State and Corporate Management of Region’s Development in the Conditions of the Digital Economy [1st ed.] 9783030463939, 9783030463946

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Advances in Science, Technology & Innovation IEREK Interdisciplinary Series for Sustainable Development

Yakhya G. Buchaev · Salikhbek G. Abdulmanapov · Arsen S. Abdulkadyrov · Arutyun A. Khachaturyan Editors

State and Corporate Management of Region’s Development in the Conditions of the Digital Economy

Advances in Science, Technology & Innovation IEREK Interdisciplinary Series for Sustainable Development

Editorial Board Anna Laura Pisello, Department of Engineering, University of Perugia, Italy Dean Hawkes, University of Cambridge, Cambridge, UK Hocine Bougdah, University for the Creative Arts, Farnham, UK Federica Rosso, Sapienza University of Rome, Rome, Italy Hassan Abdalla, University of East London, London, UK Sofia-Natalia Boemi, Aristotle University of Thessaloniki, Greece Nabil Mohareb, Faculty of Architecture - Design and Built Environment, Beirut Arab University, Beirut, Lebanon Saleh Mesbah Elkaffas, Arab Academy for Science, Technology, Egypt Emmanuel Bozonnet, University of la Rochelle, La Rochelle, France Gloria Pignatta, University of Perugia, Italy Yasser Mahgoub, Qatar University, Qatar Luciano De Bonis, University of Molise, Italy Stella Kostopoulou, Regional and Tourism Development, University of Thessaloniki, Thessaloniki, Greece Biswajeet Pradhan, Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia Md. Abdul Mannan, Universiti Malaysia Sarawak, Malaysia Chaham Alalouch, Sultan Qaboos University, Muscat, Oman Iman O. Gawad, Helwan University, Egypt Anand Nayyar, Graduate School, Duy Tan University, Da Nang, Vietnam Series Editor Mourad Amer, International Experts for Research Enrichment and Knowledge Exchange (IEREK), Cairo, Egypt

Advances in Science, Technology & Innovation (ASTI) is a series of peer-reviewed books based on important emerging research that redefines the current disciplinary boundaries in science, technology and innovation (STI) in order to develop integrated concepts for sustainable development. It not only discusses the progress made towards securing more resources, allocating smarter solutions, and rebalancing the relationship between nature and people, but also provides in-depth insights from comprehensive research that addresses the 17 sustainable development goals (SDGs) as set out by the UN for 2030. The series draws on the best research papers from various IEREK and other international conferences to promote the creation and development of viable solutions for a sustainable future and a positive societal transformation with the help of integrated and innovative science-based approaches. Including interdisciplinary contributions, it presents innovative approaches and highlights how they can best support both economic and sustainable development, through better use of data, more effective institutions, and global, local and individual action, for the welfare of all societies. The series particularly features conceptual and empirical contributions from various interrelated fields of science, technology and innovation, with an emphasis on digital transformation, that focus on providing practical solutions to ensure food, water and energy security to achieve the SDGs. It also presents new case studies offering concrete examples of how to resolve sustainable urbanization and environmental issues in different regions of the world. The series is intended for professionals in research and teaching, consultancies and industry, and government and international organizations. Published in collaboration with IEREK, the Springer ASTI series will acquaint readers with essential new studies in STI for sustainable development.

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Yakhya G. Buchaev • Salikhbek G. Abdulmanapov Arsen S. Abdulkadyrov • Arutyun A. Khachaturyan

•

Editors

State and Corporate Management of Region’s Development in the Conditions of the Digital Economy

123

Editors Yakhya G. Buchaev Dagestan State University of National Economy Makhachkala (Republic of Dagestan), Russia Arsen S. Abdulkadyrov Institute of Socio-Political Research Russian Academy of Sciences Moscow, Russia

Salikhbek G. Abdulmanapov Dagestan State Institute of National Economy Makhachkala, Russia Arutyun A. Khachaturyan Institute of Market Issues of the RAS Moscow, Russia

ISSN 2522-8714 ISSN 2522-8722 (electronic) Advances in Science, Technology & Innovation IEREK Interdisciplinary Series for Sustainable Development ISBN 978-3-030-46393-9 ISBN 978-3-030-46394-6 (eBook) https://doi.org/10.1007/978-3-030-46394-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 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, express 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

Introduction

Modern Region on the Way to the Digital Economy: Challenges for Public and Corporate Management One of the key trends of our time, which determined the wave of the market cycle for the upcoming years, is the transition to a digital economy. Joining the initiative of the leaders of scientific and technological progress, Russia launched its own national program “Digital Economy of the Russian Federation”, which was approved by the order of the Government of the Russian Federation on July 28, 2017 № 1632-r, and its passport was approved by the decision of the Presidium of the Council under the President of the Russian Federation for Strategic Development and National Projects on December 24, 2018. The strategic importance and priority of the adopted program for the Russian economy is emphasized by the fact that for its practical implementation the Autonomous Non-Commercial Organization “Digital Economy” was formed and the Ministry of Digital Development, Communications, and Mass Media of the Russian Federation was established. The regions are responsible for the implementation of the innovations provided for in the program. It is at the regional level that the basic concept of the digital economy is expected and should be adapted to the current economic reality and that breakthrough technologies are implemented in business activities, household practices, and e-government activities. The transition to a digital economy poses unprecedented challenges for the modern region. One of these challenges is the need to form and stimulate the growth of telecommunication infrastructure usage in the region. Another challenge is to launch its own digital development programs for different sectors of the regional economy and ensure their systematic implementation in order to maintain the integrity and stability of the region’s economy, as well as its balanced development and comprehensive modernization. The challenges also include the need to monitor the digital development of the regional economy. At the same time, the scientific and methodological basis for indicative assessment and analysis of the level of digitalization of the regional economy is in the process of formation. Among the challenges, it is also necessary to mention the need for proactive state and corporate management of the development of the digital economy of the region. Only a program-oriented management approach will enable the use of digitalization opportunities to improve the region’s socioeconomic situation and, in particular, its competitiveness in both the national and global economy. Another challenge is to take into account the views of stakeholders and to orient the digital economy towards addressing the social and environmental challenges of the region. The regions can provide a response to these major and numerous additional challenges of our time only with coordinated state and corporate management of the digital economy. Involvement of business in the regional programs of digital modernization and the formation of the information society is necessary, because only with the support of private capital it is possible to achieve a full-scale financing of the launched programs and only with the transition of business to the digital form of business activity with the active use of advanced technologies the full-scale transition of the region to a digital economy becomes possible. v

vi

Introduction

This monograph provides a systematic study of current problems and prospects for improving the practice of state and corporate governance of the region's development in the digital economy, based on the example of the regions of the North Caucasus Federal District as one of the most progressive districts of the Russian Federation, but at the same time retaining its peripheral position in the Russian regional economy, so that its experience is universal and can be useful for most regions of Russia and other countries of the world—both developed and developing. The book summarizes the experience of these regions, as well as offers a set of scientific, methodological, and applied recommendations for the system organization and the maximization of the effectiveness of public and corporate governance of the region’s development in the digital economy. We hope that the book will be interesting for a wide range of target audiences, including heads of regional business structures interested in modernizing their enterprises and supporting regional digital development projects, as well as for representatives of regional government bodies that develop and regulate regional strategies for the formation and development of the digital economy. Yahya G. Buchaev Dr. Sci. (Econ.), Professor, Rector Dagestan State University of National Economy (DSUNE) Makhachkala, Russia [email protected] Salihbek G. Abdulmanapov Dr. Sci. (Econ.), Professor Director of the Research Institute of Management Economics, Politics and Sociology Dagestan State University of National Economy (DSUNE) Makhachkala, Russia [email protected] Arsen S. Abdulkadyrov Ph.D. (Economics), Associate Professor, Senior Researcher Department of Social and Economic Security Center for Social Security and Riskology, ISPI RAS Moscow, Russia [email protected] Arutyun A. Khachaturyan Market Economy Institute of the Russian Academy of Sciences Moscow, Russia [email protected]

Contents

The Model of Digital Development of a Modern Region: New Challenges for State and Corporate Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Madina M. Shabanova, Nurisat M. Kurshiyeva, and Tatyana A. Kamalova

1

Factors of Development of the Region’s Labour Market in the Conditions of the Digital Economy and the Tools of Their Management . . . . . . . . . . . . . . . . Nikolay Y. Golovetsky, Victor V. Grebenik, and Victoria V. Khamalinskaya

7

Scientific and Methodological Provision of Region’s Competitiveness Assessment in the Conditions of the Digital Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arsen S. Abdulkadyrov, Ekaterina P. Zhigulina, and Evgeniya P. Samokhvalova

13

The Methodology of Indicative Assessment of the Effectiveness of Managing Region’s Development in the Conditions of the Digital Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vezirhan K. Giraev, Albina O. Ramazanova, and Muslimat H.-H. Yusupova Development of Russia’s Regions in the Conditions of the Digital Economy: Management, Effectiveness, and Competitiveness . . . . . . . . . . . . . . . . . . . . . . . . . Arutyun A. Khachaturyan, Karine S. Khachaturyan, and Anton A. Shirkin Digital Entrepreneurship as a New Subject of a Modern Region’s Economy: State Stimulation and Market Mechanisms of Development . . . . . . . . . . . . . . . . . Nazira I. Magomedova, Larisa A. Gadzhimuradova, and Aina M. Shakhbanova

19

25

31

The Regional System of Science and Education as a Source of Digital Personnel and Breakthrough Technologies for Region’s Economy . . . . . . . . . . . . . . . . . . . . Olga V. Budzinskaya

37

Infrastructure of the Digital Economy for Region’s AIC: Creation, Measuring, and Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sharip I. Sharipov, Juliana A. Akhmedova, and Gadzhi K. Kurbanov

43

Perspectives of Development Of the System of E-Government in the Region in the Conditions of the Digital Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yahya G. Buchaev

49

Digital Society in a Modern Region: Issues of Its Formation and Ways of Solving Them on the Basis of the Labour Market Management in the Agrarian Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mikhail A. Babeshin, Alexey E. Nikolaev, and Viktor A. Splender Evolution of the System of Higher Education and Problems of Development of Science in the Conditions of Digitization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yahya G. Buchaev and Zalina M. Abdullaeva

55

61

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viii

Contents

Organization of Production on the Basis of the Internet of Things: Barriers, Advantages and Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrei M. Kushnir, Taisia V. Dianova, and Olga Y. Osipenkova

67

Improving the Practice of Managerial Decision-Making in Modern Entrepreneurship on the Basis of Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . Lyudmila A. Borisova, Ruslan A. Mammaev, and Enara B. Atuyeva

73

Corporate Database Management on the Basis of Cloud Technologies, Blockchain Technologies and Technologies of Big Data Processing: Effectiveness and Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrey V. Kurkin, Akim V. Giraev, and Zaur U. Medzhidov

79

Innovative Development of Entrepreneurship in the AIC in the Conditions of the Digital Economy: Growth Points, Measuring, and Management . . . . . . . . . Ahmed G. Buchaev, Nurziyat Y. Kazavatova, and Rauf N. Gadzhiev

85

The Problem of Migration in the Conditions of the Digital Economy: New Challenges for the Labour Market, Possibilities and Priorities of Solving . . . . . . . Magomed Kh. Abidov, Fatima N. Ismailova, and Pirmagomed G. Abdulmanapov

91

Social Adaptation to Transformation of the Labour Market in the Region in the Conditions of the Digital Economy: Perspectives of Provision of Mass Digital Literacy and Accessibility of Digital Technologies . . . . . . . . . . . . Shahmardan S. Muduev, Sharafudin M. Aliev, and Gozel K. Akavova

97

Entrepreneurial Training in the Conditions of the Digital Economy: Stimulation of Demand, Organization, and Practical Experience . . . . . . . . . . . . . . . . . . . . . . . 103 Sabina E. Savzikhanova, Nigara E. Eminova, and Natalia M. Fomenko Transformation Processes in the Labour Market in a Region in the Conditions of the Digital Economy: A New Model of Organization, Digital Competencies and New Professions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Olga V. Budzinskaya State Management of Foreign Economic Activities of a Region in the Conditions of the Digital Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Salihbek G. Abdulmanapov, Nizami S. Askerov, and Abakar S. Mudunov Scenarios of Region’s Development in the Conditions of the Digital Economy and Priorities of State and Corporate Management . . . . . . . . . . . . . . . . 121 Shamil M. Tagirov, Zalmu K. Omarova, and Naida G. Omarova “Smart” Region: Managing Economic Development on the Basis of Machine Vision and Ubiquitous Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Anastasia A. Kryukova, Natalya A. Stefanova, and Ildar A. Khasanshin Perspectives of Well-Balanced Development of Regional Labor Market with the Help of Digital Modernization by the Example of Modern Russia . . . . . . 133 Farida S. Tsinpaeva, Zarema M. Abdullaeva, and Tamila D. Alikerimova Sustainable Development of Region’s AIC in the Conditions of the Digital Economy: Ecological Responsibility, “Green” Innovations and Circular Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Arsen S. Abdulkadyrov, Ahmed G. Buchaev, and Nurziyat Y. Kazavatova

Contents

ix

State and Corporate Management of Quality of Life in a Region in the Conditions of the Digital Economy: Social Programs and Social Responsibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Khadizhat M. Khadzhalova, Zaklin N. Kazieva, and Victoria V. Stofarandova Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

The Model of Digital Development of a Modern Region: New Challenges for State and Corporate Management Madina M. Shabanova, Nurisat M. Kurshiyeva, and Tatyana A. Kamalova

the rapid growth of the digital economy, which is the case in the regions of the North Caucasus Federal District of the Russian Federation, the “side effect” is the depletion of state and corporate budgets in the region and the financial crisis (pessimistic scenario). An alternative optimistic scenario is also possible which enables to avoid the crisis and possibly achieve even greater progress in building the digital economy. For its implementation, the model of state and corporate management of digital development of the modern region has been developed in order to optimize it, based on the measures of non-financial management, which ensure greater efficiency of management due to lower consumption of resources while achieving long-term (synergy) effect.

Abstract

Purpose: The article is aimed at identifying new challenges for public and corporate management in the context of the digital development of the modern region, the definition of scenarios, and the optimal model of management of this development. Design/Methodology/ Approach: The authors apply the methodology of correlation and scenario analysis, as well as economic modeling. Certain calculations are made to identify the correlation between the indicators of public and corporate management performance in the region—the balanced financial result of enterprises and the balance of the regional budget—and the indicators of digital development of the region—the share of enterprises using the Internet, the number of personal computers per 100 employees, the share of households with computers and the Internet, as well as the share of innovation-active organizations. Findings: In the course of the research it was shown that the digital development of the economy of the regions, which are not characterized by a distinct inclination to scientific and technical progress, is based on the measures of state financial support of the digital modernization of the economic system of the region and on the measures of corporate financing of own digital modernization. Originality/Value: It is justified that, although financial management measures may ensure M. M. Shabanova (&) Department of State and Municipal Administration, Dagestan State Technical University (DSTU), Makhachkala, Russia e-mail: [email protected] N. M. Kurshiyeva Economic Sciences, Department of State and Municipal Administration, Dagestan State Technical University (DSTU), Makhachkala, Russia e-mail: [email protected] T. A. Kamalova Technical Sciences, Department of Commerce and Marketing, Dagestan State University (DSU), Makhachkala, Russia e-mail: [email protected]

Keywords











Digital development Regional economy Public management Corporate management Digital economy Russian regions




JEL Codes

G34

1





O18

O31

R11

R58

Introduction

Modern business systems are entering the digital age one by one. Russia was one of the first countries in the world to launch the process of digital modernization of its social and economic systems (it is among the 63 countries with digital economies according to the IMD version by the 2018 data) —for this purpose, the program “Digital Economy of the Russian Federation” was adopted, approved by the order of the Government of the Russian Federation dated July 28, 2017, № 1632-r. The course on digital modernization has been adopted at the national level for the benefit of the

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_1

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M. M. Shabanova et al.

economic system as a whole, but it is being implemented at the regional level and may have different implications for them. The most advanced regions are likely to benefit the most from digital modernization, as they will be able to unlock their high-tech development potential more fully. They have a technological backbone and will be able to use it to enhance their competitiveness in the regional economy and beyond. Digitalization is in harmony with their social and business preferences. For the population, this will mean the formation of new high-performance, creative, and high-paid jobs, expanding opportunities for the fulfillment of human potential and increasing the availability of goods and services. Advantages for regional entrepreneurship are related to access to new markets and increased opportunities to optimize production, supply, and marketing, and for regional governments—to the growth of tax revenues. At the same time, the consequences of digital modernization for other regions, which are less progressive, making up the majority, in contrast, may be accompanied by a crisis of entrepreneurship due to a sharp decline in competitiveness due to the slow implementation of digital technologies. People in such regions can protest and resist digitalization, making it more difficult to implement and slowing it down. This will cause additional expenses for the regional public authorities and lead to an increase in the regional budget expenditures, while the prospects for increasing its revenue side will be limited. The foregoing determines the relevance of the research and seeking solutions to the problem of digital development of the modern region from the standpoint of public and corporate management in order to optimize this process, especially in those regions that do not lead the scientific and technological progress. This paper is aimed at identifying new challenges for public and corporate management in the context of the digital development of the modern region, the definition of scenarios, and the optimal model of management of this development.

2

Materials and Methods

As a result of the review of the existing scientific literature, we have revealed that some issues of state and corporate management in the digital economy are considered in the works of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017), Khachaturyan et al. (2018), Petrenko et al. (2018), Popkova (2019), Popkova et al. (2019), Popkova et al. (2018), Popkova and Parakhina (2019), Popkova and Sergi (2018), Popkova and Sergi (2019), Ragulina (2019), Sergi et al. (2019), and Sergi (2019). At this, the objects of research are

mainly the most progressive micro- and macro-level economic systems. The consequences of the digital economy arising at the meso-level (at the regional level) are practically not covered in the modern research literature as well as the issues of state and corporate management of adaptation of the region to the digital economy. In order to fill the gap in the system of economic knowledge, we apply the methodology of correlation and scenario analysis as well as economic modeling. We chose the regions of the North Caucasian Federal District of the Russian Federation as the objects of our research, as they are not among the most progressive regions of the country, i.e., the top ten regions in terms of the level of digitalization development “Digital Russia”. For example, the Republic of Dagestan is ranked in this rating (D-Russia, 2019). The hypothesis is tested by calculating the correlation between the indicators of public and corporate management performance in the region—the balanced financial performance of enterprises and the balance of the regional budget —and the indicators of digital development of the region— the share of enterprises using the Internet, the number of personal computers per 100 employees, the share of households with computers and the Internet as well as the share of innovation-active organizations. The statistical data collected by us are shown in Table 1.

3

Results

The results of our correlation analysis are shown in Fig. 1. According to Fig. 1, the growth in innovation activity of enterprises in the regions of the North Caucasus Federal District of Russia in 2018 leads to a decrease in the regional budget balance (correlation −18.38%). Internet access of households reduces both the balance of the regional budget (correlation −40.58%) and the balanced financial result of enterprises (correlation −28.50%). The balance of the regional budget decreases with the computerization of households (correlation −21.64%), with the increase in the number of personal computers per 100 employees (correlation −17.81%), and with the growth of activity of enterprises in using the Internet (correlation −42.51%). The results obtained are probably related to forced state support for modernization in the regions of the North Caucasus Federal District of Russia in 2018 through co-financing and incentives for the digitalization of households and entrepreneurship. The new challenges for state and corporate governance that arise in the context of the digital development of the modern region are systematized in Table 2. As shown in Table 2, in the context of the digital development of the modern region, there are some new

The Model of Digital Development of a Modern Region …

3

Table 1 State and corporate management performance indicators and digital development indicators of the regions of the North Caucasus Federal District of Russia in 2018 Region

Balanced financial result of enterprises, million rubles

Regional budget balance, million rubles

The Republic of Dagestan

−11920

3696

The Republic of Ingushetia

−1465 −801

The Kabardino-Balkaria Republic

Number of personal computers per 100 employees, pcs.

Share of households with a computer, %

Share of households with access to the Internet, %

Share of innovation-active organizations, %

68.4

34

62.8

76.3

2.8

117.2

100.0

37

70.5

86.3

4.8

−2697

84.4

37

68.2

81.6

3.8

47.1

90.3

44

69.4

81.7

1.8

−385.6

80.4

40

87.2

88.1

4.0

−11375

360.8

94.4

44

39.4

74.9

0.2

48780

382.8

96.7

49

69.6

74.4

5.2

The Karachay-Cherkessia Republic

904

The Republic of North Ossetia–Alania

−362

The Chechen Republic Stavropol Krai

Share of companies using the Internet, %

Source compiled by the authors based on the materials of Rosstat (2019)

Share of innovation-active organizations Share of households with access to the Internet Share of households with a computer Number of personal computers per 100 employees Share of companies using the Internet

Fig. 1 Correlation of state and corporate management performance indicators with digital development indicators of the North Caucasus Federal District of Russia in 2018. Source calculated and constructed by the authors

objects of state administration—digital infrastructure, digital society, and digital entrepreneurship—and corporate governance—intellectual resources, digital technologies, “smart” production. They find themselves in a new field for competition: the state in high-tech markets and entrepreneurship in high-tech products markets. New management technologies are also becoming available to them: for the state e-government and the “smart” region, and for entrepreneurship—intellectual support for decision-making. Answers to these challenges may be different—to reflect the high degree of their differentiation we have defined two opposite scenarios of state and corporate management of digital development in the modern region. The pessimistic scenario implies an emphasis on financial management.

Under this scenario, the government implements a set of measures to provide financial incentives for digital modernization of the region’s social and economic system, including subsidies and tax incentives for entrepreneurship. While these measures can be highly effective, they deplete the regional state budget. Similarly, corporate governance involves the acquisition of off-the-shelf technology solutions and digital devices, as well as the recruitment of highly qualified digital personnel. Corporate financial resources are also being spent rapidly. Already in the medium term, there is an acute shortage of financial resources in the region, provoking a financial crisis. The optimistic scenario involves relying on non-financial management. Under this scenario, the government

4 Table 2 New challenges for public and corporate governance, arising in the context of digital development of the modern region

M. M. Shabanova et al. Challenges

Impact of challenges on public administration

Impact of challenges on corporate governance

New management objects

Digital infrastructure, digital society, digital enterprise

Intellectual resources, digital technology, smart manufacturing

New field for competition

High-tech markets

High-tech markets

New management technologies

E-government, smart region

Intellectual support for decision-making

Source designed and compiled by the authors

Fig. 2 The model of state and corporate management of digital development of the modern region in the interests of its optimization. Source designed and compiled by the authors

digital social marketing Region population

Regional public authorities digital innovation promotion Regional scientific and educational institutions

digital skills development

cooperation, partnership cooperation, integration

market purchase

Regional entrepreneurship integration

non-financial development incentives

Digital infrastructure of the region

implements a set of non-financial stimulation measures for digital modernization of the region’s social and economic system, including social marketing measures for the development of digital competences, measures for the development of digital infrastructure, which is provided on market (paid) terms, on the basis of public–private partnership. Corporate governance includes measures for independent research and development, integration measures (clustering, participation in industrial parks, special economic zones, etc.), as well as measures for corporate training of digital personnel. In order to successfully respond to the described challenges and optimize the digital development of regions that do not lead the scientific and technological progress, we have developed the following model of state and corporate management of this process (Fig. 2). As shown in Fig. 2, the model of state and corporate management of digital development of a modern region that we have developed for the purpose of its optimization implies reliance on non-financial management measures, which are relatively new for Russian regions. The measures include, in particular, promoting commercialization of digital innovations in higher education institutions (instead of traditional R&D funding), non-financial stimulation of infrastructure development (instead of traditional state funding). The proposed model, if successfully applied in

practice, ensures the implementation of an optimistic scenario, thus avoiding a financial crisis and accelerating the digital development of a modern region.

4

Conclusion

In the course of the study, it was shown by the example of the regions of the North Caucasus Federal District of the Russian Federation that the digital development of the economy of regions not characterized by a pronounced propensity for scientific and technological progress is based on measures of state financial support for the digital modernization of the region’s economic system and on measures of corporate financing of own digital modernization. While financial management measures may provide for the rapid growth of the digital economy, as is observed in the regions of the North Caucasus Federal District of the Russian Federation, the “spillover effect” is the depletion of state and corporate budgets in the region and the financial crisis (a pessimistic scenario). Alternatively, an optimistic scenario is possible to avoid the crisis and achieve perhaps even greater progress in building a digital economy. To implement this scenario, a model of state and corporate management of digital development of a modern region has been developed for the purpose of its optimization, which is based on

The Model of Digital Development of a Modern Region …

non-financial management measures ensuring greater management efficiency due to lower resource consumption while achieving long-term (synergy) effect.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10), 78, 564–568. D-Russia. (2019). “Digital Russia” digitalization development level rating for regions. Retrieved September 18, 2019, from http://drussia.ru/vyshla-polnaya-versiya-rejtinga-regionov-po-urovnyurazvitiya-tsifrovizatsii-tsifrovaya-rossiya.html. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 82, 581–586. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44– 50. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable developmet processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72.

5 Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and Irina A. Kuznetsova, PhD in Engineering, Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components”). Theoretical and Practical Issues of Journalism, 7(1), 145–154. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will industry 4.0 and other innovations impact Russia’s development? In B. S. Sergi (Ed.), Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety vs. Rationality. Springer. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In B. S. Sergi (Ed.), Tech, Smart Cities, and Regional Development in Contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Rosstat. (2019). Regions of Russia: Socio-economic indicators—2018. Retrieved September 18, 2019, from https://www.gks.ru/free_doc/ doc_2018/region/reg-pok18.pdf.

Factors of Development of the Region’s Labour Market in the Conditions of the Digital Economy and the Tools of Their Management Nikolay Y. Golovetsky, Victor V. Grebenik, and Victoria V. Khamalinskaya

self-regulation (deregulation), the natural influence of these factors is weak. Therefore, the state management of the identified factors of the development of the regional labour market in the digital economy should be aimed at strengthening (rather than deterring, as originally assumed) their positive impact on the market—for this purpose, appropriate management tools have been proposed.

Abstract

Purpose: The authors aim is to study the factors of the region’s labour market development in the digital economy (on the example of the regions of the North Caucasus Federal District of the Russian Federation) and to develop tools for their management in order to optimize transformations. Design/Methodology/ Approach: The authors use one of the most common methods of econometrics—regression analysis. To test the hypotheses, regression curves are drawn that automatically form paired linear regression models and calculate correlation coefficients. Findings: Three factors have been identified for the development of the region’s labour market in the digital economy: the increase in the level of education in the “knowledge economy”, the growth of innovative activity of entrepreneurship and the computerization of entrepreneurship. It has been revealed that, although structural transformations of the labour market are indeed taking place in the regions of the North Caucasus Federal District of the Russian Federation in 2018, they contradict the existing ideas. Originality/ Value: It has been proved that in the digital economy, instead of the expected crisis of the labour market (rising unemployment and social tension), favourable conditions and new opportunities are being created to balance the market and establish social justice in it (preventing discrimination against employees in terms of age and level of education). However, in the case of market N. Y. Golovetsky (&)
V. V. Khamalinskaya Economic Sciences, Financial University Under the Government of the Russian Federation, Moscow, Russia e-mail: [email protected] V. V. Khamalinskaya e-mail: [email protected] V. V. Grebenik Economics, Financial University Under the Government of the Russian Federation, Moscow, Russia e-mail: [email protected]

Keywords

Labour market administration








Region Digital economy Russian regions




State

JEL Codes












J01 J08 R58

1

J23

J71

G34

O18

O31

R11

R23

Introduction

The digital economy is changing the context in which the labour market is functioning and developing. The spread of digital technologies contributes to the formation of a new essential interpretation of labour itself as a process of business processes, the subject of which can be not only a person —manual labour, mechanized labour—but also digital devices—automated labour: such as partially under the control of man (e.g. robots, manipulators), and complete or under the control of artificial intelligence (e.g. the Internet of things). The obvious consequence of this process is the structural transformation of the labour market (general hypothesis H1). One of the proposed transformations (hypothesis H11) consists in the increase of the importance of education in the labour market. The application of digital technologies requires digital competencies, i.e. professional knowledge,

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_2

7

8

skills and abilities to apply specialized digital devices and software. In addition, there is a need for high levels of media literacy, namely the possession of general competencies that enable the use of non-specialized digital technologies, such as modern office equipment. These competencies are acquired in the course of study, and most of them are obtained only through higher education. The direct consequence of this is a decrease in the competitiveness of workers without university education on the labour market, and an increase in their unemployment due to a decrease in the demand for their labour. The transformation described above makes it possible to build a logical link between the development of a digital economy and the development of a “knowledge economy” characterized by the value of university education and the recognition of the concept of lifelong learning. Another expected transformation (hypothesis H12) is related to the increase in age restrictions of the labour market and early “ageing” of personnel. The younger the worker is the more flexible thinking he or she has, and the youngest workers belong to the “Generation Z” (the so-called “digital natives”), which are most inclined and able to use digital technologies. Representatives of “Generation X” (employees over 55–65 years of age, depending on territorial peculiarities—digital outsiders) have the least inclination and ability to master and apply digital technologies in practice. There is also an intermediate “Generation Y” (so-called “digital migrants”—(workers aged from 40–45 to 55– 60 years) who are not eager but capable of mastering and using digital technologies as necessary. In the digital economy, the age limits between generations of workers are more clearly delineated. This leads to increased age discrimination in the labour market. Against the backdrop of rising retirement ages in most of today’s developed and developing countries, including Russia, we can expect an increase in unemployment due to the actual (although not recognized by law) disability of workers over 55–60 years of age (age may differ not only between countries but also between regions of the same country). Another supposed transformation (hypothesis H13) is the toughening of competition in the labour market under the influence of mass full automation. As the availability and functionality of digital labour resources increase, the entrepreneurs see their advantages over human resources. These benefits are associated with higher productivity, reduced risk of disruption (disruption of work discipline and its analogue–digital device breakdowns) and errors (the so-called “human factor”), as well as greater accountability and accuracy in the performance of work functions, allowing for the production of more high-tech products. Subsidiarity (interchangeability) of workers and digital devices in the labour market is likely to increase the intensity of the market and to exacerbate the problem of unemployment.

N. Y. Golovetsky et al.

The indicated labour market transformations are considered to be axioms that have been logically determined and are not supported by factual data. However, modern statistics does not confirm these transformations. In particular, the most progressive countries that are leaders in the global rating of digital competitiveness of the economy (e.g. the U. S.—IMD rating) are not characterized by a sharp increase in unemployment, despite the fact that the level of automation has increased many times in recent years (2012–2019), including a complete automation as well. This indicates the absence or opposite of labour market transformations in the digital economy and/or the existence of ways to manage them. In this connection, we think it is necessary to consider the described transformations not as axioms, but as hypotheses that need to be tested. In this study, we aim to investigate the factors of the development of the region’s labour market in the digital economy (using the regions of the North Caucasus Federal District of the Russian Federation as an example) and to develop tools to manage them in order to optimize transformations.

2

Materials and Method

The transformation of the labour market in the digital economy is considered in the numerous works of modern authors. For example, Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018) recognize the increasing importance of education in the labour market. Petrenko et al. (2018), Popkova (2019), Popkova et al. (2018, 2019), Popkova and Parakhina (2019) note the increasing age restrictions of the labour market and the early “ageing” of personnel. Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019) write about tougher competition in the labour market under the influence of mass full automation. However, in most cases, no statistical evidence is provided to support the transformations described. Therefore, the scientific and empirical basis for studying the labour market in the digital economy is not formed and needs to be developed. In this research, we decided to use one of the most common methods of econometrics—regression analysis. With its help, we are going to test all the three hypotheses that have the following mathematical expression: – Hypothesis H11: regression dependence of the share of employed population (aged 15–72) with higher education (y1) on the number of graduates (bachelors, specialists, masters) (x1) pronounced and direct; – Hypothesis H12: regression dependence of the average age of the unemployed (y2) on the share of organizations implementing innovations (x2), pronounced and direct;

Factors of Development of the Region’s Labour Market …

9

– Hypothesis H13: regression dependence of labour market stress coefficient (y3) on the share of organizations using personal computers (x3), pronounced and direct. To test the hypotheses, we build regression curves (as in Microsoft Excel charts) that automatically generate y = a + b * x paired linear regression models and calculate correlation coefficients (R2). The correlation of variables is recognized as expressed at R2  0.50 (correlation is more than 50%). The relationship of variables is direct at the positive value of the coefficient b in the regression model and inversely at the negative value of this coefficient. A sample of data for analysis is shown in Table 1.

3

Fig. 1 Regression curve of y1(x1). Source calculated and built by the authors

Results

The regression curves, built on the basis of data from Table 1, are illustrated in Figs. 1, 2, and 3. According to Fig. 1, the increase in the number of university graduates by 1000 people leads to a decrease in the share of the employed population with higher education by 0.1153%. The relationship between the variables is inverse (coefficient b took the value of −0.1153) and not pronounced (R2 = 0.014—correlation of 1.4%). According to Fig. 2, an increase in the share of organizations implementing innovations by 1% leads to an increase

Fig. 2 Regression curve of y2(x2). Source calculated and built by the authors

Table 1 Selection of data for the analysis of the development of labour market in conditions of digital economy in the regions of the North Caucasus Federal District of the Russian Federation in 2018 Region of the North Caucasus Federal District of the Russian Federation

Bachelors, specialists, masters, K of people

Share of employed population with higher education, %

x1

y1

The Republic of Dagestan

14.1

30.0

The Republic of Ingushetia

1.5

26.7

The Kabardino-Balkaria Republic

3.0

The Karachay-Cherkessia Republic

Average age of the unemployed, years

Share of organizations using personal computers, %

x2

y2

x3

2.8

31.8

72.5

189.4

4.8

30.4

100.0

168.1

32.8

3.8

34.1

91.5

11.7

2.3

42.7

1.8

37.1

92.3

17.3

The Republic of North Ossetia–Alania

5.4

42.2

4.0

39.6

90.7

35.9

The Chechen Republic

5.7

39.7

0.2

32.9

96.9

44.6

17.9

35.7

5.2

35.2

98.1

2.5

Stavropol Krai (District)

Source compiled by the authors based on Rosstat materials (2019)

Share of organizations implementing innovations, %

Coefficient of tension in the labour market, share of 1 y3

10

Fig. 3 Regression curve of y3(x3). Source Calculated and built by the authors

in the average age of the unemployed by 0.0499 years. The relationship between the variables is direct (coefficient b was set at 0.0499) and not pronounced (R2 = 0.0008—correlation 0.08%). According to Fig. 3, an increase in the share of organizations using personal computers by 1% leads to a decrease in the coefficient of tension on the labour market by 4.0004. The relationship between the variables is inverse (coefficient b assumed −4.0004) and not pronounced (R2 = 0.2231— correlation 22.31%). Thus, the labour market of the regions of the North Caucasus Federal District of the Russian Federation in the digital economy is influenced by the following factors. The first factor is the increase in the level of education in the “knowledge economy”. This factor is insignificant and has the opposite effect on the labour market: instead of the expected growth in demand for highly skilled workers with higher education in the digital economy, the demand for them decreases. This is due to the general growth of education level and qualification of employees on the labour market and the increased availability of self-education—in contrast to pre-digital competencies, the digital ones and media literacy can be mastered on the basis of non-formal education. In order to optimize the development of the labour market in the digital economy, it is proposed to stimulate self-education and organize regional courses on mastering digital competencies and increasing media literacy as a tool to manage this factor. This will increase access to education, but will further reduce the value of higher education. Therefore, in the long run, the digital economy and the “knowledge economy” will come into conflict. The second factor is the growth of innovative activity of entrepreneurship. This factor is also insignificant. Contrary to hypothesis H12, employees of all ages are important and in

N. Y. Golovetsky et al.

demand in the labour market for innovation activities of enterprises: both young flexible and creative workers, and experienced workers. In order to optimize the development of the labour market in the digital economy, the creation of additional workplaces for pre-retirement age workers is proposed as a tool to manage this factor. This will limit the early “ageing” of the workforce, provide an opportunity for self-realization of experienced workers and ensure highly efficient use of human potential in the region. The third factor is the computerization of entrepreneurship. The significance of this factor is moderate. Contrary to hypothesis H13, as digitalization grows, labour market tension is reduced by expanding self-employment opportunities (e.g. e-commerce-based). In order to optimize the development of the labour market in the context of the digital economy, it is proposed that the government should stimulate the computerization of entrepreneurship, for example, on the basis of regional tax incentives for property tax (digital equipment). This will make it possible to reduce the tension in the labour market in the region as much as possible.

4

Conclusion

Thus, we have singled out three factors for the development of the region’s labour market in a digital economy: an increase in the level of education in the “knowledge economy”, an increase in the innovative activity of entrepreneurship and the computerization of entrepreneurship. The general hypothesis H1 is confirmed, but with a reservation. Although the structural transformation of the labour market is indeed taking place in the regions of the North Caucasus Federal District of the Russian Federation in 2018, it is contrary to existing perceptions. In the digital economy, instead of the expected crisis of the labour market (rising unemployment and social tension), favourable conditions and new opportunities are being created to balance the market and establish social justice in it (preventing discrimination against employees in terms of age and education). However, in the case of market self-regulation (deregulation), the natural influence of these factors is weak. Therefore, the state management of the identified factors of the development of the regional labour market in the conditions of the digital economy should be aimed at strengthening (rather than deterring, as it was initially assumed) their positive impact on this market—for this purpose, appropriate management tools have been proposed.

Factors of Development of the Region’s Labour Market …

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10), 564–568, 78. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 581– 586, 82. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44– 50. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable developmet processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72. Popkova, E. G., & Sergi, B. S. (2018). Will industry 4.0 and other innovations impact Russia’s development? In B. S. Sergi (Ed.), Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited.

11 Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety vs. Rationality. Springer. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: Future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and Irina A. Kuznetsova, PhD in Engineering, Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components”). Theoretical and Practical Issues of Journalism, 7(1), 145–154. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Rosstat. (2019). Russian regions: Socio-economic indicators—2018. Retrived September 21, 2019, from https://www.gks.ru/free_doc/ doc_2018/region/reg-pok18.pdf. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In B. S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited.

Scientific and Methodological Provision of Region’s Competitiveness Assessment in the Conditions of the Digital Economy Arsen S. Abdulkadyrov, Ekaterina P. Zhigulina, and Evgeniya P. Samokhvalova

Abstract

Purpose: The paper is aimed at the development of scientific and methodological support for assessing the competitiveness of the region in the digital economy, which allows achieving both the completeness of the assessment and its high accuracy and objectivity to obtain systematic and reliable results. Design/Methodology/ Approach: The author’s scientific and methodological support of the assessment of the region’s competitiveness in the digital economy has been developed, which allows combining quantitative (contained in official statistics) and qualitative (obtained through a survey of the region’s population) data for the most complete assessment of the region’s competitiveness in the digital economy. The proposed scientific and methodological support for assessing the competitiveness of the region in the digital economy was tested on the example of the regions of the

A. S. Abdulkadyrov (&) Economic Sciences, Department of Social and Economic Security, Center for Social Security and Riskology, ISPI RAS, Moscow, Russia e-mail: [email protected] E. P. Zhigulina Department of Economics of the Oil and Gas Industry, Russian State University of Oil and Gas (National Research University, Named After I.M. Gubkina), Moscow, Russia e-mail: [email protected]

North Caucasus Federal District of the Russian Federation in 2018. Findings: It is justified that unlike the general competitiveness (taking into account the level of education, healthcare, living standards, and other indicators), for which the necessary statistics are available and are highly reliable, the assessment of digital competitiveness cannot be based only on statistical data (due to their scarcity and inaccuracy), and therefore, it should also take into account the values of qualitative indicators. This is achieved in the developed scientific and methodological support of assessing the competitiveness of the region in the digital economy. Originality/Value: The testing of the proposed scientific and methodological support on the example of the regions of the North Caucasus Federal District of the Russian Federation in 2018 demonstrated significant differences in the quantitative and qualitative assessments. As a result of their system accounting, the most objective, complete, accurate, and reliable results were obtained, according to which the highest level of competitiveness in the digital economy is peculiar to the Kabardino-Balkaria Republic. Interim assessment results can be used by all regions to identify their strengths and weaknesses as well as to develop and implement highly effective regional strategies for managing the competitiveness of the economy in the digital economy. Keywords










Assessment Competitiveness Region Digital economy Digital competitiveness Russian regions JEL Codes

D41





O18

O31

R11

R58

E. P. Samokhvalova Department of Economics of the Oil and Gas Industry, Russian State University of Oil and Gas (National Research University) Named After I.M. Gubkina (RGUNG), Moscow, Russia e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_3

13

14

1

A. S. Abdulkadyrov et al.

Introduction

In the digital economy, high-tech competition is taking place and new criteria for the competitiveness of economic systems are emerging, such as the prevalence and activity of digital technologies. Therefore, it is necessary to clarify the interpretation of competitiveness in light of its new criteria. Modern economic science has developed the practice of a separate definition of the basic competitiveness of economic systems (at the level of countries for this purpose is made an annual rating of the World Economic Forum—WEF) and their digital competitiveness (at the level of countries for this purpose is made an annual rating of the International Institute for Management Development—IMD). At the level of regional economy in most modern economic systems, including Russia, the assessment of digital competitiveness is fragmented. Ratings of the country’s regions are made according to a certain (narrow) criterion, for example, the level of development of the information society or the level of e-government. This makes it impossible to determine the overall level of digital competitiveness of each region. The results of the existing assessment practice are, firstly, imbalance of the national digital economy (there are serious differences between regions in its composition); secondly, low efficiency of state management of the digital economy development in each individual region (due to the low accuracy of the respective regional programs); and, thirdly, limited competition of regions in the digital economy (as interested persons cannot reliably determine the level of digital competitiveness of a region). In order to accelerate the development of the national and global digital economy as a catalyst for the fourth industrial revolution (global transition to the fourth technological order), a systematic assessment of the digital competitiveness of the modern region’s economy is needed. At the same time, there is a problem of ensuring the reliability of this assessment. On the one hand, reliance on accurate quantitative indicators ensures the objectivity of the assessment, but leads to its incompleteness due to gaps in the statistical accounting—not all indicators are taken into account, and some of them cannot be measured statistically at all. On the other hand, relying mainly on qualitative indicators obtained by the method of expert evaluation causes subjectivity of evaluation, although it allows the most complete coverage of the components of digital competitiveness of the region. Thus, the actual development of scientifically methodical maintenance of an estimation of the competitiveness of region in the conditions of digital economy, allowing to

reach simultaneously both completeness of an estimation, and its high accuracy, and objectivity for the reception of system and reliable results is the purpose of the present research.

2

Materials and Method

The theoretical and methodological basis for assessing the region’s competitiveness has been formed in the publications of the following authors: Aria et al. (2019), Capello and Cerisola (2019), Giordano and Dubois (2019). The fundamental basis for assessing the digital competitiveness of business systems is laid down in information and methodological materials IMD, used in the compilation of “World Digital Competitiveness Ranking.” On this basis, many authors’ methods have been formed to ensure the assessment of regional competitiveness in the digital economy, which are presented in the works of Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Gadzhiev and Buchaev (2014), Petrenko et al. (2018), Popkova (2019), Popkova and Sergi (2018), Ragulina (2019), Sergi et al. (2019), Sergi (2019). However, the incompleteness of statistical accounting of the digital economy at the level of regional economy does not allow applying the IMD methodology and other existing methods, focused on the world economy, to assess the digital competitiveness of modern regions. Consequently, it is necessary to adapt the existing methodology to the specifics of regional economy. In this paper, we believe it is necessary to combine quantitative (contained in official statistics) and qualitative (obtained through a survey of the region’s population) data to most fully assess the region’s competitiveness in the digital economy. Our logic is that statistical data could potentially be overestimated by regions in order to improve the ranking and obtain more federal funding for digital modernization of the economy. Therefore, the use of purely statistical data instead of the expected high accuracy of estimates may, on the contrary, underestimate them. In other words, the stated and actual results of digital modernization may differ, and they may differ significantly. Additional use of qualitative (survey) data will make it possible to balance formal and perceived (informal) results, and thus most reliably determine the level of digital competitiveness of a region. Our proposed scientific and methodological support of the assessment of regional competitiveness in the digital economy is reflected in Table 1.

Scientific and Methodological Provision of Region’s …

15

Table 1 Scientific and methodological support for the evaluation of the region’s competitiveness in the conditions of the digital economy Evaluation criterion

Measures for the relevant criteria Quantitative, various units of measure

Degree of digitalization of society

Qualitative, points 1–10

Name

Designation

Name

Designation

Share of households owning a computer, %*

ds1(quant.)

Prevalence of digital devices and technologies among the population

ds1(qual.)

Share of households with access to the Internet, %*

ds2(quant.)

Level of digital literacy and receptiveness of society to digital technologies

ds2(qual.)

DS=[ds1(quant.)+ds2(qual.)]+[ds1(qual.)+ ds2(qual.)]/4, shares of 1 Level of digitalization of entrepreneurship

Share of companies owning a computer, %*

de1(quant.)

Prevalence of digital devices and technologies among enterprises

de1(qual.)

Share of enterprises with access to the Internet, %*

de2(quant.)

Level of digital competence and digital receptivity of employees

de2(qual.)

DE=[de1(quant.)+de2(quant.)]+[de1(qual.)+ de2(qual.)]/4, shares of 1 Availability and quality of digital infrastructure

Regulatory and administrative indicators for digitalization, points 1-100**

di1(quant.)

Sufficiency of digital infrastructure

di1(qual.)

Information infrastructure, points 1-100**

di2(quant.)

Digital infrastructure price availability

di2(qual.)

Information security, points 1–100**

di3(quant.)

Quality (including security) of digital infrastructure

di3(qual.)

DI=[di1(quant.)+di2(quant.)+ di3(quant.)]+[di1(qual.)+ di2(qual.)+ di3(qual.)]/6, shares of 1 The scale of digitalization of the state administration

Implementation level of e-government, points 1–100***

dg(quant.)

Digitalization of public services and management

dg(qual.)

DG=[dg(quant.)+ dg(qual.)]/2, shares of 1 Digital Competitiveness Index: DC=(DS+DE+DI+DG)/4 Sources of statistical data on the example of the Russian Federation *Russian State Statistics Service Collection “Regions of Russia: socio-economic indicators” (section “Information and communication technologies” **Subindexes of “Digital Russia” index calculated by “Skolkovo” ***Monitoring conducted by “State Management” Source developed and compiled by the authors

In order to ensure the comparability of indicators (their initial units of measurement are differentiated), as well as to conduct the assessment in accordance with the generally accepted logic of competitiveness assessment (as a ratio of the indicator value in the economic system to the indicator value in the reference system), all indicators are converted into shares of 1. For this purpose, their relation in each region to the benchmark is found. When an extensive study of the entire regional economy is conducted, the national economy is taken as the benchmark. In this paper, we will test the example of the regions of the North Caucasus Federal District of the Russian Federation in 2018. Therefore, the average values for the federal district will be used as a benchmark.

3

Results

With the help of the developed scientific and methodological support, we have made estimations of competitiveness of the region in the conditions of digital economy in the regions of the North Caucasian Federal District of the Russian Federation in 2018. Its results are shown in Table 2. Based on the data of Table 2, we made a quantitative and qualitative assessment of the competitiveness of regions of the North Caucasus Federal District of the Russian Federation in 2018. As can be seen from Fig. 1, the results of quantitative and qualitative evaluations differ significantly. Thus, for

16

A. S. Abdulkadyrov et al.

Table 2 Indicators of digital competitiveness of regions of the North Caucasus Federal District of the Russian Federation in 2018 The Republic of Ingushetia

The Kabardino-Balkaria Republic

The Karachay-Cherkessia Republic

The Republic of North Ossetia

The Chechen Republic

Stavropol Krai

Type of meaning

Share of households owning a computer

initial

66.0

62.8

70.5

68.2

69.4

87.2

39.4

69.6

ds1(quant.)

–

0.95

1.07

1.03

1.05

1.32

0.6

1.05

Share of households with access to the Internet

initial

78.30

76.3

86.3

81.6

81.7

88.1

74.9

76.6

ds2(quant.)

–

0.97

1.1

1.04

1.04

1.13

0.96

0.98

Prevalence of digital devices and technologies among the population

initial

7.34

9.24

6.6

6.82

6.9

5.06

8.71

8.02

ds1(qual.)

–

1.26

0.90

0.93

0.94

0.69

1.19

1.09

Level of digital literacy and receptiveness of society to digital technologies

initial

7.59

9.34

9.47

7.65

5.95

5.73

8.35

6.66

ds2(qual.)

–

1.23

1.25

1.01

0.78

0.75

1.10

0.88

Share of companies owning a computer

initial

89.40

72.5

100

91.5

92.3

90.7

96.9

98.1

de1(quant.)

1.02

0.81

1.12

1.02

1.03

1.01

1.08

1.1

Share of enterprises with access to the Internet

initial

85.90

68.4

100

84.4

90.3

80.4

94.4

96.7

de2(quant.)

–

0.8

1.16

0.98

1.05

0.94

1.1

1.13

Prevalence of digital devices and technologies among enterprises

initial

6.91

7.12

9.05

7.32

5.7

6.46

6.39

6.32

de1(qual.)

–

1.03

1.31

1.06

0.82

0.93

0.92

0.91

Level of digital competence and digital receptivity of employees

initial

6.93

7.5

5.59

6.98

8.06

5.44

9.06

5.88

de2(qual.)

–

1.08

0.81

1.01

1.16

0.78

1.31

0.85

Regulatory and administrative indicators for digitalization

initial

45.62

43.58

40.7

48.64

41.93

40.31

52.9

51.27

di1(quant.)

–

0.96

0.89

1.07

0.92

0.88

1.16

1.12

IT infrastructure

Information security

North Caucasus Federal District

The Republic of Dagestan

Indicator

initial

46.61

46.34

41.26

49.63

40.63

42.79

48.97

56.63

di2(quant.)

–

0.99

0.89

1.06

0.87

0.92

1.05

1.21

initial

45.61

45.66

40.94

46.92

39.64

41.77

47.69

56.68

di3(quant.)

–

1

0.9

1.03

0.87

0.92

1.05

1.24

Sufficiency of digital infrastructure

initial

7.20

5.06

8.3

8.53

7.49

8.48

5.37

7.19

di1(qual.)

–

0.70

1.15

1.18

1.04

1.18

0.75

1.00

Digital infrastructure price availability

initial

7.02

5.33

6.65

9.16

6.97

6.57

7.7

6.73

di2(qual.)

–

0.76

0.95

1.30

0.99

0.94

1.10

0.96

Quality (including security) of digital infrastructure

initial

6.80

5.47

5.73

7.57

8.86

7.94

6.43

5.63

di3(qual.)

–

0.80

0.84

1.11

1.30

1.17

0.95

0.83

Implementation level of e-government

initial

31.71

42

12

81

12

24

12

39

dg(quant.)

–

1.32

0.38

2.55

0.38

0.76

0.38

1.23

Digitalization of public services and management

initial

7.32

9.06

5.17

7.85

5.8

5.15

8.78

9.41

dg(qual.)

–

1.24

0.71

1.07

0.79

0.70

1.20

1.29

Source calculated and compiled by the authors on the basis of materials of State Management (2019), Rosstat (2019), Skolkovo (2019)

example, the digital competitiveness of the Karachay-Cherkessia Republic was 1.22 by quantitative assessment and 1.08 by qualitative assessment. Similarly, the digital competitiveness of Stavropol Krai was 1.13 in

quantitative terms and 0.98 in qualitative terms. The results of the final assessment (DC) are shown in Fig. 2. As can be seen from Fig. 2, the highest level of digital competitiveness among regions of the North Caucasus

Scientific and Methodological Provision of Region’s …

17

Fig. 1 Quantitative and qualitative assessment of the digital competitiveness of regions of the North Caucasus Federal District of the Russian Federation in 2018. Source calculated and constructed by the authors

Fig. 2 Final assessment of digital competitiveness (DC) of the regions of the North Caucasus Federal District of the Russian Federation in 2018. Source calculated and constructed by the authors

18

A. S. Abdulkadyrov et al.

Federal District of the Russian Federation in 2018 is observed in the Kabardino-Balkaria Republic (1.15), and the lowest one is in the Karachay-Cherkessia Republic (0.94).

4

Conclusion

The study, therefore, justifies that, unlike overall competitiveness (taking into account education. health. standard of living and other indicators), for which the necessary statistics are available and their high reliability is ensured. The assessment of digital competitiveness cannot be based only on statistics (due to their deficiency and inaccuracy) and should, therefore, also take into account the meaning of qualitative indicators. This is achieved in the developed scientific and methodological support of the assessment of regional competitiveness in the digital economy. Its testing by the example of the regions of the North Caucasus Federal District of the Russian Federation in 2018 demonstrated significant differences in quantitative and qualitative assessment. As a result of their system accounting the most objective, complete, accurate, and reliable results were obtained, according to which the highest level of competitiveness in the digital economy is typical for the Kabardino-Balkaria Republic. Interim assessment results can be used by all regions to identify their strengths and weaknesses as well as to develop and implement highly effective regional strategies for managing economic competitiveness in the digital economy.

References Aria, M., Gaeta, G. L., & Marani, U. (2019). Similarities and differences in competitiveness among European NUTS2 regions: An empirical analysis based on 2010–2013 data. Social Indicators Research, 142(1), 431–450. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10), 564–568, 78. Capello, R., & Cerisola, S. (2019). Competitiveness through integration in the European Union Strategy for the Alpine region: A ‘balanced development’ approach. European Planning Studies, 27(5), 1013– 1034.

Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 581– 586, 82. Giordano, B., & Dubois, A. (2019). Combining territory and competitiveness in EU regional policy? Analyzing ERDF investment profiles in regions with specific geographical features. Regional Studies, 53(8), 1221–1230. Gos-management. (2019). Ratings of Russian regions on information technology development. Retrieved September 09, 2019, from http://www.tadviser.ru/index.php/. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii. Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing., 622, 44– 50. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues., 7(4), 781–791. https://doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72. Popkova, E. G., Sergi, & B. S. (2018). Will industry 4.0 and other innovations impact Russia’s development? In B. S. Sergi (Ed.), Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley. UK: Emerald Publishing Limited. Popkova, E. G., & Sergi, B. S. (Eds.), (2019). Digital economy: Complexity and variety vs. Rationality. Springer. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems. Decision and Control., 169, 167–174. Rosstat. (2019). Regions of Russia: socio-economic indicators—2018. Retrieved September 09, 2019, from https://www.gks.ru/free_doc/ doc_2018/region/reg-pok18.pdf. Sergi, B. S. (Ed.). (2019). Tech. smart cities. and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian Industrial. Tech. and financial cooperation with the Asia-Pacific region. In B. S. Sergi (Ed.) Tech. smart cities. and regional development in contemporary Russia. Bingley. UK: Emerald Publishing Limited. Skolkovo. (2019). Values of the Digital Russia Index sub-indices for 85 subjects of the Russian Federation in 2018. Retrieved September 22, 2019, from https://finance.skolkovo.ru/downloads/documents/ FinChair/Research_Reports/SKOLKOVO_Digital_Russia_Report_ Full_2019–04_ru.pdf.

The Methodology of Indicative Assessment of the Effectiveness of Managing Region’s Development in the Conditions of the Digital Economy Vezirhan K. Giraev, Albina O. Ramazanova, and Muslimat H.-H. Yusupova

state management of the development of the regions of the North Caucasus Federal District of the Russian Federation in 2018 is effective (revenues exceed expenditures). However, the opportunities of the digital economy have been practically neglected. As the digital economy develops in the regions under consideration, the efficiency of government economic management should be expected to improve. It is recommended that the digital modernization of the economy of these regions should be stimulated in order to increase the efficiency of public management.

Abstract

Purpose: The purpose of this study is to develop a method of indicative evaluation of the effectiveness of public management of the development of a region in the digital economy and its testing on the example of the regions of the North Caucasus Federal District of the Russian Federation. Design/Methodology/Approach: The author’s method of indicative evaluation of the efficiency of management of the region’s development in the conditions of digital economy has been developed. The indicators of efficiency of management of regional development in the conditions of digital economy are the use of digital economy possibilities for maximization of incomes and minimization of regional state budget expenses. Findings: It has been stated that, due to the digital economy, the income of state budgets of the regions of the North Caucasus Federal District of the Russian Federation in 2018 is 6.19 times higher (as compared to income without the use of digital technologies). At the same time, the potential for minimizing expenditures of their budgets has been rarely realized. The digital economy allows reducing state budget expenditures of the regions of the North Caucasus Federal District of the Russian Federation by 480.64 times. Originality/Value: The calculations showed that V. K. Giraev (&) Economic Sciences, Research Institute of Management, Economics, Politics and Sociology, Dagestan State University of National Economy (DGUNH), Makhachkala, Russia e-mail: [email protected] A. O. Ramazanova Economic Sciences, Department of Economics and Management, Makhachkala Branch of the Moscow Automobile and Highway State Technical University (MADI), Makhachkala, Russia e-mail: [email protected] M. H.-H. Yusupova Department of Management, Dagestan State University (DSU), Makhachkala, Russia e-mail: [email protected]

Keywords














Indicator Evaluation Efficiency Regional development management Digital economy Russian regions JEL Codes

C51

1








D61

G14

H21

O18

O31

R11

R58

Introduction

In most modern macroeconomic systems, even with a high degree of decentralization of power and, accordingly, economic independence of regions, they should follow a nationwide strategy of socio-economic development. Transition to digital economy is a strategic priority of this development and therefore is forced by modern regions. However, the regional context in which the digital economy is being modernized may be and, apparently is, different. It is possible to highlight the regions prone to building a digital economy. They are highly innovative and have sufficient venture capital investments. Regional businesses are interested in implementing projects to create digital infrastructure independently and jointly with regional government

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_4

19

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bodies. The population of such regions has a high income, which allows the mass purchase of digital devices and technologies. The social and business environment in these regions is receptive and prone to innovation, including those in the digital sphere. Digital modernization is organic in these regions and comes naturally under the influence of the market mechanism with limited control and minimal regulation by the state. They can be opposed to the regions that are not inclined to build a digital economy. They have a deficit of venture capital investment and low innovation activity. Public–private partnerships and investments in infrastructure projects in these regions are underdeveloped. The low standard of living of the population prevents the spread of digital technologies and devices. Social and business receptivity to innovation is low, and there may even be latent or overt resistance to innovation. Digital modernization in these regions encounters institutional barriers that prevent its implementation. State regulators are forced to implement large-scale regional projects to prepare and adapt the population to the conditions of the digital economy, and to stimulate digital modernization of entrepreneurship. Although, in both cases described, the transition to a digital economy contributes to the competitiveness of the region, the effectiveness of public management of the region’s development will vary greatly. In the first case, thanks to comprehensive social and business support, outstanding results in the digital modernization of the region’s economy can be achieved, while the main costs will be borne by regional venture capital investors. In the second case, the process of digital modernization will take place at a slower pace due to social and business resistance, and will require significant expenditures of the regional state budget. In both cases, there is a risk of establishment or aggravation of the budget deficit and public management crisis of the region’s development (certainly, this risk is much higher in the second case). The strategic macroeconomic course for the transition to the digital economy is designed to balance the regional economy and ensure a centralized transition of all the regions of the country to a new—fourth—technological paradigm. The crisis of public management of even one region can lead to a crisis of the entire national budget system, especially in the conditions of budget federalism implemented through interbudgetary transfers, like in modern Russia. Therefore, it is in the interests not only of the regions themselves, but of the country as a whole, to minimize the risk of the crisis described above. This highlights the problems of assessing the efficiency of digital modernization of the region’s economy. The purpose of this study is to develop a method of indicative evaluation of the effectiveness of public

V. K. Giraev et al.

management of the development of a region in the digital economy and its testing on the example of the regions of the North Caucasus Federal District of the Russian Federation.

2

Materials and Method

As a result of content analysis (substantial analysis) of the existing economic literature, we have identified three scientific and methodological approaches to assessing the effectiveness of management of the region’s development in the digital economy. The first approach focuses on the very process of digital modernization of the region’s economy. It involves an isolated assessment of the effectiveness of public administration of the digital economy separately from the rest of the public management of the region. In this case, efficiency is determined through the ratio of the advantages of digital modernization of the economy (GRP and labour productivity growth) to the costs associated with government financing of the digital economy. This approach is presented in the works of Nudurupati et al. (2016), Sanjuán et al. (2018), Abdulkadyrov et al. (2017), Popkova et al. (2019), Sanjuán et al. (2018), Sergi (2019). The second approach focuses on new markets emerging in the digital economy. Here, additional revenues of the regional state budget from the functioning of regional digital entrepreneurship are correlated with additional budget expenditures related to the financing of the digital economy. This approach is described in the research by Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Popkova and Sergi (2019), Sergi et al. (2019). The third approach is to determine the extent to which the opportunities for digital modernization of the region’s economy based on the market mechanism are realized. The focus is on the ratio of public to private investment in the digital economy: the higher the share of private investment is, the higher the efficiency as an analogue of return on investment is. This approach is considered by Petrenko et al. (2018), Popkova (2019), Popkova and Parakhina, Popkova and Sergi (2018). Thus, the existing scientific and economic publications consider the digital economy as an investment project and assess the effectiveness of this project. In our work, we interpret the digital economy as a source of new opportunities for improving public management of the region based on the results of the fourth technological revolution. In this regard, we believe that in the conditions of the digital economy, it is not the new direction of public management that should be evaluated, but the use of new opportunities in public administration of the region. As part of our methodology, we propose to be guided by the following formula:

The Methodology of Indicative Assessment of the Effectiveness … Table 1 Logic of assigning values to Kg and Ke indicators depending on b coefficient values in the regression model

Calculated indicator

21 Formulas for calculation of indicators and conditions of their application (b/y)*(100−x)

1

(b/y)/(100−x)

Kr

b>0

b=0

b 0); therefore, when calculating the indicator of the use of digital economy opportunities to maximize regional government budget revenues, we apply the formula (b/y)*(100−x). We get: Kr = (6523.2*57543.5)*(100−45.36) = 6.19. In the model y2(x), the coefficient b has taken the value of 6517.5 (b > 0); therefore, when calculating the indicator of the use of digital economy opportunities to maximize regional government budget revenues, we apply the formula (b/y)/(100−x). We get: Ke = (6517.5*57326.29)*(100−45.36) = 480.64. It allows to calculate efficiency of management of regional

22

V. K. Giraev et al.

Fig. 1 Regression curves. Source calculated and constructed by the authors

development in the conditions of digital economy in the regions of the North Caucasian federal district of the Russian Federation in 2018: Ergde = (57543.5*6.19)/ (57326.29*480.64) = 0.01. The obtained Ergde value testifies to the low efficiency of regional development management in the conditions of digital economy. It is noteworthy that the overall efficiency of this management is moderate and makes 57543.5/57326.29 = 1.00. Therefore, this management is quite effective, but the potential for improving its efficiency based on the digital economy is not fully realized.

4

Conclusion

The result of the study was the development of the author’s method of indicative evaluation of the effectiveness of management of the region’s development in the digital economy. This methodology is based on a fundamentally new modern economic science view of the efficiency of management of the region’s development in the conditions of digital economy: not through the prism of public and private investments in digital modernization and their payback, but through the prism of using the potential of increasing the efficiency of management of the region based on the possibilities of digital economy. In other words, the digital economy is not an object of management, but a tool to improve the efficiency of regional management. The indicators of efficiency of management of development of the region in conditions of the digital economy are the use of digital economy possibilities for maximization of regional state budget revenues and the use of digital economy possibilities for minimization of regional state budget expenditures. We have stated thanks to the digital economy, state budget revenues of the regions of the North Caucasus Federal District of the Russian Federation in 2018 are 6.19 times higher (as compared to revenues without the use of

digital technologies). At the same time, the potential for minimizing expenditures of their budgets has not been practically realized. The digital economy makes it possible to reduce state budget expenditures of the regions of the North Caucasus Federal District of the Russian Federation by 480.64 times. The calculations showed that state management of the development of the regions of the North Caucasus Federal District of the Russian Federation in 2018 is effective (revenues exceed expenditures). However, the opportunities of the digital economy are barely being used. As the digital economy develops in the regions under consideration, the efficiency of government economic management should be expected to improve. It is recommended that the digital modernization of the economies of these regions should be stimulated in order to increase the efficiency of government administration.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10), 564–568, 78. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 581– 586, 82. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian

The Methodology of Indicative Assessment of the Effectiveness …

23

industry. Advances in Intelligent Systems and Computing, 622, 44–50. Nudurupati, S. S., Tebboune, S., & Hardman, J. (2016). Contemporary performance measurement and management (PMM) in digital economies. Production Planning and Control, 27(3), 226–235. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), c. 781–791. https://doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72. Popkova, E. G., & Sergi, B. S. (2018). Will industry 4.0 and other innovations impact Russia’s development? In B. S. Sergi (Ed.), Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety vs. Rationality. Springer. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of

the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Rosstat. (2019). Regions of Russia: socio-economic indicators—2018. Retrieved September 21, 2019, from https://www.gks.ru/free_doc/ doc_2018/region/reg-pok18.pdf. Sanjuán, C. E., Cárdenas Garciá, M., & De Cañizares Arévalo, J. (2018). Architecture of a digital economy policy: A tool to achieve efficiency in the development of the local economy. Journal of Physics: Conference Series, 1126(1), 012064. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In Bruno S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Skolkovo. (2019). Values of the Digital Russia index sub-indices for 85 subjects of the Russian Federation in 2018. Retrieved September 09, 2019, from https://finance.skolkovo.ru/downloads/documents/ FinChair/Research_Reports/SKOLKOVO_Digital_Russia_Report_ Full_2019-04_ru.pdf.

Development of Russia’s Regions in the Conditions of the Digital Economy: Management, Effectiveness, and Competitiveness Arutyun A. Khachaturyan, Karine S. Khachaturyan, and Anton A. Shirkin

Russia, the efficiency criterion is much higher than the criterion of competitiveness. However, even though the economy of the regions of the North Caucasus Federal District of the Russian Federation becomes more digitalized in 2018, the efficiency of state management of their economy decreases (inverse regression dependence), and the accelerated digitalization scenario (scenario 3) proved to be optimal. Originality/Value: It is proved that the intuitive choice of an optimal scenario is difficult, and the developed scientific and methodological support of scenario modeling of public management of the region’s development in the conditions of digital economy has high theoretical and practical significance.

Abstract

Purpose: Authors pursue the goal to define a mass-available (simple in realization) economic and mathematical method (applied on a junction of economic and mathematical sciences) and its adaptation for the solution of a problem of nonlinear optimization and its approbation on an example of the regions of the North Caucasian district of the Russian Federation. Design/Methodology/Approach: As a result of the research, the scientific and methodological support of scenario modeling of public administration of the region’s development in conditions of the digital economy by means of adaptation of analytical hierarchical procedure of Thomas L. Saaty to the solution of the formulated problem of nonlinear optimization was formed. The advantage of this method is that the use of specialized software is not required—all calculations can be automated in the mass-available program Microsoft Excel using standard formulas. The method implies modeling with the help of matrices. We have adapted the method to solve the problem of nonlinear optimization according to the choice of the scenario of state management of the region’s development in the conditions of digital economy, which resulted in the following algorithm of its practical application. Findings: Testing of the developed scientific and methodological support by the example of the regions of the North Caucasus Federal District of the Russian Federation in 2018 showed that in modern A. A. Khachaturyan
K. S. Khachaturyan (&) Department of Economic Theories and Military Economics, Military University of the Ministry of Defense of the Russian Federation, Moscow, Russia e-mail: [email protected] A. A. Khachaturyan e-mail: [email protected] A. A. Shirkin Research Institute of the Federal Penitentiary Service of Russia, Moscow, Russia e-mail: [email protected]

Keywords











Region development Scenario analysis Economic and mathematical modeling Method of Thomas L. Saaty Digital economy Management Efficiency Competitiveness Russian regions











JEL Codes

C31

1






D41

O18

O31

R11

R58

Introduction

In the digital economy, the management of the region’s development is more complex as it is subject to bilateral changes. On the one hand, digitalization is becoming a new object of public administration. It requires financing and therefore has an impact on regional state budget spending, but also provides an additional revenue stream from digital infrastructure and digital entrepreneurship. It also contributes to the improvement of economic activity in the region, thus increasing its competitiveness. At the same time, increasing investment risks and threat to information security may

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_5

25

26

instead reduce the competitiveness of the region as a residential and business area. On the other hand, digitalization contributes to the modernization of public administration practices in the region. E-government (public administration and digital public service delivery) is emerging and developing. Based on automation, it is possible to increase productivity and reduce public administration costs. Improvement of public administration practices can improve a region’s socioeconomic situation and thus its competitiveness. However, the opposite effect is also possible, connected with the reduction of availability of state services, aggravation of social tension because of insufficient attention of artificial intelligence to social questions of the regional economic system. In this regard, two criteria for the optimality of public management of the region’s development in the digital economy can be clearly defined: efficiency (as a ratio of revenues to expenditures of the regional state budget) and competitiveness (as a socioeconomic position of the region in comparison with other regions of the country). Depending on the impact of digitalization on the region’s economy (taking into account the set criteria), its optimal scenario under public administration in the region should be determined. Taking into account that digitalization takes place in the framework of the fourth industrial revolution and therefore has a comprehensive scope (it is fundamentally impossible to limit it to certain areas of the economy), the scenarios differ in the pace of digitalization. Scenario 1: Slow digitalization. It is preferable when digitalization has a negative impact on the region’s economy, reducing its efficiency and competitiveness. The complete abandonment of digitalization of a region’s economy is not possible under the influence of globalization (pressure from national and international competition and the need to maintain digital competitiveness) and national regulation (the need to follow a national socioeconomic development strategy, which digitalization is an integral part of). The slow pace of digitalization allows the region’s economy to adapt to the current and upcoming changes and to adjust its impact on efficiency and competitiveness. Scenario 2: Moderate digitalization. In this case, the impact of digitalization on a region’s economy is controversial—it can reduce efficiency, but increase competitiveness and vice versa. The region is interested in building a digital economy, but at the same time strives to preserve its sustainability and prevent a possible crisis. A moderate (average) rate of digitalization can maximize its benefits and offset possible disadvantages. Scenario 3: Accelerated digitalization. This scenario is chosen when digitalization has a pronounced and clearly positive impact on the regional economy. It is implemented both when a region wants to accelerate its economic growth and innovation development (in the upward wave of the

A. A. Khachaturyan et al.

economic cycle) and when a region experiences a crisis of the fiscal system or a decrease in competitiveness (in the downward wave of the economic cycle). Although, theoretically, the logic of the choice among the scenarios of public management of the region’s development in the conditions of digital economy is obvious and simple, in practice, defining the impact of digitalization on the region taking into account both (often conflicting) criteria and selection of the optimal scenario of its implementation is a problem. The existing methods of pure mathematical modeling (e.g., solving the problem of nonlinear simplex optimization by simplex method) are mostly complex and inaccessible, and intuitive decision-making leads to subjectivity and lack of optimality. In the work given, we pursue the purpose of the definition of a mass-available (simple in realization) economic–mathematical method (applied on a junction of economic and mathematical sciences) and its adaptation for the solution of a problem of nonlinear optimization and its approbation on an example of the regions of the North Caucasian district of the Russian Federation.

2

Materials and Method

Selected issues of public management of the region’s development in the digital economy are discussed in the works of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018). Scenarios of digitalization of the regional economy are considered in the works of Petrenko et al. (2018), Popkova (2019), Popkova et al. (2018, 2019), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019). At the same time, the scientific and methodological support of the choice of the scenario of public management of the region’s development in the digital economy is in the process of formation, which leaves the field for scientific research. As a result of a series of experiments, we have determined a promising economic and mathematical method that is as easy to use as possible and at the same time allows achieving high accuracy in solving the problem of nonlinear optimization—the analytical hierarchical procedure of Thomas L. Saaty. The advantage of this method consists in the fact that application of specialized software (e.g., computer program MathCad) is not required—all calculations can be automated in mass-accessible program Microsoft Excel by means of standard formulas. The method implies modeling with the help of matrices. We have adapted this method to solve the problem of nonlinear optimization by choosing a scenario of public management of a region’s development in the conditions of digital economy, which resulted in the following algorithm of its practical application. At the first stage, the regression

Development of Russia’s Regions in the Conditions …

27

dependence of efficiency (y1) and competitiveness (y2) on the level of development of the digital economy (x) in the region is determined. At the second stage, the obtained paired linear regression models y1 = a1 + b1*x, y2 = a2 + b2*x, x are substituted with the values of x corresponding to each of the scenarios and calculated by the following formulas (xsci is the value of x in scenario i, i = 1,… 3; t is the time period based on the data for which the modeling is performed):

and a weighted sum of competitiveness in each scenario by formula: Cwssci = Enormsci*weight. In the sixth stage, a hierarchical synthesis by the formula is performed: HSsci = Ewssci*Cwssci. The resulting HSsci values are compared and the scenario according to which the hierarchical synthesis is maximal is selected—this scenario is optimal. Input data for approbation of the described method to the regions of the North Caucasus Federal District of the Russian Federation in 2018 are given in Table 1.

– Scenario 1: (slow digitalization): xcs1 = xt; – Scenario 2: (moderate digitalization): xcs1 = xt*1,5; – Scenario 3: (accelerated digitalization): xcs1 = xt*2;

3

This is how the yeff (y1 for x) and the y comp (y2 for x) for each scenario are defined. In the third stage, weights (weights) of the optimality criteria are determined: efficiency and competitiveness are ranked according to their importance for the region. In this case, the standard scale of Thomas L. Saaty is used (it is publicly available and well known—let us give it selectively): – – – –

the criteria are equal: 1; moderate advantage of one criterion over another: 3; strong superiority of one criterion over another: 6; very strong superiority of one criterion over another: 9.

The fourth stage is rationing. For this purpose, the arithmetic mean is determined separately for all the yeff and the ycomp, and the share of yeff and the ycomp of each scenario is arithmetic mean (the sum of shares is equal to 1). This is how Enormsci and Cnormsci are calculated. In the fifth step, a weighted sum of performance under each scenario is determined by formula: Ewssci = Enormsci*weight

Results

Let us present the results of testing the adapted method of Thomas L. Saaty for the regions of the North Caucasus Federal District of the Russian Federation in 2018. At the first stage, we obtained regression curves based on data from Table 1 (Fig. 1). At the second stage, the obtained paired linear regression models are substituted with x values corresponding to each of the scenarios (Table 2). At the third stage, weights of the optimality criteria (weights) are defined: efficiency and competitiveness are ranked depending on their importance for the region (Table 3). The results of the fourth, fifth, and sixth stages are presented in Table 4. According to the calculations made in Table 4, scenario 3 (accelerated digitalization) is the optimal scenario for the state management of the development of the regions of the North Caucasus Federal District of the Russian Federation in the conditions of the digital economy, because the hierarchical synthesis of this scenario is maximum and makes 0.19.

Table 1 State budget revenues and expenditures and the level of development of the digital economy in the regions of the North Caucasus Federal District of the Russian Federation in 2018 Region

State budget revenues of the region, billion rubles

State budget expenditures of the region, billion rubles

Socioeconomic situation, points 1– 100

Digital economy development index, points 1–100

The Republic of Dagestan

107278

103582

33.983

45.52

The Republic of Ingushetia

23000.6

22883.4

14.505

40.42

The Kabardino-Balkaria Republic

29390.4

32087.4

18.877

47.06

The Karachay-Cherkessia Republic

24039.2

23992.1

17.005

40.31

The Republic of North Ossetia– Alania

27733.9

28119.5

18.663

41.99

The Chechen Republic

77477.4

77116.6

26.013

48.61

Stavropol Krai Arithmetic mean

113885 57543.50

113503 57326.29

Source compiled by the authors based on the materials of Rosstat (2019), Skolkovo (2019)

46.233

53.58

25.04

45.36

28

A. A. Khachaturyan et al.

Fig. 1 Regression curves reflecting the dependence of efficiency and competitiveness on the level of development of the digital economy in the regions of the North Caucasus Federal District of the Russian Federation in 2018. Source calculated and constructed by the authors

Table 2 Scenario modeling for the regions of the North Caucasus Federal District of the Russian Federation (t = 2018)

Regression models

a

b

–

yeff

1.0199

−0.0006

–

y comp

−64.975

1.9846

–

Scenario modeling

x

yeff

y comp

Scenario 1: (slow digitalization): xcs1 = xt

45.36

0.99

25.05

Scenario 2: (moderate digitalization): xcs1 = xt*1, 5;

68.04

0.98

70.06

Scenario 3: (accelerated digitalization): xcs1 = xt*2

90.72

0.97

115.07

Source calculated and compiled by the authors

Table 3 Determination of weight coefficients (weights) for the regions of the North Caucasus Federal District of the Russian Federation

Weights

Efficiency

Competitiveness

Efficiency

equivalence: 1

strong superiority 6/1 = 6 (weighte = 6)

Competitiveness

strong superiority: 1/6 = 0.17 (weightr = 0.17)

equivalence: 1

Source calculated and compiled by the authors

Table 4 Solution of the nonlinear optimization problem by Thomas L. Saaty method in the regions of the North Caucasus Federal District of the Russian Federation

Criterion

Assessment

Scenario Scenario 1: slow digitalization

Efficiency

Competitiveness

Scenario 2: moderate digitalization

Scenario 3: accelerated digitalization

Initial (yeffsci)

0.99

0.98

0.97

Normalized (Enormsci)

0.34

0.33

0.33

Weighted sum (Ewssci=Enormsci*weighte)

0.34*6=2.04

0.33*6=1.98

0.33*6=1.98

Initial (ycomp)

25.05

70.06

115.07

Normalized (Cnormsci)

0.12

0.33

0.55

Weighted sum (Cwssci=Cnormsci*weightk)

0.12*0.17=0.02

0.33*0.17=0.06

0.55*0.17=0.09

2.04*0.02=0.04

1.98*0.06=0.11

1.98*0.09=0.19

Hierarchical synthesis (HSsci=Ewssci*Cwssci) Source calculated and compiled by the authors

Development of Russia’s Regions in the Conditions …

4

Conclusion

As a result of the research, the scientific and methodological support of scenario modeling of public management of the region’s development in conditions of the digital economy by means of adaptation of analytical hierarchical procedure of Thomas L. Saaty to the solution of the formulated problem of nonlinear optimization was formed. Its testing by the example of the regions of the North Caucasus Federal District of the Russian Federation in 2018 showed that in modern Russia the criterion of efficiency is much higher than the criterion of competitiveness. However, even though the economy of the regions of the North Caucasus Federal District of the Russian Federation becomes more digitalized in 2018, the efficiency of state management of their economy decreases (inverse regression dependence), and the accelerated digitalization scenario (scenario 3) proved to be optimal. This confirms that the intuitive choice of the optimal scenario is difficult, and the developed scientific and methodological support of scenario modeling of public management of the region’s development in the digital economy has high theoretical and practical significance.

References Abdulkadyrov., A. S., Ryzhov, I. V., Strokov, A. I., Kamzolov, Yu. V. (2017). Current aspects of improving the organization of production of high-tech products. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10), 564–568, 78. Gadzhiev, M. M., Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 581– 586, 82. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii. Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44–50.

29 Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues., 7(4), 781–791. https://doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., Morozova, I. A., Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: Future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov. DSc. In Economics. Professor and Irina A. Kuznetsova. PhD in Engineering. Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts. structure. components”). Theoretical and Practical Issues of Journalism, 7(1), 145–154. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems., 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will industry 4.0 and other innovations impact Russia’s development? In B. S. Sergi (Ed.), Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: complexity and variety vs. Rationality. Springer. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control., 169, 167–174. Rosstat. (2019). Regions of Russia: Socio-economic indicators—2018. Retrieved September 21, 2019, from https://www.gks.ru/free_doc/ doc_2018/region/reg-pok18.pdf. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial. Tech. and financial cooperation with the Asia-Pacific region. In B. S. Sergi (Ed.), Tech. smart cities. and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S. (Ed.). (2019). Tech. smart cities. and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Skolkovo. (2019). Values of the Digital Russia Index sub-indices for 85 subjects of the Russian Federation in 2018. Retrieved September 22, 2019, from https://finance.skolkovo.ru/downloads/documents/ FinChair/Research_Reports/SKOLKOVO_Digital_Russia_Report_ Full_2019-04_ru.pdf.

Digital Entrepreneurship as a New Subject of a Modern Region’s Economy: State Stimulation and Market Mechanisms of Development Nazira I. Magomedova, Larisa A. Gadzhimuradova, and Aina M. Shakhbanova

self-administration proved to be more preferable. At the same time, market mechanisms have shown different results. Stimulation of free competition proved to be the most effective. The growth of foreign competition and investment in fixed assets (in innovations) is also welcome. Originality/Value: On the basis of the received conclusions the scientific approach to management of development of digital business in the regions of the North Caucasian federal district of the Russian Federation in 2018 is generated.

Abstract

Purpose: The purpose of the study is to develop a scientific approach to the management of digital entrepreneurship in the modern region (on the example of the regions of the North Caucasus Federal District of the Russian Federation) through the prism of the ratio of state simulation and application of market mechanisms of development. Design/Methodology/Approach: To determine the impact of government incentives and market self-governance measures on digital entrepreneurship in the regions of the North Caucasus Federal District of the Russian Federation in 2018, the authors use the correlation analysis method. Those management measures that have the most pronounced positive impact (positive correlation) are preferable and will form the basis of the approach being developed. In order to demonstrate the specifics of digital entrepreneurship, the impact of regulatory market measures is determined not only on the market but also on entrepreneurship as a whole. Findings: It has been revealed that digital entrepreneurship is a new subject of the modern region’s economy and therefore requires a new approach to managing the development of entrepreneurship. The concept of state regulation traditionally implemented in the regions of the North Caucasus Federal District of the Russian Federation has demonstrated its inapplicability to digital entrepreneurship. The concept of market

N. I. Magomedova (&)
L. A. Gadzhimuradova
A. M. Shakhbanova Economic Sciences, Department Finance and Credit, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected] L. A. Gadzhimuradova e-mail: [email protected] A. M. Shakhbanova e-mail: [email protected]

Keywords








Digital entrepreneurship Economy Modern region State stimulation Market mechanisms of development Russian regions





JEL Codes

H25

1






K20

O18

O31

R11

R58

Introduction

Under the influence of scientific and technological progress and the reciprocal modernization of modern regions, a new subject of their economy is emerging—digital entrepreneurship. This refers to entrepreneurship, which actively uses modern and/or breakthrough information and communication (digital) technologies in carrying out its economic activities. It has several advantages over other forms of entrepreneurship. Firstly, digital entrepreneurship is more innovative and therefore contributes to the acceleration of growth and innovative development of a region’s economy and can even serve as a source of its crisis management, increasing its resilience to crises. Secondly, the application of advanced technologies allows digital entrepreneurship to be present in high-tech

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_6

31

32

N. I. Magomedova et al.

markets, thus increasing the competitiveness of the region (and, in particular, its digital competitiveness). Thirdly, digital entrepreneurship creates highly productive and well-paid jobs in the region, thus stimulating the assimilation of digital competences by employees in the labor market. This results in the fullest realization of human potential in the region, as employees can use their creativity and high-level professional competences. It also ensures development of an information (digital) society in the region, loyal and adapted to the digital economy, and improvement of living standards and quality of life. The above-mentioned advantages highlight the problem of creating favorable conditions for the development of digital entrepreneurship in modern regions. Taking into account the currently observed slow pace of development of digital entrepreneurship in the regions of modern Russia, despite the active application of government support measures, we propose a hypothesis that the management of digital entrepreneurship development is fundamentally different from traditional management of entrepreneurship. The reliance on government incentives (the concept of regulation or paternalism) in the management of digital entrepreneurship is not appropriate as incentives are ineffective or may even hinder its development. Instead, market-based self-management (deregulation or liberal concept) is preferable. The purpose of our study is to develop a scientific approach to the management of digital entrepreneurship in the modern region (on the example of the North Caucasus Federal District of the Russian Federation) through the prism of the ratio of state simulation and application of market mechanisms of development.

2

Materials and Method

There is no unified and generally accepted opinion on how entrepreneurship development in the region should be managed in economic science. Instead, there are two alternative concepts. The concept of regulation or paternalism, based on the idea of state support for entrepreneurship and artificial formation of market environment in the region, in particular, which allows limiting competition (or only foreign competition—protectionism) to support the region’s enterprises, is described in the works of Grinev et al. (2019), Salinas et al. (2019), Solodilova et al. (2016). The concept of deregulation or the liberal concept proclaiming the idea of maximum restriction of state interference in natural market processes and reliance on market self-administration, which does not allow restriction of

competition (i.e., accepting free trade) is reflected in the works of Wang et al. (2019), Wu and Hayashi (2014). The process of formation and specificity of digital entrepreneurship in modern regions is described in the works of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al (2018), Gadzhiev and Buchaev (2014), Popkova (2019), Popkova et al. (2018, 2019), Petrenko et al. (2018), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019). A literature review showed that the existing publications covered the concepts of entrepreneurship development management in the region in sufficient detail. The coexistence of two alternative concepts is quite logical and can be explained by the fact that, depending on the specifics of the region’s economy, this or that concept is preferable and the development of a universal concept is impossible. At the same time, both existing concepts have not been tested on digital entrepreneurship and are not adapted to the application for its development. Therefore, in this paper we define the applicability of each of these concepts to digital entrepreneurship in the regions of the North Caucasus Federal District of the Russian Federation and develop a scientific approach to managing the development of digital entrepreneurship in these regions, taking into account their specifics and allowing to combine both concepts or rely on the most preferable of them. We chose the regions of the North Caucasus Federal District of the Russian Federation as the objects for the research, as they have the most developed digital entrepreneurship among Russian regions. This is evidenced by the statistical data in Fig. 1. As it can be seen from Fig. 1, almost half (48.41%) of the enterprises in the regions of the North Caucasus Federal District of the Russian Federation in 2018 use special software for making financial calculations in electronic form, 37.5%—for dealing with organizational, managerial, and economic tasks, 34.4%—for access to electronic reference systems. In general, the level of development of digital entrepreneurship in these regions can be defined as high. To determine the impact of government incentives and market self-management measures on digital entrepreneurship in the regions of the North Caucasus Federal District of the Russian Federation in 2018, we use the correlation analysis method. Those management measures that have the most pronounced positive impact (positive correlation) are preferable and will form the basis of the approach being developed. To demonstrate the specifics of digital entrepreneurship, we

Digital Entrepreneurship as a New Subject of a Modern Region’s …

33

Fig. 1 Statistics on the use of special software tools in digital entrepreneurship in the regions of the North Caucasus Federal District of the Russian Federation in 2018, % of organizations. Source compiled by the authors based on the materials of Rosstat (2019)

determine the impact of regulatory market measures not only on the market itself, but also on entrepreneurship as a whole (taking into account that data on other forms of entrepreneurship are not available in isolation).

3

m3—investments in fixed assets per resident at the end of 2017, thousand rubles per person (data source: RIA-Rating). Statistical data on these indicators are given in Table 1. We have chosen the following (in all cases, the data source: Rosstat) as indicators of the development of digital entrepreneurship:

Results

To get the most complete, accurate, and reliable research results, we have formed the largest possible sample. Therefore, we have introduced and used the following designations during the calculations: r1—production volume of goods and services per resident at the end of 2017, thousand rubles per person (data source: RIA-Rating); r2—share of profitable enterprises by results of 2017, % (data source: RIA-Rating); g1—number of mixed-ownership enterprises, pcs. (data source: Rosstat); g2—investment attractiveness of a region, points 1–9 (the less, the better) (data source: National Rating Agency); m1—number of businesses at the end of the year, (data source: Rosstat); m2—number of foreign-owned enterprises, pcs. (data source: Rosstat);

d1—share of businesses that use personal computers; d2—share of businesses that use servers; d3—share of businesses that use LANs (local area networks); d4—share of businesses that use WANs (wide area networks); d5—share of businesses possessing a website; d6—share of businesses using special software; d7—share of businesses with electronic document management; d8—share of organizations that exchange data electronically among their own and external information systems. Statistical data on these indicators are given in Table 2. Detailed results of regression analysis of data from Tables 1 and 2 are given in Table 3. The results given (Table 3) are difficult to comprehend and therefore uninformative. That is why the obtained results are systematized in Fig. 2.

34 Table 1 Development indicators of entrepreneurship, state regulation, and market self-governance in the regions of the North Caucasus Federal District of the Russian Federation in 2018

N. I. Magomedova et al. Region

Business development results in general r2

r1

Factors of state incentives

Factors of influence of market mechanisms of development

g1

g2

m1

m2

m3

The Republic of Dagestan

152.49

82.30

378

9

33312

97

65.30

The Republic of Ingushetia

81.91

64.90

22

9

5066

15

28.06

The Kabardino-Balkar Republic

146.87

60.9

251

9

12681

55

47.24

The Karachay-Cherkess Republic

229.53

77.90

42

9

6750

22

38.20

The Republic of North Ossetia-Alania

137.37

62.50

1220

9

9580

37

38.15

97.20

80.20

83

9

10118

28

45.89

270.15

78.70

399

6

48295

214

49.96

The Chechen Republic Stavropol Krai

Source compiled by the authors based on the materials of Rosstat (2019), National Rating Agency (2019), National Research University “Higher School of Economics” (2019), RIA-Rating (2019) Table 2 Development indicators of digital entrepreneurship in the regions of the North Caucasus Federal District of the Russian Federation in 2018

Region

The results of digital business development d1

d2

d3

d4

d5

d6

d7

d8

The Republic of Dagestan

72.5

24.0

26.4

68.5

31.5

57.2

42.3

42.7

The Republic of Ingushetia

100.0

44.7

50.2

100.0

73.8

98.9

97.1

73.1

The Kabardino-Balkar Republic

91.5

34.9

38.6

85.2

42.0

73.2

58.8

51.8

The Karachay-Cherkess Republic

92.3

47.5

52.4

90.4

49.8

87.1

69.4

65.2

The Republic of North Ossetia-Alania

90.7

46.7

48.4

80.6

45.4

74.2

56.4

52.5

The Chechen Republic

96.9

30.3

46.6

94.4

52.3

52.3

40.9

39.1

Stavropol Krai

98.1

59.4

70.1

97.1

56.1

92.6

75.5

72.5

Source compiled by the authors based on the materials of Rosstat (2019) Table 3 Detailed results of regression analysis, %

r2

d1

g1

r1 4.54

−35.37

−23.35

d2 18.73

0.08

g2

−71.84

−29.57

−30.63

−67.21

−74.60

m1

62.96

50.62

−27.22

20.74

24.30

m2

70.19

38.19

−6.80

39.91

43.30

0.58

−13.36

16.15

3.18

26.41

m3

27.48

57.02

−76.16

−50.06

−45.05

−67.78

−79.23

−63.02

−69.50

−55.76

Source calculated and compiled by the authors

Fig. 2 Impact of government regulation and market mechanisms on the development of entrepreneurship in general and digital entrepreneurship in particular, %. Source calculated and compiled by the authors

d3

d4

d5

d6

d7

d8

−46.25

−35.83

−12.70

−25.42

−17.66

−36.61

−20.04

−40.46

−28.11

−50.58

−16.62

−26.41

0.63

−10.28

11.58

Digital Entrepreneurship as a New Subject of a Modern Region’s …

As can be seen from Fig. 2, in the regions of the North Caucasus Federal District of the Russian Federation in 2018, state regulation measures constrain the development of entrepreneurship in general and digital entrepreneurship in particular. It is noteworthy that the negative impact of these measures on digital entrepreneurship is more pronounced. Market mechanisms have a pronounced stimulating effect on the development of digital entrepreneurship in the regions under study, while they have little or no impact on entrepreneurship in general. Based on the foregoing, the scientific approach to management of development of digital business in the regions of the North Caucasian federal district of the Russian Federation in 2018 assumes realization of the following measures (in order of priority): 1. Stimulation of free competition to maximize the number of enterprises in the region; 2. Liberalism in relation to foreign competition; stimulating the growth of foreign ownership in the region; 3. Abandonment of purposeful public administration aimed at increasing the investment attractiveness of the region; 4. Isolated simulation of capital investment in enterprises as part of the management of regional innovation development; 5. Limiting the mechanism of public-private partnership and reducing the share of mixed-ownership enterprises in the region.

4

Conclusion

Thus as a result of the research the hypothesis is confirmed: digital entrepreneurship is a new subject of the modern region’s economy and therefore needs a new approach to entrepreneurship development management. The concept of state regulation traditionally implemented in the regions of the North Caucasus Federal District of the Russian Federation has demonstrated its inapplicability to digital entrepreneurship. The concept of market self-governance proved to be more preferable. At the same time market mechanisms have shown different results. Stimulation of free competition proved to be the most effective. The growth of foreign competition and investment in fixed assets (in innovations) is also welcomed. Based on the findings a scientific approach to managing the development of digital entrepreneurship in these regions has been developed.

35

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10), 564–568, 78. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 581– 586, 82. Grinev, A., Takhumova, O., & Gornostaeva, Z. (2019). Legal regulation and state support for small businesses. Indo American Journal of Pharmaceutical Sciences, 6(3), 5635–5639. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii. Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44– 50. National Rating Agency. (2019). Rating of investment attractiveness of Russian regions at the end of 2018. Retrieved September 30, 2019, from http://www.ra-national.ru/sites/default/files/analitic_article/ IPR-6-06112018.pdf. National Research University “Higher School of Economics”. (2019). Digital economics 019: A statistical summary. Retrieved September 30, 2019, from https://www.hse.ru/data/2018/12/26/1143130930/ ice2019kr.pdf. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the internet of things. Studies in Computational Intelligence, 826(1), 1137–1144. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: Future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov. DSc. In Economics. Professor and Irina A. Kuznetsova. PhD in Engineering. Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts. structure. components”). Theoretical and Practical Issues of Journalism, 7(1), 145–154. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will industry 4.0 and other innovations impact Russia’s development? In B. S. Sergi (Ed.), Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited.

36 Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety vs. Rationality. Springer. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. RIA-Rating. (2019). Rating of the socio-economic situation of the constituent territories of the Russian Federation in 2018. Retrieved September 30, 2019, from https://riarating.ru/infografika/20180523/ 630091878.html. Rosstat. (2019). Regions of Russia: Socio-economic indicators—2018. Retrieved September 30, 2019, from https://www.gks.ru/free_doc/ doc_2018/region/reg-pok18.pdf. Salinas, A., Ortiz, C., & Muffatto, M. (2019). Business regulation. Rule of law and formal entrepreneurship: Evidence from developing countries. Journal of Entrepreneurship and Public Policy, 8(2), 254–271.

N. I. Magomedova et al. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial. Tech. and financial cooperation with the Asia-Pacific region. In B. S. Sergi (Ed.), Tech. smart cities. and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S. (Ed.). (2019). Tech. smart cities. and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Solodilova, N. Z., Malikov, R. I., & Grishin, K. E. (2016). Development of the tools for the assessment of the influence of administrative regulation on the business efficiency in the region. Ekonomika Regiona—Economy of Region, 12(4), 1001–1013. Wang, Y., Chen, S., & Yao, J. (2019). Impacts of deregulation reform on PM2.5 concentrations: A case study of business registration reform in China. Wu, C., & Hayashi, Y. (2014). The effect of LCCs operations and scheduled services deregulation on air charter business in Japan.

The Regional System of Science and Education as a Source of Digital Personnel and Breakthrough Technologies for Region’s Economy Olga V. Budzinskaya

modernization and ensure a high level of global digital competitiveness of the economy of the regions of the North Caucasus Federal District of the Russian Federation.

Abstract

Purpose: The aim of the work is to define a scenario for digital modernization of the economy of the regions of the North Caucasus Federal District of modern Russia, as well as to develop recommendations for the development of a regional system of science and education as a source of digital personnel and breakthrough technologies for the economy of the regions of this district to accelerate its digitalization. Design/methodology/approach: The method of factor analysis is used to study the structure of digital personnel and the logic of formation of breakthrough technologies in the regions of the North Caucasus Federal District of the Russian Federation in 2018–2019. Findings: It has been revealed that in the regions of the North Caucasus Federal District of the Russian Federation the level of the scientific and educational system development is very high—high enough for these regions to be able to actively export advanced technologies. This is the reason for the low achieved level and slow pace of digital modernization of their economy. At present (2019), the regions of the North Caucasus Federal District of the Russian Federation are implementing the tradition-oriented scenario of digital economic modernization. Insufficient preparation of society and entrepreneurship for the digitalization program launched by the state has caused protests against this program. Therefore, despite the rather high productivity of the regional system of science and education, the advanced technologies being created are not used in the region, but are exported. The regions of the North Caucasus Federal District of the Russian Federation have the potential for transition to an internally oriented scenario (most preferable). Originality/value: Promising government regulation measures have been proposed to accelerate O. V. Budzinskaya (&) Economics Science, Gubkin Russian State University of Oil and Gas (National Research University), Moscow, Russia e-mail: [email protected]

Keywords










Regional system of science and education Digital manpower Breakthrough technologies Regional economy Russian regions Modernization Digital economy




JEL Codes

G34

1








O18

O31

I25

I26

I28

R11

R58

Introduction

The digital modernization of the region’s economy can be carried out in one of four alternative scenarios. The first scenario—which is called internally oriented—involves relying on own digital frames and breakthrough technologies while maintaining their uniqueness. This is the most preferable scenario, as it allows the region to create strong and long-term competitive advantages in the high-tech market. The region relies on its own system of science and education, which is the source of technological and human resources infrastructure for the formation and development of its digital economy. The scenario is implemented by most regions in countries with developed economies. The second—export-oriented scenario—consists of active export of advanced technologies and intensive emigration of digital human resources (incoming migrant flow). In this case, the regional science and education system is also actively developing and demonstrating high productivity and efficiency. However, trained digital personnel and developed breakthrough technologies are not used by the region itself,

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_7

37

38

but are exported. Thanks to this, the economic growth of the region is achieved, the vector of which is the system of science and education, but the potential to increase the competitiveness of the region’s economy is not fully realized, because its intellectual capital is either not unique (when exporting technologies used in the region) or is subject to leakage (when not using exported technologies in the region). This scenario is the least implemented and, as a rule, temporary. The third—import-oriented scenario—is related to the active import of advanced technologies and intensive immigration of digital frames (outgoing migration flow). This scenario is typical of regions in emerging economies that have to attract external intellectual capital because they cannot create their own in the medium term. In this case modernization is carried out, which consists in building a digital economy (digitalization). However, competitive advantages are not gained due to the lack of uniqueness of the applied intellectual capital. This is the least preferable scenario, which needs to be adjusted through the implementation of regulatory measures by regional public authorities. The fourth scenario—tradition-oriented—is a slow, poorly expressed digital modernization of the region. In this case, digitalization is stated in the strategy for socioeconomic development of the region, but it is difficult to implement it in practice, and the results are insignificant due to the unpreparedness of the regional society and entrepreneurship for changes associated with the introduction of digital technologies. This scenario is controversial. On the one hand, it allows to take into account specificity of region and to keep its uniqueness. On the other hand, this scenario leads to some isolation of the region, which does not enter the digital markets. The competitiveness of the region’s economy is not necessary, but it is highly likely to decrease over time. However, as the social and business environment is prepared, the pace of digitalization increases. Taking into account the worldwide practice of the import-oriented scenario prevailing in the regional economies of emerging economies, the author of this study suggests that this scenario also prevails in the regions of modern Russia (in particular, the regions of the North Caucasus Federal District). The aim of the work is to define a scenario under which digital modernization of the economy of the regions of the North Caucasus Federal District of modern Russia takes place, as well as to develop recommendations on the development of a regional system of science and education as a source of digital personnel and breakthrough technologies for the economy of the regions of this district in order to accelerate its digitalization.

O. V. Budzinskaya

2

Materials and Method

The process of digitalization of the regional economy based on the use of digital personnel and breakthrough technologies is reflected and discussed in the works of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al (2018), Popkova (2019), Popkova et al. (2018, 2019), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019). The specifics of the functioning and development of a regional system of science and education in the digital economy to train digital personnel, as well as to create and promote breakthrough technologies are discussed in studies of Daba and Anbesaw (2016), Lau (2014), Lim and Dall’erba (2016). The literary review revealed a high degree of elaboration of the problem of this study. Nevertheless, the development of science and education in the digital economy is seen as an independent subject of research, while digital training and the creation of breakthrough technologies are seen as another independent subject of research. The interrelationship of these processes is poorly understood. Therefore, the causal links between the development of the regional system of science and education and the digitalization of the regional economy are not sufficiently investigated and need to be clarified. Since the process of digitalization of Russian regions was launched only in 2017 (in fact, it started in 2018) in connection with the adoption of the program “Digital Economy of the Russian Federation” approved by Order of the Government of the Russian Federation (2019) No. 1632-r of July 28, 2017, only actual statistical data for 2018 and data for 2019 based on statistics for the past period of 2019 and on forecasts of experts compiling statistics are currently available. The small amount of available statistical data does not allow for the analysis of time series (regression or correlation). That is why the author of this study uses the method of factor analysis, traditionally used and preferred in the case when there are data for two adjacent time periods, and these data are closely interlinked, measured in the same units of measurement and can be combined into a common economic and mathematical model. In the context of this study, we have developed two models. The first one describes the structure of digital personnel in a region: DP ¼ dg þ ds þ db þ dd;

ð1Þ

where DP—is the total number of digital personnel in the region, people (target function);

The Regional System of Science and Education …

39

dg—number of graduates of vocational education system with key competences of digital economy, people (factor); ds—number of students enrolled in higher education programs in IT and mathematical specialities, people (factor); db—number of people with digital literacy and key competences of the digital economy, people (factor); dd—number of specialists trained in digital economy competences as part of supplementary education, people (factor). The second economic and mathematical model reflects the logic of formation of breakthrough technologies in a region. AT ¼ dt  et þ it;

ð2Þ

where AT—is the number of advanced manufacturing technologies available in the region, pcs. (target function); dt—number of advanced manufacturing technologies developed, pcs. (factor); et—number of agreements to export advanced manufacturing technology, pcs. (factor); it—number of agreements to import advanced manufacturing technology, pcs. (factor). The share of digital personnel in the population structure of the region is also determined by the following formula: SDP ¼ DP  100%=RP;

ð3Þ

where SDP—is the share of digital personnel in the population structure of the region, %; RP—regional population, people. And the maximum possible share of the region’s enterprises using advanced technologies is determined by a formula:

Table 1 Statistical data on the structure of digital personnel and the logic of formation of breakthrough technologies in the regions of the North Caucasus Federal District of the Russian Federation in 2018–2019 and their factor analysis

SET ¼ AT  100=NB;

ð4Þ

where SET—is the maximum possible share of enterprises in the region, for which advanced technologies are available, %; NB—number of businesses in the region, pcs. The growth of the target function (Δ) due to the factor is determined by finding the difference between the value of the target function and the value of the target function changed (let us substitute the value for 2019) to the value of the target function in 2018. Economic and mathematical interpretation of scenarios of digitalization of the region’s economy is as follows: – internally oriented scenario: SDP > 50%, NB > 50%, ΔAT(dt) maximum (modular); – export-oriented scenario: SDP > 50%, NB > 50%, ΔAT (et) maximum (modular); – import-oriented scenario: SDP > 50%, NB > 50%, ΔAT (it) maximum (modular); – tradition-oriented scenario: SDP  50%, NB  50%.

3

Results

Statistical data on the structure of digital personnel and the logic of formation of breakthrough technologies in the regions of the North Caucasus Federal District of the Russian Federation in 2018–2019 are reflected in Table 1, where their factor analysis is also performed.

Indicators

2018 г.

2019 г.

Δ

DP

2573458

2705712

132254 (+5.14%)

dg

15316

16719

1402 (+1.06%)

ds

3063

3344

280 (+0.21%)

db

2541760

2652210

110449 (+83.51%)

dd

13318

33439

20120 (+15.21%)

RP

9776

9823

47 (+0.48%)

SDP

26.32

27.54

1.22 (+4.64%)

AT

18

18

0

dt

15

23

8 (+4%)

et

3

103

−100 (−50%)

it

6

98

92 (+46%)

NB

131984

125802

−6182 (−4.68%)

SET

0.013

0.014

0.16 (+800%)

Source compiled and calculated by the author based on materials from the Ministry of Digital Development, Communications and Mass Media of the Russian Federation (2019) and Rosstat (2019)

40

O. V. Budzinskaya

Here is an example of calculations from Table 1 within the framework of factor analysis: ΔDP(dg) = (16719 + 3063 2541760 + 13318) − 2573458 = 1402; 1402*100/132254 = 1.06%. As it can be seen from Table 1, SDP  50%, NB 50%, ΔAT(et) is the largest (by module). This indicates that in the regions of the North Caucasus Federal District of the Russian Federation the digitalization of the economy takes place according to the tradition-oriented scenario. However, the high level of development of the science and education system is evidenced by the fact that export is a key factor in the formation of advanced technologies in these regions. Therefore, it is recommended to limit exports of advanced technologies and to ensure their application in the regional economy.

4

Conclusion

This hypothesis was disproved during the study. In the regions of the North Caucasian Federal District of the Russian Federation, the level of development of the science and education system is so high that these regions actively export advanced technologies. This is the reason for the low achieved level and slow pace of digital modernization of their economy. Thus, the share of digital personnel in the population structure of the regions under study in 2019 was 27.54%. The maximum possible share of the enterprises of the regions under consideration, for which advanced technologies are available, was 0.014%. At present (2019), the regions of the North Caucasus Federal District of the Russian Federation are implementing the tradition-oriented scenario of digital economic modernization. Insufficient preparation of society and entrepreneurship for the digitalization program launched by the state has caused protests against this program. Therefore, despite the rather high productivity of the regional system of science and education, the advanced technologies being created are not used in the region, but are exported. The regions of the North Caucasus Federal District of the Russian Federation have the potential for transition to an internally oriented scenario (most preferable). This requires government regulation aimed at restricting exports of advanced technologies and stimulating their practical application by regional businesses. Following the proposed recommendations will significantly accelerate modernization and ensure a high level of global digital competitiveness of the economy of the regions of the North Caucasus Federal District of the Russian Federation.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10), 564–568, 78. Daba, T. M., & Anbesaw, M. S. (2016). Factors affecting implementation of practical activities in science education in some selected secondary and preparatory schools of Afar Region, North East Ethiopia. International Journal of Environmental and Science Education, 11(12), 5438–5452. Federal State Statistics Service (Rosstat). (2019). Regions of Russia: Socio-economic indicators—2018. Retrieved October 01, 2019, from https://www.gks.ru/bgd/regl/b18_14p/Main.htm. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 581– 586, 82. Government of the Russian Federation. (2019). Program “Digital economy of the Russian Federation” approved by the order № 1632-r dated 28 July 2017. Retrieved October 01, 2019, from http:// static.government.ru/media/files/ 9gFM4FHj4PsB79I5v7yLVuPgu4bvR7M0.pdf. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44– 50. Lau, K. C. (2014). The science education of the East Asian regions— what we can learn from PISA. Asia-Pacific Forum on Science Learning and Teaching, 15(2), 9. Lim, J., & Dall’erba, S. (2016). An analysis of the impact of federally-funded investments in science, research and technology across regions and education groups in Arizona. Regional Science Policy and Practice, 8(4), 149–165. Ministry of Digital Development, Communications and Mass Communications of the Russian Federation. (2019). Passport of the federal project “Personnel for Digital Economy”. Retrieved October 10, 2019, from https://digital.gov.ru/uploaded/files/ pasport-federalnogo-proekta-kadryi-dlya-tsifrovoj-ekonomiki.pdf. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: Future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor

The Regional System of Science and Education … and Irina A. Kuznetsova, PhD in Engineering, Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components”). Theoretical and Practical Issues of Journalism, 7(1), 145–154. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will industry 4.0 and other innovations impact Russia’s development? In B. S. Sergi (Ed.), Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited.

41 Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety vs. Rationality. Springer. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In Bruno S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited.

Infrastructure of the Digital Economy for Region’s AIC: Creation, Measuring, and Development Sharip I. Sharipov, Juliana A. Akhmedova, and Gadzhi K. Kurbanov

the region’s AIC is sharper than the deficit of the general infrastructure of the digital economy. The largest deficit is observed in the regulatory and personnel infrastructure of the digital economy for the AIC. Therefore, it is recommended that special attention is paid to the development of these components in the interests of creating more favorable conditions for the digital modernization of the agro-industrial complex of the regions of the Caucasus Federal District of Russia.

Abstract

Purpose: The purpose of this paper is to solve the problem on the example of the regions of the North Caucasus Federal District of the Russian Federation by identifying the necessary infrastructure components, developing methods to measure infrastructure adequacy, its testing and development of recommendations for infrastructure development in these regions. Design/ Methodology/Approach: When conducting the research, we carry out our own experiment using the expert assessment method. This allowed to qualitatively assess the level of development and sufficiency of digital economy infrastructure for the agro-industrial complex of the regions of the North Caucasian Federal District of the Russian Federation in 2018. Findings: It is shown that the agro-industrial complex of the region needs special logistical (including telecommunication), technological, personnel, financial, regulatory, and marketing infrastructure. To measure the adequacy of individual components and infrastructure as a whole, a set of parameters is proposed. Originality/Value: The author’s method of measuring the digital economy infrastructure for the agro-industrial complex of the region was developed. The results of testing of this method by the example of the regions of the North Caucasus Federal District of Russia in 2019 showed that the deficit of digital infrastructure for S. I. Sharipov (&) Research Institute of Management, Economics, Politics and Sociology, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected] J. A. Akhmedova Department of State and Municipal Administration of Dagestan, State Technical University (DSTU), Makhachkala, Russia e-mail: [email protected] G. K. Kurbanov Department of Political Economy, Dagestan State Technical University (DSU), Makhachkala, Russia e-mail: [email protected]

Keywords












Infrastructure Digital economy Region Agro-industrial complex (AIC) Development regions

Russian

JEL Codes

G34

1





O18

O31

R11

R58

Introduction

The agro-industrial complex (AIC) plays an important role in the functioning of the modern region. For those regions that specialize in agriculture or food industry, agriculture is a vector of economic growth. For those regions that do not specialize in agriculture, it contributes to food security. Regardless of the production specialization of the region, there is a demand for digital modernization of the AIC (i.e., the transition to a digital AIC or AIC 4.0), as it allows to boost the productivity and efficiency of this complex, as well as to increase its productivity and establish import substitution. In such regions where the conditions for agriculture are unfavorable (e.g., arid climate, infertile soil, cold climate), digital AIC allows to maintain agriculture, thus reducing dependence on external food supplies. The general

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_8

43

44

S. I. Sharipov et al.

infrastructure of the digital economy is not sufficient for the formation and development of digital agriculture. For example, the region may have enough digital personnel in different areas, but there is a shortage of digital personnel in the agricultural sector. Similarly, many advanced technologies can be created and made freely available, but there is still a deficit of technologies that are ready for use in digital agriculture. It should be noted that the problem of infrastructure deficit in the digital economy is most typical for the agro-industrial complex. This is explained by the fact that industry is the target sector of digital modernization in the backdrop of the fourth industrial revolution, also called the transition to industry 4.0 (the name itself reflects the emphasis on industry). In the services sector, the possibilities for digital modernization of some business processes (e.g., processes related to communication with people, such as order acceptance and negotiation) are limited, while others (e.g., processes requiring digital settlements) do not require special infrastructure. And only in the AIC there is a need for specific infrastructure, which is not formed automatically and needs additional, separate management. In this regard, an urgent problem consists in the development of scientific and methodological support for the management of digital economy infrastructure for the agro-industrial complex of the region. The purpose of this work is to solve the problem by the example of the regions of the North Caucasus Federal District of the Russian Federation by identifying the most necessary infrastructure components, developing methods to measure the adequacy of infrastructure, its testing and development of recommendations for infrastructure development in these regions.

2

Materials and Method

The general issues of forming the infrastructure of the digital economy are disclosed in the works of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al (2018), Popkova (2019), Popkova et al. (2018, 2019), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019). Specifics of the organization of digital agriculture (AIC 4.0) is reflected in the works of Aubry (2019), Phillips et al. (2019), Sharma et al. (2019). At the same time, the problem of managing the infrastructure of the digital economy for the AIC is not raised and not addressed in existing research and publications and therefore requires additional research. Since Russia’s current official statistics do not distinguish the digital economy infrastructure for the AIC, we conduct our own experiment using the expert assessment method.

This allows us to qualitatively assess the level of development and sufficiency of digital economy infrastructure for the AIC of the regions of the North Caucasus Federal District of the Russian Federation in 2018. The objects of the research were 22926 AIC enterprises registered in the regions of the North Caucasian Federal District, of which 13907 were agricultural enterprises and 9019 were manufacturing enterprises, which include food industry enterprises that are not recognized separately in official statistics of Rosstat. In 2018, the share of AIC enterprises in the total structure of entrepreneurship of the North Caucasus Federal District of the Russian Federation was 18.22%. Detailed information on all regions of this federal district is shown in Fig. 1. As can be seen from Fig. 1, the largest share of manufacturing is typical of the Republic of Ingushetia (13.70%), while the lowest one is in the Chechen Republic (4.45%). The largest share of agriculture, forestry, hunting, fishing, and fish farming is found in the Republic of Dagestan (13.09%) and the smallest one is in the Republic of Ingushetia (6.61%). Therefore, in the regions of the North Caucasus Federal District of the Russian Federation in 2018, the AIC figures prominently, but is not the area of their production specialization.

3

Results

We have applied the traditional approach to infrastructure structuring of the digital economy infrastructure for the AIC and found out that most of the components (except for transport and logistics infrastructure) require special infrastructure support. For each component of infrastructure, we have defined its measurement parameters (Table 1). As it can be seen from Table 1, the logistical infrastructure is proposed to be measured with the help of such parameters as the availability of digital equipment for AIC and the availability of telecommunication services for AIC enterprises. The technological component is proposed to be measured by such parameters as the availability of ready-made digital technologies for the AIC and the availability of R&D services for the AIC. Human resources infrastructure is proposed to be measured by such parameters as the availability of digital personnel for the AIC and the availability of educational services for the development of digital competences of current employees in the AIC. The financial infrastructure is proposed to be measured by such parameters as the sufficiency of the enterprise’s own funds for digital modernization and the availability of loan (credit) resources for agribusiness enterprises (availability of special credit conditions), the attractiveness of agribusiness for venture capital investors, as well as the availability and sufficiency of state support for the agribusiness for digital

Infrastructure of the Digital Economy for Region’s AIC …

45

Fig. 1 Share of agricultural enterprises in the structure of entrepreneurship of the regions of the North Caucasus Federal District of the Russian Federation in 2018, %. Source calculated and constructed by the authors on the basis of materials of Rosstat (2019)

Table 1 Structure and recommendations for measuring infrastructure of digital economy for the agricultural sector

Infrastructure components

Measurement parameters

Physical

digital availability for the AIC

Technological

availability of digital ready-to-use technologies for the AIC

availability of telecommunication services for agricultural enterprises availability of research and development services for the AIC Human resources

availability of digital personnel for AIC availability of educational services on digital competences development of current AIC employees

Financial

own funds sufficiency for digital modernization availability of loan (credit) resources for AIC enterprises (availability of special credit conditions) attractiveness of the AIC for venture investors availability and sufficiency of state support for the AIC for digital modernization (tax, subsidy)

Regulatory

possibility of certification of the AIC produce production and sale legal certainty of digital AIC produce

Marketing

demand for digital AIC products, willingness to pay a higher price for them and benefit from them level of competition in the emerging or existing market for digital AIC products

Source designed and compiled by the authors

modernization (tax, subsidy). The regulatory infrastructure is proposed to be measured by such parameters as the possibility of certification of digital agribusiness products and the legal certainty of production and sale of digital agribusiness products (for example, the availability of quality standards). Marketing infrastructure is proposed to be measured using such parameters as demand for digital agribusiness products, willingness to pay a higher price and benefit from it (for example, remotely monitor the process of its production through a system of blockchains and ubiquitous computing or to verify the authenticity of products in the electronic database of the manufacturer) and the level of

competition in the emerging or existing market for digital agribusiness products. According to the developed method, all parameters are measured in points from 1 to 100. As a result, the sufficiency of the digital economy infrastructure for the agricultural sector of a region is determined by finding the arithmetic mean of all parameters. The results of calculations for the components and infrastructure as a whole are treated separately in order to reveal both general sufficiency of infrastructure and the level of its components’ development. At the same time, it is proposed to be guided by the following scale:

46

S. I. Sharipov et al.

Fig. 2 Digital economy infrastructure measurement results for the AIC in the regions of the North Caucasus Federal District of Russia in 2019. Source calculated and constructed by the authors

– If the value exceeds 75 points, the infrastructure is sufficient for digital modernization of the region’s AIC; – If the value is in the range from 51 to 75 points, there is an insignificant deficit of infrastructure of digital economy for region’s AIC; – If the value is in the range from 25 to 50 points, there is a significant deficit of digital economy infrastructure for agribusinesses in the region; – If the value is below 25, there is an acute deficit of digital economy infrastructure for agribusinesses in the region.

4

The described method was applied on the example of the regions of the North Caucasus Federal District of Russia, resulting in the following results (Fig. 2). As shown in Fig. 2, we have found out that the logistical infrastructure of the digital economy for the AIC (56.76 points) and the marketing infrastructure (53.10 points) are the most developed in the regions of the North Caucasus Federal District of Russia in 2019. Nevertheless, even for these infrastructure components there is a slight deficit. In terms of technological (43.2 points) and financial (34.62 points) infrastructures, there is a significant deficit, while in terms of human resources (22.17 points) and regulatory infrastructure (16.64 points) there is an acute deficit. The overall infrastructure sufficiency is defined as follows: (56.76 + 43.21 + 22.17 + 34.62 + 16.64 + 53.10)/ 6 = 37.75 (significant deficit). It is noteworthy that according to the rating of regions on the level of digitalization “Digital Russia”, the level of digitalization of the economy (with emphasis on infrastructure) of the North Caucasus

So, the results of the conducted research showed that the region’s AIC needs special logistical (including telecommunication), technological, human resources, financial, regulatory, and marketing infrastructure. To measure the sufficiency of individual components and infrastructure as a whole, a set of parameters has been proposed. Based on this, the author’s method of measurement of digital economy infrastructure for the AIC of the region has been developed. The results of testing of this method on the example of the regions of the North Caucasus Federal District of Russia in 2019 showed that the deficit of digital infrastructure for the AIC of regions is more acute than the deficit of the general infrastructure of the digital economy. The largest deficit is observed in the regulatory and human resources infrastructure of the digital economy for the AIC. Therefore, it is recommended to pay special attention to the development of these components in order to create more favorable conditions for the digital modernization of the agro-industrial complex of the regions of the Caucasus Federal District of Russia.

Federal District is 43.44 points (D-Russia, 2019). This underscores the specificity of the digital modernization of the AIC and the need for special infrastructure for it. In the regions under consideration, it is recommended to focus on training digital personnel for the AIC and for the launch of regional certification systems for digital agriculture products in the Russian Federation.

Conclusion

Infrastructure of the Digital Economy for Region’s AIC …

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Aubry, S. (2019). The future of digital sequence information for plant genetic resources for food and agriculture. Frontiers in Plant Science, 10, 1046. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10), 564–568, 78. D-Russia. (2019). Rating of regions by the level of digital development “Digital Russia”. Retrieved October 11, 2019, from http://d-russia. ru/vyshla-polnaya-versiya-rejtinga-regionov-po-urovnyu-razvitiyatsifrovizatsii-tsifrovaya-rossiya.html. Federal State Statistics Service (Rosstat). (2019). Regions of Russia: socio-economic indicators—2018. Retrieved October 11, 2019, from https://www.gks.ru/bgd/regl/b18_14p/Main.htm. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 581– 586, 82. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44– 50. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Phillips, P. W. B., Relf-Eckstein, J.-A., Jobe, G., & Wixted, B. (2019). Configuring the new digital landscape in western Canadian agriculture. NJAS - Wageningen Journal of Life Sciences, 20(1), 34–46.

47 Popkova, E.G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and Irina A. Kuznetsova, PhD in Engineering, Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components”). Theoretical and Practical Issues of Journalism, 7(1), 145–154. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will industry 4.0 and other innovations impact Russia’s development? In B. S. Sergi (Ed.), Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety vs. Rationality. Springer. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In Bruno S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sharma, R., Sharma, R., & Sharma, A. (2019). IOT based smart agriculture in digital India. Journal of Advanced Research in Dynamical and Control Systems, 11(5 Special Issue), 2052–2056.

Perspectives of Development Of the System of E-Government in the Region in the Conditions of the Digital Economy Yahya G. Buchaev

balance of readiness, demand, and level of implementation of e-government; in the regions of the North Caucasus Federal District, it is recommended to increase the level of implementation of this system. Thirdly, ensuring security (achieved in the regions of the North Caucasus Federal District of the Russian Federation), reliability, completeness, and simplification of e-government system in the region. Finally, priority electronic conversion of the most demanded components of public administration (in the regions of the North Caucasus Federal District of the Russian Federation these are state and budget services).

Abstract

Purpose: The purpose of the paper is to identify common (in the regions of Russia as a whole on the basis of improving the algorithm used) and private (in the regions of the North Caucasus Federal District on the basis of its current features) prospects for the development of e-government in the digital economy of Russia. Design/methodology/approach: With the help of the method of expert assessments, which determined the ratio of readiness, demand and the level of implementation of e-government in the regions of the North Caucasus Federal District of the Russian Federation in 2019, characteristics and promising directions for the development of e-government in the regions under study were identified. Findings: The level of development of the e-government system in the regions of the North Caucasus Federal District has been determined. The improved algorithm of electronic government system development in the region in conditions of digital economy is offered. The ratio of readiness, demand, and level of introduction of electronic government system, and also characteristics and perspective directions of development of system of electronic government in regions of the North Caucasian federal district of the Russian Federation in 2019 is defined. Originality/value: As a result of the study on the example of the regions of the North Caucasus Federal District of the Russian Federation in 2019, it is shown that the prospects for the development of e-government in the regions of Russia in the digital economy are associated with the following actions. Firstly, improvement of the algorithm of e-government system development in the region in the digital economy is possible by changing the order of its stages and including a new stage—determining the specifics of the region. Secondly, ensuring a Y. G. Buchaev (&) Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected]

Keywords








E-government Public administration Modernization Regional economy Digital economy




JEL Codes

O18

1




O31

R11

R58

Introduction

In the digital economy, advanced technologies are being introduced not only into the activities of households and business structures, but also into the activities of public authorities. When introducing and developing an e-government system, an extra advantage is achieved: in addition to increasing the efficiency of public administration, there is a marketing effect associated with increasing public and business loyalty to digital technologies. This means that the state sets an example and provides an opportunity for testing, accumulation of experience in the use of digital technologies, which increases the attractiveness of their implementation for economic entities.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_9

49

50

Although the overall strategy for the introduction and development of e-government is determined at the national (federal) level, it is implemented at the regional level, and therefore the results are different in the regions. This points to the need to study the prospects for the development of e-government in the region in the digital economy from the perspective of the regional economy. Russia is characterized by a high degree of regionalization of the national economy (regions are highly diverse), which makes it one of the most preferable objects for such studies. In the practice of regional administration of modern Russia, the following algorithm of introduction and development of e-government system is applied. At the first stage, a national plan for the introduction of digital technologies into the activities of public authorities is adopted. Although the general socioeconomic situation, trends, and level of digitalization of the regional economy are taken into account, the plan is not adapted to the specifics of each individual region. Regional strategies for implementation and development of e-government system are fully based on the national plan—they are adapted to some extent to the specifics of each individual region, but not fully. At the second stage, financial resources are allocated from the federal budget, their volume varies among regions depending on the priority of their digitalization. Financing of measures to introduce and develop e-government systems is determined by the capabilities of the federal budget and is often either surplus or deficit, which significantly complicates the practical implementation of adopted strategies. At the third stage, strategies are implemented, which is often hampered by unpreparedness of the social and business environment in the region. Thus, the availability of public services when they are converted into electronic form, instead of the expected increase, may decrease, because services are no longer provided in the traditional form, and not all economic entities have the necessary equipment (computer hardware) and technology (access to the Internet) to obtain electronic services. Besides, there is often a social and business protest against changes that do not harmonize with the economic system of a region due to psychological unpreparedness of business entities for changes or their lack of special skills (digital competences). The fourth stage includes monitoring and control of the results of implementation and development of e-government system and identification of actual problems. To solve them, social and business adaptation to the changes that have already taken place is made. According to international estimates, the level of development of e-government in Russia is 0.7969 (maximum 1), which is lower than in other countries with a digital economy, for example, in the U.S.

Y. G. Buchaev

(0.8769) or the UK (0.8999), as evidenced by the report “The Division for Public Administration and Development Management”, The United Nations, (2019). In terms of the level of development of the e-government system, Russia was ranked 30th out of 63 countries in the global digital competitiveness rating of IMD for 2018. (2019). The low level of development of the e-government system reduces the global competitiveness of the Russian digital economy, slows down its growth, and reduces the overall efficiency of the Russian economic system, and therefore represents an urgent scientific and practical problem. According to the author of this study (hypothesis), the algorithm used in modern Russia is incomplete and suggests the wrong order of stages, which prevents digital modernization of public administration in the regions. The aim of the work is to determine general (in the regions of Russia as a whole on the basis of improving the algorithm used) and private (in the regions of the North Caucasus Federal District on the basis of its current features) prospects for the development of e-government system in the digital economy of Russia.

2

Materials and Method

The benefits and principles of e-government in the region in the digital economy are set out in the works of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al (2018), Popkova (2019), Popkova et al. (2018, 2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019). The content analysis of the cited sources of research literature on the selected topic showed that they covered in sufficient detail the mechanism of operation of e-government in the region in the digital economy. However, insufficient attention has been paid to the process of implementation of the system and its subsequent development. This leads to the fact that modern regions, which have formed a digital economy, show high interest in e-government system, but can not implement plans for its development in practice due to insufficient scientific and methodological support. This research will allow to overcome the gap of scientific knowledge on the subject of e-government system development in the region in conditions of digital economy. According to the rating of Russian regions by the level of implementation of e-government, the regions of the North Caucasus Federal District show the following results (Fig. 1). As can be seen from Fig. 1, the highest level of e-government development is observed in the Kabardino-Balkar Republic (81 points out of 100 possible,

Perspectives of Development Of the System of E-Government …

51

Fig. 1 Development level of e-government in the North Caucasus Federal District regions. Source compiled and built by the author on the basis of materials of State Management (2019)

9th place among 85 regions of Russia), and the lowest—in the Chechen Republic, Karachay-Cherkessia, and Ingushetia (12 points each, 80th place). This shows that the North Caucasus Federal District regions are highly differentiated by the level of development of the e-government system. Conducting research on the basis of these regions allows us to obtain representative results, which can be extended to the regional economy of Russia as a whole. At the same time, the prospects for the development of an e-government system in Russia’s regions in the digital economy remain unclear from the rating materials given above. The available statistical data make it possible to assess the level of development of the e-government system, but do not provide an indication of its “weak points” and opportunities for its improvement. To identify them, the author used the method of expert assessments, which determined the ratio of readiness, demand, and the level of implementation of e-government in the regions of the North Caucasus Federal District of the Russian Federation in 2019, identified characteristics and promising directions for the development of e-government in the regions under study. Fig. 2 Algorithm of e-government development in the region in the digital economy environment. Source designed and compiled by the author

3

Results

The improved algorithm of e-government system development in the region in conditions of digital economy is presented in Fig. 2. As shown in Fig. 2, the proposed algorithm not only changes the order of the stages of the algorithm operating in Russia, but also adds a new stage related to the definition of the specifics of the region. An example of practical implementation of this stage by the example of regions of the North Caucasus Federal District of the Russian Federation in 2019 is shown in Figs. 3 and 4. As shown in Fig. 3, social and business readiness (availability of equipment, technologies, and skills of the population and business) for the development of e-government in the regions of the North Caucasus Federal District of the Russian Federation in 2019 is quite high (64.39 points). The demand for this system is very high (82.51 points), but the level of its implementation is low (38.62 points). This indicates the imbalance of the

Definition of the region’s specifics Stage 1

Regional plan development Stage 2

Definition of the financing procedure Stage 3

Repeated cycle Monitoring and control of the results Stage 6

Implementation of the adopted plan in the region Stage 5

Preparation of the society and business Stage 4

52

Y. G. Buchaev

Fig. 3 Ratio of readiness, demand, and level of implementation of e-government system in the regions of the North Caucasus Federal District of the Russian Federation in 2019, points. Source calculated and constructed by the author

Fig. 4 Characteristics and prospects of development of e-government system in the regions of the North Caucasus Federal District of the Russian Federation in 2019. Source calculated and constructed by the author

e-government system in the regions of the North Caucasus Federal District of the Russian Federation in 2019, which should be overcome—for this purpose the level of implementation should be increased to 4.39 points. According to Fig. 4, the e-government system in the regions of the North Caucasus Federal District of the Russian Federation in 2019 is characterized by a high level of security (74.98 points), but a low level of reliability (32.14 points), completeness (25.43 points), and high complexity (10.87 points on the criterion of simplicity). The promising areas of development of this system are ranked in order of priority for stakeholders as follows: public services (28%), budget services (25%), collection of feedback (17%), involvement in management (13%), reception of appeals (10%), and government reporting (7%).

4

Conclusion

As a result of the study on the example of the regions of the North Caucasus Federal District of the Russian Federation in 2019, it is shown that the prospects for the development of e-government in the regions of Russia in the digital economy are associated with the following: – Improvement of the algorithm of e-government system development in the region in the digital economy by changing the order of its stages and including a new stage —determining the specifics of the region; – Ensuring a balance of readiness, demand, and level of implementation of e-government in the regions of the

Perspectives of Development Of the System of E-Government …

North Caucasus Federal District is recommended to increase the level of implementation of this system; – Ensuring security (achieved in the regions of the North Caucasus Federal District of the Russian Federation), reliability, completeness, and simplification of e-government system in the region; – Priority conversion into electronic form of the most popular components of public administration (in the regions of the North Caucasus Federal District of the Russian Federation are public and budgetary services).

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10), 564–568, 78. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 581– 586, 82. Gos-management. (2019). Ratings of Russian regions on development of information technologies: Level of implementation of e-government. Retrieved October 11, 2019, from http://www. tadviser.ru/index.php. IMD. (2019). World digital competitiveness ranking 2018. Retrieved October 11, 2019, from https://www.imd.org/wcc/worldcompetitiveness-center-rankings/world-digital-competitivenessrankings-2018/. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44–50.

53 Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and Irina A. Kuznetsova, PhD in Engineering, Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components”). Theoretical and Practical Issues of Journalism, 7(1), 145–154. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will industry 4.0 and other innovations impact Russia’s development? In B. S. Sergi (Ed.), Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety vs. Rationality. Springer. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In Bruno S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. The Division for Public Administration and Development Management, The United Nations. (2019). The UN Global E-Government development Index. Retrieved Ocotber 11, 2019, from https:// publicadministration.un.org/egovkb/en-us/Reports/UN-E-GovernmentSurvey-2018.

Digital Society in a Modern Region: Issues of Its Formation and Ways of Solving Them on the Basis of the Labour Market Management in the Agrarian Sector Mikhail A. Babeshin, Alexey E. Nikolaev, and Viktor A. Splender

modern regions, which was proved by the experience of the regions of the North Caucasus Federal District of the Russian Federation in 2019. The actual problems of formation of digital society in the labour market in the agricultural sector of the regions under consideration are connected with low level of digital competition, low demand for digital personnel and low availability of digital technologies. Originality/value: The specificity of agrarian sector of regional economy has been substantiated. Certain recommendations were suggested for state and corporate management of the development of digital society in the labour market in the agricultural sector in the regions of the North Caucasus Federal District of the Russian Federation in 2019 to ensure food security.

Abstract

Purpose: The purpose of the paper is to determine the problems of formation of digital society in the modern region and ways of their solution on the basis of labour market management in the agricultural sector on the example of the North Caucasus Federal District of the Russian Federation. Design/methodology/approach: The authors made an expert assessment aimed at identifying these indicators of the development of digital society in the agricultural sector of the regions of the North Caucasus Federal District of the Russian Federation in 2019. Using the method of regression analysis, a linear dependence of the level of informatization (digitalization) of society in the regions of the North Caucasus Federal District of the Russian Federation in 2019 on the values of selected indicators was determined. Scenario modelling of simplex method was carried out on the basis of the regression model, with the help of which target values of indicators were defined to maximize the level of informatization (digitalization) of the labour market in agrarian sector in the regions under consideration. Findings: It has been revealed that the labour market in the agricultural sector makes a significant contribution to the formation and development of digital society in

M. A. Babeshin (&) Department of Economic Theories and Military Economics of the Military University of the Ministry of Defense of the Russian Federation, Moscow, Russia e-mail: [email protected] A. E. Nikolaev Department of the Cherepovets Higher Military Engineering School of Radio Electronic, Moscow, Russia e-mail: [email protected] V. A. Splender Department of Finance and Banking Activities, Military University of the Ministry of Defense of the Russian Federation, Moscow, Russia e-mail: [email protected]

Keywords

















Digital (information) society Modern region Problems Management Labour market Agricultural sector Russian regions JEL Codes

G34

1





O18

O31

R11

R58

Introduction

Digital economy is formed and developed in the social environment of the region, which determines the context and, consequently, the features and regularities of this process. Preparation of a special social environment precedes the transition to digital economy, as this transition is possible only in a specific digital environment—susceptible and loyal to new (including breakthrough) digital technologies, capable and interested in their development and practical application.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_10

55

56

It is for a reason that the state program “Information Society” was adopted by the Russian Government Decree №1815-r dated October 20, 2010 (Ministry of Digital Development, Communications and Mass Media of the Russian Federation 2019a), and the program “Digital Economy of the Russian Federation” was adopted only 7 years later—in 2017. Thanks to this, the necessary preparations were made to create a favourable social environment—an information (outdated name, its more relevant version—digital) society for an accelerated and smooth transition to the digital economy. A digital society performs a number of important and irreplaceable functions in building a digital economy. The first function is to institutionalize the practices of digital technologies. It is in the social environment that digital technologies gain practical application and become widespread. With their elitism, digital technologies may be highly valued in society, but the actual demand for them may remain low due to perceived high levels of risk (high complexity of their practical application, potential danger to the user, etc.). The social environment provides a clear understanding of the need for digital technologies and the rules for their use. The second function is to provide social support to the digital economy (creating a “synergy effect”). Implementation of state plans and directives in an unprepared social environment may face public protests, but even in the absence of expressed opportunism, a neutral social environment may also hinder their practical implementation. Social support makes it possible to accelerate the implementation of state programs (in particular, the program “Digital Economy of the Russian Federation”) and maximize their results, that is, to create a “synergetic effect” of digitalization of the economy. The third function is the formation and supply of social infrastructure for the digital economy markets. Social infrastructure refers to consumers of digital technologies, goods and services, as well as digital personnel, that is, consumers and workers with digital competences. Depending on the function in question, a digital society can be studied and managed in a context on the basis of households, goods and services markets and labour market. In the first two cases, mainly the consumers of digital technologies, goods and services that are not present on the labour market are taken into account, that is, the part of the population (economically passive population), that is the least flexible and manageable and knowingly opportunistic against digital transformations of the region’s social and economic system. Therefore, the greatest scientific and practical interest in the study of the digital society consists in workers in the labour market (economically active population). This article

M. A. Babeshin et al.

puts forward a hypothesis that in each sector of the economy there are specific needs in the digital society, problems of its formation and management in the interests of solving these problems. The aim of the work is to identify the problems of formation of digital society in the modern region and ways to solve them on the basis of labour market management in the agricultural sector on the example of the regions of the North Caucasus Federal District of the Russian Federation.

2

Materials and Methods

By now, a fairly strong scientific and theoretical basis for studying the digital society has been formed, in the structure of which two conceptual approaches can be distinguished. The first approach applies a narrow interpretation of the digital society as a population and workers in the labour market of the region. This approach is presented in the publications of Petrenko et al. (2018), Popkova (2019), Popkova et al. (2018, 2019), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019) and Sergi (2019). According to the second—more widespread—approach, the digital society is widely interpreted not only as the population and workers on the labour market of the region, but also the conditions and factors of their formation. This approach is presented in the works of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018) In the context of this study, we rely on the second approach, as the narrow interpretation of the first approach suggests a focus on the result, but does not allow us to identify causal links in the development of the digital society in the region. It is only the broad interpretation proposed by the second approach that allows us to establish causal links and systematically examine the digital society, thereby identifying the actual problems of its formation and the prospects for their solution. In accordance with our broad interpretation, we have defined a set of indicators of the development of digital society in the region. It includes the following structural components: – Conditions for the formation of a digital society in the region, determined by the policy of the state (regional public authorities): availability of digital technologies, educational services and digital infrastructure; – Labour market demand in the region as a key factor in the development of a digital society: demand for digital technologies, digital workforce, the level of digital competition among businesses;

Digital Society in a Modern Region: Issues of Its Formation …

57

– Supply to the labour market in the region by the societies: possession of digital competences, flexibility and propensity for professional development, active use of digital devices and technologies.

According to Table 2, in order to optimize the digital society on the labour market in the agricultural sector in the regions of the North Caucasian Federal District in 2019, it is necessary to achieve the following target values:

This study uses the results of the authors’ expert assessments aimed at identifying these indicators of the development of digital society in the agricultural sector in the regions of the North Caucasus Federal District of the Russian Federation in 2019. Using the method of regression analysis, a linear dependence of the level of informatization (digitalization) of society in the regions of the North Caucasus Federal District of the Russian Federation in 2019 on the values of selected indicators was determined. Scenario modelling of simplex method was carried out on the basis of compiled regression model, with the help of which target values of indicators were determined to maximize the level of informatization (digitization) of the labour market in the agricultural sector in the regions under consideration.

– demand for digital technologies (x4) increased by 1.18% to 20.75 points; – demand for digital personnel (x5) increased by 0.59% to 47.78 points; – digital competition (x6) reduced by 11.75% to 54.73 points; – in digital competence possession (x7) reduced by 12.03% to 54.73 points; – flexibility and propensity to further training (x8) increased by 28.70% to 86.69 points; – digital devices and technologies application (x9) increased by 21.78% to 87.43 points.

4 3

Conclusion

Results

The results of expert assessments and collected statistical data on the agricultural labour market are systematized in Table 1. The main (averaged) results of expert assessments are graphically illustrated in Fig. 1. As can be seen from Fig. 1, in general, the regions of the North Caucasus Federal District of the Russian Federation in 2019 are characterized by high availability and quality of educational services (85.6 points), availability and quality of digital infrastructure (72.86 points) and high activity of digital devices and technologies (71.79 points). Flexibility and propensity for advanced training (67.36 points), digital competences (67.22 points) and availability of technology (50.9 points) are moderate. Digital competition (37.24 points), demand for digital personnel (47.49 points) and digital technology (20.51 points) proved to be low. As a result, the overall level of informatization in society is moderate (68.14 points). Based on the data from Table 1, we have compiled the following multiple linear regression model: y = 188.977 + 0 * x1 + 0 * x2 + 0 * x3 + 0.2459 * x4 − 0.0525 * x5 + 1.3388 * x6 + 0.8203 * x7 − 1.8078 * x8 − 1.2873 * x9. With the help of simplex method, we have defined target values of independent variables, which it should take to achieve the optimal value (1st place) of the dependent variable (y = 1)—Table 2.

Thus, it was discovered that the labour market in the agricultural sector makes a significant contribution to the formation and development of digital society in modern regions, which was proved by the experience of the regions of the North Caucasus Federal District of the Russian Federation in 2019. It was found out that the actual problems of formation of digital society on the labour market in the agrarian sector of the considered regions are connected with low level of digital competition, low demand for digital personnel and low availability of digital technologies. In contrast to the existing belief that all the selected factors have a significant direct impact on the development of digital society in the region, it was found that such factors as technology availability, accessibility and quality of educational services and digital infrastructure (factors on the part of the state) have a neutral (zero) impact in 2019 on the labour market in the agricultural sector in the regions of the North Caucasus Federal District of the Russian Federation, while factors of digital competition and level of digital competences have a negative (zero) impact. This confirmed the hypothesis that each sector of the regional economy is specific. Instead of increasing, it is necessary to reduce digital competition and ownership of digital competences on the labour market in the agricultural sector in the regions of the North Caucasus Federal District of the Russian Federation in 2019. There is also a need to increase demand for digital technologies and for digital

66.29 69.89

71.34

48.90 51.68

83.06

Digital competences (x7)

Flexibility and a propensity for further training (x8)

Activity of digital device and technology application (x9)

72.95

65.23

47.04

28.27

52.39

31.86

65.31

89.34

54.88

43

The Kabardino-Balkar Republic

74.51

59.31

67.59

30.21

41.49

10.39

67.46

78.45

58.15

77

The Karachay-Cherkess Republic

80.88

72.27

73.81

48.52

34.13

24.11

78.42

86.72

45.82

72

The Republic of North Ossetia-Alania

58.30

79.03

73.92

45.85

48.42

34.73

81.37

82.12

55.22

82

The Chechen Republic

61.51

74.14

58.02

26.09

48.60

15.21

76.48

87.15

47.71

52

Stavropol Krai

71.79

67.36

62.22

37.24

47.49

20.51

72.86

85.60

50.39

68.14

Arithmetic mean

Source Compiled and calculated by the authors on the basis of materials from the Ministry of Digital Development, Communications and Mass Media of the Russian Federation (2019a)

On the part of the society (supply), points 1–100

42.01

39.73

Digital competitiveness (x6)

54.83

52.60

Demand for digital personnel (x5)

10.33

73.77

67.21

Availability and quality of digital infrastructure (x3) 16.97

82.88

92.52

Availability and quality of educational services (x2)

Demand for digital technology (x4)

43.03

47.90

Availability of technology (x1)

On the part of the state (conditions), points 1–100

On the part of the business(demand), points 1–100

76

75

Society informatization level, place in Russia 1–85 (y)

Result

The Republic of Ingushetia

The Republic of Dagestan

Indicator

Table 1 Indicators of development of digital society on the labour market in the agrarian sector in the regions of the North Caucasus Federal District in 2019

58 M. A. Babeshin et al.

Digital Society in a Modern Region: Issues of Its Formation …

59

Fig. 1 Average indicators of society development on the labour market in the agricultural sector in the regions of the North Caucasus Federal District in 2019. Source Compiled and constructed by the authors

Table 2 Results of scenario modelling of digital society optimization on the labour market in the agricultural sector in the regions of the North Caucasus Federal District of the Russian Federation in 2019

Indicator

Target value

Initial value in 2019

Increase in target value relative to initial one, %

x1

50.39

50.39

0

x2

85.6

85.6

0

x3

72.86

72.86

0

x4

20.7538

20.51

1.18854

x5

47.7691

47.49

0.58762

x6

32.864

37.24

−11.751

x7

54.7351

62.22

−12.03

x8

86.693

67.36

28.701

x9

87.4265

71.79

21.7809

Source Compiled and constructed by the authors

personnel, as well as to increase the flexibility and propensity for advanced training and activity in the use of digital devices and technologies. This will accelerate the formation of a digital society and encourage digital modernization of the agricultural labour market, thus ensuring food security.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10), 78, pp. 564–568. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 82, 581–586.

Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44– 50. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the

60 article by Alexander P. Sukhodolov, DSc. In Economics, Professor and I. A. Kuznetsova, PhD in Engineering, Associate Professor Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components). Theoretical and Practical Issues of Journalism, 7(1), 145–154. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will Industry 4.0 and Other Innovations Impact Russia’s Development? In B. S. Sergi (Ed.) Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety vs. rationality. Springer International Publishing. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of

M. A. Babeshin et al. knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In B. S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Ministry of Digital Development, Communications and Mass Communications of the Russian Federation. (2019a). State Program Information Society. Retrieved October 12, 2019, from https:// digital.gov.ru/ru/activity/programs/1/. Ministry of Digital Development, Communications and Mass Communications of the Russian Federation. (2019b). Rating of informatization of Russian regions. Retrieved October 15, 2019, from http://d-russia.ru/minkomsvyaz-predstavila-rejtinginformatizatsii-regionov-2017.html.

Evolution of the System of Higher Education and Problems of Development of Science in the Conditions of Digitization Yahya G. Buchaev and Zalina M. Abdullaeva

Abstract

Keywords

Purpose: The purpose of the study is to investigate the process of evolution of higher education and science in the conditions of digitalization, to identify the problems arising in this process and to determine the prospects for their solution. Design/methodology/approach: The authors rely on the methodology of institutional economic theory, which makes it possible to consider the process of institutionalization of evolutionary (modernization) practices implemented in the higher education system under conditions of digitization. The authors are also guided by a systematic approach to scientific research. According to this approach, the authors consider the system of higher education and science as a component element of the economic system of the region and as a link in the value chain of digital product. The research is based on the modern experience of the regions of the North Caucasus Federal District of the Russian Federation (2019). Findings: It is shown that in modern Russia the process of evolution of higher education system and science in the conditions of digitalization faces serious external barriers —“institutional traps”. These are caused by the directive course of the digitalization of the Russian economy, which was initiated by the state and was poorly supported by entrepreneurship and society. Originality/value: It has been substantiated that the prospects for overcoming the “institutional traps” of the evolution of higher education and science in the conditions of digitalization in Russia are connected with the harmonization of the evolution of higher education and science with the development of the labour market in the region and regional entrepreneurship.

Higher education Science Modernization Evolution Russian regions

Y. G. Buchaev (&)
Z. M. Abdullaeva Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected] Z. M. Abdullaeva e-mail: [email protected]









Development Digitalization





Region




JEL Codes

G34 R11

1










I25 I26 R58

O18

O31

O32

O33

O38

Introduction

The higher education and science system plays a key role in building the regional digital economy, providing the environment for the creation and delivery of digital human resources and technologies for the region that best meet its current needs and specificities. However, the evolution of this system under the influence of digitalization is contradictory. On the one hand, the system of higher education and science is one of the most flexible systems of modern economy, and therefore, despite the inevitable internal difficulties (e.g., the need to attract additional funding and forced reorganization), there is no doubt that this system is able to successfully evolve in the given target direction and fully perform its functions on infrastructure support of the emerging and developing digital economy. On the other hand, the higher education and science system may face serious external challenges in its evolution leading to a decline in its effectiveness. The basis for this hypothesis is the pronounced imbalance of the digital economy. For example, in the global rating of digital competitiveness IMD (2019), Russia in 2018 ranked 38th in terms of readiness of the state (the regulatory framework indicator), 39th in terms of readiness of society (the adaptive attitudes indicator), 58th in terms of readiness of investors (the capital indicator) and 62nd in terms of readiness of

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_11

61

62

Y. G. Buchaev and Z. M. Abdullaeva

business (the business agility indicator) to develop a digital economy, while in terms of readiness of the higher education and science system Russia ranked 12th (the training and education indicator) and 23rd (the scientific concentration indicator). In this regard, the scientific study of the process of evolution of higher education and science in the conditions of digitalization, the identification of the problems arising in this process and the definition of prospects for their solution becomes relevant, which determined the purpose of this study.

2

Materials and Method

The general conditions in which the higher education system and science are evolving under the influence of digitalization, as well as the importance of this process for the formation and development of the digital economy is emphasized in the works of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al. (2018), Popkova (2019), Popkova et al. (2018, 2019), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019) and Sergi (2019). The essence and peculiarities of the evolution of higher education and science in the conditions of digitization are reflected in the publications of Aguilar and Turmo (2019), Gero et al. (2019), Paganotti et al. (2019), Romeike (2019) and Syaiful et al. (2019). Nevertheless, the problems arising from the evolution of higher education and science in a digitalized world and the prospects for their solution have not been sufficiently explored, requiring further research. In our work we rely on the methodology of institutional economic theory, which allows us to consider the process of institutionalization of evolutionary (modernization) practices implemented in the higher education system in the conditions of digitization. We are also guided by a systematic approach to scientific research. According to it, we view the system of higher education and science as a component of the economic system of the region and as a link in the value-added chain of the digital product. On the basis of this, we identify “institutional traps”—economic practices that have lost their relevance, which conflict and hinder the implementation and consolidation of new (modernization) practices in higher education and science. The study is based on the current experience of the regions of the North Caucasus Federal District of the Russian Federation (2019).

3

Results

As a result of studying the modern experience of the regions of the North Caucasus Federal District of the Russian Federation (2019), we have identified two “institutional traps” that inhibit the evolution and reduce the effectiveness of higher education system and science under conditions of digitalization—they are illustrated in Figs. 1 and 2. As it is evident from Fig. 1, the “institutional trap” of evolution of higher education system in conditions of digitization is a contradiction of interests of the state (and society), regional higher education institution (HEI) and regional business. State management of higher education system in Russian regions is carried out at federal level. This is due to the fact that key higher education institutions have a legal form of organization “FGBOU HIGH SCHOOL OF THE RF Ministry of Education and Science”—federal state budgetary educational institution of higher education of the Ministry of Science and Higher Education of the Russian Federation. Guided by national interests (in Russia it is the Digital Economy of the Russian Federation programme)—the strategy of digital modernization—the government adopts standards and norms and sets requirements for training digital personnel for the region. Compliance with these requirements is mandatory for regional higher education institutions, as it determines the possibility of renewing their licenses and their ability to apply for state funding. The state order for training of digital personnel for a region is also placed. The university, in its turn, develops new teaching materials and educational programmes, provides advanced training of teachers and purchases technologies and equipment. However, regional entrepreneurship (mainly private) is in a situation of limited (weak) competition and therefore has no incentives to use digital technologies. Providing internship programmes for students in order to acquire applied digital competences, the higher education institution faces the problem of lack of existing practice bases in the region, which makes it necessary for the university to independently develop these competences among students. As a result, prepared digital personnel are released to the labour market in the region. The digital workforce places higher demands on remuneration and career development, which reduces its competitiveness on the labour market in the region. Regional business places a major demand on the digital workforce. At the same time, digital workers are forced either to retrain or become unemployed or to emigrate from the region. Consequently, the human potential in the

Evolution of the System of Higher Education and Problems … Fig. 1 The “institutional trap” of evolution of the higher education system in the conditions of digitization. Source Designed and compiled by the authors

63

State (federal government): digital modernization strategy. standards, norms, digital training requirements for the region

public contract for digital training for the region

Regional institution of higher education (HEI): − development of new educational and methodical materials and educational programs; − advanced training of teachers; − technology and equipment purchase.

placement of students in internships to master applied digital skills Regional business:

- limited (weak) digital competition;

- low digital digital personnel production

«institutional trap»

competitiveness. demand

Regional labour market unemployment

Digital personnel: − higher salary and career requirements.

Pre-digital personnel

retraining

labour migration from the region

External (national or international) labour market

Fig. 2 Source Designed and compiled by the authors

State (federal government): digital modernization strategy. standards, norms, requirements for the development and commercialization of digital technologies

External sales (exports)

«institutional trap»

Consumers in the region

difficult distribution

R&D and digital development grants low demand for digital products due to increased innovative digital cost and complexity of Regional research institutes (in technology use particular, universities): − conducting R&D in new Regional business: directions; - limited (weak) digital − advanced training of researchers; competition; − technology and equipment - low digital purchase. competitiveness. outdated digital technology

External (national or international) technology market

region is decreasing and the digital competitiveness of regional businesses is steadily low. As can be seen from Fig. 2, a similar situation occurs in the system of science. The state (federal government) accepts standards, norms, requirements for the development

and commercialization of digital technologies and allocates grants for R&D and digital technology development. Regional research institutes (in particular, higher education establishments) carry out R&D in new directions, upgrade the skills of researchers and purchase technologies and

64

Y. G. Buchaev and Z. M. Abdullaeva

equipment. They supply regional businesses with brand new digital technologies. However, owing to limited digital competition and low consumer demand for digital products due to their increased cost and complexity of use (underdeveloped digital society), regional businesses are not interested in university innovations and as a rule buy outdated digital technologies from outside. As a result, regional higher education institutions either give up the idea of commercializing the innovations they have developed or sell them externally, including for export. The digital competitiveness of regional businesses remains low. The reasons for both identified “institutional traps” are related; first, to the presence of protectionism elements in the practice of state regulation of entrepreneurship. Restrictions on foreign competition (customs duties, quotas and other barriers to entry into Russian markets) result in a low level of digital competition. Market incentives for the digital modernization of Russian business are not created, and state incentives have little impact on private business. Insufficient experience in the consumption of digital products hinders the development of a digital society. Secondly, the reason also lies in the great distance between the state and universities—in the preservation of the federal management practice of regional universities. The federal government does not fully take into account the specifics of the digitalization of each region’s economy, which makes the management of higher education and science systems unsuitable to the context. Unlike consumers and private regional businesses, higher education institutions are highly exposed to state incentives for digitalization and therefore have to evolve. However, their evolution is not in demand in the regions, which hinders the commercialization of university innovations, aggravates unemployment in the regions and increases the outflow of technology and digital personnel.

4

Conclusion

So, the hypothesis put forward is confirmed—despite the sufficient systemic integrity and internal consistency of the evolution of higher education and science in the conditions of digitalization, in modern Russia this process faces serious external barriers—“institutional traps”. These are caused by the directive course of the digitalization of the Russian economy, which was launched at the initiative of the state, poorly supported by entrepreneurship and society. In the programme “Digital Economy of the Russian Federation” and federal projects being implemented within the framework of it, close attention is paid to the issues of

evolution of the higher education system and science for training of digital personnel and fundamentally new digital technologies (“formation of technological prerequisites”). However, measures to develop the digital society are much less ambitious, and measures to digitize entrepreneurship are very limited. The “institutional traps” identified are the result of deep contradictions in Russia’s regional economy. The prospects for overcoming the “traps” are linked to the harmonization of the evolution of higher education and science with the development of the labour market in the region and regional entrepreneurship. This requires revision of protectionism measures and implementation of additional measures within the framework of the Russian economy digitalization strategy, which should be aimed at effective state and market (through a competition mechanism) stimulation of digital modernization of entrepreneurship and development of the digital society. It is also expedient to transfer regional higher education institutions to the management of regional public authorities, which take into account the specifics of the possible context and can therefore most effectively manage the evolution of the higher education system and science in a digitalized environment.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu. V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Aguilar, D., & Turmo, M. P. (2019). Promoting social creativity in science education with digital technology to overcome inequalities: A scoping review. Frontiers in Psychology, 10(JULY), 64–74. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10), 78, 564–568. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 82, 581–586. Gero, A., Tsybulsky, D., & Levin, I. (2019). Research and design triads in the digital epoch: Implications for science and technology education. Global Journal of Engineering Education, 21(1), 80–83. IMD. (2019). World digital competitiveness ranking 2018. Accessed October 15, 2019, from https://www.imd.org/wcc/worldcompetitiveness-center-rankings/world-digital-competitivenessrankings-2018/. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44–50. Paganotti, A., Voelzke, M. R., de Araújo, C. F., & Paladino, L. (2019). The use of digital technologies as a teaching resource for science

Evolution of the System of Higher Education and Problems … learning for students of the last year of fundamental education of public schools in Brazil. Journal of Science Education, 20(1), 36–49. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and I. A. Kuznetsova, PhD in Engineering, Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components”). Theoretical and Practical Issues of Journalism, 7(1), 145–154. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946.

65 Popkova, E. G., & Sergi, B. S. (2018). Will Industry 4.0 and Other Innovations Impact Russia’s Development? In B. S. Sergi (Ed.) Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E. G., Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety vs. rationality. Springer International Publishing. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Romeike, R. (2019). The role of computer science education for understanding and shaping the digital society. IFIP Advances in Information and Communication Technology, 564, 167–176. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In B. S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Syaiful, S., Mukminin, A., Habibi, A., (…), Astrid, A., Tersta, F. W. (2019). Learning in the digital era: Science education students’ perception on the SNSs use in the context of English for specific course. Elementary Education Online, 18(3), 1069–1080

Organization of Production on the Basis of the Internet of Things: Barriers, Advantages and Risk Andrei M. Kushnir, Taisia V. Dianova, and Olga Y. Osipenkova

with increased environmental efficiency of production by increasing the availability and quality of products, and the risk is associated with the energy crisis due to multiple increases in energy consumption by businesses. A comparative analysis of three available options for organizing production on the basis of the Internet of things has been carried out: partial automation of production, full automation of individual production processes and full automation of production on the example of modern Russia. Originality/value: A systematic view of production on the basis of the Internet of things has shown that in the current organization it is characterized by low efficiency, and therefore, it is inexpedient in all available options. This problem can be solved by the author’s recommendations to improve the organization of production based on the Internet of things for all available scenarios. The proposed recommendations reduce risks and increase the likelihood of benefiting from them, thus increasing efficiency.

Abstract

Purpose: The purpose of the study is to systematically review the advantages and risks of production organization on the basis of the Internet of things, as well as related barriers, and determine the feasibility of transition to this form of organization of production taking into account the interests of all stakeholders. Design/ methodology/approach: The authors operate with the fundamental principles of the systemic approach (theoretical base of research) and take into account the positions of all interested parties. The game approach serves as the methodological basis of the research. The authors use the methodology of game theory to compare the advantages, risks and risks (probability of application) of the three available options for organizing production on the basis of the Internet of things. Findings: It was found that the advantages of production organization based on the Internet of things for the enterprise of Industry 4.0 are associated with productivity growth, achieving “economies of scale”, reducing the share of faults and risks—with disruptions to machinery (production stoppage) due to insufficient provision of cyber security. Employees benefit from an increase in the intellectual component of work (routine processes are performed by machines), while the risk is associated with unemployment—the need for retraining or professional development. Advantages for consumers are associated A. M. Kushnir (&)
O. Y. Osipenkova Department of Economic Theories and Military Economics of the Military, University of the Ministry of Defense of the Russian Federation, Moscow, Russia e-mail: [email protected] O. Y. Osipenkova e-mail: [email protected] T. V. Dianova Department of Economic Theories, Moscow State Institute of International Relations (University), Ministry of Foreign Affairs of the Russian Federation, Moscow, Russia e-mail: [email protected]

Keywords












Production organization Internet of things Artificial intelligence Automation Industry 4.0 Digital personnel JEL-codes

G34

1





O18

O31

R11

R58

Introduction

Decision-making in the form of production organization is one of the key areas of corporate management. In the conditions of digital economy, technologies of the fourth industrial revolution become available, allowing to organize production on the basis of the Internet of things. This

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_12

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involves the creation of a corporate cyber-physical system— the Internet of things—which uses automation tools to exclude people from business processes based on machine communications. In this form of production organization, the enterprise belongs to Industry 4.0—the most high-tech sector of the economy. The transition process in the described new form of production organization is influenced by three factors. The first factor is scientific and technological progress. The emergence of new opportunities for improving the organization of production causes a logical desire of entrepreneurship to use them. The influence of this factor is strengthened in those regional, social and economic systems in which the receptivity and propensity of the business environment to changes (innovations) is high. The second factor is competition pressure. Even with little interest in breakthrough technologies such as the internet of things, an enterprise may be forced to change the way it organizes its production in the face of strong digital competition. Under the influence of this factor, the organization of production on the basis of the internet of things may become either a competitive advantage of the enterprise, or a condition of its arrival or preservation of its presence in the regional target market. This is due to the technological barriers to market entry in the digital economy. The third factor is state stimulation. Business can listen to national interests as part of corporate social responsibility. However, in most cases, the impact of this factor is expressed in the desire of the enterprise to gain access to state preferences, such as special tax treatment, participation in public–private partnerships, presence in the special economic zone, and so on. In practice, these factors act simultaneously, reinforcing each other, that is, creating a synergetic effect. The influence of the designated factors is favourable to Russia as the country with the formed market economy (which is testified by the country’s presence in global IMD ranking of digital competitiveness formed of 63 countries of the world). This fact creates necessary conditions for transition of the Russian business to the new form of the production organization—on the basis of the Internet of things. The relevance of this topic causes the multiplicity of its scientific research. Existing publications focus on the benefits and risks to the enterprise itself, organizing production on the basis of the Internet of things, and therefore recognize its preference. Our hypothesis is that the existing one-sided approach does not allow to reliably determine the feasibility of transition to the organization of production based on the Internet of things, and therefore requires a systematic view of it from the perspective of all stakeholders. The purpose of the study is to systematically review the advantages and risks of production organization on the basis of the Internet of things, as well as related barriers, and

A. M. Kushnir et al.

determine the feasibility of transition to this form of organization of production taking into account the interests of all stakeholders.

2

Materials and Method

A review of the research literature on the selected topic showed that in some publications, including Petrenko et al. (2018), Popkova (2019), Popkova et al. (2018, 2019), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019) and Sergi (2019), Internet-based organization of production is accepted as preferable, and recommendations are offered to apply it in practice. In other works, such as those by Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), the authors point out deficiencies in the organization of Internet-based production and give a critical view of the process. Nevertheless, in both cases the organization of production on the basis of the Internet of things is considered from the perspective of the enterprise, and the views of other interested parties are practically not taken into account. In our work we operate with the fundamental principles of the system approach (theoretical base of research) and consider the positions of all stakeholders. The game approach is used as a methodological basis for research. We use the methodology of game theory to compare the advantages, risks and risks (probability of application) of the three available options for production organization on the basis of the Internet of things. The first option consists in partial automation of production. In this case, the systematization of digital production equipment of the enterprise is achieved, but the main production functions are kept by people (workers) who have the appropriate qualification (digital personnel). The Internet of things is a network of integrated digital devices with a single centre, which collects data—on the technical condition of the equipment, the results of production, and so on. The technology of big data processing, which provides intellectual support for decision making by the production manager, is also used. For example, the Internet of things helps to identify faulty equipment, remind about the need for maintenance and repair. To date, in Russia, this option has become quite popular. Thus, ERP-, CRM- and SCM-systems are used by 19.2, 13 and 7.1% of Russian enterprises, respectively, according to the report “Digital Economy: 2019” of the National Research University Higher School of Economics (2019). At the same time, compared to other countries, even this option is much less common in Russia—for example, according to the activity of the use of big data processing technologies (the indicator “Use of big data and analytics”)

Organization of Production on the Basis of the Internet …

in 2019, Russia was 58th among 63 countries in the global digital competitiveness rating of IMD for 2018 (IMD 2019). Therefore, let us define the probability of implementation of this option as 1. The second option consists in full automation of individual production processes. This involves the creation of automated workshops or production units (branches) of the enterprise, while the other production processes are carried out by people. A vivid example of this option is a closed robotic conveyor line. In this case, the production process is pre-programmed by a manager (digital staff) and carried out without human involvement. This option is also available for modern Russia, which is among the top 25 countries in terms of robotization of industry. Thus, the number of installed industrial robots per 10,000 workers in the manufacturing industry in Russia is 3 pcs, according to the International Federation of Robotics (2018). Therefore, let us define the probability of implementing this option as 0.8. The third option consists in full automation of production. Here, the complete exclusion of a person from production processes is supposed. Artificial intelligence independently carries out planning and management of production (issues commands to digital devices) and performs quality control by means of machine vision. This variant is hardly represented in modern Russia; at least there are no official data that would testify to its application. Therefore, let us define the probability of implementation of this option as 0.5. The probability of all options has been determined taking into account such barriers as lack of financing, technology and infrastructure.

3

Results

By means of the method of expert estimations we have defined advantages and risks of realization of each separate variant of organization of production on the basis of the Internet of things from the positions of different interested

Table 1 Comparison of the variants of organization of production on the basis of the internet of things from the positions of different stakeholders using game theory methodology

Stakeholders

Comparison criteria

69

parties: enterprises of Industry 4.0, their employees and consumers (society). The results of the application of the game theory methodology to the problem under study are reflected in Table 1. Let us consider the results of the comparison in more detail. Advantages of production organization based on the Internet of things for the enterprise of Industry 4.0 are connected with productivity growth, achievement of “economies of scale”, reduction of a share of faults and risks —with breakdowns in the work of machinery (production stoppage) because of insufficient maintenance of cyber security. The advantage for workers is an increase in the intellectual component of labour (routine processes are performed by machines), and the risk is associated with unemployment—the need for retraining or professional development. Advantages for consumers are associated with increased eco-efficiency of production by increasing the availability of products and improving their quality, and the risk is associated with the energy crisis due to multiple increases in energy consumption by businesses. The game theory methodology involves comparing alternatives by finding their effectiveness through the sum of benefits and the sum of disadvantages. Additionally, a product of efficiency and probability can be found: – For the first option, the efficiency is defined as follows: (4 + 1 + 1)/(1 + 2 + 3) = 6/6 = 1. Multiplying by the probability of realization of the variant in Russia, we obtain: 1 * 1 = 1; – For the second option, the efficiency is defined as follows: (7 + 2 + 2)/(2 + 4 + 5) = 11/11 = 1. Multiplying by the probability of realization of the variant in Russia, we obtain: 1 * 0.8 = 0.8; – For the third variant, the efficiency is defined as follows: (10 + 4 + 3)/(3 + 6 + 7) = 17/16 = 1.06. Multiplying by the probability of realization of the variant in Russia, we obtain: 1.06 * 0.5 = 0.53.

Options for organizing production on the basis of the Internet of things and their characteristics points Partial automation

Full automation of individual production processes

Full automation of all production

Enterprises of Industry 4.0

Advantages

4

7

10

Risks

1

2

3

Staff (digital personnel)

Advantages

1

2

4

Risks

2

4

6

Consumers (society)

Advantages

1

2

3

Risks

Probability, a share from 1

3

5

7

1,0

0.8

0.5

Source Designed and compiled by the authors

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It is noteworthy that from the standpoint of only one interested party—the enterprises of Industry 4.0—the efficiency of all options is very high and makes up: 4/1 = 1 for the first option, 7/2 = 3.2 for the second option and 10/3 = 3.3 for the third option. Taking into account the probability, such efficiency is 1, 2.56 (3.2 * 0.8) and 1.65 (3.3 * 0.5), respectively. Thus, the organization of production on the basis of the Internet of things not only provides significant benefits to all stakeholders, but also entails significant risks for them. Fragmented automation (the first two options) is characterized by zero efficiency—the benefits are tantamount to risks —and therefore inexpedient. Only the third option (system automation) can achieve positive efficiency. However, given the probability of implementation in Russia, this option is the least preferable. In this regard, it is necessary to improve the organization of production based on the Internet of things. Within the first two options (fragmented automation), we have developed the following recommendations to improve the efficiency of production organization based on the Internet of things: – transfer of production to other regions and countries: transfer of risks to external stakeholders (contrary to the idea of corporate responsibility); – risk management: improving cyber security, fighting unemployment, developing alternative energy sources. In the third option (system automation) to improve the efficiency of the organization of production based on the Internet of things, we offer the following recommendations: – improving the availability of financial resources for the Industry 4.0 enterprises: special credit conditions, tax holidays, creation of special investment attractive economic zones; – promotion of R&D to develop missing technologies: grants, public–private partnership; – development of infrastructure of Internet of things: regulatory support, industrial Internet.

4

Conclusion

To sum up, a systematic view of production on the basis of the Internet of things has shown that in the current situation, it is characterized by low efficiency and therefore is not reasonable in all available options. This problem can be solved by the author’s recommendations to improve the organization of production based on the Internet of things for all available scenarios. The proposed recommendations reduce risks and increase the likelihood of benefiting from them, thus increasing efficiency.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu. V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10), 78, 564–568. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 82, 581–586. IMD. (2019). World digital competitiveness ranking 2018. Retrieved October 15, 2019, from https://www.imd.org/wcc/world-competitiveness-center-rankings/world-digital-competitiveness-rankings2018/. International Federation of Robotics. (2018). Level of robotization of industry in the world countries. Retrieved October 15, 2019, from https://rb.ru/story/countries-with-greatest-density-of-robots/. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44–50. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and I. A. Kuznetsova, PhD in Engineering, Associate Professor Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components). Theoretical and Practical Issues of Journalism, 7 (1), 145–154. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will Industry 4.0 and Other Innovations Impact Russia’s Development? In B. S. Sergi (Ed.) Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety vs. rationality. Springer International Publishing Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174.

Organization of Production on the Basis of the Internet … Research University Higher School of Economics (NIU HSE) (2019). Digital economy: 2019. Retrieved October 16, 2019, from https:// www.hse.ru/data/2018/12/26/1143130930/ice2019kr.pdf. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation

71 with the Asia-Pacific region. In B. S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited.

Improving the Practice of Managerial Decision-Making in Modern Entrepreneurship on the Basis of Artificial Intelligence Lyudmila A. Borisova, Ruslan A. Mammaev, and Enara B. Atuyeva

Originality/value: It has been proved that there is a close two-way relationship between state and corporate governance in the digital economy, causing their interdependence and ability to generate synergy effects in the form of accelerated construction and development of the digital economy. This connection is most distinct and strong at the level of the region, which emphasizes the special perspective of studying the regional aspect of digital economy. The analysis of modern experience of business digitalization in the regions of the North Caucasus Federal District of the Russian Federation in 2019 showed that some digital technologies have been introduced into the practice of decision-making, but the system automation of this practice has not yet been achieved—it will be facilitated by the proposed logical scheme.

Abstract

Purpose: The purpose of this work is to justify the prospects for improving the practice of management decision-making in modern business based on artificial intelligence on the example of the regions of the North Caucasus Federal District of the Russian Federation. Design/methodology/approach: In order to determine the context in which the modernization of entrepreneurship in the regions of the North Caucasus Federal District of the Russian Federation is carried out in 2018 and, in particular, the practice of management decision-making, a review and analysis of statistical data on the digitalization of business in the regions under study is conducted. Findings: It has been substantiated that the development of the digital economy in the region is cyclical: the new infrastructure available for business opens up opportunities for its modernization, promotes its flexibility and efficiency, thus increasing the level of digitalization in the region and creating prerequisites for the formation of new infrastructure. The example of artificial intelligence shows that its implementation in the practice of corporate decision-making needs infrastructure support. In turn, it increases the level of business automation, stimulating the government to create new infrastructure, as well as forming the experience of using artificial intelligence in decision-making and promoting its institutionalization.

Keywords

JEL Codes

G34

1 L. A. Borisova (&)
R. A. Mammaev Research Institute of Management, Economics, Politics and Sociology, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected] R. A. Mammaev e-mail: [email protected] E. B. Atuyeva Department of Economic Development, Marketing and Business, Dagestan State Technical University (DSTU), Makhachkala, Russia e-mail: [email protected]








Management decision-making Artificial intelligence entrepreneurship Digital economy Region







O18

O31

O32

O33

R11

R58

Introduction

The basis of corporate governance is decision-making, which determines the flexibility and efficiency of business, the ability to respond to negative events in a timely manner in the internal environment and neutralize the negative impact of environmental factors. Modern economics and management recognize the imperfection of managerial decision-making in the activities of business structures, due to a number of problems. One of them is the complexity, non-transparency of corporate and market environment of

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_13

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L. A. Borisova et al.

entrepreneurship, monitoring of which is difficult—as a result, managers, as a rule, do not identify all the pressing problems of business, which leads to an incomplete coverage of corporate governance and not all decisions that are necessary. Another problem of making managerial decisions is related to the limited analytical capabilities of a manager, for which reason he can make managerial decisions either untimely or irrationally intuitively. The rationality of a manager’s decisions is also constrained by his exposure to the “human factor”. Incomplete information support is also a recognized problem, where the manager does not fully take into account the context in which they make their decisions. This problem is caused by the complexity of data collection and analytics, as well as the fact that a full-scale collection of information can lead to higher costs of decision-making than their results in the form of business benefits. In the conditions of digital economy, the urgency of the problem of improving corporate governance practices increases even more, since, acting as a provider of growth and modernization of the regional economic system, business determines the possibilities of digital economy development in the region. In our work we propose a hypothesis that artificial intelligence allows us to improve the practice of management decision-making in modern business. We also assume that due to the lack of infrastructure support in the regions of the North Caucasus Federal District of the Russian Federation, this opportunity has not been fully realized. The purpose of this work is to justify the prospects for improving the practice of management decision-making in modern business based on artificial intelligence on the example of the regions of the North Caucasus Federal District of the Russian Federation.

2

Materials and Method

The essence and topical issues of the existing corporate governance practice are reflected in the works of Arboit (2018), El Bousty et al. (2018), Fonfara et al. (2018), Krivtsov (2014), Kummamuru and Mandaleeka (2016), Moreira and Martínez-Ávila (2018), Rinkinen and Harmaakorpi (2018), and Safar et al. (2018). The possibilities of digital economy and, in particular, artificial intelligence for modern entrepreneurship are reflected in the works of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al. (2018), Popkova (2019), Popkova et al.

(2018, 2019), Petrenko et al. (2018), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019) and Sergi (2019). The content analysis of the cited researches and publications on the given subject has shown that the problem of improvement of corporate governance practice is studied in a narrow context taking into account advantages only for the enterprise at its consideration as an abstract-isolated economic system. In our opinion, this point of view leads to the discrepancy between the theory and practice of digital modernization of entrepreneurship, as the theory recognizes the opportunities for the development of this process, but in practice there are no necessary opportunities for this, namely infrastructure. Therefore, it is necessary to study the prospects for improving corporate governance practices in a broad context, taking into account also the advantages for the region as a socio-economic environment in which an enterprise operates and develops. In order to determine the context in which the modernization of entrepreneurship in the regions of the North Caucasus Federal District of the Russian Federation is carried out in 2018 and, in particular, the practice of management decision-making, this paper reviews and analyses statistical data on business digitalization in the regions under study (Figs. 1, 2 and 3). As can be seen from Fig. 1, in the regions of the North Caucasus Federal District of the Russian Federation in 2018 almost full coverage of organizations by broadband Internet is achieved. Thus, in the Republic of Ingushetia 92.7% of organizations use broadband Internet, in Stavropol Krai— 91.2%. Cloud services are much less actively used in the regions under study—they are used by 30.2% of organizations in the Republic of Ingushetia and 26.3% in Stavropol Krai. As shown in Fig. 2, other more sophisticated digital technologies are even less actively used in organizations in the regions of the North Caucasus Federal District of the Russian Federation in 2018. They are most actively used in the Republic of Ingushetia, where RFID technologies are used by 2.5% of organizations, ERP technologies—by 7.3% of organizations, EDI-technologies—by 33.8% of organizations. According to Fig. 3, the overall level of business digitalization in the regions of the North Caucasus Federal District of the Russian Federation in 2018 is moderate. It is highest in the Republic of Ingushetia (33%) and least pronounced in the Republic of Dagestan (18%). Since data on the use of artificial intelligence is not presented separately, all the above technologies are used for intellectual support (automation) of managerial decision-making in business.

Improving the Practice of Managerial Decision-Making …

75

Fig. 1 Level of Internet coverage of organizations in the regions of the North Caucasus Federal District of the Russian Federation in 2018. Source Built by the authors on the basis of the National Research University Higher School of Economics (2019)

Fig. 2 Application of digital technologies by organizations in the regions of the North Caucasus Federal District of the Russian Federation in 2018. Source Built by the authors on the basis of materials from the National Research University Higher School of Economics (2019)

Therefore, the existing experience and infrastructure are sufficient for the application of separate automation tools in making management decisions in modern corporate structures, but do not allow to automate this process systematically based on artificial intelligence.

3

Results

We have developed a logical scheme for improving the practice of management decision-making in modern entrepreneurship based on artificial intelligence, which systematically reflects this process and takes into account the

benefits for both the enterprise and the digital economy of the region (Fig. 4). As shown in Fig. 4, artificial intelligence (AI) systematically monitors the internal corporate business and market environment in the region. Thanks to it, it identifies current problems, analyses them, forms a corporate digital database and provides support for decision-making by the manager. As a result, the following benefits are created for the enterprise: – systemic vision and complete problem identification; – accelerated decision-making for timely problem-solving; – more rational decision-making;

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The Republic of Dagestan 35 30 33 The Republic of 25 18 Stavropol Krai 29 Ingushetia 20 15 10 5 0 The Chechen 23 24 The KabardinoRepublic Balkar Republic

The Republic of North Ossetia-Alania

23

26

The KarachayCherkess Republic

Fig. 3 Business digitalization index in the regions of the North Caucasus Federal District of the Russian Federation in 2018, %. Source Built by the authors on the basis of the National Research University Higher School of Economics (2019)

Organization Regional market (external regional environment)

Fig. 4 Logical scheme for improving the practice of managerial decision-making in modern entrepreneurship based on artificial intelligence. Source Designed and compiled by the authors

Corporate digital database access, Support for decision- analytics making AI Manager

access, analytics systematic monitoring

detection, analysis

– full information support for decision-making; – reliance on breakthrough decision-making practices. This creates advantages for the digital economy of the region: growth of digital competition, institutionalization of the practice of artificial intelligence, as well as accelerated growth with the sustainability of the digital economy.

4

Conclusion

Summarizing the results of the study, we note that the development of the digital economy in the region is cyclical: the new infrastructure available to businesses opens up opportunities for its modernization, contributes to the growth of its flexibility and efficiency, thus increasing the level of digitalization in the region and creating the prerequisites for the formation of new infrastructure. The example of artificial intelligence shows that its implementation in the practice of

of the problem

Advantages for the digital economy of the region: increased digital competition; institutionalization of artificial intelligence practices; accelerated growth while the digital economy is sustainable.

corporate decision-making needs infrastructure support. In turn, it increases the level of business automation by encouraging the government to create new infrastructure, as well as by creating experience in the use of artificial intelligence in decision-making and promoting its institutionalization. Therefore, there is a close two-way relationship between public and corporate governance in the digital economy, which causes their mutual conditionality and ability to generate synergy effects in the form of accelerated construction and development of the digital economy. This connection is most distinct and strong at the regional level, which emphasizes the particular perspective of studying the regional aspect of digital economy. The analysis of the current experience of business digitalization in the regions of the North Caucasus Federal District of the Russian Federation in 2018 showed that separate digital technologies have been introduced into the practice of decision-making, but the system automation of this practice has not yet been achieved —it will be facilitated by the proposed logical scheme.

Improving the Practice of Managerial Decision-Making …

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Arboit, A. E. (2018). Knowledge organization: From term to concept, from concept to domain. Knowledge Organization, 45(2), 125–136. Buchaev, Y.G., Iakovleva, E.A., & Putihin, I.E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10), 78, 564–568. El Bousty, H., Krit, S.-D., Elasikri, M., (…), Bendaoud, K., & Kabrane, M. (2018). Investigating business intelligence in the era of big data: Concepts, benefits and challenges. ACM International Conference Proceeding Series, 24(1), 78–91 Fonfara, K., Ratajczak-Mrozek, M., & Leszczyński, G. (2018). Change in business relationships and networks: Concepts and business reality. Industrial Marketing Management, 70, 1–4. Gadzhiev, M. M., & Buchaev, Y.G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 82, 581–586. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44– 50. Krivtsov, A. I. (2014). Fair value assessment of investment capital when ensuring coherence of managerial decision–making processes. Actual Problems of Economics, 161(11), 307–313. Kummamuru, S., & Mandaleeka, N. (2016). Modeling the business system by applying cybernetic concepts. In 2016 IEEE Conference on Norbert Wiener in the 21st Century, 21CW 2016, 75–87, pp. 50– 55. Moreira, W., & Martínez-Ávila, D. (2018). Concept relationships in knowledge organization systems: Elements for analysis and common research among fields. Cataloging and Classification Quarterly, 56(1), 19–39. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14).

77 Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and I. A. Kuznetsova, PhD in Engineering, Associate Professor Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components). Theoretical and Practical Issues of Journalism, 7(1), 145–154. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will Industry 4.0 and Other Innovations Impact Russia’s Development? In B. S. Sergi (Ed.) Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E.G., & Sergi, B.S. (Eds.). (2019). Digital economy: Complexity and variety vs. rationality. Springer International Publishing. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Rinkinen, S., & Harmaakorpi, V. (2018). The business ecosystem concept in innovation policy context: Building a conceptual framework. Innovation, 31(3), 333–349. Safar, L., Sopko, J., Bednar, S., & Poklemba, R. (2018). Concept of SME business model for industry 4.0 environment. TEM Journal, 7 (3), 626–637. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-pacific region. In B. S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. National Research University Higher School of Economics (2019). Digital Economics: 2019. Retrieved December, 04, 2019, from https://www.hse.ru/data/2019/06/25/1490054019/ice2019.pdf.

Corporate Database Management on the Basis of Cloud Technologies, Blockchain Technologies and Technologies of Big Data Processing: Effectiveness and Security Andrey V. Kurkin, Akim V. Giraev, and Zaur U. Medzhidov

quantum, blockchain and big data technologies to meet the criteria of efficiency and security should be organized in a systematic manner and be based on the capabilities of the region. This forms a fundamentally new scientific concept of the organization of the process under study, which reflects the view on it not from the standpoint of an isolated enterprise, but from the standpoint of the region’s enterprises. Owing to this, security is provided with the support of the state, the risks are distributed among the enterprises of the region and full-scale financing allows maximizing the advantages. Circulation of regional enterprises allows establishing electronic document circulation and formation of an integral network of corporate databases in the region, thus further increasing management efficiency.

Abstract

Purpose: The work is aimed at developing a scientific concept of corporate database management on the basis of cloud, quantum, blockchain technologies and big data processing technologies on the principles of efficiency and security in the context of the digital economy of the region on the example of the North Caucasus Federal District of the Russian Federation. Design/method/ approach: The authors consider passports of the federal projects “Information security” and “Digital technologies” of the national program “Digital economy of the Russian Federation” from 07.11.2019. The authors analyze the statistics of financial support of digitalization of the Russian economy from 2018 to 2024. They determine the current level of development and future prospects of digital technologies in the world economy on the basis of materials of the National Research University “Higher School of Economics”. Findings: The conceptual model of corporate database management on the basis of cloud, quantum, blockchain technologies and big data processing technologies on the principles of efficiency and security was developed. Scenario analysis of corporate database management on the principles of efficiency and security in the regions of the North Caucasus Federal District of the Russian Federation with the application of game theory methodology was conducted. Originality/value: It has been substantiated that the management of corporate databases based on cloud, A. V. Kurkin (&)
A. V. Giraev St. Petersburg State Marine Technical University (SMTU), Saint-Petersburg, Russia e-mail: [email protected] A. V. Giraev e-mail: [email protected] Z. U. Medzhidov Department of Information Technologies and Information Security, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected]

Keywords
















Management Corporate database Cloud technology Quantum technology Blockchain Big data Efficiency Security Region




JEL-codes

G34

1








O18

O31

O32

O33

O38

R11

R58

Introduction

Management of corporate databases determines the information support of enterprises and therefore is the key to the reliability of its assessment of the current internal and external (market) situation, as well as making informed and rational tactical and strategic decisions. Evaluation of corporate database management is associated with the application of two criteria. The first criterion is efficiency. As well as in any economic process, the studied management

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_14

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practice should provide advantages for the enterprise, the value of which exceeds the cost of their achievement. This means that corporate database management is not an end in itself, but a tool for creating business benefits. The costs of collecting corporate information and its processing should be commensurate with and not exceed its importance. This criterion orients the management to set priorities in the field of information, that is, its sorting and ranking, and the preference for the most demanded information in the enterprise. As the volume of information in the information society grows, compliance with this criterion becomes more and more complicated and requires close interaction of the manager with the employees of the enterprise. The second criterion is security. Most of the information is attractive to business only if it is unique, which requires maintaining its confidentiality and commercial confidentiality. It should be noted that this applies not only to advanced technologies but also to other intangible assets of the enterprise, including its know-how and information on market conditions. Compliance with this criterion varies with different types of information. For example, when information is stored orally and is inseparable from the medium (informal data), it is necessary to prevent staff turnover. Information on paper media requires physical security of the company building and legal support for non-disclosure of corporate information. In the digital economy, new additional and expanded opportunities for improving the practice of corporate database management emerge based on the breakthrough digital technology Industry 4.0, the most promising of which are cloud, quantum, blockchain and big data processing technology. Although the criteria for achieving and assessing the degree of optimality of corporate database management remain the same, their application requires a new scientific and methodological approach. The digital form of data allows for automatic data classification and is subject to cyber-security risks. Fig. 1 Statistics on financial support to the digitalization of the Russian economy from 2018 to 2024. ANO “Digital economy” (2019a, b)

A. V. Kurkin et al.

This work is aimed at developing a scientific concept of corporate database management based on cloud, quantum, blockchain and big data processing technologies on the principles of efficiency and security in the context of the digital economy of the region, using the example of the North Caucasus Federal District of the Russian Federation.

2

Materials and Method

A literature review of the topic has shown that the opportunities and benefits of the application of selected breakthrough technologies in the Industry 4.0, in particular, cloud, quantum, blockchain and big data technologies in corporate database management are presented in publications by Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al. (2018), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2018), Popkova (2019), Popkova et al. (2019, 2018), Petrenko et al. (2018), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019). However, the existing literature sources contain only individual recommendations and disparate fundamental conclusions and applied solutions, while the holistic concept of corporate database management based on breakthrough technologies of Industry 4.0 is absent, the criteria for evaluating this management in the context of the digital economy are not specified, and the regional context is not taken into account. This work is intended to fill in the gaps identified. As a result of studying the passport of the federal projects “Information security” and “Digital technologies” of the national program “Digital economy of the Russian Federation” from 07.11.2019, we found that the issues of ensuring the efficiency and security of advanced digital technologies in Russia are given significant attention, as evidenced by the data on their financing (Fig. 1).

Corporate Database Management on the Basis of Cloud …

81

Fig. 2 Current level of development and future prospects of digital technologies in the world economy. Source Built by the authors on the basis of the National Research University Higher School of Economics (2019)

As shown in Fig. 1, government spending on “end-to-end” digital technologies in Russia in 2024 will increase three times as compared to 2018, and the volume of spending on information security products and services will increase from 56 billion rubles in 2018 to 167.22 billion rubles in 2024, that is three times. The materials of the National Research University “Higher School of Economics” also present the current level of development and future prospects of digital technologies in the world economy, which are illustrated in Fig. 2. As can be seen from Fig. 2, the volume of the world market of big data as services (BDaaS) in 2018 is $8.9 billion, and by 2024 it will increase to $31.7 billion, which is almost four times. The volume of the world market of quantum encryption in 2008 is 285.7 billion dollars, and by 2022 it will increase to 944 billion dollars, that is more than three times. Market capitalization of the top 100 cryptocurrencies representing distributed registry systems (blockchains) as of 2018 is estimated at $250 billion. Consequently, there are significant prospects for the development of breakthrough technologies in the Industry 4.0 at the global level.

3

Results

We have discovered that in the regions of the North Caucasus Federal District only individual private and public initiatives are being implemented to introduce digital technologies into the practice of corporate database management, which leads to restrained results. In order to accelerate the modernization of the practice, we have developed a conceptual model of corporate database management based on cloud, quantum, blockchain and big data processing technologies on the principles of efficiency and security (Fig. 3).

As can be seen from Fig. 3, the corporate database of the enterprise is formed from its internal information, and information comes from external sources—state regulators, consumers, contractors and competitors. The top manager performs analytics of this information with the help of big data processing technologies. All internal and external stakeholders have access to the corporate database through a blockchain—a chain linked to the source database, but separate segments of information. In this way each user generates their own database and stores it in their own cloud, and the corporate database is also subject to cloud backup. Quantum security technology prevents unauthorized access by unauthorized users. The developed scheme allows maximizing the benefits and guaranteeing the security of enterprise database management based on cloud, quantum, blockchain and big data processing technologies. At the same time, the issue of costs is not addressed and needs further research. For its study in this paper, a scenario analysis is conducted using the methodology of game theory in the regions of the North Caucasus Federal District of the Russian Federation (Table 1). Table 1 demonstrates that at present (2019) the advantages (cost estimate of innovative goods) in the regions of the North Caucasus Federal District of the Russian Federation is 34722.4 million rubles. Capital expenditures on technical innovations amount to 8956.8 million rubles. We have estimated the security factor (cybernetic risks) at 0.5 (in fractions from 1). The efficiency of the scenario implementation is calculated as follows: 34722.4*0.5/8956.8 = 1.94. So, taking into account the advantages of the security, risks exceed the costs by 1.94 times. In this case, not breakthrough but ordinary digital technologies (computer, Internet) are used. Scenario 1, which assumes isolated digitalization—independent implementation of breakthrough technologies by the

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Fig. 3 Conceptual model of corporate database management based on cloud, quantum, blockchain and big data processing technologies on efficiency and security principles. Source Designed and compiled by the authors

Enterprise Top manager (CEO)

Big Data analytics

reporting, taxation

State regulators

Manager 2

marketing information

Corporate database

Manager 1

Manager n

Backup copy

Consumers Counteragents Competitors

Quantum security technology unauthorized access Untargeted users

Table 1 Scenario analysis of corporate database management based on efficiency and security principles in the regions of the North Caucasus Federal District of the Russian Federation Scenario for modernization of corporate database management based on cloud, quantum, blockchain and Big Data processing technologies

Capital expenditures (expenditures on technical innovations), million rubles

Basic conditions

8956.8

Scenario 1: isolated digitalization

17913.6

Security costs, million rubles

17913.6

Advantages (cost estimation of innovative goods), million rubles

Security factor, shares from 1

Probability of scenario implementation, shares from 1

Scenario efficiency

34722.4

0.5

–

1.94

138890

0.6

1

2.33

Scenario 2: integrated digitalization

16122.2

13435.2

145834

0.75

0.9

3.33

Scenario 3: regional digitalization

13435.2

10748.2

156251

0.9

0.75

4.36

Source Compiled and calculated by the authors based on the materials of Rosstat (2019).

enterprise—promotes a slight increase in efficiency up to 2.33 and security up to 0.6. Scenario 2, referred to as integrated digitalization—breakthrough technologies implementation within the cluster of enterprises—further increases efficiency (up to 3.33) and security (up to 0.75). The most preferable scenario is the third one, involving regional digitalization. In this case, the modernization of entrepreneurship is carried out within the framework of the relevant program of the region with state financial support. This makes it possible to increase the level of security not at individual enterprises, but in the region as a whole (up to 0.9) and to distribute costs evenly among market participants, so that efficiency (despite the increased complexity and probability of 0.75) increases to 4.36.

4

Conclusion

Thus, to meet the criteria of efficiency and security, management of corporate databases based on cloud, quantum, block and big data technologies should be organized in a systematic way and be based on the capabilities of the region. This forms a fundamentally new scientific concept of the organization of the process under study, which reflects the view on it not from the standpoint of an isolated enterprise, but from the standpoint of the region’s enterprises. Owing to this, security is provided with the support of the state, the risks are distributed among the enterprises of the region and full-scale financing allows maximizing the

Corporate Database Management on the Basis of Cloud …

advantages. Digitalization of regional enterprises allows establishing electronic workflows and forms an integral network of corporate databases in the region, further increasing management efficiency.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10),78, 564–568. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 82, 581–586. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44–50. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor

83 and I. A. Kuznetsova, PhD in Engineering, Associate Professor Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components). Theoretical and Practical Issues of Journalism, 7(1), 145–154. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will Industry 4.0 and Other Innovations Impact Russia’s Development? In B. S. Sergi (Ed.) Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety vs. rationality. Springer International Publishing Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In B. S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. ANO Digital Economy. (2019a). Passport of the Federal project “Information security” of the national program “Digital economy of the Russian Federation” of 07.11.2019. Retrieved December 04, 2019, from https://files.data-economy.ru/Docs/FP_Informacionnaya_ bezopasnost’.pdf. ANO Digital economy. (2019b). Passport of the Federal project “Digital technologies” of the national program Digital economy of the Russian Federation dated 07.11.2019. Retrieved December 04, 2019, from https://files.data-economy.ru/Docs/FP_Cifrovye_ texnologii_.pdf. National Research University Higher School of Economics. (2019). Digital Economics: 2019. Retrieved December 04, 2019, from https://www.hse.ru/data/2019/06/25/1490054019/ice2019.pdf. Federal State Statistics Service of the Russian Federation (Rosstat). (2019). Regions of the Russian Federation: socio-economic indicators: 2018. Retrieved December 04, 2019, from https://www.gks.ru/ bgd/regl/b18_14p/Main.htm.

Innovative Development of Entrepreneurship in the AIC in the Conditions of the Digital Economy: Growth Points, Measuring, and Management Ahmed G. Buchaev, Nurziyat Y. Kazavatova, and Rauf N. Gadzhiev

that using the opportunities of the digital economy will increase the contribution of the innovative development of the AIC to the stability of the region, its food security and acceleration of its economic growth. For this purpose, a framework strategy for innovative development of the AIC entrepreneurship in the region in the digital economy has been developed.

Abstract

Purpose: The purpose of the work is to identify promising growth points and offer scientific and methodological recommendations for measuring and managing the innovative development of entrepreneurship in the agro-industrial complex (AIC) in the digital economy on the example of the regions of the North Caucasus Federal District of the Russian Federation. Design/methodology/ approach: The authors conduct a statistical review of the innovative development of the agro-industrial complex in the regions of the North Caucasian Federal District of the Russian Federation in 2018, and also apply the method of regression analysis to determine the innovative activity of the agro-industrial complex of the studied regions. The share of the agro-industrial complex enterprises by the criterion of number and turnover in the structure of entrepreneurship in the regions under study was analyzed. Findings: It has been substantiated that in the conditions of digital economy, the prerequisites are created to accelerate and increase the efficiency of innovative development of APA entrepreneurship. New growth points—circularity and green innovation as well as automation and APA 4.0—are becoming available. In the regions of the North Caucasus Federal District, agribusiness enterprises are highly innovative and largely determine the overall innovative development of regional entrepreneurship. Originality/value: It has been proven A. G. Buchaev (&) Department of Taxes and Taxation, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected] N. Y. Kazavatova Department of Economic Theory, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected] R. N. Gadzhiev Economics, Politics and Sociology, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected]

Keywords










Innovative development Entrepreneurship Agro-Industrial complex (AIC) Digital economy Growth point Measurement and management Region Russia







JEL-codes

G34

1








O18

O31

Q13

Q16

Q18

R11

R58

Introduction

The agro-industrial complex (AIC) is among the contradictory branches of regional economy in the conditions of digital economy. On the one hand, the development of the agro-industrial complex in the digital economy of the region may not be a priority and may be problematic. Despite the systematic modernization of the economy, the digital economy is primarily focused on industry. It is no coincidence that the most high-tech segment of the digital economy is called Industry 4.0—it reflects the industrial orientation of the economic model in the progress of the fourth technological revolution. As a result, advanced digital technologies are initially developed for industrial production, although they can later be adapted to other industries, including the agro-industrial sector. In addition to the deficit of technologies, the agricultural sector is a rare area of regional production specialization.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_15

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A. G. Buchaev et al.

Agriculture as the basis of this complex assumes high natural and climatic requirements, which exist on the territory of not every region. Dependence of competitive advantages of the agricultural sector of a region on its geography leads to the fact that for the majority of modern regions specialization in agriculture is not available, and its digital modernization is ineffective, as it requires the redistribution of resources in favor of non-priority sectors of the economy from those sectors in which it specializes. On the other hand, the AIC plays an important role in the functioning of a region and can contribute to its accelerated development, as well as opening up new opportunities for its innovative development in the digital economy. A specific feature of the agro-industrial complex is that the interest of a region in its development is determined not only by its absolute and relative competitive advantages but also by the general principles of sustainable development. The organization of a global and even national agro-industrial complex on the principle of division of labor causes high risks of food security. Therefore, each modern region should, to a greater or lesser extent, support its own AIC to prevent food crises. The working hypothesis of this study is the assumption that it is possible and expedient to use the opportunities of the digital economy to accelerate and improve the innovative development of entrepreneurship in the agricultural sector. The aim of this work is to identify promising growth points and propose scientific and methodological recommendations for measuring and managing the innovative development of entrepreneurship in the agro-industrial complex in the digital economy on the example of the regions of the North Caucasus Federal District of the Russian Federation.

2

Materials and Method

The general issues of innovative development of the region’s economy in the conditions of digitalization are reflected in the studies of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al. (2018), Gadzhiev and Buchaev (2014), Popkova (2019), Popkova et al (2018, 2019), Petrenko et al. (2018), Popkova and Parakhina (2019), Popkova and Sergi (2018), (2019), Ragulina (2019), Sergi et al. (2019), and Sergi (2019). The theory and practice of agribusiness development based on the digital economy is reflected in Altukhov et al. (2019), Huh and Kim (2018), Khaiturina et al. (2018), Kreneva et al. (2018), Matei et al. (2017, 2018), Pandithurai et al. (2018). A review of available research literature on the stated problem has shown that the regional aspect of innovative development of entrepreneurship in the AIC in the digital economy is insufficiently studied from the theoretical, methodological and applied points of view. To overcome

this shortcoming in the existing scientific and economic paradigm, in our research we conduct a statistical review of the innovative development of the AIC in the regions of the North Caucasian Federal District of the Russian Federation in 2018, and also use the method of regression analysis to determine the innovative activity of the AIC of the regions under study. The share of the AIC enterprises by the criterion of number and turnover in the structure of entrepreneurship in the regions under study is shown in Figs. 1 and 2. According to Fig. 1, the share of manufacturing enterprises in the total number of enterprises in 2018 was the highest at 13.70% in the Republic of Ingushetia and the lowest at 4.45% in the Chechen Republic. The share of agricultural enterprises in the total number of enterprises was the highest at 13.09% in the Republic of Dagestan and the lowest at 8.40% in the Republic of North Ossetia-Alania. According to Fig. 2, in 2018 the share of manufacturing enterprises in the total turnover of enterprises was the highest at 47.51% in the Karachay-Cherkess Republic, and it was the lowest at 1.02% in the Republic of Ingushetia. The share of agricultural enterprises in the total turnover of enterprises was the highest at 11.26% in the Stavropol Krai, was minimal and amounted to 2.04% in the Republic of Ingushetia. Therefore, in general, in the regions of the North Caucasus Federal District of the Russian Federation in 2018, the agricultural sector occupies an important place in the structure of the economy, but is not a branch of specialization of the economy. The data on the number and turnover of the AIC enterprises, as well as the volume of innovative products of the enterprises on the whole in the regions of the North-Caucasian Federal District of the Russian Federation in 2018, are presented in Table 1.

3

Results

Based on the data of Table 1, we conducted a regression analysis of innovation activity of the AIC enterprises in the regions of the North Caucasian Federal District of the Russian Federation in 2018 (Table 2). In particular, we present regression statistics, identify indicators of the equation of regression of the type y ¼ a þ b1  x1 þ b2  x2 þ b3  x3 þ b4  x4 , and perform dispersion analysis. The data in Table 2 allow us to compose the equation of multiple linear regression: y ¼ 333:864  0:35  x1 þ 0:269  x2 þ 486:063  x3  64:149  x4 . This means that the volume of innovative products (y). when the number of agricultural enterprises increases by 1 pcs. (x1), increases by 0.35 million rubles, with an increase in the number of manufacturing enterprises by 1 pcs. (x2), increases by 0.269 million rubles, with an increase in the turnover of

Innovative Development of Entrepreneurship in the AIC …

87

Fig. 1 Share of the AIC among the enterprises of the regions of the North Caucasus Federal District of the Russian Federation in 2018. Source Calculated and constructed by the authors on the basis of materials of Rosstat (2019)

Fig. 2 The share of the AIC in the turnover of the enterprises of the North Caucasus Federal District of the Russian Federation in 2018. Source Calculated and constructed by the authors on the basis of materials of Rosstat (2019)

agricultural enterprises by 1 million rubles (x3), and increases by 486.063 million rubles, with an increase in the turnover of processing industry enterprises, (x4) million rubles. The multiplicity coefficient of determination (R) made 0.99956; hence, the change in the dependent variable by 99.956% is explained by the change in independent variables. Value F does not exceed 0.05 and made 0.002; therefore, the regression equalization is reliable at the level of significance a = 0.05. This indicates an active innovative

development of agricultural enterprises in the regions of the North Caucasus Federal District of the Russian Federation in 2018. In order to accelerate it even more and increase its efficiency on the basis of digital economy possibilities, we have developed the following framework strategy (Fig. 3). As shown in Fig. 3, we have identified two promising growth points for the region’s AIC enterprises in the conditions of digital economy. The first one is “Circularity and Green Innovations”. The criteria for assessing the innovative development of AIC enterprises in this case are resource

88 Table 1 Number and turnover of agricultural enterprises and volume of innovation products in the regions of the North Caucasus Federal District of the Russian Federation in 2018

A. G. Buchaev et al. Turnover of agricultural enterprises, million rubles (x3)

Turnover of manufacturing industry enterprises, million rubles (x4)

Volume of innovative products, million rubles (y)

Region of the North Caucasus Federal District of the Russian Federation

Number of agricultural enterprises, pcs. (x1) (x1)

Number of manufacturing enterprises, pcs. (x2)

The Republic of Dagestan

4362

1852

5.2

23.7

182.0

The Republic of Ingushetia

335

694

0.2

0.1

22.8

The Kabardino-Balkar Republic

1279

1338

3.3

23.6

307.1

The Karachay-Cherkess Republic

619

524

5.0

36.2

40.9

The Republic of North Ossetia-Alania

805

907

0.2

15.6

26.4

The Chechen Republic

1138

450

1.2

5.7

576.74

Stavropol Krai

5369

3254

95.6

191.0

33566.7

Source Compiled by the authors based on the materials of Rosstat (2019) Table 2 Regression analysis of innovation activity of enterprises. Agricultural sector in the regions of the North Caucasus Federal District of the Russian Federation in 2018

Regression statistics Multiple R

0.99956

Square R

0.99912

Normed R2

0.99736

Standard error

647.912

Number of observations

7

Equation regression indicators a

b1

b2

b3

b4

333.864

−0.350

0.269

486.063

−64.149

Dispersion analysis df

SS

MS

F

Value F

Regression

4

954109331

238527333

568.206

0.002

Rest

2

839581.14

419790.57

Total

6

954948912

Source Calculated and compiled by the authors

efficiency (consumption of production resources and production waste) and corporate responsibility. Governance measures for innovation development in the agro-industrial sector include regulation, standardization, and the development of responsible consumption in a digital society. The second growth point is “Automation and agro-industrial complex 4.0”. The criteria for assessing the innovative development of an AIC enterprise in this case are

productivity and information support (transparency, controllability). Public management measures for innovation development of AIC enterprises include infrastructure support and incentives for digital modernization of agro-industrial complex enterprises. The results include ensuring food security in the region, sustainable development of the region’s economy, and the establishment of the AIC as a new vector of growth of the region’s digital economy.

Innovative Development of Entrepreneurship in the AIC … Fig. 3 Framework strategy for innovative development of agribusiness in the region in the context of digital economy. Source Designed and compiled by the authors

4

89

Growth Point 1 "Circularity and Green Innovations" resource efficiency; responsibility.

− Public administration measures: − rationing, standardization; − development of responsible consumption in the digital society.

Growth Point 2 "Automation and AIC 4.0" - productivity; - information support.

− Public administration measures: − infrastructure provision; − stimulation of digital modernization of agricultural enterprises.

-

Conclusion

So, the working hypothesis was confirmed during the study. In the conditions of digital economy, the preconditions for acceleration and increase of efficiency of innovative development of APA entrepreneurship are created. New growth points are becoming available—circularity and green innovation as well as automation and APA 4.0. In the regions of the North Caucasus Federal District, agribusiness enterprises are highly innovative and largely determine the overall innovative development of regional entrepreneurship. Exploiting the opportunities of the digital economy will make it possible to increase the contribution of innovation development of the agro-industrial complex to the stability of the region, its food security, and the acceleration of its economic growth. For this purpose, a framework strategy for the innovative development of AIC entrepreneurship in the region in the digital economy has been developed.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal, 11(10), 78, 564–568. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal, 11(10), 82, 581–586. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43.

Results: − regional food security; − sustainable development of the region's economy; − AIC as a new vector of growth of region’s digital economy.

Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44–50. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169(1), 65–72. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and I. A. Kuznetsova, PhD in Engineering, Associate Professor Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components). Theoretical and Practical Issues of Journalism, 7(1), 145–154. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will industry 4.0 and other innovations impact Russia’s development? In B. S. Sergi (Ed.) Exploring the future of Russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety vs. rationality. Springer International Publishing Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In B. S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited.

90 Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Altukhov, A. I., Bogoviz, A. V., & Kuznetsov, I. M. (2019). Creation of an information system—A necessary condition of rational organization of agricultural production. Advances in Intelligent Systems and Computing, 726, 800–809. Huh, J. -H., & Kim, K. -Y. (2018). Time-based trend of carbon emissions in the composting process of swine manure in the context of agriculture 4.0. Processes, 6(9), 168. Khaiturina, E., Kreneva, S., Bakhtina, T., Larionova, T., & Tsareva, G. (2018). Strategic benchmark of the digital economy in the region’s agro-industrial complex. International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM, 18(5.3), 767–774. Kreneva, S., Tsaregorodtsev, E., Tereshina, V., & Sredina, Y. (2018). Agro-industrial complex in the conditions of development of digital

A. G. Buchaev et al. society as the instrument of economic development of the region. International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM, 18(5.3), 19–26. Matei, O., Anton, C., Bozga, A., & Pop, P. (2017). Multi-layered architecture for soil moisture prediction in agriculture 4.0. In Proceedings of International Conference on Computers and Industrial Engineering, CIE, pp. 65–79. Pandithurai, O., Aishwarya, S., Aparna, B., & Kavitha, K. (2018). Agro-tech: A digital model for monitoring soil and crops using Internet of things (IOT). In Proceedings: 3rd IEEE International Conference on Science Technology, Engineering and Management, ICONSTEM 2017, 2018-January, pp. 342–346. Federal State Statistics Service of the Russian Federation (Rosstat). (2019). Regions of the Russian Federation: Socio-economic indicators: 2018. Retrieved December, 04, 2019, from https://www.gks. ru/bgd/regl/b18_14p/Main.htm.

The Problem of Migration in the Conditions of the Digital Economy: New Challenges for the Labour Market, Possibilities and Priorities of Solving Magomed Kh. Abidov, Fatima N. Ismailova, and Pirmagomed G. Abdulmanapov

migration in the region in the conditions of digital economy consist in prevention of the own personnel outflow and attraction of external personnel of digital economy, as well as prevention of unclaimed personnel inflow into the region. Their observance is facilitated by the developed regulatory concept.

Abstract

Purpose: The purpose of the work is to study the problem of migration in the digital economy through the prism of identifying new challenges to the labour market, as well as identify opportunities and priorities to address this problem on the example of the regions of the North Caucasus Federal District of the Russian Federation in 2018. Design/methodology/approach: The impact of migration on indicators reflecting the state of the labour market and the economy as a whole in the regions of the North Caucasus Federal District of the Russian Federation in 2018 is determined. The method of regression analysis has been chosen as the methodological basis for the study, which is used to build regression maps reflecting the impact of migration on the listed indicators. Findings: It was discovered that the problem of migration is growing in the digital economy, creating new challenges for the region’s labour market. These challenges include the need to retain staff in the digital economy and to find a balance between incoming and outgoing migration flows in order to maintain labour market stability. Overcoming these challenges can be achieved on the basis of the digital economy opportunities through the development of distance learning, the formation of an electronic labour market and the use of digital means of business integration, for example, distance exchange of knowledge and information. Originality/value: It is grounded that the priorities of solving the problem of M. Kh. Abidov (&)
P. G. Abdulmanapov Research Institute of Management, Economics, Politics and Sociology, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected] P. G. Abdulmanapov e-mail: [email protected] F. N. Ismailova Department of Marketing and Commerce, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected]

Keywords













Migration Digital economy Labour market Digital economy cadres Unemployment Region Russian regions JEL Codes

G34

1








J61

O15

O18

O31

R11

R23

R58

Introduction

The problem of migration, traditionally relevant to the regional economy, is growing and becoming multidimensional in the digital economy. One of its aspects is related to the attraction to the region of ready-made specialists in promising sectors of the digital economy. It should be noted that the educational services market is characterized by organization on the principle of labour division. Therefore, in practice, there can be a situation when a sharp change in production specialization of the region’s economy leads to a crisis of the labour market. The need of entrepreneurship in the personnel of the new branch causes the growth of demand, but there are no higher educational establishments preparing specialists of this branch in the region or it is possible only in the long-term time period. Thus, the crisis of overproduction on the market of educational services and the crisis of personnel deficit on the labour market can occur simultaneously.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_16

91

92

Another aspect of this problem is to prevent the outflow of the best human resources in the region due to the low competitiveness of the regional business as an employer, as well as limited opportunities for the realization of the labour potential in the immature industry 4.0. In the conditions of the digital economy, particularly in Russia, training of personnel with digital competences is carried out. This process is resource-intensive and puts an increased burden on the regional budget. Once the digital economy personnel have been trained, it may turn out that there are fewer opportunities to develop their potential in the region than in other regions. If borders are open, labour migration may occur—outflow of the most competitive human resources from the region and loss of its competitive advantages. Retention of digital economy personnel is an important task for a modern region, as it determines its future development prospects. Another aspect of the problem under consideration is the prevention of inflow into the region of specialists from industries that are poorly represented in the regional economy and are known to have low competitiveness on the labour market. Their presence leads to an increase in natural unemployment, a decrease in the general power of sellers on the labour market, a decrease in the price of labour and, as a result, a decrease in wages and a drop in the standard of living of the region’s population. Against this background, another aspect remains—the high demand of the regions, in particular, in Russia, for available labour force, as well as migration growth of the population with the inability to accelerate natural growth and demographic crisis. The basis for the competitiveness of entrepreneurship in Russian regions is low labour costs, which make it possible to reduce production costs and prices. As automation progresses, the need for cheap labour will grow even more, as robotics maintenance personnel will be required. At the intersection of the above aspects, there is a contradiction that the modern region is interested in maintaining the openness of the economy to the inflow of new personnel and at the same time tries to prevent the migration outflow of population, first of all highly qualified personnel, in particular, personnel of the digital economy, as well as to prevent the escalation of tension in the labour market. The purpose of this work is to study the problem of migration in the digital economy through the prism of identifying new challenges for the labour market, as well as identifying opportunities and priorities to address this problem on the example of the regions of the North Caucasus Federal District of the Russian Federation in 2018.

M. Kh. Abidov et al.

2

Materials and Method

The questions of theory and practice of personnel training in digital economy and their involvement in high-tech entrepreneurship are discussed in the works of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al (2018), Popkova (2019), Popkova et al (2018, 2019), Petrenko et al. (2018), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019). At the same time, migration in the digital economy needs to be reconsidered. For this purpose, let us determine the impact of migration (which is measured by the coefficient of migration growth) on indicators reflecting the state of the labour market and the economy as a whole in the regions of the North Caucasus Federal District of the Russian Federation in 2018: – tension coefficient in the labour market: number of unemployed per vacancy; – unemployment: the share of economically active population not having a job (unemployed); – labour productivity: ratio of gross regional product to the average number of the employed; – regional competitiveness: position of the region in the AV RCI-2018 competitiveness ranking. The method of regression analysis was chosen as the methodological basis for the study, with the help of which regression maps reflecting the impact of migration on the listed indicators are constructed. The data sampling based on Rosstat’s official statistics for 2018 is given in Table 1.

3

Results

Based on the data of Table 1, we have constructed regression curves that reflect the relationship of the studied indicators (Fig. 1). As shown in Fig. 1, with an increase in the migration growth rate by 1 person per 10,000 population in the regions of the North Caucasus Federal District of the Russian Federation in 2018, the labour market tension coefficient increases by 0.8785 (correlation 7.03%), unemployment increases by 0.2045% (correlation 52.16%), labour productivity decreases by 5.2903 thousand rubles/person (correlation 44.77%), the competitiveness of the regional economy decreases by 0.0206 points (correlation 42.11%).

The Problem of Migration in the Conditions …

93

Table 1 The indicators of migration and its consequences in the regions of the North Caucasus Federal District of the Russian Federation in 2018 Region of the North Caucasus Federal District of the Russian Federation

Migration growth rate, people per 10,000 inhabitants

Tension coefficient in the labour market

Unemployment, %

The average number of employees, K of people

Gross regional product, million rubles

Labour productivity, K of rubles/person

Competitiveness of the regional economy, points

The Republic of Dagestan

–42

189.4

12.0

1091.5

597097

547.04

1.59

The Republic of Ingushetia

25

168.1

27.0

179.4

50882.9

283.63

0.27

The Kabardino-Balkar Republic

–28

11.7

10.5

163.6

132707

811.17

n/a

The Karachay-Cherkess Republic

–21

17.3

13.5

169.2

73151.3

432.34

n/a

The Republic of North Ossetia-Alania

–47

35.9

11.8

189.7

125498

661.56

n/a

The Chechen Republic

–19

44.6

14.0

513.5

166711

324.66

0.88

Stavropol Krai

–16

2.5

5.2

1233.7

651925

528.43

2.32

Source compiled by the authors based on the materials from Av-Group (2019) and Rosstat (2019)

Fig. 1 Regression curves reflecting the impact of migration on the labour market and economy of the regions of the North Caucasus Federal District of the Russian Federation in 2018. Source calculated and constructed by the authors

94

M. Kh. Abidov et al.

Fig. 2 The concept of regulating migration in the digital economy in the region

The region in the digital economy State

Foreign market of educational services

Foreign market of educational services

Foreign market of educational services

*

distance learning

Higher education market

- digital economy personnel training migration control

close interconnection Electronic labour market

- digital economy personnel employment integration for accelerated digitalization

Therefore, reliance on traditional practices of deregulation of migration is unacceptable in the digital economy, as it leads to an unfavourable ratio of incoming and outgoing migration flows in the region, destabilizes the labour market and worsens the overall socio-economic situation of the region. Despite the lack of qualitative characteristics of migration flows in the regions of the North Caucasus Federal District of the Russian Federation in 2018, the established regression dependencies testify to their unfavourable combination, which implies: outflow of highly qualified personnel from the regions (outgoing flow) and inflow of low qualified personnel to the regions (incoming flow). In order to overcome the problem identified, it is recommended to strengthen government regulation of migration in the region in the digital economy. Figure 2 reflects the need for systematic regulation of migration in the digital economy in the region through a number of directions. First, it is necessary to ensure close interconnection of the market of higher education services, labour market and regional entrepreneurship in order to guarantee targeted training of exactly those personnel of the digital economy, which are most in demand in the region, to ensure their successful employment and highly effective involvement in the activities of regional entrepreneurship. State regulation of these markets is marked “*” in Fig. 2. Secondly, it is advisable to develop distance learning and increase its availability and popularity in the region. It will allow to increase flexibility of the market of higher educational services, thus carrying out training of personnel of digital economy even at the absence of own opportunities at higher education institutions of region. Thirdly, it is necessary to create an electronic labour market in the region to provide remote employment opportunities, including for job seekers from other regions (migrants).

close interconnection Regional business

- harnessing the power of the digital economy

Fourthly, it would be advisable to control migration in the region by introducing emigration and immigration criteria. For the personnel of the digital economy trained at the expense of budget funds, there should be a requirement to work for a certain period (for example, 5 years) in the region. Incoming migrants should be admitted to the region only if they have a preliminary agreement with enterprises of the region on employment. Fifth, it is necessary to facilitate the integration of regional entrepreneurship with entrepreneurship of other regions for accelerated digitalization. As a result of practical realization of the presented concept, the following advantages are achieved: – accelerated training of digital economy personnel and prevention of their shortage; – limiting undesirable migration and preventing its destabilizing effect on the labour market in the region (unemployment, tension); – expanding employment opportunities and unlocking the potential of the digital economy personnel in the region, ensuring an increase in its competitiveness and productivity.

4

Conclusion

At the end of the study, we note that the problem of migration is growing in the digital economy, creating new challenges for the labour market in the region. These challenges include the need to retain staff in the digital economy and the search for a balance between incoming and outgoing migration flows to maintain the stability of the labour market.

The Problem of Migration in the Conditions …

Overcoming these challenges can be achieved on the basis of the digital economy opportunities through the development of distance learning, the formation of an electronic labour market and the use of digital means of business integration, for example, distance exchange of knowledge and information. The priorities of solving the problem of migration in the region in conditions of digital economy are to prevent the outflow of own and attract external personnel of digital economy, as well as to prevent the influx of unclaimed personnel to the region. Their observance is facilitated by the developed regulatory concept.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Av-Group (2019). Best practices for stimulating the development and competitiveness of regions. Competitiveness Index of Russian regions–AV RCI-2018 beta. Retrieved December 05, 2019. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal 11(10),78, 564–568. Federal State Statistics Service of the Russian Federation (Rosstat) (2019). Regions of the Russian Federation: Socio-economic indicators: 2018. Retrieved December 05, 2019 from https://www.gks. ru/bgd/regl/b18_14p/Main.htm. Gadzhiev, M. M., Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal 11(10),82, 581–586. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian

95 industry. Advances in Intelligent Systems and Computing, 622, 44– 50. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety versus rationality. Berlin: Springer International Publishing. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and Irina A. Kuznetsova, PhD in Engineering, Associate Professor Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components). Theoretical and Practical Issues of Journalism 7(1), 145–154. Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control 169(1), 65–72. Popkova, E. G., & Sergi, B. S. (2018). Will industry 4.0 and other innovations impact Russia’s development? In B. S. Sergi (Ed.) Exploring the future of Russia’s economy and markets: towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In Bruno S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited.

Social Adaptation to Transformation of the Labour Market in the Region in the Conditions of the Digital Economy: Perspectives of Provision of Mass Digital Literacy and Accessibility of Digital Technologies Shahmardan S. Muduev, Sharafudin M. Aliev, and Gozel K. Akavova digital technologies through infrastructure development and formation of a regional platform for digital organization of the labour market, as well as regulatory support. Originality/value: The example of the regions of the North Caucasus Federal District of the Russian Federation in 2018 shows that in modern regions educational, psychological and infrastructural barriers to the formation and entry into the digital labour market are very high, which hinders the development of this market. The developed mechanism of social adaptation to the transformation of the labour market in a region in the conditions of digital economy allows to implement the proposed directions systematically and with high efficiency.

Abstract

Purpose: The aim of the paper is to determine the prospects and develop a scientific concept of social adaptation to the transformation of the labour market in the region in the digital economy on the example of the North Caucasus Federal District of the Russian Federation. Design/methodology/approach: To clarify the specifics of ensuring mass digital literacy and availability of digital technologies at the regional level, a correlation analysis (calculation of determination coefficients R2) of interrelation and interdependence of indicators of labour market functioning and indicators of information society development was carried out in 2018 on the example of the regions of the North Caucasian Federal District of the Russian Federation. SWOT-analysis of social adaptation to the transformation of the labour market in the regions of the North Caucasus Federal District of the Russian Federation in the digital economy was also carried out. Findings: It is shown that social adaptation to the transformation of the labour market in the region in the digital economy is a complex process involving three areas of government regulation. The first area is mass digital literacy. The second direction is psychological support for the information society through social advertising. The third direction regards increasing access to S. S. Muduev (&) Research Institute of Management, Economics, Politics and Sociology, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected] S. M. Aliev Economic and Social Geography, Natural Geographical faculty, Dagestan State Pedagogical University (DSPU), Makhachkala, Russia e-mail: [email protected] G. K. Akavova Department of Geography and Teaching Methods, Dagestan State Pedagogical University (DSPU), Makhachkala, Russia e-mail: [email protected]

Keywords














Social adaptation Transformation of the labour market Region Digital economy Digital literacy Availability of digital technologies Russian regions




JEL Codes




















D83 G34 H52 H75 I21 I22 I26 I27 M53 O18 O31 O32 R11 R58

1

I23 I24 I25 O33 O38 P46

Introduction

Under the conditions of the digital economy, deep transformation processes are being launched and intensified in the region’s labour market. Firstly, electronic recruitment and job search services are being formed and actively developed. Private organizations offer a wide range of specialized portals for organizing electronic interaction between sellers and buyers on the labour market. They enter into competition with employment services operating in the region, which practically do not use digital technologies in their activities.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_17

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Electronic services are attractive as they allow reducing transaction costs of labour market participants, but they cover only certain segments of this market. Thus, there are separate portals for job search and job placement for certain professions. Templates of information presentation are different on different services, many of them are paid, the system of navigation and sorting of data is little functional and low-productive. Presence on an electronic service is not equated to official registration in the employment service and is not a reason for receiving unemployment benefits. Therefore, despite a significant potential, the competitiveness of e-services remains low as compared to the regional employment service, which, nevertheless, gradually becomes an “institutional trap” (an ineffective self-supporting institution). Secondly, a new type of job seekers (economically active population) is emerging in the structure of the offer—digital economy staff. These are specialists of all industries of the highest qualification level, capable of applying advanced digital technologies in their professional activities. They have been carefully selected for admission to higher education institutions, met high requirements for education, and have digital thinking, which is characterized by preference for the use of digital technologies in economic activity. Personnel of the digital economy are interested in using electronic services of the labour market. Thirdly, there is a demand for digital economy personnel and experience in attracting and using them in business is being accumulated. Employers and personnel managers are interested in attracting digital economy personnel, but they are far from being able to provide all the necessary conditions for the use of their unique capabilities and offer them appropriate remuneration. They see the use of electronic recruitment services as a tool to increase their competitiveness as employers and attract digital economy personnel. It also allows reducing recruitment costs. These transformation processes are increasingly visible on the labour market in modern regions, and a new digital

practice of organizing this market is being institutionalized. Not only the personnel of the digital economy, but also all job seekers find themselves in new conditions and need social adaptation. The hypothesis of this research is that in modern regions, particularly in the North Caucasus Federal District of the Russian Federation, social adaptation to the transformation of the labour market is complicated by incomplete coverage of the digital literacy of the population and inaccessibility of digital technologies. The aim of this work is to identify prospects and develop a scientific concept of social adaptation to the transformation of the labour market in a region in the digital economy, using the example of the regions of the North Caucasus Federal District of the Russian Federation.

2

Materials and Method

Fundamental and applied aspects of the problem of social adaptation to the transformation of the labour market in the digital economy are reflected in such sources of economic research literature as Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al (2018), Popkova (2019), Popkova et al. (2018), Petrenko et al. (2018), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019). Our content analysis of the cited literature sources revealed a lack of attention to the specifics of mass digital literacy and digital accessibility at the regional level. To determine this specificity in the regions of the North Caucasus Federal District of the Russian Federation in 2018, in this paper, we conducted a correlation analysis (calculation of determination coefficients R2) of the relationship and interdependence of indicators of the labour market and indicators of the information society development, which are systematized and presented in Table 1.

Table 1 Labour market and information society indicators in the regions of the North Caucasus Federal District of the Russian Federation in 2018 Region of the North Caucasus Federal District of the Russian Federation

Employment rate, %

The Republic of Dagestan

55.5

The Republic of Ingushetia

Average time to find a job by the unemployed, months

Share of the unemployed looking for work for 12 months and more, %

Share of households with access to broadband Internet, %

Share of population aged 15–74 using the Internet, %

Share of population ordering goods and services via the Internet, %

Share of population receiving public services via the Internet, %

9.2

32.4

58.8

84.3

9.7

65.8

56.3

12.9

71.3

78.1

89.4

23.1

76.4

The Kabardino-Balkar Republic

61.2

9.6

44.7

66.7

87.5

21.7

72.0

The Karachay-Cherkess Republic

52.8

11.2

61.8

69.0

82.5

25.1

74.6

The Republic of North Ossetia-Alania

58.0

10.2

51.6

83.6

96.4

30.5

56.2

The Chechen Republic

58.8

9.3

40.5

50.2

91.6

39.7

79.5

Stavropol Krai

61.2

7.8

29.7

68.6

85.4

24.7

75.6

Source compiled by the authors based on materials of the National Research University “Higher School of Economics” (2019) and Rosstat (2019)

Social Adaptation to Transformation of the Labour Market …

3

Results

Using the method of correlation analysis, we have calculated the coefficients of determinacy of indicators from Table 1, which are presented graphically in Fig. 1 for clarity. According to the calculations and data of Fig. 1, the increased availability and activity of digital technologies (as shown by the example of the Internet) instead of the expected mitigation intensifies negative phenomena in the labour market of the regions of the North Caucasus Federal District of the Russian Federation in 2018. In particular, with the development of the information society there is an increase in unemployment, an increase in the average time to look for a job and an increase in the share of the unemployed looking for work for 12 months or more. This is evidenced by almost all positive values of determinacy coefficients (R2). The results of the correlation analysis show that social adaptation to the transformation of the labour market in the regions of the North Caucasus Federal District of the Russian Federation in the conditions of the digital economy is difficult and is developing at a slow pace. To find out the reasons for this problem, let us conduct its SWOT-analysis (Table 2). As demonstrated in Table 2, the prerequisites for social adaptation to the transformation of the labour market in the

99

regions of the North Caucasus Federal District of the Russian Federation in the digital economy are the mass availability of the Internet among the population and its active use both to order goods and services, and to receive e-government services. At the same time, barriers to social adaptation are the lack of infrastructure on the digital labour market and poor spread of digital literacy among the population. Opportunities to overcome the identified barriers are related to the development of digital labour market infrastructure and increased accessibility of digital literacy distance learning. There are threats to the reliability of digital data, psychological barriers, cyber security and protection of digital data from distortion. In order to solve the identified problem, we have developed the following mechanism for social adaptation to the transformation of the labour market in the region in the digital economy (Fig. 2). As it can be seen from Fig. 2, state regulators of the region stimulate the development of the higher education services market in the region, which supports mass mastering of digital competences and ensuring digital literacy of the population, and create an infrastructure of digital economy in the region, providing labour market entities with access to digital technologies (primarily the Internet) and forming a digital platform for the region, as well as ensuring

Fig. 1 Correlation (determination coefficients, R2) of labour market and information society indicators in the regions of the North Caucasus Federal District of the Russian Federation in 2018. Source calculated and constructed by the authors

Table 2 SWOT-analysis of social adaptation to the transformation of the labour market in the regions of the North Caucasus Federal District of the Russian Federation in the digital economy conditions

S: prerequisites

W: barriers

– mass availability of the Internet among the population; – active use of the Internet by the population.

– lack of digital labour market infrastructure; – poor spread of digital literacy.

O: opportunities

T: threats

– development of digital labour market infrastructure; – increasing the availability of distance digital literacy acquisition.

– threat to the reliability of digital data; threat of psychological barriers; – threat to cybersecurity and digital data protection from distortion.

Source designed and compiled by the authors

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Market of higher education services in the region Regional government regulators social advertising

development incentives

support for digital literacy acquisition Digital labour market in the region

Unemployed: − electronic resume; − electronic search.

digital platform for the region, security

digital technology

creation

digital

technology

Digital economy infrastructure in the region

its security (overcoming threats). The state also conducts social advertising to provide psychological support to the population. In fact, an electronic employment service is being created in the region, where registration equals official unemployment and is the basis for unemployment benefits. The core of the developed mechanism is the digital labour market in the region. There the unemployed (job seekers, economically active population) place their electronic resumes and search for vacancies electronically. Employers place vacancies electronically and conduct electronic search and selection of personnel. Interaction between the unemployed and employers takes place in a remote form. As a result, the following benefits are achieved for all participants in the digital labour market in the region: – accelerated and simplified job search by the unemployed; – fast and highly efficient selection of optimal candidates by employers; – reduction of unemployment, retention of digital economy personnel in the region.

4

Employers: remote interaction − electronic job placement; − electronic search and staff selection.

Conclusion

Thus, social adaptation to the transformation of the region’s labour market in the digital economy is a complex process involving three areas of government regulation. The first area is mass digital literacy. It involves the development of a market of educational services in the region, with an emphasis on distance learning, as it is the least costly for both universities and consumers, and is also accessible to the working population. The second direction is psychological support for the information society through social advertising. Its essence is to overcome psychological barriers to the active use of digital technologies.

Advantages: −accelerated and simplified job search and selection of optimal candidates; −unemployment reduction, retention of digital economy personnel.

Fig. 2 Mechanism of social adaptation to the transformation of the labour market in the region in the digital economy. Source designed and compiled by the authors

The third direction is increasing the availability of digital technologies through the development of infrastructure and the formation of a regional platform for the digital organization of the labour market, as well as its regulatory support. The example of the regions of the North Caucasus Federal District of the Russian Federation in 2018 shows that in modern regions educational, psychological and infrastructural barriers to the formation and entry into the digital labour market are still very high, which prevents the development of this market. The developed mechanism of social adaptation to the transformation of the labour market in a region in the conditions of digital economy allows to implement the proposed directions systematically and with high efficiency.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal 11(10), 78, 564–568. Federal State Statistics Service of the Russian Federation (Rosstat) (2019). Regions of the Russian Federation: socio-economic indicators: 2018. Retrieved December 05, 2019 from https://www.gks.ru/ bgd/regl/b18_14p/Main.htm. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal 11(10), 82, 581–586. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian

Social Adaptation to Transformation of the Labour Market … industry. Advances in Intelligent Systems and Computing, 622, 44– 50. National Research University “Higher School of Economics”. (2019). Digital Economics: 2019. Retrieved December 04, 2019 from https://www.hse.ru/data/2019/06/25/1490054019/ice2019.pdf. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety versus rationality. Berlin: Springer International Publishing. Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control 169(1), 65–72. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry

101 conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and Irina A. Kuznetsova, PhD in Engineering, Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components”). Theoretical and Practical Issues of Journalism 7(1), 145–154. Popkova, E. G., & Sergi, B. S. (2018). Will Industry 4.0 and Other Innovations Impact Russia’s Development? In B. S. Sergi (Ed.) Exploring the Future of Russia’s Economy and Markets: Towards Sustainable Economic Development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In B. S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited.

Entrepreneurial Training in the Conditions of the Digital Economy: Stimulation of Demand, Organization, and Practical Experience Sabina E. Savzikhanova, Nigara E. Eminova, and Natalia M. Fomenko

region was developed. The model demonstrated the preference of this training on the territory of the region to ensure that entrepreneurs master all necessary theoretical and practical competencies. Some recommendations were also proposed to stimulate the demand and supply of entrepreneurial training in the conditions of digital economy in the modern region.

Abstract

Purpose: The study aims to review the practical experience of entrepreneurship education in the regions of the North Caucasus Federal District of the Russian Federation and to develop recommendations to stimulate demand and organize entrepreneurship education in the digital economy in the modern region. Design/ methodology/approach: The share of entrepreneurship in the structure of employment of economically active population aged 15–72 years in the regions of the North Caucasus Federal District of the Russian Federation in 2018 has been determined. The paper considers the balanced financial result of entrepreneurship in the regions of the North Caucasus Federal District of the Russian Federation in 2018. The statistics on entrepreneurship education is analyzed and the sufficiency of its coverage for the successful functioning of entrepreneurship in the regions under study is determined. Findings: The considered practical experience of business training in the regions of the North Caucasus Federal District of the Russian Federation showed that at present entrepreneurship is not fully covered by this training due to low demand and shortage of supply. Insufficient skill level of entrepreneurs leads to business losses, which will be overcome by the development of entrepreneurial education. Originality/value: A model for organizing entrepreneurial learning in the digital economy in the S. E. Savzikhanova (&)
N. E. Eminova Department Information Technologies and Economic Security, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected] N. E. Eminova e-mail: [email protected] N. M. Fomenko Department of management theory and business technology, Plekhanov Russian University of Economics, Moscow, Russian Federation e-mail: [email protected]

Keywords












Entrepreneurial training Digital economy Region Demand stimulation Organization Entrepreneurship Practical experience Russian regions





JEL Codes

G34 O31

1












H52 H75 I22 R11 R58

I23

I25

I26

I28

O18

Introduction

Inadequate skills of entrepreneurs is a global problem. This problem is partly due to the existing scientific paradigm of economics and management according to which entrepreneurial ability is identified as a factor of production. This leads firstly to the distinction between entrepreneurial ability and labor due to which entrepreneurial activity is not perceived as a profession and Secondly to the identification of entrepreneurship not with professional competences acquired in the course of training but with the abilities and talents attributed to general (personal) competences which can be developed in training but do not always need it forming in the formation of human personality. As the global financial and economic crisis of 2008 showed in the period of stability and economic recovery it is true that entrepreneurs can rely on their own abilities and talents but in the period of recession this is not enough to

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_18

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maintain the sustainability of business and additional— professional competence in economics and management is needed. In the conditions of the digital economy the business environment is much more complex even on the upward wave of the economic cycle. Optimizing the organization and management of business based on digital technologies requires the entrepreneur to possess digital economic and management competences. Introduction of digital technologies into production and distribution is carried out under the direction of the entrepreneur, for which he must have competences in practical application of these technologies in the branch of enterprise specialization (for example, service sector, industry, agriculture). Even in the traditional (non-digital) form of entrepreneurship, the entrepreneur is forced to implement digital innovations, for example, “green” (digital consumption of resources and production waste), transactional (electronic document management with counterparties), or tax (digital calculation, tax payment and tax reporting, tax optimization). For this purpose, the entrepreneur must also possess special digital competences. In today’s world economy there is a trend toward the globalization of entrepreneurial education. For example, MBA courses are the most popular in Russia. On the one hand, this allows entrepreneurs to gain access to the world’s best practices in digitalization of business and international diplomas. On the other hand, global entrepreneurship education is not adapted to the specifics of digital business in the region, does not take into account and does not meet the unique needs of each individual entrepreneur. It only allows mastering basic universal competencies, but does not guarantee their practical application in entrepreneurial activities. On the basis of the above, the problem of developing entrepreneurship education in the digital economy in the region becomes particularly relevant. This study aims to consider the practical experience of entrepreneurship education in the regions of the North Caucasus Federal District of the Russian Federation and develop recommendations to stimulate demand and organization of entrepreneurship education in the digital economy in the modern region.

2

However, the published works do not take into account the regional context of entrepreneurship education and do not offer applied solutions for its promotion and organization in the modern region in the digital economy. This is a serious gap in the system of scientific and practical knowledge in the field of entrepreneurial education, which our work is designed to fill. Let us turn to the structure of employment of economically active population aged 15–72 years in the regions of the North Caucasus Federal District of the Russian Federation in 2018 (Fig. 1). As shown in Fig. 1, out of 4.559.000 economically active population aged 15–72 years in the regions of the North Caucasus Federal District of the Russian Federation in 2018, there were 4.459.000 economically active population aged 15–72 years. 348.72 thousand people (7.65%) or more (with collective entrepreneurship) are entrepreneurs. This indicates that entrepreneurship is a widespread type of activity. The results of entrepreneurial activity in the context of the regions under study are shown in Fig. 2. As it can be seen from Fig. 2, the majority of the regions of the North Caucasus Federal District of the Russian Federation in 2018 is characterized by negative balanced financial result of entrepreneurship. The largest losses are observed in the Republic of Dagestan (–11920 million rubles) and the Chechen Republic (–11375 million rubles). At that time, in the Stavropol Krai, the balanced financial result of entrepreneurship—the aggregate profit amounted to 48780 million rubles. Thanks to this, the total financial balance of entrepreneurship in the regions of the North Caucasus Federal District of the Russian Federation in 2018 was positive and amounted to 23761 million rubles, having decreased by 19.44% compared to 2017, when it was 29494 million rubles (Rosstat 2019). Consequently, in the regions under consideration there is a low and decreasing efficiency of business activity, which indicates its high complexity. In this regard, it is advisable to

Materials and Method

The specific nature of entrepreneurship and the importance of entrepreneurship education in the digital economy is noted and emphasized in the works of scholars such as Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al (2018), Popkova (2019), Popkova et al. (2018, 2019), Petrenko et al. (2018), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019).

Fig. 1 Employment structure of economically active population aged 15–72 years in the regions of the North Caucasus Federal District of the Russian Federation in 2018, thousand people. Source compiled and calculated by the authors based on the materials of Rosstat (2019)

Entrepreneurial Training in the Conditions …

105

Fig. 2 Balanced financial result of entrepreneurship in the regions of the North Caucasus Federal District of the Russian Federation in 2018, million rubles. Source compiled and calculated by the authors based on the materials of Rosstat (2019)

consider entrepreneurship as a profession, since relying on natural talents and abilities does not guarantee positive results of entrepreneurial activity, and this requires entrepreneurial training. According to information and analytical materials on the results of the monitoring of the quality of training in 2018, of the total number of students (193239 people) 3.17% receive education in economics and accounting (by industry) (6120 people), according to the Ministry of Science and Higher Education of the Russian Federation (2019). This indicates that less than half of the entrepreneurs receive specialized training and are qualified in their profession. Speaking of entrepreneurs as digital economy personnel, it should be noted that in Russia as a whole the number of heads of services and departments in the field of information and communication technologies is 1617.4 thousand people, 2.24% of the total number of employees, Fig. 3 Model for organizing entrepreneurship training in the digital economy in the region. Source designed and compiled by the authors

according to the National Research University “Higher School of Economics” (2019). That is, entrepreneurship education in Russia’s regions and the North Caucasus Federal District as a whole is not sufficiently developed to ensure full coverage of entrepreneurs, and training of entrepreneurs as digital economy personnel is in the process of institutionalization.

3

Results

In order to solve the identified problem and establish a full-scale training of digital economy personnel for the entrepreneurial profession, this study developed a model for organizing entrepreneurial training in the digital economy in the region (Fig. 3).

Regional government regulators stimulation Demand

requirements for the content of the educational program; training schedule preferences; education cost limit; training requirements.

supply analysis and university selection

Potential or current employer

Entrepreneurial training market in the region

Supply

learning opportunities; study schedule; education costs; credentials.

University α

learning opportunities; study schedule; education costs; credentials.

University β

learning opportunities; study schedule; education costs; credentials.

University λ

agreement engineering of business

additional educational resources designed for the entrepreneur

system of basic educational resources

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As you can see from Fig. 3, a feature of the proposed model is the implementation of entrepreneurial training even in a remote form in the region, thanks to which the entrepreneur receives not only knowledge, but also information and consulting services to conduct business, as well as acquire the necessary managerial organizational skills directly on the basis of their enterprise under the guidance of university teachers. In other words, applied training is achieved and the gap with theoretical entrepreneurial training is overcome. The market for entrepreneurship education in the region functions as follows. Each higher education institution (on the offer side) offers its own study opportunities, schedule, study prices and diploma parameters on the market. The entrepreneur (from the demand side) has requirements for the content of the educational program, preferences for the schedule of studies, requirements for the diploma and has a limit on the cost of education. They choose a higher education institution and conclude an agreement with it on entrepreneurship education. Within the framework of the contract, a system of basic educational resources is applied, additional educational resources developed for this entrepreneur, and engineering of entrepreneurship, including field training, is conducted. Taking into account modern Russian practice in the regions, it is necessary to stimulate entrepreneurship education. It is supposed to be implemented by means of state regulation of higher education institutions (proposals) with the help of such measures as standardization of entrepreneurial education in the digital economy and placement of regional state order for training entrepreneurial personnel of digital economy. It is also recommended to regulate entrepreneurs (demand) through such measures as increasing the availability of distance education in the region, implementation of state regional programs to promote the launch and running of entrepreneurship in the digital economy, as well as providing tax preferences for it.

4

Conclusion

Thus, the considered practical experience of entrepreneurship education in the regions of the North Caucasus Federal District of the Russian Federation showed that at present entrepreneurship is not fully covered by this training due to low demand and shortage of supply. Insufficient skill level of entrepreneurs leads to business losses, which will be overcome by the development of entrepreneurial education. For this purpose, a model for organizing entrepreneurial training in the digital economy in the region has been developed.

The model has demonstrated the preference for this training in the region to ensure that entrepreneurs master all necessary theoretical and practical competencies. There were also proposed recommendations to stimulate the demand and supply of entrepreneurial training in the conditions of digital economy in the modern region.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal 11(10), 78, 564–568. Federal State Statistics Service of the Russian Federation (Rosstat) (2019). Regions of the Russian Federation: socio-economic indicators: 2018. Retrieved December 06, 2019 from https://www.gks.ru/ bgd/regl/b18_14p/Main.htm. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal 11(10), 82, 581–586. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44– 50. Ministry of Science and Higher Education of the Russian Federation (2019). Information and analytical materials on the results of monitoring the quality of training in 2018. Retrieved December 06, 2019 from http://indicators.miccedu.ru/monitoring/2018/_spo/ material.php?type=1&id=25. National Research University “Higher School of Economics” (2019). Digital Economics: 2019. Retrieved December 04, 2019 from https://www.hse.ru/data/2019/06/25/1490054019/ice2019.pdf. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable developmet processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety versus rationality. Berlin: Springer International Publishing. Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control 169(1), 65–72.

Entrepreneurial Training in the Conditions … Popkova, E. G., Morozova, I. A., Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and Irina A. Kuznetsova, PhD in Engineering, Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components”). Theoretical and Practical Issues of Journalism 7(1), 145–154. Popkova, E. G., Sergi, B. S. (2018). Will Industry 4.0 and Other Innovations Impact Russia’s Development? In B. S. Sergi (Ed.) Exploring the Future of Russia’s Economy and Markets: Towards Sustainable Economic Development (pp. 51–68). Bingley, UK: Emerald Publishing Limited.

107 Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In Bruno S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited.

Transformation Processes in the Labour Market in a Region in the Conditions of the Digital Economy: A New Model of Organization, Digital Competencies and New Professions Olga V. Budzinskaya

The author’s recommendations make it possible to balance the labour market and increase the probability of establishing a balance on it.

Abstract

Purpose: The study is aimed at studying the transformation processes taking place in the labour market in the region in the conditions of digital economy, development of a new model of organization of this market taking into account new professions that have emerged in it and digital competences that are in demand in them. Design/methodology/approach: For empirical purposes, the author analyzes the structure of the labour market in the regions of the North Caucasus Federal District of the Russian Federation in 2018 from the positions of employment (satisfied supply), digitalization of business (aggregate new demand) and the number of digital workers (aggregate new supply). Oversupply and evident deficiency of digital economy personnel in the majority of regional economy branches have been revealed and the necessity of their balancing has been substantiated. Findings: The results of the research revealed a number of negative trends on the labour market in the digital economy. The example of the regions of the North Caucasus Federal District of the Russian Federation shows that nowadays there is a complication and toughening of competition, as well as growing imbalance of the labour market. In the structure of professions represented on this market, the traditional participants— non-digital specialists by industry—are complemented by digital specialists by industry and universal (cross-industry) ICT specialists. All of them compete with each other for employment opportunities, which is a complex task in the dynamic development (digitalization) of entrepreneurship. Originality/value: A new model of labour market organization in the region in the conditions of digital economy was developed and a competent approach for state regulation of this market was proposed. O. V. Budzinskaya (&) Gubkin Russian State University of Oil and Gas (National Research University), Moscow, Russia e-mail: [email protected]

Keywords











Transformation processes Labour market Region Digital economy Organization model Digital economy personnel Digital competences New professions Russian regions







JEL Codes

E24 O18

1













G34 J08 J22 J23 O31 R11 R58

J24

J42

J61

J82

Introduction

The labour market is one of the markets of production factors and therefore forms the infrastructural support of the regional economy. In the conditions of digital economy, the need for infrastructure is increasing, which makes the study of the labour market even more relevant. The traditional model of organization of this market is very simple. There are enterprises of different industries that form the demand (vacancies). There are also representatives of different professions that determine the supply. They participate in the competition for vacancies not only in their own sector, but also in other (usually related) areas. When making personnel decisions, entrepreneurs or HR managers are guided by two formal criteria: recruitment and level of competence of job seekers, as well as the price of their work (salary requirements). In the end, either the most qualified employee, a specialist in a given industry, or a representative of another industry who is ready to perform labour duties at reduced pay is selected and hired. Most industries, including education,

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_19

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health care, military, government administration and other licensed activities, have strict requirements—government industry regulations—limiting the range of job applicants to representatives of an exclusively designated industry. In these sectors, there is moderate competition in the supply of labour. In the context of the digital economy, new participants are emerging in the labour market who, because of their expressed specificity, do not fit into the traditional model of organization of this market. This leads to a distortion of the results of scientific research of the labour market in the conditions of the digital economy based on the traditional model, reduces the efficiency of its management and prevents the forecasting of its development. In this connection, the risk of labour market crisis in the conditions of digital economy increases, overcoming of which is an urgent scientific and practical problem. To solve this problem, the research is aimed at studying the transformation processes taking place in the labour market in the region in conditions of digital economy, development of a new model of organization of this market taking into account new professions that have emerged in it and digital competences that are in demand in them.

2

Materials and Method

Transformation processes in the system and practice of management in the digital economy, including the labour market in the region, are outlined in scientific papers of such scientists as Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al. (2018), Khachaturyan et al. (2018), Popkova (2019), Popkova et al. (2018, 2019), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019). Despite a rather detailed consideration of the features and changes taking place in the labour market in the digital economy, the existing research literature does not clearly identify new professions emerging in the digital economy, their respective competencies or propose a new model of labour market organization in the region. For the empirical purposes of this study, let us analyze the structure of the labour market in the regions of the North Caucasus Federal District of the Russian Federation in 2018 from the positions of employment (satisfied supply), digitalization of business (aggregate new demand) and the number of digital workers (aggregate new supply), for which we turn to Fig. 1. It can be seen from Fig. 1 that in 2018 the labour market in the regions of the North Caucasus Federal District of the Russian Federation is not balanced, far from the equilibrium state. For example, in the regions under consideration, the

highest share of employment of economically active population is typical for agriculture (18.4%) and trade (17.6%). However, the share of information and communication technology (ICT) specialists in these sectors is small and amounts to 0.4 and 5.4%, respectively, despite the fact that the share of trade in the total digitalization of business (the sum of values of the digitalization index) is one of the highest at 11.3%. At the same time, in the mining industry, where a small share of the population of the regions of the North Caucasus Federal District of the Russian Federation took place in 2018 (0.4%), the level of business digitalization is very high (9.24%), but the share of ICT specialists is small (1.63%). One of the few industries in the regions of the North Caucasus Federal District of the Russian Federation in 2018, where the indicators under consideration are balanced and there is a balance of labour supply and demand, is manufacturing industries. They have one of the highest employment rates (9.5%), a high share of ICT specialists (14.2%) and a high level of business digitalization (11.09%). Among industries with a high degree of business digitalization are water supply (7.12%), hotels and catering (9.34%) and real estate operations (4.96%). But the share of ICT specialists in these sectors is one of the smallest and amounts to 0.4%, 0.5% and 0.8%, respectively, which indicates the pronounced deficit of digital economy personnel in these sectors and the need to balance them.

3

Results

As a result of structural and logical analysis of the labour market development process in the regions of the North Caucasus Federal District of the Russian Federation, the following transformation processes that are taking place in this market under the influence of the digital economy were identified: – Tougher competition. The content of supply and demand is becoming much more diverse. That is why enterprises compete fiercely for the most suitable staff, and job seekers on the labour market compete for the best jobs and even the most job opportunities, not only by profession, although this is preferable for them; – Competition complexity. New selection criteria for employers and HR managers and new employment preferences for job seekers are emerging. The criterion of sectoral specialization is becoming more important and a new criterion—digital competence—is being added to it. Digital business prefers personnel in the digital economy in a given industry, which corresponds to their wishes,

Transformation Processes in the Labour Market …

111

Fig. 1 Labour market structure in the regions of the North Caucasus Federal District of the Russian Federation in 2018 in terms of employment, business digitalization and number of digital employees. Source compiled by the author based on the materials of Rosstat (2019)

but in practice this is not achieved in all cases due to the imbalance of supply and demand; – Increasing imbalance in the labour market. The dynamism of entrepreneurship development is growing in the digital economy, while training of personnel, especially digital economy personnel, takes a long time. Therefore, the gap between supply and demand becomes more pronounced in almost all segments of the labour market in the region. The mentioned transformation processes contribute to the formation of a new labour market structure in the region in the conditions of the digital economy, where new

Table 1 Labour market structure in terms of professions and their respective competences in the digital economy conditions

professions with the corresponding set of necessary competences emerge (Table 1). As can be seen from Table 1, three generalized professions are highlighted in this paper. The first one is non-digital specialists in industries. To a limited extent, they use certain universal digital technologies, for example, office digital equipment (computer, printer, photocopier). In a digital economy, they must not only have industry expertise, but also be able to use universal digital technologies. The second one is digital experts in industries. They actively use the whole range of universal and specialized (for example, digital medical equipment in healthcare) digital technologies. They need industry expertise, the ability to use

Profession

Specifics of digital technology use in the profession

Competence demanded by the profession

Non-digital industry experts

Limited use of selected universal digital technologies

– Industry expertise; – Ability to use universal digital technologies

Digital experts by industry

Active use of the full range of universal and specialized digital technologies

– Industry expertise; – Ability to use universal digital technologies; – The ability to use specialized digital technologies; – Flexibility, learning ability

Universal (cross-industry) ICT specialists

Technical support and maintenance of universal and specialized digital technologies

– Knowledge of how digital devices and technologies work, ability to set up and maintain them; – Programming skills; – flexibility, learning ability

Source designed and compiled by the author.

112

Labour market in the region in the digital economy Segment β Demand

Demand

Segment α

Supply

Fig. 2 Labour market organization model in the region under the conditions of digital economy. Source designed and compiled by the author

O. V. Budzinskaya

Digital business

Non-digital business

Non-digital vacancies

Digital vacancies

Non-digital vacancies

Non-digital personnel

Digital personnel

Non-digital personnel

Digital business

Non-digital business

Digital vacancies

Digital personnel

IT-specialists

universal digital technologies, the ability to use specialized digital technologies, and flexibility, learning ability. The third one is universal (cross-industry) ICT specialists. It provides technical support and services for universal and specialized digital technologies. They must be familiar with the operation of digital devices and technologies, be able to configure and maintain them, have programming skills and be flexible and trainable. In accordance with the above, a new model of labour market organization in the region in the digital economy has been developed (Fig. 2). The model developed in Fig. 2 shows that the labour market in the digital economy actually implements the neoclassical idea of “war of all against all”. All enterprises compete with each other for personnel, and all job seekers compete with each other for employment opportunities. If the digital business is underdeveloped in its segment, the digital economy personnel are forced to look for a job in non-digital enterprises in this segment or to find a job in the digital business of other industries. In other words, industry boundaries are virtually blurred, reducing labour market entropy. The obvious consequence of this process increased social tension and increased risk of labour market crisis. This proves the need for tougher state regulation of this market. Standardization of professions by competence is recommended as a promising regulatory tool. The application of a competency-based approach will make it possible to clearly define the boundaries of market segments, reduce uncertainty for all participants in the labour market and provide a guarantee of optimal personnel selection for business and a guarantee of employment for digital economy personnel in the region.

4

Conclusion

The results of the research revealed a number of negative trends on the labour market in the digital economy. The example of the regions of the North Caucasus Federal District of the Russian Federation shows that nowadays there is a complication and toughening of competition, as well as growing imbalance of the labour market. In the structure of professions represented on this market, the traditional participants—non-digital specialists by industry—are complemented by digital specialists by industry and universal (cross-industry) ICT specialists. All of them compete with each other for employment opportunities, which is a complex task in the dynamic development (digitalization) of entrepreneurship. The new model of labour market organization in the region in conditions of digital economy and the competence approach proposed for state regulation of this market allow to balance the labour market and increase the probability of establishing a balance in it.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal 11(10), 78, 564–568.

Transformation Processes in the Labour Market … Federal State Statistics Service of the Russian Federation (Rosstat) (2019). Regions of the Russian Federation: Socio-economic indicators: 2018. Retrieved December 06, 2019 from https://www.gks. ru/bgd/regl/b18_14p/Main.htm. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal 11(10), 82, 581–586. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44–50. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety versus rationality. Berlin: Springer International Publishing.

113 Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and Irina A. Kuznetsova, PhD in Engineering, Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components”). Theoretical and Practical Issues of Journalism 7(1), 145–154. Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control 169(1), 65–72. Popkova, E. G., & Sergi, B. S. (2018). Will Industry 4.0 and Other Innovations Impact Russia’s Development? In B. S. Sergi (Ed.) Exploring the future of russia’s economy and markets: Towards sustainable economic development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In Bruno S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited.

State Management of Foreign Economic Activities of a Region in the Conditions of the Digital Economy Salihbek G. Abdulmanapov, Nizami S. Askerov, and Abakar S. Mudunov

and foreign investment attraction in the development of the digital economy in the region. Originality/value: The main conclusion of this study, which has both theoretical and applied value, is to justify the need to pay greater attention to public regulation of foreign trade in the digital economy. The analyzed experience of the regions of the North Caucasus Federal District of the Russian Federation can be useful for many other regions characterized by moderate global competitiveness and reserved integration. Selected regulatory directions are universal—they are relevant for all regions in the digital economy. But depending on the specifics of the situation, different approaches for the implementation of these directions can be applied based on either stimulating or deterrent and restrictive measures.

Abstract

Purpose: The purpose of the work is to study the specifics of public management of foreign economic activity of the region in the digital economy on the example of the North Caucasus Federal District of the Russian Federation. Design/methodology/approach: In order to obtain the most accurate and reliable results in the study of the problem, a comprehensive analysis of statistical data reflecting various aspects of foreign economic activity of the regions of the North Caucasus Federal District of the Russian Federation is carried out in this research. In order to identify the specifics of these activities in the digital economy, statistical data are considered in dynamics— 2011 and 2018. Findings: So, the working hypothesis is confirmed. On the basis of the experience of the regions of the North Caucasus Federal District of the Russian Federation, which has demonstrated negative trends in foreign trade, four new directions of state management of foreign economic activity of the region, arising in the conditions of digital economy, have been defined: regulation of foreign trade in technologies as one of the most valuable resources of the region in the conditions of digital economy, general control of foreign trade in goods in the interests of ensuring economic security of the region, assistance in exploiting integration opportunities for the development of the digital economy in the region, S. G. Abdulmanapov (&) Research Institute of Management, Economics, Politics, and Sociology, Dagestan State University of National Economy (DSUNE, Makhachkala, Russia e-mail: [email protected] N. S. Askerov Department of Political Economy, Dagestan State University (DSU), Makhachkala, Russia e-mail: [email protected] A. S. Mudunov Department of Economics, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected]

Keywords








Public administration Foreign economic activity Region Digital economy Russian regions




JEL Codes

F16

1







F17

O18

O31

Q37

R11

R58

Introduction

Foreign economic activity is one of the directions of strategic state regulation of economy. In the conditions of globalization even not including the influence of the fourth industrial revolution, the implementation of this direction is one of the important and complex functions of the state apparatus due to its inconsistency and necessity to apply a flexible approach to its implementation taking into account the specificity of the economic system and its foreign trade priorities.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_20

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On the one hand, under the pressure of the global trend of international economic integration, the scientific and practical argumentation of the concept of free trade (“free-trade”) is increasing. Foreign trade cooperation is attractive both for the economies interested in expanding the markets for domestic business products—as a rule, these are developed countries and developing countries with industrial production specialization—and for the economies seeking to obtain additional foreign investment and distribution of production and distribution tasks with foreign trade partners. On the other hand, the negative impact of globalization, including the international financial and economic crises, price dumping by foreign competitors, and their absorption of domestic entrepreneurship make the concept of protecting the domestic market (“protectionism”) attractive. It is also necessary to note here the tendency of regionalization of the modern world economy, connected with selective integration of economic systems on the geographical principle. As examples, we can mention the process of European integration (EU), Eurasian integration (EAEC), and others. This practice presupposes the setting of priorities for foreign economic relations. Although the basic principles of state management of foreign economic activity are set at the national level, each modern region carries out its own regulation within the limits of the powers assigned to it in order to optimize its foreign trade relations. The working hypothesis of this study is that, in the digital economy, the needs of the region for public administration of foreign economic activities are changing and it is necessary to modernize the practice of this administration. The purpose of the work is to study the specifics of public management of foreign economic activity of the region in the digital economy on the example of the North Caucasus Federal District of the Russian Federation.

2

Materials and Method

State regulation of the economy in the digital economy is reflected in the works of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Popkova (2019), Popkova et al. (2018, 2019), Petrenko et al. (2018), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), and Sergi (2019). The theory and practice of regional foreign trade regulation is described in works by Blatter et al. (2010), Ogwumike et al. (2018), Oyarzún Serrano (2018), and Shahbaz et al. (2018). As a result of the literary review of the chosen topic, it was revealed that at present only some components of the problem of public management of foreign economic activity of the region in the digital economy are studied, which lead

to its insufficient elaboration and the need for further research. In order to obtain the most accurate and reliable results in the study of the problem, this study uses a comprehensive analysis of statistical data reflecting various aspects of foreign economic activity of the regions of the North Caucasus Federal District of the Russian Federation. To reveal the specifics of these activities in the digital economy, statistical data are considered in dynamics—2011. (long before the launch of the program “Digital Economy of the Russian Federation”) and 2018 (the period in which the first results of the program launched in 2017 may be seen).

3

Results

As a result of the systematic analysis of trends in changes in the manifestations of foreign economic activity of the regions of the North Caucasus Federal District of the Russian Federation under the influence of digital modernization, this paper identifies four new directions of public administration of this activity. The first direction is the regulation of foreign trade in technologies as one of the most valuable resources of the region in the digital economy (Fig. 1). As shown in Fig. 1, in 2011, the regions of the North Caucasus Federal District of the Russian Federation saw a moderately negative balance of foreign trade in technologies (–6096.5 thousand dollars). In 2018, there was a marked negative balance (–43593.9 thousand dollars), which increased by more than seven times. This trend can be explained by the need to borrow technologies from foreign trade partners who had previously launched programs to build a digital economy. At the first stage of implementation of this program in the region—for Russian regions, the time limits of this stage correspond to 2018–2021—a negative balance of foreign trade in technologies is admissible, but later—at subsequent stages—it is unacceptable, as it does not allow to form technological competitive advantages (“seats”) in the region and causes its dependence on technology imports. Therefore, there is a need for state management of foreign trade in technologies aimed at its maximum limit. Reduction of imports will allow to ensure technological independence of the region in the conditions of the digital economy, and limitation of exports—its long-term competitiveness due to reliance on unique technologies. The second direction is the general control of foreign trade in goods for the sake of ensuring economic security of the region (Fig. 2). According to Fig. 2, in some regions (in particular, in the Republic of North Ossetia–Alania, the amount of money spent on the implementation of the program is 30.5 million dollars—and Stavropol Krai—344.6 million dollars) and in

State Management of Foreign Economic Activities …

117

Fig. 1 The dynamics of foreign trade in technologies in the regions of the North Caucasus Federal District of the Russian Federation in 2011–2018, thousand dollars Source Compiled by the authors based on the materials of Rosstat (2019)

Fig. 2 Foreign trade balance dynamics in the regions of the North Caucasus Federal District of the Russian Federation in 2011–2018, million dollars Source Compiled by the authors based on the materials of Rosstat (2019)

the North Caucasus Federal District as a whole in 2018, under the influence of digital modernization of the economy, the balance of foreign trade turnover noticeably improved. Whereas in 2011, the Federal District in question as a whole had a significant negative balance (–310.1 million dollars); in 2018, it became positive and amounted to 30.5 million dollars. Nevertheless, in most regions, including the Republic of Dagestan (–100 million dollars), the Republic of Ingushetia (–5.4 million dollars), the Kabardino-Balkar Republic (–45.1 million dollars), the Karachay-Cherkess Republic (–151.6 million dollars), and the Chechen Republic (–42.3 million dollars), the negative balance remained or even increased. This can be explained by the increased openness of the region’s economy to foreign trade on the basis of digital technologies; thanks to digital logistics (remote tracking of cargo delivery), electronic commerce (remote interaction between seller and buyer), and electronic foreign economic communications (reduction of transaction costs). Consequently, it is necessary to restrict imports in the digital economy of the region, as well as to promote exports.

The third direction is to promote the use of integration opportunities for the development of the digital economy in the region. According to the report “Joint projects of participants in industrial clusters”, the Association of Clusters and Technology Parks of Russia, and the Ministry of Industry and Trade of Russia (2019), among the regions of the North Caucasus Federal District only in Stavropol Krai four clusters are operating, all of them are internally oriented; in other regions of the considered district, there are no clusters at all. This shows that the potential for integration has not been realized and needs to be regulated by the state in the interests of the division of labor for the development and implementation of digital technologies, for example, on the platform of the EAEC and the exchange of factors of production. The fourth direction is the attraction of foreign investments into development of digital economy in the region (Fig. 3). Figure 3 shows that despite the expanded opportunities and favorable conditions for attraction of foreign investments in the conditions of the digital economy, in 2018 (45

118

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Fig. 3 Balance dynamics of foreign direct investment in the regions of the North Caucasus Federal District of the Russian Federation in 2011–2018, million dollars Source Compiled by the authors based on the materials of Rosstat (2019)

million dollars), the balance of direct foreign investments in the regions of the North Caucasus Federal District of the Russian Federation reduced by 55.44% as compared with 2011 (101 million dollars). This points to the outflow of foreign investments from the regions under study and the increased risk of the deficit of investments in the implementation of regional projects of digital economy modernization. Foreign investment outflow needs state regulation aimed at increasing the attractiveness of the digital economy of the region for foreign investors and the development of the necessary infrastructure for venture investments.

4

Conclusion

So, the working hypothesis is confirmed. On the basis of the experience of the regions of the North Caucasus Federal District of the Russian Federation, which has demonstrated negative trends in foreign trade, four new directions of state management of foreign economic activity of the region, arising in the conditions of digital economy, have been defined: regulation of foreign trade in technologies as one of the most valuable resources of the region in the conditions of digital economy, general control of foreign trade in goods in the interests of ensuring economic security of the region, assistance in exploiting integration opportunities for the development of the digital economy in the region, and foreign investment attraction in the development of the digital economy in the region. The main conclusion of this study, which has both theoretical and applied value, is to justify the need to pay greater attention to public regulation of foreign trade in the digital economy. The analyzed experience of the regions of the North Caucasus Federal District of the Russian Federation can be useful for many other regions characterized by

moderate global competitiveness and reserved integration. Selected regulatory directions are universal—they are relevant for all regions in the digital economy. But depending on the specifics of the situation, different approaches for the implementation of these directions can be applied based on either stimulating or deterrent and restrictive measures.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Blatter, J., Kreutzer, M., Rentl, M., & Thiele, J. (2010). Preconditions for foreign activities of European regions: Tracing causal configurations of economic, cultural, and political strategies. Publius, 40 (1), 171–199. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal 11(10), 78, 564–568. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal 11(10), 82, 581–586. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44– 50. Ogwumike, O. F., Maku, O. E., & Alimi, O. Y. (2018). Human welfare and transmission channel of globalisation: Empirical evidence from Sub-Saharan African regions. Pertanika Journal of Social Sciences and Humanities, 26(3), 1729–1756. Oyarzún Serrano, L. (2018). The challenges of globalization in Latin America: State or region?|[Los desafíos de la globalización en América Latina: ¿Estado o región?]. Universum, 33(1), 164–186.

State Management of Foreign Economic Activities … Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control 169(1), 65–72. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and Irina A. Kuznetsova, PhD in Engineering, Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components”). Theoretical and Practical Issues of Journalism 7(1), 145–154. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will Industry 4.0 and Other Innovations Impact Russia’s Development? In B. S. Sergi (Ed.) Exploring the Future of Russia’s Economy and Markets: Towards Sustainable Economic Development (pp. 51–68). Bingley, UK: Emerald Publishing Limited.

119 Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety versus rationality. Berlin: Springer International Publishing. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In Bruno S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Shahbaz, M., Shahzad, S. J. H., Alam, S., & Apergis, N. (2018). Globalisation, economic growth and energy consumption in the BRICS region: The importance of asymmetries. Journal of International Trade and Economic Development, 27(8), 985–1009. Association of Cluster and Technology Parks of Russia, Ministry of Industry and Trade of Russia (2019). Joint projects between participants of industrial clusters. Retrieved October 20, 2019 from http://akitrf.ru/clusters/accreditation/. Federal State Statistics Service of the Russian Federation (Rosstat) (2019). Regions of the Russian Federation: socio-economic indicators: 2018. Retrieved October 12, 2019 from https://www.gks.ru/ bgd/regl/b18_14p/Main.htm.

Scenarios of Region’s Development in the Conditions of the Digital Economy and Priorities of State and Corporate Management Shamil M. Tagirov, Zalmu K. Omarova, and Naida G. Omarova

modernization of these regions nor their accelerated revolutionary transition to industry 4.0 is appropriate. We have recommended a scenario related to rapid but evolutionary digitalization, which would allow to achieve its clear advantages but prevent the crisis impact of the shortcomings on the regional economy. Originality/value: The analysis of the experience of the regions of the North Caucasus Federal District of the Russian Federation has shown that the digital economy can moderately promote employment, as well as significantly accelerate the innovative development of regions. Nevertheless, regardless of the scenario, digitalization leads to a decline in living standards. Therefore, social and crisis management is a key priority for the state and corporate governance of the region’s development in a digital economy. Attention should also be paid to cyber-security and ensuring a balanced regional economy.

Abstract

Purpose: The aim of the paper is to define scenarios for the development of regions of the North Caucasus Federal District of the Russian Federation in the conditions of the digital economy and to set priorities for state and corporate management of this process. Design/methodology/ approach: In the scenario modeling of the development of regions of the North Caucasus Federal District of the Russian Federation in the conditions of the digital economy, the method of regression analysis and the simplex method of multi-objective optimization are used in this paper. Based on the noosphere approach to the study of economic systems, the dependence of the level of employment (social sphere indicator), GRP per capita (economic sphere indicator), and the share of innovative products (innovation sphere indicator) on the level of business digitalization in the regions under consideration is determined in this paper. Findings: The results of the study showed that digitalization has a generally beneficial effect on the regions of the North Caucasus Federal District of the Russian Federation, but is accompanied by inevitable negative effects and risks. As a result of systematic analysis of the consequences of the development of the digital economy in the studied regions, it has been proved that neither a strong deterrent to digital S. M. Tagirov (&) Research Institute of Management, Economics, Politics and Sociology, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected] Z. K. Omarova Makhachkala Branch of the Moscow Automobile and Highway State Technical University (MADI), Makhachkala, Russia e-mail: [email protected] N. G. Omarova Department of Automobile Transport, Makhachkala Branch of Moscow Automobile and Road State Technical University (MADI), Makhachkala, Russia e-mail: [email protected]

Keywords













Scenarios Regional development Digital economy Priorities Public administration Corporate governance Region Russian regions




JEL Codes

C53

1







E17

G34

O18

O31

R11

R58

Introduction

The formation of the digital economy, like any manifestation of scientific and technological progress, has systemic and contradictory effects on the regional economy and can develop under different scenarios, which are classified using three universal criteria. The first criterion is the intensity of change. As the experience of previous industrial revolutions shows, the more radical the changes caused by scientific and

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_21

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technological progress, the more pronounced are the social consequences, both positive and negative. Simultaneously with the improvement of the quality of life of the population due to the availability of new goods and services, psychological problems arise in connection with the need to adapt to these changes. In the course of the fourth industrial revolution, the development of the digital economy will promote the spread of breakthrough technologies—ubiquitous computing, artificial intelligence, robotics. Along with its advantages, each technology has disadvantages for society. For example, monitoring offenses with the help of ubiquitous computing can lead to the violation of privacy, “home” robots will increase the availability of social services, but will cause protests against the opponents of machine society, artificial intelligence will improve the rationality of decisions, but will question the ability of man and lead to the problem of social identity. The second criterion is economic security. Financial innovations that were successfully introduced at the end of the twentieth century accelerated economic growth but eventually caused a global economic depression. Similarly, digital technologies can cause an initial recovery in production, which will be followed by a crisis of overproduction. In the context of the transition to industry 4.0, there is also a need to take into account emerging new risks related to cyber-security in the areas of intellectual property protection, privacy, and the prevention of loss of information due to breakdowns or digital device failures. The third criterion is the balance of the regional economy. Turning to the experience of industrial revolutions, it is necessary to pay attention to the constant strengthening of imbalances in the development of the global and regional economies. Just as at the global level the gap between developed and developing countries is growing, at the territorial level the differentiation of regions by the level and pace of socioeconomic development is increasing. At a low rate of digital modernization of regional economy, it is possible to maintain uniformity in regional development, but accelerated modernization will inevitably lead to institutionalization of the division of regions into advanced and lagging ones. Thus, the process of development of the region in the conditions of digital economy requires state and corporate management aimed at optimizing the scenario, according to which this development takes place. The aim of this paper is to define scenarios for the development of regions of the North Caucasus Federal District of the Russian Federation in the conditions of digital economy and to set priorities for state and corporate management of this process.

S. M. Tagirov et al.

2

Materials and Method

Some of the consequences of the formation and development of the digital economy, including the ones at the regional level, are outlined in the works of authors such as Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al (2018), Popkova (2019), Popkova et al. (2018, 2019), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019). Nevertheless, the existing publications do not summarize enough experience to establish universal patterns of regional development in the digital economy—moreover, they justify the uniqueness of the development of each individual region. This calls for applied research on the basis of different regions to develop applied solutions for public and corporate management of their development in the digital economy, taking into account the regions’ specifics. In the scenario modeling of the development of regions of the North Caucasus Federal District of the Russian Federation in the conditions of the digital economy, the method of regression analysis and the simplex method of multi-objective optimization are used in this paper. Based on the noosphere approach to studying economic systems, this paper determines the dependence of the level of employment (social sphere indicator), GRP per capita (economic sphere indicator), and the share of innovative products (innovation sphere indicator) on the level of business digitalization in the regions under consideration. It should be pointed out that the effects of digitalization on the environment are not taken into account because of their weakness in the regions under study, as revealed by the preliminary study. The sample of statistical data for 2018 is given in Table 1.

3

Results

On the basis of the data presented in Table 1, the regression curves shown in Fig. 1 are constructed. According to the built regression curves, as the level of business digitalization increases by 1% in the regions under study, the employment rate increases by 0.0505% (correlation is low: 0.63%), GRP per capita decreases by 2522.7 rubles. (correlation is low: 7.69%), the share of innovative products increases by 0.1919% (correlation is low: 9.51%). The identified dependencies allow us to apply a mathematical apparatus to determine the parameters of the three selected scenarios of digital economy development in the

Scenarios of Region’s Development in the Conditions … Table 1 Regional development indicators of the North Caucasus Federal District of the Russian Federation in the digital economy in 2018

Region of the North Caucasus Federal District of the Russian Federation

123 Level of business digitalization, %

Employment rate, %

GRP per capita, rubles

Share of innovative products, %

The Republic of Dagestan

18

55.5

197141.0

0.3

The Republic of Ingushetia

33

56.3

106756.6

0.5

The Kabardino-Balkar Republic

24

61.2

153710.

0.9

The Karachay-Cherkess Republic

26

52.8

156602.4

0.1

The Republic of North Ossetia-Alania

23

58.0

178390.3

0.1

The Chechen Republic

23

58.8

118696.4

2.8

Stavropol Krai

29

61.2

232582.0

8.3

On average in the North Caucasus Federal District

25

57.7

163411.2

2.0

Source Compiled by the authors based on the materials of the National Research University “Higher School of Economics” (2019) and Rosstat (2019)

Fig. 1 Regression curves reflecting the impact of the digital economy on social, economic, and innovation in the regions of the North Caucasus Federal District of the Russian Federation in 2018. Source Calculated and constructed by the authors

regions of the North Caucasus Federal District of the Russian Federation. Scenario 1 assumes a slow rate of digitalization of the regions under consideration, which level will not exceed 40% by 2024. This will ensure smooth changes, allow all stakeholders to adapt in time and cause minimal psychological problems among the population of the regions. Since the forms of data storage will be varied—along with the digital form, a paper form will still be distributed, which will reduce the risks of cyber-security. With the slow pace of digital modernization, regions will develop relatively evenly, which will allow achieving a balanced regional economy. Scenario 2 deals with accelerated digitalization, which will reach 70% in 2024. The changes will be much more pronounced, which will inevitably raise problems of social adaptation. Significant improvements in existing digital data protection technologies will be needed to maintain

cyber-security. The imbalance in the regional economy will widen. Scenario 3 means a revolutionary digitalization, which will reach a record level of 90% by 2024. The changes would be radical and could cause a large-scale social crisis. The problem of cyber-security will be exacerbated, as will the problem of imbalances in the regional economy. The scenarios drawn up are illustrated in Fig. 2. As shown in Fig. 2, the highest increase (as compared to 2018) in the employment rate (5.7%) and the share of innovative products (615%) is achieved in scenario C, but it leads to a 100.3% reduction in GRP per capita. The arithmetic mean of growth rate in this scenario is 173.5%. Nevertheless, taking into account high-security risks, social risks, and problems of imbalance of regional economy, this scenario is not recommended for practical implementation. This scenario assumes a minimal decline in GRP per capita (22.9%) and a moderate increase in the employment

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Fig. 2 Regional development scenarios for the North Caucasus Federal District of the Russian Federation in the digital economy, %. Source Designed and built by the authors

rate (1.2%) and the share of innovative products (135%). The arithmetic mean of growth of these indicators under this scenario is 37.8%. In our view, the most preferable scenario is scenario 2, which assumes a substantial increase in employment (4%) and in the share of innovative products (425%) and the highest average growth rate (119.9%), despite a 69.2% fall in per capita GDP. Therefore, in the regions of the North Caucasus Federal District of the Russian Federation it is recommended to stimulate the development of the digital economy, but at an average pace. The following are the priorities for government management of the process of digital modernization in the regions under study: – strengthening financial support measures for the population to prevent the recession and ensure an improvement in living standards; – increasing the minimum wage to prevent a decline in wages due to the decline in the value of human labor in an environment of total automation; – support of social adaptation of the population for crisis-free digital modernization of the regional economy; – development of cyber-security infrastructure, including with research and development grant support; – monitoring the process of digital modernization of regions and on the basis of its results—support for lagging regions to maintain a balanced regional economy. It is also recommended that the following corporate governance priorities are observed in the digital economy: – manifestation of corporate social responsibility in the form of maintaining a high level of remuneration and increasing wages in order to maintain the current living

standards of their employees in the conditions of digitalization of the regional economy; – support of employees’ social adaptation through corporate targeted training aimed at creating and developing digital competencies; – conducting own corporate R&D in the area of cyber-security.

4

Conclusion

The results of the study showed that digitalization generally has a beneficial effect on the regions of the North Caucasus Federal District of the Russian Federation, but is accompanied by inevitable negative effects and risks. As a result of systematic analysis of the consequences of the development of the digital economy in the studied regions, it has been proved that neither a strong deterrent to digital modernization of these regions nor their accelerated revolutionary transition to industry 4.0 is appropriate. A scenario involving rapid but evolutionary digitalization is recommended to achieve its distinct advantages and to prevent the crisis impact of the shortcomings on the regional economy. The analysis of the experience of the regions of the North Caucasus Federal District of the Russian Federation has shown that the digital economy can moderately contribute to employment, as well as significantly accelerate the innovative development of regions. Nevertheless, regardless of the scenario, digitalization leads to a decline in living standards. Therefore, social and crisis management is a key priority for the state and corporate governance of the region’s development in a digital economy. Attention should also be paid to cyber-security and ensuring a balanced regional economy.

Scenarios of Region’s Development in the Conditions …

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal 11(10), 78, 564–568. Federal State Statistics Service of the Russian Federation (Rosstat) (2019). Regions of the Russian Federation: socioeconomic indicators: 2018. Retrieved December 10, 2019 from https://www.gks.ru/ bgd/regl/b18_14p/Main.htm. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal 11(10), 82, 581–586. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44– 50. National Research University ‘Higher School of Economics’ (NRU HSE) (2019). Digital economy: 2019. Retrieved December 04, 2019 from https://www.hse.ru/data/2019/06/25/1490054019/ice2019.pdf. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control 169(1), 65–72.

125 Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B.S. (2018). Will Industry 4.0 and Other Innovations Impact Russia’s Development? In B. S. Sergi (Ed.) Exploring the Future of Russia’s Economy and Markets: Towards Sustainable Economic Development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: complexity and variety versus rationality. Berlin: Springer International Publishing. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and Irina A. Kuznetsova, PhD in Engineering, Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components”). Theoretical and Practical Issues of Journalism 7(1), 145–154. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling russian industrial, tech, and financial cooperation with the Asia-Pacific region. In Bruno S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited.

“Smart” Region: Managing Economic Development on the Basis of Machine Vision and Ubiquitous Computing Anastasia A. Kryukova, Natalya A. Stefanova, and Ildar A. Khasanshin

standards and quality of life, growth of social justice through more accurate and justified government support, as well as fuller satisfaction of social needs by businesses.

Abstract

Purpose: The aim of the work is to substantiate the demand and develop a promising model of “smart” region, allowing state and corporate management of economic development based on machine vision and computation in the regions of the North Caucasus Federal District of the Russian Federation. Design/methodology/ approach: To overcome the discrepancy between theory and practice and to ensure the integrity of the concept of “smart” region in their work the authors review the available statistical data on the level of development of the digital economy in the regions of the North Caucasus Federal District of the Russian Federation in 2018. Findings: The necessity and readiness (universal accessibility and active use of the Internet by business and population) of the regions of the North Caucasus Federal District of the Russian Federation in 2018 to implement the concept of “smart” region has been substantiated. The unique needs that determine the necessary specifics of the application of this concept in the regions under study— law enforcement, information, and technical support for corporate governance. Originality/value: A promising model of economic development management based on machine vision and ubiquitous computing in “smart” regions of the North Caucasus Federal District of the Russian Federation was developed. The benefits for the region’s residents are related to the improvement of living A. A. Kryukova (&)
I. A. Khasanshin Digital Economics Department the Povolzhskiy State University of Telecommunication and Informatics (PSUTI), Samara, Russia e-mail: [email protected] I. A. Khasanshin e-mail: [email protected] N. A. Stefanova Department of Digital Economics, Economics, Assistant to the Vice-Rector for Science and Innovation, Povolzhskiy State University of Telecommunication and Informatics (PSUTI), Samara, Russia e-mail: [email protected]

Keywords










“Smart” region Public administration Corporate governance Economic development management Machine vision Ubiquitous computing Region Regions of russia





JEL Codes

G34

1





O18

O31

R11

R58

Introduction

“Smart” region is one of the most progressive and actively implemented concepts of regional economic development. Nevertheless, the consequences of the practical application of this concept are contradictory. On the one hand, in truly advanced regions, where the social environment is prepared for the introduction of breakthrough digital technologies, where full financial support for the fourth industrial revolution is provided both on the basis of sufficient own investments and on the basis of attracted investment resources, the formation and development of intellectual production and consumption is justified. On the other hand, in regions with an unformed information society, lack of infrastructure and financial capital, intellectual technologies are also being introduced. Budgetary resources that are urgently needed to implement social projects are being redistributed in favor of digital modernization, while the deficit of attracted investments and underdeveloped infrastructure causes incompleteness of digital reforms and impossibility to extract expected benefits from them in full.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_22

127

128

A. A. Kryukova et al.

There is also the practice of introducing digital technologies to enhance business and government reputation. In this case, a “smart” region serves not as an end in itself, but as a tool for achieving other goals of the state and corporate governance. While these goals are indeed highly likely to be achieved in practice, which is reflected, inter alia, in the strengthening of global digital competitiveness and accelerated innovation development, this can be accompanied by negative external effects (externalities), including a low rate of return on capital or a fundamental inability to return on investment, reduced stability and sustainability, and increased exposure to crises. On the basis of the aforesaid urgency acquires reliance on scientific methodology in the realization of strategies of regional economy intellectualization. In our work we pursue the purpose of substantiation of a demand and development of a perspective model of “clever” region, allowing to carry out state and corporate management of economic development on the basis of machine vision and ubiquitous calculations in regions of the North Caucasian Federal District of the Russian Federation.

2

Materials and Method

Various fundamental and applied issues of economic development management based on advanced digital technologies are discussed in the studies of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al (2018), Popkova (2019), Popkova et al. (2018, 2019), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019). The concept of a “smart” region is presented directly in the works by Di Bella Fig. 1 Number of registered offenses in the regions of the North Caucasus Federal District of the Russian Federation per 100.000 inhabitants in 2018, pcs. Source Compiled by the authors based on the materials of Rosstat (2019)

et al. (2019), Dosso and Lebert (2019), Guzal-Dec et al. (2019), Murzyn (2019), Trippl et al. (2019). A review of the existing literature has revealed a rather high degree of elaboration of the concept of “smart” region, but its scientific and methodological justification is insufficient. In particular, the prospects and conditions of practical implementation of this concept in the activities of modern regions have not been determined. This leads to the theoreticalization of the concept under study and prevents its highly effective empirical application—either limiting its application in those regions where it is necessary and can be used, or causing unreasonable application of the concept in those regions where it is not in demand and is premature. To overcome the discrepancy between theory and practice and to ensure the integrity of the concept of “smart” region in our work we review the available statistical data on the level of development of the digital economy in the regions of the North Caucasus Federal District of the Russian Federation in 2018. First, one of the key and successfully implemented areas of state regulation of economic activity in the regions of the North Caucasus Federal District of the Russian Federation is law enforcement (Fig. 1). As shown in Fig. 1, the average number of registered offenses per 100.000 people in Russia is 1.402, while in the regions of the North Caucasus Federal District it is two times less (702). Therefore, the concept of “smart” region should be applied for technical support and multiplying the already achieved outstanding results in this direction. Secondly, insufficient information and technical support of corporate governance leads to low and even negative financial performance of enterprises of the North Caucasus Federal District (Fig. 2).

“Smart” Region: Managing Economic Development …

According to Fig. 2, in 2018 the balanced financial result of the enterprises of the considered territory made 23761 million rubles. On average in Russia, this figure turned out to be 380 times higher and took the value of 9036848 million rubles. In connection with the above statistical data, it is obvious that the business of the regions under study needs extended information and technical support for management decision-making. Thirdly, in the regions of the North Caucasus Federal District of the Russian Federation, the social environment is prepared for mass introduction of intellectual technologies, sufficient infrastructure has been formed for this purpose, and business readiness is also high (Fig. 3). According to Fig. 3, enterprises in the regions studied to use the Internet more actively (85.9% on average) than the Fig. 2 Balanced financial result of enterprises in the regions of the North Caucasus Federal District of the Russian Federation in 2018, million rubles. Source Compiled by the authors based on the materials of Rosstat (2019)

Fig. 3 The relationship between the information society and digital business in the regions of the North Caucasus Federal District of the Russian Federation in 2018, %. Source Compiled by the authors based on the materials of Rosstat, National Research University “Higher School of Economics” (2019)

129

population (60.5% on average). This underlines the high readiness of the business structures of the regions under consideration for the intellectualization of the corporate governance process, as well as the need to promote the development of the information society in the practice of public administration in building “smart” regions.

3

Results

On the basis of known and generally accepted possibilities of intellectual technologies, specific features and needs of regions of the North Caucasian Federal District of the Russian Federation in the given research the model of management of economic development is developed on the basis of machine

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“Smart” region of the North Caucasus Federal District of the Russian Federation Government economic regulators report on the socio-economic situation of households, commodity bundle, living wage

Fig. 4 Management model of economic development based on machine vision and computation in “smart” regions of the North Caucasus Federal District of the Russian Federation. Source designed and compiled by the authors

report of wrongdoing

tips for offence prevention

Government machine vision movement identification control Regional resident 1 uc

Regional resident 1 uc

localization determination

Regional resident 1 uc

consumer behavior monitoring Business machine vision sales statistics and analytics

supply chain support

Regional business structures

vision and universal calculations on the basis of the concept of “smart” region for regions of the North Caucasian Federal District of the Russian Federation (Fig. 4). As you can see from the Fig. 4, each resident of the region is connected to the ubiquitous computing system (uc), through which data about him is transmitted to the monitoring systems of the state and business. The computer vision of the state detects offenses and, with the support of ubiquitous computing, identifies the residents of the region, controls their movements, and determines their location. A report on wrongdoing and intellectual advice on its prevention are transmitted to the state. Machine vision of the business monitors the purchasing behavior of the residents of the region, it can also identify them and control their purchases using widespread computing. Entrepreneurial structures of the region receive statistics and sales analytics, intellectual support is provided for the formation of mass (nomenclature of sold products) and individual (personal offers for each client) offer. The state receives a report on the socioeconomic situation of households, on the basis of which the commodity bundle and living wage are determined and really poor people in the region are identified. The benefits for the residents of the region are related to the improvement of living standards and quality of life, the growth of social justice through more accurate and justified government support, as well as better satisfaction of social needs by businesses.

District of the Russian Federation in 2018 to implement the concept of “smart” region is justified. The unique needs that determine the necessary specifics of the application of this concept in the regions under study are law enforcement and information and technical support for corporate governance. A promising model of economic development management based on machine vision and ubiquitous computing in the “smart” regions of the North Caucasus Federal District of the Russian Federation has been developed. In conclusion, it should be stressed that the proposed model is not universal—it is suitable only for the regions of the North Caucasus Federal District of the Russian Federation. As the experience of these regions has shown, the concept of a “smart” region in each case needs to be adapted to actual economic practice and cannot be a template. Even in the regions under consideration, the recommended model for the implementation of this concept will definitely be associated with a complex transition period. Despite the fact that the model only develops the already implemented areas of state and corporate governance, its application implies radical changes for the population of the regions under study. Bilateral control by the state and business is a serious challenge even for a prepared information society in these regions. Undoubtedly, it will be necessary to form the legal field for the implementation of the proposed model of “smart” region in the North Caucasus Federal District—it is advisable to devote further research to this.

4

References

Conclusion

As a result of the study, the necessity and readiness (universal accessibility and active use of the Internet by business and population) of the regions of the North Caucasus Federal

Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh

“Smart” Region: Managing Economic Development … Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal 11(10), 78, 564–568. Di Bella, A., Petino, G., & Scrofani, L. (2019). The Etna macro-region between peripheralization and innovation: Towards a smart territorial system based on tourism. Regional Science Policy and Practice, 11(3), 493–507. Dosso, M., & Lebert, D. (2019). The centrality of regions in corporate knowledge flows and the implications for Smart Specialisation Strategies. Regional Studies, 2(1), 37–49. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal 11(10), 82, 581–586. Guzal-Dec, D., Zwolińska-Ligaj, M., & Zbucki, L. (2019). The potential of smart development of urban-rural communes in peripheral region (a case study of the Lublin Region, Poland). Miscellanea Geographica, 23(2), 85–91. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44– 50. Murzyn, D. (2019). Smart growth in less developed regions–the role of EU structural funds on the example of Poland. Innovation, 2(1), 56– 68. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable developmet processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues 7(4), c, 781–791. https://doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control 169(1), 65–72. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry

131 conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and Irina A. Kuznetsova, PhD in Engineering, Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components”). Theoretical and Practical Issues of Journalism 7(1), 145–154. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will Industry 4.0 and Other Innovations Impact Russia’s Development? In B. S. Sergi (Ed.) Exploring the Future of Russia’s Economy and Markets: Towards Sustainable Economic Development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: Complexity and variety versus rationality. Berlin: Springer International Publishing. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In Bruno S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Trippl, M., Zukauskaite, E., & Healy, A. (2019). Shaping smart specialization: the role of place-specific factors in advanced, intermediate and less-developed European regions. Regional Studies, 2(1), 39–45. Federal State Statistics Service (Rosstat), National Research University “Higher School of Economics” (NRU HSE) (2019). Information Society in the Russian Federation—2018: main characteristics of the constituent entities of the Russian Federation—2018. Reterived December 11, 2019 from https://nangs.org/analytics/rosstatinformatsionnoe-obshchestvo-v-rossijskoj-federatsii-2018vklyuchaya-regiony-pdf. Federal State Statistics Service of the Russian Federation (Rosstat) (2019). Regions of the Russian Federation: socio-economic indicators: 2018. Reterived December 11, 2019 from https://www.gks.ru/ bgd/regl/b18_14p/Main.htm.

Perspectives of Well-Balanced Development of Regional Labor Market with the Help of Digital Modernization by the Example of Modern Russia Farida S. Tsinpaeva, Zarema M. Abdullaeva, and Tamila D. Alikerimova

practical implementation, the following advantages are achieved: overcoming the gap in the structure of the economically active population of the region, balancing the demand and supply of labor, including in the digital economy of the region, ensuring the adoption of optimal personnel decisions by enterprises of the region, improving working conditions in the region, and improving social justice in the region.

Abstract

Purpose: The purpose of the study is to justify the prospects for balanced development of the regional labor market through digital modernization on the example of the North Caucasus Federal District of modern Russia, as well as in the development of practical-oriented scientific and methodological recommendations for this purpose. Design/methodology/approach: The study is of a programmatic and targeted nature. Thanks to this, it aims to use the opportunities of the digital economy to address the following identified in the statistical analysis of the current problems of the labor market in the regions of the North Caucasus Federal District of the Russian Federation, resulting in its imbalance. Findings: It is shown that the digital economy will allow achieving a balanced development of the regional labor market by overcoming its pressing problems. In the regions of the North-Caucasian Federal District of the Russian Federation, these are the gaps in the structure of the economically active population, mismatch between demand and supply of personnel on the labor market, including personnel of digital economy, the lack of optimality of making personnel decisions by employers due to incomplete accounting of labor supply in the region, incomplete compliance with requirements to working conditions in modern Russian entrepreneurship, and incomplete social justice on the labor market of the Russian regions. Originality/value: A promising mechanism has been developed for the balanced development of the regional labor market through digital modernization. Thanks to its F. S. Tsinpaeva (&)
Z. M. Abdullaeva
T. D. Alikerimova Department of Political Economy, Dagestan State University (DSU), Makhachkala, Russia e-mail: [email protected] Z. M. Abdullaeva e-mail: [email protected] T. D. Alikerimova e-mail: [email protected]

Keywords








Balanced development Regional labor market modernization Region Russian regions




Digital

JEL Codes














E24 G34 J14 J16 O31 R11 R58

1

J21

J23

J24

J28

O18

Introduction

The labor market plays a central role in the functioning and development of the region. Firstly, this market provides human resources for regional entrepreneurship. Along with other types of resources, they determine production factors, productivity, production capacity, and other business indicators. The delineation of the labor market segments and their correspondence to the structure of the regional entrepreneurship needs to determine the possibilities of meeting the labor demand. Secondly, the labor market also defines the opportunities for employment and disclosure of the human potential of the region’s residents. The composition of available vacancies, distribution of market power of labor sellers and buyers, as well as an optimal selection of candidates for employment, create conditions for the realization of abilities and talents of workers. Unfavorable conditions for self-realization can lead

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_23

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to social protests, as a rule, in the form of a general decrease in productivity, to an increase in outgoing migration flows of a region, as well as to the nonuse of opportunities for the formation of its competitive advantages based on the realization of human potential, for example, during the implementation of innovations. Thirdly, the efficiency of the labor market determines the standard of living of the region’s population and the resulting burden on regional public authorities for social support. Unemployed people react differently to their social status. Some adapt to unfavorable labor market conditions, undergo retraining, and agree on the least attractive working conditions. Others assert their rights to employment in the profession, which can cause long-term unemployment. The overall level of wages in the region also determines the need for state material support for the population—both the unemployed and those with low wages. Thus, all economic entities of the region are interested in the balanced development of the regional labor market, as its nonequilibrium state slows down the economic growth of the region and worsens its social and economic situation. The hypothesis of this research is that the new opportunities offered by the digital economy make it possible to establish long-term equilibrium in the regional labor market. The purpose of this study is to substantiate the prospects for balanced development of the regional labor market through digital modernization using the example of the regions of the North Caucasus Federal District of modern Russia, as well as to develop practice-oriented scientific and methodological recommendations for this purpose.

2

Materials and Method

The current labor market problems in the region, which lead to its imbalance, and the need to address them are emphasized in the works of Dibeh et al. (2019), Felbo-Kolding Fig. 1 Structure of economically active population in regions of the North Caucasus Federal District in 2018, %. Source Compiled by the authors on the basis of Rosstat materials

et al. (2019), Kosfeld and Dreger (2019), Mitze (2019). Most existing publications focus on the problem of migration, which is not typical for the regions of the North Caucasus Federal District of the Russian Federation. The potential of the digital economy to improve economic practices in today’s region is reflected in the publications of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al (2018), Popkova (2019), Popkova et al. (2018, 2019), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019). So, the existing literary sources give arguments in favor of imbalance of the regional labor market and relevance of digital modernization of modern regions’ economy, which only confirms the validity of the hypothesis, but does not give its scientific confirmation, the search for which requires additional research, which includes this work. This research has a program-targeted character. As a result, it aims to use the opportunities of the digital economy to address the following pressing problems of the labor market in the regions of the North Caucasus Federal District of the Russian Federation, identified in the course of statistical analysis, which lead to its imbalance. The first problem consists of gaps in the structure of the economically active population. As shown in Fig. 1, official statistics do not fully cover the issue of employment of the economically active population. The employed in Russia include only officially employed workers, while the unemployed include only job seekers registered with the employment service. Therefore, in the structure of the economically active population in Russia as a whole in the regions of the North Caucasus Federal District, there is an additional category (not typical for regions of other countries)— economically active population with an uncertain status on the labor market.

Perspectives of Well-Balanced Development of Regional Labor …

135

Fig. 2 Sectoral structure of employment in regions of the North Caucasus Federal District in 2018, %. Source Compiled by the authors on the basis of Rosstat materials

These are self-employed, working people who do not have a formal employment contract, as well as unemployed workers who are not registered with the Employment Service, for example, because their unemployment period is longer than the established period after which they are automatically removed from the register. The share of this category in 2018 ranges from 16.7% of the economically active population in Ingushetia to 33.7% in Karachay-Cherkessia. On average, in the regions of the North Caucasus Federal District, it is 30.7%, i.e., almost a third of the labor market supply. The second problem is the mismatch between the supply and demand for human resources in the labor market, including the digital economy. In general, the connection between the education market and the labor market in the regions of Russia and the North Caucasus Federal District, in particular, is relatively weak. Higher education institutions of the region prepare their staff in accordance with their capabilities, not taking into full account the labor market needs. This leads to shortages and oversupply of personnel in various branches of the regional economy. The personnel of digital economy are in the process of formation and so far are represented only by specialists in the field of information and communication technologies without digital branch specialists. When training personnel in the regions of the North Caucasus Federal District, it is reasonable to be guided by the sectoral structure of employment, which for 2018 is shown in Fig. 2. As shown in Fig. 2, in 2018 the most demanded are workers in such economic sectors of the North Caucasus Federal District as agriculture (18.43%), trade (17.64%), construction (10.51%), education (9.65%), and manufacturing (9.46%). Therefore, it is expedient to orientate higher educational establishments of the region to these branches at personnel training, including personnel of digital economy.

The third problem is not an optimum acceptance of personnel decisions by employers because of the incomplete account of the labor offer in the region. In regions of modern Russia, the practice of narrowing the circle of considered job seekers is widespread, as a result of which the labor price at the enterprise can be overestimated, and its quality (including competence) and productivity are underestimated. The fourth problem is incomplete compliance with the requirements for working conditions in modern Russian entrepreneurship. Employees are interested in strict compliance with labor standards, but in business practice, there may be situations where this is impossible, usually due to the lack of resources. This leads to shadow employment and other violations of labor laws. The fifth problem consists of incomplete social justice in the labor market of the Russian regions. Despite the official regulatory requirement to abstract employers from nonprofessional characteristics of job seekers, these characteristics (gender, age, etc.) influence decisions about employment and career development. This is evidenced by differences in unemployment rates among social categories of workers. Thus, in the regions of the North Caucasus Federal District of the Russian Federation in 2018, 51.1% of the unemployed are men and 48.9% are women. The unemployment rate is highest among people aged 20–29 years (41.8%) and lowest among people aged 60–72 years old (1.7%) (Rosstat, 2019).

3

Results

This study developed a promising mechanism for the balanced development of the regional labor market through digital modernization on the example of regions of the North Caucasus Federal District of modern Russia (Fig. 3).

136

Regional labour market Regional employment service

Unemployed

Workers 1

2 3

distance retraining Regional universities

As shown in the Fig. 3, the developed mechanism involves large-scale digital modernization of the labor market in the regions of the North Caucasus Federal District of modern Russia. The unemployed are automatically registered with the regional employment service when they register in the electronic regional job search portals. All vacancies are posted on the regional portal, so that all the unemployed take part in an impersonal (without specifying gender and age characteristics) competition. Those unemployed who are unable to find a job for a long time can take a distance course at regional universities. Employees of enterprises can remotely improve their skills and learn digital competences. They can provide electronic feedback to unions in the region to inform them about violations of labor laws. Consumers of goods and services in the region can conduct independent public monitoring of the performance of job duties by employees of enterprises and institutions in the region, the results of which are reflected in specialized forums and portals. As a result, the following benefits are achieved, ensuring a balanced regional labor market: – overcoming the gap in the structure of the economically active population of the region; – balancing of labor supply and demand, including in the digital economy of the region; – ensuring the adoption of optimal personnel decisions by the enterprises of the region; – improvement of working conditions in the region; – improvement of the level of social justice in the region.

4

2

3

distance learning, digital competence development

Regional consumers of goods and services

1

electronic feedback Enterprises

vacancies

automatic registration impersonal competition

Trade unions

public monitoring

Fig. 3 Prospective mechanism for balanced development regional labor market through digital upgrading. Source Designed and compiled by the authors

F. S. Tsinpaeva et al.

Conclusion

Summarizing the study, we note that the hypothesis inserted has been confirmed. The digital economy will indeed make it possible to achieve balanced development of the regional labor market by overcoming its pressing problems. In the regions of the North-Caucasian Federal District of the Russian Federation, these are the gaps in the structure of the economically active population, mismatch between demand and supply of personnel on the labor market, including personnel of the digital economy, the lack of optimality of making personnel decisions by employers due to incomplete accounting of labor supply in the region, incomplete compliance with requirements to working conditions in modern Russian entrepreneurship, and incomplete social justice on the labor market of the Russian regions. A promising mechanism for the balanced development of the regional labor market through digital modernization has been developed for these regions.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal 11(10), 78, 564–568.

Perspectives of Well-Balanced Development of Regional Labor … Dibeh, G., Fakih, A., & Marrouch, W. (2019). Labor market and institutional drivers of youth irregular migration in the Middle East and North Africa region. Journal of Industrial Relations, 61(2), 225–251. Federal State Statistics Service of the Russian Federation (Rosstat) (2019). Regions of Russia: socio-economic indicators—2018. Retrieved December 11, 2019 from https://www.gks.ru/bgd/regl/ b18_14p/Main.htm. Felbo-Kolding, J., Leschke, J., & Spreckelsen, T. F. (2019). A division of labour? Labour market segmentation by region of origin: the case of intra-EU migrants in the UK, Germany and Denmark. Journal of Ethnic and Migration Studies 45(15), 2820–2843. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal 11(10), 82, 581–586. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44– 50. Kosfeld, R., & Dreger, C. (2019). Towards an East German wage curve —NUTS boundaries, labour market regions and unemployment spillovers. Regional Science and Urban Economics, 76, 115–124. Mitze, T. (2019). The migration response to local labour market shocks: Evidence from EU regions during the global economic crisis. Oxford Bulletin of Economics and Statistics, 81(2), 271–298. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control 169(1), 65–72.

137 Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will Industry 4.0 and Other Innovations Impact Russia’s Development? In B. S. Sergi (Ed.) Exploring the Future of Russia’s Economy and Markets: Towards Sustainable Economic Development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital Economy: Complexity and Variety vs. Rationality: Springer International Publishing. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and Irina A. Kuznetsova, PhD in Engineering, Associate Professor “Designing the mass media as a homeostatic system by means of automation engineering: Basic concepts, structure, components”). Theoretical and Practical Issues of Journalism 7(1), 145–154. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian Industrial, Tech, And Financial Cooperation With the Asia-Pacific region. In Bruno S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited.

Sustainable Development of Region’s AIC in the Conditions of the Digital Economy: Ecological Responsibility, “Green” Innovations and Circular Production Arsen S. Abdulkadyrov, Ahmed G. Buchaev, and Nurziyat Y. Kazavatova

“green” innovations and circular production allows to overcome existing barriers to the implementation of relevant practices in the regions of the North Caucasus Federal District of the Russian Federation—to reduce the labor and resource intensity of these practices and increase their controllability through automation. This will result in a significant increase in the activity of agricultural enterprises in the regions with the implementation of measures to demonstrate environmental responsibility, introduction of “green” innovations, and ensuring circularity of production. It will also ensure the growth of the production capacities of agricultural enterprises and guarantee the reproduction of food products in the regions to maintain their food security.

Abstract

Purpose: The purpose of this work is to determine the current level of sustainability of the agro-industrial complex (AIC) development in the regions of the North Caucasus Federal District of the Russian Federation and develop a mechanism to increase it in the digital economy. Design/methodology/approach: To determine the sustainability of AIC development we will analyze statistical data: about the index of agricultural production, about the share of enterprises carrying out “green” innovations, about the share of enterprises organizing circular production in the regions of the North Caucasian Federal District of the Russian Federation in 2018. Findings: The conclusion is made that the digital economy offers enhanced opportunities for sustainable development of the region’s agricultural sector. However, its organization should apply its own schemes, rather than template schemes from other industries. The regions of the North Caucasus Federal District of the Russian Federation already pay considerable attention to environmental responsibility, introduction of “green” innovations, and circular production. This creates the prerequisites for the implementation of new initiatives based on the capabilities of digital technologies—robots and artificial intelligence. Originality/value: The developed model of sustainable development of the region’s digital agriculture based on environmental responsibility, A. S. Abdulkadyrov (&) Department of Social and Economic Security, Center for Social Security and Riskology, ISPI RAS, Makhachkala, Russia e-mail: [email protected] A. G. Buchaev Department of Taxes and Taxation, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected] N. Y. Kazavatova Department of Economic Theory, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected]

Keywords














Sustainable development Agro-industrial complex (AIC) Region Digital economy Ecological responsibility “Green” innovations Circular production Russian regions JEL Codes

G34 R58

1









Q13

Q56

Q57

M14

O18

O31

R11

Introduction

New opportunities for sustainable development of the regional economy are one of the strong arguments for its digital modernization. Robotization of production guarantees high precision control over the consumption of natural and energy resources, as well as reduces the resource intensity of the business, while at the same time controlling production waste and its automated safe disposal. Sustainable development is interpreted in terms of eliminating human

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_24

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involvement in harmful and hazardous industries, as well as in terms of reducing the environmental costs of entrepreneurship. This interpretation is based on the needs and experience of trial transition projects to industry 4.0. Application of the existing interpretation and scientific, methodological, and applied solutions based on it outside the digital industry is much less effective, which actualizes the problem of adapting the concept of sustainable development of regional business in the digital economy to the different branches of the regional economy. One of the priority tasks on the way of solving this problem is to study the features and develop recommendations for the agro-industrial complex (AIC), as it defines the food security of the region. The specificity of AIC is that the interpretation of its sustainable development should imply an additional aspect related to the prevention of decline in production. The feature of the given complex also consists of system interaction of the agricultural enterprises and the enterprises of the processing (food) industry, whose digitalization occurs on different models, in particular, because of different borders of their automation. The purpose of the given work is to define the current level of stability of development of the agro-industrial complex of regions of the North Caucasian federal district of the Russian Federation and to develop the mechanism of its increase in the conditions of the digital economy.

Fig. 1 Agricultural production index in the regions of the North Caucasus Federal District of the Russian Federation in 2018. Source Compiled by the authors based on the materials of Rosstat (2019)

A. S. Abdulkadyrov et al.

2

Materials and Method

Selected components of the problem of ensuring sustainable development of the region in the digital economy are reflected in the studies of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017, 2018), Petrenko et al (2018), Popkova (2019), Popkova et al. (2018, 2019), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019). Sustainable development of agriculture in the digital economy is addressed in the studies by Fabrizio et al. (2017a, 2017b), Popkova et al. (2019), Sergi (2019), Fabrizio et al., Shi et al. (2019), but without considering regional specificities. In this regard, it is necessary to conduct additional research on the sustainable development of the AIC based on the digital economy of the region, which is the subject of this paper. To determine the sustainability of AIC development, we will analyze statistical data. Data on the index of agricultural production in the regions of the North Caucasus Federal District of the Russian Federation in 2018 are shown in Fig. 1. According to Fig. 1, the best results are shown by the Chechen Republic, where the crop production index in 2018 was 136.8 (an increase of 36% compared to 2017), and the growth of agricultural output in general—111.4.

Sustainable Development of Region’s AIC in the Conditions …

141

Fig. 2 Share of enterprises implementing “green” innovations in the regions of the North Caucasus Federal District of the Russian Federation in 2018, %. Source Compiled by the authors based on the materials of Rosstat (2019)

Nevertheless, many regions, including Stavropol Krai, the Republic of North Ossetia-Alania, and the KarachayCherkess Republic, saw a decline in production, expressed as an index value of less than 100. For example, in the Republic of Ingushetia, the crop production index was 64.1, while the agricultural production index was 87.6. This indicates a high risk of food security in the regions under consideration. The share of enterprises that implement “green” innovations in the regions of the North Caucasus Federal District of the Russian Federation in 2018 is shown in Fig. 2. According to Fig. 2, in general, “green” innovations are quite actively implemented by enterprises in the regions of the

Fig. 3 Share of companies organizing circular production, throughout the North Caucasus Federal District of the Russian Federation in 2018, %. Source Compiled by the authors based on the materials of Rosstat (2019)

North Caucasus Federal District of the Russian Federation in 2018. The material intensity of production is reduced by 40% of enterprises, energy intensity is reduced by 50% of enterprises. Carbon dioxide emissions into the atmosphere are produced by 50% of enterprises. 50% of enterprises replace raw materials with safe and less hazardous ones. At the same time, it should be noted that industry statistics on “green” innovations are not conducted in the Russian regions, which does not allow to determine the activity of implementation of these innovations directly in the agricultural sector. The share of enterprises that organize circular production in the regions of the North Caucasus Federal District of the Russian Federation in 2018 is shown in Fig. 3.

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According to Fig. 3, circular production is actively practiced in the regions of the North Caucasus Federal District of the Russian Federation in 2018. The most outstanding results are observed in the Chechen Republic, where 100% of enterprises organize circular production. In other regions, the value of this indicator is at the level of 50%. In the Republic of Ingushetia and Karachay-Cherkess the data is not given, which suggests that there is no recycling and reproduction of natural resources used in agriculture. Based on the analysis of available statistical data, it can be concluded that the results already achieved and gaps in the practice of maintaining food security, introduction of “green” innovations, and organization of circular production open prospects for further improvement of the level of sustainability of AIC development in the regions of the North Caucasus Federal District of the Russian Federation.

Russian Federation, we have identified the following barriers on this path:

3

Automation of agro-industrial complex entrepreneurship on the basis of digital economy possibilities allows overcoming the mentioned barriers. For this purpose, it is recommended to be guided by the model developed by us (Fig. 4).

Results

As a result of a qualitative analysis of the experience of sustainable development of enterprises directly in the AIC in the regions of the North Caucasian Federal District of the

Public statistics bodies, regulators, expert and analytical organizations, interested society automatic generation and transmission of corporate environmental reports вторичные secondaryтопливноenergy энергетические resources ресурсы

non-recyclable waste

Digital AIC of the region Agricultural business AI контроль отходов industrial waste производства management; renewable agricultural resources

отходы industrial waste производства

distribution of agricultural products

agricultural raw materials контроль отходов industrial waste производства, control;внедрение “green” AI innovations «зеленых» инноваций вторичные secondary fuel топливно1 and energy энергетические и resources сырьевые ресурсы отходы industrial waste производства

transmissio

Robotic recycling business

Fig. 4 Sustainable development model of the digital agriculture in the region based on environmental responsibility, “green” innovation, and circular production. Source Designed and compiled by the authors

– High labor intensity of sorting of production wastes for the organization of their secondary processing, as well as high labor costs, which reduce the efficiency of these measures and make them inaccessible for the AIC enterprises, which have a chronic deficit of working capital; – Limited opportunities for monitoring and control of environmental responsibility measures: most agricultural enterprises are small in size, and therefore, have lower requirements for corporate reporting (compared to, for example, transnational corporations in the extractive industry and financial sector). This hinders the identification of minor violations of environmental regulations and standards by agro-industrial enterprises, as well as low return on investment in “green” innovation through marketing advantages.

2

Food industry business industrial robots Производственные роботы 3

4

non-recyclable waste

5

производство, сортировка отходов production, sorting of waste ready-made food products

distribution

Sustainable Development of Region’s AIC in the Conditions …

As shown in Fig. 4, the developed model assumes systematic sustainable development of the agricultural sector of the region, taking into account the isolation of agricultural and food industry businesses. In both these segments of the agro-industrial sector, there is cooperation with a robotic recycling business, in which production waste is transferred for recycling. In agriculture, incoming secondary fuel and energy resources are used in the production process along with agricultural resources reproduced at the enterprise itself. Only those wastes that cannot be recycled are disposed of under the control of artificial intelligence, while the rest is recycled. Finished agricultural products are sold to consumers and agricultural raw materials are transferred to the food industry. They, in turn, use recycled materials and energy for robotic food production. Artificial Intelligence controls production waste and gives the robots the command to implement “green” innovations. Only nonrecyclable production waste is recycled by robots when it is sorted. Finished food products are sold on the market. The artificial intelligence of all agro-industrial complex enterprises automatically generates and broadcasts corporate environmental reports for monitoring of state statistics bodies, regulators, expert and analytical organizations, and interested public.

4

Conclusion

Summing up the results of the conducted research, it should be noted that in the conditions of the digital economy there are extended opportunities for sustainable development of the agricultural sector of the region. But at its organization not the template schemes from other branches should be applied, but own schemes. The regions of the North Caucasus Federal District of the Russian Federation already now pay considerable attention to environmental responsibility, introduction of “green” innovations, and circular production. This creates the prerequisites for the implementation of new initiatives based on the capabilities of digital technologies— robots and artificial intelligence. The developed model of sustainable development of the region’s digital agriculture based on environmental responsibility, “green” innovations, and circular production allows overcoming existing barriers to the implementation of relevant practices in the regions of the North Caucasus Federal District of the Russian Federation—to reduce the labor and resource intensity of these practices and increase their controllability through automation. This will result in a significant increase in the activity of agricultural enterprises in the regions in the implementation of measures to demonstrate environmental responsibility, introduction of “green” innovations, and ensuring circularity of production. It will also

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ensure the growth of production capacities of agricultural enterprises and guarantee the reproduction of food products in the regions to maintain their food security.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal 11(10), 78, 564–568. Fabrizio, E., Biglia, A., Branciforti, V., et al. (2017a). Monitoring of a micro-smart grid: Power consumption data of some machineries of an agro-industrial test site. Data in Brief 10, 564–568. Fabrizio, E., Branciforti, V., Costantino, A., et al. (2017b). Monitoring and managing of a micro-smart grid for renewable sources exploitation in an agro-industrial site. Sustainable Cities and Society 28, 88–100. Federal State Statistics Service of the Russian Federation (Rosstat). (2019). Regions of Russia: socio-economic indicators: 2018. Retrieved December 11, 2019 from https://www.gks.ru/bgd/regl/ b18_14p/Main.htm. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal 11(10), 82, 581–586. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44– 50. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control 169(1), 65–72. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will Industry 4.0 and Other Innovations Impact Russia’s Development? In B. S. Sergi (Ed.) Exploring the Future of Russia’s Economy and Markets: Towards Sustainable Economic Development (pp. 51–68). Bingley, UK: Emerald Publishing Limited. Popkova, E. G., & Sergi, B. S. (Eds.). (2019). Digital economy: complexity and variety versus rationality. Berlin: Springer International Publishing. Popkova, E. G., Morozova, I. A., & Litvinova, T. N. (2018). Transformational processes in the media system under industry conditions 4.0: future outlines and perspectives (reflections on the article by Alexander P. Sukhodolov, DSc. In Economics, Professor and Irina A. Kuznetsova, PhD in Engineering, Associate Professor “Designing the mass media as a homeostatic system by means of

144 automation engineering: Basic concepts, structure, components”). Theoretical and Practical Issues of Journalism, 7(1), 145–154. Popkova, E. G., Egorova, E. N., Popova, E., & Pozdnyakova, U. A. (2019). The model of state management of economy on the basis of the Internet of things. Studies in Computational Intelligence, 826 (1), 1137–1144. Popova, L. V., Dugina, T. A., Skiter, N. N., Panova, N. S., & Dosova, A. G. (2018). New forms of state support for the agro-industrial complex in the conditions of digital economy as a basis of food security provision. Advances in Intelligent Systems and Computing, 622, 681–687. Ragulina, Y. V. (2019). Priorities of development of industry 4.0 in modern economic systems with different progress in formation of

A. S. Abdulkadyrov et al. knowledge economy. Studies in Systems, Decision and Control, 169, 167–174. Sergi, B. S. (Ed.). (2019). Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Sergi, B. S., Popkova, E. G., Sozinova, A. A., & Fetisova, O. V. (2019). Modeling Russian industrial, tech, and financial cooperation with the Asia-Pacific region. In Bruno S. Sergi (Ed.), Tech, smart cities, and regional development in contemporary Russia. Bingley, UK: Emerald Publishing Limited. Shi, Z., Zhang, W.-L., & Teng, H.-F. (2019). Digital mapping in agriculture and environment. Journal of Integrative Agriculture, 18 (2), 249–250.

State and Corporate Management of Quality of Life in a Region in the Conditions of the Digital Economy: Social Programs and Social Responsibility Khadizhat M. Khadzhalova, Zaklin N. Kazieva, and Victoria V. Stofarandova

responsibility has been developed, which shows that the roles of state and business should be redistributed to achieve maximum efficiency. Public management of the quality of life in the region in the digital economy should not be aimed at the development of telecommunications infrastructure, as it is traditionally assumed in regional development strategies, but at the use of this infrastructure for social projects. This means that social and infrastructure projects are not identified, but separated. On the contrary, it is recommended that corporate governance of quality of life in the region in the digital economy should be focused on supporting the development of the information society. As a result, the development of telecommunications infrastructure shifts from government-funded infrastructure projects to nonprofit business activities.

Abstract

Purpose: The aim of the paper is to develop a promising scheme of state and corporate quality of life management in the region in the digital economy based on social programs and social responsibility on the example of the regions of the North Caucasus Federal District of the Russian Federation. Design/methodology/approach: To determine the current quality of life in the regions of the North Caucasus Federal District of the Russian Federation in 2018, the authors are analyzing these statistics. They also review statistics on the implementation of social programs by the state, reflecting the share of spending on social and cultural events in the structure of total state budget expenditures in the regions of the North Caucasus Federal District of the Russian Federation in 2018. Findings: It has been proved that the digital economy provides opportunities, and therefore, serves as a tool for public management of the quality of life in the region. The considered experience of the regions of the North Caucasus Federal District of the Russian Federation in 2018 showed that under the traditional scheme of social programs implementation they provide limited results for the quality of life. Originality/value: A structural and logical scheme of state and corporate quality of life management in the region in the digital economy based on social programs and social K. M. Khadzhalova (&) Research Institute of Management, Economics, Politics and Sociology, Dagestan State University of National Economy (DSUNE), Makhachkala, Russia e-mail: [email protected] Z. N. Kazieva Department of Economics and Enterprise Management, Dagestan State Technical University (DGTU), Makhachkala, Russia e-mail: [email protected] V. V. Stofarandova Theory and History of Social Work Department, Dagestan State University (DSU), Makhachkala, Russia e-mail: [email protected]

Keywords


















Public administration Corporate governance Quality of life Region Digital economy Social programs Social responsibility Russian regions JEL Codes

G34 R11

1










H53 R58

J17

I12

I31

M14

O18

O31

Introduction

Quality of life is the most important benchmark and criterion for assessing the development of a region. Modern economic science has developed a contradictory approach to the study of prospects for improving the quality of life in the digital economy. Regional socioeconomic development strategies, in particular, in the regions of Russia, including the regions of the North Caucasus Federal District, consider the

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 Y. G. Buchaev et al. (eds.), State and Corporate Management of Region’s Development in the Conditions of the Digital Economy, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-46394-6_25

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implementation of the fourth industrial revolution and the transition to industry 4.0 as an end in itself. In this case, it is assumed that digital modernization itself contributes to improving the quality of life. Undoubtedly, digital technologies can reduce transaction costs and raise awareness of market conditions among stakeholders, thus ensuring that they make more informed and profitable decisions. The formation of an inclusive society through the full involvement of disabled people in economic activities provides special value. However, statistics show that the availability of digital technologies does not guarantee their practical application, and therefore, does not always lead to an improvement in the quality of life. Thus, in the regions of the North Caucasian Federal District of the Russian Federation in 2018 broadband access to the Internet was available to 65.4% of households, but only 22.2% of the population used the Internet to order goods and services (National Research University Higher School of Economics 2019). The practical application of digital technologies can be complicated by insufficient media literacy of the population or incomplete awareness of possible advantages. Based on this, we propose a hypothesis that the development of the digital economy and the use of its opportunities should be considered not as an objective, but as a tool of the state and corporate management of the quality of life in the region through the implementation of government social programs and social responsibility activities of the business. This hypothesis is supported by the continuing imbalance in the development of the regional economy of the North Caucasus Federal District of the Russian Federation in the digital economy. Thus, in 2018 the level of digitalization of the Republic of Ingushetia is one of the highest in this district—78.1% of the population has access to broadband Internet (National Research University Higher School of Economics 2019). However, the quality of life in this region is one of the lowest in the federal district under consideration (28,533 points out of 100) (RIA-Rating, 2018). The aim is to develop a promising scheme of state and corporate quality of life management in the region in the digital economy based on social programs and social responsibility on the example of the regions of the North Caucasus Federal District of the Russian Federation.

2

Materials and Method

The specifics of state and corporate governance of quality of life in the region in the digital economy are reflected in the studies of Abdulkadyrov et al. (2017), Buchaev et al. (2014), Gadzhiev and Buchaev (2014), Khachaturyan et al. (2017,

2018), Petrenko et al (2018), Popkova (2019), Popkova et al. (2018, 2019), Popkova and Parakhina (2019), Popkova and Sergi (2018, 2019), Ragulina (2019), Sergi et al. (2019), Sergi (2019). The problem of improving the quality of life in the region in the digital economy is raised in the writings of Aroca et al. (2017), Bravi et al. (2018), Chakraborty et al. (2019), Cohen et al. (2018). The issues of sustainable development of the region’s economy and the impact of corporate social responsibility of regional business on it are discussed in Hamrouni et al. (2019), Li et al. (2019), Malik and Jasińska-Biliczak (2018), Othman and Hafez (2019), Wang et al. (2019). Nevertheless, despite the high degree of elaboration of some aspects of the problem formulated, in general, public and corporate governance of quality of life in the region in the digital economy remains fragmented and needs further systematic research. To determine the current quality of life in the regions of the North Caucasus Federal District of the Russian Federation in 2018, let us refer to Fig. 1. As can be seen from Fig. 1, the highest quality of life is the characteristic of the Stavropol Territory (53,016 points), and the lowest is in the Karachay-Cherkessia Republic (25,300 points). On average, in the regions of the North Caucasus Federal District of the Russian Federation in 2018, the quality of life is at 36,956 points out of 100 and can be characterized as relatively small, with prospects for improvement. Since data on corporate social responsibility are not provided by official statistics, let us refer to statistics on the implementation of social programs by the state. The share of expenditures on social and cultural events in the structure of total state budget expenditures in the regions of the North Caucasus Federal District of the Russian Federation in 2018 is defined in Fig. 2. As shown in Fig. 2, the share of expenditures on sociocultural events in the structure of total state budget expenditures in the regions of the North Caucasus Federal District of the Russian Federation in 2018 is on average 61.04%. This shows that the implementation of social programs in the regions under consideration is a priority area of state administration that receives preferential financing.

3

Results

As a result of the study of the experience of state and corporate management of quality of life in the region in the digital economy on the basis of social programs and social responsibility in the regions of the North Caucasus Federal District of the Russian Federation in this paper identified the following pressing problems that hinder the fullest realization of the existing potential to improve the quality of life in these regions:

State and Corporate Management of Quality of Life …

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Fig. 1 Life quality index in the regions of the North Caucasus Federal District of the Russian Federation in 2018, points 1–100. Source Compiled, calculated, and constructed by the authors on the basis of RIA-Rating (2018)

Fig. 2 Share of expenditures for sociocultural events in the structure of total state budget expenditures in the regions of the North Caucasus Federal District of the Russian Federation in 2018, %. Source Compiled, calculated, and built by the authors on the basis of Rosstat (2019)

– the difficulty of applying for social benefits and state support, reducing their accessibility for the population of the region; – insufficient awareness among the population of the region of the social benefits and state support measures that are available to them, resulting in a failure to apply for them; – incomplete involvement of the population of the region in the choice among alternative social programs, resulting in the implementation of programs that are not in high demand; – low commercial efficiency of corporate social responsibility measures of the region’s enterprises due to insufficient information support. In order to comprehensively overcome the problems identified, we have developed the following structural and logical scheme of state and corporate quality of life management in the region in the digital economy based on social programs and social responsibility (Fig. 3).

According to Fig. 3, regional public authorities managing the region’s economy on the basis of the e-government system introduce artificial intelligence into their practice. It conducts Big Data analytics and selects those residents of the region who can claim (are eligible, meet the criteria) for social benefits and state support. These residents receive an automatic notification detailing the benefits and support measures available to them. If necessary, they can also request and receive remote advice and detailed guidance on how to exercise their rights through an e-government system. Residents of the region remotely submit electronic documents to receive benefits and support. E-government provides information on alternative social projects that can be implemented in the region through its digital regional portal. A general electronic vote of the population is held and the most popular selected projects are implemented. The population of the region makes an electronic rating of corporate social responsibility of regional business, which is

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Fig. 3 Structural and logical scheme of state and corporate quality of life management in the region in the digital economy based on social programs and social responsibility. Source Designed and compiled by the authors

electronic documents

Digital region Regional public authorities (e-government)

reporting on social support needs

alternative social products

consideration of corporate responsibility at business regulation

1

2

…

general electronic voting

Enterprise 1

Artificial intelligence

n

Big data analytics, selection for support Population

corporate social responsibility e-rating

4

Enterprise 2 … Enterprise n

digital society development support: media literacy courses, transfer of corporate technology to the public

taken into account in the practice of state regulation of business. The business supports the development of the digital society through the organization of media literacy courses free of charge for the population and the transfer of corporate technology to low-income residents of the region. As a result, business is involved in the development of the region’s telecommunication infrastructure on the basis of corporate social responsibility.

4

Conclusion

So, the results of the study provided scientific evidence for the hypothesis. The digital economy provides opportunities, and therefore, serves as an instrument of public management of the quality of life in the region. The reviewed experience of the regions of the North Caucasus Federal District of the Russian Federation in 2018 showed that under the traditional scheme of social programs implementation they provide limited results for the quality of life of the population. The developed structural and logical scheme of state and corporate quality of life management in the region in the digital economy based on social programs and social responsibility shows that the roles of state and business should be redistributed to achieve maximum efficiency. Public management of the quality of life in the region in the digital economy should be aimed not at the development of telecommunications infrastructure, as traditionally assumed in regional development strategies, but at the use of this infrastructure for social projects. That is, social and infrastructure projects are not identified, but separated. On the contrary, it is recommended that corporate governance of quality of life in the region in the digital economy should be focused on supporting the development of the information society. As a result, the

2 1

5 3

… n

development of telecommunications infrastructure is shifting from government-funded infrastructure projects to nonprofit business activities.

References Abdulkadyrov, A. S., Ryzhov, I. V., Strokov, A. I., & Kamzolov, Yu V. (2017). Current aspects of improving the organization of production of high-tech products. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 18–27. Aroca, P., Gonzalez, P. A., & Valdebenito, R. (2017). The heterogeneous level of life quality across Chilean regions. Habitat International, 68, 84–98. Bravi, L., Murmura, F., & Santos, G. (2018). Manufacturing labs: Where new digital technologies help improve life quality. International Journal for Quality Research, 12(4), 957–974. Buchaev, Y. G., Iakovleva, E. A., & Putihin, I. E. (2014). Formation of efficiency of scientific activities of universities in Russia. Life Science Journal 11(10), 78, 564–568. Chakraborty, T., Ganguly, M., & Natarajan, A. (2019). Predicting entrepreneurial satisfaction: The role of non-financial incentive factors and quality of life among women digital entrepreneurs. Journal for Global Business Advancement, 12(3), 328–355. Cohen, J., Bancilhon, J.-M., & Grace, T. (2018). Digitally connected living and quality of life: An analysis of the Gauteng City-Region, South Africa. Electronic Journal of Information Systems in Developing Countries, 84(1), e12010. Federal State Statistics Service of the Russian Federation (Rosstat) (2019). Regions of the Russian Federation: socio-economic indicators: 2018. Retrieved December 11, 2019 from https://www.gks.ru/ bgd/regl/b18_14p/Main.htm. Gadzhiev, M. M., & Buchaev, Y. G. (2014). Efficiency assessment of enterprise innovation activities. Life Science Journal 11(10), 82, 581–586. Hamrouni, A., Boussaada, R., Ben Farhat Toumi, N. (2019). Corporate social responsibility disclosure and debt financing. Journal of Applied Accounting Research, 20(4), 394–415. Khachaturyan, A. A., Abdulkadyrov, A. S., Zhigulina, E. P., & Sirotkina, N. V. (2017). The issues of improving the investment climate and investment attractiveness of Russian industries in the

State and Corporate Management of Quality of Life … medium-term perspective. Izvestiya Vysshikh Uchebnykh Zavedenii, Seriya Teknologiya Tekstil’noi Promyshlennosti, 371(5), 40–43. Khachaturyan, A. A., Khachaturyan, K. S., & Abdulkadyrov, A. S. (2018). Model of innovational development of modern Russian industry. Advances in Intelligent Systems and Computing, 622, 44–50. Li, D., Wei, Y. D., Miao, C., Wu, Y., & Xiao, W. (2019). Innovation, network capabilities, and sustainable development of regional economies in China. Sustainability (Switzerland), 11(17), 4770. Malik, K., & Jasińska-Biliczak, A. (2018). Innovations and other processes as identifiers of contemporary trends in the sustainable development of SMEs: The case of emerging regional economies. Sustainability (Switzerland), 10(5), 1361. National Research University “Higher School of Economics” (NRU HSE) (2019). Digital economy: 2019. Retrieved December 04, 2019 from https://www.hse.ru/data/2019/06/25/1490054019/ice2019.pdf. Othman, A. A. E., & Hafez, M. G. (2019). A framework integrating corporate social responsibility for marketing architectural design firms in developing countries. Journal of Engineering, Design and Technology, 17(6), 1174–1191. Petrenko, E., Pritvorova, T., & Dzhazykbaeva, B. (2018). Sustainable development processes: Service sector in post-industrial economy. Journal of Security and Sustainability Issues, 7(4), 781–791. https:// doi.org/10.9770/jssi.2018.7.4(14). Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control 169(1), 65–72. Popkova, E. G., & Parakhina, V. N. (2019). Managing the global financial system on the basis of artificial intelligence: Possibilities and limitations. Lecture Notes in Networks and Systems, 57(1), 939–946. Popkova, E. G., & Sergi, B. S. (2018). Will Industry 4.0 and Other Innovations Impact Russia’s Development? In B. S. Sergi (Ed.) Exploring the Future of Russia’s Economy and Markets:

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Conclusion

Development of the Digital Economy in the Region: Lessons and Implications for Public and Corporate Governance Digital economy is a generalized concept, although it is universal and covers all modern economic systems, it needs a specific interpretation and a unique approach to application in each segment of the regional economy. As the experience of the regions of the North Caucasus Federal District of the Russian Federation has shown, the models of digital development of a modern region are multiple and alternative, and the answers to new challenges of state and corporate governance may be different. The choice of specific applied solutions is determined both by the conditions of a region and its capabilities, and by strategic priorities of its socioeconomic development. The region’s development factors and management tools in the digital economy are also highly differentiated and should be considered from a noosphere perspective, including social, environmental, infrastructure, business, competition, and globalization factors. The developed scientific and methodological support for indicative assessment and management of the region’s development in the digital economy on the principles of efficiency and competitiveness allows achieving high flexibility in following basic principles, which can be useful for different regions. The results of the study showed that the system of gross regional product reproduction is transformed into the conditions of the digital economy. Digital entrepreneurship becomes a new subject of the economy of a modern region —it needs state stimulation and involvement of market mechanisms of development. The regional system of science and education is a source of digital personnel and breakthrough technologies for the regional economy, and therefore, also needs to be managed. Improvement of corporate governance practices in the digital economy in the interests of the region’s development requires the implementation of a number of innovative and

investment initiatives to modernize entrepreneurship. These initiatives include digital marketing and electronic sales as ways to optimize the distribution processes in business, organization of production based on the Internet of things, improving the practice of management decision-making in modern business based on artificial intelligence. It is also recommended to manage corporate databases based on cloud, quantum, blockchain, and Big Data technologies. It is important to note that the innovative development of entrepreneurship in the digital economy requires systematic state monitoring and risk management. The new directions of state management of the region’s development in the conditions of the digital economy are connected with solving the problem of migration, support of social adaptation on the basis of providing mass digital literacy and availability of digital technologies. Prospects also include modernization of entrepreneurial education in the digital economy, management of transformation processes on the labor market in the region on the basis of a competency-based approach, and optimization of public management of foreign economic activity of the region in the digital economy. Strategic guidelines for state and corporate management of a region’s development in the digital economy are related to the formation of “smart” regions, where economic development is managed on the basis of machine vision and universal computing. The reference points should also be the prospects for balanced development of the regional economy through digital modernization, sustainable development of the region based on environmental responsibility, “green” innovations, and circular production. The guidelines also include public and corporate governance of the quality of life in the region in the digital economy through social programs and corporate social responsibility initiatives. In conclusion, it should be noted that the region’s development scenarios in the digital economy are diverse and multifaceted, and the priorities of state and corporate governance cannot be formulated unequivocally. In the

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regions of the North Caucasus Federal District, the priorities are to ensure a balanced labor market, develop the agricultural sector to ensure food security, promote sustainable development, and improve the quality of life. We hope that

Conclusion

our recommendations will provide a scientific and methodological basis for state and corporate management of digital economy development in modern regions.