–100% of the studied regions of the country. Also, the indicator must be a relative value, that is, a numerical value that displays a comparison of two absolute values. Thus, the indicator of the level of digitalization of the regions was formed based on 6 main indicators of digitalization. The assessment of the impact of digitalization on the economic development of Russian regions is based on econometric methods and models. The study evaluated the influence of the selected group of indicators on the resulting factor. To conduct a multivariate regression analysis, the indicator of the gross regional product was determined as a dependent variable in the multivariate regression model, and a group of digitalization indicators acts as independent variables. Gross regional product (GRP) is an indicator of the economic activity of the region, characterizing the process of production of goods and services for final use [21]. Gross regional product is the gross value added created by the residents of the region and is also defined as the difference between output and intermediate consumption. It is calculated at current basic prices, which include the production prices of the industry, and the number of subsidies on products, but does not include taxes on products. STATA software will be used for econometric analysis and statistical data processing [22]. The descriptive statistics of the initial dataset are presented in Table 1.
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Table 1. The descriptive statistics of initial dataset Variable
Description
Obs
Mean
Std. Dev
Min
Max
GRP
Gross regional product (GRP) by constituent entities of the Russian Federation (gross value added in basic prices), in millions of rubles
255
1105535
2233247
44897.90
19673004.00
D_phone
Share of households with a cellular (mobile) phone in the total number of households, in %
255
66.08
15.31
2.29
D_computer
Share of 255 households with a personal computer in the total number of households, in %
69.79
8.56
39.40
96.50
D_internet
Share of 255 households with access to the Internet, in the total number of households, in %
75.89
6.87
2.10
98.10
D_state
The share of the 255 population that used the Internet to receive state and municipal services in the total population of the study region, in %
45.32
17.08
8.80
93.60
D_order
The share of the 255 population who used the Internet to order goods and (or) services in the total population of the study region, in %
31.14
11.12
7.50
80.10
100
(continued)
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S. Popova et al. Table 1. (continued)
Variable
Description
Obs
D_info
Share of 255 information transferred from or to subscribers of the reporting operator’s network when accessing the Internet (petabytes), in the total population of the study region, in %
Mean
Std. Dev
159.29
105.89
Min
Max 0.001
728.92
Completed by authors based on Rosstat, Sections “Information Society and Information”, “Communication Technologies” and EMISS [23]. The number of observations of this model (Obs) in the case under consideration is 255 since the sample of indicators is studied for the entire period from 2017 to 2019. High values of the standard deviation (Std. Dev.) and a significant spread of values between the maximum (Max) and minimum (Min) values of the GRP indicator indicate the heterogeneity of the sample. This fact is explained by the uneven development of regions and is typical for the country’s economy. Also, it is worth noting a significant excess of the values of the GRP indicator over the values of the independent variables. Therefore, the authors of the study decided to normalize the GRP indicator so that its values become comparable with the values of other indicators. To avoid the presence of multicollinear relationships between the variables in the model, the authors create a correlation matrix (Table 2). Table 2. Correlation matrix of the initial model GRP GRP
1.0000
D_phone
0.4651*
D_computer 0.3694*
D_phone
D_computer D_internet D_state
D_order D_info
1.0000 -0.3259* 1.0000
D_internet
0.2997*
0.0168
0.6307*
1.0000
D_state
0.3554* −0.0282
0.1774*
0.2725*
1.0000 (continued)
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Table 2. (continued) GRP
D_phone
D_computer D_internet D_state
D_order
0.4052* −0.1470* 0.5560*
0.4459*
D_info
0.3883* −0.4023* 0.3222*
0.1260*
D_order D_info
0.4942* 1.0000 −0.0080
0.2267* 1.0000
Completed by authors. Covariance Analysis: Ordinary. Sample: 2017–2019. Included observations: 225. Standard errors in parentheses: * p < 0.10.
Relationships were established between the dependent and independent variables, the level of which is determined as the average on the Chaddock scale [24]. There is no multicollinearity between the independent variables in the correlation matrix. Accordingly, it is possible to determine the initial view of the multivariate regression model. Initial regression equation is presented in formula 1: GRPn =f (a + b ∗ Dphone + c ∗ Dcompute + d ∗ Dinternet + e ∗ Dstate +g ∗ Dorder + h ∗ Dinfo)
(1)
In the framework of the study, many specifications of econometric models were created for analysed years of the study. As a result, the dependent variable was logarithmic. Moreover, we created four regression models - for each year separately and in aggregate for all analysed years. Also, we did a backward elimination for every equation by eliminating at each step the variable with the lowest p-value until all explanatory variables become statistically significant [25]. In Sect. 3, the results presented in the columns refer to the results of the backward elimination exercise. In all the estimations, we control for heteroscedasticity and report robust standard errors for each coefficient estimate.
3 Results and Discussion Table 3 presents the results of regression analysis. The presented table include the final models that include only statistically significant variables. Table 3. Final regression models, 2017–2019 Dependent variable
GRP2017
Constant
2.49
GRP2018 ***
(0.01) D_phone
–
D_computer
0.03
1.93
**
(0.03) – *
0.04
GRP2019
GRP2017–2019
1.62
2.37
(0.05)
(0.00)
– ***
0.05
***
– ***
0.03
*** (continued)
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S. Popova et al. Table 3. (continued)
Dependent variable
GRP2017
GRP2018
GRP2019
GRP2017–2019
D_internet
(0.06)
(0.01)
(0.00)
(0.00)
–
–
–
–
D_state
0.03
***
0.03
***
0.02
***
0.03
(0.00)
(0.00)
(0.01)
(0.00)
D_order
–
–
–
–
D_info
0.004
No. of obs
***
0.03
***
0.01
***
0.002
(0.00)
(0.00)
(0.00)
(0.00)
85
85
85
255
Prob > F
0.00
0.00
0.00
0.00
R–squared
0.43
0.43
0.35
0.36
Adj. R2
0.41
0.41
0.33
0.35
***
***
Completed by authors. Specifications marked with ‘b’ present the results after we ran the backward elimination (see Table 1 for a description of the explanatory variables). Robust standard errors are in parentheses. Statistical significance: ***p < 0.01, **p < 0.5, *p < 0.1.
It should be noted that throughout the entire period of the study, the indicators “the share of households with a personal computer in the total number of households”, “the share of the population using the Internet to receive state and municipal services in the total population of the study region” and “the share of information transferred from or to the subscribers of the reporting operator’s network when accessing the Internet (petabytes), to the total population” had a positive effect on the gross regional product. The presence of a positive relationship between these three indicators of digitalization and GRP indicates that the higher the level of digital indicators, the higher the economic development of the region. Namely, the presence of digital devices in every family increases the availability of digital services and digital literacy. The presence of the Internet allows population to unlimitedly extract any kind of information, develop and learn online. The value of the calculated coefficient of determination (R-squared) for the selected variables varies from 0.35 to 0.43, respectively. The dependent variable is 35–43% dependent on the variables included in the regression model. Over the past 5 years, the growth rate of digitalization has been growing rapidly. In accordance with this, the population of Russia has almost completely switched to the use of modern smartphones, that have access to the Internet, the government applications, applications for ordering various goods and services etc. In this regard, there is a growing trend to abandon the use of a home phone, as there are now quite a lot of cellular operators on the market that provide a different range of services on more favorable terms. The transition to digital technologies has made it possible to provide communication not only
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from different parts of the country but also from different parts of the world. Therefore, the use of smartphones can be described as “essential goods for communication with the outside world”. In the modern world, it is difficult to find a person who does not use communication services, so this can be described as a common occurrence. Demand for communications and phones will be regardless of their prices, so we can say that therefore this indicator does not have any impact on the economy of Russian regions. The next indicator is the proportion of households with a personal computer. In our study, the indicator was significant. In the context of digitalization, it is difficult to imagine production without the use of computer technology. To provide employees with computers, an organization must incur large costs, ensure information security and data confidentiality. It is at the last point that not all companies focus on what causes information leakage. However, computer technology greatly simplifies the work, but it is still necessary to stay in the trend of technology, organize refresher courses for staff to master new software technologies, increase the number of jobs, and so on. Thanks to this, the quality of products and the quality of services is growing. All this brings a certain income to the regional budget. In addition, almost every family in Russia has at least one computer. There are several main reasons for buying it - for education, for home use, for Internet access, and so on. The number of households using computers is growing every year. In our study, the indicator “the share of households with access to the Internet” was also insignificant in each of the periods we considered. Among all European countries, Russia ranks 1st in terms of the number of Internet users. There are a number of localities in the country where there is still no access to the Internet. Severe weather conditions or living in sparsely populated areas makes it difficult to access the Internet, but every year Russia is trying to fix this problem. Access to the Internet, like the use of cellular communications, is an integral part of people’s lives. Therefore, access to the Internet is always in demand, regardless of the circumstances. The digitalization of public services has made it possible to transfer some services to electronic form. The effect of the digitalization of services is identified with an increase in their quality and a decrease in costs. Digitalization allows to gradually move away from paper documents to records in modern databases. Such a transition, of course, brings problems, the most important of which is data confidentiality. The Russian government is working on the development and use of data protection technologies. The digitalization of public and municipal services allows streamlining and integrating work and production processes, managing data and information more efficiently, and allowing online public services to be delivered more efficiently. The digitalization of this sector has a positive impact on the economy of the regions. The next indicator is the share of the population who used the Internet to order goods or services. This indicator turned out to be insignificant in each of the three considered periods. This can be explained by the fact that the trend towards the use of online orders and services began to increase during the pandemic in 2020. The last indicator is the amount of information transferred from/to subscribers of the reporting operator’s network when accessing the Internet, petabytes per person. This indicator was significant in each of the three periods we considered. Every year this figure is only
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growing. This suggests that more and more information is transmitted by the population via the Internet. This indicates an increase in the level of digitalization in the regions of Russia, which has a positive effect on the economy of the regions.
4 Conclusions The formation and development of the digital economy is one of the priority areas for Russia. The digital economy is considered a new basis for the development of various spheres of society, including not only everyday life but also public administration, so there is a need for a systematic and in-depth understanding of this phenomenon. From the point of view of economic theory, the digital economy is a system of economic relations that includes data in digital form, which is a key factor in economic development. This can include the “data economy” and the “digital platform economy”, in which the use of modern digital technologies for the creation, transmission, storage, protection of data in their analysis and use for decision-making is of paramount importance. From the point of view of economic theory, the digital economy is a system of economic relations that includes data in digital form, which is a key factor in economic development. This can include the “data economy” and the “digital platform economy”, in which the use of modern digital technologies for the creation, transmission, storage, protection of data in their analysis and use for decision-making is of paramount importance. We can say that this is not a separate industry, but a way of life, a new basis for the economic development of mankind. The center of the digital economy is the sector to produce digital goods and the provision of services, which are primarily related to digital technologies. Thus, the further development of society is impossible without the formation of a digital economy and the use of innovative technologies. Digitalization is a key challenge necessary for the development of the economy in Russia. This process allows for an increase in the competitive advantages of regions. This article assessed the relationship between the conditions for digitalization and the economic development of regions. The results of the research highlight the positive influence of digital factors on the economic development of regions in Russia. It was revealed that the share of households with a personal computer in the total number of households, the share of the population that used the Internet to receive state and municipal services in the total population, the amount of information transmitted from/to subscribers of the reporting operator’s network when accessing Internet networks, petabytes per person have a positive impact on the economy of Russian regions. While indicators such as the proportion of households with a telephone, the proportion of households with Internet access, and the proportion of the population using the Internet to order goods or services (all indicators are calculated in the total number of households) do not impact the economy of Russian regions. To the knowledge of the authors, it is one of the first research, focused on the link between the indicated factors from positions of the quantitative research methods. The results will be useful for state and regional authorities in the decision-making process. The study has a number of restrictions. Our dataset was based only on Russian data. Therefore, our results cannot be generalized to other countries. Still, future similar studies in other countries could shed some light on their potential impact on the reported
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associations. Also, our dataset is restricted by a rather short time period. It relates to the country’s features. Russia officially focuses on digital transformation beginning in 2017. The analysis of longer periods of time can add to our research. The input information is limited by data from official statistics. Therefore, our study can be expanded by the analysis of other factors, indicating the digital transformation of the economy. Despite the above-mentioned limitations, and due to the increasing role of digitalization, our results are beneficial to regulators, state, and municipal authorities. Acknowledgements. This research was funded by the Russian Science Foundation. Project No. 20-78-10123.
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Managing Efficiency of Innovative Activity of Industrial Enterprises Within Digital Economy Ekaterina Burova1(B) , Svetlana Suloeva1 , and Sergei Grishunin2 1 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected] 2 National Research University Higher School of Economics, Moscow, Russia
Abstract. The paper aims to develop a system-synergetic approach to managing efficiency of innovative activity of industrial enterprises. The relevance of the research study is determined by the fact that effective functioning of an industrial enterprise within digitalization of the Russian economy is closely related to introducing innovations based on the achievements of scientific and technological progress and market-based management. Thus, managing the efficiency of innovative activity of enterprises is particularly prominent in the modern economic paradigm. The authors propose to use a system-synergetic approach in managing the efficiency of innovative activity, which will allow considering the peculiarities of a contemporary industrial enterprise as an open system operating under uncertainty. The theoretical and methodological framework for the research is based on the works of foreign and Russian researchers in the field of enterprise performance management and the existing methods for evaluating indicators of enterprise performance. When developing a model for managing the efficiency of enterprises’ innovative activity, the following tools have beten used: (1) comprehensive assessment of efficiency; (2) a factorial analysis; (3) mathematical modeling techniques. The results include: (1) systemizing the efficiency factors of enterprises’ innovative activity by stages of the life cycle of innovative products with allowance for external and internal factors; (2) the model how to manage the efficiency of enterprises’ innovative activity based on a system-synergetic approach. In comparison with the existing approaches, the approach proposed in the study is based on a multi-criteria estimation of the efficiency of the innovative activity allowing for the risk of efficiency factors. These advantages make innovation performance management a dynamic and iterative process that responds to changes in the external and internal environment of an enterprise. Keywords: assessment of efficiency · innovative activity · industrial enterprise · efficiency factors · digitalization of the economy
1 Introduction In the era of the digital economy development, one of the key factors to increase the growth rate is the effective innovative development of industrial enterprises [1]. However, unpredictability and instability of the external environment caused by the transformation © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 331–348, 2023. https://doi.org/10.1007/978-3-031-32719-3_25
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of the economic system, global competition, changes in consumer behavior, and the constant rise in prices for basic resources directly affect sustainable and successful development [2]. Such conditions make enterprises pay more attention to search and define the new concepts of innovation management that allow effectively adapting to external changes [3]. In 2011, the Government of the Russian Federation approved the Strategy for the Innovative Development of the Russian Federation for the period up to 2020 Analytical Center for the Government of the Russian Federation. However, according to Rosstat1, most of the program’s targets have not been achieved and some of them have not even been developed. Based on the research conducted by the National Research University Higher School of Economics, the main factors that prevent the effective innovation activity are external factors (Table 1) [4]. In addition, the study [5] argues that the key factors for effective operation of enterprises are internal factors. This is confirmed by the research conducted by the consulting firm McKinsey: 85% of the quantitative parameters that affect the efficiency of the development of global companies are internal and they are under the control of management, and only 15% are external factors and they are beyond its control2. Thus, the enterprise’s dependence on the unstable external environment has dramatically increased, and its competitiveness is still provided by the internal environment. Taking into consideration the fact that the innovation activity is both a priority direction of any national economy and the main development driver of a contemporary enterprise, managing efficiency of the innovation activity has become a significant factor of competitiveness at any industrial enterprise. Many scientific works focus on the issues how to manage efficiency of the innovative development of industrial enterprises. All studies can be divided into the following four groups: (1) studies considering indicators that measure the resources and results of companies’ innovative activity [6]; 2) studies describing the indicators of the innovative activity and the methodology for its measurement [7]; (3) studies aimed at the construction of multidimensional indicators to estimate efficiency of innovations [8]; (4) studies aimed at identifying factors that affect efficiency of the innovation activity [9–11]. In the classical approach to estimating the efficiency of enterprises’ innovative activity, the usage of financial indicators is mainly considered [12]. The classical approach assumes that the innovation activity is mostly assessed from the point of investment effectiveness in innovation projects [13]. The objective of the study is to develop a system-synergetic approach to managing the efficiency of the innovative activity at industrial enterprises. The proposed approach considers an enterprise as an integral open system that is in the constant interaction with the external environment.
2 Methods The theoretical and methodological framework of the research is based on the works of foreign and Russian researchers in the field of enterprise performance management, existing methods for assessing the performance indices of an enterprise. The paper
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Table 1. Assessment of Factors that Prevent the Innovative Activity: 2017–2019. Factors
Organizations that have assessed the importance of the factor preventing innovation activity, % Major and decisive
Significant
Unavailable factor
Factor missing
General economic factor Lack of one’s own funds
10.1
16.5
10.9
33.1
Lack of the state financial support
7.0
14.4
11.0
36.6
Lack of loans and direct investment
3.8
10.6
13.6
40.2
Low demand for innovative goods, works, services
4.5
9.8
13.6
38.9
The high cost of innovation
8.3
15.9
8.2
35.3
The high economic risk
6.5
14.5
9.6
35.5
Severe competition in the market
4.9
13.3
12.3
36.9
Low innovative potential of an organization
4.9
9.8
14.5
37.5
Lac of qualified personnel
3.8
10.2
16.5
39.2
Lack of information regarding new technologies
2.3
7.7
18.0
39.9
Lack of information regarding markets
2.2
7.4
17.0
41.3
Underdevelopment of cooperation ties
1.9
6.2
15.6
41.2
Discrepancy of an organization’s priority
4.1
6.2
13.8
41.0
Internal factors
Other factors (continued)
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Factors
Lack of legislative and regulatory documents that regulate and stimulate innovative activity, imperfections, rules, standards on advanced manufacturing technologies
Organizations that have assessed the importance of the factor preventing innovation activity, % Major and decisive
Significant
Unavailable factor
Factor missing
2.4
8.1
14.2
38.0
Source: [14]
proposes a system-synergetic approach to managing efficiency of the innovative activity of enterprises based on the analysis and systematization of the existing approaches to performance management. When conducting the research, systematic and analytical approaches, the comparative analysis, the method of expert assessments, and the group expert assessment have been used. The peculiarity of the developed approach is to manage efficiency of the innovative activity of enterprises taking into consideration the risks at the level of efficiency factors and in the use of a multi-parameter composite index to estimate the efficiency of enterprises’ innovative activity. The index has been developed based on the methods of the Russian Union of Industrialists and Entrepreneurs and the United Nations. Contemporary approaches to assessing efficiency of the innovation activity consider several indicators of an innovative product (IP): they reflect various aspects of enterprises’ innovative activity and take into account the relationships between indicators, innovation activity and strategic objectives of an enterprise [5, 15–20]. Such an approach has become the basis for construction of integral and composite indices to estimate the efficiency of the innovative activity of enterprises. Based on the multi-criteria approach, OECD and Eurostat developed the Oslo Manual (2018): the methodological guidelines to evaluate innovative activity. The authors of this methodology share the view that evaluating efficiency of the innovative activity should be based on the study of factors affecting efficiency. The necessity to consider and analyze the efficiency factors of enterprises’ innovative activity is emphasized in the following works [21, 22]. However, the works have not covered the issue of the interaction between the factors and the influence of this interaction on the efficiency of the innovative activity of enterprises. In addition, the methods, and indices to estimate the efficiency of the innovative activity of enterprises proposed in the scientific literature do not take into consideration the change of efficient factors under the influence of the external and internal environment.
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Despite the high relevance of the topic, there is no consistent approach to the systematic assessment and management of the efficiency of enterprises’ innovative activity that considers the interaction and constant changes of the internal and external factors [13, 23, 24].
3 Results The proposed system-synergetic approach to managing efficiency of the innovative activity at enterprises includes the following main steps (Fig. 1): The input data of the model are the strategic objectives of enterprise development that are projected on the innovative activity. The strategic objective of the innovation activity can be expressed by the target level of the performance index of the innovation activity. To construct the composite performance index (CPI) of the innovative activity of enterprises, it makes sense to use the ideas described in the Methodology for evaluating the corporate social responsibility of enterprises developed by the Russian Union of Industrial Entrepreneurs in 2014, when adjusted for contemporary conditions and peculiarities of enterprises’ innovative activity as well as the ideas that are the basis for ESG performance indicator proposed by the UN in 2015. In accordance with this methodology, key ESG performance indicators are calculated: environmental (ecological concern), social (social policy) and governance (the governance of an organization)1. Since the innovation activity is primarily associated with achievements and development of scientific and technological progress, which is of particular importance within the digital economy, it is necessary to take into consideration the scientific and technical (technological) component when constructing the CPI of enterprises’ innovative activity [25]. Then, the CPI of enterprises’ innovative activity can be represented as. CPI = f (EPI , SPI , GPI , TPI )
(1)
where EPI is the environmental performance index of the innovative activity at enterprises; SPI is the social performance index of the innovative activity at enterprises; GPI is the economic performance index of the innovative activity at enterprises; TPI is the technical-scientific performance index of the innovative activity at enterprises. Each performance index depends on various factors: PI = f (x1 , x2 , . . . , xn )
(2)
where xn is a factor of the environmental performance, n is several factors of the environmental performance. SPI = f (y1 , y2 , . . . , ym )
(3)
where ym is factors of the social performance, m is several factors of the social performance. GPI = f (z1 , z2 , . . . , zk )
(4)
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Fig. 1. The model of managing efficiency of the innovative activity at enterprises based on a system-synergetic approach.
where zk is factors of the economic performance, k is several factors of the economic performance. TPI = f g1 , g2 , . . . , gp (5) where gp is factors of the science and technology performance, p is several factors of the science and technology performance. The following methods are necessary to be used to determine the target level of the CPI of enterprises’ innovative activity: the group assessment method, the rating scale method, the calculation and analytical method, the methods of mathematical modeling. To implement the subsequent stages of the model, it is necessary to identify and evaluate the factors that affect efficiency of enterprises’ innovative activity. In scientific studies, the authors mostly define the external and internal efficiency factors of enterprises’ innovative activity to obtain information deals with the opportunity of the company to affect these factors [26]. Some scientists believe that it makes more sense to consider the efficiency factors of enterprises’ innovative activity by the stages of the IP life cycle of an investment project [27], since objectives, costs and results at each stage are different. The research proposes classification of the efficiency factors by stages of the IP life cycle with external and internal factors separated. In the era of digitalization, information resources and technologies are becoming important; therefore, they
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are separated into a special group in the proposed classification. The indicators of the quantitative assessment of the external and internal factors are presented (Table 2). Table 2. Table captions should be placed above the tables. Efficiency factors (external)
Indices of external resources
Efficiency factors (internal)
Efficiency indices
The preparatory stage: marketing research (MR) Labor market: labor market quality, access and cost
Labor force: quantity of economically active population in the region; accrued average monthly nominal wage of employees in marketing; the unemployment rate in marketing; the proportion of the population with specialized education in marketing
presence and quality of personnel employed in MR (education, experience skills in conducting research and processing results)
the proportion of employees engaged in MR in the total personnel; the proportion of employees with higher education in marketing in the total personnel engaged in conducting MR; the share of costs for the personnel involved in conducting MR in the total amount of costs for the personnel; the share of completed MR in the total amount of studies for the particular period; the wage index for employees engaged in MR
Information and communications technology market (ICT market):
Information resources and technologies (IRT):
IRT access and cost
the ICT development index; the quality of legal regulation of the ICT infrastructure in the region; the costs of the ICT use
IR quality; IT presence and quality; providing information security
the attitude of the state to conducting MR in a particular market; the availability of support programs and grants in MR
customer relationships
State and society: state policy in marketing research
the share of costs for IR in the total costs for conducting MR; the dynamics of attracting digital IRT for conducting MR; the share of costs for providing information security to carry out MR in the total costs for conducting MR
Relationships with contractors
Market of IP:
Financial resources:
the consumer market (type: indicators for market B2C, B2G, B2B and scale); assessment: market growth competition (type, intensity) rate, market opportunities; indicators for assessing market competition: the Herfindahl index; the variation coefficient of market shares of competitors
the level and sources of financing MR
the proportion of consumers participating in MR in the total number of potential consumers of IP
the share of internal funds in financing MR; the share of costs for conducting MR (in the overall cost structure)
(continued)
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E. Burova et al. Table 2. (continued)
Efficiency factors (external)
Indices of external resources
Efficiency factors (internal)
Efficiency indices
The first stage: basic research (BR) Labor market: labor market quality, access and cost
Labor force: quantity of economically active population in the region; the unemployment rate among R&D specialists; the wage of R&D specialists; the percentage of the population with higher education and an academic degree
presence and quality of scientific staff (education, experience, skills in processing Big Data)
the proportion of employees engaged in BR in the total personnel; the average experience of employees involved in BR; the scientific performance ratio is calculated on the basis of three indicators: originality of the obtained results, depth of scientific study, probability of success; the share of promising innovative ideas in the total number of BR; the dynamics of publication activity; the wage index for employees involved in BR
Information and communications technology market (ICT market):
Information resources and technologies (IRT):
IRT access and cost; pace and level of scientific and; technological progress (STP)
the ICT Development Index; the quality of legal regulation of ICT infrastructure in BR; the costs of ICT use
IR quality; IT access and quality; access to information security technologies
attitude of the state to conducting BR (restrictions or support); a number of support programs and grants for BR; a number of accredited quality standards in BR
interaction with universities and research centers
State and society: state policy in conducting BR; quality standards in BR;
the share of costs for information resources in the total costs for conducting BR; the dynamics of attracting digital resources and technologies to conduct BR; the share of the costs for ensuring IC when conducting BR in the total costs of conducting BR; reduction of terms for carrying out BR; the proportion of promising completed BR in the costs for information resources
Relationships with contractors: the growth of common projects with universities and research centers in the total amount of BR; the proportion of promising innovative ideas obtained as a result of interaction with universities and research centers in the total amount of BR; the share of costs for interaction with universities and research centers in the total costs for conducting BR
(continued)
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Table 2. (continued) Efficiency factors (external)
Indices of external resources
Market of financial resources: programs and conditions for financing BR; credit and financial legislation
Efficiency factors (internal)
Efficiency indices
Financial resources: the dynamics of financing the level and sources of BR by financial institutions financing BR in the region; the share of the costs for financing BR in budgets of different levels
the share of internal funds in financing basic market research; the share of costs for conducting BR (in the overall cost structure)
The second stage: applied research (AR) Labor market: labor market quality, access and cost
Labor force: quantity of economically active population in the region; the unemployment rate among specialists involved in R&D; the wage of employees of R&D organizations; the percentage of the population with higher education and an academic degree
presence and quality of personnel (ability to use modern technologies, including digital ones)
the proportion of employees engaged in AR in the total personnel; the average work experience of employees involved in conducting AR; the coefficient of the sci-tech effectiveness of AR is calculated on the basis of four indicators: application perceptiveness, large-scale implementation, final results, environmental friendliness; the proportion of promising studies in the total number of AR; the dynamics of publication activity; the share of received patents in the total number of conducted R&D; a number of patents per employee; the research intensity of AR; the wage index for employees engaged in AR
Information and communications technology market (ICT market):
Information resources and technologies (IRT):
IRT access and cost; pace and level of scientific and technological progress (STP)
IR quality; IT access; access to information security technologies
the ICT Development Index; the quality of legal; regulation of ICT infrastructure in AR; the costs of the ICT use to conduct AR
the share of costs for information resources in the total costs for conducting AR; the dynamics of attracting digital resources and technologies to conduct AR; the share of the costs for providing information security when conducting AR in the total costs of conducting AR; reduction of terms for carrying out AR; the proportion of promising completed AR in the costs for information resources
(continued)
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E. Burova et al. Table 2. (continued)
Efficiency factors (external)
Indices of external resources
Efficiency factors (internal)
attitude of the state to conducting AR (restrictions or support); a number of support programs, grants for AR; accredited standards of quality, environmental friendliness and safety during conducting AR; the quality of legal regulation of intellectual property policy
interaction with universities, research centres
the dynamics of financing AR by financial institutions in the region; the share of costs for financing AR in the budgets of different levels
the level and sources of financing AR
State and society: state policy in conducting AR; standards of quality, environmental friendliness and safety in AR; Intellectual Property Policy
Relationships with contractors:
Market of financial resources: programs and conditions for financing AR; credit and financial legislation
Efficiency indices
the growth of common projects with universities and research centres in the total amount of AR; the proportion of promising applied developments obtained as a result of interaction with universities, research centres and consumers in the total amount of AR; the share of costs for interaction with universities, research centres and consumers in the total costs for conducting AR
Financial resources: the proportion of internal funds financing applied market research; the share of costs for conducting AR (in the overall cost structure)
The third stage: research and development (R&D) - manufacturing and testing of an IP sample Labor market: labor market quality, access and cost; patent purity (whether the patent right of any of the patent holders is infringed); innovation competitiveness in the market; impact on other innovation
Labor force: quantity of economically active population in the region; the unemployment rate among engineering and technical personnel; the wage of engineering and technical personnel; the percentage of the population with higher technical education
the quality of engineering and technical personnel (education, experience, knowledge)
the proportion of employees engaged in R&D in the total personnel; the average work experience of engineering and technical personnel involved in R&D; the coefficient of scientific and technical performance of R&D is calculated on the basis of four indicators: application perceptiveness, large-scale implementation, final results, environmental friendliness; a number of registered copyright certificates by the developer of innovations in relation to the created prototypes; a number of IP patents per one employee; a number of IP prototypes created for each employee engaged in R&D [27]; the wage index for employees engaged in R&Dof IP
(continued)
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Table 2. (continued) Efficiency factors (external)
Indices of external resources
Efficiency factors (internal)
Efficiency indices
Information and communications technology market (ICT market):
Information resources and technologies (IRT):
IRT access and cost
IR the quality; IT access; providing information security technologies
the ICT Development Index; the quality of legal regulation of ICT infrastructure in R&D; the costs of ICT use for conducting R&D
the share of costs for information resources in the total cost for conducting R&D; the dynamics of attracting digital resources and technologies to conduct R&D; the share of the costs for providing information security when conducting R&D in the total cost for conducting R&D; the percentage of labor cost savings
Market of material resources and fixed assets:
Material resources and fixed assets:
presence, quality and cost of the level of price for raw and material resources and fixed other materials, fixed assets; assets transport conditions; the dynamics of developing the raw material market, the market of fixed assets; the range of supply of raw and other materials; access to raw and other materials, and fixed assets; the type of raw material market; access to strategic resources
presence and quality of required technologies and equipment; presence and quality of material resources
Market of IP: the market of potential consumers (possible limitations and specific needs); competition
the share of material costs in total R&D costs; the share of equipment costs in total R&D costs; the share of new equipment required for R&D; the reliability factor of new products/technologies; product/process specifications [27]; product uniqueness (lack of analogues); IP technological effectiveness
Financial resources: economic, environmental, climate and security restrictions; presence of similar products in the market
level and sources of financing R&D
the share of internal funds in financing of R&D of IP; the share of costs conducting R&D (in the overall cost structure); the share of the customer’s funds to conduct R&D
State and society state policy in conducting R&D; standards of quality, environmental friendliness and safety in R&D; Intellectual property Policy
a number of support programs, grants for R&D; presence of accredited standards of quality, environmental friendliness and safety during conducting R&D; the quality of legal regulation of intellectual property policy; the list of advanced and critical technologies
The fourth stage: IP production start-up
(continued)
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E. Burova et al. Table 2. (continued)
Efficiency factors (external)
Indices of external resources
Labor market: labor market quality, access and cost
Efficiency factors (internal)
Efficiency indices
Labor force: quantity of economically operational personnel active population in the quality (education, region; the unemployment experience, knowledge) rate of operational personnel; the wage of operational personnel; the percentage of the population with higher technical education
the proportion of employees engaged in IP production in the total personnel; the wage share of employees engaged in IP production in total income of all employees; the proportion of employees with higher education; the wage index for employees engaged in IP production
Information and communications technology market (ICT market):
Information resources and technologies (IRT):
IRT access and cost
presence and quality of information concerning the market of IP and resource providers; access to information security and cybersecurity technologies
the ICT Development Index; the quality of legal regulation of ICT infrastructure in market research; the costs of ICT use to conduct market research
the reliability of information concerning the consumer market and the market of resource suppliers; the level of time-sensitive information concerning the consumer market and the market of resource suppliers; the share of costs for information resources in the structure of IP costs; the share of costs for providing information security in producing IP (in the overall cost structure)
Market of material resources and fixed assets:
Material resources and fixed assets:
presence, cost and access to material resources presence, cost, and access to production technologies of IP
presence and quality of required technologies and equipment; presence and quality of required material resources; efficient use of production capacities; efficient use of operating resources
Market of financial resources:
prices for raw and other materials, fixed assets; transport conditions; the dynamics of developing the raw material market, the market of fixed assets; range of supply of raw and other materials; access to of raw and other materials, fixed assets; the type of raw material market; access to strategic resources
material consumption of IP; capital output ratio of IP; the share of costs for material resources in the structure of IP costs; the share of costs for fixed assets in the structure of IP costs; the share of newly acquired equipment required for IP production; the share of recycled materials used in IP production; the proportion of IP in the total product program (2); processability of IP; research intensity of IP
Financial resources:
(continued)
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Table 2. (continued) Efficiency factors (external)
Indices of external resources
Efficiency factors (internal)
Efficiency indices
programs and conditions for financing IP production
the dynamics of financing IP production by financial institutions in the region; conditions for financing IP production; credit-linked regulations; tax regulations
the level and sources of financing IP production
the proportion of internal funds financing IP production; the share of IP production costs (in the overall cost structure); cost structure for IP production
a number of support programs, grants for IP production; presence of accredited standards of quality, environmental friendliness and production safety; the quality of legal regulation of intellectual property policy; the list of advanced and critical technologies
interaction with resource providers
State and society: state policy in IP production; standards of quality, environmental friendliness and safety in IP production; Intellectual Property Policy
Relationships with contractors: a number of completed contracts to supply material resources; a number of completed contracts to provide fixed assets; the level of provision with material resources; a number of links in the supply chain of resources to produce products; the proportion of reliable suppliers in the supply chain of resources to produce products
Market of IP: the consumer market (type: B2C, B2G, B2B), scale, stability in demand, demand trends; competition (type, intensity)
indicators for market assessment: market growth rate, the scale of market opportunities; indicators for assessing market competition: the Herfindahl index; the variation coefficient of market shares of competitors
The fifth stage: IP introduction Labor market: labor market quality in marketing, access and cost
Labor force: quantity of economically active population in the region; the unemployment rate of marketing personnel; the wage of marketing personnel; the percentage of the population with higher economic education
Information and communications technology market (ICT market):
the quality of personnel involved in IP promotion (education, experience, knowledge)
the proportion of employees engaged in IP promotion in the total personnel; the wage share of employees engaged in IP promotion in the total income of all employees; the proportion of employees with higher education; the wage index for employees involved in IP promotion to the market
Information resources and technologies (IRT):
(continued)
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E. Burova et al. Table 2. (continued)
Efficiency factors (external)
Indices of external resources
Efficiency factors (internal)
Efficiency indices
IRT access and cost
the ICT Development Index in the region; the quality of legal regulation of ICT infrastructure in market research; the costs of ICT use to conduct the market research
presence of high-quality information concerning the market of IP: consumers, competitors, distribution networks; access to information security and cybersecurity technologies
the level of information confidence concerning the consumer market in the market of resource suppliers and in distribution networks; the level of time-sensitive information concerning the consumer market in the market of resource suppliers and in distribution networks; the share of costs for information resources in the structure of IP implementation; the share of the cost for providing information security while introducing IP to the market (in the overall cost for promoting IP)
a number of support programs, grants for introducing IP to the market; presence of accredited standards of quality, environmental friendliness and safety when introducing products to the market; the quality of legal regulation of intellectual property policy; the list of advanced and critical technologies; restrictions on entrance to the market imposed by the state
presence and quality of the required marketing technologies; presence and quality of the material resources that are required for IP promotion
the dynamics of financing IP promotion by financial institutions in the region; credit conditions for IP introduction; credit-linked regulations; tax regulations
the level and sources of financing IP introduction to the market
State and society: state policy in IP introduction to the market; standards of quality, environmental friendliness and safety in IP introduction to the market; intellectual property Policy
Material resources and capital assets:
Market of financial resources: programs and conditions for financing IP promotion
the share of material costs in the structure of costs for the promotion and implementation of IP; the share of costs for marketing technologies in the structure of costs for introducing IP
Financial resources: the proportion of internal funds financing IP introduction to the market; the share of IP promotion costs (in the overall cost structure); the cost structure for IP promotion
(continued)
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Table 2. (continued) Efficiency factors (external) Market of contractors: marketing technology access and cost; distribution channel access and cost
Market of IP: the market of IP (national, global); the consumer market (type: B2C, B2G, B2B), scale, stability in demand, demand trends; competition (type, intensity)
Indices of external resources
Efficiency factors (internal)
Efficiency indices
Relationships with contractors: the price for marketing interaction with sales technologies channels; development of the interaction with mass media marketing technology market the type of marketing technology market; transport conditions; the dynamics of development of the sales channel market; development and limitations of distribution networks; access to strategic distribution channels
a number of the completed contracts with distributors to supply IP; the proportion of reliable distributors in IP supply chain; the share of costs for IP distribution channels in the cost structure for IP; the share of costs for IP promotion in mass media in the cost structure for IP
Relationships with consumers of IP: indicators for market relationships with assessment: market growth consumers rate, the scale of market opportunities; indicators for assessing market competition: the Herfindahl index; the variation coefficient of market shares of competitors; stability of needs; oncentration of IP consumers; threats of new competitors; threats of the rapid emergence of analogues and / or alternative products
customer satisfaction; the share of sold IP in total sales;the share of proceeds from IP sales in the total revenue of the enterprise; IP sales profitability; the share of unsuccessful IP (2); reducing environmental burden when introducing IP to the market; changing the market share; a number of orders for IP supply
To implement a system-synergetic approach, assessment of external and internal factors should be carried out with allowance for their interaction and risks. It will allow quickly responding to changes in the external and internal environment of the enterprise and consider these changes when calculating performance indices and constructing the CPI of enterprises’ innovative activity. If the CPI, considering the impact of the external and internal factors, is less than the target level, some decisions to find possible ways to improve efficiency of enterprises’ innovative activity should be made. If the index is greater or equal to the target level, efficiency of the innovative activity of enterprises could be considered as efficient, and it corresponds to the strategic objective of the enterprise development.
4 Discussion The idea of using a multi-criteria approach to evaluate efficiency of enterprises’ innovative activity is not original. The works [5, 15–20] highlight the necessity for a comprehensive assessment of the innovative activity at enterprises, but the issues dealing with
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performance management are understudied. The proposed approach to managing efficiency of enterprises’ innovative activity allows using multi-criteria assessment based on such performance indicators as CSR and ESG. It also enables evaluating not only economic, but also social and environmental efficiency. The formulated proposal is to improve and expand the opportunities to use these indicators: (1) to manage effectiveness of the business-projection “Innovation”; (2) to supplement the components that allow evaluating the scientific and technological efficiency of the innovative activity at enterprises. The Oslo Manual (2018) provides the factorial approach to estimate efficiency. The necessity for research and analysis of the factors of enterprises’ innovative activity is described by some authors [21, 22]. There has been no detailed investigation on assessing the interaction between efficiency factors and the impact of this interaction on the efficiency of enterprises’ innovative activity. This paper proposes to complete the assessment of efficiency factors taking into account their interaction and risks that will make it possible to control and manage the target level of the efficiency of the innovative activity at industrial enterprises. The developed performance management approach has a number of advantages over the existing ones and it allows: (1) taking into account the particular characteristics of all stages of the IP life-cycle (resources, processes, results); (2) obtaining assessment of the efficiency factors taking into account their interaction and the risks at each stage of the IP life-cycle in order to identify inefficient processes and search for reserves to improve efficiency; (3) obtaining ecological assessment of efficiency of the innovation activity taking into account not only the economic aspect of efficiency, but environmental, social and scientific and technical (technological).
5 Conclusion The study proposes the systemic-synergetic approach to managing efficiency of enterprises’ innovative activity. It considers the enterprise as an integral open system that in conjunction with the external environment. The developed model of managing the efficiency of enterprises’ innovative activity allows: (1) obtaining a comprehensive assessment of efficiency, including environmental, social, economic, and scientific and technical (technological) component; (2) considering the impact of risks on efficiency factors. The proposed classification of efficiency factors by stages of the IP life cycle makes it possible: to consider the particular characteristics of each stage of the IP life-cycle by elements: resources, processes, results; to investigate the influence of external and internal factors, including the risks of IP. It should be noted that achievement of the target level of the efficiency of enterprises’ innovative activity contributes to improving the enterprise efficiency and achieving strategic objectives. Further research might explore the following: (1) development and improvement of the proposed approach to managing the efficiency of the innovative activity at enterprises; (2) development of methods to calculate the CPI of enterprises’ innovative activity; (3) development of methods for partial performance indicators in the areas of efficiency and by stages of the IP life cycle; (4) development of assessment tools and risk management procedures at the level of efficiency factors.
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Acknowledgments. The research was financed as part of the project "Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization" (FSEG-2023–0008).
References 1. Silvestre, B., Mihaela, D.: Innovations for sustainable development: moving toward a sustainable future. J. Clean. Prod. 208, 325–332 (2019) 2. Koshelev, E., Dimopoulos, T., Mazzucchelli, E.S.: Development of innovative industrial cluster strategy using compound real options. Sustain. Dev. Eng. Econ. 2(5), 80–97 (2021) 3. Goel, R.K., Nelson, M.A.: How do firms use innovations to hedge against economic and political uncertainty? Evidence from a large sample of nations. J. Technol. Transf. 46(2), 407–430 (2020). https://doi.org/10.1007/s10961-019-09773-6 4. Gokhberg, L., et al.: The Indicators OF Innovation Activities: 2021: statistics digest. National research University “Higher School of Economics” (2022) 5. Yu, L., Duan, Y., Fan, T.: Innovation performance of new products in China’s high-technology industry. Int. J. Prod. Econ. 219, 204–215 (2020) 6. Potters, L.: Innovation input and innovation output: differences among sectors. In: JRC Working Papers on Corporate R&D and Innovation 2009–10. https://www.econstor.eu/bitstream/ 10419/202111/1/jrc-wp200910.pdf Accessed 07 Sept 2021 7. Langdon, M.: An InnovationLabs White Paper Innovation Labs LLC November (2008). https://innovationmanagement.se/wp-content/uploads/2012/12/Measuring_Inn ovation.pdf. Accessed 07 Sept 2021 8. Trachuk, A., Linder, N.: Innovative activity of industrial enterprises: measurement and effectiveness evaluation. Strateg. Decisions Risk Manage. 10(2), 108–121 (2019) 9. Mardani, A., Nikoosokhan, S., Moradi, M., Doustar, M.: The relationship between knowledge management and innovation performance. J. High Technol. Manage. Res. 29(1), 12–26 (2018) 10. Wang, C., Hu, Q.: Knowledge sharing in supply chain networks: effects of collaborative innovation activities and capability on innovation performance. Technovation 94, 10–20 (2020) 11. Wu, S., Feng-Jyh, L., Chyuan, P.: The affecting factors of small and medium enterprise performance. J. Bus. Res. 143, 94–104 (2022) 12. Žižlavský, O.: Net present value approach: method for economic assessment of innovation projects. Procedia Soc. Behav. Sci. 15, 506–512 (2014) 13. Gonin, V., Kashurnikov, A.: Integrated approach to assessing the efficiency of innovative activity in power industry enterprises. π-Economy 3(221), 124–137 (2015) 14. HSE University, indicators of innovation activity: 2021, https://www.hse.ru/primarydata/ ii2021, last accessed 2021/05/03 15. Kaplan, R., Norton, P.: The Balanced Scorecard: Translating Strategy Into Action, 1st edn. Harvard Business School Press, Boston (1996) 16. Gadzhiev, M., Yakovleva, E.: Analysis of economic efficiency of enterprise. Innovations 2, 122–126 (2010) 17. Aleksandrova, T., Zhukovskaya, S.: The Development of a Methodology for Multi-Criteria Assessment of the Effectiveness of Innovative Projects. Economy 44, 233–246 (2018). Vestnik of Tomsk State University 18. Ashin, S., Tukkel’, I., Koshelev, E., Makarov, S.: Assessment of the Effectiveness of Innovation Activity, 1st edn. Izd-vo Nizhegorodskogo gosudarstvennogo universiteta, Nizhnij Novgorod (2018)
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19. Garcia-Bernabeu, A., Cabello, J., Ruiz, F.: A multi-criteria reference point-based approach for assessing regional innovation performance in Spain. Mathematics 8(5), 797 (2020) 20. Morkovkin, D., Lopatkin, D., Sadriddinov, M., Shushunova, T., Gibadullin, A., Golikova, O.: Assessment of innovation activity in the countries of the world. In: E3S Web of Conferences, vol. 157, pp. 1689–169 (2012) 21. Asiedu, M., Anyigba, H., Ofori, K.S., Ampong, G., Addae, J.: Factors influencing innovation performance in higher education institutions. Learn. Organ. 27(4), 365–378 (2020) 22. Prio, U., Kurniasari, F.: Investigating factors impelling the innovation performance. a perspective from internal corporate new business venturing on manufacturing industry. In: Conference Series vol. 3, no. 1, pp. 695–705 (2021) 23. Le, H.: Literature review on diversification strategy, enterprise core competence and enterprise performance. Am. J. Ind. Bus. Manage. 9, 91–108 (2019) 24. Yuryeva, A., Kuternin, M., Gibadullin, A.: Formation of mechanisms for the development of innovative activity in the industrial production of the Russian Federation. J. Phys: Conf. Ser. 1399(3), 033099 (2019) 25. Imaykina, O.I.: Criteria and performance indicators for managing the innovative activity of an enterprise. π-Economy, 4(151) (2012) . https://cyberleninka.ru/article/n/kriterii-i-pokaza teli-effektivnosti-upravleniya-innovatsionnoy-deyatelnostyu-predpriyatiya. Accessed 02 Mar 2022(in rus) 26. Rizal, O., Suhadak, M., Kholid, M.: Analysis of the influence of external and internal environmental factors on business performance: a study on micro small and medium enterprises (MSMES) of food and beverage. Russ. J. Agric. Socio-Econ. Sci. 66(6), 47–56 (2017) 27. Boyko, V., Falko, S.: Methods for measuring the effects of innovation activity by stages of the life cycle of innovation. Probl. Innovative Econ. 3, 1101–1110 (2020)
Forming a Methodology for Organizing Investment and Financial Reporting in the Activities of Subsidiaries in the Organization of Antitrust Compliance in Uzbekistan Mansur P. Eshov
and Dilafruz S. Nasirkhodjaeva(B)
Tashkent State University of Economics, Islam Karimov Street 49, Tashkent 100066, Uzbekistan [email protected]
Abstract. Currently, to create a national model of the economy in accordance with international standards, special attention is paid to scientific research on the approximation of national accounting standards to international standards, a radical change in the methodological foundations of classification, recognition, evaluation of elements of financial statements, and their reflection in reporting. In accordance with international standards, assets are critical elements of financial reporting. Under assets we understand economic resources, the use of which should bring income and profits. Financial assets are a crucial component of assets being part of the company’s financial instruments. Adherence to such rules means that the activities of the organization must comply with the legislation as well as any internal or external standards. Failure to comply with these requirements may harm the company and its customers. There are laws that apply to business and protect it, as well as any of its stakeholders: customers, partners, employees, and the public. Some rules apply to businesses, others to consumers. There is a corresponding compatibility for both. In this situation there is a strong need for a methodology that would regulate the investment and financial reporting in the organization of compliance activities that serve to coordinate the monopolistic behavior of the enterprise. Such may occur under the influence of market factors. Keywords: Financial Statement · Financial Sustainability · Liquidity · Solvency · Financial Ratios
1 Introduction If you run a business, your company and employees adhere to it. Here we speak about the ability to follow the rules, or, in other words, compliance. In a broader sense, it is the need for a company to act following an order, law, set of rules or regulations. Often, a dedicated compliance service provides control over compliance with all these requirements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 349–359, 2023. https://doi.org/10.1007/978-3-031-32719-3_26
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It is one of the characteristics of the modern economy to form complex business structures and groups of interrelated organizations that have legal independence but are a single economic organism. The most important source of information about the financial condition, potential, role in the economy, and the development prospects of such structures is the consolidated financial statements – the financial statements of groups of companies, in which the assets, liabilities, capital, income, expenses and cash flows of the parent company and enterprises located in the control of the parent company are presented as assets, liabilities, equity, income, expenses and cash flows of a single economic entity. As a form of presentation of information about a group of companies, consolidated reporting, performs a whole range of functions: it allows external users to form a complete picture of the financial position and performance of companies. But no less important is the consolidation of IFRS (International Financial Reporting Standards) statements for the companies themselves: its availability is a prerequisite for an IPO (the first public sale of shares); trust from parties of the states where the company operates, investors, the professional community, and the public. The formation of financial statements has historically been ex-post facto. Taking into account the fact that the activities of the Group of Companies are carried out on a continuously, which means that every moment there is a bipolar change in the financial and economic position of the Group’s enterprises, and also in the course of economic activity the structure of the Group may change (i.e., by way of mergers and acquisitions, by means of investments or vice versa by the sale of any assets of the Group, and so on) there are various risks, the minimization of which can be achieved by optimizing the accounting and reporting system according to the following parameters: – automation of the accounting process, – standardization of accounting principles for all group members, – reduction of reporting time in the general accounting system (fast close). The more branched the organizational structure of a group of companies, covering various sectors of the economy and geographically remote business units, the more pressing is the need to create, first, a unified accounting and reporting system. The complexity of the processes of formation of Groups of Companies may cause a situation in which the accounting and reporting system in the Group has the following structure: – Group members independently organize the system of accounting and reporting, form accounting policies, and develop reporting forms; – registration of participants is automated in local versions of various information systems; – various types of group reporting are formed from disparate data sources: primary accounting documents, files of various formats, databases of information management systems, etc.; – accounting systems of participants are not linked;
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– procedures for generating reporting indicators are not formalized. Comprehensive economic reforms in our country offer more opportunities for entrepreneurship. The ultimate goal of these reforms is to provide employment, produce quality, competitive goods and products, increase its volume, ultimately increase the country’s GDP, and increase per capita national income. The experience of developed countries today shows that development not only in the economy but also in other areas is in many ways directly related to intellectual property. The latter is a product of human intelligence, an intangible asset. It is no exaggeration to say that developed countries have achieved high results not primarily because of their natural resources but because of their intellectual wealth. That is, through innovative ideas, inventions, and scientific and software developments. Intellectual property, invention, and innovation are crucial for economic development. Therefore, in our country we pay special attention to the observance of patent rights and intellectual property. According to international standards, legal entities and individuals may use a particular piece of work or development only under an agreement with the owner of this intellectual property or with the appropriate permission. Currently, the regulation of temporary or lifetime use of intangible assets belonging to another person, in particular, intellectual property, is carried out based on franchising (concession or complex business) agreements. The launch of new production facilities by attracting foreign investment in the economy of our country, and the introduction of modern technologies in them will simultaneously create forms of investment in the form of intellectual property, without which it is impossible to produce competitive, high-value-added products. As a result, trademarks, industrial designs, production secrets (know-how), and recipes are becoming a significant part of the long-term assets of these subsidiaries. IFRS 15 “Revenue from Contracts with Customers” provides for the introduction of a new procedure for the recognition, measurement, and reporting of revenue. The introduction of a methodology for providing reliable, consistent, and comparable financial information about a company’s revenues, profits and distribution, and share of profits using it is a matter of urgency for all countries, especially those applying different IFRS for the first time. In our country, certain results have been achieved in the harmonization of income and profit accounting with IFRS. In particular, the procedure for income and financial results was developed following the international standards IFRS 8 "Operating Segments", IFRS 15 “Revenue from Contracts with Customers” (Sources available online: https://www.ifrs.org/) and the Regulation “On the structure of costs and the procedure for determining financial results”. It has been in operation since 2021 for joint stock companies, banks, insurance companies, and the government. Further, it is planned to introduce the procedures for the preparation, international auditing, and publication of financial statements based on IFRS for enterprises with share in capital and for other enterprises voluntarily. Ensuring the effective implementation of these tasks requires scientific research to radically improve the methodology of income and profit accounting and reporting on financial results following the requirements of international standards in accordance with the requirements of foreign investors and other information users.
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Therefore, it is important to describe income and profit indicators as an element of financial reporting and to identify their components. There are many similarities between Uzbekistan and foreign practices in assessing and analyzing the financial condition of business entities and changes in them. The reason is that the global economy also requires integration in accounting, auditing, and analysis systems on the global level. That is why international accounting, auditing, and financial reporting standards have been developed and implemented. It is the only official business language in international economic relations. For this reason, the analysis of the financial situation and changes in it in all developing countries based on the rules of the market economy is carried out in the same way, even if they differ organizationally and methodologically.
2 Materials and Methods Theoretical and methodological issues of recognition, evaluation, determination, and reflection of income and profit in the report on financial results are studied in the works of scientists of our country. In the scientific work of A. Avlokulov and S. Makhmudova [1] it is proposed to use a pragmatic concept in expanding the coverage of information on financial performance indicators and developed a three-stage algorithm for reporting control. U. Tulaev [2] and E. Orehova et al. [3] studied the issues of improving the theoretical, normative, and methodological aspects of the formation of financial results, and S. Tashnazarov [4] proposed a form of reporting on profits, losses, and other gross income following international standards. In her scientific article M. Temirkhanova [5, 6] analyzed the relationship between income from the provision of services in tourism enterprises, the functions of the profit category and the company’s profit, revenue, and asset growth. N. Alimova [7] studied the issues of improving the system of accounts, which takes into account the income from hotel services, R. Hasanova [8] developed proposals to improve the report on financial results. Although the authors’ research has important scientific and practical significance, the issues of harmonization of income and profit accounting with the requirements of international standards, recognition (assessment) of income (profit), preparation and submission of profit and loss statement have not been sufficiently studied. Russian Regulations on Accounting (PBU 9/99) defines income as follows: economic following paragraph 3 of this Regulation [9], the following receipts (cash or other property) from legal entities and individuals are not recognized as income: – commission agreements, principal, agent in favor of the principal and similar contracts, and other similar payments; – VAT, excise duty, export duty, and other similar mandatory payments; – advances in products, goods, works, and services; – receipts for prepayment of products, goods, works, and services; – collateral, if the contract provides for the transfer of the mortgaged property to the mortgagor; – deposit;
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– loan, and loan payments to the debtor. According to Uzbekistan and Russian accounting standards, income is divided into two types: 1) income from ordinary (basic) activities, 2) other income (Sources available online: https://lex.uz/acts/710346 https://www.con sultant.ru/document/cons_doc_LAW_ 6208/1f46b0f67e50a18030cbc85dd5e34849b2bf2449/, https://rufil-consulting.com/ wp-content/uploads/2021/11/Accounting-and-Tax-Guide-Russia.pdf) , [10]. Next sources [11–13] provide the following definitions: "The revenue (also referred to as sales or income) of an organization is the cash and non-cash income received by an enterprise for carrying out its production, sales, financial and investment activities”. This source notes that, depending on the nature and conditions of receipt in the accounting, the income differs from the income from ordinary activities and other income. In turn, income from ordinary activities includes proceeds from the sale of goods, payments for work performed and services rendered, receivables, license fees, rent, and royalties. Procedures for the organization of accounting and audit of financial investments, cash, and receivables, which are part of financial assets based on current international and national standards, the theoretical foundations of financial assets by foreign and domestic scientists, including T. Lee [14], W. R. Scoot [15], L. Voronina [16], I. Baholdina and N. Golysheva [17] also studied the application of international experience in the methodology of recognition, measurement, and reflection of assets in financial statements in their countries; B. Khasanov [18, 19], S. N. Tashnazarov [20], N. K. Rizaev [21, 22], A. Zaytsev et al. [23], K. B. Urazov [24], S. U. Mekhmonov [25] theoretical, methodological, and practical aspects, as well as methodological issues of their recognition, evaluation, and reflection in the financial statements. However, in the works of the above-named foreign and domestic scientists, the ways to assess the value of financial assets at market prices, to reflect them in detail in accounting and reporting, i.e., to expand their information capabilities, have not been studied in depth. Problems with the accounting and reporting of free cash and receivables of a financial nature, including the recognition, measurement, and write-off of doubtful debts of enterprises are not covered in detail. The term ‘financial assets’ is widely used in science, and its theoretical and conceptual foundations are one of the most critical objects of accounting. Therefore, there is a need to improve the theoretical and conceptual framework of financial assets. The conceptual basis of financial assets is in the absence of clear rules on national and international standards or there is necessity for their proper understanding and application. In developing the concept of financial assets, its economic significance should be taken as a basis. Recognition of financial assets and their presentation in the report is inextricably linked to the study of the nature of assets in the broadest sense. In accounting practice, assets are treated as economic resources that belong only to the enterprise. Currently, other objects of accounting are part of financial assets, the essence of which differs from each other. These include foreign currency funds, securities, contributions to the authorized capital of other types of entities, long-term and short-term loans and
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credits, and receivables of various financial nature. The assets of such business entities are radically different in their various indicators. These aspects require the improvement of the theoretical foundations of financial assets. In the logical observation of the processing of data obtained in the course of the research, critical study, analysis, and synthesis of literature, induction and deduction, comparison, classification based on known characteristics, SWOT-analysis, modeling, structural analysis, and economic analysis methods were used.
3 Results There are also some differences in the methodological aspects of financial situation analysis. But in most cases their uniformity can be observed. In Uzbekistan and abroad, the following system of indicators is used to assess the financial condition. This includes the following indicators: 1. Liquidity indicators. In international practice, this system of indicators is called ‘liquidity ratios’. 2. Indicators of financial stability. In international practice, this system of indicators is called ‘financial leverage’, in some sources – ‘leverage ratios’. 3. Profit and profitability indicators. In international practice, this system of indicators is called ‘profitability ratios’. 4. Performance indicators. In foreign practice, this system of indicators is called ‘efficiency ratios’. From the above it can be concluded that in Uzbekistan and international practice there are no significant differences in the system of indicators for the analysis of the financial situation. The problem is that they are not systematized, and there are some differences in methodological and methodological bases. 3.1 Liquidity Ratios A review of educational literature published in Uzbekistan and the countries of the former Soviet Union made it possible to identify three essential liquidity indicators. Those are absolute (intermediate), intermediate and current liquidity ratios. Absolute liquidity or Cash ratio. This indicator is based on the division of cash and short-term financial investments into short-term liabilities. This includes the following lines in the Balance Sheet created in practice. ALR = (C + STFI ) CL
(1)
where C – cash and cash equivalents, STFI – short-term financial investments (or marketable securities), CL – current liabilities. This indicator represents a portion of current liabilities that must be covered immediately.
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Rapid liquidity or Quick ratio measures the ability of a business to pay its short-term liabilities by having assets that are readily convertible into cash. This indicator can be determined by formula. RLR = (C + STFI + AR) CL (2) Herein AR is accounts receivable. Current liquidity is also called the coverage coefficient. CLR = CA CL
(3)
In this case CA is current assets. Liquidity ratios often include additional indicators. This includes the ratio of working capital. The ratio of current assets and liabilities of the enterprise on the liquidity of the Balance Sheet can also be estimated in the following composition (see Table 1). Table 1. Liquidity of Investments in Subsidiaries and Their Relative Expressions. As-sets
Compo-sition and content
Liabi-lities
Compo-sition and content
Liquidity ratios (relative expressions) 1
2
A3
Inventory, long-term receivables and other current assets
P3
Long-term liabilities
A3 / P3
A2
Short-term receivables
P2
Short-term liabilities
A1
Cash and short-term investments
P1
Accounts payable
3
4
5
A1 + A2 A1-P1 + + A3 / P1 A2-P2 + + P2 + P3 A3 / P3 (current liquidity)
A1-P1 + A2-P2 + A3-P3 / P3
A1 / P1 + A2 / P2 + A3 / P4
A2 / P2
A1 + A2 / A1-P1 + A2 /P2 P1 + P2 (fast liquidity)
A1-P1 / A2 - P2
A1 /P2 + A2 / P2
A1 / P1 (absolute liquidity)
A1-P1 / P2
x
x
A1-P1-P2 / P3
In international practice, the five indicators identified as important indicators of liquidity are fully in line with the absolute, rapid and current liquidity indicators. That is, there is almost no difference in these content indicators (in them, moreover, a single indicator of liquidity is determined). The other two are working capital to asset ratio and working capital ratio. The working capital ratio of reserves and expenses is a component of the working capital ratio of current assets. 3.2 The Sustainability Level The level of sustainability of the enterprise can be assessed based on the current state of financing reserves and expenses from appropriate sources [26, 27]. The current norms are based on the following conditions (see Table 2).
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Norms for sourcing reserves and expenses at the expense of appropriate funds NWC > BC
NWC < BC
NWC < BC
NWC < BC
NWC + LLB > BC
NWC + LLB > BC NWC + LLB < BC
NWC + LLB < BC
CL > BC
CL > BC
CL > BC
CL < BC
Absolute financial stability
Normal financial stability
Financial instability
The financial situation in a state of crisis
Explanation of indicators: NWC – net own working capital, LLB – long-term loans and borrowings, CL – current liabilities, BC – borrowed capital.
There is no difference between the practice of Uzbekistan and foreign countries in the order of determination of these indicators. The turnover of assets, capital and liabilities is measured in two terms, namely, the turnover ratio and the turnover cycle. The only difference is in their number. In the practice of Uzbekistan, a relatively large number of indicators of turnover coefficients are identified. Theoretically and practically, it is difficult to determine their exact number. Asset turnover ratios, current assets, inventories, commodities, receivables turnover ratios and turnover period are some of the indicators that need to be considered in assessing the financial condition. In foreign practice, these indicators are included in the list of the most calculated indicators.
4 Discussion Another important aspect is that in foreign practice, special emphasis is placed on the analysis of the market value of the performance of commercial enterprises and companies. These indicators include the ratio of net profit to the number of outstanding shares; the share of dividend payments in net profit; the ratio of annual dividend payments per share to their average market value; indicators of the market price of the stock, and the ratio of net profit per share. Based on these indicators, the market activity of firms and companies is assessed. In the practice of Uzbekistan, this type of analysis is not included in the assessment of financial condition and is studied separately as an important indicator of performance. The definition of these analytical indicators, the necessary aspects and importance of the comparative study are determined by the level of participation and activity of enterprises in the financial market. That is, the indicators of investment activity and attractiveness are assessed by assessing the indicators of market activity.
5 Conclusion In the current context of economic globalization, the task of bringing accounting in line with international standards, defined in the programs of socio-economic reforms adopted by our country, is of great importance. We have developed the following proposals and
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recommendations for the implementation of these tasks and to improve the theoretical and conceptual basis of accounting for financial assets: 1. Reforming the theoretical basis of financial asset accounting and drawing on the experience of developed countries. 2. Improving the methodological framework for the recognition, implementation, and compilation of IFRS. 3. Providing information users with the application of terms used in international practices and standards in IFRS and information systems, development of national standards for financial instruments. 4. Development of a new model of recognition, valuation, and reporting of financial assets following the requirements of international standards. 5. Clarification of the principles of financial reporting in the new version of the law and national standards, the development of their classification framework will serve to better reflect the role and importance of the principles and the transparency of reporting. 6. Training and improving the qualification of personnel engaged in accounting services at enterprises is needed. The above suggestions and recommendations will help to bring the composition of financial assets in line with the requirements of International Financial Reporting Standards and will help to analyze the activities of business entities. Improving the theoretical and conceptual basis of financial assets will help to eliminate existing shortcomings in accounting and reporting, and will increase the efficiency of analysis of the financial condition of subsidiaries for information users.
References 1. Avlokulov, A.Z., Elmurodovna, M.S.: Foreign Experience in Increasing the Attractiveness of Financial Statements. Int. J. Econ. Finan. Sustain. Dev. 4, 26–30 (2022). https://doi.org/10. 31149/ijefsd.v4i2.2712 2. Associate Professor Of, “Accounting And Auditing”, , PhD, Samarkand Institute Of Economics And Service, Uzbekistan, Farkhodovich, B.B., Ahmad Ugli, A.J., Master’s Degree Student Of The Department “Accounting And Auditing”, Samarkand Institute Of Economics And Service, Uzbekistan: Necessity And Main Directions Of Improving Financial Reporting In Uzbekistan. tajmei. 03, 61–67 (2021). https://doi.org/10.37547/tajmei/Volume03Issu e05-10 3. Orehova, E., Korovnikova, I., Korovnikova, G.: Improving the Methodology for Assessing the Effectiveness of Economic Entities in Modern Conditions. Bulletin of Kemerovo State University. Series: Political, Sociological and Economic sciences. 2021, 560–567 (2022). https://doi.org/10.21603/2500-3372-2021-6-4-560-567 4. Tashnazarov, S.N.: Improving accounting for liabilities in according with international standards. konomika i cociym. 897–905 (2021). https://doi.org/10.46566/2225-1545_2021_ 2_84_897 5. Mutabar Juraevna, T.: Improvement of the methodology for organizing financial accounting in travel companies. J Tourism Hospit. 07, 1000368 (2018). https://doi.org/10.4172/21670269.1000368
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6. Juraevna, T.M.: Features of establishing accounting policy in tourism enterprises. Bus Eco J. 09, 353 (2018). https://doi.org/10.4172/2151-6219.1000353 7. Alimova, N.K.: Unique features of income audit in hotels of Uzbekistan. konomika i cociym, 643–647 (2021). https://doi.org/10.46566/2225-1545_2021_1_84_643 8. Hasanova, R.B.: Organization of accounting and reporting in operating segments at cottontextile cluster enterprises. (2021). https://doi.org/10.5281/ZENODO.5112118 9. Kasyanova, S.A.: Assessement methods of an entity’s economic risks in compiling the accounting policy. Uˇcët, Analiz, Audit. 9, 6–21 (2023). https://doi.org/10.26794/2408-93032022-9-6-6-21 10. Eshpulatova, Z.B.: Ways to Improve Income Accounting. https://inter-publishing.com/index. php/AJSLD/article/view/948/819 (2023) 11. Zakirova, A., Klychova, G., Doronina, S., Abasheva, O.V., Nigmatullina, N.: Improvement of methodological support of internal control in the cash management system of the enterprise. In: E3S Web Conference, vol. 284, p. 07011 (2021). https://doi.org/10.1051/e3sconf/202128 407011 12. Avi, M.S.: Does the formal structure of the cash flow statement have an impact on the understanding of the data contained in the report explaining the company and financial dynamics? GJMBR. 22, 21–50 (2022). https://doi.org/10.34257/GJMBRCVOL22IS5PG21 13. Choi, W.: The role of financial statement in decision making. SSRN J.https://doi.org/10.2139/ ssrn.3920990 14. Lee, T.A.: Financial accounting theory. In: Edwards, J.R. and Walker, S.P. (eds.) The Routledge Companion to Accounting History, pp. 159–184. Routledge (2020). https://doi.org/10. 4324/9781351238885-7 15. R.Scoot, W.: Financial Accounting Theory 7th. (2019) 16. Voronina, L.: Financial accounting: theory and practice. INFRA-M Academic Publishing LLC., ru (2021). https://doi.org/10.12737/1171982 17. Baholdina, I., Golysheva, N.: Accounting and financial accounting. INFRA-M Academic Publishing LLC., ru (2021). https://doi.org/10.12737/1121598 18. Khasanov, B.A., Mukumov, Z.A., Alikulov, A.I., Djumanova, A.B., Eshboev, U.T., Hasanova, R.B.: Calculation of the invested capital profitability in the financial condition analysis process. Int. J. Adv. Sci. Technol. 28, 42–48 (2019) 19. Khasanov, B.A., ogli Nomozov, I.Z.: Improving liability and loan accounting in production business units (2022).https://doi.org/10.5281/ZENODO.7344755 20. Samarkand Institute Of Economics And Service, Uzbekistan, Tashnazarov, S.: Transition To International Financial Reporting Standards In Developing Countries: Possibilities And Analysis Of Implementation. tajiir. 03, 135–141 (2021). https://doi.org/10.37547/tajiir/Vol ume03Issue05-23 21. Kadirovich, R.N.: International financial reporting standards in the agricultural sector and their introduction to the practice of Uzbekistan. https://iqtisodiyot.tsue.uz/sites/default/files/ maqolalar/6_Rizaev.pdf. (2022) 22. Rizaev, N.K., Temirkhxanova, M.J., Li, S.: Improving the intangible assets accounting: in a pandemic period (2021). https://annalsofrscb.ro/index.php/journal/article/view/985 23. Zaytsev, A., Rodionov, D., Dmitriev, N., Faisullin, R.: Building a model for managing the market value of an industrial enterprise based on regulating its innovation activity. Acad. Strateg. Manage. J. 19, 1–13 (2020) 24. Ph.D., Professor, Samarkand Institute Of Economics And Service, Department Of Accounting And Auditing, Uzbekistan, Urazov, K.B., Abdurasulov, J.A.U., Master Of Accounting And Auditing, Samarkand Institute Of Economics And Service, Uzbekistan: Business Combination Process And Its Audit Review Of Financial Statements. tajiir. 03, 1–4 (2021). https:// doi.org/10.37547/tajiir/Volume03Issue07-01
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Agent-Based Modeling of Tourist Flow Distribution Based on the Analysis of Tourist Preferences Kirillov Dmitriy, Zhanna Burlutskaya, Aleksei Gintciak(B) , and Daria Zubkova Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russian Federation {aleksei.gintciak,daria.zubkova}@spbpu.com
Abstract. The purpose of this research is to develop of the structure of the agentbased model for the analysis of tourist demand. Within the study, the tourism industry is considered as a system based on the interaction of tourists and destinations. Thus, the analysis of tourist demand is based on the ability of destinations to meet the needs of tourists. This approach allows to evaluate services from the point of view of consumer value. The resulting approach includes three enlarged types of tourists, and five destinations close to real resorts of the Russian Federation. Both tourists and destinations act as agents and have their own needs. Special attention was paid to the description of the tourists’ strategies which define the visit of a particular destination. As part of the study, articles from the Scopus database were analyzed to highlight the advantages of existing models. The designed approach combines these advantages at the same time it is more flexible because considers a variety of types of recreation. As a result, a formal description of the tourists’ choice of destinations was developed as the sum of destinations features taken in proportion to their significance with consideration to the phenomenon of inertia and the destinations ability to change their parameters. Within the framework of the study, the mobile operator data is considered as an alternative data source for the development of the agent-based model. As part of further research, it is planned to implement this model in a program code and test it on real data from mobile operators. Keywords: information systems · information systems applications · decision support systems · agent-based model · tourism · mobile network operators’ data · simulation modeling
1 Introduction The tourism industry makes a significant contribution to the national economy [1]. The tourism development contributes to the fund’s receipts from tourists, the development of tourist products infrastructure, and provides related employment [2]. The cultural value of this phenomenon is also worth noting in terms of strengthening partnership between countries at the citizens level and the country’s global image cultivation. The tourism industry headway directly depends on the ability of tourist destinations to meet the needs of consumers. Because tourism is one of the most customer-oriented © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 360–369, 2023. https://doi.org/10.1007/978-3-031-32719-3_27
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industries, it is possible to define the tourist system as a combination of various agents creating a demand and various places and activities representing supply, so then [3]: • To choose a certain destination it is necessary to provide the match between the agent’s internal needs and the ability of the destination to respond to them. • To meet the agent’s needs, the destination can offer the targeted activities such as location (a unique natural or architectural sites), comfort by infrastructure and service, recreation, cultural enrichment. This approach will allow us to consider existing offers in the tourism industry from the point of view of consumer value. Considering the crowded market of tourist products, an analysis of the needs of tourists is necessary for business to ensure competitiveness [4–7]. Such interaction of agents (tourists) and destinations can be described by an appropriate agent-based model, which allows to predict tourists visits to certain destinations depending on their needs [6]. Within the research, the mobile network operator’s data is presented as a possible data source. The use of mobile operators’ data is getting popular in marketing and social research, that led us to suppose the same success in case of tourists needs and behaviour analysis. The study of the tourism process will provide insights into the existing patterns and factors influencing its change, which consequently serve as a basis for making informed management decisions within the tourist products management.
2 Materials and Methods Agent-based modelling is a type of simulation modelling based on the various agent’s interaction [7]. Agents can be represented by individuals, companies, destinations interacting with each other and exterior medium. The interaction takes place based on various rules of interaction between agents, which are based on the “if-then” principle [6]. As a result of interaction, agents can alter their state, change the vector of decision-making, adapt, and evolve, so the main difference between agent-based modelling and, for example, system dynamics is the “bottom-up” system description. The system comes to a certain state not as a result of the initially set conditions of its existence and development in time, but as a result of the conditions for decision-making by individual agents’ description [8]. This modelling principle allows tracking the emergence property of the system in various simulation outcomes. The possibility of describing at the level of the individual and the parameters set to him allows the use of agent models in social systems. The decision-making principle can be built on psychographic, demographic, social, and other indicators relevant to the research field. The studied system’s sophistication and accuracy depends on the variety of decision-making capabilities and adaptations by agents, but at the same time, the computational complexity of modelling the system increases manifold. Since the tourist system is a nexus of tourists who make decisions and destinations that they focus on, it can be implemented as an agent-based model. A tourist can focus on the selected indicators of comfort, climate, vacationers’ demographic profile, and
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destinations presented as an environment that have constant quantitative values of these parameters. Tourists are also able to learn as they accumulate previous experience and make subsequent decisions based on it [9]. As a result of modelling, it is possible to trace the dynamics of decision-making by agents with different combinations of requirements and environmental parameters, to determine the time agents spend to finally choose a specific destination and give preference to it even if there are alternatives [10–12]. Data selection. In 2008, the United Nations together with the World Tourism Organization (UNWTO) published a Methodology of classification and analysis of the tourism industry data. However, the Methodology does not consider possible data sources, as well as data processing techniques. To date, there is no unified system for acquiring and analyzing tourism industry data. Individual indicators for each country can be found on the National Statistical Services websites or in open socio-economic studies. Accordingly, there is an issue of alternative data sources assessment, which requires additional research. Within this paper, the possibility of using data from mobile network operators as a data source for building an agent model is considered. The choice is conditioned by a set of indicators necessary to determine groups of tourists based on basic metrics such as gender, age, income, education level. The review of publications over the past 10 years has revealed that the mobile network operator’s data are able to provide anonymized information about all these indicators alongside with the location data. Such a combination has the potential to be applied within the agent model. The use of mobile network operators’ data. Plenty of companies and services use mobile network operators data in marketing research [13–15]. Moreover, information about the use of mobile devices is applicable to count the number of passengers on the train or the number of cars on the highway in order to identify the peak activity of vehicles and increase traffic capacity during rush hour [16–18]. Consider the use of mobile network operators data to track and identify tourists. For instance, the Call Detail Record (CDR) data, which is the mobile network operator’s passive data, contains the declared information, but ensures non-interference in the user’s personal life [13–21]. Analyzing the users’ cellular activity, it is possible to create a mobility model, determine the approximate location, identify not only the main cities in the country, but also the approximate place of residence, work location (places that a person visits most often and for the longest time in the considered period); identify the place of large crowds of people (matches, festivals, etc.) and, accordingly, calls and SMS become more frequent [19, 20]. Although the CDR data do not contain the gender, age, and exact geographical location of mobile cellular subscribers, it is possible to identify a trend and determine the pattern of user mobility by various systems of mobile network operators, for example the billing system (censors). Initially, the CDR data are used to calculate invoices for calls and sending SMS and MMS, + calls. That is why the CDR data are easy to obtain, because they are acquired automatically and are present in all cellular services by default, which makes them in demand and convenient for conducting research. The CDR contains data that can be divided into temporal and spatial tags. Temporal tags include the date and time of the message/call, the duration of the call. Spatial tags include the identifier of the base transceiver station (BTS), which includes the location
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area code (LAC) and the cell identifier (CELL); the identifier of the network operator serving the call/message (MNC) [21]. However, there are drawbacks. The CDR data are heterogeneous with low space-time density. Due to the fact that they are created only when a cellular transaction occurs, for example, a call or an SMM, MMS sending. At the same time, it is impossible to get the CDR data about a failed call, because the data are recorded only after the end of the call or sending a message. Therefore, there is no way to carry out instant and continuous customer tracking. In any case, everyone uses a mobile phone, at least, at a minimum level while traveling. Therefore, for example, temporary data reading is used: every 4 h or once in longer time intervals (morning, daytime, evening, night or morning, afternoon) [13, 14]. Then a rule by which it would be possible to determine whether a person is a tourist or not should be set. A client can be considered as a tourist if his location does not belong to the home region and he does not return to the home region for a long time [13]. Tourists who move on their own for short distances can also be identified. For instance, a person is regarded as a traveler if he moves in one direction for a long time and makes stops within a certain (threshold) time. To calculate the daily distance range, the location from the CDR is used, which is approximate and is based on the antennas involved. Then, the distances between all pairs of transceiver towers from which the signal came to the CDR over the past day are calculated. Next, the maximum value is selected between any two receiving stations that the user’s phone contacted during the day [12]. After that, it is possible to select the average value for the sample of several days considered and take the resulting value as the usual daily distance of the user, which will help identify tourists. Additional information such as gender, age, income, education level can be obtained from data on subscriptions to various services. Thus, a mobile network operator providing mobile Internet service becomes the source of such data. Since the mobile network operators’ data are not published in open sources now, the decision was taken to use synthetic data to describe the agent model. Analysis of existing approaches. The study examined international experience in modelling tourism processes using agent-based modelling. When modelling the tourist flow, special attention is paid to the tourists’ travel mode. The study [22] examined the impact of light rail transit on tourism and land use. The paper considers a hybrid approach to simulation modelling: system dynamics to describe the interdependencies and behavior of the system at the macro level, agent-based modelling to simulate decision-making by tourists, and a geoinformation system as an environment in which tourists move and transport exists. The most common model created specifically for describing travel agents is the Plog model. The Plog model describes tourists in terms of their psychotype and behaviour, distinguishing two types with intermediate subtypes – adventurers and psychocentrics. According to the typology of Plog: psychocentric people tend to visit popular proven places; adventurers will explore local features and stay away from tourist areas. It is assumed that the number of visitors changes with the development of the resort. In the work behavioural factors were used to determine the degree of satisfaction of tourists
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from visiting the main attractions in Indonesia. A limitation of the Plog’s model, is that it ignores the motivations, activities, and mode of transport of the traveller [23]. The division of agents according to the psychological perception of destinations principle is also used in the study [24]. Agents are divided into adventurers and conservatives. These two types are also divided into a researcher who usually spends time with family or friends, a pleasure seeker who makes decisions based on his preferences for cooking, photography, or shopping, and an adventure seeker whose recreation is based on outdoor activities, sports, and hobbies. The destination agents have the following attributes in various proportions determining their ability to meet the corresponding needs of tourists: event venues, photo locations, recreation areas. In the model [25], tourists can learn as they accumulate previous experience and form subsequent decisions based on it. Emphasis is placed on the social environment in which the tourist is introduced. It is assumed that the presence of common interests, the type of recreation, the way of leisure are important factors in decision-making on a vacation destination. Thus, middle-aged tourists avoid destinations for young people’s noisy recreation, and young people prefer to rest with their peers sharing their values. The model also takes into account the influence of inertia, i.e., resistance to change. A tourist can choose a destination based on his previous experience, even if alternative options offer more acceptable conditions (up to a certain level). The study [6] considers the most flexible approach. This model presents 3 types of tourists, for each of which different levels of needs for sunbathing and swimming, culture and gastronomy have been determined. According to their needs, the attractiveness indicators of each of the 5 considered destinations on the coast have been determined, based on the ratio of the destination visitors number to the total number of tourists on the coast. The threshold of the tourist number has also been determined. Agents are mobile, and destinations form the environment in which they can move according to their choice. The selection process is influenced both by certain individual preferences and the social environment in which the agent is located. The model allows to study the distribution of tourists across different regions according to their needs depending on demographic indicators and the ability of regions to meet them.
3 Results Based on the selected indicators the structure of the agent-based model has been developed. The structure of the model presents two types of agents: tourists and destinations. Tourists are mobile agents who make a choice among the presented destinations, which in turn are static agents that form the external environment. The model assumes 3 tourist types and 5 destinations, among which a choice can be made in accordance with the attributes assigned to agents [7]. Each agent, both tourist and destination, has a set of 4 attributes: geography, recreation, culture, and service. For a destination, the “geography” attribute determines the attractiveness of the territory in terms of climatic conditions, location, landscape [25]. The “recreation” attribute reflects the availability and sophistication of opportunities for various pastimes: active recreation (skiing, snowboarding, cycling, hiking, etc.),
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attending events, cinema, theater. Various kinds of monuments, significant architectural objects, exhibitions, and excursions availability; national culture, historical heritage of a certain destination is reflected in the “culture” attribute. The “service” attribute determines the service level and the housing conditions. Agents of the “tourist” type have similar attributes. The indicator determines the significance or specific weight of the attribute when choosing a destination. The values are assigned based on the hypothesis of tourists’ various requirements related to their demographic indicators. For example, service requirements correlate with a tourist’s income level, and culture requirements correlate with age [24]. For a destination, a quantitative assessment is obtained by ranking them among themselves according to the attributes presented, except the “service” attribute. It is assumed that a tourist can assess the level of service based on reviews and information provided on the Internet and form an opinion that the level meets his requirements. However, when visiting a destination directly, the level of service may be different, so the difference in the expected and real level of service will affect further decision-making about choosing a destination [26]. Turn to the strategies description. At the first stage, the agent “tourist” makes a choice between destinations based on the attribute’s “geography”, “recreation”, and “culture” by summing the products of the tourist attribute specific weight and the indicator of the corresponding destination attribute. The results obtained are compared among the destinations and a choice is made, then a visit to the destination is carried out and the negative or positive discrepancy between the expected and real level of service is summed up with the overall attractiveness value obtained at the previous stage. Thus, the final indicator of attractiveness, which will be recorded by the tourist, is formed [8]. At the next stage of the choice, the phenomenon of inertia takes place: if another destination offers a higher primary value of attractiveness than the previous one by less than x percent, then the tourist will choose the previous destination. If more people get into a destination than with a uniform distribution or less than 50% of it, then the destination increases its level of service at the expense of additional money in the first case or, for example, credit money to avoid ruin. The Table 1 presents conditional regions in which a certain type of tourism is widespread, approximately corresponding to the real regions of the Russian Federation and comparative values of attributes on a five-point scale. Table 1. Regions. Region
Geography
Recreation services
Culture
Service
Central
2
5
4
5
Resort
5
3
3
3
National Republic
3
4
5
4
Natural
5
2
2
1
Ski Resort
3
5
1
4
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The value of the “geography” attribute is proved by the climate and the scenic beauty of the region. For example, in the case of Ski Resort, the harsh climate is compensated for by the picturesque mountain views. The “recreation” attribute reflects the ability of the region to offer opportunities for active recreation, attending events. Thus, the central region is an assembly of cafes, restaurants, theaters, where many cultural and entertainment events are held. The “culture” attribute determines the historical value of the region, preserved monuments and ethnic features. The above features can be seen in the National Republic. The “service” attribute reflects the overall level of infrastructure and service. This attribute has the highest value in the Central region since it is the most developed one. The values of attributes for conditional regions are averaged and cannot be extrapolated to real regions of the Russian Federation. As part of further research, real resorts of the Russian Federation will be identified and a methodology for evaluating each of the indicators will be developed. There are three types of tourists (Table 2): • Tourist 1 - young people (under 35 years old), average income. • Tourist 2 - middle-aged people (40–55 years old), high income. • Tourist 3 - elderly people (60–75 years old), medium-low income. Table 2. Table captions should be placed above the tables. Type
Geography
Recreation services
Culture
Service
Tourist 1
3
5
3
3
Tourist 2
4
4
4
5
Tourist 3
5
2
5
2
As a result, the basic structure of the model will take the form shown in Fig. 1.
Fig. 1. Basic structure of the model.
Thus, the following indicators will describe a tourist’s behavior:
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The primary indicator of attractiveness P p is used to compare destinations among themselves and choose between them; it depends on the indicators t i (the value of the ordinal attribute of the Tourist agent) and r i (the value of the Region agent ordinal attribute). The values of these attributes are taken from the “Regions” and “Tourists” tables. 2 (1) Pp = (ti ∗ ri ) i=0
The resulting attractiveness indicator Pf determines the final attractiveness value of the destination after the visit; depends on the primary attractiveness indicator Pp (the value is calculated in clause 1), sr (the value of the Region “service” attribute) and st (the value of the Tourist “service” attribute). Attribute values are determined by the “Regions” and “Tourists” tables. Pf = Pp + sr − st
(2)
The criterion for choosing the next destination, considering overcoming the inertia of the previous one, depends on the indicator P p (the value is calculated in clause 1). Ppj > 1,1 ∗ Ppj−1
(3)
4 Discussion Modelling the choice of destinations on the coast of Alentejo [6] made the greatest contribution to the model concept definition. This approach allows considering the needs of tourist groups and their distribution to destinations according to the peculiarities of the social environment and the capabilities of each region to meet them. However, this study does not consider the diversity of natural regions, and therefore different types of recreation. The paper presents a more flexible approach, having regard to a greater variety of recreation types, as well as factors influencing the decision-making of tourists. Applying inertia [9] in the model makes it possible to include the tourist’s psychological perception of the previous destination, which may be the most significant factor for some tourist groups when choosing a destination. However, the resulting model does not have the degree of detail presented in the study [6], and requires significant improvements when describing the procedure for choosing between tourist products belonging to the same group by type and quality. The analyzed models characterized by the emphasis on the description of user groups and their preferences, without analyzing the ability of destinations to self-study and adapt to the needs of consumers [4, 6, 9, 11, 12, 22, 24]. The developed approach describes destinations as independent agents with their own needs. Thus, the model has development prospects in terms of analyzing the economic indicators of each destination. The use of mobile network operators’ data is also fraught with certain difficulties. For now, the data of mobile network operators are private property, which necessitates the purchase of data to use them in research. In theoretical terms, within state support
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for research, this issue can be solved, but it is unlikely to receive data from all operators of the region/country at once, and the choice of a specific mobile network operator is associated with the possibility of losing the representativeness of the sample due to the artificial limitation of data by one source. As part of further research, it is planned to consider alternative ways to obtain and process mobile network operators’ data, as well as to carry out a test modelling using the data of one of the operators.
5 Conclusion The paper develops an approach to modelling tourist demand using agent-based modelling tools. The resulting approach considers the tourism industry as a set of decisions made by tourists based on personal preferences and reactions of destinations to changes in tourists’ preferences. The tourists’ choice is based on hypotheses about the different significance of the destination’s features for tourists with different demographic indicators such as income and age. As a result, a formal description of the tourists’ choice of destinations was developed as the sum of destinations features taken in proportion to their significance with consideration to the phenomenon of inertia and the destinations ability to change their parameters. This article also discusses the mobile network operator’s data possibilities for use in models of tourist processes, taking into account the existing features of data acquisition and processing. Acknowledgements. The research is funded by the Ministry of Science and Higher Education of the Russian Federation (contract No. 075–03-2022–010 dated 14.01.2022).
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10. Vinogradov, E., Leick, B., Kivedal, K.: An agent-based modelling approach to housing market regulations and Airbnb-induced tourism. Tour. Manage. 77, 14004 (2020) 11. Zhang, Y., Gao, J., Shu, C., Ricci, P.: How the spread of user-generated contents (UGC) Shapes international tourism distribution: using agent-based modeling to inform strategic UGC marketing. J. Travel Res. 60(7), 1469–1491 (2021) 12. Shi, J., Xin, L., Liu, Y.: Simulation of tourists’ spatiotemporal behavior and result validation with social media data. Transp. Plan. Technol. 43(7), 698–716 (2020) 13. Sikder, R, Uddin, J., Halder S.: An efficient approach of identifying tourist by call detail record analysis. In: Presented at the IWCI 2016 - 2016 International Workshop on Computational Intelligence, vol. 6, pp. 27127–72155. IEEE, Dhaka, Bangladesh (2018) 14. Popa, C., Mocanu, S., Dobrescu, R.: Audience indicators for geospatial marketing using traffic data from cellular networks. In 2nd International Conference on Systems and Computer Science. Presented at the 2nd International Conference on Systems and Computer Science, pp. 268–273. Villeneuve d’Ascq, France (2013) 15. Becker, R., Caceres, R., Hanson, K., Isaacman, S.: Human mobility characterization from cellular network data. Commun. ACM 56(1), 74–82 (2013) 16. Sørensen, A., Bjelland, J., Bull-Berg, H., Landmark, A., Akhtar, M., Olsson, N.: Use of mobile phone data for analysis of number of train travelers. J. Rail Transp. Plan. Manage. 8(2), 123–144 (2018) 17. Dypvik Landmark, A., Arnesen, P., Södersten, C.-J., Hjelkrem, O.A.: Mobile phone data in transportation research: methods for benchmarking against other data sources. Transportation 48(5), 2883–2905 (2021). https://doi.org/10.1007/s11116-020-10151-7 18. Janecek, A., Valerio, D., Hummel, A., Ricciato, F., Helmut, H.: The cellular network as a sensor: from mobile phone data to real-time road traffic monitoring. IEEE Trans. Intell. Transp. Syst. 16(5), 2551–2572 (2015) 19. Trestian, R., Shah, P., Nguyen, H., Vien, Q., Gemikonakli, O., Barn, B.: Towards connecting people, locations and real-world events in a cellular network. Telematics Inform. 34(1), 244– 271 (2017) 20. Xu, Y., Shaw, S.-L., Zhao, Z., Yin, L., Fang, Z., Li, Q.: Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach. Transportation 42(4), 625–646 (2015). https://doi.org/10.1007/s11116-015-9597-y 21. Mahdizadeh, M., Bahrak, B.: A regression framework for predicting user’s next location using Call Detail Records. Comput. Netw. 183, 107618 (2020) 22. Man, C.-Y., Shyr, O., Hsu, Y.-Y., Shepherd, S.: Tourism, transport, and land use: a dynamic impact assessment for Kaohsiung’s Asia new bay area. J. Simul. 14(1), 304–315 (2020) 23. Griffith, D., Albanese, P.: An Examination of Plog’s psychographic travel model within a student population. J. Travel Res. 34(4), 47–51 (1996) 24. Santoso T.A.P., et al.: Agent-based modeling on traveler behavior and travel destination development with case study of Indonesian tourism. In: 2020 6th International Conference on Interactive Digital Media (ICIDM), 1–6. IEEE, Bandung, Indonesia (2020) 25. Luo, Y., Li, Y., Wang, G., Ye, Q.: Agent-based modeling and simulation of tourism market recovery strategy after COVID-19 in Yunnan, China. Sustain. Switz. 13(21), 11750 (2021) 26. Nicholls, S., et al.: Agent-based modeling: a powerful tool for tourism researchers. J. Travel Res. 56(1), 3–15 (2017)
Being Smarter in the Pursuit of a Smart City Roy Woodhead(B) Department of Finance, Accounting and Business Systems, Sheffield Business School, Sheffield Hallam University, Sheffield S1 1WB, UK [email protected]
Abstract. The purpose of this keynote paper is to stimulate new ideas and collaborative discussions. It explores ideas of what a city is before looking at lessons we can learn from the effort of others. It argues there are two dominant strategies that need to combine. These are examples of top-down and bottom-up strategies. The paper also looks at examples that argue a Dynamic Capability perspective which seems to hold promise, especially for agile development. However, all these examples have issues and so the paper concludes a blended approach is most likely to succeed. This requires the establishment of shared services agenda between several cities and the partitioning of key issues in a smart city. Each municipality should target one theme before encouraging proof-of-concepts. There is also a call for municipalities to move from a reactive-transactional to a proactive-relational relationship with citizens that uses the idea of co-creation during a pilot project. This approach reduces the R&D cost for cities to become Smart Cities and is part of a transformation that will also unlock other potentialities. Keywords: Smart City · Co-Creation · Citizens · Municipality · Shared Services
1 Introduction Thank you for inviting me to be a keynote speaker at this prestigious conference. What I plan to do in this paper is outline ideas and thoughts I believe are needed to better manage the evolution of a Smart City. Its aim is to be visionary from within a pragmatic realist stance and hopefully stimulate interesting discussions amongst conference attendees. First, we need to agree what a city is and why we see it as we do before thinking of a smart version. One only must compare a city to a rural area to recognize an urban concentration of people in a geographical area is a key aspect of what a city is. This has efficiency advantages due to things like the cost of infrastructure being reduced because of close proximities (e.g., the length and cost of pipe carrying water per home). With the density of people being larger in a city it also enables new possibilities as it reduces business risks for companies bringing new solutions such as 5G. More importantly is the role a city plays in bringing new cultural memes into existence that later disperse over wider geographies; that is the city sets expectations for the wider country. It is also important to see a city through a temporal lens with a history, its contemporary state and where it could evolve to. Here we are in St Petersburg and have an ideal © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 370–376, 2023. https://doi.org/10.1007/978-3-031-32719-3_28
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case in point. Brook [1] explained that Peter the Great influenced the design of this very city to recreate the cutting-edge Amsterdam of its day. There was another motivation which was to use an advanced St. Petersburg to pull a then technologically backward Russia into modernization. This shows how the city has a role for the nation and is happening in various smart city attempts around the world. Furthermore, by 2050 we expect 68% of all humans will live in a city [2], and so the challenges we see today are likely to become more pressing.
2 Learning from Others Attempts to provide technological solutions that make a city smarter are too many to mention but a simple Google Search of “IEEE Smart City” found over 30 million pages (search made on 28/05/2021), and that is a search from one professional society only. When we look more closely, we see developments in the smart city space are typically point solutions [3] that target a single benefit. We argue this is because a lack of strategic guidance means an organic ‘bottom up’ approach is the current mode of development; companies specializing then look for cities that might buy their solution. A key problem to better coordination of a collection of point solutions is a lack of transcending architectural standards and so synergies are inevitably harder to achieve. Koch [4] puts forward an approach to the structuring of requirements that seem plausible but also reveals a weakness in a purely top-down logic that will inevitably end up missing some aspects. What comprises a city is simply too complex for classical top-down approaches. There have been challenges in attempts to realize the idea of a smart city. Toronto’s “Sidewalk Labs” is a great example that failed to deliver its vision primarily because of citizens’ data privacy concerns [5]. This shows how trust between the municipality’s solutions, and the citizens it purports to represent, is key to the evolution of a success smart city. Another challenge is a static vision of what a city is can quickly become out of date. Chonga et al. [6] explore the contemporary city through the lens of Dynamic Capabilities [7] and see three key capabilities as sense, seize and transform as they explain, “A smart city is an urban organization with dynamic capabilities.” This fits well with agile development as opposed to the waterfall methods. Chonga et al. (ibid) also cite Dameri and Ricciardi [8] for a more ‘smart city’ focused view of key capabilities as being: “(i) sustainability, or the ability to avoid over-exploitation of resources, (ii) robustness, or the ability to return to equilibrium after a crisis, and (iii) agility, or the ability to evolve and adapt.” This view excludes more granular requirements such as the happiness and mental health of citizens [9]. It shows how attempts to define requirements for smart city solutions will struggle to be anything other than targeting one subset of problems amongst many. Bakici et al. [10] argue yet another perspective and that a smart city rests on three main pillars: infrastructure, human capital, and information. This seems more relevant to technological solutions than the human needs of citizens.
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We can characterize many challenges as less about technology and more about alignment, motivational and trust. I. Some developers create point solutions but are often disconnected from a wider coordinated effort because singular goals fit the idea of what a project is, a means to achieve an outcome. II. The motivation of unpaid developers obviously exists but needs better coordination so that duplicated effort is minimized and the way a solution works also provides potentiality for other as yet imagined solutions. III. There is a need for coordination, but the big question is how this can be done to be motivational and promote greater levels of trust. IV. There needs to be standards and a shared infrastructure to unlock synergies.
3 Potential Next Steps The way forward seems to be around creating a cloud-based platform in which developers can target agreed requirements and so solution in a coordinated way. At the same time, they can make data available to other developers in compliance with GDPR regulations. The obvious beneficiary of a smarter approach to a smart city, would be the municipality. These have historically tended towards bureaucracies and with a reactive relationship to the citizens it is supposed to serve [11]. Furthermore, this is based on transactional thinking as the citizens contribute to the cost of the municipality from forms of localized tax and grants from Central Government. With Austerity, the idea of ‘value for money’ meant the municipality tried to deliver services from a reduced budget and this could not be achieved. Given the cost consequences of the Covid pandemic it looks likely a new form of Austerity will emerge. We know the old model did not work in many communities, so what can we do differently and how can technology help? The first thing to challenge is the reactive relationship the municipality has with the citizens it serves. If a citizen phones to complain their rubbish has not been removed then a job ticket is created, a council worker dispatched, the rubbish removed, and the ticket closed. Some services provided by the municipality are not well known such as providing secure housing for people suffering severe disability. How can a municipality move from reactive-transactions to proactive-relationships? There may be a request for feedback after a service has been delivered but most times such comments do not really change a service so much as give a view of how hard those delivering the services are working. This reinforces the reactive-transactional thinking and in so doing disconnects ‘processes’ from ‘people’. The new possibility is a move from transactional thinking to relational thinking. Goldsmith and Crawford [11] sum up this possibility calling for "empowered, engaged and enabled” citizens. This requires a search for new ways of working and a rethink of how the municipality and citizens get the necessary work done. Things like rubbish removal could be seen as a community issue requiring a community response. Why does rubbish only get collected on every other Wednesday? Because that is the most efficient way of organizing for the municipality. The bins are left for a planned period to ensure they will be full. This is not really about a great service for citizens, it is about efficiency for the council trying to deliver outcomes from within constrained budgets.
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Could a new approach be more efficient such as an Internet of Things solution with predictive analytics linked to a route-planning-optimization app, so rubbish collection is linked to bins needing to be emptied. Could robots be tasked with emptying rubbish bins daily and taking it to a collection point so a big lorry can quickly pick up the rubbish and move it to a rubbish dump? Could the rubbish bin itself be a robot? Rather than a single solution deployed city wide, perhaps a number of solutions that help deal with the different nuances involved in rubbish collection, could be established, and a model for other services.
4 Collaborating Cities In the UK, many cities are close together. Could they collaborate in coordinated efforts to increase value for citizens and reduce cost? Perhaps different cities could target one issue they all face and develop a proof-of-concept project, a pilot project, and then deployment to all the municipalities. The collection of cities should agree the criteria to recognize a winning proof-of-concept as later they will be expected to accept the production version of a solution after successful completion of a pilot project; a similar model happens in the Oil Industry where one oil major becomes the lead operator and other companies become part of the decision approval process which could be adapted to fit with a single theme in a city taking the lead. Key to this is shared infrastructure and standards to enable wider deployment later. Most of the technology to create a layered platform that addresses the needs of a city exists today. Technologies that could help include Cloud services, 4G & 5G, Blockchain, web apps, open data and so on. These technologies could innovate the way the municipality works today but that seems not to happen as part of a collaborative strategy that combines different cities. So, there is a mindset that needs to be challenged and a way of coordinating efforts to be devised. The way forward seems to be about unifying disparate parties and building a creative and shared vision of a layered model that coordinates the efforts of individual developers and companies delivering services linked to data repositories that are open to all. Immanent et al. [12] look at how requirements are defined and break the challenge into three ecosystems: business, digital and software. They then outline the process for the elicitation of requirements as: i. ii. iii. iv. v. vi.
Identifying responsibilities Identifying ecosystem assets Identifying requirements sources Analysing stakeholders Introducing the approach and tools Eliciting the requirements
Given the scale of a city and the variance amongst types of citizens, this kind of approach is unlikely to be viable on a city-wide agenda. There is a need to break the challenge into smaller bite sized chunks but in ways that later integrate. Furthermore, rather than enter a formal procurement process it might be wiser to allow solutions to emerge from self-organizing developers within a framework of standards.
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Financial incentives need to be thought through to make it easy for companies to invest in proof-of-concept solutions (i.e., Minimum Viable Products) and a process where one or more move to become a funded pilot project. This idea requires standards, so every developer brings forward projects that unlock further synergies such as making their data available to other developers. These standards could be adapted from OPC UA [13] as is common in many Industries 4.0 ecosystems and other standards needed for each service such as a standard rubbish bin. This is not an easy challenge given the complexity of multiple perspectives as well as the role dynamic context play in shaping requirements. Rather than a top-down approach perhaps we should learn from Nature and encourage multiple experiments so winning ideas can emerge victorious through a Darwinian selection process. An argument for bottom-up emergent solutions is also argued for by Dameri [14]. However, without some top-down logic the resultant variety of organically grown solutions will hinder other synergies, thus making the strategy sub-optimal. The usual way for the IT Industry to model is via a multi-layered ontology, a view of things and how they connect to an analytics engine. This will quickly become too complex unless the idea of a smart city is partitioned in some way and agenda designed for such partitions that avoid a silo mentality. The complexity of trying to build a single ontology is simply because there are so many things that need to happen in a city from the level of an individual citizen in each context to the whole city itself as a functioning entity. So, it seems obvious that we need to make explicit the design rules and standards for a longer-term grand vision of a city-wide ecosystem; a guiding star for the digital creatives to navigate towards. Furthermore, this can be broken down into partitions and a collection of collaborating cities take ownership of the delivery of the best solutions that can easily be configured for other cities. This approach could also help the municipality to benefit from a shared services model such as all agreeing to be part of a cloud services offered by a single provider. It would also benefit from closer alignment with the open-source community as their values reduce the risk of being held hostage by a large technology company that blindly seeks only profit maximization.
5 Conclusions For a city to pursue the idea of a smart city on its own increases R&D costs and risks. A wiser strategy is to break the challenge into smaller parcels and collaborate with other cities. To advance towards a smarter ‘smart city’ we need to step back and think about how we think as individuals looking at the very idea of what makes a great city. The city is a very complex phenomena with many socio-technical layers each of which interacts dynamically. To attempt a static representation is unlikely to be successful for long due to the dynamic issues each causing the need for adaptive dynamic capabilities. What is needed is a transcending high level functional view of what work must be done to make a city great for its citizens. This high-level view then needs to become part of a smart city agenda that helps developers in a bottom-up strategy within a standardized technology framework. That is, the outcomes to be achieved are defined at a high level and solutions encouraged in an open-source approach that encourages collaboration.
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This high-level view of the needed functionality (e.g., provide drinking water to all citizens or provide solutions to remove household waste etc.) should be shared amongst a collection of cities so that the endeavor can be broken into more manageable chunks. This also encourages a shared services strategy amongst different municipalities where a proof-of-concept is eventually piloted and if successful deployed to collaborating cities. The pilot project, and possibly the proof-of-concept, must include a participative co-creation approach between the municipality and its citizens to build a compelling vision for investors and inhabitants as well as identify undesirable aspects that can be mitigated in the pilot. The move from the municipality being committed to reactive transactions to more relational actions require the distribution of power and authority through a more democratic government with open and shared information symmetry among all citizens. This also requires technological solution providers to devise ways collected data can be shared whilst complying with GDPR regulations. Finally, we must recognize that a city is incredibly complex and abandon one dimensional strategy that are not part of a wider multi-dimensional strategy. That is, enable an “up and down” approach rather than only a top-down or only a bottom-up approach.
References 1. Brook, D.: A History of Future Cities. W.W. Norton & Company Inc, New York (2013) 2. United Nations (UN), 2018. 68% of the World Population Projected to Live in Urban Areas by 2050, SaysUN. https://www.un.org/development/desa/en/news/population/2018-revisionof-world-urbanization-prospects.html. Accessed 08 May 2021 3. Woodhead, R., Stephenson, P., Morrey, D.: Digital construction: from point solutions to IoT ecosystem. Autom. Constr. 93, 35–46 (2018) 4. Koch, M.: Decision support for smart ecosystem evolution. In: 2018 IEEE 26th International Requirements Engineering Conference, pp. 398–403 (2018) 5. Gray, J.: Cracks in Sidewalk Labs’ Toronto waterfront plan after fanfare. The Globe and Mail, Toronto. https://www.theglobeandmail.com/news/toronto/cracks-appear-in-sidewalklabs-plan-afterfanfare/article38103236/. Accessed 08 Sept 2021 6. Chonga, M., Habiba, A., Evangelopoulosb, N., Park, H.: Dynamic capabilities of a smart city: an innovative approach to discovering urban problems and solutions. Gov. Inf. Q. 35(4), 682–692 (2018) 7. Teece, D., Pisano, G., Shuen, A.: Dynamic capabilities and strategic management. Strateg. Manage. J. 18(7), 509–533 (1997) 8. Dameri, R.: Searching for smart city definition: a comprehensive proposal. Int. J. Comput. Technol. 11(5), 2544–2551 (2013) 9. Kamel, M., Tsouros, A., Holopainen, A.: Social, innovative and smart cities are happy and resilient’: insights from the WHO EURO 2014 international healthy cities conference. Int. J. Health Geogr. 14(3), 1–9 (2015) 10. Bakıcı, T., Almirall, E., Wareham, J.: A Smart city initiative: the case of Barcelona. J. Knowl. Econ. 4, 135–148 (2013) 11. Goldsmith, S., Crawford, S.: The Responsive City, 208. Jossey-Bass, San Francisco (2014) 12. Immonen, A., Ovaska, E., Kalaoja, J., Pakkala, D.: A service requirement engineering method for a digital service ecosystem. SOCA 10(2), 151–172 (2016)
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13. Unified Architecture. https://opcfoundation.org/about/opc-technologies/opc-ua/. Accessed 08 Sept 2021 14. Dameri, R., Ricciardi, F.: Smart city intellectual capital: an emerging view of territorial systems innovation management. J. Intellect. Cap. 16(4), 860–887 (2015)
Economy and Industry 4.0 Development
Prospects for Improving the Benchmarking Activity of Automotive Enterprises in Uzbekistan Kongiratbay Sharipov1(B)
and Umida Zaynutdinova2
1 Tashkent State University of Economics, Islam Karimov Street 49, Tashkent,
Uzbekistan 100066 [email protected] 2 Tashkent Institute of Finance, Amir Temur Avenue, Tashkent, Uzbekistan 60A100084
Abstract. This article researches the essence of benchmarking, the goals and objectives of benchmarking activities in industrial enterprises, as well as theoretical and methodological foundations, principles, and stages of benchmarking. The authors have analyzed and evaluated benchmarking activities of automotive enterprise processes and systematized them to increase the competitiveness of the domestic automotive industry. The possibilities of the automotive industry have been evaluated based on benchmarking. The proposed system of comprehensive evaluation criteria for determining the effectiveness of marketing activities is used to collect reliable data on the attitudes and behavior of customers in the automobile industry and to determine indicators of changes in the effectiveness of marketing activities, to increase the effectiveness of the enterprise’s market activity planning and marketing management. The authors of the articles present scientifically based proposals and practical recommendations for this branch of industry. When the global automotive industry enterprises conduct the research, they do it in the following priority areas to improve the scientific, theoretical and methodological foundations of marketing strategy development: first, they study the ways of increasing the efficiency of enterprises in terms of quantity and quality by applying marketing strategy; then the possibilities to increase the export potential of enterprises by way of improving the marketing strategy; next step is to study the ways to increase international competitiveness through the use of a market-oriented marketing strategy; then it comes to the improvement of marketing strategy implementation mechanisms; finally, it is necessary to study the formation and development of improved marketing strategy of national enterprises in international markets. Keywords: Benchmarking Activities · Marketing Strategist · Marketing Environment · Automotive Enterprises
1 Introduction Today, global automobile industry enterprises incur significant expenses for organizing high-tech and scientific capacity productions. The development of marketing strategies for international markets, the implementation of marketing strategies in the network, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 379–389, 2023. https://doi.org/10.1007/978-3-031-32719-3_29
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scientific research activities aimed at ensuring investment potential and competitiveness are carried out on a large scale. In this regard, special attention is paid to the development of marketing strategies aimed at expanding the potential of the automobile industry and increasing its contribution to sustainable economic development in the global market. For example, the criteria for evaluating marketing efficiency in automobile industry enterprises, the processes of their formation, and the behavior of consumers with dominant power in the market. When developing marketing strategies aimed at increasing the export potential of enterprises, it is appropriate to take into account factors such as conditions in the world market, the presence of competitors, and the company’s strategy for entering the market and occupying a certain part of the market in the formation of export prices [1]. The world’s leading scientific centers and higher education institutions (among them University of London (Great Britain), Florida Atlantic University (USA), University of Limerick (Ireland), Vrije Universiteit Amsterdam (the Netherlands), Indian Institute of Management (India). It is implemented by Plekhanov Russian University of Economics (Russia), GUU Marketing Institute (Russia), and Tashkent State University of Economics (Uzbekistan), conduct numerous research to offer the automotive industry advanced marketing strategies that would increase the competitiveness of industrial enterprises in domestic and foreign markets. As a result of global research on the development and implementation of marketing strategy in industrial enterprises, including the automobile industry: scientific and methodological bases of marketing strategy formation and its improvement have been studied (Florida Atlantic University, USA); the possibility of increasing competitiveness in the enterprises of the automotive industry by developing a marketing strategy have been grounded (University of Limerick, Ireland); the importance of the marketing strategy aimed at bringing new products to the market and expanding activities in the markets has been justified (Indian Institute of Management, India); the possibility of increasing the export potential of enterprises by applying a marketing strategy has been proved (Vrije Universiteit Amsterdam, the Netherlands); the importance of conducting marketing research and using modern methods in increasing production and sales has been grounded (University of London, Great Britain); the feasibility of organizing the production of high-tech and scientific-capacity products by applying a marketing strategy in industrial enterprises, introducing new marketing services aimed at the development of industrial enterprises, and increasing the competitiveness of the national economy has been justified (Russian State University of Economics named after G.V. Plekhanova, Russia); scientific aspects of development and implementation of marketing strategy in automobile industry enterprises and expansion of activities in international markets has been substantiated (GUU Marketing Institute, Russia); the methodology of evaluating the effectiveness of marketing strategy in industrial enterprises has been systematized (Tashkent State University of Economics, Uzbekistan); the expediency of using market-oriented strategic marketing in ensuring the modernization of the economy and transition to innovative production has been grounded (Tashkent State University of Economics, Uzbekistan). It is also worth mentioning in this paper the Resolution of the President of the Republic of Uzbekistan dated July 18, 2019 “On additional measures related to the rapid development of the automobile industry of the
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Republic of Uzbekistan”. The aim of the resolution is to increase the level of localization to 60% on average (the Resolution of the President of the Republic of Uzbekistan, 2018), to increase the volume of production by 1.4 times and the volume of exports by 2 times by further developing cooperation in the automobile industry following the Decree No. PD-60 “On the Development Strategy of New Uzbekistan for 2022–2026”. Special attention is paid to ensuring the effective implementation of the set goals and tasks, such as increasing the level of localization (Decree of the President of the Republic of Uzbekistan, 2022).
2 Materials and Methods Researchers carried out studies on the effective management of the marketing system of auto industry enterprises and the orientation of the market activity [2, 3]. In the research process, the cases related to the role of the marketing system in increasing competitiveness and ensuring this system effectiveness are in priority [2, 4]. R.J. Barak and C.R. Kniker in their studies focus on benchmarking activities and pay special attention to the processes of comprehensive market research, innovation planning, their competitiveness, price policy, promotion, sales organization, and service acceleration [5]. Researchers recognized that the national and international competitiveness of industrial enterprises depends on the level of their innovative development, the development of special methods in all directions of the marketing system [6–8]. In the studies [9–11], it is noted that the results of the strategy of implementing the goals of innovative development of industrial enterprises determine the task of significantly changing the activities of market entities and adapting them to market requirements [12]. Scientific research [3, 13, 14] recommend a system for evaluating the extent to which marketing strategies are used as the main direction of ensuring high level of competitiveness of industrial enterprises operating in the conditions of globalization of the economy. In scientific research determining the efficiency of marketing activity, the complex system of evaluation criteria reflects the feasibility of using it in practice as an effective method to collect reliable data on customer relations and behavior in industrial enterprises and to determine indicators of changes in the effectiveness of marketing activity, to plan the market activity of the enterprise and to increase the effectiveness of marketing management [15, 16]. In the research [5] the fifth generation – global benchmarking is interpreted as a future reliable tool of automotive enterprises in organizing international exchanges, taking into account the organizational culture and national processes of production [17]. The results of the research [18] show that the process of systematically developing the export potential of enterprises depends on the global marketing strategy implemented by the enterprise [19, 20]. Research methods such as scientific abstraction, comparative analysis, induction and deduction, statistical analysis, regression analysis, forecasting are used in the article.
3 Results Automotive companies need to identify, understand, and adapt to the company’s existing capabilities to improve their performance [21, 22]. As an example, the path of development of several leaders in this field should be studied and a conclusion drawn [23–25].
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Then you will have the opportunity to make a reasonable assessment of your capabilities [26]. The enterprise can apply the favorable aspects of the working methods taken as a sample in its activities. Comparative analysis based on tests can be considered one of the areas of strategic marketing research. Of course, obtaining objective indicators is not always easy. In 1972, research and consulting centers in the United States researched how enterprises in similar circumstances achieved success to find an effective solution. In 1979, the well-known American company Xerox launched the Benchmarking Competitiveness Project to analyze the price and quality of its products compared to similar Japanese companies. The project was successful. Benchmarking determines the probability of success based on indicators. Below are the types of enterprise performance evaluation: – – – –
competitive benchmarking, internal comparison, functional comparison, benchmarking process.
In competitive benchmarking, the capabilities of a process or administrative method are compared with those of competitors. In internal benchmarking, the characteristics of production units are compared with similar business processes within the organization. Functional comparison compares certain functions of two or more organizations in the same sector. Benchmarking is a process of measuring the performance of a company’s products, services, or processes against those of another business considered to be the best in the industry (Fig. 1). Step 1
Selection of benchmarking object
Step 2
Choosing a benchmarking partner
Step 3
Search for information
Step 4
Analysis (Comparative assessment)
Fig. 1. Benchmarking analysis steps. Source: Authors development according to research results.
The share of XEROX (a copier manufacturer) in the United States fell from 80% to 40% in the 1980s. This was caused by the strong competition of Canon and Minolta of Japan. That’s how the “benchmark test” project was implemented. For this, of course, competitors’ products have been examined in detail. But from this comparison, XEROX did not notice what their products lacked Therefore, all the processes of the value chain were studied and deficiencies in the logistics and sales processes were identified. Several competitors’ methods were selected and successfully implemented, reducing production costs by 50% and time to market by 66%.
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“Benchmark” in English means a mark or point of confirmation. Theoretically, a clear conceptual apparatus has not yet been formed. Therefore, representatives of each field interpret their research in their own way. In benchmarking in the automotive industry, we search for what we think are examples of best practices and apply them to our business. We know that there are many aspects of the Ford car company that are exemplary for the auto industry. As early as the 90s of the last century, comparative work was carried out to increase its position in the market. The company’s experts studied many car models to learn the advantages of each product and to determine the models preferred by consumers. For each criterion, the best car in its class was determined, based on which a strategy was developed that allowed to exceed the highest rate. As a result, the company’s products were named “Car of the Year”. There were periods when there was a gradual decline from the rising peak. The company management came to an understanding that the benchmarking process is an ongoing process and cannot be considered a one-time process. In summary, comparison allows you to determine the reason why your competing company has achieved great success in its field of activity. Such actions lead to a positive result. Analyzing only one of these indicators does not provide a complete picture of the company’s performance. Benchmarking is a very new word in business of the CIS countries. At first, many had the impression that this concept characterizes industrial intelligence, but in fact there are important differences that distinguish these two processes. Benchmarking is the study of someone else’s experience, which allows you to choose an additional criterion for your business and protect yourself from trial and error. This method of doing business was developed before 1972, but was purposefully used in the late 1980s. In a nutshell, benchmarking is looking for another company or other business that is more successful than others. Only then is it possible to answer how to improve the state of a company’s work. Nowadays, there is fierce competition in almost any market, which prevents you from doing your business the way you want. There is no chance to be distracted for a minute, because the company can quickly exit the market. Correct analysis of competitors’ actions allows you to use the most successful methods of improving your company’s performance. Experts divide benchmarking into 5 main stages: 1. Determination of areas of activity and functions that require optimization in the company. 2. Search and select the best companies in the field of activity. 3. Analysis of the indicators of one’s company. 4. Analysis of activities of external organizations. 5. Comparison of obtained data and use of results to improve their results. Benchmarking is especially common in Europe and America, but not all companies in the CIS countries are ready to fully use this method. The reason is that many company directors and managers responsible for the development believe it impossible to use the example of foreign companies. Studying the theories of marketing strategies of automobile industry enterprises and approaches to classifying the types of marketing strategies allows to determine the specifics of their use in automobile manufacturing enterprises of Uzbekistan and to develop appropriate measures.
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A methodology for developing marketing strategies is proposed for auto industry enterprises according to the marketing environment formed in the market, the state of marketing activities of enterprises, the scope of marketing activities and participation in the global added value chain (GVCs). The information system that is formed and presented by the enterprise for making management decisions should always evaluate and monitor the changes to the extent to which it meets and satisfies all the above requirements. If the data does not have a positive characteristic for at least 3 of the criteria given in Table 1, this means that the management system in this enterprise should be reorganized. Table 1. Data formation criteria for increasing the competitiveness of the domestic automotive industry based on benchmarking. T/r
Criteria
Content
1
Brevity
The information should be clear, concise, and not redundant
2
Clarity
The user must ensure that the information is free of errors and omissions and that the information is free of any changes
3
Speed
Information is necessary, that is, it should be ready when it is needed
4
Compatibility
Information should be consistent across time and departments
5
Expediency
The information must be appropriate for the purpose for which it is provided
6
Profitability
The production of information should not be at the expense of the benefit of its use
7
Unconventionalism
Information should be prepared and presented without assumptions
8
Address
The information must be passed on to the responsible party while maintaining confidentiality
9
Numeracy
The company’s database should be digitized, and benchmarking methods should be automated
Source: Authors development according to research results
According to the analysis of the characteristics of the main methodological approaches to the development of marketing strategies of automobile industry enterprises in foreign countries, it is not the relations that arise during the development of the marketing strategy and its implementation, but the flexibility to the processes related to market development is an important aspect. The main tasks of developing the results of empirical research on marketing strategies are aimed at increasing the effectiveness of marketing activities of automobile industry enterprises, increasing sales, and ensuring competitive advantage. Also, the advantage of marketing strategies in terms of competition in the enterprise, dependence on the direction of consumers and competitors, goal orientation of the state and strategies of doing business, and marketing opportunities are taken as a basis.
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3 groups of factors encouraging the development of marketing strategies for auto industry enterprises can be distinguished (see Fig. 2).
Marketing strategies
Environment (F1)
Orientation to consumption (demand) (X1)
Network competition speed (X2)
The speed of crossindustry competition (X3)
Focus on competitors (X4)
Activity status (F2)
Availability of a database on the market (X5)
Long-term goal plans (X6)
Scope (F3)
Scope of internal marketing activities (X9)
Scope of external marketing activities (X10)
Use of ICT (X7)
Use of internal marketing services (X11)
Modern technology and its modernization (X8)
Level of use of external marketing services (X12)
Fig. 2. Development model of marketing strategies in automotive enterprises. Source: Authors development according to research results.
According to the proposed model, first of all, the level of marketing opportunities in automobile industry enterprises operating in the country, the formed competitive environment, the breadth of opportunities for the implementation of marketing strategies by enterprises, the marketing activity of enterprises and its level, the level of marketingconsulting firms operating and their level of service to enterprises should be determined. According to the model of development of marketing strategies of automotive enterprises, there are the following variables for 3 groups of factors, that is, a total of 12 independent variables and 1 outcome variable for selected factors. Hidden variables (3): – the market in which the enterprise operates and its adaptability to it (F1);
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state of marketing activity of enterprises (F2); scope of marketing activities of enterprises (F3). Independent variables (12): in the marketing environment factors (F1) selected by (X1; X2; X3; X4); marketing activity status factors (F2) selected by (X5; X6; X7; X8); scope of marketing activities factors (F3) selected by (X9; X10; X11; X12).
It is necessary to determine the extent to which selected and systematized factors have been formed in the automotive industry of Uzbekistan to determine the potential of automobile industry enterprises for the development of marketing strategies, to solve the main problems and to determine the appropriate strategies. According to the proposed model for determining the potential of automotive enterprises to develop marketing strategies, all selected factors are interrelated and can be expressed in a multivariable function view: INN (F) = f (Fn) → max.
(1)
In this case: INN is marketing strategy development potential; Fn is a group of n factors that are the basis for developing a marketing strategy. The information base for the model mentioned was the dealership services, car showrooms of the GM-Uzbekistan company within the Uzavtosanoat JSC, the JV MAN Auto-Uzbekistan joint venture, and the department of the Samarkand Automobile Plant LLC. We used questionnaires collected by managers and marketers of enterprises.
4 Discussion In world practice, there have been several scientific studies devoted to the development of marketing innovations and the processes of their formation. The main ones are aimed at scientific justification of the interdependence of types of innovations. The innovative activities of most enterprises are focused on the organization of product production processes, and through innovations in production, the costs of the enterprise are reduced and competitiveness is ensured. In general, the synergy effect of any innovations implemented in enterprises is observed. The introduction of effective marketing innovations in the automotive industry is considered one of the main issues facing the automotive industry of our country, and in this regard, it is appropriate to pay attention to the following: 1. Increasing the number of marketing-consulting firms serving the market of the automobile industry, introducing effective integration of industrial enterprises with them. 2. Establishment of innovation centers, innovation incubators in cooperation with educational institutions in automobile industry enterprises, and existing ones, the establishment of separate departments dealing with marketing innovations. 3. Application of innovative marketing technologies developed by foreign companies and effectively used in their activities following national characteristics and effective use of benchmarking in this regard.
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4. Develop innovative marketing strategies aimed at effectively introducing them to the market along with the product innovation of the auto industry enterprise. 5. Establishment of special departments aimed at the development of marketing innovations in auto industry enterprises. Such many tasks will ensure the further development of the economy of our country, increase the production of competitive products, and increase the prestige of local brands in the world markets by increasing the efficiency of the activity of the auto industry enterprise.
5 Conclusion In the future, organizing the production of new models of cars based on a single unified platform, developing targeted programs to attract investment, and attracting preferential credit lines from foreign banks and export credit agencies to finance projects for the localization of manufactured cars, as well as improving the marketing activities of the industry it is appropriate to implement measures systematically. Based on the above, it is advisable to implement the following to improve the benchmarking activities of auto industry enterprises in our country: 1. To strengthen traditions related to the aesthetic perfection of cars. This marketing tradition is seen through the aesthetic attitude of consumers to the car: design, comfort, ease of use, etc. Currently, design is of primary importance in research in the field of marketing. In modern design, several main marketing traditions are distinguished: the combination of classes (SUV and station wagon); the multifacetedness of the Internet (combination of work and leisure in the car); additional conveniences and comfort (width of the roof hatch, lifting of the doors, etc.). 2. The search and creation of completely new segments called intermediate segments in automobile markets are underway. Such a tradition is the acceleration of the individualization process of automotive companies in the automobile market of some countries and the use of an individual marketing complex that uses different marketing methods taking into account the characteristics of consumers in the automobile markets of different countries. 3. Increasing the competitiveness of the products of the enterprise implies the effective use of all economic resources in the joint-stock enterprise. For effective operation, it is necessary to improve the quality of products, increase the generality of labor, reduce the capacity of funds, increase the efficiency of funds and optimize other economic indicators, improve the production and economic activity of the enterprise, and the planning and organization process. However, in all cases, the basis of production efficiency lies in maximizing the company’s profit and reducing production costs as much as possible. 4. Since the development of a marketing strategy in automotive enterprises reflects the determination of development directions that take into account the capabilities of the enterprise, its current situation in the market, factors affecting the internal and external environment under risk conditions, the decision on the choice of marketing strategy is made separately for each enterprise, only based on general requirements
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rather, it is necessary to determine with the help of the specific internal parameters of the enterprise’s activity, to develop and implement a specific marketing strategy.
References 1. Sharipov, K.A., Zaynutdinova, U.D.: Improve the efficiency of the auto industry enterprises marketing system. Am. J. Interdisc. Innovations Res. 03, 106–112 (2021). https://doi.org/10. 37547/tajiir/Volume03Issue01-17 2. Pandey, N., Kiran, R., Kumar, S., Nandkeolyar, D.: Why do Indian SMEs fail and succeed?: insights from auto-component industry. Int. J. Indian Cult. Bus. Manage. 15, 82–99 (2017). https://doi.org/10.1504/ijicbm.2017.10006294 3. Shah, S.: Impact of digital tools & digital marketing on dealers & customers in Indian auto industry. SSRN Electron. J. (2021). https://doi.org/10.2139/ssrn.3955529 4. Doumeingts, G., Ducq, Y.: Enterprise modelling techniques to improve efficiency of enterprises. Prod. Plan. Control. 12, 146–163 (2001). https://doi.org/10.1080/095372801505 01257 5. Panwar, A., Nepal, B., Jain, R., Yadav, O.P.: Implementation of benchmarking concepts in Indian automobile industry - an empirical study. Benchmarking 20, 777–804 (2013). https:// doi.org/10.1108/BIJ-03-2012-0015 6. Manjunatha, N.: Internationalization and innovation capabilities determines export performance of Indian auto component industry. Acta Technica Corviniensis – Bull. Eng. 13, 81–90 (2020) 7. Butov, A.V.: Causes of Loss Competitivenesses of the Company Ford in the Russian Federation. International Trade and Trade Policy. (2020). https://doi.org/10.21686/2410-73952019-4-171-180 8. Patron, H., Gomez, L.: A market basket analysis of the US auto-repair industry. J. Bus. Anal. 3, 79–92 (2020). https://doi.org/10.1080/2573234X.2020.1838958 9. Darawong, C.: The influence of leadership styles on new product development performance: the moderating effect of product innovativeness. Asia Pac. J. Mark. Logist. 33, 1105–1122 (2020). https://doi.org/10.1108/APJML-05-2019-0290 10. García, S., Carrete, L., Arroyo, P.: Automobile manufacturers, marketing channels and consumer loyalty. Contaduria y Administracion. 65 (2020). https://doi.org/10.22201/fca.244884 10e.2020.2411 11. Jelenkovi´c, S., Brzakovi´c, A., Mihailovi´c, B.: The role and importance of dealers (sellers) for the automobile market in Serbia. Oditor. 6, (2020). https://doi.org/10.5937/oditor2003007j 12. Zaytsev, A., Rodionov, D., Dmitriev, N., Faisullin, R.: Building a model for managing the market value of an industrial enterprise based on regulating its innovation activity. Acad. Strateg. Manage. J. 19, 1–13 (2020) 13. Gupta, P.: Bharat forge: entrepreneurial leadership triumphs disruptive times. CASE J. 18, 126–142 (2022). https://doi.org/10.1108/tcj-02-2020-0011 14. Folschette, C.: Tesla’s successful marketing strategy shows that it’s time for CEOs to get social - Talkwalker. Talkwalker (2020) 15. Naru, R., Kumar, J.A.: The role of customer relationship management in auto car industry in building customer relationship in after sales department. J. Crit. Rev. 7 (2020) 16. Israeli, A., Scott-Morton, F., Silva-Risso, J., Zettelmeyer, F.: How market power affects dynamic pricing: evidence from inventory fluctuations at car dealerships. Manage. Sci. 68, 895–916 (2022). https://doi.org/10.1287/mnsc.2021.3967 17. Barak, R.J., Kniker, C.R.: Benchmarking by state higher education boards. New Dir. High. Educ. 118, 93–102 (2002). https://doi.org/10.1002/he.58
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18. Dragan, O., Berher, A., Pustovit, J.: Estimation of marketing price policy efficiency of the enterprise of meat-processing industry. Manage. Theory .Stud. Rural Bus. Infrastruct. Dev. 40, 175–186 (2018). https://doi.org/10.15544/mts.2018.17 19. Ferman, A.M., Hasmet, M.K.: Effects of Industry 4.0 on marketing strategy, an application on Turkish auto industry: a research among auto executives in Turkey. Pressacademia. 9, 223–231 (2020). https://doi.org/10.17261/Pressacademia.2020.1298 20. Ianenko, M., Ianenko, M., Shevchuk, E.: Digital transformation of marketing activities in transport systems management during COVID-19: experience, problems, prospects. Transp. Res. Procedia. 63, 878–886 (2022). https://doi.org/10.1016/j.trpro.2022.06.085 21. Manjunatha, N.: Internationalization and innovation capabilities determine export performance of Indian auto component manufacturing industry. Gurukul Bus. Rev. 16, 47–60 (2020). https://doi.org/10.48205/gbr.v16.4 22. Joo, Y., Yoo, G.: Study on automobile culture of developed and emerging countries in Asia using text mining analysis on social media. Int. J. Adv. Sci. Eng. Inf. Technol. 11 (2021). https://doi.org/10.18517/ijaseit.11.1.14078 23. N, M.: The relationship of firm, managerial & product characteristics with standardization of export marketing strategies in auto component manufacturing industry. J. Int. Bus. Econ. 21 (2020). https://doi.org/10.51240/jibe.2020.2.2 24. Shvetsova, O.A., Tanubamrungsuk, P., Lee, S.: Organization leadership in the automobile industry: knowledge management and intellectual capital. Open Transp. J. 15, 16–30 (2021). https://doi.org/10.2174/1874447802115010016 25. Kuntonbutr, S.: The dynamic capabilities of digital transformation for improving product customization and innovation. Test Eng. Manage. 83, 6569–6582 (2020) 26. Egor, T., Victor, D., Alexandra, B., Ed, O.: Digitalization in logistics for organizing an automated delivery zone. Russian post case. In: Schaumburg, H., Korablev, V., Ungvari, L. (eds.) TT 2020. LNNS, vol. 157, pp. 143–156. Springer, Cham (2021). https://doi.org/10.1007/9783-030-64430-7_12
Socioeconomic Mechanisms of Managing Intellectual Capital of the Industrial Ecosystem Aleksandr Babkin1(B)
, Natalia Alekseeva1 , Larissa Tashenova1,2 and Akram Ochilov3
,
1 Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia
[email protected] 2 Karaganda Buketov University, Karaganda, Kazakhstan 3 Karshin State University, Qarshi, Uzbekistan
Abstract. Development of the industrial ecosystems has become the response to Industry 4.0, overall digitalization, intellectualization of industry and tougher competition. Today flexible organizational forms of industrial ecosystems allow stating that they are future of the industry. The turbulence of today’s geopolitical agenda makes it relevant to design socioeconomic mechanisms for managing the intellectual capital of an industrial ecosystem in the new reality. That is the subject of the present paper. The goal of the given research is to suggest the conceptual model of the socioeconomic mechanisms of managing the intellectual capital in the framework of the new reality. The present work is based on the analysis of the scientific publications on the given research object and subject. The work used the methods of analysis and synthesis, and the graphic method. To achieve the goal of the research, the authors identified the conditions of development of the industrial ecosystems in the new reality, revealed new needs of the industrial ecosystems in the new reality, formed the conceptual model of socio-economic mechanisms of managing the intellectual capital of the industrial eco-system in the new reality, which take into account the current realities and needs of the environment. The vector for further research is the development of the conceptual grounds for the presented model. The obtained results can be useful for researchers of industrial ecosystems and top managers of these industrial systems. Keywords: Industrial Ecosystem · Intellectual Capital · Industry 4.0 · Model · Mechanism
1 Introduction The evolution of Industry 4.0, overall digitalization and intellectualization of industry, development of the concept of added value, led to the necessity to concentrate the efforts of economics entities on the joint work [1]. The market players, who have been able to find the terms of the cooperation, rather than competition, today occupy leading positions in all the industries. By their structure and cooperation model, these successful collaborations go beyond the legally formalized concepts of concerns, holdings, and financial industrial groups [2]. Other types of cooperation and organization of business processes in the joint structures have led to the emergence of a new concept - ecosystem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 390–397, 2023. https://doi.org/10.1007/978-3-031-32719-3_30
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The specific feature of functioning ecosystems is the opportunity to coordinate work of their subjects without formalization of the organizational structure [3]. In this situation the participants cooperate on a voluntary basis to coordinate their own actions without using the pre-arranged forms of agreement [4]. The ecosystems succeed in distributing the streams of value quite efficiently. These are the conditions of efficiency that made the participants keep cooperating. In Russia the “ecosystem” concept is mostly applicable in such market segments as banking or trade. However, its success in these industries is about to expand in other industries as well. Most prospective, from the authors’ standpoint, is its application in industry. Consequently, the given paper will focus on industrial ecosystems. The “ecosystem” concept is determined by the set of participants of economic activity, represented by enterprises and organizations. These participants are involved in creating added value at the system level. The “ecosystem” concept refers to participants involved in both the undefined sectors of the economy ([5] and in the defined economic sectors. For instance, the publications [6] consider urban economy, whereas the authors [7, 8] turn to coproduction of a product and a service. The common feature of ecosystems in the works of the above-listed authors is their perception of the ecosystem as the set of participants who systematically create added value. The distinctive feature of industrial ecosystems is the use of materials and energy to create added value in specific geographical territories [9]. At the same time, industrial ecosystems are not limited to the use if materials and energy, they rely on technology and knowledge in production. Due to this, some scholars regard the industrial ecosystem as the knowledge base [10, 11]. This concept contributes to development of studies on mechanisms of creating and introducing innovations in the industrial ecosystems [12, 13, 16, 17]. Despite the differences in special studies on ecosystems of various types, the scholars agree that ecosystems are a new type of organization for economic entities, which evolved as a response to the development of market needs under the influence of digitalization. It is a flexible and informal organizational structure, compared to the previous ones. Unique interaction between the participants allows creating added value more effectively, which stimulates the participants to cooperate. Thereby we posit that there is intellectual capital of the industrial ecosystem, which is manifested in the formation of added value based on an intangible component. The problems of the intellectual capital development in the framework of expanding digital space [13] remain up to date but understudied in the industrial ecosystems. Due to this, we see the need for research dedicated to managing the intellectual capital of the industrial ecosystem. In the given paper we set a goal to propose the conceptual model of socioeconomic mechanisms of managing the intellectual capital of the industrial ecosystem in the new reality.
2 Materials and Methods The object of the research is intellectual capital of the industrial ecosystem. The subject of the research is socioeconomic mechanisms of managing the intellectual capital of the industrial ecosystem. The work is carried out based on open-source materials. The authors have conducted analysis of scientific and educational literature. The main base for selecting research
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materials has become the international abstract and citation database Scopus. The analysis covers the studies dedicated to ecosystems and industrial ecosystems in the field of knowledge «Business, Management, and Accounting». The research timeframe is determined from 2012 to 2022. The analysis of the selected sources has enabled us to understand the current state of the scientific thought in the field of industrial ecosystems, and to find out the existing research gaps. Considering the scientific and educational literature has made it possible to determine objectively the latest changes in the external environment of industrial ecosystem functioning. Thus, having applied the methods of analysis and synthesis in this study, the authors set and solved the following tasks: 1. Identify conditions for the development of industrial ecosystems in the new reality. 2. Reveal new needs of industrial ecosystems in the new reality. 3. Present the conceptual model of socioeconomic mechanisms of managing intellectual capital of the industrial ecosystem in the new reality. The algorithm of the research is shown in Fig. 1.
1. Identify the research object
2. Identify the research subject
3. Set the goal and tasks for research
4. Analyze scientific and educational literature
5. Apply the methods of analysis and synthesis to solve the tasks
6. Achieve the research goal
7. Present the obtained results in the paper
Fig. 1. The algorithm of the research (compiled by the authors).
3 Results New conditions for development of industrial ecosystems are determined by both the trends emerging in recent years, and changes in the geopolitical situation in 2022. The evolving ecosystems of different types throughout the globe has provoked development of the legislative framework that regulates such activities. Ecosystems are actively using digital platforms for their functioning, which leads to emergence of digital services and
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rules of their usage [18, 19]. Today’s legislation is unable to ensure full protection for participants of trade and market relations in the digital environment, which now hinders quality development of industrial ecosystems (Fig. 2).
Fig. 2. Conditions for the development of industrial ecosystems in the new reality (compiled by the authors).
Market regulators are seriously concerned with the development of companies’ investments in non-core activities. Investing in non-core assets is a feature of the development of industrial ecosystem participants. A specific situation has developed with intellectual property rights. This sphere of civil relations was not easy to regulate before, due to the peculiarities of the objects of such rights protection. The issue of regulating the rights and obligations of right holders and users has become even more acute and added new unresolved conflicts with the development of ecosystems. The trends of 2022 have brought the issues of import substitution to a high level of significance. If earlier countries directed their activities towards interethnic integration and business globalization, then recent events have forced business to reconsider its position. Narrowing of the ecosystems’ geographical scope thanks to the high level of the 2022 geopolitical risks will affect further development models of economic entities in the near future. Relying on the identified trends, we note the following new demands for industrial ecosystems in the new reality (Fig. 3). The need to create conditions for maintaining mutually beneficial cooperation between participants of the industrial ecosystem persists to be relevant. It is not only the need to maintain the existing level of the ecosystem that can be attributed to the new trend, but conditions for its continued existence as well. Industrial ecosystems are faced with the need of finding new partners and suppliers, new
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innovative solutions, mainly for the implementation of import substitution in the face of new sanctions and changes in logistics corridors.
Fig. 3. Needs of industrial ecosystems in the new reality (compiled by the authors).
The need for import substitution creates a demand for R&D, which requires additional resources and funding. The need to allocate beyond the limits budgets for previously unforeseen research is a new requirement of today’s industrial ecosystems. Innovation is a feature of industrial ecosystems’ functioning [15]. In this regard, the need for R&D financing immanently exists in such structures. However, the features of the current stage of business entities make this demand paramount and deserving close attention. There is still the need in a more standardized digital environment in which industries and ecosystems operate. Implementation of universal standardization will reduce uncertainty and costs, and coordinate processes of ordering and supplying the necessary resources. Based on the analysis of new development conditions and needs of industrial ecosystems, we present a conceptual model of socio-economic mechanisms of managing the intellectual capital of the industrial ecosystem in the new reality (Fig. 4). The presented conceptual model of socio-economic mechanisms of managing the intellectual capital of the industrial ecosystem is based on the presence of all the same incoming flows, they are resources, scientific and technical progress, and information. However, functioning of the industrial ecosystem is realized in new conditions and with new demands. In this connection, it is necessary to understand the aspects of organizational and economic mechanisms which need adjustments to be made to achieve the necessary management results.
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Fig. 4. Conceptual model of socio-economic mechanisms of managing the intellectual capital of the industrial ecosystem in the new reality (compiled by the authors).
As shown in Fig. 4, it is important to reproduce both organizational and economic levers of influence on the intellectual capital of the industrial ecosystem in the management process. The systems approach becomes especially relevant when establishing management mechanisms in addressing the industrial ecosystem, which is associated with the peculiarities of the format of interaction between ecosystem participants. Note that managing functions constitute the classic set of procedures. However, features of their implementation will arise due to the fundamental difference in the forms of cooperation within the industrial eco-system. The analysis of the scheme presented in Fig. 4 allows forming objective ideas on the vectors of further efforts on managing the intellectual capital of the industrial ecosystem in the new reality.
4 Discussion The authors proceed from the established ideas on the essence of the ecosystem and industrial ecosystem concepts, presented by the following authors [5, 8]. Features of the modern agenda and trends in the industry development require rethinking of the conceptual foundations of socio-economic management mechanisms. The relevance of the study of socio-economic mechanisms of managing industrial ecosystems is noted by various authors [12, 16, 17]. However, the features of the 2022 trends have not yet been considered in the research works on the issue under consideration, which makes the present study relevant. As it is noted by [9], products or business model can be produced by the industrial ecosystem. This cannot be denied. However, it is worth adding that they can also include services, works and intellectual rights, which we generally call the product. Such a list
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makes the product of the industrial ecosystem modular, intelligent and individual, which makes it possible to form its architecture at the request of a particular consumer. It is also stated [9] that reasons for maintaining collaboration of the industrial ecosystem participants are their internal motivation and belief in a synergistic effect from joint activities. Therefore, there is no need to resort to coercive methods. Agreeing with the low efficiency of coercive methods in economic activities, we also note the need for socio-economic mechanisms of maintaining and developing the industrial ecosystem, especially in need of innovation. We consider it expedient to include methods of assessing and regulating activities into the management toolkit, as well as assessment and impact algorithms both for the entire ecosystem and for its individual participants. Development, implementation and regular use of these tools will enhance the interest in the activities of the industrial ecosystem and make its environment more competitive. Changed environmental conditions have formed a new agenda for the problems of industrial ecosystems, which, in turn, requires revision of socio-economic mechanisms of managing the intellectual capital of the industrial ecosystem. The presented study is conceptual by nature, which limits the scope of its practical application.
5 Conclusion The present paper identifies conditions for development of industrial ecosystems in the new reality, which include the COVID-19 pandemic, geopolitical situation, need for import substitution, high inflation and changing role of currencies in the international and domestic markets, development of digital platforms and services, weak protection of intellectual property and entities in the Internet environment. The paper identifies new needs of industrial ecosystems in the new reality, which include the demand for new suppliers, buyers and customers, techniques of import substitution, logistics channels, as well as in the development of performance standards in the digital environment and its legal framework. A conceptual model of socio-economic mechanisms of managing the intellectual capital of the industrial ecosystem in the new reality has been formed as a result of identifying development conditions and demands of industrial ecosystems, including organizational and economic influence levers on the factors of the internal environment of the industrial ecosystem, taking into account the features of its external environment. The results obtained can be useful for researchers of industrial ecosystems as well as for the top management of the industrial ecosystems. Acknowledgments. The study was carried out at the expense of a grant from the Russian Science Foundation No. 23-28-01316, https://rscf.en/project/23-28-01316/.
References 1. Skhvediani, A., Kudryavtseva, T., Rodionov, D.: Regional industrial specialization: case of Russian electrical equipment, electronic and optical equipment industry. In: Rodionov, D., Kudryavtseva, T., Berawi, M.A., Skhvediani, A. (eds.) SPBPU IDE 2019. CCIS, vol. 1273, pp. 125–139. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60080-8_7
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Assessing Global Trends in World Energy: Genesis of the New Energy Transition Andrey Sosnilo(B) and Alexander Gorovoy ITMO University, Saint-Petersburg, Russia [email protected]
Abstract. The work is aimed at analyzing the main trends and factors that will have a significant impact on the transformation of the global energy system, as well as at assessing the development trends of renewable energy sources in the European Union, China, South Korea, Japan, Russia, and other countries. The study used methods of statistical analysis. The study received elements of scientific novelty, the comparative analysis of oil consumption forecasts until 2050 was carried out. The impact of transport electrification and ESG investments in oil consumption was considered. The dynamics of the share of energy resources produced using renewable energy sources (RES) in the total energy production in the Russian Federation in 2013–2021 was analyzed. Energy development forecasts and plans for the strategic development of the world’s largest energy companies (BP, Total, Shell, Lukoil, Tatneft) were analyzed. The analysis of the RES development in the Russian Federation, dynamics of changing electricity generation and a table of the share of renewable power plants in the energy balance of various countries was given. The practical significance of this work is due to the importance of the research results for the development and implementation of policies in the field of energy development of the Russian Federation as well as of its individual entities, especially of remote and inaccessible territories. Keywords: world energy system · energy balance · oil consumption · renewable energy sources · energy transition · renewable energy sources · ESG · forecasts
1 Introduction Research conducted by university and corporate scientists around the world over the past decade has significantly reduced the cost of generating solar and wind energy, which has affected its competitiveness in comparison with traditional methods of electricity generation. In 2022, it was announced that a group of Dutch scientists had achieved the level of 30% efficiency of solar panels using perovskite-silicon photo-voltaic cell technology [1]. By now, the cost of solar and wind energy has fallen so significantly that the energy production of using the sun and wind has come close to the cheapest methods of power generation. The ensuing energy crisis, which arose due to the geopolitical situation and imbalance between supply and demand, led to a significant increase in the cost of electricity and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 398–415, 2023. https://doi.org/10.1007/978-3-031-32719-3_31
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its shortage, reactivation of generation by burning coal and using nuclear energy. The current difficult situation with the EU countries energy supply has raised the issue of accelerating the development of RES to an extremely high level. The average annual price of 1 megawatt of solar energy was e 43.3 in Germany and $ 50 in the US in 2018, and it was almost twice higher, or e 91.7 per 1 Mwh, in 2016. (Bloomberg New Energy Finance; CERES). The cost of electricity generated by solar power plants (SPP) in such countries as China, India or Brazil has decreased significantly, reaching the level of 30% of the 2010 values and has become cheaper than the energy generated by wind farms [2]. The average cost of electricity production (levelized cost of electricity) ranged from $35 to $55 per 1 megawatt per hour over the lifetime of the plants in the world’s largest markets of the USA, EU, China and India in 2022. The cost of one megawatt produced by solar power plants has fallen almost 10 times (from $300 to $35) in a decade, and it has decreased by almost 3 times over the past 4 years (from $100) [3]. The LCOE indicator (levelized cost of electricity or full levelized cost of electricity) is used to compare the cost of various methods of power generation. All investment and operating costs are taken into account when assessing this indicator. Lazard annually releases the LCOE ratings for various fuels. The company’s data show a significant 66% reduction in the cost of wind energy production and 85% reduction in the cost of the sun energy over 7 years. Production costs in the LCOE estimates are already comparable or even lower than the values of this parameter for gas and coal for the most efficient capacities of the renewable energy sources (RES) [4]. The expansion program (REN 21), which was supported by 144 countries of the world by 2013, has become one of the key documents that determine the trends in the expansion of electricity generation from RES [5]. According to the forecasts of the International Energy Agency (IEA), RES will displace coal from the second place by 2030 and will lead in terms of electricity generation in the world [6]. Factors and trends described in the study confirm the formation of conditions for the emergence of a new energy transition to renewable energy sources. The countries of northern Europe have traditionally become leaders in the process of energy transformation and development of renewable energy sources (RES). By 2015, the amount of commissioned RES capacities exceeded the amount of traditional ones. The ratification of the Paris Agreement on climate policy and sustainable development played its role. The European Union has set an ambitious goal of reducing greenhouse gas emissions to zero by 2050, which means abandoning the use of hydrocarbon energy sources (oil and gas). Natural gas should be replaced by hydrogen, the mass production programs of which are currently launched in the European Union and are gaining momentum. The European green deal provides for a reduction in greenhouse gas emissions by 55% by 2030, and the achievement of zero emissions in the future [7]. The program has serious financial support. Starting from 2021, 40% of the budget allocated to agricultural policies will be allocated to the planned transformation. In general, the EU intends to redirect about 1–2% of its GDP to achieve these goals. The European Commission estimated the necessary costs of transformation at 260 billion euros per year, and this amount could double, starting from 2030. It should be taken into account that these plans
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can be subject to adjustments by aggravation in the geopolitical arena, energy crisis and recession, which can develop into more severe forms. The 7th environmental action program was functioning in the EU in 2014–2020, which also provided for a number of measures promoting the development of RES. Such initiatives were supported by both business structures and financial institutions. For example, the European Investment Bank (EIB) has refused to finance traditional energy projects using fossil fuels since 2021. The actions of the European Union are driven not only by the desire for environmental neutrality and fight against climate change, but also by ensuring energy security. The EU has steadily increased its dependence on imports of fossil fuels over the past decades (its imports reached 90% of oil, 69% of gas, 42% of coal, and 40% of uranium by 2017) in contrast to China, which has significantly diversified its energy supplies. In addition, the European Commission concluded that reducing emissions from the use of hydrocarbons will allow the EU to save significant funds by reducing the incidence of its citizens. Investments in RES will create additional jobs and reducing the acquisition costs from abroad will save trillions of euros in the coming decades (Fig. 1).
Fig. 1. Share of renewable energy in the EU countries in 2015–2018 [8]
2 Methods Analytical and computational techniques were used in the course of the study, statistical data and projected estimates were analyzed. The average future estimated value of the RES share in the global energy balance was calculated. The trend coefficient was calculated revealing the average change in the effective indicator with the change in the period of time. The comparative analysis of global oil consumption forecasts was carried
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out. The comparing technique of various methods of power generation costs was applied using such indicator as the LCOE (levelized cost of electricity or full levelized cost of electricity).
3 Discussion Assessment prospects for the RES development differ quite a lot, which can be explained by the multidirectional economic interest of the discussion participants. While hydrocarbon producers, refiners and sellers are interested in maintaining the ‘status quo’, and thereby most of them give lower estimates of the RES development, and indicate longer time horizons, then the RES industry representatives, together with financial institutions that sell their shares, give more optimistic assessments [9]. The scientific community also often adapts to the needs of the industry and gives different assessments depending on the sources of grant support [10–13]. Despite the hydrocarbon producers’ desire to preserve the current situation with high oil prices, individual market players take active steps to integrate with RES and create new infrastructure. BP said it would reduce oil and gas production by 40% over the next decade and invest up to $5 billion a year in renewable energy production. The company’s forecasts are extremely pessimistic. According to BP experts, hydrocarbon consumption will drop to 50% by 2050, and to 80% in the most negative scenario. Analysts explain their conclusions by a significant increase in the RES efficiency and electrification of road transport, which accounts for a large consumption amounts. Demand will decline significantly starting from the mid-2030s [14]. Total is another major oil company to release its energy outlook. The company released the ‘Total energy outlook 2020’ report with inertial and breakthrough scenarios. The inertial (Momentum) scenario assumes that its only Europe that will cope with reducing emissions to zero by 2050, and the rest will implement these tasks only partially. The breakthrough scenario (Rupture) assumes that all countries will move towards achieving zero emissions. Both scenarios assume an increase in oil consumption until the end of the 2020s, after which a gradual decline will begin. The company believes that RES will lead and generate more than 50% of electricity by 2050. In the inertial scenario, natural gas remains the largest source of electrical energy, while coal continues to be partially used. Total also predicts that the wind power capacity will grow 25 times, and the solar power 12 times even in the inertial scenario by 2050 [15]. Royal Dutch Shell and Total have also publicly announced their plans for a gradual transition to renewable energy and back up their words financially, increasing investments every year. In 2019, Exxon gave a forecast for the price of oil in 2026–2027 which, according to the company, will rise to $72, and the average price of Brent oil will be about $62 over 5 years. In the summer of 2020, the company lowered its forecast to $ 50–$ 55 per barrel over the next 5 years and $ 60 in 2026–2027 [16]. 2022 showed that oil prices could return from pandemic lows to highs much faster than experts predict due to geopolitical changes and OPEC+ commitment to concerted action to limit production. ExxonMobil was one of the most valuable global companies by capitalization 10 years ago, but Dow Jones excluded the corporation from its industrial index in August
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2020, which it has been included in since 1928. Exxon’s market cap has fallen from $400 billion in 2011 to just $175 billion in 2020. Shell invested $400 million in renewables in 2019 and planned to increase spending to $1 billion by 2020. The company believes that only 75% of energy consumption will be met by traditional energy sources by 2050. Lukoil owns several solar power plants in Bulgaria and Romania, and the company launched a solar power plant in Volgograd in 2018. Lukoil also invested in alternative energy projects in the Persian Gulf. All of the above-mentioned companies make significant investments diversifying generation of their income, to a large extent these investments are associated with RES, either directly or through partnerships [17]. Gradual awareness of the problem comes to the leadership of Russian oil companies as well. In particular, Leonid Fedun, Vice President of Strategic Development of Lukoil, stated that he expects a reduction in Russian oil and gas exports to Europe by 2040 in his interview with the Kommersant newspaper. ‘It’s inevitable. The European oil market will shrink and may be halved by 2040, according to the EU forecast [18]. Tatneft has set carbon neutrality as its strategic goal by 2050 and intends to increase ESG investments [19]. The ESG abbreviation includes factors of environmental friendliness, social development, and responsible corporate governance. Pension and hedge funds are now taking into account ESG rankings of companies when making investment decisions. For example, the PKA Danish pension fund (with $46 billion assets) sold shares of 70 coal companies and 35 oil and gas companies in its portfolio as a contribution to the Paris Climate Agreement. Alexei Kudrin, the head of the Accounts Chamber of the Russian Federation, believes that Russia will have to replace its oil exports with other goods in the 2030s as oil consumption will decline. According to the forecasts of all the leading agencies that predict consumption of oil and oil resources in the energy sector, the global oil consumption peak will take place [for the period] from 2025 to approximately 2032–2033. And then consumption will decline. This is a double challenge for Russia compared to other countries. We need to replace this oil export with some another. As a result of this global and, perhaps, civilizational crisis, some countries will move on, and other countries will not be able to cope with it. Our country is in a rather tough situation and there are great challenges ahead [20]. The global energy development scenario proposed by the International Renewable Energy Agency (IRENA) assumes a significant transformation of the energy sector and a 75% reduction in the fossil fuel consumption by 2050. The agency notes that energy consumption will continue to grow and increase from 600 exajoules (EJ) today to 710 EJ by 2050. At the same time, the amount of fossil fuels consumed remains near current levels, which indicates an increase in the share of renewable energy sources in total consumption. However, given the trends that characterize the environmental orientation of the transformation in the energy sector, most developed countries are on the path to reducing traditional fossil fuels, and this will also affect the global level decline. According to the agency’s analysts, there will be a major decline in coal consumption, it will be 41 and 87%, respectively, in 2030 and 2050; the oil prices decline will be 31% and 70% which will occur in 2030 and 2050, respectively. The natural gas consumption will increase by 3% in 2030 (over 40% natural gas growth by 2030). The Agency’s
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analysts note that RES will provide about 86% of the electricity production by 2050. Under the baseline scenario proposed by IRENA, electricity will become the main source of energy, providing 50% of total energy consumption by 2050 (compared to 20% today). It is assumed that LCOE will be reduced by 2 times for wind generation and 9 times for solar energy in the period from 2010 to 2050 [21]. It is difficult to accuse the OPEC cartel of being interested in exaggerating pessimistic estimates of declining oil demand. The organization annually releases the OPEC’s World Oil Outlook (WOO) report analyzing the current situation in the industry and its development prospects. According to analysts, oil accounted for more than 31% of global energy demand in 2019, and it is predicted to retain its leadership until 2045 in the amount of 27%; gas share is projected to be about 25%, and coal share is to be about 20% [22]. In the medium term, global oil demand will recover after the fall in oil prices at a fairly high annual rate and reach the level of 103.7 million barrels per day by 2025. Experts cite a return to pre-COVID-19 economic growth rates, as well as a significant increase in demand in the sectors most affected by restrictions during the pandemic (aviation, automotive and industrial sectors), as reasons for the growth in demand. Thus, according to OPEC forecast, consumption of hydrocarbons will decline in 5 years, especially in OECD countries, and will decrease by 35% in 2045. According to OPEC estimates, developed countries consumed about 41.4 million barrels per day in 2019, which accounted for 45.6% of global oil demand. These countries will reduce consumption over the next 5 years, and it will fall up to 40.2 million barrels per day by 2025, to 38.2 million barrels by 2030, and to 32.3 million by 2040 [22]. The policy of climate neutrality, which provides for the reduction of atmospheric emissions from the use of hydrocarbons and stimulation of electric transport development will become the reduction reasons, which will significantly reduce the fuel demand. OPEC experts condition changes in transport sector by increasing the share of electric vehicles to 16% in the total volume by 2045. However, large markets such as China can significantly increase the use of both RES (electricity is still being cut off at night in China) and electric transport much earlier due to serious environmental problems in large cities (the smog). The Asian region has become the leader in the carbon dioxide emissions production, reaching almost 50% of the global total (17.27 billion tons of CO2 ). In 2020, emissions decreased naturally due to the pandemic, but they will continue to grow with further economic recovery. At the same time, China has set the goal of achieving neutrality in terms of hydrocarbon emissions by 2060, which will require significant investments in reforming the energy sector. Sanford C. Bernstein & Co estimates that energy transformation will require an investment of about $5.5 trillion. According to the China National Renewable Energy Center (CNREC) and the State Energy Research Institute (ERI), the plan is to increase the production of energy from renewable sources by 11 times. Coal energy will have to be reduced by 25 times in 40 years, and oil and gas should be used 60–70% less than today. The results achieved by China are very revealing. If the capacity of solar generation was 43 GW in China and 39 GW in Germany in 2015, then China surpassed Germany by 4 times, and the USA by 2.5 times by the end of 2019. The total capacity of solar panels in China amounted to 204 GW, which is equivalent to the capacity of three dozen Sayano-Shushensky HPPs.
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Japan, as another leader in the global economy, has also expressed its commitment to the new green energy transformation policy. Japan’s newly elected prime minister Yoshihide Suga stated in his first parliamentary keynote speech on October 26, 2020, that achieving carbon neutrality by 2050 is his country’s goal. The key areas of Japan’s ‘green’ plan are constructing new offshore wind turbine capacities with the increase in capacity to 45 gigawatts by 2040; reducing the use of fossil fuels at all thermal power plants by 2030; increasing total hydrogen production and consumption up to 20 million tons by 2050; developing new types of reactors; introducing a production ban of cars running only on gasoline by the mid-2030s; aircraft electrification and development of environmentally friendly fuels. South Korea is not only one of the leading economies in the region and the world, but also one of the largest global polluters (top 10). However, according to President Moon Jae-in, the country is aiming to achieve zero carbon emissions by 2050. This will be a very difficult task for Korea, since 40% of the country’s energy depends on coal, and the share of RES is only about 6%. The Green New Deal involves investment of $61.9 billion, which is planned to be used to create 319,000 new jobs by 2022 and 659,000 jobs by 2025. South Korea has set the task of increasing the capacity of RES by 3 times, investing $7.2 billion to achieve it. It is planned to invest more than $10 billion in the development of environmentally friendly transport and increase the number of electric vehicles to 1.13 million, and of hydrogen-powered vehicles up to 200 thousand; which is from 91.5 thousand and 5 thousand in 2019, respectively. Costa Rica is one of the most successful countries in the use of RES in the world. According to REVE, 99.62% of the electricity in the country came from RES in 2019. Hydropower generated 78% of electricity, 10.29% came from wind farms, 10.23% from geothermal energy and 0.84% from solar power. According to the State Agency on Energy Efficiency and Energy Saving, Ukrainian households installed 27,623 solar panels and use ‘green’ electricity as of October 1, 2020. Nearly 2,000 more households switched to solar panels in the third quarter of 2020. The total capacity of all solar stations in Ukraine is 712 MW. Households invested about 560 million euros in solar panels in 2015–2020. Mayors of major cities proclaimed the ‘Global Green Deal’ at the C40 conference in Copenhagen. Regional leaders are also ready to support the policy of reducing emissions within the framework of the project. This initiative has already been joined by the leaders of the 80 largest cities of the planet, such as New York, Los Angeles, Paris, Berlin, Barcelona, etc. The essence of the initiative is to support renewable energy, electric and hydrogen transport, and reduce industrial emissions. In general, the development of RES is becoming a significant global trend with strong growth prospects [23]. Renewable energy is becoming a major employer and is constantly increasing the number of people working in this sector of the economy. According to the IRENA agency, about 11 million people work in the renewable energy sector. Solar energy employs the largest number of employed people among all areas of renewable energy, employing more than 3.5 million people. Of this number, more than 60% of the staff work in China. The RES growth is due to the decrease in the cost of purchasing solar panels, wind farms and batteries. At the same time, significant investments have been made in the
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development of innovations in these sectors, and further scaling of already created technologies and R&D results can be expected, which will lead to a further reduction in the costs of developing RES capacities in the medium term. Bloomberg predicts that constructing costs of an average solar power plant can be reduced by 70%, and of wind farm by 50% by 2050. However, their construction today is already much cheaper than new coal and gas power plants. Most experts assess the impact of transport electrification on reducing the traditional fossil fuels consumption as significant. Bloomberg New Energy Finance (BNEF) estimates that electric vehicles (EVs) will account for 57% of all passenger car sales by 2040, and the global car fleet will be more than 30% of EVs [24]. If we compare the forecasts of OPEC and Bloomberg, then the forecast of the latter is almost 2 times higher than the estimates of OPEC. BNEF predicts that total EVs sales will grow to 10 million per year by 2025 (2.2 million in 2018, 2.1 million in 2019), 28 million by 2030, and 56 million by 2040 per year. The world fleet amounted to 7.2 million pure EVs and hybrids in 2020. BNEF also predicted in 2018 that electric transport could reduce oil consumption by 7.3 million barrels per day by 2040. In 2019, analysts almost doubled their forecast, they assessed that passenger and commercial EVs, sharing services, and electric buses will reduce oil consumption by 13.7 million barrels per day [25]. In some Scandinavian countries, people are moving away from internal combustion engines not because of fashion or environmental trends, but because of pragmatic problems with selling used cars in local markets. Aircraft manufacturers are also following the trend, Airbus has developed the concept of zero emission aircraft powered by a hydrogen engine. The World Energy Outlook 2019, presented by the International Energy Agency (IEA), outlines growth in energy consumption without regulatory interventions; energy demand growth of 1.3% per year until 2040; increased pressure on the energy sector and increased emissions into the atmosphere; recognition of the low-carbon energy transition by authorities in all regions of the world, while maintaining a strong dependence on fossil fuels; decrease in the share of oil from the current value of 31.4% to 29.3%; increase in the share of natural gas from 22.9% to 25.3%; reduction of coal share from 26.7% to 23.4% as the baseline scenario. Analysts of the agency suggest that oil will remain the main energy carrier in the world until 2040. Global oil demand is expected to grow by 9.8%, or to 106.4 million barrels per day by 2040 [26]. The forecast, presented in 2020, revises the income of developing countries from oil and gas exports to the downside in 2020–2030. In the Sustainable Development Scenario, it is assumed that there will be an accelerated transition to renewable energy and progress towards meeting the goals of the Paris climate agreement. At the same time, it is predicted that oil prices will be at the $57 per barrel level by 2025 if this scenario is implemented, as well as they will drop to $53 per barrel by 2040 [27].
4 Results The comparative analysis of global oil consumption in 2025–2050 was carried out in the course of the study, the results of the analysis are presented in Table 1 (Table 2).
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A. Sosnilo and A. Gorovoy Table 1. Forecasts of oil consumption in 2025–2050 (million barrels per day). Forecast for 2025 Forecast for 2030–35
Forecast for 2040 Forecast for 2045–2050
International Energy Agency (IEA)
102.4 million B/D
105,4 million B/D
106,3 million B/D 66–50 million B/D
International Renewable Energy Agency (IRENA)
–
60 million B/D
41 million B/D
OPEC
103,7 million B/D
112 million B/D
110,6 million B/D 109 million B/D
BP
90–100 million B/D
85–97 million B/D
55–95 million B/D
30–55 million B/D
Total
–
102,3 million B/D
–
45–50 million B/D
World Energy Council (WEC)
–
103 million B/D
–
–
22 million B/D
Sources: Compiled by the author, based on data from the International Energy Agency (IEA), International Renewable Energy Agency (IRENA), OPEC, BP, Total, World Energy Council (WEC). Table 2. Forecasts of the share of RES in the global energy balance by 2050 (%) Source
Share of RES in the global balance by 2050
Bloomberg New Energy Finance (BNEF)
50%
Columbia University
50–70%
Centre for Research on Energy and Clean Air (CREA)
73%
US Department of Energy
64%
Sources: Compiled by the author based on data from Bloomberg New Energy Finance (BNEF), Columbia University, Centre for Research on Energy and Clean Air (CREA), U.S. Department of Energy.
Based on expert forecasts, the average value of the RES share is 61.75% in the global energy balance. The trend coefficient equal to 0.997% was calculated based on the data mathematical analysis. This indicator discloses the average change in the effective indicator with a change in the period of time. Thus, the increase in the share of RES in the EU occurs on average by 0.997% annually, with allowance for the current investments size. From the above data, it can be seen that, in general, the EU countries will not be able to radically change their energy balance in the next decade. However, it is also
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obvious that the ratio will change significantly in favor of RES. Planned investments will accelerate the speed of the ongoing changes. According to Eurostat, the share of renewable energy has been steadily growing in recent years. The analysis was carried out of the renewable energy share in the EU countries in 2015–2018. Indicators vary significantly depending on the country (more than 72% in Norway and Iceland, 7% in Malta and the Netherlands). Greater difficulties will be experienced by Eastern European countries, such as Poland, where the share of coal use is higher. The International Energy Agency (IEA) predicted that 45% of the global energy consumption will come from RES by 2040. India and China as one of the largest emerging economies will be heavily dependenent on oil and gas imports (China by 75–82%, India by 88–91%). According to experts, the gas share in the structure of world energy consumption has been increasing in recent years. It was 22% in 2018, and it may reach 25–27% by 2040. Natural gas will take the lead in terms of annual growth rates of 1.3–1.6%. At the same time, the increase in gas demand will take place with the exception of the European Union countries [28]. The EU plans to launch large-scale programs for the construction of hydrogen generation capacities, through the use of which it is planned to ensure zero emissions. The main growth in gas demand can be expected from China and India, which largely use coal-fired power plants, and the environmental situation in some regions of these countries is catastrophic. Growth in global gas production is also expected. It may grow by 39–48% by 2040, while LNG exports will play a significant role in this as its share could reach 60–65% [28] (Fig. 2).
Fig. 2. Forecasts of oil consumption peaks until 2040 [31].
Agencies and companies such as IRENA, ARENA, Shell predict hydrogen consumption at the level of 500–2000 TWh by 2050, some organizations predict a multiple more hydrogen consumption (Hydrogen Council predicts 16100 TWh, or 18% of world energy
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consumption). In turn, this may mean a significant increase in the role of hydrogen in the global energy balance (Fig. 3).
Fig. 3. Forecast of new capacities of centralized and distributed power generation commissioning in the world, GW [31].
The trend of increasing the importance of private electricity producers and freedom of consumers’ choice has become increasingly significant for the European market in recent years. Many households have not just the opportunity to choose from whom to buy electricity, but also the opportunity to generate and sell their own energy thanks to market regulation. According to Bloomberg New Energy Finance analysts, the share of the distributed generation in total capacity could be more than 30% in Germany and Brazil, and 45% in Australia by 2040. If we speak about the situation in the Russian Federation, the Russian Government also took a number of steps in 2009 to develop renewable energy primarily guided by the remoteness of a number of its inhabited areas. As a result, ‘The main directions of the state policy in the field of improving the energy efficiency of the electric power industry based on the use of renewable energy sources for the period up to 2024’ regulatory act was adopted. The target of 4.5% of the total electricity production received from RES was announced by 2020, and by 2024 subsequently (excluding hydropower). The supplemental RES supporting program under power supply agreements (PSA) was launched in 2013, but even it could not cover the 25 GW of the capacity required to complete the task. Nevertheless, the program contributed to the sustainable implementation of the targets for the introduction of 5.5 GW of solar and wind power plants and small hydropower plants. The commissioning of the first RES capacities started in 2015 and will reach its peak of 951 MW per year by 2021, and the average for 10 years of program implementation should reach about 548 MW annually. In general, hydroelectric power industry is quite developed in Russia due to the investments and efforts of the USSR authorities. 17–18% of the electricity is generated by the hydroelectric generating capacities in the Russian Federation. According to the
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Ministry of Economic Development, this is almost 99% of the total electricity generation based on renewable sources in the Russian Federation (as of 2018) [29]. At the same time, there remains a significant reserve for increasing the degree of economic hydro potential development, which is used by only 20%, and by only 6% in some regions of the Russian Federation (Far East). In recent years, Russia has been focusing on the construction of solar power plants, and their opening is planned in Yakutia, Tuva, and Altai. In 2019, such companies as Solar Systems, Rosatom, Hevel, as well as two foreign companies Fortum and Enel made the largest contribution to the RES development in Russia. A number of solar station tests were completed in 2020, and construction of 17 solar parks in Adygea, Volgograd and Astrakhan regions, Kalmykia and Bashkiria, Stavropol Territory should be completed soon. Pilot projects are also being launched by some oil companies. Gazpromneft PJSC has commissioned a filling station with a 5 kW solar power plant, which will meet up to 5% of the operational needs of filling stations per year, and installation of charging stations for electric vehicles has also started at some filling stations. Inter RAO has adopted the company’s development strategy, which involves significant investments in RES, including the use of a 30% quasi-treasury stake for partnerships and acquisitions of other companies. According to the Unified Interdepartmental Statistical Information System (UISIS), the share of energy resources produced using renewable energy sources in the total amount of energy production of the Russian Federation reached 19% by 2021 (Tables 3 and 4). Table 3. Share of energy resources produced using renewable energy sources in the total volume of energy production of the Russian Federation (2013–2021) [30]. 2013
2014
2015
2016
2017
2018
2019
2020
2021
17.1
16.4
15.8
17
17
17.3
17.5
19.8
19.0
Table 4. Capacity of generating facilities operating on the basis of renewable energy sources of the Russian Federation (excluding hydroelectric power plants with an installed capacity of over 25 MW) [31]. 2013
2014
2015
2016
2017
2018
2019
2020
2021
381.8
706.9
906.3
1,000.0
1,077.0
1,258.6
2,010.9
3,239.7
3,876.6
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The share of renewable energy sources (RES, including hydropower) rose to almost 26% in the global energy mix in 2019. Global installed RES capacity reached 2,351 GW by the beginning of 2019, about 50% of which comes from large hydropower. The share of RES in the global energy balance remained almost the same (28.1%) in the period from 2020 to 2021, which is higher than the level of 2019 (26.3%) [32, 33] (Table 5 and Fig. 4). Table 5. The share of RES in the energy balance of the countries of the world (%) in 2021 [32]. Country
%
Norway
99
New Zealand
80.9
Brazil
78.4
Columbia
74.5
Canada
68
Sweden
67
Portugal
65.5
Chili
47.2
Spain
47.1
Romania
44.4
Germany
41.5
Italy
41.4
Fig. 4. Values of target indicators of production and consumption amounts of electricity using renewable energy sources and the generation share of individual RES in the Russian Federation in 2014–2024 [34].
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The Power Purchase Agreement (PPA) program made it possible to increase the degree of localization of the equipment production for energy generation. In the process of the program implementation, more large-scale technological competencies were formed in the field of production and service of equipment for solar and wind energy. Some experts expressed the opinion that the network parity will be achieved in Russia by the mid-2020s, when the cost of the RES production will become equal to the traditional generation (Figs. 5, 6, 7 and 8).
Fig. 5. Share of energy resources produced using renewable energy sources in the total amount of energy resources (in %) [35].
Fig. 6. Dynamics of generated RES facilities capacity in the Russian Federation in 2013–2021 (mW) [36].
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Fig. 7. Amount of the certified electricity generation at qualified RES facilities in the retail and wholesale markets (thousand kWh) [37].
Fig. 8. Power plants based on RES (PPA RES) commissioning dynamics, MW. Sources: Russia Renewable Energy Development Association (RREDA), JSC Russian Power System Operator, NP Market Council (Qualified objects register) [38].
5 Conclusion According to Vygon Consulting, the share of RES will be 66% by 2030. The International Energy Agency predicts that the share of RES in the global electricity generation will reach 30%t by 2023. Investments in RES have doubled those in power plants operating on traditional raw materials, while oil-producing countries (Saudi Arabia, the United Arab Emirates, Canada, etc.) have set goals for the development of renewable energy at the national level. The emphasis on investment in sustainable development and clean energy was also one of the theses of J. Biden’s election campaign. According to J. Biden, the new government could allocate more than 1 trillion USD for the infrastructure package.
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Summing up, it should be noted that the existing trends in the global energy sector can have a negative impact on the budget revenues of the Russian Federation, based on its current structure. The goals set by the world’s leading economies supported by largescale investment programs, social values associated with improving the environment and reducing the impact on the planet’s climate, will only increase their influence in the future. The situation has been aggravated by the energy crisis and is accelerating the implementation of many projects and plans. Transport electrification, which is becoming a global trend, can dramatically accelerate the decline in oil consumption. ESG investments will continue to grow in importance, helping to support the most responsible companies and startups. Even earlier in time, the Government of the Russian Federation will have to develop protective measures against introduction of the carbon import tax by the European Union, which could affect more than 40% of Russian exports.
References 1. 3d news. https://3dnews.ru/1075155/v-niderlandah-sozdali-solnechnuyu-batareyu-s-effekt ivnostyu-bolee-30-?ysclid=l8ye1zrhq1589593563. Accessed 01 Mar 2022 2. Bloomberg New Energy Finance. https://www.bloomberg.com/news/articles/2016-12-15/ world-energy-hits-a-turning-point-solar-that-s-cheaper-than-wind. Accessed 01 Mar 2022 3. WEF Renewable_Infrastructure_Investment_Handbook. https://translate.google.com/web site?sl=auto&tl=ru&hl=ru&u=http://www3.weforum.org/docs/WEF_Renewable_Infrastru cture_Investment_Handbook.pdf. Accessed 01 Mar 2022 4. Lazard LCOA Ratings. https://www.lazard.com/perspective/lcoe2019. Accessed 01 Mar 2022 5. Renewables 2014 Global Status Report. Paris. https://www.ren21.net/reports/global-statusreport/. Accessed 01 Mar 2022 6. International Energy Agency. https://www.iea.org/data-and-statistics?country=WORLD& fuel=Energy%20supply&indicator=TPESbySource. Accessed 01 Mar 2022 7. European green deal. https://ec.europa.eu/info/strategy/priorities-2019-2024/europeangreen-deal_en. Accessed 01 Mar 2022 8. Energy Security Strategy, European Commission. https://ec.europa.eu/energy/en/topics/ene rgy-strategy-and-energy-union/energy-security-strategy. Accessed 01 Mar 2022 9. Rodionov, D., Konnikov, E., Dubolazova, Y., Polyanina, P., Konnikova, O.: Development of socio-economic systems in the context of information technology development. In: Proceedings of the 16th ECIE, pp. 810–820 (2021) 10. Guliev, I.: Key trends in the global electricity market. Glavnyj Energetik (Chief Power Eng.) 1, 12–16 (2022) 11. Hachaturyan, N.: Processes of the current stage of development of the glob-al electric power industry. Econ. Manage.: Probl. Solut. 1(121), 76–88 (2022) 12. Narbek, S.: Current trend in the use of renewable energy in Germany. Nauchnye issledovaniya XXI veka (21st Cent. Sci. Res.) 2(16), 81–84 (2022) 13. Rudnickij, S.: Energy transition: challenges and opportunities for the oilfield ser-vices and equipment sector. Burenie neft’ (Drilling Oil) 1, 14–17 (2022) 14. BP Energy Outlook 2020. https://www.bp.com/content/dam/bp/business-sites/en/global/ corporate/pdfs/energy-economics/energy-outlook/bp-energy-outlook-2020.pdf. Accessed 03 Sept 2021 15. Total energy outlook 2020. https://www.total.com/sites/g/files/nytnzq111/files/documents/ 2020-09/total-energy-outlook-presentation-29-september-2020.pdf. Accessed 01 Sept 2021
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16. Exxon Documents Reveal More Pessimistic Outlook for Oil Prices; The Wall Street Journal. https://www.wsj.com/articles/exxon-documents-reveal-more-pessimistic-out look-for-oil-prices-11606307763?mod=lead_feature_below_a_pos1. Accessed 04 Jan 2021 17. Rodionov, D., Karpenko, P., Konnikov, E.: The conceptual model for man-aging regional socio-economic development systems. Econ. Sci. 197, 163–170 (2021) 18. Russia can trade CO2 -free air; Kommersant No. 215 dated Nov. 24, 2020, p. 10. https://www. kommersant.ru/doc/4584070. Accessed 03 Sept 2021 19. Tatneft development strategy. https://www.tatneft.ru/ustoychivoe-razvitie/klimat-i-ustoyc hivoe-energeticheskoe-budushchee/strategicheskiy-orientir-uglerodnaya-neytralnost-k2050-godu/?lang=ru. Accessed 03 Sept 2021 20. Kudrin believes that Russia will have to replace its oil exports with some others in the 2030s; TASS news agency, 28 February 2020. https://tass.ru/ekonomika/10120963. Accessed 03 Sept 2021 21. IRENA Global Renewable Outlook 2020. https://www.irena.org/-/media/Files/IRENA/Age ncy/Publication/2020/Apr/IRENA_Global_Renewables_Outlook_2020.pdf. Accessed 03 Sept 2021 22. OPEC’s World Oil Outlook 2020 (WOO). https://woo.opec.org/pdf-download/. Accessed 03 Sept 2021 23. Rudskaya, I., Rodionov, D.: Comprehensive evaluation of Russian regional innovation system performance using a two-stage econometric model. Rev. Espacios 39(04), 40–52 (2018) 24. Bloomberg New Energy Finance (BloombergNEF) Electric Vehicle Outlook 2020. https:// about.bnef.com/electric-vehicle-outlook/. Accessed 03 Sept 2021 25. Electric vehicles and oil consumption: BloombergNEF forecast up to 2040. https://renen. ru/electric-cars-and-oil-consumption-forecast-up-to-2040-from-bloombergnef/. Accessed 03 Sept 2021 26. World Energy Outlook 2019 MEA. https://www.iea.org/reports/world-energy-outlook-2019. Accessed 03 Sept 2021 27. World Energy Outlook 2020 MEA. https://www.iea.org/reports/world-energy-outlook-2020. Accessed 03 Sept 2021 28. Energy development forecast for the world and Russia (2019). https://energo-union.com/sto rage/articles/files/2020/08/skolkovo_enec_forecast_2019_rus.pdf. Accessed 03 Sept 2021 29. Main characteristics of the Russian electric power industry; Ministry of Economic Development of the Russian Federation. https://minenergo.gov.ru/node/532. Accessed 03 Sept 2021 30. Share of renewable energy resources in total energy production; EMISS. https://www.fedstat. ru/indicator/50178. Accessed 03 Sept 2021 31. Technological development of economic sectors; Federal State Statistics Service. http:// old.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/economydevelopment/#. Accessed 03 Sept 2021 32. Enerdata. https://energystats.enerdata.net/renewables/renewable-in-electricity-productionshare.html. Accessed 03 Sept 2021 33. Kudryavtseva, T., Shneider, A., Skhvediani, A., Brazovskaya, V.: Comparative analysis of waste management and pollution elimination clustering in Russia and Finland. Ecol. Ind. Russia 26(3), 65–71 (2022) 34. Order of the Government of the Russian Federation dated January 8, 2009 No. 1 as amended on February 28, 2017. https://www.garant.ru/products/ipo/prime/doc/94737/. Accessed 03 Sept 2021 35. Share of energy resources produced using renewable energy sources in the total vol-ume of energy production; EMISS. https://www.fedstat.ru/opendata/7708234640-fiveazeroaoneas evenaeight. Accessed 03 Sept 2021
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36. Capacity of generating facilities operating on the renewable energy sources; Rosstat. https:// rosstat.gov.ru/storage/mediabank/5-4.xls. Accessed 03 Sept 2021 37. The volume of electricity generation at qualified renewable energy facilities in the re-tail and wholesale markets, confirmed by certificates (thousand kW/h); NP Sovet rynka Association. https://www.np-sr.ru/ru/market/vie/index.htm. Accessed 03 Sept 2021 38. Dynamics of commissioning of power plants based on RES (MW); Source: RREDA, JSC SO EES, NP Sovet rynka. https://rreda.ru/statistics_of_renewable_energy_in_russia?ysclid=l8w zyaaq57159605132. Accessed 03 Sept 2021
Developing the Informatization of the Technological Waste Management Process in the Lean Production System of an Enterprise Natalia Lytneva(B) and Vasily Krestov South-Russian Institute of Management – Branch of RANEPA, Orel, Russia [email protected]
Abstract. Industrial waste and losses represent a problem for industrial enterprises both in terms of technology and environment, as it is directly related to manufacturing and output of finished products, as well as to pricing and affects the profitability and efficiency of enterprises that operate in fiercely competitive internal and external markets. Negative effects can be reduced if the technological waste management process is improved using a wide array of reliable information, which has to be properly organized and reveal the data of interest for internal and external stakeholders. It is one of the promising areas at the time of economy digitalization and national project implementation. This study is aimed at researching the information support of technological waste management at industrial enterprises throughout the stages when it is produced, stored, recycled and disposed of; at developing a theoretical framework in this field given the knowledge and experience accumulated by Russian and foreign researchers; at putting forward recommendations on informatization of management decision-making for practical application in the industrial sector of the economy. In order to analyze the material on the stated problems, the authors used general scientific and applied methods and techniques, inductive method, statistical method, heuristic methods, comparison, grouping, analysis and generalization. The major results of the study include some recommendations on integrated reporting, substantiating the indicators, suggesting methodological tools for collecting, accounting and generalizing information about production waste generation, placement and disposal given the interests of various stakeholder groups in solving technological and environmental problems. The scientific importance of the work is the substantiation of the proposed model for non-financial reporting and document flow by management level as one of the main informatization elements of the technological waste management process. The concept of lean production for reduced waste and losses is formulated. Keywords: Management · Information · Waste · Losses · Assessment · Analysis · Decisions · Production · Methods
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 416–430, 2023. https://doi.org/10.1007/978-3-031-32719-3_32
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1 Introduction Operation of manufacturing enterprises and organization of production process are inevitably accompanied by generation of waste and incurrence of losses. The desire to reduce them contributed to the development of many theories and concepts of lean production aimed at creating value in the form of finished products. Such a system can be implemented at manufacturing enterprises in case production management is improved [1], and material and primary resources are rationally used at all stages from production to realization of finished products given consumer interests. The problem of lean production has become very acute in the context of the economic crisis experienced by the Russian economy due to a change in the relationships with foreign partners at the international level [2], and economic sanctions introduced by a number of countries that affected the scope and valuation base of supplied raw materials. An important role in the development of the lean production concept was played by the COVID-19 pandemic, which resulted in violation of contractual relations, bankruptcy of many manufacturing enterprises that supplied raw and other materials, lack of similar raw materials in the domestic market, growing purchase prices. Manufacturing enterprises have to look for new resources, and maximize the use of internal reserves [3, 4]. Due to these circumstances, there is an increasing need to study the mechanism managing waste and losses, as they are part of the product prime cost and affect the final financial result, which preconditions the theoretical and practical significance of scientific research in this field. According to the authors, the process of managing technological waste and losses must be accompanied with a detailed study of the following areas and calls for achieving the objectives below: learn about the conceptual apparatus in the lean production system including categories such as “waste”, “loss”, “emissions”, “irreparable reject”, “scrap”. This will allow you to control both individual production processes, and the entire production and output of finished products for diagnostics, timely detection and prevention of waste and losses, etc.; study the classification attributes of waste and its causes, depending on the specifics of the production process; develop methods and techniques for identifying and assessing technological waste as tools for an integrated approach to lean production; increase the degree of informatization of lean production management in order to reduce waste and losses, organize document flow to ensure the transparency and accessibility of information about actions that contribute to increasing or decreasing value of the product; develop and adopt management decision-making in the hierarchical management system for less waste and losses, depending on its impact on the internal or external environment of the enterprise.
2 Literature Review The initial research studies in the field of lean production in the management process system, as an enterprise management concept were conducted the first half of the twentieth century, which was the period when the foundations of the classical school of management were laid down. The studies were carried out by F. Taylor, L. Urwick, H. Fayol,
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H. Ford, H. Emerson. This concept has been investigated in more detail by Japanese scientists, such as T. Ohno, M. Imai, S. Shingo, Y. Monden, by American and European researchers, such as E. Deming, J. Wumek, D. Jones, J. Liker, M. Meskon, M. Rotor, J. Michael. The most famous works of Russian scientists in this field include the research of: Y. Adler, A. Voronin, A. Grinin, A. Kuzmin, K. Novikov, O. Turovets, D. Shekhvatov. James Wumek and Daniel Jones called the idea of lean production a breakthrough approach to organizing the management system of a manufacturing enterprise [5], believing that it ensured higher competitiveness of an enterprise without the need to attract special investments. The importance of reducing waste and losses was demonstrated with case studies of large industrial enterprises in Japan, the USA and Germany. According to the researchers, lean production principles are aimed at minimizing waste and reducing losses when organizing the value stream. In their book “Lean Manufacturing: How to Get Rid of Waste and Make Your Company Prosper” they identified five principles of lean production (Fig. 1).
Fig. 1. Lean principles according to J. Wumek and D. Jones’ theory.
For better understanding of waste management and losses incurred in the production process, they considered the concept in the interpretation of Japanese terminology – “muda”. Wumek and Jones define this term as an activity in which resources are consumed, but value is not produced. The authors called waste and losses production mistakes and highlighted their versatility [6]. They find it important to consider the differences in the types of waste and losses when making management decisions. The scientists focused on the classification of “muda” suggested by Taiichi Ohno according to the following criteria: defective products (defects); production of goods without taking into account the consumer needs and demand (overprocessing); availability of raw materials waiting in line for processing or shipment for consumption (waiting); unnecessary stages of processing; inefficient motion of workers; excessive movement and transportation of finished products; duration of the final stage of the production process [7]. The scientists supplemented the classification of Taiichi Ohno with another type of loss – inefficient manufacturing of products that do not meet consumer demand. Investigating the value stream management system, Wumek and Jones focused on the stages to reduce “muda (losses), highlighting three main management practices: assess and solve the problem that has arisen (at all stages of value creation, from the development of an idea and creation of a prototype, to its production and output); the
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process of managing the generalization and analysis of data, information flows (along the entire production chain from registering a placed order to developing a schedule for the output and shipment of products); manage the transformation of resources (processing of raw materials, their transformation into finished products). The accessibility and reliability of information flows were considered by the authors as the prerogative of making rational decisions for reducing all types of technological, organizational and financial losses. Studying the concept of lean production at different times of the scientific thought we see that not only the matters related to the development of the entire waste and loss management system have been relevant, but also the improvement of individual tools. As noted by J. Tapping, the following actions are needed for actual reduction of losses in the practice of lean production: study the consumer demand, ensure the continuity of the production process of value creation; make sure that the work is evenly distributed (smoothing) [8]. The scientist highlights that one of the process management tools is information that is generated obtained using various methods of data collection and generalization, including benchmarking. At the same time, Tapping considers it essential not to limit yourself with your own production only. He points out the importance of exchanging information both within the industry and at a higher hierarchical level in order to obtain data on your partners and competitors and make management decisions aimed at more efficient loss management when developing and implementing the lean production concept. According to H. Takeda, waste and losses occur at almost all production stages. However, many losses remain unnoticed and, accordingly, not considered. They cannot be identified using traditional methods or documented [9]. At the same time, he points out the need to identify losses, since their size directly affects the cost of manufacturing of finished products. To eliminate losses (muda), we need information that is of interest to external and internal stakeholders.
3 Materials and Methods The problems related to studying the theoretical foundations of lean production concern the use of general scientific methods and techniques. These methods allow us to generalize and analyze the principles of lean production; investigate the criteria for categorizing waste and losses in relation to the stages of product value creation; identify information risks characterizing the negative impact of insufficient disclosure of information about waste and losses on the company’s efficiency. The practical study of improved informatization of waste and loss management is based on the development of document flow, formation of a set of indicators for determining and evaluating the identified losses, their inclusion in management, accounting (financial) and non-financial statements. Among the many approaches to studying the information sources that reflect the presence of losses, we would like to highlight a systematic approach that allows you to create an information bank on waste and losses of a manufacturing enterprise for various users, given the information needs of a certain management level, and systematize information sources and disclosed data. In addition, the information support of the waste
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and loss management process should be based on a number of principles: determine the composition of the criteria characteristics of waste and losses, taking into account the interests of external and internal stakeholders; form information in certain types of sources (management, financial and non-financial reporting); ensure the availability of information on waste and losses; consider the period and time the information on waste and losses is disclosed in open sources; make sure that the information in the accounting system of the economic entity is reliable; disclose information on types of waste and losses by their occurrence, storage and disposal; monitor the impact of waste and losses on the external and internal environment of the manufacturing enterprise. Figure 2 shows systematized information support in the waste and loss management system of industrial enterprises, given the information needs of various users. To assess all the information provided on waste and losses of a manufacturing enterprise, we need to use methodological tools of an integrated approach, with waste and losses being assessed at all stages of production and output, taking into account the terms of delivery, storage, distribution of raw materials and finished products, shipment to consumers, needs for quality goods. The study implies assessing the production technologies, equipment, other main and auxiliary production facilities.
Fig. 2. A scheme of information support of waste and loss management in a manufacturing enterprise.
Complex economic analysis methods and techniques, as well as statistical methods provide information on the cost assessment of the identified waste and losses, their
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causes and impact on the external and internal environment of the manufacturing enterprise, which is necessary for taking measures to reduce losses and making management decisions on their regulation, and introduction of innovative saving technologies.
4 Results External users of information, like investors, economic entities engaged in international activities and participating in international transactions, are increasingly interested in disclosing information about how waste and loss management systems of manufacturing enterprises are organized. External users are interested in the amounts of investments, expected profit, value created, types of waste and losses, their cost assessment, their influence on cost, enterprise efficiency, and carbon footprint [10]. The source of such information is financial statements that combine various nonfinancial indicators [11] characterizing the enterprise’s business model of value creation in retrospective, current and prospective periods. Accounting and reporting are an element of the corporate policy of large companies and enterprises. Their purpose is to disclose information on sustainable development. Well-known scientists such as Vakhrushina M.A. [12], Malinovskaya N.V. [13], Plotnikov V.S. [14], Shirobokov V.G. consider integrated reporting to be one of the innovative information tools that present financial and non-financial information about business development, effective interaction of companies with financial markets and a wide range of stakeholders. To date, non-financial reporting in Russia is made up only by large companies in accordance with the basic principles formulated by the International Integrated Reporting Committee (IIRC). There is no unified reporting form, so companies provide information to serve their stakeholders’ interests, and choose the way of presenting data on their own. However, the information may be presented inconsistently, with many positions of the lean production concept not being disclosed. There are the following types of non-financial reporting: Sustainable Development Reporting (SDR); Integrated Reporting (IR); Environmental Reporting (ER); Social Reporting (CR). In contrast to sustainable development reporting and integrated reporting, environmental and social reporting have a narrower focus. Integrated reporting presents a wide range of information. It combines all the above types of reporting into separate sections with disclosure of the company’s business model, performance results in the current period, and business development strategy in the future. In other words, integrated reporting provides stakeholders with both financial information about the profitability or unprofitability of production, and non-financial data on the company’s strategy in the market, its success, and the results of economic and social policy management. Information on waste and losses is disclosed by almost all companies, but only in terms of carbon footprint [15]. Few manufacturing enterprises pay attention to the assessment of lean production [16]. According to the results of the study of the information contained in corporate nonfinancial reports generated in the electronic library of the Russian Union of Industrialists
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and Entrepreneurs [17], the disclosed information on waste and loss indicators, technologies for waste identification, storage and disposal is inconsistent. These data are included in the section “Environmental Safety” of the reports.
+
“SUEK” JSC “Polymetall ” PJSC
IR
+
SDR
+
“Siberian Chemical Combine”, PJSC “Baltika Brewery”, Ltd. “LSR Group”, PJSC (constructio n)
ER
“Nestle Russia”, Ltd. “Lukoil”, PJSC “Surgutneft egaz”, OJSC Children's World Group
+
+
+
Norms of waste and losses
SDR
+
Penalties
+
+
Recycled
SDR
+
Non-financial sanctions
+
Non-controlled leakages
+
SDR
Treatment methods
IR
“METALL O INVEST” PJSC “GMK Norilk Nikel” PJSC “Severstal” PJSC
Side sales
+
Disposal of waste and losses
IR
Waste by hazard class
“NorthWest MRSK” PJSC “Unipro” PJSC
Gross polluting emissions
+
+
Share of polluting emissions
+
+
Amount of disposed waste
Emission structure
+
SDR
Amount of waste
IR
Type of reporting
“Gazprom Neft” PJSC “Sakhalin Energy” PJSC
Companies
Amount of neutralized waste
Table 1. Criteria characteristics of waste and losses of Russian companies in integrated nonfinancial reporting.
+
+
+ ++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SDR
+
+
+
+
+
SDR
+
+
+
++
+
+
SR
+
+
+
+
+
+
SDR
+
+
+
++
+
+
+
+
+
+
ER
+
+
+
++
+
+
+
+
+
+
IR
+
+
+
++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ + +
+
+
+ +
+
+
+
+ +
+
+
+
+
+
+ +
+
+
+
+ +
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Table 1 shows a fragment of the authors’ research on big Russian companies operating in various sectors. We analyzed non-financial reporting of various types, with different criteria characterizing waste and losses, given industry and operation specifics. The results obtained allow us to assess the methodological tools for summarizing information and compiling reports, disclosing information about waste and losses for making management decisions in the field of environment, organizing lean production and output of finished products (rendering services, performing works), improving the quality and competitiveness of products. According to the study, for many companies, the identical criteria characterizing waste and loss management are: amount of waste generated in natural and cost terms; the share of polluting emissions in output; the share of emissions in the cost of the product value; the volume of gross emissions into the environment; waste and losses by hazard class; waste treatment methods and possibility of recycling. These tables clearly demonstrate the absence of consistent criteria for disclosing information on waste and losses. We believe that the following ones are some of the reasons why waste management is so poor in terms of its information value at the macro level: lack of consistent approaches to the conceptual apparatus and classification of waste and losses in the lean production system; no single coordination center for collecting, processing and building an information base that has a certain status and the right to store and host the data obtained from companies; low level of the regulatory framework for accounting, assessing, and analyzing the results of lean production; as well as automated processing of information on waste and losses when product value is being created; heterogeneity of criteria characteristics of technological waste and losses for companies operating in various sectors; the impact that the specifics of enterprises’ activities have on the organization of lean production, methods and technologies for identifying and assessing waste and losses; underdeveloped methodological tools for processing information on waste and losses, given the interests of external and internal users. According to this study, a single list of criteria for assessing waste and losses in the lean production management system need to consider the industry specifics and production features [18]. For example, from the analysis of data from the Sustainable Development Report of Sakhalin Energy PJSC [19], a representative of the oil and gas industry that ensures the continuity and stability of oil production, we see that the company uses new drilling and product extraction technologies, develops the lean production concept, contributing to the achievement of the UN Sustainable Development Goals aimed at resolving environmental and social policy problems. The company discloses information about waste and losses given the interests of stakeholders in the section “Environmental Impact Management” [19]. The main type of losses is polluting emissions (Table 2).
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Table 2. Dynamics of polluting emissions produced by Sakhalin Energy PJSC, thousand tons. Pollutants
year 2017 year 2018 year 2019 year 2020
Carbon monoxide emission
4.060
4.210
3.430
3.530
Nitrogen oxide emission (converted to NO2 )
4.260
4.340
3.930
4.040
Methane emission
1.170
1.100
0.700
0.900
Sulfur dioxide emission
0.040
0.030
0.030
0.030
Emissions of other substances
0.910
0.620
0.50
0.380
10.440
10.300
8.520
8.880
Total
The dynamics of polluting emissions is characterized by a reducing trend, from 10.44 thousand tons in 2017 to 8.88 thousand tons in 2020. However, in 2020, as compared to the previous year, there is an increase in emissions by 0.36 thousand tons, which was due to the planned repair and technical work at the company’s production facilities required for the maintenance of platforms. Nitrogen oxides represent the main share of emissions, amounting to 4.04 thousand tons in 2020, which is almost 50% of all pollutants. At the same time, the amounts of all pollutants do not exceed their concentration norms in atmospheric air. The company is actively developing lean production, cutting emissions of pollutants, carrying out measures for reducing the number of accidents, increasing the reliability of equipment, conducting technical and environmental control of the production process in the following areas: assessing the impact of pollutants on the atmosphere; controlling the impact of pollutants on changes in water bodies; controlling industrial waste management measures. The control is needed because emissions occur at almost all stages of the production cycle of Sakhalin Energy PJSC. Their share in 2020 increased at the stage of hydrocarbon production and during product transportation, so the information on the company’s activities has to be analyzed (Table 3). Table 3. Dynamics of polluting emissions by production stage of Sakhalin Energy PJSC. Production Cycle Stages
Specific Gravity, % year 2017
year 2018
year 2019
year 2020
Hydrocarbon production stage, kg/t of conditional tons
0.180
0.190
0.140
0.150
Hydrocarbon transportation kg/thous. t-km
0.060
0.080
0.060
0.080
Liquefied natural gas production stage, kg/t of c.t
0.230
0.200
0.200
0.190
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The production chain of the organization generates technical waste, whose level must comply with Russian and international requirements for environmental standards. In accordance with the norms of Federal Law No. 89-FZ “On Production and Consumption Waste” [20], the joint-stock company collects, accumulates, transports, disposes of and neutralizes technological waste. The information on waste management disclosed by Sakhalin Energy PJSC for external users in the Sustainable Development Report covers data on the amount of waste and the hazard class given the environmental impact. Waste classification considers the negative impact produced on the environment. The classes are determined by a list of criteria established by federal executive authorities for state regulation of environmental protection. Article 4.1 of the Law No. 89-FZ identifies five hazard classes of industrial waste: the first class includes extremely hazardous waste; the second class covers highly hazardous waste; the third class includes moderately hazardous waste types; the fourth class represents low-hazardous waste types; nonhazardous waste belongs to the fifth class. In its sustainable development report, Sakhalin Energy PJSC discloses information about the types of waste, their amounts, and hazard classes. The data of the report indicate the presence of low-risk waste, which belongs to hazard classes IV and V and is produced during drilling wells, plus certain types of solid municipal waste. Low-hazardous waste accounted for 26.73% of total waste amount, for nonhazardous waste this figure was 62.2%. The structure of the company’s waste in 2020 by hazard class is presented in the form of a diagram in Fig. 3.
Fig. 3. Diagram of Sakhalin Energy’s waste structure by hazard class in 2020.
Waste collection, storage, placement, neutralization and disposal are carried out by the company according to legal requirements [21]. The waste produced when wells are drilled is placed in the deep horizons of the subsurface with the presence of insulating layers for localizing and burying the waste. Production waste, classified as hazard classes I–III, is given to specialized organizations licensed for waste disposal or neutralization. Production waste that belongs to hazard classes IV–V is brought to landfills. Sakhalin Energy regularly monitors and analyzes information on waste management, organizes separate collection by types of waste and takes actions to dispose, neutralize and reduce waste. Table 4 presents information on production waste management in the joint-stock company.
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Table 4. Dynamics of waste treatment measures taken by Sakhalin Energy, thousand tons. Waste management actions
year 2017
year 2018
Amounts of all classes of waste
36.580
27.130
Shipped for disposal and neutralization
3.470
2.890
Shipped for transportation to specialized landfills, including:
1.660
1.890
in Sakhalin Region (with solid municipal waste)
0.210
0.450
outside Sakhalin Region
1.450
1.450
The dynamics of the amounts of waste produced by the joint-stock company indicates a tendency for its decline from 36.580 thousand tons in 2017 to 24.670 thousand tons in 2020. Since mainly low-hazardous and non-hazardous drilling waste is generated during the production process, its disposal is carried out by the company itself. In 2020, the disposal of such waste amounted to 18.330 thousand tons. Waste disposal in specialized landfills occurs mainly on the territory of the Sakhalin region. Sakhalin Energy PJSC is actively working on organizing lean production, looking for effective technologies for production waste disposal of the IV–V environmental hazard classes in order to reduce the amounts of waste that must be brought to specialized landfills. According to the study, reporting on sustainable development contributes to better information content of the technological waste and loss management system due to the disclosure of information on the environmental impact. At the same time, the information in such reports reflects the company’s production specifics and the industry affiliation. However, the composition of criteria that characterize waste and losses, as well as their assessment indicators, given in the Sustainable Development Reports are not a yardstick for the lean production management system, since a number of companies draw up and submit other kinds of reports to the external user: Ural Electrochemical Combine JSC, Surgutneftegaz JSC, Angarsk Electrolysis Chemical Plant JSC, Siberian Chemical Combine JSC provide only environmental reports; “PO” Cristall JSC, Nestle Russia Ltd., Kaspersky Lab, Social Information Agency, Sistema Charity Fund prepare only social reports that contain information with a different purpose of disclosure; Severneftegazprom JSC, SSC “NIIAR” JSC, “PO “Electrochemical Plant” JSC make up an integrated and ecological reports. The environmental report of Siberian Chemical Combine reveals the origin and management of waste and losses of the production process. This enterprise is involved in a nuclear fuel cycle as part of the fuel company TVEL of the Rosatom State Corporation. The management structure of the “SKH” JSC includes four plants that handle radioactive substances and nuclear materials. The main types of waste are discharges of harmful chemicals. In 2019 the volume of discharges was 16,792.90 tons, which is 17.40% of the permitted amount. The amount of the permitted discharge of harmful chemicals is defined as the volume of discharges according to the standard, given the established
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discharge limit for each substance. Out of the total volume of discharges, 28.50% are the permitted volume of discharges of “SKH”. The specification of compliance with the discharge standards by composition of harmful chemicals with the hazard class is presented in Table 5 [22]. Table 5. Assessment of compliance with the standards for harmful chemicals discharged with wastewater through the “Northern Release” in 2019. Composition of harmful chemicals
Hazard class
Volume of fluorides III
Permitted discharge, tons/year
Actual discharge tons/year
as percentage of the permitted discharge
397.970
77.760
19.50
37.750
5.180
13.70
Oil content
III
Total iron volume
IV
169.880
3.260
1.90
Volume of nitrates
IV
1365.360
16.610
1.20
Sulfate volume
IV
22572.550
4975.190
22.00
Chloride content
IV
1840.410
112.080
6.10
Volume of suspended solids
–
8981.830
536.270
6.00
The data in the table show that the company complies with the permissible norms for harmful chemicals discharge with wastewater. Siberian Chemical Combine regularly monitors compliance with discharge standards and discloses the main characteristics in the interests of external users, including investors. The information on Siberian Chemical Combine’s waste management is also disclosed in terms of the company’s impact on the external environment, and the quality of information in this section is quite high with the following criteria being analyzed: generation of non-radioactive waste during production cycle and product consumption (quantitative characteristics); grouping of waste by type with specification of the hazard class; specification of waste management methods; transfer of production and consumption waste for placement, burial and disposal; trend in volumes of neutralized waste. The company’s environmental report contains information on the management of solid and liquid radioactive waste that is generated in the production process that involves radioactive substances and nuclear materials. In addition, information is disclosed for each type of waste, which ensures that the company makes effective management decisions regulating waste management. In particular, as for solid radioactive waste, their volume is characterized by radionuclide-contaminated means of protection, technological waste, used and unusable devices, used elements of special equipment, etc. Such waste is analyzed by contamination in order to be placed in special storage systems. As for liquid radioactive waste, in its reports the company discloses information on the sources including non-technological and technological waste. The following ones are analyzed as part of non-technological
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liquid radioactive waste: trap, drainage, basin waters, washing solutions, sanitary inspection waters. This waste is subject to treatment and subsequent disposal. In the composition of technological liquid radioactive waste, the company shows medium-active waste that is subject to isolation from the eco-system.
5 Discussion As the study showed, investigating the matters of information content about waste and loss management is an urgent problem, and its solution affects the efficiency of production engineering in enterprises operating in various industries. Today Russia sees a lot of work aimed at developing and applying lean production tools and methods, which increases the interest of Russian enterprises in lean management [23]. The concept of lean production is aimed at finding a way for rational use of resources [24], which is an acute problem at the time of economic uncertainty. Reduction of waste and losses directly affects the final product, its quality, and the effectiveness of its sale to the end consumer [25]. Despite the fact that Russian enterprises lag behind developed countries in the introduction of lean production methods and techniques by about five to six times, the lean production concept and its development are gaining momentum as the Russian economy is being reformed [26]. The need to develop the concept of “lean production” relates to the need for higher competitiveness of modern enterprises, caused by intense global competition in traditional sales markets [27]. The company’s focus on increasing the quality of information content about waste and loss management will ensure transparency of data on the state and performance of business, which will result in stronger interest on the part of investors and other stakeholders.
6 Conclusion The scientific novelty of this study includes the formulated characteristics of lean production of modern enterprises in terms of their waste and loss management process; the formed information support for waste and loss management; the substantiated areas of information disclosure about waste and losses in companies’ integrated reports. Some recommendations are given on information support for waste management given the specifics of production and industry affiliation, for better identification and assessment of waste, including classification characteristics by hazard group, and by hierarchical management level. The authors’ contribution is in revealing the significance of the problem of waste and loss management in the lean production system, forming information support for waste and loss management, justifying the need to increase the information content of waste and loss management as a negative factor in manufacturing that affects product value. The theoretical significance of the studied problems is in the development of the concept of lean production, the increment of scientific knowledge about waste and loss management, the improvement of methodological tools for identifying and assessing waste and losses of industrial enterprises. The practical significance of the research results is confirmed by the possibility of using them in the activities of manufacturing enterprises, given the specifics of their activities and industry affiliation, to disclose
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comprehensive information, results and actions on organizing lean production, which will be the basis for taking decisions to reduce waste and losses. The results of the study concerning increased information content of waste and loss management will help to introduce waste-free and low-waste production technologies, innovative production facilities where waste, losses and incorrigible defects will be minimized, and waste reused for manufacturing new types of finished products.
References 1. Polyanin, A., Pronyaeva, L., Pavlova, A., Stepanova, Y.S.: Integration development processes in the wood industry based on clusterization. In: IOP Conference Series: Earth and Environmental Science, vol. 595, no. 1, pp. 1–12 (2020) 2. Dennis, P.: Hobbs Lean production implementation: A Complete Execution Manual for Any Size Manufacturer, p. 352. Grevtsov Publisher, Minsk (2007) 3. Kostyukova, E., Vakhrushina, M., Shirobokov, V., Feskova, M., Neshchadimova, T.: Improvement cost management system for management accounting. RJPBCS 9(2), 775–779 (2018) 4. Rodionov, D., Ivanova, A., Konnikova, O., Konnikov, E.: Impact of COVID-19 on the Russian labor market: comparative analysis of the physical and informational spread of the coronavirus. Economies 6(10), 136 (2022) 5. Womack, J., Jones, D.: Lean Thinking: Banish Waste and Create Wealth in Your Corporation, 7th edn., p. 472. Alpina Publisher, Moscow (2013). Transl. From English 6. Kudryavtseva, T., Shneider, A., Skhvediani, A., Brazovskaya, V.: Comparative analysis of waste management and pollution elimination clustering in Russia and Finland. Ecol. Ind. Russia 26(3), 65–71 (2022) 7. Taiichi, O.: The Toyota Production System: Beyond Large Scale Production, pp. 19–20. Productivity Press, Portland (1988) 8. Tapping, D., Shuker, T.: Value stream management for the lean office, p. 208. RIA “Standards and Quality”, Moscow (2009). Transl. from English by A.L. Raskina; Edited by E.A. Bashkardin 9. Takeda, H.: The Synchronized Production System, p. 288. IKSI, Mocow (2008). Transl. from English 10. Lytneva, N.: Conceptual approaches to integrated reporting given stakeholders’ needs. Educ. Sci. Without Borders: Fundam. Appl. Res. 9, 207–211 (2019) 11. Kyshtymova, E., Parushina, N., Lytneva, N., Polyanin, A., Plotnikov, V.: The value of the company and transformation of its evaluation under the influence of informatization. In: Proceedings of the 32nd International Business Information Management Association Conference, IBIMA 2018 - Vision 2020: Sustainable Economic Development and Application of Innovation Management from Regional expansion to Global Growth 32, Vision 2020: Sustainable Economic Development and Application of Innovation Management from Regional Expansion to Global Growth, pp. 4395–4407 (2018) 12. Vakhrushina, M., Malinovskaya, N.: Corporate reporting: new requirements and ways for development. Int. Account. 16(310), 2–9 (2014) 13. Malinovskaya, N.: Problems of developing a methodology for analyzing integrated reporting. Econ. Anal.: Theory Pract. 4(511), 645–662 (2021) 14. Plotnikov, V., Azrakuliyev, Z.: The concept of production capital in business accounting of integrated reporting. Digest-Financ. 25(253), 68–86 (2020) 15. Lavrov, D., et al.: Green electricity and heat generation in Canada: implications for Russia. Int. J. Energy Econ. Policy 3(11), 280–289 (2021)
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16. Belykh, K.: Classifying the main methods and tools of lean production. Bull. RUDN Econ. Ser. 1, 70–77 (2016) 17. Library of Corporate Non-Financial Reports of the Russian Union of Industrialists and Entrepreneurs. https://rspp.ru/tables/non-financial-reports-library/. Accessed 09 Apr 2021 18. Michael, V.: The Tools of Lean Production. Alpina Business Books, Moscow (2005) 19. Sustainable Development Report by Sakhalin Energy. https://www.sakhalinenergy.ru/en/ media/sd_report/. Accessed 09 Apr 2021 20. About Production and Consumption Waste. Federal Law No. 89-FZ of 24.06.1998 (as amended on 02.07.2021). https://legalacts.ru/doc/FZ-ob-othodah-proizvodstva-i-potreblen ija/. Accessed 09 Apr 2021 21. Kazakova, N., Prilepskaya, A.: The assessment of risks of ecological safety based on the audit of ecological expenses of Russian construction companies. In: 20th International Multidisciplinary Scientific GeoConference SGEM 2020, Sofia, pp. 529–536 (2020) 22. Environmental Safety Report by JSC “SKH”. http://www.eng.rushydro.ru/upload/iblock/c48/ Quarterly-report-for-Q2-of-2019.pdf. Accessed 09 Apr 2021 23. Davydova, N., Grashchenkova, N.: Lean production management system and sustainability of lean transformations. New Technol. 2(17), 121–130 (2021) 24. Lytneva, N., Parushina, N., Inozemtseva, P.: Lower production costs as a way to increase the efficiency of an industrial enterprise. Vector Econ. 8(14), 8 (2017) 25. Kazakova, N., Kogdenko, V.: Monitoring the main environmental safety parameters of industrial production. Ecol. Ind. Russia 3(25), 60–65 (2021) 26. Kostycheva, A.: Lean production. https://www.unisender.com/ru/glossary/berezhlivoe-proizv odstvo-ego-principy-i-instrumenty/?ysclid=l908oe9ejm337740480. Accessed 09 Apr 2021 27. Klochkov, P.: “Lean production”: concepts, principles, and mechanisms. IVD 2, 529–538 (2012)
Competencies for Digital Economy: Economic Engineer for Transport Industry Maria Anisimova, Irina Rudskaya , Angi Skhvediani , and Valeriia Arteeva(B) Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia [email protected]
Abstract. This article describes the main changes in the field of transport caused by digitalization processes. The authors discuss the main needs of the labor market and provide an overview of the competency profiles of multidisciplinary specialists in transport industry. The aim of the study is to develop a competency profile of an economic engineer for the transport industry that meets the requirements of the labor market in the context of digitalization. The analysis of the literature and professional standards shows that an economic engineer possess ten skill subcategories from four global categories: “Statistical analysis and programming”, “Special skills”, “Project management” and “Soft skills”. Also, conclusions are drawn about the need to create and refine the educational programs of universities in order to reduce the gap between the requirements of employers and the skills obtained on the basis of educational institutions. Keywords: Digital economy · Economic engineer · Transport · Labor market · Competencies · Skills
1 Introduction The development of information technologies in the field of transport is a trend of the world’s largest economies. The Government of the Russian Federation annually allocates billions of rubles for the development of transport projects as part of the Strategy for the Digital Transformation of the Transport Industry. Elements of the digital economy are especially active in large cities with a dominant transport industry based on the development of intelligent transport systems. This makes it possible to ensure a high level of mobility of citizens, to create an individual route in accordance with the requirements of passengers. Such opportunities set new principles for modern transport management, where it is necessary to operate with huge amounts of data online. Thus, with the state’s priority on the introduction of innovative information technologies in cities, and, in particular, on the development of transport systems, the labor market is undergoing transformation. Due to the new requirements of the economy and society, it is necessary to train specialists with the necessary competencies for the successful implementation of these projects. The aim of the study is to develop a competency profile of an economic engineer in the transport industry that meets the requirements of the labor market in digital economy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 431–441, 2023. https://doi.org/10.1007/978-3-031-32719-3_33
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The forecast of the Ministry of Labor, based on a survey of 46 thousand organizations, suggests that for transport specialists the risk of losing their jobs due to digitalization is very high [1]. Technological innovations are expected to reshape transport needs of the workforce size and new competencies and skills in the medium and long term. In general, it is believed that trends in the workforce should be smoothly accepted by potential new employees so that they are interested in following new market needs [2]. In the first half of 2017 the greatest demand in the field of transport and logistics in Russia was for logisticians, storekeepers, drivers, couriers, loaders, freight forwarders, order pickers, operators, dispatchers and brigadiers. However, today, due to the dramatically accelerated digitalization, employers are looking for specialists in information technology to implement digital technologies, improve business processes and keep pace with competitors [3]. Digitalization is not only changing the perception of the transport industry, but also transform the employment rules and the requirements for the employee competencies, knowledge, skills and attitudes Thus, there is a need for flexible, multidisciplinary professionals with the necessary knowledge base to create and support a new round of transportation development (e.g. intelligent transport systems, digital information platforms and new transport projects) [4]. Several main directions characterize the development of the transport industry in the context of the digital economy [5, 6]: – Infrastructural change of roads (to create an intelligent transport system on their basis), bridges, tunnels, railways (to create digital railways on their basis), ports (fully automated and robotic). – Designing vehicles/vessels, taking into account the direction of “smart transport”. – Management of transport operations (for example, capacity distribution, cargo management, tracking and tracking of delivery, customer service, etc.) using an integrated SCM (Supply Chain Management) system with ERP (Enterprise Resource Planning) to create a common supply chain ecosystem. – Maintenance and repair of transport equipment. To meet the need for development in these direction, employers need to prepare a workforce with up-to-date knowledge and competencies that meet labor market demands. At this stage, the training of specialists to solve new transportation problems, such as the creation and implementation of intelligent transportation systems or the formation of software for the mass user in the field of public transport development, is already underway. Although the labor market still lacks a workforce for the transportation sector with new digital skills with high qualifications, the education system is striving to create new and update existing educational programs to produce professionals that meet the current needs of the economy. Thus, the education system faces the task of training personnel for the digital economy, determining the cluster of in-demand professions for the next 15–20 years, developing effective forms of retraining and implementing in the activities of training centers. That is, it is necessary to organize the work of all structures of the education system in the interests of the development of the digital economy in the country. Education should be in demand, flexible, knowledge-intensive and adapt quickly to the labor market.
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2 Materials and Methods 2.1 Methodolody Modern transport infrastructure, which combines the use of digital technologies and intelligent systems, integrated into existing or planned urban planning solutions, is a technologically complex facility for the creation, operation and development of which requires qualified specialists. The future specialist must perform the following tasks statistical data analysis, the formation and evaluation of transport planning and modeling projects, the management of transport infrastructure development projects using modern information technologies. The largest universities of the Russian Federation are interested in working on the training of flexible multidisciplinary specialists, corresponding to these tasks. The creation of the master’s program “Economics and Transport Planning of Ecosystems”, carried out at Peter the Great St. Petersburg Polytechnic University to initiate the formation of a professional standard for a new specialist who meets digital trends and market demands - economic engineer in the field of transport. In order to determine the key competencies of economic engineer, the paper considers 1) professional standards used in the Russian Federation; 2) standard occupational profiles posted on government websites from other countries and academic articles concerning the skills required to work in the field of transportation planning. According to Article 195.1 of the Labor Code of the Russian Federation, a professional standard is a characteristic of the qualifications necessary for an employee to perform a certain type of professional activity. This document indicates how the qualifications of an employee can be assessed. Professional standards are used in the employment of citizens, as well as in improving their qualification. Among the professional standards for the study, the following were selected: – – – – – –
10.006 “Urban planner” 40.049 “Transport logistics specialist” 08.022 “Statistician” 08.036 “Specialist in working with investment projects” 08.018 “Risk management specialist” 08.041 “Specialist in the field of public-private partnership project management”
For each of the documents, the necessary competence, skills and knowledge were considered, corresponding to the qualification level “7”, which requires training in master’s or specialist’s programs. This skill level includes management and strategic planning skills and is designed to describe the powers and responsibilities of top management in organizations or large divisions. 2.2 Identification of Competencies and Skills According to Professional Standards in Russia The professional standard “Urban planner” is one of the key ones for the development of transport infrastructure, which forms the image of the city and ensures the economic development of the regions. Specialists who meet this professional standard organize
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planning and design of territory development, conduct research and surveys necessary for the development of urban planning documentation, form, select and justify various options for urban planning solutions for the objects under development, and also develop urban planning documentation for specific projects and tasks [7]. Such specialists should be able to analyze large amounts of professional content, summarize and systematize information in various forms and forms, model urban planning decisions and predict the consequences of their implementation, using modern means of information and information and communication technologies for these purposes. In the professional standard for “Transport logistics specialist”, several generalized labor functions are identified. One of them is monitoring the results of logistics activities for the transportation of goods in the supply chain. It includes the monitoring of key operational performance indicators and key financial indicators of logistics activities for transportation in the supply chain. Another generalized labor function is the development of a strategy in the field of logistics activities for the transportation of goods in the supply chain. It includes the development of a strategy for the development of the operational direction of the company’s logistics activities in the field of cargo transportation management in the supply chain, the development of a commercial policy for the provision of logistics services for the transportation of goods in the supply chain and the development of a risk management system for the provision of logistics services for the transportation of goods in the supply chain [8]. The main skills in these functions are system analysis and information processing, professional work with documentation and negotiation, the ability to make and present reports and implement logistics projects in accordance with the legal framework. The fundamentals of financial and strategic management are also important for this qualification. The professional standard “Statistician” for qualification level “7” represents scientific and methodological activities in statistics. Its main activities can be the development and improvement of applied statistical methodologies, statistical theory in terms of mathematical statistics and probabilistic methods for analyzing numerical and non-numerical information, the preparation of analytical reports, as well as reviews, reports, recommendations, draft regulatory documents based on statistical calculations and consulting in areas of statistical activity [9]. Thus, the statistician needs the ability to work with various sources of information, to master various methods of statistical analysis, to carry out calculations using statistical software packages and to analyze the results obtained. It is also important to be able to prepare presentations and reports. “Specialist in working with investment projects” are directly involved in implementation of investment projects. Among their main labor functions, management of the effectiveness, communications and risks of an investment project is singled out. Also, an important labor function is the management of deadlines and control over the implementation of the investment project [10]. These specialists should be able to work with legal systems and analyze the project, know the mechanisms for financing the investment project and the principles of risk management, be able to evaluate the project and form the necessary documentation and presentation materials for it. It also requires skills in working with specialized software packages and negotiating various transactions to harmonize mutual interests in the project and attract investment.
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“Risk management specialists” build an integrated risk management system. The work of this specialist includes planning, coordinating and providing integrated integrated risk management activities, work on improving the effectiveness of risk management, developing methodological frameworks and documentation in the field of risk management, as well as advising on risk management issues [11]. For “Specialist in the field of public-private partnership (PPP) project management”, there are two generalized labor functions - organizing and conducting the preparation and implementation of a PPP project and managing and monitoring the preparation and implementation of a PPP project [12]. The organization includes providing corporate, legal, financial and economic, technical preparation of the project, as well as ensuring the process of selecting a private partner for its implementation. Also, this specialist can be engaged in information support of the project and conduct public procedures for its adoption and support. Thus, the project management and control professional initiates project planning, coordinate project participants, and monitor project implementation. This specialist requires the skills to systematize and analyze data, the ability to work in specialized programs, submit reports and negotiate to reach agreements on PPP. PPP project manager needs to be familiar with the legal and regulatory framework that governs the conduct of a project, have engineering and economic skills and extensive knowledge of project cost and effectiveness assessment methods, as well as be able to manage project risks. 2.3 Identification of Competencies and Skills According to Foreign Standard Occupational Classification Among foreign sources, such occupational profiles and professions as transportation engineer, transportation planner, and infrastructure engineer have been considered to determine the professional skills required in the design of transportation facilities. Some of these jobs are not currently widely used in Russian practice. Also, to better understand and identify possible differences between Russian and foreign requirements, the skills required for a project manager were examined. First, the responsibilities and skills of transportation planners were analyzed. Transportation planning and modeling is the process of gathering information to make decisions about future development and management of transportation systems. It includes determining the need for new or expanded highways, public transport systems, cargo facilities, transport terminals, their location, their capacity and managing their demand. Typically, transportation planning involves forecasting movement patterns and demand 15–25 years into the future in order to design a future transportation system that will support spatial growth at that time [13]. The demand for travel arises as thousands of individuals make individual decisions about how, where and when to travel. Many factors influence these decisions, such as family situations, the characteristics of the person making the trip, and the choices (destination, route, and mode) available for the trip. Transport modeling is used to predict travel patterns and demand and includes many mathematical equations to model or represent how people move. Transportation modelers use special computer software to design and develop transportation routes. The transport designer can design the connection between new road structures and existing transport systems, and also develops systems with one-way traffic
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or bypass roads in case of repair of existing transport facilities or plan the optimal load on transport systems and routes before major events [14, 15]. Thus, the Transport Planner position includes the following responsibilities: – Technical formulation and management of small, medium and large transport planning and modeling tasks with minimal supervision. – Teamwork: with project managers and other department staff to ensure that required analyzes, reports and plans are completed in a timely manner as assigned. – Preparation of high-quality written reports and technical analyzes with the possibility of presenting the results to interested parties. – Advising the client and interested parties on transport issues. – Representation of the transport team implementing the project when interacting with clients, as well as with other stakeholders and government agencies. – Ensuring compliance of the appearance of the results with the required documentation format. – Applying technical skills and sharing them with junior staff. It is important enough for a transport planner to become familiar with local trends, practices and standards for transport planning, because without knowledge of the intricacies of an already formed infrastructure, it is quite difficult to form a successful project. In addition, he must also be able to travel abroad, evaluate transport infrastructure in other countries, highlight best practices and be able to adapt them to the realities of local infrastructure [16]. Experience in transport assessment, preliminary highway design and a good knowledge of transport assessment and modeling methods, as well as a practical understanding of the role and importance of transport planning in providing access to development, urban mobility and helping people are essential criteria for candidates in accordance with norms and trends towards expanding the availability of infrastructure for all segments of the population. In addition, it is necessary to be able to consider the transport infrastructure at the lowest levels, and therefore experience in conducting an appropriate analysis of intersections is required to form an assessment of the impact of road traffic on participants. The transport planner should have experience in the practical use of such special software for transport simulation, such as VISUM, VISSIM, SIDRA, SYNCHRO, HCM, and also have basic competencies in working with MS Office software (Excel, Word, PowerPoint, Project) [17]. In addition, important competencies for a transport planner include making technical presentations to a variety of audiences, including non-technical audiences, project and financial management experience, commercial competence, familiarity with electronic document management systems, quality assurance of documentation in accordance with local standards and practices (including obtaining approvals from government agencies). Soft skills are also needed to build a career as a transportation planner: language skills (especially English) in oral and written form to communicate effectively through various channels, the ability to present information in an accessible manner, great attention to project details, highly developed interpersonal and negotiation skills. Besides, the following competencies and skills are required: self-organization, self-control and adjustment of work, the ability to work in a team and organize similar work, maintain
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continuous improvement and motivate people to cooperate, a positive attitude towards work in general. The transport modeler must look critically at problems to see how work can be improved, be open to new ideas, be proactive and creative in solving problems, and encourage open and constructive discussion of work issues. It must ensure that controls and performance measures are in place to deliver efficient and valuable services. Second, the structure of the position of a transport engineer was considered. Transportation engineers perform and supervise a variety of professional engineering assignments in one or more specialized areas of transportation systems and programs, including research, planning, design, technology, materials, construction, traffic safety, traffic control, and highway and bridge maintenance, airports, railways and related facilities, technological devices and applications [18]. The work of transport engineers is aimed at optimizing traffic flow, increasing mobility and safety, and minimizing harmful emissions in all transport systems at an efficient cost. Transport engineering includes the analysis and evaluation of traffic lights, signs and markings, as well as the environmental conditions surrounding the infrastructure. Transportation engineers use computers and computer-aided design (CAD) systems to assist in planning and design. They may need to become familiar with traffic modeling and micro-simulation software to predict traffic patterns. Transportation engineers can also develop and improve advanced technologies such as variable message signs, develop new traffic management systems, and integrate vehicle-to-vehicle communication to optimize traffic flow and avoid traffic collisions. The skill structure of a transport engineer is in many ways similar to the competencies required of a transportation planner. Analytical skills are critical for a transportation engineer, as transport engineers must constantly analyze or interpret the accumulated and presented data. Considerable attention to detail is also required, as a sufficient number of factors must be taken into account when creating transport infrastructure and constantly checking the project for compliance with the real picture and the presence of design errors. Even a small inaccuracy can lead to massive work to correct the project if it is not noticed at an early stage. Companies need people who can work effectively under enormous pressure and time constraints. A transportation engineer has to work in circumstances, and in order to overcome such a problem, one must think logically and develop problem-solving skills to deal with such situations. In addition, teamwork is also important for a transportation engineer because a team of engineers will be assigned to carry out the project, and this becomes more difficult when you have to perform your work functions and manage several engineers at the same time. The profession of both a transport engineer and an infrastructure engineer revolves around a lot of overlapping data that is presented in tabular, graphical or coded form. So, the main purpose of this data is to help them plan their work. One of the most important skills is the ability to accurately interpret complex data. Third, the competencies of infrastructure engineers were analyzed. These specialists create, improve and protect the environment. They plan, design, and supervise the construction and maintenance of building structures and infrastructure such as roads, railroads, airports, bridges, harbors, dams, irrigation projects, power plants, and water and sewer systems. They also design and are responsible for the construction of tall buildings and large structures that can withstand all weather conditions [19].
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Generally, civil engineers are divided into two types: consulting engineers and contracting engineers. Consultants are responsible for project development and work primarily in the office. The contractors then take the designs and implement them during construction. Contractors work on site, managing the construction of a structure. The infrastructure engineer position includes the following responsibilities [19, 20]: – Conducting technical and feasibility studies, including field studies. – The use of a number of computer programs for the development of detailed designs and complex calculations. – Liaising with clients and various professionals, including architects and subcontractors. – Work on the compliance of the project with legislation and other standards. – Preparation of specifications and support of tender procedures. – Project management (resources, budget, team). Project managers plan, budget, track, and report on a project using project management tools, sometimes pitching a project idea or signing up to work on a project after it has already been approved. Thus, the project manager is the liaison between senior management, stakeholders, and the teams tasked with the actual execution of the project. They monitor the correctness of the project plan, regularly report on its progress and monitor the progress of the project to ensure that its implementation does not go beyond the approved budget and schedule [21]. The project manager does not usually need to perform the practical tasks associated with the project, but the incumbent should have some degree of knowledge regarding various aspects of the project.
3 Results Based on the review of professional standards, occupational profiles and literature, the main skills and competencies that are necessary for economic engineer were identified. All highlighted skills, according to the methodology [22], are divided into four categories: “Statistical analysis and programming”, “Special skills”, “Project management”, and “Soft skills” (see Fig. 1). The “Statistical Analysis and Programming” category contains knowledge and tools of statistical analysis and econometric modeling, from which the subcategories “Statistical Analysis” and “Statistical Packages and Programming Languages” were derived. The category “Special skills” included the knowledge and skills to work with the regulatory framework of the organization of public-private partnerships, as well as engineering competencies assigned to the subcategory of transport planning and modeling. The “Project Management” category highlights project organization, project economic evaluation and risk management skills and competencies. The “Soft skills” category includes the communication, teamwork, and decision-making subcategories. A total of ten decomposed skill subcategories. For each subcategory, a significant number of skills required to meet the job characteristics of economic engineer for transport industry were identified. Particular emphasis is placed on transportation planning and modeling skills, the ability to work with regulatory and legal frameworks, and competent economic evaluation of the project being developed. Also important is the
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Fig. 1. Competencies and skills structure
implementation of the organization of project activities and all communications related to it.
4 Conclusion and Discussion Digital technologies and digital services used in transport are changing the rules of employment and the requirements for competencies, knowledge, skills and relationships of employees. There is a trend towards automation of routine work and the release of labor, as well as the need for flexible multidisciplinary specialists with competencies in the creation and support of intelligent transport systems, digital information platforms and the development of new projects for transport. To create such specialists, it is necessary to train personnel with up-to-date knowledge and skills, adjusted to the demands of the labor market. Therefore, in order to carry out digitalization in accordance with national programs, it is necessary to use both employers and the education system to the maximum extent possible. In this regard, work is underway to create new educational programs that cover the need for qualified personnel, in which, in accordance with the digital vector of development of the transport industry, the state and employers are interested. Acknowledgement. This research was funded by the Grant Council of the President of the Russian Federation. Project № MK-1969.2022.2.
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References 1. Digilina, O.B., Teslenko, I.B.: Transformation of the labor market in the context of digitalization. RSUH/RGGU Bulletin. Series Economics Management Law (2020) 2. Susloparova, O.V., Modina, D.S.: Tsifrovizatsiya transportnoy otrasli v Rossii [Digitalization of the transport industry in Russia]. Innovatsionnoye razvitiye ekonomiki: tendentsii i perspektivy [Innovative development of the economy: trends and prospects], 1, 148–158 (2019). (in Russian) 3. Sakharova, Y. I. Analitika rynka truda v sfere transporta i logistiki v Rossii [Analytics of the labor market in the field of transport and logistics in Russia]. Transport Rossiyskoy Federatsii. Zhurnal o nauke, praktike, ekonomike [Transp. Russian Federation. J. Sci. Pract. Econ.] 4(71), 66–68 (2017). (in Russian) ˇ 4. Chinoracký, R., Corejová, T.: Impact of digital technologies on labor market and the transport sector. Transp. Res. Procedia 40, 994–1001 (2019) 5. Tijan, E., Jovi´c, M., Aksentijevi´c, S., Pucihar, A.: Digital transformation in the maritime transport sector. Technol. Forecast. Soc. Change 170, 120879 (2021) 6. Pernestål, A., Engholm, A., Bemler, M., Gidofalvi, G.: How will digitalization change road freight transport? Scenarios tested in Sweden. Sustainability 13(1), 304 (2020) 7. 10.006 “Urban planner” (approved by Order of the Ministry of Labor and Social Protection of the Russian Federation dated March 17, 2016 No. 110n). https://classinform.ru/profstand arty/10.006-gradostroitel.html. Accessed 15 Oct 2022 8. 08.049 “Transport logistics specialist” (approved by Order of the Ministry of Labor and Social Protection of the Russian Federation dated September 8, 2014 No. 616n). https://classinform. ru/profstandarty/40.049-spetciali. Accessed 15 Oct 2022 9. 08.022 “Statistician” (approved by the Order of the Ministry of Labor and Social Protection of the Russian Federation dated September 8, 2016 No. 605n). http://base.garant.ru/71211740/. Accessed 15 Oct 2022 10. 10.08.036 “Specialist in working with investment projects” (approved by Order of the Ministry of Labor and Social Protection of the Russian Federation dated April 16, 2018 No. 239n). https://www.garant.ru/products/ipo/prime/doc/71838730/. Accessed 15 Oct 2022 11. 08.018 “Risk Management Specialist” (approved by Order of the Ministry of Labor and Social Protection of the Russian Federation dated August 30, 2018 No. 564n),https://classinform. ru/profstandarty/08.018-spetcialist-po-upravleniiu-riskami.html, last accessed 2022/10/15 12. 041 “Specialist in the field of public-private partnership project management” (approved by Order of the Ministry of Labor and Social Protection of the Russian Federation dated July 20, 2020 No. 431n), https://www.garant.ru/products/ipo/prime/doc/74416278/. Accessed 15 Oct 2022 13. Heyns, W., Van Jaarsveld, S.: Transportation modelling in practice: connecting basic theory to practice. Transp. Land Use Integr. Perspect. Dev. Countries WIT Trans. State Art Sci. Eng. 100, 3–27 (2017) 14. DfT Transport Modeller and Planner Skills Framework. https://files.smartsurvey.io/2/0/I8Q V6T6R/Transport_and_Modeller_Framework_(1).pdf. Accessed 15 Oct 2022 15. Lyons, G.: 10-year review of the competencies expected of transport planning professionals. Transport Planning Professional (2018) 16. Civil Engineering Body of Knowledge 3 Task Committee. Civil engineering body of knowledge: Preparing the future civil engineer. American Society of Civil Engineers (2019) 17. Handy, S., Weston, L., Song, J., Maria, D., Lane, K.: Education of transportation planning professionals. Transp. Res. Rec. 1812(1), 151–160 (2002) 18. Transportation Engineer - State of Michigan. https://www.michigan.gov/documents/Transp ortationEngi. Accessed 15 Oct 2022
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19. Ryan, A., Bouchard, C., Fitzpatrick, C., Knodler, M., Jr., Ahmadjian, C.: Analytical comparison of core competencies across civil engineering positions within new England department of transportation agencies. Transp. Res. Rec. 2673(1), 427–437 (2019) 20. Ab Rasid, N., Amin, N.F.: Industry 4.0 civil engineer job skills required by employers in Malaysia. Jurnal Kemanusiaan, 17(1-S) (2019) 21. do Vale, J.W.S.P., Nunes, B., de Carvalho, M.M.: Project managers’ competences: what do job advertisements and the academic literature say? Proj. Manag. J. 49(3), 82–97 (2018) 22. Skhvediani, A., Sosnovskikh, S., Rudskaia, I., Kudryavtseva, T.: Identification and comparative analysis of the skills structure of the data analyst profession in Russia. J. Educ. Bus. 97(5), 295–304 (2022)
Development of Tools for Decarbonization of Electricity Consumption in the Russian Federation Tatiana Bugaeva(B) , Aleksandra Grishacheva, and Olga Novikova Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. The threat of global climate change today dictates a green course for the international economy and industry. It implies a set of tools to decarbonize production, as well as increase energy and environmental efficiency, to reduce the harmful anthropogenic impact on the ecosystem. The use of renewable energy sources (RES) is at the center of the global agenda. In many countries, a policy of increasing the production and consumption of renewable energy is being implemented, and since there are not so many effective technologies for carbon dioxide capture now, the construction of renewable energy capacities and an increase in the share of production at such power plants is a good alternative to combating climate change. In many countries, various government and market incentives are being developed for investment in such energy sources, as they are quite expensive. One of such solutions is the issue of renewable energy certificates. The paper analyzed the most popular world energy certification systems, which are compared with the prototype of the Russian national certification system. The economic effect of the use of certificates of origin of energy as an engine for the development of voluntary demand instead of mandatory payments is determined. In addition, the effects are formulated separately for both the energy generator and the consumer purchasing certificates. In modern conditions, the best solution seems to be the voluntary nature of such tools, because, firstly, many leading countries use them that way, and secondly, time is needed to popularize “green” certificates in Russian realities. Finally, it is necessary to create appropriate incentives for both companies generating clean energy, and companies supporting “green” production. Keywords: renewable energy sources · decarbonization · renewable certificate
1 Introduction Decarbonization has been a topic on the table for a while now. However, all the acute environmental risks and problems related to it make it even more relevant today [1]. Researchers and scientists all around the world are exploring promising ways, measures, and methods to achieve carbon neutrality. According to the research group, headed by Markus Johansson, deep decarbonization technologies require massive investment and facilitated coordination between the state and market stakeholders. What is particularly © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 442–454, 2023. https://doi.org/10.1007/978-3-031-32719-3_34
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emphasizes is that a successful transition to a low-carbon economy depends on inviting measures that can effectively reduce market and technological investment risks. A conceptual model specifies the importance of both, pricing mechanisms (primarily carbon pricing) and innovation policy, including R&D support and subsidies [2]. M.V. Zharikov identifies commercial and non-commercial approaches to greening [3]. Commercial approaches boil down to increasing energy efficiency of buildings, replacing power plants that rely on traditional fuel with RESs. The major outcome is that climate policy is expected to rest on three principles: 1) sustainable intervention (energy efficiency improvement). 2) investments in new technological trends and innovations. 3) utilization of technologies for storage, detection, and disposal of emissions. In its turn, a non-commercial approach is primarily aimed at developing an environmental strategy of a company. For example, a company cannot invest in any assets of another enterprise, unless it is guaranteed by the latter that they do not harm the environment, or use only clean energy; in other words, they have a good green reputation. However, not all researchers recognize decarbonization as a single miracle solution. For instance, V. Bushuev and D. Solovyov argue that the increase in carbon dioxide emissions does not have an anthropogenic origin only. They believe it is also determined by some natural, not energy-related factors, such as respiration of organisms, heat generation by soils; and sometimes even by natural anomalies, such as forest fires, floods, etc. Researchers do suggest an alternative way – investments in waste recycling. But the setback here is that recycling currently invites non-green polluting batteries [4]. Happily, a global effort towards decarbonization and improvement of climate policy is evident today, with new bills and initiatives constantly being developed. The leading positions in this regard are taken by the countries of the European Union. For example, in the article by [5], the authors propose a set of policy measures to achieve decarbonization of the economy. However, they note that its implementation requires allocating significant public funds in infrastructure, overcoming investment barriers, and mobilizing capital to ensure the introduction of clean technologies throughout the economy. What is more, the authors state that despite showcasing and adapting their model to specific political requirements of the EU, it can be easily adjusted and used by other regions and countries [6]. Another promising development to support renewables, and thereby reduce the negative impact on the environment, is the introduction of “green” certificates [7]. This tool is put forward by various international organizations and initiatives as a method of confirming the type of energy production that can be tracked by consumers [8]. Currently, such certificates are vastly applied in international practice. Different countries implement various tools and methods [9]. However, the bottom-line is that all systems have a single common function, which is to track the characteristics of a given megawatt-hour of electricity. At the national level, the systems of market regulation and energy performance certificates are inextricably connected to national laws, primarily environmental and electricity [10].
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The main decarbonization tools of energy consumption in the Russian Federation are: 1) free bilateral contract (SDD) – purchase and sale of electricity in the wholesale market for electricity and power (OREM) – a direct contract for “green” energy supply. 2) “green” certificates on the origin of energy – confirm the attributes of energy; can be created and transferred only upon the fact of electricity production. 3) purchase/sale agreement in the retail market (with the HEP less than 25 MW) – sale of certificates to retail consumers and repayment performed by distributor companies. Currently, the system faces some regulatory shortcomings related to the transfer of environmental rights via the above-mentioned tools. For instance, bilateral contracts do not include ecolabeling in the wholesale market; and certificates do not have a solid legal significance due to the lack of elaborate legislation on the topic. This research aims to study various certification systems and assess the possible economic effects of “green” certificates. To achieve the above-mentioned goal, it is necessary to address the following tasks: – analyze the legislative base of the Russian Federation on energy certification and carbon footprint reduction. – consider the most popular certification standards in the world and compare them with what is present in Russia. – identify the effects of certification for different stakeholders.
2 Certification of Energy Origin A green certificate is a document containing energy attributes that confirms the generation of 1 MWh of electric power from renewable energy sources. Energy attribute certificate (EAC) outlines characteristics of a private energy generation source [11]. Figure 1 shows the coverage of certificates used around the world at the beginning of 2022.
Fig. 1. Coverage of “green” certificates around the world [I-REC standard, 2022].
It is important to consider the most common systems. The European Energy Certificate System (EECS) is developed by the Association of Issuing Bodies (AIB). The
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international NPO AIB was established in 2002 and includes 27 EU countries, Iceland, Norway, Serbia, and Switzerland. AIB members are the competent authorities for the administration of guarantees of origin (GOs) schemes. Guarantees of origin are based on the Directive of the European Parliament and the Council on support for the use of renewable energy sources. The main function of guarantees of origin is to ensure that a given amount of energy sold was produced using RES [12]. Exact specifics of the circulation of certificates are determined and regulated by each country individually. Green certificates may operate in local markets (for example, Sweden and Norway), and serve as a tool to support renewable energy within the quota system. The GOs standard does not provide for the attribution of nuclear generation to renewable or “green”, however, each country remains entitled to issue guarantees of origin for the generation of electricity at non-renewable energy facilities. It is important to note that AIB is a reliable body that meets the criteria of objectivity, transparency, non-discrimination; and facilitates the international exchange of GOs certificates. The AIB members are eligible for export-import relations with each other with GOs certificates through a special electronic hub, which automatically increases their market opportunities and convenience. Renewable Energy Certificates (RECs) is a system that tracks certificates of energy origin in the USA and Canada. The term “REC” universally refers to a marketed certificate that confirms the fact that one MWh of electricity is produced by a renewable source (excepting Arizona and Nevada, where it is measured as kWh). Circulation of REC is determined by each state. In total, more than thirty states implement their own registries. Consequently, there is no single federal certificate standard. That is why, depending on the state, the set of environmental attributes, release rules, types of generation, penalties and expiration dates vary greatly. NAR, RPS, ERCOT, WREGIS, etc. are perfect examples of local registries [13]. The International REC Standard Foundation (I-REC Standard) is a non-profit organization that aims to create an infrastructure for tracking the origin of energy [14]. This standard has the widest coverage in the world. I-REC standard can be used when implementing and operating energy tracking systems by such stakeholders, as recognized issuers, and government agencies. Thus, the possibility of energy consumption from renewable sources is guaranteed. I-REC functions in accordance with single rules for the member-countries; however, some deviations are permissible for local conditions, which are regulated through accredited issuing bodies. Since 2020, Russia has been using the international voluntary system of green certificates I-REC. However, efforts have been also made to create a national certification system. Thereby, in October 2019, the association NP “Market Council” invited legal entities and individual entrepreneurs to participate in an open request for commercial proposals for the software development “Records keeping system for issuance and circulation of green certificates (trial)”. It was expected to keep record of “green” instruments (certificates of origin, bilateral agreements, etc.), which would allow documenting the origin of electricity consumed, as well as using these data to calculate the volume of indirect GHG energy emissions, confirm net consumption, and label goods. The request
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was to create software for a prototype of a trading e-platform (registry). “Distributed Registry Systems” - a company founded by the largest financial organizations in Russia, including VTB, Gazprombank, PSB, the National Payment Card System, the Moscow Exchange and the Fintech Association - was the one to be ranked first. It is the pioneer blockchain-based operator in the Russian Federation, engaged in development of innovative blockchain services and their application in the Russian market. It is assumed that NP “Market Council” will be the “head” organization in the system of green instrument circulation. The project is expected to be financed by loans, membership fees from the NP “Market Council” and companies’ fees for using the e-trading platform. The Government of the Russian Federation submitted to the State Duma the Bill of the Ministry of Energy of the Russian Federation on amendments to the Federal Law “On Electric Power Industry”, in connection with the introduction of generation attributes and certificates of origin of electric energy into civil circulation, in September 2022 [15]. The bill defines the content and procedure for exercising the rights of owners of generation attributes. In addition, legal grounds are being articulated for the measures to track the emergence of attributes, their transfer to other persons, and the exercise of such rights, including cases when generation attributes are certified. These measures are also expected to keep the record of provision, turnover and repayment of certificates of origin. Table 1 compares the international certification systems, I-REC, GOs, RECs and the draft national certification system of the Russian Federation. Table 1. Main features of international certification systems and the national energy certification system of the Russian Federation. I-REC
RECs
GOs
National system of the Russian Federation
Countries
51 countries
USA, Canada
EU
RF
Volume
1 MWh
Binding power
no
Differences in attributes
no
Availability of issuing bodies
yes
Pricing
Market pricing (supply and demand)
Market type
International system
Price
40–100 rub
Type of generation SES, WPP, HPP, for which a TES, TPP certificate is issued (biomass)
NP “Market Council” National system
1.5–6.6$
0.5–2e
-
varies
SES, WPP, HPP, TES, TPP (biomass)
SES, WPP, HPP, TES, TPP (biomass) (continued)
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Table 1. (continued) I-REC
RECs
GOs
National system of the Russian Federation
Validity period
not limited
21 months
1 year, 18 months. ≈ 5 years For repayment
Labeling
EKOenergy
EKOenergy
–
–
system operator/provider
AIIS KUE (Automated Measuring and Information System for Electric Power Fiscal Metering)
Green-E Confirming organization
In Russia - AIIS KUE (Automated Measuring and Information System for Electric Power Fiscal Metering)
staff
The project implies that certificates are issued for energy generated from RESs (solar, wind, mini-hydroelectric power plants) and low-carbon sources, such as nuclear power plants and large hydroelectric power plants (with a capacity of more than 25 MW). The decision to include nuclear power plants and large hydroelectric power plants in the renewable energy certification system may affect the chances of successful integration of a domestic system into the international one. It is explained by the fact that according to Green-e® standards, only electricity produced from the following resources can be certified: solar, air, biomass processing and water energy at HEP with low levels of exposure [16]. Decarbonization is becoming a global trend that defines the ability of markets to compete. “Green” instruments in the long run are able to become an absolute win-win for business, investors in renewables, and society in general. The section below evaluates the economic effect of issuing green certificates by reducing payments under power supply contracts. It also shows the economic effect of green certificates for an electric power producer and lists indirect economic effects for buyers of “green” certificates [17]. Potential economic effect of issuing green certificates may boil down to covering a part of consumer costs to pay for power under RES power supply agreements (PSA) at the expense of funds from selling green certificates (GC) [18]. In general, the effect can be calculated using the formula below (1): Egci = PPSAi − TRgCi
(1)
where: EgCi is effect of reducing the financial burden of payments under the PSA in i year, mln. Rub.; PPSAi is annual payment under the PSA, mln. Rub.; TRgci is total revenue in the i year from selling “green” certificates, mln. Rub.
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To assess the effect of reducing payments under the power supply agreements, it is necessary to be aware of their exact number. Table 2 shows the forecast calculations made by the NP “Market Council”. Table 2. Forecast on the dynamics of payments under the power supply agreements (HPP and RES). 2021
2022
2023
2024
2025
2026
RES power supply agreements, million rubles
104613
119992
152831
153427
152652
151800
Power supply agreements (HPP), million rubles
18 313
18 337
18 362
33 507
33 608
33 709
Further on, it is necessary to estimate the revenue from selling certificates of energy origin, following the formula 2 given. (2) TRgci = Qvol i ∗ %D ∗ Pgc where: Qvol i is volume of electricity generation from RES for the i period, MWh; %D is potential demand for green energy, %; Pgc is price of a certificate, rub. Per MWh. The I-REC standard has been operating in the Russian Federation since December 2020 (suspended since mid-March). According to the I-REC registry, about 3.75 million certificates were issued in 2020–2022. Electricity produced at nuclear power plants is not classified as clean or renewable, as it is customary in many countries. To determine the proceeds from selling green certificates, it is important to consider the market prices for HPP, WPP and SES certificates, presented in Table 3. Table 3. Prices of green certificates. HPP certificate
SES certificate
WPP certificate
90
90
rub/MWh 45
2.97 million MWh were certified, making up only 1.4% of the total amount of renewable energy generation in Russia in 2021. 1% accounts for the HPP certificates, which is explained by the superior installed capacity and capacity coefficient of hydroelectric power plants, and consequently, a bigger output compared to solar and wind power plants. On the contrary, if we consider the percentage of certificates issued for each type of renewables, the largest share will be represented by the WPP certificates, which cover 18.3% of their output, followed by SES – 7.9% and HPP – 1%.
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It is difficult to predict the potential demand for this tool after 2021. However, in accordance with the research of the Ministry of Energy with the estimated 200 million MWh per year, it is possible to calculate what percentage of output certificates will be issued in subsequent years. The calculation below is based on the data from the Russian Power System Operator for 2021 [19]. If total consumption in 2021 amounted to 1090 mln. MWh, then considering the estimated potential volume of the green certificates market of 200 mln. MWh, it is possible to forecast the demand for “green” energy of about 18% of the output. Total electricity production at RES (SES and WPP), hydroelectric power plants and nuclear power plants amounted to 437547 MWh. Since electricity from nuclear power plants is not certified in the international I-REC system, it can be assumed that production of “green” energy amounted to 2153953 MWh. Predicting the output is only possible when the targets for commissioning new HPP and RES capacities are known. The annual output will be calculated via formula 3. (3) Qvol i = Ninst i −1 + Ncomi ) ∗ ICUF ∗ 8760 where: Ninst i −1 is installed capacity of the previous year, MW; Ncomi is commissioning capacity in the current year, MW. ICUF is installed capacity utilization factor (ICUF). Using data on commissioning new capacities for 2022–2024, it is possible to calculate the approximate annual output considering the installed capacity utilization factor (ICUF) of RESs and HPPs. Table 4 presents the generation forecast. Table 4. Generation of HPPs and RESs. 2021 Qvol i RES
MWh
5875500
Qcomi HPP
MWh
209519800
2022
2023
2024
6898586
8021981
8750173
219848476.9
230686325.5
242058446.5
It is essential to calculate the effect of reducing payments for power under the programs of power supply agreements. We will take the volume of the 2021 market of certificates of energy origin as the actual one, and further on, as 18% of the output. Table 5 presents results of calculation. At a given level of demand, usage and selling of energy certificates is insignificant, but it is sufficient to compensate for the costs for 2021–2024. What is more, it will be possible to reduce the load of the RES power supply agreements by 466.53 mln. Rub., and the HPP power supply agreements – by 5.8 bln. Rub. This effect is likely to scale, given that the demand for the tool is likely to increase, including the impending cross-border carbon tax (2026). In the future, the I-REC certification system, as well as the national one, will contribute to the development of renewable energy generation and increase the efficiency of electricity production in the Russian Federation.
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2022
2023
2024
Total
SES and WES Certificates issued, mln. 842168 Rub
1265288.41 1471333.29 1604892.89
TR, mln. Rub
75.8
113.87
Charge stations, mln. Rub
104537.2 119878.1
% reduction of financial 0.07% burden
0.09%
132.42
144.44
152698.6
153282.6
0.09%
0.09%
5183682.60 466.53
HPP Certificates issued, mln. 2109425 Rub
40323003.9 42310803
44396594.5 129139826.4
TR, mln. Rub
94.92
1814.53
1903.99
1997.85
Charge stations, mln. Rub
18 218
16 522
16 458
31 509
% reduction of financial 0.52% burden
9.91%
10.38%
10.88%
5811.29
After the introduction of the I-REC standard system in Russia, generators that produce renewable energy were able to register in this system. Following that step it was possible to issue certificates of energy origin with the corresponding “green” attributes. By becoming a participant in this initiative, (clean) energy producers can contribute not only to sustainability of other companies, but also to reducing their indirect emissions. The economic effect for the generator is to receive additional revenue, that can be channeled elsewhere. However, it is necessary to understand how this very revenue is generated. As it was noted above, the I-REC system, similarly to many other international organizations, implies that the price of a certificate is set in a market manner. In other words, it depends on demand for the instrument itself. In the Russian Federation, the average price varies from 40 to 100 rubles per MWh, depending on the type of power plant that issues certificates: HPP certificates are usually cheaper than SES, or wind farms. However, it should be considered that the electricity producer will set the price based not only on the type of generation, but also on its status and that of its counterparty in the system. It is necessary to assess the options, provided that the manufacturer has the status of both a registrant, and a participant in the system. There are certain payments or fees for entering the registry and maintaining the I-REC standard account, indicated in Table 6. Thus, if the generator has signed a contract with a minor consumer, or if the counterparty is not a participant in the system, the price for the buyer will be formed according to the formula (4): P = P of issue + P of market + P pay−off
(4)
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Table 6. I-REC Standard Entry Fee. Registration of a power plant (5 years)
Release
Creating an account
Account Maintenance
Repayment
Generator
62500 rub. Without VAT
1.7 rub./MWh without VAT
–
–
–
Buyer
-
-
500 euros
2000 euros
6 cents/MWh
where: P_(of issue) is a commission of a local issuing organization for the issue of 1 certificate in the register, equals 1.7 rub.; P_(of market) is a price that is set by the balance of supply and demand; for 2022 – 40–100 rub.; P_(pay-off) is a fee of the I-REC for the certificate repayment, equals 6 eurocents per MWh. In this case, the buyer does not need to register in the I-REC system and pay registration fees or pay-off, the generator does everything for him. Otherwise, the buyer is required to pay for registration (a fee for 5 years), introduce annual charge for account maintenance and pay-off the use of each MWh of “green” energy. In that case, the generator price will be shaped as follows: P = Prelease + Pmarket
(5)
The profit obtained may be allocated to the: 1) implementation of internal projects related to energy efficiency and GHG emission reduction. 2) internal projects on construction of renewable energy facilities or modernization. 3) third-party projects related to “green” energy. 4) compensation for construction and operation costs, etc. For the end of 2021, 60% of the issued certificates were repaid. It is worth pinpointing that such a tool is a confirmation of the energy already generated, which means that the consumer is not buying energy as it is, but the product of RES generation. “Green” certificates provide certain economic and non-economic benefits for enterprises and state. From the consumer’s point of view, purchase of certificates of energy origin provides a number of indirect economic benefits. Record of the GHG emissions and its declaration play a significant role for companies today. There is the whole range of advantages that a consumer can get by purchasing “green” certificates. First, they increase the share of green energy in the energy and industrial sectors, which in the future will allow companies to: 1) reduce carbon footprint of their products.
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2) meet the sustainable development goals related to clean energy consumption, environmental consciousness and climate change. Secondly, the company’s competitiveness is boosted thanks to fact that its reputation enhances. Consequently, it attracts environmentally conscious customers and large investors. Thirdly, the company’s ESG rating grows, leading to: 1) increase in the company’s market value. 2) “green” investments or financing at a reduced interest rate. And finally, the company gets an opportunity to make a “statement”, meaning that it is possible to report all the environmental contribution and results on the RES generation in marketing campaigns or product labeling. For example, the Carlsberg Group reports on the share of renewable energy use, while Unilever has already announced 100% consumption of “green” energy. As for green labeling, there are more than several hundred systems in the world, many of which are directly related to “green” energy. Via inviting environmental labeling on products, companies enhance their status and image for consumers, and generate a competitive advantage. In addition, “green” marketing allows companies to increase revenue. In the long run, the purchase of certificates of energy origin simplifies the organization’s reporting within the Russian legislation. For instance, companies will not be required to report on the amount of carbon dioxide emissions under the Federal Law No. 296 “On GHG Emissions”.
3 Discussion Today, electricity production remains one of the main sources of GHG pollution in the atmosphere. A solution that is likely to help is a voluntary demand for renewable energy, that is, the demand shown by interested consumers whose goal is to implement corporate policies in the field of ecology, nature protection, labor, etc. This will make it possible to figure out which regions demonstrate a higher demand for “green” energy, remove mandatory burden from other buyers, and also identify priority technologies. Various measures can contribute to the development of voluntary demand in the Russian Federation, including the development of “green” instruments «green» certificates. Since the certificate of energy origin is a subject of a purchase/sale deal that reflects the attributes of renewable energy, it can be considered as a market instrument. Green certificates can help companies to make a statement in the market and gain competitive leadership. What is more, they contribute to the expansion of renewable energy capacities and increase efficiency of “green” markets. Using the showcase of Russia, it is possible to point out conceptual differences in certification systems related to the interpretation of low-carbon energy. Even the I-REC standard used in the Russian Federation does not imply the issuance of certificates for the amount of energy generated from nuclear power plants. That occurs since the world, especially the leading countries (in terms of decarbonization), do not recognize nuclear power plants as a renewable or low-carbon source. However, the energy produced at
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the NPPs is considered relatively clean, and thus has higher chances of being “greencertified” by the national certification system. Acknowledgments. The research was financed as part of the project “Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization” (FSEG-2023-0008).
References 1. Glebova, A., Daneeva, Yu.: Adaptation of the Russian energy sector to the decarbonization of the world economy. Ekonomika. Nalogi. Pravo = Econ. Taxes Law 14(4), 48–55 (2014) 2. Johansson, M., Langlet, D., Larsson, O., Löfgren, Å., Harring, N., Jagers, S.: A risk framework for optimising policies for deep decarbonisation technologies. Energy Res. Soc. Sci. 82, 102297 (2021) 3. Zharikov, M.: The price of decarbonization of the world economy. Ekonomika. Nalogi. Pravo. Econ. Taxes Law 14(4), 40–47 (2021) 4. Bushuev, V., Soloviev, D.: Climate and energy transition: interaction and interdependence. Energy Pol. 11(65), 44–55 (2021) 5. Sotiriou, C., Zachariadis, T.: A multi-objective optimization approach to explore decarbonization pathways in a dynamic policy context. J. Clean. Prod. 319, 128623 (2021) 6. Kudryavtseva, T., Shneider, A., Skhvediani, A., Brazovskaya, V.: Comparative analysis of waste management and pollution elimination clustering in Russia and Finland. Ecol. Ind. Russia 26(3), 65–71 (2022) 7. Chuangab, J., Lienc, H.-L., Dena, W., Iskandard, L., Liaob, P.-H.: The relationship between electricity emission factor and renewable energy certificate: the free rider and outsider effect. Sustain. Environ. Res. 28(6), 422–429 (2018) 8. Hustveit, M., Frogner, J., Fleten, S.: Tradable green certificates for renewable support: the role of expectations and uncertainty. Energy 141, 1717–1727 (2017) 9. Adamczyk, J., Graczyk, M.: Green certificates as an instrument to support renewable energy in Poland—strengths and weaknesses. Environ. Sci. Pollut. Res. 27(6), 6577–6588 (2019). https://doi.org/10.1007/s11356-019-07452-5 10. Finjord, F., Hagspiel, V., Lavrutich, M., Tangen, M.: The impact of Norwegian-Swedish green certificate scheme on investment behavior: a wind energy case study. Energy Policy 123, 373–389 (2018) 11. Edinaya energeticheskaya sistema Rossii: promezhutochnye itogi (operativnye dannye) [The unified energy system of Russia: interim results (Operational data)]. Moscow, Sistemny operator EES [System Operator of the EEC]. https://www.so-ups.ru/fileadmin/files/company/rep orts/ups-review/2019/ups_review_1119.pdf. Accessed 09 Aug 2022 12. The European market for renewable energy reaches new heights. https://www.ecohz.com/ press-releases/the-european-market-for-renewable-energy-reaches-new-heights. Accessed 17 Aug 2022 13. Hamrin, J.: REC definitions and tracking mechanisms used by state RPS programs. Clean Energy States Alliance (2014) 14. I-REC Standard. https://www.irecstandard.org/documents. Accessed 15 Aug 2022 15. Legislative support system. https://sozd.duma.gov.ru/bill/196167-8?ysclid=l95kklogl511994 2525#bh_note. Accessed 03 Oct 2022
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16. Lau, C., Aga, J.: Bottom line on renewable energy certificates. https://www.wri.org/research/ bottom-line-renewable-energy-certificates. Accessed 15 Aug 2022 17. Kulachinskaya, A., Akhmetova, I., Kulkova, V., Ilyashenko, S.: The challenge of the energy sector of Russia during the 2020 covid-19 pandemic through the example of the republic of Tatarstan: discussion on the change of open innovation in the energy sector 2020. J. Open Innov. 6(3), 1–12 (2020) 18. Balashov, M.: Renewable energy certificates: application potential and efficiency. Strateg. Decis. Risk Manag. 11(1), 14–27 (2020) 19. The Ministry of Energy estimates the demand for “green” certificates at 20% of output in the Russian Federation. https://peretok.ru/news/strategy/23604. Accessed 05 Oct 2022
Sino-Russian Industrial Joint Investment—Taking Oil and Gas Resource Development and Energy Cooperation as an Example YuanYuan Fu(B) Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected], [email protected]
Abstract. The purpose of this research is to study the current situation of joint investment between China and Russia in the field of oil and gas. The author uses empirical analysis methods, data analysis methods, and dynamic and static analysis methods. The novelty lies in the fact that the author combines the economy with Chinese characteristics and analyzes actual cases to give the focus of China’s joint investment in the energy economy and Russia. Digital technology has significantly changed the economy. Based on the new trend of economic development, the author investigates and analyzes the innovation index of the use of digital technology in the field of joint economic development between China and Russia, and the relevant data on the export of high-tech products. Based on the theory of energy geo-economics, the author analyzes the geographical advantages of energy cooperation between the two countries from the aspects of geographical location, capital investment, industrial structure, energy trade, and energy technology, and puts forward countermeasures and suggestions for developing Russian energy cooperation from the perspective of geo-economics. Keywords: industrial investment · Sino-Russian Industrial Cooperation · regional economic integration · capital investment · ICT Development Index · GII
1 Introduction Russia is the world’s largest energy producer, consumer and exporter. Although there are still some problems in the current energy cooperation between the two countries, “Overall, the progress made in China-Russia energy cooperation in recent years has been positive and breakthroughs have been made in certain fields, thus laying an important foundation for the two countries to further deepen energy cooperation” [1] and exporter outside OPEC. It is rich in oil and natural gas resources. It is one of the few countries in the world that can guarantee self-sufficiency in energy resources and have certain export capabilities. Russia’s energy resource sales revenue is close to 15% of GDP, accounting for about 30% of the national budget, and almost equivalent to two-thirds of export revenue. According to the “BP World Energy Statistics Yearbook (June 2021)” © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 455–467, 2023. https://doi.org/10.1007/978-3-031-32719-3_35
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published by BP, as of the end of 2020, Russia’s remaining proven recoverable reserves of petroleum are 13.1 billion tons, accounting for 6.3% of the world’s remaining proven recoverable reserves, ranking eighth in the world; the remaining proven recoverable reserves of natural gas are 54.60 trillion m3 , accounting for 25.4% of the world’s total, ranking first in the world. In 2020, petroleum production will be 611.4 million tons, accounting for 13.8% of the world’s total production, ranking second in the world [2, 3]. The annual consumption of petroleum is 146 million tons, accounting for 3.5% of the world’s total consumption, ranking fifth in the world. Russia’s natural gas production in 2020 will be 617 billion m3 , accounting for 19.5% of the world’s total production, ranking second in the world; annual consumption will be 454.6 billion m3 , accounting for 14.2% of the world’s total consumption ranked first in the world. And China is the world’s most important energy producer and consumer. After more than 30 years of reform and opening up, China’s economy has developed rapidly, especially in the past 10 years, the gross national product has maintained a growth rate of 8–10%, becoming the country with the fastest economic development in the world [4]. High economic growth has also driven high demand for energy. In 2021, “China alone will contribute 71% of the increase in global energy consumption.” According to the “BP World Energy Statistics Yearbook (June 2021)” published by BP, UK. By the end of 2020, China’s remaining proven recoverable reserves of petroleum will be 1.9 billion tons, accounting for 0.9% of the world’s remaining proven recoverable reserves; the remaining proven recoverable reserves of natural gas will be 3.1 trillion m3 , accounting for 1.5% of the world’s total. In 2020, oil production will be 203.6 million tons, accounting for 0.3% of the world’s total production. The annual consumption of petroleum is 461.8 million tons, accounting for 11.4% of the world’s total consumption, ranking second in the world. In 2020, China’s natural gas production will be 102.5 billion m3 , accounting for 3.1% of the world’s total production; annual consumption will be 130.7 billion m3 , accounting for 4.0% of the world’s total consumption. In summary, the importance of this article’s research can be seen—energy cooperation and investment between China and Russia play an important role in the world’s economic development. The purpose of this study is to study the current situation with Chinese-Russian joint investments in the oil and gas field. Sino-Russian industrial joint investment—taking oil and gas resource development and energy cooperation as an example. In order to clearly assess the status quo of Sino-Russian oil and gas joint investment, the following tasks should be completed: 1) Analyze the current situation of global oil and gas resources and trends in reserves with the help of data; 2) Analyze the current situation of oil and gas resources in the Russian Far East, trends in reserves, characteristics of the oil and gas industry chain, and energy economic distribution; 3) The distribution of China’s oil and gas resources and the characteristics of related industrial chains; 4) The trend of Sino-Russian cooperation and joint investment in the field of oil and gas, as well as suggestions for future development.
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2 Analysis of Global Oil and Gas Resources Since this century, with the increase in international energy prices, increased energy demand and the development of new oil and gas fields, global production of oil and gas resources has increased year by year. According to the “BP World Energy Statistics Yearbook (June 2020)”, global oil output increased from 3.61 billion tonnes in 2001 to 3.99 billion tonnes in 2011, an increase of 10.5% in 11 years, and natural gas production increased from 2.48 trillion m3 in 2001 to 3.28 trillion m3 in 2011, an increase of 10.5%. Natural gas production increased from 2.48 trillion m3 in 2001 to 3.28 trillion m3 in 2011, an increase of 32.3% (See Table 1). Table 1. Changes in global oil and gas production from 2008 to 2021. year
2008
2009
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020–2021
Petroleum (billion tons)
39.7
38.7
39.5
37.8
38.6
39.2
38.8
38.4
39.5
39.3
39.8
+11.5%
Natural gas (trillion cubic meters)
3.05
2.96
3.05
2.94
3.19
3.18
3.28
3.49
3.56
3.1
3.28
+33.4%
With the rapid development of the global economy at the beginning of this century, especially the advancement of industrialization and urbanization in developing countries, the consumption level of global oil and gas resources has increased year by year. From 2001 to 2011, the average annual increase in oil consumption reached 1.3%, while the average annual increase in natural gas reached 3.1% (See Table 2). Table 2. Change in oil and gas consumption growth from 2008 to 2021 year
2008
2009
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020–2021
Petroleum (billion tons)
39.9
40.3
40.6
40.8
40.7
41.2
40.9
41.3
41.98
42.5
42.87
+14.1%
Natural gas (trillion cubic meters)
3.01
2.93
3.15
3.22
3.35
3.21
3.22
3.25
3.32
3.35
3.43
+32.4%
The contribution of non-OECD countries to the growth of global oil demand is significantly higher than that of OECD countries. Among them, China’s demand has grown the fastest. When the global average demand growth is only 2.6% (2007–2011), China’s oil demand growth has already reached 25% (see Table 3). According to the forecast of the International Energy Agency, although the proportion of one-time fossil energy in the energy demand structure will decrease in the future, the absolute value of oil and natural gas demand will show an increasing trend under different scenarios (see Table 4). In the field of natural gas, the International Energy Agency even believes that global energy will enter a golden age of natural gas.
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Y. Fu Table 3. Global oil demand from 2015 to 2021 million barrels per day.
year
2015
2016
2017
2018
2019
2020–2021
OECD countries
4940
4760
4620
4560
4460
−7.1%
North America
2550
2420
2320
2370
2341
−7.9%
Europe
1550
1540
1480
1450
1440
−7.6%
Asia Pacific Non-OECD countries Russia
850
830
800
750
780
−5.6%
3830
3860
3980
4310
4350
+17%
430
420
420
460
480
+15.6%
China
780
760
820
930
980
+27%
Middle East
650
710
760
770
810
+22.1%
8770
8650
8570
8840
8910
+2.7%
The world
Table 4. Voluntary demand for oil and gas under different scenarios in the future (100 million tons). 1980
2009
New Policy Scenario
Current Policy Scenario
450 Scenarios
2020
2035
2020
2035
2020
2035
oil
30.97
39.87
43.84
46.45
44.83
49.92
41.82
36.71
natural gas
12.34
25.39
32.14
39.28
32.47
42.06
30.30
32.08
3 Reserves of Oil and Gas Resources in Eastern Russia Russia’s Eastern Siberia and the Far East are rich in oil and gas resources. There are more than 15 billion tons of primary oil resources concentrated here, which is more than 18% of Russia’s primary oil resources. Of these, 85% of the resources are distributed in the Eastern Siberia region, while the primary oil resources in the Far East are mainly located in the Okhotsk Sea. As of the beginning of 2011, the proven and probable reserves of petroleum in Eastern Siberia and the Far East had exceeded 3 billion tons (See Table 5). Eastern Siberia and the Far East are also rich in natural gas resources. The region contains about 60 trillion m3 of primary natural gas resources, which accounts for about 25% of Russia’s total primary natural gas resources, of which 86% of the resources are distributed in Eastern Siberia, while the primary natural gas resources in the Far East are mainly located in the Sea of Okhotsk. As of the beginning of 2011, the proven and probable reserves of natural gas in Eastern Siberia and the Far East totaled more than 9.4 trillion m3 (See Table 6).
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Table 5. Petroleum raw material bases in Eastern Siberia and the Far East. Subject
Total primary resources (million tons)
Cumulative mining volume (million tons)
Reserves (as of 2020, million tons)
Resources (million tons)
Proved reserves
possible reserves
total
possible resources
predict
33.69
1200
1372
2572
1852
8731 1923
Eastern Siberia, total
13189
Irkutsk Region
2546
6.40
194
235
429
187
Krasnoyarsk Krai
8048
17.43
764
948
1712
1475
4844
Sakha Republic
2595
9.87
242
189
431
191
1963
Far East, total
2274
188.80
338
119
457
360
1268
Sea of Okhotsk
1914
55.85
300
96
396
317
1133
Sakhalin Region
269
121.95
34
17
51
38
58
92
0.00
3
6
10
5
77
15463
222.49
1538
1491
3029
2212
9999
Chukchi Autonomous Region Eastern Siberia and the Far East
Note: Total primary resource is the sum of cumulative extraction, reserves and resources Table 6. Oil raw material reserves in Eastern Siberia and the Far East. Subject
Total resources for the initial test (One hundred million cubic meters)
Cumulative mining (billion cubic meters)
Reserves (as of 2020, 100 million cubic meters)
Resources (billion cubic meters)
Proved reserves
possible reserves
total
possible resources
predict
Eastern Siberia, total
522020
805.6
37330
44120
81450
50180
389580
Irkutsk Region
114500
30.4
15850
21370
37220
13060
64190
Krasnoyarsk Krai 272550
267.4
8350
10520
18870
34780
218630
Republic of Sakha
134960
507.8
13120
12230
25350
2340
106760
Far East, total
82510
1059.1
9890
2710
12590
4370
64490
Sea of Okhotsk
65040
521.2
9260
2500
11760
4370
64490
Sakhalin Region
3930
535.2
400
90
490
340
2570
Kamchatka Krai
3710
1.8
60
30
100
110
3490
Khabarovsk Krai Eastern Siberia and the Far East
960 604530
0.00 1864.7
0
20
20
0
940
47210
46830
94040
54550
454080
4 Status Quo of Sino-Russian Energy Cooperation Oil trade is one of the most basic forms of Sino-Russian oil and gas cooperation. Professor Lu Nanquan once pointed out when emphasizing the importance of Sino-Russian oil trade: “The certain progress made in Sino-Russian energy cooperation is mainly manifested in oil trade” [5]. China’s oil imports from Russia began in the early 1990s, but
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at that time, the trade between the two sides was still dominated by petroleum products. Petroleum trade accounted for only a small share. Throughout the 1990s, China-Russia oil trade has not exceeded one million tons. Since then, imports have increased year by year, and by 2011, the total amount of petroleum imported had reached 19.725 million tons (See Table 7). At the same time, the changes in bilateral oil trade also reflect the development process of energy cooperation between the two countries, and to a certain extent it has become a microcosm of China-Russia energy cooperation [6–12]. Table 7. The basic situation of Russia’s oil exports to China. Year
Russia exports oil to China (10,000 tons)
China’s total petroleum imports (10,000 tons)
Proportion (%)
Year
Russia exports oil to China (10,000 tons)
China’s total petroleum imports (10,000 tons)
Proportion (%)
2001
0.8
1136
0.07
2012
303.0
6941
4.37
2003
1.4
1568
0.09
2013
535.4
9102
5.77
2004
5.7
1236
0.46
2014
1099.4
12272
8.78
2005
3.7
1709
0.02
2015
1277.7
12682
10.07
2006
31.9
2262
1.41
2016
1596.5
14518
10.99
2007
47.5
3538
1.34
2017
1452.6
16300
8.91
2008
14.5
2732
0.53
2018
1163.8
17900
6.50
2009
57.2
3661
1.56
2019
1530
20380
7.50
2010
147.7
7027
2.10
2020
1525
23930
6.40
2011
176.6
6026
2.93
2021
1972.5
25338
7.78
As can be seen from Table 7, in the past 20 years, China—Russia oil trade has grown from less than 10,000 tons to an annual transaction volume of close to 20 million tons. During this period, it has experienced several leaps and bounds, which fully reflects the changes in energy cooperation between the two countries. In 2001, Russia exported only 8,000 tons of oil to China. Before 2005, the amount of oil trade between the two countries had been hovering between tens of thousands of tons. During this period, Russia’s geostrategic focus was completely inclined to the West, and China had just transformed into a net importer of oil. Both sides lacked the basis for mutual cooperation in the oil and gas field, both from a political point of view and from an economic point of view. In 2006, Sino-Russian oil trade increased by nearly 9 times year-on-year [13, 14]. At present, China and Russia have basically formed a consensus on the specific technical parameters of the pipeline. Price differences have become a key factor in the inability to make progress in the negotiations. Although the two sides have made continuous efforts, they have still not been able to reach a mutually acceptable price. Acceptable price. Therefore, the amount of natural gas exported by Russia to China is minimal.
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“Since 2010, Russia has randomly exported liquefied natural gas to the Chinese market, but in the first half of 2010, its exports accounted for only 4% of China’s total imports of liquefied natural gas”. In recent years, the global natural gas supply and demand situation has been constantly changing. In terms of supply, the shale gas revolution in the United States and the increase in production of natural gas producers in Central Asia and North Africa have increased global natural gas supplies; in terms of demand, the financial crisis and the European debt crisis have reduced natural gas consumption demand in Europe and other regions, while the nuclear spill in Japan has caused some countries to abandon nuclear power and switch to natural gas, which has increased the demand for natural gas consumption. The above-mentioned factors have interfered with China-Russia natural gas cooperation to varying degrees, further increasing the variables of mutual cooperation and delaying the process of cooperation. China’s oil and gas equipment has a market share of more than 30% in the global market, and it is one of the main producers of special equipment for oil drilling and production in the world. China’s oil and gas drilling products entered the Russian market relatively late, and the achievements have not been great. As of 2009, only four Chinese companies have realized the export of drilling equipment to Russia, namely Shengli Oilfield Plateau Petroleum Equipment Co., Ltd., Sichuan Honghua Petroleum Equipment Co., Ltd., Nanyang Petroleum Machinery Factory and Baoji Petroleum Machinery Co., Ltd. From 2006 to 2009, the four companies have exported nearly 100 drilling equipment to Russia, accounting for 69.1% of Russia’s total imports (see Table 8). At present, Chinese drilling equipment has been widely recognized by Russian users, which has laid a good foundation for related products to enter the Russian market in the future. Table 8. Analysis of wellheads of Russian drilling equipment from 2006 to 2009 (unit: Tai). Year
2006
2007
2008
2009
Total
Germany
2(2)
3(3)
6(3)
–
11(8)
United States
1(1)
1(1)
11(2)
3(0)
16(4)
China
1(0)
12(1)
61(37)
22(8)
96(46)
Romania
–
–
8(3)
1(1)
9(4)
Italy
1(0)
–
2(0)
4(0)
7(0)
Total
5(3)
16(5)
88(45)
30(9)
Note: The numbers in brackets are the number of devices weighing more than 300 tons
In recent years, Chinese and Russian companies have carried out a series of cooperation in petroleum engineering and technical services. At present, China National Petroleum Corporation can provide operational services such as geophysical exploration and pipeline construction to Russia. In 2006, the company obtained TNK-BP’s three-dimensional seismic data collection project. In 2010, it signed a sales and technical service contract for MCI5570 micro resistivity scanning instrument with the Russian TNG logging company, which achieved a breakthrough in the Russian logging market
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Y. Fu
with similar domestic instruments. China National Petroleum Baoji Petroleum Steel Pipe Co., Ltd. won the pipeline supply contract for the East Siberia—Pacific Pipeline project in Russia. Based on the above analysis, although China and Russia have made certain achievements in the development of oil and gas cooperation, there are still obvious shortcomings, mainly manifested as: First, the form of oil and gas cooperation is too single. At present, oil and gas cooperation between the two sides is still mainly based on trade, and infrastructure construction cooperation, investment cooperation, and engineering and technical cooperation are relatively scarce. Such forms of cooperation are very sensitive to external stimuli such as energy price fluctuations, making the energy relationship between the two countries more fragile. Second, the structure of oil and gas trade is unreasonable. Almost all the oil and gas trade between the two sides is oil trade. China only imports a small amount of liquefied natural gas from Russia, and natural gas trade is seriously lagging behind. Third, the implementation of the project and agreement is not ideal. In recent years, China and Russia have signed many cooperation projects and agreements in the field of oil and gas, but not many have been effectively implemented, and some projects and agreements are still on paper. “The exploration projects of Dongfang Energy Company, a Sino-Russian joint venture, and the Tianjin Refinery project, a Sino-Russian joint venture, are basically on hold after the signing of the agreement, making the symbolic significance of these projects far greater than the practical significance”. Fourth, cooperation in various fields is progressing slowly. The Sino-Russian natural gas cooperation negotiations have been going on for more than ten years, and the two sides are still unable to reach an agreement on prices, and China’s activities in the field of Russian oil and gas exploration have had little effect. Therefore, judging from the current basic situation and characteristics of oil and gas cooperation between the two countries, “China-Russia energy cooperation is still in its infancy.” It is precisely because cooperation is in its initial stage that the two sides attach great importance to the strategic significance of cooperation. As Russian President Vladimir Putin said: “The dialogue between Russia and China in the energy field is of strategic significance. Our cooperation projects have effectively changed the entire pattern of the global energy market [15–17]. For China, this means improving the reliability and diversity of energy supply sources; for Russia, it means creating new export routes to the fast-growing Asia-Pacific region.” Although there are still some problems in the current energy cooperation between the two countries, “Overall, the progress made in China-Russia energy cooperation in recent years has been positive and breakthroughs have been made in certain fields, thus laying an important foundation for the two countries to further deepen energy cooperation”.
5 Sino-Russian Investment-Analysis of the Processing of Oil and Gas Resources in Eastern Russia The oil processing in Eastern Siberia and the Far East mainly RELIES on the FOUR EXISTING refineries, namely THE Khabarovsk Refinery, the Komsomolsk Refinery, THE Angarsk Refinery AND the Achinsk Refinery (see Fig. 1). Among them, THE
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Khabarovsk Refinery belongs to the “Alliance” Joint-stock Group (“alnnnc”), and the other three refineries are controlled by Rosneft. The oil sources of several refineries basically come from the Western Siberia region: 81% of the oil sources of the Khabarovsk refinery come from the Khanty-Mansiysk Autonomous Region and 19% from Tomsk Oblast; 79% of the oil sources of the Komsomolsk Refinery come from the KhantyMansiysk Autonomous Region and the Yamal-Nenets Autonomous Region, and 21% of the oil sources come from the Sakhalin region (according to the product sharing agreement, the oil extracted in the Sakhalin continental shelf area is mainly used for export); Angarsk Refinery 54% of the oil sources come from Tomsk Oblast, 46% of the oil source comes from the Khanty-Mansiysk Autonomous Region; 64% of the oil source of the Achinsk refinery comes from Tomsk Oblast, and 36% comes from the Khanty-Mansiysk Autonomous Region.
Fig. 1. Processing capacity of oil refineries in Eastern Siberia and the Far East from 2000 to 2010.
Due to the increase in Russia’s domestic demand for petroleum products and the increase in the number of oil exports, the average annual increase in processing capacity of refineries in Eastern Siberia and the Far East between 2000 and 2010 reached 13.4%, and the production capacity rate increased from 47.3% to 94.2% (See Fig. 2). In the future, with the development of oil and gas resources in Eastern Siberia and the Far East, there will be new requirements for the petroleum refining and chemical capabilities of the eastern regions. On one hand, existing refineries will be upgraded, and on the other hand, new modern refineries may be established. As a key region for China-Russia energy cooperation in the future, the eastern part of Russia has a very important geopolitical and economic status. From the point of view of geographical distribution, the main oil and gas production areas are located in high-latitude areas, which increases the difficulty and cost of mining; from the point of view of resource reserves, the oil and gas industry in Eastern Siberia and the Far East has great potential for development; from the point of view of the basic situation of the entire oil and gas resource industry chain in Eastern Siberia and the Far East, whether it is upstream exploration, mining, or mid-stream transportation, as well as downstream processing and export, the development of the oil and gas industry in this region is relatively lagging behind and cannot meet future development needs [18]. These factors
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Fig. 2. Analysis of processing capacity and production capacity rate of refineries in Eastern Siberia and the Far East from 2000 to 2010. Note: Productivity rate. Pink: Petroleum processing.
constitute the basic geopolitical and economic conditions for energy cooperation with China in the eastern part of Russia.
6 Countermeasures and Suggestions for China-Russia Energy Cooperation First, to correctly view my country’s geographic position in the Russian energy strategy. Although it is clearly stated in Russia’s “Energy Strategy before 2030” that the national energy export strategy will be transferred to the east, it should be clear that Europe is still Russia’s main energy sales market. This is determined by the direction of Russia’s existing oil and gas transportation pipelines. In recent years, Russia has actively invested in the construction of “Blue Stream”, “Nord Stream” and “South Stream” and other oil and gas export projects, in order to consolidate Europe, the most important energy market. Second, reasonably predict the amount of oil and gas resources that Russia exports to China. China-Russia energy cooperation must be carried out under conditions that both parties deem safe. The Russian side has always been wary of selling raw materials to China, fearing that Russia will become China’s “raw material vassal”, and that the rapid development of China’s economy threatens Russia’s national interests. Therefore, the amount of oil and natural gas that Russia can supply to our country depends not only on the production of oil and gas resources in eastern Russia, and the negotiated oil and gas prices. It will also depend on the subjective judgment that Russia believes that it is safe to sell to China how much oil and gas resources, that is, Russia will weigh the “quantity” of this oil and gas supply [1, 19]. On the one hand, in the case of ensuring that Russia can obtain sufficient export income, the amount of oil and gas that Russia can supply to our country depends not only on the production of oil and gas resources in the eastern part of Russia, but also on the price of oil and gas negotiated by the two sides. It will not become overly dependent on the Chinese energy market; on the other
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hand, Russia is worried about supplying too much oil and gas resources to China, which will enable China to engage in re-export trade. “When analyzing the role of the Chinese market in Russia’s energy strategy, geo-economic strategy, and geopolitical strategy, it must be remembered that China not only intends to import Russian oil for domestic consumption, but also to re-export oil and its finished products to Asia-Pacific countries for sale to Russia” [20]. Third, rationally view Russia’s diversification of energy exports in the Asia-Pacific region. Looking at the global energy market, both oil and gas resource exporters and importers are working hard to ensure their own energy security by expanding and diversifying energy import and export routes [21]. In today’s highly developed economic globalization and regionalization, it is very unrealistic to absolutely monopolize the energy resources or energy markets of a certain country or region, and the market environment formed by monopoly is extremely unstable. From the perspective of Russia’s energy development strategy, exploring the energy market in the Asia-Pacific region must implement the export diversification strategy, and the Russian side can use export diversification to make a fuss and provoke disputes between importing countries in the Asia-Pacific region, so as to maximize their own interests. This is also one of the main reasons why Japan can easily intervene and interfere with the direction of the ChinaRussia oil pipeline. In the face of Russia’s diversified approach to the energy market in the Asia-Pacific region, as well as the consistent practice of deliberately creating disputes between China, Japan and South Korea to profit from it, we should rationally understand that we should not seek to monopolize Russia’s oil and gas resources exported to the east. This will not only cause vicious competition with Japan and South Korea, bring higher cooperation costs, and will increase Russia’s vigilance and add obstacles to energy cooperation between the two countries. On this issue, coordination and communication between countries in the region should be strengthened, regional energy consumption markets should be standardized, and regional energy security should be jointly guaranteed.
7 Conclusions The current situation of China-Russia energy cooperation shows that the energy cooperation between the two countries is still in the initial stage of development. The main reason for this situation is that the cooperation between the two sides in the energy field has not given full play to the existing geopolitical and economic advantages, and has basically stayed at the level of oil and gas trade. Close ties and interdependence in the oil and gas industry, market and technical services have not been formed. The geopolitical and economic advantages of energy cooperation between China and Russia are relatively prominent, mainly reflected in the complementarity of geopolitical and economic conditions. The complementarity of resource endowments, investment capital, and industrial structures formed between the eastern regions of Russia and China are key factors in the development of cooperation. The factors that inhibit the geographic advantages of China-Russia energy cooperation are mainly reflected in Russia’s overprotection of the development of the oil and gas industry in the eastern regions and its
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concerns about China’s energy cooperation. It is highly subjective, such as worrying about becoming a vassal of China, it cannot represent the views of all Russians, and it is impossible for this view to occupy a dominant position forever. At present, amidst the widespread doubts in Russian society about “whether Russia will become a material vassal of China”, there are still some Russian scholars who can look at this issue objectively and wisely, and deeply realize that what is needed between China and Russia is cooperation, not mutual suspicion and vigilance. Ostrovsky a. B. On the basis of an objective evaluation of China’s energy resources and imports, it was pointed out: “We believe that another question should be raised. Is it too late for Russia to enter the Chinese market with the advantages of its own energy resources? The analysis of the issue of energy cooperation between China and Russia further shows that in addition to some objective limiting factors affecting energy cooperation between the two sides, differences in their understanding of energy and geographical interests are also one of the reasons why cooperation is difficult to deepen. Acknowledgments. First and foremost, I would like to show my deepest gratitude to our supervisor, Dr. Demidenko Daniil Semenovich. he is respectable, responsible and resourceful scholar. Without his enlightening instruction, impressive kindness and patience, I could not have completed my thesis. I also owe a special debt of gratitude to all the professor in our institute, from whose devoted teaching and enlightening lectures. I have benefited a lot and academically prepared for the article.
References 1. Belogoryev, A.: China’s energy strategy and Russian gas. Acad. Energy 1(44), 40–44 (2021) 2. Makarov, A., Mitrova, T., Kulagin, V.: Long-term forecast of world energy development and Russia. Econ. J. High. Sch. Econ. 2(16), 172–204 (2020) 3. Korzhubaev, A., Filimonova, I.: Prospects for the integrated development of the oil and gas industry in Eastern Siberia and the Far East. Gas Ind. 6, 10–16 (2020) 4. Koroleva, E., Jigeer, S., Miao, A., Skhvediani, A.: Determinants affecting profitability of state-owned commercial banks: case study of China risks 9. Risks 8, 1–19 (2021) 5. Khalin, V., Chernova, G.: Digitalization and its impact on the Russian economy and society: advantages, challenges, threats and risks. Adm. Consult. 10, 46–63 (2018) 6. Alexeeva, O., Lasserre, F.: An analysis on Sino-Russian cooperation in the Arctic in the BRI era. Adv. Polar Sci. 29(4), 269–282 (2018) 7. A new Sino-Russian high-tech partnership. Australian Strategic Policy Institute. https://www. aspi.org.au/report/new-sino-russian-high-tech-partnership. Accessed 08 Sept 2021 8. Dittmer, L.: The Sino-Russian strategic partnership. J. Contemp. China 10(28), 399–413 (2021) 9. Flikke, G.: Sino-Russian relations status exchange or imbalanced relationship? Probl. Postcommunism 63(3), 159–170 (2018) 10. Yi, F., et al.: Sino-Russian cooperation on soybean development in the Russian far east. Am. J. Econ. Sociol. 79(5), 1553–1586 (2020) 11. Bekkevold, J., Bobo, L.: Sino-Russian relations in the 21st century. https://www.springerprof essional.de/sino-russian-relations-in-the-21st-century/15975324. Accessed 08 Sept 2021 12. Liu, Y., Pustokhina, I.V.: Sino-Russian economic cooperation: challenges and prospects. Hum. Capital Vocational Educ. 2(9), 60–73 (2020)
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13. Liu, X.: The direction of oil and gas policy in the Putin era and the prospects for energy cooperation between China and Russia. Int. Petroleum Econ. 7, 47–54 (2017) 14. Putin, V.: Russia and China: A New World of Cooperation. People’s Daily, 003rd edn (2019) 15. Favorable factors and constraints of China-Russia energy cooperation. http://en.kremlin.ru/ events/president/news/60669. Accessed 08 Sept 2021 16. Korzhubaev, A., Filimonova, I., Sokolova, I.: Forming a new reality. Oil Russia 1, 6–12 (2018) 17. Igumnov, P.V.: Oil and gas projects of the Sakhalin region. Power Manag. East Russia 4, 12–16 (2018) 18. Arsentieva, I.: Recommendations on the organization of cooperation between the eastern regions of Russia and the border territories of the PRC. Russia China: Probl. Strateg. Cooperation 9, 128–147 (2019) 19. Baskaev, K.: Oil and gas of the East of Russia. Oil Russia 1, 32–35 (2020) 20. Belogoryev, A.: Chinese games. Problems and risks of Russian gas exports to China. World Energy 8, 76–77 (2017) 21. Wang, X., Shi, J., Wang, Z.: Accurately cognising the digital economy and facilitating its healthy and sustainable development in China. Sustain. Dev. Eng. Econ. 3(4), 61–75 (2022)
Use and Processing of Digital Data in the Era of Industry 4.0 Aleksei Gintciak(B) , Zhanna Burlutskaya, Darya Fedyaevskaya, and Artem Budkin Laboratory of Digital Modeling of Industrial Systems, Peter the Great St. Petersburg Polytechnic University, Saint-Petersburg, Russia {aleksei.gintciak,darya.fedyaevskaya}@spbpu.com
Abstract. The article discusses innovative approaches in the field of “new” data types. In the context of global digitalization, information and communication technologies have become available to the public in many countries, which has led to the emergence of “new” types and sources of data. The purpose of this article is to review “new” data types, as well as their sources, collection, and processing technologies. The research is based on 25 articles from the Scopus, RSCI, and the Higher Attestation Commission databases. The paper considers the following data sources: mobile operators (MNO), social networks, applications, and websites. Data acquisition technologies are divided into IP-based systems, billing data acquisition, connection with the controller data acquisition, the device and SIM card data acquisition, satellite navigation system, social media data acquisition. During the study, the international experience of using new data types to solve various tasks is analyzed. The areas of application include, but not limited to statistics, marketing, incident management on the roads, assessment and improvement of network coverage, sociological research, modeling and predicting user behavior. Such a wide range of applicability of digital data confirms the relevance of this research. Based on the analyzed sources, security problems were identified at the stages of collecting, processing, and storing “new” data types, and the approach proposed by the European Statistical System - Reference Methodological Framework was considered in detail. This paper is an analytical basis for data acquisition and processing within building socio-economic models of various sectors of the economy as part of the industry 4.0 development. Keywords: information systems · data systems · modelling · Industry 4.0 · digital data
1 Introduction The world is currently undergoing global digitalization, which is associated with the advent smartphones and other smart devices, the spread of the Internet of Things. We are witnessing the spread of big data technologies, artificial intelligence, distributed ledger technology, robotics, and other advanced and fast-growing technologies [1]. The digital society is being formed around the world. It is the stage of the information society development, within which the digital format of information, digitization and transmission methods are the major figures [2, 3]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 468–480, 2023. https://doi.org/10.1007/978-3-031-32719-3_36
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Datafication—the process of quantifying and monetizing human life using digital information. Digital information is one of the digital society model concepts [2]. With the advent of information and communication technologies, most of our social interactions have been transferred to the digital environment [4]. Thus, using digital technologies, we leave a digital footprint that can be converted into data. The digital footprint of each user is stored as its digital twin. Based on these data, behavioral models of user groups are built, which are used both in marketing research and in the healthcare sector, which makes big data processing a priority task for ensuring the well-being of the population [1]. The digital society development is associated with the information and communication technologies implementation [2]. The widespread use of digital technologies is the reason for the emergence of “new” data sources and “new” data types. Such data is stored by government and private sector. Thus, there are potential competitors to companies working with data. It is especially important for them to follow the development trends in this field to remain competitive. Data experts define quality as validity, accuracy, timeliness, punctuality [5]. To maintain the data handling quality level, entities need to evaluate the potential advantages of “new” data types and sources, as well as configure channels for transmitting such data. Such data implementation contributes to the development of innovative methods and approaches to handling them. The purpose of this work is to analyze existing approaches to processing new types of data, as well as their sources and other characteristics. The study was carried out drawing on the Scopus, RSCI, and the Higher Attestation Commission databases. This article is of interest to researchers, industry specialists, and people willing to study the topic of new data types. The study is an analytical basis for data acquisition and analysis within building socio-economic models of industrial sectors of the economy in the era of Industry 4.0.
2 Materials and Methods The trend to data processing and analysis study increases annually (Fig. 1). 46,678 scientific publications were found in the SCOPUS scientific publications database via the keywords “new types of data” when querying and limiting it to the field of computer science. In 2021 alone, 4,002 articles were published, intellectual field of which covers energetics, engineering, and physics (13.9%); information technology and computing (11.2%); biology, medicine, and chemistry (46.7%); economics, management, and sociology (7.1%) (Fig. 2). Thus, it follows that digital data types are used in all key sectors of the economy.
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Fig. 1. The number of scientific publications in the SCOPUS database with the keywords “new types of data” within the computer science intellectual field.
Fig. 2. Research areas of the SCOPUS database publications referring to the new data types for the 2021 period.
Aspects of the study include new data types and their features; new data sources and acquisition tools; new data types processing and analysis specifics. MNO data, including additional data transmission capabilities due to the transition to the fifth-generation mobile communications; data transmitted through radio network controller and other network controlling nodes; applications; social media; websites are considered as data sources. To search for information about cell sites data and 4G, 5G, LTE, a query with the keywords “users’ data mobile network cellular operator” was used, which outputs 2,956 search results. The following keywords were selected as limiting keywords: “mobile
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network operator data”, “mobile network operators”, “Mobile Operators”, “big data”, “5G Communication System”, “Cellular Network”, “LTE”, “5G”, “Digital Storage”, “Cellular Operators”, “Mobile Data Offloading”, with the help of which the analyzed material was reduced to 1.208 results. The search for information about the use of data from social media was carried out using the keywords “social media”, “data”, “analysis”, followed by manual sampling.
3 Results The analysis of the SCOPUS and RSCI databases papers allowed to select 23 articles in which the goals set for the study were most fully reflected. From the database of scientific publications of the Higher Attestation Commission, 2 articles by Russian authors describing the legal aspects of using new data types were identified. The data fitting the definition of “new data types” described in the articles under study are shown in Table 1. Table 1. New data types. Data type
Data source
Articles
1. Messenger data
5G, 4G, LTE
[6]
2. Browser data
5G, 4G, LTE
[6, 12, 13, 26]
3. Social media data
5G, 4G, LTE; social media
[6, 16–19]
4. Streaming platforms data
5G, 4G, LTE
[6, 19]
5. Connection data
MNO
[6, 19, 24, 26]
6. Billing data (calls, SMS)
MNO
[6, 25]
7. Geolocation data
5G, 4G, LTE; MNO; social media
[5–13, 16, 17, 19, 21, 25, 26]
8. Personal data
MNO; social media
[19, 21, 24, 26]
9. Data on the use of MNO services
MNO
[13]
New data types, taken even from the same data sources, are acquired using different tools. Data acquisition technologies are shown in Table 2. The Gn and S1-U interfaces are transmitting messenger data, as well as browser data, social media data, streaming platforms media items. Data transmission by the Gn interface is performed via TCP (Transmission Control Protocol) or IP (Internet Protocol), between the SGSN (Serving GPRS Support Node) and GGSN (Gateway GPRS Support Node) [5] nodes. GPRS (General Packet Radio Service) belongs to the general packet radio service. It is designed for efficient data transmission, signaling, and optimization of the network and radio resources use. SGSN belongs to the service node for GPRS support and location tracking of user equipment. It also performs users’ security and access control functions. GGSN provides inter-network
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Data acquisition technology
Data source
Gn interface [6]; S1-U interface [6]
Messenger data Browser data Social media data Streaming platforms media items Messenger data Browser data Social media data Streaming platforms media items Calls SMS Connection data Location
Billing Record [6]
Measurement Record [6]
Connection to a radio tower or other controller data
Censor software [6]
Location
A-GPS (Assisted GPS) [7]
Location
Combining Cell ID (CID) and uplink Timing Advance (TA) (also known as Enhance CID) [7]
Location
Application
Correction of data transmission power in the radio channel to initiate reconnection or re-selection of cellular communication
Multiliterate technology based Location on Angle of Arrival (AOA) or Time Difference of Arrival (TDOA) [7] SUPI (Subscription Permanent Location Identifier) [8] User data
User authentication Checking the access rights to use the network in which the subscriber is located
Permanent Equipment Identifier (PEI, also known as International Mobile Equipment Identity (IMEI) up to 5G) [8]
Device identification
Location User data
communication between the GPRS network and the communication package. TCP is one of the main Internet data communication protocols.
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The S1-U-interface has similar functionality, but in the LTE architecture it connects the eNodeB and SGW (Serving Gateway) stations [6]. The data of the Internet Protocol-based system is called IP Detail Record (IPDR). It provides IP protocol-based data using Gn and S1-U interfaces and DPI (Deep Pocket Inspection) filtering [6]. DPI checks packages not only according to standard patterns, by which it is possible to determine whether a package belongs to a particular application by the header format, etc. DPI performs data mining, including packet payload, behavioral traffic analysis, etc. Billing Record Data is a type of user data that includes calls, SMS, connection, and geolocation data [6]. They are acquired by the billing system of MNO. Billing data about the Internet connection is the same as IPDR, so the validity of the data can be checked on their basis. Measurement Record (MR) records data about the connection to the radio tower or any other controller within the mobile communication system [6]. This type of data recording is used to adjust the data transmission power in the radio channel to initiate reconnection or re-selection of mobile communications. Censor software analyzes the location by combining GPS and WI-FI hotspot data [6]. Such a tool acquires rather more user data into a database than sources that record only network connection data. In terms of information quality, it turns out to be better than location data via a network (cellular network data). Such data is acquired only during a new entry in the Internet usage log, and this is how they differ from IPDR, which are recorded every second when the device is online. A-GPS (Assisted GPS) device determines the location by processing the GPS signal. To accelerate the process, the server uses additional information (the exact time, satellite Doppler shift, etc.). When there is no access to GPS, other means are used [7]. Combining Cell ID (CID) and uplink Timing Advance (TA) [7] (also known as Enhance CID) with which the server determines the approximate location of the device. Most often, this method is used instead of A-GPS. Multilateration technology based on Angle of Arrival (AOA) or Time Difference of Arrival (TDOA): the device evaluates the difference in arrival time and mobile communication, which it transmits to the server determining the user’s geolocation multilaterally [7]. In the 5G era, SUPI (Subscription Permanent Identifier) and PEI (Permanent Equipment Identifier, also known as International Mobile Equipment Identity (IMEI) up to 5G) are implemented on each SIM card. For instance, MNO can legitimately use them to block stolen devices [8]. In this case, PEI is used to identify the device and does not relate to the subscriber permanently. And SUPI is used to authenticate a user on the network to determine his belonging to a network and verify the access right to use the network in which he is located. Their combination provides access to subscriber identification and tracking. The use of new data types is practiced in solving various tasks. However, data processing experts believe that the most productive way to use new data types lies in combination with data of the “old” type [5, 9]. Table 3 shows the options for using the new data types.
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Data type
Application
Source
Location
Statistical estimation of the population
[17]
Population mobility assessment: tourist traffic, places of residence, communication patterns, use of vehicles
[9, 16, 17]
Marketing: increasing the advertising efficiency
[12, 13]
Determining the level of traffic congestion
[6, 14]
Managing road incidents
[14]
Use in cases of administrative offenses
[25]
Identification and tracking combination
Blocking a lost phone
[8]
Location and network connection combination
Cellular connection coverage analysis
[7, 12, 20]
Data about friendly networks in social networks
Modeling and predicting user behavior
[16]
User-generated content
Building a model predicting user behavior
[17]
Assessment of user’s behavior psychological aspects
[18]
User personal data
Sociological research
[19]
Site sessions and browsing history
Marketing: improving advertising performance
[13]
The use of MNO data for official statistics is described in the sources [5, 9–11]. MNO data on the location of people allows us to draw conclusions about the population mobility: tourist traffic, places of residence, communication patterns, use of vehicles [9]. The source [10] describes a method of the population statistical estimation via the Reference Methodological Framework (RMF), being developed by the European Statistical System. Even though work on the system has not yet been completed, experts admit the effectiveness of its using when working with MNO data. Statistics is a significant part of management; only based on complete and reliable data can informed decisions be made. The LTE Union opens great opportunities for Statistical Offices (SO), since a wide range of data is held by private enterprises, including MNO. Without them, the quality of official statistics data will be lower than potential statistics providers [5]. However, MNO see SO as rivals in data monetization [5, 11], therefore it is necessary to establish B2G channels to stabilize the data flow from the private sector to the official statistics offices.
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Digital marketing also uses digital data types to create and target advertising campaigns. To improve the KPI of advertising (clicks, unique visitors, impressions, orders), marketers evaluate user data, such as place of residence, visits, online purchases, etc. For example, ads for upcoming events are shown to users who have visited their venues [12]. Marketers also utilize their expertise of the correlation of the MNO, the frequency of scrolling, and the regional level of income with the probability that the user will click on the ad shown [13]. To improve the advertising efficiency, marketers use a Bayesian network model [13] to estimate the conditional probability that a user would click the ad in an application after he/she had already clicked on another ad in the same application. Using a large amount of user’s behavior data, which is stored by MNO, experts create intelligent systems for assessing traffic congestion [6]. Yandex Maps users provide the service with depersonalized data about their location, which allows the service to compile a traffic congestion map using the data aggregation method with an indication of accidents blocking traffic on one or more lanes. Yandex systems also assess the level of congestion in large cities (Moscow, St. Petersburg, etc.) by the scale of 10 points (where 0 points - free flow, and 10 points - traffic jam). Moreover, having a large spatial and temporal perspective, experts developed a model for assessing and predicting drivers’ behavior on the roads based on GSM network data [14]. Thus, software predicting difficult road sections that may be associated with accidents, crowded events (matches, festivals, etc.), or terrorist attacks, was developed for the road incident management system. New and more efficient applications bring the management of 5G networks to a new level of complexity [15]. Crowds and congestions of people that occur due to mass cultural events (sports matches, parades, fairs, etc.) have a great impact on the service of the mobile network [15]. To smooth out the impact of such events on the network quality, researchers propose forecasting systems [15] of such events and network balancing methods [12]. Location data coupled with users’ device’s connection to network data (stability, availability, integrity) provide experts with a base for identifying crowded places that lead to the network quality decrease. The system proposed by the authors describes an algorithm for load distribution between network cells. Social media data represent a great potential for conducting scientific research based on them [16]. With advances in information and communication technologies, people have transferred part of their daily experience to the digital environment, including social media [4]. Users tend to share content that would be comparable to trends and socially acceptable to their environment. In addition to the above-mentioned ways of using social media users’ data, they can be applied to determine various behavior patterns: making friendly networks [16]; urban mobility based on published content geotags [16, 17]; generated content [16]. Moreover, the source [6] describes the experience of assessing the psychological aspects of users’ activities in social media. The analysis of psychological qualities of a person is carried out using specially developed software. In LIWS, there are 93 assessed user qualities based on the texts generated by them: standard aspects (number of words, more than six letters long words, etc.); psychological thought process (cognitive functions, emotionality, perception, social process), other aspects (punctuation, swear words). Psychological qualities in MRC are calculated using the Medical Research Council’s
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psycholinguistic database, which has 150,000 words with linguistic and psycholinguistic features of each. Thus, the user’s psychological disorders are identified based on the analysis carried out. This information can be used for various purposes. For instance, in case of a security threat, the user’s psychological instability can be proved via such an analysis. User data, such as gender, age, etc. are effectively applied in conducting sociological research in various fields. Thus, an analysis of streaming services subscribers was carried out using data from the Association of MNO in Germany [19].
4 Discussion The features of digital data types processing are associated, among other things, with new sources. Thus, the MNO data is heterogeneous. This is due to obtaining the same data type in different ways. For example, location data is acquired in seven different ways mentioned, as well as with differences in the network architecture [10]. Digital data types require the processing development approaches as opposed to nondigital ones [9]. It is necessary to ensure the experts and MNO engineers close cooperation to produce high-quality data cleansing. The sources [5, 10] describe the Reference Methodological Framework (RMF), which offers a tool for working with data consisting of three layers: Data layer (D-layer), Convergence-layer (C-layer), Statistic layer (S-layer). The authors suggest using this tool when working with MNO data. D-layer embeds processing modules, the implementation logic of which heavily relies on the specific MNO infrastructure, the type and format of the available input data, or in any case on the details specific to a particular technology. The C-layer is designed to eliminate dependence on input-agnostic and output-agnostic data. It separates the complexity and heterogeneity of other areas, providing independent development, evolvability, and portability of processing techniques. Statistic layer (S-layer) includes statistical analysis modules that depend on a specific goal and intended indicators. In one of the deployment scenarios proposed by the authors, the lower part of the processing workflow, including the entire D-level, C-level, and the lower part of the S-level, can be physically performed in the MNO premises. Intermediate data that is created at some logical point of the S-level is then transmitted to the SO, where the upper part of the S-level functions is performed. If it is necessary to determine the user’s location, it is important to consider that in the interval between two consecutive observations, the mobile network does not know where the mobile device was located, whether it moved or where it moved. However, if the signaling data is available from the D-level, it can be assumed that during this interval the mobile device remained limited to a certain area, which we term the limiting area in RMF, consisting of a predefined set of neighboring radio networks [5]. In some cases, interpolation is applied. Based on the observed (or interpolated) event locations for the selected set of mobile devices, the next task is to estimate the (unknown) spatial density. This is the way experts can cope with spatial and temporal uncertainty. When calculating the population coverage, it is necessary to take into account the uncertainty that lies in discrepancy of the mobile devices number considered by the MNO [5]. Thus, there are errors of insufficient coverage, excessive coverage, and double counting, corresponding to people who do not have a mobile device, devices that
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are not portable by people (frequently called Internet of Things (IoT) or “Machine-toMachine” (M2M) devices), and people who have multiple devices or subscriptions in dual SIM phones respectively. From the one operator’s perspective, the level of insufficient coverage partly depends on its subscriber base market share. The problem can be solved by data filtering: non-personal devices connected to the Internet of Things and Machine-to-Machine (M2M) communication are removed in order to reduce the re-covering error. The source [14] also describes errors that occur when the network architecture is misunderstood. In many works, a rather simplified method of location determination is used, since they do not take into account some very basic aspects of mobile network operation, in particular the fact that radio networks with quite different transmission power and coverage ranges are superimposed in so-termed multilayer radio deployments, with small and large cells (operating in different frequency ranges and with different 2G/3G/4G radio technologies) coexisting together in the same area [20]. In addition, the mobile device does not always select the strongest signal received, and in any case, the strongest signal does not always correspond to the nearest antenna (for example, due to different transmission power) [5]. Only a few innovative works have begun to consider models of overlapping locations that take into account the multilevel nature of the radio networks deployment and the fact that the radio networks coverage areas overlap by design. Social media users’ data also needs to be filtered and cleansed. The considered cases of analyzing user behavior based on social media data [16–18, 21] contain descriptions of problems related to data acquisition and processing. Although social media generate a large bulk of data (text messages, photos, videos, the structure of users’ relationships), they have a long interval between data updates, compared to MNO. Thus, if the MNO data can be analyzed over a period of 2–3 months, then the social media data are needed to be acquired for several years [17]. At the same time, social media data are available and free, which is their advantage. Modern data types emerged alongside with innovative technologies, so they require new approaches. Their acquisition and processing technologies are more complicated than common ones, but despite all the features, such data has numerous numbers of advantages over non-digital data, in particular because of which they are used. These advantages are relevance, accuracy, timeliness, punctuality, wide coverage, detailed elaboration, insight [5, 25]. Moreover, the data are private, so they require mechanisms to maintain the users’ confidentiality. When forming a digital society, there is concern about the personal data security. The digital environment requires innovative approaches to ensuring information security [22]. Private enterprises do not fully ensure the data storage security [23]. This is proved by public scandals due to personal data leaks, sale, and illegal use. The Cambridge Analytica Scandal [5, 21] (the Cambridge scandal of a consulting firm using Facebook users’ data for a political campaign) is the example of such global scandals. The data of 533 million users were used: phone numbers, Facebook ID, full names, location, dates of birth, profile information, emails.
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To intercept data, software that is used both by government agencies for legal purposes and illegally for criminal purposes was developed. Thus, the news about the use of a special Carnivore software in the United States by the Federal Bureau of Investigation (FBI) [24], which allows intercepting and tracking e-mails and messages of any person, received negative publicity. Attempts to force some Internet service providers to install certain elements of the mentioned software on their servers were also widely covered. In Russia, the “Mayachok” and “Lokator” services [25] are used to determine the device location. At the same time, to use the data obtained by bailiffs in legal proceedings, they must be obtained in accordance with the procedure established by law [24, 25]. In addition to the legally established grounds for the need to ensure data security, experts have developed technological tools to prevent thefts or unintended databases modification. For example, Differential Privacy (DP) [26] is a powerful system for data protection by adding noise that does not allow efficient data copying but maintains the data quality for its owner. The source [21] describes the mechanism of the Knoxly system based on machine learning, the prototype of which was used by the Google Chrome system. Knoxly has full control over the data while using the network, determines the personal or sensitive information that the user wants to publish or send, and sends a notification (warning) about the danger of actions [27].
5 Conclusion This article considers 25 articles describing innovative approaches in the field of new data types. New data types, their features, technologies for obtaining and processing them, as well as existing limitations and problems were analyzed within the research. As a result of the study, 9 types of data were identified, their acquisition technologies and sources were described. Then the new data types were classified by the type of data being analyzed, and examples of their use were demonstrated. Data related to the use of digital and information and communication technologies were identified as “new” data types. The study identified categories of data that can be acquired by private and state-owned enterprises: messenger data, browser log, social media data (content, clickthroughs, etc.), streaming platforms data, connection data, calls and SMS data, location data, personal data, data on the MNO services use. Data acquisition technologies are divided into IP-based systems, billing data acquisition, connection with the controller data acquisition, the device and SIM card data acquisition, satellite navigation system, social media data acquisition. The study analyzes the international experience of using digital data in various fields, including innovative approaches to handling them. It also describes the features of using innovative approaches when working with data, taking into account the increasing complexity of processing unstructured data flows, as well as existing security problems of data acquisition and storage. In the further study, we intend to consider the legal and technical aspects of ensuring the data storage security, processing, and exchange of “new” data types in more detailed way.
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References 1. Smirnov, A.: Digital society: theoretical model and Russian reality. Monit. Public Opin.: Econ. Soc. Changes 1, 129–153 (2021) 2. Talkanbaeva, R.: Digitalization Should Start with the Regions. Bulletin of the Academy of Public Administration under the President of the Kyrgyz Republic (2019) 3. Rodionov, D., Zaytsev, A., Konnikov, A., Dmitriev, N., Dubolazova, Y.: Modeling changes in the enterprise information capital in the digital economy. J. Open Innov.: Technol. Market Complex. 7, 166–186 (2017) 4. Dobrinskaya, D.E.: What is a digital society? Sociology of science and technology. Moscow State Univ. Bull. Ser. 18 Sociol. Polit. Sci. 25(4), 175–192 (2021) 5. Ricciato, F., Wirthmann, A., Giannakouris, K., Reis, F., Skaliotis, M.: Trusted smart statistics: motivations and principles. Stat. J. IAOS 35, 589–603 (2019) 6. Huang, J., Xiao, M.: State of the art on road traffic sensing and learning based on mobile user network log data. Neurocomput. Recent Adv. Mach. Learn. Non-Gaussian Data Process. 278, 110–118 (2018) 7. Bejarano-Luque, J.L., Toril, M., Fernández-Navarro, M., Jiménez, L.R., Luna-Ramírez, S.: Statistical model for mobile user positioning based on social information. Electronics (Switzerland) 10 (2021) 8. Haque, A., Madathil, V., Reaves, B., Scafuro, A.: Anonymous device authorization for cellular networks. Presented at the WiSec 2021 - Proceedings of the 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks, pp. 25–36 (2021) 9. Ricciato, F., Meersman, F., Wirthmann, A., Seynaeve, G., Skaliotis, M.: Processing of mobile network operator data for official statistics: the case for public-private partnerships. In: Cervera, J., Glaztel-pop, C., Ivanov, V., Plapp, L., Vaju, S. (eds.) Conference GNIS 2018, Bucharest, Romania, pp. 2–13. ESS (2018) 10. Ricciato, F., Lanzieri, G., Wirthmann, A., Seynaeve, G.: Towards a methodological framework for estimating present population density from mobile network operator data. Pervasive Mob. Comput. 68, 101263 (2020) 11. Salgado, D., Oancea, B.: Towards a methodological framework for the integration of mobile phone data in the production of official statistics. In: Cervera, J., Glaztel-pop, C., Ivanov, V., Plapp, L., Vaju, S. (eds.) Conference GNIS 2018, Bucharest, Romania, pp. 2–13 (2018) 12. Vaju, S.: Towards a methodological framework for the integration of mobile phone data in the production of official statistics. In: Cervera, J., Glaztel-pop, C., Ivanov, V., Plapp, L., Vaju, S. (eds.) Conference GNIS 2018, Bucharest, Romania, pp. 2–13 (2018) 13. Torres, R., Fortes, S., Baena, E., Barco, R.: Social-aware load balancing system for crowds in cellular networks. IEEE Access 9, 107812–107823 (2021) 14. Peng, J., Qu, J., Peng, L., Quan, J.: An exploratory study of the effectiveness of mobile advertising. Inf. Resour. Manag. J. 30, 24–38 (2017) 15. Steenbruggen, J., Borzacchiello, M.T., Nijkamp, P., Scholten, H.: Data from telecommunication networks for incident management: an exploratory review on transport safety and security. Transp. Policy Spec. Issue Transp. Pricing Policies 28, 86–102 (2013) 16. Villegas, J., Baena, E., Fortes, S., Barco, R.: Social-aware forecasting for cellular networks metrics. IEEE Commun. Lett. 25, 1931–1934 (2021) 17. Cao, G., Wang, S., Hwang, M., Padmanabhan, A., Zhang, Z., Soltani, K.: A scalable framework for spatiotemporal analysis of location-based social media data. Comput. Environ. Urban Syst. 51, 70–82 (2015) 18. Osorio-Arjona, J., García-Palomares, J.: Social media and urban mobility: using Twitter to calculate home-work travel matrices. Cities 89, 268–280 (2019)
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Specifics of Implementing Digital Technologies in Investment and Construction Projects in China Zhimin Ju and Natalia Solopova(B) Moscow State University of Civil Engineering (National Research University), Moscow, Russia [email protected]
Abstract. This article presents strategic recommendations and implementation framework to promote the qualitative development of China’s construction industry based on digital innovation, with the aim of 1. Improving managerial competencies. 2. Reducing electricity consumption by applying the concept of energy saving. 3. Protecting the environment. 4. Building a solid foundation for future sustainable development. Through data analysis and complex analysis methods it was defined that the results of ICP implementation in China have always been criticized by the public due to careless management, high energy consumption and low production capacity. With the help of research methods, data description and review of existing research, the ways of introducing digital technologies in the implementation of ICP in China are set. Based on identifying the connection between digital technologies and the transformation of the construction industry, we analyzed the digital innovation level of development in the construction industry; suggested a strategic path for introducing innovations in digital technologies. With the development of digital technologies such as the Internet, big data, BIM and artificial intelligence, the use of informatization and digital tools, and the integration of advanced intelligent building technology and equipment, an intelligent and eco-friendly building model, the IoT full life cycle equipment resource model, and the core technology resource integration model, an improved and process-oriented construction management the following model will be gradually implemented. This study’s key is to define the effectiveness of digital technologies implementation to investment and construction projects (ICP) in China and to suggest the optimal direction for development of the Chinese construction industry. Keywords: construction industry · investment and construction projects · digital technologies · technological innovation · sustainable development · fourth industrial revolution · Industry 4.0
1 Introduction Construction is the basis of China’s national economy, and it is a significant driving force in the onset of a new technological revolution [1–4]. In recent years, the use of digital technologies in the implementation of ICP has become more and more extensive © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 481–491, 2023. https://doi.org/10.1007/978-3-031-32719-3_37
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[5, 6]. By 2025, the political and industrial system for the coordinated development of smart building and industrialization of construction, for the most part, will already be established. As well as foundation of the Internet platform for the construction industry in China will be established. Many Chinese researchers in adjacent fields have analyzed the specifics and statistics of China’s construction industry and described in detail the status of the introduction of digital technologies in the implementation of ICP in China [7–9]. However, the direction in which digital technologies should be applied and new models of project delivery have not yet been explored. With the rapid development of digital technologies and the digital economy, the construction industry is undergoing an important transition and a key period of new opportunities. At present, the application and integration of digital technologies such as BIM, cloud computing, Big Data, the Internet, and Digital Twin have encouraged the construction to be more creative. Which resulted in contributing to the flexible allocation of resources, as well as to the efficient use and measurement of energy [9, 10]. The rapid development of new infrastructure, such as 5G infrastructure, artificial intelligence, and the Industrial Internet of Things (IIo), will boost construction. Digital concepts are gradually emerging, such as smart buildings, smart cities, smart construction sites and smart building ecosystems [10–12]. In this process, it is necessary to clarify the differentiated needs of different building formats during the digital transformation, and to coordinate the efforts of the extractive and processing industries. The issue of applying new models for the delivery of construction projects, such as IPD, was previously unexplored by researchers [13]. To achieve the goal of sustainable development of the Chinese construction industry, the application of digital technologies in ICP is used. The following problems will be solved during the research: 1. Conduct analysis of the status and problems of ICP implementation in China. 2. Analyze Chinese and foreign experience in the use of digital technologies in the implementation of ICP. 3. Substantiate the need to introduce advanced digital technologies and ICP management models. 4. Offer guidelines for using digital technologies in the implementation of ICP in China.
2 Materials and Methods Descriptive analysis. (1) This article uses the following methods: a descriptive analysis method to collect and organize the use of digital technologies in China, descriptive analysis of digital technologies’ development direction, development of ICP in construction, digitalization of construction enterprises and digital application of ICP. The article/It also describes and analyzes in detail how frequent and successful are attempts at using Chinese digital construction projects in the world. (2) Relevance of digital technologies application in ICP [6, 14] See Fig. 1.
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50 45 40
Data A
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Data B
30 25 20 15 10 5 0 0
5
10
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Fig. 1. Relevance of the use of digital technologies in ICP.
The digital transformation of the construction industry is conditioned by data-driven management innovations that use intelligent “data + algorithms” to improve decision making and achieve high levels of construction efficiency and resource preservation. (2) Application of digital technologies in ICP. In recent years China has gradually applied new information technology represented by BIM in the field of construction. Advanced BIM design, integration of design and construction, digital integrated delivery of a construction site, prefabricated modular buildings, etc. are an important addition to the future application of digital technologies in the construction industry, leading to an increasing number of standards for the future development of construction. However, there are still some common problems in BIM development, such as: difficulty in extending the entire BIM model process; the characteristics of informatization of BIM technology are not entirely explored, and so on [16]. Guided by the results of construction management through wireless communication, cloud computing, information platforms, etc., it is essential to collect comprehensive construction data to form a digital archive for construction, create an engineering ecological
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construction value chain based on digital information technologies such as artificial intelligence, machine learning, and also to put into operation the general industrial upgrade in the construction industry. The Internet of Things, cloud computing and other emerging technologies in China continue to innovate, providing momentum for the digital development of ICP. According to the research, the cloud computing market reached 209.1 billion yuan in 2020, and the BIM market was estimated to be about 8.235 billion. Yuan, and the IoT market made a breakthrough - 1.7 trillion yuan in China. In 2019, thanks to the “Internet + ” principle, ICP digitization has opened new space in tens of billions of markets, and with the continuous development of Internet technologies, the scale of the domestic digitization market in the construction industry is expected to reach 100 billion [2]. See Fig. 2.
Fig. 2. Digital ICP market size in China.
An integrated digital ICP implementation model, such as the BIM-based IPD model, will greatly simplify the ICP implementation process, reorganize the construction industry production chain, create new ICP roles and expand construction requirements, promote upstream and downstream cooperation in the production chain based on information technologies and improve the overall level of informatization and industry competition in mechanical engineering and construction in the energy sector [13]. With the advent of the industry 4.0 era, the construction industry is rapidly developing in China, and construction management is gradually moving towards modernization, standardization and intelligent use of resources [9]. Resources such as science and technology, information and personnel have become the source of the industry’s development. Thanks to the highly developed information
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technology of the “big cloud Internet of Things”, the management of the full life cycle of the ICP can be carried out using the technology of multidimensional databases for: 1. 2. 3. 4.
Implementation of highly integrated project planning. Construction and operation management. Integration of technological processes into the design process. Creation of a complete database of projects, companies, industries, and countries for the entire life cycle. 5. Contribute to the healthy development of the construction industry. Among all surveyed construction companies (the BIM reporting team surveyed 31 provinces and cities), up to 95% of them use BIM technology. For the number of enterprises using BIM technology see Fig. 3.
Fig. 3. The number of enterprises using BIM technology.
For the technological uses of BIM that companies have implemented, the distribution of different types of BIM uses is relatively balanced, as shown in Table 1. Through the gradual improvement of artificial intelligence algorithms, China will become a highly smart-tech country by 2035, and Chinese smart eco buildings will become the important key points in construction management, equipment management and core technology. The innovative use of cloud computing, big data, the Internet of
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Reasons for using BIM
Percentage of BIM usage
The customer requires the use of BIM
45.95%
Projects with complex building structures
42.86%
Projects with certification requirements
41.59%
Improving enterprise management
37.79%
Strengthening the influence of the company’s brand
37.56%
Things, artificial intelligence, 5G, blockchain and other digital technologies, as well as the integration of a lot of data in the construction field, will contribute to the development of building “consciousness” [3].
3 Results Through the research methods, we drew attention to the importance of digitalization in ICP and showed that the use of digital technologies in ICP in China is currently only implemented in large companies and large projects. Through a more advanced use of cross-border transfer of personal data, the use of digital technologies in ICP is being streamlined and updated. The use of digital technologies should be coordinated with an appropriate management model based on BIM, such as the IPD model [13]. In the digital age, data is a key asset. In the implementation of smart building, it is required to first transform digital assets into scientific basis for decision-making for enterprises relying on data to support the application of digital technologies, so that the decision-making process becomes more scientific and accurate and is more in line with the requirements of enterprise development and implementation of ICP. It is necessary to improve construction management, working conditions, working hours and increase the productivity of enterprises through technical means such as the use of intelligent equipment, the production and use of robots. It is necessary to create a project management and control platform using information technology such as the Internet and the Internet of Things, so that design, construction, management and services can be easily linked, and then form a closed cycle of high efficiency and high-quality development. In addition, it is necessary to promote digital technologies and learn from them to create engineering models, model the construction process, find defects in engineering design and problems that need to be paid attention to during construction, and offer targeted solutions to improve the quality of construction [3, 8, 16]. Digital architecture is the core engine for the transformation and upgrading of the construction industry and the key foundation for the success of engineering projects. The digital construction model of “new design, new construction, and new operation” that China is promoting will drive industrial transformation and innovative development. It can improve the construction industry to the level of industrial refinement, meet individual needs on a large scale, and deliver healthy construction products that reach industrial quality. See Fig. 4.
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Development plans and strategies aim to improve core competitiveness and promote enterprise transformation and modernization.
Fig. 4. In digital architecture, “three new approaches” (new design, new construction, new operation) Source: Compiled by the authors.
Optimization and modernization of the industrial chain of engineering construction should open all aspects of research, design, construction, supervision, supply of building materials, operation and maintenance, achieve high-quality and highly efficient development of the entire industry with overall coordination of the entire process, fine management and mutual support of each component [9]. In particular, the following optimizations and updates need to be made: 1. Optimization and updating through the deep integration of industry chain information. Creation of a mechanism for sharing scientific information. There is a lot of information available to apply from the design stage to the construction process, from budgeting to the final project financing process, from the quality management process to the structural safety system, and from the initial assessment process to the final appraisal review. 2. Optimization and renewal through innovation in the management model. Establish collection, verification, release and management mechanism for information exchange, establish an industrial chain data management system, fulfill the main responsibilities, and ensure the authenticity, sufficiency, failure risk reduction and security of information resources. 3. Through the integrated application of digital technology, a platform for full-life cycle, multi-subject collaborative management has been created. Taking BIM technology as a software platform, combining digital productivity such as big data, and integrating technologies such as 3D and drones, to achieve technological upgrades, to achieve the optimal investment goals of improving project quality, shortening construction periods, and reducing project costs, as shown in Fig. 5.
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Fig. 5. Key digital technologies Source: Compiled by the authors.
4 Discussion Thanks to the research of scientists in adjacent fields, the importance of digitalization in the development of construction enterprises, the importance of digital technologies in construction projects, the development trends of digital technologies, as well as the organizational and management model that supports digital technologies are described in detail. This contributes to the sustainable development of the Chinese construction industry and leads to an increase in the profitability of construction projects [15]. Through a series of data collection and literature analysis methods, as well as the sorting out of China’s digital architecture-related policies, this paper summarizes the urgent problems to be solved in China’s construction industry and the direction of digitalization for sustainable development. The sustainable development of the construction industry requires a high degree of integration of BIM and related digital technologies, and the improvement of the original project delivery model. Within the framework of the new delivery model (such as the IPD model), the advantages of digital technology can be better utilized to apply During the entire life cycle of investment and construction projects. As the main engine of the construction industry, “digital construction” can reinforce the transformation, upgrading and sustainable development of the construction industry, promote the integration of digital information and interaction of the entire construction industry, and promote the sustainable operation of enterprises. See Fig. 6. The six main directions for building “Digital China” at this stage: 1. 2. 3. 4.
Leading innovative cultivation of new driving factors of development. Promoting sustainable development. Support for environmentally friendly “green” development. Deepening open cooperation.
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5. Collaborative construction and use of data. 6. Risk reduction and prevention.
Fig. 6. Development trend of digital construction.
In the follow-up research, we should further summarize the application barriers of digital technology, learn from the problems existing in the application of BIM, combine practice and field investigation, to seek solutions to the obstruction of digital buildings, and promote the application and development of digital technology in new delivery models. Development, to achieve the goals of the best quality, the lowest cost and the shortest construction period for investment and construction projects, thereby promoting the sustainable development of the construction industry.
5 Conclusion The research presented in this article gives us a deeper understanding of the digitalization of construction enterprises, construction projects and construction management organization, and further promotes the application of digital technologies in construction projects, ensures the efficient and sustainable development of China’s construction industry, and provides theoretical and practical recommendations for digitalization. Construction industry in other countries. Digital transformation has become inevitable for the qualitative development of the construction industry. Digital buildings need to be implemented using information modeling technologies, as well as big data, the Internet of things and cloud computing [17–19]. Prediction for design and construction based on new technologies: with the development of new sciences and technologies, more and more new technologies gradually enter
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all aspects of design and construction. Currently, four technologies are widely affecting the architectural design industry: the first is virtual reality and augmented reality, the second is artificial intelligence, the third is interactive technologies, and the fourth is 3D printing [7, 10, 20]. Digital construction is a new, gradually developing development direction for the construction industry. Our goal is intelligent construction, i.e., Industry 4.0 (1.0 is a force that can replace labor, 2.0 is an industrial production line, 3.0 is a robot arm that replaces people, and 4.0 is the mass introduction of intelligent technologies in industry) [21, 22]. The construction industry in different parts of our country is still at different stages of development, bringing it to a single quality standard is seen as a difficult task. In general, it can be concluded that digital technologies are an effective means of solving the existing problems of the PRC construction industry.
References 1. Central People’s Government of the People’s Republic of China. http://www.gov.cn/zhengce/ 2020-08/17/content_5535307.htm. Accessed 08 July 2021 2. Yan, L., van Sander, N., Hertogh, M.: Understanding effects of BIM on collaborative design and construction: an empirical study in China. Int. J. Project Manag. 4(35), 686–698 (2017) 3. National Bureau of Statistics of the People’s Republic of China. http://www.stats.gov.cn/eng lish/Statisticaldata/AnnualData/. Accessed 09 July 2021 4. National Internet Information Office. Digital China Construction Development Report (2017). https://difang.gmw.cn/cq/2018-04/23/content_28441004.htm. Accessed 09 Sept 2021 5. Mannino, A., Claudio, M., Re Cecconi, F.: Building information modelling and internet of things integration for facility management. Lit. Rev. Future Needs. Department of Archit. Built Environ. Constr. Eng. 11(7), 3062 (2021) 6. Victorov, M.: Digitalization of the processes of implementation of investment and construction projects. Econ. Manag. 4(35) (2020) 7. BIM Investment, Returns, and Risks in China’s AEC Industries. https://doi.org/10.1061/(ASC E)CO.1943-7862.0001408. Accessed 08 Sept 2021 8. De Vieira, C., Ariovaldo, D., Gomes, D.S.: A systematic literature review on integrative lean and sustainability synergies over a building’s lifecycle. Sustainability 9(7), 1156 (2017) 9. Kravchenko, T.: BIM-technologies in the management of construction projects. Young Sci. 3, 176–179 (2019) 10. Love, P., Matthews, J.: The ‘how’ of benefits management for digital technology: from engineering to asset management. Autom. Constr. 107, 102930 (2019) 11. Ghaffarianhoseini, A., Tookey, J., Ghaffarianhoseini, A., et al.: Building Information Modelling (BIM) uptake: clear benefits, understanding its implementation, risks and challenges. Renew. Sustain. Energy Rev. 75, 1046–1053 (2017) 12. Jin, R., Zou, Y., Gidado, K., Ashton, P., Painting, N.: Scientometric analysis of BIM-based research in construction engineering and management. Eng. Constr. Archit. Manag. 8(26), 1750–1776 (2019) 13. Liu, B., Wang, M., Zhang, Y., Liu, R.: Review and prospect of BIM policy in China. In: IOP Conference Series: Materials Science and Engineering, vol. 245, no. 2, p. 022021. IOP Publishing (2017) 14. Travush, V.: Digital technologies in construction. ACADEMIA. Archit. Constr. 3, 107–117 (2018)
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Measurement and Evaluation of China’s Regional Innovation Efficiency: Analysis Based on Network Super SBM-Malmquist Model Shuquan Li(B)
and Marina Ianenko
Peter the Great St. Petersburg Polytechnic University, Polytechnic, 29, St. Petersburg, Russia [email protected]
Abstract. The verification data of innovation efficiency comes from the status quo of innovation efficiency in thirty-one provinces in China from 2010 to 2020. To observe the development and changes of China’s regional innovation efficiency more clearly, the study takes 11 years as the overall time to study China’s regional innovation efficiency. The analysis method is to use the network super SBMMalmquist model to measure and analyze the efficiency of innovation. Results show that recent years the innovation efficiency of China is increasing, the speed of technological efficiency is not as fast as the technological progress index. Technological progress has a greater impact on innovation efficiency than technological efficiency. There are regional differences in the growth rate of innovation efficiency in various provinces of China. According to the research conclusions, in terms of increasing the investment in fiscal innovation expenditure and reforming the investment method of fiscal innovation expenditure, countermeasures and suggestions to further improve the efficiency of regional innovation in China put forward. Keywords: innovation efficiency · regional innovation · SBM-Malmquist model · innovative technology
1 Introduction Innovation is the first driving force leading development. The World Intellectual Property Organization released the “Global Innovation Index Report 2022” in September 2022. The report shows that China ranks 11th, an increase of one place from last year, and a steady increase for ten consecutive years. China’s innovation and development show a good positive relationship, and innovation input transformed into more and higher-quality innovation output. The Chinese government has invested huge of financial support to improve the national level of scientific and technological innovation. The internal expenditure on research and development (R&D) has increased from US$104.33 billion in 2010 to US$353.66 billion in 2020. With the investment of huge of resources, the efficiency of scientific and technological innovation has gradually become the focus of attention. China has paid increased attention to the spatial balance of technological © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 492–503, 2023. https://doi.org/10.1007/978-3-031-32719-3_38
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innovation and development. The report of the 20th National Congress of the Communist Party of China clearly emphasized “promoting coordinated regional development.” Restricted by various geographical and natural environments and social and economic development conditions, there are obvious differences in the development of various regions in China. China has adopted a series of measures to coordinate regional development. An in-depth understanding of China’s current level of regional technological innovation efficiency and its regional differences will help promote coordinated regional development, formulate scientific development strategies for industrial development, and provide policy recommendations for optimizing resource allocation.
2 Literature Review International research institutions have released an index system for innovation and development, which is representative of the Innovation Alliance Scoreboard (IUS) issued by the European Union, the Global Innovation Index (GII) established by INSEAD and the World Intellectual Property Organization, and the China Regional innovation ability index system issued by the China Science and Technology Development Strategy Research Group, which has further improved and developed the scientific and technological innovation index system. Fare [1] pointed out in the 1990s that technical efficiency refers to the ratio of the ideal minimum input to the actual input of a production unit under the same output. CruzCázares [2, 3] believed that innovation and technical efficiency should conducted simultaneously, and systematically analyzed the impact of technological innovation efficiency on corporate performance. Nasierowski [4] used a non-parametric method to illustrate the relative impact of a country’s technological innovation efficiency and productivity. Chen [5] used DEA to measure the efficiency of China’s regional innovation system. Broekel’s [6] study highlighted the positive relationship between regional innovation performance and the intensity of cooperation among regional organizations. Li Zheng et al. [7] defined innovation efficiency as the input-output ratio of innovation activities and pointed out that innovation efficiency is the innovation output of each innovation subject unit under a certain innovation situation. In terms of research methods, technological innovation is a complex activity with multiple inputs and multiple outputs. In the process of measuring innovation efficiency, there are analysis methods such as Theil index method, Poisson regression, dynamic spatial econometric model, stochastic frontier analysis (SFA), data envelopment analysis (DEA), among which DEA used more. Yang Qian et al. [8] used the super-efficiency EBM model to evaluate the efficiency of technological innovation in China’s major national strategic regions and used the Theil index method to reveal the regional differences in technological innovation efficiency. Zhang Chaolin et al. [9] explored the internal mechanism between stock liquidity, agency efficiency and corporate technological innovation based on the poisson regression. Yang Bo et al. [10] used the super-efficient SBM-Malmquist model to measure the technological innovation level of universities across China. Li Hongxin et al. [11] used a spatial metrology model to explore the impact of industrial structure optimization and upgrading on technological innovation efficiency. You Daming et al. [12] used the DEA model to compare the technological
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innovation efficiency of large and medium-sized industrial enterprises in China. Song Laisheng et al. [13] used the SFA method to analyze the influencing factors of China’s technological innovation efficiency. Wang Yixin et al. [14] used the DEA-Tobit two-stage model to analyze the technological innovation efficiency and key influencing factors of industrial enterprises. In conclusion, the current research on innovation efficiency focuses on various industries and specific geographical areas, and there are few measures for the level of technological innovation efficiency in various provinces and regions across the country. This paper uses the SBM-Malmquist model based on the super-efficiency network, takes the innovation efficiency of thirty-one provinces in China as the analysis object, and builds an innovation efficiency evaluation index system to measure the innovation efficiency of each province, and summarizes its geographical distribution characteristics. Reveal the main problems existing in China’s innovation and development, explore their causes, and then put forward corresponding improvement measures and suggestions.
3 Research Design 3.1 SBM Network DEA Model Tone [14] proposed the SBM model (Slack Based Measure) in 2001, the advantage of which is that it successfully solves the problem of ignoring relaxation variables in the efficiency evaluation process of the radial model. The network SBM (slack based measure) model opens the traditional single-stage data envelopment analysis (DEA) model to encapsulate the “black box” of each decisionmaking unit DMU, measures the efficiency value of the multi-stage production process, and can find inefficient specific reason. The specific expression is as follows: there are n DMUj (j = 1, 2, …, n, n = 31), a DMU has K (k = 1, 2, …, K) nodes, mk and rk correspond to node k respectively the number of input indicators m and the number of output indicators r. The possible set of production in the technology R&D stage under constant returns to scale (CRS) is: n n λ1j xj1 , Z (1,2) ≤ λ1j z (1,2) (1) X1 ≥ j=1
j=1
The production in the achievement transformation stage assembled into: n n n X2 ≥ λ2j xj2 , Z (1,2) ≤ λ2j z (1,2) , Y 2 ≤ λ2j xj2 j=1
j=1
j=1
Therefore, the non-oriented network SBM model is: k− 2 1 mk si0 k=1 (1 − mk i=1 xk ) i0 ρ ∗ = min k+ 2 1 rk sr0 k=1 (1 − rk r=1 k )
(2)
(3)
yr0
Among them: ρ* is the overall efficiency of the DMU; ρ* = 1 means that the DMU is effective as a whole; ρ* < 1 is invalid; each DMU has m types of inputs and r types of outputs, which are recorded as xi and yr ; Sk− and Sk+ are respectively Slack variables of input and output, Sk− ≥ 0, Sk+ ≥ 0.
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3.2 Malmquist-Luenberger Index The network SBM model is a static calculation of the efficiency value, while the Malmquist index is a dynamic analysis of the efficiency, which makes up for the deficiency of the static analysis. It indicates the change of the DMU productivity from t to t + 1 period. Malmquist index based on unexpected output Malmquist-Luenberger index. Its reference set is: p p (4) S g = S 1 ∪ S 2 ∪ · · · ∪ S P = (xj1 , yj1 ) ∪ (xj2 , yj2 ) ∪ · · · ∪ (xj , yj ) It decomposed into technical efficiency change (EC) and technical change (TC): E G (xt+1 , yt+1 ) = EC × TC g MI = Mg xt+1 , yt+1 , xt , yt = E G (xt , yt )
(5)
Among them: MI is the Malmquist index; g is the Malmquist model, which uses the frontier jointly constructed by all periods as the reference frontier; xt and xt+1 are the input of period t and t + 1 respectively; yt and yt+1 are Output; Et (xt , yt ) is the technology in period t and the technical efficiency level in period t; Et + 1 (xt+1 , yt+1 ) is the technology in period t + 1 and the technical efficiency level in period t + 1; MI > 1 It indicates that innovation efficiency increases, and vice versa decreases; EC > 1 indicates that technical efficiency improves, and vice versa; TC > 1 indicates technological progress, and vice versa. 3.3 Selection of Indicators and Data Sources In view of the availability of data, this paper uses the internal expenditure of R&D funds and the number of personnel involved in scientific and technological activities as indicators of innovation input. The relevant data of the indicators come from the China Statistical Yearbook. In this paper, thirty-one provinces (autonomous regions and municipalities) in China used as the subject of decision-making evaluation, and relevant data from 2010 to 2020 used as sample data. The specific index system shown in Table 1. Table 1. Description and definition of variables. First-level indicators
Secondary indicators
Logo
Code
innovation input
Internal expenditure of R&D funds, logarithm
RDIE
X1
Number of scientific and technological personnel, logarithm
RDNST
X2
Number of patent applications, logarithm
Npa
Y1
Technology market turnover, logarithm
Ttm
Y2
innovation output
Among them, the internal expenditure of R&D funds refers to the financial support provided by a region for the smooth development of innovation activities; the number
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of R&D personnel refers to the scientific research personnel who specialize in scientific and technological activities and basic research, as well as the personnel who provide scientific and technological activity management and related services for scientific research personnel. The development of activities provides direct support and services; the number of patent applications used to measure the number of achievements in scientific and technological innovation activities; the turnover of technology markets is the main manifestation of the value of innovation activities.
4 Result Analysis The analysis of the calculation results of China’s regional innovation efficiency takes the innovation input and innovation output of thirty-one provinces (autonomous regions, municipalities) in China (considering the availability of data, excluding Hong Kong, Macao, and Taiwan) as the target. Missing values in the Tibet Autonomous Region in individual years filled with regression substitution. The panel data restricted to conduct Malmquist index analysis based on the period from 2010 to 2020. First, Stata 15.1 software used for data analysis, and the statistical results of the main variables shown in Table 2. Table 2. Descriptive statistics of main variables. Variables
N
mean
sd
min
max
RDIE
341
6.321
0.670
4.062
7.542
RDNST
341
4.980
0.568
3.209
6.070
Npa
341
4.546
0.709
2.210
5.986
Ttm
341
6.471
0.601
4.507
7.975
According to the changes of total factor productivity, technical efficiency, and technological progress of China’s regional innovation efficiency, the TFP and its changing trend based on China’s regional innovation efficiency from 2010 to 2020 can obtained. This paper uses DEARUN Version3.0 software, based on Based on Network Super SBM-Malmquist Model calculated and decomposed. Table 3 partially lists the results of China’s innovation efficiency in 2019–2020. In the Table 3, effch represents the technical efficiency change index, techch represents the technological progress index, and tfpch represents the Malmquist productivity index. The table shows China’s regional innovation efficiency from 2019 to 2020. Dynamic research on China’s regional innovation efficiency requires the establishment of observations for a certain period. Therefore, the article statistically analyzes the data of 11 years from 2010 to 2020. Taking 11 years as the overall time to study China’s regional innovation efficiency can more clearly observe the development and changes of China’s regional innovation efficiency. The research results prove that from 2010 to 2020, the level of China’s innovation efficiency has improved significantly; the overall level is on the rise. In the future, the coordinated development of various regions needs
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Table 3. Regional Innovation Efficiency in China from 2019 to 2020. Year
DMU
e-t+1-t+1
e-t-t
e-t-t+1
e-t+1-t
effch
techch
tfpch
2019–2020
DMU1
0.432
0.358
0.379
0.406
1.209
0.880
1.063
2019–2020
DMU2
0.423
0.277
1.014
0.340
1.530
1.396
2.137
2019–2020
DMU3
0.250
0.167
0.200
0.210
1.494
0.798
1.191
2019–2020
DMU4
0.187
0.215
0.149
0.261
0.874
0.807
0.705
2019–2020
DMU5
0.229
0.152
0.184
0.190
1.505
0.801
1.205
2019–2020
DMU6
0.227
0.170
0.183
0.210
1.334
0.808
1.078
2019–2020
DMU7
0.422
0.347
0.359
0.407
1.219
0.851
1.037
2019–2020
DMU8
0.285
0.184
0.227
0.233
1.542
0.795
1.225
2019–2020
DMU9
0.276
0.204
0.225
0.250
1.353
0.816
1.103
2019–2020
DMU10
0.200
0.123
0.156
0.158
1.621
0.780
1.265
2019–2020
DMU11
0.183
0.108
0.142
0.140
1.699
0.774
1.316
2019–2020
DMU12
0.211
0.139
0.166
0.178
1.515
0.784
1.187
2019–2020
DMU13
0.114
0.077
0.088
0.101
1.473
0.770
1.134
2019–2020
DMU14
0.155
0.105
0.120
0.136
1.472
0.774
1.139
2019–2020
DMU15
0.286
0.158
0.230
0.200
1.805
0.799
1.442
2019–2020
DMU16
0.135
0.080
0.105
0.103
1.692
0.775
1.311
2019–2020
DMU17
0.335
0.229
0.278
0.278
1.459
0.827
1.206
2019–2020
DMU18
0.181
0.109
0.144
0.138
1.660
0.794
1.318
2019–2020
DMU19
0.271
0.169
1.017
0.215
1.608
1.713
2.755
2019–2020
DMU20
0.308
0.179
0.246
0.226
1.728
0.794
1.372
2019–2020
DMU21
1.077
0.207
1.117
0.263
5.200
0.904
4.699
2019–2020
DMU22
0.093
0.068
0.072
0.088
1.366
0.773
1.056
2019–2020
DMU23
0.254
0.214
0.207
0.261
1.188
0.815
0.969
2019–2020
DMU24
0.439
0.314
0.359
0.388
1.400
0.813
1.139
2019–2020
DMU25
0.175
0.114
0.139
0.145
1.537
0.789
1.213
2019–2020
DMU26
1.905
2.285
1.027
1.165
0.833
1.029
0.857
2019–2020
DMU27
0.433
0.319
0.370
0.378
1.359
0.848
1.153
2019–2020
DMU28
0.414
0.298
0.337
0.365
1.389
0.815
1.133
2019–2020
DMU29
0.519
0.351
1.043
0.421
1.477
1.295
1.913
2019–2020
DMU30
0.248
0.111
0.198
0.141
2.240
0.792
1.774
2019–2020
DMU31
0.420
0.234
0.342
0.291
1.796
0.809
1.453
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to balance the relationship between the investment structure of innovation resources and the demand for regional innovation resources. Moreover, this chapter completes the statistical results of innovation efficiency indicators for the later research on the impact of innovation policy tools on regional innovation performance and prepares for the data.
5 Discussion 5.1 Overall Evaluation of Provincial Innovation Efficiency Mobile learning is a pedagogical process enriched by context-sensitivity of being (location) and doing (activity) of a learner or a teacher. It is a comprehensive process to create meaningful instructional and learning interactions in consideration of where learners and teachers will be and what they will be doing. Simple transferring decontextualized traditional curriculum activities via a mobile device can have limited effectiveness. The mobile leaning pedagogical question is not only about making instructions or guidance available for just-in-time learning, but also about creating an expert presence for a specific individual that facilitates context-based learning and performance in an authentic situation. China has a vast territory, and there are large development differences among different provinces. To further evaluate China’s regional innovation efficiency, this paper summarizes and analyzes the regional innovation efficiency measurement results of thirty-one provinces in China from 2010 to 2020, which can be obtained Draw the following conclusions: First, the regional innovation efficiency of each province has increased. From the perspective of the vertical changes in the regional innovation efficiency of each province, except for a very small number of provinces such as Beijing, Zhejiang, and Qinghai, the regional innovation efficiency of other provinces has shown an upward trend year by year since 2012, and the upward trend of some provinces has also shown circuitous fluctuations, such as: Inner Mongolia, Guizhou, Guangxi, Chongqing, etc. The regional innovation efficiency of these provinces has declined to varying degrees in 2013–2014, 2015–2016, and 2018–2019, which characterized by “rising-decreasing-rising”, and the overall trend is also rising. Judging from the data from 2019 to 2020, the average regional innovation efficiency of each province is 1.4, and the regional innovation efficiency is at a prominent level, but there is still room for improvement. Second, there are large gaps in regional innovation efficiency among provinces. From the average value of regional innovation efficiency of each province from 2010 to 2020, the provinces with higher average regional innovation efficiency concentrated in the eastern coastal areas, while in the central and western regions, only Hainan, Sichuan, Chongqing, Guizhou, and other regions have higher average regional innovation efficiency. The average value of regional innovation efficiency in other provinces is low. The inter-provincial differences in regional innovation efficiency are large, and the largest difference is above 0.6. Third, the time effect of innovation input and innovation output is asymmetric. Therefore, the article uses the data from 2010 to 2020 as the target time range and conducts an overall study to analyze the innovation performance of China’s provinces (autonomous regions, municipalities) in these 11 years. Guangxi, Zhejiang, Hainan, Tibet, Guangdong,
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Anhui, Guizhou, Qinghai, Tianjin, and Shandong have the fastest growth in innovation performance. Fourth, from the ranking of the average regional innovation efficiency of thirtyone provinces in China from 2010 to 2020, five of the top ten provinces are from the eastern region, namely Beijing, Shanghai, Jiangsu, Zhejiang, Guangdong, and Beijing, Zhejiang The regional innovation efficiency of has been in the DEA effective state for eleven years, which shows that in the process of regional innovation and development in Beijing and Zhejiang, there are very few cases of redundant input and insufficient output. It is worth noting that these five provinces and Hainan Province also have prominent levels of scientific and technological innovation resources (see Figs. 1, 2, 3, 4, 5 and 6).
BeiJing
2.500 2.000 1.500 1.000 0.500 0.000 2010-2011 2011-2012
2012-2013
2013-2014
2014-2015
effch
pch techch 2015-2016
2016-2017
techch
2017-2018
effch 2018-2019
2019-2020
pch
Fig. 1. Innovation efficiency in BeiJing.
ShangHai
2.500 2.000 1.500 1.000 0.500 0.000 2010-20112011-2012 2012-2013
2013-2014
2014-2015
effch
pch techch 2015-2016
2016-2017
techch
2017-2018
effch 2018-2019
pch
Fig. 2. Innovation efficiency in Shanghai.
2019-2020
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JiangSu
3.000 2.500 2.000 1.500 1.000 0.500 0.000 2010-2011 2011-2012
2012-2013
2013-2014
2014-2015
effch
pch techch 2015-2016
2016-2017
techch
2017-2018
effch 2018-2019
2019-2020
pch
Fig. 3. Innovation efficiency in Jiangsu.
ZheJiang
4.000 3.000 2.000 1.000 0.000 2010-2011 2011-2012 2012-2013
2013-2014
2014-2015
effch
pch techch 2015-2016
2016-2017
techch
2017-2018
effch 2018-2019
pch
Fig. 4. Innovation efficiency in ZheJiang.
2019-2020
Measurement and Evaluation of China’s Regional Innovation Efficiency
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GuangDong
3.500 3.000 2.500 2.000 1.500 1.000 0.500 0.000 2010-2011 2011-2012
2012-2013
2013-2014
2014-2015
effch
pch techch 2015-2016
2016-2017
techch
2017-2018
effch 2018-2019
2019-2020
pch
Fig. 5. Innovation efficiency in GuangDong.
HaiNan
6.000 5.000 4.000 3.000 2.000 1.000 0.000 2010-2011 2011-2012
2012-2013
2013-2014
2014-2015
effch
pch techch 2015-2016
2016-2017
techch
2017-2018
effch 2018-2019
2019-2020
pch
Fig. 6. Innovation efficiency in HaiNan.
5.2 Countermeasures and Recommendations First, the government should continue to increase investment in fiscal innovation expenditures. The empirical results show that the proportion of innovation expenditure in fiscal expenditure has a significant positive impact on regional innovation efficiency, and fiscal innovation expenditure has a significant role in promoting regional innovation efficiency. The support of financial funds for innovative activities is indispensable for any region, especially the support for research projects with public basic nature. Fiscal innovation expenditures should identify innovation areas that need key support, while considering other innovation areas, so that regional innovation activities can develop in an all-round way. When fiscal innovation expenditures focused on key national strategic areas, financial support for innovation entities such as enterprises and universities should increase at the same time to stimulate the innovation vitality of different innovation entities.
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Second, the government should reform the way in which fiscal innovation expenditure invested. The government should strengthen the system of allocating funds for financial innovation and improve it. In order to make financial innovation funds more effective in supporting the innovation activities of innovation subjects such as enterprises and universities, the government should avoid the form of free allocation of innovation funds to innovation subjects in the process of allocating financial innovation funds, but should use more incentive payment methods, such as the establishment of innovation funds, subsidies to innovation subjects in stages and quotas, etc., so that financial innovation expenditures can maximize their benefits. For financial funds supported by government departments for a fee, set up a scientific and reasonable mechanism for entering and exiting the market, and build a virtuous circle of capital investment mechanism of “capital investment-capital operation-capital withdrawal-capital reinvestment” to improve the driving and guiding effect of financial innovation capital investment on social funds.
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14. Yixin, W., Kong, R.: Research on the efficiency and key influencing factors of scientific and technological innovation of industrial enterprises above designated size from the perspective of value chain-based on the DEA-tobit two-stage model. Sci. Technol. Manag. Res. (3), 7 (2019) 15. Tone, K., Toloo, M., Izadikhah, M.: A modified slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 287(2), 560–571 (2020)
The Influence of Announcement and Event Dates of M&A Deals on Return of Company’s Shares in Oil and Gas Industry Ekaterina Koroleva1(B) , Maria Tikhomirova1 , and Vladlen Shakhov2 1 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected] 2 LLC Glavstroy-SPb Specialized Developer, St. Petersburg, Russia
Abstract. The number of mergers and acquisitions reached record levels in the first six months of 2021. Activity is mainly observed in developing countries, including Russia. In the framework of the study, we analyze the impact of the date of the announcement and the completion of the merger or acquisition transaction on the shares’ profitability of the buyer company or the seller company. We studied the behavior of Russian oil and gas companies’ shares for 287 mergers and acquisitions in the period from 2002 to 2020. The study was conducted using the event study with the use of either the date of the transaction or the date of the announcement as the date of the event. The study considered three possible observation windows 1 month, 6 months and 1 year. The Russian economy has gone through the COVID19 crisis relatively well. Besides, the oil and gas sector are a strategically important sector for the Russian economy and most M&A transactions take place in there. This feature makes Russia an interesting case for the research. The conducted research revealed that during the analyzed period, the market reacted equally to information about the announcement and execution of mergers and acquisitions in the Russian oil and gas industry. This article presents a unique comparative analysis of the announcement and the completion of the merger or acquisition transaction dates impact on the profitability of the buyer or seller company’s shares. Therefore, the paper contributes to the developing of the event study analyses and pays attention to the influence of M&A transactions in the different spheres of economy. Keywords: merger · acquisition · event study · oil and gas industry · return of shares
1 Introduction Today, the external expansion of the company becomes the main way of its development and allows it to adapt to the rapidly changing conditions of the business environment [1, 2]. Any company’s decisions, both operational and strategic, are usually focused on the future growth of its value. This also applies to such important decisions as decisions on mergers or acquisitions of the company. Mergers and acquisitions suppose changes © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 504–518, 2023. https://doi.org/10.1007/978-3-031-32719-3_39
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in future financial results and, accordingly, lead to a revaluation of the company’s value by the market. The number of mergers and acquisitions reached record levels in the first six months of 2021 [3]. Activity is mainly observed in developing countries, including Russia. The current market situation indicates that countries are actively recovering from COVID-19 restrictions and companies are intensively redistributing their resources [4]. Today, research in the field of companies’ mergers and acquisitions [5–7] is aimed at analyzing the returns of shares of buying or selling companies before and after the procedure. The authors analyze what happens to the company’s stock returns and explain the market’s reaction to certain events. E.B. Magenheim and D.C. Mueller [8], as well as M. Rahchamani [9] noted that it is quite difficult to measure the effect of various events, because it is hard to identify the fact when the information became available to the market. Several authors [10, 11] focus their research on the analysis of events around the date of the merger or acquisition announcement by companies. Others [12], on the contrary, investigate the impact of the fact of a merger or acquisition on the profitability of shares of both companies. Today, there is no research that would compare the impact of two key merger or acquisition dates – the date of the announcement and the transaction date on the shares’ profitability of sellers and buyers. Which event will the market react more strongly to? This study is aimed at filling the identified gap. The purpose of the study is to provide a comparative analysis of the impact that the date of the announcement and the completion of the merger or acquisition transaction have on the profitability of the shares of the buyer company or the seller company. We analyzed the behavior of shares of Russian oil and gas companies for 287 mergers and acquisitions in relation to two events – the date of the announcement of the transaction and the date of the transaction itself using event analysis. The study considered three possible observation windows - 1 month, 6 months and 1 year. The relevance of the study on the example of Russia is due to the following aspects: the Russian economy has relatively well suffered the COVID-19 crisis [13]. In 2020, Russia’s GDP decreased by 4%, while in European countries, GDP decreased by 7.4% [14]. The restrictions related to COVID-19 did not affect the total value of mergers or acquisitions (59.9 in 2020 versus 62.9 in 2019) in the country. The oil and gas sector are a strategically important sector for the Russian economy [15–19]. Moreover, most M&A transactions take place in the oil and gas sector [20, 21]. There were 4 mega-deals worth more than $10 billion in the oil and gas sector in 2020 alone [13, 22]. All the above confirms the expediency of studying mergers and acquisitions on the example of the Russian oil and gas industry. The conducted research revealed that during the analyzed period, the market reacted equally to information about the announcement and execution of mergers and acquisitions in the Russian oil and gas industry. The presented results allow us to combine two groups of studies focused on the date of announcement of transactions [10, 11, 23] and the date of actual transactions [12, 24]. The market reaction is comparable for the two events, which makes the studies of both groups relevant. We also supplement the algorithm for implementing event analysis in determining the event window [25, 26]. The study was conducted using the example of three different event windows – 1 month, 6 months and 1 year. In all analyzed cases, the best results were obtained for an event
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window of 1 month. This underlines the importance of building predictive models based on historical data that accurately reflect the latest events in the company. The paper is structured as follows. Section 1 provides an overview of theoretical and empirical background. Data and methodology are discussed in Sect. 2, the results - in Sect. 3. Finally, Sect. 4 provides the discussion and conclusions.
2 Data and Methodology The study analyzed mergers and acquisitions involving Russian companies in the oil and gas sector in the period from 2002 to 2020. The information base of the study was stock exchange quotations and quotations of the Moscow Exchange index [27]; information about participants in M&A transactions, including data about the transaction, the date of the announcement of the transaction, the date of the transaction [27]. We analyzed a pool of 4261 transactions to form a set. The recurring transactions, transactions between nonpublic companies, as well as transactions characterizing privatization, share repurchase or participation in joint ventures (i.e., not directly related to mergers and acquisitions) were excluded from the analyses. All unfinished transactions were also excluded. As a result, 287 transactions were selected for further analysis, of which: 194 transactions were made by oil and gas companies as a buyer and 93 transactions were made as a seller. Event study analysis is often used to assess the impact of events on stock returns [25, 28, 29]. The approach allows in the short term to assess the impact of the transaction by analyzing the changes in the profitability of the company’s shares. Two-time intervals are defined – the observation window and the event window. The observation window characterizes a certain period before the merger or acquisition of companies. Based on the analysis of this period, a model is constructed that reflects the relationship between the return of a stock and the return of a market index. The model allows you to predict the profitability of a stock if the conditions for the event window remain unchanged. The event window is the time interval in which the event being analyzed is located directly. In the framework of the study, an event is understood as a merger or acquisition transaction. In the event window, the predicted and real returns on shares are compared and as a result, excess returns (abnormal returns, AR), the unexpected changes in stock prices are calculated:
ARit = Rit − aτ − β τ Rmt
(1)
Rit represents the actual return of “i” shares on day “t”, Rmt represents the return of the market index on day “t”. The value of the abnormal yield (AR) obtained using the formula shows the change in the share price in one day [30, 31]. After calculating (AR), it is necessary to conduct a test to determine the statistical significance of the change. To do this, we use the Z-test: Z=
ARit σ
(2)
ARit represents the estimation of excess profitability of “i” shares on day “t”, the σ is standard deviation.
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To recognize ARit as a significant value with 95% probability, it is necessary that the value |Z| > 1,96. Also, the effectiveness of events can be assessed using cumulative anomalous return (CAR). The accumulated value of the excess return (cumulative abnormal return) in the event window from the first day of the window (t1) to the last day (t2) is calculated as: CARi (t1 , t2 ) =
t2 t=t1
ARit
(3)
The advantages of this method include the availability of information, the disadvantages are the complexity of determining the size of the observation window and the event window to build a predictive model [32]. Also, this approach does not reflect the effects of the M&A transaction on the company’s activities, only the market reaction in the short term. The fact that such a research methodology is applicable only to public companies is also criticized [33]. As part of the study, the event window was determined based on an experimental approach. In several studies [34, 35] the event window includes not only the specific date of the event and a certain number of observations after, but also the date before the event. This decision is because information about M&A transactions may be known or felt by the market somewhat earlier than the date of the event. Regarding the previously generated data set, this approach is not relevant, since the available information allows us to assess both the impact of the news about the announcement of the transaction and the fact of the transaction. Therefore, the date of the event is taken as the starting date for the event window in this work. To determine the size of the event window we selected the largest transactions among Russian companies in the oil and gas sector for 2019. This year was used for analysis, for a few reasons. In the case of using data for 2020, distortions are possible due to the nonstandard behavior of the stock market in the context of the COVID-19 and large-scale quantitative easing programs. Another reason for choosing this period as an experimental one is the presence of a variety of transactions within this period. Since the purpose of the study is to analyze the transactions of both sellers and buyers, it is necessary that transactions be made on both sides. Transactions involving both individuals and legal entities are also analyzed. Table 1 shows a list of transactions that were considered when determining the size of the event window. Table 1. Transactions that were analyzed to determine the event window. №
The Buyer
The Seller
Transaction date
1
Information about the buyer was not disclosed
PAO Gazprom
26.07.2019
2
Information about the buyer was not disclosed
PAO Gazprom
21.10.2019
3
Cnooc Limited
PAO Novatek
25.04.2019
4
Japanoil Corporation
PAO Novatek
29.06.2019
5
PAO Gazprom
The Shareholder
07.11.2019
6
PAO Lukoil
Newage Corporation
10.09.2019
Created by authors.
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We have visualized data on the behavior of the stock price in the event window. The results are presented in Table 2. The time interval [t0 ; t+7 ] is highlighted in red. Table 2. The results of determining the event window by visualizing data around the event window.
№
The Buyer
The Seller
Transaction date
Visualization
1
Information about the buyer was not disclosed.
PAO Gazprom
26.07.2019
The possibility of using the interval [t0; t+7] for analysis Yes
2
Information about the buyer was not disclosed.
PAO Gazprom
21.10.2019
Yes
3
Cnooc Limited
PAO Novatek
25.04.2019
Yes
4
Japanoil Corporation
PAO Novatek
29.06.2019
Partly
5
PAO Gazprom
07.11.2019
Yes
6
PAO Lukoil
The Shareholder Newage Corporation
10.09.2019
No
Created by authors.
As a result of the analysis, the interval [t0; t+7] was chosen as the event window, as 4 out of 6 transactions are characterized by a sharp changes in stock prices in this interval. The disadvantages of the event study analysis were also the complexity of determining the observation window. We decided to analyze the behavior of the stock price in relation to three observation windows: 1 month, 6 months and 1 year.
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According to this and based on the hypothesis of the study, the behavior of shares of oil and gas companies during 287 mergers and acquisitions will be analyzed in relation with the date of the announcement of the transaction and in relation with the date of the transaction itself, considering three possible observation windows. In the process of collecting the necessary pool of information, a lack of data on certain positions was revealed. The final set of analyzed transactions is presented in Table 3. Table 3. Analysis of the number of transactions used in the study. The number of transactions relative to the date of its announcement The observation window
1 month 6 months 1 year
Total
285
Oil and gas company – the seller
282
269
93
91
89
Oil and gas company – the buyer 191
191
180
The number of transactions relative to the date of its completion The observation window
1 month 6 months 1 year
Total
232
230
223
74
74
72
Oil and gas company – the buyer 158
156
151
Oil and gas company – the seller Created by authors.
As part of the study, the hypothesis was tested based on 1,521 observations. It is necessary to find the information for each individual transaction, make a forecast model and based on a comparison of the real values of stock returns with forecast values, determine the significance of excess returns, cumulative excess returns for the period of the last date of the event window to conduct a comprehensive study and identify the results. As a result, the data will be aggregated to allow further comparison of the behavior of stock returns relative to the date of announcement and the actual date of transactions and confirmation/rejection of the hypothesis.
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3 Results The interval [t0; t+7] was defined as the event window. Within the interval, the days immediately after the event were identified in which excess profitability was a significant value. Table 4 presents aggregated data regarding the date of the announcement of the transaction. Table 4. The number of transactions in which there was a significant excess return, relative to the date of the announcement of the transaction.
The share of transactions in which AR is significant
The number of transactions in which AR is significant Total transactions
The share of transactions in which AR is significant
1 month
The number of transactions in which AR is significant Total transactions
6 months
The share of transactions in which AR is significant
t0 t +1 t +2 t +3 t +4 t +5 t +6 t +7
1 year The number of transactions in which AR is significant Total transactions
The day after the date of the announcement of the transaction
26 22 19 11 22 12 18 19
9,67% 8,18% 7,06% 4,09% 8,18% 4,46% 6,69% 7,06%
27 25 22 12 25 15 24 20
9,57% 8,87% 7,80% 4,26% 8,87% 5,32% 8,51% 7,09%
31 33 29 18 33 28 28 26
10,88% 11,58% 10,18% 6,32% 11,58% 9,82% 9,82% 9,12%
269 269 269 269 269 269 269 269
282 282 282 282 282 282 282 282
285 285 285 285 285 285 285 285
Created by authors.
In general, a small percentage of mergers and acquisitions in the oil and gas industry were identified, whose announcement significantly affected the profitability of oil and gas companies, which acted as either a buyer or a seller. When comparing different observation windows, we note that in the case of using 1 month as an observation window, the number of transactions with a significant excess return turned out to be slightly higher. The greatest reaction of the market regarding the date of the announcement of the merger and acquisition transaction occurs directly on the day of the announcement or on the 4th day after the announcement of the news.
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Table 5 presents aggregated data regarding the date of the transaction. Table 5. The number of transactions in which there was a significant excess return, relative to the date of the transaction.
Total transactions
The share of transactions in which AR is significant
The number of transactions in which AR is significant
Total transactions
The share of transactions in which AR is significant
1 month
The number of transactions in which AR is significant
6 months
The share of transactions in which AR is significant
t0 t +1 t +2 t +3 t +4 t +5 t +6 t +7
1 year The number of transactions in which AR is significant Total transactions
The day after the transaction date
21 22 18 9 12 10 26 17
9,42% 9,87% 8,07% 4,04% 5,38% 4,48% 11,66% 7,62%
20 18 22 9 17 12 26 15
230 230 230 230 230 230 230 230
8,70% 7,83% 9,57% 3,91% 7,39% 5,22% 11,30% 6,52%
33 28 29 19 22 20 31 26
232 232 232 232 232 232 232 232
14,22% 12,07% 12,50% 8,19% 9,48% 8,62% 13,36% 11,21%
223 223 223 223 223 223 223 223
Created by authors.
The number of transactions in which there was a significant excess return, relative to the date of the transaction, is comparable to the number of similar transactions relative to the date of the announcement of the transaction. When analyzing the results obtained using a predictive model based on a 1-month observation window, the number of transactions with a significant excess return is higher than when using a 1-year and 6-month observation window. This trend appears both in the analysis of the announcement date and the date of the transaction, which may indirectly indicate a higher accuracy of models calculated on data within 1 month. The greatest reaction of the market regarding the date of the merger and acquisition transaction occurs directly on the day of the announcement or on the 6th day after the announcement of the news.
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Table 6 presents aggregated data on the significance of the transaction announcement’ date impact on the profitability of the shares of an oil and gas company acting as a buyer or seller, in the context of the analyzed period. The impact of the transaction on the Table 6. The number of transactions in which the excess yield is significant relative to the date of the announcement of the transaction.
The observation window 6 months
Total transactions
The share of transactions in which AR is significant
The number of transactions in which AR is significant
Total transactions
The share of transactions in which AR is significant
The number of transactions in which AR is significant
Total transactions
The share of transactions in which AR is significant
1 month
Year
The number of transactions in which AR is significant
1 year
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Total
2 0 3 0 3 7 5 3 2 9 8 14 10 0 5 5 6 5 4 91
5 16 11 1 12 19 8 19 8 23 25 31 25 11 14 15 9 11 6 269
40% 0% 27% 0% 25% 37% 63% 16% 25% 39% 32% 45% 40% 0% 36% 33% 67% 45% 67% 34%
1 4 3 1 3 14 5 4 3 11 7 18 10 0 5 6 5 5 4 109
5 20 11 1 14 24 8 19 8 24 26 31 25 11 14 15 9 11 6 282
20% 20% 27% 100% 21% 58% 63% 21% 38% 46% 27% 58% 40% 0% 36% 40% 56% 45% 67% 39%
1 7 3 1 8 13 4 7 7 13 6 22 8 4 9 10 5 6 2 136
5 21 11 1 15 24 8 19 8 25 26 31 25 11 14 15 9 11 6 285
20% 33% 27% 100% 53% 54% 50% 37% 88% 52% 23% 71% 32% 36% 64% 67% 56% 55% 33% 48%
Created by authors.
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profitability of the stock was recognized as significant if on one of the days within the event window there was a significant excess profitability of the shares. In the case of a 1-year observation window, 91 out of 269 (34%) transactions significantly affected the profitability of the shares of an oil and gas company acting as a buyer or seller. In comparison with other results, this value is the smallest. This may be because the observation window of 1 year is too long and does not reflect the current state of the Table 7. The number of transactions in which the excess return is significant relative to the date of the transaction.
The number of transactions in which AR is significant
Total transactions
The share of transactions in which AR is significant
The number of transactions in which AR is significant
3 11 6 1 8 11 8 18 7 21 21 28 23 9 14 14 8 8 4 223
0% 45% 50% 0% 25% 27% 50% 22% 57% 38% 38% 54% 43% 22% 29% 43% 38% 13% 50% 38%
0 5 3 0 2 4 3 6 4 11 7 18 11 3 4 5 2 1 2 91
3 11 6 1 9 16 8 18 7 21 22 28 23 9 14 14 8 8 4 230
0% 45% 50% 0% 22% 25% 38% 33% 57% 52% 32% 64% 48% 33% 29% 36% 25% 13% 50% 40%
0 6 3 0 3 9 2 9 6 14 13 18 9 7 8 9 4 2 2 124
Created by authors.
The share of transactions in which AR is significant
The share of transactions in which AR is significant
0 5 3 0 2 3 4 4 4 8 8 15 10 2 4 6 3 1 2 84
1 month
Total transactions
Total transactions
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Total
The number of transactions in which AR is significant
Year
The observation window 6 months
1 year
3 11 6 1 9 16 8 18 8 22 22 28 23 9 14 14 8 8 4 232
0% 55% 50% 0% 33% 56% 25% 50% 75% 64% 59% 64% 39% 78% 57% 64% 50% 25% 50% 53%
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market. In the case of a 6-month observation window, 109 out of 282 (39%) transactions turned out to be significant and significantly affected the profitability of shares of an oil and gas company acting as a buyer or seller. The best results were obtained for an event window of 1 month. In almost 50% of cases, there was a significant market reaction to information about the announcement of a merger and acquisition transaction, which was reflected in the profitability of shares of an oil and gas company acting as a buyer or seller. Table 7 presents aggregated data on the significance of the transaction date impact on the profitability of the shares of an oil and gas company acting as a buyer or seller, in the context of the analyzed period. For a 1-year observation window, the smallest number of transactions were received (84 out of 223 or 38%) with a significant excess return. For a 6-month observation window, 91 out of 230 (40%) transactions significantly affected the profitability of the shares of an oil and gas company acting as a buyer or seller. For a 1-month observation window, the following values were obtained: in 53% of cases, information about mergers or acquisitions significantly altered the profitability of shares of an oil and gas company acting as a buyer or seller. In general, we obtained comparable results in Tables 6 and 7. There are clearly no significant differences in the market reaction between the dates of the announcement and completion of mergers and acquisitions in the oil and gas industry in Russia during the study period. We would like to draw attention to the fact that the share of transactions with a significant excess return varies significantly from year to year. This is because of the economic situation in the country, which is also influences the market reaction to different events. When analyzing the significance of excess profitability, it is important to consider the quality of the forecast model based on historical data. The results described above can be based on predictive models that have a relatively low coefficient of determination. From this perspective, we calculated the coefficient of determination for each predictive model. Table 8 presents an analysis of the sensitivity of the results depending on the requirements for the coefficient of determination of the predictive model. When the requirement for the coefficient of determination of predictive models is greater than 0.5, the data set is reduced by about 50%, while the share of significant transactions changes slightly. If the coefficient of determination is greater than 0.6 or 0.65, a similar situation is observed. With a further increase in the requirements for the coefficient of determination of the forecast model, the number of transactions is rapidly decreasing, and the proportion of significant transactions changes randomly. In general, it can be argued that the exclusion of transactions based on models with a low coefficient of determination does not significantly affect the proportion of significant transactions.
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The share of transactions in which AR is significant
Total transactions
The number of transactions in which AR is significant
1 month The share of transactions in which AR is significant
Total transactions
The number of transactions in which AR is significant
6 months The share of transactions in which AR is significant
Total transactions
1 year The number of transactions in which AR is significant
Coefficient of determination of the predictive model
Table 8. Sensitivity analysis of the number of transactions in which excess profitability is significant, depending on the value of the coefficient of determination of the forecast model.
Results regarding the date of announcement of the merger or acquisition transaction Not 91 269 34% 109 282 39% 136 285 48% limited >0,5 59 173 34% 67 187 36% 87 180 48% >0,6 33 116 28% 40 113 35% 68 139 49% >0,65 27 89 30% 35 94 37% 53 114 46% >0,7 24 70 34% 27 75 36% 39 88 44% >0,75 16 48 33% 22 55 40% 32 70 46% >0,8 12 35 34% 17 38 45% 20 48 42% >0,85 3 12 25% 4 13 31% 11 28 39% Results regarding the date of the merger or acquisition transaction Not 84 223 38% 91 230 40% 124 232 53% limited >0,5 56 142 39% 54 146 37% 83 148 56% >0,6 34 92 37% 36 90 40% 68 121 56% >0,65 26 73 36% 29 76 38% 51 96 53% >0,7 22 57 39% 21 60 35% 37 75 49% >0,75 11 35 31% 13 39 33% 28 58 48% >0,8 8 26 31% 8 28 29% 20 39 51% >0,85 3 10 30% 6 16 38% 7 19 37% Created by authors.
4 Discussion and Conclusion Studies of the M&A transactions impact on the value of shares conducted using the market method (event study) with the use of either the date of the transaction or the date of the announcement as the date of the event. No comparison of the results was carried out. The study conducted by the authors made it possible to close the identified gap and
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prove that there are no significant differences in the market reaction between the dates of announcement and completion of mergers and acquisitions in the oil and gas industry in Russia during the study period. This point highlights the relevance of studies focused both on the date of announcement of transactions [10, 11] and on the date of actual transactions [12, 24]. The study also made it possible to supplement the algorithm for implementing event analysis in determining the event window [25, 28, 34, 35]. The analysis of mergers and acquisitions was carried out using the example of three different event windows – 1 month, 6 months and 1 year. The best results were obtained for an event window of 1 month. This underlines the importance of building predictive models based on historical data that accurately reflect the latest events in the company. Nevertheless, the presented study has a few limitations. The study was conducted on the example of the Russian oil and gas industry. Accordingly, it cannot be extended to other industries and other countries. Future similar studies in other countries and industries may expand the understanding of the significance of the dates of the announcement or the actual transaction in relation to mergers and acquisitions of companies. One of the disadvantages of event analysis is the analysis of the impact of events in the short term [28]. Large-scale events such as mergers or acquisitions of companies have an impact on the performance of companies in the long term [24]. The approach used in the study did not allow us to evaluate this effect. Despite the identified limitations, this article presents a unique comparative analysis of the announcement and the completion of the merger or acquisition transaction dates impact on the profitability of the buyer or seller company’s shares, conducted using event analysis. Acknowledgments. The research was financed as part of the project “Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization” (FSEG-2023-0008).
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27. MOEX Index (2021). https://www.moex.com/ru/index/IMOEX. Accessed 09 Aug 2021 28. Tao, R., Su, C., Yaqoob, T., Hammal, M.: Do financial and non-financial stocks hedge against lockdown in Covid-19? An event study analysis. Econ. Res. 35(4), 1–22 (2021) 29. Magradze, Y.: Analysis of the territory energy security in the context of sustainable development (case of Georgia). Sustain. Dev. Eng. Econ. 1, 96–112 (2021) 30. Galanov, V., Galanova, A., Shibaev, S.: Random and regular stock price change depending on a time span. Econ. Soc. Changes-Facts Trends Forecast 10(4), 228–241 (2017) 31. Svoboda, M., Rihova, P.: Stochastic model of short-time price development of shares and its profitability in algorithmic trading. In: Proceedings of the International Conference: Quantitative Methods in Economics: Multiple Criteria Decision Making XVIII, Bratislava, Slovakia, pp. 362–368 (2016) 32. Kim, J., Kim, J., Lee, S.K., Tang, L.: Effects of epidemic disease outbreaks on financial performance of restaurants: event study method approach. J. Hosp. Tour. Manag. 43, 32–41 (2020) 33. Euchner, F., Goldenius, V.: Can Optimism in Press Releases Increase Abnormal Stock Returns?-An Event Study and Text-Analysis of Press Releases Issued by FinTech Companies under IFRS and US GAAP (2012). https://gupea.ub.gu.se/bitstream/handle/2077/61414/ gupea_2077_61414_1.pdf?sequence=1&isAllowed=y. Accessed 07 Sept 2021 34. Lane, J.C., et al.: Safety of hydroxychloroquine, alone and in combination with azithromycin, in light of rapid wide-spread use for COVID-19: a multinational, network cohort and selfcontrolled case series study. 2(11), 698–711 (2020) 35. Simakov, O., et al.: Deeply conserved synteny resolves early events in vertebrate evolution. Nat. Ecol. Evol. 4(6), 820–830 (2020)
Investments in the Fixed Capital of the Fuel and Energy Complex as a Factor in Growing Innovations and Increasing Digitalization Olga Nadezhina and Alexandra Geraseva(B) Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia [email protected], [email protected]
Abstract. This paper is aimed at describing and analyzing the current state of the fixed assets of the largest Russian energy players, as well as at finding out if there is a relationship between the changing investments into fixed assets and the financial results of the companies in general. Regression analysis is used as the main research method for determining the dependence and influence of investments in the fixed capital on the net profit of the energy companies. The fuel and energy complex is the most important structural component of the national economy, with all other socio-economic spheres of the country being dependent on it. One of the factors for the success of this component is the development of the fixed assets of the industry. While transiting to a new technological paradigm, we should understand the importance of innovation and digitalization for main production facilities, which require a lot of investment. Electrification and energy consumption projections indicate both an increasing use of renewable energy sources and the importance of classical power plants. Due to the distinctions of the energy sector in terms of its functioning and management, investment activities seem both difficult and necessary. Modern trends in the digital transformation of management and creation of innovative equipment contribute to the growing efficiency and competitiveness of the energy industry. The outflow of investment capital, its formation at the expense of the companies’ own funds and the increasing depreciation of the fixed assets make the Russian energy industry fall behind the best world practices, not only in terms of digitalization, but also in terms of the sector efficiency. This study discusses the current situation, development scenarios, trends, projections on the global and domestic state of the energy sector and uses a statistical method for analyzing the possible impact that investments in the fixed capital of the energy industry can have on the financial results of Russia’s largest energy companies. The research topic is widely covered in the works of some other authors. It is considered from the perspective of high investment risks in this industry and the limitations in the development of the energy sector, given the specifics of economic activity and certain legal issues. Further research is essential for obtaining more relevant results because we need to consider in more detail the effect produced due to the introduction of innovative projects, better technological solutions and digital actions that influence not only the financial component of the companies, but also the production and technical indicators, as well as the expansion of the companies representing the industry.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 519–529, 2023. https://doi.org/10.1007/978-3-031-32719-3_40
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O. Nadezhina and A. Geraseva Keywords: fuel and energy complex · energy industry · fixed assets · innovations · digitalization
1 Introduction The fuel and energy complex plays an important role both in the entire world and in Russia, in particular. Today the energy sector is one of the key industries in the economy. Many studies, for example, by authors such as Kremer-Matyškeviˇc [1], Simionescu [2], closely relate energy production and consumption to the changes in the country’s gross domestic product. I.e., the advancement of the energy industry is not only one of the indicators of economic development, but also largely affects this process. This industry takes up a special place in Russia’s national economy, as it ensures its sustainability not only in terms of its own powerful energy system permitting independent energy existence, but also in terms of the export component of various types of both primary energy and electricity [3]. No industry or economy as a whole can make progress without changes and modernization. These concepts are the key to achieving the necessary pace of development, essential for healthy operation of the country’s economy and its industries in the post-COVID era at a time of global political instability. Despite many events that, to a certain degree, have slowed down the development of the world economy and created difficulties in socio-economic and political relations, most countries are either striving for or in the process of transition to a post-industrial society, which implies the predominance of the service sector over industrial production in the economy, the transition to the knowledge industry and a high-quality innovative type of production. Such changes are impossible without developing the fuel and energy complex, firstly, because the industry itself is subject to these transformations, and, secondly, because all innovative processes are accompanied by large consumption of various types of energy electricity and heat energy, relying on various energy resources [4]. The forecast for the future of the energy industry in the world and in Russia [5] describes several scenarios according to which the fuel and energy industry will be developing in the country. The most important scenarios are a conservative scenario, an innovative one and energy transition. The latter two rely on various technologies, renewable energy sources, energy efficiency and decarbonization (no or low-carbon technologies). “Unlike fossil fuels, renewable forms of energy are not limited to geological reserves. Their use and consumption do not lead to inevitable depletion of reserves [6]. The work by Brazovskaya V. and Gutman S. discusses the positive effect of innovative technologies, the intellectualization of the electric power industry and renewable sources of energy. In the authors’ opinion, these fields of the energy sector are essential because of higher demands for reliability, quality and environmental friendliness [7]. The conservative scenario implies that the above measures are not taken and the current situation is preserved. Despite the slower rate of the average annual population growth, the trends towards energy conservation, other factors, and the consumption of electricity in the world will continue to go up in all the scenarios. This is due to the growing electrification in all regions around the world. Figure 1 shows the following
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trend: the developing countries in Asia and the Middle East account for higher potential consumption in the 2040 perspective.
Fig. 1. Electricity consumption scenario by regions in the world.
The forecast for Russia’s energy sector suggests a trend towards higher consumption of primary types of energy and electricity, with thermal power plants remaining to be the main production facilities. Obviously, to provide consumers with the required amounts of energy, its production, whose dynamics is shown in Fig. 2, must be increased. Higher production and consumption of electricity inevitably entails the need for expanding production capacities, which will result in the growth of the total installed capacity of all electric power facilities. This process includes the creation of new facilities, restoration of worn-out and modernization of the existing plants and equipment. In addition, the use of renewable energy sources is growing, with solar and wind generation being the most widespread. In addition to the potential growth in electricity consumption due to increased electrification, Russia has some territorial, meteorological, and infrastructural distinctions. The economic development of Russian regions is inconsistent, which is why there is no abundant energy supply of the required number of objects. Due to this situation in the energy sector, the country also needs a lot of additional investments, and, as a result, a special representative of the industry - small-scale energy is developing. This generation method has become widespread in remote regions. The study by Sokolov A.A. and Rudneva O.S. [8] considers the prospects for small hydro generation in the steppe regions of Russia. Despite many geographical difficulties, this type of generation is less expensive, the equipment can be installed in a shorter time, and the return on investment is much higher.
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Fig. 2. Electricity production scenario by the main types of power plants.
The main goal of investment activity is to provide a real opportunity for innovation process and maximize the market value of the enterprise [9]. The fuel and energy complex are unique due to some of its features, including monopoly, which requires external strict control over the operations of companies; capital intensity and the need for massive investments with long payback periods; technological dependence with its inevitable need for innovation and modernization of the production component of the enterprise [10]. The energy industry cannot develop properly unless the condition of the fixed assets of energy enterprises is improved [11]. Fixed assets are means of labor capable of ensuring the production and sale of particular products, work and services (use value) in the process of their use (operation), retaining their physical form during repeated use and transferring their value to the newly created product in parts in the form of depreciation deductions [12]. Fixed assets include such objects as buildings and structures, machinery, equipment, transmission devices, and more. The main production assets are usually highlighted on their own. They include what is directly involved in the production process: machines, equipment, inventory, transmission devices. Herein, today it is no longer enough to simply replace worn-out equipment or do repair on time. In order to be able to improve the overall efficiency of the company, “given the conditions of the new technological paradigm and the fourth industrial revolution, different digital technology-based approaches to management are required” [13]. In addition, digital transformation has to be introduced in the entire energy industry, with particular attention being paid to production facilities as they are the main production funds that play a major role in energy enterprises. Innovations and digital technologies are understood as follows: automated technological process control, digital twins of real plants, remote technical maintenance using big data (Big Data) and IoT technologies (Internet of Things), SF6 insulation used in open switchgear, Digital Worker solutions etc. [14]. The need for taking these measures in order to improve the efficiency and competitiveness of the energy industry is also reflected in the order of the Russian Federation Government on the 2030 strategic direction of digital transformation of the fuel and energy complex.
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According to the results of the study of the digital maturity of Russian companies [15, 16] by industry, as shown in Fig. 3, the fuel and energy complex is in the 6th place, falling behind the world’s best practices by 1.2 points. The study covers some areas of the company’s activities such as strategic initiatives, the use of technology, operational activities, customer experience and corporate culture. The leading positions are occupied by representatives of the banking sector and trade, whose activities are regulated by state to a lesser extent. At the moment, in order to minimize the disadvantage, energy enterprises are actively working out digital and innovative solutions that can be introduced in real production and equipment.
Fig. 3. Digital maturity assessment by industry.
Despite the course for modernization, digitalization and innovation, the national energy industry lacks external investment in the fixed assets that can cover the costs of these processes and activities. Most of the investments of energy companies are made from the internal funds of the enterprise itself and are directed primarily to resolve the most acute problems, while innovation processes take a subordinate position [17]. According to Rosstat [18], in 2018 the companies’ own funds allocated for investments in fixed assets amounted to 63.5%, whereas attracted funds comprised 36.5% of the total investment and the state part equaled just 5.2%. At the same time, it is already possible to measure the positive economic impact of the current level of investment in fixed assets of the companies, as well as to project what results could be achieved from additional investments in this industry. This paper is aimed at describing and analyzing the current state of the fixed assets (capital) of the largest representatives of Russia’s energy industry, as well as determining the presence or absence of a dependence between changing investments in the fixed assets and the financial results of the companies as a whole. The current state of fixed assets of the energy industry, namely companies supplying electricity, gas and steam in Russia, is based on the data reflecting the degree of depreciation of the fixed assets published on the Federal State Statistics Service website [19].
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Depreciation of fixed assets is a partial or complete loss of the original technical and operational qualities and use value. Physical deterioration is an inevitable process, which can only be staved off by doing repairs in due time. There is also obsolescence, aging caused by scientific and technological progress, the emergence of newer technology, making equipment less efficient. Fixed assets may not have any physical depreciation, having been in preservation status for a long time [20]. In such a case, it also does not seem reasonable to continue using the equipment or keep it in reserve.
2 Materials and Methods To attain the goal of describing and analyzing the current state of the fixed capital of national energy companies, as well as to find out about the relationship between net profit and investments in fixed capital, some data on the depreciation of fixed assets in general and their production part were compared in dynamics over the past 10 years. In addition, the indicators of the increasing values were calculated. Next, we analyzed the existing dependence between investments in the fixed capital of energy companies and the net profit of the largest companies in the sector. The investments in the fixed capital by types of economic activity are also presented on the Federal State Statistics Service website. The structure of investment sources is indicated there too. The relevant data for 10 years, from 2011 to 2021 inclusive, were taken for analysis. Net profit was chosen as an indicator of financial results, since it shows the real financial condition of a company after it pays taxes, duties, deductions, and other obligatory payments. Six largest representatives of the energy industry, responsible for generating energy at various types of stations and using various primary types of energy sources, were selected for the analysis. Among them there are companies owning mostly hydroelectric power plants [21], nuclear power plants [22], renewable energy sources [23], combining several generating and transmitting energy enterprises [24], the largest private company producing electricity and heat [25], and a company that manages assets not only in Russia, but also in the CIS and Europe [26]. The data on the financial results of the companies were gathered from the annual financial statements presented on the official websites and prepared in accordance with the Russian Accounting Standards (RAS). The most popular statistical method of quantitative analysis was chosen to discover the dependence between the indicators – regression analysis, which was carried out using data analysis in Microsoft Excel. Due to the reliability and transparency of the method, a pre-prepared data sample can be used not only to obtain a report on the presence or absence of a dependence between the resulting factor and the variable(s), but to see the type and degree of this dependence. In this case, a linear dependence model was chosen. Several indicators, which in one way or another characterize the dependence between the factors, were obtained as a result of the regression analysis. First, the quality of the resulting model should be assessed: whether it fully describes the dependence, whether it can be used for explaining the influence of the variable factor on the resulting one. The regression model is the better, the more of the dispersion is explained by a changing regular component. Traditionally, the measure of quality is the
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ratio of the variance of the explained part to the variance of the unexplained part. This indicator is called the coefficient of determination and denoted as R-square [27]. The next important indicator is Fisher’s F-test (F significance), which defines the overall reliability of the model. Its norm is no more than the value of the difference between one and the chosen significance of the model (reliability level) [28]. Another important metric for the regression model is the P-value, which characterizes the probability of rejecting a valid hypothesis. Here, again, the smaller the value the better. In many cases, a certain standard is chosen for considering that the hypothesis is correct. It is either 0.005 or 0.01. A coefficient will be indicated for the variable factor in the final part of the regression analysis. Special attention should be paid to the sign in front of it, as it reflects the dependence to which it affects the resulting coefficient. Another coefficient indicated in the same part is the y-crossing. Its value shows what the resulting indicator will be if the variable factors in the theory are equal to zero. This indicator will help us to conclude whether there can be any third-party indicators that affect the result, and whether the model has to be developed further. Interpreting the results of the regression analysis, namely the values of the above indicators, we can conclude about the reliability of the model, about the presence, type and closeness of the relationship between the factors.
3 Results Having considered and analyzed the depreciation of fixed assets in the energy sector of Russia, it can be concluded that the depreciation of the fixed production assets and fixed assets in general is growing. In some periods, the value of the asset depreciation indicator decreased (see Table 1). It may be due to the predominating processes of writing off worn out and obsolete equipment and updating the fixed assets. In the period under review, the greatest depreciation of the fixed production assets occurred in 2017, while the highest depreciation of the fixed assets was recorded in 2020. These dynamics suggest the need for updating and modernizing the fixed assets in the energy sector, introducing more advanced technologies, machinery, and equipment with a longer useful life, as well as systems capable of forewarning of the need for maintenance and repair work that reduce the risks of irreversible physical wear. Table 1. Depreciation of the fixed assets in the energy industry. Indicator/Period
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
FPA, %
41.9
39.2
39.2
39.6
40.2
41.7
48.6
44
44.9
46.4
Total FA, %
42.2
42.2
43.2
43
44.7
46.9
43.2
49.6
51.3
52.9
Growing FPA depreciation, %
–
−2.7
0
0.4
0.6
1.5
6.9
−4.6
0.9
1.5
Growing FA depreciation, %
–
0
1
−0.2
1.7
2.2
−3.7
6.4
1.7
1.6
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The results of the regression analysis presented in Table 2 with the same name are based on determining the relationship between investments in the fixed capital of the energy industry and the net profit of the largest companies in this industry. The results obtained prove that the assumption that such a dependence exists can be considered true. The R-squared value is used to assess the quality of the model, which is approximately 70%. While the model can be considered relatively qualitative (describing the dependence), it is worth thinking over the possible need to expand the data sample both by time and by the number of companies whose financial results are included in the analysis. The reliability of the model is determined by the F-criterion. In this case its value is 0.001, respectively, with the credibility being 90%. Thus, we can assume that the model can be trusted. Table 2. Results of the regression analysis in MS Excel. Regression statistics Multiple R
0.83
R-square
0.69
Normalized R-square
0.66
Standard error
52689.25
Observations
11
Analysis of variance df
SS
MS
F
Significance F
Regression
1
5.62E + 10
5.62E + 10
20.26
0.0014
Remainder
9
2.5E + 10
2.78E + 09
Total
10
8.12E + 10
Coefficients
Standard error
P-Value
Y-intersection
53758.51
28979.79
0.01
Investments
0.126568
0.02
0.0014
The variable factor that has been chosen – investment in fixed capital – is not the only one that contributes to the change in the resulting indicator – the companies’ net profit. This is evidenced by the value of the y-intersection. The most important conclusion is that there is a direct relationship between changes in investment and net profit. The coefficient of the variable factor has a positive value. Therefore, increasing investment in the fixed capital of the industry makes net profit grow too, and vice versa. It should be highlighted that the alleged relationship between the factors is confirmed by the analyzed dynamics in the depreciation of fixed assets and regression analysis, that is, the assumptions about the possible qualitative changes in the fixed assets can be a factor improving the financial performance of the companies that depend on high quality and reliable energy systems, which are built with advanced technologies, modern equipment, and competent management.
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4 Conclusion The fuel and energy complex remains the most important sector of the national economy, which should be constantly developed and innovated. Energy production, energy efficiency and competitiveness can be improved only in case the fixed assets, directly involved in production processes, are being forever developed. Investments in modernization, development and digitalization are insufficient, being made largely by the companies themselves, while other sources of investment can hardly be ensured both due to the specifics of the industry and the difficulties in economic and political relations. Currently existing systems and practices can improve the indicators most important for the industry, such as reliability, uninterrupted operation, and safety. If introduced, they will optimize and improve the operations of energy companies, as well as the quality of energy supplied to consumers. According to the results of the analysis, investments in fixed assets directly contribute to the financial well-being of energy enterprises, and their constant increase positively influence the companies’ activities. Further research should be focused on expanding the number of variable (influencing) factors so that the ways in which financial indicators change can be considered more carefully. Another productive feature, most closely related to the production, technological component of the energy industry, could be discussed. In addition, expanding the possible impact or the result of the introduction of certain systems, types of equipment and technologies will help to better demonstrate the benefits and value of these solutions for the energy companies.
5 First Section This paper investigates the impact of investing in fixed capital on financial performance in terms of net profit and the relationship of these investments with innovative processes and digitalization in the energy industry. The study has no exact analogues, since the authors cited in this paper focus on learning about the relationship of investment processes in the energy sector with more global indicators such as gross domestic product. According to a more detailed study of the dependence between investments in the fixed assets of energy companies and their net profit, not only there is a relationship between the two indicators, but they also contribute to better understanding of the importance of the fixed assets in this sector. The energy industry as a driver of the national economy is an important topic by many researchers. In their work, Shcherbakova N.S., Churilova V.V., Bogachenko D.V. [29] present a similar idea of the role the fuel and energy complex of Russia plays in the economy of the entire country. The authors considered similar distinctions of this industry, as well as its technical, technological, economic and institutional problems. In her work, [29] discusses in more detail the impact of investments in the fixed assets have on modernization and innovations in the electric power industry. The work focuses on the existing risks for innovative development. In particular, it analyzes the dynamics of the prices for energy sources such as gas and coal, with regular price fluctuations making investments in the industry more difficult and riskier. In her article, Cherkasova V. [30] elaborates on the idea that investing in the fixed capital of the fuel and energy complex is essential despite all the complexity. The author
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looks at the differences in the impact of national and foreign investments, analyzes the number of patent applications, dependence on public capital, political aspects, and studies the positive dynamics in the applications filed by energy and industrial companies. The complexity of investment processes in the country’s energy complex caused by the natural monopoly in this industry, the global epidemiological situation and political situation are considered in the study “The Energy Regulation and Markets Review. Russia” [31]. Despite the positive prospects of the energy sector, the above processes slow down or, to some extent, prevent foreign investments. On the other hand, this situation facilitates local investments, which are aimed not only at improving standard energy generation and distribution technologies and methods, but also developing renewable sources of energy, which makes the energy complex more competitive and innovative. Acknowledgments. The research was financed as part of the project “Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization” (FSEG-2023-0008).
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Risk Assessment of Decarbonization Projects in the Context of Digital Transformation of the Oil and Gas Industry Vladislav M. Krasilnikov(B)
, Alexander A. Iliinsky , and Alexandra A. Saitova
Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. The oil and gas sector of the industry today is undergoing a major transformation under the influence of two paradigms simultaneously – Industry 4.0 and the Sustainable Development Goals. In particular, two global processes are taking place – digital transformation and decarbonization. In this article, digitalization and decarbonization are considered in close connection, because it is the synthesis of these trends that can radically transform traditional approaches to doing business in the energy complex (reasoned in the course of work). The aim of this work was to clarify the list and determine the relevance of risks and threats that most acutely affect the development of projects for the digitalization and decarbonization of the oil and gas complex. A risk assessment methodology was proposed depending on the frequency of occurrence of specific types, as well as on their variability. As a result of the study, risk groups were identified that deserve the utmost attention when implementing digital transformation and decarbonization projects. The idea put forward contributes to a deeper analysis of risks and threats. The methodology can serve as a trigger to improve the quality of a comprehensive risk assessment when planning and implementing projects for the decarbonization of the oil and gas sector. The result should be subject to criticism by like-minded people, formalization of key points, as well as further development. Keywords: Digital Transformation · Digitalization · Decarbonization · Oil and Gas · Risk Assessment · Risks
1 Introduction The realities of the world are currently putting the oil and gas industry in serious crisis conditions. In addition to the depletion of existing oil fields, the political and economic strategies of the leading countries aimed at improving the environmental friendliness of production and social processes are becoming a problem. In each, there is a trend of sustainable development, which combines environmental, economic, and social factors, which appeared and was determined for the first time in the World Strategy for Conservation of Nature [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 530–543, 2023. https://doi.org/10.1007/978-3-031-32719-3_41
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The goals of Russia’s sustainable development are described in a number of official documents, among which it is worth highlighting the Decree of the President of the Russian Federation “On the Concept of Transition of the Russian Federation to Sustainable Development” (On the concept of the transition of the Russian Federation to sustainable development. Decree of the President of the Russian Federation of April 1, 1996 No. 440. URL: http://www.pravo.gov.ru/. Last accessed 15.02.2022), as well as the State Programs of the Russian Federation “Economic Development and Innovative Economy” (On approval of the State Program of the Russian Federation. Economic Development and Innovative Economy. Decree of the Government of the Russian Federation of April 15, 2014 No. 316. URL: http://www.pravo.gov.ru/. Last accessed 15.02.2022) and “Development of industry and increase of its competitiveness” (On the approval of the State program of the Russian Federation. Development of industry and increasing its competitiveness. Decree of the Government of the Russian Federation of April 15, 2014 No. 328. URL: http://www.pravo.gov.ru/. Last accessed 15.02.2022) [2]. The transformation of the Russian oil and gas industry and its desire to meet new trends is facilitated by two processes at once – decarbonization and digital transformation. The first is designed to reduce the impact of industry on the environment, and ideally eliminate any impact at all; the purpose of the second is to optimize and increase the efficiency of production processes, as well as the rationalization of resource consumption. The mentioned directions of development are closely interconnected, and in the case of the industrial sector, they cannot proceed independently. Sectoral energy transition, reduction of CO2 emissions, as well as an increase in the efficiency and rationality of resource consumption in industry are possible subject to the development and implementation of new digital technologies [3]. Using the example of the implementation of the development of the Novoportovskoye field by Gazprom Neft PJSC, we can see how information modeling has reduced the time for putting the field into operation by 30–40%, thereby reducing the volume of resources used and emissions [4]. There are also judgments that the digital transformation itself, according to the analysis of the effects of its implementation, leads not only to an increase in economic efficiency, but also to an increase in energy consumption [5, 6]. It follows that it is the optimization of the management processes of decarbonization through the digital transformation of the industry that will bring the desired result. Based on the foregoing, this article proposes to consider decarbonization as one of the aspects of the digital transformation of the oil and gas sector. To successfully conduct business in the current picture of the world, it is not enough for oil and gas companies to simply increase investments in the exploration and development of new hydrocarbon fields, which, by the way, are becoming more and more difficult to physically reach due to remoteness and geographical conditions. To improve the efficiency of current processes, a new look is needed on the organization of key business processes and the implementation of strategies that take into account digital decarbonization technologies. The introduction of any new technology designed to optimize production or other processes carries certain risks. Any project related to decarbonization should be perceived as a large-scale investment project, the definition of which, in addition to goals and objectives, also includes costs, risks, payback and its period.
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Decarbonization strategies for oil and gas companies, both on site and as a whole, are to determine a more optimal range of technologies and methods that are planned to be implemented. For the success of the strategy being built, it is necessary to analyze all possible risks and costs, as well as to predict the positive effect that the implemented change will eventually have. The search for the “optimum” point is the main task of asset management for the possibility of introducing modern technologies. A high degree of innovation, payback, and at the same time risks are inherent in venture projects. Decarbonization projects should be classified as such, since the development and implementation of the latest carbon-neutral technologies requires significant material and human resources. Often venture projects do not reach their logical conclusion for the reason that the company simply cannot cope with the costs that such projects entail. Based on the above, we can conclude about the relevance of the chosen research topic. For successful management of innovative projects, it is always necessary to measure the volume of investments and all possible risks. The aim of the study is to describe the basic principles for creating a new methodology for assessing risks and threats that most acutely affect the planning and implementation of projects for the decarbonization of the oil and gas sector. To achieve the goal, you must complete the following tasks: 1. Review scientific papers highlighting the problems of implementing projects for the decarbonization of the oil and gas sector; 2. Analyze all risk factors and set the frequency of mentioning risk classes 3. Fix the variability of different types of risks; 4. In accordance with the frequency of occurrence of risks and their diversity, make a rating; 5. Provide guidance on considering specific risks when planning and implementing decarbonization projects.
2 Literature Review Since it was decided to consider the processes of decarbonization as a part of the digital transformation of the oil and gas complex, the work that affects the risks of digitalization can also be useful in the course of studying the subject area and analysis. The synthesis of all the studied materials can lead to the creation of a list of recommendations for the risk management of decarbonization projects in the oil and gas sector. The literature base is scientific works that touch upon the problem of risks and their assessment in the implementation of decarbonization projects and related processes. Agyekum E.B. et al. [7] as a part of the work “Decarbonize Russia–A Best–Worst Method approach for assessing the renewable energy potentials, opportunities and challenges” conducted a survey among experts, the results of which indicate risk factors that deserve close attention. At the same time, the publications of Ghimire L.P., Kim Y. “An analysis on barriers to renewable energy development in the context of Nepal using AHP” [8], as well as “Towards accelerating the deployment of decentralized renewable energy mini-grids in Ghana: Review and analysis of barriers” by Bukari D. et al. [9] give us an understanding
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that the degree of importance of a certain group of risks depends on the region in which new projects are being implemented. Chebotareva G., Strielkowski W., Streimikiene D. in their work [10] draw conclusions about the consistency in risk assessment among countries of the world, referring to the results of the implementation of the Dia-Core project, 2016 [11]. The significance and influence of some risk groups is also confirmed by data on implemented decarbonization projects. The problems and obstacles to the digital transformation of the industry are discussed in the works of Flaksman A.S. et al. [2], Henriette E. et al. [12], as well as Koroteev D. and Tekic Z. [13].
3 Materials and Methods Based on the problems discussed that decarbonization initiatives in Russia face, certain risks can also be identified. In the work of Agyekum E.B. and his co-authors, seven negative factors affecting the process of decarbonization in Russia are identified [7]. These factors, as well as their importance (according to 30 interviewed experts), expressed in terms of specific gravity, which in turn was obtained by the best-worst method, are listed in Table 1. Table 1. Importance of problems standing in the way of decarbonization in Russia. Challenges
Weight, %
Low attention to clean technologies from government
31.4
Unequal playing field
17.9
Strict local content requirement
13.5
Regulation and implementation uncertainties
13.5
High cost of renewable energy projects
13.5
The most important risk factor, according to the authors, is the low concern of the governments with the development of clean technologies. It is also worth noting that factors have different importance in different countries. Thus, for example, weak government attention is similarly considered the most important problem in Nepal and Ghana, but in India, geographic and environmental factors and economic barriers are more concerned [7–9]. Like previous authors, Papadis E. and Tsatsaronis G. in their study point to problems associated with insufficient government concern and the lack of regulatory mechanisms. The high capital intensity of decarbonization projects in the energy sector and unstable forecasts put potential investors at risk of losing all their investments [6]. The authors considered all three factors of sustainable development and also identified the following problems: 1. Absence or underdevelopment of a transnational market to facilitate cross-border trade in clean energy;
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2. Low availability of materials required for the implementation of technological R&D projects; 3. The long duration of the implementation of projects for the decarbonization of the energy sector; 4. Low excitability threshold of society and populist parties in the case of a sharp increase in the cost of energy. Along with the opening opportunities, Cherepovitsyn A.E. and Rutenko E.G. draw attention to the following challenges facing the process of decarbonization of the oil and gas sector: • complete abandonment of investments in projects with a high level of greenhouse gases; • lack of competencies and innovative solutions; • strong competition in new markets, as well as low competitiveness; • lack of partnership opportunities; • high level of requirements from society, investors, and the governments [14]. In the context of the development of CCUS technologies in Russia, both industrywide risks and special ones are distinguished. The first include the lack of proper economic motivation for doing business; this point was also noticed in the sources discussed above. The problem of recognition of Russian CCUS projects by EU countries is referred to as special, which in turn is a consequence of the lack of a regulatory framework [15]. Based on the data of companies involved in renewable energy, Chebotareva G. identifies the following problems, which, in her opinion, are the most important on the way to the development of renewable energy projects: • shortage of qualified specialists; • still stable and rapid development of traditional energy; • wrong choice of places for the implementation of renewable energy facilities [16]. Political risk and the perceived dependence of the energy sector on government regulation are considered the most important issue for EU member states, based on the risk rating presented by the Dia-Core project [11]. Figure 1 shows the distribution of risks by importance, compiled on the basis of a survey of participants in the RES development process. In a number of southern European countries, financial risk comes to the fore in importance. Also, the majority of respondents agreed in assessing the risk of access to the power grid [10].
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Fig. 1. Rankings of RES risks by the level of hazards (EU countries): first “highest” (1), second “medium” (2), and the third “lowest” (3) levels [10].
The inefficiency of state support is also proved by the fact that the average level of risk for a RES project increases in the case of concessional state lending (Table 2). Table 2. Distribution of risks in RES projects in the case of concessional government lending. Source: Risk assessment in renewable energy projects: A case of Russia [10]; Note: WPP e wind power plant; SPP e solar power plant; HPP e hydro power plant. Project name
Adygeyskaya 1–3: onshore WPP
RES type
Wind power
Initiator
State corporation “Rosatom”
Period
2016–2020
Pre-investment stage (period)
2016
Investment stage (period)
2016–2020
Post-investment stage (period)
From 2020
Risk profile (avg.)
Min. (0,00) Max. (1,00) No data available
Project name
Wind measurement complex in Stavropol region
RES type
Wind power
Initiator
Private company JSC “Vetrogeneriryuschaya companiya” (continued)
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Project name
Adygeyskaya 1–3: onshore WPP
Period
2013–2015
Pre-investment stage (period)
2013
Investment stage (period)
2013–2015
Post-investment stage (period)
From 2015
Risk profile (avg.)
Low (0,332) High (0,777) Max. (1)
Project name
Kalmykskaya SPP
RES type
Solar power
Initiator
Private company LLC “Solar Systems”
Period
2015–2029
Pre-investment stage (period)
2015
Risk profile (avg.)
Min. (0,00)
Investment stage (period)
2015–2019
Low (0,384)
Post-investment stage (period)
From 2019
No data available
Project name
SPP Staromaryevskaya
RES type
Solar power
Initiator
Private company LLC “Solar Systems”
Period
2014–2018
Pre-investment stage (period)
2014
Investment stage (period)
2014–2018
Post-investment stage (period)
From 2018
Risk profile (avg.)
Min. (0,00) Avg. (0,599) Max. (1)
Project name
SPP in Abakan
RES type
Solar power
Initiator
Private company PJSC “Kracnoyarskaya HPP”
Period
2013–2014
Pre-investment stage (period)
2013
Investment stage (period)
2013–2014
Risk profile (avg.)
Min. (0,00) Min. (0,00)
Post-investment stage (period)
From 2014
Min. (0,00)
Project name
Beloporozhskaya HPP-1
RES type
Hydropower
Initiator
Private company LLC “NGBP”
Period
2015–2019
Pre-investment stage (period)
2015
Risk profile (avg.)
Max. (0,979)
Investment stage (period)
2015–2019
Avg. (0,510)
Post-investment stage (period)
From 2019
No data available
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Figure 2 shows the increase in the value of expected risks for subsidized projects and projects without government support.
Fig. 2. Distribution of average risk values by stages of RES projects [10].
Such studies also emphasize the importance of political risks, which can be expressed in the change in development strategies of states and the withdrawal of support for decarbonization projects. Moreover, the decarbonization process may face legal market barriers and legal uncertainty [17]. In several researches classifications of risks of digital transformation of industrial enterprises were given [18–21], they were summarized and presented in Table 3. Table 3. Risk classification of digital transformation in industrial sector Barrier class Name of the barrier Description of the barrier External
State
Difficult economic situation, lack of uniform standards for the implementation and use of digital technologies, lack of state support for the implementation and use of digital technologies
Competitive
Large costs on the part of counterparties; commitment of the end user to familiar products and services, lack of experience of successful implementation and use of digital transformation technologies by other enterprises
Technological
Lack of digital projects that take into account the characteristics of the enterprise; low level of digital infrastructure development (continued)
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V. M. Krasilnikov et al. Table 3. (continued)
Barrier class Name of the barrier Description of the barrier Internal
Resource
The need for significant investments in the implementation of digital technologies; the need for significant costs for the use of digital technologies
Human
Lack of sufficient awareness of the benefits of introducing and operating digital technologies; lack of qualified employees with the skills to implement and operate digital technologies
Psychological
Lack of own experience in the implementation and use of digital technologies; having a habit of working without the use of digital technologies; distrust of the reliability of the use of digital technologies
Organizational
Digital technologies must be integrated into the existing infrastructure of the enterprise
4 Results As a result of the review of scientific papers, a database of facts of the occurrence of risks and their variations in the implementation of projects for the decarbonization of the oil and gas sector was formed. For further work, it is necessary to compile a table structuring the information received, calculate the number of recorded cases of mentioning different types of risks and their variations, the frequency of mentions (see Table 4). Table 4. Summary table of risks and their variations based on source analysis. Risk type
Number of cases
Mention frequency, %
№
Variation
Political
13
35.14
1
Government indifference
2
Unequal playing field
3
Lack of regulatory mechanisms and regulatory framework
4
Lack of subsidies (soft loans)
5
Change of development strategy (end of support)
1
High capital intensity of projects
Economic
10
27.01
(continued)
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Table 4. (continued) Risk type
Social
Technological
Ecological (geographic)
Total
Number of cases
2
6
6
37
Mention frequency, %
5.41
16.22
16.22
№
Variation
2
Forecast instability
3
No transnational market
4
End of investment
5
Long implementation time
6
Low competitiveness
7
Low partnership opportunity
1
Low threshold of social excitability
2
The sharp rise in the cost of energy for the consumer
1
Low availability of materials
2
Lack of qualified personnel
3
Lack of innovative solutions
4
Underdevelopment of management mechanisms
1
Harsh environmental conditions
2
Wrong choice of location for implementation
100
On the basis of structured information, we obtain diagrams showing the difference between mentions of different types of risks (Fig. 3) and their variations (Fig. 4).
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Fig. 3. Difference between mentions of risk types, %.
Fig. 4. Difference between number of variations of risk types.
4.1 Principles of the Proposed Methodology To determine the relevance of risks, the number of mentions of a particular type will be correlated with the total number of factors identified during the review of scientific works of other authors. Also, the assessment will take into account the diversity of identified risk factors. As a result, we get a combination of two parameters, the ratio of which will be designed to more accurately assign weights to risks in their comprehensive assessment.
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Conclusions can be drawn based on two key parameters: • the number of times a specific type of risk is mentioned, expressed as a percentage of other types; • the number of variations of a particular type, its diversity.
5 Discussion The results obtained in the course of the study can be interpreted in such a way that political and economic risks are most often of concern. At the same time, the variability of various typical aspects arises precisely in the same directions. This conclusion confirms the correctness of placing a large proportion in the complex risk assessment of the variables responsible for the mentioned types. It should be noted that in all analyzed sources, social type risks are mentioned less often than others. This can lead to two possible conclusions at once: • companies implementing decarbonization projects today are doing a satisfactory job of communicating the importance and usefulness of their activities, as well as strictly monitoring the economic and environmental impact of the transition to clean energy on society; • or society has independently come to realize all its benefits from the processes of decarbonization of the energy sector. The criteria proposed in this study make it possible to take into account the variability of the risk group. The results of the studies used in this work as sources of information eventually get the opportunity to be considered from the point of view of new criteria, more pointwise. If earlier the facts of the occurrence of risks of various types were considered only in private, now it becomes possible to create a more detailed model for taking into account risks in the implementation of projects for the decarbonization of an oil and gas sector enterprise. The proposed methodology for assessing risks in planning and implementing decarbonization projects can become a reference point for a new direction in the development of risk management, which will also take into account the diversity of risk groups and threats.
6 Conclusions During the study, publications of Russian and foreign scientists were studied and analyzed. In the considered works, the issue of digital transformation, decarbonization was raised. A review of planned or implemented cases of decarbonization of the oil and gas sector was also carried out. As a result of the analysis, conclusions were drawn about the significance of specific groups of risks and threats. The result of all the work was the proposed risk assessment methodology, taking into account the frequency of occurrence and the depth of variability. With the potential viability of the proposed methodology for assessing the risks of implementing projects for the decarbonization of the oil and gas sector, it is necessary to: • develop a system of variables involved in the evaluation process;
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• conduct periodic in-depth monitoring of project development and implementation to identify new aspects of emerging problems; • use information about related projects in the assessment. The proposed principle should be criticized by other scientists. For the successful development of the idea, clarifications and improvements are needed. Acknowledgments. The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program ‘Priority 2030’ (Agreement 075-15-2021-1333 dated 30.09.2021).
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Industry 5.0 and Digital Ecosystems: Scientometric Research of Development Trends Aleksandr Babkin1
, Larissa Tashenova1,2(B) , Dinara Mamrayeva2 and Elena Shkarupeta1
1 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected] 2 Karaganda Buketov University, Karaganda, Kazakhstan
Abstract. Ensuring competitiveness in the conditions of modern economic development, due to the Fourth Industrial Revolution, the gradual and progressive transition to Industry 5.0, is possible through the creation, active using of a variety of information and communication tools, digital ecosystems and technologies that allow creating products with high added value, as well as containing customized component, the presence of which provides indisputable competitive advantages to the enterprises. It should be noted that in many respects customization can be implemented by the use of modern and high-performance digital platforms by companies. Studies show that the research of this scientific problem is an actual scientific direction today. The purpose of this scientific article is to research the trends of scientific development in the field of Industry 5.0 and digital ecosystems based on a scientometric analysis. The main tasks are the following: the research of search queries in the categories “digital ecosystems” and “Industry 5.0” in Google Trends to study the general search trends over the past 5 years for the studied economic phenomena; characteristics of queries “digital ecosystems” and “Industry 5.0” based on the results of issuance (993 and 109 respectively) on the WoS platform; conducting a scientometric analysis of the data array obtained from the WoS database for the categories “digital ecosystems” and “Industry 5.0” based on the using of the VOSviewer software product. The following research methods were defined: the comparison method, on the basis of which the frequency of search queries “digital ecosystems” and “Industry 5.0” in Google Trends was studied; content analysis method, the use of which made it possible to systematize the entire array of publications in the Web of Science database (Clarivate Analytics), give it a detailed description and use it to implement scientometric analysis; method of systematization, through which research trends were determined within the framework of the analyzed scientific issues for the next 2–3 years. The article also proposes an author’s method for conducting this kind of analysis, which is flexible, adaptable and can be used in the analysis of a data array not only from the WoS scientometric database, but also from Scopus, PubMed, Lens and Dimensions. Keywords: Digital Ecosystems · Industry 5.0 · Scientometric Analysis · Vosviewer
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 544–564, 2023. https://doi.org/10.1007/978-3-031-32719-3_42
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1 Introduction At present, issues related to the research of digital sustainable systems, the features of their interaction with humans, the trends in the formation of new Internet technologies, the creation and using of which is possible in various industries and areas of economic life, are becoming especially relevant. It can be noted that more and more publications appear in the scientific literature, reflecting aspects of the new industrial revolution - the Fifth, the so-called Industry 5.0, which today is an integral prerogative of the economic development of many countries. Of course, both for Industry 4.0 and for the Fifth Industrial Revolution, the most important component of their implementation is digital platforms, which, in the context of modern economic development, should be considered from the perspective of digital ecosystems. Bibliometric and content analyzes, a systematic review of scientific literature, as the researchers note, make it possible to identify innovations, developments, and information platforms that are characteristic of each of the periods of technological development [1– 3]. At the same time, it should be noted that it is possible to designate a certain pool of works devoted to the study of the distinctive features of Industry 4.0 and 5.0, including from the standpoint of studying the aspects of technological competitiveness, sustainable development and their conceptual foundations [4, 5]. In a number of publications, special attention is paid to “absolute innovation management”, through which it is possible to ensure the availability of developments to a wide range of enterprises, to make them to some extent an “everyday” component of their activities, including due to the synergy of the existing innovation ecosystem [6], as well as the role of automation and robotics in industries of industrial production [7–9]. Obviously, Industries 4.0 and 5.0 largely contributed to the emergence of customized services, payment systems, created the basis for the development of new augmented reality tools, as well as information technologies that are widely used in society, including a new formation - Society 5.0 [10–12]. Undoubtedly, the realization of the possibilities of both the Fourth and Fifth industrial revolutions is impossible without the development of human and innovative capital, the formation of modern corporate cultures of the enterprises [13–17], the creation of smart factories, the introduction and adaptation of new technological tools within the framework of industrial production [18–20]. At the same time, it is important to take into account the restrictions that are dictated by the pandemic of the coronavirus infection COVID-19, which to some extent change the way the usual activities of enterprises [21, 22]. As noted above, digital ecosystems today are becoming an integral part of the functioning of companies, since, due to their technological features, they provide connectivity between many departments and activities and contribute to the creation of new products that have a high level of competitiveness in the market [23–27]. It should be noted that the research of the relationship between digital ecosystems and cloud storage [28], the specifics of building their architecture, modeling features, is today very relevant and practice-oriented [29–36]. It is well known that modern ecosystems in the conditions of modern technological development are becoming a fundamental part of smart systems [37–39], educational
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platforms [40], examples of which are widely reflected in scientific articles by scientists around the world [41–43]. They are perfectly integrated into the business systems of companies, contribute to knowledge management [44–46], are effective communication systems in complex integrated structures, including those represented by backbone innovative and active industrial clusters, enterprises in the context of widespread digitalization and sustainable development [47–52]. Given the high relevance of the economic phenomena under consideration, the purpose of the article if to research the trends in scientific development in the field of Industry 5.0 and digital ecosystems based on scientometric analysis using the VOSviewer software product. The main tasks are the following: analysis of search queries in the categories “digital ecosystems” and “Industry 5.0” in Google Trends; characteristics of queries “digital ecosystems” and “Industry 5.0” based on the results of issuance on the WoS platform; conducting a scientometric analysis of the data array obtained from the WoS database for the categories “digital ecosystems” and “Industry 5.0”. The object of the research is an array of information obtained from the Web of Science database (Clarivate Analytics) as part of the implementation of a search query for such keywords and phrases as “digital ecosystems” and “Industry 5.0”. The subject of the research is the publication activity of scientists studying aspects of Industry 5.0 and digital ecosystems.
2 Materials and Methods 2.1 Research Methodology The basis of the research was the materials of the open press sources and the Internet, as well as publications of the scientometric database Web of Science (Clarivate Analytics), in particular in the direction of “digital ecosystems” - 1094 units, “Industry 5.0” - 109; among them: articles published in scientific peer-reviewed journals, as well as in the proceedings of international conferences, sections of books and monographs (with the exception of the search query “digital ecosystems”). The research methodology included 3 key sections: the first - the analysis of “digital ecosystems” and “Industry 5.0” in the Google Trends; the second is the research of search queries “digital ecosystems” and “Industry 5.0” based on the results of issuance, using the tools built into the WoS platform; the third is scientometric analysis of the data array obtained from the WoS database in the VOSviewer program, including based on the visualization of the results, as well as the characteristics of the resulting clusters combined homogeneous elements into one whole unit. Consider each of the sections in more detail, defining its specific features. The choice of VOSviewer was based on the fact that it is open source software for visualizing and analyzing trends in the scientific literature; this program also allows clustering and network analysis of bibliometric information. VOSviewer is a popular research tool with a clear interface and deep analytics capabilities. Section 1 of the methodology - “Analysis of “digital ecosystems” and “Industry 5.0” using the Google Trends source” included several stages: the first is the definition of 2 search queries “digital ecosystems” and “Industry 5.0” and adding them to the analysis system; the second is the specification of parameters: region of coverage – “Worldwide”, period – “Last 5 years” and “For 12 months”, categories – “All categories”; the third is to
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analyze the dynamics of the popularity of requests and add a picture that interprets these trends to a scientific article; similar actions were implemented when studying dynamic trends for the queries “Industry 4.0” and “Industry 5.0”. Section 2 of the methodology “Studying the search queries “digital ecosystems” and “Industry 5.0” based on the results of issuance on the WoS platform” was built on the step-by-step implementation of the following actions: 1. definition of the search formula: “digital ecosystems” and “Industry 5.0” (each request was processed separately; search mode – “All fields”; sources – “All”); based on the results of the entered queries, 1094 and 993 results were received, respectively; further, in order to obtain more correct results, it was decided to leave only “Articles” and “Proceedings Papers” for the search query “digital ecosystems”, as a result of which the results narrowed down to 993 results; 2. using the Results Analysis tool, in particular the following parameters: “Web of Science Categories”, “Years of Publication”, “Authors”, “Affiliation”, “Funding Institution” and “Country/Region” for which the assessment was made publications on digital ecosystems and Industry 5.0. Section 3 of the methodology was aimed at conducting a scientometric analysis at the request of “digital ecosystems” and “Industry 5.0” in VOSviewer by implementing the following tasks: 1. preparation of information arrays (for each of the requests – “digital ecosystems” and “Industry 5.0”), by uploading it from the WoS platform for the received results within the framework of the search formulas used above; it should be noted that when generating the data, the “Export” tool was used (format – “Text file”; record content – “Full record”; upload mode - all the results obtained); 2. loading data arrays (separately for each of the requests) into the VOSviewer program; 3. clarification of the analysis parameters: analysis type – “Co-occurrence”, analysis unit – “All keywords”, counting method – “Full counting”; clarification of the frequency of occurrence of keywords - 1; 4. interpretation of the results (visualization); 5. analysis of the resulting clusters; 6. determination of trends in the publication activity of scientists within the areas: “digital ecosystems” and “Industry 5.0”. As research methods, the authors identified: a comparison method, on the basis of which the frequency of search queries “digital ecosystems” and “Industry 5.0” was obtained over the past 5 years; content analysis method, the use of which made it possible to systematize the entire array of publications in the WoS database, giving a detailed description of the works related to digital ecosystems and Industry 5.0, and use it to implement scientometric analysis; method of systematization, through which key areas of research were identified within the framework of the analyzed scientific issues for the next 2–3 years. 2.2 Theoretical Fundamentals The theoretical basis of the research was the scientific works of domestic and foreign scientists involved in bibiliometrics/scientometrics analysis, studying aspects related to
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the conceptual foundations of Industry 5.0, its component composition, development features, as well as exploring the essence of digital ecosystems, their architecture, the specifics of application in various sectors of economic activity.
3 Results 3.1 Analysis of Search Queries «Digital Ecosystems» and «Industry 5.0» in Google Trends Service The research of trends related to the analysis of the formation and development of digital ecosystems, their active using in various sectors of the economies of countries and regions of the world, especially in industrial production, is becoming more and more relevant every year, as evidenced by the data on the number of requests from the Google Trends service over the past 5 years (Fig. 1). A similar situation is observed in such an economic category as “Industry 5.0’”, which implies synergy between people and automated machines, introduced into scientific circulation relatively recently. The research of trends related to the analysis of the formation and development of digital ecosystems, their active using in various sectors of the economies of countries and regions of the world, especially in industrial production, is becoming more and more relevant every year, as evidenced by the data on the number of requests from the Google Trends service over the past 5 years (Fig. 1). A similar situation is observed in such an economic category as “Industry 5.0’”, which implies synergy between people and automated machines, introduced into scientific circulation relatively recently.
Fig. 1. Number of Google searches over the past 5 years for “digital ecosystems” (red) and “Industry 5.0” (blue). Note - obtained by the authors according to the Google Trends Service
On average, the number of requests to Google Trends over the past 5 years for the query “digital ecosystems” is 14; “Industry 5.0” - 48, while in the last 12 months - 16 and 51, respectively. If, for example, we compare the number of queries on Google over the past 5 years in categories such as Industry 4.0 and Industry 5.0, then their average number was 60 and 2, respectively (blue and red; Fig. 2). In general, over the analyzed period of time, we are seeing much more attention to the scientific category “Industry 4.0”, while “Industry 5.0” is only gaining popularity both among ordinary users and among the scientific community.
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Fig. 2. The number of Google searches over the past 5 years for the search terms “Industry 4.0” and “Industry 5.0”. Note - obtained by the authors according to the Google Trends Service
3.2 The Research of Search Queries “Digital Ecosystems” and “Industry 5.0” Based on the Results of Issuance on the WoS Platform According to the results obtained, the main sections of Web of Science, within which the largest number of articles related to digital ecosystems, their development, implementation and using in various fields of science and technology, are: Computer Science Theory Methods (361 publications), Computer Science Artificial Intelligence (309), Computer Science Information Systems (244), Engineering Electrical Electronic (237), Business (177), Management (177) and Engineering Industrial (123). The smallest number of papers was recorded in such sections as: Environmental Studies, Green Sustainable Science Technology and Operations Research Management Science - 6.6 and 5 publications, respectively (Fig. 3).
Fig. 3. Distribution of the number of publications based on the results of the query “Digital Ecosystems” by WoS categories. Note - obtained by the authors according to the analysis
As for the distribution of articles in the Industry 5.0 direction, the leading positions are occupied by the following categories: Engineering Electrical Electronic (19), Computer Science Information Systems (15), Engineering Multidisciplinary (12), Telecommunications (12), Computer Science Interdisciplinary Applications (10) and Computer Science Theory Methods (10), which, of course, is related to the multidimensionality of the considered economic phenomenon, its multidisciplinarity and the scale of the effect determined by the entry of mankind into a new technological era - the Fifth Industrial
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Revolution, where the role of man and his technological connections with automated and robotic machines (Fig. 4). This, in turn, determines the small number of published works (for 2022), but, at the same time, the increasing relevance.
Fig. 4. Distribution of the number of publications based on the results of the query “Industry 5.0” by WoS categories. Note - obtained by the authors according to the analysis
Talking about the number of publications on the platform for the period 2016–2021, in the direction of “Digital Ecosystems” we will observe their reduction, which is partly due to a change in the scientific course towards the study of a new phenomenon cybersocial ecosystems, inextricably linked with the Fifth Industrial Revolution, while in “Industry 5.0” is their gradual increase, as evidenced by the 23 papers (relative to the total number of publications) already presented in the database at the time of the study (March 2022) (Fig. 5).
Fig. 5. Number of publications in WoS for the period 2006–2022 according to “Digital Ecosystems” and “Industry 5.0” queries. Note - obtained by the authors according to the analysis
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Table 1 presents the authors whose works, according to the analysis, are more related to Industry 5.0 and digital ecosystems. The table also shows their most cited articles. At the same time, it is important to note that the main period of publications within the first direction of “Industry 5.0” falls on 2020–2022, then within the second “Digital Ecosystems” - for 2008–2012. Table 1. TOP-10 authors in the context of search queries “Industry 5.0” and “Digital Ecosystems” The authors
Number of records Most cited publications*
Search query “Industry 5.0” Carayannis E.G
5
Carayannis, E.G., Draper, J., & Bhaneja, B. (2021). Towards Fusion Energy in the Industry 5.0 and Society 5.0 Context: Call for a Global Commission for Urgent Action on Fusion Energy. Journal of the Knowledge Economy, 12 (4), 1891–1904. https://doi.org/10.1007/s13 132-020-00695-5
Haleem A
3
Javaid M
3
Javaid, M., Haleem, A., Singh, R.P., Haq, M.I.U., Raina, A., & Suman, R. (2020). Industry 5.0: Potential Applications in COVID-19. Journal of Industrial Integration and Management-Innovation and Entrepreneurship, 5 (4), 507–530. https://doi. org/10.1142/s2424862220500220
Aguayo-Gonzalez F
2
de Miranda, S.S.F., Aguayo-Gonzalez, F., Avila-Gutierrez, M.J., & Cordoba-Roldan, A. (2021). Neuro-Competence Approach for Sustainable Engineering. Sustainability, 13 (8), Article 4389. https://doi.org/10.3390/su1308 4389
Avila-Gutierrez M.J
2
Avila-Gutierrez, M.J., Aguayo-Gonzalez, F., & Lama-Ruiz, J.R. (2021). Framework for the Development of Affective and Smart Manufacturing Systems Using Sensorised Surrogate Models. Sensors, 21 (7), Article 2274. https://doi.org/10.3390/s21072274
Fernandez-Carames T.M 2
Fraga-Lamas, P., Lopes, S.I., & Fernandez-Carames, T.M. (2021). Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case. Sensors, 21 (17), Article 5745. https:// doi.org/10.3390/s21175745
Fraga-Lamas P
2
(continued)
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The authors
Number of records Most cited publications*
Kopacek P
2
Doyle-Kent, M., & Kopacek, P. (2021). Adoption of Collaborative Robotics in Industry 5.0. An Irish industry case study. Ifac Papersonline, 54 (13), 413–418. https://doi.org/ 10.1016/j.ifacol.2021.10.483
Kotecha K
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Rahate, A., Mandaokar, S., Chandel, P., Walambe, R., Ramanna, S., & Kotecha, K. Employing multimodal co-learning to evaluate the robustness of sensor fusion for industry 5.0 tasks. Soft Computing. https://doi.org/10.1007/ s00500-022-06802-9
Kumar S
2
Choi, T.M., Kumar, S., Yue, X.H., & Chan, H.L. (2022). Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond. Production and Operations Management, 31 (1), 9–31. https://doi.org/10. 1111/poms.13622
Search query “Digital Ecosystems” Chang E
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Dillon T
11
Hussain F.K
21
Dong H
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Chang, E., Dillon, T., Calder, D. Ieee. (2008). Human System Interaction with Confident Computing. The Mega Trend. 2008 Conference on Human System Interactions, Vols 1 and 2, 1–11. https://doi.org/10.1109/hsi.2008.458 1399 Dong, H., & Hussain, F.K. (2011a). Focused Crawling for Automatic Service Discovery, Annotation, and Classification in Industrial Digital Ecosystems. Ieee Transactions on Industrial Electronics, 58 (6), 2106–2116. https://doi.org/10.1109/tie.2010.2050754 Dong, H., & Hussain, F.K. (2011b). Semantic service matchmaking for Digital Health Ecosystems. Knowledge-Based Systems, 24 (6), 761–774. https://doi.org/10.1016/j.knosys. 2011.02.005 Dong, H., Hussain, F.K., & Chang, E. (2011). A Service Search Engine for the Industrial Digital Ecosystems. Ieee Transactions on Industrial Electronics, 58 (6), 2183–2196. https://doi.org/10.1109/tie.2009.2031186 (continued)
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Table 1. (continued) The authors
Number of records Most cited publications*
Dreher H
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Zhu, D., & Dreher, H. (2009). Discovering Semantic Aspects of Socially Constructed Knowledge Hierarchy to Boost the Relevance of Web Searching. Journal of Universal Computer Science, 15(8), 1685–1710
Damiani E
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Gianini, G., Damiani, E., Mayer, T.R., Coquil, D., Kosch, H., Brunie, L. Ieee. (2013, Jul 24–26). Many-player Inspection Games in Networked Environments. 7th IEEE International Conference on Digital Ecosystems and Technologies (DEST), Menlo Park, CA
Ishikawa H
10
Ishikawa, H., Kato, D., Masaki, E., Hirota, M. Acm. (2018). Generalized Difference Method for Generating Integrated Hypotheses in Social Big Data. Proceedings of the 10th International Conference on Management of Digital Ecosystems (Medes’18), 13–22. https://doi.org/ 10.1145/3281375.3281404
Ghinea G
9
Martins, L.C.B., Victorino, M.C., Holanda, M., Ghinea, G., Gronli, T.M. Acm. (2018). UnBGOLD: UnB Government Open Linked Data. Proceedings of the 10th International Conference on Management of Digital Ecosystems (Medes’18), 1–6. https://doi.org/ 10.1145/3281375.3281394
Nimmagadda S.L
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Namugenyi, C., Nimmagadda, S. L., & Reiners, T. (2019). Design of a SWOT Analysis Model and its Evaluation in Diverse Digital Business Ecosystem Contexts. Knowledge-Based and Intelligent Information & Engineering Systems (Kes 2019), 159, 1145–1154. https://doi.org/10. 1016/j.procs.2019.09.283
Blanke T
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Anderson, S., & Blanke, T. (2012). Taking the Long View: From e-Science Humanities to Humanities Digital Ecosystems. Historical Social Research-Historische Sozialforschung, 37 (3), 147–164
Note - compiled by the authors based on the results of the analysis in WoS * There are no citations for some articles, as they were published in late 2021 and early 2022
As for affiliation, a large part of works on digital ecosystems are associated with CURTIN University (88 publications; Australia), in second place is UNIVERSITY OF
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LONDON (27 works; Great Britain), and UNIVERSITY OF TURIN closes the top three (25 articles; Italy). As part of the direction related to the research of the nature and specific features of “Industry 5.0”, the most active universities whose scientists conduct research in the analyzed direction are: GEORGE WASHINGTON UNIVERSITY (USA), AMRITA VISHWA VIDYAPEETHAM (India), JAMIA MILLIA ISLAMIA (India), TECHNICAL UNIVERSITY KOSICE (Slovakia) and UNIVERSITY OF NAPLES FEDERICO II (Italy) (Table 2). Table 2. TOP-5 affiliations in the context of search queries “Industry 5.0” and “Digital Ecosystems” Affiliation
Number of records
Search query “Industry 5.0” GEORGE WASHINGTON UNIVERSITY
4
AMRITA VISHWA VIDYAPEETHAM
3
JAMIA MILLIA ISLAMIA
3
TECHNICAL UNIVERSITY KOSICE
3
UNIVERSITY OF NAPLES FEDERICO II
3
Search query “Digital Ecosystems” CURTIN UNIVERSITY
88
UNIVERSITY OF LONDON
27
UNIVERSITY OF TURIN
25
BUCHAREST UNIVERSITY OF ECONOMIC STUDIES
19
UNIVERSITY OF MILAN
19
Note - compiled by the authors based on the results of the analysis in WoS
Among the research funding organizations, there are mainly: European Commission (listed in 42 articles as a source of funding), National Natural Science Foundation Of China NSFC, Ministry Of Education Universities And Research Miur, Ministry Of Education Culture Sports Science And Technology Japan Mext and Japan Society For The Promotion Of Science (Table 3).
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Table 3. TOP-5 funding organizations in the context of search queries “Industry 5.0” and “Digital Ecosystems” Funding organization
Number of records
Search query “Industry 5.0” European Commission
7
National Natural Science Foundation Of China NSFC
3
Ministry Of Education Universities And Research Miur
2
National Science Foundation NSF
2
Slovenian Research Agency Slovenia
2
Search query “Digital Ecosystems” European Commission
35
Ministry Of Education Culture Sports Science And Technology Japan Mext
22
Japan Society For The Promotion Of Science
20
Grants In Aid For Scientific Research Kakenhi
15
Australian Research Council
11
Note - compiled by the authors based on the results of the analysis in WoS
The leading countries in terms of the number of publications in the Industry 5.0 direction are: the USA (21 papers), Italy (17) and India (16) (Fig. 6).
Fig. 6. Distribution of the number of publications for the search query “Industry 5.0” by the regions of the world. Note - compiled by the authors based on the results of the analysis in WoS
Speaking about the distribution of the number of publications for the search query “Digital Ecosystems” by regions of the world, here the location of the countries within the TOP-5 is as follows: Australia, Italy, England, France and Germany (Fig. 7). In general, it is possible to distinguish differences in the country distribution of papers published in both areas under the research, while in a number of articles, according to the results of the analysis carried out by the authors, there is frequent cooperation in
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Fig. 7. Distribution of the number of publications for the search query “Digital Ecosystems” by the regions of the world. Note - compiled by the authors based on the results of the analysis in WoS
the form of co-authorship, which also makes it possible to prepare publications that are relevant and have a pronounced scientific novelty. 3.3 Scientometric Analysis of the Data Array Obtained from the WoS Database on the Search Masks “Digital Ecosystems” and “Industry 5.0” in the VOSviewer Program The data array loaded from the WoS database for the search query “digital ecosystems” allowed us to obtain a visualization that clearly reflects the cluster distribution of the main scientific areas (Fig. 8). In particular, the obvious result obtained in the course of the scientometric analysis was a clear identification of the digital ecosystems themselves, then sustainable development, technologies, digital economy, technologies and innovations, which are their integral components.
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Fig. 8. Results of visualization as part of the scientometric analysis in the direction of “digital ecosystems” Note - developed by the authors
In total, 30 clusters were obtained in total; characteristics of the 10 largest of them are presented in Table 4. Table 4. Characteristics of the TOP-10 largest clusters obtained as part of the analysis in the direction of “digital ecosystems” Cluster number
Keywords
Cluster 1 (14 keywords) cloud computing; community cloud computing; community clouds; cooperation; cyber engineering; dynamics; final trust; initial trust; mechanism; model; risk; sustainability; time; trust maintenance Cluster 2 (14 keywords) climate change, collaboration, communication technologies, cross, e-governance, ecosystems, ict, information, information and communication, networks, smart communities, society, value co-creation Cluster 3 (13 keywords)
bibliometric analysis, business ecosystems, cognitive processes, digital ecosystems, innovation ecosystems, knowledge management, knowledge resources, mobile broadcast query process, performance, system, value creation, wireless, wireless data dissemination
Cluster 4 (13 keywords)
accountability, agents, architecture, decentralized, distributed, ecosystem, evolution, federated identity, identity, intelligence, trust, web services, world (continued)
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Cluster number
Keywords
Cluster 5 (12 keywords)
boundary resources, competitive advantage, design, digital ecology, ecological thinking, future is research, Gregory Bateson, infrastructures, mechanisms, platform, responsible digitalization, technology
Cluster 6 (11 keywords)
analysis, anti-virus service, co-authorship, collaborative writing, digital ecosystem, grammar, grammars, language, on-demand, virus removal, wiki
Cluster 7 (11 keywords)
agent interaction, bio-inspired computation, budworm dynamics, causal tree, chaos, complexity, food webs, shifts, subsidies, tree augmented naïve bayes net
Cluster 8 (11 keywords)
assessment of the level of development, collaborative working, digital economy, digitalization conditions, ecosystem of the digital economy, index of activity of the subject, regions digitalization matrix, self-organization, semantic web, social-ecosystems, swarm intelligence
Cluster 9 (10 keywords)
business intelligence, complex environment, domain independent ontology, entropy, model driven preservation, modelling, modelling strategy, ontology, policy, sheer curation
Cluster 10 (9 keywords)
agent, attack tolerance, de architectural analysis, ecology, error, jxta-overlay, metabolic networks, pathways, scale-free architecture
Figure 9 shows the visualization for Industry 5.0.
Fig. 9. The results of visualization as part of the scientometric analysis in the direction of “Industry 5.0”. Note - developed by the authors
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Within the framework of this scientific direction, 23 clusters were obtained, the first five of which are the largest, since they combine 22, 20, 19, 19 and 18 words, respectively (Table 5). Table 5. Characteristics of the TOP-5 largest clusters obtained as part of the analysis in the “Industry 5.0” direction Cluster number
Keywords
Cluster 1 (22 keywords) bibliometrics analysis, bibliometrics, companies’ culture, companies’ performance, competences, corporate culture, creation, emerging economies, evolution, future trajectory, human resource management, impact, innovations, knowledge management, management fashion, organizational culture, performance, policy, scopus, sustainable development, systematic review of the literature, technological competitiveness Cluster 2 (20 keywords) AI, algorithms, awareness, CBM, communication, corrective maintenance, devices, digital circular economy, digital transition, edge AI, edge computing, FOG, green IOT, IIOT, preventive maintenance, reliability, reliability centered maintenance, self-corrective maintenance, survey, things Cluster 3 (19 keywords) actor-network theory, concept of operations, ethics, human factors, human-machine cooperation, human-machine teams, industrial revolution, industry 4, industry 5, joint cognitive systems, operator 4, smart factory, smart operator, technology engineering, technology-driven, value sensitive design, value-driven, value-sensitive design Cluster 4 (19 keywords) data-driven innovations, economy, employee assessment, firm performance, human capital, human-resource management, individual trajectories of professional development, information society, innovation, knowledge society, labor performance, machines, motivation, organizational innovation, revolution, satisfaction, smart society, society 5.0, technology Cluster 5 (18 keywords) advance technologies, automation, care, COVID-19, customer journey, digital innovation, hospitality 5.0, hygiene and cleanliness, industrial IOT (IIOT), IOT, mobile technology, robots, service, system, systems science, telemedicine, things IOT, virtual / augmented reality
It should also be noted that within the framework of the analysis, it is necessary to highlight similar terms/keywords that occur both within the “digital ecosystems” and “Industry 5.0” directions, which once again confirms their connectivity, among
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them: sustainable development; knowledge management; bibliometric analysis; competitive advantage; technological competitiveness; smart (smart communities, smart factories, smart society, smart operator); technology (v tom qicle – technology engineering, technology-driven, mobile technology); innovation/innovations, digital innovations; IIOT, IOT.
4 Discussion The aspects reflected in this article allow us to get a general idea of the current trends in research and publications in the context of the scientific areas “Digital Ecosystems” and “Industry 5.0”. Nevertheless, it is necessary to highlight a number of limitations within the framework of the research conducted by the authors: firstly, when studying trends using the Google Trends service, the query was formulated only in English, while it is possible that some users could enter such phrases into the search engine, as “Digital Ecosystems” and “Industry 5.0” in Russian; secondly, the Web of Science (Clarivate Analytics) database was used as a source for obtaining a data array for scientometric analysis, while works, for example, on the Scopus database, were not taken into account. In general, the existing limitations are not critical and do not significantly affect the results obtained during the research. Obviously, the choice of WoS was due to the depth of the archive and the high reliability of the data; request in Google Trends in English is due to the fact that most publications on this topic are also presented in English. Nevertheless, in future research it is necessary to take into account the materials of the Scopus database, including for conducting a comparative analysis. At the same time, it is important to understand that there is a tendency for a rapid increase in works, therefore, even after a month, the results of the analysis may be different. Further research by the authors will be aimed at studying the specifics of using cybersocial ecosystems in the digital economies of countries and regions of the world, including in industry, as well as within the framework of the active formation and development of cluster systems.
5 Conclusion The study conducted by the authors made it possible to obtain results that are scientifically novel and of practical importance for the further development of areas of work related to digital ecosystems and Industry 5.0. The study of trends in search queries “Digital Ecosystems” and “Industry 5.0” in Google Trends gave reason to believe that the studied economic phenomena are popular requested categories, especially over the past 5 years; more and more users are looking for trends related to the development and implementation of digital ecosystems in the activities of enterprises in economic sectors, as well as in the implementation of smart projects. Similar dynamics can be traced when studying the search query “Industry 5.0”. In many respects, it is connected with the elucidation of the features of the functioning of cyber-physical systems, the use of alternative networks in the structures of augmented reality, the Internet of things and the Industrial Internet of things, the research of the specifics of building decentralized targeted industries, smart factories, and others.
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An analysis carried out on search queries in the WoS database showed that the main jobs are in the fields of knowledge related to digital engineering, technology, computer science, and management. It should also be noted that over the past 7 years, there has been, in general, a steady upward trend in publications in the areas of “Digital Ecosystems” and “Industry 5.0”. Scientometric analysis, implemented using the VOSviewer software product, made it possible to identify key research trends on the analyzed issues, and also, visualizing the results in the form of cluster structures, made it possible to highlight their promising areas that will be relevant in the coming years, among them: the study of cloud services; ecosystems, including those focused on the active interaction of machines and humans; knowledge management; decentralized systems; smart technologies (including smart factories and productions); information and communication technologies; architecture of digital solutions; Industry 5.0; mobile technologies and robotics, etc. Acknowledgments. The study was carried out at the expense of a grant from the Russian Science Foundation No. 23-28-01316, https://rscf.en/project/23-28-01316/.
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Assessment of the Digital Production Management Potential Based on Costs Statistical Analysis in Machine Industry Elena Shkarupeta1 , Vladimir S. Tikhonov2 , Anton N. Sunteev3 Yulia V. Veis2(B) , and Aleksander V. Babkin1
,
1 Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic
University (SPbPU), 195251 St. Petersburg, Russia 2 Samara State Technical University, Molodogvardejskaya str., 244, 443100 Samara, Russia
[email protected] 3 TPC Prodmash LLC, Novo-Sadovaya str., 321, 443125 Samara, Russia
Abstract. The trend of recent years is the active introduction and use of digital technologies, including in the industrial field. As a result of the digitalization of production management processes at enterprises of the machine-building complex, it is possible to solve key tasks of ensuring competitive advantages. Generating the potential of digital production management in machine industry based on changes in the cost structure will allow for the development of digital technologies. The process of identifying internal reserves should include a phased sequence of actions from cost analysis to the digitization of the enterprise. Cost analysis is formed on the basis of continuous monitoring of the structure and economic content of the cost of domestic engineering. As a result, a mechanism for its reduction and redistribution of costs can be developed. Redistributable costs will be directed to the implementation of projects in the field of digital technology. The article analyzes the dynamics of all articles of the production cost of production of the machine industry for 20 years and describes the factors influencing their change. The key costs items causing the increase of production costs are identified. Statistical analysis of the production cost of production is directed at finding internal reserves to reduce the production cost of production and create further prerequisites for the implementation of the identified reserves in digital technology in machine-building enterprises. Keywords: digital control · potential of digital production management · digitalization of industry · production systems management
1 Introduction At the St. Petersburg International Economic Forum, V.V. Putin touched the subject of the digital economy. He spoke of its importance for the development of the country’s economy and the growth of GDP.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 565–579, 2023. https://doi.org/10.1007/978-3-031-32719-3_43
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To increase the technological advantages in the field of the digital economy (financial, personnel, intellectual) the following areas are highlighted: • generating a new flexible regulatory framework for the introduction of digital technologies in all areas of activity; • providing state support to companies that develop and help implement digital technologies in the activities of interested enterprises; • increase in the number of specialists in the field of digital technologies; The President of Russia emphasized that in terms of digitalization in Russia, the most important for the country sectors of the industry are extractive, manufacturing, machine-building, instrument-making, and transport and unfortunately all of them are leaving of according to digital parameters in companies with EU countries [1]. Currently, in Russia, machine-building enterprises are experiencing tough competition from foreign enterprises. The growth of competition forces each market participant to fight for their place. The competition is won by the one who offers the best conditions and characteristics of the product to its consumers. The optimal combination of these factors determines the outcome of a competition. The best price conditions depend on the effective management of costs in the direction of their reduction, and the best characteristics of the product are associated with the effective management of processes in the direction of their improvement. The trend of recent years is the active introduction and implementation of digital technologies, including in the industrial field. As a result of the digitalization of production management processes at enterprises of the machine-building complex, it is possible to solve key tasks of ensuring competitive advantages [2]: • maintaining the lowest possible level of production costs by analyzing and structuring costs with the subsequent identification and use of reserves to reduce them; • maintaining leadership in product performance through monitoring and timely adjustment of production and management processes using digital technologies; Solving these problems allows us to form and use the potential of digital production management based on the following factors [3]: • the presence of internal reserves of redistribution of costs in favor of the implementation of digital technologies; • implementation of the digital environment in all production and management processes; • digital monitoring of processes and correction of actions in case of deviations; In this case, the potential of digital control should be understood as the total amount of reserves to reduce costs in quantitative and qualitative terms, which will be directed at the introduction and use of digital technologies in the enterprise processes. The main indicator structurally characterizing the costs of the enterprise is the production cost of production. Financial results of activity, rates of production and reproduction, the financial condition of an enterprise depend on its level. Analysis of the production cost of production is of great importance because it allows identifying trends in this indicator, to determine the influence of factors on its change, to identify reserves of redistribution of costs in favor of the intensification of technologies and to develop
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corrective measures to use opportunities to reduce production cost. Production cost of the most characterizes the rationality and cost-effectiveness. The following indicators appear as objects of analysis of production costs [4]: • • • • •
total production cost of production as a whole and by costs element; costs level per ruble of output; production cost of certain types of products; individual costs items; costs for responsibility centers;
The calculation of this indicator is necessary for many reasons, including to determine the profitability of individual types of products and production as a whole, to determine wholesale prices for products, to implement internal production cost accounting, and to calculate the national income nationwide.
2 Purpose of the Study In the process of research related to the formation and optimization of the costs of machine-building enterprises, as well as the possibility of transition to digital technologies in production, the following key issues are highlighted: • application of management accounting information to assess the structure and dynamics of costs changes (planned and actual production cost of production); • the use of statistical reporting, which presents aggregated data on the costs of production and sale of products, for the subsequent identification of irrational costs; • identifying internal reserves to reduce the costs of the enterprise and the allocation of key expenditures that can be redirected to other areas of development, in particular, digital technology; • assessment of the possibility of creating, using and developing the potential of digital production management based on the implementation of internal reserves to reduce the production cost of production of machine-building enterprises. As a result of the assessment of the current situation and prospects of development of the industrial sphere of Russia, as well as the possibility of transition to the path of active digital development, the main objectives of the study are formulated: • statistical analysis of costs in machine-building based on their structuring and assessment of the dynamics for a certain period; • identification of the most significant costs elements occupying the largest share in the production cost or constantly increasing; • determining the possibility of redistributing costs in favor of using and developing the potential of digital control based on internal reserves to reduce the production cost of industrial products.
3 Research Method The following General scientific methods were used in the research: system analysis, comparison method, comparisons, observation, expert evaluation, classification, segmentation, and the objective nature of studying the problem.
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As the main sources of information for the economic analysis of production costs were used: • management accounting data (planned and actual costing of products); • statistical reporting, which presents data on the costs of production and sale of products.
4 Results Russia is already actively adapting to the conditions of digitalization: it ranks first in Europe and sixth in the world in terms of the Internet users number. Over the past three years, about 60% of the population have started using smartphones, which is higher than in Brazil, India, and Eastern European countries. The number of users of state and municipal services portals doubled in 2016 alone and reached 40 million people [5]. Digital transformation is the key factor in global economic growth. In particular, according to the McKinsey Global Institute estimates, up to a 22% increase in China’s GDP by 2025 can be achieved through the development of Internet technologies. In the US, the expected increase in value generated by digital technology by 2025 may be about $2 trillion. The potential effect of the digitalization of the Russian economy will be about from 4 to 9 trillion rubles of an increase in the country’s GDP by 2025. Such optimistic forecasts are associated not only with the effect of automation of current processes but also with the implementation of breakthrough business models and technologies (digital platforms, digital ecosystems, BigData technologies, artificial intelligence, Industry 4.0 technologies). First of all, this concerns information and communication technologies (ICT) [5]. Nowadays, the share of the digital economy in the GDP structure of different countries is as follows (Table 1) [5]. Table 1. The structure of the digital economy in the country’s GDP (% of GDP) Name
USA
China
EU
India
Brazil
Russia
GDP share
10,9
10
8,2
6,3
6,2
3,9
Digital household expenditures
5,3
4,8
3,7
3,2
2,7
2,6
Companies’ investment in digitalization
5
1,8
3,9
2,7
3,6
2,2
Government expenditures on digitalization
1,3
0,4
1
0,6
0,8
0,5
ICT export
1,4
5,8
2,5
5,9
0,1
−1,8
ICT import
−2,1
−2,7
−2,9
−6,1
−1
3,9
Countries which are demonstrating success in the digital economy have their own history and there is no clear leader in the development of all of its areas. Germany, about 10% of whose population is employed in high-tech industries, is the founder of industrial technology and the concept of Industry 4.0. Digital giants have been created in South Korea and Japan as accelerators of innovations based on the multinational corporations
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Samsung, LG, Toyota, Sony, and so on. The United States is more committed to produce innovations and introduce them in most areas of activity. China is successful both in identifying and implementing promising digital solutions and in developing its own export-oriented projects [5]. The digital economy in different countries is developing in different ways, but it is possible to highlight common features: • availability of favorable conditions for the development of innovation; • significant investment in digital technology. Russia is still far behind the leading countries in the development of digitalization in the business sector, including industrial production. Private companies’ investments in digitalization are slightly higher than 2% of GDP, which is two times lower than in the USA. The most lagging behind in terms of digitalization in the Russian Federation are the key industries: mining, manufacturing, and transport (Fig. 1) [5].
Transportation Mining Manufacturing Chemical industry Financial services Education IT, Communication 0 EU
1
2
3
4
5
6
7
8
Russia
Fig. 1. Level of digitization by fields of activity in the Russian Federation and the EU (expert assessment)
The lag of Russia in most areas is primarily due to insufficient investment in the development of digital technologies in industrial fields. This situation in the field of mining and processing is typical both for the EU countries and for the world as a whole. Industry 4.0 technologies should change this situation and ensure, in particular, for Russia, a significant breakthrough and ensure an approach to the leaders [5]. In order to conduct an economic costs analysis and production costs, a system of indicators reflecting their level and dynamics is defined (Fig. 2). Analysis production costs of products usually begins with a study of the total costs of the whole and the main elements. The study of the structure of costs for individual elements, as well as the analysis of items of expenditure actually produced products is one of the stages of in-depth production cost analysis. The purpose of this analysis is to identify ways and sources of costs reduction and, consequently, increase profits [6]. The first step in the analysis of the costs structure is to determine the percentage of their individual elements in the total amount, as well as changes over a certain period. Next, a comparison is made of specific indicators of actual costs for the economic
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elements of the reporting period with similar indicators of the previous period, as well as planning data. This comparison allows us to study the structure of production costs [7].
Fig. 2. The system of generalizing indicators used in the analysis of production costs
When analyzing the production cost, special attention should be paid to the items of expenditures that occupy the largest share in the production cost of production [8].
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Analysis of deviations in the context of individual costs items, analysis of the costs data structure, allows you to identify those cost items that led to a change in the production cost index. Analysis of the costs structure helps in assessing the use of production resources and allows you to identify those resources, the production of which causes an increase in production costs. Analysis of the dynamics of the costs structure allows you to respond in a timely manner to deviations from the norms and standards in the conduct of the production process, to identify trends in costs, to develop measures for their effective reduction. The maximum possibility of using the potential of digital management is provided by additional investments that can be formed from the internal reserves of the enterprise by redistributing costs according to the results of structural statistical analysis [9]. The machine industry enterprise has a high potential for digital technologies through the identification and using of internal reserves to reduce production costs. The identified reserves will allow first of all to determine the key directions of digital technologies implementation, and redistribution of costs (for example, from the sphere of material costs for the creation of intellectual production) to form additional sources of financing of digitalization processes. Statistical analysis in this case is a tool for identifying internal reserves through structural cost estimation and finding ways to redistribute costs [10]. Table 2 presents the dynamics of the costs structure of the economic elements of the machine industry complex for the period from 1995 to 2015. This period was taken in connection with the fact that it includes difficult moments in the development of the domestic economy, such as the economic crisis of 1998, the financial crisis of 2008. And economic sanctions 2014. The key changes were caused by the currency and import dependence of the domestic economy, which affected the production, price and, accordingly, the production cost of production. The largest share in the production cost of production is direct material costs, the share of which has increased over the period. In 1998, 2010 and 2015, there was a reduction in this cost item, which was probably due to the optimization of the use of materials or their replacement with cheaper options. The maximum increase is typical for labor expenditures (more than 4%), the largest reduction – for depreciation (about 3%). The reduction of depreciation funds may be due to the sale of unused property, as well as the use of equipment that is completely worn out and is no longer actually appropriate for use in the production environment. This is especially true in the conditions of significant development of digital technologies, which require the most advanced types of machines and equipment. The cumulative effect on the deviation of material costs is manifested for most enterprises in changing three main factors: standards of use, purchase price, replacement of a material resource.
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Table 2. Dynamics of the cost structure by economic elements in the production cost of production “Manufacture of machinery and equipment” [11, 12] Name
Cost structure of enterprises by year, % 1995 1998 2000 2005 2008 2010 2013 2014 2015
Production and sales costs, total 100
100
100
100
100
100
100
100
100
Material costs
59,2
57,0
65,3
60,7
61,7
57,7
59,4
65,2
63,3
Labor expenditures
15,7
18,5
15,3
21,4
20,4
21,5
19,8
18,8
19,1
Social contributions
6,9
6,0
6,2
5,1
4,5
4,7
5,4
5,2
5,4
Depreciation
5,5
5,7
2,5
2,1
1,9
2,7
2,7
2,8
3,3
Other costs
13,6
11,9
10,7
10,7
11,4
13,4
12,7
8,0
8,9
In Table 3 detailed material costs of industrial enterprises. The largest reduction is typical for fuel and energy expenditures (2 times). The reduction was caused by the consequences of the global financial crisis of 2008, when many enterprises were forced to cut expenditures, “freeze” construction, etc. The largest share in material costs is raw materials and materials (80%), which reflects the high material intensity of the machine industry. A slight decrease in raw material expenditures was observed in 2008, 2013 and 2014, in 2008 compared to 2005, a decrease of more than 4%. Fuel and energy costs are about 5–7%. Table 3. Dynamics of the material costs structure for the production and sale of products [11] Name
Material cost structure by year, % 2005
2008
2010
2013
2014
2015
Material costs, total
100
100
100
100
100
100
Raw materials
86,6
82,3
89
86,8
84,6
86,9
Fuel
3,3
2,3
2,4
2
1,8
1,7
Energy
5,9
3,7
4,7
3,7
3,2
2,7
Other costs
4,2
11,7
3,9
7,5
10,4
8,7
The second largest share in the production cost structure of machine industry products are labor expenditures, which account for about 20% of total costs. Dynamics of change of indicators on labor resources for 1995–2015 presented in Table 4.
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Table 4. Indicators on labor force of enterprises of machine industry [11, 12] Name
Values of indicators by year 1995 2005 2008
2010
2011
2012
2013
2015
Average number of employees (thousand people)
4876 905
667
658
636
603
602
Average monthly wage of employees (rubles)
392
835
8380 16940 20103 22778 25671 28231 32248
Duration of working time (hours 1365 1713 1545 per year per 1 workman.)
1697
1704
1708
1692
1675
Throughout the period, the decreasing dynamics of the average annual number of employees of machine industry enterprises and the positive dynamics of the average monthly wage are observed. The number of workers employed in the machine industry complex decreased by 8 times. Such changes are caused by the reduction in the number of production enterprises and the influence of interest in other areas of activity with higher added value (mining, energy, etc.). A separate category of other costs has not undergone any special changes. On average, this costs item is about 10% of their total. The structure of the main elements of other costs is presented in Table 5. In 2015 compared to 2008, other costs increased 3.4 times. This increase is due to R&D expenditures, the payment of compulsory insurance payments, the costs of labor and the environment, and the rent. In turn, this has led to an increase in the costs of the item “Taxes and fees”, which are included in the production cost of production (works, services). In the context of the active introduction of advanced technologies and the transition to digital development, the most significant category of costs is the costs of research and development (R&D). One of the most important costs items without which the machine industry will not be able to develop and compete with other States is R&D costs (Table 6). Table 5. Indicators on labor force of enterprises of machine industry [11, 12] Year
2005
Other costs, total
Including: Depreciation of intangible assets
Rent
Compulsory insurance payments
Voluntary insurance payments
Representation expenditures
Taxes and fees included in the production cost of production
Expenditures for payment of works and services of a non-productive nature
40,9
0,12
7,99
2,05
1,22
0,17
2,47
14,9
(continued)
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Year
Other costs, total
Including: Depreciation of intangible assets
Rent
Compulsory insurance payments
Voluntary insurance payments
Representation expenditures
Taxes and fees included in the production cost of production
Expenditures for payment of works and services of a non-productive nature
2008
94,6
0,24
17,57
3,76
1,43
0,27
3,87
27,6
2010
102,5
0,42
16,22
2,83
1,26
0,23
4,57
43,6
2011
98.0
0,51
17,24
4,19
1,39
0,28
4,43
30,7
2013
104,5
0,73
19,27
3,54
1,64
0,27
3,94
39,1
2015
139,3
0,74
20,45
4,06
1,82
0,29
4,26
48,8
Table 6. Indicators on labor force of enterprises of machine industry [11, 12]. Research and development costs, billion rubles [11–13] Name
Cost Per year 2005
2008
2010
2011
2013
2015
All costs
12,63
23,92
32,84
41,25
59,35
74,69
Internal operating costs
11,99
23,28
31,95
40,01
56,67
70,88
11,2
The cost of labor
4,4
8,91
13,24
17,32
21,36
Insurance premium
1,14
2,17
2,72
3,91
4,79
5,96
Equipment costs
0,64
0,94
1,24
1,07
1,85
2,25
Other material costs
2,91
6,26
9,88
14,33
18,29
20,19
Other operating costs
2,89
5,01
6,91
7,46
14,42
20,12
Capital expenditure
0,64
0,64
0,89
1,24
2,68
3,81
Land and buildings
0,05
0,03
0,02
0,035
0,13
0,37
Equipment
0,45
0,53
0,77
1,08
1,69
2,49
Other capital expenditures
0,14
0,08
0,1
0,13
0,86
0,95
Research and development costs have been rising steadily during the period under review. The largest share of expenditures is wages. Capital costs are mostly directed to the purchase of equipment (more than 60% of all capital costs). In General, the increase in research and development costs has led to an increase in the volume of scientific and technical work carried out by enterprises. The expenditures of implementing digital technologies in machine industry are considered in comparison with other countries (Table 7) [5]. In the costs structure of Russia for the introduction and development of digital technologies, the smallest share is made up of data processing systems (automation
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Table 7. The cost of implementing digital technologies [11, 12] Name of cost
Cost per country, million dollars USA
China
Germany India
Brazil Russia
Internal service
37 769 5 698
5 426
2 010 2 474
1 045
Software
31 777 2 432
4 228
1 084 2 079
652
IT-services
55 666 3 866
10 273
1 958 3 934
696
Telecommunication service
22 366 11 811 2 856
2 085 2 685
1 460
Devices
8 494
5 757
1 858
1 175 643
822
Data processing systems (data centers) 9 875
2 904
1 755
6 133 952
460
of control systems, remote monitoring systems). The largest share of the costs goes to the development of telecommunications services (communications, Internet). Currently, Russia is not among the leaders in the development of the digital economy in terms of such indicators as the level of digitalization and the share of the digital economy in GDP, which is 3.9% (2–3 times lower than that of the leading countries). At the same time, several positive trends are already noticeable. One of the most important indicators – the volume of the digital economy – has been growing rapidly in recent years. For example, the country’s GDP grew by 7% from 2011 to 2015, and the volume of the digital economy increased by 59% – by 1.2 trillion over the same period. Due to digitalization provided 24% of the total GDP growth. In Russia, almost from scratch managed to create large digital companies, some of which have achieved international fame (online Bank “Tinkoff Bank”, services “Yandex” and Mail.ru, social network “Vkontakte”, “Kaspersky Lab” and many others). The leader in the costs of digitization is the United States (Fig. 3) [5]. The results of the study allowed us to trace the dynamics of changes in all production cost items of machine industry products, and the main factors that influenced its change. The main sources of internal reserves are – material, labor and other costs. The identification of these reserves will form an internal investment reserve Fund in the form of identified savings, which is planned to be directed to the introduction of digital technologies in the activities of machine-building enterprises [13–15]. A brief set of measures to digitalize the machine industry complex of Russia is formulated in Table 8 [5]. The formation of reserves through the redistribution of costs based on statistical analysis of the production cost is possible in the following directions (Table 9) [5]. The final decision on the choice of the direction of redistribution and the share of replacement costs for each category is made on the basis of additional study of the production and organizational structures of the enterprise. In the case of a significant reduction in labor processes for the majority of domestic enterprises can be difficult flexible and painless transition to a new digital technologies platform. This is primarily due to the socially-oriented business enterprises, which are key in the region of their activities. Consequently, the massive reduction of employees can lead to serious social problems [16, 17].
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Fig. 3. The ranking of countries in terms of the cost of digitalization [5] Table 8. Sources of digital technologies potential and profit growth through digitalization [5] Event
Implementer
Expected effect
Optimization of production and logistics operations
Real-time monitoring of production lines Optimization of logistics routes and prioritization of shipments
Speed up and simplify processes Improving accuracy, quality
Increase efficiencies labour organization
Efficient and fast job search and Reduced staff turnover filling of vacancies Increase in labour Remote work capabilities productivity New professions and jobs
Productivity increase equipments
Reduce equipment downtime and repair expenditures Increase equipment load
Increase in production
Improving efficiency R&D and product development
Rapid prototyping and quality control Analysis of large amounts of data in the development and improvement of products
The emergence of new consumers Increase in sales
Reduction of resource expenditure and production losses
Reduction of energy and fuel expenditure Reduction of production losses of raw materials
Reduction of resource and labor intensity
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Table 9. Directions of redistribution of costs of the machine industry enterprise in favor of digitalization [5] Cost category/Share of redistribution, %
The direction of redistribution
Basic tools of digital technologies production
Material costs/from 10 to 50%
Optimization of production and logistics functions, reduction of excess inventory and losses
Online monitoring systems, “Intelligent” production, “Smart” logistics
Labor costs/from 30 to 80%
Optimization of labor processes Automation of processes, and growth of labor productivity Professions of the future, Remote work
Depreciation/100%
Cost recovery for the implementation of digital technologies
Digital technologies of equipment and processes
Other costs/50 to 90%
Optimization of additional costs in the field of creation, production and sale of products
Rapid prototyping, Analysis of large data sets
Therefore, the primary categories of redistributable costs should be material and other costs.
5 Conclusion Production cost of production – is expressed in cash costs for its production and sale. The production cost of production as a synthetic indicator reflects all aspects of production and financial and economic activity of the enterprise: the degree of use of material, labor and financial resources, the quality of work of individual employees and management as a whole. Reducing the production cost of production of machine industry enterprises is of great importance to improve production efficiency. This follows from the economic essence of this category, which is expressed by the totality of labor, material and financial resources used in the production of machine industry enterprise. The final results of the production activity of the enterprise are also affected by the production cost price by changing the nature and transition from extensive methods to intensive methods. Cost reduction factors affect the production cost of production of the machine industry complex, while providing an increase in the volume of production, improving their quality, improving the structure and range of products. They also create conditions for increasing the company’s profit, increasing its profitability and strengthening its financial position. Their action is determined by many factors of production, economic and organizational order. The presented research results form the basis for development of digital management potential. The formation of the potential of digital production technologies in machine industry based on changes in the cost structure will ensure the development of digital technologies. The process of identifying internal reserves should include a step-by-step sequence of actions from costs analysis to digitalization of the enterprise.
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Redistributable costs will be directed to the implementation of projects in the field of digital technologies. As a result, the total costs of the enterprise, and hence the generated profit will remain at the same level. Acknowledgements. The study was carried out at the expense of a grant from the Russian Science Foundation No. 23-28-01316.
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Indicators and Digital Technologies for Assessing the Condition of Urban Soils Gutman Svetlana , Vargasova Maria, and Evseeva Ksenia(B) Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. Today urban soil degradation is an acute problem for many countries. The paradox is that even though urban soils are extremely valuable in terms of property and construction, they tend to be completely forgotten when it comes to the functions they perform for humankind, nature and wildlife. Rapidly developing digital technologies are a powerful tool that improves the process of environmental monitoring, in particular, the monitoring of the condition of urban soils. This paper discusses a system for monitoring the condition of urban soils given the possibilities of digital technologies, which facilitate progress in achieving the goals of sustainable urban development. This study is aimed at identifying the indicators and digital technologies that can be used for assessing the condition of urban soil. In order to achieve this goal, the authors used general scientific methods: analysis, synthesis, analogy, deduction and induction. The methodological basis for this study is the system of balanced scorecard (BS) proposed by R. Kaplan and D. Norton. The authors have compiled a strategic map for introducing the concept of a sustainable smart city based on a balanced scorecard (BS). The component “ecology” is studied and the main goals of sustainable development are analyzed according to a scheme of anthropogenic impact on urban soils. The paper discusses today’s digital technologies and proposes some urban environment indicators that can be used for assessing the condition of urban soils and components of urban environment. Not only will the suggested monitoring system provide for a qualitative and quantitative assessment of the condition of the urban environment, but it will also help to manage decisions more effectively, develop the city and improve the environmental literacy of the population as a whole. Thanks to new digital technologies, cities will be able to reach a new level of sustainable development that allows the needs of the present to be met without compromising the ability of future generations to meet their own needs. Keywords: Internet of Things · green infrastructure · sustainable urban development · tree monitoring · urbanization · urban soils
1 Introduction The concept of sustainable development appeared because society has to make global decisions in three spheres of life at a time: social, economic and environmental spheres [1]. The term “sustainable development” was coined by the World Commission on Environment and Development. The 1987 report “Our Common Future” defined sustainable © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 580–592, 2023. https://doi.org/10.1007/978-3-031-32719-3_44
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development as “development that allows the needs of the present to be met without compromising the ability of future generations to meet their own needs” [2]. A very important criterion for sustainable development of the world is achieving a balance between human activity and maintaining the reproductive capabilities of the biosphere [3, 4]. The greatest concentration of activity is in cities, whose growth and development are accompanied, as a rule, by a significant burden imposed on the natural environment and by irrational use of natural resources, which deteriorates the environmental situation. Urbanization changes the way land is used and induces soil compaction. Vegetable life is pushed away by buildings, roads, and sidewalks. Urbanization can bring about dangerous processes on a global scale due to anthropogenic causes - desertification, soil depletion and deforestation. These processes have a significant impact on all the aspects of sustainable development of the city – economic, social, environmental. Desertification and soil depletion inflict enormous damage to agriculture and some other economic activities. However, these problems cause even greater damage to the animal world. Because of the lack of water, biodiversity is significantly reduced. In many cases, desertification, partial or complete loss of natural forests are the result of human activity. The problem of soil erosion can lead to the penetration of silt into lakes, streams, and other water resources. Fresh water can become contaminated in certain territories and the health of residents can deteriorate while depletion of vast areas makes population migrate. In order to resolve global problems of sustainable development the UN has set 17 Sustainable Development Goals (SDGs) in three spheres of life - environmental, economic, and social. Certain targets have to be met so that these goals can be achieved. The strategy of sustainable development of cities as socio-economic systems implies the achievement of Goal 11 (make cities inclusive, safe, resilient and sustainable), put forward in the “2015 Agenda”. In addition to the socio-economic development of the city, Goal 11 embraces environmental target 11.6: “By 2030, reduce the adverse per capita environmental impact of cities.” Also, the solution to environmental problems, such as soil degradation and biological diversity, desertification of territories is reflected in Goal 15, with one of the targets (15.3) being “By 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world” [5]. As mentioned earlier, growing population and, as a result, crawling megacities is one of the causes of the harmful impact produced by man on the environment. Various areas of cities are polluted by liquid, gaseous and solid waste. Cities also face the challenge of the lack of natural resource potential, including the lack of green spaces, water and air pollution, and accelerated geodemic processes, such as floods, landslide, etc. As a result, the environment is deteriorating, while the abiotic nature of the system increases and territories lose their stability (Fig. 1). Thus, various influences on soil pose quite serious problems, including negative impact on human and animal health, climate change, infections of soil caused by pathogenic microorganisms, etc. Thus the condition of urban soils has to be monitored and the dynamics of environmental indicators have to be constantly tracked. When decisions are made under conditions of rapid urbanization, authorities should determine how to transform cities into a more sustainable, reliable and safe environment. In the modern
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world, such changes rely on ICT innovations. Thus, in the era of digital transformation, sustainable development of a city based on smart technologies is essential. Russia, like other countries, implements the concept of sustainable development. In 2020, an overview of the main achievements of the SDGs was presented for the first time at the UN forum (Organization Of the United Nations) As for Goal 15.3 “End desertification and restore degraded land”, Russia is actively working on the protection, rational use, and reclamation of disturbed and degraded lands. The national project “Ecology” includes a federal level project “Clean Country”, which is aimed at re-cultivating and restoring the land occupied by dumps and landfills. Thanks to this national project, in 2019 Russia managed to reclaim as many as 730 illegal dumps in the Moscow region only (Organization Of the United Nations).
Fig. 1. Anthropogenic impact on urban soils and indicators corresponding to global problems (Bockheim 1974)
The Digital Earth project is very special [6]. According to this project, it is planned to create a domestic digital platform for collecting, processing, storing and distributing remote sensing data obtained from space about the Earth. In this way it is possible to create topographic maps, maintain cadasters, use services in the field of nature management, land use, regional management, control and prevention of emergencies, natural disasters and man-made accidents. The main applications of this product will be: agriculture, forestry, water resources, emergencies, ecology, construction and infrastructure [6]. Thus, the concept of the SDGs together with digital technologies will facilitate progress in the environmental, economic and social spheres of life. Sustainable development based on digital technologies will make cities more flexible, able to quickly adapt to new conditions, mitigate adverse impacts and stimulate positive socio-economic and environmental changes.
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2 Literature Review Scientific literature lacks a single approach to interpreting and assessing the development of a sustainable smart city, but the key features of sustainable smart cities can be identified: sustainability, quality of life and intelligence [7–11]. In this study, we will rely on the definition and review of SD assessment methods presented by Gutman, S., Rytova, E. [8]. A sustainable smart city will be understood as “a city in which information and communication technologies collect and analyze data for identifying and implementing technological innovations that increase the sustainability (socio-economic and environmental) of the urban environment”. Currently, in their study of “sustainable cities” researchers focus on the potential of urban soil. The emphasis is put not only on ensuring its natural functions, but also on the safety of auxiliary functions important for humans and the environment [12]. Changes in soil mantle caused by urban expansion negatively affect the environment (for example, biodiversity, hydrological balance, biogeochemical cycle of carbon and other elements). In addition to the loss of vegetation cover and depleting carbon pool of the ecosystem, urbanization results in the accumulation of pollutants and their spread into groundwater. It also influences thermal balance, energy consumption, surface compaction, gas emissions, etc. [13]. To date, different terms can be used for describing urbanized soils: anthropogenically disturbed urban soils, technogenic soil, urbanozems and others [14–17]. Scientists Zelikov V.D. [18], Zemlyanitsky L.T. [19], Gantimurov I.I. [20] began studying the first research papers devoted to urban soils and grounds back in the 1960 s. The interest in this topic grew in the 1980 s. In Russia, Bashirov, Obukhov, Dolotov, Ponomoreva wrote about the specifics of urban soils and their morphology. Foreign authors were from Poland – Konecka - Betley et al., 1985, who wrote on the preliminary classification of soils in agglomerations [21]; from Great Britain – Bridges, who described the soils of the center in Washington, DC; from USA – Short et al., 1985 [22]; Craul, 1992 [23]; Bockheim in 1974 [24], who discussed the values of anthropogenic soils and nature in the city. 1847 saw the first work on soil science. Its author was Ferdinand Senft – “Soils in urban, industrial and mining conditions with reduced fertility due to the deposition of toxic waste” [25, 26]. In 1951, the first maps of urban soil types were created. As for the assessment of urban SD, especially in terms of land degradation, the existing methods do not suggest any uniform criteria for identifying degraded and polluted lands [27–29]. Data acquisition is also a problem. For instance, different countries collect different indicators and there is no single methodology even for collecting the same indicators. The absence of data, their incompatibility, availability or lack in public access, differences in methodologies – all of it complicates the possibility of choosing universal criteria for assessing sustainable urban development in various countries. In order to assess changes in soils in a qualitative or quantitative way, control the intake and content of all kinds of harmful substances in soils (heavy metals, radionuclides, nitrates, pesticide residues, and other chemical pollutants of inorganic and organic origin) we have to resort to digital technologies that provide not only soil monitoring, but also support decision-making in the field of sustainable urban development. This is a way not only to monitor the soil condition, but also to conduct a comparative analysis in various functional areas of the city (for example, industrial, residential and recreational ones).
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Today, many tasks related to the assessment or monitoring of soil mantle, especially in agriculture, are solved using digital technologies. A common example is geoinformation technologies (GIS). The first GIS appeared in the 90 s of the 20th century. These are systems for collecting, processing, accessing, visualizing and analyzing spatiallylinked information. Most GIS support a fairly wide range of standard operations. In Russia, the most common commercial packages are ArcGIS, MapInfo, GIS Map, Arc Info, ArcView. All packages are used for land cadasters, in land management tasks, real estate accounting, engineering communication systems, geodesy and subsoil management and in other areas. GIS technologies produce a cartographic output, including a map, monitoring layers, and attributive and additional information. In her article Gileva L.N. discusses the need to use GIS technologies for monitoring degraded lands, identifying land plots that are actually used and including them in the tax, monitoring the process of land development after reclamation works [16]. Russian scientists A.M. Berlyant [30], L.M. Bugaevsky [31], A.V. Koshkarev [32], I.K. (GN) et al. do research in the field of GIS technologies. Their studies focus on investigating and assessing the condition of soils and crops. Using GIS technologies, it is possible to automate the data processing process, which, in turn, will allow you to determine the location of the contaminated site on the map much faster. Thus, qualitative and quantitative analysis of urban soils is essential today. There is a need for continuous monitoring of the current state and making decisions on the management of urban environmental degradation in order to achieve the goal of sustainable urban development and solve a number of global problems threatening humanity. Introducing digital technologies in the field of environmental monitoring can provide the benefits of real-time data transmission from multiple measurement points with low costs. However, no specialized applications are capable of providing this process today. The relevance of the problem, its theoretical and practical significance determine the purpose of the study, which is to develop the main elements of a system for monitoring and assessing the condition of urban soils and green spaces, taking into account the use of digital technologies.
3 Materials and Methods To achieve the goals of this study, general scientific methods were used: analysis, synthesis, analogy, deduction and induction. The analysis method was used to extract information from research sources, including materials from international and Russian sources of statistics, as well as state standards of maximum permissible concentrations (MPC). The synthesis method was used for summarizing the results of content analysis, where key differences were identified in approaches to assessing the concept of a sustainable city. The method of analogies was adopted to suggest recommendations for forming a system of urban soil monitoring and assessment. Methods of deduction and induction were employed to write conclusions obtained as a result of the research. The methodological basis of this study is the system of balanced scorecard (SBS) proposed by R. Kaplan and D. Norton [33]. If adapted, the classical structure of balanced scorecard is good for exploring the issues related to mapping out a development strategy at different levels. That includes bringing the system of indicators of sustainable
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development of the city in accordance with the goals and development strategy of the region, which contributes to the sustainable development of the latter [34].
4 Results and Discussion Urbanized soils, as well as green components of the urban environment, differ from their natural counterparts, as they are constantly impacted by man, and experience contrasting anthropogenic load and changing climatic conditions. This affects the properties and functions of these soils, as well as the ecosystem services to be provided, that is, the quality of the benefits that are given by nature. Traditional monitoring methods are stationary laboratories, where information is collected from few samples and reference points, which are not suitable for a comprehensive assessment of the condition of soils and green spaces in the urban ecosystem. There is a need to use technologies which allow us to quickly make decisions on management and development of the urban environment, judging namely from the condition of urban soils. However, before exercising control, an appropriate criteria and regulatory framework have to be formed for obtaining the results to be assessed. Thus a system has to be created for express monitoring and assessment of urban soils given constant change, since one of the major goals in the development of a city is to preserve and improve environmental safety. Below is a strategic map for implementing the concept of a sustainable smart city, which was developed on the basis of the system of balanced scorecard (SBS) devised by Kaplan R. and Norton D. [33]. This map can be used not only for describing the very
Fig. 2. Strategic map of a sustainable smart city [compiled by the authors using data: [8, 34]
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concept of city development, adjusting goals and their performance indicators, but also for monitoring the progress of these goals (see Fig. 2). The strategic map shows the causal relationships between individual goals, sub-goals and benchmarks. The extent to which the concept of a smart city is implemented can be assessed by evaluating its intangible assets, i.e. its lowest component – technological development or innovations. Next, concrete results are measured at the city level (economic development) for a comprehensive assessment of the concept. This work considers one projection – ecology in detail. Table 1 shows the main goals and problems of the city.
Ecology
Table 1. Indicators for monitoring the condition of soils and environment [compiled by the authors according to: [35–37] Goals Assess the input of harmful chemicals into soils
Problems Accumulation of harmful chemicals and salts in soils
Determine the concentration of heavy metals (HM)
Soil contamination (HM)
Evaluate and analyze soil pollution by oil products
PAH content in soils (total benzo(a)pyrene equivalent (BaPeq)) Content of microbial organisms in soils
Analyze the level of activity of pathogenic microorganisms Assess temperature changes in soils Evaluate and analyze pollutants in the atmosphere Assess water condition
Indicators According to St. Petersburg Government: indicator of total soil pollution Zc < 32 According to the statistics of Primorsk Krai: excess of Zn, Cd and Pb by 5 or more times According to Novgorod Region: excess of BaPeq from 3.8 to 15 times
Rate Permissible rate Zc < 16
The indicator is unavailable in Russia
N/A
Soil temperature rise
The indicator is unavailable in Russia
N/A
Emissions of pollutants from automobile traffic
In the city of Moscow according to Rosprirodnadzor amounted to 131.0 thous. tons Excess of MPC of harmful substances is 2 or more times
N/A
Indicators of water pollution (TM, NFP, ammonium nitrogen (NNH4), nitrite nitrogen (NNO2), etc.)
Rate: Zn=23 mg/kg; Cd= from 0.5 to 2 depending on soils; Pb=32 mg/kg; BaPeq rate for any functional zones is 0.6 mg/kg
Zn=1 mg/l BaP= 0.000005 mg/l etc.
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Given the problems considered, it can be concluded that there are three types of data: official statistics in the public domain, data obtained from specialized organizations, and data currently unavailable in Russia. New cutting-edge smart technologies have to be applied to overcome these barriers. In particular, there is need for a regulatory framework, coordination of new technologies with supervisory authorities so that they could be effectively used throughout the Russian Federation. The authors studied the digital technologies currently used in urban eco-systems for assessment and analysis of urban soils and components of the urban environment. Table 2 shows the results. With all digital technologies involved, it is possible to comprehensively analyze the overall condition and advancement of an urban ecosystem according to the following indicators [35, 38]: 1. Risk of further contamination from contaminated surface and ground waters; 2. Progress in the control over contaminated sites; 3. Impact of soil pollution and compaction on the ecological quality of inland waters and on the availability of water resources; 4. Pressure on coastal ecosystems; 5. Quality of air, water and soil; 6. Effect of the developed infrastructure on soil quality and etc. These digital technologies can be used for collecting and evaluating the indicators of the state of the components of the urban environment. The technologies are mainly applied to solve major environmental problems of urban soils. For example, a conductivity potentiometer makes the assessment of soil salinity a matter of several minutes. These indicators if used together the results of the existing studies allows us not only to determine the source of a particular pollution, but also develop measures for resolving various problems. It is essential to develop its own indicators for each technology, so that the proposed monitoring system will work effectively. Table 3 presents the indicators and digital technologies designed for assessing the condition of soils and environment. The global problems shown in Fig. 1 are related to the anthropogenic effect produced on urban soils. Table 3 shows new indicators in addition to the previously proposed ones that are related to these problems. These indicators can be used for assessing the effectiveness of systems as they reflect reality as reliably as possible. The ability to competently perform express monitoring, receive and interpret data, and then enable various users (residents, managers and designers) to make the right decision for the development and management of a modern city is the main task of the Committee on Environmental Management, Protection and Safety. It is important not only to continuously analyze the condition of the urban environment, but also to take the necessary steps quickly and in a timely manner. Thus, thanks to various technologies, such as portable XRF and XRD spectrometers, which are suitable for detecting the presence and composition of heavy metals in urban soils or Tree Talker for monitoring and evaluating green spaces, it becomes possible not only to assess environmental pollution, but also improve the condition of the city and the health of its residents. Sustainable development of the biosphere, safety and well-being of the population and future generations can hardly become possible if the problems of monitoring the condition and protection of soils are not resolved.
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Digital Technologies
Description
Tree Talker
Suitable for measuring the following parameters: - water transfer in the trunk xylem (sap movement), - temperature and humidity of wood, - how a tree moves and grows, a growth simulation model - air temperature and relative humidity, - soil temperature and amount of water
Infrared gas analyzer
Suitable for measuring gases in soil and assessing microbial activity
Conductivity potentiometers
Suitable for assessing soil salinity
Portable XRF and XRD spectrometers
Suitable for assessing soil pollution with chemical elements (from magnesium Mg to uranium U)
Unmanned aerial vehicle with hyperspectral camera
Suitable for producing high-resolution images in various ranges and evaluate the state of green spaces by vegetation indices
Fluorometer
Suitable for assessing and analyzing soil pollution with oil products
Spectrophotometers
Suitable for assessing and analyzing the content of organic matter in soil and water
Optical emission spectrometer
Suitable for assessing and analyzing the content of metals and non-metals in soil and water
The results of the work match those obtained by scientists from many countries around the globe. The theoretical part is in harmony with the research of scientists such as [39]. The special attention paid to ecosystem services is consistent with works [7, 40–43]. It should be noted that the results of this paper correlate with the works of those scientists who focus on studying forest and water resources [39–41]. The results obtained in this paper can be a solution to the problems of soil quality considered in works [44].
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Table 3. Indicators and Digital Technologies for Assessing the Condition of Soils and Environment Global Problems
Indicators
Digital Technologies
Low fertility results in degradation of green spaces
Condition of green spaces; Soil temperature, °C; Number of emergency trees per year, pcs; Number of trees with a damaged root system, pcs Condition of soils where green spaces are
Tree Talker
Content of bacteria and viruses Content and types of (pathogenic) in soils microbes; Content of gases in soil; Dynamics of photosynthetic activity of plants
Infrared Gas Analyzer
Soil salinity
Types and quantity of salts in soil; Change in soil properties; Death of plants due to soil salinity
Conductivity Potentiometers
Accumulation of heavy metals in the soil
Accumulation of heavy metals in soil and their types; Soil contamination with pesticides
Portable XRF and XRD spectrometers
Condition and trend of biodiversity
Condition of green spaces; Number of parks and gardens; Number of parks in need of concrete intervention for a better condition
Unmanned aerial vehicle with a hyperspectral camera
Accumulation of harmful chemicals in soil
Territories with the highest oil Fluorimeter pollution. Types and quantity of oil products in soil
Organic matter content in water and soil
Soil condition at gas stations; Soil condition of residential areas; Quantities and types of organic substances in soil; Territories classified as “Hazardous”
Spectrophotometers
Accumulation of harmful chemicals in soil
Number of sources of potential pollution; Percentage of samples exceeding MPC
Optical emission spectrometer
5 Conclusion The rapid growth of urban agglomerations is accompanied by serious environmental problems that grow as a result of ill-conceived urbanization processes and irrational use of natural resources. Currently, the condition of urbanized ecosystems, which are extremely unstable and have lost their ability for self-repair, is of great concern. Urban
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soils have high variability due to anthropogenic activity, which increases the biochemical complexity of soils tremendously. The paper considers urban soil degradation, and new cutting-edge smart technologies that can be used for competent monitoring urban soils and green spaces and assessing the condition of urban ecosystems in order to make the right decisions concerning sustainable urban development. These include digital technologies which will help us to increase the accuracy of measurement and improve the condition of urban soils. Thanks to new digital opportunities, cities will be able to reach a new level of sustainable development. In this regard, a system of indicators has been developed for each specific problem of urban soils. To date, some technologies have already been actively used and tested in some cities of Russia. Tree Talker measures water transfer in trees, radial trunk growth, spectral characteristics of leaves and microclimate parameters. In order to verify the effectiveness of the technology, namely, to trace how the technology counteracts the deterioration of soil and tree fertility, we can compare and analyze the actual and planned values of such indicators as the condition of green spaces; soil temperature, °C; the number of emergency trees per year, pcs; the number of trees with damaged root system, pcs, etc. Such a system of indicators is suitable not only for identifying weak points of the city, but also contributes to the development and continuous improvement of the entire monitoring system. It will be logical if such monitoring systems will become widely used in Russia, while new services and applications will become available to residents for making independent examination of the components of the urban environment and food and developing the city in accordance with the Sustainable Development Goals for the benefit of current and future generations. There are other similar studies (we analyzed them above) and they emphasize the importance of preserving urban soils. Special attention is paid to both digital and nondigital ways of combating the negative impact produced by man on nature. This study takes a new approach to the assessment of urban soils using digital facilities. The proposed monitoring system can be used for a qualitative and quantitative assessment of the condition of the urban environment, while the suggested system of indicators allows you to effectively manage decisions, develop the city and general environmental literacy of the population. Acknowledgements. The research was financed as part of the project “Development of a methodology for instrumental base formation for analysis and modeling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization” (FSEG-2023–0008).
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Conceptual Basis of Digital Platform Development for Managing Innovative Investment Projects Elena Shkarupeta1 , Yulia V. Veis2(B) , Oksana Yu. Eremicheva2 Irina B. Kostyleva2 , and Vladimir S. Tikhonov2
,
1 Peter the Great St.Petersburg Polytechnic University, St. Petersburg, Russia 2 Samara State Technical University, Samara, Russia
[email protected]
Abstract. The main trend of modern economic development is the introduction of digital services and technologies in all spheres of activity. Corporate structures, the state, medium-sized businesses are now in dire need of personnel with a high level of competence, the ability to quickly learn and adapt to the ever-changing conditions of the production process and management process. Especially high requirements are imposed on project managers associated with the development of innovative and investment projects. Not all higher and secondary special educational institutions are ready to become a platform for the implementation of new promising technologies that can prepare specialists of a new level. The paper presents the experience of development and integration into the educational process of a digital service “Monitor IPT”. The proposed service is a tool for tracking the development of innovative projects on the project path, the environment for assessing the development of individual and team competencies in the implementation of interdisciplinary project teams. This digital service is based on a practice-oriented approach to education combined with a project approach. An important result of the study is the possibility of developing individual project tracks in the proposed digital service, which is especially important for innovative projects. The study will allow to implement practice-oriented project training in the implementation of innovative projects using a flexible digital service that can quickly adapt to the ever-changing requirements of the digital economy for training. Keywords: digital services and technologies · development of innovative projects · interdisciplinary project teams · practice-oriented project training
1 Introduction Most Russian universities implement the practice of conducting business accelerators, which are less focused on education, and more on the activation of small and medium enterprises in the student community.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 593–604, 2023. https://doi.org/10.1007/978-3-031-32719-3_45
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The largest Russian universities (for example, Moscow State University named after M.V. Lomonosov, Moscow Institute of Physics and Technology, Moscow Polytechnic University, Bauman Moscow State Technical University, HSE, FEFU, KFU) have created specialized centers aimed at implementing engineering, investment and innovative projects for real business. Moreover, their activities are mainly aimed at adapting scientific developments to the requirements of industrial partners. However, end-to-end educational technologies of practice-oriented project management training are not fully applied. There are separate elements of technology without a rigid sequence of necessary processes that ensure the comprehensive development of competencies and the integration of innovative projects in the consumer environment. In foreign practice, practice-oriented training is used to a greater extent due to the long experience in implementing such educational models, which are also designed primarily for the development of professional competencies in the field of practical actions without focusing on the process of leading the team to the need for these actions. World experience in the implementation of project training with the participation of private business has proved its effectiveness. Innovative and rapidly growing new universities, such as The Hong Kong University of Science and Technology, The Singapore University of Technology and Design, Ecole Polytechnique Federale de Lausanne and others [1], are becoming a modern form of such interaction. The Russian analogue of such a university is Open University Skolkovo. An important aspect of this process is the development of a digital platform that can become a universal tool for the implementation of practice-oriented teaching technologies with the project approach.
2 Purpose of the Study The aim of the study is to analyze the experience of development and integration into the educational process of the digital service “Monitor IPT”, implemented on the basis of the Samara State Technical University. The proposed service is a tool for tracking the development of innovative projects along a project path, an environment for assessing the formation of individual and team competencies during the implementation of interdisciplinary project teams, a tool for generating individual project paths and an individual student’s track. This digital service is based on a practice-oriented approach to education, combined with a project approach. Issues on innovative development are adequately covered in Russian and foreign studies. Theoretical and practical aspects are highlighted in the works of O.S. Belokrylova [2], O.A. Boris [3], N.E. Egorova, A.V. Babkina, G.S. Kovrova, S.V. Muravyova [4], M.E. Kosov and E.V. Yagudin [5], Sh. T. Ogotsky [6], D.M. Tadtaev [7], A.A. Ter-Grigoryants [8], R.A. Fathutdinov [9]. Many scientists consider the development of innovations in socio-economic systems, including I. Ansoff [10], I. Bergman and D. Charles [11], D.K. Doichen [12], P. Drucker [13], M. Kautonen [14], B. Lundvall [15], B. Santo [16], A. Twiss [17], J. Schumpeter [18] and others. The development of investment projects is covered in the scientific works of P.L. Vilensky, V.N. Livshits, S.A. Smolyak [19], Yu.B. Winslaw [20], A.D. Kasatov [21, 22], M.I. Rimer [22], Yu.V. Yakutin [23] and other authors.
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Sufficient theoretical state of knowledge of this topic does not solve the main problems that arise during the implementation of innovative investment projects. Many problems arise in the field of training for the implementation of these projects. Practiceoriented training moves to the forefront among educational technologies, implying not only the development and implementation of innovative and investment projects, but also the development of individual educational paths. At the same time, the overall workload of the processes for tracking educational paths being implemented, the difficulty in assessing team and individual competencies, and tracking the progress of a project are increasing. At the same time, the development of the digital economy poses new challenges for educational institutions - the formation of a student’s digital footprint, which should become the basis for the formation of an end-to-end learning path throughout life. A solution that can solve these problems may be a digital platform for the implementation of innovative investment projects on the basis of the university: • application of management accounting information to assess the structure and dynamics of costs changes (planned and actual production cost of production); • the use of statistical reporting, which presents aggregated data on the costs of production and sale of products, for the subsequent identification of irrational costs; • identifying internal reserves to reduce the costs of the enterprise and the allocation of key expenditures that can be redirected to other areas of development, in particular, digital technology; • assessment of the possibility of creating, using and developing the potential of digital production management based on the implementation of internal reserves to reduce the production cost of production of machine-building enterprises. As a result of the assessment of the current situation and prospects of development of the industrial sphere of Russia, as well as the possibility of transition to the path of active digital development, the main objectives of the study are formulated: • statistical analysis of costs in machine-building based on their structuring and assessment of the dynamics for a certain period; • identification of the most significant costs elements occupying the largest share in the production cost or constantly increasing; • determining the possibility of redistributing costs in favor of using and developing the potential of digital control based on internal reserves to reduce the production cost of industrial products.
3 Materials and Methods Quantitative and qualitative methods of analysis are used in the work. When analyzing the implementation features of the “IPT Monitor” digital platform, statistical and scientometric methods are used, as well as a content analysis method. To substantiate the results of the study, methods of expert and factor analysis are used.
4 Results A digital platform has been developed at Samara State Technical University, which currently includes 3 digital services: “Electronic tutor - Measurate”, “Individual educational paths”, “Monitor of the activity of interdisciplinary project teams (IPT)”.
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The implementation of the “IPT Activity Monitor” module is based on the classical project development path. Since the start of the implementation of project activities within the framework of innovation and investment projects, a rigid project trajectory in the logic of the project cycle has been implied [24]. During the process of project implementation, the vision of it was transformed taking into account the needs of potential consumers and industrial partners and the transition from a rigid to a flexible scheme for creating a project trajectory. Currently, the project path includes 3 sections, including a sequence of steps [25]: Section 1 - Concept and project planning: • • • • • • • • • • • • • • •
analysis of the situation, trends, existing developments; consumer analysis; target audience dentification; market research; marketing analysis; project concept development; business models designing; monetization models; naming; benchmarking of world experience in the subject area of the project; image/layout/sketch, visual solutions to the project; potential investors analysis; feasibility study; risk analysis. Section 2 - Development and implementation of the project:
• • • • • • • • • • •
digital modeling (3D modeling); analysis and evaluation of product properties in the required field; application of appropriate modeling software packages; description of the technology; drawing up a technological map and a description of technological processes. manufacturing and assembly of the product prototype; adaptation of the digital model of the product to the technological processes of prototyping; copyright registration; the concept of life cycle from the point of view of the designer, manufacturer, consumer, society, software (PLM - product lifecycle management); domestic PLM systems; development of projects: production/- operation/disposal. Section 3 - Project Commercialization:
• • • •
business/investment commercialization planning; financial modeling; KPI project for decision making; assessment and methods to reduce project risks;
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• • • • • • • • • • •
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raising funds; requirements for the project management system; preparation of the presentation of the business plan for commercialization; brand development; rehearsal DemoDay; exposure; presentation of a business plan to experts and investors; legal registration of activities; organization of production (own/contract); supply contracts/sales of products; product promotion/marketing.
The specifics of this module of the implemented digital platform is the ability to individualize the project path taking into account its specifics and reject stages that do not meet the needs of the industrial partner or stages that can be outsourced to project stakeholders. On the basis of the university, various projects are being implemented [26], presented in Fig. 1. Individualization of the project development path allows avoiding recurrence at the main stages of technological development. The project training manager supports the learning process and forms individual educational paths with the participation of the head of the IPT and the project team. In addition, the digital platform in this module assumes a regular assessment of progress along the project path by both internal experts and external experts from stakeholders. Project activities planning is carried out taking into account the distribution of the activity content (project units - PU). Each work contains 100 PUs, which are distributed among the project team members responsible for the implementation of this work in accordance with their functional roles and level of responsibility. Distribution of PU according to the modules of project activities is carried out in the following order: Module 1 - 23 activities on 100 PUs with a total workload of 2300 PUs. Module 2 – 13 activities on 100 PUs with a total workload of 1300 PUs. Module 3 - 11 activities on 100 PUs with a total workload of 1100 PUs. The total workload of the project for 3 modules is 4700 PU. The distribution of the activity content of the entire project between the participants is carried out proportionally in order to ensure balanced loading between team members. Evaluation of the work performance is carried out by an expert by setting the actual volume of work (in PU) in the process of intermediate examination and based on the results of intermediate attestation sessions (IAS). A point-rating assessment of each of the participants is carried out by calculating the percentage of actual work performed in accordance with the initial distribution at each moment of the examination (interim examination and IAS). The overall team rating is formed as a total assessment of the work performance by each IPT participant.
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Using this service allows you to avoid the additional costs of financial resources during the implementation of the project and as closely as possible meet the expectations of potential and real consumers of the project product.
Fig. 1. Innovation and investment projects implemented on the basis of the Samara State Technical University
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In addition to the IPT activity monitor, there is an expert assessment by external experts during the IAS. Experts evaluate the project and make rating sheets displayed in the digital platform [25]. An individual and team rating is formed on the basis of the arithmetic mean value of expert evaluations (on a scale of 1 to 5) of the presentation and reporting of the module in the ratio of 60% (report)/40% (presentation): Р
Э
(1)
Э
where PIPT – the final rating score for the performance of the IPT; Эr – arithmetic mean value of expert assessment based on the results of module reporting evaluations; ЭIAS – arithmetic mean value of expert assessment based on the results of the presentation evaluation upon completion of the module in the IAS. The scoring of activities (exit criteria) planned in accordance with the project documentation is formed according to the following scale, presented in Table 1. Table 1. The scoring of works (exit criteria) of the project Score
Interpretation of evaluation Level of implementation and content of the assessment
Correspondence of the results
1
Activities (exit criteria) not 0% (content, timing, cost, Results not presented fulfilled quality)
1–2
Activities (exit criteria) is formally completed
Up to 50% (content, terms, cost, quality)
The results do not correspond to the declared topic, subject area and planned documentation of the project
2–3
Activities (exit criteria) completed
More than 50% (content, terms, cost, quality)
The results to a greater extent correspond to the declared topic, subject area of the project planning documentation
3–4
Activities (exit criteria) completed
More than 75% (content, terms, cost, quality)
The results to a greater extent correspond to the declared topic, subject area of the project planning documentation
4–5
Activities (exit criteria) completed
More than 90% (content, terms, cost, quality)
The results fully correspond to the declared topic, subject area of the project planning documentation (continued)
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Score
Interpretation of evaluation Level of implementation and content of the assessment
Correspondence of the results
5
Activities (exit criteria) completed
The results fully correspond to the declared topic, subject area of the project planning documentation
100% (content, terms, cost, quality)
The minimum number of experts participating in the assessment procedure is 6 people, including at least 1 expert in the following areas: project management; information technology, industrial technology, economics and management, marketing and PR, public administration. Based on the results of the projects evaluation, a decision is made on their further progress on the project path in accordance with the scale of performance presented in Table 2. Table 2. Structure of the project performance evaluation Score
Further project promotion
Additional conditions
1–2
The project is forcibly completed without moving to the next stage
There remains the possibility of participating in new competitions taking into account the recommendations of experts or a change in the subject area of the project
2–3
The project returns to the previous stage in The process of the stage is repeated order to improve and adjust the results based on expert recommendations
3–4
The project conditionally proceeds to the next stage with the re-reporting and presentation of the results of the previous stage in the mini-IAS of the next stage based on expert recommendations
In the absence of changes according to the results of the mini-IAS of the next stage, the project is returned for revision
4–5
The project moves on to the next stage
According to the recommendations of experts, the project elements are adjusted
The application of the rating when making decisions on the continued financing of the project during the next stage is carried out according to the scale presented in Table 3. The remaining funding gets back on the assumption of an increase in the rating based on the results of the next stage, as well as refinement and adjustment of the results of the previous stage in accordance with the recommendations of experts.
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Table 3. The structure of evaluating the effectiveness of the project when deciding on its financing Score Further project financing Additional financing conditions 1–2
Funding ends
2–3
Funding continues
In the amount of 50% of the planned cost of the project stage
3–4
Funding continues
In the amount of 75% of the planned cost of the project stage
4–5
Funding continues
In full
In addition to assessing the effectiveness of work in accordance with the project documentation, one should not forget about assessing the competencies of team members. The project team includes a project manager, project training manager, team members and experts involved to enhance project promotion along the project path and develop additional competencies as part of the project development. Since most of the team members are students and undergraduates of the university, the digital platform provides a module for the development of individual educational technologies. Team members, together with the project training manager and team leader, make a list of the necessary competencies for the successful implementation of the project. After diagnostic assessment of team members, an individual track is formed for each participant, taking into account individual characteristics, the degree of development of the main specialty and role in the team. The level of assessment of the competencies development includes both a team and an individual assessment. On the basis of the platform, a separate module “Electronic Tutor - Measurate” is provided for assessing the development of competencies. Competency assessment involves team members, including self-assessment, and internal experts - project activity curators and employees of the project training center. The rating assessment of the IPT activity (IPT Assessment) is developed on the basis of the integration of assessment blocks: 1. Expert evaluation of the results of the development of the project path (IPT Activity Monitor) - Grade A (team/individual). Grade A (team) is the final assessment result by 2 categories of experts, taking into account weighting factors: A1- assessment is carried out by internal experts - team members (IPT manager, software manager, etc.) on a 100-point scale. A2 - assessment is carried out by internal experts - engaged employees of SSTU on a 100-point scale. A1 × 0.4 + A2 × 0.6 (2) AssessmentAteam = average
average
Grade A (individual) is formed on the basis of Grade A (team) for each project activity, adjusted for the planned volume (%) of participation in the project implementation. 2. A scoring of the results of the educational path development (Modular course “Project Activities for the IPT”) - Grade B (team).
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B1 assessment is carried out by employees of the unit responsible for the implementation of the project training program for the participants of the IPT (SRO). B Assessmentteam = AssessmentB1n × K% 1n (3) Assessment B1n – assessment (% of actual development/implementation) of the results of the development of sections (n) of the modular course. B2 assessment is based on the results of business games (BG) in the course of IAS. exp ertsIAS exp ertsBG IPT AssessmentB2 = B2m + B2k + B2t (4) average
average
average
AssessmentB = AssessmentB1 × 0.4 + AssessmentB2 × 0.6
(5)
3. Scoring the competency profile of the participants of the IPT (Electronic Tutor Measurate) - Assessment C. 4. Expert evaluation of the results of project activities (IAS) - Assessment D. Assessment D - the presentation on the results of project activities by invited internal and external experts on a 5-point scale, taking into account the “cost” of 1 point (100 points/number of evaluation criteria by experts). average D1m × 100 AssessmentD = (6) Ncriteria The integrated rating assessment of the IPT activity is formed taking into account the weighting coefficients of each assessment unit: AssessmentIPT = AssessmentA × 0.3 + AssessmentB × 0.3 + AssessmentC × 0.2 + AssessmentD × 0.2
(7)
The digital platform development is currently at the stage of testing and active implementation. The “Electronic Tutor - Measurate” module was originally implemented in 2017. The practice of using this module has revealed enough opportunities for further improvement: team and individual competencies of project participants ranking system, individualization of each participant’s role in the project team. The second module in the implementation of the digital platform was IPT Monitor in 2018. Currently, this module is being actively developed taking into account the creation of flexible individual tracks of the project with the participation of the main paths” is under development. Currently, a mechanism is being designed for integrating this module into the general scheme of the digital platform.
5
Conclusion
In general, during the digital platform development on the basis of the Samara State Technical University, 25 innovation and investment projects in the form of interdisciplinary project teams have been implemented. Most projects are implemented with the participation of industrial partners. 5 projects have initiated by industrial partners. The project activity involves more than 200 students of various levels of training in 49 fields of study. In the process of implementing project activities, 21 patents have been obtained, confirming the innovative component of ongoing projects.
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Based on the study, the following conclusions can be drawn: 1. The role of practice-oriented project training currently prevails among educational technologies. Particularly relevant is the use of this approach in the implementation of innovative investment projects with the participation of industrial partners. 2. Using a digital platform allows you to create a digital footprint of a participant in the educational process, being the basis for the development of an individual end-to-end path of his development as a specialist with the ability to plan further competencybased growth and development. 3. The digital platform, currently developed on the basis of the Samara State Technical University, includes three main modules: “Electronic Tutor - Measurate”, “IPT Activity Monitor”, “Individual Educational Paths”. Each of the presented modules carries a unique functionality that can become the basis for training a new level specialist for the digital economy. 4. The “Electronic Tutor - Measurate” module allows you to evaluate the team and individual competencies of each participant in the project activity as part of a digital platform, taking into account the stage of the project and the role in the IPT. 5. The “IPT Activity Monitor” module allows you to create an individual project path focusing on the requests of industrial partners, potential and real consumers and other stakeholders, track changes in the project track and adjust project progress on time. 6. The module “Individual educational paths” allows you to create individual educational paths based on the project development path, taking into account the interdisciplinary competencies necessary for the implementation of the project. As a further stage of development in the digital platform, it is planned to expand the participants of the project practice-oriented training and involve schoolchildren in this process. In this context, it is logical to integrate the module “Pre-university tutorial” into the digital platform. This module should become the basis for the development of the student’s competency profile, preparing him for entering and studying at the university. Acknowledgements. The study was carried out at the expense of a grant from the Russian Science Foundation No. 23-28-01316.
References 1. Huisman, J., Smolentseva, A., Froumin, I.: 25 Years of Transformations of Higher Education Systems in Post-Soviet Countries Reform and Continuity Palgrave Studies in Global Higher Education (2018). https://www.researchgate.net/publication/324739515_25_Years_of_Tra nsformations_of_Higher_Education_Systems_in_Post-Soviet_Countries_Reform_and_Con tinuity 2. Belokrylova, O.S.: Innovative type of development and modernization. Sodeistvie-XXI century, Rostov-on-Don (2011) 3. Boris, O.A.: Socially-oriented innovative enterprise: theory and practice of holistic development (Stavropol: Publishing and Information Center “Fabula”) (2014) 4. Egorov, N.E., Babkin, A.V., Kovrov, G.S., Muraveva, S.V.: Comparative assessment of innovative activity of region’s economy actors on the basis of the triple helix model. Procedia Soc. Behav. Sci. 207, 816–823 (2015)
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5. Kosov, M.E., Yagudina, M.E.: Tax regulation of innovation (Moscow: UNITI), p. 175 (2013) 6. Ogotsky, S.H.T.: On the development of a monitoring and management system for the full cycle of the national innovation process (Moscow: Publishing house MIPT), p. 369 (2005) 7. Tadtaev, D.M.: Background and conditions for the formation of an integrated innovation and investment policy (Stavropol), p. 26 (2018) 8. Ter-Grigoryants, A.A.: Theory and methodology of managing the innovative development of socio-economic systems (Stavropol), p. 472 (2013) 9. Fathutdinov, R.A.: Innovation Management, p. 263. Petersburg, St. Petersburg (2013) 10. Ansoff, I.: Strategic Management, p. 343. Petersburg, St. Petersburg (2009) 11. Bergman, E., Charles, E.: Innovative Clusters: Drivers of National Innovation Systems, p. 245. Organization for Economic Cooperation and Development, San Antonio (2003) 12. Doychen, D.K.: Scientific approaches to the formation of regional strategies for innovative development Federal relations and regional socio-economic policy 5, 10–13 (2007) 13. Drucker, P.: Business and Innovation, p. 432. Williams, Moscow (2007) 14. Kautonen, M.: Regional Innovation System Bottom-up: A Finnish Perspective. A Firm Level Study with Theoretical and Methodological Reflections. Acta Universitatis Tamperensis, no. 1167, p. 270. Tampere University Press, Tampere (2006) 15. Lundvall, B.: National Systems of Innovation. Towards a Theory of Innovation and Interactive Learning, p. 404. Anthem Press, London (2010) 16. Santo, B.: Innovation as a means of economic development (Moscow: UNITY-DANA), p. 239 (2001) 17. Twiss, B.: Management of Scientific and Technical Innovations (Moscow: Economics), p. 271 (1989) 18. Schumpeter, J.: Theory of Economic Development: Selected, p. 157. EKSMO, Moscow (2008) 19. Vilensky, P.L., Livshits, V.N., Smolyak, S.A.: Evaluation of the effectiveness of investment projects. Theory and Practice, p. 888. Delo, Moscow (2001) 20. Vinslav, Y.B.: Management of integrated structures: theoretical and methodological aspects (Moscow: TsentrLitNefteGaz), p. 510 (2017) 21. Kasatov, A.D.: Methodology of investment management of integrated corporate structures in industry (Samara), p. 345 (2015) 22. Rimer, M.I., Kasatov, A.D.: Investment Planning (Moscow: Higher Education and Science), p. 232 (2001) 23. Yakutin, Y.V.: Improving the integration interaction of business entities as a factor in increasing the efficiency of Russian corporations (Moscow), p. 406 (2001) 24. Regulation P-369 dated 05/10/2018 “On interdisciplinary educational programs of higher education - undergraduate programs, specialty programs, master’s programs of Samara State Technical University” (in a new edition). https://samgtu.ru/sites/default/files/2018/proekti/ Pol_o_MDOP_2018.doc 25. Regulation P-404 dated 03/29/2019 “On interdisciplinary educational programs of higher education - undergraduate programs, specialty programs, master’s programs of Samara State Technical University” (in a new edition). http://cpo.samgtu.ru/sites/cpo.samgtu.ru/files/pol ozheni_404.pdf 26. IPT: Interdisciplinary Project Teams / Project Training Center. IPT. Source: http://cpo.sam gtu.ru/mezhdisciplinarnye-proektnye-komandy
Data Management and Digital Solutions
Development of a Methodology for Integral Assessment of the Effectiveness of Medical Organizations Under Conditions of Changes in the Main Business Processes in the Health Care System Olga S. Chemeris1(B) , Alissa S. Dubgorn1 , and József Tick2 1 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected] 2 Óbuda University, Budapest, Hungary
Abstract. The present article proposes the author’s methodology of integrated assessment of the effectiveness of medical organizations, summarizes the results of domestic and foreign studies in terms of determining the indicators of such effectiveness, identifies the problems of its assessment and unresolved problems in the current organization of such examinations. The aim of the research is to develop a method of calculation of integral assessment of medical organizations efficiency based on the methodology of hierarchy analysis which takes into account qualitative and quantitative characteristics of economic (methodological and economic), social and medical (structural) efficiency assessment in the framework of one model. The article demonstrates the results of domestic and foreign researches on the use of health care resources and management methods used to improve the effectiveness of medical organizations, as well as the results of the study of Russian relevant regulatory legal acts and current methodological documents on the approaches to assess the effectiveness of medical organizations in terms of implementing patient-oriented model of reorganizing the entire Russian health care system. The article also proposes a method of accumulation and formalization of data on medical organizations, presents an algorithm for calculating the resulting evaluations of the criteria and sub-criteria that have a significant impact on the results of formalization of implicit knowledge and their subsequent use in the evaluation of the effectiveness of medical organizations. Methodological approaches to the development of a valid multi-criteria model for assessing the effectiveness of their activities under the conditions of changes in the basic business processes in the health care system are substantiated. Keywords: Performance Measurement · Business Processes Of Medical Organizations · Evaluation Methodology · Evaluation Criteria · Economic Resources · Expertise · Accumulation Of Knowledge
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 607–620, 2023. https://doi.org/10.1007/978-3-031-32719-3_46
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1 Introduction The new patient-oriented model of reorganizing the work of the whole Russian health care system [1] is based on the changes taking place in the main business processes (within the medical organizations (hereinafter - ME), as well as in the whole environment of interaction between the subjects of the system). Budgetary medical institutions function under the most difficult conditions of resource shortage with an increase in the population’s demand for medical services. At the same time, there is an increase in competition due to the spread of private medical practice [2], as well as increasing requirements of the recipients of medical services to the quality of their provision. In connection with the need to ensure a balance (between the resource capabilities of health care facilities and the requirements of accessibility of medical services), one of the key objectives of the state health care system is the introduction of optimal management technologies aimed at improving the effectiveness of health care facilities, taking into account their resource availability (material, technical, financial, informational and personnel). The urgency of this problem is confirmed by the long-standing search for an acceptable management model at all levels of the health care sector, which is able to guarantee the maximum efficiency of MI functioning at minimum costs. However up to the present time the problem of objective measurement of efficiency as a decisive factor of economic activity of ME remains unsolved [3]. In the present article this issue was investigated along with related ones associated with the absence of unified approaches that regulate the analysis and evaluation of the effectiveness of the organization of the IO resource management processes, as well as the established set of criteria for these purposes. The concept “efficiency” in different researches is interpreted differently [4], however always, efficiency of systems of health care and obligatory medical insurance (further MHI) is always differentiated in several aspects: economic (methodological-economic), social and medical (structural). Methodologies of an estimation of efficiency of activity of ME are various, but in all of them an estimation of each indicator is made at macro-, meso, and microlevel that gives an idea of the current situation in regional systems of health care and obligatory medical insurance [5]. The important conditions for maintenance of qualitative procedure of examination at an estimation of efficiency of activity of ME and for establishment of adequate reliable values of a parameter of a standard, with which the functioning medical organizations will be compared, are: – the choice of an effective methodology for such evaluation; – taking into account the principle of complementarity in selecting the composition of the expert group (internal and external experts). It is necessary to note, that strengthening market processes and financial difficulties in Russia create obvious complexity of a combination of all components of efficiency of functioning of the medical organizations determined by dissatisfaction of the population with quality of received medical services which makes social efficiency of functioning of medical organizations. At the same time, expenses and volumes of rendered medical services condition economic efficiency, confirming necessity to decrease expenses, preserving a level of volumes of services, or to increase volumes of rendered medical aid, preserving volumes of financing.
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2 Materials and Methods To solve the problems and achieve the goals of this article, the authors analyzed open sources of information with the results of research on the use of health care resources and management methods used to improve the performance of health care institutions, and also studied relevant regulations and existing methodological documents; a systematic and content analysis with elements of structuring information from bibliographic database sources on approaches to assess the performance of health care institutions was performed.
3 Results Efficiency of elements of the health care system and compulsory medical insurance (hereinafter - CMI) is considered in all sources in several aspects, grouped according to different indicators [4–6]: – economic (methodological-economic), which is characterized by indicators of achieving the results of treatment of diseases in patients through the use of various methods and schemes of their treatment; – social, which characterizes the indicators of public health: mortality of the population, from the so-called controllable causes, as well as the primary receipt of disability, temporary disability (in case of disease), etc.; – medical (structural), which is characterized by indicators of programs of state guarantees for free provision of medical care of all types, forms and conditions to the population. For today the system of health care effectiveness assessment is fixed at the federal level in the documents [7, 8], however the list of criteria and the indicators contained in them do not coincide [6], though they should be used for efficiency assessment of separate ME or territorial systems of health care. It is worth noting that the existing approaches to the assessment of MI functioning in the conditions of changes in the main business processes in the health care system are based on the achievement of indicators and norms, fixed by the program of state guarantees of free medical care (hereinafter - SHCG), often being identically equated with each other when considered. Also, in work [6] there are contradictions in the levels of responsibility in the execution of these programs (the implementation of SHCG is under the control of the Ministry of Health of the Russian Federation, but the RF State program “Development of Healthcare” is interdepartmental). Thus, there is a contradictory situation, when the Ministry of Health is responsible for the process of providing medical services to the population, but is not responsible for its result and quality. In this connection it is worth highlighting the problem of selecting specific performance indicators for each health care facility within the territorial health care system. Despite the wide coverage of the problems of analysis and estimation of efficiency of functioning of health care subsystems at the federal and regional levels, the solution of the issues of difficult perception of the research subject is possible only at systematization of existing criteria of estimation of efficiency of resources use [9] MO, development
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of the uniform classification of parameters of such estimation and realization of the comparative analysis of activity of MO in the municipal formations of subjects of the Russian Federation. The existing system of indicators of the socio-economic effect of health care development does not solve the problem of assessing the effectiveness of the use of funds in this sphere [6]. In the model for assessing the effectiveness of the implementation of territorial programs for the provision of medical care by hospital institutions in the work [9] the authors propose the criteria of quality and accessibility of medical care, enshrined in SHCG, as well as the process of providing medical services using human, organizational, managerial and material and technical resources. From the point of view of all constituent elements of the effectiveness of the activity of ME under conditions of changes in the basic business processes in the health care system, the work [10] offers a system of analytical indicators of a complex assessment of the effectiveness of the activity of such an institution. It is necessary to note, that to receive the concrete medical result and after granting to the population of qualitative medical service to reach the certain social effect is possible at the organization of work, corresponding to system of indicators of the analysis of economic efficiency of MO, which should consider efficiency of use of all resources (material, financial, personnel and information). In the study [11] the structure of the form of presentation of analytical materials on efficiency and effectiveness of MO performance for the subsequent analysis is defined by expert way. It is worth noting that when conducting a qualitative assessment of the efficiency of MO functioning, it is necessary to provide: firstly, the formation of a list of all relevant evaluation criteria, secondly, the determination of admissible (benchmark) values and, thirdly, the indication of exactly which expert(s) should evaluate medical organizations according to these proposed criteria. Thus, apart from the introductory part, the form of presentation of information about an MI must contain the following sections assessed according to the groups of criteria described in work [11]: information support of MO (in terms of assessment of openness and availability of information about MO); resource support of MO (indicators of personnel and material and technical support); processes of diagnostics and treatment itself (according to indicators of performance of early diagnostics of socially significant diseases and work performed on their treatment); processes of prevention (including, inter alia, prevention of diseases). It should be noted that more preferable are those approaches based on the assessment and mapping [12] of processes in MO, because they take into account the specificity, degree and proportion of involvement of specific resources in the functioning of MI, as well as allow to establish specific cause-effect relationships and ensure more operational management and regular improvement of the current business process. In order to increase the quality level of the management decision-making procedure in relation to the assessed MIs and the degree of objectivity of the results, it seems expedient to use the group expert method of assessment. At that, the experts, depending on the problem area of practical and theoretical competences, can compose several subsets determined for the hierarchy by a set of corresponding evaluation criteria. From
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the point of view of knowledge complementarity, it is important to involve people with different subject areas of practical and theoretical competences in the expertise. It seems expedient to involve people with different subject areas of practical and theoretical competences in such an assessment procedure: – one expert-scientist in the field of medicine (Es): a medical researcher, a specialist in a particular problem medical field, having solid theoretical and practical knowledge and skills in direct implementation of providing medical services to the population – one expert in medical management (and marketing) (Em), who evaluates more precisely, for instance, the organization of planning, control and management methods, the correct distribution of work, the time-related manipulation of the monetary, material and human resources in the medical field, as well as the social and economic application of the results. – one expert-expert in economics (Ee), more correctly evaluating, for example, all financial criteria for achieving commercial objectives of the provided medical services and the effect for the region, the correspondence of the economic development of the region, its environmental features, scientific and technological directions of the innovative structure, as well as being able to establish the correspondence of the expected material and labor costs to the requested amounts of financing, etc.; – two experts-practitioners in the field of functioning of medical organizations (Ep), who are specialists in the sphere of implementation of medical services and who should not be interested in the results of the expertise, but they are practical specialists in the sphere of implementation of MO activities on rendering medical services to the population. These experts can be involved to evaluate all groups of criteria. At the same time, in our opinion, the scales established for qualitative criteria must have verbal (qualitative) gradations, but must necessarily be translated into measurable (quantitative) ones. The operation of assessments, which to a certain extent are unified for representatives of different fields of knowledge, while taking into account the complementarity of interaction in the evaluation will make it possible to obtain more reliable results of the examination. Exploring the known types of scales and their properties (according to Stanley Smith Stevens classification), the geometric mean is taken in this paper as a measure of central tendency (aggregated score on a scale of relations). This is due to the fact that the scale used will be metric (measurable) when determining the score, and the geometric mean in this case allows us to calculate the numerical mean of the formalized score of a particular MO (MOR). In this case, there is no reason to believe that in this case there will be contradictions of the chosen measure with the measure of the central tendency (median) in the ordinal scale. In practice, the implementation of a similar quantitative averaging approach using similar types of scales in the current methodology of the U.S. National Foundation allows us to claim that it fully satisfies the laws of statistics. Thus, in the present study, the aggregated opinion of the circle of experts is proposed to be calculated using the geometric mean according to the formula (1): N MOR MOR n1 MOR n2 MOR nN QABC = QABC QABC . . . QABC (1) where:
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nN; N – the total number of experts participating in the procedure for evaluating the level of performance of the MO. It is proposed that the judgements of all the experts participating in the evaluation procedure be averaged by the person making the final decision with respect to specific MO, taking into account their qualifications and “weight”, the coefficients of which it is advisable to calculate by means of hierarchies. In this case, the aggregate assessment (in the case of involving n experts with different weight coefficients of significance) will have the following form [13]: aijA = aija1 aija2 . . . aijan
(2)
where: aijak – specific MO evaluation (MOR), conducted by the k-th expert with weighting coefficient ak ; provided that the identity a1 + a2 + . . . + an = 1. The complementary interaction of experts must ensure the most accurate determination of the level of performance of the MO under conditions of changes in the main business processes in the health care system. It should be noted that difficulties during the expertise are connected with the fact that it is necessary to take into account qualitative and quantitative evaluation criteria within the framework of a single model; for this purpose, a scale of point estimates is often used. However, traditional expert methods do not always allow expert judgments to be checked for consistency and logic. This circumstance reduces the confidence in the evaluation results obtained. Thus, it seems expedient to take this circumstance into account and to develop such a methodology of integral assessment of MO performance, which will be able to work with expert judgments that include elements of consistency and logicality checks. It is proposed, first of all, to make a hierarchy of the problem. The model of integral assessment of MO performance in this case will look as follows (Fig. 1). The presented model is proposed to be reduced to the assessment of MO in terms of efficiency in the context of economic (methodological and economic), social, medical (structural) aspects according to qualitative and quantitative criteria based on the application of the above described principle of necessity and sufficiency of formalized knowledge to obtain reliable and justified results. The proposed algorithm for the expert review implies that a responsible person representing a medical organization (to be further evaluated) fills out a questionnaire, which is then given to each expert. A logically grouped set of criteria (and sub-criteria) must be selected for each question in this questionnaire. The qualitative characteristics and their degrees of influence or correspondence should be established expertly. On the basis of predicates, it is proposed to divide all qualitative criteria into one-syllable statements to determine all aspects of MO functioning efficiency: economic (methodological-economic) Ax , social By and medical (structural) C z . Answers to such statements will ensure the formation of an assessment Q for each of them (QAx and QBy , where x = 1..X i y = 1..Y ). It is proposed to evaluate the efficiency of MO functioning in several stages.
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Fig. 1. Model for integral assessment of the performance of the MO [Compiled by the authors]
All preparatory stages of the evaluation procedure and the mathematical tools of its carrying out are based on formalization of evaluations according to the systematized criteria by means of the predicate data model (or finite predicate algebra means). Implementation The proposed multistage model of such expertise, if implemented, will provide reliable results of the integral assessment of the performance of a health care institution under the conditions of changes in the main business processes in the health care system. In our opinion, it is reasonable to use elements of the pattern recognition theory when performing the procedures for assessing the effectiveness of activities of medical institutions. The problem statement at such recognition assumes use of mathematical language, it is explained by aspiration to replace experiment with logical reasoning and proof mathematically. At the same time, the inconsistency of initial data on the object has a significant impact on the level of validity, and the difficulty of achieving the goals – on the level of performance of traditional models, which in these conditions are low, which often forces to abandon economic and mathematical modeling and leads to low socio-economic efficiency of investment [14]. It is proposed to avoid such problems by reducing the problem to the recognition of those MO, which have the necessary and sufficient attributes for the transition to a qualitatively higher level. In this case more than one formula of predicate logic by the number of characteristics of each criterion should be set as a requirement. In this way lower level nodes will be formed, and in the process of synthesis the structure will develop according to a similar principle. Next, the scores obtained are proposed to be compared with the established benchmark, which must meet the acceptable values. To determine the scores QAx and QBy it is necessary to select appropriate criteria, which should be characterized by predicates. These predicates will include one-word answers to questions from the questionnaire: Axk and Byk , where k = 1..K. Thus, the resulting estimates of the criteria for removing each type of uncertainty can be calculated by formulas (3,4): QAx =
x=1 X
QAxk ∗ ωAxk
(3)
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QBy =
y=1 Y
QByk ∗ ωByk
(4)
where ωAxk – weighting of the sub-criterion Axk in the criteria Ax; ωByk – weighting of the sub-criterion Byk in the criteria By . The estimate QCz is calculated similarly. If a situation arises where there is an inconsistency in the experts’ judgments, the answers to such uncoordinated (disputed) answers must be reassigned to the experts for reassessment and clarification. The set weight values of all statements for each criterion for the aggregated group in total should be equal to 1, as well as the weight values of the criteria themselves by the degree of their importance for achieving the goal. In the framework of the methodology of the analysis of hierarchical structures, the check can be performed using the consistency index, calculated by the formula (5) [13]: CI = (λmax − R)/(R − 1), subject to CI ≤ 0, 1
(5)
где CI – consistency index; λmax – maximum eigenvalue; R – dimensionality of the matrix of pairwise comparisons. The maximum eigenvalue is calculated by formula (6) [12]:
λmax = eT ∗ U ∗ W
(6)
Resulting scores by criteria QAx , QBy and QCz are formed based on one-question answers (Yes/No) of experts to qualifying statements V d , where d = 1, . . . , D According to the number of such statements clarifying the state of MO functioning V d you can build a variant truth table to determine possible answers. Each of the one-syllable answer choices (“yes” or “no”) corresponds to a linguistic gradation Lg(A;B), where g = 1, . . . , G, each of which (their number is determined by the experts) corresponds to a value from 0 to 1, determined by the formula (7): L=
g−1 G−1
(7)
where L – linguistic gradation of qualitative criteria (with low level corresponding to 0, medium - 0.5, and high - 1); g – the number of linguistic gradations (g = 1..G). Similarly, truth tables of admissible values for all qualitative criteria are compiled. The special importance in formalizing implicit knowledge and its subsequent use in artificial intelligence systems is given to the values obtained. An example of possible answers of experts, formalized by means of algebra of finite predicates, is shown in Table 1.
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Table 1. Table of truth of the logical-algebraic model of variants of expert answers [Compiled by the authors] V1
V2
…
V d-1
Vd
M
0
0
…
0
0
V1 · V2 · . . . · Vd −1 · Vd
0
0
…
0
1
V1 · V2 · . . . · Vd −1 · Vd
L2
0
0
…
1
0
V1 · V2 · . . . · Vd −1 · Vd
…
…
…
…
…
…
…
Lg
1
1
…
1
1
V1 · V2 · . . . · Vd −1 · Vd
L1
The table shows: Vd – the answer is “yes” to the statement V d of the sub-criterion, corresponding to the truth table value “1”; Vd−1 – the answer is “no” to the statement V d-1 of the sub-criterion, corresponding to the truth table value “0”; M – minterms (their number depends on the number of comparisons of answer choices V d for each of the gradations L g . Next, it is necessary to determine the functions for assessing the efficiency of MO functioning on the basis of the obtained values of assessments QAx , QBy and QCz . For this purpose an inverse symmetric matrix of their pairwise comparisons is formed (Table 2), where aij – valuation ratio Q by the criterion of Ax medical organizations (MO). Inverse symmetric matrix of estimates QBy is constructed in a similar way. Next, it is necessary to determine the main eigenvector of the matrix, which is formally defined as an eigenvector. It corresponds to the maximal eigenvalue (8): W = (w1 , . . . , wX +Y )
(8)
Table 2. Matrix of pairwise comparisons of alternatives MO regarding the estimates of the qualitative criterion QAx U(Ax )
MO1
MO2
…
MOR
MO1
1
a12
…
a1r
MO2
a21
1
…
a2r
…
…
…
…
…
MOR
ar1
ar2
…
1
In the case of consistency of the matrix component of this vector, the relative estimates of MO for the criterion in question are determined. Thus, the evaluation function will take the following form (Table 3).
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MO
MO1
MO2
…
MOR
W(MO/Ax )
w1
w2
…
wR
In order to achieve the reliability of the vector elements it is necessary to check the matrix of pairwise comparisons for consistency using the consistency index (according to the methodology of hierarchy analysis). Thus, the following are constructed and processed x + y + z matrices of pairwise comparisons, and the results of these actions are presented in tabular form (Table 4). Table 4. Matrix of pairwise comparisons of MOs for each criterion Ax, By, Cz [Compiled by the authors] MO
w MO/A1
…
w MO/Ax
w MO/B1
…
w MO/By
MO1
w11
…
wx1
wx+1 1
…
w1
…
w MO/Cz
…
w1
MO2
w12
…
wx2
wx+1 2
…
w2
w2
…
w2
…
…
…
…
…
…
MOR
w1r
…
wxr
wx+1 r
…
…
…
…
x+y wr
x+y+1 wr
…
…
wr
x+y x+y
w MO/C1 x+y+1
w1
x+y+1
x+y+z x+y+z
x+y+z
Calculation of the integral evaluation of MO functioning efficiency on the basis of predicate algebra logic and hierarchical methods of pairwise comparisons is also described below step by step. Formalization of the obtained results is possible with the use of predicate model, and the estimates QAx and QBy take the following form according to formulas (9, 10): MO VMo ∗ Lg (9) QAxk = Mo=1
QByk =
MO Mo=1
VMo ∗ Lg
(10)
The decision-maker, taking into account the opinion of the experts, sets the weight t of each statement VMo in the sub-criteria. In sum, for each criterion, the weights of the statements add up to 1. In total for each criterion, the weight values of all statements in total are 1, and according to the given gradations the predicate form of reproduction of each criterion Axk and Byk will have a form according to formula (11): G M(Lg ) (11) Q Lg = g=1
The resulting assessment of the efficiency of the MO functioning will be calculated by the formula (12): D QMOR = LMOR ∗ ω + Vd ∗ tVd (12) d =1
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The final scores obtained are then compared with the benchmark, which provides for compliance with the admissible values. At the same time, those that do not meet the acceptable values according to the criteria of MIs are eliminated (requiring optimization of activity, re-profiling or restructuring) and do not participate in the ranking of those MIs that have a sufficient level of efficiency in each of the aspects. The obligatory condition for the considered IOs is compliance with the benchmark (Et) which is a set of parameters evaluated by experts (QMOEt ) on all criteria from K−1 to K−n. The results are processed using hierarchical methods of pairwise comparisons with the use of a predicative model of data representation. Determination of weight values of criteria (qualitative and quantitative) is established according to the degree of their importance on the basis of the constructed matrix of pairwise comparisons of criteria of assessment of IO (Table 5). It is also necessarily checked for consistency. Matrices of pairwise comparisons of all groups of criteria are constructed and checked in the same way. Table 5. Matrix of pairwise comparisons of the Axk criteria of the MO evaluation [Compiled by the authors] Leading criterion
A1
…
Ax
B1
…
By
A1
1
…
d1n
d1n+1
…
d1n+m
…
…
…
…
…
…
…
An
dn1
…
1
dnn+1
B1
dn+11
dn+1n
1
…
dn+1m
…
…
…
…
…
…
.
Bm
dn+m1
…
dn+mn
dn+mn+1
…
1
dnn+m
If the matrix is matched, the result of its processing is the main eigenvector, which is determined by the formula (13): Wcriterion = (W1 , W2 , . . . Wx+y+z )
(13)
The components of this vector determine the “weight” of each quantitative and qualitative criterion of MO effectiveness assessment. Hierarchical weighting is performed at the next stage, implying calculation of the final integral assessment of MO performance through calculations according to the formula (14): MOR = QAMOR + QBMOR + QMOR QABC
(14)
The resulting performance evaluation of each evaluated alternative IO must satisfy condition (15): MOR MOm QABC ≥ QABC
(15)
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Formally, the hierarchical weighting is a matrix multiplication, and the final integral assessment of the MoD performance is obtained by mathematical calculation according to the formula (16): WMOR =
X x=1
WMOR /Ax · WAx +
Y y=1
WMOR /By · WBy +
Z
WMOR /Cz · WCz
(16)
z=1
Thus, the studied material allows us to draw a conclusion about the necessity of forming a unified system of parameters, within which it will be possible to analyze the final medical result and the social effect in an integrated manner, taking into account the economic resources consumed by the medical organizations.
4 Discussion The assessment of efficiency of functioning of interdepartmental institutions in the conditions of actual changes of the basic business processes in the health care system and prognostic assessment of their productive potential it is necessary to carry out by means of complex solutions which will consider uniform, measurable indicators of activity of interdepartmental institutions, based on reliable data and taking into account including territorial features of health care system.
5 Conclusion The offered complex technique of an integral estimation of efficiency of functioning of medical organizations, organized on the principle of necessity and sufficiency on the basis of the predicate model of data representation and hierarchical methods of pair comparison is capable to eliminate the revealed drawbacks of modern domestic and foreign techniques of an estimation of alternatives and allows to operate with qualitative and quantitative criteria of explicit and implicit knowledge of results within the limits of the uniform model, characterizing level of efficiency of compared MO. This will make it possible to obtain reliable examination results and form a reasonable conclusion and recommendations for correcting actions with regard to the evaluated MO in order to increase their performance efficiency. The implementation of the proposals described in the article on the organization of the procedure for assessing the effectiveness of medical organizations under the conditions of changes in the main business processes in the healthcare system will contribute to the creation of sustainable competitive advantages of domestic medical organizations. Acknowledgments. The reported study was funded by RFBR according to the research project № 20–010-00955.
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Emergence of the New Start Up Ecosystem: How Digital Transformation Is Changing Fintech and Payment System in Emerging Markets? Samrat Ray1 , Elena V. Korchagina2(B) , Andrey E. Druzhinin2 , Vladislav V. Sokolovskiy2 , and Pavel M. Kornev2 1 Calcutta Institute of Engineering and Management, Calcutta, West Bengal, India 2 Peter the Great St. Petersburg Polytechnic University, Saint-Petersburg, Russia
[email protected]
Abstract. The use of crypto for conducting business presents a host of opportunities and challenges. By and large as with any frontier, there are both unknown dangers and strong incentives. Nowadays, the use of virtual currencies is more frequent in the financial transactions is the main reason for consider to use it. Both the primary and secondary data have been collected by the authors. Surveys and thematic analysis of the information have been done. It gave the clarity of the subject. In the results chapter, survey results have been presented in graphs. Survey has been conducted on the bankers and the responses have been further analyzed with SPSS. The statistical analysis has been performed ensuring the objectives of the research is satisfied. In the discussion chapter, the survey results and the statistical analysis have been further discussed. It has been observed that the digital transformation has helped in making the payments more effective. Correlation has been determined indicating that the digital transformation boost the growth of digital economy. The research helped in understanding the impact of cryptocurrency and the expansion of bitcoin on the digital economy of the emerging market during the pandemic. The research helped in understanding with the help of using secondary qualitative analysis by generating themes that digital economy impacts the emerging markets in a positive way. The research paper also helped in understanding that cryptos help the emerging markets a lot. Keywords: Digital Transformation · Cryptocurrency · Financial Inclusion · Bitcoin · Banks · Emerging Markets
1 Introduction The development of the Fintech and implementation of innovative means have changed the payment system of the emerging markets. The development of Fintech has especially helped countries like India during the covid-19 situation to make payments without making any direct contact. The development of “cryptocurrency” along with the “bitcoin industry” has significantly helped the businesses of emerging countries like India during the pandemic to grow and expand. The usage of cryptos and bitcoins in India supports © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 621–638, 2023. https://doi.org/10.1007/978-3-031-32719-3_47
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the “financial inclusion” of emerging markets. The research thus tends to understand the impact of cryptos and the bitcoin industry in emerging markets like India during the pandemic [1]. “Cryptocurrency” is one of the emerging currencies of the modern world. This is a form of “medium of exchange” that is performed digitally and also is not governed by any governmental organization like “Banks” [2]. One of the most prominent forms of cryptocurrency is “Bitcoin”. This is a kind of digital currency that is transacted digitally and is sent to users from any part of the world [3]. There is no intervention of government bodies making the currency a risky investment. The implementation of using cryptocurrencies in emerging markets like India can allow the country to boost its economy during the pandemic. As stated by Kumar [4] by 2030, the cryptocurrency economy of emerging markets like India is predicted to develop 2X quicker, with the potential to create eight lakh employment. It has the potential to contribute $184 billion in economic value through investments and cost reductions. The value of Bitcoin, which is the most popular cryptocurrency during the pandemic, became a “high valued asset” of 2020. During the pandemic, the value of bitcoin increased by 500% in the first six months of 2020. With many countries already giving them legal status to cryptocurrency emerging markets like India as the pandemic comes to its end has implemented norms to make it a legal tender. The implementation of cryptos and the expansion of the bitcoin industry has significantly impacted the digital economy of emerging markets [5, 6]. This has helped the central banks of the economies to adapt to the digital currencies for conducting their transactions [7]. This has helped in increasing the activities of the emerging markets like India during the pandemic which helped in boosting the growth of the economies. Thus, the implementation of cryptocurrency and expanding the bitcoin industry has helped in the development of the digital economy of emerging markets like India during covid. This is an issue because, many developed countries like Japan, the United States and the United Kingdom have accepted cryptocurrency as a “legal tender”. However, emerging markets like India are finding it difficult to accept cryptocurrency as a “legal tender” due to the lack of development of the latest technologies to monitor the currencies along with the lack of transparency among the government [8]. The introduction section provides the background about the impact of cryptocurrency and the expansion of the bitcoin industry has on the digital economy of emerging markets like India, especially during the pandemic. The aim of the research as well as the objectives that the research tends to fulfil is also being generated to help in understanding the impact of cryptos on the digital economy of the emerging markets.
2 Materials and Methods The methodology is one of the major chapters of the assignment as this chapter focuses on the different aspects of the data collection and analysis related to the requirements of the study. In this section, several aspects of the proceedings of the research and the pattern of the data analysis will be done. Research philosophy, approach, and design will be discussed along with the time plan.
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Digital transformation has changed different aspects of the Fintech and payments system and that impacts the emerging markets. Hence, this topic must be analyzed well and in detail [9]. Research design is the format and the pattern of data collection and analysis. There are three types of research design: Explanatory, Exploratory, and descriptive. The explanatory design focuses on the explanation of the data whereas the exploratory focuses on the exploration of the new data and information, In this paper, the descriptive design has been taken. It is due to the fact that description and details of the collected information and the opinion of the people have been important as this topic is quite modern and the up to date data analysis is required [10]. There are two types of data: “Primary Data” and “Secondary Data”. Primary data helps the researcher to go out and collect the data and opinions of the people related to the topic and type secondary data is the collection of the information and data from the pre-published secondary sources [11]. In addition to that, “Quantitative data” is the data based on numbers and statistical figures, and “Qualitative data” is based on the information. In this paper, both the Primary Quantitative and secondary Qualitative data have been collected. Primary quantitative data have been collected as it focuses on the opinion of the people and the secondary qualitative data have been collected as it focuses on the different aspects of the topic [12, 13]. The changes in digital transactions and the effect in emerging markets and recent businesses. As per the thought of [14], the surveys have been conducted for the primary data and the secondary data have been collected using secondary sources like Google scholar and Proquest. There are majorly two types of data analysis in the primary data: “Quantitative analysis” and “Qualitative analysis”. In the secondary analysis, “Thematic analysis” is one of the major analyses. In this paper, survey analysis has been taken [15]. It is due to the fact that the survey gives the opinion of the people on the changes in payments and the effect on the market. On the other hand, thematic analysis has been taken into account as themes give the different sides of the topic and depth in the knowledge. In addition to that, SPSS analysis has been taken into account for the quantitative analysis method [16]. Ethical considerations have been one of the major aspects of the research paper. In this paper, the ethical aspects have been followed. The data accuracy has been maintained. In addition to that, the privacy of the data has been taken into account [17].
3 Results The study included a formalized survey of 75 representatives of the financial and banking industry about the impact of digital technologies and cryptocurrencies on the economy of emerging markets. The results of respondents’ answers to the questions are presented below.
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Fig. 1. Gender of participants [studies by authors]
Most of the participants are male respondents in comparison to the female.
Fig. 2. Age of participants [studies by authors]
Most of the bankers are having an age range between 31 to 40 years.
Fig. 3. Digital transformation and growth in fintech [studies by authors]
Based on the responses, the digital transformation has brought rapid growth within fintech.
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Fig. 4. Digital facilities to customers [studies by authors]
53.3% respondents are in favor of the banks providing digital facilities to the customers.
Fig. 5. Operational efficiency [studies by authors]
The majority of the responses determine that the digital transformation has created operational efficiency.
Fig. 6. Effectiveness of payments process [studies by authors]
The payments process has become more effective according to 68% of the respondents with the digital transformation.
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Fig. 7. Flexibility in fintech [studies by authors]
Majority of the participants agree with the fact that digital transformation has brought flexibility in fintech.
Fig. 8. Acknowledgement of digital payments system [studies by authors]
Most of the bankers believe that the digital payment systems are well acknowledged by the customers.
Fig. 9. Growth of digital economy [studies by authors]
According to most of the responses, the digital transformation helps in growth of the digital economy.
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Fig. 10. Future of digital payments [studies by authors]
41.3% bankers strongly agree that digital payment systems can be the future of emerging markets (Tables 1, 2, 3 and 4). Table 1. Descriptive Statistics [studies by authors] Descriptive Statistics Mean
Std. Deviation
N
Are the banks providing digital facilities to the customers?
1.47
.502
75
Is the digital transformation creating operational efficiency?
1.49
.503
75
Are the banks providing digital facilities to the customers?
Is the digital transformation creating operational efficiency?
1
−.014
Are the banks providing digital facilities to the customers?
Pearson Correlation N
75
75
Is the digital transformation creating operational efficiency?
Pearson Correlation
−.014
1
Sig. (2-tailed)
.903
N
75
Sig. (2-tailed)
.903
75
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S. Ray et al. Table 2. Correlation for digital facilities and operational efficiency [studies by authors]
Pearson Correlation
Sig. (1-tailed)
N
Do you agree that digital payments systems will be the future in emerging markets?
Are the payments process becoming more effective with digital transformation?
Do you agree that digital payments systems will be the future in emerging markets?
1.000
.200
Are the payments process becoming more effective with digital transformation?
.200
1.000
Do you agree that digital payments systems will be the future in emerging markets?
Are the payments process becoming more effective with digital transformation?
Do you agree that digital payments systems will be the future in emerging markets?
.043
Are the payments process becoming more effective with digital transformation?
.043
Do you agree that digital payments systems will be the future in emerging markets?
75
75
Are the payments process becoming more effective with digital transformation?
75
75
Theme 1: The digital economy has positively impacted the economy of an emerging market.
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Table 3. Analysis of variance [studies by authors] Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
3.844
1
3.844
3.038
.086b
Residual
92.343
73
1.265
Total
96.187
74
a. Dependent Variable: Do you agree that digital payments systems will be the future in emerging markets? b. Predictors: (Constant), Are the payments process becoming more effective with digital transformation? Coefficientsa Model
1
Unstandardized Coefficients
Standardized Coefficients
B
Std. Error
Beta
(Constant)
1.613
.390
Are the payments process becoming more effective with digital transformation?
.485
.278
.200
t
Sig.
95.0% Confidence Interval for B Lower Bound
Upper Bound
4.138
.000
.836
2.390
1.743
.086
−.070
1.040
a. Dependent Variable: Do you agree that digital payments systems will be the future in emerging markets?
The implementation of the digital economy has impacted the business of organizations of emerging markets positively. The digital economy has helped in improving the “GDP” of the emerging markets by more than $100 million during the pandemic. As stated by [18] this helped in the expansion of businesses allowing the creation of jobs by more than 40%. The digital economy has also helped in reducing the social gaps among the rural and the urban people by developing technologies in the rural sector that helped the people to access the internet and solve their financial needs quite easily [19, 20]. Theme 2: Cryptocurrencies has helped organizations of an emerging market to continue their business during the pandemic. The implementation of cryptocurrencies has helped organizations of the emerging economies to conduct their business without any disruption during the pandemic. The usage of cryptocurrencies by organizations have helped to increase their financial transactions to more than 60% during the pandemic. This has led organizations of the emerging markets to generate revenue of more than $ 200 billion during the pandemic [21]. With the increase in the value of cryptocurrency by more than 300% during the pandemic, the
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S. Ray et al. Table 4. Regression analysis [studies by authors]
Correlations Has the digital transformation brought rapid growth in fintech?
Has the digital transformation brought rapid growth in fintech?
Do you agree that the digital transformation impacting the fintech and payments systems helps in growth of digital economy?
Pearson Correlation 1
.025
Sig. (2-tailed)
.834
N
75
Do you agree that the Pearson Correlation .025 digital transformation Sig. (2-tailed) .834 impacting the fintech 75 and payments systems N helps in growth of digital economy?
75 1 75
organizations have generated profits of more than $50 million in 2020. Thus helping the businesses of the emerging market to expand themselves. Theme 3: The cryptocurrency and bitcoin industry has helped in shaping the digital economy of emerging markets during the pandemic. The implementation of cryptocurrency and the expansion of the bitcoin industry has helped in shaping the digital economy of the emerging markets during the pandemic. The digital economy with the help of the cryptocurrency and bitcoin industry has helped in the increase of the economic activities of the emerging markets that have helped in the growth of the economy financially. As stated by [22], the digital economy has also helped the businesses of the emerging markets to reduce their “transaction costs” thus allowing businesses to make good investments. Thus, cryptos and bitcoin have helped in shaping the digital economy. Theme 4: Bitcoins has proven to have a positive impact on the businesses of emerging markets. The implementation of Bitcoins has a positive impact on the businesses of emerging markets since it offers some benefits. Bitcoins have helped businesses from incurring the fees that they had to give to the banks while doing transactions. Bitcoins have helped businesses of the emerging markets to conduct international payments without incurring any transaction costs. Bitcoins have also helped businesses to offer their financial services from any part of the world as these are mobile. Since bitcoins have a “peer-to-peer” network, the transactions are done with the help of “Blockchain technology” making the transactions to be safe [23, 24]. Theme 5: Emerging markets with the implementation of cryptocurrencies have transformed their payment system.
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As stated by [14] cryptocurrency has helped in creating ample opportunities for investments for everyone as the digital payment system is available to everyone of whatever caste and religion a person may belong to. The implementation of cryptocurrency involves the use of “Blockchain Technology” thus making the digital economy a safer place for people to do their transactions [25]. The digital economy with the help of cryptos has enabled the reaching of financial services to vast lands at a fast pace thus satisfying the needs of the consumers. Thus, allowing people to rely less on physical transactions and more on digital payments [26, 27]. Survey has been conducted on the bankers and the responses are recorded. Statistical analysis has been performed based on the survey results in order to meet the objectives of the research. Discussion about the results of the survey have been further performed in the next chapter.
4 Discussion In Fig. 1, the participants are 75 bankers and they are asked to reveal their gender at the initial stage of the survey. Based on the analysis, it has been detected that Most of the participants associated with this survey are male. 84% of respondents associated with this survey are male and 16% of the respondents are female. In Fig. 2, the age of the participants has been asked. It is detected that the age of most of the respondents are between 31 years and 40 years. The range of ages of the bankers is necessary to be evaluated for determining the effectiveness of the survey. In Fig. 3, the researchers asked the bankers whether the “digital transformation” can cause rapid growth in “fintech” [28]. 54.7% of the respondents agree with the fact that it can make a major impact on significant growth in “financial technology”. The “digital facilities” provided by the banks have been evaluated in Fig. 4 in this aspect. Most of the respondents think that the banks are providing relevant “digital facilities” to the customers. 46.7% of the respondents have disagreed with this fact [29]. In Fig. 5, the researchers asked the respondents whether “digital transformation” can create operational efficiency. The majority of the responses determine that the digital transformation has created operational efficiency. 49.3% of the overall respondents disagreed with the fact that it can create operational efficiency. In Fig. 6, the respondents gave responses regarding the effectiveness of the payment process. It has been asked whether the payment process is efficient in the digital transformation. 68% of the respondents think that it is an effective process in terms of “digital transformation”. In Fig. 7, the respondents have been asked whether the digital transformation provides flexibility in fintech. Majority of the participants agree with the fact that digital transformation has brought flexibility in fintech. Only 12% of the overall respondents think that it is not a flexible method in fintech [30]. In Fig. 8, the 75 bankers have been asked regarding the acknowledgement of a “digital payment system” by the bankers. It has been determined that most of the respondents think that the payment systems are acknowledged by the customers [30]. Minority of the respondents have disagreed with this fact. In Fig. 9, it has been detected that the researchers asked the respondents of the survey regarding the “future of emerging markets”. Based on the responses of most of
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the responses, the digital transformation helps in growth of the digital economy. 16% of the respondents have disagreed with this fact. 13.3% of the participants do not think that the digital payment system will be the future in the “emerging markets” [31]. In Fig. 10, the respondents have been asked whether the “digital transformation” is impacting the payment system and fintech in the growth of the economy. 41.3% of the overall bankers strongly agree that digital payment systems can be the future of emerging markets. Statistical analysis has been conducted based on the survey results. Correlation among the digital facilities and the operational efficiency is determined to be −0.014 [32]. It indicates that weak negative correlation is present between the variables of digital facilities and operational efficiency [33]. The digital facilities have minimal impact over the operational efficiency. Regression analysis has been performed for the effectiveness of the payment process and digital payment system in the emerging markets. In this case hypothesis has been formed which is as follows: H0: The effectiveness of the payment process does not contribute for the growth of digital payments in future. H1: The effectiveness of the payment process contributes for the growth of digital payments in future. The “F value” in the ANOVA is calculated to be 3.038 which is not close to 0. In this case, the alternative hypothesis can be accepted. It determines that the continuous drive of effectiveness of the payment process due to digital transformation can be beneficial for the growth of digital payments in the future [34]. Correlation among the digital transformation in the growth in the digital economy is calculated as 0.25. A positive correlation has been determined in this scenario indicating that the changes brought by the digital transformation boost the growth of the digital economy [35]. The expected outcome as stated by [36] for Theme 1 is that the digital economy would have a positive impact on the economy of the emerging market. On the other hand, Hasan [37] stated that the digital economy would have no impact on the economies of the emerging economy. The analysis has helped to understand that the digital economy has positively impacted the economies of the emerging markets. The implementation of the digital economy by the emerging markets have seen an improvement in the GDP of the economies during the pandemic as it was more than $100 million. Implementation of the digital economy has helped in the creation of jobs by more than 40% during the pandemic [38]. The digital economy has also impacted the economies of the emerging markets in the sense that the social disparities between the rural and urban people have been reduced as technologies are being developed helping the rural areas to solve their financial issues easily. The digital economy has also helped the economies of the emerging markets to have a “growth rate” of nearly 10% during the pandemic. Thus, the digital economy has helped in developing the “small and medium industries” of these countries allowing the sector to expand and also providing this sector with more opportunities and growth. The digital economy has helped this sector in generating a revenue of more than $40 million during the pandemic. This has happened because this sector was able to fulfil the demand of their consumers who are located in far off places very easily by providing digital financial services [39]. The expected outcome as stated by [40], for Theme 2 is that Cryptocurrencies has helped organizations of the emerging markets to conduct their business without any
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interruption during the pandemic. Similarly, [17] stated that cryptos have in expanding their business during the pandemic. The analysis helped to understand that the implementation of cryptocurrency has indeed helped organizations of the emerging markets to conduct their business during the pandemic without any interruption. Organizations by implementing the use of cryptocurrencies have helped in increasing their financial transactions by more than 60% at the time of the pandemic. This has happened because cryptocurrency is a digital currency that helps organizations to expand their financial services to people to a vast number of people without any direct contact. Cryptocurrencies have also helped organizations of the emerging markets to generate revenue of more than $200 billion in 2020. The analysis also stated that as the values of the cryptocurrency increased by 300% during the pandemic, organizations of the emerging market who have adapted using cryptos have generated a profit of more than $50 million during this period. Cryptocurrency has helped organizations of the emerging markets to expand their business at the global stage as the world now uses cryptocurrencies in performing their business. Cryptocurrency has helped businesses of the emerging market to create more employment opportunities to help the people to survive during the pandemic. The expected outcome for Theme 3 according to [41], is that the cryptocurrency and the bitcoin industry has helped in shaping the digital economy of the emerging markets. On the contrary, Johnson [42] stated that due to the lack of technology and government intervention the cryptocurrency and the bitcoin industry has not helped in shaping the digital economy of the emerging markets. The analysis helps to understand that the cryptocurrency, as well as the bitcoin industry, have helped in shaping the digital economy of the emerging markets. The usage of cryptocurrencies and expansion of the bitcoin industry in the emerging markets have helped to shape the digital economy in such a way that the digital economy has helped in increasing the economic activities of the emerging markets. The analysis helped to understand that by using cryptocurrency and by expanding the bitcoin industry in the emerging market, the digital economy has contributed to the growth of the markets. The shaping of the digital economy with the help of cryptocurrencies and by expanding the bitcoin industry in the emerging markets have helped in the creation of ample opportunities for doing good investments that are of very low risks but offers high returns. Shaping of the digital economy has helped in reducing the “transaction costs” that the businesses of the emerging markets had to bear before the development of the digital economy. The shaping of the digital economy with the help of cryptos and bitcoin has happened in such a way that the transactions that are now happening are based on using “Blockchain technologies” providing safety and security to the consumers in doing their financial transactions [43]. The expected outcome for Theme 4 as stated by Deng and Zhang [44], is that Bitcoins have the businesses of the emerging markets in a positive way. Similarly, Wu [45] stated that Bitcoins have provided many benefits for the businesses of the emerging markets. The analysis helps to understand that the implementation of Bitcoins have helped the businesses of the emerging business as it provides benefits to the businesses of the emerging markets. Bitcoins have helped businesses of the emerging markets to be free from incurring the fees that the businesses had to give while doing any transactions via banks. This happens since bitcoins are not controlled by banks. Bitcoins also have businesses of the emerging markets to do their business in the international market
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and help in solving the financial problems without paying any transaction costs which was not possible earlier because the businesses did all their transactions with the help of banks and had to pay a transaction fee [46]. The analysis also stated that Bitcoin also helped businesses of the emerging markets to expand their business and offer their financial services to the consumers of the world freely and without consuming any time as bitcoins are mobile. The analysis helped to understand that since the transactions that are done with the help of Bitcoin is done with the help of a “peer-to-peer network”, the transactions that are done is safe and secure. The analysis also stated that the fraud and corruption among the business in terms of conducting financial transactions would be decreased as Bitcoins are digital allowing every data to be stored in the cloud. This makes the transactions done with the help of Bitcoin to be transparent which helps businesses to maintain their stability in the market [21]. Cryptocurrency has been one of the modern approaches and one of the newest technological changes that have changed the currency system and the different aspects of digitalisation. It has been seen that the modern trend has changed, and cryptocurrency has taken a significant position in the different aspects of transaction and digitalisation. It is one of the hot topics in recent times and the researchers said that in the future, the transaction and payment processes will be changed to cryptocurrency to a significant amount. In recent businesses and the new market, ecosystems have been changing in recent times and digitalisation has changed the aspects of digital payments and transactions [47, 48]. Bitcoin is one of the major threats in recent times and people are investing in bitcoin. It will take the significant position of a currency element in recent years [49]. Understanding This trend, the emerging markets have started to implement new payment systems. In recent times, online payments have taken the market and the new emerging businesses have adopted the different aspects of online payments. Now, in regard to the digital transformation, cryptocurrency has taken a significant position and the chances of the acceptance of bitcoin and cryptocurrency as a form of currency in the near future are increasing [33]. Considering this factor, major changes in the payment systems have been implemented by the new and emerging businesses. It has been seen that some of the new businesses have agreed to implement cryptocurrency and bitcoin as one of the available payment methods. Thematic analysis has been performed considering the research topic. It is observed that the digital transformation has been creating operational efficiency. Based on the statistical analysis it is observed that changes brought by digital transformation helps in growth of the digital economy.
5 Conclusion In this paper, both the primary and secondary data have been taken. Surveys of 75 bankers have been done and they have been asked the question related to the changes in the digital ecosystem. The secondary data have been collected from secondary sources. Google scholar and Proquest have been chosen as the secondary sources, In the analysis section, SPSS analysis has been done. It gave the numeric interpretation of the data as well as the subject and thematic analysis has been done as the themes give a better overview of the knowledge.
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In the quantitative analysis, it has been observed that most of the participants are within the age range of 31 to 40 years. The digital transformation has brought rapid growth within fintech and the banks have been providing digital facilities to the customers. Operational efficiency has been developed with the digital transformation in fin tech and payment systems. The digital transformation is beneficial for the growth of the digital economy and it can also be the future of the emerging markets. And considering the statistical analysis, it is observed that a weak negative correlation is present among the digital facilities and the operational efficiency. Regression analyses further verifies that the effectiveness of the payment process can be beneficial for the growth of digital payments. Correlation analysis determines that the changes brought about by the digital transformation fosters the growth of the digital economy. The digital economy may take over the emerging markets in the future. The research tends to understand the impact of cryptocurrency and the expansion of the bitcoin industry on the digital economies of the emerging markets during the pandemic. Section 3 and 4 are based on the results and discussions that have been generated with the help of themes. The results chapter helped to understand the digital economy impacts the economies of the emerging markets positively. The result section helps to understand the fact that the digital economy allows the differences between the urban and the rural people to be reduced due to the development of the region with the latest technologies that allow them to solve their problems of financial needs. The research paper concludes that the implementation of cryptocurrencies has helped the businesses of the emerging markets to continue their business without any disruption. Cryptos and the expansion of the bitcoin industry have helped in shaping the digital economy of the emerging markets. Bitcoins have positively helped the businesses of the emerging economy to expand. The research thus implements the fact that cryptocurrency and the expansion of the bitcoin industry not only helps the digital payment system to transform, it also the emerging markets to compete with the world during the pandemic.
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Stock Market Reaction to the Blockchain-Related Technologies Adoption: An Event Study Analysis Varvara Nazarova(B)
and Artem Shumeiko
National Research University Higher School of Economics, Saint Petersburg, Russia [email protected]
Abstract. Cryptocurrencies and blockchain-related technologies are becoming more popular every year and public companies discover and adopt these technologies. This paper aims to investigate how announcements about adoption blockchain-related technologies made by public companies influence their stock price. The announcements from the period from 2017 to 2022 are considered and divided into categories: means of payment, investment, NFT, mining, metaverse and blockchain. The methods used in this paper are event study with the modified version of the Corrado rank-test and regression analysis with the OLS method with the dependent variable standardized cumulative abnormal return and the interest variables P/E and profit margin. The results of the event study have shown that announcements about investment in cryptocurrency or using it as a means of payment exhibit significantly positive results, whereas mining activities has a significantly negative reflection in the stock price. The regression analysis reveals the significantly negative relationship between the dependent variable and profit margin. Keywords: Blockchain-related technologies · Cryptocurrencies · Event Study · Non-fungible Token (NFT) · Stock Price
1 Introduction In the past couple of years, cryptocurrencies along with NFTs (short for non-fungible tokens) and DeFi (short from decentralized finance) have become very popular, more people have started to get involved in cryptocurrencies. Even though cryptocurrencies are seen by many people as insecure, non-regulated, and highly volatile financial assets, a boom of these technologies draws attention of retail investors, institutional investors and large corporations like Microsoft, Apple, and Tesla. Even some countries monitor cryptocurrencies and their application, for instance, in 2021 the Republic of El Salvador has adopted Bitcoin as a national cryptocurrency. The popularity of cryptocurrencies seems to rise exponentially and appeal a lot of people as more and more marketing campaigns praise crypto-related technologies. The crypto market capitalization has risen from only $18 billion in January 2017 to 2,800 billion in November 2022 [1]. Some people compare the invention of cryptocurrencies © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 639–649, 2023. https://doi.org/10.1007/978-3-031-32719-3_48
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to the invention of the Internet, anticipating a similar fast growth of the community and massive adoption of them in real life. Companies gradually adopt blockchain-related technologies and announce it to the public, which may reflect their stock price. Investors of some companies highly anticipate any blockchain-related technology adoption to buy such stock [2], while in other companies the mention of cryptocurrency has no effect on the stock price [3]. Hence, the extent of such announcements and the sentiment among investors differs across companies. Cryptocurrencies have been on radar of institutional and retail investors for not long ago, thus this topic is not yet fully explored in the academic papers. In the past couple of years, many companies adopted blockchain-related technologies. There is a relatively small body of literature that is concerned with effects of blockchain-related technologies adoption by companies on their stock prices. Much of the current literature on event study in crypto field pays particular attention to the effect of different events on cryptocurrencies’ prices but not stock prices. Cheng et al. [4] find out that investors’ response to the public firms’ initial SEC 8-K disclosures about blockchain is mainly positive in the seven-day event window but is generally reversed over the month following the disclosure. In another research, Cahill et al. [5] conduct an event study and discover that the average abnormal return is 5.1% for blockchain-related announcements. They also reveal that abnormal returns are closely related with the Bitcoin performance. In an academic paper by Liu et al. [6], it is mentioned that on the day of the announcement, the market reacts well to block-chain news. When compared to announcements that aren’t related to technical innovation, the market reacts more positively to blockchain technical innovation announcements. The market reacts more positively to strategic statements than to operational announcements. Stock market reactions to blockchain announcements are un- affected by enterprise characteristics such as scale and innovation capability. Xu [2] examines the announcements of blockchain adoption in China’s listed companies and concludes that such announcements have a significant positive impact on their stock prices. This paper seeks to investigate the effect of announcements about adoption of blockchain- related technologies by companies on their stock prices. This work is intended to estimate the abnormal returns for stocks of public companies that made announcements about the adoption of any blockchain-related technology. The research sample is composed mainly of American companies and includes period from 2017 to 2022. The method used in this paper is an event study with a market-adjusted returns model and Corrado rank-test. After that, a regression analysis is used to estimate the relationship between standardized cumulative abnormal returns on variables and financial ratios such as profit margin of the company, price to earnings ratio and others.
2 Methodology 2.1 Data In this research paper, the effect of various announcements made by companies from 2017 to 2022 that are related to the blockchain-related technologies is estimated. The data used in this paper are historical daily adjusted close stocks prices of these companies. The data required for analysis are collected from Yahoo! Finance [7], a widely known
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aggregator of financial news and financial data. A total of 84 announcements from 63 companies is considered in this paper. It is important to define an announcement and where it comes from. An announcement is a news article published in the trusted sources of financial disclosure and distributors of press releases like PR Newswire [8] or Business Wire [9]. Official websites of the companies are also considered as trusted sources of information for the purposes of this paper. News agencies, business magazines and other sources of financial news as Reuters, Fortune, Bloomberg and Yahoo! Finance are also used as sources of news. Each announcement belongs to one of the six categories of news: Investment, Mining, Meta- verse, NFT, Means of Payment or Blockchain. Description and examples for every category are provided be-low: • Investment – any news about buying bitcoin and rarely ether (the second largest cryptocurrency) are included in this category. For instance, Microstrategy have made not less than four public announcements about bitcoin purchases during the period 2020–2021 [10]. Other companies have invested in bitcoin, for instance, Tesla, Block, Marathon Digital Holdings and others. • Mining – announcements that contain information about mining activity or buying mining company. Examples include Tesla [11], CleanSpark, Block and others. • Means of payment – any news about permitting holding cryptocurrency on debit or credit cards or paying for goods in cryptocurrency with such cards. For instance, in March 2021 T announced that it will accept bitcoin to buy its electric cars. Other announcements come from companies as Visa, Mastercard, PayPal and others. • NFT – this category is related to the releasing, supporting or purchasing digital art. For instance, giants such as Facebook and Twitter have given opportunity to their users to use NFT as profile pictures. Fashion companies as Gucci and Victoria’s Secret have released NFT in 2022, which present virtual luxury clothes. • Metaverse – metaverse announcements are related only to the purchase of virtual land. Examples include companies such as Victoria’s Secret, Adidas, HSBC, Walmart and others. • Blockchain – these types of announcements are usually related with the companies that have a complex supply chain. For instance, Walmart, Shell, and FedEx announced their objectives, which blockchain will bring to them. For instance, it may be valuable for the purposes of easing disputes between customers sending and receiving goods as well as streamlining and improvements in cross-border payments, record management and supply chain management. 2.2 Event Study The main method of this paper, event study, is a widespread method that is used by researchers to estimate an impact of an announcement (event) on the price action of the stock. This paper relies on the notation described by Campbell et al. [12]. For every announcement, a period of 60 trading days is chosen as an estimation window. To estimate the cumulative abnormal return (CAR) multiple event windows are selected: (t1, t2): (−1, 1), (0, 1), (0, 3). It is a common practice when conducting an event study to choose a main event window, hence, the window (0, 1) is the primary one for this study. It is assumed that two trading days are generally enough for investors to
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decide whether to buy the stock, sell or hold. Nevertheless, in this paper the reaction of investors within the period of 1 day before and 3 days after the announcement occurred is estimated. The impact of any post-event announcement is not taken into account. Returns of the stock are calculated as formulated below: Rit =
Pit Pi(t−1)
,
(1)
where Rit is the return of the firm i stock at time t, Pit is the price of the firm i at time t, Pi(t−1) if the price of the firm i at time t − 1. The OLS market model is used to predict the stock returns within the event window, where the market returns are represented by the S&P500 stock market index: Rit = αi + βi · Rmt ,
(2)
where Rmt is the market return at time t. The difference between the actual stock return and its expected value is known as abnormal return (AR). The OLS market model is used to predict abnormal returns. The formula is as follows: ARit = Rit − E(Rit ),
(3)
where ARit is abnormal return of a firm i at time t, E(Rit ) is the expected stock return derived from the OLS market model of a firm i at time t. The total of the abnormal returns inside the event window is determined as the cumulative abnormal return (CAR): CARi (t1 , t2 ) =
t2 t=t1
A Rit ,
(4)
where CARi (t 1 , t 2 ) is the CAR of the firm i within the event window (t 1 , t 2 ), where T 1 < t1 ≤ t2 ≤ T 2. Cumulative average abnormal return (CAAR) is the average CAR among the firms. It is calcu- lated the following way: CAAR =
1 N CARi , i−1 N
(5)
The null hypothesis of any event study testing framework is that there is no event effect. For this paper it means that on average stocks of companies that adopt blockchainrelated technologies are not affected significantly by the announcement made by these companies. All significance tests in the field of the event study can be grouped into parametric and non- parametric tests. According to the study of Brown and Warner [13] on the event study and properties of daily stock returns, daily stock returns as well as
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daily stock excess returns have non- normal distribution with fat tails. With this in mind, it seems reasonable to choose a non-parametric test. In this paper the Corrado and Zivney [14] test is used. This test does not require symmetry of the cross-sectional abnormal return distribution. The test is efficient when using within the small (less than 5 days) event windows [15]. This test fits the event study con- ducted in this paper due to the low number of observations and hence non-normal distribution of the average abnormal returns. Kolari and Pynnonen [15] mention that ranking abnormal returns has its pitfalls as the magnitude of the returns is not captured. Despite some criticism of the test, it is still robust in comparison with parametric tests and other non-parametric tests [16]. At first ranks of ARs are calculated and standardized. Standardization is an improvement of the initial Corrado test made by Corrado and Zivney [14], in which authors show the dominance of non-parametric rank test over parametric tests. Transformed abnormal returns are calculated with the following formula: rank SAR∗it , (6) Ki,t = 1 + L1 + L2 where
SAR∗it
=
SARit , for t = 0 , SAR∗it SSAR , for t = 0
(7)
where ARi,t , SSAR = SARit = SARi
2 1 n SARi0 − SAR , i=1 n−1
(8)
where L 1 is the length of the estimation period and L 2 is the length of the event window, SARit is the standard deviation of the ARs within both the estimation and the event window, S SAR is the standard deviation of the event day SAR, SAR is the average SAR on the event day. The variance of the returns is calculated using the formula: SK2 =
T2 Nt 2 1 K t − 0.5 , t=T0 N L1 + L2
(9)
1 Nt Ki,t , i=1 Nt
(10)
where Kt =
with N t defining the number of returns across companies. The rank statistic for a multi-day event period is different from the original one-day event window rank statistic in study by Corrado and was described in Campbell and Wesley [17]. To calculate the rank statistic the following formula is used: K T1 ,T2 − 0.5 , (11) trank = L2 SK
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where K T1 ,T2 =
1 T2 Kt , t=T1 +1 L2
(12)
is the mean rank across time and firms within the event window. 2.3 Regression Analysis The second part of the research is dedicated to forecasting standardized cumulative average return (SCAR) and finding the key variables that affect it. The method used in this paper is an econometric modeling, namely, linear regression with the OLS method. The dependent variable is CAR that is standardized by its variance for all stocks. The standardization measure equalizes the abnormal returns of companies, which may differ due to the different volatility of stocks. Variables of interest include traditionally used regressors as P/B and profit margin [18]. It is supposed that they play a significant role in defining the investor’s interest in the stock. The control variables include market capitalization of the company, its financial leverage and the type of announcement (NFT, mining etc.). Ho¨hler and Lansink [19] and Liu et al. [6] also used such financial ratios as control variables in their papers. Financial leverage is calculated as the ratio of total liabilities to the total shareholders’ equity. The regression model is the following: P D + β3 · + β4 , Ei Ei log(Market Capi ) + β5 · Profit Margini + ε SCARi = α + β1 · Categoryi + β2 ·
(13)
where SCARi is the standardized cumulative abnormal return for a stock i within the event window (0, 1), Categoryi is a dummy variable defining announcement type, P/Ei is the price-to-equity ratio of the firm i, D/Ei is the debt-to-equity ratio of the firm i, log(Market Capi) is the natural logarithm of market capitalization of the firm i, Profit Margini is the trailing 12 months (TTM) net profit mar- gin of the firm i. It is expected that the relationship between the Profit Margin and SCAR is positive as investors prefer to invest in profitable fast-growing companies. The relationship between P/E and SCAR is expected to be positive as investors are more inclined to invest in the growth stocks rather than in value stocks. The chosen method is divided into two parts. At first, the model with independent variable Profit Margin and control variable Category is used and is called the base model. After that, an extended model with the second independent variable P/B and control variables D/E, log(Market Cap) is constructed. For both models a robustness check is applied. Morgan [20] in their article prove that the variance of stock returns as well as abnormal stock returns is not constant over time, therefore, returns are heteroscedastic. Due to this fact the data are tested for the heteroscedasticity using the Breusch and Pagan test. In case of detected heteroscedasticity robust standard errors are implemented to get unbiased standard errors of β’s coefficients. Moreover, a VIF test is applied to check for the amount of multicollinearity of the regression variables.
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3 Results and Discussion Table 1 reports the cumulative average abnormal returns (CAAR) (in %) of all categories of announcements for different event windows. The table also includes the average abnormal returns (AAR) (in %) within the event windows. The ∗ sign shows the statistical significance of the results for different significance levels. Table 1. Event study results Means of
13
1.17
0.39
1.05
0.53
0.01
0.00
Mining
8
-1.29
-0.43
-1.58
-0.79
-1.4
-0.35
NFT
23
1.03
0.34
0.28
0.14
0.85
0.21
Blockchain
11
0.36
0.12
0.36
0.18
-0.14
-0.04
payment
Investment
16
0.48
0.16
2.05
1.03
2.43
0.61
Metaverse
13
-0.03
-0.01
-0.93
-0.47
-1.01
-0.25
It is seen from the Table 1 that the categories Means of payment and Investment are considered positive by investors within the primary event window and another event window. Means of payment is significant at 10% level with average abnormal return of 0.39% within the (−1, 1) event window. The results within primary event window show AAR of 0.53 at 5% level. Investment announcements are also positively perceived by market participants as the AAR for the primary event window is 1.03 and is significant at 10% level, AAR for the (0, 3) event window is significant with the value of 0.61% at 5% level. On the other side, Mining is among those that are not seen as positive by the market participants. The AAR within the event window for the Mining type of announcements is -0.79% at 10% level and -0.35% at 10% level within the (0, 3) event window. Announcements related to NFT, Blockchain and Metaverse do not provoke the stock price to change significantly. The interpretation of the positive reaction of the market to the news about adoption cryptocurrency as means of payment may be the following. Companies as Visa, Mastercard or PayPal give their customers an opportunity to buy cryptocurrency and hold it on their debit and credit cards and wallets. It generates an additional cash flow for these companies from commissions they get [11], hence investors want to buy an undervalued stock. The implementation of cryptocurrencies is not expensive, while the number of enthusiasts willing to pay with their virtual money is growing. Companies that announced that they are willing to start mining bitcoin have significantly negative CAARs. The cause of a such market sentiment may be hidden in the cost of the hardware for mining digital currency, which pays off not very soon because of the competitive environment [8]. At the same time, it may be related to the crypto winter, which is a prolonged period of falling prices, within which the announcements were made.
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Investment activities undertaken by public companies have significant positive CAARs. It may be induced by the bull cryptocurrency market, which led investors think that such investments are supported by technical or fundamental analyses of crypto market or both. The outcome of this paper partly correlate with the results from other authors. As mentioned earlier, other authors, especially Cahill et al. [5] and Cheng et al. [4] report that blockchain- related announcements provoked significantly positive reactions from investors. These results are similar to the results of this paper: blockchain-related type of announcements has a positive cumulative abnormal return on average, though, the results are not statistically significant. Such difference in the results of this paper and others may lie in the statistical framework: authors of above-mentioned papers use parametric t-test which more often than non-parametric tests rejects the null hypothesis as mentioned in the introduction. The results of the regression analysis are shown in Table 2: the coefficients before the respective regressors, as well as their statistical significance. The SCAR for the primary event window (0, 1) is used as a dependent variable. Table 2. Regression analysis results Blockchain
-0.485 (0.681)
-0.140 (0.733)
Metaverse
-1.811 (0.854) -0.478 (1.248) -0.437 (1.072)
-1.703 (0.884) -0.276 (1.318) -0.483 (1.091)
Mining
-1.498 (0.763)
-1.286 (0.603)
Profit Margin
-0.210 (0.070)
-0.221 (0.084) 0.017 (0.020) -0.042 (0.136) -0.084 (0.168) 3.076
Means of payment NFT
P/E D/E ln(Market Cap) Constant adj. R2 No. of observaƟons
1.279 (0.428) 0.332
(4.257) 0.347
84
84
Robust standard errors in parentheses p < 0.10, p < 0.05, p < 0.01
The regression identifies the factors which have an impact on the standardized cumulative ab- normal returns. The base model uses a category of an announcement as a dummy variable with Investment as a reference category and Profit Margin. The results
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show that announcements related to Metaverse and Mining exhibit statistically significant negative difference in relation to the Investment announcements. Profit Margin is significant at 1% level. It means that the companies which are more profitable than others on average have lesser SCAR. One of the possible explanations is that investors on average anticipate that adoption of blockchain-related technologies for companies with high profit margin may reduce the profit and hence reduce the target stock price, therefore, investors prefer companies with smaller profit margin. The second regression introduces other key financial ratios as P/E, D/E and Market Capitalization. The results show that the coefficients before these financial ratios expose no statistically significant difference from zero. The results of the linear regression aim to demonstrate the effect of different categories and fundamental financial indicators of the company on the standardized cumulative abnormal returns. The initial hypothesis about the considerable influence of financial ratios was partly confirmed as profit margin has a significant impact on abnormal returns. The event study results, and regression analysis results complement each other and give a useful information for the market participants needed for developing trading and investment strategies based on the different types of news. The results of the study confirm the conclusions of the authors regarding the impact of cryptocurrency on the stock price [2, 4]. The announcements related to adoption cryptocurrency as a means of payment or investing in cryptocurrency have on average a significantly positive average abnormal return, whereas announcements related to mining entail significantly negative average abnormal returns [19]. From the regression analysis it is seen that in a set of interest variables only Profit Margin is statistically significant at 5% level. The relationship between standardized cumulative abnormal return and this variable is negative. Similar results were obtained in the works of Cahill et al. [5], Liu et al. [6], Ho¨hler, J. and Lansink, A. O. [19]. Our research contains some flaws that will require additional research in the future. To begin with, our sample size is relatively modest. Future researchers could expand the sample size in the future to perform their study. Moreover, other statistical frameworks may be applied for conducting the event study analysis. Other variables may be used as variables of interest and more variables may be used as control variables for the regression analysis. These results could be considered as a good basis for developing investment and trading strategies for asset management companies, hedge funds and retail investors. They may be particularly valuable for high-frequency trading firms that parse news articles and build its strategies based on it. Announcements related to the adoption of cryptocurrency as a means of payment or investing in cryptocurrency should attract attention of traders as the stocks rise on average after such announcements are made.
4 Conclusion The key purpose of this paper was to investigate the effect of news about adoption of any blockchain-related technology on stock prices of the companies that have adopted it. To conduct the research, two main methods were used, namely event study and linear regression. The OLS market model was used to estimate the expected returns. The
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results of the event study have revealed that investment in cryptocurrency and using it as a means of payment by public companies has on average a significant positive reaction of investors, whereas mining activities cause investors to sell such stocks. Furthermore, it is worth mentioning that the non-significant results should also be considered as valuable. It may protect traders from developing inefficient strategies based on the speculative trading of such stocks. The regression analysis reveals that the profit margin of the company is negatively related to the standardized cumulative abnormal returns of the stock. This paper adds to the literature on new technology valuation by examining the market reaction to companies making blockchain-related announcements.
References 1. Cryptocurrency Charts: Global Cryptocurrency Charts, 2022. https://coinmarketcap.com/ charts. Accessed 01 Mar 2022 2. Xu, M.: The impact of blockchain technology on stock price: an emprical study. In: SHS Web of Conferences, vol. 96, p. 04008. EDP Sciences (2021) 3. Chiu, T.-T.: Blockchain Adoption and Investment Sensitivity to Stock Price. PhD thesis, Norwegian School of Economics (2021). https://doi.org/10.1016/j.frl.2022.103201 4. Cheng, S.F., De Franco, G., Jiang, H., Lin, P.: Riding the blockchain mania: public firms’ speculative 8-k disclosures. Manag. Sci. 65(12), 5901–5913 (2019). https://doi.org/10.1287/ mnsc.2019.3357 5. Cahill, D., Baur, D.G., Liu, Z.F., Yang, J.W.: I am a blockchain too: how does the market respond to companies’ interest in blockchain? J. Bank. Financ. 113, 105740 (2020). https:// doi.org/10.1016/j.jbankfin.2020.105740 6. Liu, W., Wang, J., Jia, F., Choi, T.-M.: Blockchain announcements and stock value: a technology management perspective. Int. J. Oper. Prod. Manag. 42(5) (2022). https://doi.org/10. 1108/IJOPM-08-2021-0534 7. Yahoo! Finance: Victoria’s secret plans NFTs and entry into the metaverse. https://finance. yahoo.com/news/victoria-secret-plans-nfts-entry-122832073.html. Accessed 01 Mar 2022 8. PR Newswire.: PR Newswire’s news distribution, targeting, monitoring and market- ing solutions help you connect and engage with target audiences across the globe. https://www.prn ewswire.com/. Accessed 20 Mar 2022, Accessed 01 Mar 2022 9. Business Wire. Press release distribution, EDGAR filing, XBRL, regulatory filings. https:// www.businesswire.com/. Accessed 20 Mar 2022 10. Microstrategy.: Microstrategy acquires additional bitcoins and now holds over 105,000 bitcoins in total. https://www.microstrategy.com/en/investor-relations/press/microstrategyacquires-additional-bitcoins-and-now-holds-over-105000-bitcoins-in-tota. Accessed 20 Mar 2022 11. Tesla, block and block stream to mine bitcoin off solar power in Texas. https://www.cnbc. com/2022/04/08/tesla-block-blockstream-to-mine-bitcoin-off-solar-power-in-texas.html. Accessed 15 Mar 2022 12. Campbell, J.Y., Lo, A.W., MacKinlay, A.C., Whitelaw, R.F.: The econometrics of financial markets. Macroecon. Dyn. 2(4), 559–562 (1998) 13. Stephen, J., Brown, S., Jerold, B.W.: Using daily stock returns: the case of event studies. J. Financ. Econ. 14(1), 3–31 (1985) 14. Corrado, C.J., Zivney, T.L.: The specification and power of the sign test in event study hypothesis tests using daily stock returns. J. Financ. Quant. Anal. 27(3), 465–478 (1992). https:// doi.org/10.2307/2331331
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15. Kolari, J.W., Pynnonen, S.: Nonparametric rank tests for event studies. J. Empir. Financ. 18(5), 953–971 (2011). https://doi.org/10.1016/j.jempfin.2011.08.003 16. Corrado, C.J.: Event studies: a methodology review. Account. Financ. 51(1), 207–234 (2011). https://doi.org/10.1111/j.1467-629X.2010.00375 17. Campbell, C.J., Wesley, C.E.: Measuring security price performance using daily NASDAQ returns. J. Financ. Econ. 33(1), 73–92 (1993) 18. Höbarth, L.L.: Modeling the relationship between financial indicators and company performance. An empirical study for US-listed companies. PhD thesis, WU Vienna University of Economics and Business (2006) 19. Höhler, J., Lansink, A.O.: Measuring the impact of COVID-19 on stock prices and profits in the food supply chain. Agribusiness 37(1), 171–186 (2021). https://doi.org/10.1002/agr. 21678 20. Morgan, I.G.: Stock prices and heteroscedasticity. J. Bus. 49(4), 496–508 (1976)
Analytics in the Era of Digital Transformation Evgenii S. Artemenko(B) Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. The article is devoted to a comprehensive study of the evolution of analytics in the context of digital transformation. As a research task, the author identified an attempt to analyze the key elements that distinguish digital analytics from its traditional predecessor, the competencies that a company must have in order to utilize digital analytics, and the tools used by digital analytics, including big data analysis and the Internet of Things. The main objective of the study is an analysis of the reasons for the emergence of digital analytics, the place it occupies in the overall architecture of business processes, its role in operational activities and the strategic development of the company. Empirical methods (description, classification, analogy, abstraction, and generalization), theoretical methods (analysis, synthesis, induction, and deduction), as well as elements of economic analysis were used as research tools. As a result of studying the evolution of analytics in the context of digital transformation, the mechanisms of functioning of digital analytics, its place in the overall architecture of business processes and the opportunities it offers companies were analyzed. In addition, the process of changing the business model after the introduction of digital analytics was detailed and the impact of the new analytical mechanism on changing the content and format of employees’ work was considered. Elements of novelty are contained in the presentation of the analytical mechanism as a process with feedback and the definition of the place of traditional analytical tools in the era of digital transformation. The practical significance of this study lies in the systematization of the principles of construction and operation of the digital analytics mechanism, identification of what kind of position digital analytics will occupy in business processes and the opportunities that the latest generation of analytics will provide for a company. Keywords: Digital Analytics · Digital Transformation · Big Data · Information Systems · Automation
1 Introduction When analyzing the transformation of analytics under the impact of digitalization, we should note its increasing influence on individual decision making and operational actions of company employees. Analysts are now responsible not only for strategic decisions, but also for local tactical ones. Shifting the focus of attention to the operational level leads to a change in the key function of the analytical infrastructure - the information support of the main activity is replaced by the task of implementing specific © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 650–661, 2023. https://doi.org/10.1007/978-3-031-32719-3_49
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steps to achieve the goals set. The most important feature is the exclusion of the human factor - the process of making decisions and implementing operational actions is carried out automatically, maximizing the importance of analytics for the business as a whole [1–3]. The purpose of the study is an attempt to analyze the key elements that distinguish digital analytics from its traditional predecessor, the competencies that a company must have in order to utilize digital analytics, and the tools used by digital analytics, including big data analysis and the Internet of Things. The main objectives of the study are an analysis of the reasons for the emergence of digital analytics, the place it occupies in the overall architecture of business processes, its role in operational activities and the strategic development of the company. Particular attention is paid to determining the place of digital analytics in the organizational structure and its impact on changing the content and format of people’s work.
2 Materials and Methods The ability of a company to effectively use the analytical apparatus for processing the information it has in a highly competitive environment becomes a decisive factor for successful development. Since the relationship with analytics is different for all organizations, it makes sense to look at the whole evolutionary path up to the moment when it gained the dominant position among management tools. Before we proceed with the historical overview, it should be noted that the development of analytics took place in the context of the development of both computer technology and decision support systems. 2.1 Stage I: Traditional Analytics The only data sources for traditional analytics were a company’s internal resources accounting reports, information about commercial transactions, management accounting systems, etc. All information was well-structured and was essentially an array of descriptive statistics, on the basis of which predictive analytics was built. The latter was based on trend equations and regression analysis tools. The task of creating and storing a single array of data was the responsibility of the IT department, which provided it to all stakeholders on the basis of formal requests. And although the amount of information collected was small by modern standards, and the structure of the data set was extremely simple, the work of transforming it into material usable for analysis was time-consuming and labor-intensive. This alone limited the possibilities to use analytics, as well as its actual impact on business processes. It is worth noting that the data received from IT services was often unsuitable for further analysis without additional processing. The problem lay in the architecture of corporate information systems, which were tasked primarily to support the current activities of the company - any questions relating to analytics were considered as secondary. In order to get meaningful results, specialists had to do a lot of work on the aggregation, transformation and combination of data collected from different parts of the information system. Considering the process of obtaining traditional analytics, it can be stated
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that most of the time was spent on the accumulation of the necessary information in one place, rather than on its analysis. Given the significant time lag between the events that generated the data and the production of usable results, the creation of prescriptive analytics was impossible [4, 5]. Of particular interest is the consideration of the place that analytical structures occupied in the organizational architecture of companies. Analysts formed isolated departments under the direct subordination of top management, isolated from all other departments. In their work, they had little to no contact with real business processes and information technology. Their main and only task was to generate ideas for optimizing internal processes; neither clients, nor products, nor the market in which the company operated were within their scope of attention. In the eyes of top management and rankand-file employees, the presence of an analytical department in the organization structure was an optional extra that had no impact on current work and company development. Despite its almost clandestine existence, traditional analytics, using a significant part of modern analysis tools, played a useful role in the development of companies for many years. At the moment, we can state that this approach to analysis is irrevocably outdated and requires a radical revision of the “rules of the game” [6]. The result of traditional analytical methods was the creation of a set of reports, control panels (dashboards) and mechanisms for alerting users, based on a set of KPIs and a balanced scorecard (BSC). At the same time, the procedure for creating reports was long, formalized and bureaucratic - specialists of the analytical department had to receive a request from the user, understand the problem, make the necessary calculations and provide an answer in an understandable form. Users themselves, having no access to information, could not influence the analytical process and had to wait for the final result. In case they were not satisfied with it, the procedure was started all over again. To be fair, it should be noted that such a workflow was fully consistent with the rhythm of the underlying business processes. Attempts to increase the speed of obtaining results from the analytical department did not make sense, because companies had no opportunity to put the acquired knowledge into practice - the process of implementing new ideas took weeks, and sometimes even months. A natural consequence of this work system was the limited use of analytical tools and a significant underestimation of the potential benefits of accelerating the analytical cycle. In most cases, companies were simply unaware of the opportunities presented by the information at their disposal [7]. 2.2 Stage II: Big Data Analytics Introduced in 2008, the term “Big Data” ushered in a new era in understanding the role of information technology infrastructure in company operations. Being a reflection of one of the two most important trends of our time (the second one is virtualization), the meaning of the term remains rather vague. The term “big data” refers both to an array of information that cannot be processed by traditional analytical methods and to data processing itself. Although “big data,” as its name implies, involves the accumulation of a significant amount of information, the main difficulty is not its size but its form, which does not correspond to the structured format of databases. Big data can include any kind of information - text documents, photos and videos, web logs and social media content, machine code, etc. There is no single repository
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either - analysts need to collect fragmented data from multiple locations outside their companies. Additional complexity is created by the constantly increasing update rate of the information array external to the organization and the need to synchronize it with the company’s own data. And while the issue of generating, transmitting and storing a huge array of data in a volume that suits the vast majority of companies has already been resolved, the issue of analyzing it and getting meaningful conclusions that can be applied in practical activities remains a task that requires new technological approaches and modernization of analytical methods [8]. When analyzing the tools used when working with big data, it can be noted that they represent a fusion of traditional analytical methods (cluster and regression analysis, data integration methods) with relatively new approaches to analysis (machine learning, artificial neural networks, visualization methods). Despite the existence of advanced research tools, in reality, big data analytics still uses the same sources as traditional analytics - various reports, retrospective descriptive statistics, some elements of predictive analytics and, as an exotic feature, prescriptive analytics. The reasons for this are: – – – –
poor quality of the data used; objective difficulty of translating a huge array of data into a form suitable for analysis; insufficient qualification of specialists; underdevelopment of analytical tools.
Ultimately, the results obtained, balancing between triviality and unreliability, often fail to meet customer expectations [9, 10]. The growing interest in big data and its analysis methods does not cancel the need of companies for everyday analytics that provide answers to operational questions and solve local problems. Admittedly, “small data”, i.e., the data used by traditional analytics, is sufficient to overcome the vast majority of problems encountered in the course of dayto-day operations. Consequently, the analytical department must be able to work with all types of data and use all available tools for their processing - training individual employees to work exclusively with big data and developing special analysis methods for them would be not only pointless but also extremely detrimental to the company. An important consequence of beginning to work with big data was the formation of data science and the emergence of specialists professionally engaged in data research. The second stage saw a dramatic change in the place of analysts in the corporate architecture - from employees shielded from participating in real business processes, they turned into consultants capable of influencing the people making the final decisions. The result of this process has been the transformation of big data into a commercial product in its own right - companies have emerged in the marketplace that define themselves as “data scientists” whose offerings consist entirely of data and analytics [11]. Big data analytics becomes a truly breakthrough development tool only if it builds on the concepts, knowledge, and experience of the traditional analytics era. The latter circumstance indicates that the big data phase is of an intermediate nature. It is only the merging of traditional analytics and big data analytics that brings us to the final (for now) stage of development [12, 13].
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2.3 Stage III: Digital Analytics The stages discussed reflect the natural evolution of analytical mechanisms, so digital analytics is a continuation, not a replacement, of previous analytical formats. Using the methods of traditional analytics and big data analytics, it seeks to integrate all previous knowledge into a single scheme integrated into business processes. The main objective of digital analytics is to search and collect data based on predefined criteria. The resulting information must be transformed into knowledge that can be used to form specific business strategies, market plans, products, etc. In most cases, the practical fulfillment of this scheme requires a company to fundamentally change its approach to doing business - from an instrument for increasing the efficiency of current activity, analytics becomes the core around which all other processes are built. Naturally, this transformation is impossible without a direct order and subsequent support from the company’s top executives. The need to turn analytics into a key business process in most cases leads to a complete overhaul of existing analytics platforms [14]. The era of big data has created the need to work with a variety of information sources, and most of them are open and provide data in an unstructured form. The transformation of fragmented, unstructured information into knowledge that is suitable and useful for practical applications is one of the main technological challenges of the era of digital analytics - an issue that gives rise to a large number of innovative technologies. New data processing methods, on the one hand, can provide a company with a competitive advantage, but, on the other hand, will make it dependent on the amount of data and the ability to access it. Only transition to active implementation of predictive and prescriptive analytics can compensate this dependence. It should be noted that the described processes do not reduce the needs of the organization in various reporting and descriptive analytics. Such a fusion of traditional and modern approaches leads to the emergence of digital analytics - an integrated analytical complex responsible for operational activities. As the core of the corporate information system, such a complex will provide the necessary information to all stakeholders in an online format twenty-four hours a day, seven days a week. In turn, applications for mobile platforms will make corporate analytics available anywhere in the world - at the “decision-making point” [15, 16]. Today, digital analytics is the last but not the final stage in the development of corporate analytics, absorbing all the best from traditional analytics and big data analytics and placing greater demands on the technological component of the analytical process (as an example, a request for parallel data processing, a combination of different memory systems, etc.) [17, 18].
3 Results The final result of analytics has traditionally been information that was later used to make specific decisions, or something that would be an element of the final decision. Human involvement inevitably adds an element of subjectivity and self-interest when analytical conclusions are tailored to the desired outcome. Digital analytics assumes that the analytical process and the actions arising from it are carried out immediately after the system receives the necessary information, leaving no room for manual correction of the decisions made.
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The creators of any operational-analytical process are human - only a human can formalize all existing dependencies and establish a stable operation of the information system. However, after the full launch, human interference in its work should be reduced to a minimum. Artificial intelligence forms the necessary amount of data, processes it and on the basis of the received information performs the required actions. In addition to impartiality, its advantage is the speed of decision-making, when both analytical and operational transactions can be performed millions of times a day. Automated information systems not only increase the efficiency of relevant processes, but also create a demand for even more detailed information content, which allows to fine-tune the current business processes of a company. The evolutionary spiral described above takes analytics beyond its traditional use assessing events that have happened and making forecasts. Digital analytics becomes a key element of the business process, capable not only of performing complex actions and making decisions without human intervention, but also of generating instructions for the other participants in the system [19]. The discussed changes are the final stage of a long process of transition from classical descriptive analytics to predictive analytics. Whereas the former had as its key task a detailed description of the events that took place and the identification and quantification of all the factors that influenced the obtained result, the latter is concentrated on obtaining the most reliable forecasts of the situation development based on the information available at the moment of forecasting. An important circumstance is the passive position of a decision-maker - they can assess and predict but cannot influence the development of a situation. Digital analytics breaks established boundaries and learns a prescriptive function. Operational analytics not only identifies factors that can influence future development, but, through managing these factors, shapes the events necessary for the company. Summarizing the above, we can say that while descriptive analytics was concerned with the past and predictive analytics was concerned with the future, digital analytics is interested in the present, or rather in the necessary actions in the present which are likely to lead to the required result in the future. The stages of evolution and the key tasks facing analytics at each of them are presented in Table 1. Table 1. Key analytical tasks (source: author) Analytics type
Analytics tasks
Descriptive
Analysis of events in the past
Predictive
Forecast of events in the future
Prescriptive
Recommendations for achieving the events necessary for the company
For many years, traditional analytics focused on operational problems. The transition to digital analytics does not eliminate the need to solve traditional analytics problems but greatly expands the scope of the latter.
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The key difference between the two aggregate states is the complexity of digital analytics. While replicating traditional analytics in terms of the mathematical apparatus used, the new-format analytics must be fully digitized, subject to extensive scaling, and provide high speed processing of information and delivery of final results. Realization of this task in practice is extremely difficult and requires interdisciplinary qualification from executors, as well as skills of modeling the architecture of business processes [20]. The most important feature of digital analytics is the speed of decision making. Deeply integrated into business processes and functioning in automatic mode, the analytical apparatus must issue recommendations within the interval required to make a decision in a particular situation, up to working in real time. Thus, it is the decisionmaking time that becomes the parameter that determines the speed of the analytical mechanism. The traditional approach to using analytics to solve operational problems can be considered outdated because it involves following a predetermined procedure with respect to certain time regulations. Digital analytics must adapt to the decision time for each specific situation. Highlighting decision time knowledge as a key parameter for implementing digital analytics brings us back to the basic condition for digitalizing analytics. Until a company begins to use traditional analytics involving batch processing on a continuous basis, the task of going digital is impossible. The first necessary step on the road to digitalization is mastering core analytics functionality. Only after that can you move on to integrating individual analytics mechanisms into your company’s information system. The main parameters that make it possible to differentiate the stages of development of analytics are presented in Table 2. Table 2. Key differences in the stages of analytics development (source: author) Parameters
Traditional analytics
Big Data analytics
Digital analytics
Analytics type
Descriptive
Predictive
Prescriptive
Sources of information
Inside
Outside
Inside and outside
Position in the organizational structure
Isolated consultants for top management
Consultants for decision-makers
Decision-makers
Duration of the analytical cycle
Weeks
Days
Real time mode
The main task
Support for internal business processes
Commercialization of data and results of their analysis
Search for opportunities for company development
Digitalization of analytics does not differ from similar processes in all other elements of activity. Existing employee competencies must ensure the maintenance of a sustainable analytical process based on batch data processing, which, in turn, becomes
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the foundation for further digital transformation. A widespread mistake is to assume that new technologies can replace traditional knowledge. In fact, digitalization takes business processes to a new qualitative level in terms of scalability and speed of data processing, but at the same time, it increases the requirements for the skills of performers, as it involves more fine-tuning and adjustment of all elements of the system. Without a clearly structured and regulated framework, the task of digitalization of any business process turns into utopia [21]. Digital analytics is an intelligent decision-making system that automatically takes actions within a specified timeframe according to a self-formed strategy. Once set up and running, operational and analytical processes begin to function autonomously, making thousands of decisions every day. The key challenge facing digital analytics is turning a structured set of data into knowledge that can be applied for the benefit of a company. Creating a system that can independently process an array of data, generate new knowledge, scale it, and adapt it to changing market conditions in real time is extremely challenging. Despite the high degree of autonomy of described structures, humans will play a key role in their existence. At the same time, human significance increases considerably, because from qualified personnel interacting with the system, people will turn into creators - those who must come up with the concept, develop the structure, build a system of relationships, configure the internal architecture and, once launched, control the functioning process [22, 23]. The emergence of digital analytics is a consequence of the rapid development of computer and analytical technologies - it should be perceived as the next stage in the evolution of the analytical process. But no matter how inspiring the prospects are, we have to remember that man-made artificial intelligence is not yet able to think for itself. We are merely dealing with a very complex algorithm that requires careful testing before it can be launched. Given the actual freedom of action and the impossibility of operational control, a poorly functioning operational and analytical process can cause fatal damage to a company. The quality of the thousands of micro-decisions the system will make is ultimately the responsibility of its creators [24, 25].
4 Discussion The described evolution of analytics is inevitable and will eventually affect the vast majority of companies, but until it happens, many executives will remain unaware of how seriously digital analytics can change business models. Let’s take a look at the main areas of further development of analytics. Analytics is becoming an end, not a means. The traditional task that companies had when introducing a new product or service to the market was to successfully market it in order to maximize profits. In the course of further trade, they accumulated data on sales volumes, the average characteristics of the target audience, the most frequent defects and complaints from consumers. This information provided an opportunity to understand how to improve the company’s offer, but in itself it was a consequence of market transactions - the product itself was only indirectly involved in the process of forming the necessary data set. The spread of the Internet of Things has dramatically changed the situation - more and more objects of the physical world are collecting data about their surroundings.
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Often, there is a situation where the main function of a product becomes information gathering - it turns into a channel for transmitting data about the buyer. The original value of the offer for which the product was put on the market becomes of secondary importance to the producer. Their main task becomes the collection of data subject to analytical processing. Nowadays, the true purpose of a market transaction is not the commercial benefit of a particular transaction, but the acquisition of analytical data, the processing of which will allow the company to strengthen its market position. Despite the fact that the described algorithm originally implied the use of the obtained analytics exclusively within a company in order to increase the efficiency of its interaction with the market, the practice of selling the obtained analytical data to end users is now being successfully developed. The acquired material object is perceived by the buyer as a device that provides access to the necessary information. In this case, the carrier of consumer value, for which the customer is willing to pay money, is analytical data. Thus, there is an opportunity to earn twice on the collected information - during the direct sale of the data and as a result of personalization of the offer, the consequence of which is an increase in the efficiency of interaction with customers. The more information customers collect about the products they buy, the easier it is for companies to tailor subsequent products to the needs of a particular consumer [26, 27]. Data remains a key success factor. The transition to digital analytics is inevitable, but the path towards it is different for each company. Some are close to implementing it, while others don’t even know it exists. It can be argued that the launch of third-level analytics will be a decisive factor in the competitive struggle, because it opens the way to breakthrough innovations. Digital analytics is much larger than traditional analytics, but it is based on the same key principle - the data used must be of high quality (accurate and relevant). For the analytical process being analyzed, this requirement has become even more significant, since it involves an automated decision-making procedure. The work algorithm, which implies the use of newly collected information for immediate transition to practical actions, does not include the stage associated with the search and elimination of errors in the underlying data. Data quality, which was an important factor at every step in the development of analytics, has become the determining factor in the final digital phase. An automated analytical process that does not have a built-in error-finding mechanism is technically impossible to work with if it is based on non-ideal data. Regardless of the qualifications of the creators of the analytical process and the technical solutions underlying it, an error in the starting data completely compromises the final result. If a company is unable to generate a quality dataset in the sparing environment of the traditional analytics process, it has no chance of doing the same in the tightly constrained timeframe of digital analytics. Errors in raw data inevitably lead to errors in the analytical procedure, which in turn leads to errors in the final decisions, which are made automatically, without human involvement. The implementation of the described chain of events will inevitably lead the company to significant financial losses, and possibly to collapse [28, 29]. Digital analytics fosters creativity. The increasing pace of digitalization raises the question of the future role of people in business processes - how likely is it that new
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technologies will be able to function without human involvement? In the context of digital analytics, this question sounds like this: will there be room for creativity and out-of-the-box solutions once automated algorithms are launched? The exact answer to this question can be obtained only after the mass transition to third-level analytics is completed, but there are already a number of arguments to the contrary. Automation of decision-making allows you to quickly get feedback on the performance of each of its elements and determine the effectiveness of their work. The traditional approach to building business processes implied the choice of a single option after a preliminary collective discussion. Regardless of the quantity and quality of ideas generated, only a single option was always implemented in reality. Digital analytics makes it possible to simulate a multitude of proposed alternatives and to make a choice not intuitively, but based on an understanding of their quantitative results. In other words, digital analytics serves as a convenient tool for assessing the creative potential and possible risk of any generated idea. This brings us to one of the core challenges of digital analytics - well-functioning operational analytics processes must take over much of the daily grind and give people time to be creative. Automated analytics frees employees from the need to collect and verify data, allowing them to engage in work that is beyond the reach of artificial intelligence: developing ideas to improve business processes. It can be argued that digital analytics not only does not hinder, but acts as the main driver for the development of creative potential of a company [30, 31]. In conclusion, it should be noted that although the introduction of digital analytics usually revolutionizes business processes and the format of employees’ work, it is based on well-known analytical methods. The innovation in this case is automation, which significantly increases the speed of decision-making, and the integration of analytics into operational processes as a mechanism that provides timely recommendations for specific actions with an accurate calculation of their consequences.
5 Conclusion As a result of studying the process of evolution of analytics in the context of digital transformation, the following results were obtained. First of all, the process of analytics evolution up to the moment of it’s gaining the dominant position among management tools was described. The procedure of using big data and the Internet of Things in the analytical process was formalized. Secondly, the mechanisms of functioning of digital analytics, its place in the overall architecture of business processes and the opportunities it offers to companies were analyzed. An attempt was made to figure out the reasons for the transition from traditional analytics and big data analytics to digital analytics. Thirdly, the process of changing the business model following the implementation of digital analytics was elaborated. Furthermore, possible directions of further development of analytics were considered. Finally, the impact of the new analytical mechanism on the change in the content and format of employees’ work was assessed. The practical significance of this study lies in the systematization of the principles of construction and operation of the digital analytics mechanism, identification of what kind of position digital analytics will occupy in business processes and the opportunities that the latest generation of analytics will provide for a company.
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As directions for future research, the author considers the tasks of determining the permissible boundaries of the automation of the analytical process, systematizing and quantifying the risks generated by the transition to digital analytics, developing mechanisms to minimize the negative consequences of errors inherent in automated analytical algorithms.
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Perspectives for the Implementation and Development of AI in Banking Sphere Ekaterina P. Mochalina1 , Galina V. Ivankova1 , Yulia A. Dubolazova2(B) , Alexey Davydov1 , and Vladislav Bolonkin1 1 Plekhanov Russian University of Economics, Moscow, Russia 2 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected]
Abstract. The AI and ML technologies are integrated into a wide range of areas of daily life nowadays. There are several reasons for the explosive growth in the need for such technologies: AI increases the efficiency and speed of forecasts and calculations, enables to provide more detailed assessment of risks, AI and ML make possible to reduce the likelihood of human error. Paper studies AI and ML application in banking sphere and reflects of their possible ways of changing. The purpose is to evaluate the perspectives and directions of implementation of AI in Bank industry. Banks are the pioneers in the industrial application of AI technologies. To complete the goal authors, use the information analyzes of the latest IT innovations in Banks branch. Authors illustrated it by the example of Credit Scoring technology. “AI-first” trend in banking sphere recently gave a strong impulse the move towards ecosystems. Digital ecosystems have changed the way consumers perceive goods and services: a common access point (an app, for example) is a new part of life. Ecosystem technologies are the future: it can be clearly visible via the fact that even non-banking businesses and various aggregator apps are adopting a range of financial services, supplementing them with user-friendly products, providing customers with atypical user experiences, and disrupting traditional methods of promoting products and services delivery. But being customer-focused, banks will need to create a personalized portrait of their customer based on big data. To build an effective model of changing demand for certain services, it is necessary to clearly understand their age, gender, social and behavioral characteristics. So, it can be expected the appearance of an increasing operational efficiency trend through ultimate automation of manual tasks (zeroops thinking) and replacing or supplementing human decisions with advanced diagnostic mechanisms in various areas of banking activity. Keywords: Artificial Intelligence · ML · Ecosystem · Credit Scoring technology
1 Introduction The trend of informatization is rapidly spreading through different branches of modern economy. The latest inventions in a branch of information technologies are implementing into operational processes of business and help to increase efficiency and cut the costs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 662–672, 2023. https://doi.org/10.1007/978-3-031-32719-3_50
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However, the quantity of data that is needed to be analyzed is raising too [1]. To solve the problem companies, introduce special software on the base of AI. AI technology can be defined as intelligence of non-biological origin, capable of achieving a narrow range of tasks. It differs from ordinary program: direct algorithms for performing the task are not set in advance in that case [2]. It is customary to single out narrow (weak) and wide (strong) AI [3]. The key difference lies in the range of the tasks. If a weak AI is configured to perform similar tasks (AI for playing chess, checkers, Go, etc.), then a strong one is able to perform the entire range of diverse tasks available in its habitat (the information field of AI work). ML (Machine Learning) is a key technology that enables AI to achieve its goals [4]. This technology allows to develop an algorithm for the most rational implementation of the task by repeatedly repeating the process and evaluating the result [5]. It should be noted that another key characteristic of strong AI is the ability to determine the order and the necessity of performing tasks, in the framework of a huge variety of possibilities (an analogue of the human will). At the same time, when classifying ML, controlled and uncontrolled processes are distinguished [6]. Under control, it is supposed to limit the data analysis environment to set the direction of the search for the algorithm for solving the problem. At the present stage, controlled ML processes within the framework of weak AI are applied [7]. Programs with this technology are used in many areas of the economy, but the authors focus on AI development trends within the banking sector. Authors set the aim to analyze the process of implementation of the new IT-technologies including AI and ML in Russian Federation. The importance of this analysis is that within the framework of the digital development program of the Russian Federation, there are aims for informatization of the banking industry. Moreover, in the Russian Federation, this process is characterized as uneven. Thus, the analysis of modern prospects for the introduction of IT technologies in the banking industry is important for understanding the possibilities of fulfilling the strategic goals of the Russian Federation in this area. The banking sector directly interacts with almost every economic agent at different levels. Serving individuals, companies and governments, banks are an integral part of our lives [8]. Throughout history, many innovations were originally introduced in this area: Back in the 1960s, banks provided fast cash transfers over large areas through ATMs, and electronic card payments, which greatly facilitated the procedure, became available already in the 70s. 24/7 online banking became widespread in the 2000s, followed by mobile “banking on the go” in the 2010s. The next stage of development is the evolution of AI in business processes, as well as the emergence of ecosystems to obtain the necessary information base for such technologies. AI also has several driver benefits that accelerate the adoption of these technologies, namely: reducing the cost of storing and processing data, expanding access and connectivity for everyone, as well as the rapid development of complementary technologies. Implementing AI leads to a higher level of automation and, when deployed after risk control, can often improve human decision making in terms of both speed and accuracy. Thus, the banking industry is one of the first objects of the introduction of new technologies, thereby reflecting the trends in their development in terms of applications. What’s more, McKinsey estimates that the adoption of AI could create $1 trillion in additional value for banks every year [7].
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2 Materials and Methods The process of implementation of modern technologies into different branches of economic activity is uneven. At the industrial stage of development, innovative technologies were primarily implemented into production processes only after they found their application in the service sector and everyday life [9]. The post-industrial stage prioritizes information as a key innovative factor of production. Modern AI and ML solutions are used primarily in the information environment, in contemporary solutions for IT companies, after they adapt to use as services for the B2B sector (implemented in real production) and for the B2C sector (transformed into products for customers). Considering the factor of transformation and transfer of B2B banking services to the format of mobile Internet banking, the use of credit scoring technologies, as well as client actions tracking are a priority for the implementation of AI and ML [10]. At the same time, due to the diversity and differentiation of modern banking services, there is no single direction for the development of innovative technologies in this industry [11]. Some banks focus on implementing options for investment strategies, while others adapt the bank’s daily tasks – issuing loans, forming deposits, as well as offering optional banking products [12]. There is also a differentiation of approaches to software creation, some companies transfer tasks for building software architecture and its maintenance to third-party companies (outsourcing), others implement tasks by their IT departments (insourcing) [13]. Due to the high level of differentiation of banking products and the difference in approaches to the introduction of new technologies into processes, there is no possibility of a clear definition of the comparative criterion. The purpose of this study is to determine the direction of development of modern technologies in the banking industry. Thus, the most relevant research methods are a field study of the capabilities of various banking systems, as well as the study of banks’ information about modern solutions related to AI and ML. At the first stage, materials on the technical possibilities of introducing such technologies into modern banking processes are studied. Further, the authors describe and evaluate the already implemented projects of the three largest banks of the Russian Federation in this area. At the last stage, conclusions are formed about the direction of using AI and ML technologies in the banking industry of the Russian Federation.
3 Results The banking system is a complex structure consisting of many interrelated processes [14]. Today, the level of development of AI is not high enough to optimize all processes [15]. However, the introduction of these technologies or at least some of them can significantly reduce costs and increase the efficiency and competitiveness of banks. Authors distinguish several areas for application of AI within the banking industry that modern companies are developing, they can be divided into four main categories [16]: – Credit scoring – Investment forecasting – Promotion of internal products and services of the bank
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– Ecosystem Products Credit scoring is the process of evaluating a potential borrower, a bank client, for the possibility of repaying a loan [17, 18]. An assessment of the probability of an event of non-payment or an appearance of a delay on a certain time horizon is made on a base of methodology. Bank scoring systems have been actively developed since the 20th century. The event itself, which is a criterion of the client’s solvency, depends on several factors (parameters) that determine the high or low probability of the event. In conditions of uncertainty and asymmetry of information, when scoring, banks rely on indirect factors that, with a certain statistical probability, indicate the reliability or unreliability of the borrower. Such criteria are age, gender, possession of property, income level, marital status, and so on. However, this approach also has several risks. The first is the method of revealing information. Most banks use questionnaires and income statements. At the same time, the information in the questionnaires may turn out to be false, and a thorough check takes a significant amount of time and does not exclude the human factor [19]. The second risk is the falsification of documents and social engineering methods, which, in some cases, allows to bypass the scoring system. Thus, the key risk is the credit scoring method itself (Fig. 1).
Quesoneer
Credit history
Credit score
Bank's decision
Fig. 1. Classical credit score model (created by authors)
To fix the problem, it is possible to select another scoring object. Nowadays, the amount of cash in circulation is significantly reduced, the turnover of money on bank cards with a high probability can reflect the real income of a person [20]. Credits to the account can reflect the place of work and spending on the card can indicate the presence of property, marital status, and so on. Under these conditions, it is possible to integrate an AI-based system that will allow analyzing the status of a client’s account and its dynamics for converting into a credit rating. However, this approach does not exclude the use of credit history (Fig. 2) [21]. Software products using AI are also applicable in investments [22]. Many banks provide an opportunity for clients to act as an investor and buy securities through them, acting as a broker. At the same time, each of these banks provides an interactive space to facilitate transactions. It exists both in the investor’s personal accounts and in mobile applications [23]. Directly, the increase in the number of client-investors for banks is a key indicator of the effectiveness of this service. However, an indirect factor influencing customer retention is the subjective success of their investment. Thus, the bank acts not only as a broker, but also as a kind of mentor for investors. Such activity consists in assembling ready-made investment portfolios and offering investment solutions [24]. At this stage, the company’s specialists participate in the development of such proposals, providing additionally paid consulting services. However, due to lack of funding, novice investors cannot afford such services. This, combined with a lack of financial literacy,
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Income
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Fig. 2. AI credit score model (created by authors)
can result in investment losses, and create a negative experience that is projected into refusing to use such services. The securities market is a dynamic system and requires constant updating the information. Thus, the creation of “investment advisor” on the base of AI can be quite relevant in the framework of the implementation of such services [25]. An important characteristic of modern banks is the huge variety of services. Nowadays, there are enough representatives of the banking industry to attract the customers [12]. Banks promote special offers and products. The examples of such products are increased cashback for certain types of goods, preferential terms for a loan, simplified document management for business, and optimized accounting. Each of services was made for a certain category of clients. However, it incurs additional costs for the bank, which may not be justified (if there are not enough clients). The role of AI in this process is to identify the needs of each client for a particular service and its promotion for him. Within the framework of the database, the client’s spendings on a bank card are analyzed. In particular, if a certain category of customers’ orders food delivery more often than they buys products, then for this category a more interesting paid service may be an increased cashback for delivery or a free delivery subscription if the delivery’s network is a partner. The fourth category is not traditional banking type of service. It has appeared as complementary services that have arisen because of the information transformation of the banking industry [26]. In the 2010s, due to informatization, wearable electronics systems (smartphones, tablets, smart devices and the Internet of Things) have been developed. The banking industry has gradually migrated much of its most used customer processes to mobile apps. By the mid-2010s, most of the contemporary service market had moved to the Internet space: services for ordering a taxi, food delivery, music subscriptions, online stores, and so on (Fig. 3). This market situation has obliged banks to adjust their services to the developing IT market by introducing cashback and online payment systems, as well as significantly
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Fig. 3. Transitional internet-services model (created by authors)
increasing the security of funds on settlement accounts [27]. At the beginning of the 2020s, the market with IT solutions for businesses in various industries became oversaturated and a primary aggregation of services appeared. Large IT companies have begun to create special applications that combine popular services and allow them to cover the demand of a new generation [28]. The banking industry, in turn, has two key prerequisites for the transition to the IT sector [29]. Firstly, due to active informatization, more and more economic activity takes place in the internet. So, the creation of solutions for participating in these processes determines the further profitability of companies. The second factor is the analysis of customer activity. The presence of own services that cover the main needs of the average user. It allows to evaluate customer’s interests, income, and other metrics necessary for both marketing and scoring tasks (Fig. 4) [14].
Fig. 4. Aggregation model (created by authors)
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Thus, in the future, banks can organize or become the central link of the largest market ecosystems. The authors are going to evaluate the implementation of AI technologies in the banking industry using the example of four largest banks in Russian Federation: Sberbank, VTB, Tinkoff and Alfa-Bank.
4 Discussion Sberbank is the largest national bank of the Russian Federation and is also one of the ten largest European banks. The introduction of modern technologies into the structure of the company’s operational processes is ongoing, for which the company has special R&D departments. At the present stage, work is underway to develop a special credit scoring system based on the analysis of large data arrays of bank cards transactions. This technology was officially patented and presented as a concept at the end of 2021. The bottom line is to completely move away from the use of questionnaires and credit history analysis when making a decision on issuing a loan product. The technology involves the analysis of banking transaction data for a certain period, followed by clustering into types of income and expenses. A special algorithm allows to estimate the real average income of a person, eliminating the risk of concealing official income, as well as the main directions of a person’s expenses [30]. Indirectly, based on the type of expenses, this algorithm allows to evaluate the place of work, interests, availability of property and even social ties of people, up to the daily routine. Also, this algorithm reveals the risks of people’s tendency to irrational spending, borrowing, and gambling. It should be noted that this algorithm is not implemented in the processes due to legal restrictions. In mid-2021, a law was adopted to unify the credit scoring process, which involves the use of conservative methods [31, 32]. It should be noted that Sberbank, since 2020, has been a brand of SBER. The rebranding was carried out due to a change of the course of company’s development. The management decided to develop within the framework of a large ecosystem that includes many services. Over a year and a half of development, online cinema systems, carsharing, music platforms, logistics, mobile communications, insurance, as well as a number of technological products of the Internet of Things were introduced. The banking system of Sberbank has become the center of this ecosystem. This solution made it possible to achieve retention of users within the Sberbank system, as well as to attract new customers. Moreover, the company received a sufficient field for analyzing the individual characteristics of its customers. Many services use weak AI systems, for example, in music services and online cinemas, they help to choose the most suitable solutions for leisure, in logistics - to optimize supply chains, and in insurance - to calculate the cost of an insurance policy and premium. Thus, Sberbank is actively introducing AI technologies into the activities of its ecosystem, however, some of the solutions are waiting for implementation due to the lack of legislative opportunity [33]. Consider VTB Bank. VTB is the second largest bank in Russian Federation. VTB was one of the first Russian banks to start using AI when opening and upgrading its branches and ATMs. In 2021, the bank developed and implemented a ML model that, using Big Data analysis, makes it possible to predict the demand for banking services in
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specific parts of the city. The implementation of the project will reduce the average time for the availability of VTB branches for a client to 15 min. More than 5,000 parameters are used to determine the potential number of customers and sales volumes in new locations [34]. In 2020, the VTB brokerage department integrated an AI system into the algorithm of a robot-adviser in the company’s investment application. At the moment, the algorithm performs the role of an investment portfolio manager offering various development scenarios. The task of the algorithm is to find a strategy and create a portfolio with the maximum return, taking into account the given restrictions: the maximum possible change in value, the share of securities in the portfolio, and others. The algorithm goes through all possible strategies and portfolios to find the highest potential return with the lowest risk. For this, various ML tools are used: linear models, deep neural networks, and others. It should be noted that VTB is more focused on the use of innovative technologies within the framework of internal banking services and processes [35]. Alfa-Bank is a major Russian bank and is also represented on the European market. At the end of 2021, Alfa Bank began using AI to make credit decisions for retail clients. AI analyzes the client’s transactional activity and data from various sources - for example, the use of bank products by clients. The technology helps to form a loan decision faster, and the work of algorithms is more accurate. It should be noted that due to legislation, this project works solely to determine the credit card limit for the client. The second well-known case of the use of AI by a company is the creation of a voice assistant navigator for a mobile application - Alf. Alf understands voice commands and helps users of the Alfa-Bank mobile app to perform financial transactions. Alf knows how to answer questions about the account balance, credit card debt, the amount of the next mortgage payment, and it also helps to navigate quickly to the desired section of the mobile application. When communicating with people using ML, the voice algorithm better adapts to the tasks of clients [36, 37]. Tinkoff Bank is also a major bank in the Russian Federation. Since the end of 2020, the company has separated special divisions for big data analysis and artificial intelligence. The company’s goal is the total transformation of the system into "Smart Banking". Several initiatives have been implemented so far. Firstly, investment strategies developed by AI are actively used in the management of Tinkoff mutual funds. Also, AI systems are used to maintain security. At the moment, anti-fraud technology is being tested, which consists in identifying atypical interactions between a client and his smartphone. It is based on the fact that each smartphone user has their own characteristic habits, and the neural network will analyze them. We are talking, in particular, about the angle at which the user holds the smartphone, the features of tactile interaction with the screen, etc. After analyzing these parameters, the neural network can determine whether the owner is holding the phone or not. If the bank detects that the smartphone is not used by the client, his activity in the mobile bank will be put under special control and in case of an attempt to transfer funds using uncharacteristic details and the transactions will be blocked [38, 39].
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5 Conclusion Thus, in the context of informatization, representatives of the banking industry are actively making decisions on the introduction of AI and ML technologies into the processes of their activities [40]. The implemented software products allow to reduce costs, as well as open new horizons for the development of the company’s services, providing additional profit generation. However, now there are no technologies that would allow to fully optimize the complex and multifaceted structure of the banking industry, the implementation takes place separately in the internal processes of activity. The main areas are credit scoring, investments, internal bank services and ecosystem solutions. It should be noted that the first two spheres are the most popular in the Russian Federation. Based on the study of the implementation of AI in Russian banks, the main features can be identified: – Redefining valuation systems with the introduction of AI, in particular lending and insurance; – Use of AI in brokerage applications; – Freezing of developments due to the lack of legislative regulation; – Lack of a unified strategy for the development of AI in the industry. Despite the rapid penetration of technologies, in Russian Federation it is still very fragmented. Programs are aimed only to solve specific problems. And the idea of a strong AI is based on the opportunity to go through the edge of the particular program field. So, we can conclude that this feature is not a part of current AI programs. At the same time, there is no significant progress between banks in the joint development of AI-based services. Based on the experience of aggregating services, the authors suggest the emergence of joint developments of banks, as well as the unification of analytical systems, at least in the field of credit scoring and investment. The authors see the prospect of developing the study in assessing the economic impact of various types of AI and ML implementation in the banking sector. It is important to understand that each of the presented solutions may not pass the economic selection. The introduction and operation of such technologies is an extremely high cost for companies and not every one of them can achieve payback. Thus, in the future, the authors are going to assess the economic feasibility of introducing various types of modern technologies in the banking sector of the Russian Federation9. Acknowledgments. The research was financed as part of the project "Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization" (FSEG-2023–0008).
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Modern Digital Assets: Trends of the Central Bank Digital Currencies Kseniia Lakovich(B) , Igor Lyukevich, and Olesya Lakovich Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. Central Bank Digital currencies (CBDCs) are one of the most important trends of the late 2010s, which can radically change the role of financial regulators in the financial services industry. The purpose of the study is to identify the distinctive characteristics of the central bank’s digital currencies in comparison with other forms of money and determine the possible impact of them on the economy of a country. The study reveals the process underlying blockchain transactions and the main risks of cryptocurrencies such as high volatility, anonymity of transactions contributing to the development of illegal activities, a threat to the welfare of citizens and the financial stability of the economy. At the same time, the introduction of CBDCs potentially promises to significantly reshape payment infrastructure at both the domestic and international levels. We define a central bank digital currency as central bank money in a digital form that will be used along with cash and non-cash money, performing all the functions of money and being equivalent to all three forms of the same currency (1 digital ruble = 1 ruble in cash = 1 non-cash ruble on the deposit accounts). During the research 14 core features of CBDCs have been identified, covering the CBDC instrument, the underlying system, and the broader institutional framework in which they exist. On the Russian market the question of issuing a digital ruble becomes particularly relevant as the share of non-cash trade turnover in total consumer spending is constantly growing. In the course of the study, the authors identified the main factors that can assess the degree of readiness of businesses and citizens to implement digital ruble, according to which it can be recognized that there is a high potential for the adoption of innovative financial technologies in Russia. It is worth noting that in February 2022, the Bank of Russia started assessing the possibility of issuing a digital ruble and conducting tests. Keywords: digital assets · cryptocurrencies · Central bank Digital currencies · CBDC · Bitcoin
1 Introduction The current socio-economic processes lead to the emergence of a new economic reality - a modern economic model, characterized by the following key trends: – the transition to digital technologies not only of business but also of all spheres of human life due to the rapid growth of computerization and the development of new means of communication; © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 673–692, 2023. https://doi.org/10.1007/978-3-031-32719-3_51
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– the rapid formation of trends and market leaders, which are becoming a growth factor for entire industries; – global penetration into the world market of trends for new services that create entire sectors of the economy: the emergence of “green technologies”, market placements, and sharing services; – the departure of finance to the Internet and the emergence of fundamentally new financial assets - digital ones, which are generated by the network itself, such as cryptocurrencies and stablecoins. In the context of growing initiatives to issue private currencies, alternative systems for value trading, and other challenges, the central banks of most countries are beginning to explore the possibilities of digital transformation of the economy. Thus, central banks around the world seek to change one of the oldest elements of the economy (money) by introducing the central bank digital currency (CBDC). Scope of the research corresponds to the issues of financial accessibility, which is an important condition for sustainable development. Moreover, financial accessibility has the closest relation to the assessment of the people’s quality of life in the context of the Sustainable Development Goals. The low or uneven level of accessibility of financial services to the population indicates that there are groups of people in the country who cannot use financial services to the proper extent. This usually concerns the most vulnerable segments of the population, as well as residents of remote and hard-to-reach territories [2]. It is obvious that in countries with a high quality of life, focused on sustainable Development Goals, this situation is unacceptable. Therefore, the level of financial accessibility can be one of the indicators of the quality of life of the population of a particular economic territory. Ensuring equal opportunities in access to financial services actualizes the increase in digital accessibility, which at least helps to overcome the problems of physical access to these services. In addition, some new digital financial solutions are able to reduce the tax burden and at the same time form the tax prerequisites for the sustainable development of territories [3, 4]. However, any innovations entail new risks and challenges, requiring adequate approaches to regulation, in the absence of which the positive effects can be leveled. In this discourse, the question of the practical implementation of digital financial technologies and related services is relevant. The solution in this area is the introduction of central banks digital currencies (CBDC), which is one of the practical approaches to overcoming the natural financial decentralization. Thus, Cunha, Melo, Sebastião [5], presented a detailed overview of scientific research and practical developments concerning the meaning, advantages, disadvantages, and future of digital currencies, with an emphasis on digital currencies of central banks. The issue of introducing digital currencies is also being investigated considering the achieved level of financial accessibility, including Náñez Alonso, Jorge-Vazquez, Reier Forradellas [6], who have summarized the arguments of most national banks both defending and refusing to take decisive steps towards the introduction of CBDC into circulation. Arguments such as geographical dispersion of access to financial services, as well as an increase in the level of penetration of banks and the level of access to financial services, are used by developing countries in favor of the introduction of CBDC [6]. It is curious that for developed economies with a high level of financial accessibility may not have the advantages of CBDC at
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all compared to existing payment systems. However, for countries with low consumer protection of financial services and insufficient level of financial accessibility, the latter may be increased with the introduction of CBDC. Thus, the purpose of the study is to identify the characteristics of the central bank’s digital currencies and determine the possible impact of them on the economy of a country. The use of digital currencies is an important step towards the digital transformation of financial systems. However, it remains to be seen how much digital finance will be accepted by society. It can be expected that with these changes, the tastes of consumers will also change, who will increasingly prefer digital access channels. Similarly, as shown in Cohen N. et al. when cash is excluded from circulation, banknotes are replaced with alternative money. At the same time, the expansion of digital access to financial services may be impractical if the demand for them is insufficient [7]. According to Moon [8], Korea is successfully implementing a program of transition to a cashless society. It is based on the widespread use of the Internet and mobile phones along with the development of financial technologies. This leads to the emergence of new digital payment tools and services that are rapidly replacing the use of cash in payments. But the widespread use of these services and the complete transition to a cashless society would not have been possible without the active coordination and support of both the government and the central bank. Additionally, global digitalization and the ever-increasing needs of consumers to quickly obtain various high-quality products and services with minimal effort through convenient digital channels have become the key reasons for the growing interest of central banks of the world in issuing digital currencies. At the same time, the COVID 19 pandemic had a significant impact on the growth of the share of online and non-cash payments, which accelerated the transition to a new digital form of mutual settlements. This trend may lead to a situation where a significant part of payments will be denominated in a private digital currency, which means that there is a risk of undermining the monetary sovereignty of the government.
2 Key Issues A review of the literature shows that increasing digital accessibility as a driver of financial innovation carries certain risks, but, of course, can be considered as a factor that positively affects the level of financial accessibility due to the neutralization of problems of physical access to financial services. Research in the field of digital financial services and technologies does not allow us to state unequivocally that the supply of digital services determines the corresponding demand, which depends on many factors. Thus, the assumption introduced that the expansion of digital penetration will reduce the level of demand for traditional banking services requires specification and verification. At the same time, it is interesting that countries with initially different levels of financial accessibility seem to have mixed results from expanding the supply of digital financial services. However, the relationship between digital and financial accessibility is being investigated in the context of individual innovations (for example, smart contracts and digital currencies), while general patterns in this regard have not received due attention. Identified gaps and controversial provisions determined the methodology of our research.
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According to the definition of the Official Monetary and Financial Institutions Forum (OMFIF), a central bank digital currency is central bank money in a digital form [9]. Unlike privately issued cryptoassets, this is not a parallel currency. It serves as new means of payment and as an alternative to cash. CBDCs are denominated in the official monetary unit of the issuing country and are a direct liability of the central bank. At the same time, if cash is issued in the form of banknotes, each of which has a unique number, and non-cash money exists in the form of records on accounts in commercial banks, then the digital currency will have the form of a unique digital code that will be stored on a special electronic wallet. The transfer of digital currency from one user to another will take place in the form of moving a digital code from one electronic wallet to another. Besides maintaining relevance and policy influence within the increasingly digitized payments landscape, the introduction of CBDCs potentially promises to significantly reshape payment infrastructure at both the domestic and international levels. Nevertheless, this issue still hides many questions that we are going to reveal. Understanding the importance of crypto assets, including CBDCs, in developing the financial services sector this study focuses on addressing the key issues: 1. What are the features of cryptocurrencies? 2. What are the risks hidden in cryptocurrencies? 3. What are the development trends and key characteristics of Central Bank Digital Currencies? 4. Is the Russian economy ready to introduce the Digital ruble into the financial market? 5. What is the experience of implementing Central Bank Digital Currencies in different countries?
3 Results From an economic point of view, money is an instrument that performs four main functions: a medium of exchange, a way of payment, a unit of accounting, and a store of value. Law supplements the theory of money with an important factor—money in the legal sense can only be signs which are issued by the government. However, the question of the expediency of securing the state’s monopoly right to issue money has long been a subject of discussion in science. The most famous adherent of the theory of private money is the Austrian economist F. A. von Hayek [10], who believed it is necessary to deprive the government (including the central bank) of the right to issue and regulate money and introduce currency competition, in which any individual can issue money and, following the laws of the market, the strongest and most stable currencies will win in the competition. He believed that the state is not able to give society money of a better quality than those that are created without its participation. State money is less reliable and suitable for exchanging, and the government’s monopoly right to issue money leads to an increase in the power of the state, which the author considered harmful. These ideas have become especially actively discussed with the advent of cryptocurrencies. The increased pressure from non-bank payment platforms is the reason why the central banks of different countries are trying to create their own digital currencies. With the development of the technologies, the use of cash has declined and opened the way for innovations in the financial sector. The amount of worldwide non-cash operations
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reached 708.5 billion transactions in 2019, that shows an increase of 80% since 2014. The value of global cashless payments grew from 900 trillion US dollars in 2014 to 1,370 trillion in 2018 and are expected to reach 1.8 trillion by 2025 [11]. However, a total cashless society still seems unlikely in the near future because cash remains highly trusted by people. According to a survey of Deutsche Bank, in developed countries, one-third of the population uses cash as a preferred payment method, and one of the main reasons for that is that people appreciate faster payments and better control over spendings. Additionally, cash is considered to be a convenient and secure method of payment which is accepted almost everywhere [12]. After the appearance of the world’s first Bitcoin cryptocurrency in 2009, the market has developed rapidly and currently includes a wide range of various tools based on the use of distributed ledger technology (DLT). The broadest term for such instruments is a crypto asset - an asset that exists in digital form or is a digital representation of another asset and was created using distributed ledger technology. At the same time, crypto assets can be issued by a central bank (Central bank digital currency) or a private issuer. Centralized digital money is characterized by a central point of control over the money supply, while decentralization of digital money means the control of various sources over the money supply. Privately issued assets include electronic money (eMoney), unsecured cryptocurrencies, stablecoins and tokenized assets. One of the innovations in the field of payment systems is cryptocurrencies. To understand the essence of cryptocurrency, we should first study the mechanism underlying the financial instrument and ensuring the functioning of the cryptocurrency. The term blockchain is widely known as the acronym DLT, but it has its own specific distinctive characteristics. Blockchain was developed in 2008 to serve as the public transaction ledger for Bitcoin. The fundamental feature of Blockchain is that it is “a chain of cryptographically-linked data blocks to efficiently and securely time-stamp digital data in distributed systems” [13]. Blockchain technology is greatly secure as Blockchain information is stored in blocks of information, and it is impossible to alter or erase these data because blocks are reproduced across multiple ledgers. While money transfer requires the involvement of intermediaries in the form of the sender’s bank, the recipient’s bank, and the card operator, which leads to the charging of additional fees, blockchain technology allows you to bypass intermediaries, since cryptocurrencies are based on cryptography and the decentralized system. Thus, transfers operate directly between peers since users have a unique public and private key. Figure 1 illustrates a blockchain transaction process.
Fig. 1. A blockchain transaction (Laboure, 2020).
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In addition to the fact that cryptocurrencies are highly secure thanks to blockchain technology (Fig. 2), they also have such features as: • • • •
broad access; they are represented only in a digital form; the asset is a digital “token” and does not have backing or intrinsic value; as the transactions are not intermediated by a clearing party it can make financial dealings very cheap; • however, cryptocurrency mining requires expensive equipment and high energy costs; • cryptocurrencies can be highly volatile and carry a lot of risks especially for nonprofessional investors. Bitcoin’s volatility has always been extremely unstable. Cryptocurrency mining began in 2009, so that year its price was actually $0. Throughout 2010, bitcoin failed to reach the $1 mark, but the price began to rise even then. The world’s first purchase using Bitcoin took place in 2010, when American Laszlo Hench bought two pizzas for 10,000 BTC (then 1 BTC cost $0.0025). In 2011, Bitcoin finally managed to reach the value of $1, and by the middle of the year it had already reached $32. On October 20, 2021, bitcoin was trading at a record price of $66.4 thousand (Fig. 3). In February 2022, the price of the crypto asset is $41.8 thousand. At the end of December 2021, the total market capitalization of cryptocurrencies and tokenized assets traded on cryptocurrency exchanges amounted to about $2.3 trillion, and the peak value was reached in early November 2021 – the capitalization volume exceeded the $3 trillion (CoinGecko.com, 2022). At the same time, the total market capitalization of cryptocurrencies relative to the volume of global financial assets is about 1%. Bitcoin accounts for the largest amount of capitalization (about 0.9 trillion US dollars, 38%), followed by Ethereum (about 450 billion US dollars, 19%). Figure 4 shows the dynamics of capitalization of the three main cryptocurrencies and the Tether stablecoin, which account for about 70% of the stablecoins market.
Fig. 2. The “Money flower”: accessibility, form, issuer, transfer (Cohen, Rubinchik, Shami, 2020).
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Fig. 3. The dynamics of Bitcoin price (Cunha, Melo, Sebastião, 2021).
Fig. 4. The Dynamics of the market capitalization of the top 3 cryptocurrencies and stablecoin (Tether) (CoinGecko.com, 2022).
The market value of cryptocurrencies is determined by two groups of factors. On one hand, this is the market’s assessment of the prospects of payment service technologies in the economy (both in the legal and illegal sectors) and expectations of their further spread. On the other hand, these are speculative factors associated with the high volatility of cryptocurrencies, the hype around them and the desire of market participants to get a quick income. Thus, speculative demand is the main factor influencing the price of cryptocurrencies and contributing to the formation of a bubble. A number of scientific papers prove that the increase in the level of use of cryptocurrencies leads to an increase in their value [14, 15]. There can be defined three main groups of risks hidden in cryptocurrencies: • threat to the welfare of citizens; • threat to financial stability; • threat related to illegal activities. Threat to the welfare of citizens. The risks of cryptocurrencies for private investors are associated with the possibility of a complete loss of investments in cryptocurrencies since
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cryptocurrencies are characterized by high volatility. At the same time, the volatility of this crypto asset is influenced by the statements of media personalities, media messages, and the fact that a significant concentration is in the hands of a small number of owners, which creates opportunities for deliberate manipulation in the market. What is more, cryptocurrencies have the characteristics of financial pyramids, since the growth of their price is supported by speculative demand from newly entering the market participants. Owners who “come out” of the pyramid sell their investments at a profit at the expense of newcomers who purchase cryptocurrency in the hope of increasing its price in the future and stimulate its growth by their own demand. These risks can not only have a serious impact on the price of the cryptocurrency, but also lead to a sharp drop in its value and a complete loss of the investor’s invested funds. Investors’ losses may arise not only because of a drop in the price of cryptocurrencies, but also as a result of improper fulfillment of obligations by exchanges, as well as due to fraudulent actions, cyber threats and hacker attacks on exchanges. Threat to financial stability. The spread of cryptocurrencies carries significant risks for the country’s economy and financial stability. The potential use of cryptocurrencies as a means of payment for goods and services creates a risk of undermining monetary circulation and loss of sovereignty of the national currency. It is thought that the widely accepted private cryptocurrencies will reduce the effectiveness of the monetary policy, weaken the central bank’s ability to be the lender of last resort which will lead to an increase in the inflation rate. To contain inflation, it will be necessary to maintain a higher level of the key rate on a permanent basis. This will reduce the availability of credit for citizens and businesses. Threat related to illegal activities. Cryptocurrencies may provide support for criminal activity, due to their anonymous nature. At the same time, there are no approaches that allow deanonymizing all participants in transactions with cryptocurrencies, which can affect the financing of such criminal activities as laundering of proceeds from crime, drug trafficking, terrorist financing, illegal sale of weapons, corruption, and others [16]. The introduction of CBDCs potentially promises to significantly reshape payment infrastructure at both the domestic and international levels. To fulfil the foundational principles, a potential CBDC would need certain features. Fourteen core features have been identified (Table 1), covering the CBDC instrument, the underlying system, and the broader institutional framework in which they exist. On the one hand, a digital currency is similar to banknotes, since it has a unique digital code (just as a banknote has a series and number) and is issued by the central bank, so the digital money of the central bank is sometimes called “digital cash”. Continuing the analogy with cash, it should be possible to use the digital currency in offline mode, that is, in the absence of access to the Internet and mobile communications. The availability of digital money offline will not be achieved immediately after the introduction of CBDC, since this requires the development of a special infrastructure and takes much time. The digital currency will be used along with cash and non-cash money, performing all the functions of money. All three forms of the same currency will be absolutely equivalent –just as now 1 ruble in cash is equivalent to 1 non-cash ruble, so 1 digital ruble will always be equivalent to each of them. At the same time, the owners of the money will be
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Table 1. Core CBDC features. Instrument features Convertible
The possibility of exchanging CBDC at par with cash and private money
Convenient
CBDC payments should be as easy as using cash, tapping with a card or scanning a mobile phone to encourage adoption and accessibility
Accepted and available
A CBDC should be usable in many of the same types of transactions as cash, including point of sale and person-to-person. This will include some ability to make offline transactions (possibly for limited periods and up to predetermined thresholds)
Low cost
CBDC payments should be at very low or no cost to end users
System features Secure
Both the infrastructure and participants of a CBDC system should be extremely resistant to cyber-attacks and other threats. This should also include ensuring effective protection from counterfeiting
Instant
Instant or near-instant final settlement should be available to end users of the system
Resilient
A CBDC system should be extremely resilient to operational failure and disruptions, natural disasters, electrical outages, and other issues. There should be some ability for end users to make offline payments if network connections are unavailable
Available
End users of the system should be able to make payments 24/7/365
Throughput
The system should be able to process a very high number of transactions
Scalable
To accommodate the potential for large future volumes, a CBDC system should be able to expand
Interoperable
The system needs to offer sufficient interaction mechanisms with private sector digital payment systems and arrangements to allow easy flow of funds between systems
Flexible and adaptable
A CBDC system should be flexible and adaptable to changing conditions and policy imperatives
Institutional features Robust legal framework A central bank should have clear authority underpinning its issuance of a CBDC Standards
A CBDC system (infrastructure and participating entities) will need to conform to the appropriate standards and regulatory documents that should be developed by states planning to implement CBDC
able to freely transfer rubles from one form to another. Table 2 distinguishes the main differences of digital currency, cash, bank accounts and cryptocurrency.
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Cash
Bank accounts
Cryptocurrency
Shape
Digital code
Protected paper
Digital record in the banking database
Encrypted record in the blockchain system
Issuer
Central bank
Central bank
Commercial bank No single issuer – any company
Personalization
Personalized or payable to a bearer
Payable to a bearer
Personalized
Payable to a bearer
Value stability
+
+
+
−
A mean of payment
+
+
+
+
A store of value
Without interest rate
Without interest rate. Risk of irrevocable loss
With interest rate High volatility
A mean of payment
Online/offline
Offline
Online
Online
Thus, being a form of digital money, CBDCs are denominated in national monetary units, which are directly in the area of responsibility of central banks. CBDCs have the potential to become a new form of money due to the fact that the value embedded in a monetary unit is not necessarily determined in the same way as fiat money. There are the following development paths of changing the form of digital currencies: 1. Digitalization of fiat currencies is a way of developing a CBDC where the form of value remains the same and is determined as in the case of fiat currencies. The main changes relate to other components of CBDC (in particular, products and/or infrastructure). 2. Digital transformation of fiat currencies (new rules of old money) is a way of developing a CBDC where the form of money fundamentally remains the same (e.g., the value is also determined by the state). However, changes occur in the rules for determining value (e.g., the way of determining value is formalized and further established/changed, according to transparent fixed mechanisms). 3. Evolution of currencies is a way of developing a CBDC that meets new rules for determining value. The Bank for International Settlements (BIS) asks central banks to work on the development of digital currencies, as it fears that the largest technology companies will be the first to occupy this niche, making their way in the financial services sector [17] (The Bank for International Settlements, 2021). BIS notes: “If CBDCs remain in the hands of central banks, they can form the basis for a very effective new digital payment system, providing wide-scale access and high standards of data management and privacy”.
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In the Russian market, the high demand for remote services and non-cash payments stimulates the processes of digitalization of the financial market (Fig. 5). In these conditions, the question of issuing a digital ruble becomes particularly relevant.
Fig. 5. The share of non-cash trade turnover in the expenses of Russian citizens (SberIndex, 2022).
According to Fig. 5, the share of non-cash trade turnover in total consumer spending is constantly growing. In the fourth quarter of 2021, the share of non-cash trade turnover in Russia amounted to 60.8%, and for the whole of 2020 it reached 59.3%. This is 4.9 percentage points higher than the level of 2020 (54.4%) and another historical maximum. It is worth noting that the process of moving away from cash continues is happening, even despite a significant increase in the volume of paper money in circulation (+2.8 trillion rubles to 13.4 trillion rubles). Sber experts estimate that in 5 years the share of non-cash payments may approach its limit of 85%. As the successful implementation of CBDC crucially depends on how many consumers are motivated to adopt this new digital form of public money, it is important to know which factors influence that adoption. Let us consider the side of potential users of the digital currency, their interest and market readiness to use the Central Bank Digital Currencies in Russia. Consumer readiness: literature analysis presented by Carapella, Flemming [18] allows us to conclude that the success of the implementation and the willingness of users to accept CBDC depends on the same factors that affect the acceptance of noncash retail payment services and innovations by customers. In the scientific literature on topics close to CBDC [19–21] the following are distinguished among the main factors of consumer readiness: 1. Adoption of financial innovation. This factor implies that the higher the level of use of payment innovations and the penetration of fintech innovations in the country, the higher the probability that consumers will start using new payment instruments.
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2. Financial inclusion. In terms of financial inclusion, it is important to understand how widespread financial services are within a country. 3. Financial literacy. A low level of financial literacy among people can negatively affect the desire to try new things, as well as the understanding of the tool, the perception of its safety and necessity. 4. Satisfaction with current offers. The higher the customer satisfaction with the tools offered on the market, the more consumers will be immune to changes since they will not make sense to them. 5. Trust in financial institutions. The higher the consumer’s confidence in the Central Bank (as a potential provider of CBDCs) and commercial banks (potential partners/competitors), the higher the probability of accepting a new instrument from the provider. 6. Trust in technology that underpins a CBDC. 7. Digital literacy. For an innovation to be accepted by consumers, they must be prepared from a technological point of view. Business readiness: having analyzed the work of Alsheibani et al. [22] the following groups of criteria can be distinguished for assessing the readiness of a business: 1. Organizational readiness. Organizational readiness includes the availability of the necessary investment/financial and human resources for the introduction and creation of new products, as well as the organizational structure of the business that allows for rapid innovation. 2. Internal benefits. This criterion considers the technical and financial advantages of introducing a new payment instrument in relation to existing ones (e.g. transaction processing speed, security, user convenience, the amount of commissions for companies). 3. Environment. This criterion considers the demand for the tool from consumers and competitors, as well as the presence of a legal environment. Table 3 allows us to assess the readiness of participants of the Russian financial services sector to implement the digital ruble using the criteria listed above. Thus, after analyzing the data obtained, we can say that there is a high potential for the adoption of innovative financial technologies in Russia. However, it should be borne in mind that consumers should be extremely aware of the advantages of the new tool in comparison with existing offers on the market. In the absence of significant differences from existing products and services, clients may be prone to abandon the use of CBDC in favor of the usual types of money (Fig. 6). On 13 October 2020, the Bank of Russia presented the concept of a digital ruble in its consultation paper, making the issue of evaluating the innovation potential of a Russian CBDC relevant. The Central Bank has not yet made a final decision on the issue of the digital ruble. At the same time, within the framework of the Moscow international forum “Open Innovations”, the special representative of the President of the Russian Federation, Dmitry Peskov, said that “The digital ruble may appear in Russia in three to seven years”. In February 2022, the Bank of Russia started assessing the possibility of issuing a digital ruble and conducting tests. 12 Russian banks participated in the testing. The first transfers in digital rubles between citizens were successfully carried
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Table 3. The readiness of participants of the Russian financial services sector. Consumer readiness 1. Adoption of financial innovation According to the EY Fintech Adoption Index (2019) [23] which is equal to 82% as a percentage of the digitally active population in Russia, as well as the measurements of the use of e-wallets conducted by BCG which revealed that in 2010–2018, the volume of current payments in Russia grew by an average of 22.1% (Boston Consulting Group, 2019) [24]. Russia is constantly in the top 3 in terms of the penetration of financial technologies, thereby showing that consumers are more likely to be ready for new payment instruments. However, it should be borne in mind that in the case of an oversaturation of the market, consumers may lose interest in innovations 2. Financial inclusion
According to SberIndex (2022) [25], in the fourth quarter of 2021, the share of cashless trade turnover in Russia amounted to 60.8%, and for the whole of 2021 it reached 59.3%. Additionally, according to the latest data from Global Findex (2017) [26], about 80% of the Russian population own bank accounts
3. Financial literacy
According to the most recent data for 2020, in Russia, the level of financial literacy was 12.37 out of 21 possible points (NAFI, 2020) [27]. It should be noted that financial literacy has stagnated, reaching a growth of only 2% over the past couple of years. This suggests that the level of financial literacy in Russia is slightly higher than average and there is a possibility that consumers will not voluntarily use a new financial instrument without accompanying training
4. Satisfaction with current offers
According to surveys among cardholders conducted by SKOLKOVO-NES in 2019, 53% of respondents like to pay with a card, about a quarter (24%) like to pay with a smartphone using existing solutions. Only 12% prefer to pay in cash. This indicates the high quality of financial services in the country [28]
5. Trust in financial institutions
According to the Edelman Trust Barometer (2021), the general level of public trust in Russia is in the “red” zone of distrust (31 out of 100 possible points), trust in business and the government fluctuates around 34 points. Therefore, there is a possibility that users will not accept the CBDC, since the country has a low level of trust in the state [29] (continued)
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Consumer readiness 6. Trust in technology
The level of penetration of financial technologies in Russia is at an extremely high level. In Russia, security is one of the barriers to the use of mobile wallets, as some users simply do not know how the payment is made and what to do in case of loss of a smartphone
7. Digital literacy
According to the results of 2020 – the year of the pandemic and remote work – the level of digital literacy of Russians has increased: the share of Russians with an initial level of digital literacy has decreased, the share of Russians with a basic level has increased (NAFI, 2021) [30]. The index of digital literacy of Russians in the first half of 2021 amounted to 64 points on a scale from 0 to 100
Business readiness 1. Organizational readiness
Russian banks are among the leaders in terms of digitalization and the development of financial innovations for customers. The largest banks have transformed into ecosystems, and financial services are organized on the principle of microservices, which makes the introduction of new solutions easier
2. Internal benefits
The current benefits of banks and merchants from participation in the financial services market are positive for the Russian market (Krivosheya, 2021) [31]. However, there are market segments (e.g., small and medium banks, SMEs) where cashless instruments are associated with high acquiring costs and uneven distribution of benefits. A CBDC may also associate with these problems if the issue is not thought through in advance
out. Customers not only opened digital wallets on the digital ruble platform through the mobile application, but also exchanged non-cash rubles from their accounts for digital ones and then carried out digital ruble transfer operations among themselves. Nevertheless, the Russian economy adheres to a strategy in which the experience of the pioneers in the issue of CBDCs will allow to bypass many barriers. The analysis shows that the complexity of the task of implementing the concept of Central Bank Digital Currencies is compensated by a number of advantages that it provides to the financial market (Table 4). The results of the SWOT analysis indicate the strengths and weaknesses of the introduction of the digital ruble, allow us to draw conclusions about the potential opportunities and risks of using the Central Bank Digital Currencies on the example of the digital ruble. The central banks of the countries are at different stages of implementing digital currencies (Fig. 7). So, if China, Sweden, and South Korea have already started piloting the
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Fig. 6. Preferred payment and transfer methods (Edelman Trust Barometer, 2021). Table 4. SWOT analysis of the introduction of the “Digital ruble”. STRENGTH
WEAKNESSES
• Increasing the role of the government • Increase in the Central Bank’s income from seigniorage • Alternative to cryptocurrencies • Combining the advantages of cash and non-cash funds • Reducing the costs of issuing and distributing banknotes • Ease of use • Reducing the number of shadow operations • High security of the payment system Secure transactions (smart contracts)
• Low level of financial literacy of the people • Assigning all responsibilities to the Central Bank • Lack of implemented examples on the international market • Loss of competitive meaning among existing • Electronic payment systems
OPPORTUNITIES
THREATS
• Increasing the speed and reducing the cost of transactions • Increasing financial accessibility • Convenience and ease of converting the digital ruble into • Cash and funds on bank accounts • 24/7 access • Using offline • The emergence of the basis for the creation of new digital products within the framework of the innovative • Infrastructure of the financial market • Confidentiality of consumer information
• The destruction of the traditional banking system • Insecure infrastructure • Mass withdrawal of deposits from commercial banks to CBDC • Deposit outflow and the risk of loss of liquidity by commercial banks • The emergence of competition between the regulator and commercial banks for customer funds • Cybersecurity and cybercrime • Reducing the authority of the government
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CBDC project, then in Canada, Norway, Great Britain, Denmark, Switzerland, Iceland, the project of introducing a digital currency is at the research stage.
Fig. 7. CBDC project status [32].
Over the last four years, the share of central banks actively engaging in some form of CBDC work grew by about one third and now stands at 86% (Fig. 7). About 60% of central banks (up from 42% in 2019) are conducting experiments or proofs-of-concept, while 14% are moving forward to development and pilot arrangements. A study by the Bank for International Settlements highlighted that 20% of central banks aim to launch a digital currency in the next six years (Fig. 8).
Fig. 8. Central banks’ engagement in CBDC work [33].
The analysis shows that the development of its own digital currency is quite a relevant task among central banks. There are several examples of Central Bank Digital Currencies implementation in international practice (Table 5).
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Table 5. International practice of implementation CBDC. Country
The process of introducing CBDC
Italy
In December 2020, the Italian Banking Association (ABI) has started an experiment with a digital euro based on blockchain. The ECB is considering the possibility of a digital euro pilot project in 2021
Sweden
The eKrona project was initiated in 2017. In 2019, the central bank signed a contract with Accenture for testing electronic wallets, distributed registry technology and interaction with banks. In February 2020, the eKrona pilot project was launched. Sveriges Riksbank announced that it is ready to introduce CBDC Market by 2025
France
In December 2020, the Bank of France successfully conducted an experiment with the Central Bank, for the first time using this technology to calculate the shares of funds on a private blockchain platform. During the implementation of the project, investors purchased simulated shares in the amount of 2 million euros using CBDC [34]
China
China’s CBDC, known as “DC/ EP” (Digital Currency – Electronic payment), can be stored and used by consumers through digital wallets, while the digital yuan does not require a bank account. A pilot project for the introduction of the digital yuan was launched in Beijing, Tianjin and Hebei Province in October 2020. Citizens of these regions were given 10 million digital yuan. People can pay for purchases with digital currency [35]
South Korea At the end of 2021 it is planned to start a full-scale pilot test token of the Bank of Korea Turkey
In January 2021, The central Bank announced the testing of its own Central Bank Digital Currencies [36, 37]
Ecuador
The digital currency project called Sistema de Dinero Electronico (electronic money system) was launched in the period 2013–2018. The system provided individuals with access to mobile credit accounts denominated in a currency approved by the central bank. In 2018, based on the results of the project implementation, the government decided to transfer the electronic money project from the management of the Central Bank to private companies. In 2019, the Interbank network BANRED launched a mobile wallet that allowed “paying or making requests for collection to other users of the service”
Uruguay
Banco Central del Uruguay launched a pilot project on digital currency in 2017. For six months, the electronic peso was available to the public as an addition to cash. In total, 20 million electronic pesos (about 650 thousand US dollars) were issued in the form of a digital version of the Uruguayan peso. The Uruguayan central Bank is currently studying the possibility of the Central Bank to provide anonymity, influence on monetary policy and the economy
The study of the experience of the development of the CBDC is a necessary condition for the optimal adaptation of the national financial system to external challenges associated with the possible successful and rapid implementation of this concept by different countries, including the Russian Federation.
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4 Discussion This study contributes to the study of the topic of Central Bank Digital Currencies with a focus on Russian financial market. However, the study does not consider the crisis phenomena that may affect the decision of states to introduce digital currencies. Although we have proved that Russia is ready for the introduction of the digital ruble, Nunez Alonso, S.L et al. made calculations according to which the Baltic Sea area (Lithuania, Estonia, and Finland) is the closest in terms of infrastructure readiness to the countries that have already implemented CBDCs (Bahamas - which has already implemented CBDCS in its territory, China - which has already completed two pilot tests, and Uruguay - which has completed a pilot test). Based on this, the next step of the research will be to carry out own calculations and study the motives influencing the development of digital currencies of central banks. At the moment, we identify the following motives and relevant variables evaluating this motive relative to each country: geographic dispersion of access to financial services (inhabitant per square kilometer), access to financial services (commercial bank branches (per 100,000 adults), Increase the Banking Penetration Rate (financial sector credit to the private sector (% of GDP)), financial sector not to become obsolete (Digital Readiness Index), security reasons: avoid money laundering and terrorist financing (shadow economy, percent of GDP), institutional quality (The government effectiveness index), consumer protection (UNCTAD B2C E-commerce Index), fall in use of cash (Broad money (% of GDP)), the stage of implementation of the CBC project in the country, as well as mentions of CBDC in search engines in the country. The next hypothesis for statistical calculations is that all of these factors have a direct impact on the introduction of Central Bank Digital Currencies in the country, and the highest correlation of these indicators with similar indicators of countries in which the CBDC project has already been implemented indicates the favorability of this country to the introduction of Digital currencies. Acknowledgments. The research was financed as part of the project “Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization” (FSEG-2023–0008).
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4. Victorova, N., Rytova, E., Koroleva, L., Pokrovskaia, N.: Determinants of tax capacity for a territory (the case of the Russian federal districts). Int. J. Technol. 11(6), 1255–1264 (2020) 5. Cunha, P.R., Melo, P., Sebastião, H.: From bitcoin to central bank digital currencies: making sense of the digital money revolution. Future Internet 13, 165 (2021). https://doi.org/10.3390/ fi13070165 6. Náñez Alonso, S.L., Echarte Fernández, M.Á., Sanz Bas, D., Kaczmarek, J.: Reasons fostering or discouraging the implementation of central bank-backed digital currency: a review. Economies 8, 41 (2020). https://doi.org/10.3390/economies8020041 7. Cohen, N., Rubinchik, A., Shami, L.: Towards a cashless economy: economic and sociopolitical implications. Eur. J. Political Econ. 61, 101820 (2020) 8. Moon, W.: A coinless society as a bridge to a cashless society: a Korean experiment. In: Cash in East Asia, pp. 101–105. Springer, Berlin/Heidelberg, Germany (2017) 9. Digital Monetary Institute. The future of payments. https://www.omfif.org/wpcontent/upl oads/2020/12/The-Future-of-Payments.pdf. Accessed 22 Jan 2022 10. Hayek, F.: The Denationalisation of Money. Institute of Economic Affairs, Londan (1976) 11. World Payments Report (2021). https://worldpaymentsreport.com/resources/world-pay ments-report-2021/. Accessed 22 Jan 2022 12. Laboure, M.: The Future of Payments - Part I. Cash: the Dinosaur Will Survive. For Now. https://www.dbresearch.com/PROD/RPS_EN-PROD/PROD0000000000504353/The_Fut ure_of_Payments_-_Part_I__Cash%3A_the_Dinosau.PDF?undefined&realload=ljt5doLuI BN4fazbZdomd3uOz76paYNYsPkK9Dyfe1uCitp/hLYgghUujVVRAW7t. Accessed 22 Jan 2022 13. Rauchs, M., et al.: Distributed ledger technology systems: a conceptual framework (2018). https://doi.org/10.2139/ssrn.3230013 14. Cong, L.W., Li, Y., Wang, N.: Tokenomics: dynamic adoption and valuation. Rev. Financ. Stud. 34(3), 1105–1155 (2021). https://doi.org/10.1093/rfs/hhaa089 15. Liu, Y., Tsyvinski, A., Wu, X.: Accounting for Cryptocurrency Value (2021).https://doi.org/ 10.2139/ssrn.3951514 16. Central bank cryptocurrencies. https://www.bis.org/publ/qtrpdf/r_qt1709f.pdf. Accessed 22 Jan 2022 17. The Bank for International Settlements. BIS Innovation Hub work on central bank digital currency (CBDC). https://www.bis.org/about/bisih/topics/cbdc.html. Accessed 22 Jan 2022 18. Carapella, F., Flemming, J.: Central Bank Digital Currency: A Literature Review. https:// www.federalreserve.gov/econres/notes/feds-notes/central-bank-digital-currency-aliteraturereview-20201109.html. Accessed 22 Jan 2022 19. Parasuraman, A.: Technology readiness index (TRI) a multiple-item scale to measure readiness to embrace new technologies. J. Serv. Res. 2(4), 307–320 (2000) 20. Lu, Y., Yang, S., Chau, P.Y., Cao, Y.: Dynamics between the trust transfer process and intention to use mobile payment services: a cross-environment perspective. Inf. Manage. 48(8), 393– 403 (2011) 21. IMF Working Paper: A survey of research on retail central bank digital currency. IMF Working Papers, vol. 2020(104), p. 66 (2020). https://doi.org/10.5089/9781513547787.001 22. Re-thinking the Competitive Landscape of Artificial Intelligence. https://scholarspace.manoa. hawaii.edu/items/a421ac9a-765f-457d-98af-f70eb1767810. Accessed 22 Jan 2022 23. EY. Global FinTech Adoption Index (2019). https://www.ey.com/en_gl/ey-global-fintech-ado ption-index. Accessed 22 Jan 2022 24. Boston Consulting Group. “Russian miracle” in cashless payments 2019. https://www.bcg. com/russian-miracle-in-cashless-payments. Accessed 22 Jan 2022 25. SberIndex. Ranking of non-cash payments in cities and regions, IV quarter 2021 SberIndeks. https://sberindex.ru/upload/research/203/61f175c6d51ff.pdf. Accessed 22 Jan 2022
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26. The World bank. The Global Findex database (2017). https://globalfindex.worldbank.org. Accessed 22 Jan 2022 27. NAFI. The results of the second wave of measuring the level of financial literacy of Russians. https://nafi.ru/projects/finansy/rezultaty-vtoroy-volny-issledovaniya-urovnyafinansovoy-gramotnosti-rossiyan/. Accessed 22 Jan 2022 28. SKOLKOVO-NES. Payment instruments: attitude and perception of Russians. https://sk.sko lkovo.ru/storage/file_storage/863eef40-d03e-4553-97bc-c1bad7b0bdba/SKOLKOVO_Pay ment_instruments_FullReport_2019-12_Ru.pdf. Accessed 22 Jan 2022 29. Edelman Trust Barometer (2021). https://www.edelman.com/sites/g/files/aatuss191/files/ 2021-03/2021%20Edelman%20Trust%20Barometer.pdf. Accessed 22 Jan 2022 30. NAFI. Forced digitalization: a study of digital literacy of Russians in (2021). https://nafi. ru/analytics/vynuzhdennaya-tsifrovizatsiya-issledovanie-tsifrovoy-gramotnosti-rossiyan-v2021-godu. Accessed 22 Jan 2022 31. Multilateral interchange fee efficiency evaluation: the case of the Russian retail payment cards market. https://www.hse.ru/sci/diss/346378619. Accessed 22 Jan 2022 32. Capgemini Research Institute. World payments report (2021). https://worldpaymentsre port.com/wp-content/uploads/sites/5/2021/10/World-Payments-Report_2021_Web.pdf. Accessed 22 Jan 20222 33. Ready, steady, go? – Results of the third BIS survey on central bank digital currency. https:// www.bis.org/publ/bppdf/bispap114.pdf. Accessed 22 Jan 2022 34. The Banque de France conducted a successful experiment with IZNES on the use of central bank digital money for interbank settlement purposes. https://www.banque-france.fr/en/com munique-de-presse/banque-de-france-conducted-successful-experiment-iznes-use-centralbank-digital-money-interbank. Accessed 22 Jan 2022 35. The flipside of China’s central bank digital currency. https://s3-ap-southeast-2.amazon aws.com/ad-aspi/2020-10/Digital%20currency_1.pdf?I70Ql0IhgfgSIJeH6YKTN0ml.Y6M LHLI. Accessed 22 Jan 2022 36. CoinGecko.com. Cryptocurrency Prices by Market Cap. https://www.coingecko.com. Accessed 22 Jan 2022 37. Central bank digital currencies: Drivers, approaches, and technologies. https://voxeu.org/art icle/central-bank-digital-currencies-drivers-approaches-and-technologies. Accessed 01 Mar 2022
Correlation-Regression Model for Analysis of Overdue Debt and AI-System for Prediction the Finance Risk of Russian Commercial Banks Nikolay Lomakin1(B) , Anastasia Kulachinskaya2 , Uranchimeg Tudevdagva3 , Natalia Bescorovaynaya4 , Natalya Mogharbel1 , and Ivan Lomakin5 1 Volgograd State Technical University, Volgograd, Russia
[email protected] 2 Peter the Great St.Petersburg Polytechnic University, St. Petersburg, Russia 3 Technische Universitat Chemnitz, Chemnitz, Germany 4 Volgograd Branch of the Plekhanov Russian University of Economics, Volgograd, Russia 5 Volzhsky Polytechnic Institute (Branch) Volgograd State Technical University, Volzhsky,
Russia
Abstract. The article discusses the theoretical foundations for the emergence of overdue loans and forecasting financial risk in Russian banks under market uncertainty. The relevance of the study is due to the fact that the growth of bad debts of commercial banks on loans is currently the most acute problem. The dynamics of the volume of overdue debt on loans from commercial banks of the Russian Federation for 2012–2021 was analyzed. In the course of the study, it was revealed that the volume of bad debts is influenced both by internal factors, such as poor management of the loan portfolio, and by external factors, such as abrupt changes in the level of inflation, the growth of the exchange rate, etc. In order to study the influence of factorial signs on the effective sign - the growth rate of overdue debts, a correlation-regression model was compiled. In addition to the effective feature Y - the growth rate of overdue debt, the model included such factorial features as: X1 - the growth rate of GDP per capita, X2 - the growth rate of the average per capita income of the population, X3 - the growth rate of foreign trade surplus, X4 - inflation index, X5 is the growth rate of capital outflow, X6 is the growth rate of cash, X7- is the interest rate on loans, X8 - is the US dollar exchange rate, X9 - is the price of a barrel of oil URLS dollars, X10 - is the growth of wages. Keywords: Overdue debt · Correlation-regression model · AI-system · Perceptron · NPL forecast · Financial risk
1 Introduction The purpose of the work is to calculate the forecast values of the volume of overdue loans in Russian commercial banks. To achieve this goal, the following tasks are formulated and solved: 1) explore the theoretical foundations for the formation of overdue loans © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 693–706, 2023. https://doi.org/10.1007/978-3-031-32719-3_52
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and methods for predicting the quality of the loan portfolio, 2) calculate the predicted values of the volume of overdue loans in two different ways, both using a correlationregression model and a neural network, 3) compare the accuracy of forecasts obtained using different methods. It is known that financial risk is a risk that is associated with the probability of loss of financial resources, for example, cash. With the development of monetary circulation, the problem of minimizing financial risks is of particular importance. Identification, calculation and minimization of financial risks are an integral part of working with financial assets, especially in conditions of market uncertainty [1]. As you know, market uncertainty can be expressed as certain conditions in which the process of making economic decisions takes place, changes in which are difficult to predict and evaluate. Incomplete information, or lack of it, is always encountered, especially in the field of finance. Market uncertainty cannot be eliminated altogether, but it can be reduced. The relevance of the study lies in the fact that the growth of troubled debts of commercial banks on loans to legal entities, individual entrepreneurs and individuals is currently the most relevant and debated issue in the banking community. Theoretical foundations for the appearance of overdue loans in the banking sector have been studied. According to experts, the urgency of the issue of reducing the share of overdue debts will only grow over time, until banks and the state develop effective methods to deal with it. It’s very important to use a statistical analysis to manage the amount of overdue debt of commercial banks in the Russian Federation. In order for the work with bad debts to be effective, commercial banks need to take into account a number of factors that can directly affect it. Factorial signs are revealed, a correlation-regression model is formed, which makes it possible to reveal the direction and strength of the influence of factorial signs on the growth rate of overdue debts. A hypothesis has been put forward and proved that with the help of the AI system, it is possible to obtain a forecast of the financial risk of a commercial bank receiving net profit, taking into account the dynamics of the share of overdue loans in the bank’s portfolio. According to experts, in 2018 the share of problem loans in the portfolio of Russian banks was 16,7%. In 2019–2020, according to S&P, the share of bad debts of Russian banks decreased, although not too significantly - to 16 and 15%, respectively. The share of non-performing loans increased by 5 percentage points, from almost 13% to 18% over the period from 2013 to 2016, and only then the share of “bad” loans began to decline [2]. In this regard, the study of the factors that determine the dynamics of the volume of bad debts of Russian banks is of great importance. The formation of a multifactorial correlation-regression model made it possible to reveal the strength and nature of the action of factorial features included in the model. According to S&P experts, the reserves of Russian banks cover bad debts by less than 60%. This level is quite low. As practice shows, the creation of new provisions for problem loans can lead to the fact that the capital adequacy ratio of banks falls below the minimum level required by the regulator. Although, the Russian banking system as a whole fulfills the requirements for capital adequacy. S&P analysts believe that if
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Russian banks formed the necessary amount of reserves for problem loans, then the total capital adequacy ratio would drop by 2 percentage points, to 10,2%, with the minimum requirements of the Central Bank of 8% [3]. In the current situation regarding the dynamics of a high share of problem loans, the use of an AI system to predict the financial risk of loss of net profit by Russian commercial banks will play an important role. Low debt recovery in the event of default by the borrower is another risk for banks. Studies show that on average in Russia it is 42%, while in European countries it is 76%. One more important point is that S&P indicates that Russian households spend about a quarter of their income on servicing loan debts. This situation indicates a high risk of significant losses for those banks that are involved in unsecured consumer lending, if the economic growth slows down and the standard of living of the population begins to fall. Research shows that artificial intelligence technologies are increasingly being used. So, for example, Bataev A.V., Gorovoy A.A. and Denis, Z. conducted a comparative analysis of the use of neural network technologies in the world and Russia, stating that the prerequisites for the rapid use of artificial intelligence are the processes of largescale development of information and communication technologies, an increase in the volume of processed information, as well as the development of production capacities of computers in data processing centers, and other factors [4]. The authors have identified the features of the processes of segmentation and positioning when creating an educational brand in the neural network economy. The researchers noted that the features of the processes of segmentation and positioning when creating an educational brand in the neural network economy are largely due to such factors as: applied computing, computers and business [5–8]. Practice shows that the use of artificial intelligence systems is interconnected with digitalization processes in all areas, including financial. One of the important aspects of digitalization, according to Goncharova, N., is the development of methods for providing financial services to people with dementia in the context of digitalization: partnership between citizens and the Russian state [9]. Considering the problems of lending, further development of the banking sector, it should be noted the achievements in the use of blockchain technology to identify customers by financial institutions. For example, Bataev A., Plotnikova E., Lukin G. and Sviridenko M. evaluated the economic efficiency of the blockchain for identifying customers by financial institutions. The authors noted that the financial sector is one of the drivers of the digitalization of the economy, which led to the emergence of fintech, an innovative industry based on the fusion of modern digital and financial technologies [10]. Assessment and reduction of financial risk remain in the focus of attention of experts in the financial sector. So a group of authors Shokhnekh A., Lomakin N., Glushchenko A., Sazonov S., Kovalenko O. and Kosobokova E. proposed a digital neural network to manage financial risks in business through real options in financial and economic system [11]. The authors Lomakin N., Lukyanov G., Vodopyanova N., Gontar A., Goncharova E. and Voblenko E. developed a neural network model that makes it possible to forecast the profit of enterprises in the real sector of the economy that are at risk. The analysis
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showed that the risk of financial income of enterprises (sigma) in chronological sequence increased unsustainably from the level of 0.4 from the second quarter of 2015 to a maximum of 3.1 with subsequent consolidation to 2.8 billion rubles, while its average value was 2.09 billion rubles [12]. Certain aspects of the use of neural networks in the financial sector intersect with issues of economic analysis in the financial management system. The authors Morozova T.V., Polyanskaya T., Zasenko V.E., Zarubin V.I. and Verchenko Y.K. note that in the conditions of the development of the modern economy, for the effective operation of an enterprise in the face of ever-increasing competition, it is necessary to respond in a timely manner to various kinds of changes in all factors affecting the enterprise [13]. To prevent the growth of overdue loans in the credit sector, it is important to assess the creditworthiness and financial stability of the enterprise. Rybyantseva M., Ivanova E., Demin S., Dzhamay E. and Bakharev V. considered separate approaches for assessing the financial stability of an enterprise [14]. Practice shows, that the main direction in the development of the credit and banking system is the use of artificial intelligence systems. Represents an incremental scientific knowledge study by Siyi Wang and colleagues regarding risk and return forecasting for non-performing consumer loan price portfolios. They developed a system for risk analysis and pricing of portfolios of such loans. The authors solved this problem by creating a bottom-up architecture where they model the distribution of the repayment ratio of each individual loan and then model the distribution of the total portfolio repayment ratio [15]. In the deep risk model proposed by Hengxu Lin, Dong Zhou, Weiqing Liu and Jiang Bian, a deep learning solution is proposed to analyze latent risk factors while improving the estimation of the covariance matrix. Experiments were carried out on stock market data and demonstrated the effectiveness of the proposed solution. The method allows you to get 1,9% higher than the identified variance, as well as reduce the risk of the global minimum variance portfolio [16]. Of practical interest are the studies of Ni Zhan, Yijia Sun, Aman Jakhar and He Liu in the development of graphical models for financial time series and portfolio selection. The authors explored various graphical models for building optimal portfolios. Graphical models such as PCA-KMeans, autoencoders, dynamic clustering, and structural learning can capture time-varying patterns in a covariance matrix and allow you to create an optimal and robust portfolio. When comparing derived portfolios from different models with the underlying methods, charting strategies produced steadily increasing returns at low risk and outperformed the S&P 500 index. This work suggests that charting models can effectively learn time dependences in time series data, which proves their usefulness in management assets [17]. Financial risk assessment using the VaR model provides high performance to support managerial decision-making in the financial sector. A team of scientists consisting of Kei Nakagawa, Shuhei Noma and Masaya Abe proposed an approach based on the use of the RM-CVaR model. It is known, that dispersion is the most fundamental measure of risk that investors seek to minimize, but it has several drawbacks. Notional Value at Risk (CVaR) is a relatively new risk measure that overcomes some of the shortcomings of well-known variance risk measures and has gained popularity due to its computational
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efficiency. CVaR is calculated as the expected value of a loss that occurs beyond a certain probability level (β) [18]. The use of artificial intelligence is increasingly manifested in the use of advisory robots, and the financial sector is no exception. Catherine D’Hondt, Rudy De Winne, Eric Ghysels and Steve Raymond conducted a study on the use of the "Alter-ego" system with artificial intelligence in the field of robotic investment. The authors introduced the concept of "AI Alter Ego", which are shadow robot investors. Based on the use of a unique data set that covers brokerage accounts for a large number of investors in a sample from January 2003 to March 2012, including the 2008 financial crisis, the benefits of robo investing were assessed [19]. One of the promising areas is the use of deep neural networks in the banking sector. For example, Krzysztof R. et al. proposed neural risk assessment in networks of unreliable resources [20]. According to the authors, it is advisable to use a GNN-based algorithm that learns only with random graphs generated using the Barabashi-Albert model. Clarkson J. et al. proposed the DAMNETS neural network, which is a deep generative model for Markov network time series. Time series networks are found in many areas, such as trade or payment networks in the economy [21]. The use of generative models is useful for Monte Carlo estimation and dataset enhancement, which is of interest for both data privacy and model fitting. Bingyang H. studied distribution-resistant estimation of expected function values from time data. He approximated test functions with neural networks and proved sample complexity using Rademacher complexity [22]. Neufeld A. suggested using robust data-driven statistical arbitrage strategies using deep neural networks [23]. Further research into the problem of reducing the risk of the bank’s loan portfolio can be continued in the following directions. First, the development of approaches to the study of model risk in loan portfolio models. Meyer C. has researched model risk in loan portfolio models. Using his approach, he proposed how to deal with uncertainty in all model parameters in a comprehensive yet easy-to-implement way [24]. Second, improve the capital structure and portfolio selection. Shan Huang investigated two problems of stochastic control in capital structure and portfolio selection. The model proposed by him defines the noise that hides the risk of bank solvency from banking regulators [25]. Thirdly, improving the risk assessment of the bank’s assets and liabilities. For example, Cheridito P., Ery J., Mario V. W. proposed a neural network approach to assess the risk of a portfolio of assets and liabilities over a given period of time. This may require imputed portfolio valuation. The conditional valuation of the portfolio must take into account the state of the world at a later time. Such accounting is difficult to perform if the portfolio contains structured products or complex insurance contracts [26]. Fourth, the optimization of losses in the recovery of the loan. Noteworthy is the study of scientists Arno Botha, Conrad Beyers, Peter de Villiers, who proposed to optimize possible losses before making a decision on loan repayment [27].
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The conducted research makes it possible to obtain an increment of knowledge that allows closing the scientific gap regarding the influence of factors that act on the complex processes of the formation of overdue debts in Russian commercial banks in modern conditions. At the same time, the scientific approach is based on two approaches, not only the classical one - using a multifactorial correlation-regression model, but also the digital one - the perceptron artificial intelligence system.
2 Materials and Methods In the presented work, such research methods were used as: monographic, analytical, statistical, multivariate correlation-regression model, as well as the perceptron artificial intelligence system. Based on data from the Central Bank of the Russian Federation for the period from 2012–2021 years, a selection of the main macroeconomic indicators was formed, reflecting the dynamics of the development of the economy and the banking sector of the country. Based on the data of the Bank of Russia, the performance indicators of credit institutions for 2012–2021 were collected [28]. Using data collected by the Central Bank of the Russian Federation and the State Statistics Committee, time series of financial and macroeconomic indicators were formed, reflecting the development of the Russian economy and its financial sector for 2012–2022 (Table 1). Table 1. Financial and macroeconomic indicators of the development of the Russian economy [25] X1 GDP per capita, growth rate, %
X2GDP billion rubles
X3foreign trade surplus, growth rate, %
X4- Loans to legal entities and individuals in GDP, %
X5Capital outflow, growth rate,%
X6 Growth rate of cash,%;
3.6
72986
-9.3
44.0
11.9
8.3
-10.4
79030
-30.5
51.6
152.2
-33.9
83087
-44.0
52.7
-6.5
85616
-63.8
47.5
23.1
91843
60.8
5.1 1.7
X7 Key rate of the Central Bank (at the end),%
X8 - US dollar exchange rate, rub
X9price of a barrel of URLS oil, USD
X10Net profit of banks, billion rubles
5.50
40.4
107.9
994
8.6
10.50
61.1
97.6
589
-62.5
2.7
11.50
72.7
51.2
192
-67.6
0.9
10.50
60.7
41.9
929.7
46.0
30.3
6.6
7.75
57.6
53.0
1279.5
103862 185.8
46.6
171.8
9.5
7.75
69.5
70.0
1344.8
109608 -80.3
46.6
-65.5
10.6
6.25
61.9
63.6
1700
-12.1
107315 59.3
58.9
123.0
3.4
4.25
73.9
41.7
1600
-0.5
130795 234.2
63.9
42.9
6.0
7.5
74.3
77.8
205
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The collected data were included in the correlation-regression model. The following factors were included in the model: X1 – GDP per capita, growth rate, %; X2 – GDP billion rubles; X3 – foreign trade surplus, growth rate, %; X4 – loans to non-financial organizations and individuals in GDP, %; X5 – capital outflow, growth rate, %; X6 – growth rate of cash, %; X7 – key rate of the Central Bank (at the end), %; X8 – US dollar exchange rate, rub.; X9 – the price of a barrel of URLS oil, dollars; X10 – net profit of banks, billion rubles; Y – the amount of overdue debt, billion rubles. Using the parameters of the regression model, based on the values of factorial features for 2022, made it possible to obtain the predicted value of the current feature. The result of the study was the forecast values of the volume of overdue loans received in two different ways: based on a correlation-regression model, a neural network. Studies have shown that 2 out of 10 factorial features in the correlation matrix had extremely low values. In order to optimize the model, the factorial features X6 and X7 were removed. To obtain the predictive value of the effective feature - the amount of overdue debt using an artificial intelligence system, we will form a dataset of the model. The neural network dataset has been formed (Table 2). Table 2. Neural network dataset X1 GDP per capita, growth rate, %
X2GDP billion rubles
X3foreign trade surplus, growth rate, %
X4- Loans to legal entities and individuals in GDP, %
X5Capital outflow, growth rate,%
X8 - US dollar exchange rate, rub
X9price of a barrel of URLS oil, USD
X10Net profit of banks, billion rubles
Prognoz, arrears on a loan mlrd. Rub
3.6
72986
-9.3
44.0
11.9
40.4
107.9
994
435
-10.4
79030
-30.5
51.6
152.2
61.1
97.6
589
1025
-33.9
83087
-44.0
52.7
-62.5
72.7
51.2
192
1022
-6.5
85616
-63.8
47.5
-67.6
60.7
41.9
929,7
2892
23.1
91843
60.8
46.0
30.3
57.6
53.0
1279,5
2994
5.1
103862 185.8
46.6
171.8
69.5
70.0
1344,8
3184
1.7
109608 -80.3
46.6
-65.5
61.9
63.6
1700
3198
-12.1
107315 59.3
58.9
123.0
73.9
41.7
1600
3051
-0.5
130795 234.2
63.9
42.9
74.3
77.8
205
4480
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N. Lomakin et al.
The perceptron neural network was formed on the Deductor platform, trained by the backpropagation method in over 10000 epochs. The trigger signal is a sigmoid (Fig. 1).
Fig. 1. Formation of the architecture of the perceptron neural network
3 Results As a result, of the study, forecast values of the volume of overdue loans received in two different ways were obtained: based on a correlation-regression model. a neural network. Using financial macroeconomic indicators, which were formed with Central Bank data, made it possible to build a correlation-regression model, as well as a perceptron artificial intelligence system. 3.1 Correlation-Regression Model The correlation matrix of paired correlation coefficients was calculated, using the XL and the “Data Analysis” package. The matrix of paired correlation coefficients after optimization will take the form (Table 3). Table 3. Correlation matrix after optimization X1
X2
X3
X4
X5
X1
1
X2
0.18142985 1
X3
0.33374173 0.66595821 1
X4
-0.3839476
X5
0.17973296 0.13476575 0.56013837 0.21648559 1
X8
-0.4405413
X9
0.17807681 -0.2514276
X9
X10
Y
0.64218425 0.49696445 1
0.64817549 0.42341127 0.72417632 0.20219975 1 0.09178054 -0.1549882
X10 0.49200454 0.10325607 -0.1352095 Y
X8
-0.4306086
0.30556810 -0.5569776
1
0.14330347 -0.1896229
-0.2582022 1
0.38238364 0.89739862 0.59761646 0.43555180 0.06411647 0.55471455 -0.4604784 0.23478675 1
To determine the quality of the correlation model, regression statistics were calculated (Table 4) and (Table 5).
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Table 4. Indicators of regression statistics Regression statistics Multiple R
1
R - square
1
Normalized R - squared
65535
Standard error
0
Observations
9
Table 5. Analysis of variance df
SS
MS
F
Significance F
Regression
8
14192968
1774121
#number
#number
Remainder
0
0
65535
Total
8
14192968
In order to calculate the predictive value of the effective feature for the future, it is advisable to use a regression model (Table 6). Table 6. xxxxx Odds Y-intersection Variable X1 Variable X2 Variable X3 Variable X4 Variable X5 Variable X8 Variable X9 Variable X10
14647.49143 -30.800727 0.193087161 -7.64904969 -170.108059 18.56374618 -219.834141 -85.3661440 -2.93747574
Standard error
t-statistic
P-Value
Bottom 95%
Top 95%
14647.49143
14647.49
0
65535
#NUMBER!
0
65535
#NUMBER!
0
65535
#NUMBER!
0
65535
#NUMBER!
0
65535
#NUMBER!
0
65535
#NUMBER!
0
65535
#NUMBER!
0
65535
#NUMBER!
0
65535
#NUMBER!
-30.800727 0.193087161 -7.64904969 -170.108059 18.56374618 -219.834141 -85.3661440 -2.93747574
-30.8007 0.193087 -7.64905 -170.108 18.56375 -219.834 -85.3661 -2.93748
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The coefficient of determination R2 = 1.0 shows that 100% of the total variation in the total amount of overdue debt is due to the influence of the factors included in the model. The assessment of the statistical significance of the regression parameters is examined using Student’s t-statistics. We put forward the hypothesis H0 about the statistically insignificant difference of indicators from zero and determine the Student’s t-test. In fact, the value of t-statistics exceeds the tabular values, so the hypothesis H0 is rejected, i.e. the event is not random, differs from zero and is statistically significant. Fisher’s F-criterion evaluates the reliability of the regression equation as a whole, as well as the tightness of the links. The actual value of Fisher’s F-test, equal to #number, indicates the limited capabilities of the xl-table when calculating the statistical significance of the regression equation. Based on the analysis, it was revealed that the volume of overdue debt in commercial banks of the Russian Federation on loans to legal entities and individual entrepreneurs is most affected by changes in such factors as 0.89739862 (X2 - GDP billion rubles) and 0,59761646 (X3 - foreign trade surplus, growth rate, %). Using the parameters of the regression model, you can get the forecast value for the next period - a year, Y = a + b ∗ X1 + c ∗ X2 + d ∗ X3 + e ∗ X4 + f ∗ X5 + g ∗ X8 + h ∗ X9 + i ∗ X10 (1) Y =14647.49143 − 30.800727 ∗ X 1 + 0.193087161X 2 − 7.649049692 ∗ X 3 + 170.1080591 ∗ X 4 + 18.56374618 ∗ X 5 − 219.8341414 ∗ X8 + 85.36614405 ∗ X9 − 2.937475 ∗ X10
(2)
Substituting the values of factorial signs for 2022, we get the predicted value of the effective sign - the amount of overdue debt, 7159,9 billion rubles. 7159.9 =14647.49143 − 30.800727 ∗ (−0.4) + 0.193087161 ∗ 120331.47.649049692 ∗ 117.0 − 170.1080591 ∗ 50.0 + 18.56374618 ∗ 643.0 − 219.8341414 ∗ 109.0 − 85.36614405 ∗ 103.4 − 2.937475749 ∗ 164.0
(3)
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3.2 Neural Network Perseptron The neural network graph is shown below (Fig. 2).
Fig. 2. Neural network graph
Using the “what-if” function and substituting the values of factorial indicators for 2022 into the model, it is possible to calculate the predicted value of the effective indicator - the amount of overdue debt is 4466.261 billion rubles, while this parameter was 4480 billion rubles a year earlier. The neural network promises a decrease in the volume of overdue debts by 13,7 billion rubles, or by 30,7%. The analysis showed that the largest amount of overdue debt was formed in the consumer lending segment, which accounts for more than 60% of the total overdue debt. As of September 2021, the overdue debt of Russians to banks will reach 955 billion rubles, already in the next year 2022 it can overcome the threshold of 1 trillion rubles. Often, when a consumer loan is the second or third in the consumer’s basket, then given that it is not collateral, in the event of financial difficulties, the client at the beginning stops payments on this particular obligation. An objective assessment of the quality of the forecast will be obtained at the end of 2022, a comparative analysis of the obtained forecast values is presented in Fig. 3.
Fig. 3. Comparative analysis of the obtained forecast values of the volume of overdue debt, billion rubles
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The correlation-regression model showed a forecast value that has a larger deviation from the actual + 51,8%, while the neural network calculated the forecast value of overdue loans, which has a deviation from the actual – 0,31%.
4 Discussion Correlating the results with the questions that were presented in the introduction, we can say that for future research it is advisable to use more advanced neural network models, for example. CNN. The Convolutional Neural Network is a Deep Learning algorithm that can take input parameters, assign importance (digestible weights and biases) to different areas / objects depending on the purpose of the study. This study provides an increment of scientific knowledge that allows closing the scientific gap regarding the identification and assessment of the influence of factors that cause the formation of overdue debts and the financial risks of this process in Russian commercial banks in modern conditions. The development of the computing power of modern cloud clusters has made it possible to use modern neural algorithms based on CNN, using parallel computing of the Hadoop and Spark open frameworks, in order to form complex forecasts in the financial sector, including for predicting financial risks associated with bank’s overdue loans.
5 Conclusions The result of the study was the forecast values of the volume of overdue loans received in two different ways: based on a correlation-regression model, a neural network. Using the parameters of the regression model, based on the values of factorial features for 2022, made it possible to obtain the predicted value of the current feature - the amount of overdue debt 7159,9 billion rubles. As the study showed, the correlation-regression model showed a predictive value, which has a deviation from the actual value of + 51,8%, while the neural network provided a predictive value of the volume of overdue loans, with a deviation from the actual value of 0,31%. The use of financial macroeconomic indicators, which were formed on the basis of data from the Central Bank, made it possible to build a correlationregression model, as well as a perceptron artificial intelligence system. Comparison of the values, forecast volumes of overdue loans in commercial banks, led to the conclusion that the correlation-regression model shows a deviation from the actual value by + 51,8%, while the neural network provided a forecast value with a deviation from the actual value of 0,31%. This study provides an increment of scientific knowledge that allows closing the scientific gap regarding the identification and assessment of the influence of factors that cause the formation of overdue debts and the financial risks of this process in Russian commercial banks in modern conditions. Further research may focus on the use of more advanced tools - convolutional neural networks, which have high performance due to the use of parallel computing, which are able to solve large-scale problems when processing big data. The priority direction for the development of the credit and banking system is the wider use of artificial intelligence systems.
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Acknowledgments. The research was financed as part of the project "Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization" (FSEG-2023–0008).
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Forecasting of the Global Market of Software that Uses Artificial Intelligence Algorithms Djamilia F. Skripnuk(B)
, Kseniia N. Kikkas , and Viktor I. Merkulov
Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. The article describes the methodology for analyzing and modeling the development of the software market that uses artificial intelligence and machine learning algorithms. The artificial intelligence (AI) market is growing rapidly in a variety of segments: the consumer market, entrepreneurship, government, and military sectors. Companies have already mastered the first AI pilots and are now looking for new ways to use the technology. Market development analysis and modeling refer to the analysis of the dynamics and development trends of artificial intelligence technologies in the world. The methodology for researching the development of the global market for AI and machine learning is carried out in the following sequence: factors (economic, political, legal, scientific) that affect the development of the are identified; the market is estimated by endogenous and exogenous variables; the influence of certain factors on the growth of endogenous and exogenous variables is assessed by expert assessments; the AnyLogic software builds a model of the system dynamics of the influence of factors on the development of the global market for artificial intelligence and machine learning. The flexibility of AnyLogic allows reflecting the dynamics of complex and heterogeneous economic and social systems at any desired level of abstraction. Based on the results obtained from the created model, we can conclude that the market for artificial intelligence and machine learning is now growing extremely rapidly and, under the influence of any factors that slow down the development of the market, it still maintains a positive trend of further development. Keywords: Global Artificial Intelligence Market · Market Development Factors · Global Machine Learning Market · Digitalization
1 Introduction Software that uses artificial intelligence algorithms surrounds us almost everywhere and performs a variety of procedures. Areas of its use include unmanned vehicles, smart homes, and various applications. Thanks to them, computers can perform various tasks and operate with large amounts of data. The scope of application of artificial intelligence algorithms is quite wide. It covers both known and perspective technologies. Artificial intelligence (AI) provides a wide range of solutions, from vacuum cleaners to space stations. Also, artificial intelligence should not be perceived as something monolithic and indivisible. Some areas in which artificial intelligence is applied are becoming new © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 707–721, 2023. https://doi.org/10.1007/978-3-031-32719-3_53
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sub-sectors of the economy and contribute to its further development. The volume of the world market for software using artificial intelligence algorithms in 2021 reached about 51 billion US dollars, an increase of 21.3% compared to 2020. The data was published in November 2021 by the research company Gartner. The market under consideration at Gartner includes applications with built-in artificial intelligence capabilities e.g., machine vision software. An analysis considers virtual assistants to be the largest segment of the market— spending on them on a global scale by the end of 2021 amounted to about 6 billion US dollars which is 12% more than a year ago. That is why forecasting the global software market using artificial intelligence algorithms is an extremely important and interesting topic for research and modeling today. It is worthwhile, to pay a lot of attention to the problems of AI market development. It is necessary to analyze the issues of expanding the areas of the practical application of artificial intelligence technologies, the issues of the safety of their use, as well as the results obtained during AI application [1]. New breakthroughs in the development of more efficient and faster artificial intelligence technologies are expected not only in the manufacturing of high-tech products but also in various spheres of the world economy in the near future. The results of comprehensive market research of AI in the world and in Russia showed that the number of patent families in machine learning, one of the main areas of development, is growing dynamically, occupying a predominant share of all artificial intelligence patents. Russia ranks 16th in the world in terms of the number of invention applications. Machine learning technologies are the main means of implementing artificial intelligence [2]. An overview of the practice of applying artificial intelligence technology in the financial sector, and the identification of the problems of transforming the financial ecosystem under the influence of this technology is required for the market analysis [3]. It is also necessary to consider the concept of artificial intelligence as a technology for creating intelligent machines, in particular intelligent computer software that is being implemented into the economy [4]. Economic relations in the modern economy are influenced by the technologies of the fourth industrial revolution at different levels, but most concentrated they affect the position of an individual person – an employee, manager, or entrepreneur. The technology of artificial intelligence is already the future of world medicine [5]. It is important to emphasize the dynamics of the world market for artificial intelligence systems and technologies, to show the relationship of its dynamics with a rapid increase in the performance of information processing algorithms, which became possible thanks to fast computers based on graphic processors, an avalanche-like growth of data and the emergence of almost unlimited possibilities for their storage. The technology of artificial intelligence is already the future of world medicine [6]. The consideration of the dynamics of the world market for artificial intelligence systems and technologies, emphasizes the relationship of its dynamics with a sharp jump in the performance of information processing algorithms, which became possible thanks to fast computers based on graphic processors, an avalanche-like growth of data and the emergence of almost unlimited possibilities for their storage and access [7]. The analysis indicates that economically developed countries are increasingly actively implementing artificial
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intelligence technologies, the use of which provides competitive advantages and development prospects [8]. Artificial intelligence and robotization of technological, production processes will increasingly lead to an increase in the number of released specialists, whose functions will be taken over by robotics, thereby increasing the burden on the labor market. Research on the main expected positive and negative consequences of robotization and automation of production on the labor market is essential [9–11]. The statement [12] that artificial intelligence technologies can also complement and expand human capabilities, which leads to higher productivity, greater demand for human labor, and improved quality of work should be taken into account. From a theoretical perspective, the impact of AI on employment and wages is ambiguous and can be highly dependent on the type of technology being developed and deployed, how it is being developed and deployed, and market conditions and policies. Machine learning technology has proven to be extremely useful in various fields such as data mining, artificial intelligence, text recognition, statistics, computer vision, mathematical optimization and its importance is constantly growing [13]. Artificial intelligence is able to transform the global economy through technological innovation [14], scientific knowledge [15], and entrepreneurial activity [16]. The gradual growth of automation and artificial intelligence in the last decade is due to two main factors: the growing availability of big data and hardware accelerators (GPUs and tensor processors). These factors make artificial intelligence the main technology responsible for extreme automation and interaction and thus bring the world closer to the fourth industrial revolution. It should be emphasized that artificial intelligence has demonstrated significant technological progress over the past five years, and much faster than previously thought [17–19]. It is obvious that progress will continue in the same vein and even accelerate. The questions about the relationship between people and machines and the rising necessity of these issues may become more relevant as technology advances and artificial intelligence becomes an integral part of our environment [20–22]. Choosing the best solution and analyzing which system is better: direct human-human interaction or human-machine interaction, and how companies save a lot of money by replacing real people with technological options [23–26]. The main factors that should accompany further growth are labor savings and efficiency gains, complex software applications, software evolution, and robotic systems. Despite the growing concern and intensified debate about the implications of automation of labor in the future, there are two points of view: the view that automation means the end of work for people, and the view that technology will always increase the demand for the labor force as it was in the past [27–30]. According to the analysis, artificial intelligence will not displace human labor but will form new professions, perhaps even create new working places in existing production facilities, as the total volume of production will increase. AI technologies have an exciting and great potential to improve health, environment, safety, and education. At the same time, they raise serious and complex ethical, legal, and security issues [31]. The general trend in the use of artificial intelligence is becoming increasingly important for innovation in the United States [32], and it shows the scale and spread of innovation. If artificial intelligence proves to be as transformative as electricity, it will rely in part on innovators and technological advances to apply ideas and innovations [33]. A
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breakthrough in the dissemination of innovations that uses artificial intelligence is likely to expand existing market capacity and potentially accelerate labor displacement [34]. Analysis of the global market of software that uses AI and modeling of market development is the actual topic. Market development analysis and modeling refer to the analysis of the dynamics and development trends of artificial intelligence technologies in the world; areas of application of artificial intelligence; global large companies using artificial intelligence technologies; turnover of the world market of artificial intelligence; safety of using artificial intelligence technologies; the impact of artificial intelligence on the global labor market; model of the world market of artificial intelligence. This analysis is the subject of this article.
2 Materials and Methods Within the framework of the research methodology for the development of the global artificial intelligence market, the analysis and modeling of the global software market using artificial intelligence and machine learning algorithms were carried out in the following sequence. In the first stage, a graph is built and the factors influencing the development of the artificial intelligence and machine learning market are identified (see Fig. 1). The following subsections provide instructions on how to insert figures, tables, and equations into your document.
Fig. 1. Factors influencing the Artificial Intelligence Market (complied by authors).
The graph considers the growth in the variety of systems used, the growth of participants, countries of companies, the volume of labor, scientific and financial resources, and other factors. To assess the variability of alternative options and subsequent decisionmaking, it is necessary to consider the factors that influence the development of the artificial intelligence and machine learning market in general. In the process of analyzing scientific works, four large groups of factors have been identified: economic, political, legal, and scientific. For convenience, some factors have been previously combined into one. Using this approach, it seems possible to build a model of the current state of the artificial intelligence and machine learning market and, based on the resulting model, obtain a forecast of the value of market development for a period of five years.
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In the second stage, the market development analysis is evaluated by the following endogenous and exogenous variables: y1 – economic factors; x1 – the number of programmers in the world, people; x2 – the number of job places for developers of artificial intelligence technologies in the world, places; x3 – the level of competition of companies using artificial intelligence technologies, %; x4 – the average salary level of artificial intelligence technology specialists, USD/ month; x5 – the average cost of artificial intelligence solutions in the world market, USD/ solution; x6 – investment attractiveness of the field of artificial intelligence systems in the world, USD; x7 – the number of companies using artificial intelligence in the world, companies; x8 – the number of countries using artificial intelligence or having companies using artificial intelligence, countries; x9 – the cost of Internet access services in the world, USD/ minute; y2 – political factors; x10 – security; x11 – the number of laws in the field of artificial intelligence and high technologies in the world, legal acts; y3 – legal factors; x12 – the number of patents in the world, patents; y4 – scientific factors; x13 – the number of scientific publications in the world, publications; x14 – the number of new technologies in the world, technologies. x15 – the number of scientific programs in the field of artificial intelligence in the world, programs. In the third stage, each of the factors was given an assessment of importance on a scale from 1 to 10 in the context of the impact on the development of the artificial intelligence market, based on a subjective understanding of the importance of the factors based on a literature review. Then these assessments were normalized by dividing by the average, grouping them into accumulators “Economic”, “Political”, “Legal” and “Scientific”. As a result, the weight coefficients of the model were obtained. In the fourth stage, based on the table of indices and weights, equations are formed for each group of factors. In the fifth stage, using the AnyLogic software form a model of the system dynamics of the influence of factors on the development of the global market of artificial intelligence and machine learning. The simulation system dynamics model was chosen because it allows modeling complex systems at a high level of abstraction, without considering small details: individual properties of individual products, events, or people, which is suitable for the task of evaluating artificial intelligence market indices.
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3 Results In accordance with the first and second stages of the analysis methodology, statistical data on factors for 2020 and 2021 was selected and indices of change of variables were determined. The results are shown in Table 1. Table 1. Indices of change of variables to the base period (complied by authors). Index
Name
Change index
2021
2020
iy1
Economic Factors
-
-
-
ix1
Number of programmers
1,04
55300000
53000000
ix2
Number of job places for AI and ML developers
1,47
8840
6000
ix3
The level of competition of companies using AI
1,08
-
-
ix4
Average salary level of AI technology specialists
0,96
2440
2520
ix5
Average cost of AI solutions
1,04
58000
60000
ix6
Investments in the AI systems
1,23
4241638
3456344
ix7
Number of companies using AI
1,07
31%
29%
ix8
Number of countries using AI or having companies using AI
1,02
180
176
ix9
The cost of Internet access services
1,38
57
72
iy2
Political factors
-
-
-
ix10
Security
1,05
-
-
ix11
Number of laws in the field of AI and high technologies
1,20
-
-
iy3
Legal factors
-
-
-
ix12
Number of patents
1,41
395000
279145
iy4
Scientific factors
-
-
-
ix13
Number of scientific publications in the world
1,11
251000
226000
ix14
Number of new technologies in the world 1,00
33
33
ix15
The number of scientific programs in the field of AI
1,2
-
-
iy5
AI and ML market
-
-
-
Expert assessments of the influence of certain factors on growth were given. Each of the factors was given an importance rating on a scale from 1 to 10 in the context of the impact on the development of the artificial intelligence market, based on a subjective understanding of the importance of the factors based on a literature review. Then
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these assessments were normalized by dividing by the average, grouping them into accumulators “Economic”, “Political”, “Legal” and “Scientific”. As a result, the weight coefficients of the model were obtained. The calculation results are shown in Table 2. Table 2. Indices of change of factors to the base period (complied by authors). Index
Name
Value
Rating
Weight coefficient
iy1
Economic factors
-
10
0,32
ix1
Number of programmers
1,04
10
0,17
ix2
Number of job places for AI and ML developers
1,47
9
0,15
ix3
The level of competition of companies using AI
1,08
5
0,08
ix4
Average salary level of AI technology specialists
0,96
7
0,12
ix5
Average cost of AI solutions
1,04
5
0,08
ix6
Investments in the AI systems
1,23
10
0,17
ix7
Number of companies using AI
1,07
8
0,14
ix8
Number of countries using AI or having companies using AI
1,02
2
0,03
ix9
The cost of Internet access services
1,38
3
0,05
iy2
Political factors
-
5
0,16
ix10
Security
1,05
4
0,33
ix11
Number of laws in the field of AI and high technologies
1,20
8
0,67
iy3
Legal factors
-
8
0,26
ix12
Number of patents
1,41
10
1,00
iy4
Scientific factors
-
8
0,26
ix13
Number of scientific publications in 1,11 the world
9
0,35
ix14
Number of new technologies in the world
1,00
10
0,38
ix15
The number of scientific programs in the field of AI
1,20
7
0,27
iy5
AI and ML market
-
-
-
Based on the table of indices and weights, equations were formed. Economic group index: iy1 = 0,17ix1 + 0,15ix2 + 0,08ix3 + 0,12ix4 + 0,08ix5 + 0,17ix6 + 0,14ix7 + 0,03ix8 + 0,05ix9
(1)
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Political group index: iy2 = 0,33ix10 + 0,67ix11
(2)
iy4 = 0,35ix13 + 0,38ix14 + 0,27ix15
(3)
Scientific group index:
Since one index was formed for the group of legal factors, the index of the legal group of factors is equal to: iy3 = 1ix12 = ix12
(4)
Further, in the AnyLogic software, a model of the system dynamics of the influence of factors on the development of the world market of artificial intelligence and machine learning “Analysis_of_AI_market” was built (see Fig. 2).
Fig. 2. Creating a model in AnyLogic software (complied by authors).
To do this, following the index table and equations, the model parameters were set, and the dependency equations were constructed. Previously obtained indices were used to build the model. To build such a model in AnyLogic, components from the “System Dynamics” section of the “Palette” tool were used. After all the parameters were placed and the links were configured, the model took the following form (see Fig. 3). Links show the influence of factors on the index of a group of indices or the final index, the artificial intelligence market index. In Fig. 3, the following notation is used: AI_market_iy5 – artificial intelligence market development index; economics_iy1 – growth index of economic factors (calculation according to formula); policy_iy2 – growth index of political factors (calculation according to formula); legal_iy3 – growth index of legal factors (calculation according to formula); science_iy4 – growth index of scientific factors (calculation according to formula);
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Fig. 3. Model parameters and links in AnyLogic software (complied by authors).
iy1_d – growth index of economic factors for the entire time of the experiment (total increase to the base period, not included in calculations, used for monitoring); iy2_d – growth index of political factors for the entire time of the experiment (total increase to the base period, not included in calculations, used for monitoring); iy3_d – growth index of legal factors for the entire time of the experiment (total increase to the base period, not included in calculations, used for monitoring); iy4_d – growth index of economic factors for the entire time of the experiment (total increase to the base period, not included in calculations, used for monitoring); cumulative_iy1 – accumulator, (required to calculate the dynamic variable year); year – dynamic variable of time accounting, year; ix1 – growth index number of programmers in the world; ix2 – index of growth in the number of jobs in artificial intelligence in the world; ix3 – index of growth in the level of competition of companies using artificial intelligence in the world; ix4 – growth index of the average salary level of artificial intelligence specialists in the world; ix5 – growth index of the average cost of artificial intelligence solutions in the global market; ix6 – index of investment growth in the field of artificial intelligence solutions in the world; ix7 – growth index of the number of companies using artificial intelligence in the world; ix8 – index of growth in the number of countries using artificial intelligence or having companies that use artificial intelligence; ix9 – growth index of the cost of Internet access services in the world; ix10 – security growth index; ix11 – growth index of the number of laws in the field of artificial intelligence and high technologies in the world; ix12 – world patent growth index; ix13 – index of growth in the number of scientific publications in the world; ix14 – index of growth in the number of new technologies in the world;
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ix15 – index of growth in the number of scientific programs in the field of artificial intelligence in the world. The AI_market_iy5 index is calculated using the formula AI_(market_iy5) = 0.32 economics_iy1∧ year + 0.16 policy_iy2∧ year + 0.26 legal_iy3∧ year + 0.26science_iy4∧ year
(5)
The cumulative_iy1 accumulator is used to calculate the year variable and then consider the year in the model. This is necessary to correctly obtain the growth index for the base period. The model parameters are set as follows (Fig. 4).
Fig. 4. Values of model parameters in the AnyLogic software (complied by authors).
The time limit of the experiment was also set to find out how the artificial intelligence market growth index would change in a year or five years. The time setting is shown in Fig. 5. Next, the model was started. The result is shown in Fig. 6. From the results obtained, in 2021 the global market for artificial intelligence and machine learning has developed by ~20% (iy5 = 1,2). Economic factors increased by 14% in general, political ones by 15%, scientific factors practically did not increase and the highest increase among the number of patents for these technologies is 41%. If all
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Fig. 5. Values of model parameters in the AnyLogic program (complied by authors).
Fig. 6. Model of development of the world market of artificial intelligence and machine learning, indices 2021 (complied by authors).
factors keep their pace, we will get that in five years, the market will grow by about 2.79 times compared to 2020 (see Fig. 7). If we assume that the trend is unlikely to continue and that the increase in the number of patents will decrease by 10% every year, then we get an average increase of 1.17. With this condition, it turns out that in five years the artificial intelligence market will grow by only 1.7 times compared to 2020. It is possible to simulate the situation of the impact of the economic crisis on the market, for example, because of a pandemic. This will significantly affect economic factors and it can be expected that, first, the desire of employers to create additional vacancies, expand production and intensively invest in development will decrease, we will reduce the indices ix2 and ix6 to +1% per year respectively. In this case, the iy5 index will be equal to 1.67 (see Fig. 8). Further, it can be assumed that the issuance by states and organizations of laws regulating the field of activity around artificial intelligence algorithms and machine learning is aimed not at support, but restrictions. To do this, we add a new parameter “law”, which will change depending on the growth of the economy. Then the model and forecast for the next five years will look like on the Fig. 9.
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Fig. 7. Model of development of the world market of artificial intelligence and machine learning until 2025 (compiled by authors).
Fig. 8. A model for the development of the artificial intelligence and machine learning market until 2025 with a decrease in the growth of vacancies and global investment (complied by authors).
As can be seen in Fig. 9, in this case, the speed of development of the market will significantly decrease (in 2025, the market will grow by only 35% compared to 2020), but in this case, there will be still an increase.
4 Discussion In general, according to the results obtained from the constructed model, it can be concluded that the market for artificial intelligence and machine learning is now growing extremely rapidly and, under the influence of any factors that slow down the development of the market, it still maintains a trend of further development. Given the dynamics of the growth of factors, only strict legislative regulation, both at the local and global levels, can greatly slow down its growth, for example, within the framework of future agreements adopted by the Organisation for Economic Co-operation and Development, such as the “Principles of Artificial Intelligence”.
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Fig. 9. A model for the development of the artificial intelligence and machine learning market until 2025 with the adoption of restrictive laws (complied by authors).
5 Conclusion When studying the problem described in articles in general, one can find out that the market for artificial intelligence and machine learning is now developing rapidly. According to various estimates, it demonstrates growth from 16–17% on average to 45% per year. The number of companies using technologies based on artificial intelligence algorithms and applying them in functional management is increasing as well as the amount of labor resources, investments in systems (from a total of 12 billion USD in 2017 to 58 billion USD in 2021), and scientific research in this area. The legal bearings of the issue are also developing. As an integral part of the IT market, the AI market is also subject to the big market trend, with global IT spending totaling 3.8 trillion USD in 2021, up to 4% from 2020. There is also some concern about the enormous changes in the labor market, from massive layoffs to the introduction of new professions in future. In general, the global market for artificial intelligence technologies is now in a phase of overrated expectations with a high level of risk for investors. However, despite this, investments are growing and according to forecasts, the market size by 2025 will increase by 150 times compared to 2016, 20% of workers engaged in non-routine tasks will rely on the help of artificial intelligence. The main leader in the use of AI in the world market is the United States, but in the future, the championship will go to China, where growth is expected from 0.8 to 1.4 percentage points to GDP growth annually. The AI algorithms themselves show great potential in decision making, science, medicine, intelligence, military systems, text re-cognition, statistics, computer vision, and process optimization through data analysis. There are also difficulties, among them mainly the complexity of building the algorithms themselves, as well as shortcomings in providing sufficient data processing facilities. Acknowledgments. The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program ‘Priority 2030 (Agreement No. 075-15-2021-1333 dd 09/30/2021).
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Evaluation of Data Visualizations with Bloom’s Six Levels of Understanding Enrico Pezzella1
and Ed Overes2(B)
1 St. Petersburg Polytechnic University named after Peter the Great, Saint Petersburg, Russia 2 Zuyd University of Applied Sciences, Heerlen, The Netherlands
[email protected]
Abstract. Data visualization is a very relevant topic in today’s world full of data, people see this data in different forms and visualizations. Many studies examine different visualizations and bring new ones to the community. Nowadays it is vital to evaluate the types of visualizations and make decisions about what visualization to use based on the conducted analysis. That is why the evaluation of various types of visualization is an important and interesting topic that has been touched upon in various scientific papers. Since there is no clearly defined list of visualization types in the scientific community, and more importantly – methods for their evaluation, this issue is relevant for further study. This paper describes an experiment conducted amongst 9 respondents to examine the visualization evaluation method using Bloom’s taxonomy (which includes six levels of understanding). The study was inspired by an article written by Burns et al. where the authors apply a framework (based on these six levels of understanding) to evaluate the efficacy of data visualizations. Our experiment consisted of a set of questionnaires about the “classic” data visualization (bar chart) and two more modern and complex ones - infographics and data comics. We wanted to compare them by testing the understanding and perception of presented data by our participants, so all used visualizations have been created on the same data set related to Covid-19 statistics. Even though our research has several limitations we obtained interesting results and insights not only about the types of visualizations but also about the evaluation methodology itself. Keywords: Visualization · Data Visualization · Evaluation of Visualization · Infographics · Data Comics · Bloom’s Taxonomy · Levels of Understanding
1 Introduction Data visualization is vital in today’s world with a trend of data-driven decision making [1–4], and it also strongly influences the interaction with a wide audience [5, 6]. There is a challenge when it comes to explaining the results of a research [2, 7]. That is why data scientists and other specialists need to become excellent storytellers, so data, visuals, and narratives should be combined to persuade an audience to make specific strategic moves or conclusions. The idea of storytelling with data also leads us to the trend of infographics and data comics applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 722–731, 2023. https://doi.org/10.1007/978-3-031-32719-3_54
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Nonetheless, the amount of data that different organizations and people have at their disposal is increasing day by day [7, 8] and the key question for data visualization specialists that comes is how to evaluate whether a certain visualization is suitable for storytelling. In this matter, of course, many aspects of visualization are important: positioning, color, context, etc. [9]. In this study, we want to examine a specific methodology (which will be described in the next section) applied to the evaluation of several different data visualizations. We designed three questionnaires with a bar chart, infographics image, and a data comic on one data set in different sequences to make sure that the assumptions that our respondents make would not be affected by the already seen data.
2 Materials and Methods 2.1 Literature Review Based on the set of various studies taken into work, we can conclude that there is no agreement on the list of all types of data visualization. Usually, there are classified basic types of data visualizations such as bar charts, pie and donut charts, linear and dot graphs [7, 9, 10]. On the one hand, based on the analyzed studies, there are more advanced visualization methods that can be identified [11] – infographics [10, 12–16] and data comics [17–19]. The last ones have begun to develop actively, which is confirmed by the growing number of studies about them in recent years. In scientific papers and books that analyze the abovementioned basic visualization methods infographics and data comics are often not considered at all. The situation is similar on the other hand – when analyzing infographics or comics, typical graphs, diagrams, etc. are considered only as part of a complex visualization. Despite the absence of a visualization types list, some researchers have attempted to classify them. So, in the work of Segel et al. there are highlighted 7 narrative visualizations, such as magazine-style, annotated chart, partitioned poster, flow chart, comic strip, slide show, and film/video/animation. It is emphasized here that they are not mutually exclusive and may well be combined to create even more advanced visualizations [20]. In a later work, Tong et al. also attempted to classify visualizations, but from a completely different perspective, without a specific representation of visualization types [21]. There are several approaches and principles to evaluate visualization. They are inherently grouped into two categories – qualitative and quantitative [16, 22–25]. In general, to understand whether the main goal of visualization is achieved using this graph, infographics or other data visualization questionnaires are created with both open and closed answers. For example, A. Burns et al. presented a taxonomy based on which it is possible to determine the success of visualization. It defines several levels of understanding: Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation (which are not hierarchically connected) [22]. So, according to this work, the “Knowing” level describes the ability of a learner (or viewer) to “recall or recognize factual information”. The “Comprehension” level shows whether learners begin to understand the presented information or data (in other words when they can understand the main idea or identify keywords). At the “Application”
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level learners present the ability to apply the knowledge to solve tasks (for example, calculate, translate, etc.). At the “Analysis” level learners are expected to show that they can compare the spatial representation of visualization parts, for example, identify a trend based on shown data. The “Synthesis” level is opposite to the “Analysis” because it focuses on the ability to “put ideas together to create something new”, so the learners might be asked to make predictions on, for example, identified trends from the “Analysis” level. And the last level is the “Evaluation” level which evaluates the ability to review and judge the visualization based on given or self-delivered criteria. Since the use of such visualization assessment methods is quite expensive because it requires testing for each case [23], it makes sense to consider the results of empirical experience in comparing different visualizations, or their use in a specific field. Referring to the main topic of this work, there are enough papers describing the basic principles and datasets for which one graph or another should be used. For example, it is preferable to use pie and donut charts in cases when the data set represents a 100% total of indicators so that a reader can see the values as parts of a whole and the total amount of these indicators is less than 5 [7, 9]. However, many authors do not recommend using such visualizations because of possible manipulations with 3D representation and the complexity of perception and comparison (for example, a three-dimensional representation of such a diagram can distort the understanding of a pie-part that is in the back [10]). Bar charts are also one of the most common ways of data presentation and visualization, which are widely used for comparing data of different types of groups or categories [7]. This type of visualization offers a good way to show sorted data, which is crucial in grouped data comparison and analysis [7, 9]. The line chart can show the dynamic change of one variable over another [7] (usual quantity over time) for one or more groups of lines [9]. As the bar chart is universally accepted as the most easy-to-use, this type of data chart is a popular and understandable way of data visualization. According to the described methods for data visualization evaluation, several experiments that evaluate the use of such a visualization as infographics in different fields were conducted. For example, McCrorie et al. conducted their research by giving the participants (males aged 50+) a questionnaire for quantitative and qualitative measurements of understanding infographics with cancer-related data, for example: «Over the next year, which of these groups of people, if any, do you think is most likely to be diagnosed with cancer?» (as a test question) and «Define “cancer incidence» (for text-written answer) [12]. Another research related to the education sphere included only quantitative methods to measure the feedback from undergraduate students involved in a study session with infographics [14]. Although the researchers did not use any special methodology or pattern for the evaluation, it is possible to conclude that infographics is a very effective and practical method of communicating information to a wide audience using various visual elements, such as images, graphs, tables, texts, etc. Thus, infographics are actively and effectively used to present data-driven stories in the social sphere [12, 15], medicine [15] and education [13, 14]. This is achieved primarily due to the idea of presenting complex data in a convenient and, above all, interesting form [15]. Infographics are accepted as an efficient way to understand data both by aged people [12] and students [14].
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Data comics as a new method of presenting data is actively developing for almost the same reasons that infographics gained their popularity. The methods of data comics evaluation do differ from the infographics’ methods because there is an example of the use of experiment and observation methods. According to analyzed studies, there are qualitative and quantitative questionnaires carried out by researchers. In a study from 2015, Zhao et al. compared the effectiveness of data comics with PowerPoint presentations that offered 5 test questions to participants aged between 21 and 30 about engagement, speed, space-efficiency, ease of use, enjoyability, and asked for free feedback. After qualitative and quantitative results analysis, data comics showed themselves to be more interesting for the perception of visualization compared to a PowerPoint presentation [19]. There was also an experiment that involved Ph.D. and master students to participate in creation of data comics by the end of which data comics were described as a “visual interface into a more written format” [18]. Based on a study by Bach et al. where the authors described the results of the lab study and in-the-wild study (analyzing both answers from indoor interviews and the engagement of participants in outdoor presentation). Even in comparison with infographics, comics show greater involvement in memorizing information and attract more people to read them [16]. 2.2 Methodology and Procedure The setup for the research is presented in Fig. 1. Three different groups of three people took part in it. The participation was voluntary, and no compensation in any form was proposed.
Fig. 1. Research set up.
Participants were given questionnaires with three sections according to bars, infographics, and data comics type visualizations. The questionnaires consisted of both quantitative and qualitative questions according to six levels of understanding of Bloom’s taxonomy. We included two questions to test the “Knowing”, “Application” and “Analysis” levels and one question for “Comprehension”, “Synthesis” and “Evaluation” levels for each type of visualization. The order of data visualizations presented was different for each group of respondents, so every visualization was placed in a new position. This was done to ensure that the sequence of given images did not affect the perception of information and to make a more correct analysis of the answers after the lab study. Also, the questions in every section (that correspond to the levels of understanding) were slightly different for the same reason. Participants could also return to the visualization of each question in a section.
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The biggest part of the participants that we asked to take part in the research were master’s degree students at Saint-Petersburg Polytechnic University (7 out of 9). One of the respondents was a master’s degree student at Moscow Institute of Physics and Technology and the last respondent was a Higher School of Economics graduate. The average age is 23 years; gender distribution – 5 female and 4 male respondents. 2.3 Design of Visualizations One of the ideas of the data set to be used in the research was any kind of Covid-related statistics. We thought that would increase the engagement of the respondents because of the relevance of the topic. This type of data set is also not specific to any science field, so the respondents would not face difficulties because of education level. We chose a ready-made infographic image from statista.com with access to raw data. Based on this data we made bar charts using MS Excel and designed a data comic using open-source tools (see Fig. 2). We decided to limit the number of frames by four to create short storytelling with a small amount of information to visualize. The data itself included the share of vaccines given out by China, India, the European Union, South America, the United States, and Africa and the share of the world population of listed countries and continents.
Fig. 2. Design of data visualizations.
2.4 Hypothesis Based on Bloom’s taxonomy characteristics and our general goal to test its application for the evaluation of different types of visualizations, we have set two hypotheses that we will verify in our field research: 1. We expect that on the “Knowing”, “Comprehension” and “Application” levels there will be no significant difference in the results for all visualizations.
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2. For infographics and data comics we expect a higher involvement and more precise answers according to “Analysis”, “Synthesis” and “Evaluation” levels of understanding.
3 Results In this section, both the qualitative and quantitative results of the research will be discussed. We gave similar questions for each visualization in each level of understanding to the participants so that we could analyze them equally. 3.1 Knowing For the level “Knowing” the answers were mostly correct. We see 100% right answers for the first question (for example: “Which country or continent gave out the biggest part of world vaccine doses?”) in all visualization sections, 100% of right answers for the second question in the Infographics visualization section, but 89% (which is 8 out of 9) of right answers for the second question in the Data Comics and Bars visualizations sections. We assume that in terms of this level of understanding it could not be concluded that Infographics lead to a significantly better understanding with such a small difference. 3.2 Comprehension To examine the next Bloom’s taxonomy level of understanding (“Comprehension”) respondents were asked to briefly describe the data they have seen (“How would you describe the data to your friends and family without showing the visualization? (in 2–3 words)”). As expected, there is also no significant difference between the answers for each type of visualization. It is worth mentioning that in Infographics and Data Comics sections 22% of respondents emphasized China’s impact on world vaccination. 3.3 Application For the “Application” level we made 2 questions that required simple calculations to answer. In the Data Comics section, respondents showed a better ability to solve specific tasks with the data and ended up with almost 100% correct answers. In the Infographics part, respondents were asked to calculate the share of vaccines given out by China and India; we found that 22% of respondents calculated the share of the population instead of vaccines. The answers regarding Bar charts also were not fully correct but included one very approximate answer and another one without any calculations. 3.4 Analysis For the “Analysis” level we also made 2 questions to see how respondents make conclusions out of given data. For Infographics, we have noticed that respondents mostly correctly answered our question where we asked them to identify what piece of data was used to calculate a specific part of our infographics – 78%. We also noticed that respondents (mostly) made correct conclusions based on our data in the Data Comics section
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with a very concrete description of the connection between the share of vaccines and the share of the population. With almost the same questions in the Bar Charts section, respondents gave fewer correct answers (only 22%) which leads us to the conclusion that respondents see it harder to make conclusions out of data in simple bar charts. 3.5 Synthesis Our question regarding the “Synthesis” level of Bloom’s methodology was tricky to answer because it required identifying a trend based on the data (with some predictions). We found “No trend” answers in all sections of the questionnaire – 1 in Infographics, 2 in Data Comics, and 2 in the Bar Charts. In both Infographics and Data Comics sections respondents mentioned the correlation between the share of population and the share of vaccines given out (22% in each part), there was also one interesting conclusion made in the Bar Charts part – one respondent stated that “Africa either started vaccinating people later than others, or doing it at a slower rate” (it does not correspond to the “Synthesis” level, but represents that the respondent perceived the data deep enough to make such a conclusion). 3.6 Evaluation At the last level of Bloom’s taxonomy respondents were asked to apply the information that they had seen in real-world situations. “What topic would you propose to discuss at a conference with African authorities based on given data?” in the Data Comics part; “Imagine being a lawmaker. What would you propose to change in the world restrictions policies based on this data?” in Infographics and “Imagine being a WHO (World Health Organization) specialist. What would be the point of discussion at a conference with African authorities?” in the Bar Charts section. Even though the Infographics image was designed by professionals 2 respondents did not make any discussion about the given topic. Data Comics and Bar Charts sections, in turn, respondents made very concrete and thoughtful claims.
4 Discussion The conducted research was aimed at data visualization evaluation according to Bloom’s taxonomy. We wanted not only to test the taxonomy itself (in terms of applicability for visualizations evaluation), but also to see what visualization that we used (infographics, bars, and data comics) leads to a better understanding of presented data according to this taxonomy. We assume that the bigger the percentage of correct answers and the more detailed answers for open questions are the better the data has been perceived. As expected, we did not identify any significant difference in the understanding of Bar Charts, Infographics, and Data Comics visualizations according to the “Knowing”, “Comprehension” and “Application” levels of Bloom’s taxonomy (Hypothesis #1). We could not fully prove our second hypothesis which stated that Bar Chart visualization will have lower rates of understanding according to the remaining levels of the taxonomy. We
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have noticed a slightly worse percentage of correct answers on the “Analysis” and “Synthesis” levels, but on the “Evaluation” level respondents showed the same involvement as for the other two visualization types. Apart from questions that we designed for the levels of understanding we also asked for overall feedback from our participants. Surprisingly, respondents liked more simple bars (four out of nine), than the data comics (three out of nine) and infographics (two out of nine). We also asked: “What data visualization was the most effective for understanding in your opinion?” and the results matched our expectations here: four out of nine picked data comics, three selected bars, and only two - infographics. Even though the results are quite interesting, several limitations that were present during the experiment should be considered. The first and the main limitation is the number of respondents. This means that some of the results might change with the expansion of the survey. The second limitation worth mentioning is the remote survey procedure that did not allow to measure the time the participants spent analyzing, reading, and answering each question. After all, our future research in this field will include working on limitations and will bring an opportunity to see how they affected our current results.
5 Conclusion The amount of data is increasing day by day and the need to deliver this data to users in an understandable way is very important. That is why data visualization is a very relevant and interesting topic that will remain relevant for a long time, but the question of how to evaluate visualizations also comes along. In this work, we tried to apply Bloom’s taxonomy for data visualizations evaluation by giving our respondents a bar chart, an infographic, and a data comic based on covid data. We were able to get interesting insights into how data could be perceived through different types of visualization using this methodology. Our findings in this field could set a basis for future research. One of the ways for forthcoming work could be testing other types of visualizations using different data sets. Since we use specific data about covid it would be interesting to test the understanding of information in some other specific fields.
References 1. Egor, T., Victor, D., Alexandra, B., Ed, O.: Digitalization in logistics for organizing an automated delivery zone. Russian post case. In: Schaumburg, H., Korablev, V., Ungvari, L. (eds.) TT 2020. LNNS, vol. 157, pp. 143–156. Springer, Cham (2021). https://doi.org/10.1007/9783-030-64430-7_12 2. Killen, C.P., Geraldi, J., Kock, A.: The role of decision makers’ use of visualizations in project portfolio decision making. Int. J. Project Manage. 38(5), 267–277 (2020). https://doi.org/10. 1016/j.ijproman.2020.04.002 3. Pavlov, N., Zotova, E., Shaban, A., Overes, E.: Digital technology of generating survey data on the characteristics of an innovative product in a distance learning system.In: Proceedings of the International Scientific Conference - Digital Transformation on Manufacturing, Infrastructure and Service, pp. 1–5. ACM, New York (2020). https://doi.org/10.1145/3446434.3446537
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Development of the Company’s IT Infrastructure in the DAMA-DMBOK Standard Implementation Oksana Iliashenko(B)
, Victoria Iliashenko , and Alexandra Shuvalova
Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. In the article, the authors conduct a detailed analysis of the possibilities for developing the IT infrastructure of companies in the period of digitalization. The authors highlight the main trends in the management of digital companies, considering data as one of the key assets of the company. The article proposes to use the DAMA-DMBOK standard to implement the data-driven approach. The process of managing a company based on the analysis of data collected by the company is called the data-driven approach. This requires the development of the company’s IT infrastructure based on a holistic corporate data management strategy. Data Governance is defining as the activity of providing direction and control over the management of information assets, which allows you to bring the company’s work to a qualitatively new level. The purpose of the study is to increase the value of information assets of companies based on the use of a data-driven approach. To achieve this goal, it is proposing to develop the IT infrastructure of companies based on the DAMA DMBOK standard, which contains the best practices for implementing a data-driven approach. Because of the study, the authors proposed the model of the company’s IT infrastructure based on data-driven approach and DAMA DMBOK. The proposed model can be customizing for various industries and businesses. The dynamism of markets and the growing awareness of the importance of data as a differentiating factor in the competitive struggle is forcing organizations to rethink the distribution of authority and responsibility in data management. This trend is clearly visible in the financial, government, electronics and retail sectors. Keywords: IT-infrastructure · Data-Driven Approach · Data Governance · Data Management · DAMA-DMBOK · Data as a company asset
1 Introduction Modern digital companies strive to reduce time and financial costs by automating processes and implementation digital platform for management and increase business value of the company. The introduction of automated information solutions based on modern information technologies is an extremely expensive and time-consuming process, forcing the company to mobilize financial, human, and material resources [1, 2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 732–744, 2023. https://doi.org/10.1007/978-3-031-32719-3_55
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An efficient enterprise IT structure not only helps the company earn more, but in many cases literally keeps it on its feet. Human resource management systems, customer service processes, market analysis tasks are constantly becoming more complex, and it becomes simply impossible to do without the help of information technology [1]. That is, the success of the business largely depends on how competently the company’s IT infrastructure is formed and how well it will work. It is also clear that in cases where the system does not cope with the duties assigned to it, quite serious problems are inevitable. First, about the concept of enterprise IT infrastructure [2]. This term can be called the combination of various information resources, without which the organization will not be able to function normally, and employees will not be able to perform their work efficiently. A computer with software connected to the World Wide Web is the simplest infrastructure. The IT infrastructure of an enterprise includes various elements [3]: – The server is a computer that will build service software without the help of a worker. – SCS (structured cabling systems) is the basis of the company’s IT infrastructure. It connects computers and devices to each other, in addition, they are involved in data transfer. – LAN (local area network) is an integrated system that occupies a relatively small area (office, inside the institute, etc.) and consists of hardware software. – UPS (uninterruptible power supply) - something that protects the entire computer system from damage when the power supply is suddenly turned off. – PBX (automatic telephone exchange) - equipment that allows subscribers to communicate with each other. – Network equipment is what allows the entire network to work efficiently. – A network switch is a set of devices that combines several computers that exchange various data with each other. – Network hub or “hub” - connects computers and servers to each other in one network. – A router which distributes data between connected devices. – Workstation - personal computer, scanner, information media. In general, a fully equipped place for an IT specialist. Software is a system with which a user can use a personal computer. Everything can be grouped into three groups: – Hardware is anything that connects to a computer and helps it function properly. This includes the cooling system, power supply, motherboard, keyboard, monitor, server, router, etc. – Software is the programs that enable the computer to operate. Without it, it would just be dead iron. These include utilities, files, libraries, drivers, web servers, CMS and CRM systems. – A network that creates a connection between the entire system. It consists of hardware and software elements that protect the entire system and allow it to function normally. These can be firewalls, switches, servers, etc. The development of a complex IT infrastructure is continuously connected with the flow of data that comes from various sources for further processing [4]. Therefore, the use of a data management standard for the implementation of a complex IT infrastructure of a company and its further development is an urgent issue.
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2 Materials and Methods 2.1 Methods The article required the study of special literature aimed at research in the field of data governance, data management. The authors analyzed the main framework of DAMA DMBOK. The existing practices of forming IT-infrastructure in the company were analyzed. After analyzing best practices and DAMA-DMBOK standard, authors have suggested model of IT-infrastructure based on data driven approach. The next subsections provide instructions on how to form IT-infrastructure model based on data management. The article uses general scientific methods, methods of comparative analysis, Architectural Development Method (ADM). To develop the IT-infrastructure model based on data-driven approach, we propose to implement the following steps: 1. Literature analysis and best practices analysis in the field of data management. The result of this stage is understandable the current situation and identifying problem areas in the formation of the IT-infrastructure model based on data management; 2. Clarification of the research objectives. This will allow preparing more accurate formulation of research objectives; 3. Formation of IT infrastructure taking into account 11 knowledge areas of the Dama Wheel Framework [5]. 4. Development of the main levels (layers) of the IT-infrastructure model based on data-driven approach with ADM: • • • •
Business architecture; Data architecture; Application architecture; Technological architecture (infrastructure).
The results of the modeling is the reference IT-infrastructure model based on datadriven approach. 2.2 Materials The process of managing a company based on the analysis of data collected by the company is called the data-driven approach. The main concepts within this approach are the concepts of Data Governance and Data Management. What is the difference between these concepts? One of the main differences between the two business functions is that Data Governance is a strategy while Data Management is a tactic. This means that in addition to establishing a common data management paradigm for an organization, leaders must define specific information management practices in order to achieve their goals. In addition, Data Governance is not controlled by technology solutions. On the contrary, businesses are using technology to address the core issues that a data management plan poses to them. Finally, it is important to understand that Data Governance and management work in parallel and complement each other.
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Consider the ontology of the concept of Data Governance. Today there are several close interpretations of the concept of Data Governance. Let’s dwell on some of them. Data Governance – planning, oversight, and control over management of data and the use of data and data-related resources… we understand that governance covers ‘processes’, not ‘things’, the common term for Data Management Governance is Data Governance [5]. Data governance is the collection of decision rights, processes, standards, policies and technologies required to manage, maintain and exploit information as an enterprise resource [6]. Data governance is the orchestration of a company’s staff, technologies and processes to transform data into an enterprise asset that yields business value for the organization [7]. Data Governance is a system of decision rights and accountabilities for informationrelated processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods. Data governance is the practice of organizing and implementing policies, procedures and standards for the effective use of an organization’s structured/unstructured information assets. Data Governance is an approach to data management that defines data as the main asset of an organization. This is an approach that requires the implementation of specific process roles and a solution to manage data as a valuable asset [8]. The DAMA DMBOK Association identifies 11 areas of expertise in enterprise data management: Data Governance, Data Architecture, Data Modeling and Design, Data Storage and Operations, Data Security, Data Integration and Interoperability, Data Warehouse and Business Intelligence, Metadata, Data Quality [5]. Consider the best practices for applying the data-driven approach used in Russian and foreign companies. There are many data management software solutions on the market today. To get acquainted with the best samples, the easiest way is to contact analytical agencies. Gartner regularly releases Magic Quadrants, which list about a dozen vendors in the Leaders group alone [Data and Analytics Strategy Template (gartner.com)]. In recent years, the Informatica platform has been the leader. She, it should be noted, is in the first positions in the ratings of IDC, the second main analysts in the IT world. In their survey of vendors of data cataloging software solutions, Informatica is well ahead of all other vendors. The second position is occupied by Oracle, then IBM. The data management integration platform Informatica is focused on the implementation of data management tasks at the strategic and tactical levels of working with company data/This is a platform consisting of components integrated with each other that perform the main functionality (Table 1). Oracle has developed the concept of Data Governance and identified 6 main areas for which the company has developed appropriate software (Table 2). IBM has unveiled its data management and control program that can turn data into business information [9] and enable value to be extracted from data. IBM believes that modern data management solutions should address metadata management issues, data quality issues, and comply with the EU General Data Protection Regulation (GDPR)
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Table 1. Functionality of modules of the Informatica integration platform for data management. Area of Data Governance/Data Management
Main functions
Software tools
Data loading and data flow management
Loading and data processing, includes ETL tools and allows you to extract data from source systems, transform it and load it into data warehouses or data marts
Informatica Developer
Management and standardization of business terminology
Maintaining the company’s business glossary, controlling, and monitoring data quality metrics
Informatica Axon
Metadata management
Metamodels and semantic data management. Maintaining a catalog of data connected to the company’s Data Lake, loading metadata from external source systems
Enterprise Data Catalog (EDC)
Data Quality Management
Data analysis, monitoring, reporting and transfer of results to a business glossary
Data Analyst
[10]. Today there are such solutions: SAP Data Governance, Collibra Data Governance Center and IBM InfoSphere Information Governance Catalog [11]. All these solutions have some common functionality: a glossary, a data catalog and the ability to generate data [12]. Their difference is in the additional features provided by vendors. Rostelecom in 2018 presented its view on the system of solutions and processes for corporate data management. In their opinion, the experience of Data Governance programs in Russia and the world shows that the main focus areas are usually the tasks of data quality management; master data and metadata management. In 2021, Rostelecom introduced a new IT product for business—the Data Management Platform. This product is designed to store, process and manage corporate data in organizations of any size. This platform made it possible to build a corporate data warehouse, create a database for data monetization based on a client profile, and ensure the company’s transition to a data-driven model. The data management platform was created to solve numerous business problems: streaming and batch data processing (configuring and monitoring ETL/ELT processes); formation of a lake of “raw” unstructured data; building a classical data warehouse and a non-relational segment of quasi-structured data; data quality management; using data to generate BI reporting and dashboards, machine learning and data monetization services; organization data management with the construction of a single glossary and data map.
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Table 2. Functionality of modules of the Oracle integration platform for data management. Area of Data Governance/Data Management
Main functions
Software tools
Information policy management
Formation of applications for planning, collaboration, reporting applications
Data Governance Stewardship
Data Quality Management
Cleansing, enriching, standardizing, collating and merging data
Oracle Enterprise Data Quality
Master Data Management (MDM)
Define and standardize a business entity
Oracle GoldenGate
Metadata Management
Metamodels and Semantic Management
Oracle Enterprise Metadata Management
Lifecycle Data Management
Recording management, archiving and e-discovery
Oracle Cloud Infrastructure Process Automation
Data security management for which the software is developed
Data access control, auditing
Oracle GoldenGate Plug-in for EMCC
3 Results In the context of digitalization, IT infrastructure is the foundation of the functioning and the engine of development of any organization, company, including both private and public sector. IT infrastructure or information technology infrastructure is a combination of components that are necessary for the operation of corporate IT services and IT environments, as well as their management [13]. In other words, IT infrastructure of company is its IT assets, the hardware and software from the employees’ workstations to the data centers used within the company. In a constantly and rapidly changing economic environment, organizations are forced to adapt quickly to new realities. Clearly, a key change in this adaptation is the adoption and use of digital technology. IDC predicts that around 90% of organizations around the world will prioritize investments in digital tools to expand physical spaces and assets, and by 2024, more than half of all investments will be related to digital transformation [14]. To the present-day digital transformation of business is considered to be an ongoing process and it does not end with the implementation of a single standalone tool or solution. At the same time, the issue of developing an organization’s IT infrastructure
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as the framework on which the process of digital transformation unfolds is significantly influenced. At the present stage, there are challenges and tasks related to IT infrastructure development: development of flexibility and speed of adaptability while maintaining stability, introduction of new technologies and risk reduction, rational technology investment in a limited budget, ensuring comprehensive IT security while maintaining transparency and openness of IT infrastructure [15]. Specifically in terms of IT infrastructure components - these are problems associated with the lack of powerful computing platforms for fast processing of large data volumes, with the lack of effective adaptive data storage architectures, including cloud storage, with the need to ensure high performance computing of large volumes of data during their transfer, including their virtualization and automation, with data quality and respectively selection of quality data from the entire array, as well as the need to ensure reliability. Obviously, a significant block in the development of IT infrastructure and solving operational problems occupies the management, optimization and protection of data sets. There is an explosive growth of data volumes and connections at the edge, which creates a whole new set of tasks for IT teams and forms of requirements to the IT infrastructure of the company. This is because modern companies face a number of challenges in working with corporate information, such as: the rapidly growing volume of documents, the emergence of new partners, customers, the need to implement highquality and rapid interaction between employees regardless of their location, ensuring the implementation of end-to-end processes [16]. As a result, in case of poorly implemented or generally unprepared IT infrastructure, companies incur losses associated with the cost of data acquisition and storage, their recovery in case of loss, improving their quality and leveling out other risks associated with data and data management. The impact of data and its value is forcing businesses to rethink how data management and IT infrastructure deployment should occur in the future with minimized development losses. In essence, the key challenge that IT professionals can realize is to design, implement and maintain an IT infrastructure that will be essentially a company-wide, autonomous platform for high-quality enterprise data management within complex, progressive digital environments [17]. Thus, IT infrastructure for such purposes should meet the requirements of high scalability - to take into account the growing needs of the company, standardization infrastructure components should be compatible and meet the requirements of information security, flexibility and fast adaptability - to be quickly and easily adjusted for changes in processes both within the company and the external environment, integrality - infrastructure components can be integrated and work together, economic efficiency to be cost-effective [8]. Companies should consider possible solutions related to IT infrastructure, focusing on management, use, analysis and protection of huge flows of data in use in real time, at the same time ensuring economic feasibility of technological solutions.
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IDC predicts that by 2023, 80% of enterprises will use AI-enabled cloud management services to manage, optimize and protect dispersed resources and data [9]. Thus, we are talking about creating a predominantly cloud-based, or at least hybrid IT infrastructure, that is, access to the infrastructure is provided via the Internet and through virtualization, where computing resources are used without installing local components. In this case, speaking of data, the development of IT infrastructure system project is directly related to the creation of an integrated information system that involves the integration of existing and emerging components, based not only on interoperability, but also on the structure of the data used, also, the way they transform and organize access to data. The implementation of high-quality data architecture is a fundamental approach to implementing an evolving IT infrastructure. The data architecture is expected to be like a bridge between the business strategy and its technological implementation within the company. Properly orchestrated data design, quality assurance, integration and interoperability practices are the foundation for reliable systems and applications [18]. The data architecture as part of the enterprise architecture then contributes to the following objectives: – fast preparation and adaptation of processes to the launch of new products and services of the company and maximum use of business opportunities in the implementation of new technologies; – solving the question of information deficiency for company processes at fulfillment of business requirements; – providing management of the process of granting and circulation of data and information in the scales of the enterprise; – to help improve consistency between business and IT processes. At the top level, working with data involves 3 main steps: extract, transform and load (Fig. 1). Data is extracted from both external and internal sources. The stage of data transformation is necessary to clean the data and bring it to a structured form for further work with it [5]. The data loading stage includes loading them into the storage for further visualization using analytical tools. As data circulates within the company and is constantly being used by users in real time, we are talking about the future of data in motion, the need for fast speed in retrieving, transforming, and loading the data that was requested by user. IDC predicts that by 2025, 70% of companies will invest in alternative computing technologies to drive business differentiation by reducing the time to retrieve useful information from complex data sets [19]. In addition, IT infrastructure must meet some technological requirements when dealing with data. These include ensuring that processes such as: data collection and accumulation, data cleansing and standardization, collation, and consolidation, i.e., defining entities, distributing data across the organization and ensuring sharing, and ensuring subsequent data consistency and usage control are in place [20]. These requirements serve as the basis for configuring and integrating applications and services within the company, in order to achieve maximum consistency and adaptability of IT infrastructure components [21]. Speaking about specific tools, it is the implementation of data profiling tools, metadata repository, enterprise data bus, data services providing processing of requests
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Fig. 1. ETL-process Source: [5].
for adding, deleting, updating or selecting data, virtualization and data modeling server (Fig. 2).
Fig. 2. Process of data analyzing Source: author
Figure 3 proposes a model of the company’s IT infrastructure, the basis of which is data management. The data management block includes work with metadata, a set of structured and unstructured data, non-identifiable and identifiable data. The blue layer includes analytics applications for working with data, which must meet functionalities such as: • Real-time, batch, streaming • Data linking, fusion, filtering • Link analytics, entity resolution, complex analysis. In the era of digital business transformation, every large enterprise uses several information systems. Often, they operate with intersecting data arrays, whether it is information about products, customers, sales statistics, or something else. If there is no connection between applications, this results in a huge waste of time and resources.
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The ESB (enterprise service bus) module performs the function of an enterprise service bus, a specialized set of software that acts as a single center for messaging between information systems and applications. The service bus allows to easily configure message routes, stores the history of messages, and fixes the path of each of them [22] (Fig. 4).
Fig. 3. Model of the company’s IT infrastructure based on data management [22].
Fig. 4. Data transfer model without and with ESB service [23].
To receive data from another application, it is necessary to go through a complex multi-level chain of operations. The continuous exchange of messages between them threatens to turn into real chaos. On the user side, this will manifest itself as a long wait and constant application crashes.
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And if at least one of the systems needs to be updated, changed or distributed between departments, this will inevitably affect all other services. The ESB bus changes things completely. With it, applications no longer need to indirectly contact each other - each of them interacts only with the integration platform. This immediately eliminates the need for a huge number of access methods - you need exactly as many interfaces as there are services. If changes need to be made to one of the systems, this will not affect the operation of other enterprise applications in any way. Only the ESB data bus will be responsible for all these tasks. Thus, the ESB approach, unlike the traditional point-to-point architecture (when services directly interact with each other), is more flexible. Integration scripts can be modified with minimal developer intervention. Simplifying application integration by implementing an ESB saves time and money, improves service performance, and ultimately improves the efficiency of the organization and increases the profits of the enterprise. One of the main functions of the ESB is that it receives data from some applications and sends them to others in accordance with the given rules, builds the paths of information flows, their sequence. The service data bus contains configuration tools with which you can set the desired information flow control parameters. Data from different systems can be presented in various formats - XML, CSV, JSON, DBF and others. With the classic point-to-point approach, this makes it difficult for applications to “communicate” with each other. Enterprise Service Bus solves the problem by converting the data from the wrong format to the right one. For example, if you need to send the same message to both ERP and CRM, ESB will transform the data as needed and transfer it to the appropriate systems. Thanks to its scalability, the ESB can work with a different number of information systems and different amounts of data, distributing the load between applications. The integration bus provides data transfer of any size, breaking large arrays into smaller ones. Processing information piecemeal in the event of a failure prevents data loss and the need to retransmit already sent packets. Scalability also provides the ability to increase the information capacity of the enterprise indefinitely, and the IT landscape does not have to be homogeneous. The proposed IT infrastructure model will provide companies with the opportunity to solve problems when implementing a data-driven approach: • • • •
cataloging corporate data; reduction of development time in corporate IT systems; improving the reliability of data, analytics and reporting based on them; improving the quality of corporate data.
4 Discussion The DAMA-BOOK standard is a flexible standard that allows it to be used by various industry companies. In the future, as part of the research, it is planned to adapt the reference model of the company’s IT infrastructure based on data to the industry specifics of organizations, taking into account their features of integration and work with data.
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Another topical issue from our point of view is the issue of valuation of data assets. This is due to two aspects: • the data value is contextual (what is of value to one organization may not be of value to another); • the data value may be dependent on time parameters (what was valuable yesterday may not be of value today). We plan to develop mathematical models for the evaluation of data assets, taking into account the specified parameters.
5 Conclusion Within the framework of the article, the authors analyze the possibilities of managing companies based on a data-driven approach. To implement this approach, the development of the company’s IT infrastructure is required. The authors propose to use the DAMA-DMBOK standard for the development of the company’s IT infrastructure. As part of the study, the authors analyzed existing solutions from leaders in the development of the integrate data management platforms. The analysis of the best practices for the development of foreign and Russian companies based on the data-driven approach was carried out. The authors also studied the DAMA DMBOK standard and proposed to use the Data Wheel framework for the development of IT architecture. The paper proposes a model of the data analysis process, on its basis, developed a model of the company’s IT infrastructure. The model takes into account the functionality and instrumental support of integration infrastructure services, data management processes, data analytics, and the possibility of using ESB. The proposed IT infrastructure model is a reference one. From our point of view, this model will provide an efficient, cost-effective and secure organization of the processes of collecting, storing and using data. The model will allow implementing the tasks of the company’s Data Governance – to optimize data management, to make data a business asset of the organization, to increase the data value, to improve the quality of management decisions based on the data analysis. Based on the frameworks of each of the Dama Wheel knowledge areas (Data Quality Management, Metadata Management, Data Warehouse and Business Intelligence, etc.), we plan to offer a description of these areas for various fields of activity: energy, medicine, education, agriculture, etc.
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3. What is information technology?. https://www.techarget.com/searchdatacenter/definition/IT. Accessed 21 Mar 2022 4. Mogilko, D., Iliashenko, V., Chantsev, V., Von Schmit, A.C.F.M.: Data management in the business processes system. In: Proceedings of the 33rd International Business Information Management Association Conference, IBIMA 2019: Education Excellence and Innovation Management through provision 2020, pp. 8603–8609 (2019) 5. DAMA-DMBOK (2nd Edition): Data Management Body of Knowledge. DAMA International. Technics Publications (2020) 6. Modern Data-analytics. https://www.gartner.com/smarterwithgartner/7-key-foundations-formodern-data-and-analytics-governance. Accessed 11 May 2022 7. Bruskin, S.N.: Methods and tools of advanced business analytics for corporate information and analytical systems in the era of digital transformation. Mod. Inf. Technol. IT Educ. 12(3–17), 234–239 (2016) 8. Ilin, I.V., Iliashenko, O.Yu., Levina, A.I.: Application of service-oriented approach to business process reengineering. In: Proceedings of the 28th IBIMA Conference - Vision 2020, pp. 768– 781 (2016) 9. IDC FutureScape: Worldwide IT Industry 2022 Predictions. https://www.idc.com/getdoc.jsp? containerId=US48312921. Accessed 21 Mar 2022 10. Guide to the General Data Protection Regulation (GDPR). https://ico.org.uk/media/for-organi sations/guide-to-the-general-data-protection-regulation-gdpr-1-0.pdf. Accessed 21 Mar 2022 11. Data management, privacy, and security in connected systems. https://www.interact-lighting. com/b-dam/b2b-li/en_AA/interact/articles/data-management/interact-sec-wp.pdf. Accessed 21 Mar 2022 12. Egyed, A., Schaefer, I. (eds.): FASE 2015. LNCS, vol. 9033. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46675-9 13. Ilin, I., Lepekhin, A., Levina, A., Iliashenko, O.: Analysis of factors, defining software development approach. In: Murgul, V., Popovic, Z. (eds.) EMMFT 2017. AISC, vol. 692, pp. 1306–1314. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-70987-1_138 14. Enterprise Data Management—Driving Large-Scale Change in Your Organization. https:// www.sisense.com/blog/enterprise-data-management-driving-large-scale-change-in-yourorganization/. Accessed 25 Mar 2022 15. Ilyashenko, V.M., Ilyashenko, O.Yu.: Formation of the management system high-tech medical organization based on data-driven approach. Glob. Sci. Potential, M.: TMBprint 10(139), 320–328 (2022) 16. Litvinenko, O.A.: Conceptual aspects of enterprise informatization management system. Sci. Tech. Bull. SPbSU ITMO 1(71), 120–123 (2011) 17. Understanding the Big Data Principle in Data Analytics. Beginner’s Guide: Extract, Transform, Load (ETL). https://towardsdatascience.com/beginners-guide-extract-transform-loadetl-49104a8f9294. Accessed 25 Mar 2022 18. Rubakov, S.V.: Sovremennyye metody analiza dannykh. Upravleniye naukoy i naukometriya (7), 165–176 (2008) 19. IDC FutureScape: Worldwide Future of Digital Infrastructure 2022 Predictions. https://www. idc.com/getdoc.jsp?containerId=US47441321. Accessed 25 Mar 2022 20. Yusupov, R.M., Musaev, A.A.: On the evaluation of the effectiveness of information systems: methodological aspects. Inf. Technol. 23(5), 323–332 (2017) 21. Gromov, Yu.Yu., Ivanova, O.G., Yakovlev, A.V., Odnolko, V.G.: Data management: textbook. Tambov: Izd-vo FGBOU VPO «TGTU», 192 p. (2015) 22. ESB (Enterprise Service Bus). https://www.ibm.com/cloud/learn/esb. Accessed 25 Mar 2022 23. Enterprise Service Bus (ESB) Introduction and Use Cases. https://www.cleo.com/blog/kno wledge-base-enterprise-service-bus-esb. Accessed 25 Mar 2022
Visualization of Business Processes Through Data Comics Saida Dospan(B) and Anastasia Khrykova Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. Levels of achieving organizational goals of any company reflect the effectiveness of the enterprise management information system, especially with regard to human resources management aspects. Increasing the productivity of each employee reduces the cost of services and improves the quality of their provision. The main purpose of this article is to determine whether data comics is better and easier business process visualization format compared to a modeling language or not. The hypothesis was checked based on a case study about an internal business process of an IT company in Saint Petersburg. The object of study of this work is a company N - an enterprise in the oil and gas industry and its activities in terms of electronic document management. The subject of the work is the electronic document management system used at the enterprise. The sources for searching articles related to the topic of the study were Google.Scholar, ResearchGate, Scopus. The keywords for the search were Visualization of Business Processes, Data Comics, and combinations. Since data comics is quite a new method of visualization and it was necessary to go through practical research, the criteria for filtering were > 2015. However, for theoretical purposes, earlier works were reviewed. The analysis of the survey results is carried out and recommendations for its practical implementation are given. The results of the case study are encouraging for the use of data comics, especially for internal use in the IT company because of many processes that some employees do not correctly understand. Making this study let respondents acquainted with a new and unknown method of data comics (no one of the employees have ever come across it). Creation of data comics for one business process requires not more than a working day and there is no need to hire a new employee (a graphic designer, for example), as data comics modeling is an easy process and there a lot of special tools to draw them (canva.com, makebriefscomics.com, etc.) The further research may be based on a project of implementation data comics in the IT company firstly for internal purposes. After the implementation and getting results data comics may be applied to external business processes and involve customers of the company. Keywords: Business processes · Visualization · Data comics · BPMN modeling language
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 745–758, 2023. https://doi.org/10.1007/978-3-031-32719-3_56
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1 Introduction In the modern world, successful and stable enterprise development depends on a variety of factors. One of the main factors is the quality of visualizing business processes. Using graphs, diagrams, AI and ML methods, and other tools for visualization provide a deeper understanding of different processes. Moreover, visualization allows stakeholders and other participants of business processes to find out existing problems quickly. It is especially important for big companies where different departments depend on each other. In addition, using different visualization notations (BPMN, IDEF0 and etc.) enables companies to create a unified structure that is better understandable compared to a text document. One of the main reasons of the increasing need to leverage effective visualization tools in a company is digitalizing business processes in almost every modern entity, from production [1] to education [2]. In [1] authors studied the digitalization of refining processes on the example of producing winter diesel fuel. They defined the digitalization of oil refining processes as the construction and solution of a mathematical model. In [2] authors analyzed the distance learning trend which was triggered by COVID-19. They focused on a digital technology of generating survey data on the characteristics of an innovative product in a distance learning system. Although business processes in the given examples belong to different industries every one of them requires suitable visualization tools. Using an appropriate visualization approach enables them to illustrate these business processes in a proper way for stakeholders for further decision making and do studies’ results more accessible and engaging. Visualization is not only an important tool for providing information for decisionmaking. Visualization also has a big influence on service quality for customers and employees. Changing their preferences due to rapid technology development and increasing market competition requires new solutions. Due to this fact, the main hypothesis of this research was checked based on a case study about an internal business process in which every employee is involved. According to a study done by [3] when correctly implemented, visualizing of business processes can help an organization gain business value and hone its competitive edge. To correctly implement business-process management and visualization, a company should follow these steps [4]: 1. Qualify goals beforehand. 2. Obtain buy-in from up and down the organization chart. 3. Select BPM software carefully. 4. Monitor and measure after implementation. However, modeling problems can cause process execution errors in a production environment, creating extra costs for the organization [5]. To reduce costs and avoid risks, and also to make visualization clear and readable for everyone who is involved in the working process, a new method/format may be used for those purposes. Making business processes accessible to users constitutes a crucial challenge throughout their entire life cycle: users should be enabled to understand business process models (Analysis & Design phase), keep an overview on running process instances (Operation phase), perceive process adaptations (Operation phase), and comprehend as well as interpret results of analyzing processes (Evaluation phase) [6]. What sounds easy
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for small process models quickly becomes an enormous challenge in the context of complex wallpaper process models because they can consist of hundreds of process activities, data flows, and resources and can have thousands of running process instances in different execution states. Obviously, for such scenarios, it becomes very hard to recognize or even understand, e.g., deviations from the regular process execution path [6]. A new and not really explored format of visualization is data comics which can be applied to the visualization of business processes. Data comics which are a preferable and more understandable way for people to get complex information rather than infographics draw from the tradition of comics, and combine techniques from infographics, data visualization, journalism, and other formats of visual explanations [7]. Thus, the main goal of this research is to determine whether data comics is better and easier business process visualization format compared to a modeling language or not.
2 Materials and Methods Research in this field will enable enterprises to analyze the effectiveness of their business processes visualization and identify their weaknesses and strengths. The results of this research work will be helpful for companies who look for new approaches to improve current business processes visualization tools. Scientific articles, master theses, and case studies about visualization of business processes over the past ten years were analyzed. The main sources are Elsevier and Google Scholar. Key definitions and conceptual frameworks of data visualization were analyzed by [8]. According to [8] Data Visualization Literacy (DVL), like other literacies, aims to promote better communication and collaboration, empower users to understand their world, build individual self-efficacy, and improve decision making in businesses and governments. The core revised DVL frameworks typology, which consists of seven types (insight needs, data scales, analyses, visualizations, graphic symbols, graphic variables, interactions), exercises that facilitate learning and DVL assessment was given by [8]. Visualization definitions, purposes, and a brief history were analyzed by Dix [9]. Visualization is making data easier to understand using direct sensory experience [9]. [9] noted that this is still about insight and understanding, but also about the perception (‘sensory experience’) and deliberate design (‘making’). [9] reported that there are two kinds of the target audience for creating visualizations: First, there is the data analyst, whose job it is to sift through data whether the academic interpreting experimental results, the forensic accountant looking for anomalies in a bank’s accounts, the city planner working out the best route for a new cycleway, or the intelligence officer piecing together emails, tweets, and passport data to prevent a terrorist attack. The other group is the eventual data consumer, the client, audience, newspaper reader, or the CEO. [9] analyzed ways to visualize classic visualizations of the main kinds of data structure: hierarchical data, clustered data, multi-attribute data, and big data. According to [10] visualization impacts management’s decision-making process. [10] found suitable graphical visualizations are necessary to support managers in understanding, evaluating, and comparing the performances of management decisions according to all objectives in all plausible scenarios. [10] proposed two visualization methods:
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a novel extension of empirical attainment functions for scenarios and an adapted version of heat maps. They help a decision-maker in gaining insight into realizations of tradeoffs and comparisons between objective functions in different scenarios [10]. According to [11], the need for quick reflexes regarding decision-making becomes a factor as well. Any delay in obtaining accurate and up-to-date information hampers the ability of decision-makers. The interconnection between visualization and the decision-making process was also proved by [12] in the Russian Post case study. In 2015, a three-year project was completed on the largest implementation of the 1C automated system in Russia in terms of the number of workstations. This project allowed the Russian Post to switch to a single method of operational, accounting, and tax accounting in all divisions throughout the country. One of the main advantages was the possibility of control by the financial director in the online mode (due to a single centralized accounting system) of all ongoing financial transactions, as well as the balance on special accounts [12]. Digital visualization also plays an important role on frontlines [13]. Authors in [13] studied frontline employees’ perspectives and actions during a strategic change initiative in faculty members at an internationally accredited business school in a leading regional Eastern Mediterranean university (8000 students, 1000 academic staff). They focused on how digital visualization tools like Sankey, Forced Node, Treemap, and Mapping Table diagrams and their features (colors, shapes, widths, network graphics, links, drilling up/down, popups, etc.) were enrolled in the actual process of realizing the curriculum transformation strategy [13]. Authors analyzed how the distinctive features of digital visualizations and the associated affordances could play a potential role in the strategy realization work of frontline employees [13]. As for call centers, another main frontline channel in every modern institution, the importance of call centers for business development, reengineering call center business process based on the case studies bank call centers was searched in [14] and [15]. Creating a new call center management system due to rapid technology development was studied by [16]. [17] gave a big contribution for analyzing TUI Nordic call center work. [11] considered implementation of business intelligence system in call centers in the CallLogic project. The challenges of visualization software for an increasingly complex world were considered by [18]. There are main challenges of visualization software according to the authors: the ecosystem beyond visualization in terms of incorporating external technology, effectiveness and user experience of a new software, entry barriers to existing solutions, building blocks and abstraction, one visualization system to fit all needs, game engines as data visualization platforms, medium-term funding and sustainability, evaluation of software and quantification of success, building a typology of visualization software [18]. Features of data visualization in a complex production process based on the example Volvo Cars in Torslanda (Sweden) were researched by [19]. Authors provide a concept study for a visual interface framework together with the software Sequence Planner for implementation on a complex industrial process for extracting process information in an efficient way and how to make use of a lot of data to visualize it in a standardized human machine interface for different user perspectives [19].
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These sources allow to conclude that nowadays visualization of business processes plays an important role in business development. It requires further scientific research and up-to date technology support. As one of new and effective approaches for visualizing business processes data comics can be considered. Data comics are characterized by great flexibility and numerous advantages beneficial to communication, including: a) tight integration of visual and textual explanations; b) sequential presentation of information; c) visual content for recall and quick navigation [7, 20], and d) different levels of reading, i.e., quick overview as well as access to details, from skimming to close reading. As a static medium, comics are ideal for storyboarding and ideation [21]; there are minimal technical barriers to production, and they can be shared in many forms such as scientific papers, conference posters, slide shows, grant proposals and blogs [20]. Due to the fact that data comics consists of following four essential components [22]: visualization, flow, narration, words and pictures (Fig. 1), comics provide a unique medium for the communication of complex content [20]. Their expressive and compelling power for visual storytelling comes from their unique combination of text and pictures [23, 24] and its linear reading order, able to break down complexity into sequential steps that a reader can consume at their own pace. By providing several panels on the same page, comics can also support overview and detail, allowing for simultaneous explanation and exploration [20]. The effectiveness of comics was analyzed based on empirical studies in presenting scientific phenomena [23] and data visualizations [21].
Fig. 1. Four essential components of data comics
The effectiveness of data comics for visualizing business processes was also proved through empirical study in finance. Financial products are generally poorly understood, and this can lead to financial distress for consumers that can impact their long-term financial well-being. Comics represent a viable solution to simplifying the way in which these products are presented, thus improving the comprehension of these terms [25]. It is especially important when finance in different parts of the world goes through digitalization. Financial entities need to leverage appropriate visualization methods for showing the data and information the clients need. It is one of the main tools which enables them to attract and maintain customers.
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The authors in [25] conducted research how comics can improve behaviour with insurance products, specifically funeral policies. By adopting a mixed-methods experimental approach, the quantitative results of the research showed that comics do in fact lead to more uptake of the funeral policy product. The qualitative aspect of the study showed that comics (Fig. 3) are better perceived than traditional text documents (Fig. 2) [25].
Fig. 2. Extract from original text version of the policy wording. Source: Creative Contracts © and Hollard © [25]
What happens if data comics is applied together with commonly used ways of visualization of a business process in an information technology (hereinafter referred to as IT) industry? This study covers how data comics influences comprehension, recall, and engagement with the format for internal use in an IT company.
3 Results The main hypothesis of the research work: data comics is better and easier business process visualization format compared to a modeling language. This hypothesis will be checked based on a case study of an IT company in Saint Petersburg which has different projects based on developing a new software and supporting installed one. There are several groups of employees in this enterprise: management, business analysts, developers, QA-engineers, a support group. Most of workers do not have a specialization in business analysis, so they sometimes are struggling because they
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Fig. 3. Extract from comic visualisation version of the policy wording [25]
cannot properly understand what business analysts show in their business diagrams. It is very important for every team member to clearly understand what they are about to work on, but also it is important to understand the internal processes of the company. – – – – – –
Following steps were completed to check the hypothesis: Determining the internal business process; Visualizing this process through data comics and a modeling language; Creating a survey for comparison of these two visualization tools; Forming a group of respondents; Analyzing survey’s results.
The company uses electronic document management systems (hereinafter referred to as EDMS) for internal use, so if an employee wants to take a vacation, to go on a business trip, to make a request for new working devices – he/she must use EDMS to create an application. For checking the hypothesis, the process of taking a vacation was chosen as the most popular process and document in the EDMS. This process is described in a corporate Wiki using business modeling language (Fig. 4): A new visualization of a business process was worked out using data comics (Fig. 5) which was designed using canva.com: Respondents were asked to answer questions from their workplaces, and it took them approximately 10–15 min. Firstly, they were asked to present themselves and then they were divided in 2 groups: Business-analysts and specialists who are experienced enough to understand business process visualizations;
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Fig. 4. AS-IS visualization of a business process
Fig. 5. TO-BE visualization of a business process using data comics
Employees who are not experienced enough to understand business process visualizations. Secondly, participants were asked if they feel uncertain or have the struggle with business process visualizations. The answer for these questions could be YES or NO.
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After that they were offered 2 images: BPMN visualization and data comics visualization to pick up from. The next question was based upon understanding: participants were asked to place steps of a process in the correct order (Fig. 6). Steps were mixed, and participants had to place numbers of steps in the right order.
Fig. 6. A survey using Google.Forms
The final question was the main one: did they prefer BPMN model and would data comics help them better understand processes. The answer could be YES or NO, but also participants were offered to share some thoughts on data comics visualization. In the Table 1 questions which were offered to survey participants are presented: Table 1. Questions of the survey №
Understand BP visualization
1
Introduce yourself
2
Are you a business analyst?
3
Do you understand the basic principles of business process visualization?
4
Do you sometimes feel uncertain about BP visualization?
5
Have you ever heard of data comics?
6
[image of process] – [image of data comics] Which image do you find easier to understand? (continued)
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№
Understand BP visualization
7
Let’s check if you correctly understood the process: please write the steps of business process from the first to the last
8
Do you think that data comics visualization of business process would help you to better understand the specific of work rather than BPMN (or any other visual language)?
The survey was made through Google.Forms (Fig. 7) and all the data was exported into MS Excel:
Fig. 7. The questions of the survey in Google.Forms
17 respondents (Fig. 8) participated in it, including 6 business analysts and 11 other specialists (developers, QA-engineers, product managers). 10 participants understand principles of business analysis (including 6 business analysis) and 7 participants who don’t understand business process visualizations. 3 participants who understand business process principles sometimes feel uncertain when looking through business process visualizations. 6 participants of 7 who don’t understand business process principles are facing problems when they come across business process visualizations (Fig. 9). In other words, it means that half of respondents are having problems when they are working with business process visualizations.
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Fig. 8. Number of respondents according to their position
Fig. 9. Number of participants according to their position
Most of respondents (12 people or 70%) preferred data comic rather than business process visualization through BPMN modeling language (Fig. 10). All participants correctly understood the process and came up with each step. The same result (12 for data comics and 5 for business process visualization through BPMN modeling language) was for the last question, so most of employees found data comics helpful and more understandable:
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Fig. 10. The division between data comics and BPM language
4 Discussion The further research may be based on a project of implementation data comics in the company firstly for internal purposes. After the implementation and getting positive results data comics may be applied to external business processes for engaging more customers.
5 Conclusion The new genre of data comics combines many features with the potential of making data-driven stories accessible and understandable. To verify the comics’ effectiveness on reader’s understanding, the test was done comparing data comics with business process visualization in the modeling language. The results are encouraging for the use of data comics, especially for internal use in the IT company because of many processes that some employees do not correctly understand. Most of participants (70%) preferred data comics rather than the modeling language, so the hypothesis of the study should be accepted. Business analysts mostly preferred BPMN visualization, but other specialists voted for data comics, so it could be reasonable to use it. Making this study let respondents acquainted with a new and unknown method of data comics (no one of the employees have never come across it). Right know all internal business processes in the company (how to create vacation application, business trip, sick leave in the EDMS) are described using BPMN. Many employees have questions how to create such documents and BPMN models don’t help much, so the implementation
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of data comics would help to reduce misunderstandings and help employees feel more confident. Creation of data comics for one business process requires not more than a working day and there is no need to hire a new employee (graphic designer, for example), as data comics modeling is an easy process and there a lot of special tools to draw them (canva.com, makebriefscomics.com, etc.).
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13. Azad, B., Zablit, F.: How digital visualizations shape strategy work on the frontlines. Long Range Plan. 54(5), 101990 (2021). https://doi.org/10.1016/j.lrp.2020.101990 14. Elhag, S., Alshahrani, E., Alsharif, Z.: An automated experience-based business process reengineer: case study bank call center. Int. J. Sci. Res. (IJSR) 8, 1868–1871 (2018) 15. Cohen, Y.: Improving operational measures in a financial institute call center: a case study. Braz. J. Oper. Prod. Manag. 14(2), 204 (2017). https://doi.org/10.14488/BJOPM.2017.v14. n2.a8 16. Chikwanda, K.: Call center management systems. Int. J. Multi-Discipl. Res. Paper-ID: CFP/358/2017 (2017) 17. Wolff, P.: Development of Customer Recognition Plugin for Contact Center Application. Master’s Thesis (2016). https://www.theseus.fi/bitstream/handle/10024/107511/Wolff_Patrick. pdf?sequence=1. Accessed 11 Dec 2021 18. Reina, G., et al.: The moving target of visualization software for an increasingly complex world. Comput. Graph. 87, 12–29 (2020). https://doi.org/10.1016/j.cag.2020.01.005 19. Albo, A., Bengtsson, K., Dahl, M., Falkman, P.: A framework concept for data visualization and structuring in a complex production process. Procedia Manuf. 38, 1642–1651 (2019). https://doi.org/10.1016/j.promfg.2020.01.120 20. Wang, Z., Ritchie, J., Zhou, J., Chevalier, F., Bach, B.: Data comics for reporting controlled user studies in human-computer interaction. IEEE Trans. Visual Comput. Graph. 27(2), 967– 977 (2021). https://doi.org/10.1109/TVCG.2020.3030433 21. Wang, Z., Dingwall, H., Bach, B.: Teaching data visualization and storytelling with data comic workshops. In: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA 2019), Paper CS26, pp. 1–9. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3290607.3299043 22. Bach, B., Riche, N.H., Carpendale, S., Pfister, H.: The emerging genre of data comics. IEEE Comput. Graph. Appl. 37(3), 6–13 (2017). https://doi.org/10.1109/MCG.2017.33 23. Farinella, M.: The potential of comics in science communication. J. Sci. Commun. 17(01), Y01 (2018). https://doi.org/10.22323/2.17010401 24. McCloud, S.: Understanding Comics: The Invisible Art. HarperCollins, Northampton (1993) 25. Harcourt-Cooke, C., Els, G., van Rensburg, E.: Using comics to improve financial behaviour. J. Behav. Exp. Financ. 33, 100614 (2022). https://doi.org/10.1016/j.jbef.2021.100614
Data Comics for Business Process Visualization Erick Leonel García Ibañez(B) Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. Two third-party studies, related to the legal and veterinarian sector, discuss the level of engagement and business process comprehension acquired by subjects who used Data Comics against other different kind of media. These studies consist of developing quantitative research following two different approaches. In the first one only one media was used: comics; and includes a pre-test and a posttest evaluation. In the second approach the participants were exposed to different kind of media, but only a post-test evaluation was developed. The review debates some strengths and weaknesses of data comics, as well as the results of the studies. The aim of the review was to evaluate the effectiveness of visualization to explain business processes against different kinds of materials, the results were positive in favor of Data Comics. Keywords: Visualization · Data comics · Business processes · Storytelling
1 Introduction Before we go deep into Data Comics, we need to understand some basics such as Visualization and Storytelling. Visualization has existed for centuries, even in the 15th century when Leonardo da Vinci used to draw about his research and inventions. Some of the earliest examples of visualization were meant to show and explain, not analyze [1]. McKenna et al. [7] published an article to help novices through the visualization design process, the authors consider four activities in the process: understand, ideate, make, and deploy. Storytelling and visual expression are integral parts of human culture: storytelling has even been referred to as ‘the world’s second-oldest profession’ [8]. Then again, Kosara and Mackinlay mention that Storytelling is the next logical step in visualization research [1]. Stories and written documents share the same purpose, which is to make a point in a detailed way so the viewer can understand what is being said. We all must have heard the phrase ‘an image is worth more than a thousand words, but also according to Gershon and Page [8] a story is worth a thousand pictures. A story could be defined as a sequence of steps following a specific order, each of which may contain words, visualizations, video, images, or a combination of the previous items. Probably there is still a lack of an accepted definition of what constitutes a ‘comic’ as many authors point out they can be extremely malleable [4]. However, they also share some unique recognizable features [4] and next on we will review some important definitions and related ideas to the main topic. Comics represent a casual yet effective © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 759–771, 2023. https://doi.org/10.1007/978-3-031-32719-3_57
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and engaging way of telling stories using a juxtaposition of illustrations, text, and visual annotations [2]. Comics are an old art familiar to many [3] and they have been among us for several decades as a means of entertainment, but they were given a new approach quite recently. Data Comics draws from the tradition of comics and combines techniques from infographics, data visualization, journalism, and other formats of visual explanations [9]. As they become more popular, researchers publish new articles detailing applications for data comics, some of these applications are science communications [4], data analysis through computational notebooks [10], and business process visualization [6, 11]. In recent years data visualization scholars and practitioners have drawn attention to the need for data to be humanized [5]. This requirement fuels the demand for the use of data comics, as they can incorporate empathy and help audience to connect to information [5]. This review spotlights business process visualization among other data comic applications, later some examples will be analyzed and discussed. Bach et al. [3] consider four essential components that make some content a Data Comic. Figure 1 can be useful to exemplify these components by using a Comic, which describes a business process (admission procedure for the master program) from Peter the Great St. Petersburg Polytechnic University [12]. 1. Visualization: the comic in Fig. 1 includes realistic visual forms that represent the process of applying to a master’s program. In Fig. 2a there is a representation of a person (realistic picture) showing his will to enroll in a master’s program. Visualization may range from realistic pictures to abstract concepts. 2. Flow: the comic contains many panels, each one of them delivers one message at a time and helps the reader to understand the direction of the story. In Fig. 1, the admission procedure is divided into steps, which creates a directed flow so the reader can understand the right sequence. Also, in Fig. 2b we can see how the sequence is identified by a blue row. Comics may range from undirected non-explicit flow to directed flow. 3. Narration: in the case of the example the main goal of the comic is to communicate the process that an applicant must follow, each panel has a message. For example, in Fig. 2c the reader realizes that he needs to pay for the training. Comics must be engaging to readers (in this case potential applicants). Narration can range from factual arrays to narrated graphic novels. 4. Words and Pictures: in Fig. 2d you can see how the narrative of the comic is based on pictures (avatars of the actors involved in the process and images related to the topic), and words (dialogues and labels) working together to make a point for the reader. In Fig. 2, you can see how each element is represented in the Comic. The characters that appear in Figs. 1 and 2 were created with the ComicGen tool [14], designed by Gramener. These figures were specially developed for this article to help the reader have a better understanding about Comics. Panels play an important role in data comics, they are the central narrative in a comic and the camera through which the reader perceives the ‘world’ [15]. This means that everything the author wants to share must be inside a panel. In Fig. 1 we can see 9 panels, each one of them shares parts of the whole story, which in this case is a business process. Data comics are based on the notion of a sequence of panels [9].
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Fig. 1. Examples of data comics used to explain the admission procedure for a master’s program at Peter the Great St. Petersburg Polytechnic University.
Bach et al. [15] introduced a series of design patterns for specific purposes. In Fig. 3 [15] we can see the following patterns: narrative, temporal, faceting, visual encoding, granular, and spatial. The comic created for the application process to a master’s program (Fig. 1) is an example of a narrative pattern. Figure 4 shows an example of a temporal pattern for panels, this example describes the expansion process of the Inca Empire according to Ogburn [16]. More information related to this matter might be found in the article Design Patterns for Data Comics [15]. One of the most convincing papers related to Data Comics is the one written by Bach et al. [3], titled The Emerging Genre of Data Comics. This article explains the basics of Data Comics using Data Comics, which is a good opportunity to show the reader he can use them to explain anything, even to explain itself. According to the Learning Pyramid [17], also called Cone of Experience, after two weeks we only remember 10% of what we have read and 30% of what we see. Although ‘seeing’ is three times as effective as Reading, both are considered Passive Learning,
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Fig. 2. Individual representation of the elements of a Comic.
Fig. 3. Some examples of design patterns in data comics
therefore the low percentage of learning by using this method, as you can see in Fig. 5. On the other hand, there are different ways to achieve Active Learning, such as designing collaborative lessons, which allows participants to retain 70% of the content several days after the event. This is where Comics play an important role, placing images instead of only text, making Data Comics more enjoyable and fun [9] for the public. In Fig. 5 you can see the representation of the Learning Pyramid. Furthermore, data comics could play a bigger role, especially in education if placed in the COVID-19 crisis. Pavlov et al. [26] highlighted the importance of distance learning systems during the pandemic crisis in both schools and universities. As professor-student interaction was moved from the typical classroom into a virtual meeting platform, it has become more difficult for students to stay focused for hours in front of a screen.
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Fig. 4. Example of a temporal design pattern that represents the expansion of the Inca Empire. This temporal panel was developed based on an Inca expansion figure by [16], in page 3
Fig. 5. The Cone of Experience, originally developed by Edgar Dale [17] in the 1960s
Educational comics could offer the following benefits: supporting students cognitive and affective development [18], encourage students to think [19], increase student’s test results [20], and encourage student’s curiosity [20]. Thus, the use of data comics would allow teachers to create more attractive presentations, which will improve the learning outcome. The purpose of the review was to evaluate the effectiveness of visualization to explain business processes against different kinds of materials, the results were positive in favor of Data Comics. Section 2 examines the mechanism used in the applications that will detailed in this document. Such mechanism includes the methodology that was applied
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for the research, the criteria for the selection of the sample, the size of the sample, and the steps followed to perform the research. In Sect. 3, the results of the applications are explained in detail. Section 4 consists of discussing the results of the studies and the literature review, which might also include some pros and cons about the use of Data Comics according to the articles I read, and my experience creating Data Comics for this review. Finally, in Sect. 5 some important conclusions will be explained, followed by some future work possibilities.
2 Materials and Methods Before going further into the studies and applications of data comics, it is highly important to note that the process that is about to be represented must be clear and properly documented. Nikonorov et al. [13] suggest that before the digitization of any process a mathematical model must be made. This idea is perfectly suitable for the representation of a business process by a data comic. Therefore, the first step in the creation of data comics is to make sure that a proper model of the business process has been created. Figure 1 is a suitable example of an application of Data Comics, which I used to illustrate and describe a business process in education. However, Comics can be used for visualization of business processes in many industries because they are easier to understand, especially for non-experts [9] and vulnerable audiences [5]. Now we will discuss two applications: in the legal, and veterinarian sector. 2.1 STUDY N° 1: Using Comics to Communicate Legal Content The first application that will be discussed in this section consists of quantitative research. The study was made for the legal sector, more detailed information regarding this experiment can be found in the original article written by Botes [6]. In Fig. 6 we can see a Comic that represents a business process called ‘How to Terminate a Purchase Agreement’, created by The Visual Law Lab Ltd. The process starts when a client decides to buy a used car in a dealership, followed by the car breakdown. Then, the comic shows the steps the client must follow to get the cancellation of the contract, and finally, some text regarding the possible amount of money payable by the consumer and bank is considered in the last panel. The purpose of the comic in Fig. 6 is to increase comprehension of the business process by using visualization. To determine the level of comprehension of the process an experiment was conducted, in which participated 46 clients from the car dealership Volkswagen Hatfield in Pretoria. The clients responded to a questionnaire to find out the effect of the comics on the learning process. The questionnaire was sent via email to 50 buyers, and 46 of them answered the form. The clients who agreed to participate in the study had to answer a pre-test and post-test. Pre-test consists of solving the quiz before reading the comic, whereas the post-test is performed after the introduction of the comic. The results were analyzed to find out the effect of the comic on the business process understanding.
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Fig. 6. Credit agreement cancellation process comic according to the study made by Botes [6]
2.2 STUDY N° 2: Using Comics in the Veterinary Industry The second application for comics that will be discussed in this article was made for the veterinary industry, more detailed information related to the study can be found in the original document by Cooper et al. [11]. The study consisted of quantitative data collection to evaluate the business process comprehension and engagement. Cooper and her colleagues outlined two studies, but this paper will focus on the research that compares three communications tools (written, cartoons, and photographs) in the informed-consent process (ICP). The article does not mention data comics, the authors use the word cartoon instead. I decided to include the article in this review because the cartoon they developed, which you can see on the right side of Fig. 7, contains the four components required by Bach et al. [3]. The study was realized in Tanzania, and 22 lives-stock keepers agreed to participate, 21 male and 1 female. The sample was divided into 3 group. In the first group 7 people were included and they used a cartoon media, in the second group 8 people were included and they used a photographic media, the third group included 7 participant and they used a written text as source of data. The volunteers who participated in the study had to respond a survey with 12 questions. The ICP is detailed in Fig. 7, two of the communication tools are shown in this figure: photographs (left side), and toons (right side). The process begins when the farmer wants to know more about what is making their cattle sick. Then, the farmer must choose 1–3 sick animals for sampling, two samples must be taken: milk, and blood. If necessary, the sick cattle will be restrained on the ground using ropes. Finally, farmers will receive advice related to sick animals from a veterinarian, free of charge.
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Fig. 7. Informed-consent process (ICP) according to the study made by Cooper et al. [11]
3 Results 3.1 STUDY N° 1: Using Comics to Communicate Legal Content In Fig. 8 we can appreciate some detail related to the sample that participated on the survey. The data is divided into two categories: age and highest qualification. Regarding the age, the sample contains a diverse population with ages ranging from 20 to over 50. The most representative segment are the people who between 26–30 years old as they represent the 26% of the sample, the rest of the segment do not have a representation higher than 15%. On the highest qualification category there are four segments, the most representative one is Matric (47.8%) and least one is Grade 8 (4.3%). Figure 9 shows the results of the questionnaires before and after using data comics. It is very clear an average overall increase in correct answers, and therefore an improvement in comprehension of the contract terms [6]. The most outstanding improvement occurs in the first question, which goes from 50% to 95.6% of correct answers before and after using data comics respectively. According to the author of the study [6], question #1 dealt with the most technical aspect of the contract, unlike the rest of the questions that were more practical and had a commonsense approach. 3.2 STUDY N° 2: Using Comics in the Veterinary Industry In Fig. 10 are shown the results of the survey applied to the farmers who participated in the study. In the results we can appreciate the criteria of the evaluation (comprehension, number of questions, time, and engagement) and the media to which the participants
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Fig. 8. Age and education profile of respondents for study performed by Botes [6].
Fig. 9. Number of correct answers before and after introduction of comic. Statistics belong to the legal application for data comics [6]
were exposed (cartoon, photographic, and written). For each criterion three metrics are details: the median score, the maximum score, and the minimum score.
Fig. 10. Results of the study of the communication tools. Statistics belong to the veterinary application for data comics [11]
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4 Discussion 4.1 Regarding the Third-Party Studies Two third-party studies, related to the legal and veterinarian sector, discuss the level of engagement and business process comprehension acquired by subjects who used Data Comics against other different kind of media. These studies consist of developing quantitative research following two different approaches. In the first one (study N° 1) only one media was used: comics; and includes a pre-test and a post-test evaluation. In the second approach (study N° 2) the participants were exposed to different kind of media, but only a post-test evaluation was developed. Although the different approaches, both studies show better results when the participants were exposed to comics. In study case N° 1 the average improvement for business process comprehension is 19%, which strongly indicates that introducing comics improves understanding of legal content. Also, it is important to highlight that in the post-test all the questions got an average score higher than 93% in comparison with the pre-test where only question got an average score higher than 93%, and the lowest average score for one question was 50%. These statistics can be verified in the Table 2. As for the case study N° 2, in Table 3 are shown the results of the questionnaire applied to the farmers who participated in the study. In the results table, two criteria are highlighted. First, the people who were exposed to Cartoons had the highest average comprehension level (8/12), they achieved the highest grade (11/12) between the 22 farmers, and they exceeded the average score (7.3/12). Second, people who used Cartoons also reported higher engagement, Cooper et al. [11] even mentions that subjects who used the written form were bored. The difference of average engagement showed by the group who was exposed to Cartoons (21) compared to the other two groups (16.5 and 14.4) was big, as you can see in Table 3. Finally, we can also emphasize that the largest median time (10.3) and individual time (12.28) correspond to the group that was exposed to Cartoons, meanwhile the other two groups have the same median time (7.2). 4.2 Pros and Cons About Data Comics There are some facts that make Data Comics more suitable for learning compared to other techniques. Wang et al. [9] makes a comparison of this technique against Infographics, and they concluded that Data Comics are rated more engaging and enjoyable. Farinella [4] also agrees on Data Comics increasing engagement and facilitating learning as they have a multimodal nature. Likewise, they are easier to comprehend [6, 9]. Data comics can be read by everyone, even unqualified people. This makes Comics a very suitable choice for explaining complicated business processes. Comics can be used in different fields, such as education [18–23], veterinary [11], law [6], or finance [24, 25]. These characteristics allow readers to get a higher level of attention, thus improving the learning process. Despite the strengths detailed previously, it takes more time and space to create Data Comics [15] in comparison to text. Creating Comics requires a lot of patience, attention to detail, and even some artistic skills, finding someone who matches these requirements may not be an easy task.
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Some difficulties were found when creating the Data Comics for this literature review. First, once Comics are finally done it does not mean the job is finished. Since business processes can be changed or improved, Comics related to those processes must be updated as well. Comics might be great for visualization, but we also must evaluate if the cost of keeping them up to date is worth it. The second problem is related to the tools used for creating Data Comics, which are still scarce and limited. So far there isn’t one single application that possesses all the required tools for creating a data comic. For example, when creating some data comics for this review (Fig. 1) the following applications had to be used: ComicGen, Microsoft Word, and Paint. Switching from one application to another takes extra time and effort.
5 Conclusion Two independent studies have proven that data comics are better than text and photography for improving engagement and comprehension in readers. Results from the study in the legal sector (Table 1) show that the average knowledge of the process improved by 19.02% after using data comics. On the other hand, results from the study in the veterinary industry (Table 2) show that the subjects who used data comics had the best median understanding score (8/12). Both studies were realized in Africa, although in different countries. Despite the benefits of using Data Comics, there are still some difficulties of creating them. Therefore, it is not yet suitable for everyone, a lot of effort and skills (patience, attention to detail, artistic skills, imagination) are required. Likewise, tools for creating Data Comics are still scarce and limited. Thus, there is still a lot to do in this matter and some future work may go in this direction. Next on, I will detail two possible scenarios for future research on the area of data comics and business processes. First, according to Zhu [26] there has not been a consistent and universally accepted definition for the term “effective visualization”. In his paper ‘Measuring Effective Data Visualization’, he comes up with a way of measuring the effectiveness of data visualization in terms of three principles: accuracy, utility, and efficiency. Thus, we submit the following hypothesis for further research: “Data Comics can be effectively used to improve financial education by following the principles defined by Ying Zhu in the article Measuring Effective Data Visualization”. This future research might use a pre-test and post-test, and result could be analyzed by using normalized gain [27]. Secondly, Temirgaliev et al. [28] proposed the development of an automated delivery zone and a simplified algorithm for parcel processing in a study elaborated for the Russian Post. Their idea of ‘Mail of the future’ is where Automated Post Stations (APS) are installed in different facilities for self-service and self-receipt. The use of data comics could benefit the massification of the proposed APS as they can be easier to understand especially for people who are not familiarized with technology. A pilot project could be implemented to find out how beneficial would be the use of data comics so that the user can easily understand and properly manipulate the equipment.
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Methodology for Prognostic Effectiveness Evaluating of Digital Twins Implementation as an Example of the Railway Traffic Management Task Andrey V. Timofeev1
, Aleksander B. Titov2(B) , Alexander M. Kolesnikov3 and Alexandra K. Antonova2
1 LLP “EqualiZoom”, Astana, Kazakhstan 2 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected] 3 Saint-Petersburg State University of Aerospace Instrumentation, St. Petersburg, Russia
Abstract. As part of the development of the technological concept of industry 4.0, of considerable interest is the issue of predicting the economic and business effect that a particular company may receive from the digital twin’s implementation in the management circuit of its key business processes. Currently, there is no widely accepted methodology for obtaining a predictive estimate of this type. The way to create such methodology, which is based on predictive estimates of the relative effect of the implementation of digital twins on a set of formal metrics, looks promising. The current configuration of business processes is used as the base. This paper proposes a transparent methodology of the mentioned type, which is designed to obtain a predictive estimate of the quantitative type for the value of the economic and business effect that a company will receive after the digital twin’s concept implementation. This assessment has a transparent interpretation and is quite naturally associated with a group of metrics that are commonly used to evaluate the company performance. For example, the metrics that a company uses within the Balanced Scorecard framework can be used as a group of such metrics. It also proposed an original composition of digital twins, designed for the railway traffic management. On the example of this digital twin composition step by step demonstrated the mechanism of using the proposed predictive methodology to quantify the value of the economic effect that a railway company will receive from the digital twin’s implementation. The methodology proposed in the article to obtain a predictive estimate of the digital twins implementation economic effect is quite transparent and simple. Therefore, it can be used in the practice of financial planning without much difficulty. Keywords: Digital twin · ML PdM · C\F-OTDR system · Railroad traffic management · Productivity improving
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 772–789, 2023. https://doi.org/10.1007/978-3-031-32719-3_58
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1 Introduction Digital Twins (DT’s) are one of the main components of the Industry 4.0 concept. Generally speaking, a DT is a virtual model of some object that reflects a physical object or process in a targeted slice (in other words, in a certain semantic focus). DT provides an information bridge between the physical and digital world in a mode close to the real time scale (the specific time scale used by DT depends on the objective for which it was created). DT technology is naturally and deeply related to the concept of the Industrial Internet of Things (IIoT), as the IIoT is a tool that delivers relevant information about an object to the DT, thereby updating it. At its core, DT enables remote monitoring and control of all kinds of equipment and systems. But in a broader sense, the DT can be the basis of a real-world simulation model that is used in testing and predicting real-world facility or process changes under a variety of what-if scenarios. Using DT, companies can more effectively realize significant benefits, such as optimized facility maintenance, improved facility operations through the selection of optimized facility management strategies, reduced risks, and overall improved productivity. The creation of a digital twin is based on the use of data from multimodal sensors that capture the dynamics of the behavior of objects (assets and processes). Usually the parameters of vibration, temperature, pressure, density, etc. are measured. At the same time, the parameters of the external environment in which the object is operating (temperature, humidity, atmospheric pressure) are also necessarily recorded. These data are transmitted to the processing center (digital platform) through special communication channels, including wireless. The digital platform serves as a data repository combining and storing heterogeneous data received from the sensor network with data of a higher level of abstraction (various control systems, logistics management systems, warehouse management systems, MES, ERP). By combining this heterogeneous data into a holistic structure, a relevant basis for decision-making can be obtained. Including the use of methods based on the concept of the Data Driven Approach. The digital twin concept was first proposed by NASA in 2010 [1]. Initially an attempt was made to create multi-scale probabilistic models for avia transportation systems, in order to reflect the life of its flying, material twins. Over time, the concept has evolved, with one application being the development of a digital twin for large-scale infra-structure system management tasks, including energy and transportation systems, digital communications, water supply, and waste control. In a landmark development, the UK National Infrastructure Commission [2, 3] came up with the idea of creating a digital twin that would act as a digital model of that country’s entire infrastructure. It is pointed out that this model can be useful for planning and understanding the underlying processes that take place within the real infrastructure. In particular, it is emphasized that central to the realization of the concept of using digital twins is the acquisition of the relevant data needed to study the effectiveness of using machine learning to solve problems that are traditionally solved by more simplistic methods of analysis. In addition, the need to demonstrate exactly how a digital twin can be used to make effective decisions in the management of national infrastructure is indicated. Currently, the topic of digital twins is widely discussed and studied in the global scientific community [4–46] in relation to different, diverse multi-scale objects and systems. There are many definitions of “digital twin”. According to the authors, the most capacious is the following: “A digital
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twin is a digital representation of a real-world entity or system. The implementation of a digital twin is an encapsulated software object or model that mirrors a unique physical object, process, organization, person or other abstraction. Data from multiple digital twins can be aggregated for a composite view across a number of real-world entities”, [47]. Thus, the DT concept promises a lot [4–46], but its practical implementation is not inexpensive. Therefore, despite the many prospects that can be achieved with the implementation of DT, the main question for practice remains: what exactly will the implementation of DT give to a particular enterprise? This article attempts to answer this question by proposing a formal method for evaluating the planned economic and business effectiveness of DT implementation at a specific enterprise. The solution is given in quantitative form, easily interpreted and decomposed for detailed analysis. The example of DT implementation in a railway traffic management demonstrates specific, practical steps that a railway company needs to take in order to answer the main question: what exactly will the implementation of DT give the railway company? What economic and business benefits will the company gain after the implementation of the DT-concept? The final decision to implement DT in a railway company depends on the answer to this question.
2 Materials and Methods Consider the set, consisting of N enterprise KPIs (metrics), whose values are expressed numerically. Here is the value of the i-th type metric for the index of the company’s business process structure, where. That is, are metrics values for the for the basic business processes structure variant (without DT), and the set are metrics values for the business processes variant with DT. The idea of the method is to calculate the DT implementation performance indicator using the following Eq. 1: The values of represent the metric weights, which determines the significance of the corresponding metric. The mechanism for determining the set is as follows: Here is the score (weight) assigned to the i-th metric as part of the implementation of the Balanced Scorecard program. According to [1] the interpretation of is obvious: the value characterizes the percentage of relative increase (improvement) of the weighted sum of KPI (metrics) of the system, calculated for case, in relation to their basic values, calculated for case. Thus, the greater the value of, the greater efficiency gain on a given set of KPIs gives the implementation of DT. At = 0 there is no effect from introduction. At < 0 the effect of implementation is negative. In other words, the indicator can be called an index of the enterprise efficiency relative increase (EERI) as a result of the implementation of DT’s. In the literature, for example [48], in the context of the DT implementation effectiveness assessing it is customary to consider the following set of metrics: 1. 2. 3. 4. 5. 6.
Productivity,. Cost (railroad maintenance cost),. Maintenance cost (trains maintenance cost),. OEE (Overall Equipment Effectiveness),. Risks,. Reliability of equipment,.
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Production times,. New business opportunities,. Customer service,. Product quality,. Supply and delivery chains,. Profit,.
So, is this of metric set that we will focus on when we look at the example of obtaining an a priori assessment of DT’s implementation effectiveness in field of rail transport.
3 Results In this section the original DT set designed for railway traffic management will be discussed in detail. Then, for the proposed DT set, the procedure of EERI index () estimation will be described step by step as a practical example of its use. DT-Concept for Railway Traffic Management Digital models for railway traffic management tasks are widely enough [49–60] considered by the world scientific community. However, almost all known papers on this subject consider, albeit in depth, only one specific task or a specific section in this area. For this reason, it makes sense to describe a more general concept of DT-concept implementation for railway traffic management tasks, which is based on fiber-optic monitoring of the railroad track area and on the implementation of ML PdM (Machine Learning Predictive Maintenance) techniques in the practice of train’s maintenance. Under this concept, the set of DT’s used to rail traffic management is split into two sets: set A and set B. The set A includes those DT’s, which are related to the management of the state of the rails (tracks). The set B includes those DT’s that are related to the management of the internal state of train units (trains). Simplified, this concept is presented in Fig. 1. Namely this approach to the implementation of DT-concept in the railway traffic management practice seems to the authors the most promising. Let’s consider this approach in more detail. Data for DT’s from set A is formed according to measurements of fluctuations of the seismoacoustic field in the area of the ballast prism of railroad tracks. To measure the target fluctuations of the seismoacoustic field, fiber-optic monitoring (C\F-OTDR systems) of the railroad track area [61–66] is used, which provides a high solidity analysis of the seismoacoustic field of the ballast prism. In fact, the C\FOTDR system is a highly efficient, spatially distributed acoustic sensor, the data from which allows to solve many tasks of railway traffic management. Some of these tasks are proposed to be solved in the framework of the use of appropriate DT, including: rail traffic managing (trains movement control, speed control & optimization, monitoring the block-sections availability, crossing control, technological activities control etc.), railway tracks condition model (state control), railroad equipment ML PdM. Here, based on the C\F-OTDR monitoring technology [61, 66], the seismoacoustic field in the area of the ballast prism is fully covered. The results of C\F-OTDR monitoring abundantly provide data on the location of trains, types of technological work, and other mechanical activities on the tracks. This information is contained in the reflectometric data, which are
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Fig. 1. Digital Twins concept for railway traffic management tasks (authors’ creation).
fed to the C\F-OTDR-interrogator processing unit, where, using special data processing methods [61, 62, 66], they are extracted and accumulated for further processing. The C\F-OTDR monitoring system (ORMS) is based [61, 62, 66] on the vibration sensitivity of the infrared stream injected into a standard single-mode fiber (DFOS) by means of a coherent semiconductor laser at the wavelength of 1550 nm. The simplified scheme of ORMS represented on Fig. 2a. The laser of ORMS is probing the DFOS with usage of coherent infrared stream. This probing is carried out in the pulsed mode. Pulses have a length of ~ 30–200 ns, with an interval of ~ 30–300 µs. The DFOS is put into the ground, at the depth of 30–50 cm, at the distance of 5–10 m from the monitoring object. When a pulse is moving along the optical fiber, the Rayleigh elastic backscattering is realized on natural inhomogeneities (impurities) of the optical fiber. Due to high coherence of the used laser of 3B class, this backscattering leads to formation of the so-called stable interference structures of chaotic type, otherwise called as speckles or
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speckle images (Fig. 2a). A sequence of speckles (Fig. 2b) is accepted in the point of emanation using an ordinary welded coupler and a PIN-photodiode.
Fig. 2. (a) ORMS simplified scheme; (b) General scheme of speckle-based formation of an ORMS useful signal (authors’ creation).
The central moment of the ORMS -concept is the phenomenon that seismic vibrations arising on the surface of the DFOS due to propagation of seismoacoustic waves from the sources of elastic oscillations, changes DFOS local refractive index. Changes of the local refractive index are reflected in the time-and-frequency structure (TFS) of the respective speckle. Knowing the pulse duration and the velocity of wave propagation in the optical fiber, it is easy to determine the section where the deviation of the TFS-speckle took place. Analysis of the speckle structures sequence with usage of special methods makes it possible not only to reliably detect the target source of seismoacoustic emission [62], but also to determine its type [66] and area of occurrence. In particular, location of the target source of seismoacoustic emission is determined with the accuracy of up to 1..10 m at the distance of up 40 km from the interrogator location. Actually, as a result of logical processing, several thousands of the so-called C-OTDR channels are formed on the monitoring distance, each of which transfers information on seismoacoustic activity at the well-defined point of the DFOS. The width of the typical C-OTDR channel is 1..5 m.
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DT’s from set B, as part of the ML PdM concept, reflect the degradation models of the basic units and subsystems of the train. Thus these DT’s are designed to optimize service of each train key nodes and should be developed as part of the progressive ML PdM concept. A group of multimodal sensors, including: pinpoint vibration sensors, pinpoint temperature sensors, voltmeters and ammeters, are used as suppliers of objective information about the condition of the target train assemblies for DT-models from set B. The information processing scheme that is provided within the ML PdM concept for DT from set B is shown in Fig. 3.
Fig. 3. Data processing within the ML PdM concept (authors’ creation).
All DT’s used in railway traffic management tasks are embedded in a common information space, otherwise known as the DT Data Bus, within which DT’s are able to ex-change information messages, transmitting messages and data to each other and to higher-level information systems in the hierarchy. Different mechanisms can be used to implement the DT Data Bus. The simplest of which is the TCP-IP protocol. Of course, DT Data Bus, in some cases, can be implemented as a cloud service, but this applies only to the applications that are not intended to operate in real time. The scheme of DT Data Bus organization is presented in Fig. 4.
Fig. 4. Data Bus with Railroad Digital Twins (authors’ creation).
A Prognostic Evaluation of the DT-Concept Effectiveness Implementation in the Railway Traffic Management System Let us consider an example of applying the methodology described in Sect. 1 for the predictive evaluation of the effectiveness of DT implementation in railway traffic management practice. As mentioned in Sect. 1, we will rely on a set of metrics from the
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(Basic)
set ϒ: first, for each metric ρi ∈ ϒ we will define the values of ρi , ρi . And then, according to [1], we will calculate the target EERI index. Figure 5 shows a general scheme of data processing in the calculation of the EERI index for the case of railway traffic management. This figure schematically shows the main data sources that are used in the formation of target metrics required to calculate the EERI index.
Fig. 5. Data processing scheme for the EERI index calculating (authors’ creation).
The Productivity Improving Metric (ρ1 ) For the DT in question, this metric (KPI) can be interpreted as the railway throughput rate (RTR). The RTR is almost completely determined by the parameter “automatic block signal spacing size” (Tabsss ). In fact, Tabsss is the minimum time for trains to be separated one after the other on sections equipped with automatic blocking or semiautomatic blocking in the presence of passing block posts. Daily capacity (RTR24 ) of a railway line equipped with security systems is unambiguously conditioned by the value of Tabsss . In fact, this value is a full-fledged characteristic for the daily capacity of the line: the smaller it is, the greater the daily capacity: RTR24 = 24/Tabsss . The calculations considered
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a 384 m long passenger train (900 tons/16 wagons). The initial data for the evaluation calculation of ρ1 value are given in Table 1. Table 1. The initial data for the evaluation calculation of value available in the Word template Initial parameter
Value for the baseline variant
Value for DT variant
The average speed of movement on the section, km per hour
90
90
Section duration, m
100 000
100 000
Train length, m
384
384
Braking path duration for a given speed (smooth mode), m
1020, (68)
1020, (68)
Accuracy of train boundaries definition by the monitoring system, m
2600, (68)
50, (62)
Block-section length, m
2600,(68)
1120
The number of block sections delimiting the trains in the normal basic scheme
3,(68)
3,(68)
As a result of the calculation, which was carried out based on the formulas (and methodology) from [68], for a speed of 90 km per hour, we get the estimate of value shown in Table 2. Table 2. The result of the ρ1 value calculation Calculated parameter
Value for the baseline variant
Value for DT variant
Distance between the centers of the trains of the calculated pair, m
8184
3744
Automatic block signal spacing size (Tabsss ), in minutes at a speed of 90 km per hour
5.44
2.49
ρ1 (RTR24 )
4.4
9.63
(DT )
(Basic)
So, ρ1 = 9.63, ρ1 = 4.4. Obviously, the value of this metric depends on the train average speed. This dependence is presented in Fig. 6. Here dashed graph corresponds to the “soft block section” (DT) case, the solid graph corresponds to the “hard block section” (Basic) case. For the sake of certainty, we will use the value of this metric for the speed of 90 km per hour.
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Fig. 6. Dependence of ρ1 (RTR24 ) metric value on average train speed.
Railroad Maintenance Cost Metric (ρ2 ) Reduced maintenance costs of the monitoring system are realized due to the very principle of fiber-optic monitoring system C\F-OTDR. In [68] it is stated that, depending on the application conditions, the use of C\F-OTDR monitoring system is 2–3 times cheaper than the basic variant based on the use of rail circuits, both in installation and (DT ) (Basic) = 3.0, ρ2 = 1.0. in maintenance. Therefore, we take ρ2 Trains Maintenance Costs Metric (ρ3 ) This metric sense: lower train’s maintenance costs by predicting maintenance issues before breakdowns occur. We will have lower train’s maintenance costs, by introducing ML PdM methodology into train service plans. Implementation of ML PdM reduces (DT ) (Basic) = 1.1, ρ3 = overall maintenance costs by 5 to 10% [69]. Thus, we will take ρ3 1.0. OEE (Overall Equipment Effectiveness) Metric (ρ4 ) Improved OEE through reduced downtime and improved performance. In terms of rail traffic, this is equivalent to reducing the daily “downtime” of railway track length L due to compaction of the passing trainsflow. In fact, we are talking about the need to N24 −1 increase the parameter ε24 = i=1 Li L , where N24 is the number of trains passed during the day on a section of railway track length L, with the length of trains form a set {Li |i = 1, ...N24 }. For example, for a section of L = 100 km, for a passenger train (DT ) (Basic) of length 384 m, OEE = 2.18. Therefore, we take ρ4 = 2.18, ρ4 = 1.0. Risk Metric (ρ5 ) The meaning of this metric for the tasks of railway traffic management is to measure the number of failures of the railway tracks structural elements and the downtime associated with the replacement and repair of these elements. The lower the breakdowns amount,
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then the better for the company’s efficiency. Reduction of the number of failures is achieved by solid control of the ballast prism, which is ensured by application of C\FOTDR monitoring system. Based on the information collected, the monitoring system makes a conclusion about the condition of the railroad tracks structural elements for each segment of 2–5 m in length. Thus, if the maintenance of the railroad tracks is implemented within the ML PdM concept, when any segment of the railroad track begins to show signs of instability, unscheduled (urgent) preventive or rehabilitation work is carried out on it. According to a report by Deloitte Insights [69], PdM promises a 10 to 20% increase in equipment uptime and availability. Interpreting this estimate for the railroad transportation case means that there will be a 10–20% increase in the availability of a normally functioning railroad track [70]. Consequently, the risk of a railroad incident (DT ) = 1.1, will decrease by about these values. Let’s take conservative estimates: ρ5 (Basic) ρ5 = 1.0. Reliability of Equipment Metric (ρ6 ) According to a report by Deloitte Insights [69], PdM promises, a 10 to 20% increase in (DT ) = equipment uptime and availability. Thus, we can use conservative estimates: ρ6 (Basic) = 1.0. 1.1, ρ6 Production Times Metric (ρ7 ) The value of this metric, as applied to the realities of railway traffic control, is already taken into account in the ρ1 metric. Therefore, we assign a value of importance (weight) k7 = 0 to this parameter. New Business Opportunities Metric (ρ8 ) We believe that with the implementation of DT new business opportunities that could (DT ) (Basic) = 1.0, ρ8 = 1.0. be quantified - not planned. Therefore, ρ8 Customer Service Metric (ρ9 ) We believe that with the introduction of DT, changes in customer service, which could (DT ) (Basic) = 1.0, ρ9 = 1.0. be expressed numerically, is not planned. Therefore, ρ9 B Product Quality Metric (ρ10 ) The value of this metric, as applied to the realities of railway traffic control, is already taken into account in the ρ1 metric. Therefore, we assign a value of importance (weight) k10 = 0 to this parameter. Supply and Delivery Chains Metric (ρ11 ) According to a report by Deloitte Insights [69], PdM promises a 5 to 10% reduction in overall maintenance cost. This fact implies the optimization of a minimally sufficient stock of components for repair. Therefore, we can expect a 5–10% reduction in inventory (DT ) (Basic) = 1.0. cost. We use a conservative estimate of ρ11 = 1.07, ρ11 Profit Metric (ρ12 ) The value of this metric, as applied to the realities of railway traffic control, is already
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taken into account in the ρ1 metric. Therefore, we assign a value of importance (weight) k12 = 0 to this parameter. EERI Index Calculation Results (v) So the sets ϒv = ρi |i = 1, ..., 12 , v ∈ Basic , DT are defined. Now we can proceed directly to the calculation of the EERI index. The results of this calculation, according to [1], for the weight tuple K = (k1 , k2 , ..., k12 ) = (80, 90, 80, 70, 80, 30, 0, 30, 50, 0, 30, 0) are shown in Table 3. Table 3. EERI index calculation Metric
Short name
ki
(DT )
ρi
(Basic)
wi
ρi
wi
Productivity
ρ1
80,0
2,18
0,15
0,32
Cost (railroad maintenance cost)
ρ2
90,0
3,00
0,17
0,50
Maintenance cost (trains maintenance cost)
ρ3
80,0
1,10
0,15
0,16
OEE (Overall Equipment Effectiveness)
ρ4
70,0
2,18
0,13
0,28
Risk
ρ5
80,0
1,10
0,15
0,16
Reliability of equipment
ρ6
30,0
1,10
0,06
0,06
Production times
ρ7
1,00
0,00
0,00
New business opportunities
ρ8
30,0
1,00
0,06
0,06
Customer service
ρ9
50,0
1,00
0,09
0,09
1,00
0,00
0,00
1,07
0,06
0,06
1,00
0,00
0,00
Product quality
ρ10
Supply and delivery chains
ρ11
Profit
ρ12
EERI index
0,00
0,00 30,0 0,00
(DT )
ρi
(Basic)
ρi
1,70
Thus, for = (80, 90, 80, 70, 80, 30, 0, 30, 50, 0, 30, 0) the EERI index is 1.70. This means that for a given importance of the metrics (defined by the tuple K) the railroad company will obtain a 70% increase in performance after the implementation of the DT concept in its business processes. When all metrics are “equal” for the company, which in this case corresponds to = (100, 100, 100, 100, 100, 100, 0, 100, 100, 0, 100, 0) the value of the EERI index is 1.53. That is, the effect of the implementation of DT in this case is somewhat less, but nevertheless remains quite satisfactory.
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4 Discussion In this paper, we propose a method to estimate the future effect of DT implementation in the business processes of a company. The proposed method of the integrated effect forecasting from DT implementation, first of all, has sense to implement in those companies, which already use target management methods in some form, for example, management by target indicators (KPI). Among target management methods, the Balanced Scorecard, as well as its various modifications, is widespread. As a rule, most of the metrics that underlie the approach proposed in this work, in one form or another are calculated (evaluated) exactly in the companies with target management [71]. This fact will significantly simplify the implementation of the proposed methodology, which has a comparative nature, where the current state of a group of target metrics is used as a base, with which the forecast state (corresponding to their statuses after DT implementation) of these metrics is compared. A certain role, in the framework of the proposed approach, is played by a weight tuple, which are assigned to metrics in the process of forming the resulting EERI index. The content of this tuple, in general, coincides with the weights of the corresponding metrics from the Balanced Scorecard program. In the case when the metrics are not ranked by their importance for the company, the weights in the tuple are taken equal. When calculating metrics, the specific features of the specific application area in which the company that implements the DT concept in its business processes must be taken into account. For example, some metrics can be assigned zero importance weights, as demonstrated in the example of using digital twins in railroad traffic management tasks. In general, the proposed method for evaluating the expected effect of DT implementation is very transparent to understand and analyze, and therefore its results can be easily interpreted, as demonstrated in Sect. 3.
5 Conclusion Created a transparent methodology for predicting the value of the relative economic and business effect that will be obtained after the implementation of digital twins in the company’s business processes composition. It is shown that as a group of metrics that are used in the formation of a predictive estimation of the economic effect, it is reasonable to use the metrics previously created in the framework of the implementation of the balanced scorecard (BSC). For the first time a composition of digital twins, designed for the management of railway traffic, is proposed. The example of this digital twins composition step by step demonstrated the mechanism of using the proposed predictive methodology to quantify the economic effect value, which will receive a railway company after the digital twins implementation. The methodology proposed in the article to obtain a predictive estimate of the digital twins implementation economic effect is quite transparent and simple. Therefore, it can be used in the practice of financial planning without much difficulty. As a direction for future research, the authors plan to study the robustness of the proposed method to gross, stochastic errors that may occur when calculating some of the metrics used in determining the EERI index value. Of particular practical interest is the approach to the
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implementation of DT in the practice of railway traffic management described in this article. The authors plan to develop the proposed concept, especially in terms of using ORMS to manage the block sections occupancy.
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Digital Transformation of Business: Industrial Solutions
Digital Transformation of Small Business Development Management in the Region Svetlana Baranova(B) and Daria Ostroukhova Central Russian Institute of Management - Branch of the RANEPA, Orel, Russia [email protected]
Abstract. The study provides the analysis of the existing national and international scientific developments in the field of innovations in management and identifies current trends in applying an effective digital management system to business entities of the small sector of the economy as well as in creating a digital infrastructure for small business development management in the region. General scientific and special methods were used, such as generalization, synthesis and analysis of accumulated scientific results on organizing the cooperation between small businesses and the state, changing the nature of managing the regional economy through the use of electronic technologies to expand access to additional financing for small businesses and improve the efficiency of small businesses. An authordeveloped approach to building a digital management system for the development of small businesses in the region is proposed. The approach is based on the use of modern digital services and contributes to expanding access to additional financing and improving measures to support and develop small businesses through the use of the My Business Service Center. The original methodological approach to building an effective digital management system for the development of small businesses in the region makes it possible to arrange the use of modern digital services and improve the management system, apply new approaches to the interaction of various business structures in order to increase the availability of financial resources and optimize systematic measures to support small businesses. Keywords: Small Businesses · Digital Transformation of Management · Regional Structure of Small Business Management
1 Introduction The current socio-political and economic environment for business operations forces small businesses to respond quickly to changing conditions in various market segments and facilitates the introduction of new approaches to business organization and management. Considering the digitalization of management as a separate stage of automation and digitalization of activities, one should really be aware of the positive effect of applying information and communication technologies, including the accumulation and analysis of big data adapted for forecasting and optimizing business processes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 793–800, 2023. https://doi.org/10.1007/978-3-031-32719-3_59
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The problem of implementing new approaches (innovations) in management became relevant at the end of the 20th century. Certain aspects of forming and the sequence of transformation of economic processes are described in the works of C. Freeman [1], devoted to research into the technological specification and dynamics of innovations based on new technologies, new knowledge and changes in “organizational institutions”, and works of C. Christensen [2], whose studies included the assessment of business performance efficiency and the necessity of innovations. The formation of the digital space and the use of digital technologies in management were considered in the works of E. Toffler, H. Toffler [3], where the authors revealed the essence and significance of “information that can replace material resources”, D. Teece [4] (2012), who emphasized availability of a good infrastructure as a prerequisite for introducing innovations, A. Gawer [5], who studied the possibilities of transforming business operations using design engineering, P. Drucker [6], who determined the prospects for the development of the information sphere as a whole. Relevant are the works of M. Porter, J. Heppelmann [7] on creating information (digital) products of “augmented reality” which form a “new information environment”, and the works of K. Schwab [8] devoted to the issues of the “Fourth Industrial Revolution”, and other authors. Analyzing the main approaches to implementing digital technologies in the economy, modern Russian researchers focus on the specifics of business operations and management in various economy sectors. Regarding small business as a separate economic segment, A.V. Lukyanova [9], V.V. Maslennikov, Yu.V. Lyandau, I.A. Kalinina [10], A.A. Fomin [11], V.I. Tarasov [12] and other authors pay a lot of attention to employing digital technologies for achieving competitive positions in small business, building an effective digital management system for business entities, and creating a digital infrastructure. L.G. Batrakova [13] in her research considers such significant issues as cooperation between small businesses and the state, changing the nature of the regional economy management through the use of electronic technologies. In modern conditions, the most relevant issue is setting up a new e-government system “Electronic Government” [14] and Regional Management Centers as integrated units for monitoring and generating analytical materials [15]. Thus, a range of research lines in the field of improving small business performance efficiency has been formed, including: – searching for optimal ways of cooperation between small businesses and the state; – developing and applying digital services for managing individual business entities and small business in general; – studying the possibility of using alternative sources of additional financing, increasing the importance of using digital services when choosing optimal financial solutions.
2 Materials and Methodology To study the state of knowledge of the problem, general scientific methods were used, such as generalization, synthesis and analysis of the accumulated scientific results on building a modern system for managing the development of small business in the region through the use of digital services.
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We have analyzed the existing national and international scientific developments in the field of innovations in management and identified current trends in applying an effective digital management system to business entities of the small sector of the economy as well as in creating a digital infrastructure for managing the development of small business in the region. General scientific and special methods were used, such as generalization, synthesis and analysis of accumulated scientific results on organizing the cooperation between small businesses and the state, changing the nature of managing the regional economy through the use of electronic technologies to expand access to additional financing for small businesses and improve the efficiency of small businesses. For the practical implementation of the proposed approach to building an effective regional management system, we studied the structural elements that have a direct managerial impact on the development of small business in the Orel region and the possibility of using digital services in their cooperation.
3 Discussion Digitalization of production processes in small and medium-sized businesses is impossible without the introduction of information and systematized technologies. The development of new directions in building a digital infrastructure for small businesses does not make the research into theoretical and methodological provisions for ensuring efficiency growth less relevant, since about 86% of scientific publications of Russian researchers are devoted to this issue [16]. Most of the studies touch upon the problems of budgetary funds and effectiveness of the government support measures. At the same time, foreign authors, considering small business as an integral part of the market economy, focus on finding alternative sources of additional funding, creating specialized financial structures and increasing the importance of using digital finance. Lu Jolly Zhou, Xinyu Zhang, Yezhou Sha [17] see an increasing role of angel investments in developing China’s small business financing system, Yan Yuan, Zhao Rong, Nana Xu, Yiyang Lu [18] point out the need to use credit cards, emphasizing their relevance for the regions with lower involvement in digitalization. There is also confirming evidence for the effectiveness of the Ashoka Investment Fund, which attracts funds from large private sponsors and Western charitable foundations [19], the Echoing Green Fund [20], and the Schwab Social Entrepreneurship Fund [21] with a similar focus. A number of authors pay attention to the study of the digital economy and its integration into small businesses management. So, A.A. Shpilyova and A.V. Lukyanova investigated the problems and prospects of small business digitalization [22, 23]. V.Y. Burov notes that the institution of small business, being an integral part of the effective operations of the market economy, shows particular interest to digital technologies in terms of socio-economic transformations that have taken place in recent years in the Russian Federation [24]. Researchers draw promising conclusions that small businesses are following three main directions during the transition to digital technologies: accelerating digitalization, digitalizing sales functions, and finding digital partners to enter the market [25]. Opting for one of the three ways of digital transformation, according to the authors of this study, is determined by the existing level of digital maturity, the
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culture of learning, and the history of the introduction of digital technologies for small businesses.
4 Results Implementing strategic models for the development of territorial entities, economic sectors and individual business entities requires prompt changes and flexibility of managerial activity, where digitalization has virtually no alternative. In modern economic environment, the awareness of the need for competent management is actively supplemented by new approaches in the development of management technologies that contain elements of digitalization and the use of intellectual resources. Digital transformation of small business management is seen as significant and timely in the conditions of the COVID-19 pandemic crisis, accompanied by a ban on offline activities and the extensive introduction of e-commerce. The priority of building an effective managing system for regional development of small businesses made us realize the importance and relevance of conducting a study into possible uses of digital technologies in this direction. Figure 1 summarizes the structural elements that have a direct managerial impact on the development of small business in the Orel region. Digital services in the system of small businesses organization and operation are currently provided by the My Business Service Center and include the State Digital Business Support Platform, which contains [26]: – online consulting support “Online consultations with the My Business Centers”; – financial support “Preferential loans with financial participation”, aimed at expanding access to additional financing; – banking products “Bank Guarantees”, which increase the accessibility to banking services; – financial support “Refinancing with state participation”, aimed at improving small businesses efficiency; – property support “Preferential lease of state property”. A promising modern direction in the digital management development is the National Program “Digital Economy of the Russian Federation” [27], which includes building a platform for interdepartmental cooperation for data exchange between departments and state institutions. Provided that individual business entities are integrated into this platform, a fundamentally new system of entrepreneurial cooperation will be formed. A separate area of the project implementation is “Digital Public Administration”, aimed at: – providing citizens and organizations with access to priority public services and digital services; – building the National Data Management System (NDMS); – developing e-government infrastructure; – implementing end-to-end platform solutions in public administration. Considering data as a separate type of public administration assets, it is necessary to emphasize that one of the priority goals for the National Program “Digital Public
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Fig. 1. Structural elements of small business development management in the Orel region
Administration” is to meet the needs of individuals and legal entities for access to information, the availability of which is necessary for organizing interaction and developing entrepreneurial cooperation [28, 29]. The creation of the National Data Management System will expand the functionality of the My Business Service Centers by: – conducting research into the direction of small businesses operations and development in the region, based on the data of the “Register of SME Support Infrastructure Organizations”; – identifying priority areas for development through creating and applying a modern online service which contains information on regional markets; – using modern digital services - accumulating, analyzing, processing and structuring data on the potential and actual capacity of the regional market, the dynamics of changes, consumer preferences, etc. Hence, the possibility of achieving key indicators of socio-economic development is seen in building a new system of an integrated digital space for managing the region,
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focused on the needs of users - representatives of small businesses, government bodies and other stakeholders (Fig. 2).
Fig. 2. Management of small business development in the region using the National Data Management System
5 Conclusion Managing the development of small business by applying the National Data Management System will make it possible to: – coordinate institutional infrastructures of direct interaction; – build a consistent system for providing the data necessary to analyze the performance of business structures; – improve management efficiency by identifying priority areas for the development of small and medium-sized businesses, developing and implementing regional business development and scaling programs;
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– expand access to additional financing and increase the level of investment activity in the region by releasing information on the scale of business operations, performance efficiency, the efficiency of using additional financing by individual business entities, the implementation of various regional programs and projects, priority business areas; – expand the functionality of the My Business Service Centers by providing consulting support when choosing areas of operation and creating business structures. The use of the National Data Management System will make it possible to transform the modern system for managing the development of small businesses in the region and improve its efficiency through the use of digital services.
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18. Yuan, Y., Rong, Z., Xu, N., Lu, Y.: Credit cards and small business dynamics: evidence from China. Pac. Basin Financ. J. 67, 101570 (2021). https://doi.org/10.1016/j.pacfin.2021.101570 19. Official website of the Ashoka Foundation. https://www.ashoka.org/en-us. Accessed 27 Aug 2021 20. Official website of the Echoing Green Foundation. https://echoinggreen.org/. Accessed 27 Aug 2021 21. Official website of the Schwab Social Entrepreneurship Foundation. https://www.schwab found.org/. Accessed 27 Aug 2021 22. Lukyanova, A.V.: Tendencies and possibilities of digitalization of small and medium-sized businesses. Eurasian Sci. Assoc. (7-1), 20–30 (2019) 23. Shpilyova, A.A.: Processes of digitalization in small and medium-sized companies under pandemic conditions. Econ. Entrep. Law 11(2), 299–312 (2021) 24. Burov, V.Y.: Small entrepreneurship in a digital economy: problems and prospects. Formalization as the basis of digital economy: Mat. All-Russian Scientific and Practical Conference with International Participation, Irkutsk, pp. 89–95 (2018) 25. Seliverstov, Y.I., Rudychev, A.A., Dmitrieva, Y.A.: Digital transformation of business by small and medium enterprises as a competitiveness growth factor. Bull. Altai Acad. Econ. Law (11-3), 531–539 (2020) 26. My Business. The State digital platform for supporting entrepreneurship. https://msp.eco nomy.gov.ru/. Accessed 27 Aug 2021 27. The national program «Digital Economy of the Russian Federation». Minutes of the meeting of the Presidium of the Presidential Council for Strategic Development and National Projects. https://digital.gov.ru/ru/activity/directions/858/. Accessed 27 Aug 2021 28. Rodionov, D., et al.: Methodology for assessing the digital image of an enterprise with its industry specifics. Algorithms 15, 177 (2022). https://doi.org/10.3390/a15060177 29. Rodionov, D.G., Konnikov, E.A., Nasrutdinov, M.N.: A transformation of the approach to evaluating a region’s investment attractiveness as a consequence of the COVID-19 pandemic. Economies 9(2), 59 (2021)
Concept of Forming the Digital Strategy for Business Structure Development Irina Avdeeva(B) and Ilya Mikhalev Central Russian Institute of Management, Branch of RANEPA, Oryol, Russia [email protected]
Abstract. The purpose of the research is to specify the necessity of strategic planning in the development of digital economic systems in today’s changing conditions. As the most efficient and promising area is digital space, at present every company has to focus on a strategy of its development in the digital environments. In the current economic situation, planning and forecasting of company actions in the digital space is extremely urgent. In order to achieve the research goals, the authors studied the literature on strategic planning in enterprises and development of the digital promotion strategy, and they analyzed efficiency of the tools necessary for digital promotion. In addition, the authors studied theoretical issues of digital marketing, found out key advantages and disadvantages of the methodology of strategic planning undertaken by international companies. Consequently, the outcome of the research has been the algorithm of the company’s digital promotion strategy. The paper represents the analysis of several major international and Russian companies, the methodology of their strategic planning. It considers how a business strategy is implemented in the digital environment. The methodological basis of the study is a complex of methods, which is determined by its interdisciplinary type. Thus, the main method of the study is the qualitative comparative analysis. In the course of the research, the authors worked out the algorithm of creating the digital promotion strategy. This algorithm is adapted to specifics of the company’s activity with allowance for its individual features. Implementing the developed strategy, it should be noted that every stage of the strategy is mandatory, and only the complex step-by-step implementation can most likely lead to a positive result. It is also important that the promotion strategy in the digital environment should reflect the general strategy of the company development on the market, with no contradictions. Keywords: Strategic Planning · Strategy · Promotion Strategy · Digital Space · Digital Strategy · Business Model · Change Management
1 Introduction Digital transformation is a purposeful strategic process of business change in the conditions of permanent economic modernization. Efficiency of the existing models and business processes decreases, the current communication methods malfunction, because of changes in customer behavior and customer model to consume goods and services. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 801–815, 2023. https://doi.org/10.1007/978-3-031-32719-3_60
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The problem raised in the paper is up-to-date, as implementation of the complex digital promotion strategy is able to allow companies to project correctly their strategic goals onto the promotion strategy in the digital environment, by achieving the companies’ commercial and social goals. Maintaining a competitive advantage of any company cannot do without timely development and introduction of an online promotion strategy. Issues of forming the digital strategy for development of economic systems at different levels are considered in publications of such scientists as [1–12], etc. Although most issues of digital transformation for economic systems at different levels are studied and revealed, there are no complex studies devoted to forming the digital strategy of modern business structures. We assume that it is impossible to achieve high results in today’s rapidly changing economic situation without planning actions and forecasting consequences. Therefore, the essential part of managing the modern economic systems at different levels is strategic planning under conditions of constantly changing environment, which confirms relevance and practical significance of the research. Ultimately, the digital strategy based on the algorithm will enable companies of any field of activity to achieve goals, following the purpose of the company’s general development strategy. The introduction of the digital strategy will take into account not only a commercial component of the company, but a social goal as well. It should be noted that though companies work in different fields, and each company has to follow its own individual strategy, the internet space has common requirements, terms, and factors which are determined by the external environment. Due to this, we set an objective to work out an optimal model of the strategy which could be adapted to a company of any profile with allowance for its individual features.
2 Materials and Methods Theoretically and methodologically, the research is based on scientific works devoted to strategic planning and digitalization algorithms for modern companies. It is worth stating that strategic thought dates back to antiquity. One of the first publications which analyzed the algorithm of winning a war is the ancient Chinese treatise by Sun Tzu. The origin of the word “strategy” should be found in an Ancient Greek dictionary, where “strategos” means commander, warrior, general from “stratos” – army and “ago” – act. What alo matters is an instrument of strategy implementation – tactics, which means the art of building up troops (from “tásso” – to arrange the army). Both concepts were originated from the art of war and referred to combat operations. This is remarkable, as it characterizes the use of these terms which imply management, planning of development and progress (personality, group, business, enterprise, industry, state), when the purpose is to overcome external and internal obstacles and to win (to succeed or to achieve a goal).
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It is Niccolo Machiavelli who contributed a lot to the evolution of the “strategy” concept. In his political treatises “The Prince” and “The Art of War” Machiavelli reflected on the role of the ruler, and techniques of war. The Renaissance philosopher stressed the significance of the strategic prediction: “When the evils that arise have been predicted, they can be quickly dealt with”. This concept should be specified depending on its application. It requires addressing to a wide range of literary sources. In the period of 1960-1990s a variety of approaches to strategic management and understanding of business strategies evolved. A great contribution to the formation and development of theoretical and methodological grounds of strategic planning was made by foreign scientists – J. Quinn, H. Mintzberg, M. Porter, M. Mescon, A. Strickland, I. Ansoff, A. Thompson, J. Hamel, K. Hofer, A. Chandler, D. Higgins etc. In Russia the foundations were laid in the works of E.P. Golubkov, A.D. Kornilova, V.I. Vernadsky, N.I. Andrusov, A.S. Famitsyn etc. The methods and tools that are used by the authors of this paper are studying data from scientific sources, i.e. research papers and books.
3 Research Results The concept of digital space, or digital environment, is currently widely used. Obviously, it is related to the technology outburst, the emergence of gadgets, Internet, online services, and technology solutions, which permeate our life. Modern technologies and internet are directly integrated in the human life, almost in each of its segments. Today’s technological breakthrough leads to dynamic development and progress of the digital economy. With the rapid growth of. Internet users, the information economy determines new requirements for organizations in the field of forming marketing communications with customers. In accordance with the Digital 2020: Global Digital Overview, the number of Internet users has risen by 7%, compared to 2019, and amounts to 4,54 billion people (+298 million new users from January 2019). In Russia the number of Internet users amounts to 118 million, which equals to 81% of the Russian population. 69% of them goes online daily, according to VCIOM (Russian Public Opinion Research Center) data for 2020. Today every company introduces innovative digital services, from social media and monitoring to digital platforms. The driver of all these innovations is primarily the customer and his changes in consuming preferences. There are six trends of digital transformation: customer centricity, innovations, collaborations, humans, data, and values.
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A strategic plan should be specified by actual data and comprehensive studies. Strategic planning is a type of company’s management. It dates back to the 50s last century, when entrepreneurial activity took advantage of advanced planning. It was a breakthrough in management, as entrepreneurs became thinking ahead, linking their performance with large-scale tasks. Then, in late 1960s advanced planning was replaced with strategic planning, thereby company performance included not only elements of the internal environment, but political and social aspects, competition, consumer demands etc. Strategic planning evolved steadily, by creating different models of the strategic analysis, numerical and formal methods of solving tasks. This kind of planning gained popularity in early 1980s, it was regarded as a drastic solution to any company’s problems. However, with time, the formal methods of strategic planning became limited in use. This can be caused by the following factors: – – – – –
more importance of the human factor in entrepreneurship; development of the concept of corporate culture; contradictions between methods of strategic planning and modern management; increased uncertainty in business environment; decrease in predictability of change.
Currently, the idea of strategic planning is enhanced by using a creative approach. It is considered as a management function. Planning is no longer a universal recipe for success for everybody, it has laid the foundation for proper enterprise performance, where the system of management functions is implemented and efforts of all the enterprise staff are joined to achieve goals. However, it raises the question of the difference between a strategic and common long-range plan. We emphasize that during strategic planning the main parameters of an enterprise are assessed with a SWOT matrix, then changes in the competitive market position of an enterprise are analyzed by using a special scheme, and prospects are found out. Primarily, strategic planning is unique, because it is not based on the assumption that the future will be better than the past. It is worth noting that a strategic plan is characterized by multivariance, as it is developed to adapt an enterprise to rapidly changing conditions. Consequently, it results into another difference: strategic planning is the function of the enterprise trend development (strategy is the primary guideline for making a plan, whereas long-range planning is the function of time).
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In practice, top managers of most companies criticize the process of strategic planning as extremely bureaucratic, insufficiently insightful and poorly adapted to today’s fast changing markets. Some managers regard strategic planning as a relic of the past, and prosperity-oriented enterprises should invest into market analytics, when in turmoil. It is quite logical, but in accordance with the statistics, almost a tenth part of listed companies vanish annually, which means a four-fold increase in failures since 1965. Facing the challenges, it makes no sense to invest all the funds into attempts to adapt to the changing world. It is more important to prepare for a possible crisis in advance. The strategic preparedness is achieved with structured thinking aimed at revealing potential threats, failures, and opportunities, which is strategic planning. However, the problem of most companies is not strategic planning, but inefficient process of this planning. Although there is no unified approach, it is noticeable that companies which benefit a lot from their strategic planning activity have common features (Fig. 1).
Fig. 1. Common features of strategic planning in modern companies.
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After the successful process of strategic planning, it is also important to transform the strategy into results, and to control them regularly. The external environment is extremely volatile and changeable; therefore a company should invest not only into the initial stage of the strategy development, but constantly keep work in several areas. At present, Russian experience of strategic planning in companies is less effective, than in some other developed countries. Due to this, we will make a diagnostic overview of several international and major Russian companies, analyze their methodology of strategic planning, and consider how company’s strategy is implemented in the digital environment. For the analysis we select such companies, as CVS-Pharmacy (US pharmacy chain), Apple (US technology manufacturer of consumer electronics, smartphones, software), and SIBUR (Russian petrochemical company). The detailed comparison of the companies is shown in Table 1. Table 1. Diagnostic overview of the companies. Companies
CVS-Pharmacy
Apple
SIBUR
Location
USA, Woonsocket, Rhode Island
USA, California
Russia, Moscow
Sector
Pharmacy chain
Electronics, IT, software
Petrochemical company
Mission
CVS is convenience, value, and service
The company is rapidly innovating its activity, providing professionals, professors, students with cutting-edge user-friendly hardware, software, services, and gadgets, relevant to all customers’ demands and needs
Together we create better future for people and the planet
Vision
We help people to live longer, healthier, happier lives. Health is of great value
We believe that we are on the face of the earth to make great products and that is not changing. We always focus on innovations. We believe that beauty in simplicity
We want to make people’s lives better by accepting the challenges of the changing world as a driving force for progress (continued)
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Table 1. (continued) Companies
CVS-Pharmacy
Apple
SIBUR
Key strengths
- the largest pharmacy chain (over 9,600 outlets); - online pharmacy stores in the USA; - some CVS Pharmacy stores have MinuteClinics and diabetes centers (over 1,100 outlets), which provide healthcare services; - all CVS stores deal with electronic prescriptions; - a wide range of prescription pills, over-the-counter drugs, beauty care products and cosmetics, photo printing services, and processed products
- the large retail chain Apple Store (over 400 outlets) distribute Apple products in a variety of countries; - Apple Store online, iTunes Store and App Store delivery throughout the world; - the unique brand reputation, resembling the cult brand, is established due to innovative technologies and aesthetic design of electronics; - the leader of product placement in films; - the 3d in the Forbes 500 Bests Employers ranking
- using innovative approaches and leveraging cutting-edge technologies in production, which benefit people and protect environment; - petrochemical production is provided with the company’s own raw materials; - the company adopted the environmental programs aimed at minimizing wastewater and improving its quality, reducing harmful emissions; - online product ordering
Strategy
CVS Pharmacy Strategy aims to secure the nation in terms of pharmacy provision and maximize the company’s reach in all the segments
Apple’s strategy is to encourage people to buy not a single product, but a whole ecosystem of products. Apple aims to become not only a technology company, but to from a lifestyle
The long-term strategy aims to maintain sustainable growth, contributing to Russia’s transition from the resource-based economy to the processing economic model (continued)
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Companies
CVS-Pharmacy
Correlation between In the social media the strategy and its (primarily in digital identity YouTube) Pharmacy CVS covers social issues; it provides information to make people aware of important news, thereby making them feel secure due to CVS Pharmacy products Customers will trust CVS drugstores and CVS Health clinics, as in the media the company is positioned as responsible, helpful, and accessible to everyone
Apple
SIBUR
The company shows in social media how Apple electronics is applied in lives of common people. This confirms that Apple is close to customers and understands their needs. Through gaining the trust of customers in the media environment, the company implements its strategy to create an ecosystem of their brand products for every customer
In the social media SIBUR is open to show internal manufacturing processes, introduce the company’s top management and staff to media subscribers, encourage them to participate in interactive activities on the website, thereby gaining trust on behalf of potential customers, partners, and would-be employees. For maintaining the brand reputation and implementing the strategy of sustainable growth, it is important to be honest and open with people in social media
Let us consider the strategies of each company under analysis and compare them with the strategies implemented in the digital environment. 1.CVS Pharmacy’s strategy aims to secure the nation in terms of pharmacy provision and maximize the company’s reach in all the segments. The main strategic lines: – development of Minute Clinic (medical service of the walk-in format). In partnership with Teladoc Inc. The company is developing Direct-to-Consumer Telehealth Service; – improvement of sales outlets to maximize purchasing convenience for customers; – opening of new outlets; – increasing awareness of corporate training. Due to the aforementioned, the digital strategy of the company should be targeted at proving that residents could entrust them their health. In social media (e.g. YouTube) CVS Pharmacy covers socially significant issues and provides the residents with information about the latest healthcare news, understanding that CVS takes care of their
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safety. The company’s social media strategy leads to customers’ trust for CVS pharmacies and CVS Health clinics, because in the social media the company positions itself as responsible, helpful, and accessible to everyone. 2.Apple’s strategy is to encourage people to buy not a single product, but a whole ecosystem of products. Apple aims to become not only a technology company, but to from a lifestyle. The main strategic lines: – a decrease in “brand premiums”, lower product price for the target audience (due to innovative technology Apple is planning to attract more young customers to make them loyal to the brand and keep them for life); – Apple’s augmented reality (AR) transforms the world around us by visualizing things that would be impossible or impractical to see; – development of products compatible with iPhone to create a stronger connection with technological innovations (this gives customers a familiar experience with new products and reduce the financial burden on the company due to the transition to a new product). In order to correlate properly a strategy and its projection in the digital environment, Apple shows in social media how Apple electronics is applied in lives of common people. This confirms that Apple is close to customers and understands their needs. Through gaining the trust of customers in the media environment, the company implements its strategy to create an ecosystem of their brand products for every customer. Additionally, using YouTube platform with detailed reviews of technology, product presentations and brand news, Apple interacts with customers, persuading them that the company is open and interested in building customer loyalty. 3.The long-term strategy aims to maintain sustainable growth, contributing to Russia’s transition from the resource-based economy to the processing economic model. The main strategic lines: – – – – – – – –
responsible business; environment protection; society and partnership; sustainable product portfolio; climate impact reduction; import substitution; enhancing long-term access to raw materials; digitalization.
Implementing its strategy in social media, SIBUR is open to show internal manufacturing processes, introduce the company’s top management and staff to media subscribers, encourage them to participate in interactive activities on the website, thereby gaining trust on behalf of potential customers, partners, and would-be employees. For maintaining the brand reputation and implementing the strategy of sustainable growth, it is important to be honest and open with people in social media. Resulting from the comparative analysis, we can find out strengths and weaknesses of strategic plans of companies specialized in a variety of activities and assess how they correlate a strategy with its digital projections.
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Using a digital strategy at an enterprise should be clearly pre-planned, otherwise, most powerful tools of promotion will not be able to be effective. Taking advantage of every tool of the digital strategy for promotion of goods or services can be effective only with a complex approach. The effect from using some separate tools is worse than the results from using the complex which provides all the Internet capacities and allows using them in compliance with the general company strategy. It is impossible to work out a strategy which would be relevant for years and fit in every company, The reason is that technology is evolving at a frenetic pace and leading in every industry. However, it is possible to create an optimal methodology of a strategy, which would be adaptable to business specifics and take into account its features. We propose an algorithm of the business strategy for company promotion. The basis for its development is deep understanding of business structure and goals, and also a profile of customers who buy goods or services. In accordance with the common buildup scheme, a digital strategy is based on the questions “What?” and “How?”, which implies the following: what the company owns at the moment, what goals the company sets and how (with what measures and changes) the company can achieve these goals. Then we will consider the main stages of the digital promotion strategy, adaptable to any business specifics: 1. Collection and analysis of initial data. The objective of this stage is to study in detail the current state of the company. On the one hand, it is necessary to interview the company’s CEO and the marketing department. The company’s employees know its products better; this helps highlight important aspects during the strategy development. On the other hand, the third-party collection of information also matters. It includes monitoring reviews, reading thematic forums, studying activity in social media. All this helps clarify the company’s identity. At this stage of the analysis, it is essential to find out: - the company profile (age, branch, organizational structure, sales figures, SWOT analysis); - mission, goal, positioning; - reputation (reviews from the company staff and external reviews); - a range of goods and services (competitive advantage, seasonal demand, substitute goods, processes of manufacturing/purchasing); - main competitors (comparative analysis of competitive goods and services, analysis of competitors’ identity in media space, reputation); - information about customers (determination of target segments, personification, customer profile); - sales analysis (customer journey, used software, customer objections); - marketing overview (social media/website diagnostics, experience of traffic increasing tools, use of USP and offers). 2. Goal setting for digital promotion with allowance for overall business strategy targets. At this stage it is necessary to determine goals of digital marketing and sales. These can be the following: - quantity (sales, leads, subscribers, coverage etc.);
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- percentage (website conversion into leads, conversion of leads into sales, change in the average bill etc.). The goals are determined by every key product/service of the company, and by customer segments. In terms of digital environment, they can be specified to a number of targeted visits to the website for each product. In addition, it is possible to forecast how to reach certain figures, considering market opportunities and correcting the process of goal achievement. When setting goals, it is vital to follow the SMART principle (specific, measurable, achievable, relevant, time-bound). 3. Detecting problem areas and impact points. By this stage the strategy developer has had been aware of the current state of the company (main figures, resources, experience) and of the goals achieved after the adoption of the digital strategy. Consequently, the strategy developer can analyze problem areas, which are constraints on the way of goal achievement (e.g., low website traffic, missing target segments, low conversion etc.). After the list of constraint hypotheses has been completed, the latter can turn into key impact points which will be affected by the digital strategy. The final list represents priority hypotheses in terms of “resources/potential to achieve the goal”. 4. Development of tactics and selection of tools to achieve goals. Customer Journey Map. At this stage it is advisable to study data of the previous stages and divide potential customers by combinations “Product/service + Target audience”. For each priority combination it is necessary to reveal key changes and tactics for implementing the digital strategy. To achieve a certain goal for a particular segment of the target audience when selling a product (service), it is worth writing detailed chains of the strategy with an accurate content-plan, in-depth analysis, and a set of applied digital tools. There are four groups of digital tools: performance, CRM, branding, PR in digital. To find out which tools are to be used in a certain situation, you should know in advance how customers behave, which moments you can influence them, and what is the result of the customer’s contact with a company. Due to this, the digital strategy is based on the Customer Journey Map, when all the tools are aligned into a single logical chain. The Customer Journey Map should be built at the stage of tactics development, when the company’s products and the target audience have been described, goals and problems have been determined. The only thing to be done is to work out a detailed action plan based on CJM. Using CJM it is possible to monitor and analyze the journey of a potential customer, understand his needs, information search channels, emotions, satisfaction with interaction with a company, and finally, draw a conclusion how to affect his decisions, at which stages start working and which tool settings will be appropriate.
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CJM diagram is shown in Fig. 2.
Fig. 2. Customer Journey Map
Below we present the algorithm of building the Customer Journey Map with the stage description: 1. Define a product/service and target audience for the CJM to create a customer profile. CJM for each segment will be different. The target audience has been determined at the stage of collecting information and analyzing initial data. Therefore, at the described stage you can specify the available information by gathering answers to particular questions, i.e. “Why is this product appealing to the selected customer?”, “What product criteria are especially important for the customer? What makes him ask for detailed information?”, “What problems does the customer face and how the product can help to solve them?”. It is also advisable to mention thoughts, emotions, fears and expectations of the customer related to the product or the company. 2. Mark points and channels for interaction with the customer. You should elaborate on the process of selecting, decision making, and purchasing. It is necessary to find out and mark the points of contact between the customer and the company. These points can be both offline (office, outlet, courier) and online (website, apps), depending on the interaction channel. You also have to follow all the interactions of the existing customers with the company while selecting and purchasing a product. Data can be obtained from sales departments, analysis of queries in WorldStat, customer surveys etc. Later this information can help prepare heating materials and create offers. 3. Determine critical points and consumer barriers. You should detect consumer barriers, which emerge when consumers contact the company, and find ways to overcome them. The points with numerous barriers are called critical. In such points the consumer suffers negative emotions related to a product or a company. 4. Develop methods to remove barriers. It is necessary to determine particular actions for improving the company performance to create a positive customer experience,
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ongoing successful interaction between a customer and a product. It is recommended to measure costs to reduce barriers and select the best methods. Think over alternatives for further efficient interaction with users. Analyze objections and find out lacking information in the media space of the company, which changes are essential to make to minimize emergence of new barriers. In addition, you have to think like a customer, decide how it will be convenient for the customer to get in touch with the company, and at the intersection of customer’s preferences, company’s opportunities and the market situation, select the best combination of conversion types, fields to fill out, calls to action. Put down CJM in a formal way and revise it, if necessary. During implementation, CJM needs correcting, as customer journeys tend to change due to economic and social circumstances. You should be ready to change the format of interaction with the customer. It is important to monitor the points of interaction between the customer and the company to prevent emergence of new barriers. Make a team, determine budget and time. This stage implies that the main resources to implement the strategy are the following: a team which involves those who work out a plan of actions and put it into practice. The team involves both internal employees of the company and external contractors. Internal employees are to be in charge of strategy control and budget allocation, whereas external contractors implement the digital strategy through efficient tool management. Budget implies analytics costs, advertising, paid traffic, SMM experts, SEO etc. Timing implies time expenditure of managers and experts for strategy development and implementation. It is noteworthy that budget and timing are specified approximately, but they are vital for accurate details of strategy implementation tactics. Strategy analytics and optimization. To implement the developed strategy, a manager should divide a block of tasks among performers, determine key performance indicators (KPI), pre-plan control points and get down to work. Every month it is advisable to estimate tactical KPI and correct further planning. After completing each ad campaign, it is necessary to analyze its efficiency and make corrections for further improvement. It also refers to the goals which should be updated when they are achieved. Regular data analysis will allow revealing problems and growth points on a timely basis, which leads to efficiency of the digital strategy.
4 Discussion Summing up, we can state that a strategic plan is focused on change management to achieve goals set inside an organization with allowance for changes in the external environment. This is confirmed by the quote of the famous American scholar Peter Drucker: “Strategic planning does not deal with future decisions. It deals with the futurity of present decisions”. We assume that strategic planning is a key tool of sustainable socio-economic growth of an organization undertaken globally. It should be developed in the conditions of digital transformation. Although strategic planning can have numerous features, its main concept deals with corporate vision and activities. This opinion is in line with the definition that strategic planning is a systematic and formalized effort of a company to define primary goals, objectives, policies, strategy, and also to develop detailed plans
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for implementing the policy and strategy aimed at achieving the goals and objectives of the company. Digital platforms connect suppliers and consumers of information or services by establishing various network interactions and creating the potential for new digital services [13, 14]. Digital platforms create not only new markets, but new communities. Today different economic structures cease to be the central body: this function is carried out by a set of algorithms and software tools controlled by the platform, which controls and develop a decentralized network of different platform actors [15]. We assume that being innovative business models, digital platforms contribute to higher risks of disruptive innovations. On the one hand, the sharing economy use digital platforms to create technological infrastructure for innovative interaction between producers and consumers. Namely, one of the challengers is effective use of limited and (or) declining resources: premises, equipment, various labor tools, and labor itself. On the other hand, sharing of resources can exacerbate social inequality, granting privileges for those who own property and make profits on its rent.
5 Conclusion The authors draw a conclusion that the algorithm of implementing the digital strategy is specified to achieve certain goals, related to a selected segment of the target audience and a product category. Besides, the authors come up with recommendations that help solve the main problems of the company and contribute to achieving all the goals set within the strategy. It should be noted the digital strategy divides the Internet advertising campaign into stages, based on the detailed customer journey. The strategy is centered on customer behavior. This behavior can be affected by properly configured and timely connected digital tools, which rely on the 4P elements of the marketing mix.
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5. Bykov, I.A., Gladchenko, I.A.: Communicative aggression as a communication strategy in digital society. In: Proceedings of the 2019 IEEE Communication Strategies in Digital Society Seminar, ComSDS, pp. 34–38 (2019). https://doi.org/10.1109/COMSDS.2019.8709649 6. Kuokkanen, H., Sun, W.: Companies, meet ethical consumers: strategic CSR management to impact consumer choice. J. Bus. Ethics 166(2), 403–423 (2019). https://doi.org/10.1007/s10 551-019-04145-4 7. Deepa Viswasini, R., Abilasha, R., Ramani, P., Gheena, S., Hannah, R., Sundar, A.: Impact of digital marketing strategy for successful laboratory and clinical practitioners-a questionnaire based study. Int. J. Pharmaceut. Res. 12(2), 2677–2688 (2020). https://doi.org/10.31838/ijpr/ 2020.SP2.293 8. de Arroyabe J.C.F., Arranz, N., Schumann, M., Arroyabe, M.F.: The development of CE business models in firms: the role of circular economy capabilities. Technovation 106, 102292. https://doi.org/10.1016/j.technovation.2021.102292 9. Katsikeas, C., Leonidou, L., Zeriti, A.: Revisiting international marketing strategy in a digital era: opportunities, challenges, and research directions. Int. Mark. Rev. 37(3), 405–424 (2021). https://doi.org/10.1108/IMR-02-2019-0080 10. Rahim, H.A., Kamaruddin, S.B.A., Ibrahim, S., Ghani, N.A.M., Musirin, I.: Exploration on digital marketing as business strategy model among Malaysian entrepreneurs via neurocomputing. IAES Int. J. Artif. Intell. 9,1, 18–24 (2020). https://doi.org/10.11591/ijai.v9.i1.pp1 8-24 11. Zhu, Y., Lynette Wang, V., Wang, Y.J., Nastos, J.: Usiness-to-business referral as digital coopetition strategy: insights from an industry-wise digital business network. Eur. J. Mark. 54(6), 1181–1203 (2020). https://doi.org/10.1108/EJM-01-2019-0011 12. Zineb, K., Bouchaib, B.: General approach for formulating a digital transformation strategy. J. Comput. Sci. 16(4), 493–507 (2020). https://doi.org/10.3844/JCSSP.2020.493.507 13. Hagiu, A., Yoffie, D.B.: What’s Your Google Strategy? Harv. Bus. Rev. 87(4), 74–81 (2009) 14. Janssen, M., Estevez, E.: Lean government and platform-based governance—doing more with less. Gov. Inf. Q. 30(Suppl. 1), S1–S8 (2013). https://doi.org/10.1016/j.giq.2012.11.003 15. Moazed, A., Johnson, N.L.: Modern Monopolies: What It Takes to Dominate the 21st Century Economy. St. Martin’s Press, New York, 272 p. (2016)
The Distinctions of the Automated Accounting Information System for Sole Proprietors Trading on Online Marketplaces Tatiana Nepryakhina(B) Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia [email protected]
Abstract. The growth of e-commerce, and, in particular, the soaring sales on marketplaces, cause digital transformation of all related business processes. Small businesses are increasingly using the services of online marketplaces. The prevalence of sole proprietors matching the conditions of microbusiness in the structure of sellers in recent years suggests a trend towards the development of a specific stratum of small business in online commerce. This study is aimed at identifying the distinctive features of the automated accounting information system for sole proprietors that trade on online marketplaces. This survey conducted among sole proprietors reveals that even if there is no formal requirement for submitting accounting reports to regulatory institutions, sole proprietors do need to use bookkeeping and management accounting indicators in order to control their activities. The analysis of the ERP systems used in e-commerce has shown that they do not fully meet the needs of sole proprietors as they neither consider their intuitive desire to analyze business processes, nor have a holistic integration into the Internet. The logic of the systems is focused exclusively on the needs of legal entities for bookkeeping and tax accounting. The survey and analysis of the systems are helpful in identifying the distinctions of the automated accounting information systems for sole proprietors trading on online marketplaces and propose the points for automation of business processes. The results of the study can be used for putting forward general recommendations on accounting systems for sole proprietors operating in various fields, as well as improving the existing ERP systems. Further research is needed to clarify the structure of the integrated accounting system for sole proprietors, as well as to evaluate its effectiveness in case it is integrated into an ERP system. Keywords: Sole Proprietor · Automated Accounting Information System · E-commerce · Marketplaces · ERP systems
1 Introduction 1.1 Relevance of Research Small business is an important element of the Russian economy. The share of microenterprises and sole proprietors is 97% of all small businesses, while the number of microenterprises and sole proprietors is the same. The total revenue turnover in small business is © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 816–827, 2023. https://doi.org/10.1007/978-3-031-32719-3_61
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39%, with wholesale and retail trade accounting for 61%. Small business provides 38% of jobs, of which 29% is due to microenterprises. The leader of employment is the trade sector. Trade as a type of economic activity among microenterprises amounts to 38%, and 34% of it is e-commerce [1]. The largest proportion of e-commerce is accounted for sole proprietors (17%). New sole proprietors choose this type of trade in 42% [2]. As for legal entities, this type of activity is not included in the ranking of the most popular OKVED (Russian National Classifier of Economic Activities). Obviously, trade is the domain of small business. E-commerce takes up a considerable share of trade and the development of this field is hardly possible without modern digital technologies. The role of digitalization in trade has been considered by various authors, who pay special attention to marketplaces. Analyzing the statistics of the growing digital economy, we should note that the contribution of information and communication technologies (ICT) in trade in 2020 was 13.1%, taking up the second place and yielding other industries. Digital technologies are actively used in trade, especially digital platforms (30.3% of the average 17.2%). One of these platforms is marketplace. A marketplace is a business model that unites sellers and buyers who make transactions through an online platform that offers a wide range of different product categories and services, from the moment an order is placed to the moment it is received [3]. Russia has seen an increase in the volume of the e-commerce market by 33.2% since 2018 [4]. The share of e-commerce in the total turnover of retail trade is 9.2%, being 26% of non-food retail trade. In particular, the main growth is ensured by online trading (B2C trading) – buying tangible goods on the Internet, where a purchase is understood as ordering goods through a website or mobile application from the user’s device, regardless of the method of payment or receipt of the order. The share of e-commerce from the GDP in Russia (3.4%) exceeds that in Europe, but is significantly less than in China (11.7%), the UK (7.6%), South Korea (6.8%) and the USA (3.8%) [5]. 89% of the global e-commerce market is accounted for 10 leading countries (Russia is not on this list), with China, the United States and the United Kingdom taking up 76%. However, according to Data Insight, the Russian retail e-commerce market is expected to be the fastest growing market within 2021–2025. At the same time, the share of the five marketplaces (AliExpress, Ozon, Wildberries, Yandex Market, Sbermarket) in the total volume of the Russian e-commerce market in the second quarter of 2022 amounted to 69% by the number of orders and 44% in monetary terms. This is 6 and 7 percentage points higher, respectively, than a year ago [5]. Surely, the rapid growth of the e-commerce market brings about certain point solutions, such as improved digital platform technologies, expanding functionality, and proposal of new “rules” of e-commerce (AKIT consumer service standards [6]). However, such solutions are the result of the need and commercialization according to the concept of state regulation of digital platforms and ecosystems for citizens. It is really a quick and good way for business to meet its final needs: revenue growth and cost reduction due to new customers and convenient services. However, do such solutions stimulate small and medium–sized businesses? This very aspect is a subject to a more detailed analysis. The predominance of sole proprietors in the structure of e-commerce sellers
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(51%) [5], as well as the legal specifics of this form of business, e.g. not having to keep accounting records or submit accounting reports to authorities, as well as being able to use the earnings for meeting the sole proprietor’s own needs, as well as full property responsibility, reveals the need to investigate sole proprietorship as a special a type of small business, in particular, in the field of e-commerce. The authors [7] believe that small business can be involved in the digital economy in the following ways: – digitalization of small business based on information and communication technologies, which leads to new business models; – creation of digital technologies by small business subjects; – as a result of development and implementation of state programs aimed at digitalization of small business. While the first two methods are the result of exogenous influence on the market, the interaction of the state and small business is the very endogenous bond that helps to promote national interests in the system of market relations, to demonstrate concern towards people of the country, especially in sole proprietorship. The results show that many micro-entrepreneurs pursue non-economic goals [8]. Making a profit/getting wealthy was the fifth category of the firms’ main goals, and the entrepreneurs stated that they primarily focus on bringing certain benefits to society and their own family. This finding also means that some micro-entrepreneurs are not interested in making their business grow. The activities of sole entrepreneurs have not been studied enough, but the specifics of individual rule and the strive for simplifying the accounting of information make these business similar to microenterprises, and this feature can be a distinction of this form comparing to other types of small business. Microenterprises are not just a smaller copy of larger firms. Their structure and access to both human and financial resources make them behave differently from other businesses. In addition, due to individual rule, which is a common thing, the owners of microenterprises, as well as sole proprietors, are looking for ways to automate the processes in which information about the company’s resources is collected, processed and stored for further analysis and management decision-making. For this purpose, companies use automated accounting information systems. Such systems are designed to automate all business processes and help companies to easily and economically perform all their business operations [9]. In order to increase the market competitiveness and business efficiency of the company, its managers should rely on information technology and introduce their own accounting systems or purchasing accounting software [10]. Despite the growing use of modern Internet technologies in trade, the companies are poor in the internal use of software in their own activities, in particular, electronic document management systems (43.3% of the average 53.8%) [4], as well as financial payments in electronic form (36.5% of the average 41.8%). Despite the fact that organizational, management and economic solutions are well developed (61.2% of 57.2%), there are no real working systems for private entrepreneurship. Accounting information systems in IT 4.0 have become an important tool helping enterprises improve their competitiveness and efficiency, as well as adapt to changes [11]. The need of small businesses and especially microenterprises for such systems should be researched, but modern studies do not focus on sole proprietors, although their activities, as noted below, also need to be considered.
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1.2 Literature Review Multiple scientific research studies confirm that the activities of microenterprises differ significantly from other types of small businesses, first of all, due to fact that they have fewer transactions and because decisions are made directly by the owner [12–15]. According to the studies, small companies, and especially microenterprises, do not use accounting tools a lot or at all [16], their management is more intuitive and decisions are taken based on the skills, abilities, vision and opinion of the company’s owner [15]. The owner’s priority participation in the development and implementation of accounting systems is also common [17]. One of the most important factors making micro- and small firms fail is their inefficient managing of finance, which is caused by the insignificant use or outright misuse of the company’s accounting and financial information [13] and the lack of clear goals [18]. Such empirical evidence imply that intuitively comprehensible accounting tools have to be introduced for microenterprises. Given the distinctions of microenterprises highlighted by researchers, such intuitive administration and active participation of the owner in the development of an accounting system may be suitable for the accounting of a business run by a sole proprietor. Studies show that microenterprises often use management accounting tools [12]. However, in many cases when these tools are used, the enterprises do not make records or even suspect about doing so [14]. In addition, the effective research shows that management accounting tools and methods, such as budgeting, cost system and information acquisition, are vital for decisionmaking and performance of microenterprises [19]. Microenterprises that use the practice of management accounting are more efficient. The calculation system has become one of the main tools that positively affect performance. It is known that cost calculation systems, such as activity-based cost calculation, result in a higher level of productivity [9]. However, micro- and small enterprises use very heterogeneous practices, since many of the firms in the studied sample stated that they either do not have any budgets or use only cash as an efficiency indicator. Thus, microenterprises need management accounting tools to improve their efficiency and decision-making, and in practice many companies informally use management accounting data. However, do sole proprietors need the same thing? This question has not been investigated to a sufficient extent and is one of the objectives of this study. An assumption is put forward about the similarity in the logic of microenterprises, as a form of small business, and sole proprietors. A distinction of sole proprietorship is that there is no formal obligation to submit accounting reports to the tax office and keep accounting records. It is known that microenterprises are not very skilled in using book-keeping and management accounting tools, and the need of sole proprietors to use book-keeping and management accounting tools and indicators is not considered in scientific literature at all. Today accounting tools can hardly be applied without appropriate automated accounting information systems. Automated accounting information systems are intermediary between accounting and information systems [19]. Such a computer system is designed to help an enterprise achieve two goals related to decision-making and control. However, as early as at the stage of determining the purpose of the computer system, the difference in the logic of decision-making of a company and of an entrepreneur should
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be understood. The author believes that a distinctive characteristic of decision-making in case of a sole proprietor, which makes it different from a company, is that control is used as a strategy for converting uncertainty into goals [20]. The result of resource control is that it sets the development goal of the sole proprietor, whereas in case of a company the goal determines how many resources are needed to achieve this goal. Based on this paradigm, an automated accounting information system (AAIS) for sole proprietors should be integrated and capable of consolidating all business processes with a centralized database from which accounting and non-accounting data can be withdrawn to control the resources and clarify the goals. The accounting standards, on which the AAIS is based, do not consider the real business processes occurring in the company. They are primarily aimed at preparing financial statements, and there are no universal integrations of management accounting tools for specific activities. The proposed principle of sole proprietorship “resource control determines the goal” identifies an alternative way for sole proprietors to apply an integrated automated accounting information system that allows them to keep accounting and non-accounting data within the existing business processes and establish the strategic goals of their business. The literature does not propose versatile solutions for integrating the AAIS into management accounting tools for a certain type of activity of sole proprietors, so we need to clarify the points of automation of certain business processes that could become the basis for an integrated AAIS for sole proprietors trading on online marketplaces. The ERP environment is a suitable computerized system for applying an integrated AAIS by enterprises. The ERP environment that enterprises use includes a group of computer applications in the form of software modules representing integrated administrative and operational business processes. According to various accounting research studies the ERP environment is a form of applied information technology that impacts the AAIS, having a stabilizing effect [21]. Full-fledged ERP systems are one of the stages of digitalization of business processes. Not only does digitalization help large businesses, but it is also vital for small and medium-sized companies, as it allows them to grow and develop much faster with less costs [22]. The government is to play a significant role in this transformation. On its initiative and under its direct control, an ecosystem should be formed to allow the entrepreneur to find prompt answers to the questions that arise in the process of digitalization of the company’s activities [23]. According to a survey of entrepreneurs conducted by Otkritie Bank, the drivers of digitalization of business processes are [24]: support from the state (43%), lower prices for business services (37%). Obstacles can be: low awareness about possible digital technologies (27%) and budgetary constraints in the company (23%). It is common for sole proprietors, especially at the initial level to see that all drivers and obstacles become bigger due to their budget constraints. In this research we need to consider the existing ERP systems recommended for accounting in the field of e-commerce and analyze the compliance of the existing solutions with the needs of sole proprietors.
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1.3 Purpose This research is aimed at identifying the specific features of an automated accounting information system for sole proprietors operating in the field of online commerce through marketplaces. This aim can be achieved if the following objectives are accomplished: – Determine the need to use book-keeping and management accounting indicators for sole proprietors. – Analyze the existing ERP systems for e-commerce and determine the compliance of the systems with the needs of sole proprietors. – Specify the points of automation of certain business processes that could become the basis for an integrated AAIS for sole proprietors that trade on online marketplaces.
2 Methods and Materials In this study we conducted a survey among some Russian sole proprietors. The survey contained eight questions: 1. Sole proprietors do not have to keep accounting records or submit financial statements, however, some financial indicators can be used by entrepreneurs to analyze their activities (for example, self-cost, profits). Do you use such indicators in your activities? 2. What are your main priorities in business? 3. What is your education? 4. How do you find the information you need about the specifics of your business? 5. How long in total (given possible breaks and reopening) do you operate as a sole proprietor throughout the entire time of your business activity? 6. Do you use accounting programs for tax accounting and tax reporting? 7. Do you need to use non-accounting information to analyze your operations (for example, the quantity of orders, product weight, volume of sales, norms, etc.)? 8. What borrowed funds do you use in your activities? The questions were generated in Yandex Forms. The link to the survey was sent to the “Center for the Development and Support of Entrepreneurship in St. Petersburg”, to the “Federal Corporation for the Development of Small and Medium-Sized Businesses”, as well as to the branches of the portal for the support of small and medium-sized businesses called “My Business”. These centers cannot publish external surveys officially, but it was possible to publish them in work chats and personal mailing lists. The survey was expected to reveal the attitude of sole proprietors to the state policy in terms of informing entrepreneurs about support measures, consultations provided to entrepreneurs, and the information policy adopted by the state. In addition, we wanted to know if the entrepreneurs needed internal accounting and control of their resources in order to analyze their own activities and set goals for further development of their business. 239 responses were received in the survey. The data obtained from the analytical agency “Data Insight” were used to study the available ERP systems. Our main research methods were questionnaire, comparison, analysis, and synthesis.
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2.1 Research Results The survey of sole proprietors shows that the priority for 44% is control (control over current activities, setting goals for expanding business and make it more profitable, analyzing the market and offering services and goods that are in demand). In 37% it is reputation (complying with legislation, paying taxes on time, making due payments to suppliers and demanding the same from buyers), as the entrepreneurs need positive relationships with the state and counterparties, and want to gain internal control over the activities and growth of business. Only 19% choose dynamism (being able, if necessary, to change the field of activity, finding additional ways of making profit, using the status of a sole proprietor to obtain favorable conditions for loans, subsidies, contracts with large state and commercial organizations), as a result of uncertainty. Despite the fact that sole proprietors do not have to book-keep, most of the entrepreneurs (54%) keep the record of their self-cost and profits by themselves and 23% with the help of an accountant, while the same proportion of the respondents (23%) do not keep the record of their self-cost or account it in an intuitive way based on receipts and write-offs. This trend emphasizes the desire of the entrepreneurs to control their activities using the well-known methods: accounting and financial statements. 38% of the respondents use accounting programs for submitting tax reports, 42% do not need such programs, and 20% use programs only for primary documentation. 41% of the entrepreneurs do not use non-accounting information in the form of the quantity of orders, volume of sales, norms and other indicators. This trend may reflect the results of “false” entrepreneurial activity, i.e. the sole proprietor’s dependence on one dominant client and the lack of need to control activities, low level of financial literacy, poor knowledge of accounting and marketing tools, an aggravating problem of selfemployment, such as the lack of desire to expand business. In this case self-employment is an alternative to wages. At the same time, 33% use accounting programs to analyze non-accounting indicators, and 26% note that accounting programs cannot process such information. The survey results show that the entrepreneurs seek to control and preserve their reputation, most entrepreneurs use various financial indicators, even if they do not have to submit financial statements to the tax office. They also keep the record of non-accounting information and highlight the weaknesses of the existing automation systems. These results confirm the findings of the research studies on the need to use accounting and management indicators in the activities of microenterprises and they can also be true with regard to the operations of sole proprietors. In order to compare the ERP systems used in e-commerce, the recommended systems should be analyzed using official statistical publications in this field. Data Insider annually updates the list of services available for the e-commerce ecosystem. The blocks of this system offer solutions to most of the tasks that sellers face when trading online. For keeping records and control of resources, there is a block “Logistics”, a category “Automation of Logical Processes”, a subcategory “Accounting Systems”, a block “Store and Customer Management”, a category “Customer and Order Data”, a subcategory “ERP”. For the subcategory “Accounting Systems” Data Insider offers only three services: My Warehouse, Business.ru, Big Bird, for the subcategory “ERP”: 1C, SAP, ORACLE, FLEXBBY, Microsoft Dynamics 365. Many services are affordable. “My Warehouse”
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and “1C” are among the most popular ones, as they have ready-made universal integrations with marketplaces. However, in all the solutions, 1C is an accounting program aimed at systematic and continuous accounting, and My Warehouse is an applied CRM system for warehouse accounting that does not cover a full-scale financial management block. Thus, the digitalization solution of a sole proprietor’s business processes is expressed in three blocks of services: one program for sales, another for the warehouse, and the third for finance, with financial accounting and tax accounting being mainly limited. Such solutions are dictated primarily by the accounting legislation applicable to legal entities, and if these have become better adapted for small businesses due to the recommendations on simplified accounting and additions to tax legislation, no alternative solutions are suggested for sole proprietors. According to the results of the survey, an integrated accounting system for sole proprietors should meet the need to identify benchmarks, be able to control resources, set goals for business growth, and also allow the user to maintain interaction with the state through simple and independent formation of tax reporting data. For e-commerce, including in case of online marketplaces, such an accounting system must also go through a full-fledged digitalization process. This can be done if the state puts forward recommendations on the use of an integrated accounting system by sole proprietors. Such general recommendations should be suitable for identifying any field of activity of sole proprietors given their business processes. The structure of these should comply with the accounting principles applicable for formal tax reports, while the system itself should be able to integrate easily on the Internet. Figure 1 shows an option we propose for automating the business processes of sole proprietors trading on online marketplaces. The movement of business processes goes from left to right, the preliminary stage is decision-making, the main structure shows business processes that characterize blocks of operations, accounting and non-accounting information is obtained and documented in management and tax accounting registers. Table 1 contains the explications of such automation. The points of automation are indicated by symbol “A”, while the points of automation that may need to be manually adjusted are marked by symbol “PA”. The results of the analyzed registers should be relevant so that sole proprietors could use them in decision-making. The decisions taken should allow the sole proprietor to optimize their business processes and adjust strategic goals. Such recommendations could allow the developers of the ERP systems to customize the digitalization of each business process not in the form of an expensive adjustment of individual parameters, but in the form of versatile software products for a group of industries that would be focused on meeting the needs of sole proprietors.
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Fig. 1. Automation of business processes of sole proprietors trading on online marketplaces
3 Discussion The results of the survey of sole proprietors confirm the studies investigating the specifics of microenterprises and presented in the literature review. Although there is no need to submit financial statements to the tax office, sole proprietors still use accounting and management indicators in their activities. According to the responses, the sole proprietors are quite independent in doing business, and few of them turn to experts for help. In addition, our research confirms the theory of the logic of the entrepreneur who is willing to control their business activities and set goals accordingly. The survey revealed that accounting programs are used exclusively for the purposes of submitting tax reports and obtaining primary documentation. The entrepreneurs note the complexity of processing non-accounting information when such programs are used. The ERP systems for e-commerce that exist today are meant exclusively for legal entities and are primarily used for book-keeping and tax accounting. Despite extensive digitalization of the trading business and, according to statistics, sole proprietorship being a predominant business form among sellers on marketplaces, the solutions proposed today do not meet the needs of sole proprietors. Due to the lack of such solutions, significant adjustments must be introduced to the automation of the integrated accounting systems for sole proprietors. Despite the existing theoretical basis on business processes applied in the activities of a company, these automation solutions are customized individually for the company’s characteristics. They require significant investments and are not versatile. The study proposes the points of automation of business processes for trading on online marketplaces, which would make it possible to make general recommendations on automation of accounting systems for sole proprietors, given the distinctions that have been identified.
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Table 1. Explications of symbols in Fig. 1 Symbol
Explication
A1
Automation based on a Bank-Client system
A2
Automation with the applied solution “Recognition of Primary Documents”
A3
Calculation of the quantity, weight and other characteristics based on the primary documents and using the program for non-accounting information
A4
Automation of information in the electronic platform system about writing-off a commodity item
A5
Automation of the information in the electronic platform system about the quantity of orders
A6
Automation of the information in the electronic platform system about the goods accepted to the warehouse of the electronic platform / delivery to the drop box / delivery to the client
A7
Automation of the information in the electronic platform system on the quantity of goods, their weight, and other characteristics necessary for non-accounting information
A8
Automation of the information in the electronic platform system on the quantity of the goods sold, their price, and returns
A9
Automation of the information in the electronic platform system on the quantity of the goods sold, and returns
A10
Automation of the accounting information needed for accounting registers
PA1
Need to change the method of acquisition of accounting information to comply with the law or decision
PA2
Need to change the method of acquisition of non-accounting information to comply with the law or decision
PA3
Preliminary entry of additional categories for cost allocation
PA4
Generation of information about the settled disputes with the customer
PA5
Filling in other non-accounting information necessary for accounting registers
Source: compiled by the author
4 Conclusion Based on the research aimed at investigating the specific features of the automated accounting information system of sole proprietors trading on online marketplaces, the following results have been obtained: 1. It has been revealed that sole proprietors need to use accounting and management indicators in their activities. Another priority is the relationship with the state concerning the matters of compliance with legislation and payment of taxes. This result confirms the existence of a close contact between the state and sole proprietors.
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2. The existing ERP systems for e-commerce are limited to book-keeping and tax accounting. It is difficult to integrate them on the Internet, they have many indicators, but not all of them are understandable or relevant for the accounting system of sole proprietors. 3. The literature review and the survey data suggest that an integrated accounting system for sole proprietors should meet the need to identify the necessary benchmarks, and have to be able to control resources, choose goals for business growth, and allow the entrepreneur to keep in touch with the state through comprehensible independent formation of tax reporting data. Given these distinctive features, we propose a system for automating business processes of sole proprietors operating in the field of online commerce on marketplaces. Thus, an automated accounting information system for sole proprietors trading on online marketplaces should meet the intuitive needs of an individual entrepreneur in controlling business processes through relevant book-keeping and management accounting indicators. It should be suitable for easy integration through an ERP system to the Internet space. In addition, sole proprietors depend on the state for compliance with legislation, so the general recommendations for automation of integrated accounting systems for sole proprietors operating in certain fields should accompany the digitalization of business processes and offer automated accounting. Creators of ERP systems can use the proposed recommendations for implementing an alternative system to the existing accounting programs that will correspond to the logic of decision-making applicable to sole proprietors. Acknowledgments. The research was financed as part of the project “Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization” (FSEG-2023–0008).
References 1. SME statistics, https://mcp.pf/analytics/. Accessed 20 Nov 2022 2. Statistics of the Unified Register of Small and Medium Business Entities, https://rmsp.nalog. ru/statistics.html. Accessed 20 Nov 2022 3. Kulikova, O.M., Suvorova, S.D.: Marketplace: business model of modern trade. Innov. Econ.: Prosp. Dev. Improv. 6(48), 50–55 (2020) 4. Abdrakhmanova, G.I., Vasilkovsky, S.A., Vishnevsky, K.O.: Digital Economics: 2022: Brief Statistics Book. National Research University Higher School of Economics, NRU HSE (2022) 5. Market research e-commerce in Russia 2021/Data Insight, https://datainsight.ru/eCommerce_ 2021. Accessed 20 Nov 2022 6. AKIT customer service standards. https://akit.ru/business/standards?ysclid=l7yoe3352974 7982524. Accessed 20 Nov 2022 7. Nepryakhina, T.M.: Specifics of external and internal control of the activities of sole proprietors and self-employed. In: Sheshukova, T.G. (ed.) Development of Accounting, Analytical and Control Systems in the Context of Global Economic Processes: Collection of Scientific Articles. PSNIU, vol. 13, pp. 213–223 (2020)
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8. Najera, R.T., Collazzo, P.: Management accounting use in micro and small enterprises. Qual. Res. Acc. Manag. 18(1), 84–101 (2021) 9. Wang, D.H.M., Huynh, Q.L.: Effects of environmental uncertainty on computerized accounting system adoption and firm performance. Int. J. Hum. Appl. Sci. 2(1), 13–21 (2013) 10. Abdelraheem, A.A., Hussaien, A.M., Mohammed, M.A., Elbokhari, Y.A.: The effect of information technology on the quality of accounting information. Grow. Sci. 7(4), 191–196 (2021) 11. Lee, J., Kao, H.A., Yang, S.: Service innovation and smart analytics for Industry 4.0 and big data environment. In: The 6th CIRP Conference on Industrial Product-Service Systems, pp. 3–8 (2014) 12. Shields, J., Shelleman, J.: Management accounting systems in micro-SMEs. J. Appl. Manag. Enterp. 21(1), 19–31 (2016) 13. Dyt, R., Halabi, A.K.: Empirical evidence examining the accounting information systems and accounting reports of small and micro business in Australia. Small Enterp. Res. 15(2), 1–9 (2007) 14. Alattar, J.M., Kouhy, R., Innes, J.: Management accounting information in micro enterprises in Gaza. J. Acc. Organ. Change 5(1), 81–107 (2009) 15. Liberman-Yaconi, L., Hooper, T., Hutchings, K.: Toward a model of understanding strategic decision-making in micro-firms: exploring the Australian information technology sector. J. Small Bus. Manag. 48(1), 70–95 (2010) 16. Najera, R.T., Collazzo, P.: Determinants of the use of accounting systems in microenterprises: evidence from Chile. J. Acc. Emerg. Econ. 11(4), 632–650 (2021) 17. Carey, P.: External accountants’ business advice and SME performance. Pac. Acc. Rev. 27(2), 166–188 (2015) 18. Peters, M., Buhalis, D.: Family hotel businesses: strategic planning and the need for education and training. Educ. Train. 46(8/9), 406–415 (2014) 19. Thong, V.Q.: Factors defining the effectiveness of integrated accounting information system in ERP environment – evidence from Vietnam’s enterprises. Ho Chi Minh City Open Univ. J. Sci. 7(2), 96–110 (2020) 20. Sarasvathy, S.D.: Causation and effectuation: toward a theoretical shift from economic inevitability to entrepreneurial contingency. Acad. Manag. Rev. 26(2), 243–288 (2002) 21. Rom, A., Rohde, C.: Management accounting and integrated information systems: a literature review. Int. J. Acc. Inf. Syst. 8, 40–68 (2007) 22. Zargaryan, Z.S., Savostin, N.N., Savtsova, A.V.: On the prospects of small business in Russia. Russia, Europe, Asia: digitalization of the global space: Collection of scientific papers of the II International Scientific and Practical Forum, Stavropol, 09–12 October, pp. 368–371 (2019) 23. Polyanin, A.V., Soboleva, Y., Tarnovsky, V.V.: Digitalization of processes in small and medium business. Manag. Consult. 4(136), 80–96 (2020) 24. Small Business Digitalization Index 2019. https://backend.academyopen.ru/media/checkup/ material/BDI_1_volna.pdf. Accessed 20 Nov 2022
Study of the Impact of the Digital Transformation of the Economy on SMEs Vadim Karapetov(B) and Andrei Stepanchuk Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia [email protected]
Abstract. The purpose of this paper is to study the impact of digitalization on operation of small and medium-sized businesses in the Russian Federation. Besides, the research allows studying the rate and speed of digital transformation. This will help determine, which business indicators are affected by digitalization of the economy, as well as find out what relations and trends of changes are. This is very important now, at the age of digitalization of everything surrounding us; moreover, in Russia, in 2019, the project aimed at developing digital transformation of the economy and the country as a whole was approved. For this paper, the authors used empirical data that are rarely considered in other studies, and the main methods for this research were comparative and descriptive analyses. Russian and foreign sources regarding issues of digitalization of the economy were studied, with calculations to be carried out for a number of indicators, to obtain the necessary data. As for conclusions to be made as the results of the study, it is definitely worth mentioning that three hypotheses related to the facts that digital transformation has the positive impact on business, and efficiency of enterprises increases, have been proven. The latter hypothesis also confirms the assumption that, in the context of cloud services for entrepreneurs, digitalization reduces the threshold for entering business, so among newly emerged SMEs, the share of individual entrepreneurs grow year by year. It is also worth noting that researching digital transformation makes significant contribution to development of the digital economy and digitalization of business, in particular. Since researching digital transformation is underdeveloped in terms of the Russian Federation, each new study considering the issues from different angles allows both business and the state to come closer to full understanding of the effect of digitalization. Keywords: Digitalization; Digital Transformation · Small and Medium-Sized Business · Technology in the Economy
1 Introduction At the beginning of the 21st century, the process of global digitalization in all spheres of our life received the powerful start. Digitalization is in focus of many key aspects of our life: social, political, economic, etc. It gradually penetrates in education, communication, politics, public life and business [1]. All this together shapes our lives, though this article will consider digitalization in the economy and business both through global © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 828–838, 2023. https://doi.org/10.1007/978-3-031-32719-3_62
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transformations and local solutions intended for individual enterprises. Relevance of the article concerning this issue is determined by the fact that digitalization of the economy, and our life as a whole, depends on digitalization of business. Nowadays, digitalization of the economy is a priority task for many developed and developing countries, including the Russian Federation [2]. Importance of digitalization of the economy is also confirmed by the record number of recent search queries for this phrase in the Google search engine [3]. Digitalization is always achieved by implementing many reforms to be called digital transformation. In turn, digital transformation is the process of introducing digital technologies, which is joint with optimization of the management system of main technological processes. At present, the third wave of transformation, in which the most striking changes in the business environment get noticeable, is taking place [4]. Organizations are willing to adopt positive properties of digitalization, since it is one of the most significant aspects of the company’s growth and development in the modern world. Digitalization penetrates not only into technological processes through application of various robots and automated equipment, but also into managerial, administrative, and logistical processes [5]. This is determined by widespread development of the Internet and the use of mobile applications, which is described and proved in the article [6]. Moreover, this article considers small-sized and microenterprises in South Africa, where the market economy is under development, as well as the whole country, so the results of this study can be projected to Russia, because the economic effect of digitalization occurs in both developed and developing countries [7]. Business is completely immersed in digitalization, from all its sides [8]. Moreover, we should not forget that society, and hence customers/consumers, are also subject to digitalization, people have begun to use mobile technologies and social networks in masse, therefore many of them consider processability of a product/a company to be the important factor for making choice [9]. Like everything new, these changes occur gradually: at first, digital transformation covers large, capital-intensive industries - large business companies with good profit indicators, aimed at powerful development. Then, technologies begin to “descend lower” and become available for wider use, with small - and medium-sized enterprises to transform. SMEs are the backbone of the country’s economy and play a key role in shaping the business environment, and in implementing innovative solutions [10]. Services, programs and other solutions adapted for applying by private entrepreneurs related to SMEs are developed. It is worth noting that all of the above is almost impossible without thoroughly studying of business needs in context of digitalization. In any country, without exception and including the Russian Federation, programs to support digital transformation of business should be developed and are developed. We need special legislative framework, improved tax conditions, incubators, support for scientific research and other measures of the state support. Moreover, digitalization can be not only on the part of the state, companies that provide their solutions and services related to digitalization also play an important role [11]. These companies are supported by the state, for the purpose of creating conditions conducive to rapid development of the area.
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In our country, in framework of digital transformation, the national program “Digital Economy of the Russian Federation” was created, to control and support the following areas: • • • • • • • • •
Normative regulation of the digital environment; Personnel for the digital economy; Information infrastructure; Information security; Digital technologies; Digital public management; Artificial intelligence; Provision of the Internet access through development of satellite communications; Development of the human resources potential of the IT industry.
This program is launched quite recently, to be approved in 2019, that’s why at present, there are few scientific papers and statistical data, which definitely prevents studying and improving digital transformation of business in the Russian Federation. This viewpoint is also confirmed by other scientists’ articles indicating, in particular, insufficiently qualitative interpretation of laws and too little information [12]. However, carefully reviewing available literature makes it possible to identify several interesting studies. This article [13] indicates the positive impact on efficiency and profitability of the business from its digitalization. Researchers have proved that application of the latest technologies, ICT [14], social networks and many other technological factors, together with the statutory framework, positively affects the SMEs’ efficiency [15, 8]. Similar conclusions and theses on digital transformation among small- and mediumsized businesses provides fundamental support for business growth and results in increasing competitiveness among companies can be found in the article [16]. In support of this, studies by authors from different countries of the early and late periods, from 2006 to 2021, are presented. In the article [17] its authors use regression analysis of the sample of enterprises of different scale. The authors set for themselves the task of, first of all, confirming or refuting a number of hypotheses by applying regression analysis: • H1: Dependence of efficiency parameters on business digitalization level is positive. • H2: The strongest effect of digitalization is observed in companies related to technology, communications or finance. • H3: Digitalization impact scale is not related to the company’s size. • H4: The most mature companies with high-level digitalization receive significantly less effect from digitalization. When analyzing the companies, the authors of the article came to conclusions that not only confirm efficiency of the digitalization impact on efficiency of the organization’s operation, but also indicate that there is no correlation between the company’s size and the digital transformation effect scale. The same cannot be said about the field of the company’s activities, since there is noticeable difference among effects in companies from various industries.
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[18] in their study proved by applying the econometric model the direct dependence between digitalization indicators growth and economic efficiency of enterprises in the energy sector. This evidence gives us reason to believe that the same dependence is relevant for any other business entities. The authors of the article [19] revealed that if the entrepreneur wants to achieve the necessary level of digitalization of the business, it is needed to introduce technologies not only into production processes, but also into organizational and managerial ones. In this case, digital synergy allowing the organization to work most efficiently is achieved. Smoothly moving from reviewing other authors’ articles to this paper, we would like to note that in our article we are going to analyze the relationship between business and digitalization, assess the impact of digitalization factors on the companies’ performance indicators that have not been studied in previous studies, though they show the levels of efficiency and development of SMEs in Russia. The main objective of this article is identifying the quality of the digital transformation impact on SMEs’ efficiency. In particular, the following hypotheses will be verified in this paper: • H1: Digital transformation of business has the positive effect on the amount of specific revenue per one SME per year. • H2: Digital transformation of business positively influences on the amount of specific revenue per one employee per year. • H3: Development of digital online accounting, financial and management accounting services positively impact on the share of individual entrepreneurs in the overall structure of SMEs. These hypotheses are derived after cumulative analysis of the literature covering issues of digital transformation of various businesses in different countries. The completed study can become the basis for qualitative and quantitative description of digitalization of business in many countries.
2 Methods 2.1 Description of Research Methods This study examines and analyzes indicators related to small- and medium-sized businesses (SMEs) in the Russian Federation. Comparative and descriptive types of analysis were used as methods for conducting the study. Unfortunately, the small amount of the data did not allow making factor or regression analyses in relation to studying the level of digital transformation of the economy and its impact on real business in the Russian Federation. Based on the hypotheses formed, it was decided to choose the following indicators describing SMEs’ efficiency: 1) Labour Productivity Labor Productivity =
Total Revenue Number of Employed People
(1)
where Labour Productivity is the amount of revenue per one employee, Total Revenue is the total revenue of all SMEs in the Russian Federation, Number of
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Employed People is the average number of employees for all SMEs in the Russian Federation. 2) Specific Revenue per one company: Specific Revenue per Company =
Total Revenue Number of Companies
(2)
where Specific Revenue per one Company is the amount of revenue per one company, Total Revenue is the total revenue of all SMEs in the Russian Federation, Number of Companies is the total number of SMEs in the Russian Federation. Next, the indicators describing digital transformation of business and the economy of the Russian Federation as a whole were selected. One of these indicators is BDI (Business Digitalization Index) developed by the NAFI Analytical Centre together with the Otkrytie Bank and the Skolkovo Foundation. Besides BDI, there is another index developed in the Russian Federation, and completely reflecting the level of digitalization of business: this is ISIEZ (Business Digitalization Index) developed by the HSE, to be calculated considering several indicators. However, the data on the HSE index belong to a longer period than the data on BDI, so it was decided to focus on the indicator developed by the NAFI Analytical Centre. In the world, there are several other indices reflecting digitalization level, but they are either not calculated for the Russian Federation, or was calculated too long time ago. These include: IDI, EGDI, DESI, GCI, WDCI, NRI [19]. As additional indicators reflecting digitalization level, the following were also selected: • • • •
Digital economy development costs as % of GDP [20]; Application of ICT in organizations [20]; Use of the Internet by the population to order goods and services [21]; Internet use in organizations [21].
Some of the above indicators are widely used in scientific articles [22]. For more convenient analysis, in course of the study, other auxiliary indicators are calculated. 2.2 Description of Research Methods To begin with, it is necessary to sort out digitalization indicators, and only then assess their impact on SMEs. As mentioned above, one of main comprehensive indicators for assessing digital transformation is BDI. Unfortunately, calculation of this index began only in 2018, in any case, there is no information about its earlier calculations in open sources. Besides the Business Digitalization Index, individual non-comprehensive indicators describing digital transformation were selected: they are listed in paragraph 2.1 as additional indicators, and are taken from official statistical collections on the digital economy for 2021 and 2022. These collections were compiled by the Ministry of Finance of Russia, Rosstat and the HSE. For descriptive reasons, numerical information of the above-mentioned indicators was presented in the form of diagram (Fig. 1).
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Fig. 1. Digitalization indicators
All the indicators are calculated in %, except for BDI, which is calculated in pp. But since the lower and upper values for BDI are 0 and 100, respectively, it can be displayed on the same diagram with the percentage indicators [20, 21]. For calculating SMEs’ performance indicators, the following data were collected and calculated: • • • • •
Number of SMEs at the end of the year; Average number of employees per year; Revenue of SMEs in comparable prices in 2016; Specific revenue per one company – calculated by formula (2); Labour productivity – calculated by formula (1).
The data on these indicators are presented in Table 1. It is also worth noting that the amount of revenue was adjusted taking into account the price index and calculated in prices of 2016, since this year is the starting point of our study. Moreover, it is noticeable that there are no data for 2016, 2017, 2018 for several indicators. This is because of absence of the data on revenue for this period, and indicators such as unit revenue per one company and labour productivity cannot be calculated without them.
3 Results and Discussion Before describing the results, it is worth noting SMEs’ significance in the total volume of business and economy. This provision is confirmed by multiple studies mentioned in the literature review, as well as by statistics. In particular, in the Russian Federation, SMEs’ share in GDP is about 23%, which is quite much, although in comparison with European countries, the potential for growth is noticeable. Moreover, according to the
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V. Karapetov and A. Stepanchuk Table 1. SMEs’ Indicators
Year
Number of Average SMEs at the end number of of the year, units employees per year, persons
2016
5,841,509
15,922,438
2017
5,998,371
16,130,582
2018
6,042,898
15,917,053
2019
5,924,681
15,357,010
2020
5,702,150
15,509,813
2021
5,839,009
14,638,722
Revenue of SMEs in comparable prices in 2016, billion rubles
Specific revenue per one company, million rubles
Labour productivity, million rubles
-
-
-
-
-
-
-
-
-
36,095.45
6.09
2.35
36,638.1
6.43
2.36
41,139.8
7.05
2.81
statistical data, more than 2/3 of SMEs are involved in working with representatives of large business, which also confirms the significant role of small - and medium-sized business. These concepts help correctly assess the contribution of overall digitalization of the economy to development of SMEs. Having conducted the comparative analysis, we can safely state that digital transformation positively impacts on SMEs, but first we need to evaluate and describe the changes within the framework of digital transformation. For the greatest clarity, the diagram shown in Fig. 1 demonstrates, firstly, growth of the government costs for digitalization of the economy, and this growth has been the most active since 2019, as it was this year when the national program “Digital Economy of the Russian Federation” was approved. Due to the state support and the general development of mankind, the digitalization level growth constantly continued, even though until 2019, all indicators responsible for digital transformation also showed positive dynamics. If we consider in detail the BDI comprehensive indicator, we’ll find out that from 2018 to 2021 its growth amounted to 50%. This is the high-level indicator that makes us discard all doubts about digital transformation development of the economy in the Russian Federation. As for the number of SMEs, there are signs of stagnation, with the noticeable downward trend, which is completely due to the severe crisis situation to have overtaken business all over the world. The crisis of 2019–2020 is associated with the COVID-19 pandemic, which covered the entire globe, and completely paralyzed the economy of most countries. Russia has not become an exception, slight decrease in the number of SMEs over 6 years is noticeable, but if we assess the prospects, the downward trend may change, since the consequences of the COVID-19 pandemic have actually leveled off. The values of the average number of employees began to decline in 2017, long before the crisis related to the pandemic, the decline definitely intensified in 2019 and 2020, but the peak minimum extreme for the study period is 2021, i.e., the time when in many regions and countries, the restrictions related to the COVID-19 pandemic was removed, and business recovered its operation as much as possible. However, the global trend towards digitalization of the economy also explains the long-term decline in the average
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number of employees. This is determined by automation, robotization and digitalization of production. Currently, business needs in general fewer people to perform the same functions; besides, many employees’ competencies grow, which eliminates the need for additional employees. It is also worth noting that the pandemic accelerated digital transformation, as many people began to work online, and online shopping services reached their record levels. In course of the study, it was also found out that the number of employees per one SME reduces annually. Revenue is likely to be the most interesting and informative indicator among the above, but the strong impact of the pandemic and the lack of centralized data on the revenue of SMEs significantly affects the information content. In spite of the reasons above, even according to the data for three years (from 2019 to 2021), it is possible to draw certain conclusions and notice the trend. By the way, the revenue values are adjusted in view of the price index. Even despite the pandemic and the CPI adjustment to 2016 prices, SMEs’ revenue is obvious to continue increasing. All this also confirms that the digitalization impact is rather fruitful, and despite strong economic shocks, both in absolute and relative terms, revenue continues to grow. Now, we turn to the estimated indicators that were calculated as part of this study. Firstly, this is the indicator of labour productivity, which is calculated by formula (1). It is substantial that from year to year, with decreasing number of employees and increasing revenue, we observe increase in labor productivity, which again indicates the positive impact of digital transformation on SMEs. Moreover, these conclusions prove the H2 hypothesis to be formulated in the introduction of this paper. Digital transformation of business really positively impacts on the amount of specific revenue per one employee per year (labour productivity level). The next indicator is specific revenue per year per one SME; it is rarely found in scientific papers, instead, profit margin is most often used, but, unfortunately, it is impossible to find data on profit margin of the entire SME sector per year in the Russian Federation. This indicator does not reflect 100% efficiency for sure, since the share of profit in revenue may decrease even when turnover increases, but revenue more or less partially shows the level of the company’s economic activity. Specific revenue similarly shows its growth from year to year, since the number of enterprises has the downward trend during the study period, and their revenue, on the contrary, grows. Similarly, to the H2 hypothesis, the H1 hypothesis is also confirmed, since the data and analysis of calculations demonstrate the positive impact of digital transformation of business on the amount of specific revenue per one SME per year. The indicator “Use of cloud services in organizations (including legal entities and individual entrepreneurs), in % of the total number of companies”, which shows how many organizations out of the total number of companies use cloud services (data storage, communication between employees, electronic document management, online accounting, financial and management accounting services). Growth of this indicator exceeded 50% over the period from 2016 to 2021, and reached the level of 33.2%, which indicates high speed of digital transformation. In course of the study, it was found out that the share of individual entrepreneurs in the total volume of newly opened SMEs increases. For 6 years of observations, this growth was 35%, the data are presented in Table 2.
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V. Karapetov and A. Stepanchuk Table 2. Information on newly created SMEs
Year
Total, units
Legal entities, units/%
Individual entrepreneurs, units/%
2016
1,090,081
478,131
43.86%
611,950
2017
1,113,312
428,694
38.51%
684,618
61.49%
2018
1,133,926
349,905
30.86%
784,021
69.14%
1,062,132
305,711
28.78%
756,421
71.22%
238,295
28.97%
584,377
71.03%
240,595
23.48%
784,226
76.52%
2019 2020 2021
822,672 1,024,821
611,950
Taking into account the data on “The use of cloud services in organizations (including legal entities and individual entrepreneurs), in % of the total number of companies” and information on the share of individual entrepreneurs in the total number of newly opened SMEs, one can conclude that the H3 hypothesis is confirmed, since development of digital online accounting, financial and management accounting services really positively impact on the share of individual entrepreneurs in the overall structure of SMEs. All these technological solutions facilitate the process of creating the company based on the individual entrepreneur status, as well as simplify the subsequent conduct of business. Such services are also aimed at reducing costs and optimizing business management for aspiring entrepreneurs. Moreover, confirmation of our results can be found in the article [23].
4 Conclusions Summing up all the above, it is worth noting that three hypotheses regarding the impact of digital transformation of the economy and business on operation and efficiency of SMEs have been proven. All hypotheses confirm that this impact is positive, and in almost all cases the accelerated growth of indicators/indices of digitalization of the economy leads to accelerated increase in SMEs’ performance indicators. Data on 11 indicators (six of which relate to organizations and their activities, and five – to the processes of digital transformation) over 6 years - from 2016 to 2021 - were studied. It was not possible to obtain open data for all 6 years of the study period for all indicators. We hope very much that this situation will be corrected in future, and many scientists will have the opportunity for studying the impact of digitalization in as much detail as the study methods, rather than the availability of data, will allow. Moreover, in the process of verifying the hypotheses announced in the introduction of this paper, several additional factors, indices and indicators were explored. Additional conclusions were drawn and described in the paper. This study revealed the problem of lack of data in the field of SMEs and digitalization of the economy. Statistical collections from the same publisher can sometimes contain different content of indicators, besides, the rate of studying and describing digitalization is too low. Really few publications on correlation between digital transformation and SMEs are available in open sources [8].
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Based the results of the study, several recommendations can be proposed: 1) Currently, there is a noticeable lack of reliable data and qualitative research concerning on the issues of digitalization, so the state should develop actions to stimulate collection and provision of the data. 2) For accelerating development of the economy, and, in particular, in the field of SMEs, it is necessary to create training centers and platforms, as well as motivation programs, for the purpose of guiding entrepreneurs on the path of digital transformation. Some methodological aspects of the technology for consulting entrepreneurs in terms of the digital economy have already been developed by Russian scientists [24]. 3) Companies promoting the digitalization of the economy need to be provided with preferential taxation and simplification of bureaucratic procedures related to digital transformation. Acknowledgments. The research was financed as part of the project "Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization" (FSEG-2023–0008).
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Formalizing the Materiality Assessment for Audit Procedures Yu. Yu. Kochinev1 , Elena R. Antysheva1(B) , and Bokhodir Isroilov2 1 Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia
[email protected] 2 Tashkent State University of Economics, Tashkent, Uzbekistan
Abstract. The paper considers issues of assessing materiality for planning audit of financial statements. It is asserted that in accordance with the requirements of the International Standard on Auditing (ISA) 320 “Audit Materiality”, the auditor needs to establish materiality for financial reporting framework, and also materiality for audit procedures. The analysis of scientific literature confirms that the publications elaborate on materiality assessment for financial statements as a whole. Thereby it is relevant to specify the materiality assessment for performing audit procedures. The definition provided in ISA 320 implies that materiality for audit procedures is related to the materiality for financial statements as a whole and detection risk. The purpose of this paper is to formalize dependence between both concepts. Formalizing the dependence has been undertaken in the paper with the following methodology. Detection risk is regarded as geometric probability of a point (the aggregate of material misstatements) falling into a segment that represents the sum of misstatements undetected by the auditor. Thus, relying on the ISA 320 definition of materiality, the authors have obtained the expression which links materiality for performing audit procedures, materiality for overall financial statements, and detection risk. The obtained formula will allow the auditor community to assess reasonably the amount of materiality for audit procedures, which is provided by ISA 320. Keywords: Audit · International standards on auditing · Audit Procedures · Materiality for audit procedures · Materiality for financial statements as a whole · Detection risk
1 Introduction It is known that materiality is one of the main concepts in auditing, since, ultimately, it is its level that determines modification of an auditor’s report. Reference to the need to apply the concept of materiality and its definition are given in the International Standard on Auditing (ISA) 200 “Overall Objective of the Independent Auditor, and the Conduct of an Audit in Accordance with International Standards on Auditing” (paragraph 6). According to this definition, misstatements are considered material if they (individually or in the aggregate) could affect the economic decisions of users taken on the basis of financial statements [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 839–846, 2023. https://doi.org/10.1007/978-3-031-32719-3_63
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The auditor’s specific responsibilities in terms of applying the principle of materiality in planning, conducting an audit, and evaluating detected misstatements are described in ISA 320, “Materiality in Planning and Performing an Audit”. The standard introduces the concepts of materiality for financial statements in general (paragraph 10) and materiality for audit procedures (paragraph 11). Regarding materiality for financial statements, ISA 320 states that it should be determined by applying a certain percentage to a benchmark selected by the auditor. The benchmarks may include such elements of financial statements as assets, liabilities, equity, revenues, expenses, gross profit, pe-tax profit, etc. (paragraph A5 of ISA 320). The percentage, according to ISA 320, is determined on the basis of the auditor’s professional judgment. Materiality for audit procedures under ISA 320 is set by the auditor less than materiality for financial statements as a whole in order to reduce the likelihood that the auditor will not detect misstatements that in the aggregate exceed the materiality for financial statements. ISA 320 states that in formulating an overall audit strategy, the auditor should determine materiality for the financial statements as a whole, as well as materiality for performing audit procedures. The issue of the auditor’s determination of materiality for financial statements is discussed in detail in numerous literature sources. Let us point out the work [2], which most fully analyzes the methodologies related to materiality assessment. According to [2], the known methodologies related to materiality assessment of overall financial statements, can be divided into 3 groups: methodologies of choosing indicators which characterize financial reporting as a whole (benchmarks); methodologies of selecting materiality levels for the given benchmarks; methodologies of allocating the materiality level of the basic indicator for similar transactions, account balances, which form the benchmark. In the work [3], the author determines the main methods of choosing benchmarks for financial statements (critical component, stable base, basic array, key indicators), for which the auditor will assess materiality for financial statements as a whole. In the papers [4– 6] the authors point out that relying on international standards, auditors should apply a multi-level materiality model based on the development of several methodologies, i.e. the methodology of establishing materiality for financial statements as a whole and the methodology of assessing the applicable level of materiality (materiality for performing audit procedures in terms of ISA 320). The research works [7, 8] present the legal analysis of today’s Russian legislation and explain the choice of the interval, within which the materiality level for overall financial reporting is optimal (5–10% of the benchmark value). The authors [9, 10] confirm that ISA 320 does not have an algorithm of materiality assessment, and they draw attention to the international experience of using criteria to calculate materiality for financial reporting as a whole. The same issue of overall financial statements is discussed in other publications [6]; [11]; [12, 13]; [14]; [15]. The issue of the auditor’s assessment of materiality for audit procedures has not yet been addressed in the known literature. However, ISA 320 contains a requirement for mandatory determination of this indicator, but it does not contain any recommendations
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on this issue (other than the above-mentioned statement that materiality for audit procedures should be less than materiality for the financial statements as a whole) [16]. Thus, formalizing the materiality assessment for audit procedures is a very relevant task. From the definition of materiality for audit procedures given in ISA 320, it follows that this indicator should somehow be related to materiality for the financial statements as a whole through the detection risk [17]. In this paper, we have attempted to derive a formal dependence linking materiality for audit procedures with materiality for financial statements as a whole and the detection risk.
2 Methods In order to formalize the dependence between the above-mentioned indicators, the given paper applies the known provisions of the probability theory, namely determination of probability geometrically. Geometric probability shows the chance that a point falls on segment 1 which is a part of L segment. By the probability theory it is known that this chance is equal to the ratio of the lengths of segments 1 and L. To make a model, we use the following variables: Q is the segment, every point of which can have the aggregate of material misstatements contained in financial statements; S is the segment equal to the materiality set by the auditor for financial statements as a whole; K is the segment equal to the aggregate of the misstatements that the auditor has detected; P is a value of materiality for performing audit procedures; Rmm is risk of material misstatement; RD is detection risk. As the data source the authors use the definition of materiality for audit procedures provided in ISA 320, and also the definitions of misstatement and detection risks provided in ISA 200. Resulting from the listed definitions of risks, we have obtained the expression for Rmm as for geometric probability of falling a point (the aggregate of material misstatements) on segment Q-S, which is a part of segment Q. The authors have obtained the expression for RD as for geometric probability of falling a point (the aggregate of material misstatements) on segment S-K, which represents the sum of misstatements undetected by the auditor. Resulting from the definition of materiality given in ISA 320, there has been obtained the expression which links materiality for audit procedures P, materiality for financial statements as a whole S, and detection risk RD .
3 Results Let us refer to the definitions of audit risk and its components (risk of material misstatement and detection risk) given in ISA 200 (paragraph 13): – audit risk is the risk that the auditor expresses an inappropriate audit opinion when the financial statements are materially misstated; – risk of material misstatement is the risk that the financial statements are materially misstated before conducting an audit;
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– detection risk is the risk that the procedures performed by the auditor will not detect a misstatement that exists and that could be material, either individually or when aggregated with other misstatements Let us note that the definition of “risk” through the concept of “risk” is correct only if it is previously determined what risk is in general [18]. In different situations, the concept of “risk” is defined differently. From the subsequent provisions of ISA 200 and ISA 315 “Identifying and Assessing the Risks of Material Misstatement through Understanding the Entity and Its Environment”, dedicated to assessing the components of audit risk, we can conclude that the standards understand risk as the probability of occurrence of an adverse event [19]. Then, audit risk should be understood as the probability of the event that the auditor will express an inappropriate audit opinion when the financial statements are materially misstated. The definition of audit risk and its components (risk of material misstatement and detection risk) as probabilities follows, in particular, from the provisions of ISA 200, which explains that risk assessment is more a matter of professional judgment of the auditor (i.e., audit risk and its components are often subjective probabilities), although the risk can also be a subject of accurate measurement (by statistical probability [20]). Let us get expressions for the risk of material misstatement and the detection risk using the geometrical definition of probability (Fig. 1).
O
K
S
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Fig. 1. Geometric interpretation of the risk of material misstatement and detection risk
On the ray shown in Fig. 1, coming out of the zero point, the aggregate sum of the actual misstatements contained in the financial statements can get to any point on segment Q. S is the materiality for the financial statements as a whole, as determined by the auditor. We will assume that the aggregate sum of misstatements contained in the statements is material if it exceeds the value of S - materiality for the financial statements as a whole. Figure 1 shows that the aggregate sum of actual misstatements present in the financial statements is significant if it has a value that falls in any point of segment Q – S. According to the geometric definition of probability, we find that the probability of the sum of actual misstatements falling within segment Q – S is equal to the ratio of segments Q – S and Q. Thus, the risk of material misstatement (let us denote it by Rmm ) can be defined as the ratio of segment Q – S to segment Q: Rmm =
(Q−S) Q ,
(1)
When Q = S, the risk of material misstatement according to expression (1) is zero (no misstatements exceeding the materiality level). When S = 0, the risk of material
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misstatement according to expression (1) is one, because in this case any misstatement is material. We obtain an expression for the detection risk from the following considerations. From the definition of audit risk given in ISA 200, it follows that the detection risk occurs when the sum of misstatements in the financial statements is material (exceeds the materiality for the financial statements as a whole). If the sum of misstatements in the financial statements is material, then, for an adequate auditor’s report, the sum of misstatements detected by the auditor should at least be equal to S - materiality for the financial statements as a whole, as in this case the auditor will conclude that an appropriate modification of the auditor’s report is required. The fairness of this statement follows from the provision of ISA 200 (paragraph 6), which states that during the audit it is almost impossible to detect all material misstatements, it is sufficient to detect only those that will allow the auditor to avoid errors in the preparation of the auditor’s report. Let us plot point K to the left of point S on the ray coming from the zero point (Fig. 1). Let K be the aggregate sum of misstatements that the auditor has detected. Since S is the aggregate sum of misstatements that the auditor needs to detect in order to form an appropriate audit opinion, then segment S – K represents the sum of misstatements undetected by the auditor. From this, it follows that the detection risk can be defined as the ratio of segment S – K to segment S: RD =
(S−K) S ,
(2)
Let us check the validity of expression (2). When K = S, the detection risk according to expression (2) is zero (a significant number of misstatements are detected). When K = 0, the detection risk according to expression (2), as it should be, is equal to one. From expression (2), we obtain: K = S − S ∗ RD , orK = S(1 − RD ),
(3)
Let us introduce the designation P - materiality for performing audit procedures. ISA 320 states the following condition in determining materiality for audit procedures: materiality for audit procedures must be less than materiality for the financial statements as a whole. As a result, the auditor, by not detecting (due to the detection risk) some misstatements and comparing the identified aggregate sum of misstatements with the materiality of the audit procedures, will avoid an error in the auditor’s report. This condition will be satisfied if the value of materiality for performing audit procedures is P = K. We obtain the following expression: P = S(1 − RD ),
(4)
For example, materiality for the financial statements as a whole is set at S = 1 000 thousand rubles. The acceptable level of the detection risk is estimated by the auditor at RD = 0.2 (20%). Then, the materiality for performing audit procedures will be:
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P = S(1 − RD ) = 1000000 ∗ (1 − 0, 2) = 800 thousand rubles. Let us investigate the fairness of the obtained expression (4) on a simple numerical example. Let the accounting value of all the elements of the audited aggregate J = 10 000 thousand rubles and let it have 10 misstatements, each of which has a value of q = 60 thousand rubles. Then the aggregate sum of misstatements in the audited aggregate would be Q = 10 ∗ q = 10 ∗ 60000 = 600 thousand rubles. The auditor has determined materiality for the financial statements as a whole in the amount of s = 5%. Then materiality in monetary units will be S = 500 thousand rubles (10 000 000 * 0.05) and, accordingly, the aggregate sum of misstatements in the audited aggregate will be material. Having performed the audit, the auditor detected 7 misstatements amounting to 420 000 rubles (7 * 60 000). By comparing it with the value of S, the auditor would consider the detected sum of misstatements to be immaterial and make an incorrect auditor’s report. The actual detection risk in this case would be 16% ((500 000 - 420 000)/500 000). The auditor is not aware of any undetected misstatements in the audited aggregate, but if the risk of material misstatement they assessed is high, the auditor has cautiously assessed the possible detection risk to be 20% (0.2). Then, materiality for the audit procedures would be: P = S(1 − RD ) = 500000 ∗ (1 − 0, 2) = 400thousandrubles. Having compared the detected sum of misstatements with the P value, the auditor will modify the auditor’s report accordingly. Thus, the assessment of materiality for audit procedures can be performed using dependence (4), based on the auditor’s assessment of the detection risk.
4 Conclusions The research conducted on the basis of geometric probability has resulted into the expression which links materiality for audit procedures, materiality for financial statements as a whole, and detection risk, which represents the probability that after audit procedures material misstatements (individually or in aggregate) will not be detected by the auditor. The formula obtained by the authors will allow the auditor community to assess reasonably the amount of materiality for audit procedures, which is provided by ISA 320. The received expression can lead to the conclusion about possible lines of further research. The logical development of the work accomplished by the authors can be studies aimed at reasonable assessment of the numerical value of detection risk.
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Acknowledgments. The research was financed as part of the project “Development of a methodology for instrumental base formation for analysis and modelling of the spatial socioeconomic development of systems based on internal reserves in the context of digitalization” (FSEG-2023–0008).
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Using Predictive Modeling to Reduce Uncertainty in Managing Industrial Enterprises Irina A. Goryacheva, Olga A. Myzrova(B) , and Larisa O. Serdyukova Yuri Gagarin State Technical University of Saratov, Saratov, Russia [email protected]
Abstract. Under present conditions of economy management, characterized for rapid IT development, market repurposing and changes in the competitive scope, decay of the purchasing capacity, and growth of uncertainty and risks related to business activity, it is important to find new tools to realize the manufacturing control aimed to timely detect the ongoing internal and external changes. This article proves the necessity of predictive analyst for effective decision making used to eliminate the probabilistic negative impact on the results of an enterprise activities under conditions of dynamic and diversified market environment. The authors propose a method targeted to reduce the uncertainty in the management processes while achieving the goals set by an enterprise based on an algorithm of predictive analysis in order to ensure the purchasing capacity on the basis of identified prevailing consumption model. Keywords: Predictive Analytics Methodology · Uncertainty · Management Processes · a Forecast Pattern · Probabilistic Assessment of the Set Goal
1 Introduction Rapid advances of IT and networking technologies, mainstreaming of communications media, growth of engineering and technological achievements, decay in the purchasing activity, and growth of uncertainty and risks in business activities determine the need for upgrading efficiency of the management processes which encompass the whole market cycle, starting from planning to analysis of outcome and goals achieved by enterprises. All these issues indicate the need for searching new tools supporting managerial decisions under conditions of dynamic and diversified market environment. Lack of structural dimensions and criteria used in sorting out data exchange across horizontal and vertical networks leads to information overloads, and thereby, to reduction of management quality at enterprises determined by emerging overloads or lack of data. Thus, in the recent years we observe a growing interest to the issues related to reducing uncertainty within the management processes by introducing the method of predictive analytics applied to fulfill timely management decisions aimed to improve and adapt the whole set of business activities of an enterprise to the ongoing internal and external changes. Under conditions of growing uncertainty, it is critical to solve © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 847–858, 2023. https://doi.org/10.1007/978-3-031-32719-3_64
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the problem with the choice of methods and tools of predictive analysis to shape the corporate strategies and implement the strategic goals of industrial enterprises. One of the current issues debated in scientific papers is finding efficient methods for control and development of new approaches to dynamic diagnostics, estimation and prediction referring the status of an enterprise, including its external environment under uncertainty conditions, which proves their relevance and demand in the practical activities of industrial enterprises [1–6]. The following investigations [1, 8] emphasize the lowering effectiveness of corporate strategies and decreasing planning horizon used as guidemarks in making strategic decisions, which is determined by modifications in the properties of the business environment, i.e. VUCA – a combination of volatility, uncertainty, complexity and ambiguity. The above-mentioned issues demonstrate inefficiency of conventional approaches to the development of corporate strategies and require new mechanisms applied to control uncertainty of the business environment and a new system of dynamic diagnosing for the timely response to the ongoing changes. Many researchers focus on the issue of strategic control required for managerial decisions and business environment under uncertainty conditions, which also requires a regular assessment of factors destabilizing the environment and their impact on the performance of industrial enterprises. A special emphasis is placed on the necessity to optimize monitoring of the external environment and detect the weak signals by means of the corporate foresight, which can ensure widening the horizon of long-term planning, intensification of intercorporate communication when shaping development strategies, and rapid adaptation to unpredictable changes in the business environment under uncertainty [1, 4, 5, 8, 9]. When working out the strategies and their alternative versions, to consider the likely scenarios of the changes in business environments, the enterprises should be provided with an efficient prediction system and related management methodology to implement predictive managerial decisions applied to the possible changes [1, 4, 10]. An important aspect, in terms of high interdependence between efficiency of strategic planning tools and competitive advantages of an enterprise under growing uncertainty, is development of an effective system for analysis and prediction of the changes in external and internal environments of an enterprise [9, 11]. To solve the above-mentioned problems, it would be reasonable to apply the methods of predictive analytics in order to reduce uncertainty in enterprise management, improve the corporate strategies and change the vector of an enterprise development, that will open wider opportunities for business and bring it to higher qualitative levels.
2 Materials and Methods We assume that significance of the tools of predictive analytics is connected primarily with the need to improve the strategies and current management mechanisms at industrial enterprises, which under uncertainty of the business environment are unable to promptly and adequately react to the ongoing changes and ensure continuous and sustainable adaptation of the whole set of processes in terms of business activity of an enterprise. Using the methods of predictive analytics can help in solving a whole set of problems connected with dynamic and diversified business environment, including high risks
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and uncertainties in achieving the targets related with an enterprise business activity. Predictive analytics is characterized for a number of advantages, including the following: – sufficient extension of the horizon of long-term performance planning of an enterprise; – optimization of business environment monitoring due to mechanisms which promptly define the trends of ongoing changes and detect the weak signals in the business environment; – prompt analysis of the processes connected with control of organizational and economic changes within the development period, finding out the causes and factors reducing efficiency of an enterprise performance; – -intensification of intercorporate communication in order to prepare a strategic plan. – The necessity of the methods of predictive analytics applied to upgrade the quality of the management processes under conditions of dynamic and diversified market environment is determined by an array of objective internal and external factors, such as: – deterioration of the management media quality (volume, accuracy, promptness in receiving the data), which results in lower management quality and lower performance of an enterprise [12, 13]; – delays in the data exchange between hierarchical levels of an enterprise management reduce the possibility of prompt managerial decisions needed to improve imperfections in performance parameters [12, 13]; – distribution of problems between various subdivisions results in low interfunctional communication and increasing information costs at readdressing the object of a process as a result of disagreements [14, 15]; – poor interfunctional relations caused by internal misunderstandings makes it difficult to choose between reducing the costs through the optimization procedure and advantages arising from satisfying the growing customers’ demands [4, 16]; – long-term product modification procedure and product design procedure lead to insufficient flexibility in interacting with customers and growing dissatisfaction of customers [4, 5]; – time shift when organizing transition processes leads to uncompensated deviations from planned parameters and established development vector, which may result in partial achievement of the set goals [5]; – low level of monitoring part of business processes may lead, at uncontrolled stages, to nonconformity under possibility of serious process-related risks [13, 14]; – insufficient understanding of cause-and-effect relations when forming the mechanisms for an enterprise management, which mitigate the possible negative consequences affecting performance indicators of an enterprise [15]; – lack of perspective analysis, effective mechanisms to predict internal and external environment parameters of an enterprise, and detect the weaknesses may lead to the growing risks and lower the quality of managerial decisions [2, 3, 4, 16]; – the growing unpredictability and rapid changes in the business environment lead to high risks and uncertainty in managing industrial enterprises, and necessity for taking strategic decisions outside the planning cycles [5, 6, 9].
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2.1 Methodology of Predictive Analysis and Enterprise Environment Estimation for Reducing Uncertainty of Management Processes To promptly respond to predictive management decisions used to mitigate negative impacts, caused by environment destabilization factors, on the performance results of an enterprise under growing uncertainty, it is important to work out the methods ensuring management processes based on methodology of predictive analytics. The aim of the proposed methodology is to provide operational data, analysis and foresight relating the changes in external and internal environments of the enterprise both in the real-time mode and in the future through a variety of dynamic parameters including the sequence, amount, and character of transition from one status into another. These parameters help in forming object-oriented management decisions distinguished by the choice of the management components depending on environment patterns, customer demands and their value to achieve the enterprise goals. The algorithm for the methodology of predictive analysis and evaluation of the components (parameters) of the enterprise environment applied to reduce uncertainty in the management processes are presented in Fig. 1. When forming the amount of statistical data, it is necessary to define the likely time frame as the basis for predictive analysis, which is essential in providing a hindsight that will have an impact on the outcome of the analysis. Conducting preliminary research targeted to reveal the likely states of the external environment and object of forecasting, time frame parameters, and to proactively provide a foresight and foresight background, allows for creating a database on the status of external and internal environments of the enterprise. When conducting the diagnostics procedure under growing uncertainty, it is critical to find out and describe the factors of external and internal environments [14] having impact on the enterprise performance, which proves the necessity to classify the given factors. The authors assume that such classification should be made by the following indicative characteristics: the rate of the impact on the performance results, manifestation of these characteristics, lifetime, and accuracy of data that exclude any additional research and redundancy at estimating the rate and directions of their impact on the resultant parameters of the enterprise performance. The next step is creating the environment profile which allows for estimating the relevance of classification components to be accounted for in predictive models of the enterprise performance, and in finding out the dominant parameters of an enterprise and efficiency factors, as well as the main trends of their behaviour. A three-level matrix-histogram to the status of external and internal environments is to be developed based on the system of indicators characterizing their status. The choice of indicators reflecting the qualitative and quantitative influence of efficiency factors is motivated by a profound comparative data analysis of internal and external environments of enterprises. A three-level Matrix-histogram to the status of external and internal enterprise environments is a certain signal element of the management system which implies a class of dynamic parameters presented as an information field corresponding to a subset of dynamic parameters with their estimation within a specified time frame. In this case, dynamic parameters are taken for the parameters of estimated characteristic, and the
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results of assumed estimation of fuzzy dynamic parameters are taken for estimated results. A three-level matrix-histogram highlights three states of an object of analysis, characterized by the level of the following indicators: 1 – implies that the parameter value remains invariable in relation to the previous period, 2- implies that the parameter value reduces, 3 – implies that the parameter value increases. The bars of the matrix correspond to the relative values of estimated states of dynamic parameters of an object, while the rows correspond to the target time intervals. The monitoring procedure based on created three –level matrix-histogram within the set time span allows for tracking, searching out and demonstrating behavior trends (changes in the state) of external and internal enterprise environments. Meanwhile, the environment status is diagnosed by generalizing the data relating the whole flow of the incoming dynamic information, including data relating the various parameters (indicators), which is beyond the analysis within a single conventional information field. Identification of the control profile implies arranging the list of the management test points supporting control connected with performance of the chosen flow pattern. The authors assume that the limiting time frames, when taking managerial decisions under uncertainty, determine the applicability of the methods for situational management. Meanwhile, the choice of the management decision is conducted through selection from the available number of decisions in accordance with the predictive power behind the management decision. Combining the management decisions is made depending on the configuration of the external environment, consumer demands and the assumed values in order to achieve the target business performance variables. An important verification issue is providing mathematical estimates for reliability and accuracy of predictive models. The method implies a two level estimation procedure. A multivariate regression is applied for quantitative estimation of prediction accuracy. The variable for the prediction error is considered as the function from the length of the observed time frame and from the length of the predictable period. Additionally, the prediction error variable can be applied to predict the tracking signal, with its value to be compared with the predetermined upper and lower control boundaries. If the tracking signal goes beyond the control boundary, then the predictive model should be improved. The control boundaries are supposed to be as follows: ±4 – strict control, ±6 – average control, ±8 – lax control, whereas the final total errors for the valid tracking signal is minimal, i.e. the positive errors are approximately equal to the negative errors. The prediction accuracy is achieved using the tracking signal calculated according to the following formula: Tracking signal = RSFE/MAD,
(1)
where RSFE is the total amount of errors, MAD is the average absolutr divergence RSFE =
n
et
(2)
|et | ÷ n
(3)
t=1
MAD =
n t=1
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where etis is the difference between the actual and predictable variables at the point t; n is the number of observations. The estimated tracking signal should be compared with the predetermined upper and lower control boundaries calculated experimentally. The boundaries for the strict control are established at 4 MAD, and for the lax control are at 8 MAD. A single MAD corresponds to about 0,8 of the standard divergence, so that 2 MAD equals 1,6 of the standard divergence, 3 MAD equals to 2,4 of the standard divergence, and 4 MAD equals to 3,2 of the standard divergence value. It follows that for the controlled prediction 89% of errors are within the limit of 2 MAD, 98% are within the limits of 3 MAD, and 99,9% are within the limits of 4 MAD. In the case, when the given process undergoes serious changes, it is necessary to find out the point of their emergence and modify the available database within the established time span. This sequence is followed until the whole time series have been analyzed. If for the last level in the series the tracking signal variable does not go beyond the threshold values, then the predictive parameters are estimated, and the prediction is made for the determined time frame. Alternative predictive models are designed based on the algorithm of the proposed methodology. Then the forecasts are analyzed for a second time and estimated in terms of the whole set of factors used in order to improve the qualitative parameters which were not taken into account when designing the predictive model. Thus, the predictive model should constantly improved according to the incoming source data. If the predictive models are found reliable, then the optimistic, actual and pessimistic enterprise development scenarios are created. Then follows a comparative estimation of predictive and current variables characterizing the status of an enterprise and its external environment, which is used to improve the corporate strategies. A variable choice of the development vector is conducted to upgrade the consumer value based on identified dominating customer behavior model. Meanwhile, it is necessary to take into account that the personal market choice of a customer is determined by a generated system of values, including the functional, association, emotional, cognitive (informational), relative and representative values within the framework of prioritized consumer model. Thus, the proposed methodology for predictive analysis and parameter estimation of the enterprise environment allows for the following: - identify and conduct a comprehensive analysis of the trends and prospects for probability models relating the mid-term and long-term external and internal changes; - foresee and promptly mitigate the destabilizing environmental factors which reduce the possibility of achieving the targets of an enterprise; - under the foreseeable limitations of business activity of an enterprise, determine the possible ways for its development which to the maximum degree satisfy the customer demands.
3 Results: Evaluation of the Method The proposed methodology was evaluated at the machine-building enterprise in Saratov region.
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Let us create a predictive model for the outcome indicators related to gross profit. To develop a predictive model we used the methods of histogram equalization, harmonic analysis (Fourier series), and moving average. Let us proceed to smoothing the time series by means of moving averages. All the levels in the series were involved in the calculation procedure. The wider smoothing interval leads to the trend being more smooth. At larger values of the smoothing interval, vibrations of the smoothed series significantly decrease, which leads to reducing number of observations. Let us present a diagram to empirical and theoretical (predictive) values of gross profits in Fig. 2.
gross profit thousand rubles.
120000 100000 80000 60000 40000 20000 0 2000
2005
actual values
2010 year
2015
2020
centered moving average
Fig. 2. Gross profit prediction using the moving average method
Let consider the method of histogram equalization to express the main gross profit trends of a machine-building enterprise in Saratov region. For the main prediction data, we recommend to use the polynomial of degree 3, since it provides a rather accurate picture of the development process and is less sensitive to the influence of random factors. Thus, we receive the following equation. y = 228, 52x3 − 5 585, 28x2 + 42 288, 84x − 26 394, 46 Calculation of the gross profit data and evaluation of the prediction accuracy are presented in Table 1. The gross profit prediction using the method of histogram equalization is presented in the diagram Fig. 3. Testing the accuracy of the predictive model is based on the tracking signal with the values of the interval at −4; 4, which corresponds to boundary control. Thus, the predictive model does not require any adjustment.
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Fig. 3. Gross profit prediction by the histogram equalization method
Table 1. Gross profit prediction bymeans of histogram equalization metod Year Gross profit, thousand rubles
e
RSFE
|e|
Summary MAD error
Tracking signal
Actual value
Fitted value
2004 13759
10538
3221,38
3221,38
3221,38
3221,38
3221,38
2005 32333
37670
−5337,26
−2115,88
5337,26
8558,64
4279,32 −0,49
2006 65058
56375
8683,42
6567,54
2007 54966
68022 −13055,7
2008 64418
73983
8683,42 17242,06
−6488,16 13055,7
−9564,74 −16052,9
30297,76
9564,74 39862,5
10042,28 26095,18 65957,68
5747,35
1,00 1,14
7574,44 −0,86 7972,50 −2,00
2009 101724 75629
26095,18
10992,95
0,91
2010 64860
74331
−9471,06
571,22
2011 76341
71461
4880,42
5451,64
9471,06 75428,74
10775,53
0,05
4880,42 80309,16
10038,65
0,54
2013 67063
68389
−1325,5
4126,14
1325,5
9070,52
0,45
2014 61256
66486
−5229,94
−1103,8
5229,94 86864,6
8686,46 −0,13
2015 63995
67124
−3129,02
−4232,82
3129,02 89993,62
8181,24 −0,52
2016 75901
71674
4227,14
−5,68
4227,14 94220,76
7851,73
81634,66
0,00
To analyze the influence of seasonal variability or other random factors on the gross profit rate, let us conduct a harmonic analysis (Fourier series). Calculations of derived data used to construct the Fourier series functions by the gross profit of the machinebuilding enterprise in Saratov region are presented in Table 2. As a result, we receive the function of the Fourier series with one, two or three harmonics for the gross profit. The diagram to the gross profit prediction for the machine-building enterprise in Saratov region using the method of Fourier series is presented in Fig. 4.
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Fig. 4. The diagram for empirical series and the Fourier function with two harmonics for the gross profit
The overall diagram to the prediction scenario of the gross profit at the machinebuilding enterprise in Saratov region is presented in Fig. 5.
200000 180000 realistic 160000 scenario 140000 120000 pessimistic scenario 100000 80000 optimistic scenario 60000 40000 empirical 20000 evidence 0 2005 2008 2011 2014 2017 2020 Fig. 5. The diagram to predictive scenario of the gross profit at the machine-building enterprise in Saratov region.
Consequently, using the developed predictive models we can identify the trends in the mid-term and long-term changes of resultant parameters of an enterprise performance, and parameters of the business environment that will give the enterprise a possibility to conduct a variable-based accounting of alternative states of the business environment (in line with pessimistic, real life, and optimistic scenarios), and a possibility to work out response strategies in order to reduce uncertainty and risks in the performance of an enterprise.
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Table 2. Calculation of derived data to constract the fourier series function by the gross profit of the machine-building enterprise in saratov region Fourier function/harmonic 1
Fourier function/harmonic 2
Fourier function/harmonic 3
Fourier deviation from harmonic 1
Fourier deviation from harmonic 2
18056,1224
17728,285
17262,257
5115,878
5443,715
5909,743
17446,4504
16934,154
16686,2086
1979,45
1467,154
1219,209
16732,7959
16336,137
16612,3957
1638,796
1242,137
1518,396
16023,8063
15975,141
16434,5266
2693,806
2645,141
3104,527
15427,4188
15755,256
15830,5947
5361,581
5033,744
4958,405
15034,4281
15546,724
15145,0011
1104,572
14904,6634
15301,323
14918,5181
3834,663
4231,323
3848,518
15057,8802
15106,545
15215,2822
1799,88
1848,545
1957,282
15470,7526
15142,916
15608,9442
5561,247
5889,084
5423,056
16080,4246
15568,128
15816,074
16794,0791
16397,42
16121,1606
2885,079
2488,42
2212,161
17503,0687
17454,404
16995,0188
3768,069
3719,404
3260,019
18099,4562
18427,293
18351,9541
6585,544
6257,707
6333,046
18492,4469
19004,743
19406,4663
18622,2116
19018,871
19401,6756
1513,212
1909,871
2292,676
18468,9948
18517,66
18408,9224
2791,995
2840,66
2731,922
761,4246
62,44692
592,2757
249,1284
574,7431
Fourier deviation from harmonic 3
993,9989
497,074
976,4663
4 Discussion and Conclusion The conducted research allows us to conclude as follows: 1. Transformation of the business environment and its new qualitative characteristics, such as volatility, uncertainty, complexity and ambiguity, requires reconsidering information and methodological support of the management processes in order to upgrade the level of adaptation and speed of response to the ongoing internal and external changes, which can ensure growth of the outcome indicators of an enterprise due to maximum satisfying the growing customer demands under permissible values of labour, material and financial resources. 2. We have proved the importance of predictive analytics for management decisions used to mitigate the possible negative impact on the outcome of an enterprise under conditions of growing uncertainty. Using the methods of predictive analysis can help solving the amount of problems caused by the dynamic and diversified market environment, including high risks and uncertainty when solving the target problems related with business activities of an enterprise. 3. The proposed methodology, based on the methods of predictive analysis used to reduce uncertainty in the enterprise management, will provide a possibility to regularly amend corporate strategies, formulate strategic priorities within the framework of predictive modeling and timely changes of the development vector of an enterprise with regard to the trends and predicted states of internal and external enterprise environments.
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4. Predictive response to external and internal changes is provided due to the signal system of the management structures (characterized for ability to reveal weak signals), and combination of management decisions by selecting the management components, configuration of external environment, and customer demands, and is applied to help the business achieve the targeted outcome.
References 1. Bodwel, W., Chermack, T.: Organizational ambidexterity: integrating deliberate and emergent strategy with scenario planning. Technol. Forecast. Soc. Chang. 77(1), 193–202 (2010) 2. Rohrbeck, R., Battistella, C., Huizingh, E.: Corporate foresight: an emerging field with a rich tradition. Technol. Forecast. Soc. Chang. 101(1), 1–9 (2015) 3. Rohrbeck, R., Schwarz, J.O.: The value contribution of strategic foresight: insights from an empirical study of large European companies. Technol. Forecast. Soc. Change 80(5), 1593–1606 (2013) 4. Ruff, F.: The advanced role of corporate foresight in innovation and strategic management reflections on practical experiences from automotive industry. Technol. Forecast. Soc. Change 101(1), 37–48 (2015) 5. Vecchiato, R.: Strategic planning and organizational flexibility in turbulent environments. Foresight 17(3), 257–273 (2015) 6. Vecchiato, R., Roveda, C.: Strategic foresight in corporate organizations: handling the effect and response uncertainty of technology and social drivers of change. Technol. Forecast. Soc. Change 77(9), 1527–1539 (2010) 7. Bennett, N., Lemoine, G.J.: What VUCA really means for you? Harv. Bus. Rev. 92(1/2), 27–35 (2014) 8. Roland, B.: How to Survive in the VUCA World. Hamburg: Roland Berger (2013) 9. Bereznoy, A.: Corporate foresight in multinational business strategies. Foresight STI Govern. 11(1), 9–22 (2017) 10. Bishop, P., Hines, A., Collins, T.: The current state of scenario development: an overview of techniques. Foresight 9(1), 5–25 (2007) 11. Dibrell, C., Craig, J.B., Neubaum, D.O.: Linking the formal strategic planning process, planning flexibility, and innovativeness to firm performance. J. Bus. Res. 67(9), 2000–2007 (2013) 12. Goryacheva, I.A.: Logistic-oriented aspects of improving organizational management structures for industrial enterprises. Bulletin of Moscow State Regional University. Ser. Econ. 2, 47–53 (2015) 13. Naydis, O.A.: Management of the internal environment of an industrial enterprise on the basis of inter-factor production and economic relations. Bull. Moscow State Reg. Univ. Ser. Econ. 2, 86–93 (2015) 14. Goryacheva, I.A., Shilovskaya, M.S.: System analysis of environmental factors in the operation of logistics system. Izvestiya Saratov Univ. New Ser. Ser. Econ. Manag. Law 15(1), 49–56 (2015) 15. Yashin, N.S.: The problems and prospects for the development of Russian management in the conditions of economic modernization. Sci. Soc. Ser. “Econ. Manag.” 3(6), 82–84 (2012) 16. Danielson, M.R.: The Impact of Corporate Foresight and Strategic Orientation on Performance. Aarhus University, Aarhus (2014)
Modelling as a Basis for the Transformation of Service Enterprises in the Digital Economy Yuri Gusev1(B) , Tatyana Polovova2 , and Alexey Pinsky1 1 All-Russian Scientific-Research Institute “Center”, Moscow, Russia
[email protected] 2 Moscow Metropolitan Governance Yury Luzhkov University, Moscow, Russia
Abstract. Technological changes determine other changes in all spheres of the modern economy. New technologies, in particular, technologies in the field of digitalization are replacing the previous basic technologies in many sectors of economy. In this regard, the development and implementation of digital technologies at service enterprises, whose activities are entirely focused on the end user of the product/service, is becoming extremely relevant. The purpose of the research is to identify the key areas of business process change for building a new model of enterprises based on digital technologies that can ensure the effective functioning and development of enterprises in the digital space. The article considers a methodological approach to form the structure of the processes of service enterprises as the basis for digital transformation, the transition from the model of traditional options for services provision to options with virtual elements and the use of digital technologies in the field of technical and technological, service (maintenance), organizational and technological and management innovations. Some aspects of the formation of a hybrid (integrated) model of service enterprises. The results are brought to conceptual provisions that can be used as the basis for the development of a model of service enterprises. Keywords: Service sector enterprises · Digital transformation · Value innovation · Business process
1 Introduction Digital economy is a global project of transformation of all spheres of the government into a single IT infrastructure based on centralized data processing and a unified database of information, which opens up opportunities for high–speed information exchange and transparent business functioning. Digital economy is characterized by high growth rates and high innovation and investment activity. This allows it to become a central vector of global economic development, providing a key role in increasing labor productivity, as well as the formation of new markets and spheres of activity, achieving inclusive sustainable growth [1]. Until the beginning of the 21st century, the volume of international trade in computer technology exceeded the volume of trade in telecommunications technology. However, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 859–870, 2023. https://doi.org/10.1007/978-3-031-32719-3_65
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then the situation changed. The growth rate of the telecommunications equipment market has twice exceeded the market of computing devices. This indicates an increase in the economy’s need for digital data transmission channels and technologies. Information technologies enjoy pride of place in manufacturing technology, becoming not only a key tool in working with intellectual assets, but also the key technologies through which most of the added economic value is created. The process of economic development becomes multi-vector on the platform of digital innovations [2]. Thus, digital transformation is taking place in all spheres of activity and digital space is being formed [3, 4]. In this context, an important place is occupied by the program “Digital Economy of the Russian Federation”, the purpose of which is “… to create an ecosystem of the digital economy of the Russian Federation, in which digital data will be a key factor of production in all spheres of socio-economic activity” [5]. Thus, digitalization of enterprises in all sectors of economy is becoming a defining trend [6, 7], based on digital platforms – integrators of business processes of enterprises [8]. In this regard, it becomes relevant to develop conceptual methodological provisions regarding the formation of a structured model of enterprises due to the need to transform business processes as a result of, firstly, the integration of digital technologies and, secondly, a change in the consumer’s vision regarding the value characteristics of a product/service [9]. These trends in the digitalization of economy create opportunities for theoretical and methodological research in terms of modelling the transformation of service enterprises in the context of adaptation to global changes in digital technologies and preferences of potential consumers of a product/service.
2 Methods The studied problems are based on dialectical methods of cognition, from a methodological point of view being a tool for a comprehensive and objective study of modern trends in the field of economy digitalization. This allowed us to analyze and synthesize the theoretical material, sum it up, classify, etc. The research of the service sector enterprises transformation was conducted from the perspective of the economy digitalization concept, transformation of enterprises in connection with the development of digitalization and networking of service sector enterprises. For the conducted research, scientific works by Russian and foreign scientists were studied, which allowed, having alternative investigations, to conduct a more reasonable generalization of the aspects of economy digitalization under consideration. Many of them are further considered and generalized, in particular, from the position of: – concepts of socio-economic systems transition to the digital economy (G.N. Andreeva, T.G. Bogatyreva, S.Yu. Glazyev, M.N. Rudenko, V.V. Trofimov); – digitalization of service sector enterprises (I.P. Boyko, M.A. Zhukova, S.A. Lukyanov, Z.K. Omarova, V.A. Tsvetkov); – theoretical aspects of digital transformation interconnected with the trends in sterilization (D. Bell, N. Wiener, B. Gates, P. Drucker, H. McLuhan, E. Toffler);
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– fundamental trends in the servicisation of enterprises. (G.A. Karpova, T.A. Lavrova, V.I. Sigov, S.A. Uvarov and other authors. The presented research is based on the analysis of three main hypotheses in the context of the transformation of business processes of enterprises. The first hypothesis of the research is that the spread of digital technologies leads to the development of economy servicisation as a technological basis for digital transformation, the formation of a new segment in the form of digital services; The second hypothesis boils down to the fact that the development of digital technologies is becoming a serious factor in changing consumers’ vision regarding the value characteristics of a product/service. The third hypothesis of the research is that the development and strengthening of the role and importance of the digital economy require structuring and changing the content of business processes, the use of new digital platforms adequate to the changed realities. The subject of the research in the context of the hypothesis are the following aspects of digitalization: the introduction and development of digital technologies, the formation of digital infrastructure; restructuring of an enterprise model; enterprise development and service activities based on digital platforms. The research is aimed at identifying the key areas of business process change to build a new model of enterprises based on digital technologies that can ensure the efficient functioning and development of enterprises in the digital space. The realization of this goal led to the formulation and solution of the following research tasks: • to clarify and specify the impact of digitalization on changing nature of the service enterprises’ activities; • to determine the relationship between digital technologies and the creation of innovation values of enterprises; • to develop a conceptual scheme of a model of enterprises adequate to the trends in digital transformation of the economy. Digital transformation is focused both on system (infrastructure) projects and on creation and development of digital resources, digital platforms, development of the digital services market, etc. [6, 10–12]. This methodology is of great importance for the development of digital technologies in the service sector, in particular, tourism, catering, hotel business, etc. [13, 14]. Currently, the possibilities of the service sector have increased due to digital technologies in the economy and the determination of the place of this sphere in infrastructure development projects of cities and megapolises. This is influenced by Internet communication technologies, service innovations and other technological and organizational solutions. To solve the tasks set out above, the following research methods were used: a desk study of previous Russian and foreign works on digitalization of enterprises; a desk study on scientific materials in terms of theoretical aspects of digital transformation of enterprises, interrelated with the trends of servicisation. The studied problems are based on dialectical methods of cognition, from a methodological point of view being a tool for a comprehensive and objective study of modern trends in the field of economy digitalization. The approach to substantiating the modern
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model of service enterprises based on the trends of digital transformation of socioeconomic systems is founded on general scientific methods of cognition. This allowed us to analyze and synthesize the theoretical material, sum it up, classify, etc. As a general methodological basis, a qualitative approach was used, which made it possible to classify, analyze and structure the most general and key aspects of theoretical and methodological developments regarding the transformation of enterprises in a context of digitalization of the economy. This is due to the fact that the new economic reality requires a new coordinate system to create value innovation for enterprises. Therefore, the emphasis of scientific research approaches to the management of innovative activities of enterprises is obviously shifting to the space of systems, processes, mechanisms of value creation as a result and on the basis of digital technologies. The evidence base for substantiating the hypothesis is founded on a comparative analysis method, which made it possible to compare different approaches to the transformation of the innovative structure of enterprises and the construction of infrastructure adequate to the challenges of the external environment. In the context of new imperatives of changing the vector and trajectory of enterprise development, the transfer of concepts and ideas to the level of changing their business processes will significantly reduce the essential gap between the strategic intentions of enterprises and their real capabilities. The comparison of existing approaches to modelling the structure of service sector enterprises adapted to the digital economy was carried out using the method of comparative analysis, as well as the experience of the authors as experts. This served as the basis for substantiating the evidence base of the hypothesis. The authors’ position on the new model of enterprises is substantiated by general scientific methods of abstraction and generalization of materials, various points of view and scientific views on the publications under study, using general scientific methods of abstraction and generalization. Within the study of digitalization processes at the micro level, the tools of dialectical methods of cognition are used, in particular, collection and processing of information, analysis of bibliographic sources, generalization and other methods. The use of systematic and integrated approaches in the study for the constructive solution of problematic issues allowed to draw a conclusion about the feasibility of restructuring enterprises and to propose a methodological approach for their transformation in the context of the introduction of digital technologies.
3 Results and Discussion Most scientists consider digitalization as a tool for systemic socio-economic transformation and the creation of a new reality with new values, management paradigms, social norms and economical laws [9, 12]. Thus, S. Glazyev, quite rightly, notes that “widespread computerization and large-scale expansion of the spheres of computer systems application initiated the relevant digital revolution topic that has occurred today” [15]. In other words, we are talking about the transformation of the economy. In this context, we should consider the concept of a new industrial revolution “Industry. 4.0”, focused on technological changes and practical transformation of business and the entire infrastructure of the economy” [16]. The concept largely determines
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the research interest in study and development of the theory of digital transformation, the practice of implementing digital technologies, economic problems of business development [17, 18]. Until 2024, the Russian government has identified five basic directions for the development of the digital economy in Russia (regulatory control, personnel and education, formation of research competencies and technical reserves, information infrastructure, information security) [19]. Thus, digital economy sets a vector for many years to come, according to which socio-economic systems of all levels will be developed. Currently, researchers actively discuss not only the general problems of the economy digitalization [19, 20], the introduction and development of information and digital technologies, the use of big data in business [21], but also the economic problems of functioning and development of enterprises in the context of digitalization [17, 18]. At the same time, other scientists’ research does not sufficiently present an analysis of the impact of the economy digitalization on the management of enterprises and the transformation of business processes in connection with the introduction of digital technologies, a new vision of consumers regarding the value characteristics of a product/service. We see the relevance in the research in the field of developing conceptual methodological provisions on the formation of a structured model of enterprises. In this article, the main emphasis is placed on the issues of structuring business processes of service enterprises and management changes in the context of digitalization. Economy servicisation development. According to economic scientists, the consequence of servicisation is primarily factors such as the transformation of value chains, leading to competitive advantages, changes in enterprise strategies and culture [22–24]. At the same time, it should be noted that for the B2C sectors and especially for B2B sectors, servicisation is largely caused by the digitalization of interaction with suppliers and buyers, with the need to model and search for offers that are adequate to their needs. In addition, there is a process of expanding the role of services in aggregate production. Thus, the contribution of the service sector to world value added increased from 61% in 1970 to 68% in 2019 (from the speech “Service for service: servicisation of industry requires new industrial policy”, delivered at the 23rd Yasin (April) International Academic Conference on Economic and Social Development). The special role of servicisation in digital transformation is associated with the transformation of business models of enterprises. “Value innovation” of a company’s product/service. Digital technologies, being a source of innovation development, become the basis for the formation of “value innovation” of an enterprise’s product/service, as a new way of implementing an enterprise strategy, which is based on a qualitative increase in the value of a product/service for consumers, implemented through innovation with a significant amount of additional utility, leading to the creation of a new market space free from competition. Based on the purpose of the study, the product/service of enterprises is considered as a complex concept that characterizes customers satisfaction activities (i.e., as a process) and at the same time as a result of meeting needs while ensuring quality-price compliance (i.e., as a value) of the product/services.
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In accordance with this, a number of features are distinguished in the interpretation of “product/services of enterprises”: – a product/service as an object of management, which is understood as a set of conditions, result, process and time of formation of a set of benefits or profits that bring positive result to the consumer and are aimed at meeting his needs; – a product/service as a benefit (economic phenomenon) in the form of a set of actions aimed at satisfying the needs of the client; – a product/service as an object of receiving benefits by the client. Digital platforms. At the same time, a key place in innovation should be occupied by digital platforms based on IT technologies, which represent special organizational and economic forms of entrepreneurship [11]. Defining the essence of the concept of “digital platform”, N. Srnichek notes that “at the most general level, platforms are digital infrastructures that allow two or more groups to interact; therefore they are positioned as intermediaries connecting various users – buyers, advertisers, service providers and goods, manufacturers…” [19]. The availability of technological platforms makes it possible to increase the objectivity of assessing the competitiveness of an innovative product/service, the level of demand, the priority of implementing appropriate digital technologies, thereby ensuring an increase in the efficiency of business processes as a result of joint efforts of enterprises in the development and implementation of digital technologies [25]. As a technological integrator of services as a whole, the digital platform becomes the basis for services, applications and solutions that potential customers will interact with and pay for. Digital platforms create market advantages in the following forms: – income sources formation based on new products/services; – cost reduction in traditional areas of business; – joint innovations, creation of new products/services (for example, within service networks) with market participants; – increasing the speed of product placement in target markets; – assortment expansion due to servicization of enterprises’ activities. Structuring an enterprise’ model. For the successful transformation of enterprises, it is necessary to have a clear understanding of the structure and the corresponding functionality, i.e. we are talking about the formation of a new enterprise model adapted to digital technologies. In the process of transition to the digital plane, the modelling process is necessary to abstract from previous models and focus on aspects that allow introducing new or transforming previous processes based on digital technologies. The type and detailing of the model depends largely on the abstract description of the elements of the system being formed, in which each key element is identical to the elements of a real enterprise. At the same time, such a model should be sufficiently informative so that there is an opportunity to build (structure) a new enterprise. Due to the lack of a unified theoretical model of a service sector enterprise, a system design of the model is proposed, highlighting the main functional modules of service
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sector enterprises, taking into account the communicative interaction of the enterprise with other market participants. The above presented interpretation of the phenomenon of “product/service” allows us to identify fundamentally important functions and aspects in the content, the purpose of which is determined by the relevant business processes, operating conditions and content. Firstly, focus on the “logistics”, “service” and “production” processes. Secondly, in view of the fact that the processes occur within time limits, then “time” itself is allocated as a condition for functioning. Thirdly, the final exchange process in the form of the purchase and sale of a product/service (“trade”). Fourthly, the focus on “value” for the client, which is created and used in the process of providing the service and is the result of customers’ expectations. The identified functions and conditions for the creation and implementation of a product/service in their entirety create a synergistic effect. In order to clarify and substantiate the processes that ensure the functioning and development of the enterprise, their decomposition and identification of cause-and-effect relationships in the structure of the model are carried out. The focus is on the key modules that define the future model of the enterprise in the context of digitalization and innovative development. As a result of the conducted research, the main modules were identified in the form of business processes and key aspects in the activities that provide a holistic view of service enterprises: – – – – – –
“logistics” module; “trade” module; “production” module; “service” module; “time” module; “information” module (database, website, mobile application, data collection and analysis system).
The “time” module is explained by the dynamism in the system of functioning of processes that take place in time, which is becoming more and more valuable for consumers. It should be noted that with the advent of digital platforms, in addition to information, knowledge and competencies are considered as a strategic resource of enterprises, which in the future, if appropriate technologies are available, will be digitized (formalized). The selected modules allow us to group all the main business processes of a service enterprise: management, logistics, production, sales, customer service, marketing, ensuring the activities of the enterprise. Modules are combined into a single model through various areas of relations (for example, between an enterprise and customers while receiving services, events or actions, an enterprise and stakeholders) (see Fig. 1). In the context of digitalisation of the service sector and the implementation of the basic components of the enterprise model, it is necessary to choose a dominant paradigm. It seems that an integrated approach can be used as such a paradigm, consisting in the unity of strategic management (capable of solving dynamic tasks), the method of expert
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Feedback
Feedback Digital technologies
Innovation Business processes [modules] of the enterprise
INPUT Resources Information Digital platforms
EXIT Products, Services Profit Value innovation
Consumers and other stakeholders (preferences)
Consumers and other interested parties (preferences, requirements)
Service
Fig. 1. Summarizes the conceptual scheme of the enterprise model
multi-criteria assessment (for solving poorly formalised and multi-criteria tasks) and the “blue ocean” strategy, focused on obtaining competitive advantages due to new segments and a market space free from competition (the ideologists of which are Chan Kim and Renée Mauborgne [26]. In this regard, the very understanding of the management essence is changing. The new reality, through digital transformation and the outpacing growth of the servicisation of activities, makes it possible to fundamentally restructure and optimise the management of the enterprise. Thus, there is an increase in the share of services in the gross value added created, relative to the share of traditional products/ services. The introduction of digital technologies will allow enterprises to effectively solve the following strategic tasks: to prepare scenarios for forecasting the future state of the external environment, to obtain real-time data, to visualise data on production volumes, to conduct real-time analysis and speed up the decision-making process, to reduce the likelihood of human errors and total costs, to provide remote monitoring and monitoring of equipment and others. The issues of digitalisation in the service sector in the context of innovative solutions of enterprises, interaction with consumers and other aspects of digital transformation are studied by Russian and foreign scientists. The study of the issues on the designated topic allowed us to conclude that in the economic literature, scientists have set out in sufficient detail the points of view from the following positions concepts of digital economy and mechanisms for socio-economic systems how to transit to it. In this regard, the research conducted by G.N. Andreeva, D. Bell, S.Yu. Glaziev, M.L. Kaluzhsky, K. Kelly, K. Maskus, N. Negroponte, M.N. Rudenko, D. Tapscott, E.Toffler, et al. M.L. Kaluzhsky, K. Kelly, K. Maskus, N. Negroponte, M.N. Rudenko, D. Tapscott, E.Toffler, et al.
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The term “digital economy” was introduced by Nicholas Negroponte, pointing out that in connection with the transformation of information from material media into digital form: atoms are replaced by bits [27]. In foreign theory, the ideas of studying the essence of the digital economy, digitalisation and digital transformation go back to the concepts outlined in the works by D. Bell [28] and E. Toffler [29], and the definition of “digital economy” was used for the first time in the work by D. Tapscott [30]. The unique author’s definition of the digital economy is given by K. Kelly, who understands the digital economy as a set of communications that become the main link of digital technologies, means of communication and, as a result, the economy itself [31]. In the report of the European Commission in 2014 “Expert Group on Taxation of the Digital Economy”, the definition of the term “digital economy” is formulated briefly: “An economy that depends on digital technologies” [32]. According to another report of the European Commission (2018) “Fair Taxation of the Digital Economy”, the digitalisation of the economy will lead to the improvement of several spheres of life at once: scientific, economic and social [5, 33]. Also, the position of percepting the digital economy as a knowledge economy is supported by the well-known economist K. Maskus: “We live in a global knowledge economy, and those who can turn knowledge and scientific discoveries into a commodity will have a future in it. Innovation, adaptation and use of new technologies are key drivers of growth in both national and global economies” [34]. Thus, it is concluded that the digital economy as a knowledge economy and the main source of growth are signs of innovative activity of enterprises in modern conditions of economic development. These are the landmarks of their transformation. In this context digitalisation of service enterprises, it is necessary to highlight the works by I.P. Boyko, M.A. Zhukova, S.A. Lukyanova, Z.K. Omarova, V.A. Tsvetkova. The influence of digitalisation on the emergence of new and changing existing business models is considered, the prospects for the development of digital technologies are discussed. According to a number of scientists, there are many traditional enterprises that have businesses and assets in the “off-line” world. At the same time, they actively use modern technologies as their infrastructure. In this case, its entire information infrastructure is assessed as part of the digital economy [35]. Theoretical aspects of digital transformation, interconnected with the trends of servicisation, are reflected with a sufficient degree of argumentation in the works by D. Bell, N. Wiener, B. Gates, P. Drucker, H. McLuhan, E. Toffler. In terms of studying the fundamental trends of enterprise servicisation, the research conducted by the following scientists are especially noteworthy: G.A. Karpova, T.A. Lavrova, V.I. Sigov, S.A. Uvarov and some others. A significant number of studies in the field of digitalisation of business processes in the service sector allow us to conclude that there are serious, relevant and reasoned generalisations, results and proposals of practical importance. At the same time, it should be noted that a number of issues and problematic aspects of digital transformation require further study, in terms of digitalisation in the service sector, it is necessary to form theoretical and methodological provisions regarding their
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restructuring, taking into account the processes of servicization and transformation of business processes. To this end, an attempt has been made to substantiate and propose the main methodological provisions for the development of a model of service enterprises that are adequate for digital transformation at all levels of the national economy.
4 Conclusions Digitalization of the economy, representing a modern stage of economic development. This has significantly increased the efficiency of enterprises. The results of the research presented in this article indicate that the digitalization of the economy has an impact on the content of business processes, signaling the need for their transformation and “adjustment” to new process technologies. In this sense, the first hypothesis about the servicisation of the economy as a technological basis for digital transformation, the formation of a new segment in the form of digital services has been confirmed. Currently, service sector enterprises that provide and support the digitalization trend in one format or another are gaining competitive advantages. At the same time, there is a need for an extension study on how significant the impact of these digitalization processes is on concretization and change of business processes and economic development of enterprises. Certain problems of modelling business processes in the context of digitalization are associated with the difficulties of establishing changes based on certain digital technologies and platforms. The development of digital technologies is becoming a serious factor in changing the vision of consumers regarding the value characteristics of a product/service. This aspect is especially important for service companies. The orientation of innovation activity to the creation of a product/service with high consumer properties becomes possible when creating a “value innovation” using cloud technologies and digital platforms. The transfer of digital technologies makes it possible to fundamentally change the relationship of “production of services to the end consumer”. The second hypothesis is also confirmed. According to the results of this research, the prospects for the development of the digital economy are largely related to the need to structure and change the content of business processes, the use of new digital platforms adequate to the changed realities. The development of digitalization of service enterprises by changing the content of business processes or supplementing them with new functionality makes it possible to change the vision of consumers regarding the value characteristics of a product/service. The analysis of publications on this issue and our own research allowed us to substantiate and propose a methodological approach to the formation of an enterprise model in the context of their transformation, the structural construction of an infrastructure management system and adaptation to the conditions of digital economy. The third hypothesis of the research about the need to structure and change the content of business processes, the use of new digital platforms is also confirmed. The conducted research, definitely, cannot provide answers to all the questions related to the problematic aspects of the development of digitalization and servicisation of service enterprises. Nevertheless, the results of the research represent an approach to
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developing a model to support the transformation of service enterprises in the context of digitalization of economic sectors. New imperatives of the development of the modern digital environment, options for the transformation of business processes in the context of the introduction of digital technologies, as well as the creation of innovation values of service enterprises taking into account digital technologies provide many directions for further research: how to form an effective infrastructure for creating value innovation, including together with the parties concerned (stakeholders), how to take into account the specifics of the digitalization of the Russian economy, what are the models of management of innovation activities of enterprises in the context of the digital economy development.
References 1. Gnezdova, J.V.: Development of digital economy in Russia as a factor of global competitiveness increase. Intell. Innov. Investments 5, 16–19 (2017) 2. Gribanov, Y.: Key aspects of the theory and methodology of digital transformation of social and economic systems. Bull. Altai Acad. Econ. Law 2, 83–89 (2019) 3. Afanasev, M., Dneprovskaya, N., Kliachin, M., Demidko, D.: Digital transformation of the knowledge management process. In: Proceedings of the 19th European Conference on Knowledge Management (ECKM), vol. 1, pp. 1–8 (2018) 4. Smirnov, E.N.: Evolution of innovative development and prerequisite of digitalization and digital transformations of the world economy. Voprosy innovatsionnoy ekonomiki 8(4), 553– 564 (2018) 5. Tsifrovaya ekonomika RF. https://digital.gov.ru/ru/activity/directions/858/?utm_referrer= https%3a%2f%2fwww.google.com%2f. Accessed 21 Nov 2021 6. Dneprovskaya, N., Urintsov, A., Afanasiev, M.: Study the innovative environment of the digital economy. In: Proceedings of the 15th International Conference on Intellectual Capital, Knowledge Management & Organisational Learning (ICICKM 2018), vol. 1, pp. 67–76 (2018) 7. Sigala, M.: A market approach to social value co-creation: findings and implications from “Mageires” the social restaurant. Mark. Theory 1, 27–45 (2018) 8. Gusev, Y.V., Polovova, T.A., Karnaukh, I.: Strategic focus as a tool to ensure economic stability and of non-financial corporations as socio-economic systems in Russian economy modern. J. Appl. Econ. Sci. 5(43), 968–982 (2016) 9. Rozhdestvenskaya L.N., Rogova O.V., Cherednichenko L.Y.: Digital technologies in managing food industry enterprises. In: Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth (MTDE 2020): Advances in Economics, Business and Management Research, pp. 591–597 (2020) 10. Digital transformation in the financial services sector. https://www.financierworldwide.com/ digital-transformation-in-the-financial-services-sector#.X0jpl25uKUk. Accessed 21 Nov 2021 11. Gnezdova, J.V., Kugelev, I.M., Romanova, J.A.: Conceptual model of the territorial manufacturing cooperative system use in Russia. J. Internet Bank. Commer. 4, 82 (2016) 12. Gribanov, Y., Shatrov, A.A.: Essence, contents and role of digital transformation in development of economic systems. Bull. Altai Acad. Econ. Law 3, 44–48 (2019) 13. Lee, W.-H.: A technology acceptance model for the perception of restaurant service robots for trust, interactivity, and output quality. Int. J. Mobile Commun. 16, 361–376 (2018)
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Strategic Diagnostics of Directions Circular Transformation Industrial Complex Ekaterina Kaplyuk(B) and Kristina Rudneva Southern Federal University, Rostov-On-Don, Russia [email protected]
Abstract. The purpose of this research is to study the institutional framework of the Russian Federation in the field of circular economy at the national and regional levels. The authors suggest that business models in a circular economy should be built in accordance with the principles of resource. To this end, the article uses elements of normative and institutional analysis, based on the use of which the authors highlight the role of the public sector in conserving natural resources and promoting the well-being of the population, which enhances its role in promoting the concept of a circular economy. The results obtained in terms of highlighting the formats for implementing and supporting circular business models - “top-down” and “bottom-up”, which can also be used to support the transition to a circular economy, as well as the described practical experience in the implementation of circular models and their classification. The scientific novelty lies in the study of the institutional framework of the Russian Federation in the field of the circular economy at the national and regional levels, it was revealed that at this stage of economic development, the policy in the field of introducing the circular economy is implemented on the basis of the “top-down” principle (the results are presented at the macro and meso levels). As a result of the analysis, the consistency of policy emphasis on increasing the level of environmental friendliness and resource efficiency of production through the introduction of new technologies was noted. Keywords: Circular Economy · Circular Economy Business Model · Resource Efficiency · Social Responsibility · Framework · Industry
1 Introduction The increasing pace of industrial development in the Russian Federation is still identified with the technogenic type of development, including all the risks and consequences that it entails. Today, the introduction of innovative and environmentally friendly technologies, the development of environmentally friendly industries, the development of a system for the efficient management of production and consumption waste, the creation of a recycling industry, including the reuse of such waste, are important state tasks [1]. Despite the trend in the spread of “green” technologies, today the principle of using the best available technology, which is a key principle in the field of environmental economics, is not always applicable. More and more, both in the scientific field and at © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 871–884, 2023. https://doi.org/10.1007/978-3-031-32719-3_66
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the level of state initiatives, there is a declaration of the need to comply with the principles of sustainable development in its classical sense - stable socio-economic development that does not destroy its natural basis and ensures the continuous progress of society [2]. At present, the concept of a circular economy in the scientific field is increasingly acting as an answer to questions regarding resource consumption and resource conservation, as global economic and social problems, the solution of many of which is focused on the transition from a linear economic model to a circular one. In accordance with this, the purpose of this article is to study the prospects and institutional framework for the introduction of a circular economy in Russia, in connection with which the authors perform a number of interrelated tasks within the framework of the study: 1) explore the theoretical basis of the circular economy; 2) consider the world experience in the implementation of circular business models; 3) identify and describe the role of the public sector in replicating circular business models in the real sector of the economy; 4) analyze the norms of institutional regulation in the field of the circular economy, established at the national and regional levels in the Russian Federation. The concept of circularity itself is not new. The cyclical idea of the organization of production and the circular flow of materials and energy was reflected by Kenneth E. Boulding in the scientific work “The Economics of the Coming Spaceship Earth”, in which the need to match the economic and ecological systems, taking into account its limited resources, was identified. And the circular economy is seen as a cyclical system that is capable of continuously reproducing material forms, even if it cannot avoid energy input [3]. The term “circular economy (circular economy - CE)” was defined by Allen Kneese et al. in 1988 in an ecological-economic perspective based on the principle of material balance, according to which all material flows can be taken into account, but these will be economic values, not physical flows [4]. Different from the concept Kenneth E. Boulding. Pearce i Turner (1990) divided CE into four functions: the value of amenities, the resource base for the economy, the sink of residual flows, and the life support system [5]. Ellen MacArthur Foundation defined the circular economy as an industrial system that supports the concept of restoration through the intelligent design of materials, products, systems and business models [6]. The concept encourages business activity to practically optimize products, components and materials for maximum usefulness and value by distinguishing between technical and biological cycles. The principles of the circular economy have also been discussed by researchers to express the conceptual content of the circular economy, which promotes the emergence of industrial and economic systems, relies on cooperation between actors and resources, uses waste and energy as resources to minimize the input materials and energy of the system. In China, the concept of the circular economy has adopted the principles of “3R”: reduce, reuse and recycle materials/energy [7, 8]. The European Union has presented its vision for a circular economy since 2014, and in 2020 the New Circular Economy Action Plan was launched, which “shows the way to a climate-neutral, competitive, responsible consumer economy”. Today, an increasing number of challenges are facing the subjects of the economy, and the concept of the circular economy finds a large number of supporters. In a study by Eva Ripanti, Benny Tjahjono, Ip-Shing Fan [9] term “circular economy” is used to describe an industrial economy capable of “regeneration”. Unlike the “linear economy”
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model and the organization of business processes according to the “take, make, recycle” algorithm, the circular economy includes minimizing the consumption of final resources and raw materials in the production of products and returning the recovered content of products back to the place of production after their expiration. Chang H. Kim, Adrian T.H. Kuah, K. Thirumaran examined the morphology of circular economy business models in the electrical and electronic equipment sector in Singapore and South Korea [10]. The transition to a circular economy requires manufacturers to rethink business models for more efficient production and supply chains. Many enterprises have adopted Circular Economy Business Models (CEBM) with different architectures to achieve sustainability and cost efficiency. However, unexplored CEBM design options can be a major barrier to moving towards a circular economy. In Singapore, results-oriented CEBM and collaboration with local governments and communities became the main design options. On the other hand, in the South Korean context, rental or leasing business models using a membership system with a personalized home visiting service and the use of digital capabilities are widespread. These results are expected to help manufacturers and practitioners understand and adopt better business model management options, highlighting the agenda for further exploration of digital service and consumer adoption of CEBM. The changes taking place in the economy constantly challenge the industrial sector, requiring continuous transformation and adaptation to a changing environment. The circular economy reduces the risks of resource shortages, reduces the amount of waste, and increases the speed of recovery. A growing body of work focuses on integrating the circular economy at the business model level, but there is limited understanding of an industry-wide approach to the innovativeness of the CEBM [11]. The scientific position of the authors of this study is reflected in the thesis that the circular economy involves an evolutionary change in business processes, and as a result, business models of economic entities. A large number of studies of business models of the circular economy are emerging in the scientific field, which in turn actualizes their critical reflection. Maryam Hina, Chetna Chauhan, Puneet Kaur, Sascha Kraus, Amandeep Dhir reviewed 126 systematic literature reviews (SLR) studies of CEBM to identify their gaps and limitations [12]. SLR is a method that allows a thorough examination of the current state of affairs in any particular area of research, while identifying research gaps to facilitate future research and knowledge development, and within said research, SLR is used to holistically assess and summarize the current progress of the relevant literature on driving forces and barriers to CEBM implementation [13]. The authors have identified key thematic areas that cover multiple drivers and barriers to CEBM and have formed a conceptual framework for identifying patterns and relationships between them. It is necessary to focus on those studies that are devoted to identifying the limitations of the circular economy and their scientific justification. The study [14] focuses on the rebound effect of the circular economy and proposes a conceptual framework that combines the main characteristics and mechanisms of the rebound effect in the context of the circular economy. Four main conclusions were identified as a result of the study of the circular rebound effect: the need for new forms of management, research and accounting for the replacement of direct effects with indirect ones, which indicates the need for tools for early detection of possible “bounces”.
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Vasileios Rizos, Julie Bryhn identify Circular Economy business models as critical levers for moving towards a CE [15]. The number of studies examining the barriers and opportunities for adapting the circular economy has been growing in recent years. But available empirical data is still limited, and sector-specific estimates are missing. These include a lack of transparency rules in supply chains, poor enforcement of waste regulations, limited use of cycle criteria in public tenders, and a lack of CE standards. Inconsistent requirements arising from different policy areas can also create problems for companies implementing CE practices. The industrial type of development that dominates the countries intensifies environmental problems that are global in nature [16]. A number of studies [17–19] aimed at conducting focus groups and individual interviews with representatives of municipalities and regional waste management centers to identify the problems of introducing a circular economy in the absence of the participation of local authorities (namely, in terms of municipal waste management, improving efficiency, resource allocation and business development). Industrialized countries are actively implementing circular models, while for low- and middle-income countries this is a completely new concept that requires support, including at the institutional level. It should be noted that for the last group of countries, circular models are of high relevance, which is explained by population growth, an increase in urban areas, an increase in anthropogenic pressure, and an increase in the amount of solid waste.
2 Materials and Methods 2.1 Research Methodology Public policy and the adaptation of business models of economic entities are key elements of the transition to a circular economy, but in modern research, the interaction of these elements in order to accelerate the implementation of the concept of circularity is poorly studied. As part of the study, we set the task to conduct a systematic review of regulations, to consider the possibility of interaction between public policy and business models in the context of a circular economy. Raphael Wasserbaur, Tomohiko Sakao, Leonidas Milios [20] say that there are many possible interactions between public policy and business models. The interaction between command and control rules and elements of the value proposition of business models is most often studied. As a result, entrepreneurs can optimize their circular business models to use the policy framework. Technological development can drive a shift towards circular business models, forcing governments to focus on circular business models and keep them competitive. Of scientific interest is the presence at the federal level of a clear understanding of the targets for the strategic development of the country’s territories, which is a growth-forming factor in the effectiveness of the functioning of the regional system. That is, the macro-level strategic planning documents determine the target settings of the regional strategizing program documents, which, in turn, form the production system of the region, which is formed under the influence of innovative initiatives of individual industrial enterprises and their associations. Authors [21] in a study on expanding institutional theory to implement a circular economy in the fishing industry in Vietnam emphasize the importance of two factors
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- sociological and economic. Within the framework of the sociological variant, three mechanisms arise: the compulsion to follow institutional norms, under pressure from the regulator; setting standards based on social norms and cultural factors; imitation of leaders, arising from cognitive factors that force them to copy the actions of other firms. In the context of the economic option, three more mechanisms emerge, two of which are identified by the technological factor - copying business models based on a critical mass of followers and based on the adaptation of models of prestigious firms; the third mechanism is caused by the economic factor - the introduction of business models based on copying actions that bring a positive economic effect. Thus, the authors identified 4 different factors of influence - regulatory, socio-cognitive, economic and technological. The first two factors are aimed at increasing the legitimacy of the accepted practice, while the last two contribute to increasing its effectiveness. The public sector has a social responsibility to conserve natural resources and promote the well-being of the population, which enhances its role in promoting the concept of a circular economy. Two formats for the implementation and support of business models are generally recognized - “top-down” and “bottom-up”, which can also be used to support models within the circular economy. The top-down approach includes the legal framework and public policy, as well as the supporting infrastructure and social awareness of economic actors and consumers. Consequently, the public sector is responsible for the collective awareness of environmental issues, as well as for the public benefit of industrial activity, through strict control over industrial enterprises [17]. Top-down policies are implemented at the macro level through the implementation of concepts, strategies and national projects, and at the meso level through development strategies and support programs for relevant ministries. The bottom-up policy is implemented through enterprise strategies and support programs from line ministries. Accordingly, in the current economic conditions, the top-down policy is of the greatest interest. It is regulatory policy that provides the initial impetus, as firms experiencing deep uncertainty will be more likely to adopt the practice due to legitimacy pressures rather than perceived efficiency. In other words, regulatory and socio-cognitive factors (legitimacy) have a stronger influence than economic and technological factors (efficiency) under conditions of uncertainty [22]. In our study, we focus on the analysis of the institutional conditions for the transition to a circular economy and the corresponding business models. The analysis of the institutional conditions for the development of a circular economy is based on the study of legal frameworks and strategic documents that stimulate the transition to circularity and aimed at its legitimacy, and then their qualitative assessment was carried out. 2.2 Theoretical Fundamentals It should be emphasized that the implementation of the principles of the circular economy in the real sector is based on the transformation of business models of enterprises that require taking into account changes in the main functional processes. In Russian practice, as well as in the world, the approach of the Ellen MacArthur Foundation to the definition of circular business models is widespread. “Circular business models is an umbrella term for a wide variety of business models that aim to use fewer materials and resources to produce products and/or services; extending the life of existing products
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and/or services through repair and refurbishment; completing the life cycle of products through recycling, capitalizing on the residual value of products and materials” [23]. The scale of the required functional changes will be determined by the company’s strategy - whether it needs to reconfigure current business processes, or whether a new business model is needed and the complete development of individual The study of world practical experience in implementing the circular economy is based on the classification of business models (Table 1). Table 1. Description of business models within the circular economy [24]. Model
Description
Circular suppliers
The implementation of the model involves the replacement of limited resources with renewable ones [25, 26]
Resources recovery
The implementation of the model is based on the introduction of technological innovations for the recovery and reuse of resources [27, 28]
Sharing platforms
The model implements the sharing of resources, goods, assets [29, 30]
Product life extension The model is implemented by manufacturers of production equipment to increase productivity and is focused on increasing the life cycle through repair, modernization, reconstruction and refurbishment [31, 32] Product as a service
The model suggests an alternative to buying a product by renting it with pay-per-use [33–35]
Systematized experience in the implementation of circular business models allows us to conclude that their implementation and development is aimed at improving the efficiency of production processes, increasing profits, reducing dependence on resources (for example, energy) and optimizing their use. The economic level of development of a particular country determines the national and institutional features of the transition to the concept of a circular economy. Developed countries, changing the existing structure of production and consumption, take a leading role in the introduction of circular systems, and in the future will support the transition to a circular economy in developing countries through financing, technology transfer [23]. Based on the foregoing, it is of scientific interest to study the institutional framework of the Russian Federation in the field of the circular economy at the national and regional levels, since at this stage of economic development, the policy in the field of introducing the circular economy is implemented on the basis of the “top-down” principle (Table 2). To assess the nesting of state policy, the analysis of regulatory legal acts of the regional level was carried out on the example of the subjects of the Southern Federal District. This localization was chosen due to the development of the industrial complex in this area. Separate elements of the circular economy can be traced in the norms of state regulation of the industrial sector. Thus, it is possible to identify priority goals, objectives and activities in the regulatory legal acts of the national and regional levels, which contribute to the development of the circular economy in Russia. Such norms are present
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Table 2. Norms of institutional regulation in the field of circular economy established at the national and regional levels National level Industrial Policy Law rational use of material, and natural resources, availability of resources and their introduction of resource-saving and concentration on the development of priority environmentally friendly technologies industries National Security Strategies of the Russian Federation ensuring environmentally oriented economic growth, developing environmentally friendly industries
reduction in the volume of generation of production and consumption waste, development of the industry for their disposal and reuse
Development of industry and increase of its competitiveness ensuring the technological development of the domestic industry based on the creation and implementation of breakthrough, resource-saving, environmentally friendly industrial technologies for the production of competitive science-intensive products Strategy for the development of the industry for the processing, recycling and neutralization of production and consumption waste for the period up to 2030 maximum use of raw materials and raw materials; waste prevention;
increase in the share of products manufactured using recycled materials,
resource saving and maximum involvement of waste in economic circulation Environmental protection The main priority of the state policy in the Activities include: field of environmental quality regulation is the stimulating “green” technologies in introduction of a circular economy production reduction in the volume of production waste generation, development of the industry for their disposal and reuse Regional level Rostov region
Krasnodar region
Strategy for socio-economic development of the Rostov region for the period up to 2030
Strategy for socio-economic development of the Krasnodar Territory until 2030
Ensuring activities at industrial enterprises of the region for the processing of industrial production waste and their recycling
Ensuring environmental protection and increasing the level of environmental safety; ensuring the introduction and use of environmentally friendly technologies, compliance with environmental standards; ensuring efficient management of production waste (continued)
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Environmental protection and rational use of natural resources
On industrial policy in the Krasnodar Territory
increasing the protection of the environment from anthropogenic impact to ensure the safety of human life, rational use and protection of natural resources
financial support for organizations implementing projects to improve the environmental safety of industrial production On environmental protection in the Krasnodar Territory Industrial environmental control is carried out in order to ensure the implementation in the process of economic and other activities of measures for environmental protection
Republic of Adygea
Republic of Kalmykia
Strategy of socio-economic development of the Republic of Adygea until 2025
Strategy for the socio-economic development of the Republic of Kalmykia for the period up to 2030
Ecologization of production technologies in all sectors of the economy
environmental safety, reasonable use of natural resources based on the principles of sustainable development
Implementation of resource-saving and waste-free technologies in all areas of economic activity; Development of systems for the use of secondary resources, including waste processing Astrakhan region
Environmental protection Creation of conditions for the recycling of all production and consumption waste prohibited for burial Volgograd region
Strategy for socio-economic development of Environmental protection in the Volgograd the Astrakhan region for the period up to 2035 region improving the quality of the use of available resources through the introduction of new energy-saving technologies, the transition of industrial enterprises to lean production
commissioning of 255.0 thousand tons of MSW processing facilities in the Volgograd region (continued)
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Table 2. (continued) reduction of pollutant emissions from stationary sources (industrial enterprises) Republic of Crimea Strategy for socio-economic development of the Republic of Crimea until 2030 Ensuring environmentally oriented economic growth. Reducing the anthropogenic impact of industry, on the environment, incl. Resourceand energy-saving technologies, reducing the volume of production and consumption waste, etc a systematized and compiled by the authors on the basis of the analysis of the legal acts of the
Russian Federation
both in acts regulating the development of industry or socio-economic development in general, and in acts directly aimed at greening industry and the economy. This division is observed both at the national and regional levels. Thus, the norms characterizing the circular economy in non-specialized acts can be identified at the national level within the framework of the Law on Industrial Policy, the strategy for the development of the manufacturing industry and the national security strategy, and at the regional level strategies for the socio-economic development of the territory (present in all regions except the Volgograd region) and the Law on Industrial Policy in the Krasnodar Territory. Specialized legal acts in the field of ecology at the national level are represented by the national project “Ecology”, the Law on Production and Consumption Wastes, which at the regional level corresponds to the law on environmental protection (Krasnodar Territory), as well as the state program “Environmental Protection”, similar programs are also present at the regional level (the norms within the framework of the circular economy in state programs are allocated for the Rostov region, the Volgograd region, the Republic of Kalmykia). It should be noted that the analyzed legal acts reflect a sufficient level of regulation of the circular economy in the industrial sector, however, only certain priority areas and development tasks have been formed that can be attributed to the business models of Circular suppliers - in part in terms of the use of renewable resources, including renewable energy sources, Resources recovery - in terms of introducing new technologies into production processes that increase resource efficiency and ensure environmental safety of production, as well as the recycling of industrial waste, but do not reflect the possibilities and ways of transition to these business models.
3 Results Within the framework of the state policy in the field of sustainable development, ensuring environmental safety, including resource conservation and rational use of natural resources, is defined as a priority. At the same time, a separate strategy is planned to
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form an industrial sector that ensures the disposal and processing of waste. The priority goals and objectives in the field of the circular economy, indicated in the selected legal acts, can be structured depending on the direction of development of the circular economy (Table 3). Table 3. Directions for the development of the circular economy, according to institutional regulation The direction of development of National regulation the circular economy
Regional regulation
Ecologization
6 Environmentally oriented economic growth. Environmentally friendly technologies for industry
7 Increasing the level of environmental safety of industrial production. Ecologization of industrial technologies
Resource efficiency, including energy efficiency
8 Increasing the resource and energy efficiency of the economy. Rational nature management
7 Implementation of resource and energy saving technologies. Rational use of natural resources
Recycling of materials
4 Maximum involvement of waste in economic circulation and resource saving
4 Recycling of industrial waste. Use of secondary resources
Waste management
6 Development of the waste disposal industry. Waste reduction
3 Processing of industrial wastes and their effective use. Reduction of industrial emissions
a systematized and compiled by the authors on the basis of the analysis of the legal acts of the Russian Federation
Based on a quantitative comparison of the national and regional levels, one can note the consistency of policy emphasis on increasing the level of environmental friendliness and resource efficiency of production through the introduction of new technologies. At the same time, waste management in the industrial sector at the national level is more regulated. As a result of the analysis of the norms of the regional level, it should be noted that in all subjects of the Southern Federal District, with the exception of the Volgograd region, there are norms in the field of efficient use of resources, including in terms of energy efficiency and material intensity of production. It is also worth noting that at the regional level, the direction of “waste management” affects the sphere of consumption to a greater extent than the sphere of production, despite the fact that the industrial sector forms the largest share of waste. Manufacturing enterprises act as stationary sources of
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pollution, which necessitates regulatory action in order to transform business processes, and as a result, business models, in the concept of circularity.
4 Discussion Turning to the results of the analysis, we can say that within the framework of state policy, a framework (framework) has been built to see the main points around which the business processes of enterprises in the concept of a circular economy will be modernized. The framework built within the framework of the state policy will become the basis for the adoption by enterprises of the environmentally responsible behavior adopted by world leaders and the impetus for the implementation of the bottom-up policy. The results of the analysis, in addition to the priority areas in the field of the circular economy, made it possible to identify certain negative factors affecting the development of this area. The problems highlighted for the Russian economy at the national level include the lack of requirements for waste treatment before disposal, which makes it difficult to further process and neutralize; informational, systemic, logistical problems of organizing industrial waste treatment; lack of regulatory regulation of intrasubject, intersectoral and interdepartmental interaction in the organization of activities for resource conservation and industrial waste treatment. On a regional scale, the actualization of environmental problems arises against the backdrop of the development of industrial sectors, the growth of the production volume of which increases the volume of waste production (as well as air pollution), the system for processing and reuse of which has not been formed, and the need for significant investments in the modernization of industrial technologies is also highlighted to meet environmental standards. In addition, it should be noted that there is no adequate system of indicators and indicators that would allow to completely quantify the level of greening of the industrial sector and characterize the effectiveness of government measures for the transition to a circular economy. The available indicators (quantified for the most part in state programs) are isolated from the industrial sector and reflect the general environmental pollution, emissions, the number of emissions permits issued, the forest cover of the territory, and others.
5 Conclusion In the current economic conditions, the concept of the circular economy finds an increasing number of followers who recognize its ability to respond to global economic and social challenges at the macro, meso and micro levels. Industrially developed countries are currently implementing a bottom-up policy, which is reflected in the described experience in introducing circular business models. In less developed countries, a top-down policy is currently being formed that will give impetus to the formation of environmentally responsible leaders in the manufacturing sector, as it is regulatory policy that provides the initial impetus, since, experiencing deep uncertainty, firms are more likely to adopt the practice due to pressure of legitimacy, not because of perceived effectiveness. Acknowledgments. The study was prepared within the framework of the President’s grant No. MK-1478.2022.2 “Business models for managing industrial associations in a circular economy.”
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Key Trends in the Digital Transformation of Business and Their Impact on the Business Processes Svetlana V. Shirokova(B) , Olga V. Rostova , Anastasiia Prosvirnina , and Anastasia Odainic Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia [email protected]
Abstract. Nowadays, we live in the world of rapidly developing technology. That leads to the changing requirements of the clients and businesses themselves. While talking about the businesses of almost all the areas, the greatest adjustment to these requirements is digitalization. Information and communication technologies have become the key support factor of any business. The main reason is that automation leads to the increase in efficiency, which, in its turn, results in the profit enhancement. This goal is achieved by the shortening of communication chain, main business processes optimization, lower transaction costs, and the errors and mistakes decrease. Digitalized business is simpler to run and requires less efforts from the managers. Besides, it offers wider market outreach. This article highlights key trends in the digitalization area and identifies information technology project management standards. This article considers the digital transformation in general as well as its recent trends. The recent trends include CRM, IoT, Generative AI, and other. Besides, the article explains the reasons why digitalization process is of crucial importance for modern business as well as the steps and results of such process. Several examples are also included. The steps of digitalization are considered further in the paper with an emphasis on the CRM implementation and the areas of businesses most implicated by it. Those are customer management, marketing, and service. Each of them targets special groups which will be thoroughly explained. Additionally, the article explains the business processes related to the implementation of IT-solutions. The paper highlights project management standards mostly used by the companies during digitalization processes. For instance, PRINCE, ISO, PMBOK, ISEB, and DSDM standards. Keywords: CRM system · Digitalization · PRINCE2 · DSDM · ISO · PMBOK · ISEB
1 Introduction Needless to say that the 21st century is the era of digitalization. Businesses of all the areas are aimed at increasing their income as well as at improving efficiency. This process was also boosted by the COVID-19 pandemic. Its effect has already been discussed in various articles [1, 2], so it will not be the main subject of this paper. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 885–895, 2023. https://doi.org/10.1007/978-3-031-32719-3_67
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The areas of digital transformation can be divided in four groups (Fig. 1). The Fig. 1 is the authors’ creation.
Fig. 1. Digital transformation areas
Those are: • • • •
Process transformation; Business model transformation; Domain transformation; Cultural transformation.
Each of them is to reach balance for the implementation to be considered successful. For this purpose, PRINCE2, ISO, PMBOK, ISEB, and DSDM standards are often used, which is explained thoroughly further in the paper. Also, in the field of digital product development, one of the rapidly developing areas is the use of flexible methodologies [3]. This paper also emphasizes on the recent trends, such as CRM, IoT, and Artificial Intelligence. Even though CRM systems have already become quite popular among businesses, their implementation rate is still rising. One more improvement for the wellknown system is AI. AI-based CRM offers an opportunity to predict customer lifetime value and to adapt treatment of customers [4, 5]. Adding AI to the CRM helps with the sales enablement due to the algorithms being able to analyze customer behavior and build a model of the possible, usually highly likely, prognosis. Another trend to consider in this area is IoT CRM. This kind of extension enhances end-to-end processes by connecting products, devices and equipment. Those subjects were thoroughly researched, and the results are presented further in the article. Besides the analysis of the existing papers on the topic, the experience of operating businesses was also examined. The main purpose of this article is to consider recent changes in the concept of the CRM systems, such as implementation of Artificial Intelligence and IoT. Additionally, the article is aimed at the consideration and comparison of the project management standards, such as PRINCE2, ISO, PMBOK, ISEB, and DSDM standards, as well as discussion of the effect of one of them on the implementation of the CRM system on the example of a shipping company.
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2 Materials and Methods Digital transformation is conducted by people, and the key to the success of any project is reasonable project management method. The method to be discussed is called PRINCE2 (PRojects IN a Controlled Environment). PRINCE2 is a structured approach to project management, so it is a method for managing projects within a well-defined framework. Not only does this methodology explains the steps to be taken, it also offers an explanation on how to do that. PRINCE2 considers the relationships between the customer, the supplier and the user. This product-based methodology considers project as a temporary venture set up to deliver one or more commercial products according to the presented Business Case. The method implies seven key principles, themes and processes [6]. The seven principles include: 1. 2. 3. 4. 5. 6. 7.
Continued Business Justification; Learning from experience; Defining roles and responsibilities in the project; Managing by stages; Managing by exceptions; Focus on products; Tailor to the project environment. The seven themes include:
1. 2. 3. 4. 5. 6. 7.
Business Case Organization Quality Plans Risk Change Progress
The seven processes are presented in the picture below (Fig. 2). The Fig. 2 is the authors’ creation.
Fig. 2. PRINCE2 Seven Processes
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Thus, the key features of PRINCE2 are product-based planning, dividing the project into manageable stages, adaptability, and flexibility. It also covers the answers project management team and exceptions management. The second standard in consideration is called PMBOK [6]. PMBOK stands for the Project Management Body of Knowledge. This standard can be characterized as process-oriented approach. It describes: • • • • •
Project life cycles; Organizational structure; The groups of processes (initiation, planning, execution, control, and completion); Core and supporting processes; 9 knowledge areas. The nine aspects related to project management are:
1. Project Integration Management Identification, definition, combination, and coordination of processes and activities. Project objectives establishment. 2. Project Scope Management Requirement collection, scope definition, work breakdown structure creation, scope verification, scope control. 3. Project Time Management Activities definition, activities sequence establishment, the required resources estimation, required time estimation, a schedule development, schedule control. 4. Project Cost Management Costs estimation, budget determination, costs control. 5. Project Quality Management Prototype development and testing. 6. Project Human Resource Management A human resource plan development, hiring the project team, a project team development, project team management. 7. Project Communication Management Stakeholders identification, communication plan development, information distribution, stakeholders expectations management, performance report. 8. Project Risk Management Risk management planning, risks identification, qualitative and quantitative risk analysis, risk response planning, risks monitoring and control. 9. Project Procurement Management Procurement planning, procurement conduction, procurement administration, procurement closure. For all nine aspects both entries and outputs are defined, as well as relationships between them. The newest version of the standard also describes tools and techniques of the input and output data transformation, highlights the difference between project life cycle and product life cycle, and presents the graphic format of the processes. In this article have been explored important aspects of customer relationship management for the new Internet services.
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While conducting this research, an extensive literature review was examined. Various papers on the subject of digitalization and CRM implementation were studied [7–9]. Besides, real-life business cases were researched with an emphasis on the results of AI-powered CRM systems implementation [10]. The next standard worth mentioning is Project and Product Management for Enterprise Innovation (P2M) [11]. The key feature of this standard is that it is aimed the improvement of the organization, not the product. It considers how the project will influence on the business strategy of the company [12]. The concept of this standard is based on three ideas: Complexity, Value, and Resistance. All of them form context-based restriction triangle. It can be considered as environment of the innovation. The complexity of the problem correlates directly with the value of solution and the amount of resistance faced by the implementation of this solution in the enterprise [13]. Another method in consideration is called DSDM, standing for Dynamic Systems Development Method, which is an Agile method. Like PRINCE2, It is focused on the full project lifecycle. The approach proposes eight principles: 1. 2. 3. 4. 5. 6. 7. 8.
To focus on the business need; To deliver on time; To collaborate; To never compromise with quality; To build incrementally from firm foundations; To develop iteratively; To communicate continuously and clearly; To demonstrate control.
3 Results Undoubtedly, following project management standards simplifies the task of project managers and have an impact on the success of the project in a positive way [14]. Even with a great variety of standards, the article focuses on PRINCE2. This standard allows not only to consider the project elements, steps and stages, but also states the approach to the practical management of the project. Thus, this methodology proposes tools for making possible the implementing of a project justification, building a project plan, assessing risks, considering problems and changes, and evaluating the progress of a given project at any stage. Such evaluation has crucial importance for realizing where the project is going. Besides, unlike other project management standards presented in the paper, PRINCE2 was originally developed for the IT-projects, that is why it considers features represented in such projects [15]. The results of such PRINCE2 standard implementation are presented on the example of the CRM system introduction to the transportation corporation. The company in question presents with the linear hierarchy with a CEO at the top of the chain of command. He gives orders to the Chief Logistics Officer, Chief Engineering Officer, Chief Accounting Officer, and Chief Legal officer. Each of them has a department under supervision.
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The project of CRM implementation was considered necessary. The aim of it was to automate the process of goods shipping planning and increase its efficiency. The required features were determined at the beginning and the main requirement was stated: the system was to be user-friendly and easy to adjust to in a short period of time. The company lacked time resources waiting for the employees to adapt to the new system. Besides, it was stated that the service, sales and analytics tools needed to be included. The system also had to include such features as route control, transportation control, and customer, partner and transport operator footprint creation. The reasons of why the implementation should be conducted were also determined. Among others, lack of tools for shipment planning was highlighted, lack of ability to take into account the dimensions of the cargo and the volume of the cargo compartment transport was mentioned, and a problem of inability to quickly change routes in case of unforeseen circumstances was named. Various business cases were also considered. After comparison of different scenarios of doing nothing, implementing minimal changes and conducting thorough transformation, the decision of CRM implementation was made. After that, the expected benefits, possible risks, time frames, project costs and investment appraisal were calculated and described. The expected benefits included quantitative and qualitative factors. The quantitative factors included the income increase and the costs decrease. As for the qualitative factors, the following aspects were highlighted: – – – – – – –
Increase in the number of the shipments; Increase in the number and volume of goods transported; Reduction of the transportation time; Optimization of the transportation routes; Reduction of the required gasoline and fleet maintenance costs; Absence of the unnecessary runs of vehicles; The employee efficiency improvement as the result of the system taking over some of the work and helping to improve the work process.
As for the risks, they can be divided in 2 groups: financial and nonfinancial. Financial risks include possible project costs increase and inefficient financial management while dealing with significant amount of money. Nonfinancial risks included the employees unwillingness to learn the new how to work with the new information system, incorrect estimation of the project costs, and other. The time frames were limited up to 6 months. However, according to the calculations following the PRINCE2 standard, the 9 steps of the information system implementation would take 79 business days. The costs were estimated considering the equipment already owned by the company. The required equipment needed to be purchased included modern computers, servers, and a tracking system. Those are the essential elements of the modern CRM system introduction to the company. The increase in the number of annual shipments has had an impact on the costs. The company’s annual costs increased. However, the increase in costs had a smaller effect on the company’s quantitative indicators due to the reduction of runs and vehicle downtime. The cost reduction effect was achieved mainly due to the smaller amount of the truck runs, which resulted in lower fuel consumption and less vehicle wear and tear.
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According to the calculations of the analysts, the investment payback period would be less than a year – about 9 months. The project was conducted in accordance with the PRINCE2 standard. A three-level project team was created. Organizational structure is presented in the below (Fig. 3). The roles and responsibilities were also outlined. The Fig. 3 is the authors’ creation.
Fig. 3. Organizational structure of the project
Besides, a plan of the project was developed. According to PRINCE2 standard, it consisted of 9 steps. The steps of the project realization included the following: 1. Determination of the implementation goal and expediency of project initiation; 2. Project initiation (company’s activity and business processes analysis, current information architecture analysis, project startup); 3. Requirements gathering (kick-off meeting, meetings aimed at the requirements gathering); 4. Writing and negotiation of the TOR; 5. Purchasing the necessary assets (IS license, a package of services for GPS/GLONASS vehicle monitoring, equipment; looking for a supplier); 6. Implementation of the information system (development of the environment, deployment of the information system, initial configuration of the system); 7. System testing; 8. System debugging; 9. User training (Preparation of instructions, preparation of technical documentation, user training). More than that, the connection of the plan and product structure were created. An executor for each of the steps was chosen. After the milestones were set, a level of importance was chosen for each of them. Risk management was also considered and
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possible reactions to the risks and contingent situations were chosen. For example, GPS equipment malfunction would result in the changes of the supplier as soon as the contingent situation arises. The project duration incorrect calculation would require recalculation and some other changes. Such solutions were stated for all the possible contingent situations. Moreover, for each of such situation’s probability, severity level and the detection difficulty were estimated. As a result, the project was successful. The number of shipments has been increased, as well as the employees efficiency. The costs of the transportations decreased, and the income started to demonstrate positive growth. The company managers were satisfied with the results of the project. The proposed goals were achieved: the number of the shipments was increased, the amount and the volume of the transported goods were also increased, the time of the shipments was decreased, and the routes were optimized. All of that resulted in lower costs and higher income. PRINCE2 standard allowed the company to implement a CRM system efficiently. Absence of unforeseen situations did not influence the project in a negative way since possible reactions were considered well in advance.
4 Discussion Usage of all the above-mentioned standards is proven to be successful while implementing Informational Systems, including CRM. It is also recommended by various experts [16–18]. However, PRINCE2 is more of use for running global projects, and in terms of CRM implementation it might be too complex. DSDM, however, is aimed at the delivery of product. This approach might be helpful while considering different CRM solutions and comparing them. It offers an opportunity to assess how the CRM of choice would work for the business processes of the company. If talking about the company efficiency improvement, it might be fair to conclude that P2M standard is the best fit as a project management method chosen. No matter the chosen way of implementation, the main goal of this process is successful optimization of business processes resulting in the income increase [18]. The key trends are showing promising results in this term. The first researched trend is AI-powered CRM systems. Generally, this technology leads to the same results as the usual CRM system: increased sales, reduced time and costs, improved customer satisfaction, and enhanced employee satisfaction. However, AI integration into CRM improves the quality of all the mentioned factors [19]. The main reason is that under the circumstances of constantly increasing amount of data it is becoming impossible to process all the customer data without introducing AI or machine learning. AI, however, is able to convert unstructured information into structured, making it possible to analyze it, as well as determine patterns and make prognosis. Besides the huge amount of unstructured data to be processed, the business processes are becoming more and more complex. Moreover, the character of relationships between both company employees and customers is also changing. It means that AI algorithms are of crucial importance to detect the new patterns accurately. While CRM presents the data the way that requires time and effort to process, AI-powered CRM provides
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companies with analyzed information. As a result, it increases the efficiency of sales managers and analysts work by automatization. In addition to artificial intelligence, Internet of Things technology has also shown an impact on CRM systems. Generally speaking, IoT is a network connecting devices to the Internet. Its connection to the CRM may not be obvious since CRM is about collection of customer’s data. However, the connection of both technologies leads to the more promising results in term of past enterprise analysis and connecting it to the real-time data collected from the devices. One of the crucial advantages of this integration is optimized customer service [20]. For example, it would make it possible to trace and fix the errors in the products before the customer notices them. That has a high probability of leading to a better customer service and level of satisfaction. Among other benefits of CRM and Internet of Things technology integration are: • Connection with customers throughout product life cycle; • Automatic feedback tracking for the launched marketing campaign; • Simplified process of providing proactive measures resulting in better service and level of customer satisfaction; • Real-time data available for analysis; • Reduced marketing research cost.
5 Conclusion Taking everything into consideration, digitalization has a great impact on the business processes of the enterprise. It is of key importance to ensure a thoroughly thought project management and consider all the business processes to be affected. Among other recommendations a close work with the resistance to the innovation can be advised. Any Informational system implementation team should be well-coordinated and fully prepared to manage the risks and pay attention to all the members of influenced by the innovations members. It is of vital importance for the project manager and the team to be ready to adjust the project subjectives to the subresults in order to achieve the best outcome. Even though Informational systems themselves have a great impact on the company, the efficiency is not likely to be achieved if the project management standard was not implemented and the project was not coordinated efficiently. Modern technologies have a lot to offer to the businesses nowadays, however, overcomplication of the system would not lead to the improvement of the business performance. On the contrary, the business processes are likely to have their efficiency decreased. Besides, overcomplicated systems are not user-friendly which results in longer and more difficult adaptation of the staff. The idea is to find balance between the modernity and simplicity of the information system that is to be implicated. Thus, the key to success is the accurate choice of the informational system taking into account all the characteristics of the business and meticulous coordination of the project [21]. If the complexity of the project is not justified by the company’s goals, it is advised to search for the less complex IT solution. Lockdown experience by almost all of the countries has influenced the world of logistics that will never come back to
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normal [22]. However, the flexible companies that can change considering the trends have great changes of becoming the leaders on the market [23–26].
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Using Design Thinking to Build Skills for Working with Agile Methodologies in a Digitalized Environment Ekaterina A. Kharkina1 , Olga V. Rostova1 , Svetlana V. Shirokova1(B) Anna V. Valyukhova1 , and Anastasiia S. Shmeleva2 1 Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia
[email protected] 2 Marriott International Yerevan, Yerevan, Armenia
Abstract. This article discusses the design thinking as a tool for developing skills in working with flexible methodologies in the face of rapid changes associated with digitalization. In the era of the digital economy, adaptability is a key characteristic that determines the competitiveness of a company. Agile methodologies are becoming more and more common in product development and project management. In this regard, a question rises of how to prepare modern specialists to work with flexible agile methodologies, instill the appropriate values, and prepare them for teamwork in conditions of uncertainty and risk. One of the possible tools is design thinking, a solution generation technique that supports the values of agile methodology. The main purpose of this article is to review and validate design thinking as a tool for developing the skills necessary to work with agile methodologies. The practical significance of this article lies in revealing the possibilities and potential of design thinking and encouraging to use this technique in the educational process. The article considers the concept of agile methodologies, their principles and features, discusses the role of design thinking as an approach for learning to work with agile methodologies. Analytical materials published in the systems Google Scholar and EBSCO were used to conduct the study. Through a selection of international literature, the article provides the results of case studies, theoretical reflections and reports to improve understanding of the context, opportunities, experiences, application effects, disadvantages and advantages of the design thinking approach in business and education. The study showed that the use of design thinking in the educational process improves the skills of students that are important for working with flexible methodologies. The benefits and challenges of design thinking as an approach that immerses students in the basics of flexible methodologies were identified. Keywords: Agile Methodology · Design Thinking · Digital Technologies · Creative Thinking · Educational Process
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 896–907, 2023. https://doi.org/10.1007/978-3-031-32719-3_68
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1 Introduction The socio-economic changes of the 21st century resulted in the demand for multidisciplinary knowledge, for adaptability to rapidly changing conditions. One of the key events that transformed society was the COVID 2019 pandemic, which demanded the ability to approach complex problems, to use creative thinking to solve problems. A concomitant phenomenon of the pandemic has been accelerated digitalization, the transition of many processes to online, and the development of online services. Many companies are planning to develop the acquired solutions and are confident that rapid technological changes will continue in the future [1, 2]. Staying competitive in an evolving business and economic environment requires the flexibility that allows to use digital technology to meet newly emerging challenges. Employees need the ability to solve problems under conditions of uncertainty and risk, which is typical for organizations in the era of digital change [3]. Many industrial enterprises are already facing the problem of a shortage of personnel with digital skills and, at the same time, the ability to apply flexible methodologies in the implementation of projects [4]. Today’s educational institutions face the question of how to prepare students in the digital age to work in a changing world, to teach them technological problem-solving skills and how to build effective communications. In order to do this, it is necessary to apply approaches in the educational process that will allow students to learn how to find non-standard solutions. Agile project management methodologies have proven to be a good tool for organizing work under uncertainty. Agile methodologies for digital product development are based on completing tasks within a short time frame, involving effective communication between team members and high adaptability to changing working conditions. Such methodologies implement the organization of group work with the absence of a clear distribution of tasks between team members, allowing each participant to contribute and be responsible for decisions made. Design thinking techniques can be used to prepare students to work with agile methodologies. Design thinking is a method that integrates successful practices of organizing group intellectual work, soft-skills development, formation of critical and creative thinking. The purpose of this article is to investigate the possibilities of applying this approach to develop students’ skills in working with agile methodologies.
2 Materials and Methods The theoretical and methodological basis of this research was provided by Russian and foreign authors’ publications on agile methodologies for project management, as well as the application of design thinking for solving non-standard tasks of various levels of complexity. The information-empirical base of the study was formed by scientific analytical materials presented in Google Scholar and EBSCO systems. The search resulted in articles, books and online publications with the keyword “design thinking”. “agile” and “education”, published from 1990 to 2022. The chosen time span included many publications on the selected topics and allowed to study the experience of applying agile
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methodology and design thinking in modern conditions. Most of the sources describing empirical applications of design thinking are case studies covering both business and higher education fields. Agile development is a set of methods for project management with the aim of increasing the speed of creating finished products through interactive communication between team members and rapid response to changes. The Agile approach emphasizes teamwork, changes in accordance with updating requirements and conditions, regular interaction between clients and developers. This approach allows the team to respond quickly to customer requests and to alter the product, reducing the number of unsatisfied customers. The most striking benefits for the client are improved relationship with the developer and enhanced product quality due to flexibility, ability to adapt [5, 6]. The Agile Manifesto outlines the values and principles on which the methodology is based. The Agile Manifesto includes the following [7]: 1. People and interactions: Communication, interaction and the quality of relationships between people in the development process are more important than formal processes and technical aspects. 2. Working product: A working, proven solution is more important than carefully written documentation. 3. Cooperation with the client: Communication, feedback from the client and attention to their requirements are more important than formal documents and agreements. 4. Flexibility to change: The ability to adapt to changing conditions, to change the work in progress, is more important than working to a well-defined plan. Design is an interdisciplinary activity that integrates scientific, technical, aesthetical and attitudinal components to influence economic and production processes [8]. Design thinking is a method of developing new product based on human emotional intelligence. More broadly, design thinking is a process in which we seek to understand the user, analyze, synthesize, and rethink the problem to generate insights and find non-obvious alternative solutions [9]. In 1969, Herbert Simon, an American scientist, in his book Sciences of Artificial, formulated the idea of design as a process of transforming existing conditions into desired conditions, transforming the world around us [10]. Design has been developed as a special technology, an approach capable of influencing objects and processes. In 2005, a School of Design Thinking or d.school was founded at Stanford University, USA. With the help of this organization, a large community of students and teachers who actively apply design thinking has been created. This technique has become widespread throughout the world. Its basic principles are: 1. the use of design thinking in the work of multidisciplinary teams; 2. fostering a bridge between students, teachers and industry; 3. working with large projects and using prototyping to generate solutions [11]. The main goal of design thinking is to generate new ideas through unconventional, creative thinking and freedom from stereotypes and analytical principles. An important component of the method is a creative approach to problem solving, in which the most unusual ideas lead to the desired solution. According to the methodology of the Stanford
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University School of Design Thinking, the process of solving a problem with design thinking consists of five stages: Stage 1. Empathy. The stage of observing user behavior, conducting interviews to immerse in the client’s experience. The empathy stage is associated with the ability to put yourself in the place of the client and is built on a confidential dialogue, feedback, and attention to detail. It is necessary to study the problem, the context of the task, the personal characteristics of the client. Stage 2. Focusing. The stage of systematizing the information and highlighting the key issues. This step provides an explicit formulation of the problem that needs to be solved. Stage 3. Idea generation. The stage of working through non-standard solutions, rejecting critical thinking and analysis, brainstorming. Stage 4. Prototyping. The stage of checking the working capacity of ideas in practice, checking the functions and compliance of the product with the required tasks. A prototype is required to simulate a future solution, verify customer’s satisfaction and pre-test capabilities. Stage 5. Testing. A stage of testing the best solutions and processing feedback. This stage reveals the shortcomings of the solution and allows to assess its compliance to the customer’s needs [11]. Design Thinking helps to find innovative solutions that do not lie on the surface. Using the steps formulated by Design Thinking School, it is possible to immerse a team in a subject area in a short period of time, to use empathy to gather information, systematize and generate ideas, to test and select the best solutions for tasks in any field.
3 Results 3.1 Companies’ Experience in Applying Design Thinking At the first stage of this study, the experience of companies in applying the design thinking methodology was analyzed. IDEO was founded in 1991 and popularized the use of design thinking in business, applying it to product development, customer experience improvement and transformation of business environment. Many companies were introduced to the design thinking approach through their cooperation with IDEO and the use of their set of techniques [12]. Design Thinking is currently used by 59% of organizations surveyed by PwC as a collaborative model for managing innovation. According to respondents’ reports, the use of design thinking has made it possible to bring together people from different areas of expertise within the company to solve problems and generate new ideas. Respondents identified innovative behavior and culture (65%), fresh ideas (63%) as key factors influencing the success of innovative processes within an organization. Confident leadership, a clear business model and an increasing budget for innovation have taken on less demanded positions [13]. In a study of Russian-French experience of applying design thinking in companies, more than a half of the participants from Russian (61% of respondents) noted that, in
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general, design thinking tools in their organizations are mostly used only by individual specialists who have experience of working with this approach. In French companies, design thinking is used on a regular basis by individual structural divisions, as evidenced by 45% of respondents from France. The Russian figure for this criterion is significantly lower, at just 28%. The majority of Russian and French participants of the research (84% and 87% of respondents, respectively) note the relevance and practical significance of design thinking skills for professional activities of modern managers and specialists operating in various economic sectors [14]. A large Russian company applying design thinking in its work is Sberbank, which has been using the methodology since 2012. As a part of Agile transformation, the company implemented design thinking for customer experience design. Design thinking is used in the tasks of launching new product, improving user experience in bank’s offices and for internal processes [15]. One example of the projects completed is the optimization of the top 5 operations in ATMs. The company organized a design thinking session, bringing together users and specialists in design, development, customer experience and payment systems. Script paths were generated, cosmetic changes to the UX design were developed, the titles were changed, and input masks were added for the convenience of users. 3.2 Application in the Educational Process The next step was to investigate the possibilities of applying design thinking methodology in the educational process. More than 60 universities and colleges in the United States use design thinking in seminars, courses and continuing education programs [16]. Design thinking has become a pedagogical discovery for higher education due to its wide application in many disciplines [17]. In 2014 STEM (Science, Technology, Engineering and Mathematics) students at Stanford University worked with secondary school students to help them to become familiar with the design thinking. The application of the approach allowed students to get acquainted with the principles of design thinking, to learn the features of working in a natural science environment, and to prepare for scientific work under conditions of uncertainty. Students and schoolchildren gained skills in problem solving, interpersonal communication and increased confidence in their own creative solutions and innovative abilities [18]. The design thinking approach is not new in Russia, but it has not received the same widespread use in educational programs. According to a study of the Corporate Culture of the student community of universities, a small part of teachers use active and interactive teaching methods aimed at developing communication skills and the ability to work in a team [19]. In 2013, Sberbank’s Corporate University launched Russia’s first educational program “Design Thinking: from Insights to innovation”, based on the methodology of Stanford University. The course was aimed at developing an agile culture of clientcentricity, decision flexibility and experimentation. The curriculum includes individual and group work, the use of virtual and augmented reality technologies, and knowledge control. Upon completion of the face-to-face training program, participants solve an
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actual problem by applying design thinking [15]. One of the business services of the Sberbank Corporate University is Sbergile, a digital transformation of a company that includes training in product management and design thinking skills, and the organization of agile teams. Design thinking training is an integral part of this program as part of the formation of product management, setting up inter-team interaction. A Design Thinking Lab was implemented within HSE University, involving courses and seminars on design thinking methodology, aimed at mastering empathy skills, enhancing field research tools. In 2021 the Design Thinking Lab and Perekrestok retail chain have teamed up to run a six-week course for students to work on cases to improve customer experience. Using the design thinking approach, the teams developed 58 projects, 14 of which were selected by Perekrestok for implementation [20]. 3.3 Benefits of the Approach At the next stage, the advantages and difficulties of using this approach were identified. Design thinking is not only a human-centric search business methodology, but also a form of approach to education that combines learning, entertainment and involvement - the so-called Edutainment. Design thinking training builds the ability to work on projects, organize and direct collaborative creativity [3]. This approach helps not only to find creative solutions, but also to build strong connections within the team, thereby developing opportunities for collaborative creativity. Joint creativity is the association of participants, cooperation in order to find innovative solutions [21]. Collaborative creativity is one of the components of the agile approach and design thinking. The cocreation process is a more market-oriented way of thinking about innovation, bringing together the goals of producers and consumers, i.e. innovators and users. Design thinking allows you to evaluate tasks in a non-unilateral way, taking into account not only business requirements and technical conditions, but also the personal wishes of the client (Fig. 1). The assessment of customer requirements in product development occurs on the basis of needs already identified through data, which are subjective and transformed through the prism of prejudice. The needs that are not expressed in data, remain unfulfilled. The advantage of design thinking in solving this problem is clientcentricity, that is, focusing on the client’s experience and needs [22]. This feature can be extremely useful, for example, when creating IT solutions. During the development of an information system, it is necessary to take into account the wishes and difficulties of not only end users, but also the technical support staff responsible for maintaining the system. One of the undoubted benefits of design thinking is the strengthening of interpersonal relationships through the development of group communication skills within a disparate student community. Differences that affect connections between students include individual, population, social characteristics, including gender, age, ethnicity, language skills, religious beliefs, socioeconomic status. In international educational programs, ethnic differences affect the socialization of students, making it difficult to implement group work. Ethnic heterogeneity within a group of students is associated with greater segregation in friendly relations, the difficulty of developing teamwork skills [23]. Students tend to choose friends from the same ethnic group rather than establish inter-ethnic communication [24]. Lack of group work and co-creation experience leads to
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Fig. 1. Design thinking approach to innovation. The picture is created by the authors of the study.
adverse consequences, which may include withdrawal from all aspects of the educational experience, lack of involvement in the learning process. The nature of relationships with other subjects of the educational process directly affects the personal and professional development of students [19]. The general context and common tasks, the language of communication in which the members of the community are immersed, are very important for the formation of relationships. Design thinking sessions can be fertile ground for the development of interpersonal connections, which will immerse students in a common context, provide them with tasks to solve. Through the use of co-creation, open discussions and communication within the group, the design thinking method can strengthen a divided student community. A study at the Hanoi University of Technology confirmed the positive effect of the design thinking method in the educational process by a number of indicators. Before and after applying design thinking, students assessed the key areas covered in the approach using a questionnaire. The value of empathy for idea generation increased by 9.5% (M(mean) = 3.53 in pre-test, M = 3.87 in final test); a look at the problem from different perspectives by 13% (M = 3.49 in the preliminary testing, M = 3.94 in the final testing); teamwork by 7.6% (M = 3.69 in the preliminary test, M = 3.97 in the final test). Participants in the study noted an improvement in relationships with classmates, the development of a deeper understanding and respect for the problems of others, a willingness to work on solving problems with fuzzy conditions [25]. 3.4 Challenges of Application The COVID-19 pandemic has impacted education, making digital transformation an integral part of the educational process. The result of the rapid shift to online learning is the significant development of remote teaching via digital platforms. Many universities have fully digitalized their operations or confirmed moving most of their courses online, and the online learning trend is expected to continue to grow [26]. The nature of student participation and engagement in online discussions presents a significant challenge to the effective application of design thinking. Some students actively participate in discussions, are interested in group communication, others prefer only to listen and observe. The size of a group of learners also influences the willingness to participate in group work – small and medium groups are expected to be more involved than large groups [27]. A study by Dan Lee showed that 72% of students note the positive impact of online classes on the feeling of connection with other students, on maintaining teamwork in the
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learning process. However, 27% of students note an insufficient level of communication among students, a small number of opportunities for involvement in the discussion [28]. One of the possible solutions is to update the format of classes in streaming platforms to provide an expanded level of communication between students. Conflicts between members within a team can affect the implementation of design thinking [29]. A group of researchers from Stanford University conducted a systematic study of relationships in student design thinking teams. The researchers observed the two teams while working on projects outside the classroom. The authors stated, “Conflicts between group members seem to be ubiquitous in teamwork and immediately surface” [16]. Alfatouni, Vakkari, and Noshtader also point out that conflicts in teamwork arise from unequal distribution of tasks and team sizes [30]. It is also confirmed that students may experience confusion and frustration when they first apply design thinking, and “even those who have already practiced design thinking experience periods of frustration during design work.”. The reason for this feeling lies in the versatility of the design thinking process, which leads to anxiety as you collect and comprehend a large amount of information. The authors note that this feeling subsides when moving from generated ideas to elaborate prototypes. Students who have difficulty working in situations with a high level of uncertainty are prone to stress during the transition between these stages. The recommended solution is to distribute such participants into groups with more confident students [31]. Table 1 presents the results of the analysis of the advantages and difficulties of applying the design thinking approach. Table 1. Benefits and Challenges of Implementing Design Thinking. The table is created by the authors of the study. Benefits
Challenges
Developing empathy
Difficulty of implementation in a remote format
Developing skills for co-creation and teamwork
Lack of student involvement in group work
Strengthening relationships in the community
Risk of conflict within a team
Openness to change, flexibility
Stress when working under uncertainty
4 Discussion This article is intended to introduce design thinking methodology as a basis for developing skills and learning the values of agile methodologies, experience of applying design thinking, advantages and disadvantages of the approach. Although some critics may consider design thinking to be quirky, ambiguous [32], the design thinking methodology is finding more and more supporters. The study of potential of design thinking to support change in higher education argues that in the context of the COVID-19 pandemic and significant changes in the
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educational process, a deep understanding of innovative processes and the identification of creative, people-centered approaches to problem solving are required. To sustain development and transformation in line with the changing reality, institutions need to provide students and teachers with the tool and skills that will meet the demands of a rapidly changing world around them [33]. Design thinking is a strong tool that will instill in students the necessary priorities for working with the agile development. Teachers can use design thinking to develop students’ problem-solving skills and develop original solutions. Design thinking can be used as a basis for organizing and improving teamwork. Participation in design thinking sessions can be a rewarding experience, unlocking the student’s inner potential, bringing the joys of camaraderie and exploration. “Design thinking should be integrated into the learning process. Its strength as a teaching tool lies in its ability to work with a variety of interdisciplinary academic material” [34]. The limitations of design thinking, the potential problems that can be encountered in conducting design thinking sessions, should be considered. Teachers need to be prepared to deal with conflicts within teams, the different nature of student involvement in the work process, the unpreparedness of participants or their individual traits.
5 Conclusion The article gave the concept of design thinking methodology, described the goals and stages of design thinking, considered the application of this methodology in the educational process. Thus, the results of the study showed that design thinking is a universal approach that embodies the values of agile development methodologies, such as: empathy and high-quality communication with the client; teamwork and co-creation; creation of ready-made solutions; adaptability to changes and work in uncertain environments. Design thinking methodology helps to identify the specific personal needs of the client and develop solutions productively using empathy, co-creation and personal characteristics of the participants in the process. The development of digital technologies has radically transformed business processes, thereby changing project roles, team structures, and project implementation methods. The competitiveness of modern organizations depends on the speed of adaptation to new conditions and technologies. As new technologies are introduced into an everwidening list of industries, their use reveals the need for new rules of operation, faster and more flexible forms of organization. Agile project management methodologies are becoming increasingly popular around the world, with organizations and professionals shifting to agile methodologies, also within the digital transformation [35]. As a result of these changes, design thinking is in demand more than ever. The experience of design thinking allows to master the in-demand skills: learn how to quickly generate solutions to non-standard problems, offer options for solving problems in crisis situations, when there are drastic changes in the world, using co-creation and empathy. The possibilities of applying design thinking are diverse, and the method is becoming more widespread, but it is necessary to pay attention to the potential of applying this methodology in the educational process. Its merits and strengths are becoming increasingly apparent due to the many scientific papers, including pedagogical ones, which
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allow teachers to evaluate the effectiveness of the method for teaching, to select options for the use of the method in their curriculum. The application of design thinking in the educational process allows to improve students’ creative skills, develop skills to work with tasks with ambiguous conditions, work in situations of uncertainty, thereby preparing students to work with flexible methodologies in the era of digital economy. The article can serve as an introductory material for teachers who decide to include design thinking in their curriculum as an element of teaching agile methodologies. However, it is also necessary to pay attention to the limitations of the application of this method: the difficulties of working in conditions of online education, the diverse nature of student involvement, the risk of conflicts in groups and the stress that students experience when working in conditions of uncertainty. Future areas of research could include exploration of other diverse approaches within pedagogical tools that encourage students to develop the skills needed to work in flexible environments.
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Industrial Enterprise Digital Transformation Navigator: Stages and Tools for Strategic Change Vladimir V. Glukhov1 , Tatiana A. Gileva2(B) , Margarita P. Galimova2 , Dier Karimov3 , and Ekaterina D. Malevskaia-Malevich4 1 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia 2 Ufa State Aviation Technical University, Ufa, Russia
[email protected] 3 National University of Uzbekistan named after Mirzo Ulugbek, Tashkent, Uzbekistan 4 North-West Institute of Management, Branch of the Russian Presidential Academy of
National Economy and Public Administration, St. Petersburg, Russia
Abstract. Digital transformation is essential if an industrial enterprise wants to be competitive. Strategic management is a methodology for managing development in an unstable external environment. However, the principles and methods of strategizing used in the digital environment should be refined and extended. The knowledge and experience accumulated in the course of digital transformation are a precious resource for defining the goals and development path of an enterprise. Nevertheless, since this knowledge and experience are so diverse and inconsistent, informed strategic decision-making is difficult. The most challenging task is to develop and apply tools that allow the enterprise to respond to changes as fast as possible (adaptive strategy, agile roadmaps, etc.). That is why this work is aimed at building a landscape of strategizing methods and tools systematized according to the phases of the digital transformation process, which can be used as a navigator for choosing a digital transformation trajectory of an enterprise given its digital maturity and specifics of the digital environment. The strategizing landscape of digital transformation is presented in the form of a matrix that structures modern methods and tools of strategic decision-making according to the phases of the transformation process given that an adaptive approach is used. A checklist is produced for mapping out the digital transformation strategy of an enterprise. The paper suggests general guidelines for navigating through the methods and tools presented. Keywords: Enterprise · Digital transformation · Strategic management · Methods and tools · Digital strategizing landscape · Navigation · Road map
1 Introduction Digital transformation is a strategic focus of development for a majority of organizations, including industrial enterprises. In the digital environment, competitiveness depends not as much on the size and strength of companies as on their ability to be flexible and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 908–920, 2023. https://doi.org/10.1007/978-3-031-32719-3_69
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adapt quickly to continuous changes, which can be quite unpredictable. In its essence, strategic management is a methodology ensuring competitiveness at a time when the external environment is highly instable. However, the digital environment has brought new challenges. As a result, some traditional principles and methods of strategizing that companies use for digital transformation management have to be refined and developed further. Analysis of the theory and practice of digital transformation in Russia and around the world shows that three aspects are the prerequisites for building a systemic landscape of the digital transformation process, accentuating its key phases and forming navigation through a rich arsenal of methods and tools available now. First. Despite enormous successes achieved by digital leaders, for many pre-digital companies [1], up to 70% of digital initiatives fail to attain their goals, which often results in multi-billion losses. According to Gartner, it takes large companies at least twice as much time and money than was initially planned to pursue the digital transformation path [2]. That is why after a period of euphoria caused by digital transformation, there is often time for frustration. To a large extent, this is due to the mistakes made when digital changes are introduced, such as failing to understand the essence of digital transformation and seeing it as equivalent to just adopting digital technology, implementing local digital projects that have nothing to do with the strategy of general transformation of business, as well as rushing after digital leaders and losing rationality in decision-making [3,6]. Second. Pursuing the essence of digital transformation. In theory, digital transformation is generally recognized today as something much bigger than just introducing new technologies. It is about using digital technologies for achieving significant business improvements aimed at enhancing customer experience, optimizing operations, creating new business models, and dramatically increasing flexibility through organizational learning and decision-making processes based on high-quality data that become available in a shorter timeframe [7]. However, failing to comply with these rules in practice is still one of the main reasons why many digital transformation programs do not produce the expected results. Thus, Strategy Partners analysts note that most Russian companies understand digital transformation of a company primarily as a way of stimulating business processes and automating operations, with no more than 1/3 of companies moving from adopting individual digital solutions to introducing new digital business models and products, and working with digital personnel and culture [4]. Having no digital strategy is one of the most significant obstacles to successful digital transformation of Russian mediumand high-tech enterprises [3]. According to Komanda-A (KMDA) company, Russian enterprises have the following priority areas for digital change: digitalization of business processes (61% of respondents), data-driven management (51%), customer experience management (50%), digital infrastructure and technology (42%). “Soft” factors, such as lack of competencies and knowledge, internal resistance and fear of change and having no strategy take an especially prominent place among the obstacles to successful digital transformation (53 5, 45% and 42%, respectively). But at the same time, activities in these areas are not classified as priorities.
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A similar situation is described in the guidelines [8]. According to them, an essential prerequisite for successful digital transformation is organizational culture. However, the process of change in organizational culture is not monitored further, since the key performance indicators (KPI) do not contain a single indicator that would show any significant features of the organizational culture. Third. A very large number of recommendations have been put forward. Almost all world-famous consulting companies, and many others have developed approaches to digital transformation of enterprises. In addition, a lot of theoretical and methodological research has been done in this area, and rich empirical experience was analyzed and generalized. Given the current conditions, several behavior patterns are possible. Firstly, it is using the services of a consulting company. Secondly, it is choosing and implementing one of the proposed approaches independently. Thirdly, it is systematizing the accumulated knowledge and experience, forming a vision and designing the digital transformation process in the company. In addition, whatever option is chosen, top managers and key employees can and should be trained in CDTO programs. Not all industrial enterprises can use the services of top consulting companies while the very choice of a consultant is attended by the need to understand the essence and scope of work ahead. Therefore, it seems relevant to generalize and systematize the accumulated knowledge and experience, and then form a general landscape of digital transformation, identify the principles, methods and tools so that a digital transformation trajectory could be customized to meet the requirements of a particular enterprise. Thus, this study is aimed at building a landscape of strategizing methods and tools, systematized according to the phases of the digital transformation process, which can be used as a navigator for choosing the digital transformation path of an enterprise, given its digital maturity and the characteristics of the digital environment. The objectives of the work are as follows: – substantiate the outline of the process of digital transformation of industrial enterprises; – study the features of strategic planning and management methodology in the digital environment, affecting the composition and content of the principles, methods and tools that ensure the competitiveness of industrial enterprises in the digital economy; – build a landscape of digital transformations and set out the rules for navigating through a system of goals and choosing the digital transformation trajectory of industrial enterprises.
2 Materials and Methods 2.1 Outline of Digital Transformation of an Industrial Enterprise In order to identify the main phases of digital transformation of industrial enterprises and setting the key goals, let us compare some approaches to digital transformation in Russia and around the globe (Table 1). As with any decision-making process, an analysis phase precedes the choice of strategy. Studies [5, 8, 9] highlight the need for analyzing the digital maturity of the company, the current market situation and the prospects for its change. Studies [2, 6] imply the analytical phase by default. The first phase of the strategizing process as such
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Table 1. Comparative analysis of approaches to digital transformation (Compiled by the authors) World Economic Forum, Bain & Company [6]
Gartner [2]
Guidelines for digital transformation of public corporations [8]
KMDA [5]
1. Digital strategy
1. Ambition
1. Current situation and the 1. Measure the prospects of digital digital maturity of transformation for the the company state-owned company
2. Business model
2. Design
2. Target vision, goals and KPI of digital transformation of the state-owned company
2. Form a strategic vision
3. Enablers/Foundation of 3. Deliver digital transformation
3. Initiatives and roadmap for digital transformation of the state-owned company
3. Create a digital transformation management body
4. Orchestration
4. Scale
4. Personnel, competencies 4. Develop a digital and culture for digital transformation transformation of the strategy state-owned company
5. Refine
5. Digital transformation management model for the state-owned company
5. Prepare employees for digital transformation
6. Digital transformation financial model of the state-owned company
is the choice of a digital strategy consistent with the outlined vision and goals of digital transformation [5, 6, 8]. Today, in addition to the traditional approach, the usefulness of setting ambitious goals is increasingly emphasized [2, 10]. The most generalized approach among the above (Table 1) is the one developed by analysts of the World Economic Forum in collaboration with consulting company Bain & Company [6]. Here is a brief comparison of its four basic stages with the key points highlighted in other approaches. The approaches proposed by Russian developers focus on the matters of organizational and financial support of changeovers (setting up a body controlling digital transformation, recruitment and staff training, developing some management and financing models), whereas foreign consulting companies concentrate on the management phases of the transformation process (orchestration, scaling and refinement) [2, 6]. However, whenever a digital strategy is formed it is always in the spotlight. The digital strategy and the business model are the main tools of digital transformation. They determine the choice of digital technologies for additional consumer value and competitive products, set the list of the most desirable competencies, etc. It should be
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noted that despite the unconditional “primacy” of the business strategy in relation to the digital technologies that support it, the process is iterative. Thanks to digital technologies, new products can be produced, new formats of interaction with consumers and partners can be chosen, and new business models can be put into practice. That is why, in order to understand the business opportunities arising from these technologies, enterprises often carry out the so-called pilot projects, which, if successful, are scaled up to the entire company. This point is strongly emphasized in the approach used by Gartner [2], while model [6] combines these processes, the same as some others in the orchestration phase. Along with digital technologies, Enablers of transformation form the grounds for it and include data analysis, an operating model, partnership, talent management, organizational structure, corporate culture, etc. [6]. Many studies discuss the content and features of a digital strategy, as well as analyze the most successful business models in the digital environment [11,16]. Business ecosystems play an essential role in the digital economy [17], so creating an ecosystem is sometimes considered to be a separate phase of digital transformation. In any case, this is an extremely important strategic decision that must be considered and accepted by every company. Studies [18,20] discuss a strategic management model of an enterprise’s digital transformation that takes into account the capabilities of ecosystems. The traditional contradiction between sustainability and flexibility of a strategy is becoming more and more obvious today. The following ones are considered to be recommendations for increasing the flexibility of a digital strategy: the modular principle of construction [14, 18], the principles of building an adaptive strategy [15], agile management technologies that are used not only during the implementation phase, but also when the strategy is being developed [1], erasing boundaries between long-term and medium-term planning [12], agile strategic roadmaps [21]. Roadmaps are the tool that connects the phases when the strategy is being developed and implemented. This follows from the definition of digital transformation as a journey rather than a destination [2, 19, 22]. For this reason, the IDC company has proposed an approach that involves developing a roadmap with a breakdown into three time horizons: immediate, medium-term and long-term [21]. Another necessary prerequisite for successful transformation is clear indicators of goals and progress of transformation (KPI). Since the aspects and processes that are evaluated have their own specifics, active research work is being carried out in this field [23, 24]. 2.2 Specifics of Strategic Planning and Management in Digital Environment Strategic management as a methodology for managing enterprise development was formed in response to the increasingly instable external environment in the 1970s. The spread of digital technologies has led to a new qualitative leap in this direction, and today theorists and practitioners face an extremely tough challenge of adapting and developing methods and tools for strategic management of enterprises in the digital environment [25, 26]. The first thing that is noted by most researchers is the need to abandon the calendar approach in strategic planning. The necessary management flexibility cannot be ensured any longer if strategic plans are linked to rather long periods of time and adjusted once
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a year. The strategy should be monitored and adjusted given the events that can have a significant impact on the competitiveness of the enterprise. Strategic cycles are getting shorter, and the process is becoming situational rather than calendar. Focus should be shifted from what is stable to what is changing and to how these changes can neutralize historical sources of advantages, and create new opportunities at the same time [12, 27]. The results of modern research in this area can be broken down into two interrelated groups: 1. analysis of opportunities and constraints of the traditional strategic management methods and their adaptation; 2. development of new approaches and tools for strategizing in the digital environment. As for adaptation of the classical methods of strategic management, the following ones have to be distinguished: – the increasing role of the mission, vision and strategic priorities of an enterprise. Since a clear long-term “route marking” is getting impractical, now the above have the value of a landmark, a guiding “north star”. Having such a landmark, project teams working in various fields can move towards achieving the goals of the enterprise and adjust their objectives in a flexible way given the changes occurring around. In addition, the best results are invariably demonstrated by those companies whose mission is more ambitious [9, 28]; – development of methods for analyzing the external environment. Analysis is still a necessary and crucial phase of the strategic management process. However, changes are needed here as well. First of all, this concerns the scope of analysis constrained mainly by the limits of a certain industry. Today, industry boundaries are being blurred or fading away, with business ecosystems playing an essential role in this process. New promising areas are increasingly available outside a particular industry, or at the junction of industries. In this regard, the role of technological intelligence in the digital environment is significantly growing [28, 29]. Another area for developing environmental analysis is an increasing bearing cone. Study [30] proposes to continuously monitor not just 4−6 factors (as in PEST analysis and its modifications), but eleven. In addition, there is need for analyzing and considering the relationships between different groups of factors. Thus, the TPESTRE method used by Gartner company assumes that a “tapestry” of interrelated strategic assumptions is created and used as a basis for constructing possible scenarios of development [31]. Technological, political, economic, socio-cultural, ethical, regulatory and environmental factors are considered to be the basic Drivers of Change. As a consequence of the above, the role of scenario analysis and planning, and management by weak signals is growing significantly [32]. At the same time, a number of approaches and methods is still relevant. These include the method of time-tested SWOT analysis, the hierarchical principle of building a strategy (identification of corporate, business and functional strategies), the need for developing and monitoring the metrics of digital transformation process (KPIs). However, the role and structure of the KPI system are also being actively discussed and developed. Thus, the following structure of the KPI system is proposed [23]: – KPIs of the enterprise determine the main strategic and investment decisions;
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– Customer KPIs are the characteristics of value, desired behavior, etc., which set priorities in customer relations; – KPIs of partners and suppliers measure the efficiency of the business ecosystem; – workplace analytics measure the productivity and involvement of people and teams, and characterize the contribution of human capital to the achievement of business goals. Further improvement of the KPI system should rely on machine learning algorithms that make more precise adjustment of the indicator system to the characteristics of the enterprise and the market situation [24]. A digital maturity assessment model is one of the strategizing tools for identifying priority areas and projects for enterprise development in the digital environment. Today, there are quite a lot of different models, which are analyzed and compared in [9]. As companies that use ambitious goals and strategies achieve greater progress, the FAST system is replacing the traditional goal-setting requirements of SMART [10]. In addition to setting ambitious goals, for which the normal level of achievement is 60−70%, the possibility of discussion, transparency and more frequent adjustments are among the features of the FAST system. As noted above, close attention is devoted to the key tools of strategic management: strategy, business model and roadmap. The main principles for developing a digital strategy should be highlighted: flexibility and customer orientation, modularity, bimodal architecture, as well as agile Strategy Sprints and the principle of Fail Fast [22, 27, 33, 34]. At the same time, despite the rapid changes in technologies, consumer preferences and markets, the strategy should proceed from the possibility of maximizing the competitive advantages the enterprise has. The probability that the strategy will be successful largely depends on how concise it is and whether the strategic priorities are visualized. Gartner has developed the concept of a “one-page strategy” and possible templates for its presentation [35]. A necessary prerequisite for maintaining the required flexibility is the use of tools (platform software solutions) for supporting the strategic management process. Given the analysis above, let us build a landscape of methods and tools that contribute to the successful digital transformation of an industrial enterprise.
3 Results The following are selected as a theoretical basis for designing a landscape of strategizing for an industrial enterprise’s digital transformation: – the outline of the digital transformation process, combining four key phases necessary for the development and implementation of successful transformations; – Gartner’s approach to building an adaptive strategy in the digital environment [36]. Gartner’s research has identified four main practices that ensure transition from calendar strategic planning to an adaptive, event-oriented approach [34]: – embrace and explore uncertainty (G1); – start execution as early as possible (G2);
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– respond to changes as they happen (G3); – involve everyone in strategy (G4). The strategizing landscape of digital transformation (Fig. 1) is a matrix in which various methods and tools are systematized according to the phases of digital transformation and selected with maximum consideration of the adaptive approach required by the digital environment.
Fig. 1. Strategizing landscape of digital transformation (compiled by the authors)
The matrix is designed for navigation through the knowledge and experience gained during successful digital transformation, which is done by identifying the methods and tools relevant in the digital environment and linking them to the appropriate phases of digital transformation of an enterprise. It contains some fairly well-known methods and tools (analysis of the drivers of change, visualization of the user’s path, prioritization methods, models for assessing digital maturity, successful business models of the digital economy, etc.). However, new lesser-known, but useful approaches are constantly emerging (Minimum Viable Strategy, One-Page Strategy [35], Experiment- and Option-Based Strategies [34]). In order to act as a navigator, the landscape presented in Fig. 1 must be constantly augmented and updated. Structured questionnaires, or checklists, are another tool for structured information analysis and navigation in a rapidly changing world. Table 2 offers a checklist for developing a roadmap of digital transformation strategy of an enterprise based on the results of the analysis. To sum up, we should note what extensive management practice suggests: – firstly, it is impossible and impractical to use all top methods at the same time;
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Table 2. Checklist for developing a roadmap of digital transformation strategy of an enterprise (Compiled by the authors) Phase/objective of developing the roadmap
What should be considered
1. Analyze market
How do digital technologies change your sector? Who are your customers? What is the key value for them? Who are your rivals? What makes you different from your rivals?
2. Digital vision, ambitious goals
What new digital products and services do you want to create? What channels of interaction with customers should be used? How is the market position seen by the enterprise? What goals would you like to attain?
3. Assess and use the enterprise’s competitive edges
What are your competitive edges? How can they be best used to achieve your goals and strengthen your market position?
4. Define the strategic priorities and develop the KPI system
What will be included in the portfolio of your business, products and services? How will digital and traditional products and services be combined? What strategic initiatives are most important (choose no more than 3–5)?
5. Identify gaps
What digital technologies do you need? What competences? Who are your major suppliers and partners? What necessary resources and opportunities can you get from taking part in ecosystems?
6. Digital strategy roadmap
Define projects in terms of three horizons: Immediate, Midterm, and Long term. Set clear KPI values for projects of the first horizon. Scenarios should be developed for projects of the second horizon. Projects of the third horizon are defined according to the results of technology reconnaissance and weak signals analysis
7. Monitor and revise strategy
To what extent are the results of the project consistent with the plan? What events are expected to be and/or have been significant for business? What adjustments should be made to the project portfolio, strategic initiatives and goals given the changing situation?
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– secondly, there are no ideal or universal methods and tools. They all have both strengths and weaknesses, which should be considered when a methodological and instrumental basis is formed for specific conditions. Now enterprises that design their own individual transformation trajectory in the forever changing digital environment should focus on three types of landmarks: 1. Transformation phases. This work relies on the outline of the digital transformation process proposed in [6] and consisting of four big phases (Digital strategy, Business model, Enablers, Orchestration); 2. Principles and rules of decision-making. The main principle ensuring the competitiveness of enterprises in the digital environment is flexibility. Therefore, it is proposed that Gartner’s adaptive approach to strategic planning should be used as rules [34]; 3. Methods and tools of decision-making in each phase should be chosen in accordance with certain rules. As recommendations, the authors of this paper suggest a strategizing landscape of digital transformation (Fig. 1).
4 Discussion Due to the size constraints of the paper, the content could be disclosed only to a limited degree, the same as the benchmarking of the methods and tools presented in Fig. 1. This should be done further in order to move to the next level of navigation and produce specific recommendations on how certain tools can be selected and applied, given the characteristics of a particular enterprise and the conditions under which it operates. Further research should solve this problem. Another relevant research area is to specify and elaborate the methodological grounds of the newly developed tools. Many of them are only presented conceptually and their application is not developed well enough. We believe that today such tools include Minimum Viable Strategy, Experiment- and Option-Based Strategies, Agile Roadmaps, etc.
5 Conclusion In this paper, navigation is understood as a process of methodological support aimed at creating a roadmap that can be used in the digital transformation strategy of an enterprise. This process includes theoretical justification and systematization of experience gained by successful companies and suggests recommendations, structured according to the phases of the digital transformation and concerning the rules, formats and methods of strategic decision-making. The paper provides an outline for digital transformation of an industrial enterprise that can be used for determining the key phases and objectives of digital transformation. The necessity of developing an adaptive digital strategy of the enterprise in the first phase is emphasized. The features of strategic planning in the digital environment are highlighted: the urgency of the transition from the calendar nature of planning to event-based planning, as well as the danger of limiting the scope of analysis to industry boundaries. The paper
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shows the directions for developing traditional methods and tools of strategic management, including analyzing environmental factors, expanding the scope of scenario planning and management based on weak signals, increasing the role and ambition of the mission and the system of strategic priorities of an industrial enterprise. The results of the researched methods and tools of strategic management in the digital environment are summarized: models for assessing the digital maturity and determining priority areas and projects of digital transformation, approaches to the formation of an adaptive digital strategy, including the use of agile technologies not only in the process of its implementation, but also during development. A strategizing landscape of digital transformation of an industrial enterprise has been built. It systematizes today’s methods and tools by the phases of digital transformation with maximum consideration for the adaptive approach required by the digital environment. The paper gives general recommendations regarding navigation through the methods and tools presented. A checklist is proposed for developing a roadmap for digital transformation strategy of an enterprise. Acknowledgements. The study was conducted with the financial support of the Russian Science Foundation as part of scientific project No. 23-28-00395.
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Business Digital Maturity Assessment in Strategic Decision Making Aleksandr V. Kozlov , Irina M. Zaychenko(B)
, and Darya P. Kolotova
Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russian Federation [email protected]
Abstract. In the context of the digital transformation of the economy and almost all socio-economic phenomena and processes resulting from it, the issues of determining the readiness of a business for such a transformation become quite relevant. In qualitative analysis, the use of a verbal description does not allow for assessing the degree of business digitalization accurately and objectively, so strategic decision makers need quantitative assessments. One of the recently popular approaches in this area is the use of the concept of “digital maturity”. Digital maturity characterizes the degree of digital transformation of a business or its individual enterprises, as well as its information and communication capabilities, both actual and potential, which is certainly important for an integral assessment of business readiness for digital transformation. Thus, the purpose of this article was to develop a methodology for assessing the digital maturity of a business in the field of strategic decisions. To assess the digital maturity of companies, a system of indicators was developed, consisting of eight groups. To assess the degree of digital maturity for each specific indicator in the listed blocks, a 10-point scale is used, then a weighted coefficient is calculated to obtain a more objective assessment result, then an integral indicator of business digital maturity can be calculated based on the results obtained. The result of calculations of the integral assessment of the digital maturity of a business can serve as basic information when making strategic decisions related to assessing the quality of decisions made in the field of managing digital business transformation processes, choosing a strategy for digital business transformation, analyzing the degree of digital transformation of an enterprise in an industry or regional sense. Keywords: Digital transformation · Digital maturity · Assessment of the degree of digital maturity
1 Introduction M. Porter postulated at the time that sound strategic decisions on business development are the key to success in the long run [1]. The main tool of strategic analysis, as a stage of strategic decision-making, is the search for a balance between the internal state of the business and the dynamics of the external environment. An important feature of the modern business landscape is the processes of digital transformation, both within individual enterprises and in the business infrastructure [2–10]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 921–934, 2023. https://doi.org/10.1007/978-3-031-32719-3_70
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In this case, it becomes extremely important to consider the impact of digitalization processes on strategic decision-making. Qualitative analysis, the use of a descriptive approach does not allow us to assess the process of digitalization of business accurately and objectively, therefore, strategic decision-makers need quantitative assessments. One of the most popular approaches in this area recently is the use of the concept of “digital maturity” [11, 12]. The digital maturity indicator of a business allows you to perform a retrospective analysis, trace the trajectory of the development of digital transformation processes, and identify the strengths and weaknesses of the business in this context. Thus, the objective of the study is the development of a method for a business digital maturity assessment in the framework of the strategic decision-making process. There are the following tasks to be fulfilled to achieve the goal: 1. Analysis of the process of a business digital transformation as an element of strategic decision making. 2. Analysis of business digital maturity concepts in the context of strategic management of an enterprise digital transformation. 3. Overview of existing approaches and methods for a business digital maturity assessment. 4. Propose an original approach to a business digital maturity assessment. 5. Elaborate on the method of a business digital maturity assessment based on business performance indicators. In the Introduction of the article, the relevance of the research is substantiated, and the goals and tasks that need to be implemented to achieve the goal are formulated. The Materials and Methods section describes the research methodology and theoretical provisions of the role of assessing the digital maturity of modern business in the context of strategic management. A scheme of indicators reflecting the digital maturity of the business, formulas for calculating the integral indicator, and ways of its application for strategic decision-making is proposed. The Discussion section shows the difference between the proposed approach to assessing digital maturity from existing ones, and the limitations of the approach and method of assessing the digital maturity of a business are noted. The Conclusions section provides an overview of the overall results of the study and defines the directions for further research.
2 Materials and Methods 2.1 Research Methodology The scientific literature presents many methodological approaches to assessing the digital maturity of companies. The BCG study (Digital maturity assessment to improve efficiency. URL: https://www.bcg.com/ru-ru/capabilities/digital-technologydata/digital-maturity. Last accessed 15.03.2022) presents the BCG Digital Acceleration Index, which helps to assess the level of digital development and lay the foundation for the digital transformation. The level of digital development of a company, that is, its ability to create value using digital technologies, serves as a key predictive parameter for assessing the chances of success of the digital transformation. The Digital Acceleration Index (DAI) of the BCG company is a diagnostic tool that allows you to assess
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the level of development of digital competencies and make comparisons with comparable competitors, industry averages, digital leaders and so on. The index also evaluates the readiness of a business to transform and become an organization that combines the capabilities of technology and man for growth, innovation, efficiency, and resilience. Following the approach to the digital maturity assessment presented in research (Industrie 4.0 Maturity Index. Managing the Digital Transformation of Companies – UPDATE 2020. Acatech, National Academy of Science and Engineering (Germany). URL: https://en.acatech.de/publication/industrie-4-0-matu-rity-index-upd ate-2020/. Last accessed 15.03.2022), it is necessary to consider such assessment factors as: • • • • • •
computerization, connectivity, visibility, transparency, predictive capacity, adaptability.
Deloitte identifies only 5 criteria for assessing digital maturity: strategic initiatives, technology application, operational activities, customer experience, and corporate culture (SAP and Deloitte Research: how to increase the level of digital maturity – from strategy to implementation. URL: https://www2.deloitte.com/ru/ru/pages/consulting/art icles/sap-deloitte-research.html. Last accessed 15.03.2022). The concept of the digital maturity of an industrial enterprise as an integral part of digital transformation is presented in work of E. Popov [13]. The author has developed a six-level typology of digital maturity, namely: absence, existence, application, use, substitution, and autonomy. The author’s vision of the roles of the entrepreneur, manager, and performer in the process of digital transformation is proposed. This allows for resolving the contradiction in the relationship between the principal and the agent, associated with the availability of information about the processes performed by the agent on behalf of the principal. One of the most recent works by J. Lufman is interesting [14]. It presents six components of the maturity of strategic alignment: Communications. They determine the effectiveness of the exchange of ideas, knowledge and information between IT companies and business organizations. Value. It uses balanced different dimensions to demonstrate the contribution of information technology and IT organizations to businesses in terms of how people understand and accept business and IT. Governance. It determines who has the right to make IT decisions and which IT process managers implement at the strategic, tactical, and operational levels to determine IT priorities. Partnership. It evaluates the relationship between a business and an IT organization. Scope and Architecture. They measure the provision of flexible IT infrastructure and its evaluation, the application of new technologies. Skills. Methods of working with personnel are measured, such as hiring, retention, training, performance reviews, encouraging innovation, and career opportunities.
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The international management consulting firm Arthur D. Little (Digital Transformation – How to Become Digital Leader. URL: https://www.adlittle.com/sites/default/ files/viewpoints/ADL_HowtoBecomeDigitalLeader_02.pdf. Last accessed 15.03.2022) has developed the Digital Transformation Index. The study identified seven areas of assessment, presented below: • • • • • • •
strategy & governance, products & services, customer management, operations & supply chain, corporate services & control, information technology, workplace & culture.
Summarizing and comparing the concepts of digital maturity, we can see that the definition of digital maturity in existing studies focuses more on the process, the degree of its development, as well as its capabilities, and achievements [15]. It seems to us that in the context of digitalization, a more structured and justified approach is based on the allocation of the following evaluation blocks following the functional blocks of strategic analysis: • • • • • • • •
digitalization strategy, digital business models, human resources for digitalization, organizational flexibility, ICT resources, digital business culture, digital value chain, digital environment of consumers.
Table 1 shows the characteristics of the estimates (indicators) in of the selected blocks. The methodology for calculating the integral indicator of the digital maturity of a business is given in the Results section. 2.2 Theoretical Fundamentals The theoretical basis for the formulation of directions (allocation of blocks) for assessing the digital maturity of a business concerning strategic management tasks are the works of M. Porter on the formation of sustainable competitive advantages by analyzing the value chain [16], S. Gupta research on the specifics of digital business strategy [17], the theory of the use of modern strategic analysis tools [18, 19]. The Strategic Alignment Maturity Assessment model, developed by J. Luftman [20], provides companies with a tool to evaluate their strategic alignment actions and shows how they can improve their alignment practices. One of the first maturity models in the field of information security was the Paulk Capability Maturity Model, developed in 1993, which led to the constant emergence of new maturity models in information security research. In this five-level model, M. Paulk
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describes how organizations engaged in software development change their capabilities, focusing on improving software processes [21]. Reference models of maturity are described by T. Thordsen [22]. They can be used by companies to assess the current state of a set of opportunities and identify measures for improvement. The article [23] examines the company’s culture as an element of digital maturity, which is certainly an important aspect not only of assessing digital maturity but also of doing business in general. There are other models for assessing digital maturity [24–28]. However, there are also studies in which digital maturity is often understood by researchers as synonymous with digital readiness, which is assessed even at the level of the country as a whole [29]. The author of the article [30] adheres to the point of view that digital transformation is the way an organization passes through the levels of its digital maturity. Digital maturity determines the degree of a company’s digital transformation and its digital capabilities, which is important for assessing both actual and potential business digitalization opportunities [31].
3 Results To assess the digital maturity of companies, a matrix was developed, implying the use of weighted coefficients to obtain the result. A 10-point scale is used for analysis, where 10 is the highest score and 1 is the lowest. The list and characteristics of the evaluation blocks, as well as the corresponding weight coefficients, are presented in Table 1. Each evaluation block (eight in total) is divided into sub-blocks. First, in the process of assessing the level of digital maturity of an enterprise, an assessment is determined on a 10-point scale for each subblock. These estimates are multiplied with the corresponding coefficients (from Table 1) and make up the sum, that is, the block estimate (Zj ) according to the formula (1). zj =
Nj i=1
kij × Pij .
(1)
Here k ij is the coefficient of significance of indicator i of block j of the evaluation matrix; Pij are the value of the indicator i of block j of the evaluation matrix; Nj are the number of indicators in block j of the evaluation matrix. The second stage of the analysis is the multiplication of estimates by blocks with corresponding coefficients, summation, and obtaining an integral indicator of the assessment of the level of digital maturity Z (score on a 10-point scale) according to the formula (2). Z=
M
Here rj are significance coefficients; M is a number of evaluation blocks.
j=1
rj × zj
(2)
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Table 1. Matrix for assessing the level of digital maturity of the company (compiled by the authors). №
Digital Maturity Assessment Assessment characteristic Unit
1
Digitalization strategy
1.1
Leadership
The level of the leader’s 0.38 qualifications, readiness for serious changes, and making “inconvenient” decisions on the way to the digitalization of business
1.2
Innovativeness of the strategy
Degree of innovation in the company’s existing development strategy and the level of its compliance with the requirements of the modern digital market
1.3
Feasibility of a strategy
The level of understanding of 0.28 how to implement the existing digital business transformation strategy
2
Digital business models
2.1
Validity of digital business models
Level of compliance of the capabilities of the created models with the results that the company strives for, innovation and digital component of the developed models in general
0.65
2.2
Implementation of existing digital projects
Degree of involvement in the production process and full use of the digital potential of existing models
0.35
3
Human resources of digitalization
3.1
Remote work
k1
k2 0.13
0.34
0.128
0.125
Quality of working conditions 0.17 in the conditions of remote work, the use of this opportunity to reduce production costs while maintaining and preferably increasing labor productivity (continued)
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Table 1. (continued) №
Digital Maturity Assessment Assessment characteristic Unit
3.2
Qualification and compliance Level of qualification of 0.16 employees and compliance with their positions, the availability of skills (including education) in the field of IT
3.3
Efficiency of interaction
Completeness of the use of existing digitalization drivers to increase the efficiency of staff interaction with each other and labor productivity
3.4
Staff flexibility
Degree of flexibility of the 0.16 staff, the ability to perform not only their functions but also the tasks of colleagues if necessary
3.5
Employees’ professional competences and skills
Completeness of staff access 0.17 to all necessary tools, models, and equipment, the level of conditions created for comfortable collaboration, both face-to-face and remotely
3.6
Equipping staff with digital devices
Degree of provision of administrative staff with computers and related equipment
k1
k2
0.17
0.17
4
Organizational flexibility
4.1
Organizational structure
Degree of interconnection and 0.39 interdependence of the parts of the whole, the functioning of the enterprise as a single organism “Industry 4.0”, the activity of the unit responsible for innovation and digital transformation
0.12
4.2
Digital flexibility
Level of compliance of the 0.33 organizational structure with the realities of “Industry 4.0”, the degree of digital flexibility of the business (continued)
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№
Digital Maturity Assessment Assessment characteristic Unit
4.3
Equipment
5
ICT resources
5.1
Data storage and availability
Degree of reliability of the security system and the safety of data, provided they are available to employees
0.6
5.2
Completeness and relevance
Relevance, usefulness, and completeness of the information used in the process of functioning of the company
0.4
6
Digital business culture
6.1
Propensity for innovation
7
Digital Value chain
7.1
Digitalization of production
Degree of automation and innovation in the production process, level of its efficiency
7.2
Digital logistics
Novelty and effectiveness of 0.22 the digital technologies used to implement logistics processes
7.3
Quality control
Level of proficiency in modern quality management tools, minimizing losses by reducing the number of errors in production
k1
k2
Degree of modernization of 0.28 old equipment, the acquisition of new innovative equipment (automation and robotization of production processes) 0.125
0.118 Degree of innovation in the corporate culture of the company, the commitment of employees to the idea of digital transformation of the business, contributing to the process voluntarily
1.0
0.127 0.25
0.24
(continued)
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Table 1. (continued) №
Digital Maturity Assessment Assessment characteristic Unit
k1
7.4
Intellectual processes
0.29
8
Digital environment of consumers
8.1
Marketing communications
Degree of adjustment of the 0.22 two-way communication system with the client (excluding the need for personal presence), the solution of the main marketing tasks with its help
8.2
Customer Data analytics
Relevance of the digital tools used for analyzing customer data, the degree of innovation of the approach
8.3
Online-commerce
Degree of commitment to 0.19 e-commerce tools in the implementation of financial and other types of transactions
8.4
Omnichannel
Degree of integration of 0.19 various digital communication channels into a single whole
8.5
Customer self-service
Quality of the implemented conditions for partial or full self-service of customers using modern technologies
Degree of commitment to the Data-driven approach – decision-making based on up-to-date data, conciseness, and clarity, hitting the target
k2
0.127
0.21
0.19
After deriving a general assessment of all the characteristics of the form, we can conclude about the level of digital maturity of the company under study by referring to the results table (Table 2). The results of the calculations of the integrated assessment of the digital maturity of the business can serve as initial information in solving the following strategic tasks: • assessment of the strategic decisions made in the field of business digitalization process management in the presence of data on the dynamics of this indicator for several years; • comparative analysis in the field of digitalization of a particular business in the sectoral and/or regional context; • choosing a business digitalization strategy.
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Table 2. A scale that characterizes the degree of digital maturity of the company (compiled by the authors). № Rating (10-point scale) Characteristics of the degree of maturity of the company 1
1–3 points
Zero maturity level. Closed infrastructure, the simplest automation systems, goods (services) exist separately from each other, information is collected manually, is not structured, is stored in paper form, there is no specific approach to management, the simplest tabular and graphical forms are used, personnel work on specific assignments and have no right to make mistakes, qualifications are confirmed by the presence of a document on education, the non-necessity of developed personal qualities, including leadership, the non-necessity of skills working with computer and information systems
2
4–6 points
Low maturity level. A goal appears, tools are selected to achieve it, consultations with experts have been held, pilot projects have been launched, a product portfolio has been created, rules, production standards, the definition of an automation system, regulations, structuring of work with data, combining data into groups, the use of regression analysis methods, personal qualities of employees, competencies in the areas of “IT”, “Change management”, “Working with data”, differentiation of responsibilities by functionality have appeared, there is competition between employees in the culture
3
7–8 points
The average level of maturity. The company’s digital platform is approximately 50% developed. There are such things as enterprise architecture, cloud technologies, modern computing programs, a system for collecting feedback from consumers, analyzing and responding to information received, more than 30% of processes at the enterprise are automated, monitoring of processes is fragmented, an automated data exchange system, they are not collected manually, interconnected and high-quality, more than 30% of personnel are engaged in task setting and management, most of the processes are carried out using information technology, grouping people according to their qualifications, advanced training in the field of IT, hiring specialists in this field (continued)
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Table 2. (continued) № Rating (10-point scale) Characteristics of the degree of maturity of the company 4
9–10 points
High level of maturity. Full disclosure of the digital potential of the enterprise. There is an automated process for the production of innovative products, customer orientation, predictivity, product analytics and continuous improvement, end-to-end integration of processes, decision-making is completely based on data, modeling, monitoring and optimization do not stop and are implemented in all areas of activity at the enterprise, data is relevant, available in real time, the use of machine learning principles, the search for cause-effect relationships, the widespread use of innovative models, each employee has the whole set of competencies, inherent in the position, the corporate culture is based on honesty and openness, there are no harsh penalties (except unique force majeure situations)
4 Discussion The topicality of the study subject stems from the necessity to make decisions regarding business digital transformation based on some quantitative indicators. Responsible managers may assess the existing level of business digital maturity to monitor and evaluate the dynamics of the digitalization process, compare with other businesses from in the industry and provide benchmarking. The study is in line with the important and popular research direction presented in the academic information space, referring to business digital maturity assessment. The authors analyzed existing approaches to the definition of the concept of business digital maturity and postulated that digitalization is a part of the process of business strategic management. It defines the main concept proposed in the paper method which is the calculation of integrated indicators based on sub-indicators according to the eight sectors/facets characterizing the strategic position of a business. The calculation formula takes into account the weighted coefficients for every sector and sub-indicator according to their importance. The limitations of the study are as follows: firstly, the proposed approach is based on experts’ evaluation. That means strong requirements for their competencies in the field, and deep knowledge of the state of art in all components inside of the business. Secondly, the assessment scale is a four-level, which is not typical for similar studies. The general concept of a business‘s digital maturity could be defined in another way which lead to necessity to reformulate basic concept and use different tools to assess business digital maturity. Future studies could be done in the direction of drilling deeper into every sector characterizing strategic position of a business and adjustment of sub indicators’ list, rearrangement of weighting coefficients.
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5 Conclusions Thus, the article proposes the method of assessment of a business‘s digital maturity which is a part of the process of business strategic management. The integrated indicator allows us to perform a retrospective analysis, trace the trajectory of the development of digital transformation processes, and identify the strengths and weaknesses of the business in this context. The table of sectors and sub-indicators for assessment is organized according to distinguished eight areas characterizing the strategic position of a business, namely digitalization strategy, digital business models, human resources of digitalization, the flexibility of the organizational structure, ICT application resources, digital business culture, digital value chain, and the digital environment of consumers. The formula of the integrated indicator is a convolution of sub-indicators of all eight sectors with weighted coefficients for every sector and sub-indicator according to their importance. The evaluation process is based on a four-level scale developed by the authors. Every level is scrupulously described. The results of the calculations of the integrated assessment of the digital maturity of the business can serve as initial information in solving the following strategic tasks: • evaluation of strategic decisions made in the field of business digitalization process management in the presence of data on the dynamics of this indicator for several years; • comparative analysis of the state of affairs in the field of digitalization of a particular business in the sectoral and/or regional context; • choosing a business digitalization strategy. The paper distinguishes the limitations of the method and directions of future research. Acknowledgments. The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program ’Priority 2030 (Agreement No. 075–15-2021–1333 dd 09/30/2021).
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Digital Solutions for Multimodality in the China-Europe Route Igor V. Ilin1(B) , Sofia E. Kalyazina1 , Anastasia I. Levina1 , and Bulat D. Khusainov2 1 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected] 2 Institute for Economic Research, Nur-Sultan, Kazakhstan
Abstract. The field of logistics operations, including multimodal transport in the China-Europe direction, is facing, on the one hand, an increase in freight traffic and, on the other hand, various crisis factors, such as the COVID-19 pandemic. Under these circumstances, the digitalization of freight transport is becoming increasingly important. It is digital solutions that can provide the calculation of optimal parameters for logistics operations. The article reviews the state of affairs in the sphere of multimodal transportation in China-Europe direction and reveals the directions of digitalization development to improve the speed, quality, safety and transparency of transportation. The purpose of the study is to identify problems in multimodal transport in the direction of China-Europe, which require greater use of digital solutions, and to propose ways to solve them. The research methodology is the case study methodology. As a result of the study, the key digital technologies in the area under consideration are identified, the ongoing and planned projects are considered, and the existing problems, including those requiring greater use of digital solutions, are formulated. The practical significance of the study is related to the fact that the importance of transportation on the route China-Europe is growing. With the growth of cargo traffic, a number of identified problems are intensifying. The deepening of digitalization and the possible optimization of cargo flow, logistics, and document flow on this basis can significantly improve the economic efficiency of all participants in the supply chain. The novelty of the study is due to the fact that there is a high degree of variability in the area in question, related, among other things, to the impact of the COVID-19 pandemic and changes in the political sphere on a global scale. Keywords: Logistics · Digitalization · Multimodal Transportation · Internet of things · International Transport Corridor · Railway Communication
1 Introduction The global production transformation has made delivery routes more complex, which has led to the need to find more system and network solutions to coordinate the stages of production, involving related operations such as packaging, labeling, loading and unloading, warehousing, customs and insurance consulting, the use of information technology and tracking systems, strategic planning methods, etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 935–944, 2023. https://doi.org/10.1007/978-3-031-32719-3_71
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Digitalization is precisely the factor that opens up new opportunities in logistics, makes it possible to improve business models and expand the range of services provided. Important trends in the modern development of logistics are the increased role of transport and logistics outsourcing and transport and logistics centers in coordinating the global transport and logistics process and the strengthening of regional integration processes with the abolition of tariff restrictions in mutual trade [1, 2]. In particular, it is important to create transport and logistics centers with giving them a multimodal status. The practice of transport and logistics outsourcing is carried out through intermediaries of the transport and logistics process - logistics service providers, who carry out transport and logistics operations within the entire supply chain. Multimodal carriage is a type of international carriage carried out by two or more modes of transport under one contract of carriage, a single transport document and a single tariff by a multimodal transport operator of a level corresponding to a logistics provider of levels 3 and 4 PL (United Nations International Multimodal Transport Convention of 1981). The development of multimodal transportation requires the availability of an appropriate transport and logistics infrastructure along the entire chain, capable of ensuring the uninterrupted flow of goods on any mode of transport with the provision of a whole range of related transport and logistics services. The practice of placing such centers in hubs in international transport corridors contributes to increasing the integration, multimodality and efficiency of the services provided in the system of transport and logistics centers [3]. At the same time, the transport and logistics aspect of cooperation in international transport corridors is developing into a tool for ensuring the global geo-economic and geopolitical dominance of a number of countries and associations. The aim of the article is to identify areas of digitalization in the field of multimodal transport in the direction of China-Europe.
2 Materials and Methods The research methodology is a case study. The cases of leading railway companies in the direction under consideration, as well as the experience of companies that are carriers and freight forwarders in the China-Europe direction, are considered. The study was conducted using an inductive approach. Intra-case analysis and the technique of cross-case analysis were used to analyze the data collected. Also, an analysis of relevant sources on the topic of the study was made.
3 Results The key digital technologies used in transportation logistics are the Internet of Things, smart roads, drones, and warehouse robotics [4, 5]. The transparency of the Internet of Things allows free access to data and routing of goods at all stages of transit [6]. A smart road is a unified concept that includes various technologies to manage road transport. The concept includes many elements with the following devices: video cameras; navigation components; weather sensors; electronic road signs; road markings; traffic sensors; traffic intensity control sensors; traffic light and lighting control systems;
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and parking meters [7]. The collected data allows to create maximum safety during the movement of traffic and to produce intelligent management of traffic flows. Unmanned aerial vehicles are capable of taking inventory in a logistics warehouse, transporting cargo by air or performing security tasks [8]. Warehouse robotics takes care of order processing and fulfillment, distribution of goods, and optimization of logistics supply chains [9]. Modern electric freight transport has the ability to travel up to several hundred kilometers without an additional charge. Additional advantages of electric vehicles are reduction of environmental pollution and operating costs. An unmanned vehicle is equipped with a number of sensors, laser scanners, and tracking systems controlled by an onboard computer, allowing the vehicle to travel without the intervention of the driver or unauthorized people [10]. Cargo marking is the application of a special barcode to the cargo, which is read when it arrives at the point of delivery or redirection. With the digitalization of transport processes, GPS trackers come into play [11]. The automation of customs processes is the introduction of technology to improve the information and communication system of government agencies in different countries. The digitalization of logistics makes it possible to calculate the optimal mode of freight transportation, combining the advantages of each mode of transportation, using the minimal environmental impact of freight trains combined with the mobility of trucks. Wherever possible, transportation by rail serves as the main transportation stage. This reduces CO2 emissions by 80% compared to traditional road transportation [12]. Trucks carry the cargo from the place of its receipt to the place of loading on the main means of transportation (preliminary transport) and the cargo after unloading from the main vehicle to the final delivery point (further transport). The bundling of shipments in logistics centers has an additional effect, as it reduces the number of empty trips. Digital solutions provide a detailed report of the cargo status in real time and guarantee supply chain transparency, safety and cost savings [13]. Additionally, digitalization provides the following capabilities: – – – – –
Analysis, customized reports and statistics Online monitoring of temperature and humidity Geo-fence setting Internal motion, lighting, vibration and intrusion detection sensors Reverse device logistics via air or sea freight.
In the direction of China-Europe, China is working on the implementation of the Silk Road Economic Belt project (a megaregional project that could have a significant impact on global economic development in the future) with a land transport core in the form of the Eurasian transport highway to access the EU and Persian Gulf markets bypassing the Southern Sea Route. The EAEU forms its own system of international transport corridors, consisting of East-West and North-South corridors, to “switch” the transit cargo flows “Asia-Pacific Region - European Union” and “European Union - India - Gulf countries” on its territory, which allows to strengthen the negotiating position with China on specific issues of the project of coupling transport and logistics system of the EAEU and the Silk Road Economic Belt. The U.S. seeks to recreate the Silk Road through the international transport corridor “New Silk Road”, linking the EU with Central Asia,
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India and Pakistan through Afghanistan, creating competition to Chinese and Russian initiatives in the region [14]. Container transportation on the China-Europe-China route is currently based on “paper” shipping documents. The introduction of electronic document flow requires a unified format of data exchange and mechanisms to ensure the legal significance of shipping and accompanying documents, coordination of issues of their digitization. The number of containers transported by rail along the route PRC - Kazakhstan Russia - Belarus - EU is steadily growing [15]. In general, the market for rail transport from China to Europe is containerizing rapidly. This phenomenon has several interrelated reasons: 1. The share of cargo relevant for transportation in containers on the PRC-Europe direction reaches about 80%. More than 50% of EU goods are in the “machinery, equipment and industrial products” group, 10–15% are metal products, 5–10% are glass and ceramic goods, ready-made building materials, clothes, shoes and textiles. Cargoes to the Russian Federation include about 25% machinery and industrial goods, 15–20% - metal products, building materials, about 10% - finished chemical products and chemical raw materials. 2. Reduction of container transportation tariff (due to subsidies to support railway export transportation by Chinese provincial administrations), from an average of $ 9 ths/FEU (40-foot container) in 2011 to $ 5.5 ths/FEU significantly increased demand for the service. 3. Railroad tariffs have reached a level where the competitive advantages of rail transport over maritime transport, such as speed, scheduled transportation and cargo security, have increased their weight significantly. For large consignments of high value goods, even a twofold increase in transportation costs does not have a very noticeable effect on the cost of production. At the same time capital turnover is accelerated. 4. The increase in the number of routes and frequency of container trains expands opportunities in this freight segment and stimulates interest in it on the part of cargo owners. 5. The main counterpart of China in Europe is Germany, which provides reception of about 60% of all containers in this direction. In shipments from China to Germany almost 100% of cargo is transported in containers, including building stone, ore and non-metallic raw materials, fuel and similar cargoes, due to the dominance of container transshipment technologies in both Chinese and German ports [16, 17]. In November 2014, the EEU countries created the largest railway operator in the region, the United Transport and Logistics Company (UTLC). UTLC was formed by merging the packages of national rail carriers - Russian Railways JSC, Kazakhstan Temir Zholy National Company JSC and Belarusian Railway State Association - and is designed to provide an integrated service of through transit by rail through the EEU using its own terminals on the external borders according to the “single window” principle. UTLC is capable of providing annual transportation of more than 1.7 million TEU (20-foot container) containerized cargo, terminal handling of over 3 million tons of noncontainerized cargo and freight forwarding of around 40 million tons. UTLC is also assigned a special role in the modernization of the railway infrastructure of the Eurasian Economic Union with the expected investment of more than 6 billion dollars [18].
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“United Transport and Logistics Company - European Railways Alliance” (UTLC ERA) has set itself the task of increasing the volume of transportation in the transit corridor to 1 million TEU by 2025. With the rapid growth of commodity turnover between China and Europe, the work of transport companies becomes impossible without prompt processing of cargo data. UTLC ERA interacts with all participants in the rail transport process, using its information databases, as well as data from co-executors and agents. These data are received in various formats, often untimely (this depends on the level of equipment of co-executors). However, already now, based on a single database of transit traffic using the company’s own information system, it is possible to: – promptly control and manage the process of cargo transportation (if an emergency situation occurs on the way, the dispatch center and the client department see it, can intervene and prepare additional documents in advance, to avoid confusion, correct the situation as quickly as possible, take the necessary measures); – plan the costs and efficiency of transportation, minimizing costs; – to automate the process of calculations and reconciliation of services rendered with co-executors (the use of cars for the trip, idle time in the dispatch centers, etc.); – provide a tracking service, open access to transportation data for clients and coexecutors using a mobile application/web service where all transportation data is visible. An application for the Chinese messenger WeChat is also being prepared. It is also necessary to optimize empty railcar returns and reduce costs. With the imbalance of freight flows from China to Europe, about half of the cars go back empty (China exports more goods to Europe than it imports from it). This imbalance can be eliminated by searching for product categories that were not previously sup-plied from the EU to Asian markets, as well as by searching for a freight base en route and selecting optimal routes in the Europe-China direction. Empty wagon runs, of course, are an additional cost for transport companies. As the volume of transit traffic increases, the capabilities of the automated information system, which allow to calculate optimal order chains for the loading of cars, taking into account the free fleet in the return direction, become more and more demanded and are currently being developed at UTLC ERA, analyze the economic efficiency of various routes and thereby solve the issue of optimal use of carrying capacity. Today less than 1% of the total volume of international freight traffic between the East (Asia-Pacific countries, APR) and the EU passes through the EAEU, while in 1982 the USSR accounted for up to 20% of the corresponding volumes. Up to 90% of AsiaPacific countries’ trade with the EU is carried by the Southern Sea Route through the Suez Canal. The annual volume of sea freight containers from the EU to China is 4.5 million TEUs and 11.2 million TEUs in the opposite direction and takes an average of 35–40 days. In the future the Suez Canal may not be able to cope with the growing cargo flows, thereby freeing up volumes for the EAEC. The cost of land transit through the EAEU is, on average, twice as high as sea transit, but the main competitive advantage of land transit through the EAEU is its speed, which is two to three times higher than sea transit. In this regard, overland transit across the EAEU territory is still economically justified only for goods for which the speed of delivery is of fundamental importance -
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high value-added goods and perishable foodstuffs. Thus, the primary task of transport and logistics cooperation of the EAEU in the field of transit should be effective tools for “switching” sea cargo volumes in the EU-China direction to the EAEU land territory with the formation of competitive delivery routes - the Eurasian transport alternative. The emerging system of the EAEU transport corridors consists of two Eurasian corridors - “East-West” and “North-South”, which are interconnected with Pan-European corridors №2 and №9, respectively. International transport corridor “East-West” begins in the port of Nakhodka and Vladivostok, follows the route of the Trans-Siberian Railway to Moscow, and then joins Pan-European corridor No. 2 through Minsk and Warsaw with exit to Berlin. International transport corridor “North-South” begins in Helsinki and then coincides with the Pan-European corridor №9 - through the ports of St. Petersburg and the Leningrad region to Astrakhan, and then through the territory of Iran with an exit to the port of Bandar Abbas on the Persian Gulf in the direction of India. Eurasian corridor “East-West” is focused on the transit traffic not only of Chinese, but also Japanese, South Korean, Indonesian, Malaysian and Singaporean cargo to the EU [19]. The TransSiberian Railway, which has long been the main land bridge between Europe and Asia, remains the backbone of the Russian link in the East-West international transport corridor. Transsib transportations allow to shorten a way of delivery from Europe to Asia by 8 thousand km, which makes 10–20 days saving on the way [18]. An indisputable competitive advantage of the EAEU in case the two projects are paired is the option of overland delivery from China to the EU through the single customs territory of the EAEU, bypassing the sea route, burdened by China’s territorial disputes with Southeast Asian countries and its “bottleneck” - the Strait of Malacca. As the global economy begins to recover from the effects of the COVID-19 pandemic, freight rail service between China and Europe has, according to officials and industry experts, become the most important channel for the continent’s livelihood. Freight train service between China and Europe has become increasingly popular among importers and exporters on both sides, as commercial activity by shipping companies and airlines is still suspended or limited due to the pandemic. The country’s railway operator, China Railway, reported that freight trains made 5,122 trips between China and Europe in the first half of 2020, 36 percent more than in 2019, and the monthly record was broken several times. The China-to-Europe freight train route was opened in 2011 and is considered an important part of the One Belt, One Road initiative, aimed at boosting trade between China and countries linked to the initiative. Since Chinese companies had almost no capacity to rent cargo bays on domestic and European passenger airlines after the coronavirus outbreak, China Post launched regular freight trains to a number of European cities from Chongqing central city in March and from Yiwu city in Zhejiang province in April. By August 2, 2020, the state-owned company had made 22 transcontinental freight rail trips to Lithuania and Poland. About 8,481 tons of mail and goods were shipped in 1,303 rail containers in both directions. In the first half of the year, the five freight assembly centers of Zhengzhou, Chongqing, Chengdu, Xi’an and Urumqi sent 4,003 trains with 40 and 50 cars each, accounting for 78.15 percent of the total trains sent from more than 50 Chinese cities to Europe. Work is underway to improve the efficiency of the transport network of China-Russia and China-Europe freight trains. Russian Railways reported that container traffic on their network doubled
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in 2020. Thus, on the route China - Europe - China, Russian Railways carried 502,600 TEU. Chinese railroads increased the number of trains by about 3 thousand, and the total number of trains in the Eurasian communication amounted to more than 12 thousand. UTLC ERA also reported that back in early December 2020 it passed the milestone of 500,000 TEUs in traffic on the China-Europe-China route. Germany’s DB Cargo Eurasia reported that its revenue exceeded 100 million euros, doubling in 2020 com-pared to 2019. The National Port Administration Office of China’s General Administration of Customs, due to the growing demand for rail transport, will support stakeholders by building hubs along rail routes and help group cargo and sort domestic and foreign goods at sites controlled by customs to optimize transcontinental railroad capacity [20, 21]. Transit countries have also played an important role in ensuring the high quality, speed and efficiency of rail freight shipments. Kazakhstan, Mongolia, and Russia have transported huge volumes of cargo through their infrastructure. Kazakhstan, along with the route through Russia, increased alternative traffic - through the Trans-Caspian International Transport Route, in cooperation with Azerbaijan and Georgia [22, 23]. The role of other countries also grew. Uzbekistan actively developed its railway infrastructure, and Iran and Afghanistan are now connected by railways [24]. The first train arrived from Istanbul to Xi’an. In 2020, new European cities joined the Silk Road railroad as hubs [25–27]. For example, Amsterdam and Liege. Even Great Britain is now connected to China by rail via European ports [28]. An important development was the new service through Poland and Ukraine, which unloaded the Brest-Malaszewicze crossing. An equally interesting alternative in 2020 was the route through the Kaliningrad region. Freight trains go through Lithuania after crossing into Belarus. Kaliningrad Railway and Kaliningrad Oblast ports made the greatest contribution to unloading the borders in 2020. From the seaport, cargo is loaded onto feeder ships bound for Scandinavia, Benelux or Great Britain. Trains also run through Kaliningrad to hubs such as Duisburg. Chengdu - Rotterdam, Kaliningrad - Rostock - Verona, and Xi’an - Neuss routes have appeared - these are currently the fastest rail connections between China and Germany. The scheme in Fig. 1 shows the final conclusions on the identified problems of digitalization of transport on the route China-Europe and possible approaches to solve them.
Fig. 1. Digitalization of China-Europe transportation
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4 Discussion The extent and long-term impact of the pandemic on the multimodal transport market in the direction of China-Europe requires further research. It is advisable to find out whether it is possible to expand the results achieved. It should be taken into account that integration, digitalization and improved regional station infrastructure have also contributed to the development of transportation. It is necessary to consider that the advantages of maritime transport - low cost and huge cargo capacity - do not lose their importance. But in 2021, maritime transport has become more expensive. The situation in the maritime shipping market is extremely volatile, freight rates are rising, and in many cases delivery times have also increased due to vessels idling in queues to the ports. The main increase in freight traffic now accounts for the route China-Europe-China. During the nine months of 2021 the volume of transit was more than 569 thousand TEU (an increase of 47%). To date, networks cannot cope with the in-creased load, the delivery time has increased in connection with this. Nevertheless, despite the maximum load on the infrastructure, transportation by rail routes is still faster. Emerging difficulties in rail transportation concern the search for empty equipment, the availability of seats on the trains. There are also infrastructure problems, which include a shortage of personnel, including managers and crane operators, and rolling stock.
5 Conclusion All of the above information makes it clear that successful, optimal logistics operations in the face of increasing traffic flows and the need to choose the best route require the increasing use of digital solutions. The main challenges that require increased use of digital solutions are: – Providing a common data exchange format for all participants in logistics chains. – Providing all players with the means to transfer information in a timely manner. – To elaborate mechanisms to ensure the legal significance of transport and accompanying documents, to harmonize issues concerning their digitization. – Solutions to optimize the empty return of cars. – Expansion of opportunities of artificial intelligence which will allow in addition to the real-time determination of cargo location to take decisions on the optimum re-direction of cargo to the vacant sections of the track in case of cargo delay. – Strengthening of financial and settlement capabilities for participants, subject to increased settlement security and confidentiality, with the use of blockchain technology. Solving these problems will increase the speed, quality, safety and transparency of multimodal transportation between China and Europe. Further research will be related to the analysis of the state of affairs in the area considered, taking into account the rapidly changing external conditions.
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Digital Transformation in Russian Transport Companies Igor V. Ilin1(B) , Nina V. Trifonova1 , and Bulat D. Khusainov2 1 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected] 2 Institute for Economic Research, Nur-Sultan, Kazakhstan
Abstract. Digital transformation applies to individual enterprises as well as to entire industries. A variety of technologies, business models and market demands become the levers for digital transformation. The transportation industry plays an important role in the supply of industrial business and largely determines the price of products. The formation of a single digital space for the transport complex is an important task in Russia. The introduction of mandatory electronic waybills in 2023 further increases the pace of digital transformation. The digital transformation of various companies is becoming an integral part of the current economy. The digital transformation of companies involved in the transport sector in one way or another is one of the issues that is acute for business. The transportation component of each organization, because of its communication nature, is the most important component of the production and economic module. In this article, the digital transformation of companies involved in the transportation sector is examined on the basis of a literature review. In turn, the digitalization of road transport and transport and logistics services implies compliance with several prerequisites. The transport component of any organization due to its communication nature is the most important component of the production and economic module, requires a different approach to management, which not only takes into account technological aspects, but also focuses primarily on the customer. The purpose of this article is to identify the main areas of digital development of transport companies in Russia by conducting a literature analysis. Using the examples of Russian companies, as well as foreign experience, it is possible to formulate the main directions of digital development of the Russian transport complex, trends in digital technology, as well as to justify the effectiveness of digital transformation in transport companies. Keywords: Digital transformation · Transport companies · Transport sector · Transformation
1 Introduction The digital transformation of various companies is becoming an integral part of the current economy [1, 2]. Various technologies, business models, and market demands are becoming levers for the digital transformation of not only individual companies, but entire industries as well. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 945–954, 2023. https://doi.org/10.1007/978-3-031-32719-3_72
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Looking at the transport sector in Russia, it is impossible not to say that companies have already embarked on the path of digital transformation. The events of recent years have significantly accelerated digital transformation, as organizations have been forced to use various digital tools to enable online business interactions [3, 4]. Also, the introduction of digital technologies into business processes can improve productivity, customer satisfaction, and a company’s reputation. Foreign and domestic experience, as well as the policy pursued by the state, does not leave it possible to isolate ourselves from new technologies and the adoption of digital transformation. The purpose of this article is to identify the main areas of digital development of transport companies in Russia by conducting a literature analysis. This analysis will highlight existing trends, as well as talk about the effectiveness of digital transformation in transport companies.
2 Materials and Methods The transport component of any organization due to its communication nature is the most important component of the production and economic module, requires a different approach to management, which not only takes into account technological aspects, but also focuses primarily on the customer. In order to reflect the holistic situation in this area in the framework of digital transformation, a literature analysis in the bibliographic database Scopus and ScienceDirect was conducted. In order to search the Scopus bibliographic database, the following search string was used: TITLE-ABS-KEY (digital AND transformation AND of AND transport) AND (LIMIT-TO (PUBSTAGE, “final”)) AND (LIMIT-TO (OA, “all”)) AND (LIMITTO (PUBYEAR, 2022) OR LIMIT-TO (PUBYEAR, 2021) OR LIMIT-TO (PUBYEAR, 2020)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (EXACTKEYWORD, “Digital Transformation”) OR LIMIT-TO (EXACTKEYWORD, “Transport Infrastructure”) OR LIMIT-TO (EXACTKEYWORD, “Logistics”) OR LIMITTO (EXACTKEYWORD, “Transport Policy”) OR LIMIT-TO (EXACTKEYWORD, “Transport Services”) OR LIMIT-TO (EXACTKEYWORD, “Transportation”)). The following keywords and restrictions were used to search ScienceDirect: – – – –
Keywords: digital business transformation transport logistics Years: 2020, 2021, 2022 Subject areas: Business, Management and Accounting Access type: Open access & Open archive
The time interval 2020–2022 was chosen in order to describe the current situation in the dynamics of not more than 3 years. The received articles, in our opinion, more accurately reflect the current situation within the studied issue. During the analysis, 34 articles from Scopus and 48 articles from ScienceDirect were studied in order to select sources to better reflect the situation in the industry. Seventeen sources were selected for the final analysis. The greatest number of articles on the topic was written and published in 2021. Among all papers, 47% were Articles, 47% were Conference Papers, and 6% were Reviews.
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All selected articles based on the results of the analysis were divided into 4 semantic categories, presented in Table 1. Table 1. Categories of literature sources, Author’s Category
Literary Source
Digital transformation for different modes of transport
[1–4]
Platforms and ecosystems
[5–8]
Intelligent Transport Systems
[9, 10]
Digital Transformation of Transport in Russia
[13–16]
Digital Transformation of Transportation Abroad
[11, 12, 20]
The global trend of digitalization of the economy, which is one of the important priorities of the Russian Federation in the context of the development of the information society, has affected the transport sector. The digital transformation of companies involved in the transport sector in one way or another is one of the issues that is acute for business. Transportation plays an important role in the supply of industrial business and largely determines product prices. According to expert estimates, transportation costs in supply, production and distribution processes now average up to one third of the price of the final product [1]. Researchers prefer to divide organizations by the type of transport used to describe the digital transformation. For example, in the article Popova I. et al. consider the possibility of using such digital technology as RFID in railway transport, the implementation of which will reduce costs while increasing the prestige and safety of rail transport, increasing the competitiveness of this type of transport [3]. Remencova T. et al. examine the possible results of the digital transformation of small airports. The digital transformation of the airport is not only about new technology, but also about the complete business transformation in the digital world [2]. The work of Tijan E. et al. touches on drivers (11pc.), success factors (25pc.) and barriers (16pc.) to digital transformation in the maritime transport sector [4]. Consideration of the creation of a set of digital services and software and hardware modules that constitute a digital platform, as well as holistic ecosystems is considered in the works of many researchers [5–7]. The result of using the principles of digital transformation of technological and management processes (the introduction of these technologies) Ivanov I. et al. see an increase in the efficiency of regional and municipal passenger transport, comfort and safety of transportation [8]. The articles Rudskoy A. et al. and Okrepilov V.V. et al. consider the introduction of intelligent transport systems. They make it possible to solve the main problems of the transport network and develop it effectively [9, 10]. The results of the Chinoracky R. et al. study is an assessment of how digital technology affects the transport industry in Slovakia [11]. And a research team from the
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University of Zilina looked at the integration of digital technology in airport transport in Slovak airports [12]. Considering the domestic experience of digital transformation, the following works can be highlighted. The research group of Saint Petersburg State University of Architecture and Civil Engineering considered the tools for implementing the concept of digital transformation. It was highlighted that digital platforms allow the interaction between entities in the Russian Arctic in the digital economy [14, 15]. Andreev D.V. et al. in their work substantiate the fact that no branch of modern life today cannot be imagined without the active implementation of digital technology on the example of Yakutia [16]. The articles of Uskov V. et al. and Vasilenko M. et al. consider the existing structure of the current state of transport infrastructure in Russia, which will allow to assess the changes in the current situation in the transport sector [17, 18]. 80% of Russian companies in the transport industry are implementing new business models based on digital technology. 55% of organizations have already begun to implement their own digital transformation strategies. Digital transformation has embraced an industry such as logistics. Previously, few dared to make serious and fundamental changes in approach. However, the Covid-19 pandemic has exposed many existing problems in transport logistics and accelerated the transition to automation. One of the most striking trends in domestic logistics is the shift from stand-alone solutions to platform solutions. A landmark example of a platform solution was the service for the search and selection of carriers (an analogue of Uber for trucking) and the organization of multimodal transportation. Platforms facilitate the integration of business processes of chain members, connect producers with consumers, manage inventory, and provide a range of other services. Digital transformation is not only changing individual logistics organizations, but it is also a subject of dialogue between government agencies, departments and businesses.
3 Results The formation of a single digital space of the transport complex in Russia is supported by the Ministry of Transport of Russia. The implementation of the departmental project “Digital Transport and Logistics” entails digital transformation of the transport complex of the Russian Federation. Various types of transportation, the implementation of the environmental paradigm and unmanned technologies in transport, etc. are supported by this project to organize a single digital space of the transport complex of the country by 2024 [19, 20]. The creation of a unified digital platform for the transport complex (UDC) is worth mentioning separately. The platform is an information environment of the transport complex that is neutral to foreign trade and transport processes and provides the highest possible degree of optimization of logistics costs of foreign trade entities without reducing the level of state control. It ensures the availability, reliability, safety and quality of air, water, road and rail transport.
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UDC is the main element that ensures the formation of a unified digital space for the transport complex and the key mechanism for creating an industry-wide data management system, which is an integral part of the National Data Management System (NDMS). The state segment of the CPTC will establish uniform standards, rules and regulations of information exchange, will ensure the legal significance of data on transport infrastructure and vehicles. The platform will act as an aggregator of information about transport, which guarantees non-discriminatory access to the data of all interested participants of the industry. CPTC will allow to control the quality of transport services and will create the basis for digital security in transport. The Unified Digital Transport and Logistics Environment (UDTLE) is an organized set of digital platforms, systems and infrastructure, a unified, standardized environment for information support of transport and logistics processes. A reliable ECTS and the availability of legally significant data from the connected facilities will reduce the time of cargo delivery by increasing the speed of information exchange and its interoperability, fully automating administrative procedures and ensuring a complete electronic document flow. The UDTLE will not only provide a trusted environment for intra-industry communication, but will make Russia an attractive partner for the countries of the Eurasian continent, allowing it to offer the best and most transparent conditions for the transportation of all types of goods across its territory. The formation of the UDTLE will ensure Russia’s leadership as the main transit zone between Asia and Europe. Functionality The Digital Platform of the Transport Complex of the Russian Federation will provide various types of digital services for the state and business. Examples of Digital Platform of the Transport Complex of the Russian Federation services for the state could be the formation and provision of a register of state information standards for the purpose of organizing the interaction of different types of transport. A service of the Digital Platform of the Transport Complex of the Russian Federation could be the formation of unified registers of objects and subjects of transport security, transport infrastructure and vehicles, such as civil aviation pilots, unmanned vehicles, etc. Such services may include the maintenance and provision of regulatory and reference information of the transport industry, which is necessary to translate existing multiple state and business systems into unified industry standards. On the basis of the integration of corporate, industry and international standards, technological and technical existing competences into the Digital Platform of the Transport Sector of the Russian Federation the implementation of information interaction between different sources of information in the transport sector, the integration of information received from intelligent transport systems, the organization of legally relevant paperless document flow in transport processes and other tasks are expected. With the use of integrated mechanisms the Digital Platform of the Russian Federation Transport Complex is intended to implement the processing and analysis of large amounts of data, the simulation of transport processes on the basis of information from various sources and various systems, the creation of applications based on distributed database technologies. Based on the aggregation of
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information resources on the basis of the Digital Platform of the Transport Complex of the Russian Federation, it is possible to implement smart transport infrastructure objects. In turn, digital transformation captures more and more companies. For example, Russian Railways is considering the following technologies for digital transformation: Internet of Things (IoT), Big Data (BIG DATA), BLOCKCHAIN, Intelligent Systems (AI/ML), Virtual and Augmented Reality (VR/AR) and New Data Technologies (incl. Quantum Communication). This list of technologies is a priority in development for Russian Railways until 2025. Since several business areas in this organization can be considered, there are various digital projects accordingly. Each of them involves certain changes and contains the planned effect. For example, the business area "Passenger Transportation" and the key digital projects "Multimodal Transportation and Development of Additional Services" and "CRM and Customer Data Management System". These projects entail such changes as the implementation of comprehensive passenger transportation involving third-party carriers and the introduction of a platform for selling additional services with high margins to passengers. The list of these services includes both selection of the class of service and purchase of content on the trip, insurance, etc. Thanks to the implemented changes, we should expect an increase in commission income and revenue from passengers. Thus, as part of the 2019 forum, Russian Railways presented 6 business areas for digital transformation. The organization expects to have a multiplicative effect on the Russian economy, as well as increasing the level of the entire transport system of the country [21, 22]. A separate place in the digital transformation is the assessment of the digital maturity of companies, which evaluates 7 main blocks: digital culture, human resources, processes, digital products, models, data, infrastructure and tools. As part of the digital culture block of the transportation organization, attention must be paid to the level of organizational culture that supports continuous improvement and innovation processes. Assessment of the Human Resources block ensures that personnel are fit for successful work in the digital economy. The application of process management practices, analysis and continuous updating of processes is also one of the most important blocks for assessing the digital maturity of a transportation company. Also, the Digital Products block requires an analysis of existing products in the organization. The following blocks (models, data) respectively assess the validity of the models and the quality of the data used. The infrastructure and tools assessment provides information about access to the digital architecture. All these blocks provide a holistic assessment of a transportation company’s digital maturity, reflecting strengths and weaknesses for digital transformation. At the moment, companies in the transportation industry assess their plans for digital transformation as follows (Fig. 1): Continuing with the theme of digital transformation of the transportation industry, we cannot overlook the growing number of business models based on digital technologies. The use of e-tickets, paperless freight transportation, and other facts of digitalization have already become firmly entrenched in air and rail transportation. The introduction of mandatory electronic waybills in 2023 further increases the pace of digital transformation in various companies.
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87%
Heading into "full digitalization"
66%
Plan to introduce new business models,…
14% 55%
Initiated digital transformation strategies
31%
No digital transformation strategy 0%
20%
40%
60%
80%
100%
Fig. 1. Transportation companies’ plans for digital transformation, Author’s creation
In turn, the digitalization of road transport and transport and logistics services involves several prerequisites: telematics services, big data (Big Data) and a digital logistics space. Organizations aimed at helping the digital transformation are gaining momentum. This includes Nedra. New Digital Resources for Assets is an IT company that assists in the digital transformation of large oil and gas companies. The company develops and implements modern technological products that are necessary to automate processes in the areas of exploration, oil production and logistics - portals, geological and economic assessment projects, using mathematical algorithms, neural networks, machine learning, as well as integration and infrastructure solutions. The list of processes to be automated includes logistics, among others. In August 2021, the number of implemented projects in the company’s portfolio was more than 20. The list of trends in the digital transformation of business in the transportation industry cannot fail to include big data processing systems and artificial intelligence. Researchers predict that the number of data to be collected and analyzed in real time will reach 30% by 2025. Intelligent analysis of data and events will make it possible to optimize traffic flows, develop both transport and logistics infrastructure, etc. So, the main directions of digital development of the transport complex are [23, 24]: – Development of digital solutions for customer interaction and information support; – Increase the level of digital penetration across the entire lifecycle of transport infrastructure and vehicles for all modes of transport; – Increasing the level of digitalization in the organization of transport complex management. The greatest success in organizations undergoing digital transformation is reflected in the area of interaction with customers [25–29]. This fact is realized through digital channels, the development of platform aggregators. However, the existing barriers to digitalization of the transport industry are a lack of personnel and financial resources.
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4 Discussion The digital transformation of companies involved in one way or another in the transportation sector is one of the issues that is acute for business. Transportation plays an important role in providing industrial business. This article discusses the implementation of intelligent transport systems to solve the main problems of the transport network and the growth of business models based on digital technology. Outlining the main trends of digital business transformation in the transport industry includes not a complete list of technologies in connection with indicating the most striking examples at the moment. The description of the assessment of digital maturity of companies is superficial, but in-depth description of methods is planned in future works. It is stipulated that all the blocks used for assessment provide a holistic picture of the digital maturity of the transportation company, which reflects the strengths and weaknesses for digital transformation. Examples of both organizations aimed at assisting companies with digital transformation and the organizations themselves conducting digital transformation are provided. Expansion of the list of analyzed companies is planned in future studies.
5 Conclusion By incorporating digital technology into business processes, companies can improve productivity, customer satisfaction, and reputation. Digital transformation applies to both individual enterprises and entire industries. After conducting a literature review, we have identified the main areas of digital development for transport companies in Russia. Companies operating in the transport sector are resorting to various business models to carry out digital transformation. The main trends include the development of digital solutions for customer interaction, increasing the level of digital penetration throughout the entire lifecycle, and increasing the level of digitalization. These trends boil down to a desire to match the current level of digital technology development in the global market and to improve one’s own work. Cloud solutions, offthe-shelf solutions, platforms, ecosystems, big data analytics and other digital technologies enable companies to achieve their goals, as the digital transformation of enterprises facilitates this through progressive technologies.
References 1. Palkina, E.: Conceptual basis of using digital technologies for reducing transport costs in product price. E3S Web Conf. 258, 02022 (2021). https://doi.org/10.1051/e3sconf/202125 802022 2. Remencová, T., Sedláˇcková, A.N.: Modernization of digital technologies at regional airports and its potential impact on the cost reduction. Transp. Res. Procedia 55, 18–25 (2021). https:// doi.org/10.1016/j.trpro.2021.06.003
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Information Management as a Basis for Change Management in Enterprise Digital Transformation Projects Alexey B. Anisiforov, Arkady A. Evgrafov(B) , Alena S. Ershova, and Dayana M. Gugutishvili Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected]
Abstract. The paper considers the problems of forming the information infrastructure of an enterprise and organizing an information management system in the course of the digital transformation of an enterprise. Its role, place and goals in the enterprise management system are determined. The main object of research is the information management system of an enterprise, approaches, methods and tools that are used to manage the IT infrastructure of an enterprise, the features of its formation and the possibility of adapting to changes during a digital transformation project. The role of the information management system in the processes of preparation for the implementation of digital transformation projects of the enterprise is determined. All problems and tasks of information management are proposed to be considered in the context of a unified enterprise management system, the construction of which in the enterprise architecture ensures the achievement of the goals of digital transformation and at the same time supports current business activities through the effective and coordinated management of all enterprise resources based on their informational reflection. The features of the information management system in the enterprise architecture are also considered, the construction of which will provide a reliable foundation for the implementation of the digital transformation project. A certain approach is proposed to the organization of all information processes, including at the stage of preparation for project implementation. The development of the organizational, economic and information infrastructure of an enterprise is considered as the central task of information management in digital transformation projects of an enterprise, which involve constant changes in the management system that radically change the way business is done and its information support. Keywords: Information Infrastructure · Information Management · Information Resources · Enterprise Architecture · Business Architecture · Business Process Management · Information Service First Section
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 955–964, 2023. https://doi.org/10.1007/978-3-031-32719-3_73
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1 Introduction The rapid growth of costs for informatization in companies has contributed to understanding the importance of information resources and the need to manage them in the interests of business. This becomes obvious during digital transformation, where information management becomes the most important component of the management of all necessary and radical changes in information processes in an enterprise. A huge number of IT solutions, complex and expensive IT infrastructures, information security problems and the organization of information services for management personnel make information management one of the most difficult and costly areas in the management of any company, the organization of which depends on the achievement of digital transformation goals. This makes the information management system a pillar in the implementation of such projects and confronts it not only with the task of current support, but also with the necessary changes in the business model and information infrastructure in the process of digital transformation, based on the architectural model of enterprise management. It is the Enterprise Architecture (EA) that becomes the foundation of the digital transformation of the enterprise, which allows carrying out all activities in the preparation and implementation of the digital transformation project, and the information management system ensures their implementation.
2 Materials and Methods 2.1 Information Management as the Most Important Part of the Overall Management System at the Enterprise Information management is the most important part of the enterprise management system, ensuring the achievement of the organization’s goals through the effective and coordinated management of all enterprise resources based on their informational reflection. The main goal of information management is to ensure the effective development of the organization by regulating various types of its information activities [1]. Already in the second half of the last century, information systems (IS) for various purposes and a number of information and communication technologies (ICT) were widely used in enterprise management. At the same time, the need to create special units in enterprises responsible for the operation of IS and ICT and to train specialists capable of doing this became obvious. Further development of business information support systems led to the emergence of special units - information services (IT services), whose main task is to manage the information infrastructure of the enterprise and provide the business with the necessary information services. The success of any business depends on how successfully information processes are built at the enterprise, internal and external communications, and this dependence is intensified with the development of digitalization processes in the modern economy. And often, in order to build a modern information infrastructure management system, it is necessary to revise not only the purpose and organization of the IT service, but also the overall organizational and information infrastructure of the enterprise. The same is true for digital transformation processes. The information infrastructure of an enterprise is not just a set of IT solutions, technical means, processes, documents, regulations, services, etc. This is an integrated system
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that ensures the activities of the organization. Like any system, it must be purposefully designed and properly operated. Its size, complexity and speed of ongoing changes are such that without building a management system that covers all stages of the life cycle of such a system, it is impossible to manage it. The digital transformation of business involves radical changes not only in business processes, but also the most efficient use of new organizational and digital technologies. This is a whole range of technologies and methods such as artificial intelligence, machine learning, big data (Big Data), robotization of business processes (Robotic Process Automation/RPA), cloud computing (Cloud Computing), blockchain (Blockchain), virtual reality (VR) and augmented reality (AR), Internet of things (IoT) and industrial Internet of things (IIoT), edge computing (Edge Computing), digital twin (Digital Twin), process analytics (Process Mining), etc. Integration of such technologies into the information infrastructure of the enterprise requires new competencies from IS personnel to ensure their practical use. This is digital disruption or simply disruption - the main locomotive and basis of digital transformation [2, 3]. This is nothing less than a revolutionary transformation of industrial relations, caused by the introduction of disruptive digital technologies and new business models associated with them. This process is accompanied by significant changes in consumer behavior and the very shape of the market. Disruption and digital transformation require entrepreneurs to seriously reassess how they do business. But they provide a great chance to gain a decisive advantage, even in the most highly competitive field. The widespread use of communication tools, digital platforms and other solutions that provide information exchange and communications with a high level of information security requires a fundamental change in the business model and its information support in the business management system [4]. Any internal and external changes in the business environment, such as: organizational changes in the enterprise, launching new products on the market, technical and technological changes, the emergence of a new direction of activity, changes in the logistics system, external economic factors and much more, require immediate transformation as a business -model, and the corresponding changes in IT support [5]. This, in turn, leads to a serious reorganization of the work of the information service and the entire information management system at the enterprise as a platform for changes, and makes its leading role in the process of digital transformation of the enterprise obvious. 2.2 Information Management in Enterprise Architecture as a Platform for Enterprise Digital Transformation Digital transformation offers business development strategies that require radical changes in the way it is organized and conducted, i.e. practically reengineering of the business model. However, the business development strategy cannot be considered in isolation from the IT infrastructure development strategy, since radical changes simultaneously affect both the business model and the IT infrastructure, and therefore, when implementing digital transformation projects, it is necessary to rely on a foundation that supports such a relationship [6, 7]. Such a foundation should be the Enterprise Architecture that provides this connection. The main goal of digital transformation can be
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formulated as “bringing all business activity in line with the rapidly changing requirements of the modern world”. The process of digital transformation is long, it is implemented gradually and is often associated with major infrastructural changes. However, the company continues its business activities, fulfills its obligations to customers, and this poses very difficult tasks for the information management system - the current information support of the business and making changes during the implementation of the digital transformation project. In other words, the information management system is faced with the task of providing businesses with the most radical external and internal changes and remaining competitive in an emerging environment. “Revolutionary” changes both in business and in the IT infrastructure of an enterprise make a digital transformation project, especially for large industrial enterprises, extremely complex, expensive and lengthy, consisting of many gradually implemented projects. The scale of the enterprise, its organizational structure, production technologies, territorial features, product nature, type of production, internal production processes, organization of supply and marketing processes, partnerships, etc., predetermine an original approach to each project and require serious work to prepare it for launch. In addition, the level of digitalization of management processes, the level of maturity of corporate governance and the competence of personnel has a significant impact on the implementation of such projects. A digital transformation project cannot be considered as a one-time event, it is several stages of complex work, and most often it is a portfolio of projects, each of which involves the achievement of certain goals. The sequence of execution of stages (subprojects) is subject to information and logical connections in the management system, both at the business level and in the system of its information support. The implementation of this portfolio of projects, as discussed earlier, should be based on a certain foundation that will allow for the necessary changes, and also provides a strategic interaction between business and IT. This foundation is the Enterprise Architecture (EA), which supports changes in business architecture and IT architecture, providing service support for the business model. At the same time, a well-organized information management system is absolutely necessary, which ensures all changes and interconnections. Thus, EA is the basis for all infrastructural changes in the process of digital information, and the information management system is a platform for organizing these changes in the enterprise. These changes underlie changes in the information infrastructure of the enterprise and the organization of information service processes [8, 9]. Obviously, when preparing a project, it is necessary to know the current state of both the EA and the information management system. The EA should be analyzed for its compliance with the stated goals of the digital transformation project, which will make it possible to understand the directions and content of the necessary changes, and the information management system should be audited to assess the level of information services provided, manage the IT infrastructure, and identify the necessary changes in the IT architecture to form strategy for its development. It is also important to take into account the fact that digital transformation launches a continuous process of changes in business that threaten its stability and sustainability [10, 11], increases the risks associated with data protection, because economic activity cannot be interrupted during the
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implementation of the project and is carried out in parallel with it. This requires close attention to changes in the information security system.
3 Results 3.1 Business Architecture Management and Analysis of Its State Digital transformation requires a lot of infrastructural changes in the enterprise management system. The enterprise architecture provides the opportunity to make such interrelated changes. This is a concept that describes the current and target state of the application architecture, business processes, IT infrastructure, consistent with the company’s business strategy. The management model, based on the opportunities that EA provides to the management of the enterprise, is commonly called the architectural management model. The architectural management model allows you to use the methods and tools of EA to support strategic and operational management, synchronizing the processes of managing and developing IT and business in the course of digital transformation. A characteristic feature of the EA concept is that it is focused on supporting the organization also in terms of developing a development strategy and effective management of both business architecture and IT infrastructure, since they are the focus of enterprise digital transformation projects. Figure 1 shows the place and tasks of information management in the project of digital transformation of the enterprise.
Fig. 1. The place and tasks of information management in the project of digital transformation of the enterprise
Enterprise architecture management is the most important subject area of information management in digital transformation projects. It is loaded with many features such as: business requirements management, business architecture, corporate IT infrastructure, corporate strategy and strategic goals, good practice compliance management, standards management, etc.
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An analysis of the state of the business architecture is necessary to assess the level of process maturity, which will not only eliminate architectural errors, but also take into account the results of the analysis in the digital transformation project. It must be carried out in all areas - the organizational structure of management and its compliance with the goals and objectives of the enterprise, the system of business processes and regulations for their execution, the document management system. Of key importance are the existing business process management system, as well as the level of digital competencies of managerial personnel, without which it is impossible to manage business processes today [12, 13]. Business process management is an important tool for improving enterprise architecture through the continuous and mutual adaptation of IT services and business architecture in the process of digital transformation. Changing the business model of an enterprise as part of digital transformation in the future will require a number of decisions that determine the direction of development of the production and information infrastructure, which can significantly affect the economic model, affecting the cost structure of the organization and sources of income. To create, manage and develop a business process system, various methods and IT solutions are used, including business process management systems (BPMS - Business Process Management System). “The digital transformation of an enterprise is most directly related to BPM - a digital business model is impossible without digital business processes” [14]. The implementation of BPM is becoming an essential component of the digital strategy. BPM makes it possible to purposefully change, improve business processes and maintain their current effectiveness and efficiency [15]. It is also necessary to single out many tasks related to the organization of project implementation, the solution of which will make it possible to adjust the concept of digital transformation of a particular enterprise. It is also necessary to outline the directions of modernization and development of the business model in accordance with the strategic objectives of the business, to assess the possibility and necessity of creating new business lines and conditions for interaction with partners. Methods, models and tools for describing, forming and managing EA are widely known, are actively used by enterprises and can be used in digital transformation projects. EA management is impossible without the formation of the goals of architectural transformation and description of the architecture. This is also important for digital transformation projects, the implementation of which requires change management within the company, ensuring the adaptability of the system in a new management paradigm that ensures the strategic sustainability and development of the enterprise, which is currently the goal of enterprise digital transformation. It should be noted that the lack of a mature Enterprise Architecture can significantly complicate the processes of its digital transformation. Management of all the listed areas of business architecture analysis and assessment of its state is possible only in an information management system that has the necessary methods and tools to provide its information support. 3.2 IT Infrastructure Management, Analysis of Its Condition and Formation of a Development Strategy The architectural management model is supported by the IT infrastructure of the enterprise, which includes many elements: activity models, technical and communication
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tools, databases, information exchange models, application solutions, a set of information services, data management and protection tools, etc. It is a platform without which a business cannot function effectively in the digital world. The success of the business depends on how effectively the information support processes within the company and external communication with customers and suppliers are built [16], and before starting a digital transformation, you need to carefully check the existing IT infrastructure, its reliability, ability to change quickly and “purity” [17], determining the possibility of its modernization or the need for partial or complete replacement. It is on the creation, management and formation of an IT infrastructure development strategy that matches the business strategy that the main focus is on the digital transformation of an enterprise. The complexity and diversity of the tasks of managing the information infrastructure of an enterprise, its information resources lead to the need to develop an IT development strategy and coordinate it with the business development strategy, as well as constant audit and monitoring of information infrastructure elements. This is absolutely necessary to align the information needs of the enterprise with the capabilities of the IT infrastructure (IT architecture). The creation, maintenance and operation of such a complex infrastructure requires not only serious costs, but also a lot of preparatory and current work. This is the implementation of quality management standards and best practices, the development of an IT infrastructure development strategy and the implementation of engineering and technical measures aimed at changing it, as well as constant monitoring and control of its condition and evaluating the effectiveness of service support for the business model. The implementation of these activities is possible only on the basis of the creation of a well-organized and regulated information management system. The task of assessing the current state of the IT architecture and making forecasts for its development is called the task of information monitoring, and periodic monitoring and analysis of the state of individual elements of the architecture is called an information audit [18]. Corporate IT architecture includes information architecture and application architecture, infrastructure architecture (the most complex set of technical means, a system of communications and technologies) and service architecture. The most difficult area in the monitoring and auditing system is the IS architecture and its compliance with the goals and current tasks of the enterprise [19]. Careful documentation of the IS architecture to some extent facilitates the monitoring process, and modern methods for monitoring and auditing elements of the corporate IT architecture, based on a number of standards, approaches, concepts, methodologies, best practices and tools, allow for constant monitoring and analysis of its state. Each of the elements of the IT architecture for monitoring requires unique methods and tools, each accompanied by a variety of information and operational risks and threats that must be taken into account when building a corporate information protection system. It is possible to effectively manage these risks and threats only when building an information security system that covers all components of the EA. This allows us to single out the security architecture as an independent element. A critical area of analysis is also the assessment of the composition, reliability and performance of the technological architecture, its compliance with the application architecture. This allows you to identify new business opportunities arising from the rapid development of IT, which offer new
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business models based on the use of data analytics, network, and cloud technologies, etc. [20, 21]. To achieve the goals of digital transformation, it is often not enough to create and change your own information infrastructure that provides internal management and interaction with the outside world; it is often necessary to change the processes of partners, suppliers and consumers, i.e. an integrated approach is needed to the use of IT in all processes of the company, not only inside, but also when interacting with the outside world: customers, partners and the state. Throughout the implementation of digital transformation projects, the organization of an information monitoring and audit system for the IT architecture will become the most important condition for ensuring business continuity and an element of information security policy based on systematic monitoring of the state of objects, phenomena, processes and incidents in all elements of the IT infrastructure in order to assess their state, control or forecast and formation of decisions on the necessary impacts. Solving the problems of information monitoring and audit of the corporate IT architecture is possible only on the basis of a well-organized and regulated information management system at the enterprise. The activities of the information service (IT service), within which all the tasks of information management are solved, must be subject to certain rules and regulations for the execution of work processes. Access to information resources, response to user requests, interaction with service customers, conditions for providing information services, actions in emergency situations, etc. it is necessary to strictly document and, in their execution, be guided by the existing system of standards and best practices. The following findings can be drawn together at the result of this section: 1. Digital transformation requires a lot of preparatory work to analyze the state of the enterprise, which is possible only in the Enterprise Architecture. 2. To implement a digital transformation project, a developed Enterprise Architecture is required, which will become the foundation for all necessary changes. 3. For the effective implementation of changes, it is necessary to have an information service, the staff of which has the appropriate digital competencies. The information service maintains an organized and regulated information management system that plays the role of a platform for the digital transformation process.
4 Discussion The following topics may be suggested for discussion. 1. EA is the foundation for the implementation of the digital transformation project 2. The launch of a digital transformation project should be preceded by a lot of work to analyze the state of the EA or its creation. 3. Simultaneous implementation of a digital transformation project and effective support for current business activities creates serious information risks and significantly complicates the digital transformation process. The project affects the entire management system of the enterprise, and information management becomes the key to the implementation of this project.
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5 Conclusion The process of digital transformation of an industrial enterprise links the transformation of the organizational and economic model and management culture and the transformation of the information infrastructure of the enterprise into a single project. Most often, the failures of its implementation are associated with an insufficient level of corporate maturity of the company, and the reasons are inefficient planning, inconsistency of goals and unclear formation of development strategies, as well as poor preparation for the launch of the project and support for the necessary changes in the information management system. The changes seriously affect both the information infrastructure of the enterprise and the organization of information service processes. This seriously increases the demands on the management of the enterprise as a whole and proves the important role of the information management system as a reliable platform on which to rely on when making changes during the implementation of projects. In addition, the business model of the enterprise is also subject to change, therefore, to coordinate changes in business and IT, a strong foundation is needed that provides methodological and organizational unity and instrumental support for the entire range of work in the implementation of digital transformation projects. This foundation is the Enterprise Architecture, which ensures fast and efficient digital transformation and proper business support during the project. It should be noted that careful preparation of a digital transformation project largely determines its success and efficiency, which, ultimately, will ensure that the enterprise achieves its goals.
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8. McCalman, J., Paton, R.A., Siebert, S.: Change Management: A Guide to Effective Implementation, 4th edn. Sage, London (2015) 9. Ilin, I., Iliashenko, V.M., Dubgorn, A., Esser, M.: Critical factors and challenges of healthcare digital transformation. In: Rudskoi, A., Akaev, A., Devezas, T. (eds.) Digital Transformation and the World Economy. SESCID, pp. 205–220. Springer, Cham (2022). https://doi.org/10. 1007/978-3-030-89832-8_11 10. Ananyin, V., Zimin, K., Lugachev, M., Gimranov, R., Skripkin, K.: Digital organization: transformation into the new reality. Bus. Inf. 2(44), 45–54 (2018). https://doi.org/10.17323/ 1998-0663.2018.2.45.54 11. Ilin, I., Borremans, A., Levina, A., Esser, M.: Digital transformation maturity model. In: Rudskoi, A., Akaev, A., Devezas, T. (eds.) Digital Transformation and the World Economy. SESCID, pp. 221–235. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-898328_12 12. Csedo, Z., Kinga, K., Zavarkó, M.: How does digitalization affect change management: empirical research at an innovative industrial group. Eur. J. Bus. Manag. 9(36), 1–5 (2017) 13. Mogilko, D.Y., Ilin, I.V., Iliashenko, V.M., Svetunkov, S.G.: BI capabilities in a digital enterprise business process management system. In: Arseniev, D.G., Overmeyer, L., Kälviäinen, H., Katalini´c, B. (eds.) CPS&C 2019. LNNS, vol. 95, pp. 701–708. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-34983-7_69 14. von Rosing, M., Rosenberg, A., Omar, R., Chase, G., Kemsley, S., Prickril, G.: Future trends for BPM. In: Applying Real-World BPM in an SAP Environment, pp. 613–634. SAP PRESS (2010) 15. Ilin, I.V., Lyovina, A.I., Antipin, A.R.: Business architecture development and process and project maturity. In: Becker, J., Kozyrev, O., Babkin, E., Taratukhin, V., Aseeva, N. (eds.) Emerging Trends in Information Systems. PI, pp. 51–63. Springer, Cham (2016). https://doi. org/10.1007/978-3-319-23929-3_5 16. Targowski, A., Kozlowski, A.: Enterprise Information Infrastructure. All Books and Monographs by WMU Authors, vol. 467 (2002). https://scholarworks.wmich.edu/books/467 17. Lanzolla, G., Anderson, J.: Digital transformation. Bus. Strateg. Rev. 19(2), 72–76 (2008). https://doi.org/10.1111/j.1467-8616.2008.00539.x 18. Anisiforov, A.B., Dubgorn, A.S.: Organization of enterprise architecture information monitoring. In: Proceedings of the 29th International Business Information Management Association Conference - Education Excellence and Innovation Management through Vision 2020: from Regional Development Sustainability to Global Economic Growth, Vienna, Austria, 03–04 May 2017, pp. 2920–2930 (2017) 19. Anisiforov, A., Dubgorn, A., Lepekhin, A.: Organizational and economic changes in the development of enterprise architecture. In: E3S Web of Conferences, vol. 110, p. 02051 (2019). https://doi.org/10.1051/e3sconf/201911002051 20. Silkina, G.Y.: Information and communication technologies in ensuring of innovative development. In: Proceedings of the 29th International Business Information Management Association Conference - Education Excellence and Innovation Management through Vision 2020: from Regional Development Sustainability to Global Economic Growth, Vienna, Austria, 03–04 May 2017, pp. 1165–1176 (2017) 21. Ilin, I., Iliashenko, O., Iliashenko, V.: An architectural approach to managing the digital transformation of a medical organization. In: Devezas, T., Leitão, J., Sarygulov, A. (eds.) The Economics of Digital Transformation. SESCID, pp. 227–249. Springer, Cham (2021). https:// doi.org/10.1007/978-3-030-59959-1_15
Analysis of Economic Consequences of Digital Solutions in Logistics on the Example of Russian Railways Holding Olga S. Chemeris1(B) , Alexandra D. Borremans1 , and József Tick2 1 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected] 2 Óbuda University, Budapest, Hungary
Abstract. Taking into account current trends in the digitalization of the logistics industry, the article assesses the state of digital processes in the Russian Railways Group, describes the advantages and disadvantages of IT solutions within the implementation of digital railway projects, and analyzes the economic consequences of their implementation. The article also identifies the main problems of customers and contractors, as well as describes their main needs, which are of interest from the point of view of eliminating problems by improving the applied digital solutions. In the course of the research, materials on the relevant topics were studied, the main provisions of the Digital Railway concept were considered, and the main activities carried out by the Russian Railways holding company in the field of digitalization were studied. The article considers the problems of organizing resource support in the implementation of production processes on the example of the Russian Railways holding. To solve them, the components of resource flows are highlighted and the relevance of automation and harmonization of the fulfillment of obligations of all subjects of the network organizational structure formed by the Russian Railways holding is shown. It is proved that to improve the quality of management decision-making, it is advisable to expand the possibilities of automation of business processes of intra-holding resource flows and it is recommended to use smart contracts and data storage technology in the form of a corporate distributed registry based on blockchain. This article is of practical interest to specialists in the field of logistics, especially in the field of rail transportation. Keywords: Digital Economy · Digital Railway · Decision-Making · Network Structures · Resource Flow Management · Mathematical Modeling · Smart Contracts
1 Introduction As a result of the economy’s transition to market relations, the entire transport system has been streamlined. It is worth noting that both the labor productivity of modern enterprises and the efficiency of goods and services exchange depend on the efficiency of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 965–977, 2023. https://doi.org/10.1007/978-3-031-32719-3_74
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transport operation. Digitalization is of no small importance in this. Digitalization is a concept of economic activity that is based on digital technologies being introduced into various industries in all countries without exception [1, 2]. The tipping point occurs when the adoption rate of digital strategies in an industry reaches 40%. In modern companies, the technological regime is subject to changes, digital business processes are being introduced. The Boston Consulting Group (BCG) made an economic assessment of the restructuring of management in Russian enterprises (with regard to the digitalization of their activities): from the employment of personnel to the formation of their competencies. The economic effect of such a transition based on the application of IT technologies has been estimated by BCG at 1.5% of GDP per annum, which, at current prices, is approximately RUB 10 trillion over the period to 2025 [3]. Transportation companies that adopt digitalization strategies tend to be global players in the relevant services and use different types and forms of international business in their operations. The Russian Federation is the world’s largest country in terms of the length and area of its roads; in addition, rail transport is one of the most important types of transport in the country, which handles about 82% of the total freight turnover of all types of transport (except pipelines) [4]. It is worth noting that railway transport in Russia accounts for almost 1/3 of all passenger turnover in the country. The purpose of the article is the study of the current state of implementation of digital technologies in logistics and digital solutions within the framework of the holding’s relevant projects is of interest and is currently relevant.
2 Materials and Methods The digitalization of Russian railways is an effective tool for increasing labor productivity through the use of IT technologies, the implementation of which makes it possible to improve the performance indicators of the entire logistics complex. The development, testing and implementation of advanced IT technologies entails significant changes in business and the entire management system, which is especially noticeable in the activities of large business structures, including in state corporations such as, for example, the Russian Railways holding, which is the subject of the natural monopoly of Russia in the field of freight rail transportation. The digital transformation of the holding company and the entire domestic logistics sector is implemented through the introduction of industry-specific railway projects and programs (such as the “Long-term Development Program of JSC Russian Railways until 2025” and “Digital Railway”). Through a comparative analysis of their implementation in the sectoral areas of railway transport development, it is possible to identify conceptual differences in the approaches used in them to formalize the space of multiple solutions. For example, the long-term development program of the Russian Railways holding contains a number of tasks localized by type of activity, and the second digitalization program formulates tasks for the use of infrastructure management technologies and the organization of the entire transportation process, as well as interaction with participants in the transportation process.
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3 Results The formation of Russia’s digital economy ecosystem envisages the implementation of over 50 projects in various sectors based on the use of advanced innovative developments based on IT technologies (Big Data storage and management, blockchain, distributed registries, industrial Internet of Things, quantum computing, etc.)[5]. The development and practical implementation of innovations in the field of digitalization of activities increasingly stimulates the overcoming of imbalances in the technological development of the national economy - the Triple Helix model of the modern economy: the systemic association of universities, business, government [6]. In order to meet the state requirements of digital transformation, to operate effectively in the market of logistics services in the current conditions and to increase the transit potential of Russia, the transport and logistics complex needs to continuously develop, increasing the speed, quality and convenience of passenger and cargo transportation services. However, this becomes impossible without the modernization of the corporate culture of management in railway transport and the provision of innovative transport and logistics services of high quality. Thus, the state policy to create the necessary conditions for the development of the digital economy determines the digital transformation of all elements of the transport and logistics process. Through the efforts of the Russian Railways holding, not only is the introduction of breakthrough technologies carried out in the company, but the corporate digital culture is changing, the efficiency of existing and new business processes is increasing, and the list of services provided by the company is expanding. One of the key elements of the strategy for the digital transformation of the logistics sector is the formation of digital platforms for managing the transportation process. Thus, as part of the digitalization strategy adopted by Russian Railways holding, eight digital platforms [7] were created, which are complexes of interconnected technological solutions that ensure the interaction of contractors. One of the digitalization projects is the creation of a digital platform for freight transportation (Fig. 1).
Fig. 1. Digital freight platform
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It should be noted that in the process of developing and implementing digital platforms, the regulations for interaction and optimization of all business processes between structural divisions are being revised. Freight transportation is also planned with the participation of specialists from various structural divisions, with automated systems for centralized preparation and execution of transportation documents, operational management of the transportation process itself, etc. being used to summarize the analytical information obtained, which is intended to significantly facilitate operational end-toend production planning of railway freight transportation. The introduction of artificial intelligence (AI) into management allows the holding to unload the payroll and develop the introduction of AI technologies into its digital projects [8]. According to the Digital Railroad project, the transition to the targeted implementation of IT technologies creates a stable basis for the creation of new information services, which implies reducing the share of operating costs of the Russian Railways holding on information systems to 5% per year. Modern trends in the use of the latest technologies require the digitalization of programs and management systems for logistics complexes. At the moment, there is no complete digitalization of all systems in the Russian Railways holding, because the offline offices of the Territorial Centers of branded transport services still play a very important role. Nevertheless, the holding has several systems for processing and dispatching cargo. The online service “Personal account”, through which the company constantly monitors the wishes of shippers and consignees and takes them into account in its work, is actively developing. A digital personal account allows you to calculate transportation and additional services that can be organized at the site when organizing transportation by rail, to receive information about the location, technical condition of wagons, containers and current operations that are carried out during transportation. It is worth noting that all users have access to training material in their personal account and an ecological calculator that allows you to calculate the reduction in the level of hydrocarbons (greenhouse gases) when transported by rail in relation to other types of transport. An analogue of a personal account is the Russian Railways Cargo 2.0 mobile application. Registration in such a personal account is possible through the synchronization of credentials with the automated system ETRAN. The dynamics of connection of organizations and users to the personal account is shown in Fig. 2.
Fig. 2. Dynamics of organizations’ and users’ connections to the Personal Account
By November 2021, 697 enterprises were registered at the landfill of the branch of Russian Railways holding – Oktyabrskaya Railway (this is more than 1000 users);
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monthly, due to the expansion of the functionality and capabilities of the personal account, the number of users is increasing, the number of documents issued electronically using a simple electronic signature is also growing (and since the beginning of 2021 it has increased more than 3 times) (Table 1). As I.L. Sakovich, first deputy head of Oktyabrskaya Railways for economics, finance and corporate coordination, noted in his report at ICDT-2021 international conference in November 2021, over 91% of documents in the holding are issued electronically with the constant development of digital services. Table 1. Demonstration of Problem and Delay Shipment Indications based on Power BI Number of documents signed and executed
01.2021
02.2021
03.2021
04.2021
05.2021
06.2021
07.2021
08.2021
09.2021
10.2021
1 080
2 466
3 071
3184
4 237
3 089
2 985
2 840
3 068
3 404
However, the current platforms operate in a complex and fragmented manner and do not form a unified system. Each has a number of drawbacks: there are limitations of the RZD-Gruz 2.0 system and personal account, the service of counterparties on the Freight Transportation electronic trading platform, as well as insufficient connectivity and accessibility from different devices of the ETRAN automated system. It is worth saying that the epidemiological situation has strengthened the practice at Russian Railways holding of holding meetings on the well-known remote platforms (Zoom and Skype), viewing tasks and reports in Bitrix 24 system, which enables a more flexible and prompt response to external factors and ensures constant communication between employees. Digitalization is becoming an effective tool for increasing productivity. After analyzing the main segments of Russian Railways’ clients, it was found that more than 70% of freight transportation clients are small and medium-sized companies, but up to 80% of revenues are generated by large enterprises. Thus, it is impossible to single out the main category since both segments occupy an important position for the company. To determine the main pains of the client, the current path of the shipper was considered, and the main problem of clients and contractors associated with digitalization was summarized: a complex document flow and the need to work in several systems. The most important thing for shippers is to fill out an application quickly and conveniently, provide a guarantee of data confidentiality, a loyalty system, and an optimal cost, while for service providers transparency of transportation, the ability to view current applications, documentation on them, and simplified document flow are important. It is determined that the client gets stuck already at the stage of placing orders, because in 90% of cases it is necessary to go to an offline office—this is not very convenient, especially considering modern realities, after which it is necessary to carry out operations for concluding contracts, payment, tracking in 4 or more systems, while the client independently searches for the operator of the first and last mile, as well as the forwarder. These problems today are determined by the low customer focus of Russian Railways. Reducing the number of
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forced unnecessary transactions and combining them on one platform will significantly reduce operating costs and attract new customers to the holding. In the locomotive complex of Russian Railways, large-scale turnovers are currently gaining two promising interconnected digitalization projects - Digital Depot and Smart Locomotive [9]. They are able to ensure the transition from a system of scheduled preventive maintenance (with accompanying forced unscheduled repairs) to a system of repairs based on the technical condition of facilities. Within the framework of this concept, it is assumed that potential for failure of units and parts should be identified by a predictive analytics system that combines mathematical models, modern IT technologies and depot operating experience. This will allow minimizing the failure of units and parts of the locomotive on the line, which at present, without the introduction of the described system, is fraught with economic losses and downtime of locomotives in an unused fleet for unscheduled repairs. The Smart Locomotive project is a predictive analytics system that monitors equipment according to its technical condition and predicts the possible failure of specific locomotive units equipped with relevant sensors. The following end-to-end technologies were applied in the creation of the system: artificial intelligence, industrial Internet of Things [10]. The project is based on CloverGroup’s own platform, the purpose of which is to process and analyze large amounts of data from manufacturing and service enterprises. Based on the platform, a digital solution Ctrl@Maintenance has been created, which represents an intelligent system for risk-oriented production management and planning at all stages of locomotive maintenance and repair (the system includes a library of rules as well as MX-models (MathExperience mathematical models). Since the beginning of 2020 and up to now, the Smart Locomotive system analyzes the operation of 23 types of equipment according to more than 300 parameters depending on the locomotive series; it is capable of finding more than 60 types of equipment failures, automatically identifying violations that have been committed during operation due to incorrect actions of locomotive crews. This system processes over 1.5 terabytes of data monthly [11]. However, the company encountered difficulties when implementing the project: in the short term, the system eliminates the possibility of organizing online transfer of the data read out on the locomotive and taking all its parameters during each maintenance of the locomotive significantly increases costs. In addition, there are difficulties related to the specifics of the locomotive complex [12]. At the current stage of implementation, it is impossible to assess the efficiency of the Smart Locomotive project and even the degree of its impact on traffic safety in isolation from another project that has a direct impact on all indicators of the locomotive complex. The Digital Depot project is a pilot project implemented by Russian Railways since 2018. Currently, it is being implemented at the Bratskoye service locomotive depot (SLD) in Vikhorevka, Irkutsk Region, by the LocoTech group of companies together with NIITKD, 2050.digital and Ctrl2GO. As of today, more than 30 different digital solutions and technological changes, combined into a single intelligent system, have been tested in the Bratskoye SLD as part of the implementation of this project. It is planned that the implementation of the entire set of digital solutions will reduce the time of repair operations by 2 times, and labor productivity will increase by up to 30%.
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In continuation of work to improve internal efficiency, the holding is implementing a pilot project to organize acceptance of transportation cars using fully unmanned technology - an upgraded ASKO PV 3D system that allows scanning the rolling stock contour, determining cargo displacement on the rolling stock and assessing compliance with dimensions and compliance with transportation conditions, tracking online cargo placement while on the rolling stock, transmitting information automatically to the center for making operational decisions. In addition, the system determines the remains of previously transported cargo on the rolling stock, and remote monitoring of the rolling stock takes place in real time using high resolution video cameras. The unified integration IoT-platform EIPP is a system-forming solution for combining all digital solutions of the Digital Depot project into a single set of information systems and hardware and software. Mobile applications make it possible to record the status of the locomotive and monitor the quality of work performance remotely and directly on the locomotive in online mode. The holding calls the following digital solutions efficient: smart frame for reading sensors from chipped line equipment and locomotives so-called RFID tags, specialized system for measuring tire profile with RIFTEK laser profilers, etc. All processes involving the implemented digital solutions can be monitored in real time, using special interconnected mobile applications installed both on mobile devices (smartphones or tablets) and on stationary devices (computers and terminals). The entire set of digital solutions helps minimize time and labor costs for locomotive condition data collection and increases the objectivity of the information obtained. The implementation of mobile applications together with the previously implemented automated control system Network Schedule allows systematizing and reducing the time of distributing production tasks and making effective management decisions. However, now there are also disadvantages of the implemented digital solutions, which are summarized in Table 2. It is also worth noting that not all solutions originally conceived for implementation within the Digital Depot project were ultimately adopted in the final version of the project and recommended for replication in other depots. At the same time, the costs of developing such solutions are irrecoverable and will not pay off. Solutions found to be ineffective or not taken into the replication recommendations are presented in the source [9]. Evaluation of the effectiveness of successfully implemented solutions is difficult due to the closed nature of the economic indicators of LokoTech Group, including those related to the operational activities of the Bratskoye locomotive service depot, on the basis of which the Digital Depot project is being implemented. Considering the total investment in the project, estimated at about 2 billion rubles [13]. To pay off the project within an acceptable period of 10 years, the net undiscounted cash flow should reach 200 million rubles per year, mainly due to the reduction of losses from idle locomotives. Such an increase in the indicator in a single service locomotive depot seems unlikely even if all the goals of the project are achieved. In addition, the implementation of the project in a single service location does not exclude the effect of negative factors affecting the oversimplification of locomotives being repaired and, as a result, loss of revenue. The production processes of the Russian Railways holding and their organization, first of all, must ensure a continuous transportation process and at the same time maintain
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Table 2. Disadvantages and economic consequences of Digital Solutions implementation at the current stage of Digital Depot project implementation Digital Solution
Economic losses that may arise due to deficiencies identified during the implementation phase
RFID system to identify line equipment
Implementation costs - up to 6 million rubles, at replication for the entire fleet - up to 41 million rubles The system does not cover all linear equipment: as of the first quarter of 2021, less than 13% of the linear equipment of the serviced locomotive fleet was equipped with RFID tags. The IT infrastructure does not allow identifying the exact location of the equipment within the shop. Improvement is required
Mobile applications
Development and implementation costs - up to 27 million rubles The use of smartphones is difficult for some specialties of employees (locomotive mechanics, motor mechanics servicing traction motors). Fixed stations (terminals) are provided as an alternative to smartphones for such employees. The applications are not integrated into the information systems of JSCo “Russian Railways” responsible for the release of locomotives to the line, such as the module “Locomotive Receiver” of the Automated Management System for the Locomotive Complex (ASUT-T)
Trusted environment of Russian Railways Direct consolidation and security costs - RUB 24.8 mln The developments of the Digital Depot are not integrated into the Trusted Environment of Russian Railways. The information systems of Russian Railways and the Digital Depot are separate and not integrated with each other Universal repair position (URP)
Losses from maintenance downtime associated with shunting movements - up to 2 million rubles/year Shunting movements from the DRS are not excluded: locomotives are forced to move from the DRS to the repair position of the shop for shunting, which increases time losses for repairs (continued)
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Table 2. (continued) Digital Solution
Economic losses that may arise due to deficiencies identified during the implementation phase
Biometric personnel identification system Cost of implementation - 0.5 million rubles, cost BIOTIME of superadministration for the period of completion - 40 thousand rubles per month Technical difficulties related to IT infrastructure. During the system debugging phase, its functions are forced to be duplicated manually by assigned “superadmin”
a given level of service. This becomes a prerequisite for solving the primary task of the holding’s enterprises, which is to fulfill the function of providing resources for the work of the holding’s network integrator. When implementing the movement of resource flows (flows of material resources and services), production and economic and legal relationships within the company are the network architecture of a multi-agent organizational structure. In this case, reactive actions (all algorithmic processes that are a consequence of external influence on agents in the context of regulatory and legal regulation) related to procurement and resource use processes are carried out by each agent of the network under consideration. The study [7] indicates methods and simulation models of resource flows, which are carried out by network agents of the structure and are mathematical algorithms that allow multiple reproduction of production processes when carrying out variant calculations. Their result is quantitative information, which can be the basis for making an effective management decision. In view of the fact that the network structure of Russian Railways holding is complex, the holding’s resource support must be quantitatively substantiated and economically feasible. The efficiency of intra-holding support and implementation of production programs is determined by an appropriate criterion, as which the work [7] suggests using the integral coefficient of sustainability of a network integrator, which depends on the performance of the entire network structure. Therefore, it is important to formalize and automate the actions of the subjects, which are involved in the production process. It is possible to achieve efficiency in organizing resource provision within the network structure of the holding company through the use of smart contracts ("smart" or network contracts [14–16], which are software protocols of relations between the subjects of law. In this case, the essential obligations of the parties are transformed into a set of triggers and subsequent automated actions. Exploring and organizing the resource support, as a corporate distributed registry we can propose the use of a network multi-agent network, which is formed by the Russian Railways holding (Fig. 3). Figure 3 shows the following designations: I - network structure integrator (Russian Railways holding and structural divisions); D - orbit of subsidiaries and affiliates of Russian Railways holding; E g - orbit of general performers (legal entities / IE) under
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Fig. 3. Multi-agent network structure of a holding company
contracts with integrator and S&A; E s and E si - orbit of subcontractors; Ink and Out corresponding incoming and outgoing resource flows; Pi - accountability and publicity of activities in accordance with the legislative and regulatory impact on the entity; O i - compliance with regulatory and legal requirements; C i - regulatory and legislative impact; I i - impact and control by the integrator). The multi-agent network is studied in more detail in [7]. The nodes of the distributed corporate registry network can be considered as the represented subjects of law (agents) of the network structure, and for decentralized data distribution - the network relationships existing in the Russian Railways holding. The system of information blocks formed in a multi-agent network, when implementing the movement of resource flows, is proposed to be considered as an algorithm for fulfilling the obligations of all parties, which is uniform and independent of subjective interventions. The linkage of such blocks is carried out in the harmonization of triggers for making managerial decisions and the transformation of protocols into a single program code. The proposed system makes it possible to link the obligations of the parties and calculate the most effective algorithms for solving the set tasks. This will improve the sustainability of resource provision of the network integrator (Russian Railways holding) and create automated mechanisms for influencing the holding’s intranet resource flows. Thus, to achieve the goals of automation, it is proposed to apply the digitalization of intermediate and routine operations, using the concept of the Internet of Things, the chipping of rolling stock elements, video control systems with a recognition system, and so on, tested at Russian Railways.
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4 Discussion The described unified consolidated algorithm of resource supply of the holding company will allow launching the processes of self-adjustment of the network structure depending on the needs of a network integrator. And the use of smart contracts and quantitative information in the implementation of procurement and implementation processes and automation of fulfillment of incoming obligations will make it possible to perform qualitative digitalization of resource flows in order to most efficiently load the production capacities of all entities of the network structure. Such automation will affect the value of the integral coefficient of stability of the network integrator, which will be expressed in the maximization of the results of resource provision and their approximation to the planned indicators.
5 Conclusion The digital transformation of the logistics sector reduces the time, labor and financial losses that are directly related to the search for data for the formation of optimal logistics schemes based on the effective modeling of horizontal links (both production and economic, as well as trade-economic) between the various participating companies. In the field of transportation management, the development of digital logistics contributes to the optimization of transportation processes, significantly reducing the costs of their planning and implementation [17, 18]. The conducted analysis allows us to conclude that there is a high level of digital maturity of the Russian natural monopoly entity in the field of freight rail transportation - the Russian Railways holding company [19], and the development of digitalization technologies allows us to talk about the achievement of economic effects through the implementation of effective IT solutions [20] and the refinement of projects that are less successful at the current stage in order to replicate the best digital technologies throughout the holding’s network. In order to calculate the most effective algorithms for solving certain tasks of the holding, this article considers a system that provides automation and digitization of management decisions in the field of resource flows using smart contracts, and also describes the technology for storing protocols of their digital information in the form of a distributed register using blockchain [21, 22], which simplifies the process of recording transactions and accounting for assets on the business network. A multi-agent network structure and linking all digital information protocols into a single program code seem appropriate as a corporate distributed register of information storage for organizing resource support for the Russian Railways holding. Further development of the study may consist in analyzing the economic consequences of the current implementation of digital solutions, considering the experience of digitalization of Russian Railways holding, as well as in the development of a unified simulation model that will be able to link all components of the movement of resource flows in a smart contract involving the use of a distributed register of information storage based on blockchain technology.
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O. S. Chemeris et al. International Scientific Conference “Far East Con” (ISCFEC 2020), Advances in Economics, Business and Management Research, pp. 2960–2964. Atlantis Press (2020).https://doi.org/ 10.2991/aebmr.k.200312.422 Borremans, A.D., Zaychenko, I.M., Iliashenko, O.Yu.: Digital economy: IT strategy of the company development. In: MATEC Web of Conference, vol. 170, p. 01034 (2018). https:// doi.org/10.1051/matecconf/201817001034 Vysotskaya, N., Sorokina, O.: Process transformation digitalization management system russian railways OJSC taking into account new conditions. Entrepre. Guide 13, 9–19 (2020). https://doi.org/10.24182/2073-9885-2020-13-3-9-19 Zaostrovskikh, E.A., Sanzhieva, A.S.: Digitalization as a solution of the main problems of clients and contractors of jsc «Russian Railways». In: Sotsial’no-Ekonomicheskiye Osnovy Ustoychivogo Razvitiya Regionov, pp. 70–73 (2021).https://doi.org/10.31433/978-5-90412131-0-2021-70-73 Kapranova, L.: The digital economy in russia: its state and prospects of development. Econ. Taxes Law 11, 58–69 (2018). https://doi.org/10.26794/1999-849X-2018-11-2-58-69 Kalugin, V.A., Pogarskaya, O.S., Malikhina, I.O.I.O.: The principles and methods of the appraisal of commercialization projects of the Universities innovations. World Appl. Sci. J. 25(1), 97–105 (2013) Tikhonov, P.M.: Grapho-analytical model of resource provision of network organizational structure in the regulated procurement process (on the example of the holding company ‘Russian Railways’). RSUPS Bulletin 1(77), 129–136 (2020) Haefner, N., Wincent, J., Parida, V., Gassmann, O.: Artificial intelligence and innovation management: a review, framework, and research agenda. Technol. Forecast. Soc. Chang. 162, 120392 (2021). https://doi.org/10.1016/j.techfore.2020.120392 Dinets, D.A., Solovyev, P.Y.: Ekonomicheskie posledstviya proektov cifrovizacii v lokomotivnom komplekse OAO «RZHD». Molodaya nauka Sibiri: ehlektronnyj nauchnyj zhurnal [Young Sci. Siberia Electron. Sci. J.] (1), 662–671 (2021) Vanin, P.A., Nesterov, A.S., Kholodilin, I.Y.: Integration of IIoT and AR technologies to educational process through laboratory complex. In: 2018 Global Smart Industry Conference (GloSIC), pp. 1–6 (2018).https://doi.org/10.1109/GloSIC.2018.8570108 Gritskevich, T.I., Leukhova, M.G., Gagin, P.V., Kalichkin, K.K., Yakimova, N.S.: Introduction of IoT solutions to business processes at locomotive enterprises: efficiency and transformation of social communications. In: Solovev, D.B., Savaley, V.V., Bekker, A.T., Petukhov, V.I. (eds.) Proceeding of the International Science and Technology Conference “FarEastSon 2021”: October 2021, Vladivostok, Russian Federation, Far Eastern Federal University, pp. 875–883. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-8829-4_86 Babkov, Yu.V., Kim, S.I., Zhuravlev, S.N., Pronin, A.A.: To the issue of creating a ‘smart’ locomotive. In: AIP Conference Proceedings, vol. 2389, no. 1, p. 030003 (2021). https://doi. org/10.1063/5.0063647 Loading on the Russian Railways network amounted to 1.2 bln tonnes in 2020. https://com pany.rzd.ru/ru/9397/page/104069?id=258580. Accessed 10 Mar 2021 Smart Contracts and Their Application in Supply Chain Management. Thesis, Massachusetts Institute of Technology. https://dspace.mit.edu/handle/1721.1/114082. Accessed 10 Mar 2021 Kupriyanovsky, Y., et al.: Smart container, smart port, BIM, Internet Things and blockchain in the digital system of world trade. Int. J. Open Inf. Technol. 6(3), 49–94 (2018) Ilin, I., Maydanova, S., Levina, A., Jahn, C., Weigell, J., Jensen, M.B.: Smart containers technology evaluation in an enterprise architecture context (business case for container liner shipping industry). In: Schaumburg, H., Korablev, V., Ungvari, L. (eds.) TT 2020. LNNS, vol. 157, pp. 57–66. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64430-7_6
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Application of Robotic Process Automation Technology for Business Processes in the Field of Finance and Accounting Alena S. Ershova1 , Dayana M. Gugutishvili1(B) , Alexander A. Lepekhin1 , and Andrea Tick2 1 Peter the Great St.Petersburg Polytechnic University, St. Petersburg, Russia
[email protected] 2 Óbuda University, Budapest, Hungary
Abstract. Robotic process automation refers to the use of software robots that are programmed to perform repetitive and time-consuming tasks. This makes them ideal for numerous applications in the field of finance and accounting. RPAfriendly tasks include sending email, opening applications, copying and pasting information from one banking system to another. Effectively implemented, RPA can significantly reduce manual work so that human employees can focus on more complex banking operations, human interaction, and decision making. In fact, automated process automation in the banking sector can reduce the need for repetitive manual tasks, data reconciliation and transcription - up to 70%. Today RPA is poised to change the way financial institutions do business and make that change faster than any other technology currently available. RPA is gaining momentum with its promise to increase business efficiency, increase productivity and lead to an overall increase in profits. The purpose of this article was to study the possibilities of using robotic automation of processes in the field of finance and accounting. In this article, the RPA technology as a whole was analyzed, its advantages and disadvantages were discovered. The areas of application of this technology in various fields were studied, as well as typical processes of financial organizations were analyzed in more detail and options for automating these processes using RPA technology were presented. The relevance of this article is due to modern trends in the field of digitalization and the growing popularity of this particular technology. The article will be of practical value for representatives of the financial sector, as well as for IT professionals specializing in the development of software products for financial institutions. Keywords: Robotic process automation · RPA technology · Business process robotization · Automation
1 Introduction Over the past 15 years, large companies have widely introduced the first five levers of transformation. To the extent that they become institutionalized, that is, an accepted and normal part back-office management [1]. However, only in the last few years has © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 978–991, 2023. https://doi.org/10.1007/978-3-031-32719-3_75
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the real power of service automation has been started. Some followers of heavy service automation we studied automated more than 35% of their transactions. This trend of service automation is called “Robotic Process Automation” (RPA). RPA refers to the use of software robots (or similar virtual assistants) that are programmed to perform repetitive and time-consuming tasks. RPA-friendly tasks include sending email, opening applications, copying and pasting information from one banking system to another. Effectively implemented, RPA can significantly reduce manual work so that human employees can focus on more complex banking operations, human interaction, and decision making. Robotic process automation is entering a new phase of maturity. It went beyond days basic “screen cleaning and scripting” to automate repetitive tasks for a solution that can work along with existing EAI and BPM deployments to automate more complex processes and actions [2]. The adoption of RPA by business process outsourcers was originally driven by the need to reduce costs (and errors) associated with hiring people to work in service centers to perform general or worldly tasks related to IT. The simple idea is that RPA allows you to process a large amount of transactions in a very predictable manner without having to involve more staff, because instead of using people to complete tasks, you run one or more software robots that do the work instead. If the number of tasks increases, instead of recruiting, training and deploying more employees, you just perform additional robots as needed. This offer is still valid, but RPA technology has become more complex. Now offers Service organizations and enterprises a way to integrate systems, adapt business processes, quickly scale and provide more complex services safely, reliably and with more control. It is not requiring investment and effort that other approaches to integration and business process management demand. The purpose of the article is to make research on the abilities of application of RPA in the field of finance and accounting.
2 Materials and Methods In recent years, the term “Optimization of business processes” sounds more and more often at various seminars, conferences and other events designed to enable companies from one or different industries to share their experience in improving business and to adopt technologies from others. This concept includes all new and new methods and technologies to improve the operational efficiency of companies. This direction is in the strategic focus of the overwhelming majority of companies - this point of view is indirectly confirmed by the fact that the majority of large companies providing consulting services allocate entire departments for this area [3]. It used to be said that a company could gain a competitive edge in their industry by executing first-to-market, offering products of superior quality, or being the price leader. The business sages of the past believed that a company could compete in only two of these three areas. However, during the 1970s and 1980s, Japanese manufacturers turned traditional business wisdom on its ear. Historically, the product lifecycle started when a company (typically, the market leader) brought its new product offerings to market first. Then competitors followed by offering similar products of higher quality; and then more companies entered the market offering comparable quality at a lower price. The Japanese
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showed that companies could, in fact, compete in all three strategies simultaneously and become industry leaders. Traditional business has realized that “faster”, “better”, “cheaper” are not the only variables consumers weigh when making buying decisions. Superior product service has become a key business process. Companies have evolved from a product-based company into a services company that makes high quality, innovative products [4]. To dominate an industry, an organization must continually create innovations and remain agile in order to respond quickly to change and effectively execute any changes. They need to have the ability to pursue strategic initiatives with confidence including alliances, acquisitions, outsourcing, and global expansion. They also need the means to consolidate their business during economic downturns, using cost effective new tools for business process integration. Achieving these outcomes first requires equipping executive management with process controls and accurate information to make educated decisions for strategic course corrections and realignment [5]. Once decisions are made, these Business Process Optimization projects require an organization to examine and pursue opportunities to reduce costs, cycle times, while increasing service levels or product quality [6]. One of the modern ways to increase the efficiency of operation is the direction of Robotic Process Automation (RPA) - a new form of automation of business processes. RPA is a way to automate routine business operations, in which a software robot imitates employee actions in a various system user interface (GUI) [7, 8]. In this context, a robot is a program capable of performing standard and repetitive operations that employees currently perform using a keyboard, screen, and mouse. In other words, the robot emulates the user’s “hands” that control the means for entering information on the automated workplace. Robots of this type are not intelligent and, at least for the time being, practically do not use technologies related to Artificial Intelligence. The scope of these robots are standard typed operations with a clearly defined algorithm of actions and standard conditions [9, 10]. The main difference between the RPA technology and the standard procedure for automating business processes is that the operation of the robot does not require the integration of the systems used. Classical automation uses the Application Programming Interface (API) technology - an interface for transferring data between systems [11]. When using RPA, the data interface is the same graphical user interface. Data is transferred from one system to another, either through a clipboard, or, as a rule, at the software level of the robot. This difference is an advantage of the RPA technology, since no revision (or creation from scratch) of the API is required. In addition, in many modern accounting systems, the refinement of integration mechanisms requires substantial resources that are incomparable in size to the cost of developing robots [12]. The reasons for the emergence of this technology have long been known and practically do not differ from the causes of the appearance of physical robotization of employees. In any production, there is always a problem of efficiency - employees performing various operations with their hands get tired, they can make mistakes and are limited by time resources. Robots successfully solve this problem - they do not get tired, can work without interruption 24 h a day, and do not make mistakes in standard, typed operations [13].
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As a form of automation, the same concept has been around for a long time in the form of screen scraping but RPA is considered to be a significant technological evolution of this technique in the sense that new software platforms are emerging which are sufficiently mature, resilient, scalable and reliable to make this approach viable for use in large enterprises (who would otherwise be reluctant due to perceived risks to quality and reputation). Unlike other forms of automation, RPA has the intelligence to decide if a process should occur. It can analyze data presented to it and make a decision based on the logic parameters set in place by the developer. In comparison to other forms of automation, it does not require system integration. RPA is a broad field and there is a wide array of technologies in the market that greatly differ from one another. However, most RPA products will comprise of RPA developer tools, a controller, and the robot itself. Businesses can leverage RPA in a multitude of different ways. Flexible and easy to implement, some businesses may find that they use it in a way that is unique to their organization. Determining what processes should be automated is a key strategic point. There is no point in automating a process just for the sake of it [14].
3 Results and Discussion Robotic Process Automation finds its relevance across multiple industries wherever there is a need for intelligent automation and set process, which is governed by triggers. While the advantages of robotic process automation or agnostic of industry and domain, there are few places where robotic process automation can come in as a big advantage. And the first area of implementation is customer support. As consumers, we are accustomed to expecting immediateness and accuracy in our typical interactions with companies, such as finding an answer to a question, placing or changing an order, or simply updating our data. However, when we are faced with more complex problems, we seek to seek help from a person. Getting the perfect balance between them is a serious problem for companies, and at the same time, it provides a significant opportunity to increase not only customer satisfaction, but also the efficiency of processing request data. And here can help robotization. Big data management is one of the areas in which RPA has one of the highest adoption rates. Organizations accumulate huge amounts of customer data every day. Clients are aware of this and begin to expect expanded experience in exchange for providing a large amount of personal information for use by companies. Thus, when you are forced to repeatedly repeat your data when you call, receive an email with an error in the name or receive a suggestion of inappropriateness, dissatisfaction quickly arises [15]. Robotic process automation helps with eliminating the extra efforts of customers by collecting, analyzing, cross-referencing and exchanging information between platforms and channels without obtrusiveness. Using software robots to replicate people’s actions, RPA fulfills its purpose to help automate internal processes to improve customer service. Examples include opening a customer information tab on the operator’s desktop as soon as a call arrives, sending a personalized email based on a predefined set of criteria, or even regular health checks to make sure all systems and services work and work [16].
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Another important area of application of RPA technology is healthcare. Health service providers around the world perform a variety of procedures and tasks, such as managing bills and claims, registering patients, delegating medical professionals, collecting reports and patient data, and providing prescriptions. Traditionally, these tasks are managed or controlled manually or using off-the-shelf software [17–19]. With this approach, the implementation and monitoring of these procedures, along with the main tasks, can be tedious, time consuming and error prone. In addition, medical facilities must ensure compliance with various rules and regulations. In addition, the rules of health care are constantly changing. Consequently, health facilities should be aware of the latest changes and take appropriate measures to comply with regulatory requirements. Failure to comply may result in significant fines or, in some cases, the closure of hospitals. Such incidents affect the reputation of health facilities. Human resource management is also a promising area for the introduction of RPA technology. In HR operations, RPA HR usage scenarios include automating the simplest, repetitive keystroke administrative and administrative actions. Personnel processes that can be simplified with the help of the RPA include employee relations, training and development, recruitment and recruitment, benefits and rewards, and recruitment activities. In most personnel departments, implementations such as ADP do not achieve the goal of direct processing, even though they were sold as capable of doing so. In RPA on frames, the focus is on the level of microtasks or keystrokes, combining processes that large systems did not solve properly or could never do [20]. Finally, another extremely promising area for the introduction of this technology is finance, as well as the related area of accounting. In a typical digital conversion scenario, such as automating end-to-end financial processes in Procure to Pay, Order to Cash, and Record to Report cycles, most processes can be automated using capture and workflow automation solutions. However, we still find gaps in automated processes, when there are actions and tasks between systems, such as entering and re-entering information from one application to another, entering supplier portals to collect information, and manually updating financial data or ERP applications from Excel spreadsheets [21]. Such gaps can be overcome with the help of RPA technology. We propose to consider in more detail some options for using RPA in the context of operations related to finance and accounting. Examples of the use of robotic automation of processes in finance and accounting are defined as documented actions or process steps that are opportunities for implementing an RPA. They are documented at the level of advanced employees, fixing the work steps performed on their computers or other electronic devices of the end users. The financial and accounting scenarios for using the RPA facilitate the preparation necessary to automate the movement of information across systems [22]. It does not have to be complicated. Consider use cases like financial transactions and accounting schemes used by consultants to set up automated data processing scenarios in several IT systems. 3.1 Accounts Payable Let’s create a scene for robotics in finance and accounting with an automated learning example: user’s day in life, the payables clerk.
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The accounting process before RPA is as follows: 1. User has Accounting system, Excel, a PDF account from a client, and Outlook open on his computer. 2. User retrieves customer data (name, address, account number, invoice amount) from the PDF account received in Outlook 3. He then inserts it into Excel for internal financial reporting. 4. User then inserts the same data into Accounting system. 5. He can cut a check for the seller. 6. Then, he needs to copy and paste information from Accounting system into Outlook to send an email to the company, in which he reduced the check to confirm that the payment was processed. The simple process diagram is listed in Fig. 1.
Fig. 1. Accounts Payable
User’s work life could be easier if he sat down and analyzed the process just discussed above, documented the accounting workflow and figured out which fields need to be copied and pasted from each application to another. Add to this the ability to automatically copy and paste? Well, this is exactly what the base scenario of automation of robots looks like in financial and accounting operations. You can literally remove all the copying and pasting work if you spend some time standardizing the process, and then add the RPA action to it. Let us rewind time a bit and start the whole scene again, but with RPA set. Suppose UiPath, since this is our RPA leader. In this new reality, a robot accountant can find this source PDF file in Outlook user. User’s robot software assistant can navigate across
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multiple screens (even controlling her mouse and keyboard) to copy and paste everything into relevant documents. He can even e-mail the final confirmation, significantly reducing user’s workload and increasing the total AP cycle time. Now imagine that throughout the organization! This is an example of a non-serving RPA, meaning that the entire process has been automated. This is much more difficult to do compared to the serviced solution. A simplified fast, but no less effective version of robotics in the field of finance and accounting can greatly affect your organization [23] and is known as the visited RPA. This means that the robot is basically installed as an assistant, not a human. We call this the bionic approach. Think of this approach as a crawl (or your first steps) before breaking into a steady sprint. With this approach, user would still open her e-mail and the necessary attachments herself, after which she would include her automated accounting software that would move the data to where it needed to go. Common functions in finance and accounting that benefit from RPA include payables, receivables, accounting and reporting, budgeting and forecasting, cost management, internal audit and regulatory compliance, taxes, treasury management and payroll. Accounts payable with its repetitive work are generally better suited for automated accounting than a process like budgeting that requires a lot of human evaluation [24]. 3.2 Consumer Loan Processing In this example of the use of robotics in banking, we will follow user, a consumer credit processor who is preparing to do his daily work: process a request from a potential borrower. The high-level version of the process is illustrated in Fig. 2. It usually takes user at least 20 min per customer per loan application. This is because 80% of her work is manual: he needs to copy and paste information between email, several loan processing systems, credit bureaus, and several government websites. This is a difficult, time consuming, time consuming and tedious job. Consider the process of using banking pre-RPA: 1. When a customer requests a new consumer loan or line of credit, a call center representative, a branch employee or a website writes data to the loan processing system 1. 2. As soon as user receives information, he conducts a manual credit check. It does this by transcribing data from the loan processing system to an external website for a credit report. 3. User then saves the credit report in PDF format and appends it to the Loan Processing System 1. 4. User then copies and inserts the credit score in the field in the Credit Processing System 1. 5. Once the credit check is complete, User transcribes the data from Credit Processing System 1 to the other two major banking systems. 6. User then goes to the government website to confirm the client’s address and evaluate the documents provided. It is not surprising that he does this by copying all the information from the Loan Processing System 1 and pasting it onto the website to confirm the address of the client requesting the loan.
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Fig. 2. Consumer loan processing
7. Once the information is confirmed, User prints it in PDF format and attaches it to the Loan Processing System 1. Again, all this is done manually. User takes the same steps about 20 times a day: this is his maximum ability. Of course, 80% of its capacity is spent - or perhaps lost - for manual copying and pasting. Now consider the exact same scenario, upgraded with banking robotics. Banking RPA uses this, almost magically, for a hard worker such as user: 1. User receives a loan package in the system just as before. 2. User launches his UiPath robot. It then enters the credit processing system 1 and automatically retrieves all the information needed to process the credit check. 3. The robot opens a credit reporting site. It runs a credit check, extracting information from the Loan Processing System 1.
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4. The robot creates a copy of the credit report in PDF format, attaches it to the Loan Processing System 1 and copies the credit score in the “Credit Score” field in the system. 5. The robot then retrieves the loan data obtained in the Loan Processing System 1 and writes it to two other major banking systems. 6. The robot enters a government website, enters the necessary data to verify the address, and checks the property valuation and the client’s address. 7. Finally, the RPA bot saves the address verification and evaluation file to the loan processing system 1. What user had done 80 steps earlier, now needs to be done with one click. What used to be the full 20 min of his day now takes only five. It is important to note that user is not only faster; now he can focus on providing exceptional banking services to customers, rather than just transfer data. Not surprisingly, lenders such as user regard banking in banking as such a boon to their productivity and morale [25]. 3.3 Investment and Asset Management Robotic automation of processes in operations with investments or asset management is defined as the introduction of software robots using UiPath, Blue Prism or Automation Anywhere to reduce and eliminate the manual efforts required to handle repetitive and routine tasks performed on end-user devices. The investment management RPA runs on the interface of existing software applications for scraping and decrypting data related to several simple and repetitive tasks that make up the bulk of the working day of the investment management back office. The implementation of RPA is associated with the following benefits: – No new infrastructure investment - RPA acts as a layer on top of existing core asset management applications. – Higher quality of work and data - RPA in investment management reduces human errors when moving data by 100% – Increased productivity - RPA provides 24/7/365 transaction processing capability - robots can work during off-hours without overtime. Expect 50% reduction in transaction processing time. – Cost reduction. Robotics in investment and asset management can reduce processing costs by up to 80%. – Increased implementation speed - Robots can be deployed in as little as two weeks after the use case has been correctly defined. No more waiting for years to implement. – Higher Staff Satisfaction—The asset management RPA frees staff from unnecessary and boring processing work, which allows them to focus on core value-generating tasks, such as helping customers. Let’s look at a real example of using RPA, which we implemented in investment management below. This example of using RPA for asset management explains how to use automated process automation to speed up analysis and report generation to overcome repeated data movement errors to create reports. The result was a decrease in manual work by 80%.
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User works in the intermediate office of an asset management firm as an investment analyst. He updates the broker stock valuation report. Usually, she manually processes each assessment in his company, which takes at least 15 min for his assessment report. In its current state (before RPA), it must transcribe information between emails, Excel, and the cloud web portal. He then creates a PowerBI report for internal analysis. The process of using pre-RPA to manage investments and assets is as follows: 1. Every day brokers evaluate changes, user receives emails with new weekly estimates. 2. He opens every email and saves attachments to a network drive. 3. Then he opens the first attachment and copies the broker’s estimates for the day to an Excel master spreadsheet. 4. He repeats this process for each investment. 5. When he finished copying brokers’ ratings for the week, he opens the browser. 6. He goes to the standard website, copies the assessment of changes on the website and adds it to the table of main totals. 7. After each update, he saves a table of main totals. 8. He opens the PowerBI, then opens the PBI report created to read the weekly summary table. 9. User updates the report and the pie chart changes to reflect the new estimates. However, with the help of robotics, working with user’s assessment reports will be completely different. The RPA usage scenario for asset management will handle the work as follows, taking specific intervals to allow user to check for errors: 1. User still receives emails from brokers every day with new weekly estimates. 2. UiPath opens Outlook, searches for these emails and saves attachments to a network drive. 3. UiPath reads each cell in attachment lines within a second. 4. UiPath enters information from attachments into the main general table in 6 s. 5. UiPath opens the browser, goes to the standard web site, copies the weekly rating from the site and inserts it into the general table. 6. Then UiPath pauses and asks user if the information is correct. (User clicks “Yes” if the information is correct, and “No” if you need to change something.) 7. User clicks Yes, and the robot continues. 8. UiPath opens the weekly PowerBI Estimated Report and clicks the Refresh button to update the pie chart based on the table of main totals. 9. The PowerBI report pie chart changes to reflect new information. 10. UiPath again pauses to ask user if there are any errors in the pie chart. (User clicks No if the pie chart is correct, and Yes if he needs to make a change.) 11. User clicks No, and UiPath ends the sequence by saying “Finished” to tell him that the sequence was successful. 12. Now he can send a PowerBI report to her manager and colleagues. The above steps took only 3 button presses compared to the 100 + clicks needed before RPA. Previously, user took 15 min to update a separate stock report, but now he needs 1 min - saving about 14 min on a stock report. During an 8-h workday, user could update about 30 reports. With RPA, he can now update hundreds of reports every day.
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3.4 Insurance Claims In RPA insurance, it is defined as the implementation of software robots that can be configured for each computer without using a code. These software robots help their real knowledge workers perform monotonous and repetitive insurance duties, such as data entry. RPA effectively introduces artificial intellectual (artificial) labor to insurance operations, that is, robot assistants who can perform basic computer teams of employees, performing various “low-value-added” tasks that currently waste these employees’ time, energy and morality. RPA platforms can handle actions down to mouse and keyboard levels. Once they are set up properly, they can do everything from opening applications to clicking, copying and pasting information from one application to another, sending emails and similar actions. So, we would like to present a use case. In this case, user’s robotized insurance automation process claims through numerous company systems. Each application takes from 20 to 40 min. He usually has a waiting list or a waiting list. This queue is the reason why a client’s claim can take several days, and why it takes so long for policyholders to receive their money after the insurance claim. The process of using pre-RPA insurance is as follows: 1. User receives a letter with a new claim. Each claim form - PDF - contains a new claim number. Your Outlook is configured to search for these emails. 2. He received an order for receipt. This creates a backlog of 10 to 20 applications or more. 3. User opens the oldest application in the queue. First he learns how to get an insured account. 4. It’s not a problem. 5. If you are several people, they do not know their number. He then attaches a PDF to the claims system. 6. He moves the PDF from the queue to the finished folder. She then sends the claim by email. 7. He has lunch breaks. When he returns, there are 10 more complaints in his email box. Therefore, user never catches up; bottleneck in the claims process. This whole process is done manually. And all this manual processing reduces productivity and increases costs, which can be avoided, because claim handling companies are engaged in clumsy repetitive tasks, rather than using their skills better (Fig. 3). Now let us look at the same process using robotics and see the difference in performance. The insurance use option after RPA processes the job as follows: 1. User receives a letter with a complaint, as before. However, this time he opens all the relevant systems and starts the robot. 2. UiPath is looking for user’s claim folder in Outlook. It finds a new email and saves the attachment to the network drive folder. 3. UiPath opens the oldest requirement in the queue, searches for the account of the policyholder in the company’s system and compares the information. If it detects any discrepancies between the claim information and the account, the robot stops and
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Fig. 3. Insurance claims processing
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saves the claim in the new Verification Required folder. User can focus her efforts on these “exceptions.” Since this robot works in cycles, it continues the following application. If the information in this claim matches the account information in the system, the robot copies all the information about the claim to the claim system and attaches a PDF. UiPath then uses a pre-created (template) email to send a claim to the claim processing department, notifying them that it is complete and ready to be paid. User is going to lunch, but the robot is working. When he returns, the robot has completed the processing of claims. It received, retained and processed all 10 new claims received during his absence. After lunch, user opens the Validation Required folder to correct any inconsistencies between these statements and the corresponding accounts.
Each statement that used to take user 20–40 min earlier now takes only 4 min from the robot. Since the robot is running continuously until all applications have been processed, the queue is completed within an hour. Also, note that the robot processed claims, while no one paid for the work. UiPath eliminated a bottleneck, eliminated multiple manual work and accelerated the processing of claims. Thus, insurers receive the necessary payments faster. The presented use case and the real corporate examples mentioned above are just a few examples of how RPA makes insurance operations faster and more cost-effective, saving companies money while simultaneously stimulating customer experience. Thus, the expediency of using RPA technology for automating business processes is clearly shown.
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4 Conclusion Today RPA is poised to change the way financial institutions do business and make that change faster than any other technology currently available. Robotic process automation is gaining momentum with its promise to increase business efficiency, increase productivity and lead to an overall increase in profits. In this article, the possibilities of using RPA technology in various fields of activity, especially in the field of finance and accounting, were studied. Specific examples of the use of RPA for the most common business processes specific to financial organizations were presented. In addition, the advantages of using this technology were outlined. As a direction for further research, we can define the development of recommendations for the effective implementation of RPA technology in the company’s processes.
References 1. Levina, A.I., Borremans, A.D., Lepekhin, A.A., Kalyazina, S.E., Schröder, K.M.: The evolution of Enterprise Architecture in scopes of digital transformation. In: IOP Conference Series: Materials Science and Engineering, vol. 940, p. 012019 (2020). https://doi.org/10.1088/1757899X/940/1/012019 2. Levina, A., Novikov, A., Borremans, A.: BPM as a service based on cloud computing. In: Murgul, V., Pasetti, M. (eds.) EMMFT-2018 2018. AISC, vol. 983, pp. 210–215. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19868-8_21 3. Ernst, M.: Method and apparatus for dynamic optimization of business processes managed by a computer system (1999). https://patents.google.com/patent/US5890133A/en. Accessed 08 Nov 2022 4. van der Aalst, W.M.P., Bichler, M., Heinzl, A.: Robotic process automation. Bus. Inf. Syst. Eng. 60(4), 269–272 (2018). https://doi.org/10.1007/s12599-018-0542-4 5. Hansen, W.-R., Gillert, F.: RFID for the Optimization of Business Processes. John Wiley, Hoboken (2008) 6. Anisiforov, A., Dubgorn, A., Lepekhin, A.: Organizational and economic changes in the development of enterprise architecture. In: E3S Web Conference, vol. 110, p. 02051 (2019). https://doi.org/10.1051/e3sconf/201911002051 7. Lacity, M., Willcocks, L., Craig, A.: Robotic Process Automation: Mature Capabilities in the Energy Sector, vol. 19 (2015) 8. Syed, R., et al.: Robotic process automation: contemporary themes and challenges. Comput. Ind. 115, 103162 (2020). https://doi.org/10.1016/j.compind.2019.103162 9. Lu, H., Li, Y., Chen, M., Kim, H., Serikawa, S.: Brain intelligence: go beyond artificial intelligence. Mob. Netw. Appl. 23(2), 368–375 (2017). https://doi.org/10.1007/s11036-0170932-8 10. Lacity, M., Willcocks, L.P.: Robotic Process Automation and Risk Mitigation: The Definitive Guide. SB Publishing, Ashford, UK (2017) 11. Lepekhin, A., Borremans, A., Iliashenko, O.: Design and implementation of IT services as part of the “Smart City” concept. MATEC Web Conf. 170, 01029 (2018). https://doi.org/10. 1051/matecconf/201817001029 12. Aguirre, S., Rodriguez, A.: Automation of a business process using robotic process automation (RPA): a case study. In: Figueroa-García, J.C., López-Santana, E.R., Villa-Ramírez, J.L., Ferro-Escobar, R. (eds.) WEA 2017. CCIS, vol. 742, pp. 65–71. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66963-2_7
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13. Zaychenko, I., Smirnova, A., Borremans, A.: Digital transformation: the case of the application of drones in construction. MATEC Web Conf. 193, 05066 (2018). https://doi.org/10. 1051/matecconf/201819305066 14. Willcocks, L.P., Lacity, M.: Service Automation Robots and The Future of Work. SB Publishing, Ashford, UK (2016) 15. Desai, D., Jain, A., Naik, D., Panchal, N., Sawant, D.: CRM using RPA UiPath. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Bestak, R. (eds.) Data Intelligence and Cognitive Informatics, pp. 729–743. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-64601_56 16. Mullakara, N., Asokan, A.K.: Robotic Process Automation Projects: Build Real-World RPA Solutions using UiPath and Automation Anywhere. Packt Publishing Ltd, Birmingham (2020) 17. Brooks, J., Brooks, L.: Automation in the medical field. IEEE Eng. Med. Biol. Mag. 17, 76 (1998). https://doi.org/10.1109/51.687969 18. Ilin, I., Levina, A., Lepekhin, A., Kalyazina, S.: Business requirements to the IT architecture: a case of a healthcare organization. In: Murgul, V., Pasetti, M. (eds.) EMMFT-2018 2018. AISC, vol. 983, pp. 287–294. Springer, Cham (2019). https://doi.org/10.1007/978-3-03019868-8_29 19. Iliashenko, O.Y., Iliashenko, V.M., Dubgorn, A.: IT-architecture development approach in implementing BI-systems in medicine. In: Arseniev, D.G., Overmeyer, L., Kälviäinen, H., Katalini´c, B. (eds.) CPS&C 2019. LNNS, vol. 95, pp. 692–700. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-34983-7_68 20. Balasundaram, S., Venkatagiri, S.: A structured approach to implementing robotic process automation in HR. J. Phys.: Conf. Ser. 1427, 012008 (2020). https://doi.org/10.1088/17426596/1427/1/012008 21. Rozario, A.M., Vasarhelyi, M.A.: How robotic process automation is transforming accounting and auditing. CPA J. 88, 46–49 (2018) 22. Issac, R., Muni, R., Desai, K.: Delineated analysis of robotic process automation tools. In: 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC), pp. 1–5 (2018) 23. Lacity, M.C., Willcocks, L.: What knowledge workers stand to gain from automation. Harvard Bus. Rev. 6 (2015) 24. Iden, J.: Lightweight IT and the IT function: experiences from robotic process automation in a Norwegian bank. Bibsys Open J. Syst. 25, 1–11 (2017) 25. Cewe, C., Koch, D., Mertens, R.: Minimal effort requirements engineering for robotic process automation with test driven development and screen recording. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 642–648. Springer, Cham (2018). https://doi.org/10. 1007/978-3-319-74030-0_51
Digital Transformation and Business Processes Reengineering of the Education Services Jorge P. Olivos Salazar1 , Oksana A. Balabneva2 , and Alexandra D. Borremans2(B) 1 Pontifical Catholic University of Peru, Lima, Peru 2 Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
[email protected]
Abstract. This article describes the stages of developing a web application for online education management for the Martial Arts Academy. This article is aimed at solving the problem of differentiation of services, creating innovations in a system that provides computer services for basic learning processes. This topic is new and relevant in connection with the trend towards a healthy lifestyle and the process of business digitalization in Russia. The section “Materials and methods” describes the steps for optimal design of the needs of the research and design of the system, where methods such as analysis, research and generation of hypotheses and design were used. In the following chapters, the company’s strategic planning is carried out by analyzing the internal and external situation, applying the SWOT analysis method for the enterprise and the environment, and how these factors interfere with reduction strategies. The last chapters relate to business process reengineering. Business processes were categorized. A possible organizational structure and architecture of the information system are proposed. The future state of the global architecture is proposed through transactions, services, processes, capabilities, and viewpoints. Ultimately, a universal methodology was proposed for any company with similar characteristics and business market. Also, the results of this study are practically significant, as an interesting business opportunity was found; For financial reasons, the city of St. Petersburg is attractive for investing in the sports business and developing new strategies for increasing market share with the introduction of new technologies. In conclusion, the conclusions and recommendations on the report made for the main types of services within the company are considered. Keywords: Business Processes · Service Development · Software Development · Digitalization · Information Technologies
1 Introduction One of the most important concepts of the last decades has been how knowledge management can add value to customers in various industries. Knowledge management is the name given to a set of systematic and disciplined actions that an organization can take to get the most out of the knowledge available to it. “Knowledge” in this context includes both the experience and understanding of the organization’s employees, as well © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 992–1007, 2023. https://doi.org/10.1007/978-3-031-32719-3_76
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as information materials such as documents and reports available within the organization and in the outside world [1]. Effective knowledge management, as a rule, requires an appropriate combination of organizational, social and managerial initiatives along with, in many cases, the introduction of appropriate technology [2]. Sports business have not been excepted to trend of knowledge management. The concept of martial arts academy is not new in Russia, nevertheless, many companies have not been using IT technologies and Knowledge Management Process such as part of their strategies to grown and capture new market shares [3]. The effective use of software design in companies would naturally lead to improve organizational performance. Organizations that improve their Knowledge Management through innovation and technology integration can improve their operational efficiencies based on lessons learned [4]. It is necessary to focus on the processes by which knowledge is transformed between its hidden and explicit forms [5]. Organizational learning occurs as individuals participate in these processes, as their knowledge is transmitted, formulated and made available to others. The creation of new knowledge takes place through the processes of unification and internalization [6]. In the city of Saint Petersburg, that there are several academies, companies, and organizations dedicated to this offer different combat sports and martial arts. However, there is a diverse trend to seek the competitiveness of this sector. Therefore, among several companies do a great job, but the development of the industry requires going through a process of adaptation to IT System and new trends in education online [7]. In this sense, there is a latent need for research and development an innovative design to achieve a new tool to offer their main service. Many companies do not use IT technologies and Knowledge Management Processes, such as part of their growth strategies and capturing new market shares [8]. Organizations that improve knowledge management through innovation and technology can improve their performance based on lessons learned [9]. This article is devoted to the development of a web application for software databases that manages several business processes, business services and knowledge management within a sports academy for educational sports programs.
2 Matherials and Methods All the work on the article was divided into several stages. Analysis of an existing enterprise: identification of problems and opportunities for their improvement. Research and hypothesis generation. The research model discussed at this stage was developed using current conceptual information about various methods that other authors had to use in previous studies in other industries, such as medical institutions [10]. Existing studies and other documented cases in the literature were analyzed, as well as the needs of users in business requirements were investigated. Designing. This stage begins with modeling the company as part of the reengineering methodology process [11]. The second stage is dedicated to the architecture of the company, where modeling of the entire company, as well as the main management positions and their points of view, business services and technological components is carried out [12].
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Results. At this stage, the refinement and stabilization of the construction of the reference model was carried out, through interviews, in order to collect additional empirical data confirming the hypotheses put forward at previous stages.
3 Results 3.1 Analysis of an Existing Enterprise The Academy is a sport business company, which wants to satisfy entertainment needs of their clients. The purpose is to offer confidence, wellness, being in shape, stress reduction, increase cardiovascular capacity, and flexibility. To inspire hope, self-control and confidence in all customers. Customers: Parents with children, juvenile and teenagers (3–18 years old) who support them financially. Adults between 18 and 50 years old, whom lives in Saint Petersburg. Current Technology: Academies are designed with high quality in those kinds of sports. Zones are implemented to offer different innovative trainings and classes in martial arts and combat sports with infrastructure, equipment and modern services. The company use basic program for registration clients and manage data base. Industry: Services for members of families. Economy Activity: Sport entertainment for children, juvenile, adults from 3 to 50 years old. Market: Martial Arts Academies oriented to provide classes, trainings, activities, seminars and events to promote sports and martial arts for people between 3–50 years old in the city of Saint Petersburg. The research plan is shown in Fig. 1.
Fig. 1. Research plan (created by the authors)
Vision “To be an innovative sports company through the promotion of martial arts and fight
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sports. To provide confidence, wellness and a unique learning experience in an excellent environment.” Mission “We are focused in martial arts and orient our sports business with innovation and passion to improve people’s life based on friendship, hope, and loyalty”. Values – – – – –
Balance to maintain the focus on the objectives. Determination to achieve innovation based on trust and loyalty. Respect for building a safety sport environment (people, society and environment). Loyalty to act flawlessly, transparently and truthfully. Sacrifice in providing maximum effort and responsibility
Internal Analysis In that analysis, the objective is to try to find potentialities and weaknesses inside the company, with this result we can see a clear status of main aspects inside the company. The analysis is presented in Tables 1, 2, 3, 4, 5. Table 1. Marketing factors for internal analysis (created by the authors) Factor
Existence
Company services
+
Diversity of sales in a few products or clients
+
Markets research
+
Market or submarket shares
+
Service mix and expansion potential
−
Channels of marketing
+
Effective sales organization
−
Branding, reputation, and quality
−
Creative and effective sales promotion and advertising
−
Pricing strategy and pricing flexibility
−
After-sale service and follow-up
−
Brand loyalty
+
Based on the results of the interview and other methods of collecting information, a SWOT analysis was conducted, the results of which are presented below [13]. Strengths: – S1: High quality infrastructure for clients in all academies. Suppliers share the values of the same academy
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Factor
Existence
Capacity to raise short-term capital
+
Capacity to raise long-term capital: debt/equity
−
Corporate-level resources
−
Cost of capital relative to industry and competitors
−
Tax considerations
+
Investors relations
+
Cost of entry and barriers to entry
+
Price-to-earnings ratio
+
Working capital; flexibility of capital structure
−
Effective cost control, ability to reduce costs
+
Accounting system for cost, budget, and profit planning
+
Table 3. Operational factors for internal analysis (created by the authors) Factor
Existence
Capacity to raise short-term capital
+
Materials cost and accessibility
+
Inventory control systems
−
Location of facilities; layout and utilization of facilities
+
Facilities utilization of capacity
−
Effective use of subcontracting
+
Vertical integration, value added, and profits
−
Efficiency and cost/benefit of equipment
+
Effective operation control procedures
−
Technological capabilities relative to competitors
−
Research and development/technology/innovation
+
– S2: Innovations in activities, classes and training in combat sports and additional activities. – S3: Certified trainers with experience in sports and competitions. – S4: Friendly technology in the media for communication with clients and athletes. – S5: High ability to invest in marketing to attract parents, teens and adults through social media and other channels. Weaknesses: – W1: The Academy has been running for two years, some activities are relatively new.
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Table 4. Human Resources factors for internal analysis (created by the authors) Factor
Existence
Human Resources Management personnel
+
Organizational Culture
+
Labor relations compared to industry
−
Efficient and effective personnel policies
−
Development programs
+
Employee absenteeism
−
Expertise / Specialized skills
+
Table 5. Organizational factors for internal analysis (created by the authors) Factor
Existence
Organizational structure
−
Company’s image and prestige
+
Company’s objectives
+
Systems communication in the company
−
Control system
−
Clear procedures and techniques in decision making
+
Top-management skill, capacities, and interest
+
Strategic planning
−
– W2: Athletes know the type of business very well; they can do other activities separately. – W3: Operating costs are very expensive for several academies. Capital labor costs are very high. – W4: The brand is not well known to the public. – W5: Low flexibility to make changes to sports programs in all academies. Environmental Analysis Macroeconomics Analysis – Demographics According to the FSGS Director in St. Petersburg, in the year 2016, the population was 5.253.634 people. The population growth in this year was 0.9% with respect the year 2015. In the same year 232,663 people arrived in this city (22,391 of them came from other countries) [14]. About the age pattern, in 2016 the following diagram shows the distribution of ages by gender. In this year, 2.690.700 people were men and 2.188.900 were women (Fig. 2).
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Fig. 2. The Age Pattern by gender [15]
– Economic In January 2018, St. Petersburg City inflation was at the level of 101.3% change from the corresponding period of the previous year, down from 105.2% change from the corresponding period of the previous year previous month. According to official Government statistics, in 2017, the employed population for St. Petersburg City was 2,990.5 thousand persons. In 2015, personal income per capita for St. Petersburg City was 39,845 rubles. Most of these people work in the field of production and services. Recently, there has been an increase in demand for industrial specialties (construction and manufacturing) [16]. Immigration Trends: At the moment, there are about 3 million able to work residents in the city. These are people whose age ranges from 16 to 65 years. Competition Analysis Rivalry: The main service is similar in current competence, although it is true that there is a great invest in marketing promotion in the industry. The whole market is fragmented, incurring in low profit margin, many competitors have to reduce costs in order to acquire new students, competing in a price struggle. This means that innovation is not promoted in the market, there is an opportunity to improve the service. The promotion in marketing activities demands high budgets, not many competitors are able to invest in aggressive campaigns to capture market shares [17]. Suppliers: The company has four types of suppliers, infrastructure, systems, sporting goods and graphic design formats. In the first place, there are those who will provide the company with the installation, maintenance, remodeling and repair services for each type of sport. Secondly, there will be those suppliers that will be in charge of supply IT systems to provide necessary informatics hardware and software to promote e-learning systems [18]. Thirdly, it should be mentioned that to provide a complete experience to the students it is necessary to equip them with their own sporting goods, such as clothing and uniforms. Finally, the implementation of all our promotions, campaigns and logos must go through specialists who can offer high-level artistic designs.
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For those reasons, power of suppliers is medium since there are many suppliers that offer these products and services. In the future, in order to reduce operative costs, the company will negotiate better price conditions and smaller order sizes. Customers: The company has a market target from 3 to 45 years of age, who live, study or work in areas around Saint Petersburg. It is important to mention that for infants, youth and adolescents between the ages of 3 and 18 who are economically dependent, they are considered final users, however, those who assume the role of purchase are their parents (Table 6). Table 6. Target Markets (created by the authors) Potential Market
Target Market - General
Target Market - Specific
Parents who have children between 3, 5 and 18 years old
Parents who have children between 3, 5 and 18 years of age, residents in Saint Petersburg, who need to find a way of recreation for their children
Parents who have children between 8 and 11 years old, residents in Saint Petersburg, who need to find a form of recreation for their children contributing to their comprehensive education
People between 18 and People between 18 and 45 years of age, with different 45 years of age, with different occupations occupations, who work or live in Saint Petersburg
People between 18 and 45 years of age, with different occupations, who work or live in Saint Petersburg and are eager to carry out sports activities
The negotiation power of the clients is medium because there are many substitutes, it generates that some students could change the academy since there is no additional cost to replace service. It is important to mention that services substitutes are not differentiated. New arrivals: There are no extensive barriers to the entry of new competitors. Currently, in the case of the Brazilian Jiu Jitsu less than six academies are certified by the International Federation of Brazilian Jiu Jitsu. In order to make the sport more competitive, companies need to promote differentiation by high level certifications, this will make it more difficult to increase the number of new arrivals. During this period, there is encouragement to look for more events that capture the association to have greater control, to inform and regularize barriers to entry. Opportunities: – O1: The audience is interesting and they are well informed about Brazilian Jiu-jitsu, MMA and other sports. – O2: A new trend in the market, more and more families in St. Petersburg are investing in entertainment events – O3: The concept of a well-integrated Flight Academy is relatively new in St. Petersburg
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– O4: In the current market with several large competitors, other companies have no experience in Brazilian jiu-jitsu. – O5: There are several suppliers of martial arts infrastructure in Russia. Threats: – T1: International companies can enter the St. Petersburg market; they could invest in this sport. – T2: There are several alternatives in which families and parents can invest their money. – T3: This year there is a trend: people prefer to travel during the holidays than to stay in the city. – T4: The cost of renting a place in the city of St. Petersburg is getting more expensive. – T5: Traditional academies have regular customers and athletes. Strategic Management Organizational objectives The following objectives are proposed for the company – Achieve a 10% market share during the first three years. – Obtain a return on capital invested from 70% to the fifth year. – Achieve sales growth of 10% in the fifth year Based on the results of the analysis, three phased development strategies were put forward: 1. Short-Term Perspective - Market Penetration. During this time, the main goal will be to increase sales by using all available services. The main focus will be on a marketing campaign to increase the number of visits by students to academies, visits to corporations, universities, institutes, colleges in order to attract potential customers, advertising campaigns, free classes by periods and a great emphasis on direct marketing. 2. Medium-term perspective - Transition period. It is necessary to support and develop new promotions in order to maintain the participation already won and continue to penetrate the market. In addition, it is necessary to add events, campaigns, sizes and efforts to invest in seminars with the participation of international professors, it is necessary to maintain attractiveness and attract new customers. Conduct research according to customer satisfaction in order to be able to expand new disciplines and offer sports alternatives or functional supplements. 3. Long-term perspective - Product development The competitive advantage focused on continuous innovation will develop, it is always aimed at offering a differentiated product to the customer, attracting new and retaining customers that we already have, therefore it is necessary to update the team improvement campaigns, along with the development of alternatives in accordance with the needs emerging in the market.
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3.2 Business Process Reengineering The next stage of the work was the description of the company’s business processes [19]. All processes were classified into the following categories: planning and control processes, operational processes, occupational health, safety and central business-related processes. Table 3 shows the processes by category and their description (Table 7). Table 7. Business Processes (created by the authors) Process
Description
Strategic planning Development of new businesses and activities The company will always look for new alternative programs for the development of new classes. This will be handled by the heads of the Academy Strategy Management
The Company’s executive staff will develop a management strategy for each period
Control and resources The Purchase Process
Purchases, requirements for classes and academies will be carried out. This will be done by administrators
Warehouse Management
Relationships with suppliers will be established, as well as supply partnerships and beneficial relationships for the effective management of academies
Operating-Retail and Store The Process Of Booking A Class
For those new students, test classes will be booked, who will only need to provide their data and have the opportunity to participate in classes of their choice. Reservations can be made by mail or by phone
The Hospitality Process
The central process in which he will seek to increase customer loyalty, in the same way this process will be responsible for quality control of service
Operational And Educational Process The Process of Developing an Educational Plan
A continuous improvement team will be selected, which will be focused on meeting all customer needs based on an analysis of the effectiveness of academies
Educational Process
This process will be carried out, during which the client will use all the amenities and will be taught the knowledge of each class
Support - Marketing Customer Selection Process
This will be done through the sales and marketing department, which will send materials, advertising, contacts via social networks and other promotions
Marketing, Strategy and Management
Set a group of marketing goals of a sports business to promote products and sports services The marketing department is responsible for developing a marketing strategy designed to achieve the overall goals of the organization
(continued)
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Process
Description
Customer Satisfaction Management
Understanding the expectations and requirements of all your customers, as well as the relationship between our programs and their expectations
Support Service - HR Department The Process of Recruitment, Staff and Administration
A personnel management process focused on achieving the goal of improving the quality of service. The company’s employees will be selected according to their values, inclinations and views. With the best employees, we will have the best processes
Support – Accounting Accounting Process
A specialist accountant will be hired to complete this process
After a detailed description of each of the processes, an improved organizational structure of the company was presented, as shown in Fig. 3.
Fig. 3. Organizational structure (created by the authors)
In order to visualize the organization ArchiMate-based tool was used [20]. Figure 4 shows the organizational structure with business services for each of the actors.
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Fig. 4. Organizational structure with business services (created by the authors)
After an accurate description of the business processes was carried out, and the roles performing and responsible for them were determined, the architecture of the software product was developed [21]. Its components are shown in Fig. 5.
Fig. 5. Components of the software product (created by the authors)
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The enterprise architecture is a comprehensive and exhaustive description (model) of all its key elements and inter-element relationships [22]. The general enterprise architecture model is shown in Fig. 6.
Fig. 6. General enterprise architecture (created by the authors)
Fig. 7. Database schema (created by the authors)
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Having described all the functional and non-functional requirements imposed on the system, it became clear what data is needed in order to ensure its full functionality [23]. The database schema is shown in Fig. 7.
4 Discussion For future conclusions, it is proposed to conduct a study using resources that already work in other similar systems, it is also important to conduct surveys and more complex materials to assess the degree of satisfaction of customers who can use the e-learning system to improve their training plans and martial arts [24]. For future research, it is also recommended to conduct an economic study to analyze the cost of software in new markets related to e-learning systems [25]. In the same aspect, it is important to analyze how to conduct complex market research in the environment of IT systems in the service sector.
5 Conclusion Thanks to the analysis of the environment, an important business opportunity was found; for economic reasons, the city of St. Petersburg is favorable for investing in sports business and developing new strategies to increase market share using new technologies. The proposed methodology is suitable for any company with similar characteristics and a business market. Knowledge and learning management systems play an important role in building new relationships between traditional business process management topics and new trends in big data and customer service. This process was done in order to get a general idea of business services and assemble the architecture of the enterprise. The purpose of structuring the business processes presented in the article was to increase the efficiency of working with the client. To get information about the company, the article was based on on-site experience, in several interviews with experts and clients. It can be noted that in this type of business, one activity could be very easily imitated to gain competence.
References ˇ 1. Connelly, C.E., Cerne, M., Dysvik, A., Škerlavaj, M.: Understanding knowledge hiding in organizations. J. Organ. Behav. 40(7), 779–782 (2019) 2. Di Vaio, A., Palladino, R., Pezzi, A., Kalisz, D.E.: The role of digital innovation in knowledge management systems: a systematic literature review. J. Bus. Res. 123, 220–231 (2021) 3. Abubakar, A.M., Elrehail, H., Alatailat, M.A., Elçi, A.: Knowledge management, decisionmaking style and organizational performance. J. Innov. Knowl. 4(2), 104–114 (2019) 4. Al-Emran, M., Mezhuyev, V., Kamaludin, A., Shaalan, K.: The impact of knowledge management processes on information systems: a systematic review. Int. J. Inf. Manage. 43, 173–187 (2018) 5. Akram, M.S., Goraya, M.A.S., Malik, A., Aljarallah, A.M.: Organizational performance and sustainability: exploring the roles of IT capabilities and knowledge management capabilities. Sustainability 10(10), 3816 (2018)
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Rental Processes Digitalization in Commercial Real Estate on the Example of the Development Company Alexander K. Frolov(B) , Konstantin V. Frolov, and Ulyana Yu. Muhina Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia [email protected], [email protected], [email protected]
Abstract. To survive during crises, companies need to increase resilience by reducing costs and increasing revenues. This is especially true for Russian companies that are entering the third crisis in the last two years. In these times, only most resilient companies will survive. That means cutting costs and increasing efficiency, which could be achieved by digital transformation of the business. For small and medium-sized enterprises this is unachievable due to high costs of implementation. In this article, we consider an architecture variant and methodological foundations that allow us to create a solution that meets the needs of small development companies, and the proposed approach is a bridge to large solutions in the future, which does not necessarily imply the scrapping of the system that provided the company with a rise to the heights of leadership. As such this article could be used as guideline or a proposition for business analysts working for such businesses. The article provides an analysis of the business processes of a company engaged in the development and leasing of commercial real estate. For each of the main processes, subprocesses are described (including in the form of a diagram). The problems of AS-IS with the exponential growth of the number of facilities owned by the company are revealed. Solutions implemented by digitalization or automation of a part of business processes are proposed, diagrams of TO-BE business processes are constructed, including the proposed solutions. Implementation of systems proposed in the article allowed examined company to reduce labor costs and increase work efficiency. Keywords: Digitalization · Digital Transformation · Digital Twin · Digital Services
1 Introduction Modern financial crises leave a strong imprint on many industries. The development industry (and the rental industry associated with it) like many other industries alongside them, suffered as a result of the “Coronacrisis” and the subsequent crisis associated with the disruption of global logistics chains (the rise in price of building materials and other goods). In Russia, the imposition of sanctions also increased pressure on © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 1008–1020, 2023. https://doi.org/10.1007/978-3-031-32719-3_77
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business (a decrease in the number of suppliers, an increase in the cost of products due to “parallel” imports). At the same time, the rental cost cannot be significantly increased (and some-times even need to be reduced or postponed) due to the risks of losing tenants. Digitalization and automation can come to the aid of business in such difficult times— they allow businesses to optimize the costs of development and maintenance of owned properties (increased labor productivity, more efficient planning), as well as increase profitability by offering additional digital services [1]. In this context, it is relevant to use the methodology and IT solutions specially developed for digital twin [2] for Capital Construction, the elements of which can be successfully applied to support the solution of rental tasks. But what should organizations that are not so big and rich enough to afford implementation and usage solutions of this class do? In this article, we consider an architecture variant and methodological foundations that allow us to create a solution that meets the needs of small development companies, and the proposed approach is a bridge to large solutions in the future, which does not necessarily imply the scrapping of the system that provided the company with a rise to the heights of leadership.
2 Materials and Methods This paper uses the engineering of AS-IS processes of the company at the main stages of work, identifying their weakness-es and offering TO-BE processes using the principles of digitalization and automation. These proposals are presented based on the results of brainstorm sessions with and without potential stakeholders and also by utilizing business analysis. Next, information systems, frameworks and technologies that can be used to implement TO-BE processes are proposed (mainly those with which the development team has experience, as well as those that are maximally represented on the market). For each of the obtained systems, a qualitative assessment of changes is given, and provided that the system has already been implemented at the enterprise, a quantitative assessment of changes is given. Business Process Modeling Notation (BPMN) is used to engineer processes. The processes are built on the principle of AS-IS–TO-BE modeling. 2.1 Case Study Let’s look at the processes that are subject to automation and digitalization using the example of a St. Petersburg development company. This company has been operating on the domestic market for more than 10 years, owns more than 10 commercial facilities (warehouses, production facilities, office premises are located on them). These objects are located in different districts of the city, differ in quality, condition (the level of necessary injections to bring the object into the form in which it can be rented), communications and development opportunities. The company’s clients are businesses of different sizes and from different industries, in this regard, it is necessary to find an individual approach to each of them and bring the premises in line with its requirements. The number of objects owned by the company is constantly growing, so the team of development specialists is constantly busy at new facilities. It should be noted that
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in addition to the planned tasks of the development of facilities, the operational work of emergency crews providing situational response to various situations related to the operation of facilities is essential. In the context of the classical representation of the objectives of digital construction, operation and commercial use of Real Estate objects, the described statement is limited to the area marked with a dotted line in the Fig. 1.
Fig. 1. Place of development in objectives of digital construction. Diagram by K. Frolov, A. Frolov, U. Muhina.
The complexity of the work is constantly growing, the number of employees is increasing, as well as the number of tenants and the number of facilities. In this regard, the complexity of business management and its derivatives increases. It is more difficult for management to make decisions and meet the requirements of future and current tenants, and a digital management system can be a lifeline for the company in such a situation. 2.2 Key Business Processes and Problems Within the framework of the process category designated as the subject of analysis, we will single out those process groups that are subject to detailed consideration in order to justify efforts to ensure the effective operation of a commercial enterprise. From our point of view, such process groups include: – – – – –
Planning of resources, being spent on the implementation of commercial activities; Purchase of commercial real estate; Development of acquired real estate; Search for tenants and distribution of vacant areas; Rental of premises and provision of services.
Each of these processes has its own specifics, as well as the difficulties and problems associated with it. Next, we will analyze each of them in more detail, as well as the possibilities of digitalization and automation, noting that planning for this scenario is excluded from consideration due to its obvious simplicity. Each of these process categories (except planning) can be decomposed to a level that allows to identify those processes within each of the groups that can be automated. In addition, such a decomposition makes it possible to identify business services related to processes, the automation of which, together with processes, makes it possible to implement scenarios of modern digitalization. Let’s look at each of them (Fig. 2) in more detail.
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Fig. 2. High-level processes. Diagram by A. Frolov, K. Frolov, U. Muhina.
2.2.1 Commercial Realty Purchasing During the search for a new facilities for purchase, the company usually receives several offers, as well as independently searches for objects to purchase. To determine the cost effectiveness, a detailed analysis is required, taking into account the thresholds of the acquisition price, the cost of development, maintenance, tax (including excise, licensing, certification, etc. – transaction) costs and profitability of facilities. Important: costs and profitability cannot be considered as static values, these are values that have a dynamic nature, and their value cannot be fixed as deterministic, but has an indefinite character. That is why scenario analysis of the commercial side of various options is not trivial and requires the involvement of advanced analysis tools [3]. Such an analysis is necessary from the moment of the initial study of the options for the acquisition transaction, since it allows you to determine acceptable purchase and development prices that will allow to count on the profitability of all the company’s efforts. Several officials are involved in the preparation of the analytical characteristics of the facility due to the fact that a significant part of those taken into account in the commercial portrait of the transaction is characterized by uncertainty, the determinization of which requires expert assessments. As a rule, the higher the place of an expert in the management hierarchy, the better his expert judgment. That is why the analytics generated on the basis of a given data structure is subject to refinement during analysis at each of the management levels. In particular, the following chain of information processing may be acceptable. The primary analytical picture is formed by an experienced employee who has participated in several transactions. He collects all the basic information about the object (including historical information), communicates with tenants, takes photos, and searches for additional information from open sources. From this information, he creates a document that he passes to the senior analyst. The senior analyst analyzes the document in which he notes the key features of the site, calculates the approximate cost of development, potential income and expenses related to real estate, and most importantly – forms a draft conclusion on the effectiveness of the transaction with the specified parameters. Then the document is passed to one of the managers, who can make a purchase decision. This manager decides whether to purchase a facility, specifies the maximum cost of the object to purchase, and selects a source of financing for the purchase. This processes are displayed in Fig. 3. In our example, a development company demonstrates growth that is not linear. Rapid growth has led to a shortage of time to collect information about facilities: while maintaining the full-time number of employees and even with a conservative increase in their resources, there are not enough resources to collect and process information about premises. The growth of the regulatory part of employees, harmonized with the growth of the data flow, is doubtful due to the obvious need to adapt personnel to specific
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Fig. 3. Buy realty subprocesses AS-IS. Diagram by A. Frolov, K. Frolov, U. Muhina.
tasks, even if we assume the impossible: all new employees already have the appropriate qualifications. 2.2.2 Commercial Realty Development Immediately after the purchase of the object, the parameters of the development are clarified: resource requirements, deadlines (taking into account, among other things, various approvals) and finances. Planning of development activities is carried out using project management methods [4], and with the parallel implementation of several projects that create a resource conflict, and using portfolio management methods [5] that support the parallel implementation of several projects and project programs. As a result of project planning, a schedule of work is formed, involving the formalization of resource needs, including specialists [6], a proposal is created for the selection of suppliers and service partners [7]. Based on the fixed needs for materials and Fixed Assets, plans are created for their supply [8] from their own warehouses or from suppliers as a result of the purchase. Within the framework of project management, the list of tasks that should be set as a system-forming one is presented as follows (Fig. 4): – – – – – –
Maintaining the project structure; Planning resource requirements; Calculation of the cost plan based on the terms of the project; Material and technical resources—equipment; Operational monitoring of project execution; Project closure.
2.2.3 Searching for Tenants At this stage, it is important to convey the advantages of commercial real estate to customers. This is done by employees of the sales department (Fig. 5). They find potential tenants, communicate with them, show the premises, agree on conditions. Due to a significant increase in the company’s property, these employees also have more work [9]. To speed up the search for tenants, the company began investing in marketing, and began with the development of a website. This site allows to view all the company’s facilities, get acquainted with the services, explore the areas available for rent, and familiarize yourself with the rental conditions. If desired, the site visitor can immediately leave his
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Fig. 4. Develop realty subprocesses AS-IS. Diagram by A. Frolov, K. Frolov, U. Muhina.
contacts and book a premise, after which one of the sales department employees will contact him. The site automatically generates brochures for each facility, based on the information on the facility page, as well as the corporate style. This allows not to keep a designer on the staff of the company, which reduces the expenditure part of the balance.
Fig. 5. Find tenants subprocesses AS-IS. Diagram by A. Frolov, K.Frolov, U. Muhina.
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2.2.4 Maintenance of Rented Premises At this stage, tenants have already entered the premises. Every day they are provided with access to the premises, utilities (electricity, water, heat) and other services (security, video surveillance, Parking spaces, Internet access). Monthly tenants pay for the premises and services, as well as exchange documents with the landlord. Usually, the payment for the occupied area remains unchanged for a long time (for several years), utilities and other payments change quickly. One-time services (for example, redevelopment) are paid separately. Key processes are displayed in the Fig. 6.
Fig. 6. Maintain premises subprocesses AS-IS. Diagram by A. Frolov, K. Frolov, U. Muhina.
In business practice, electronic document management has become a trend for a long time [10], providing its users with obvious advantages. Above, we have identified the target technology of design and use – 6D design, which involves the use of electronic document management (EDI) by default. In the context of the tasks set, we will limit ourselves to legally significant electronic document management (LSEDI), the business coverage space of which is smaller than the classical EDI within the 6D model. In our scenario, the LSEDI is integrated with the accounting system, which allows accountants to send fiscal documents to recipients without leaving familiar program [11].
3 Results To implement the digitalization of processes, it is necessary to implement or develop software solutions of various kinds that would meet the requirements of the main stakeholders as much as possible, and also be cost-effective. The following are examples of TO-BE diagrams for each of the high level company processes implemented by specific solutions. For each solution reason for choosing specific implementation is described in detail. 3.1 Commercial Realty Purchasing To optimize the process of buying real estate, it is proposed to use the achievements of digitalization to collect and process information. To do this, we propose to apply the ideas of a digital twin [12, 13] to formalize information about facilities (Fig. 7). Most of the parameters of a digital portrait of a particular site get their values in standardized
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ways, based on understandable algorithms, and some of the parameters will get their values as a result of expert work. For the primary level of employees involved in the digitization of objects (analysts), a system is being developed that offers, depending on the geography, type of object, assessment tasks, an electronic template that must be filled in by the analyst. Taking into account the comments given earlier, not all template parameters can get their values as a result of the analyst’s work. Such parameters get their values at the subsequent stages of the formation of a digital portrait of the object. It is important to note that the preparation of the template is not only the formation of a parametric skeleton, but also the creation of a contextual reference explaining in detail the requirements for filling, taking into account the qualifications of the employee involved in the process. This reduces the time to prepare the primary digital portrait of the object, even taking into account the fact that the employee is not qualified enough. As they say, Automated learning is in action. The digital portrait of the object creates prerequisites for the development of a decision support system by specialists at higher levels of management – senior analysts and managers. This will increase the objectivity of the decisions made, re-duce the risks of error and reduce the time to analyze the business picture within the framework of standard business cases [14, 15]. The diagram shows an example of a modified process of acquiring commercial real estate with the software digital twin [16] of the site, implemented by the 1C: Rental and Real Estate Management [17]. There is also a report for the senior analyst, on the basis of which he prepares his own conclusion and justification. So far, analysts are filling out the object card directly in 1C, but in the future it is planned to create custom forms that will speed up the input of information, including on mobile devices. As a result of the implementation of the system, the time for making a purchase decision was reduced by three days in general, which shows the effectiveness of the proposed solution.
Fig. 7. Buy realty subprocesses TO-BE. Diagram by A. Frolov, K. Frolov, U. Muhina.
3.2 Commercial Realty Development The uneven involvement of technical and engineering staff in the tasks of development (including changes to an already prepared facility to meet the requirements of an alternative tenant), the formation of so-called emergency teams for these employees is obvious
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(Fig. 8). In practice, it is difficult to find examples of the sufficiency of the company’s own re-sources to perform both development tasks and maintenance and repair tasks. In this regard, the urgency of attracting external teams of specialists to perform technical work, primarily maintenance and repair, is obvious. With the indicated evidence of such cooperation, the task of forming a digital passport of an object that takes into account the work done on development and re-development is urgent. A digital passport created with the requirement of BIM modeling in the context of 6D design is a necessary technical prerequisite for delegating maintenance and repair work to an external partner and accepting it from him upon completion. In this context, the creation and use of digital project network technologies is promising, within the framework of which both the company, whose resources are used for development and maintenance, and partners performing design, audit, maintenance and repair tasks interact. A certain difficulty in the scenario discussed above is the planning of work within the framework of maintenance and repair (emergency teams and maintenance teams). Indeed, such a problem is related from the point of view of the solution algorithm as a problem of schedule theory and its solution in the context of the formulation of the optimal choice problem meets serious problems. In this regard, we can talk about the formulation of the coordination selection problem, which is practically solved through the tickets system. The corresponding subsystem, designed to support the coordination mechanism for the involvement of teams of external contractors, should process formalized requests from the company, bearing in mind that the commercial aspects of interaction have been previously settled. A formalized request to an external contractor must contain a link to the digital passport of the object, urgency parameters, photo and video artifacts, etc. Our proposal is to implement the OpenProject [18] project management system.
Fig. 8. Develop realty TO-BE. Diagram by A. Frolov, K. Frolov, U. Muhina.
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3.3 Searching for Tenants However, now the task of moderating the site falls on sales managers – after all, the information on it must be kept up to date. The solution to this problem is downloading information about real estate objects from the cards of their digital twins [19, 20] (Fig. 9). This will keep the information up-to-date without the intervention of sales staff, which means it will relieve them of a significant share of the load. Additionally, it will be possible to upload a room plan to the website, which will also improve the quality of informing site visitors. This addition is planned to be implemented by integrating 1C: Rental and Real Estate Management and website using CommerceML 2 [21] protocol. Further stages of the site development are the launch of marketing campaigns in search engines and social networks based on portraits of tenants, which are made up by sales managers based on the capabilities of the premises. For maximum effectiveness, such campaigns should be personalized and lead to automatically generated landing pages with information extraction from the main site [22]. The top of the development of such a system will be the provision of personal offers to visitors based on the history of offers and offers at other facilities generated using special algorithms. Such opportunities will open up after a long collection of information about real estate for rent thanks to digital twins of facilities [23].
Fig. 9. Find tenants TO-BE. Diagram by A. Frolov, K. Frolov, U. Muhina.
3.4 Maintenance of Rented Premises This stage is the only one that brings profit to the company, and in times of crisis it is necessary to look for new ways of earning in addition to reducing costs. One of the most effective ways is to provide your customers with additional services that their business would benefit from (Fig. 10). For example, it is convenient to use digital twins of premises prepared for this purpose to manage utility costs—the data in them comes directly from the terminal devices placed in them. They can measure the amount of water consumed, the quality of electricity and much more. At the same time, each tenant has a personal account where he can see consumption analytics, as well as subscribe to paid services (such as accident notification services or notifications about exceeding the consumption of a given resource limit). Security system functions can be installed in such a personal account:
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security cameras with face recognition, a parking pass by car number, and possibly control of employees’ lateness using information from the checkpoint. This service is already being implemented on the base of asset management system Sensorum Dash, capable not only of gathering information from different metering devices, processing, storing and displaying said information, but also of maintaining private account for every tenant. One–time services can also be ordered through personal account - tenant only need to make an application with a de-scription of the need or problem (for example, connecting telephony), then it will get to the responsible specialist who will begin to execute the task, and then contact the tenant about the results of its execution. Such applications can also be initiated automatically when special rules are set (for example, if sensors detect an accident, an “emergency” application is automatically created, a rapid response team arrives, corrects the problem and creates a report for the tenant). This system could be deployed as automatically generated OpenProject custom-rules accounts [7]. Since an increasing number of companies renting premises are beginning to develop their own digital services and products, the renter can provide them with a data center with round-the-clock support. The introduction of such digital services (always with a previous survey of tenants and an assessment of the need for them) can help both the landlord – by increasing revenue from premises, and tenants – by reducing the risks and cost of services (due to centralized management).
Fig. 10. Maintain premises TO-BE. Diagram by A. Frolov, K. Frolov, U. Muhina.
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4 Conclusion The article provides an analysis of the business processes of a company engaged in the development and leasing of commercial real estate. For each of the main processes, subprocesses are described (including in the form of a diagram). The problems of AS-IS with the exponential growth of the number of objects owned by the company are revealed. Solutions implemented by digitalization or automation of a part of business processes are proposed, diagrams of TO-BE business processes are constructed, including the proposed solutions. Since some of the proposed solutions have already been implemented in the company, qualitative and quantitative metrics have been proposed for such solutions that show their effectiveness. The remaining solutions are either being designed, or are at the implementation stage, or are still being coordinated with stakeholders within the company. A logical further development of the presented systems would be to combine them on the basis of the ERP III (Enterprise resources planning, 3rd generation) system [24, 25], interacting with services hosted on the Intranet network. This will allow integrating even more of the company’s business processes into a single digital environment and increase the level of digitalization. Since a significant part of the services are designed to interact with the company’s customers (tenants) directly, performance, UX/UI and functionality will play a significant role. It follows from this that it is necessary to work out the architecture of the system being developed, to conduct an additional analysis of customer needs, as well as to organize a fault-tolerant and productive infrastructure that will contain the company’s services.
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Author Index
A Alekseeva, Iuliia 41 Alekseeva, Natalia 390 Alferiev, Dmitrii 248 Anisiforov, Alexey B. 955 Anisimova, Maria 431 Antonova, Alexandra K. 772 Antysheva, Elena R. 839 Arteeva, Valeriia 431 Artemenko, Evgenii S. 650 Avdeeva, Irina 801 B Babkin, Aleksandr 390, 544 Babkin, Aleksander V. 565 Balabneva, Oksana A. 992 Barabanov, Anton 87 Baranova, Svetlana 793 Bescorovaynaya, Natalia 693 Bikezina, Tatyana V. 27 Bolonkin, Vladislav 662 Borremans, Alexandra D. 965, 992 Budkin, Artem 468 Bugaeva, Tatiana 442 Burlutskaya, Zhanna 360, 468 Burova, Ekaterina 331 C Chemeris, Olga S. 607, 965 Chernova, A. 157 Chertes, Polina 203 D Davydov, Alexey 662 Degtereva, Victoriya A. 27 Degtereva, Viktoriya A. 52 Demidova, Svetlana 257 Dmitriy, Kirillov 360 Dospan, Saida 745 Druzhinin, Andrey E. 621
Dubgorn, Alissa S. 607 Dubolazova, Yulia A. 662 E Efremova, Marina 320 Egorova, Svetlana 230 Eremicheva, Oksana Yu. 593 Ershova, Alena S. 955, 978 Eshov, Mansur P. 349 Evgrafov, Arkady A. 955 F Fedyaevskaya, Darya 468 Feofilova, Tatyana 41 Frolov, Alexander K. 1008 Frolov, Konstantin V. 1008 Fu, YuanYuan 455 G Galimova, Margarita P. 908 Geraseva, Alexandra 519 Gileva, Tatiana A. 908 Gintciak, Aleksei 360, 468 Glukhov, Vladimir V. 908 Gorovoy, Alexander 398 Goryacheva, Irina A. 847 Grishacheva, Aleksandra 203, 442 Grishunin, Sergei 331 Gugutishvili, Dayana M. 955, 978 Gusev, Yuri 859 Gutman, Svetlana 173 Guzikova, Liudmila A. 265 Guzov, Iurii 307 H Hoang, Thuy Dam Luong 278 I Ianenko, Marina 492 Ibañez, Erick Leonel García
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 I. Ilin et al. (Eds.): DTMIS 2022, LNNS 684, pp. 1021–1023, 2023. https://doi.org/10.1007/978-3-031-32719-3
759
1022
Author Index
Iliashenko, Oksana 732 Iliashenko, Victoria 732 Iliinsky, Alexander A. 530 Ilin, Igor V. 935, 945 Imani, Mehdi 41 Ipatova, Daria 13 Isroilov, Bokhodir 839 Ivankova, Galina V. 662 Ivanov, Maxim 52 Ivanova, Marina 111
Levina, Anastasia I. 935 Li, Shuquan 492 Liubarskaia, Maria 13 Lo, Thi Hong Van 278 Lomakin, Ivan 693 Lomakin, Nikolay 693 Lukina, Olga V. 27 Lytneva, Natalia 416 Lyukevich, I. 157 Lyukevich, Igor 188, 673
J Ju, Zhimin 481
M Malevskaia-Malevich, Ekaterina D. Mamrayeva, Dinara G. 66 Mamrayeva, Dinara 544 Maria, Vargasova 580 Markevich, Vladimir 203 Melnikova, Zhanna 203 Merkulov, Viktor I. 707 Mikhalev, Ilya 801 Miroshnikova, Tatyana 257 Misbakhova, Chulpan 257 Mochalina, Ekaterina P. 662 Mogharbel, Natalya 693 Mozaleva, Natalia 96 Muhina, Ulyana Yu. 1008 Myzrova, Olga A. 847
K Kalmykova, S. 140 Kalyazina, Sofia E. 935 Kaplyuk, Ekaterina 871 Karapetov, Vadim 828 Karimov, Dier 908 Kharkina, Ekaterina A. 896 Khrykova, Anastasia 745 Khusainov, Bulat D. 935, 945 Kichigin, Oleg 96 Kikkas, Kseniia N. 707 Kochinev, Yu. Yu. 839 Kolesnikov, Alexander M. 772 Kolotova, Darya P. 921 Konnikov, Evgeniy 203 Konyshev, Evgeny V. 66 Korchagina, Elena V. 621 Kornev, Pavel M. 621 Koroleva, Ekaterina 320, 504 Kostyleva, Irina B. 593 Kozlov, Aleksandr V. 921 Krasilnikov, Vladislav M. 530 Kravchenko, Valentina 3 Krestov, Vasily 416 Kryzhko, Darya 203, 248 Ksenia, Evseeva 580 Kuchumov, Artur 230 Kudryavtseva, Tatiana 218 Kulachinskaya, Anastasia 693 Kulakova, Natalya 230 Kulkaev, Grigory 96, 111 Kurochkina, Anna A. 27 L Lakovich, Kseniia 673 Lakovich, Olesya 673 Lepekhin, Alexander A. 978
N Nadezhina, Olga 519 Nasirkhodjaeva, Dilafruz S. Nazarova, Galina 96 Nazarova, Varvara 639 Nazmetdinova, K. 140 Nepryakhina, Tatiana 816 Novikova, Olga 442 O Ochilov, Akram 390 Odainic, Anastasia 885 Okrushko, Ekaterina 130 Ostroukhova, Daria 793 Overes, Ed 722 P Pezzella, Enrico 722 Pinsky, Alexey 859 Polovova, Tatyana 859 Popova, Sofia 320 Prosvirnina, Anastasiia 885 Putinceva, Natalia 13
349
908
Author Index
R Radygin, Evgeny 41 Rasskazov, Sergey V. 130 Rasskazova, Albina N. 130 Ray, Samrat 621 Razletovskaia, Viktoriya 307 Rodionov, Dmitriy 203 Rodionova, Maria 218 Rostova, Olga V. 885, 896 Rubtsova, Maria 87 Rudneva, Kristina 871 Rudskaya, Irina 248, 431 S Saitova, Alexandra A. 530 Salazar, Jorge P. Olivos 992 Selentyeva, Tamara 278 Serdyukova, Larisa O. 847 Seredin, Evgenii 173 Seredin, Vladislav 173 Shakhov, Vladlen 504 Sharipov, Kongiratbay 379 Sharipova, Renata 188 Shirokova, Svetlana V. 885, 896 Shkarupeta, Elena 544, 565, 593 Shmeleva, Aleksandra 203 Shmeleva, Anastasiia S. 896 Shumeiko, Artem 639 Shuvalova, Alexandra 732 Skhvediani, Angi 218, 431 Skripnuk, Djamilia F. 707 Sokolova, Ekaterina 52 Sokolovskiy, Vladislav V. 621 Solopova, Natalia 481 Somga Bitchoga, Nicolas Francois 265 Sosnilo, Andrey 398 Stepanchuk, Andrei 828 Stepnov, Igor 307
1023
Suloeva, Svetlana 331 Sunteev, Anton N. 565 Svetlana, Gutman 580 Svetlichnyy, Stanislav 257, 307 T Tanina, Anna V. 66 Tanina, Anna 111 Tashenova, Larissa V. 66 Tashenova, Larissa 390, 544 Testina, Yana 230 Tick, Andrea 978 Tick, József 607, 965 Tikhomirova, Maria 504 Tikhonov, Vladimir S. 565, 593 Timofeev, Andrey V. 772 Tirabyan, Karina 87 Titov, Aleksander B. 772 Trifonova, Nina V. 945 Tudevdagva, Uranchimeg 693 V Valyukhova, Anna V. 896 Vasetskaya, Natalia 130 Vasilieva, Elena 87 Veis, Yulia V. 565, 593 Volodin, Aleksandr 52 Vyshinskaia, Natalia 87 W Woodhead, Roy 370 Z Zaychenko, Irina M. 921 Zaynutdinova, Umida 379 Zhuravleva, Irina 291 Zubkova, Daria 360