Developments in Information & Knowledge Management for Business Applications: Volume 2 [2] 3030766314, 9783030766313

This book provides practical knowledge on different aspects of information and knowledge management in businesses. For e

797 106 26MB

English Pages 715 [704] Year 2021

Report DMCA / Copyright

DOWNLOAD PDF FILE

Table of contents :
Preface
Contents
Study on Wide-Ranging Ethical Implications of Big Data Technology in a Digital Society: How Likely Are Data Accidents in the COVID-19 Reality?
1 Introduction
2 Views on Big Data Technology
2.1 The Use of Big Data Technology on a Large Scale and Associated Commercial Opportunities
2.2 The Use of Big Data Technology for Managing COVID-19 Pandemic in a Digital Society
3 Views on Big Data Technology
3.1 The Normal Accident Theory and Normal Accidents
3.2 Normal Accidents Associated with Big Data Technology
4 Views on Data Accidents Associated with Big Data Technology
4.1 Systemic Data Accidents
4.2 Noncommercial Data Accidents
4.3 Commercial Data Accidents
4.4 Application of Normal Accident Theory to Real Data Accident Scenarios
4.5 Successful Risk Mitigation Strategies
5 Discussion and Contributions
6 Conclusion
References
Application of Business Rules Mechanism in IT System Projects
1 Introduction
1.1 Objectives and Scope of Work
1.2 Composition of Work
1.3 Review of the Literature
2 The Concept of Business Rules in the Context of Software Development
2.1 Outline of Problems in Creating IT Systems
2.2 Arguments for Finding New Solutions in Software Development
2.3 The Concept of Business Rules
2.4 Business Rules
2.5 Implementation of Business Rules
3 Technological Aspect of Business Concepts
3.1 Business Rules Management Systems
3.2 Repetition of the Algorithm
3.3 Review of Business Rule Management Technologies
4 Design and Operation of a Business Rules Engine on the Example of the JBoss Drools System
4.1 Basic Information About the Drools System
4.2 Managing Business Rules Using the Drools Platform
4.3 Key Operating Mechanisms of Drools Expert
4.4 BRMS Drools—Summary
5 Application of the Corporate Rules Engine in the Design of an IT System
5.1 Problem Presentation
5.2 Requirements for the Proposed System
5.3 Description of the System Architecture
5.4 Identification of Key Processes
5.5 Technologies and Tools Used in the Project
5.6 System Design—Summary
6 Analysis of the Application of the Business Regulatory Force in the IT System
6.1 Description of the User Interface
6.2 Description of the Module for Adding Business Rules
6.3 Analysis of Selected Use Scenarios: Defining Business Rules
6.4 Advantages of Using the Rules Motor in the Created System
6.5 Problems Encountered in Designing System and Application Errors
6.6 Analysis of the Use of the Commercial Rules Engine
7 Summary
References
Data-as-a-Service versus Information-as-a-Service: Critical Differences in Theory, Implementation, and Applicability of Two Growing Cloud Services
1 Introduction
1.1 Relevance
1.2 Goals and Objectives
2 Theoretical and Conceptual Background
2.1 Cloud Computing in General
3 Data Versus Information
4 Data-as-a-Service (DaaS)
4.1 Definition of DaaS
4.2 Distinction
4.3 Characteristics of DaaS
4.4 Suitability of DaaS
4.5 Benefits of DaaS
4.6 Downside/Challenges of DaaS
4.7 Pricing Models
4.8 Examples of Real-World Applications
4.9 Drivers
5 Information-as-a-Service
5.1 Definition
5.2 Distinction
5.3 Characteristics of Information-as-a-Service
5.4 Suitability
5.5 Benefits
5.6 Downsides
5.7 Pricing Models
5.8 Examples of Real-World Applications
5.9 Drivers
6 Comparison of DaaS and IaaS Along Discussed Dimensions
6.1 Criteria for Suitability of the Respective Format
6.2 Summary of the Key Findings
7 Conclusion
7.1 Synopsis
7.2 Further Research
References
Management and Measuring Customer Loyalty in Digital Marketplace—Analysis of KPIs and Influence Factors in CLTV
1 Introduction
1.1 Relevance
2 Theoretical and Conceptual Background
2.1 Customer Relation Management
2.2 Customer Loyalty Program
2.3 Bonus Schemes
2.4 Requirements for the Conception of a Bonus Program
3 Bonus Programs in Austrian, German and Swiss Retail
4 Results Evaluation and Conclusions
References
Cost-Effective Solutions in Cloud Computing Security
1 Introduction
1.1 Relevance
1.2 Goals and Objectives
2 Theoretical and Conceptual Background
2.1 Cloud Computing as a Cost-Effective Solution
2.2 Cloud Computing Services
2.3 Deployment Models in the Cloud
3 Concerns Regarding Cloud Computing and Security
3.1 Challenges in Cloud Computing Security
3.2 Some Major Challenges
3.3 Security Issues in Cloud Services
4 Perspective on Available Solutions
4.1 Security Solutions Available
4.2 Solutions to Privacy and Security Using Cryptography
4.3 Security and Privacy Issues Tackled with Cryptography
5 Models and Methods of Cost Calculations of Security in Cloud
5.1 Encryption Method of Cost Calculation
5.2 The Economic Model of Security Threats
6 Conclusion
6.1 Synopsis
6.2 Further Research
References
Marketing Communication and Its Role in the Process of Creating Rational Awareness of Generation Z Representatives
1 Introduction
2 Materials and Methods
3 Results
3.1 Perception of Contemporary Marketing
3.2 Attitudes Toward Sustainable Product Strategies
3.3 Opinion on Marketing Communication as a Basis for Creating Customer Awareness
4 Discussion and Conclusions
References
How is Data Visualization Shaping Our Life? The Application of Analytics from Google Trends During the Epidemic of COVID-19
1 Introduction
2 Data Visualization and the COVID-19 Epidemic
2.1 What Can Big Data Do for Epidemic Control?
2.2 Big Data of Search Volume Could Guide the Economy and People’s Livelihood
2.3 Case Study:Multiple Applications of Epidemic Control Supported by Big Data in China
3 Methods and Hypothesis
4 Results of Evaluation
5 Conclusion
References
Analysis of the Practices of Financial Intelligence Units (FIUs) and Other Anti-money Laundering Agencies Within EU
1 Introduction
2 Defining the Phenomenon of Money Laundering and Explanation on its Characteristics
2.1 Phases of the Money Laundering Process
2.2 Multicriteria Decision Making Model Within Banks as a Successful Criterion for Risk Elimination of Money Laundering Within the Financial Sector
2.3 Decision Support Systems
2.4 Principles of Banking and Financial Operations, Including Anti Money Laundering Principles as a Part of Modern Private Banking
2.5 Liquidity and Solvency Principles
2.6 Principle of Efficiency
2.7 Profitability Principle
2.8 Anti-money Laundering Principles (I.E. Prevention of Criminal Use of the Banking Institution)
2.9 Harmonization of Banking Principles
3 Multicriteria Decision Making
3.1 Defining Terms in the Problem of Decision Making
3.2 Methods of Multicriteria Analysis
3.3 Combining AHP and TOPSIS Methods in Multi-criteria Evaluation Optimization Investment Benefits of Banks
4 Conclusion
References
Modern Approaches to Leadership Development—An Overview
1 Introduction
2 Modern Approaches to Leadership Development
2.1 Personnel and Leadership Development
2.2 Systematic Approach to Development Programs
2.3 Evaluation of Development Programs
3 Conclusion
3.1 Synopsis
3.2 Further Research
References
Crowdfunding and Uncertain Decision Problems—Applying Shannon Entropy to Support Entrepreneurs
1 Introduction
1.1 Relevance
1.2 Development and Future Expectations in Crowdfunding
2 Theoretical and Conceptual Background
2.1 Triadic Relationship in Crowdfunding
3 Research Method
3.1 Answers of Participants
3.2 First Results
4 Mathematical Decision Function to Analyse the Complex Structures of the Decision Process in Crowdfunding
4.1 Decision Drivers and Decision Aims
4.2 Example of Two Decision Drivers (Determinants for Actors): Time of Realization and Costs of Realization and Mathematical Approach of Their Intensity
4.3 Example to Explain the Intensity and Ranking of Two Decision Drivers in One Decision Aim Based on Probability Theory and Shannon Entropy
5 Conclusion and Further Research
References
The Impact of Electronic Services on Traditional Services
1 Introduction
2 Traditional Services
2.1 Shift in the Economy: The Importance of Services
2.2 Internal Communication Tools
2.3 Service Quality
3 Electronic Services
3.1 Definition of Electronic Services
3.2 Service Quality
3.3 The Long Tail
4 Examples in the Industries
4.1 Traditional Banking Service Versus E-Banking
4.2 Traditional Shopping Service Versus Online Shopping
4.3 Netflix
4.4 Uber
5 Conclusion
References
Use of Digital Technologies in Business in Slovakia
1 Introduction
2 Goal and Methodology
3 Use of Digital Technologies in Doing Business
3.1 Digitization in the Area of Taxes
3.2 Use of Digital Technologies in Financial Services
3.3 Electronic Payment System
3.4 Transactions with Virtual Cryptocurrency
3.5 Crowdfunding
3.6 Insurance Services InsurTech
4 Personal Data Protection
5 Conclusion
References
Business Information Through Choice-Based Conjoint Analysis: The Case of Electric Vehicle Home Charging
1 Introduction
2 Background
2.1 Choice-Based Conjoint Analysis
2.2 Previous Studies
3 Study Design
3.1 Attributes and Levels
3.2 Non-considered Attributes and Prohibited Combinations
3.3 Implementation of the Study
4 Analysis
4.1 Part-Worth Utilities
4.2 Importance of Attributes
4.3 Interaction Effects
5 Market Simulations
6 Conclusion
References
The Combination of “Loft” and “High-Tech” Styles in the Formation of Interior Spaces for IT Companies
1 Historiography of Loft Design and High-Tech Development
1.1 Sources and Historical Aspects of the Emergence of Loft and High-Tech Styles in Design
1.2 Examples of Loft Design with the Implementation of Stylistic Features of High-Tech
1.3 Modern Trends in the Design of Functional Areas of IT Companies
2 Theoretical Provisions of the Combination of Stylish and Constructive Features of the Loft and High-Tech
2.1 Compositional and Constructive Characteristics of the Design in the Loft Style
2.2 Stages of Formation of High-Tech Style and Integration into Other Styles
2.3 Ways of Symbiosis of High-Tech Compositions in Loft Design
3 Provisions of Implementation of Loft and High-Tech Symbiosis in Design of Functional Zones of It Company
3.1 General Requirements for the Design of Interior Spaces for IT Companies
3.2 Types of Integration Zones of Functional Spaces of IT Companies
3.3 Implementation of Concepts Combining Stylistic and Compositional Features of Loft and High-Tech in the Design of IT Companies
4 Conclusion
4.1 Synopsis
References
Is Pillar 3 a Good Tool for Stakeholders in CEE Commercial Banks?
1 Introduction
2 Related Work
3 Methodology
4 Results
5 Conclusion
References
Factors Behind the Long-Term Success in Innovation—In Focus Multinational IT Companies
1 Introduction
1.1 Problem Area
1.2 Research Questions
2 Research Methods
2.1 Analysis of the Number of Patents
2.2 Analysis of Investments into Research and Development
2.3 Analysis of Factors of Corporate Culture in a Chosen Multinational IT Company
2.4 Review of Business Models
2.5 Interview in the IBM Innovation Centre
3 Theory Behind Innovation
3.1 Innovation Explained
3.2 Innovation Process and Innovation Management
4 Number of Patents and Its Connection to Innovation
4.1 Selection of Representative IT Companies for Our Research
5 Analysis of Investments into Research and Its Influence on Innovation
5.1 The Absolute Value of Investments in Research and Development
5.2 R&D of Total Revenue Ratio
6 Business Model and Strategy of Innovative Companies
6.1 Use Case: IBM
6.2 Approach to the Business Model of the Four Selected IT Companies
6.3 Deduction Based on the Analysis of the Business Models
6.4 Connection Between Innovation Management Model and Innovativeness
7 Analysis of the Internal Environment of an Innovative Multinational IT Company
7.1 Corporate Culture
7.2 Survey at IBM
7.3 Innovation Centre
7.4 Deduction from the Interview in the Innovation Centre
7.5 Innovation Hubs
8 Discussion and Conclusion
References
eServices and Gaming Industry—Value-Creating Ecologies as Main Factor for Customer Acceptance of Digital Servitization
1 Introduction
2 Servitization and Gaming Industry
2.1 E-Service Definition
2.2 Classification of eServices
2.3 Servitization of the Game Industry
2.4 eService Systems
2.5 Product-to-Service Transition
3 Servitization and Value Co-creation
3.1 From Supply Chain to Value Ecology
3.2 Value Creation and Co-creation in Value-creating Ecologies
3.3 Value and Value Co-creation in the Game Industry
3.4 Customer-to-Customer Value Co-Creation in the Game Industry
3.5 Development of Ecosystem and Acceptance of Servitization
4 Conclusions
References
Sharing Economy Business Models: Informational Services Innovation and Disruption in Uber and Airbnb
1 Introduction
1.1 Relevance
1.2 Goals and Objectives
2 Theoretical and Conceptual Background
2.1 Sharing Economy and Theoretical Definitions
3 Business Models of the Sharing Economy
4 Uber and Ride Sharing
4.1 Business Model of Uber
4.2 SWOT Analysis of Uber
5 Airbnb and Room Sharing
5.1 Business Model of Airbnb
5.2 SWOT Analysis of Airbnb
6 Conclusion
6.1 Synopsis
References
Safe and Secure Communication between Two Cyber-Physical Systems: A Framework for Security
1 Introduction
1.1 Proposed Framework for Safety and Security of CPS
2 Related Work
3 Proposed Methodology
3.1 Source CPS
3.2 Liveness
3.3 Message Queue Telemetry Transport (MQTT Protocol)
3.4 Controls
3.5 Security
3.6 Targeted CPS
4 Results and Discussion
4.1 LOIC
4.2 Immunity Check
5 Conclusion
References
Investigation the Scope of Social Inequality by Accessing Telecommuting from Home Under Corona Pandemic
1 Introduction
2 Material and Methods
3 Results
3.1 Scope of Home Office with the Start of the COVID-19 Pandemic
3.2 Scope of Social Inequality to Access of Home Office with the Start of the COVID-19 Pandemic
3.3 Inequality Accessing Home Office Prior Time of COVID-19 Pandemic
3.4 Inequality Accessing Home Office Within the Time Frame of COVID-19 Pandemic
4 Discussion
5 Conclusions
References
Building Online Stores Using PHP
1 Introduction
2 Rules for Creating Online Stores
2.1 Advanced Forms
2.2 Responsive Web Design
2.3 Page Structure
2.4 Positioning
2.5 GDPR
2.6 ISO Norms
2.7 Load Balancing
2.8 VAT
2.9 Learn More About Creating Online Stores
3 Analysis of Selected Online Stores
3.1 Characteristics of Selected Industry
3.2 Analysis of the Oponeo.Pl
3.3 Analysis of the Opony.Pl
3.4 Analysis of the 24opony.pl
4 Design and Implementation of the Created Online Store
4.1 Characteristics of the Online Bakery Industry
4.2 Description of the Technologies Used to Design the Online Store
4.3 Construction and Functionality of the Created Website
4.4 Page Characteristics
5 Limitations
6 Summary
References
Information Exchange Platform Based on a Q&A Model
1 Introduction
2 Information Exchange Systems
2.1 Systems Based on a Question-and-Answer Model
2.2 Discussion Forums
2.3 Wiki
2.4 Portals Promoting Popular Posts
2.5 Instant Messaging
2.6 Use of Q&A Systems by Organizations
2.7 Summary
3 Software and Technologies Used for the Implementation of the Practical Part
3.1 Software for the Implementation of the QASystems Platform
3.2 Programming Technologies and Methodologies
3.3 Summary
4 Architecture of the QASystems Platform
4.1 Analysis of the Requirements of the Created Application
4.2 Data Layer Design
4.3 Design of the Logic Layer
4.4 Design of the User Interface Layer
5 Description of the QASystems Application
5.1 Logging in and Registering to the System
5.2 Viewing Questions and Answers
5.3 Adding and Editing Content
5.4 Searching for a Question
5.5 User Data
5.6 Admin Panel
5.7 Summary
6 Conclusions
References
Recommend Papers

Developments in Information & Knowledge Management for Business Applications: Volume 2 [2]
 3030766314, 9783030766313

  • 0 0 0
  • Like this paper and download? You can publish your own PDF file online for free in a few minutes! Sign Up
File loading please wait...
Citation preview

Studies in Systems, Decision and Control 376

Natalia Kryvinska Aneta Poniszewska-Marańda   Editors

Developments in Information & Knowledge Management for Business Applications Volume 2

Studies in Systems, Decision and Control Volume 376

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Systems, Decision and Control” (SSDC) covers both new developments and advances, as well as the state of the art, in the various areas of broadly perceived systems, decision making and control–quickly, up to date and with a high quality. The intent is to cover the theory, applications, and perspectives on the state of the art and future developments relevant to systems, decision making, control, complex processes and related areas, as embedded in the fields of engineering, computer science, physics, economics, social and life sciences, as well as the paradigms and methodologies behind them. The series contains monographs, textbooks, lecture notes and edited volumes in systems, decision making and control spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. Indexed by SCOPUS, DBLP, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

More information about this series at http://www.springer.com/series/13304

Natalia Kryvinska · Aneta Poniszewska-Mara´nda Editors

Developments in Information & Knowledge Management for Business Applications Volume 2

Editors Natalia Kryvinska Department of Information Systems Faculty of Management Comenius University Bratislava, Slovakia

Aneta Poniszewska-Mara´nda Institute of Information Technology Lodz University of Technology Łód´z, Poland

ISSN 2198-4182 ISSN 2198-4190 (electronic) Studies in Systems, Decision and Control ISBN 978-3-030-76631-3 ISBN 978-3-030-76632-0 (eBook) https://doi.org/10.1007/978-3-030-76632-0 © 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, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The second volume of this series subline continues to explore different aspects of information and knowledge managing as well as doing business with information. We survey further the key aspects of managerial implications of the informational business. The novel methodologies and practices for the business information processing, as well as application of mathematical models to the business analytics and efficient management, are examined. The first chapter “Study on Wide-Ranging Ethical Implications of Big Data Technology in a Digital Society: How Likely Are Data Accidents in the COVID-19 Reality?” is dedicated to the wide-ranging ethical implications of big data technology in a digital society. Until now, much discussion of big data is focused on its transformational potential for technological innovation and efficiency; however, less attention was given to its ethical implications beyond the generation of commercial value. Thus, the authors investigate the wide-ranging ethical implications of big data technology. The subsequent chapter “Application of Business Rules Mechanism in IT System Projects” deals with the use of the mechanism of business rules in information systems. It facilitates the implementation of business logic, which is expressed in the form of precise and legible rules. The work is divided into two parts. The first part deals with the approach to business rules based on the literature studies. The second part of the work deals with the practical use of trade rules mechanisms. Within this part, the BiblioRule information system was designed. In the next chapter titled “Data-as-a-Service versus Information-as-a-Service: Critical Differences in Theory, Implementation, and Applicability of Two Growing Cloud Services,” the authors indicate that by an exponential increase of data produced and processed in all industries, data-oriented services are having increasing importance. Companies are seeking for services related to administration of data, data analysis, and preparation of insights. Accordingly, the authors focus on detailed understanding of both data-as-a-service and information-as-a-service on cloud. The authors seek to find a clear definition and a specification for the DaaS, IaaS, as well as contrast two concepts, highlighting which service is better in which situation for what kind of company. v

vi

Preface

The chapter authored by Wolfgang Neussner “Management and Measuring Customer Loyalty in Digital Marketplace—Analysis of KPIs and Influence Factors in CLTV” explores that in a highly competitive digital business environment, the management of customer loyalty becomes particularly important, especially when new customers can only be won at considerable expense and in manageable numbers. As the author claims—it is essential to strengthen customer loyalty to the company in order to ward off competitive marketing campaigns in the best possible way. Thus, this research analyzes and compares the concept of bonus programs in the retail industries of Austria, Germany, and Switzerland. The research is supplemented by the development of purchase decision processes. Besides, the implementation in companies is quantitatively surveyed and compared with the theoretical concepts. The next chapter presents a study on the “Cost-Effective Solutions in Cloud Computing Security.” As the author notices—the popularity and advances in technology recently have created a great deal of interest in cloud computing, especially for enterprises. However, as the author explores—despite the cloud computing platform offers a cost-efficient solution, there is a big drawback when it comes to security and the real costs behind it. Thus, this work examines some of the most efficient existing solutions for security in cloud computing, presents a short overview of the progress in literature and research that tackle such issues, and investigates the models and methods of calculating the costs of security in cloud computing. While engaging qualitative and quantitative methods, different analyses were carried out to finally present the state-of-art in cloud security research and present the existing cost calculation methods. In the chapter “Marketing Communication and Its Role in the Process of Creating Rational Awareness of Generation Z Representatives” authored by Katarína Gubíniová, Peter Štarchoˇn, Lucia Vilˇceková, Gabriela Pajtinková Bartáková, and Jarmila Brtková observes significantly negative responses to the principles of marketing in the form as described so far. Phrases such as “marketing tricks” and “marketing lies” are used more and more frequently in both domestic and foreign professional literature. Consumer market representatives themselves characterize many of today’s marketing activities as “intrusive”, “disturbing”, “misleading,” and “deceptive”. Based on such associations, marketing both as a scientific discipline and as a functional area of management often becomes despised. Hence, the authors evaluate in this paper a perception of marketing and marketing communication on the basis of quantitative and qualitative analysis on the representative sample of 1248 Generation Z respondents (i.e., age range 18–29 years) in terms of how positive/negative they perceive the current activities of organizations in marketing communication assessing their impact on creating rational awareness about products. The chapter authored by Yuanxin Li “How is Data Visualization Shaping Our Life? The Application of Analytics from Google Trends During the Epidemic of COVID-19,”discusses how has the diverse information acquisition methods greatly enriched user behavior habits. The paper provides also a case study of the application of the methods of big data in China, to fight against the epidemic. As the author claims—during the epidemic, users tended to search actively from passive information viewing. Further, the author establishes hypotheses, studies how to achieve

Preface

vii

“value-added” data in big data search, and guides government decision making through data visualization. In the next chapter on the “Analysis of the Practices of Financial Intelligence Units (FIUs) and Other Anti-money Laundering Agencies Within EU,” Darko Panevski, Tomáš Peráˇcek, and Katarína Rentková focus on the chain of suspicious transactions analysis in Europe, from banks, to financial intelligence units, to courts, and examine the way in which financial data are interpreted and shared across the security chain, leading to security decisions like frozen assets, closed accounts or criminal convictions. At present, there is NO comparative research on the work of European FIUs. The authors’ ultimate goal was to make an independent conceptual contribution to the understanding of financial security by analyzing the implementation of different lows mechanisms and information systems models, in order to find better solutions that will ensure efficiency, effectiveness and easy access to the information required. In the chapter authored by Helena Kiß and Rozália Sulíková “Modern Approaches to Leadership Development—An Overview,” it is examined which modern approaches to leadership development predominate in both theory and practice. In the modern economy, organizations are distinguishing themselves by qualified leaders. These decisively determine the success or failure of an organization and thus are an elementary part of personnel development. But before leaders can effectively perform, they need be identified as potentials and further developed. Hereby, it is of importance to identify the professional and interdisciplinary qualifications of leaders and to prepare them to successfully meet future organizational requirements and thus to contribute to its success. As the authors state, the result shows that there is an enormous wealth of opportunities that organizations can embrace. It would therefore make sense for further research to empirically investigate whether and which measures are known to the organizations and, above all, if they are actually being used effectively. In the next chapter named “Crowdfunding and Uncertain Decision Problems—Applying Shannon Entropy to Support Entrepreneurs,” it is presented an introductory motivation by making transparent recent developments in crowdfunding, as it has become an interesting and widely accepted alternative to finance start-ups and projects. Furthermore, there are provided insights into the decision making of the involved crowdfunding actors in their triadic relationship. Based on empirical data collected from five in-depth expert interviews, the authors identified several factors, which influence the behavioral decision process of an entrepreneur when making a choice on a crowdfunding platform for their crowdfunding campaign. On an example of two of those identified factors, the authors formalize the entrepreneur’s decision process using Shannon entropy and give a numerical example. The chapter “The Impact of Electronic Services on Traditional Services” studies the quality of e- and classical services—in the last years, and companies have seen that the quality of the services they provided is becoming more and more important. They try to reach as many clients as possible and try to improve their services. New technologies (Internet, computers, smartphones) are something that companies are taking advantage of, and there has been a huge change in the way services are provided. There are less physical contacts between companies and clients and even

viii

Preface

less contacts by phone (already being exceeded). Companies like Amazon, Netflix, or Uber are good examples of how the way companies provide services is changing, taking advantage of new technologies, and making everyone’s life easier. In the research performed by Daniela Nováˇcková, Jarmila Wefersová “Use of Digital Technologies in Business in Slovakia,” it is defined, by the descriptive analysis, a phenomena and the processes related to the use of digital technologies within the framework of business in some areas in Slovakia. The study opens a discourse on the digital economy that changes the system of business and represents a challenge for new jobs. With the help of data obtained from the websites of major entities, while applying the principles of legal logic and systematics, accuracy, and the possibility of generalizing, the authors have concluded that digital technologies are widely used in Slovakia in business, with the banking sector at the forefront. Due to the increasing use of digital technologies, the authors also addressed in this study an issue of personal data protection. The chapter presents an analysis of impacts from laws of the European Union on the formation of a digital market in Slovakia. The chapter called “Business Information Through Choice-Based Conjoint Analysis: The Case of Electric Vehicle Home Charging” and authored by Marvin Klein, Christine Strauss, Christian Stummer demonstrates the generation of information through choice-based conjoint analysis using the example of electric vehicle charging. As the authors claim—there are several alternatives for electric vehicle charging, with home charging being the main charging point for most of today’s electric and plug-in hybrid electric vehicles. Therefore, a large number of consumers consider home charging as mandatory when buying a car. Before blindly investing in the construction of charging stations close to citizens’ homes, decision makers (e.g., policy makers) need to learn about the impact of possible measures. Thus, the chapter examines whether performance improvements in alternative vehicles (e.g., in terms of range or charging time) or governmental incentives (e.g., price subsidies) could compensate consumers for not having home charging stations. Findings reveal that, in general, both electric and plug-in hybrid electric vehicles profit from the construction of home charging stations, but its perceived benefit decreases continuously with faster charging times at public charging stations. However, at the present time, when the technological progress of electric vehicles remains low, monetary subsidies for environmentally friendly vehicles appear to mainly support only sales of plug-in hybrid electric vehicles. In the next chapter “The Combination of “Loft” and “High-Tech” Styles in the Formation of Interior Spaces for IT Companies,” Olena Ivanochko and Jennifer R. Calhoun focus on an idea of the office design, to reflect the accepted style of the company, and express that image in every detail, inclusive of the interior. The outcome is to provide an exclusive brand image. Nowadays, interior design is especially popular for IT companies. Arranging an office space for an IT company, provides a creative interior project, which perfectly fits the corporate style of the company in its exclusive design, and original approach to the employee workspace and the creation of a unique image. Particular attention is paid to ergonomics and office zoning. The interior of the room has a significant impact on those who stay in it for a long time.

Preface

ix

The next chapter named “Is Pillar 3 a Good Tool for Stakeholders in CEE Commercial Banks?” a Pillar 3 is explored as a tool for market discipline enhancement, which is supported by the analysis of the website data dedicated to Pillar 3 disclosures of the banking institution operating in CEE country. The analysis of the web portal is based on the time spent on the web page by the web users and evaluating their interest in relation to time spent and content of the Pillar 3 disclosures. The data consist of preprocessed data from a log file of a web portal of banking institution. The web portal pages were joined into logical web parts that were joined into specific categories. The results show statistically significant differences in the average time spent on the web parts, and the most average time was spent on the web part Annual Reports and Emitent Prospects. The Pillar3 Q-terly Info web part had the second least average time spent. This part was analyzed in more detail and its categories with the most time spent by the web users were identified. They are Individual financial statements, shareholders, and risk management, which can indicate different importance of these web categories for stakeholders based on its content or its high volume. The research performed in the chapter “Factors Behind the Long-Term Success in Innovation—In Focus Multinational IT Companies” is focused on aspects of longterm innovativeness of multinational IT companies. Annually, there are several lists of the most innovative IT companies published, and many of IT companies are appearing in the first places for several years in a row. Thus, the research is focused on those factors that enable these companies to maintain their innovativeness for several years. In order to identify the factors, the authors have compared investments into R&D of the four most innovative multinational IT companies and we conducted a survey about factors of corporate culture in IBM as one of the representatives of innovative multinational IT companies. Additionally, they reviewed business models of these companies in order to find out similarities and attitudes, which might have a positive influence on a long-term generation of innovations. The interview in the Innovation Center of IBM allowed to gain deeper insight into its processes. Thanks to our research, we were able to identify specific factors of long-term innovativeness of multinational companies, which may be a source for further investigation as innovation management offers a wide range of perspectives since the field is continuously developing. The chapter “eServices and Gaming Industry—Value-Creating Ecologies as Main Factor for Customer Acceptance of Digital Servitization,” Vladorchuk et al. show that the development of value-creating ecologies can be considered as the main factor for the customer acceptance of servitization in the creative industries. And regarding servitization, it is important to design services in a way that they can generate additional value even for those customers who do not consume such services. It is important to enabling the customer to become a cocreator having him involved as a partner in this process. In the chapter “Sharing Economy Business Models: Informational Services Innovation and Disruption in Uber and Airbnb,” the authors investigate two most known sharing economy platforms, Uber and Airbnb, with the focus on the technological functioning of their business models. The chapter examines the business models of the Uber and Airbnb platforms based on a review of previous studies. Based on the

x

Preface

actual market situation in which companies currently find themselves and the challenges they may face in future, a SWOT analysis will be provided. Advancement of technologies, especially the Internet, cloud computing, smartphones, business intelligence, and geolocation have significantly influenced the enhancement of the sharing economy and trigger a novelty of their business models. Contributors of the sharing economy, mostly the technological revolution have caused the disruption of traditional business models, in the way the platforms create a demand-supply relationship with customers, derive revenues, as well as the functionality of the platforms in providing services. Further the chapter “Safe and Secure Communication between Two Cyber-Physical Systems: A Framework for Security” the authors examined CPSs that how can they build a safe and secure communication path between two CPSs and secure the correspondence of CPS with the help of MQTT protocol. The authors have planned the progression of its correspondence they set two CPS at two different places, which controls the water from the well to various tanks. In this development, diverse checks for securing the ports have been used. To test the presented framework for security, the authors have used two different tools for generating cyber-attacks named as LOIC and XOIC. In the next chapter “Investigation the Scope of Social Inequality by Accessing Telecommuting from Home Under Corona Pandemic,” the research focuses on the changing scope of teleworking and its influence on access differences accompanied by inequality. The research fundamentally bases on an extensive literature review using qualitative content analysis as outlined by Mayring to interpret the findings of 41 more comprehensive studies. Teleworking access under crisis crystallized more social inequality in contrast to the past and keeps employees under pressure and threats. This literature research can reveal relevant data and policy implications for tackling social inequalities that will moreover arise in the labor market under the long-term pandemic of COVID-19. The chapter authored by Daniel Pietrasik et al. “Building Online Stores Using PHP” discusses the basics of an ideal e-commerce solution for a company that would like to use its website as the main source of sales. The goal of this work is to analyze other websites, to define which sections the website should contain and what layout it should have to facilitate customer orientation and thus support sales. The work explains to readers the basic theoretical knowledge with the help of domestic and foreign experts, and it also presents ISO standards, the Data Protection Act (GDPR), VAT in the European Union, and more. And the final chapter “Information Exchange Platform Based on a Q&A Model” analyzes information exchange platforms found on the Internet, how they operate, what benefits they offer, and what disadvantages they pose. To understand the operation more deeply, the authors created own questions-and-answers system. In later sections, the authors discuss how this system is created, how users can interact while on the platform, and what best practices we used based on our previous analysis of other similar platforms. Based on the own research and the system created, the authors conclude that while creating a Q&A platform is with the right knowledge easy to implement, and it acts as a very useful tool to support the free exchange

Preface

xi

of information. Especially in the field of business, such a system may be a major advantage in the field of marketing, recruiting, and customer support or when used internally to support the exchange of ideas and experiences between employees. Bratislava, Slovakia Łód´z, Poland June 2021

Natalia Kryvinska [email protected] Aneta Poniszewska-Mara´nda [email protected]

Contents

Study on Wide-Ranging Ethical Implications of Big Data Technology in a Digital Society: How Likely Are Data Accidents in the COVID-19 Reality? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Izabella V. Lokshina and Cees J. M. Lanting Application of Business Rules Mechanism in IT System Projects . . . . . . . Sylwester Balcerek, Vincent Karoviˇc, and Vincent Karoviˇc

1 33

Data-as-a-Service versus Information-as-a-Service: Critical Differences in Theory, Implementation, and Applicability of Two Growing Cloud Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Ilias Wagner and Zuzana Tacacs Management and Measuring Customer Loyalty in Digital Marketplace—Analysis of KPIs and Influence Factors in CLTV . . . . . . . 151 Wolfgang Neussner Cost-Effective Solutions in Cloud Computing Security . . . . . . . . . . . . . . . . 177 Lumbardha Hasimi Marketing Communication and Its Role in the Process of Creating Rational Awareness of Generation Z Representatives . . . . . . . . . . . . . . . . . 203 Katarína Gubíniová, Peter Štarchoˇn, Lucia Vilˇceková, Gabriela Pajtinková Bartáková, and Jarmila Brtková How is Data Visualization Shaping Our Life? The Application of Analytics from Google Trends During the Epidemic of COVID-19 . . . 223 Yuanxin Li Analysis of the Practices of Financial Intelligence Units (FIUs) and Other Anti-money Laundering Agencies Within EU . . . . . . . . . . . . . . 241 Darko Panevski, Tomáš Peráˇcek, and Katarína Rentková Modern Approaches to Leadership Development—An Overview . . . . . . . 271 Helena Kiß and Rozália Sulíková xiii

xiv

Contents

Crowdfunding and Uncertain Decision Problems—Applying Shannon Entropy to Support Entrepreneurs . . . . . . . . . . . . . . . . . . . . . . . . . 289 Valerie Busse and Christine Strauss The Impact of Electronic Services on Traditional Services . . . . . . . . . . . . . 305 Dragana Saric and Marian Mikolasik Use of Digital Technologies in Business in Slovakia . . . . . . . . . . . . . . . . . . . 335 Daniela Nováˇcková and Jarmila Wefersová Business Information Through Choice-Based Conjoint Analysis: The Case of Electric Vehicle Home Charging . . . . . . . . . . . . . . . . . . . . . . . . . 357 Marvin Klein, Christine Strauss, and Christian Stummer The Combination of “Loft” and “High-Tech” Styles in the Formation of Interior Spaces for IT Companies . . . . . . . . . . . . . . . . 381 Olena Ivanochko and Jennifer R. Calhoun Is Pillar 3 a Good Tool for Stakeholders in CEE Commercial Banks? . . . 421 ˇ Petra Blažeková, Lubomír Benko, Anna Pilková, and Michal Munk Factors Behind the Long-Term Success in Innovation—In Focus Multinational IT Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 Martina Halás Vanˇcová and Iryna Ivanochko eServices and Gaming Industry—Value-Creating Ecologies as Main Factor for Customer Acceptance of Digital Servitization . . . . . . 485 Alex Vladorchuk, Michaela Schaffhauser-Linzatti, Marian Mikolasik, and Iryna Ivanochko Sharing Economy Business Models: Informational Services Innovation and Disruption in Uber and Airbnb . . . . . . . . . . . . . . . . . . . . . . 521 Lucia Šepeˇlová, Jennifer R. Calhoun, and Michaela Straffhauser-Linzatti Safe and Secure Communication between Two Cyber-Physical Systems: A Framework for Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541 Shahbaz Ali Imran and Sabina Akhtar Investigation the Scope of Social Inequality by Accessing Telecommuting from Home Under Corona Pandemic . . . . . . . . . . . . . . . . . 559 Katja Zöllner and Rozália Sulíková Building Online Stores Using PHP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 Daniel Pietrasik, Loretta Pinke, Peter Veselý, and Oskar Karlík Information Exchange Platform Based on a Q&A Model . . . . . . . . . . . . . . 643 ˇ Emil Pytka, Ondrej Cupka, René Pawera, and Miloš Šajbidor

Study on Wide-Ranging Ethical Implications of Big Data Technology in a Digital Society: How Likely Are Data Accidents in the COVID-19 Reality? Izabella V. Lokshina and Cees J. M. Lanting

Abstract This chapter is dedicated to the wide-ranging ethical implications of Big Data technology in a Digital Society. Exponential growth in the commercial use of the Internet has dramatically increased the volume and scope of data gathered and analyzed by datacentric business organizations. Big Data emerged as a term to summarize both the technical and commercial aspects of this growing data collection and analysis processes. Until now, much discussion of Big Data is focused on its transformational potential for technological innovation and efficiency; however, less attention was given to its ethical implications beyond the generation of commercial value. In this chapter, the authors investigate the wide-ranging ethical implications of Big Data technology. The authors inform that strategies behind Big Data technology require organizational systems, or business ecosystems, that leave them vulnerable to accidents associated with its commercial value and known as data accidents. These data accidents have distinct features and raise important concerns, including about data privacy in the time of the COVID-19 pandemic. In this chapter, the authors suggest methods of successful risk mitigation strategies. Keywords Big data technology · Ethical implications · Datacentric business organizations · Business ecosystems · Data accidents · Risk mitigation strategies · COVID-19

1 Introduction Recently, as the use of the Internet has grown, so too has an interest in the use of data gathered and analyzed by arriving at a more digitized and technologically connected society [1–10]. In the 1998 film “Enemy of the State”, a rogue government agency I. V. Lokshina (B) MMI, SUNY Oneonta, Oneonta, NY, USA e-mail: [email protected] C. J. M. Lanting DATSA Belgium, VBR, Louvain 3010, Belgium e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Kryvinska and A. Poniszewska-Mara´nda (eds.), Developments in Information & Knowledge Management for Business Applications, Studies in Systems, Decision and Control 376, https://doi.org/10.1007/978-3-030-76632-0_1

1

2

I. V. Lokshina and C. J. M. Lanting

is shown as having unlimited access to private data from a variety of data sources. At that point, the scenario was so disturbing to the Federal Bureau of Investigation (FBI) that a public relations campaign was launched to assure society that the plot was pure fiction [11]. Nevertheless, as recent exposures about data privacy have made clear, the surveillance shown in the film is very real at this time [2–10, 12–16]. What has changed is the role of governments as collectors and users of data, and the increasing importance of commercial entities as the drivers of both data gathering and analysis, i.e. data analytics [2–10, 13–15, 17, 18]. The COVID-19 emergency has taken place in an already digital society. The amount of pandemic-related data gathered and processed globally has been enormous. Additionally, advanced computational models, some based on machine learning, have shown tremendous potential in tracing sources or predicting the future spread of COVID-19, necessary for the planning of resources. Without correct data and models, the risk to public health cannot be assessed correctly, causing a problem that the authorities cannot allocate time, assets, and resources appropriately, leading to both penury, and oversupply, and waste. Therefore, it is essential to leverage Big Data technology and intelligent analytics and put them to effective use for the benefit of public health. Reliance on digital data sources has been of great value in outbreaks caused by new pathogens significantly improving data collection, however, precise data is still rare and hence forecasts are still less reliable and effective [19]. The needs and conditions for responsible data gathering and analysis at a global scale must be clear as Big Data technology has become critical for managing the COVID-19 pandemic in a digital society. The use of data that has been collected from digital sources for prediction and surveillance is very important in the fight against the COVID-19 pandemic, but it is equally important to use this data in compliance with data protection regulations and with due respect for privacy and confidentiality and recognizing its possibly limited validity or bias. Previous generations of information technology were dominated by technology companies with commercial strategies based on their expertise in hardware or software. Currently, many leading Internet companies have commercial strategies built around the collection and analysis of data. For datacentric business organizations like Google, Facebook, and others, the collection of data has become a target instead of a way to achieve any additional business goals [2–10]. This comes at a point when there is a growing interest from researchers in wide-ranging ethical implications of the increasing use of data in different areas, including data privacy [2–10, 12, 13, 15, 17, 20–24]; cyber-hacking [12, 16, 24–28]; government regulation [2–10, 17, 22, 23, 29]; and intellectual property [2–10, 12, 13, 15, 21, 30]. Besides, there are concerns about the level of government surveillance of commercial social networks, for instance revealed due to leaks by Edward Snowden [31]. In these circumstances, the term “Big Data” has obtained popularity in both business and public policy circles as summarizing industrial and commercial aspects of these

Study on Wide-Ranging Ethical Implications of Big Data …

3

state-of-the-art data gathering and analysis processes which involve also private data collection [2–10, 13, 15, 16, 28]. Until now, there has been much discussion of Big Data technology focused on its potential positive effects for both business and society [1–10, 32]. This extends beyond improvements to commercial efficiency and expands to claims about its transformational effect on such areas as healthcare and delivery of public services [2–10, 33]. Nevertheless, there has been less discussion on Big Data technology devoted to ethical issues. This is not surprising given the limited research on Internet ethics [2–10, 34] and limited understanding of wide-ranging ethical implications of new technologies started being deployed [2–10, 35]. Therefore, in this chapter, the authors have given thought to wide-ranging ethical implications of Big Data in a digital society, including during the COVID-19 pandemic, and evaluated ethical issues related to Big Data technology by applying the normal accident theory [2–10, 32, 36–41]. Normal accidents are “normal” in the sense that the occurrence of negative developments is inevitable and taking place unexpectedly in organizational systems that are complex, interactive, and tightly coupled [37–41]. The normal accident theory has been applied to investigate nuclear power plant accidents [42], underground coal mine devastations [36, 43–45] plane crashes [46], structural collapses [2–10, 36], failures in hospitals, anesthesia systems, the practice of medicine and perfusion producing shocking medication reactions [47], as well as most recent financial system fiascos [43–45, 48]. The authors believe that in high-risk systems distinguished by multiple and unexpected interactions, no matter how effective safety mechanisms are, normal accidents are inevitable [47]. These negative events take place regardless of the number of safety mechanisms, the quality of the care provided, or the awareness of operators. In other words, in complex systems, errors are made by humans [36, 43–45, 47]. The authors suggest that emerging datacentric business organizations, which can enable or be enabled by Big Data, have system features as identified in the normal accident theory. However, the consequences of normal accidents in these datacentric business organizations are less obvious than with natural disasters, which makes identification and remedy more difficult [32]. In this chapter, the authors discuss data accidents, which extend the normal accident theory by exploring the scale and interconnection, as well as uncertainty and distrust created by Big Data technology in a digital society. The authors investigate mostly wide-ranging ethical implications of Big Data since the consequences of data accidents must be weighed against the effectiveness and value achieved by society with the deployment of Big Data technology [2–10, 32]. This chapter is comprised of eight sections and organized as follows. Section two identifies key features of Big Data technology and datacentric business organizations that enable and are enabled by an exponential growth in data collection and analysis processes. This section also shows that large-scale data collection can help curb the COVID-19 pandemic; however, this process should respect data privacy and gain

4

I. V. Lokshina and C. J. M. Lanting

public trust. Section three analyzes normal accidents associated with state-of-theart and emerging technologies, outlines the normal accident theory, and explains specifics of datacentric organizational systems, or business ecosystems, that enable data accidents. Section four extends the normal accident theory by investigating distinct features and potential consequences of data accidents. This section also provides a classification of data accidents and analyzes scenarios of systemic, noncommercial, and commercial data accidents including examples related to COVID19. Section five explains what should be done to mitigate the risks and potential implications of Big Data technology given the growth in the industrial and commercial value of state-of-the-art and emerging technologies. This section also recommends successful strategies and best practices to maintain responsible data collection and processing standards on a global scale to safeguard business ecosystems, followed by the conclusion, acknowledgment, and references.

2 Views on Big Data Technology In this section, the authors present their views on Big Data technology. Although data has become strongly associated with information technology, managing and making sense of data remains an old problem. Historians, politicians, and military leaders have relied on information as a source of power, as well as a driving force, for centuries, while denying access to information has always been a lever to diminish power. However, what has changed is the speed and volume of data gathered to analyze. When, in the 1850s, managers of the United States railroads were searching for new organizational designs to help overcome the challenges of data overflow in the growing business, they were talking about the volume of data below a single megabyte in modern terms [49]. On the other hand, an important feature of data in modern business is a constant exponential growth in volume. To use just one contemporary statistic, 90% of all present data has been generated in the last two years [2–10, 22, 23]. Under these circumstances, Big Data first appeared as a term to describe the technological innovation supporting this tremendous increase in the volume of data that is collected and analyzed [2–10, 12, 27]. Beyond the volume of data, enabled to be collected, Big Data technology has also changed the complexity and velocity of data to be gathered and analyzed [2–10, 21]. Velocity refers to processing power, i.e. speed at which data may be collected and analyzed, with near real-time analysis becoming possible for very large datasets and complex analysis. Complexity, due to the volume and variety of data, is also very significant as it marks a transition from gathering data in only text format towards collecting data in metadata, video, audio, and image formats [2–10, 12, 50]. More recently, Big Data went far beyond its industrial roots to include wideranging commercial opportunities enabled by the analysis of data, i.e. data analytics [2–10, 22, 23, 33]. Therefore, Big Data as a term has been identified by politicians as

Study on Wide-Ranging Ethical Implications of Big Data …

5

the means for achieving economic growth. In the UK, Big Data has become one of the eight key government priorities [32, 51]. In the United States, Barrack Obama’s use of campaign data for the analysis has led to nicknaming him as the Big Data President [52]. This technological innovation has been supported by several economic factors. First, the cost of storing data has been reduced to the point that it might be economically feasible to store all forms of data even while there is no direct use for it. Second, the required hardware and software have now become readily available in the marketplace [2–10, 12]. Much of the software that currently supports Big Data have first appeared not from traditional technological companies with commercial strategies built around their expertise in hardware or software; but from datacentric business organizations where their specific demands have forced them to resolve their own Big Data technology issues. Particularly, to support the use of Big Data technology on a wide scale, much of this technology has become available through open-source modules, which helped datacentric business organizations to adopt Big Data technology for their specific needs [2–10, 12, 32, 53–55].

2.1 The Use of Big Data Technology on a Large Scale and Associated Commercial Opportunities In this subsection, the authors examine the use of Big Data technology on a large scale and related commercial opportunities. Big Data technology creates the potential for numerous commercial opportunities and innovations. The most common examples come from the major Internet companies for which data gathering and analysis, i.e. data analytics, are core competencies. For instance, [56] specified that Google has managed to develop an advanced spellchecker, not because of its competence in natural language processing or a unique approach to spelling, but due to collecting and analyzing a massive database of real spelling corrections. Other examples of commercial opportunities based upon similar technology usage scenarios include recommendation engines used by Amazon and Netflix that benefit from vast databases of user preferences on books and films to provide recommendations for future reference [9, 10, 56]. However, the commercial benefits of Big Data technology are not limited to Internet companies. Indeed, the McKinsey report that served to promote Big Data as a commercial strategy indicated the potential in healthcare, finance, and public services [2–10, 22, 23, 33]. For instance, in healthcare services distinguished by using fragmented datasets, Big Data technology has been proposed to help control increasing costs and accelerate research and development (R&D) processes in new pharmaceutical systems [2–10, 57].

6

I. V. Lokshina and C. J. M. Lanting

Specific features of commercial Big Data usage scenarios that have resulted in many advantages being less publicized as they are continuous improvements to existing processes. In other words, many Big Data usage scenarios are to continually improve existing processes rather than invent new procedures. A good example derives from the transition in airlines from relying on pilots to provide estimated time of arrival (ETA) information to employing a data-driven system, which combines several data sources including weather, radar, and flight schedules [58, 59]. Formerly, busy pilots could make generally accurate estimations; however, 30% of their estimations have been over 5 min off. Currently, by combining multiple data points with the use of automated algorithms, airlines obtained the power to virtually eliminate gaps between estimated and actual arrival times, saving millions of dollars per year for the airports [2–10, 59]. What brings all these examples together and makes them Big Data technology is a commercial value created by large-scale data analysis, i.e. data analytics, and not only by data gathering, together with subsequent merging data in multiple datasets. While these examples can illustrate commercial benefits from Big Data, the wideranging ethical implications of Big Data technology that may result from commercial manipulation remain unclear. Although the focus is often placed on the volume of data gathered, Big Data technology is less about the large volume of data and more on the capacity to search, aggregate, and analyze numerous large datasets [2–10, 22, 23, 60]. Up to this point, there are still not many concerns about the wide-ranging ethical implications of Big Data technology. Specifically, there are only a few concerns documented about the privacy implications of this technology in a digital society. There is nothing new about privacy concerns in the public domain related to new technologies be it photography [2–10, 61], personal computers [2–10, 16, 24, 28, 62], or the Internet [2–10, 13, 15, 16, 28, 32, 53–55]. However, typically, the nature of these concerns has masked the reality of how new technologies have used and analyzed data [2–10, 13, 15, 22, 23, 63]. Specifically, concerns about data privacy implications have been driven by fear over the active and visible data gathering process before passive and autonomous data gathering process, associated with Big Data technology [2–10, 12, 13, 15, 22– 24]. Therefore, concerns over data privacy implications directly related to Big Data have been less discussed among the users of Big Data technology or described in the literature because the users of this technology have less insight into running data gathering and analysis scenarios [2–10, 12, 13, 15, 22–24]. On the other hand, in this chapter, the authors investigate the lack of perspective when the nature of privacy risk is unknown. Returning to the main notion of Big Data technology, the authors restate that large volumes of data might be gathered and stored because it is economical and there is a potential for future analysis of this data, i.e. intelligent analytics, to create commercial value [12, 15]. However, the data use scenarios are undetermined, therefore wide-ranging societal implications of Big Data technology remain unknown and nontransparent.

Study on Wide-Ranging Ethical Implications of Big Data …

7

2.2 The Use of Big Data Technology for Managing COVID-19 Pandemic in a Digital Society In this subsection, the authors consider the use of Big Data technology to manage the COVID-19 pandemic. On January 30, 2020, the World Health Organization (WHO) director-general declared the coronavirus disease 2019 (COVID-19) outbreak a public health emergency of international concern (PHEIC). Several weeks later, the outbreak has been categorized as a pandemic. COVID-19 has already caused a hundred times more cases than the previous coronavirus-induced PHEIC, i.e. the 2002–2003 severe acute respiratory syndrome (SARS) outbreak, and the COVID-19 numbers are expected to grow. However, compared with the 2002–2003 outbreak, the COVID-19 disaster has taken place in a much more digitized and connected ecosystem. The amount of all data produced by 2003 is generated today within a few minutes. Furthermore, advanced computational models like those using machine learning have shown great potential in tracing sources or predicting the future spread of infectious diseases [2–10, 19, 64–66]. Therefore, it is imperative to leverage Big Data technology and intelligent analytics and put them to good use for the benefit of public health. Relying on digital data sources like data from mobile phones and other digital devices is of great value in outbreaks caused by newly discovered pathogens since precise data is still rare and hence forecasts less reliable and ineffective [19]. A recent study has shown the possibility of forecasting the spread of the COVID19 outbreak by combining data from the Official Aviation Guide with data on human mobility from the WeChat app and other digital services owned by Chinese tech giant Tencent [31]. Mobile phone data have shown potential in predicting the spatial spread of cholera during the 2010 Haiti cholera epidemic while leveraging Big Data analytics demonstrated effectiveness during the 2014–2016 Western African Ebola crisis [67]. However, during the recent epidemics, the large-scale collection of mobile data from millions of users, especially call data records and social media reports, have raised privacy and data protection concerns as well. In 2014, privacy concerns urged the GSM Association, i.e. an industry organization that represents the interests of mobile network operators worldwide, to issue guidelines on the protection of privacy in the use of mobile phone data for responding to the Ebola outbreak [68]. In the information-intensive reality of 2020, ubiquitous data points and digital surveillance tools can easily worsen those concerns. China, the country first and heavily affected by COVID-19, has reportedly used ubiquitous sensor data and health check apps to reduce the disease spread [69]. According to a New York Times report, there has been little transparency in whether and how this data was cross-checked and reused for surveillance purposes [70]. For instance, the report has stated that Alipay Health Code, an Alibaba-backed government-run app that supported decisions about who should be quarantined for COVID-19, also seemed to have shared information with the police [70].

8

I. V. Lokshina and C. J. M. Lanting

In Italy, the European country recording one of the largest numbers of the COVID19 cases, a local data protection authority has been urged in March 2020 to issue a statement to clarify the conditions of lawful data use for mitigation and containment purposes. In its statement, the authority warned against the privacy-infringing collection and processing of data by non-institutional actors, i.e. private employers. Two weeks later, the European Data Protection Board issued a statement on the importance of protecting personal data when used in the fight against COVID-19 and flagged specific articles of the General Data Protection Regulation (GDPR) that provide the legal grounds for processing personal data in the context of epidemics [71–73]. For instance, Article 9 allowed processing personal data “for reasons of public interest in the area of public health, such as protecting against serious crossborder threats to health,” provided such processing is proportionate to the aim pursued, respects the essence of the right to data protection and safeguards the rights and freedom of the data subject. As Big Data technology has become critical for managing the COVID-19 pandemic in the modern digital world, the conditions for responsible data collection and processing at a global scale must be clear. The authors consider that the use of digitally available data and algorithms for prediction and surveillance, i.e. identifying people who have traveled to areas where the disease has spread or tracing and isolating the contacts with infected people, has been of great importance in the fight against the COVID-19 pandemic. However, it is equally important to use these data and algorithms responsibly, in compliance with data protection regulations, and with due respect for privacy and confidentiality. Failing to do so could undermine public trust, which would make people less likely to cooperate in providing, when necessary in the public interest, travel and contact data, follow public health guidance or recommendations, and eventually, would result more likely in worse health outcomes [74]. Careful data management practices should govern both data collection and processing levels. At the data collection level, the principle of proportionality regarding affected people should apply, which means that the data collection intensity must be proportional to the seriousness of the public health threat. This data collection intensity must be limited to only what is necessary to achieve a specific public health objective and be justified on a scientific basis [2–10, 22, 23]. For instance, gaining access to data from personal devices for contact tracing purposes can be justified if it takes place within specific bounds, has a clear purpose, i.e. warning and isolating people who may have been exposed to the virus, and has no less invasive alternative, i.e. using anonymized mobile positioning data, is suitable for that purpose. On the other hand, “do it yourself” health surveillance like it was termed by the Italian data protection authority should be avoided as its contribution to public health safety is doubtful. At the data processing level, data quality and security controls are required. Data integrity weaknesses that are common when data from personal digital devices are used can introduce minor errors in one or multiple factors, which in turn can have an outsized effect on large-scale predictive models [4–10, 22, 23].

Study on Wide-Ranging Ethical Implications of Big Data …

9

Besides, data breaches, insufficient or ineffective anonymization, and biases in datasets can become major causes of distrust in public health services. Data privacy challenges not only are technological but also depend on political and judicial decisions. Requesting or warranting access to personal devices for purposes like contact tracing can be more effective than simply leveraging anonymized mobile positioning data. However, convincing providers to allow access to or even assist in decrypting cryptographically protected data, similar to what has occurred in the 2016 United States Federal Bureau of Investigation (FBI)—Apple encryption dispute, may be counterproductive, especially when the agreements between international authorities and service providers would lack transparency and proportionality [2–8]. Similar trade-offs could apply to health apps that require users to register with their names or national identification numbers. National authorities should realize that because personal data might contain valuable and sensitive information about the social interactions and recent movements of infected people, these must be handled responsibly. Overriding consent and privacy rights in the name of disease surveillance may fuel distrust and ultimately turn out to be disadvantageous. There have been reports that China’s digital epidemic control might have worsened stigmatization and public distrust [110]. This risk of distrust is even greater in countries where citizens place a much lower level of trust in their government like Italy, France, and the United States [75]. Therefore, whenever access to these data sources is required and considered to be proportional, a society should be adequately informed [2–10, 22, 23]. Secrecy about data access and use should be avoided. Transparent public communication about data processing for the common good must be pursued. For instance, data processing agreements should disclose what data has been transmitted to third parties and for what purpose. Reports from Taiwan have shown a promising way to leverage Big Data analytics to respond to the COVID-19 crisis without fueling public distrust [76]. Taiwanese authorities integrated their national health insurance database with travel history data from customs databases to assist in case identification. Other technologies like QR-code scanning and online reporting have been used also for containment purposes [76]. These measures have been combined with public communication strategies involving frequent health checks and support for those under quarantine [76]. The authors consider as more countries prepare to use digital technologies in the fight against the COVID-19 pandemic, Big Data technology, represented by large-scale data collection and intelligent analytics, is among the best methods, provided applied appropriately [2–10, 17, 22, 23].

10

I. V. Lokshina and C. J. M. Lanting

3 Views on Big Data Technology In this section, the authors present their views on accidents associated with state-ofthe-art and emerging technologies. The authors note that technological development has always produced a divide between the economic advantages and societal implications. The question is not whether technological progress in certain ways defines society, but the extent to which it does define society [2–10, 77]. Although currently, data appears to be the main element in the discussion about wide-ranging ethical implications of information, communications, and computing technologies, this was not always the case. By defining a notion of ethical overload, [78] identified wide-ranging ethical issues that could relate to computer technology. The concerns were associated with speech recognition, biotechnology, and the effect on the workplace structure from working at home that currently has become an important ethical overload in the COVID-19 reality, while there has been little reference to the role of data itself [2–10, 78]. This example demonstrates the challenges related to predicting the consequences of state-of-the-art and emerging technologies, even when these technologies are wellunderstood themselves. The researchers have described these challenges by stating that under similar circumstances “the new immorality is to act in ignorance of future consequences” [2–10, 32, 78]. Although physical accidents and natural disasters have regularly taken place in society, the latest complex, embedded technologies require special attention [2–10, 79–81]. The authors consider that normal accident theory could help explain the consequences of these latest technologies, as well as the need for a better understanding of why disastrous situations and catastrophic events are likely to take place [2–8, 36, 43–45].

3.1 The Normal Accident Theory and Normal Accidents In this subsection, the authors focus on the normal accident theory and associated normal accidents. An introduction of the normal accident theory has been launched from the report for the President’s Commission on The Accident at Three Mile Island after the 1979 nuclear accident at a power plant on that site [37]. The report concluded that the accident was not caused by discrete technical faults or human errors as it could have been expected but by a sequence of organizational failures that take place within systems like nuclear power plants that are complex, highly interactive, and tightly coupled [37–39]. Therefore, the focus of the document was not on identifying who to blame, but rather on investigating how organizational failures could develop causing such an accident to take place [37–39]. Generally, systems in which normal accidents can take place have two key features: complex interactivity and tight coupling [32, 36–45, 47, 48].

Study on Wide-Ranging Ethical Implications of Big Data …

11

Complex interactivity refers to the possibility of a chain of unknown and unplanned failures that take place in a sequence. This is contrary to linear interactivity which refers to a series of obvious, anticipated, and planned events that take place according to a known order [36–41, 43–45, 47]. Linearity does not suggest that a linear system is simple, i.e. missing complexity, but rather that events are well-understood and linearly take place. For instance, a production line for pharmaceuticals or the flight of airplanes are linear systems as these processes can be easily explained; however, they are not simple at all. An accident could also take place in these circumstances; however, based on its linear nature, consequences can be easily identified and fixed. For instance, when an unexpected situation takes place in a linear system like a production line, it can be simply identified and corrected by the staff. On the other hand, complex interactivity takes place when interactions are not fully understood by the staff who must take time-constrained decisions that are necessary to mitigate an accident [32, 36–41, 47]. Tight coupling refers to interactions that are discrete and take place very quickly. Therefore, the parts within an organizational system that is tightly coupled impact each other and trigger failures which transform into an accident [32, 37–41, 47]. In these circumstances, an accident is defined as a major, abnormal system failure, contrary to an incident defined as a minor, routine system failure. Therefore, an accident is a failure of the entire system and not of a certain part of the system; the outcome is that a chain of discrete system failures needs to arise before a system accident can take place [32, 36–41, 43–45, 47]. A challenge for the normal accident theory is that application of the term “system” is not properly defined despite its notion is placed in a center of the theory [82]. However, applying this term, the normal accident theory can change its focus from a discrete failure, i.e. a failure of a certain part of the system including human error, to a failure of the entire system. This means that now an organizational system where the accident can take place is the primary cause of a normal accident, and not the staff [83]. The term “normal accident” means that under specific circumstances an accident can become inevitable. This definition created an important disagreement between normal accident theorists and high-reliability academics since high-reliability thinkers suggested an option to design organizational systems that are complex, highly interactive and tightly coupled, and additionally, able to survive a normal accident [37–41, 82, 84, 85]. Normal accident theorists responded that numerous incidents, even complex incidents like multiple system failures, can be prevented while a normal accident is inevitable due to a failure of the entire system [37–41, 82, 84, 85]. Therefore, the main message of normal accident theorists is not about avoiding the risk but on how to deal with the consequences of a normal accident. This suggestion has put in question the overall relationship between technology and society; however, the notion of the normal accident theory remained the same. For instance, disasters caused by natural sources like pandemics known in the past and COVID-19; catastrophes prompted by industrial and technological sources like former accidents at nuclear power plants; and devastations caused by deliberate

12

I. V. Lokshina and C. J. M. Lanting

sources like terrorism and malicious acts of violence, have been inevitable and even growing: some of them could have been identified and narrowed, but never escaped [2–8, 36, 39, 40, 43–45, 86–88]. The notion that a normal accident is likely to take place in an organizational system has marked the normal accident theory against all other theories concerning technology risk. Therefore, in this chapter, the authors apply this view to the wideranging ethical implications of Big Data technology.

3.2 Normal Accidents Associated with Big Data Technology In this subsection, the authors define normal accidents associated with Big Data technology. The authors do not suggest that Big Data technology can create normal accidents. Instead, they assume that Big Data can play a critical role in shaping organizational systems where normal accidents are possible to take place. To demonstrate this assumption, they analyze Big Data technology against the fundamentals of the normal accident theory, i.e. complex interactivity and tight coupling. Besides, the authors expand the normal accident theory by considering specific circumstances in emerging datacentric business organizations that can enable or be enabled by Big Data technology where normal accidents are likely to take place. They also investigate uncertainty and distrust created by Big Data technology in a digital society since the consequences of normal accidents would have to be weighed against the effectiveness and value achieved by society from the deployment of this technology. Therefore, first, the authors state that Big Data technology displays features of a tightly coupled system. Such an argument seems like a counter-intuitive statement considering that the Internet is a prototype of a loosely coupled system that has been engineered to survive the destruction of some of its parts in a Cold war era. However, there is a tightly coupled infrastructure, independent of the Internet but only using it, which stands behind Big Data technology and companies that tie together datacentric business organizations, dependent on this technology. The authors also note there is an ongoing transition from datacentric business organizations that could control their technology stack toward cloud computing where storage and processing power are provided as service. While datacentric business organizations could develop software to gather data, to maximize efficiency they become more dependent on the deployment of large data centers operated by companies providing cloud services like Amazon, Microsoft, or Google. For all but very large business organizations, both the efficiency and flexibility offered by these multibillion-dollar data centers make it a prerequisite to apply third-party storage and likely computing technology [2–10, 22, 23, 32]. However, when there is a failure in data centers, the consequences are far more wide-ranging, unexpected, unpredictable, and dramatic than when datacentric business organizations use their own, distributed infrastructure that also, they can control. For instance, an accidental loss of data by a software developer who balanced

Study on Wide-Ranging Ethical Implications of Big Data …

13

traffic between many servers using Amazon Web Services on Christmas Eve 2012, resulted in a sequence of failures that made online services like Netflix unavailable on Christmas morning [4–8, 89]. In their explanation of the failure, Amazon has highlighted that only a small team of software developers had access to this data; at first, the software developers did not recognize an error; and initially, the entire software development team was mystified by an error message generated by the system [90]. However, the problem was resolved not with the use of technical resources, but by implementing a new process to ensure that changes to the system are double-checked to avoid accidental loss of data in the future. Second, the authors note concerning loose coupling that a distributed method by which Big Data has been collected and stored created intrinsic complexity among numerous datacentric business organizations and various technologies. Returning to a previous example of Netflix’s failure, the authors note that Netflix was dependent on the infrastructure of another datacentric business organization to deliver much of its content. Besides, Netflix was reliant on the business organization with whom it competed directly, i.e. Amazon’s Instant Video service. While a few datacentric business organizations can achieve vertical integration, most of them have become dependent on a growing network of third-party technology [2–10, 22, 23, 32]. Taking a hypothetical example of a mid-size online retailing company, besides hosting its website, its dependence on other websites, i.e. thirdparty technology, could include payment services like credit card companies, Paypal and Apple Pay; integration with social media; content delivery networks to provide video content; integration with a third-party customer relation management (CRM) services; courier and delivery services; web analytics tools and advertising servers; etc. [2–10, 17, 22–24]. Besides complexity that is essential for data collection, there is also a growing concern over efforts to find qualified employees who can effectively analyze data [2–10, 91]. Third, the authors state that a distinct feature of a normal accident and Big Data technology is a lack of shared understanding over risks. Datacentric business organizations that have been previously used as examples of a normal accident may be designated as systems that have understood what can create an accident, although they would likely disagree on the extent to which it can be considered as a normal accident. Besides, datacentric business organizations that frequently collected large amounts of data were governments that are expected to be motivated to respect data privacy. Both the lack of commercial incentives and restricted access to data has implied an increased level of control over data by datacentric business organizations. Instead, commercial requirements that enable or are enabled by Big Data technology have been developed around two fundamental processes: data collection with consequent analysis, i.e. data analytics, and data sharing [2–10, 13, 15, 17, 22–24]. The authors consider this transformation has established a different culture with risks for data privacy. Besides, it has put pressure on processes that generate

14

I. V. Lokshina and C. J. M. Lanting

a commercial value which can likely trigger normal accidents that the authors investigate in this chapter.

4 Views on Data Accidents Associated with Big Data Technology In this section, the authors suggest that normal accidents associated with Big Data technology have particular features that make them different from physical accidents that have been discussed in the normal accident theory [39, 92]. The term “data” is used on purpose instead of the term “information” as Big Data has been associated with unstructured data, i.e. data as opposed to structured data, i.e. information. From a technical viewpoint, applying structure to data requires defining particular features of data to gather before a data collection process is started. For instance, it must be defined in advance that certain data is either an image, sound, or text; or it is a name, currency, time, etc. [2–10, 22, 23]. The process of structuring data before its collection indicates that some consideration was granted to the final use of data. This may be a semantic viewpoint, but really helpful position to reinforce the main standard of Big Data technology informing that data can be gathered despite, and potentially, without knowledge of, the aim for its final use [2–10, 32, 79]. Additionally, the authors state that data accidents associated with large scale data collection should be considered based on the following classification [108]: • Systemic data accident involving a systemic incomplete or unreliable collection of data and limitations in the interpretation of data, such as • Systemic data accident involving a systemic incomplete or unreliable collection of data, i.e. • Systemic data accident resulting from the systemic incomplete collection of data, and • Systemic data accident resulting from the systemic unreliable collection of data. • Systemic data accident resulting from systemic limitations in the interpretation of data. • Data accident involving reliably collected and correctly interpretable data, such as • Data accident resulting from violations of data security compromising authorized access to data, i.e. “denial of service”. • Data accident resulting from violations of data security compromising data privacy by an unauthorized read access to data, i.e.

Study on Wide-Ranging Ethical Implications of Big Data …

15

• Data unintended disclosure, violating data privacy by exposing confidential data, where disclosure has limited consequences for the organization and others, and • Data unintended disclosure, violating data privacy by exposing confidential data where disclosure has serious consequences for the organization and others. • Data theft compromising data privacy by unauthorized access to data, i.e. • Data leak, undetected unauthorized access violating data privacy, • Data disclosure blackmail, where violating data privacy is used as a lever to unlawfully obtain funds or other benefits or concessions, and • Data exposure, where violating data privacy is a tool to intentionally expose data. • Data accident resulting from violations of data security compromising data validity by unauthorized manipulative access to data, i.e. • Data integrity loss, undetected unauthorized manipulative access violating both data privacy and integrity, • Data destruction blackmail, where unauthorized manipulative access to data is used as a lever to unlawfully obtain funds or other benefits or concessions, and • Data integrity loss, unauthorized, irrecoverable manipulative access violating both data privacy and integrity. Several important examples of data accidents associated with large scale data collection that demonstrate the relevance and use of the proposed classification are considered in the following subsections. These examples include systemic data accidents involving a systemic incomplete or unreliable collection of data and limitations in the interpretation of data corresponding to the COVID-19 data collection, interpretations, and decisions that are considered as special case data accidents associated with Big Data technology; as well as data accidents involving reliably collected and correctly interpretable data, containing undetected unauthorized access violating data privacy like in the Manning and Snowden data leak scenarios, along with violations of data security compromising both data privacy and integrity by unauthorized manipulative access to data like in the Johnson & Johnson and the World Anti-Doping Agency (WADA) data manipulation scenarios [9, 10, 32, 93–96].

4.1 Systemic Data Accidents In this subsection, the authors suggest the COVID-19 data, the following interpretations, and decisions derived from these interpretations should be defined as systemic data accidents associated with Big Data technology. These should be considered as special case data accidents because the problems in large scale data collection and

16

I. V. Lokshina and C. J. M. Lanting

interpretations are known, but considered inevitable; and the resulting interpretations and extrapolations are considered “best-effort”, “the best we have”, or “the all we get”. Additionally, the COVID-19 data is mostly gathered voluntarily; therefore, it is possibly or even likely incomplete. Also, the COVID-19 data can be incomplete due to errors before data is collected, i.e. some people dying at home or in an elderly home which may be considered rather as “natural death” than as COVID-19 fatalities. Additionally, the COVID-19 data can be incomplete because of the initial anonymization of data which may suppress severity and historical data, i.e. the number of people tested may be unknown when only the number of tests is recorded. Besides, there is feedback between collected data, interpretations, extrapolations, resulting actions, and the effect on the contamination and, consequently, on the causes behind the sources of properly collected data, which constitutes a difficulty to assess the challenge. This effect on the data sources makes it similar to non-systemic data accidents like scenarios of suspected silent manipulation, i.e. when manipulation is suspected but both nature and scope are unknown. Nevertheless, it is critically important to define the COVID-19 data, the following interpretations and decisions derived from these interpretations as a data accident since implications of problems with large scale data collection and interpretations are neither preventing local, regional, and national authorities nor stopping datacentric business organizations from making crucial decisions such as numbers of required hospital beds or intensive care unit (ICU) beds based on extrapolations from this data. The feedback between extrapolations, strategy, actions, reduction in COVID-19 spreading, and for instance, the number of required ventilators is also a problem. The number of ventilators, being built in the United States is likely based on a gross over-estimation of real need, without considering changes in both the therapy and spread due to measures to reduce the spreading of the COVID-19 pandemic. Besides, in assessing the deadliness of COVID-19, confusion is caused by failing to distinguish COVID-19 cases from COVID-19 infections. The case fatality rate is computed by dividing the number of deaths by the total number of confirmed cases. But to obtain an accurate COVID-19 fatality rate, the number in the denominator should be the number of infections instead of the number of confirmed cases. In the early days, only part of infected people was identified as confirmed cases that resulted in an inaccurate fatality rate which drove public policy and sowed fear and triggered the widespread lockdowns. The lockdowns have serious effects on public health. The United Nations (U.N.) estimated that 130 million additional people will starve this year because of economic damage resulting from the lockdowns [97]. Also, the parents stopped bringing their children in for immunization against diseases like diphtheria, pertussis, and polio. Many patients who have had cancer and needed chemotherapy did not come in for treatment, and the others skipped recommended screening or diabetic monitoring. Additionally, the lockdowns have severe psychological effects, especially among young adults and children, who are denied much-needed socialization.

Study on Wide-Ranging Ethical Implications of Big Data …

17

In this example, the local, regional, and national authorities are aware of problems in the COVID-19 data collection and resulting interpretations and extrapolations. Therefore, the main issue raised by this instance is that since authorities have approved the COVID-19 data manipulation given incomplete data collection and limitations in its interpretation that impact both the derived decisions and public opinion, then how society can expect that commercial applications of Big Data technology will not suffer from similar situations.

4.2 Noncommercial Data Accidents In this subsection, the authors consider noncommercial data accidents associated with Big Data technology. First, keeping in mind [39], the authors examine two recent examples of data loss accidents that have been described in the literature. Both examples are instances of information leaks that took place through two individuals associated with United States government organizations. One case is associated with Bradley (Chelsea) Manning who has been leaking United States diplomatic cables to Wikileaks [32, 93–95]. The other case is associated with Edward Snowden who has been leaking classified National Security Agency (NSA) data and procedures to various media organizations [32, 94–96, 98]. These instances do not illustrate direct commercial use of Big Data; however, they can help investigate issues around key features of data accidents. The authors note that in the Manning and Snowden scenarios the term “accident” is used to describe outcomes when original data losses have been caused not by “accidental” system failures but unexpected interactions in organizational systems. Nevertheless, the authors consider these scenarios were accidents as they were caused by the failures of entire systems designed to prevent data losses, not by discrete events [94–96]. Like with a previous scenario given for Amazon [89], data accidents have taken place by a combination of unexpected failures. In the Manning case, the Wikileaks incident has taken place over a junior staff member, Bradley (Chelsea) Manning, who has copied data, primarily diplomatic cables, to a CD-ROM and then contacted media [93–95, 98]. In the Snowden case, the precise method by which Edward Snowden has retrieved data from the NSA databases remains unknown, though it included copying data from a computer to a USB memory drive [94, 95, 99]. This simple method has not been taken into consideration because computers used to store sensitive data and confidential information are expected to have USB ports disabled. What is important, the fact that any of the Manning and Snowden data accidents took place unidentified until data was leaked to media. While accessing and downloading data, the full meaning of Snowden leaks has been unclear due to uncertainty in scope and Snowden’s intention to publicly distribute. In both examples, authorities have been unaware of data loss until it was leaked to the media. Besides, it has not been performed by a foreign government or a criminal

18

I. V. Lokshina and C. J. M. Lanting

organization that involves multiple actors. Instead, it has been performed by a single junior staff member who conducted regular organizational tasks; however, could quickly access and transfer large data volumes. Therefore, the authors suggest the main issue raised by these instances is that if single junior staff members in highsecurity organizations could access, download and circulate large data volumes, with much of it, like in the Snowden scenario, classified “Top Secret”, then how society can expect that commercial applications of Big Data technology will not suffer from similar situations. Next, the authors examine two recent examples of data manipulation accidents that have been described in the literature. Both examples are instances of database breaches when data has been accessed and altered in place, posing a serious threat for involved datacentric business organizations and concerned individuals [9, 10]. One scenario is associated with data manipulation in one of Johnson & Johnson’s insulin pumps in 2015, which enabled hackers to overdose on users with insulin. The other scenario concerns Russian hackers who breached the World Anti-Doping Agency (WADA) systems in 2016, changed in place medical data of many athletes, and then circulated this altered data damaging their reputation [9, 10]. These instances cannot illustrate direct commercial use of Big Data; however, they can help investigate issues around key features of data accidents. Healthcare and pharmaceutical industries are at risk of data manipulation accidents when patients’ lives are affected by tampering with data on what medications the patients are prescribed, how often they should take the medications, and what allergies the patients have. When healthcare data is manipulated on a large scale causing a great deal of harm, this is considered cyberterrorism [9, 10]. Data manipulation accidents not only prompt datacentric business organizations to lose their profits and customers, or patients, or users, but also show how cybersecurity and public health and safety are increasingly connected. In the Johnson & Johnson scenario, a security vulnerability in one of its insulin pumps enabled malicious actors to overdose several patients with insulin [9, 10]. Data manipulation accidents create opportunities to change reality for smear campaigns. By accessing data in organizational systems and altering it in place, data manipulation accidents are used to destroy the personal and professional reputations of various individuals. In the Russian hackers’ scenario, the WADA systems have been breached and the medical data of many famous athletes have been changed in place and then released [9, 10], with a clear objective, masking usage of forbidden enhancing products and procedures by others. Additionally, data manipulation accidents are intended to influence public opinions and impact decisions. When data is changed, inevitably, choices made based on this data are also manipulated. By performing calculated alterations to data, the decision-making process of people who use this data can be strategically controlled by malicious actors [9, 10]. In the global race for the development of a vaccine, understanding vaccine research and other specifics about the pandemic has become a main target for intelligence agencies around the world. Currently, “state-backed hackers” more likely to focus on highly sensitive information such as the COVID-19 vaccine research rather than

Study on Wide-Ranging Ethical Implications of Big Data …

19

just organizational data. For instance, throughout 2020, a Russian state-backed hacking group known as APT29 targeted various medical organizations involved in the COVID-19 vaccine development in Canada, the United States, and the United Kingdom, to access data in their organizational systems to use, leak, or manipulate [8, 9]. In these instances, data manipulation has been performed by a foreign government or a criminal organization, involving multiple actors. Therefore, the authors suggest the main issue raised by such instances is that if malicious actors could access data in organizational systems, alter data in place and then circulate, then how society can expect that commercial applications of Big Data technology will not suffer from similar situations.

4.3 Commercial Data Accidents In this subsection, the authors consider commercial data accidents associated with Big Data technology. The authors note this is difficult to directly estimate the potential for commercial data accidents because firstly, unlike in the Manning and Snowden scenarios discussed earlier, most commercial data accidents are not made public. Secondly, commercial data accidents have the key features that distinguish them from traditional “physical accidents” discussed earlier as well. These features make data accidents very difficult to identify, assess, and remedy. The first difference is the absence of physical artifacts that makes data accidents extremely difficult to identify when they take place. In prior noncommercial data accident scenarios, the first indication that data accidents took place became a fact that data was used at the time it was used. Therefore, if a sequence of incidents, i.e. minor failures involved in a nuclear accident, could be identified at a physical location, data accidents can be only discovered by the following data uses. The second difference is that the consequences of data accidents are neither geographically specific nor geographically positioned. Datacentric business organizations that collect data can be nationwide, at least in standard legal terms, but the Internet and Big Data technology enable them to collect data on people from around the world with very few limitations. Once data is lost, legally, or illegitimately, it can spread all over the world very quickly. An absence of geographical borders has some concerns in terms of authority when it applies to both reducing the consequences of data accidents and stopping the spreading [9, 10, 25, 100]. The third difference is that a specific time when the consequences of data accidents can be identified is very difficult to foresee. After physical accidents took place, the consequences can be estimated at a certain level. However, after data accidents took place, the consequences can be only noticed due to the following spreading of this data and its analysis [9, 10, 32, 36, 43–45]. And even then, the consequences could last for years in the future as new methods of data analysis can become available.

20

I. V. Lokshina and C. J. M. Lanting

4.4 Application of Normal Accident Theory to Real Data Accident Scenarios In this subsection, the authors inform that certain researchers believe that the normal accident theory has potential limits to be effectively applied to real data accident scenarios associated with Big Data technology. These researchers mention that the main limitation is the lack of real normal accidents proposed by the normal accident theory. They consider that many datacentric business organizations are highly reliable [109] and can avoid normal accidents [4–8, 37–41, 79–81]. For instance, [82] indicated that the absence of system accidents foreseen by [39–41] demonstrated that some potential scenarios of system accidents could be at some level attempts to manipulate data to promote the normal accident theory [39–41, 82]. Many researchers suggest that the Y2K scenario can be used to illustrate the potential limits for the application of normal accident theory to real data accidents. The Y2K problem has been a normal accident, which in many cases was designed out before it took place. Additionally, the Y2K scenario was a normal accident that demonstrated key features comparable to Big Data technology. Therefore, for many researchers, the Y2K scenario became the first example of considering the moral and societal implications of state-of-the-art and emerging technologies [32]. However, the real implications of the Y2K scenario were rather insignificant when compared with the anticipated extreme outcomes [101]. Therefore, the authors can agree that the case for data accidents is at some level compromised by the shortage of real data accidents [2, 3, 23, 102]. Recently, [40] evaluated analogous consequences in the context of the nuclear industry [40, 41]. The interpretation was that societal pressure despite only a few accidents that took place, helped develop a regulatory system that made it hard to establish nuclear power as an energy source. Therefore, apart from France and Japan, nuclear power continues to remain a restricted power source. Additionally, this regulatory system served to create a very different management structure with high costs, a moratorium on new nuclear power plant construction, and government ownership [40, 41]. Besides, this regulatory system revealed considerable costs to decommission nuclear power plants and sites. Therefore, keeping in mind the Fukushima nuclear accident in Japan, the authors assume that [39] simply suggested insufficient timeframes. The authors inform that the notion remains the same, i.e. the normal accident theory can be effectively applied to real data accidents associated with Big Data technology. Additionally, the authors notify that underestimating the potential consequences of state-of-the-art and emerging technologies due to unexpected and inevitable future is unwise and risky.

Study on Wide-Ranging Ethical Implications of Big Data …

21

4.5 Successful Risk Mitigation Strategies In this subsection, the authors discuss what should be done if data accidents would take place. For physical systems where the risk is catastrophic and the consequences of failures can greatly offset the potential advantages, [39] suggested that such systems must be terminated [39, 40]. However, [39] also admitted that in practice this recommendation could be unfeasible and less productive [39, 40]. In his theory, [39] assumed that the risk of emerging technologies can be assessed [39, 43–45]. However, given the abstraction of data accidents, a comprehensive and sensible risk analysis of data accidents is from very difficult to impossible. Additionally, the greater integration of data gathering and analysis processes in the social life indicates that the future direction of Big Data technology moves towards even bigger data. However, the authors inform that the volume and scope of Big Data and the presence of technology that potentially enables more effective data analysis and storage will not increase the risk of data accidents. Instead, the risk of data accidents can arise from uncertainty and distrust that surround the data gathering process creating a modern “gold-rush” situation when datacentric business organizations with the largest data collections can win, even when the commercial value of volume data remains unknown. One risk mitigation strategy can be to accept the costs derived from reducing data privacy and advantages obtained from reducing public information asymmetry. However, it cannot be achieved without compromising the legal and regulatory system that governs customer data collection and usage [2–10, 17, 32]. The entire history of governing commercial activities associated with consumers demonstrates that the regulation takes place “with a rear-view mirror” and with anticipated retroactive consequences like the regulation of pharmaceuticals and tobacco. Therefore, changes made to the regulatory system may take place retrospectively. Another, more challenging risk mitigation strategy can be to establish a relationship between data gathering and regulation, although the pace of regulation is often pushed by the lobbying power of concerned industries and effective enforcement. First, there is an understanding among policymakers that the current regulations are inadequate given that consumers themselves are much involved in data collection and use [9, 10, 13, 15, 17, 24]. Second, there are concerns about the enforcement of regulation in the setting when data show increasingly cross-border features and the relevance of regulation is unclear [4–10, 32]. Third, due to social networks and other online services operating like service organizations and presenting themselves as public services while trying to avoid appropriate regulation and legislation, they should be possibly subject to regulation like regular business organizations [103]. Finally, the regulation itself is going through a transformation process when the regulation becomes not only more common but also the spirit of regulation changes [2–10, 17, 103].

22

I. V. Lokshina and C. J. M. Lanting

5 Discussion and Contributions In this section, the authors provide a discussion of the wide-ranging ethical implications of Big Data technology in a digital society, followed by the major contributions of this chapter in the literature as well as in general information technology (IT) and business domains. Therefore, in attempts to foresee future organizational environments or business ecosystems, the authors accept the risk as when discussing potential data accidents, the authors use hypothetical scenarios and potential implications. The authors do not intend to predict future data accidents because attempts to predict would be misinterpreting the inevitable and unexpected nature of data accidents. Besides, the authors consider that potential data accidents that can be foreseen can be monitored and mitigated as well. Additionally, the authors emphasize that the term “normal” is used for data accidents to indicate that data accidents are inevitable. Therefore, as the normal accident theory suggests, data accidents have the potential to take place. However, the normal accident theory does not determine when and how these data accidents can take place. The term “mitigation” does not refer to specific instances, i.e. to preventing another data leak, data manipulation, or financial meltdown; but instead, it suggests reassessing basic organizational environments, where normal accidents have the potential to take place. The mitigation strategies must address essential requirements to simplify and decouple these organizational systems, or business ecosystems [32, 80, 81]. The authors focus on the notion of normal accident theory and Big Data technology concerning mitigation strategies. The mitigation strategies require to change both the acceptance measure and the degree of organizational understanding that creates tolerance only when potential consequences of data accidents associated with Big Data technology have been addressed, also keeping in mind that the features of potential data accidents are distinct and uncertain. Therefore, the authors suggest that Big Data technology creates externalities that generally can be influenced by relevant regulatory processes. However, the distinct and uncertain features of data accidents together with technical and commercial limitations associated with Big Data technology, develop challenges for effective regulation [2–10]. Accordingly, the authors demonstrate the notion by using the entire process of updating data protection legislation in the European Union to replace the currently outdated regulation from the 1990s and to standardize legislative approaches to data privacy across the European Union and associated countries [2–10, 21, 71–73, 104]. First, a challenge of regulation across borders has led to attempts to apply regulation to the major, United States-based, social networks. However, this resulted in problems ranging from the practical concerns over the limitations of the remit of national authorities to the threats of a trade war [4–8, 14, 15, 32, 71, 72, 105]. Second, the attempts to regulate the ability of datacentric business organizations to gather data in a format that enables Big Data analysis, i.e. data analytics, have raised issues about the level at which society is supportive or actively opposed to such data

Study on Wide-Ranging Ethical Implications of Big Data …

23

collection. For instance, if the new European Union regulation could result in Google and Facebook limiting their features or even withdrawing from the European market, how likely the consumers would appreciate improved data privacy for themselves and others or rather criticize the European Union for limiting the scope of their online activities? [2–10, 14, 15, 21, 43, 71–73]. Accordingly, the authors suggest that datacentric business organizations may have to change their privacy-related practices as these look incomplete and, maybe, insufficient to meet the requirements of the new European General Data Protection Regulation (GDPR)—Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons about the processing of personal data and the free movement of such data and repealing Directive 95/46/EC [2–10, 41, 71, 72]. The authors note that there are proposals for different approaches that put collected data at and under the control of the users. For instance, the approach proposed by Tim Berners-Lee with the SOLID project introduces the concept of personal online data stores (pods), hosted anywhere the user desires [9, 10]. Applications that are authenticated by Solid, a decentralized data-linked system, are permitted to request data if the user has given the application permission. The authors also inform that during the COVID-19 pandemic, the large-scale data collection and intelligent analysis must respect data privacy and public trust. Best practices should be identified to maintain responsible data collection and data processing standards at a global scale, and need to be applied to all types of related data collection [19, 66]. For instance, in the COVID-19 reality when contact tracing of people is implemented to trace contacts with people found positive or suspected of having the virus, which is among key strategies to contain the spreading, it is important to maintain data privacy principles while establishing contact tracing procedures as part of COVID-19 detection. Therefore, the main data privacy aspects should be consent, transparency, purpose limitation, proportionality, and security safeguards [2–10, 19, 22, 23, 66]. The term “consent” is used to obtain agreement on providing, when required, contact tracing data to collection points, in the same way as forms or employee declarations. The term “transparency” is used to inform of how data can be used as part of the contact tracing process. The term “purpose limitation” is used to notify that personal information will be treated in strict confidence and can be used only for the purposes to identify people to who the virus may have spread. The term “security safeguards” is used to ensure that security measures are taken to protect personal data from being shared unnecessarily or leaked. Implementing these measures would support a secure contact tracing process and help maintain data privacy on a global scale [2–10, 19, 22, 23, 66]. However, the authors are worried about data security problems when private data could be exploited by malicious actors and stalkers. Private data can be anonymized and encrypted, but these are not sufficiently secure measures to protect private data that is stored in a centralized location and accessed remotely. Additionally, there is a chance of spoofed versions of contact-tracing software to be distributed. For instance,

24

I. V. Lokshina and C. J. M. Lanting

agreements with Apple and Google can mitigate the issue but cannot eliminate the problem [2–10, 19, 22, 23, 66]. Given the current understanding of COVID-19, contamination can occur up to two weeks before people become symptomatic and can be transmitted via objects and air. To preserve privacy, not only the access to but also the retention time of contact data should be respected; contact data must be deleted after a recommended time keeping in mind the incubation time and the resulting delay in discovery of possible virus transfer. Moreover, the private data can be used for other purposes, for instance, by intelligence agencies. In normal circumstances, such invasive contact tracing would be unacceptable. However, a temporary increase in surveillance should be tolerated representing a compromise between privacy and public health to safeguard public health, public health infrastructure, businesses, and business ecosystems [2–10, 19, 22, 23, 66]. Third, the insight into implications of Big Data technology has become more challenging due to the issues of data ownership, i.e. the rights to use data after datacentric business organizations have it collected [2–8]. The ownership typically depends on the method by which data has been collected, and the on-going discussion in terms of regulation aims to address asymmetries that are present here [2–8, 17, 32, 71–73]. For instance, when people sign up for online services like social media, they provide a “blanket consent” that their private data can be used for a frequently unnamed range of purposes for an unknown length of time. Such sign-ups represent a legal consent that permits the use of data and, basically, the ownership or at least the right to use and trade for the entire length of time as these people remain the members of these online services. However, the use of data by these online services unlikely meets the requirements of traditional academic research that is based on informed consent [2–8]. For instance, the continued use of collected user data after ending the membership is an issue that should be properly resolved. Additionally, while people expect to have at least limited ownership rights on personal data directly collected by datacentric business organizations, another important issue with Big Data technology is autonomous data collection. For instance, the commercial value of data gathered autonomously by sensors in the cars, houses, and public buildings cannot be promptly understood even by datacentric business organizations themselves [2–8]. Additionally, the ownership discussion over commercial data can be unrealistic as people may not know that personal data has been collected. Fourth, the authors underline the need to know if the collected data is personal data or not. This issue is very important as data protection legislation typically does not provide the rights over personal data to people after it was aggregated and anonymized. here [2–8, 14, 17, 32]. Fifth, the authors note that a paradox of Big Data technology is that the methods to anonymize datasets, can be also used to deanonymize the same datasets. This paradox creates further ethical issues associated with both data privacy and data ownership that earlier did not exist. Despite data anonymization and unlike in traditional data analysis that aims to produce aggregate insights, the commercial benefits

Study on Wide-Ranging Ethical Implications of Big Data …

25

of Big Data technology are heavily driven by the need to analyze personal data here [2–8, 12, 15, 17, 21, 32]. Besides, the authors recall the ethical issues related to the COVID-19 pandemic data collection and resulting interpretations and extrapolations. The concern is that since authorities approved the COVID-19 data manipulation given incomplete data collection and limitations in its interpretation that impact both the derived decisions and public opinion, then how society can expect that commercial applications of Big Data technology would not suffer from similar situations. Sixth, the authors suggest that the following two mitigation strategies should be considered. One approach is a behavioral transformation in datacentric business organizations addressing the wide-ranging ethical implications of Big Data technology discussed earlier in this section. Additionally, the authors remind about a difference between the major industrial companies with commercial strategies in general and those datacentric business organizations that have data collection and analysis as a goal. Therefore, another approach is to change the focus of the industrial companies that do not plan to become data conglomerates away from Big Data technology. Seventh, the authors inform about an issue related to the role of consumer behavior itself. For instance, datacentric business organizations like Google and Facebook have already transitioned to the philosophy that at some level respects the value of data privacy in consumer decision-making [2–10, 32]. Additionally, the literature suggests that consumers consider data privacy and security before they share personal data online, especially with fishy strangers [16, 106]. Therefore, given the commercial drivers behind Big Data technology, this mentioned in the literature decreased consumer tolerance to online data collection indicates the potential to move away from the Big Data economy [9, 10, 12, 14, 32, 53–55]. The authors recap that the normal accident theory has potential limits for its effective application to data accidents associated with state-of-the-art and emerging technologies discussed earlier. The first limit is that normal accident theorists seemed to be partially wrong in the past, especially with the Y2K scenario [43–45]. However, this was not a weakness of the normal accident theory. On the contrary, this was a result of attempts to use the theory to predict future data accidents and prevent potential consequences instead of an effort to explain. In his defense, [41] underlined that many researchers did their analysis of emerging technologies wrong. Therefore, the Y2K scenario demonstrated challenges introduced by the risk analysis produced by researchers without a sufficient understanding of the distinct features of these emerging technologies [4–8, 32, 41]. The second limit is related to a view that construct “Big Data” should be used only for reference purposes. The literature also suggests a different view that construct “Big Data” is an ill-defined phrase with an unclear and confusing application that creates an association with technological advancement without the need to properly understand its nature [9, 10, 13, 15, 17, 18, 107]. The authors consider the use of the term “Big Data” has currently surpassed its understanding [4–8, 32]. However, the term emerged from a practical need to recognize a transformation in data collection and analysis processes that already took place in a digital society [2–10, 17]. Therefore, in this chapter, the authors used

26

I. V. Lokshina and C. J. M. Lanting

the term when they discussed the organizational strategies and new organizational behaviors in business ecosystems to address the wide-ranging ethical implications of Big Data technology in the digital society instead of inventing new terms [108]. Finally, this chapter makes the following major contributions in the literature as well as in general information technology (IT) and business domains. First, the authors investigated the wide-ranging ethical implications of Big Data technology in a digital society. Second, the authors determined that strategies behind Big Data technology require organizational systems, or business ecosystems, that leave them vulnerable to accidents associated with its commercial value and known as data accidents. Third, the authors established that data accidents have distinct features and raise important concerns about data privacy in a digital society, particularly, in the time of the COVID-19 pandemic. Fourth, the authors created a classification of potential data accidents and proposed successful risk mitigation strategies.

6 Conclusion In conclusion, the authors note that exponential growth in the commercial use of the Internet has dramatically increased the volume and scope of data gathered by datacentric business organizations. Big Data emerged as a term to summarize both the technical and commercial aspects of this growing data collection and analysis processes. Previously, much discussion of Big Data was focused on its transformational potential for technological innovation and efficiency. However, less attention was devoted to its wide-ranging ethical implications beyond generating commercial value. In this chapter, the authors investigated the wide-ranging ethical implications of Big Data technology in a digital society. The authors informed that strategies behind Big Data technology require organizational systems, or business ecosystems, that leave them vulnerable to accidents associated with its commercial value and known as data accidents. These data accidents have distinct features and raise important concerns about data privacy in a digital society, especially during the COVID-19 pandemic. In this chapter, the authors suggested classification and methods mitigate the risk of potential data accidents.

References 1. George, G., Haas, M., Pentland, A.: Big data and management. Acad. Manag. J. 57(2), 321– 326 (2014) 2. Lokshina, I.V., Durkin, B.J., Lanting, C.J.M.: Data analysis services related to the IoT and big data: strategic implications and business opportunities for third parties. Int. J. Interdisciplinary Telecommun. Netw. 9(2), 37–56 (2017)

Study on Wide-Ranging Ethical Implications of Big Data …

27

3. Lokshina, I.V., Lanting, C.J.M., Durkin, B.J.: IoT and big data-driven data analysis services for third parties, strategic implications and business opportunities. Int. J. Soc. Ecol. Sustain. Develop. 9(3), 37–56 (2018) 4. Lokshina, I.V., Durkin, B.J., Lanting, C.J.M.: The IoT- and big data-driven data analysis services: KM, implications and business opportunities. Int. J. Knowl. Manage. 14(4), 88–107 (2018) 5. Lokshina, I., Durkin, B., Lanting, C. J. M.: IoT-and big data-driven data analysis services for third parties: business models, new ventures and potential horizons. In: Meghanathan, N. (ed.) Strategic Innovations and Interdisciplinary Perspectives in Telecommunications and Networking, pp. 256–289. (2019a) 6. Lokshina, I., Gregus, M., Thomas, W.: Application of integrated building information modeling, IoT and blockchain technologies in system design of a smart building. Proc. Comput. Sci. 160, 497–502 (2019) 7. Lokshina I., Zhong H.: Digital communications and a smart World. In: Kryvinska, N., Greguš, M. (eds.) Data-Centric Business and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol. 20, Springer, Cham (2019) 8. Lokshina, I., Lanting, C., Durkin, B.: Evaluation of strategic opportunities and resulting business models for SMEs employing IoT in their data-driven ecosystems. In: Jennex, M. (ed.) Knowledge Management, Innovation, and Entrepreneurship in a Changing World. pp. 148– 186. (2020) 9. Lokshina, I.V., Lanting, C.J.M.: Addressing ethical concerns of big data as a prerequisite for a sustainable big data industry. Int. J. Interdisciplinary Telecommun. Netw. 10(3), 34–52 (2018) 10. Lokshina, I.V., Lanting, C.J.M.: A Qualitative evaluation of IoT-driven eHealth: knowledge management, business models and opportunities, deployment and evolution. In: Proceedings of Hawaii International Conference on System Science (HICSS-51), pp. 4123–4132. Waikoloa, Hawaii (2018b) 11. Miller, Z.: Former NSA Chief was worried about “Enemy of The State” Reputation| TIME.com (2013). Retrieved from http://swampland.time.com/2013/06/07/formernsa-chiefwas-worried-about-enemy-of-the-state-reputation 12. Jennex, M.E.: Big data, the internet of things, and the revised knowledge pyramid. ACM SIGMIS Database: the DATABASE for Adv. Info. Syst. 48(4), 69–79 (2017) 13. Marr, B.: Big data: Using SMART Big Data Analytics and Metrics to Make Better Decisions and Improve Performance. Wiley, Chichester, UK (2015) 14. Molok, N., Chang, S., Ahmad, A.: Information leakage through online social networking: opening the doorway for advanced persistence threats. In: Australian Information Security Management Conference (2012) 15. Sedayao, J., Bhardwaj, R.: Making big data, privacy, and anonymization work together in the enterprise: experiences and issues. Big Data Congress (2014) 16. Zaslavsky, A. Perera, C., Georgakopoulos, D.: Sensing as a service and big data. In: Proceedings of International Conference on Advances in Cloud Computing (ACC), pp. 21–29. (2012) 17. Committee on Commerce, Science, and Transportation.: Committee Announces Paper Hearing on Big Data and the Coronavirus. (Paper hearing titled Enlisting Big Data in the Fight Against Coronavirus on Thursday, April 9, 2020). Press Release (April 2, 2020). Retrieved from https://www.commerce.senate.gov/2020/4/committee-announces-paper-hea ring-on-big-data-and-the-coronavirus 18. Ylijoki, O., Porras, J.: Perspectives to definition of big data: a mapping study and discussion. J. Innov. Manage. 4(1), 69–91 (2016). https://doi.org/10.24840/2183-0606_004.001_0006 19. Scarpino, S., Petri, G.: On the predictability of infectious disease outbreaks. Nature Commun. 10, 898 (2019) 20. Hong, W., Thong, J.: Internet privacy concerns: an integrated conceptualization and four empirical studies. MIS Q. 37(1), 275–298 (2013)

28

I. V. Lokshina and C. J. M. Lanting

21. Jain, P., Gyanchandani, M., Khare, N.: Big data privacy: a technological perspective and review. J. Big Data 3(25) (2016). https://doi.org/10.1186/s40537-016-0059-y 22. Lokshina, I.V., Durkin, B.J.: Redesigning the healthcare model to address obesity problem using the integration of processes and mobile technologies: facing a worldwide epidemic in an innovative manner. Wireless Personal Commun. Springer, US 96(4), 5483–5498 (2017) 23. Lokshina, I.V., Durkin, B.J.: Using wireless and mobile technologies in e-healthcare on a wide scale: the issues and challenges to overcome. Int. J. Serv. Econ. Manage. 8(3), 133–151 (2018) 24. Lu, R., Zhu, H., Liu, X., Liu, J., Shao, J.: Toward efficient and privacy-preserving computing in big data era. IEEE Netw 28(1), 46–50 (2014) 25. Abdullah, N., Håkansson, A., Moradian, E.: Blockchain based approach to enhance big data authentication in distributed environment. In: International Conference on Ubiquitous and Future Networks. ICUFN, pp. 887–892. (2017) 26. Bambauer, D.: Ghost in the network. Univer. Pennsylvania Law Rev. 162(5), 1050 (2014) 27. Jacobs, A.: Pathologies of big data. ACM Queue 7(6) (2009) 28. Perera, C., Ranjan, R., Wang, L., Khan, S., Zomaya, A.: Big data privacy in the internet of things era. IT Professional 17(3), 32–39 (2015). https://doi.org/10.1109/MITP.2015.34 29. Fink, M., Harms, R., Hatak, I.: Nanotechnology and ethics: the role of regulation versus self-commitment in shaping researchers’ behavior. J. Bus. Ethics 109(4), 569–581 (2012) 30. Bateman, C., Valentine, S., Rittenburg, T.: Ethical decision making in a peer-to-peer file sharing situation: the role of moral absolutes and social consensus. J. Bus. Ethics 115(2), 229–240 (2013) 31. Witte, D.: Privacy deleted: is it too late to protect our privacy online? J. Internet Law 18(1), 1–28 (2013) 32. Nunan, D., Di Domenico, M.: Big data: a normal accident waiting to happen? J. Business Ethics 145, 481–491 (2017). https://doi.org/10.1007/s10551-015-2904-x 33. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.: Big data: The next frontier for innovation, competition, and productivity (2011). Retrieved from http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innova tion/Big_data_The_next_frontier_for_innovation 34. Schlegelmilch, B., Oberseder, M.: Half a century of marketing ethics: shifting perspectives and emerging trends. J. Business Ethics 93(1), 1–19 (2010) 35. De George, R.: The Ethics of Information Technology and Business. Blackwell Publishing, Oxford, UK (2003) 36. Likhterman, S., Lokshina, I., Batugina, N.: The use of fuzzy models to estimate stability of mine constructions. In: Martens P.N. (ed.) Roof-bolting in Mining , ABRW Band 15, pp. 433-443. (1998) 37. Perrow, C.: Normal accident at three-mile Island. Society 18(5), 17–26 (1981) 38. Perrow, C.: Normal Accidents: Living with High Risk Technologies. Basic Books, New York (1984) 39. Perrow, C.: Normal Accidents: Living with High Risk Technologies, 2nd edn. Princeton University Press, New Jersey (1999) 40. Perrow, C.: Disasters evermore? reducing our vulnerabilities to natural, industrial, and terrorist disasters. Social Res: An Int Quarter 75(3), 733–752 (2008) 41. Perrow, C.: What’s needed is application, not reconciliation: a response to Shrivastava Sonpar and Pazzaglia. Hum. Relat. 62, 1391 (2009) 42. Pidgeon, N.: Retrospect: normal accidents. Nature 477(7365), 404–405 (2011) 43. Lokshina, I.V., Insinga, R.C.: Decision support system for ventilation operator based on fuzzy methods applied to identification and processing of gas-dynamic images. J. Electr. Eng. 54(9–10), 277–280 (2003) 44. Lokshina, I.V.: Intellectual support of investment decisions based on a clustering of the correlation graph of securities. WSEAS Trans. Syst. 1(2), 284–290 (2002) 45. Lokshina, I.V.: Expert system based on the fuzzy diagnostic model to support coal mine ventilation operator’s decisions. In: Grmela, A., Mastorakis, N. (eds) Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation. pp. 118–122. WSEAS Press (2001)

Study on Wide-Ranging Ethical Implications of Big Data …

29

46. Helmreich, R.: Managing human error in aviation. Sci. Am. 276(5), 62 (1997) 47. Dain, S.: Normal accidents: human error and medical equipment design. The Heart Surgery Forum#2002–180891, 5(3), 254–257 (2002). Retrieved from www.hsforum.com/vol5/issue3/ 2002-180891.html 48. Palmer, D., Maher, M.: A normal accident analysis of the mortgage meltdown. Res. Sociol. Organ. 30, 219–256 (2010) 49. Rosenthal, C.: Big data in the age of the telegraph (2014). Retrieved from http://www.mck insey.com/insights/organization/big_data_in_the_age_of_the_telegraph 50. Kuechler, W.: Business applications of unstructured text. Commun. ACM 50(10), 86–93 (2007) 51. HM Government.: UK data capability strategy: seizing the data opportunity—Publications— GOV.UK (2013). Retrieved from https://www.gov.uk/government/publications/ukdata-cap ability-strategy 52. Hurwitz, J.: The Making of a (Big Data) President. Businessweek.com (2012). Retrieved from http://www.businessweek.com/articles/2012-11-14/the-making-of-a-big-datapresident 53. Kaczor, S., Kryvinska, N.: It is all about services—fundamentals. Drivers and Bus. Models The Soc. Serv. Sci. J. Serv. Sci. Res. Springer 5(2), 125–154 (2013) 54. Kryvinska, N.: Building consistent formal specification for the service enterprise agility foundation. Soc. Serv. Sci. J. Serv. Sci. Res. Springer 4(2), 235–269 (2012) 55. Molnár, E., Molnár, R., Kryvinska, N., Greguš, M.: Web intelligence in practice. Soc. Service Sci. J. Service Sci. Res. Springer 6(1), 49–172 (2014) 56. Marcus, G.: The web gets smarter. The New Yorker (2012). Retrieved from http://www.new yorker.com/culture/culturedesk/the-web-gets-smarter 57. Groves, P., Kayyali, B., Knott, D., Van Kuiken, S.: The “Big Data” Revolution in Healthcare. Center for US Health System Reform, McKinsey & Company, Accelerating value and innovation (2013) 58. Mayer-Schonberger, V., Cukier, K.: Big data: a revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt, Boston, MA (2013) 59. McAffee, A., Brynjolfsson, E.: Big data: the management revolution. Harvard Bus. Rev. 90(10), 60–68 (2012) 60. Boyd, D., Crawford, K.: Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Info. Commun. Soc. 15(5), 662–679 (2012) 61. Warren, S., Brandeis, L.: The right to privacy. Harvard Law Rev IV 5, 193–220 (1890) 62. Zuboff, S.: In the Age of the Smart Machine: Machine: The Future of Work and Power. Basic Books, NY (1988) 63. Mundie, C.: Privacy pragmatism. Foreign Affairs 93(2), 28–38 (2014) 64. Diamond, J.: In: Guns Germs and Steel: The Fate of Human Societies. W. W. Norton & Company (1997) 65. Lindenbaum, S.: Kuru, prions, and human affairs: thinking about epidemics. Annu. Rev. Anthropol. 30, 363–85 (2001) 66. Wheeler, N.: Tracing outbreaks with machine learning. Nature Rev Microbiol 17, 269 (2019). https://doi.org/10.1038/s41579-019-0153-1 67. Bates, M.: Tracking disease: digital epidemiology offers new promise in predicting outbreaks. IEEE Pulse 8(1), 18–22 (2017) 68. GSMA.: GSMA guidelines on the protection of privacy in the use of mobile phone data for responding to the Ebola outbreak (October 2014). Retrieved from https://www.gsma.com/ mobilefordevelopment/wp-content/uploads/2014/11/GSMA-Guidelines-on-protecting-pri vacy-in-the-use-of-mobile-phone-data-for-responding-to-the-Ebola-outbreak-October-2014. pdf 69. The Economist.: To curb covid-19. China is using its high-tech surveillance tools. Non-state firms are best equipped to track people’s movements and contacts (2020). https://www.econom ist.com/china/2020/02/29/to-curb-covid-19-china-is-using-its-high-tech-surveillance-tools 70. Mozur, P., Zhong, R., Krolik, A.: In Coronavirus fight, China gives citizens a color code, with red flags. The New York Times (March 1, 2020). Retrieved from https://www.nytimes.com/ 2020/03/01/business/china-coronavirus-surveillance.html

30

I. V. Lokshina and C. J. M. Lanting

71. European Commission (n.d.) 2018 Reform of EU data protection rules (2018). Retrieved from https://ec.europa.eu/commission/priorities/justice-and-fundamental-rights/data-protec tion/2018-reform-eu-data-protection-rules_en#background 72. European Data Protection Board.: Statement by the EDPB Chair on the processing of personal data in the context of the COVID-19 outbreak (March 16, 2020). Retrieved from https://edpb.europa.eu/news/news/2020/statement-edpb-chair-processing-per sonal-data-context-covid-19-outbreak_en 73. Proposal for a Regulation of the European Parliament and of the Council, European Commission (2012). Retrieved from http://ec.europa.eu/digital-agenda/en/news/proposal-regulationeuropean-parliament-and-council-establishing-connecting-europe-facility 74. Ward, P.: Improving access to, use of, and outcomes from public health programs: the importance of building and maintaining trust with patients/clients. Frontiers Public Health 5, 22 (2017). https://doi.org/10.3389/fpubh.2017.00022 75. OECD.: Government at a Glance (2019). Retrieved from https://www.oecd.org/gov/govern ment-at-a-glance-22214399.htm 76. Wang, C., Ng, C., Brook, R. (2020). Digital smartphone tracking for COVID-19—balancing public health and civil liberties. JAMA. Retrieved from https://jamanetwork.com/journals/ jama/fullarticle/2762689 77. Heilbroner, R.: Do machines make history? Technol. Culture 8(3), 335–345 (1967) 78. Coates, J.: Computers and business? a case of ethical overload. J. Bus. Ethics 1(3), 239–248 (1982) 79. Anjaria, K., Mishra, A.: Information leakage analysis of software: how to make it useful to IT industries? Future Comput. Info. J. 2(1), 10–18 (2017). https://doi.org/10.1016/j.fcij.2017. 04.002 80. Leveson, N.: Engineering a Safer World. MIT Press, Systems Thinking Applied to Safety (2016) 81. Leveson, N., Dulac, N., Marais, K., Carroll, J.: Moving beyond normal accidents and high reliability organizations: a systems approach to safety in complex systems. Organiz. Stud. 30(2–3), 227–249 (2009) 82. Shrivastava, S., Sonpar, K., Pazzaglia, F.: Normal accident theory versus high reliability theory: a resolution and call for an open systems view of accidents. Hum. Relat. 62(9), 1357–1390 (2009) 83. Cummings, L.: Normal accidents: living with high-risk technologies. Book Rev. Adminis. Sci. Quarter. 29(4), 630–632 (1984) 84. Rijpma, J.: Complexity, tight–coupling and reliability: connecting normal accidents theory and high reliability theory. J. Contingencies Crisis Manage. 5(1), 15–23 (1997) 85. Sagan, S.: The limits of safety: organizations, accidents, and nuclear weapons. Princeton University Press, Princeton, NJ (1993) 86. Bloomberg.: The Coronavirus May Be “Disease X” Health Experts Warned About (22 Feb 2020). Retrieved from https://www.bloombergquint.com/business/coronavirus-may-be-thedisease-x-health-agency-warned-about 87. Pillinger, M.: Virus travel bans are inevitable but ineffective. Foreign Policy (20 Feb 2020). Retrieved from https://foreignpolicy.com/2020/02/23/virus-travel-bans-are-inevitable-butineffective/ 88. Science.: The coronavirus seems unstoppable. What should the world do now? (25 Feb 2020). https://www.sciencemag.org/news/2020/02/coronavirus-seems-un-stoppablewhat-should-world-do-now 89. Cockcroft, A.: The netflix tech blog: a closer look at the christmas eve outage (2012). Retrieved from http://techblog.netflix.com/2012/12/a-closer-look-at-christmas-eve-outage.html 90. Amazon.: Summary of the December 24, 2012 Amazon ELB service event in the US-East Region (2012). Retrieved from http://aws.amazon.com/message/680587/ 91. Brown, B., Court, D., McGuire, T.: Views from the front lines of the data-analytics revolution. Mckinsey.com (2014). Retrieved from http://www.mckinsey.com/insights/business_techno logy/views_from_the_front_lines_of_the_data_analytics_revolution

Study on Wide-Ranging Ethical Implications of Big Data …

31

92. Snook, S.: Friendly Fire: The Accidental Shootdown of U.S. Black Hawks over Northern Iraq. Princeton University Press (2000) 93. Goel, S.: Is part of Chelsea Manning’s legacy increased surveillance? the conversation (January 20, 2017). Retrieved from https://theconversation.com/is-part-of-chelsea-manningslegacy-increased-surveillance-71607 94. Shane, S.: The Age of Big Leaks, The New York Times (February 2, 2019). Retrieved from https://www.nytimes.com/2019/02/02/Sunday-review/data-leaks-journalism.html 95. Shane, S., Perlroth, N., Sanger, D.: Security breach and spilled secrets have shaken the N.S.A., The New York Times (November 12, 2017). Retrieved from https://www.nytimes.com/2017/ 11/12/us/nsa-shadow-brokers.html 96. Szoldra, P.: This is everything Edward Snowden revealed in one year of unprecedented topsecret leaks. Business Insider (September 16, 2016). Retrieved from https://www.businessi nsider.com/snowden-leaks-timeline-2016-9 97. United Nations.: Senior officials sound alarm over food insecurity, warning of potentially ‘Biblical’ famine. In: Briefings to Security Council. Press Release (April 21, 2020). Retrieved from https://www.un.org/press/en/2020/sc14164.doc.htm 98. Leigh, D.: How 250,000 US embassy cables were leaked. The Guardian (November 28, 2010). Retrieved from https://www.theguardian.com/world/2010/nov/28/how-us-embassycables-leaked 99. Waterman, S.: NSA leaker Ed Snowden used banned thumb drive, exceeded access. The Washington Times (2013). Retrieved from https://www.washingtontimes.com/news/2013/jun/14/ nsa-leaker-ed-snowden-used-banned-thumb-drive-exce/ 100. Allen & Overy.: Big data—Annual review 2013—Allen & Overy (2013). Retrieved from http://www.allenovery.com/publications/en-gb/annualreview2013/global-local/Pages/ Big-data.aspx 101. MacGregor, D.: 10 Public response to Y2K: social amplification and risk adaptation: or, “how I learned to stop worrying and love Y2K”. In: Pidgeon, N., Kasperson, R., Slovic, P. (eds.) The Social Amplification of Risk (pp. 243). New York, Cambridge University Press (2003) 102. La Porte, T., Consolini, P.: Working in practice but not in theory: theoretical challenges of high-reliability organizations. J. Public Adm. Res. Theor. 1, 19–47 (1991) 103. Bygrave, L.: Data Privacy Law: An International Perspective. Oxford University Press, Oxford (2014) 104. Ashford, W.: Infosec 2014: Act now, but no new EU data protection law before 2017, says ICO (2014). Retrieved from http://www.computerweekly.com/news/2240219908/Infosec-2014Act-now-but-no-new-EU-data-protection-law-before2017-says-ICO 105. Farivar, C.: EU data protection reform could start ‘trade war’, US official says (Wired UK) (2014). Retrieved from http://www.wired.co.uk/news/archive/2013-02/01/eu-data-pro tection-us-trade-war 106. Johnson, M., Egelman, S., Bellovin, S.: Facebook and privacy: it’s complicated. In: Symposium on Usable Privacy and Security (SOUPS) 2012, July 11–13 (2012) 107. De Mauro, A., Greco, M., Grimaldi, M.: A formal definition of big data based on its essential features. Library Rev. 65(3), 122–135 (2016). https://doi.org/10.1108/LR-06-2015-0061 108. Lokshina, I., Lanting, C.: Study on Wide-ranging Ethical Implications of Big Data Technology in a Digital Society: How Likely Are Data Accidents during COVID-19?. J. Business Ecosystems. 2(1), 32–57 (2021) 109. Roberts, K.: Some characteristics of high reliability organizations. Organ. Sci. 1, 160–177 (1990) 110. Wu, J., Leung, K., Leung, G.: Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet 395, 689–697 (2020)

Application of Business Rules Mechanism in IT System Projects Sylwester Balcerek, Vincent Karoviˇc, and Vincent Karoviˇc

Abstract This work deals with the use of the mechanism of business rules in information systems. It facilitates the implementation of business logic, which is expressed in the form of precise and legible rules. The work is divided into two parts. The first part, consisting of two chapters, deals with the approach to business rules based on the literature studies. It presents the advantages and disadvantages of applying the idea of business rules. The second part of the work deals with the practical use of trade rules mechanisms. Within this part, the BiblioRule information system was designed. The aim of the BiblioRule system is to improve the functioning of the library. BiblioRule allows library administrators to easily modify selected requirements. BiblioRule was implemented in Java. It uses the components of the JBoss Drools platform, Drools Expert as a rules module and Drools Guvnor as a business rules management system. Keywords Business rules · IT systems · IT projects

1 Introduction The use of information technology in the modern world is undoubtedly a common occurrence. While most people may not be aware of this, computer systems help people implement many activities. The range of their applications is very wide, from systems controlling technological processes in factories, through systems supporting the work of bank branches and ending with tool applications such as laminating S. Balcerek Lodz University of Technology, Lodz, Poland e-mail: [email protected] V. Karoviˇc (B) · V. Karoviˇc Faculty of Management, Comenius University in Bratislava, Bratislava, Slovakia e-mail: [email protected] V. Karoviˇc e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Kryvinska and A. Poniszewska-Mara´nda (eds.), Developments in Information & Knowledge Management for Business Applications, Studies in Systems, Decision and Control 376, https://doi.org/10.1007/978-3-030-76632-0_2

33

34

S. Balcerek et al.

foils. The process of creating information systems is very often a complicated action. Application designers and developers face many challenges, among which the problem of changing requirements for IT systems is very important. The source of these changes is often the user or customer of the software being executed. To meet the new requirements, system developers must modify previously created procedures and adapt them to the current needs of the user. It is often a complicated and time consuming activity due to the complexity of the architectural design of the system. Additional problems can occur when it turns out that system designers have not understood exactly what the client is waiting for. Therefore, in the development of IT systems, a solution is sought through which the development becomes flexible, with an uncomplicated procedure of entering new requirements. An important factor will also be the development in a clear and legible way, which will allow all parties interested in the system to specify the requirements precisely. The use of the business rules mechanism in the design of an IT system may prove to be a solution to the above problems. It is a technology whose use can bring benefits, especially where the requirements of the logic of the IT system are changing dynamically. It is also very important that users of terms called business rules can define their requirements without requiring the participation of a programmer. This work deals with the topic of using mechanisms of business rules in information systems. The theoretical assumptions on which this technology is based were analyzed. In addition, a description of the practical application of the business rules mechanism was created based on the IT system project implemented as part of the work.

1.1 Objectives and Scope of Work The aim of this work is to analyze the application of the mechanism of business rules in IT systems projects. The results of the analysis will be used, inter alia, to create a prototype of an IT system whose business logic will be based on the use of a business rules mechanism. The created application is then assessed for compliance with the concept of business rules. An equally important goal is to review and evaluate the possibilities of integrating business regulatory technologies with other technologies and IT tools.

1.2 Composition of Work The first chapter contains information on the theoretical foundations on which the technology of enterprise IT systems is based. It includes a discussion of the concept of corporate rules, the definition of the concept of corporate rules, as well as the advantages and disadvantages of using the corporate rules approach.

Application of Business Rules Mechanism in IT System Projects

35

The second chapter deals with the technological side of the concept of business rules. Here you will find, among other things, a description of the business rules management system, as well as an overview of several products designed to work with business rules. The third chapter describes the business rules mechanism and business rules management system called JBoss Drools. The information contained in this document provides a better understanding of the technology. The fourth chapter presents a description of the IT system project implemented in this work. Reports were presented regarding the requirements of the proposed system and a description of its architecture. The fifth chapter describes the created IT system and analysis of the use of the mechanism of business rules in its design. The summary contains the conclusions resulting from this work. An assessment was also made of the application of business rule mechanisms in information systems.

1.3 Review of the Literature The literature on this topic can be divided into two main categories: the first deals with the topic of trade rules, and thus theoretical issues, and the second deals with the practical use of the trade rules mechanism. The literature on the concept of business rules contains books. All works used in the work are written in English. They deal with the theoretical foundations of business technology. The second category includes books and electronic resources that include websites and articles published on those websites. As mentioned above, this part of the literature is also in English. It contains many valuable tips for creating business rules, configuring the business rules mechanism, and integrating it with other information technologies.

2 The Concept of Business Rules in the Context of Software Development The implementation of a business activity requires many decisions regarding various aspects of the problem being solved. Which solution is chosen depends on many factors, such as legislation or customer needs. Entrepreneurship requires flexibility in action and a quick response to change. Software that helps with the implementation of the project must also meet the stated objectives. The success of a business largely depends on the extent to which IT systems meet the needs of the business. The concept of business rules in this chapter is an example of an idea to ensure the best match between IT tools and business requirements.

36

S. Balcerek et al.

2.1 Outline of Problems in Creating IT Systems The IT system that supports or enables work is today one of the most important elements of various types of organizations, with a special emphasis on e-business oriented businesses. With the growing competitiveness of IT systems, newer and higher requirements are set for their creators, which should be met as soon as possible, without compromising the quality of products. However, it turns out that these two requirements do not have to go hand in hand and may be mutually exclusive. The creation and development of information systems is a complicated and often longterm process, which takes place in several stages. In general, we can distinguish the following phases [1–4]: • analysis of system requirements—includes, among other things, understanding the needs of the user and creating an analytical model that specifies all required functions, processed data and constraints imposed on the performed functions, • system design—aims to create a system diagram, by transforming a technologically independent analytical model into a model describing the details of the program design; defines the data structure and cooperation of system elements in the context of selected information technology, • implementation—transforms the design model into work program code by creating, connecting and running all system components, • verification and approval—checks the correctness of the program execution and the correctness of the obtained results and also checks whether the created product is in accordance with the client’s expectations, • maintenance—contains changes that may occur after the system has been put into operation, such as the removal of previously undetected errors. An important point of work on the development of the system is the creation of its architecture, which defines the division of the system into specialized parts responsible for specific tasks. Parts of the system with a similar purpose form layers characterized by a certain independence between them. This means that each of the program layers can be analyzed and developed independently, without affecting the elements of the other layer. The three-layer architecture is most often used in information systems (Fig. 1), which store layer separation in the created systems: • presentation—its main task is to present data and enable interaction with the user, • logic—is the core of the whole system, the place where all processes are performed and decisions are made, • data—responsible for data storage, processed in the system. Each of the layers is implemented using technology suitable for that layer. For the presentation layer, this can be, for example, JavaServer Faces or ExtJS. Java EE or.NET technology can be used to create the logical layer, while a database management system, such as the MySQL or PostgreSQL layer, can be responsible for the data layer.

Application of Business Rules Mechanism in IT System Projects

37

Fig. 1 Schematic representation of a three-tier information system architecture

Of these three layers, logic is particularly important because it reflects the needs of future users. This reflection is a business process which, according to [1], is “a defined and repetitive operation of an organization, described by a series of steps with a known method of starting and ending, leading to what valuable results are”. Because of the responsibility of the logical layer for performing these processes, it is often called the business logical layer. The requirements for the business logic layer change more frequently than the requirements for other layers [5]. One reason may be a change in customer needs

38

S. Balcerek et al.

that will force the introduction of new features into the system or existing changes, and the logical layer is important for these activities in terms of system architecture. Changes in IT system requirements are a natural process of software development. The reasons for the changes can be various—errors highlighted in the previous stages, which limits the lack of resources needed to meet all the requirements or the above changes in customer needs. However, any change destabilizes the software development process and can be a source of high costs, making working on the system more advanced. Cooperation in the period of dynamic development of IT systems places demands for the implementation of business changes in the systems in the shortest possible time. No manager wants to be ahead of the competition by offering another product with more functionality. At this point, programmers and system designers will create a non-trivial task of modifying the system while meeting deadlines. However, these requirements are not always met, because during the process of adapting the system, they are shown that the created project is not as flexible as it seemed in the initial phase of the project. It should be clear that the changes currently being introduced in the program may not necessarily be appropriate in the future and it is likely that the system will be subject to improvement and adaptation to market needs. In such a case, it is worth considering using a different approach or a different methodology for software development, which will allow changes to be made in a short time and without the need for a large intervention in the logical code of the system [6–8].

2.2 Arguments for Finding New Solutions in Software Development At present, business in the broadest sense of the word is inextricably linked to computer technology. Depending on the type of project or design, the connection is stronger or weaker. An important aspect of this collaboration is the proper flow of information between the company’s management, the client, and the designers and programmers. It is necessary to clearly state who will be responsible for the implementation of the requirements. It often happens that information about the functioning of the IT system in the organization is not generally available and only a certain group of people or, in the worst case, only one person has access to it. This can lead to many problems, starting with a lack of general understanding of what the system implements and how, ending with a huge mess that occurs when the person responsible for the system has to stop working with it for some reason (e.g. illness, dismissal) [9–11]. Access to knowledge is only needed for a small group of people. Knowledge that is not structured in any way and available to others is, in principle, of little use. In addition, if information about the requirements of the system exists only in the minds of the designers and not in the form of clearly defined expectations, then there is a high risk that the created product will not achieve the set goals. One tends to

Application of Business Rules Mechanism in IT System Projects

39

simplify work, and the lack of clearly written requirements undoubtedly causes such temptations to programmers. The phenomenon of poorly defined specific requirements and the lack of understanding for the parties involved in the business are not the only problems that appear on the way to achieving the goal of the organization. If you look at the problem only from the IT point of view, it may happen that the IT system is not well designed. This is reflected in the fact that changes in the business logic of the program are cumbersome, laborious and time consuming. The close links between the layers of the system, the rigid coding of business rules and processes that make them dependent on many other elements of the system and their dissemination across many modules make it a great challenge for the maintenance and development of such a system. The above factors appear to be a sufficient reason to look for new, effective solutions in the design and use of information systems. It is necessary to take into account both the business aspect, i.e., inter alia, the specification of requirements, their analysis, information flows, as well as the IT aspect, which means the use of appropriate technologies, the introduction of changes in the system in response to dynamic changes in requirements.

2.3 The Concept of Business Rules Informatics, and especially one of its areas, which is software engineering, is a scientific discipline that has been developing very dynamically for several decades. This development is often influenced by the imperfections of existing tools, high competition in the manufacturer’s market, as well as the finding of IT applications in various areas of life. Advances in computer science are also supported by the emergence of new ideas for the software development and maintenance process. In this chapter, we will address one such concept of business rules.

2.3.1

What is the Concept of Business Rules?

The activities of business organizations are based on a set of specific requirements that should be met during product development in such a way as to meet their objectives. Requirements are defined at the enterprise level of the enterprise based on the collection of customer expectations or market analysis. Based on the information obtained, designers and programmers then prepare an IT system that meets the required requirements. In view of the arguments presented in the previous section, it can be assumed that the above scenario is often too optimistic. One way to circumvent these barriers may be to use a business rules-based approach in the software development process.

40

S. Balcerek et al.

The concept of company rules according to [12, 52, 53] consists in a formal way of managing the company’s requirements, which are expressed in the form of specific rules. In other saunas, the discussed approach representing knowledge of the problem in a form well known in the field of artificial intelligence is the rule [15]. The expected operation of the system is determined by a set of such rules defining corporate policy, defined by corporate managers. Answers to the question of what the concept of business rules is also provided by von Halle [12], who argues that it is a methodology as well as a special technology through which rules can be managed, disseminated, automated and changed in line with the organisation’s business strategy. These seemingly simple statements have implications for the software development approach. The notion of business rules strongly rejects the disclosure of the knowledge contained in the rules only to programmers and keeping them in the program code, from which it will be difficult to get them other people involved in the implementation of the commitment meet the needs of the client. To summarize the answer to the question posed in the title of the subsection, it is worth emphasizing that» in the business rules approach, rules are used to represent requirements. They should be expressed in a clearly defined and comprehensible form. It is also assumed that the expression of the rule in the IT system should be more direct and not part of the code. In addition, the aim of the business rules approach is to allow system logic to be determined not only by programmers but also by people who are most involved in the practical use of the system, i. J. Organizational or human resource managers.

2.3.2

History of Ideas for Business Rules

The idea of a business rules concept was born in the first half of the 1990s. At that time, the first framework of this approach was outlined, while in the late 1990s, technologies related to this topic began to emerge. However, the beginnings of information systems based on the representation of knowledge in the form of a rule should be sought earlier, as the first professional systems were created with valid force in the 1970s. Business rules were not created in response to the emergence of a new class of software specifically designed for knowledge management. This concept is based on the work of experts who have experience in creating systems for business needs. Their aim was to develop business solutions that enabled automated transparent requirements management using appropriate information technologies [16]. The work related to the concept of business rules is inextricably linked with the Business Rules Group, an independent non-profit organization. It deals with the formulation and development of standards related to business rules and the relationship between these rules and the way the organization and organization of IT systems. During the work of the group, several documents were created that introduced and formalized the subject of the business rules, including: “Defining Business

Application of Business Rules Mechanism in IT System Projects

41

Rules—What Are They Really?”—the first publication issued, or the “Business Rules Manifesto”, a document describing the requirements of the business rules.

2.3.3

Characteristics of the Idea of Business

An IT system designed in accordance with the business rules approach should fulfill several functions that will facilitate its creation and use. The rules to be introduced in this system should have several important elements, for example [12]: • Separating the rule from other components rules should form an independent element of the system. In this way, the change forces the system to be reworked, but only changes the rule. The separation of rules represents this advantage and you can manage them yourself without any problems with the program code. • Enhancements to the rule expression should be made in an understandable format, preferably despite technical constraints, so that you can immediately state “what” is intended for the rule. The provisions of this rule shall make them available to any person participating in the undertaking. • The appropriate arrangement of rules should take into account the rules in the IT system so that they can be easily and quickly modified if necessary. This condition is implemented using technology designed for this type of task, such as business rule mechanisms, that allow software to be managed according to established rules and defining rules without the need to modify application logic. • Rule mapping Representation of business requirements in the form of IT-managed rules. It is important to maintain the correct reflection between the “high-level” client request and the implementation of the “low-level” rule.

2.3.4

Advantages of Using Business Rules

Many advantages of using business rules in building information systems are mentioned in the literature. Among them, it should be emphasized [12]: • the rules of simplicity—are well-understood statements for people who do not face technological problems; clearly reflect the functional requirements of the system, • less complicated system architecture system designers do not have to focus on creating complicated and flexible solutions, because they have such functionality provided by the use of specific technology that allows enforcement of rules, • slight changes to requirements—dynamic requirements can be reflected in the system without the need to interfere with software code, • easier access to specifications—information on the functionality of the system is not hidden in its code; By placing a rule in the rules archive, the requirements

42

S. Balcerek et al.

Fig. 2 Benefits of using business rules—accelerating the implementation of new requirements (based on [15])

are achievable for all team members not only for the programmers who create the system, but also for the people who deal with the business side of the company. • Rapid adaptation of the system to change requires the use of a corporate regulation engine significantly reducing the time to modify the system and achieve the desired effects.

Application of Business Rules Mechanism in IT System Projects

43

A graphical representation of some of the advantages of the business concept is shown in Fig. 2. It should be noted that the process of introducing new requirements is much shorter if a business rules approach is used.

2.3.5

Disadvantages of Access

Like any concept, it has certain disadvantages. In [5, 17] and [18], attention has been paid to the disadvantages that may be associated with the use of a business rules approach. The most important postulates against this approach are: • Difficulties in adapting this approach require a different way of thinking, especially from programmers who will have to express requirements in a declarative way, and not as before, by placing the rule in the program code, • the efficiency of the technology—using the rules engine requires the incurrence of certain costs, such as the cost of effective implementation of requirements, data in the form of rules, • administration and limited interaction between rules—entering a very large number of rules into the system can cause problems with their administration— for example, answering a simple question: “which rule is unnecessary after the introduction of a new policy”; then it is difficult to see that there can be links between such rules, extreme and there are cases, as you know, recursion during data editing due to the mutual influence of the rule on each other, • the problem of the black box—by expressing the requirements in a declarative form the computer gets a command “what to do” and not “how to do it”; can cause, especially for programmers, a loss of control over the operation of software, • Difficulties in analyzing rules and verifying their correctness “This is due to two previous shortcomings” A large number of rules, complicated dependencies and lack of nuclear operation of the system can lead to many problems with determining the correctness of a rule. 2.3.6

Business Rules and Business Processes

Access to business rules is in some way related to another concept, namely I am not a business process. The difference between these concepts is always obvious, and therefore the functions that these concepts represent sometimes overlap in business rule management technologies and business processes. In [19], a business process is defined as a continuous set of activities that is used to achieve a specific result. More emphasis is placed on how the work should be done (“like”) than exactly what should be done (“what”). In this way, a certain difference between rules and processes is evident in that it declaratively expresses a rule, which specifies what should be done, not how. However, the process defines a certain sequence of events that should lead to the achievement of the goal. The result of determining whether a given activity meets certain conditions is the direction of the process.

44

S. Balcerek et al.

Both approaches can sometimes appear to be competitive because they share the same data model and can produce similar results. However, this is not entirely correct. Technologies implementing concept concepts and business processes can work together to increase the efficiency of business management. As already mentioned, the business process determines a set of certain activities, the feasibility of which depends on the fulfillment of certain conditions. This is where the “meeting” of these two concepts takes place. Business rules determine whether an activity is performed or not. Therefore, the rule is a certain element of decision-making in the process—indirectly responsible for which activity should be performed in the following order. What is the advantage of this combination? Specifically, the use of rules allows you to control the process and then change the rules as needed. Based on the findings and rules, rules determines the engine, often through an inference mechanism, the decision regarding the next step of the process. In the event that organizational policy changes and new conditions need to be identified that determine the steps of the process, changing the rule can significantly save costs and time to reorganize the process. As an example of the relationship between the business approach and business processes, a simplified procedure for ordering a product in an online store can be considered. A diagram of this process is shown in Fig. 3. Initially, the data needed later in the process is collected. This can include customer purchase history, sign-in frequency, and other details. In the step “Determining the discount” it is decided whether the customer will receive a discount for the product. For this purpose, the business rules are applied on the basis of the facts gathered and the rules adopted, it is determined whether the conditions for granting a discount are met. It should be noted that these rules are independent of the other steps of the process, they can change at any time when the conditions for granting a discount change. This example shows that business rule and process approaches can work together and be an effective tool to support an organization’s work. However, it should be borne in mind that the rules are not closely linked to business processes, that they may exist in complete independence.

2.3.7

Examples of Use

Examples of implementing business policy concepts can be found in many IT systems. Several were mentioned in [21]. One example is the use in banking systems to manage the mortgage lending process. Based on many parameters (e.g. earnings, domestic value, age of the borrower, previous credit of the spat), the system helps determine whether a loan will be granted. Another example of use is software that works in a supermarket. It helps in managing the amount of goods in the warehouse, on the shelves of the store, it is also useful in predicting customer demand for products in the future. Based on data purchased last weekend, the system may suggest an increase in the stock of the

Application of Business Rules Mechanism in IT System Projects

45

Fig. 3 Schematic of an example of ordering a product in an online store (based on [20])

product in stock. An additional feature of such software is the ability to determine discount strategies for customers. Business-based approaches used in specific organizations include: • Cisco Active Network Abstraction—is a system designed to manage network resources. Through business rules, it allows the user to define the logic associated with the response to events occurring in the managed network [22]. • Bank of the West bank, which uses the concept of business rules in the lending process. Bank managers have the possibility to design and implement various lending policies in the form of rules [23]. • California Motor Vehicles Division—is the institution that maintains the California Vehicle Register. The calculation of different types of claims, for example after vehicle registration, is subject to many, often changing, regulations. Using the business rules approach allows for a rapid implementation of a policy specifying the method of calculating fees [24]. • The Singapore Tax Office—the use of technology implementing the approach to business rules allows the Office’s staff to respond to economic and tax changes, as reflected, for example, in the introduction of a new tax calculation policy [25].

46

S. Balcerek et al.

• Route Management System (Siemens)—a system supporting rail traffic management. The different legal provisions in the countries where the system is to be used mean that it needs to be adapted quickly. This function ensures that a business rules approach is validly used [26].

2.4 Business Rules So far, a description of the concept of a business rule and its main features has been presented. However, the very concept of trade rules was treated intuitively and no formal definition was provided. A more or less clear explanation of the concept of business rules can be found in the literature dealing with the approach to business rules. You will examine these definitions in this subsection.

2.4.1

Business Threads

Many authors cite the definition of a business rule formulated in the above—Group Business Rules. This definition can be found in [27], one of the first and most important documents on the idea of trade rules. The purpose of the rule is to control or influence the behavior of this company. rulesa cannot be divided without losing the information “it is atomic (atomic)”. In [12], business rules are defined as a set of conditions that govern business events in such a way that they occur to an extent acceptable to the requirements [12]. An example of such a business event could be the placement of orders. The order cannot be processed until the “acceptance conditions” are met, for example by entering the delivery address. These “eligibility conditions” are set by managers, which they express in the form of rules in which the event can be successful and should not. Cited complement thread with the decision available in [28], which defines business rules as a means by which an organization implements a company’s strategy, organizes policies, and also complies with legal obligations. A remarkable and interesting diary is given by Morgan in [29]. It describes a business rule as a compact statement on certain aspects of an undertaking [29]. It also states that the rule is defined by terms directly related to the company, using common, unambiguous language understood by all parties concerned: »Professionals, business analysts as well as employees who relate to the technical side of the company—architects or system programmers. Graham agrees with the counselor quoted. However, in his book [30] he expands Morgan’s definition. The most important points of this development were that the business rule is indivisible (atomic), correctly formulated and expressed in a declarative way. In addition, the language used to express the rules may contain business-specific terms. The author explains what he means by defining a well-formulated rule; if it is to be used in rule management software, it must be possible to express it “in a way understood” for that program. Graham also draws attention to the important conclusion of the beneficiary from its definition and, in

Application of Business Rules Mechanism in IT System Projects

47

fact, part of it, referring to the declarative way of expressing rules, which means that rulesy does not describe business processes, but indicate what activities are allowed during the process [30]. In the book [16], Ross provides several definitions of the term business rules formulated by various authors. Among them are several, of which he is the author. In one of them, he states that the business rule is separate, which allows the policy to be applied to the company. It can be considered as a non-technological requirement and the user, as a non-technical form, which is expressed as a statement, as a nontechnological and non-technical form. It is a statement describing the conduct of the company. Among other definitions given by Ross, it should be noted that they were formulated by Chrisholm [16]. Defines a business rule as a single term that captures data or information that an organization owns and draws new information from or uses to create a particular action. By analyzing the cited descriptions, it is possible to distinguish some characteristic terms. The collection of these conditions can be the basis for drawing up a new definition of business rules that will be most appropriate for the person. Everyone can look at business rules from a different perspective: The business itself or the IT system that supports it, but we should not forget that the rule first stated “what” should be done or “what” forbidden, as opposed to “how” to do it. In addition, it is important to ensure that all stakeholders involved in a rulesa project understand the term rule, which determines the set of decisions that can be made, and that its interpretation by one of the project team members can adversely affect the results of software that supports the project’s decision-making processes.

2.4.2

Manifesto of Trade Regulation

An interesting source of information on business rules requirements is the “Business Rules Manifesto” [31], produced by the Business Rules Group. Contains a list of instructions for expressing and using business rules. It is also a good introduction to the concept of business rules. The current version of the “Manifesto” contains 40 points, which are divided into several categories (articles). The following points are more interesting and worth mentioning: • “rulesy are the most important citizens of the world” (p. 1.1). • “rulesy are direct restrictions on the functioning of the organization and can also support its functioning” (p. 2.1). • “rulesy is the basis of what it knows about the company itself”, ie, the basis of business knowledge (p. 3.4). • “rulesy de lowers the line between measures taken and unacceptable measures “ (p. 7.1). • “rulesy should come from sales representatives” (p. 9.1).

48

S. Balcerek et al.

The examples given from the “Business Management Rules”, as well as other examples not mentioned here, are a good complement to the “business rule definitions” mentioned above and allow a better understanding of the application of the concept of business rules in context. Business management, both commercially and technically.

2.4.3

Class Regulation

Terms referred to as business rules may be subject to classes “due to different criteria. The classes of these terms may be useful in their formulation and analysis. In his work [12], Von Halle cites several schemes of a class of business rules formulated by different persons or organizations, and presents his own view of the class of business rules. The game “this division is shown in Fig. 4. As you can see, von Halle introduces high-level business rules into three subgroups: concepts, facts, and rules per se. A term is a natural word or phrase. Ross clarifies von Halle’s definition of the term [16], emphasizing that the term should have a specific and unambiguous meaning in the context of the undertaking in order to be recognized and cooperation of project implementers. Examples of this term may be: customer’, ec employee’, order’(a term as a term defining a particular entity), customer address’, „employee’s account number’ (a term as a characteristic of that concept), Thursday’(a term value), “working days of the week” (term as a set of values “Mon, Tue, Thurs, Fri”). Fact is an expression which, by combining terms with verbs (hearings) and other terms, expresses a certain important observation. Examples are: “A customer can order,” “A manager is a type of employee,” and “Programmers are members of an organization’s IT department.” Facts and dates can be logical data modeling in the application. They also allow rules to be considered in terms of their meaning “they create the semantics of rules”. The third group in the class of business rules presented by von Halle are rules. Rules are defined here to distinguish the name. The principle is a declarative formulation for which using computational mechanisms» Get new information (fact) or decide what steps to take. Examples of rules are: “Order value is the sum of delivery

Fig. 4 Scheme of business rule classes proposed by von Halle (based on [12])

Application of Business Rules Mechanism in IT System Projects

49

costs and product prices”, “The customer cannot order the product until he has paid for the previous order”, “The time to pay for the order must not exceed 14 days”. Von Halle made another division “this time about the principles themselves”. There are five categories of principles [12]: • restrictive (orders)—wording that expresses the need to meet or the need for failure, for example, “Customer can not order more than 10 products at once”, • instructions—wording expressing a warning in relation to circumstances that should or should not be, for example: “The customer should not order more than 10 products at the same time”, • Triggers—Formulations in which the statement of conditions is true initiates a new event, such as “If the ordered product is missing, stop the ordering process.” • computational—formulation, which is an algorithm to obtain the value of a given term, while the algorithm uses mathematical operations (sum, difference and others), for example “The value of the order is equal to the product of the product you tax and the total product price and cost pliers” • request—formulations whose conditions are true lead to the discovery of a new fact, for example “If the customer does not pay the order for 14 days, gets a blocked status”, “if he sometimes has a blocked status”, he can not order more than 5 products at once. The presented classes of “terms”, referred to as business rules, formulated by von Halle, as well as others available in [27, 29] and [16], are intended to make it easier to find requirements and formulate rules. Morgan writes: “It is important not to waste time talking about the” rule of one group or another [29]. Classes are meant to help express clear and well-defined rules.

2.5 Implementation of Business Rules Gaining the benefits of applying business policy concepts requires proper action at two levels: business responsible for defining requirements and IT enabling software to process the rule. In this section, we will look at ways to apply business rules in the field of IT. The book [12] presents three ways of implementing the trading rules in the system. All can be used, but one way clearly outperforms the others. The first way you can express business rules in software is implementation in a database management system (DBMS) in the form of so-called triggers, storage procedures, or other DBMS-specific options. As soon as the database transaction takes place, the rule is executed and possible steps in these rules are taken. The disadvantage of this approach is that it is not possible to easily share the definition of rules between different database systems changing a rule in one system will not change it in all systems. Another way is to express rules as part of the methods in the application code. However, in the light of previous postulates, in particular those which state that rules

50

S. Balcerek et al.

should be easily accessible and not participate in the Code, this approach should be rejected. Apart from this postulate, however, the above method seems to be quite demanding to implement in terms of programming and system design, which requires sophisticated solutions. The third and most interesting way is to use an external software component. It has several advantages over other solutions. These include, but are not limited to: • the ability to manage a set of rules regardless of the processes performed and the data processed, • better performance because» this type of software is designed exclusively for rule processing and management, • the ability to share rules across multiple systems—much like a database can be shared by many systems. This type of software is called a business policy management system (BRMS), business logic server, or business policy module. The surname is not entirely correct, as the rules mechanism is part of the business rules management system. In the book [30] you will find the functions that characterize the BRMS software. These include: • maintaining a rules register, which is a policy set for the company, • the possibility of integration with IT systems supporting the work of the organization, • grouping rules into independent but related files, • draw conclusions on the basis of regular sets, • enable business analysts and even regular users to create rules and policies related to business areas. The use of external BRMS technology is a good solution to the connection problem requiring business and IT limitations. It provides a relatively elegant connection between two perspectives: the implementation of the IT system by programmers and the definition of business processes and requirements by sales managers. Due to many advantages and usability, the third part of the implementation of business rules in the system. An external software component called the business policy management system will be considered later in the work.

3 Technological Aspect of Business Concepts The software development process in modern times is relatively economical. A large number of different types of programming tools are used to implement an IT project, from source code editors, through data modeling programs to application servers. The concept of business rules introduces a new set of tools called the business rule management system. An overview of the characteristics of this type of software is given in the following sections.

Application of Business Rules Mechanism in IT System Projects

51

3.1 Business Rules Management Systems The application of business rules in the software development and development process requires the fulfillment of several requirements envisaged by this approach. These requirements were presented and discussed in the previous chapter. However, the most important of them should be mentioned here: • expression of requirements for the functionality of the IT system in a form comprehensible to interested parties • separation of business rules from software code, • The ability to set a rule for people who are not connected to the information technologies. An analysis of the above points leads to the conclusion that the interpretation of the rule by software is only one of many actions that need to be taken in order to achieve the desired results. Mechanisms are needed that allow rules to be stored, modified, and often tested in general for all business policy management operations. The purpose of the mechanisms to be implemented is to facilitate the development of software based on the business rules approach. The mechanisms outlined here that are responsible for implementing many functions are called business rule management systems. Here you will find a description of the functions of the rule management system business.

3.1.1

The Difference Between a Business Rules Management System and a Business Rules Mechanism

However, before describing the business policy management system, it is necessary to clarify some ambiguities associated with the naming of the software that allows the business policy approach to be applied. In the world of information technology, there are mainly two names that describe these business rule mechanisms and business rule management systems. These names are often used interchangeably, except for program types: sometimes different software vendors enter their own names to distinguish them from competing products. The business rule machine is recognized as part of the business rule management system. It allows you to call the procedures responsible for enforcing the rules and determine which rules should be analyzed depending on the context of the operation being performed. Rules engine allows you to compare the data on which the IT system works with defined rules. The purpose of this connection is to obtain the result in the form of a decision to perform or stop the system in the performance of a certain operation, for example by sending an e-mail. At a time when enterprise rule technologies were in their infancy and began to be used, manufacturers’ attention was focused primarily on the rule enforcement mechanism (engine) and algorithms that allow for efficient rule processing, i. an

52

S. Balcerek et al.

assessment of which rules should apply at a given time. Over time, software development has been tracked by tools that have enabled the general understanding of business policy management. Manufacturers of business rule engines have added features that allow rules to be tested, versioned or stored in a dedicated repository [32]. As a result of the increased functionality offered by manufacturers of business rules technology, business rule engines have become one of the elements of a larger business rule management system. The role of the rules engine itself is not yet obsolete, it is the most important element of BRMS systems. Therefore, the use of interchangeable terms does not constitute a major abuse. The formulation of business rule mechanisms in the title of this work can also be interpreted as “business rule management systems”.

3.1.2

Characteristics of Business Rule Management Systems

There is no standard or definition among manufacturers of business policy management systems (BRMSs) that accurately describes this type of software. However, there are some similarities between the main manufacturers’ systems. In [30] Graham distinguishes four main business management rules (Fig. 5): • knowledge base (rule, knowledge base), • motor rules, • rules repository

Fig. 5 BRMS architecture diagram [33]

Application of Business Rules Mechanism in IT System Projects

53

Fig. 6 The most important tasks of the business rules engine

• tool set. The knowledge base is an element of the BRMS system that defines what the system knows about the problem it is solving. In other saunas, the knowledge base contains the facts and rules needed to solve a problem. Facts can be represented in the form of relationships between certain objects and characterized by certain attributes. The rules motor, also called the inference motor, is the most important element of the system. Responsible for the interpretation of business rules and actions consisting in the acceptance of appropriate (determined by the rules) actions in relation to the data. It also sets out which rules should be enforced at a given time and in what order. The most important tasks performed by the rule engine are shown in Fig. 6. An important task of the engine rules is to lead the inference process, i. J. Derive new facts based on the current state of knowledge and the rules ensemble. The two most common inference strategies are chaining back and forth. The idea of direct deduction is to make it based on available information rules and facts will generate new facts until the goal is achieved (it is not possible to generate new facts). On the other hand, the retrospective inference process is the opposite of inference. At the beginning there is a clear hypothesis, the truth of which must be proved by proving the truth rules. At present, most trade rule mechanisms allow for preliminary derivation and some of them also for retrospective derivation. Implementing inference in most existing business rule engines now uses the rete algorithm to effectively implement inference. A discussion of this algorithm is given in Sect. 4.2. Another element of the business rule management system that Graham mentions in [30] is the rules repository. Provides rules storage, control, and versioning based on changes made to rules. The repository is often associated with a package that provides permission to access the rule for different systems. The fourth part of the BRMS system is all kinds of tools that help maintain the set of rules, their definition, as well as the runtime environment in which the whole system works.

54

S. Balcerek et al.

In addition to these components, BRMSs often include mechanisms responsible for determining the correctness of rule design, testing and simulating rule enforcement, as well as checking for conflicts between rules, such as whether rules are mutually exclusive. There are also functions that allow to explain how the rules mechanism decided that action should be taken. An important feature of the BRMS system is the fact that the rules mechanism and more precisely its implementation is independent of the rules dataset, t. J. From knowledge of the problem. This means that rule changes do not force changes to the engine code.

3.1.3

Relationships Between Business Rule Management Systems and Expert Systems

It was mentioned in the previous business chapter, the approximation and reasons for the beginning of the concept of business rules management can be traced back to the 1970s. This was the time when the first expert systems began to be created. According to Mulawka, expert system is a computer program that performs costsaving tasks with high intellectual requirements and does the same as a person who is an expert in this field [34]. One of the first expert systems, created in 1974, was called MYCIN. His task was to diagnose blood diseases and make a suitable diagnosis. The principle of operation of this system was based on a dialogue with a doctor, who thus provides the system with specific information. The representation of knowledge in expert systems can take many forms. Rule representation is often used, but in [34] you will find other forms: using semantic networks, computational models, frameworks, or sentence or predicate calculations. Business rule management systems show similarities with expert systems in terms of architecture and operation. When analyzing information about BRMS systems, you may ask whether these are in fact expert systems that have adopted a new name for marketing purposes. In his book [12], von Halle makes five remarks which distinguish between systems BRMS systems expert. Although not all of these views are to be justified, it is worth quoting here. The differences indicated by von Halle are shown in Table 1. Von Halle also emphasizes that the differences between expert systems and BRMS systems are in applications. Expert systems are used for diagnostics, such as consulting systems, classification, evaluation or control of various processes. On the other hand, business rule management systems are mainly used in the field of electronic order management or business process support [12]. Graham disagrees with von Halle’s views. Referring to the above views, the book [30] states that the complex decisions made by an expert system are not as costeffective as von Halle states. He also believes that many of the expert systems involved in the creation required the application of business rules to information from the database. In addition, it also states that all popular business rule management systems have deduction mechanisms, so it is not just the domain of expert systems. Graham

Application of Business Rules Mechanism in IT System Projects

55

Table 1 Differences between the expert system and the BRMS system according to von Halle [12] Expert system

BRMS system

Making the decisions you make

Making simple (“everyday”) decisions

They can handle knowledge with a degree of uncertainty

They work on certain knowledge

Reasoning is the main thing a process that uses usually

Rules are not used only for inference, but also for example to face (calculation rules) or perform certain actions (rulesy causing actions)

use of the rule is not binding with database operations

The use of the rule is limited often for surgery updating data in the database

Usually include engine reasoning

Operations based on rules do not require use inference engine

therefore disagrees with von von Halle’s assertion that the rules in BRMS do not apply in inference processes. However, there is one argument put forward by von Halle, with whom Graham agrees that BRMS systems cannot handle knowledge uncertainty. None of the current business rule management systems supports mechanisms to promote uncertain knowledge. As Graham goes on to say, this argument may distinguish BRMS systems from expert systems, but outside of it he sees no significant technical differences [30]. The views expressed by Graham seem very precise. In terms of software architecture, expert systems as well as business rule management systems have very similar components. So you may be wondering if these are just two different names for the same software? The line between these two types of software is very thin. In addition to the above argument that BRMS systems do not support the management of knowledge uncertainty, it may also be important that BRMS systems are not, as in most expert systems, used directly by humans (for example through “dialogue” with the user) but rather by other (external) IT systems. There may be a whole chain of components between the user and the BRMS, so the user may not be aware of the use of such a system [20]. For the purposes of this work, it is difficult to focus on (expert) knowledge of the problems required by systems other than BRMS. However, an interesting extension of this topic may be to examine the application of business rule management systems in the context of issues that are addressed by expert systems.

3.1.4

Interpretation of Rules by the BRMS System

Interpretation of the knowledge contained in the business rules is a key activity of the BRMS system. In most cases, BRMS treats business rules as so-called Production rules. These are terms in the form: IF X THEN Y.

56

S. Balcerek et al.

The first part of this production rule X is called the predecessor, state, or left side—LHS. The second part of Rule Y is referred to as the successor, act, or rightwing—RHS. The interpretation of the rules of production can be done in various ways, for example:—if a certain condition is met, some action is taken—or—if the statement is true, then another can be derived from it [30]. The advantage of using production rules as representatives of knowledge in business rule management systems is that people can easily understand them. In addition, given that each rulesa contains only a small amount of knowledge, the production rules are independent of each other and adding and removing them from the rules database is not a complicated process [30]. Systems based on production rules are called production systems or production systems. A simplified structure of such a system is shown in Fig. 7. The main components of the production system are [35]: • set of fact types—set of user-defined data types (structures, classes); an example of a type of reality can be a home, a person, while the reality itself can be considered as assigning the value of a certain property (field, attribute) to a type of reality, for example: » yellow house, tall person, • working memory—is a type of database or structure in which facts are stored; determines the current state of the system, • production regulation, • production memory (also called Production memory)—also called knowledge base, is a structure that stores the rules of production, its content changes very rarely, • The rule selection strategy—consists of algorithms responsible for selecting a specific rule, which should be activated when many rules meet the conditions contained in the LHS. Currently, almost all of the most popular BRMS systems have the abovementioned functions—therefore they are production control systems. A formal description of the production rules and production systems is given in [35].

Fig. 7 Structure of the production rules system

Application of Business Rules Mechanism in IT System Projects

57

3.2 Repetition of the Algorithm The business rules engine, which is one of the components of the peani BRMS system, is a very important task of the mechanism responsible for enforcing business rules. The efficiency of this operation is ensured by using the rete algorithm. This section introduces the operation of this algorithm. The rete algorithm was created in 1974 by Charles Forgie University of Carnegie Mellon University. It has become the basis of common production systems such as OPS5, ART or CLIPS. Currently, modifications of this algorithm are used in many popular rules motors. The word “rete” means “network” in Latin.

3.2.1

Examining the Problem

To better illustrate the problem to be solved by the rete algorithm, an example situation is considered: let the knowledge base contain a set of rules R. In addition, there is a set of facts F in the state’s memory and decide to activate a rule whose conditional portion (LHS) is true. This action takes place in the so-called Inference cycle [35]. The scheme of this cycle is shown in Fig. 8. The most visible and easiest way to implement the inference mechanism is to assign parts of all LHS rules to existing facts (step “Corresponding rules”). Due to the existing facts, the conditional parts (LHS) of many rules can be violated, so it is possible to activate many rules—all these rules are formed into a set of rules A, which are the subject of activation. In the next step (“Selecting a rule to activate”), only one rule from set A that is forced is selected based on the rule selection strategy (step “Activating the selected Fig. 8 Inference cycle performed by the business rules engine

58

S. Balcerek et al.

rule”). As a result of activating the given rule, it is possible to insert new facts into the memory of state F, and therefore the state of the whole system will change. As a result, it may turn out that the conditional parts of the rules remaining in rule set A subject to activation are not welded in the context of the current state of the system and these rules should not be activated. Then the whole cycle is repeated again, followed by reassigning the rule, creating set A and activating a specific rule. The above scenario is not very effective—after each activation of the rules, it is necessary to re-check all the rules based on current facts. Increasing the amount of rules in the rules database or facts in the state’s memory means that the rules mechanism has to perform more actions, leading to low time efficiency. The rete algorithm eliminates the above problem. It is based on two important observations that ensure high performance: • The status memory during the execution of subsequent inference cycles does not change significantly in the setting of control A to be activated. The rete algorithm uses this fact and stores the results of the comparison of rules with the facts from previous cycles. In the case of facts that have been added or removed from the state memory, compliance with the rule is made as a result of the activation of the rule. As the other facts have not changed, there is no need to adapt them to the rules. • Shared conditions—some rules may have common conditional parts; the calculation of the result of such a condition is performed only once, not sequentially for each rule. 3.2.2

Operation of the Exchange Algorithm

The process of operating a rete algorithm can be divided into two phases: the network construction phase (hence the name of the rete) and the phase of performing network activities. In the first phase, a network of connected hoses is built. Such a network takes the form of an acyclic controlled graph. Each of the nodes represents one or more conditions that occur in the LHS portion of the rules. There are several species of snakes [35]: • The bark enters the snake net, receives a factual fact at the entrance and sends it to another snake. • A single-threaded node, also called an A-node or an α-node. It is the only condition that appears in the part of the LHS rule; if the LHS portion consists of several conditions joined, for example, once AND AND OR, then each condition created is followed by several conditions; snake A-snake has its own memory in which facts are stored that meet the state represented by this snake. • A two-threaded snake, also called a B-snake or β-snake. It is used to determine the result of testing conditions that must be met by more than one fact. Hoses of this type have two memory elements—one for each inlet for which the facts are given.

Application of Business Rules Mechanism in IT System Projects

59

• The end node is also called the P-node; is a chart sheet. Achieving such a rule means that all the conditions in the part of the LHS regulation have been met. Such a rule is then added to rule set A to be activated. Figure 9 shows an example of a graph generated according to the rete algorithm for two rules: IF X and Y THEN action 1, IF X and Y and Z THEN action 2. It should be noted that in the present case, there is cooperation of hoses with the same condition (X and Y). If such optimization does not exist, the X and Y condition check would be performed twice—separately for the first and second rules. After creating the hacks, the rules engine is ready to perform the next phase of the rete algorithm. The elements are gradually downloaded from the state memory (fact set F) and presented at the network entry to the root, which it transmits to the child branches. If it behaves like “poultry “, if the presented element (fact) meets the state that represents the given Awzieze, then it remembers it in the memory of this node and then goes to the next snake. If the condition is not met, the above is not taken into account these steps. During the next operation of the algorithm, the elements from the state memory get to the B-nodes. These nodes are responsible for limiting the facts that reach their entrances. The annex should be understood as testing one set of facts (received on the first registration) in relation to a second set of facts (received on the second registration). The set of facts obtained as a result of testing at Node B is transmitted to the following subgroups.

Fig. 9 Example of nodes generated according to the rete algorithm for two rules (based on [36])

60

S. Balcerek et al.

If a particular Pact node reaches a P-node, it means that all the conditions of that rule have been met and such a rule can be activated. However, before this occurs, rules is added to set A subject to activation. After presenting the elements from the state memory to the network, the rules engine determines, based on the rule selection strategy, which rule should be activated. However, this process is not part of the rete algorithm itself is an important point among the tasks that the engine educates. Specific choice rules from a set of rules to be activated can be made in different ways, for example: depending on the priority of the rule, depending on the order of the rule in the set, or other criteria. The rules engine activates the rules from the activation rule set until all of them are activated, with each rule being activated once during one cycle of presenting facts in the network. During the process of activating subsequent rules, it may happen that the action of the activated rule modifies (introduces a new or deletes an existing) fact from the state memory. This can, for example, affect other rules in the set of rules to be activated, so that the engine needs to take steps to resolve the situation. In this case, the engine presents the fact in the network and if the correct conditions are met, the new rule can be activated. Activating this particular rule may cancel the execution of another rule that is currently in the rule set to be activated. The engine continues to enforce the remaining rules according to the above scenario. Of course, there can be a situation where an explosion occurs during the activation of the rule, such situations are usually undesirable. The solution to this situation varies depending on the implementation of the algorithm.

3.2.3

Recovery Algorithm—Summary

All currently popular trading rule mechanisms use the rete algorithm or one of its improved variants, which include: rete II, rete III or rete OO. Each of the following versions of the rete algorithm introduces some conveniences and increases its efficiency, but the main idea of the operation is mostly in line with the description in this section. A key advantage of the rete algorithm is that rule checking on network nodes is performed only when facts are added (added or removed). In the simplest (socalled Naive) implementation, in the case of the most modern factual adjustments, all conditions of all existing rules would be checked. Rete thus brings significant time savings in operation. However, the high efficiency achieved requires certain costs. The rete algorithm has been criticized for overusing the memory required to store results corresponding to the conditions represented by network nodes. Due to the significant increase in the number of rules, memory usage is extremely increasing, which may call into question the use of the rete algorithm. However, various new variants of the algorithm try to balance the memory using some implementation solutions [37].

Application of Business Rules Mechanism in IT System Projects

61

3.3 Review of Business Rule Management Technologies Business rule management systems are products whose development has been relatively dynamic in recent years. Currently, there are many systems in the software market that implement the assumptions of the business rule concept. Most of them have many similar functions, including creating and managing business rules. In addition, they use the rete algorithm or updated versions of the algorithm. Current BRMS systems have no restrictions on running on a specific platform— there are systems for Windows, Linux and both. Most BRMS vendors use Java, and more and more systems of this type using the.NET environment are appearing. In this section, you will find an overview of the most popular business policy management technologies. More detailed information about the found products can be found on the manufacturers’ websites.

3.3.1

IBM ILog JRules

ILog JRules [38] -rm IBM is one of the most modern BRMS products. It is designed to work in both Java and.NET. It is part of the IBM WebSphere package. The main components of the ILog Jrules system include: • Rule Studio—development environment, which allows decoding and modulation; is based on the Eclipse environment, • Rule Team Server—is a policy management environment that also allows authentication and authorization of rules created for enterprise users. • Decision validation service—a mechanism enabling tests to be performed on regular files as well as simulations of various data, • Rule Execution Server—a runtime environment designed to execute applications using business rules; allows monitoring of the rules processing process using the JMX mechanism; has full integration with Rule Studio and Rule Team Server, • Rules for Cobol—a module that allows you to compile a rule into the Cobol language and use it with older applications created in this language. ILog JRules also has a very interesting tool called Rule Solutions for O—ce. Its great advantage is that it allows business rules to be managed in a way that is understood by users who are not involved in programming tasks. Rule Rule for O—ce is an add-in for Word and Excel—Microsoft. It is integrated with the Rule Team Server, which provides great flexibility in creating business rules. The overall presentation of the components of the ILog JRules package and their purpose is shown in Fig. 10.

62

S. Balcerek et al.

Fig. 10 Main components of the IBM ILog JRules system [39]

3.3.2

Advisor to FICO Blaze

The FICO Blaze Advisor [40] is a tool that provides comprehensive business policy support. Versions are available for Java,.NET and Cobol. The current version of the system is number 6.8. The most important components of the system are shown in Fig. 11. Blaze Advisor is very willing to use in organizations in the insurance sector— about 65% of all applications. The Structured Rule Language (SRL) is used to create the rule. This will allow you to define rules templates and rules itself as close as possible to the natural language. This functionality is implemented using mapping phrases that people use for instructions, using a computer. rules decoding can be performed using the IDE—Integrated Environment, recommended by technical personnel, or using an environment named by the application Rule (RMA) creators [42]. Blaze Advisor has an extensive system of rules versions and rules version authorizations. An interesting feature is the assignment of time validity to the rules—when

Application of Business Rules Mechanism in IT System Projects

63

Fig. 11 Components of the FICO flame adviser system [41]

the specified period expires, the rules automatically cease to apply as if they were not encountered. Rules quality control is ensured through rules verification and validation mechanisms. During the verification, the rules are checked to see if the rules are unique, mutually exclusive, whether there are infinite loops, or situations where the logical result is always true or always incorrect. In the validation process, regular tests are performed on a set of test data and the results obtained are compared with those results [42]. To process the rules, the Blaze Advisor system uses the rete III algorithm, which is a property of FICO. The improvement offered by this version of the algorithm is the linear dependence of memory occupancy on the size of the set of rules and data (facts). Blaze Advisor also allows you to use a sequential algorithm to handle rule, which is applied when setting up a rule it is not too expensive and extensive and the exchange algorithm is too difficult for this type of case [42]. An interesting feature offered by the Blaze Advisor system is the ability to simulate changes in army’s business strategies. Historical data collected rm¦ are used for this purpose. Different sets of rules representing different strategies can be used for such data. The analysis of the obtained results enables a more precise determination of the optimal business policy of the given company [42].

3.3.3

Microsoft BizTalk Business Rules Framework

The Business Rules Framework is part of Microsoft BizTalk Server [43]. It is designed to work in a.NET environment on Windows systems. The main modules of the BizTalk Business Rule Framework include:

64

S. Balcerek et al.

• Business Rules Composer—a graphical interface enabling the creation of business rules, their grouping into groups, as well as the creation of forestry used in the rules, which is mapped to the relevant environmental instructions, • Engine Deployment Wizard—allows you to add and exchange rules files in the rules repository, as well as import and export as XML files. • Run Time Rule Engine—environment for processing rules, allows derivation into the future, implements a mining strategy. Testing of rules is enabled by the Business Rules Composer module. During the tests, it is possible to monitor different phases of the procedure: presentation of facts, checking of conditions, completion of a set of rules for activation and activation of rules. In addition to the three basic modules, the BizTalk Business Rules Framework includes a number of tools to facilitate your work and perform certain business policy management tasks. These tools include: • Rules Language Converter—a tool enabling the migration of rules sets defined in one rules language to another rules language; this function is implemented by exporting rules to the business rules intermediate language based on XML format, • Rule Set Update Service—a mechanism that allows you to dynamically add rules without having to rebuild all application code. • Rule Tracking Interceptor—a tool for receiving events from the rules engine; they can be used to monitor the processing of rules as well as to detect errors (debugging), • Authorization devices—module for performing authorization for rules files; you can use it to give different users different access rights (read, edit, delete). A disadvantage of the BizTalk Business Rules Framework may be that it is not available as a stand-alone application, but with the entire BizTalk Server system, which can increase the investment cost of the product.

3.3.4

Oracle Business Rules

Oracle Business Rules [44] is one of the main components of the Oracle Fusion Middleware Suite. It integrates easily with Oracle Business Process Management, which is designed to support business processes. Works in Java environment. The main components that make up Oracle’s business rules are: • The JESS-based regular motor uses the rete algorithm in operation and is compatible with the JSR-94 specification. • Rule author—is a graphical tool (Fig. 12 that allows you to create and modulate rules; programmers can use it to decrypt terms used in rules and mapped to appropriate instructions). Entrepreneurs created in this way can easily use Java or XML phrases to define rules. • Rules SDK (Software Development Kit)—a set of application programming interfaces (APIs), the main task of which is to allow applications to access the rules

Application of Business Rules Mechanism in IT System Projects

65

Fig. 12 Creating a business rule in oracle rule author [45]

repository. It also allows you to add additional archives to the system in addition to the default Oracle MDS. You can use RL to define a rule in Oracle Business Rules. It has a format similar to Java. It is interpreted directly by the rules engine, so in the case of dynamic modifications or rule additions, it is not necessary to compile a rules. The RL language replaces the original LESS engine-based JESS engine language. Oracle Business Rules uses the so-called Dictionaries. These are XML files that act as containers for all the information that the user has defined (for example, by the rule author): rules, facts, global variables, and also the data model. which many contradict which rules can and facts. The application can be canceled mutually. All razors are stored in a storage compartment [46].

3.3.5

Free Tools

The above business policy technologies are commercial products. To use them, you need to purchase the appropriate license. The cost of such tools is often very high. However, there are free products in the enterprise technology market that can become a good alternative to commercial products. Free business policy tools include both.NET and Java tools. At present, however, the number of rulesian machines for the Java platform far exceeds the number of engines running in the.NET environment. The two main business rule mechanisms running in the.NET environment are Drools.NET and NxBRE. Both use the rete algorithm in their operation. In addition, they have the most important functions available in commercial products. The other two free.NET control engines are Simple Engine and Flex Rule. They have slightly

66

S. Balcerek et al.

less options than the products mentioned above, but can be used successfully for problems of medium complexity. More free business rule engines have been created for the Java platform. The most famous of these is the Drools product from JBoss. It is a system that is quite advanced in its functionality, and although it is available under a license for free, its capabilities do not differ significantly from commercial products. More information on Drools technology will be given in Chapter “Cost-effective Solutions in Cloud Computing Security”. Other well-known rules engines available in Java are Open Rules, JRuleEngine, JLisa and Hammurapi Rules. These are technologies intended for use in smaller and more specific IT projects [47].

3.3.6

Technology Overview—Summary

All of the above business rules have similar functions. The functions that distinguish these systems focus mainly on the devices offered by the user, as well as on the degree of integration with other technologies. The key factor that can decide to use a given business rule management system is often its price. The above-mentioned paid technologies are products whose price is often increased by the value of the entire package, which also includes the BRMS system. As an alternative to such products, the systems may be made available under free licenses. Their functions, although often smaller than in commercial products, are nevertheless suitable for use in smaller projects. There are also systems that match the capabilities of paid instruments. One of them—JBoss Drools—will be discussed in the next chapter.

4 Design and Operation of a Business Rules Engine on the Example of the JBoss Drools System Using a business rule engine in the software development process often requires a number of decisions. Among them, it is important to answer the question of which of the products available on the market offers the most efficient function at a reasonable price. An alternative to paid products are systems developed as part of the Open Source movement. Use of this type of software does not require the payment of license fees. The functionality of such systems is often comparable to commercial products. A business rules management system called JBoss Rules, Drools, also found» is one of the best—known representatives of business policy technology, available under the free ASL license—Apache Software License.

Application of Business Rules Mechanism in IT System Projects

67

The extended functionality and free license of the Drools system decided to use it in the practical part of this work. This chapter discusses the most important elements of this technology.

4.1 Basic Information About the Drools System The beginnings of the Drools system date back to 2001, when a never-released version 1.0 was created, using a “naive” implementation in line with reality. In the next version 2.0, the rete algorithm was used, which would undoubtedly improve its performance. The development of Drools from a common business policy management tool to a business policy management system has been in place since version 4.0. It was the basis for the creation of version 5.0, which was named by the creators of the Business Logic Integration Platform. It consists of four components (Fig. 13): • • • •

Drools Expert—modules forming the correct engine of business rules, Drools Guvnor—module responsible for managing business rules, Drools Flow—modules that are a workflow tool used to model business processes, Drools Fusion—event processing module—complex event processing (CEP).

All these modules have a very good integration, which means that the selected combination of tools can be applied to a specific problem to achieve the best solution [5]. Given the topic, the use and discussion of the Drools platform in this work focuses mainly on the use of modules Expert and Guvnor, which are a system of managing business rules. Fig. 13 Drools platform components

68

4.1.1

S. Balcerek et al.

Drools System Architecture

The JBoss Drools package was created and developed for the Java environment. The above-mentioned Expert and Guvnor modules must be used to use the business rule management system. The architecture of the system using the BRMS Drools system during its operation is shown in Fig. 14. As you can see, the system works on two application servers—one of which provides services for the user application, while the other for the Drools Guvnor application. The user’s application uses the business rules engine in its operation, which is responsible for making decisions based on the facts in the system. Drools Guvnor, on the other hand, is responsible for storing, versioning, and validating business rules. The names drools-core, drools-compiler and drools repository indicated in Fig. 14 are the names of the library packages that are responsible for enforcing, interpreting, and translating rules. The name Jackrabbit in Fig. 14 stands for Apache, which uses the Guvnor module as a rules repository [48]. Each of the four elements of the Drools platform can work independently of the others. Also in the example shown in Fig. 14, the creator of the system could limit himself to using the rules engine itself—Drools Expert. However, if it is desired to use the entire BRMS system, you should also use the Drools Guvnor tool. It should be noted that the architecture of the system (Fig. 14), which uses the BRMS Drools system, is complementary to the structure of the Business Rules Management System (BRMS), mentioned in Sect 4.1.2.

Fig. 14 System architecture using BRMS Drools

Application of Business Rules Mechanism in IT System Projects

4.1.2

69

Control Formats in the Drools System

The use of business rules requires the introduction of certain rules that apply when creating a rule. These rules define the rules—so that the information contained in the rules can be correctly interpreted by the computer. The Drools package allows you to denature business rules in several forms: • DRL format, • DSL/DSRL format • decision tables. DRL format. Rules stored in DRL format are called technical rules. The reason for using this name is that the rule in this format is intended primarily for people who have practical experience with software development. An example rule saved in DRL format is shown in Fig. 15. The first line after a keyword rule is the name of the rule, like Java classes, are grouped into packages. The rule name must be unique within the package. Everything between the keywords then and then (except for the notes marked with the symbol) means the state of the rule, the fulfillment or rejection of which has the logical value of truth or falsehood. The condition of the rule determines the pattern that should be met in order for the action to be performed. In the given example, the existing facts in the rules engine match the rules with the rules—first it checks whether the given fact (instance of a certain class) is a copy of the class or the name of the Customer attribute. If so, it is still being checked, this copy is worth New Year’s Eve. Fulfilling this condition means that the rule is activated. A rule, which is an action that is performed when a condition is met, is placed between the keywords and then to the end. All valid Java codes can be found here. In the example above, the method call was made using the setPromotion reference on the object labeled $ customer. The consequence of the rule should be simple steps, especially if a statement should not be used here. A new rule should be created instead.

Fig. 15 Example of a rule defined in DRL format

70

S. Balcerek et al.

In addition to using Java code as a result of rules, three fact-modifying expressions can be used in state memory. These include expressions: insert adds a new fact, download removes an existing fact, modifies modifies the properties of that fact. The use of these terms changes the set of facts available, so it is possible that the rules to be activated may not be activated and vice versa [5]. DSL/DSLR format. Formulating a rule in DRL format can be too difficult for a user who is not involved in the software development process on a daily basis. With these users in mind, the developers of the Drools platform have provided a few DSL/DSLR formats. DSL is an acronym for domain-specific language, which can be understood as the language of the relevant area of the problem. The use of DSL/DSLR formats in the rule term consists in the creation of a mapping file in DSL format, which contains a “translation” of terms that the user understands in terms that the computer can interpret (rules machine). The second step is to create a DSLR file that contains rules similar to the DRL rules, but instead using technical language, convenient mappings dated in an associated DSL file are used. An example of a rule saved in DSLR format is shown in Fig. 16. You can easily see that the structure of this rule is very similar to the structure discussed above in DRL format. In this case, however, both the conditional part (line 6) and its consequence (line 8) are easy to understand. In the example from Fig. 16, in the first line, there is a keyword expander that informs the rules engine in which file it should search for mappings of structures occurring in the rules. The rulesa example points to a file named dslrule.dsl. Its contents are shown in Fig. 17. The DSL mapping file contains patterns in which

Fig. 16 Example of a rule in DSLR format

Fig. 17 Example of the contents of a map file «for DSLR format control

Application of Business Rules Mechanism in IT System Projects

71

the rules mechanism tries to find one that matches the selected expression. Terms appearing in the conditional portion of the rules (in DSLR) are compared to terms beginning with a label (in DSL). A similar situation will occur for consequences that match the terms beginning with the label [then]. There can be many expressions labeled [reg] ay and [then] in a DSL file. In these examples, the rules search engine searches for term mappings. The customer name is a value, where value means a variable. This expression will, of course, be correctly aligned with the expression from lines 1 and 2 in Fig. 17. Similarly, with an expression indicating the consequence of the rules [5]. As you can see, in order to create easy-to-read rules (in DSLR format), despite all the programmer’s actions, it should be necessary to define a possible expression mapping. However, during the use phase of the system, eligible costs may be demonstrated, especially if the user of the system is a non-technical person. Decision tables. Using the DSLR format to label transparent data is not the only tool offered by Drools platform developers. Another allows you to create rules in the form of decision table elements. The decision-making board is a type of information system. A formal statement of the concept of the decision table is given in [49] and [50]. To understand how the decision table works in Drools, you only need information, and it is a table structure in which the columns determine the conditions under which the decision will be taken and the actions that make up that decision. |. The following lines indicate the values of the subsequent conditional attributes and the selection of one or more actions to be performed after the conditions are met. The Drools platform recognizes decision tables represented in the form of XLS and CSV tables. A sample sheet with rules determining customer discounts is shown in Fig. 18. The conditions listed in this decision table apply to the attributes of the name and age of objects of the Customer class. The steps to be taken once the conditions have been met are to determine the appropriate amount of rebate for the customer. Examples of rules that can be read from the table are: The discount for customers named New Year’s Eve is 15.14 cent and the discount for customers with a permanent duration (at least 18 years) named Anna is 31.67 cent.

Fig. 18 Example of a decision table for the Drools system

72

S. Balcerek et al.

Fig. 19 Congolese congressional terms contained in the example decision table

The use of decision tables requires, as in the DSLR format, some guidance for the rules mechanism regarding the interpretation of the data contained in the table. This information is placed directly in the XLS file. In fact, these are terms similar to those used in DRL rule names. In this example, they were hidden between rows 4 and 8 of the worksheet. Figure 19 shows the situation under consideration. Using decision tables to define business rules in the Drools system can be very convenient, especially if the user handles many rules under the same conditions, but with different combinations of parameters. In other cases, better results can be obtained by using the DRL or DSLR format to create the rule.

4.2 Managing Business Rules Using the Drools Platform As has been emphasized many times, a good BRMS system should have tools that allow, among other things, versioning, testing and offering a relatively simple way to create business rules. On the Drools platform, a tool called Guvnor is such a tool. Drools Guvnor is a web application with a graphical interface (Fig. 19) created by GWT technology. The recommended startup environment is a Jboss application server, but after proper configuration, you can use any other application server or servlet container. Guvnor uses the JCR standard implemented by Apache Jackrabbit to store files. Guvnor offers a number of editors (Fig. 20) with which you can request business rules, each of the above formats for creating a rule has a dedicated editor. Additionally, you can import rule collections that were created in another tool. To facilitate the management of the rules, a categorization of rules has been introduced—each rule belongs to at least one category. It is also possible to assign rules to rule sets to make it easier to determine which rules are currently in use and which are in the testing phase, for example (Fig. 20). Checking the proper functioning of rules offers a test scenario mechanism. It consists of creating a set of rules tests that are performed based on the facts entered by the user. In such tests, it is possible to determine the expected effects in terms of which rules should be activated and what their effects should be. Maintaining version control of rules files with Guvnor means that all changes you make are remembered, and you can restore the previous version of the rule at any time. Guvnor has the ability to specify access levels for individual users for the operations performed. This is an important feature because it ensures that each user can

Application of Business Rules Mechanism in IT System Projects

73

Fig. 20 Drools Guvnor interface

only perform operations that are allowed for them, such as reading a set of rules, but not modifying them (Fig. 21). Using Drools Guvnor as a business policy management tool can certainly make it easier to define and maintain the quality of policy files. The undoubted advantage of the application is the ability to easily access business rules from virtually any computer. This is possible through a graphical web interface that is available in most web browsers. However, as the developers themselves admit, in the initial phase of use, the skills of an experienced user (such as a programmer) need to be used to properly configure the required elements, as a non-technical person may not be able to understand how a program works in a short time [51].

4.3 Key Operating Mechanisms of Drools Expert Expert software is a valid business engine for the business policy management system of the Drools platform. It is important that this element is responsible for enforcing the rule and implementing concrete measures. This subchapter describes the most important mechanisms responsible for the operation of Drools’ business rules mechanism. Figure 22 shows the cooperation of these mechanisms.

74

S. Balcerek et al.

Fig. 21 Editors of different rules formats in Guvnor

Fig. 22 Cooperation of the most important mechanisms of the drools expert tool

Application of Business Rules Mechanism in IT System Projects

4.3.1

75

Session

Measures taken to implement the decision-making process, such as adding facts to the state’s memory or enforcing a law enforcement order, are carried out within the methods offered by the session. On the programming side, the session is a copy of the implementation of the StatefulKnowledgeSession or StatelessKnowledgeSession interface. The use of the first of these interfaces means that the session used will have the character of a state. This means that between the following prompts to activate rules (fireAllRules method), the appearance of the state memory in which the facts are located is preserved. You will not be required to resubmit facts when you make another activation request memory because they will still be there. Using a state session to interact with a rules engine is especially effective when you require rules activation at certain intervals for events that have not changed or their rate of change is low. Otherwise, adding the same facts would always be a loss of computer resources. The StatelessKnowledgeSession implementation provides a stateless session. This means that the status memory is always cleared between subsequent calls that require activation. Subsequent calls for activation requests must be preceded by the addition of facts to the state memory. The advantage of using a stateless session is that the rule allows the engine to perform better optimization than a state session because the contents of the state memory do not change.

4.3.2

Knowledge Base

The structure in operational memory, which is a magazine for various denominations, used by the rules engine is called the knowledge base. This is an implementation of the KnowledgeBase interface. These definitions include, but are not limited to, rules, fact types, and names of functions that rules can share and use. However, the knowledge base does not contain facts. As mentioned, they are added directly to the session, which can be created using the methods provided by the KnowledgeBase interface. The knowledge base instance should be kept in memory, because creating it consumes a lot of resources, for example, when reading a rule from files. The session does not have to meet this condition—it can be created multiple times.

4.3.3

Dynamic Adjustment of the Knowledge Base

In the previous point, it was noted that the knowledge base is a structure in memory that, among other things, stores sets of rules. As it is created, a rule is added to the knowledge base. However, you may need to modify the rules and apply them to the knowledge base immediately. The solution requires renewing the knowledge base. However, if it is created only once, for example when starting an application, you will need to restart the system, which can cause unwanted downtime.

76

S. Balcerek et al.

The creators of the Drools platform offer a mechanism that avoids such extreme situations. This requires the use of a so-called agent—an object that implements the KnowledgeAgent interface. The agent’s activity is based on a cyclic scan of selected sources in order to look for possible changes that may have occurred since the last scan. The default scan interval is 60 s, but the configuration allows you to select any value. If the agent determines that a resource modification has been made, for example, the rules have changed, the knowledge base is rebuilt. For sessions that will be created after the reconstruction, therefore, the adjusted resources will be taken into account [52].

4.3.4

Bidding Strategy

Chapter “Data-as-a-ServiceVersusInformation-as-a-Service: Critical Differences in Theory, Implementation, and Applicability of Two Growing Cloud Services”, describing the features of the business rule management system, emphasized that » due to the simultaneous presence of several rules to be activated, the rules engine will select additional rules based on the conflict resolution strategy. The Drools business rules engine offers four ways to choose from a rules set to activate. These include: • Priority assigned based on a rule attribute defined in the key meaning, it is possible to set a priority that indicates to the rule engine that the rule should be activated sooner or later than other rules. The higher the value of the protrusion attribute, this rule has a chance to be activated sooner. The default value is 0. • The number of activations that the rules motor checks, how often the rules has been activated before. The higher the number of activations, the greater the significance of this rule. • Presumed rules that impose conditions, such as conjunction rules, are more accurate than single-condition rules. The more rulesa has the conditional part stored, the sooner it will be activated than the others. • Load order In the event of failure of any of the above strategies, the Drools rules engine can determine the order in which control will be activated based on the order in which the rule was loaded into memory. The previously loaded rulesa will be activated before the others. Drools technology allows the programmer to deviate from his own policy selection strategy. In the vast majority of cases, however, existing solutions work well together [21].

4.4 BRMS Drools—Summary As a business rule management system, the Drools platform meets the basic objectives set for this type of instrument. A great advantage of the discussed system is

Application of Business Rules Mechanism in IT System Projects

77

its free license, which allows its use in open (open) and commercial projects. Decision tables and an easy-to-read DSL format undoubtedly provide the user with a convenient and simple way to define business rules. A view of the Drools system from the point of view of programming allows us to express the opinion that its use in created applications and integration with other technologies is not demanding. Clear and uncomplicated programming interface—application programming interface, API characterizing the platform, allows to understand the mechanism of operation is a relatively straightforward task. Guvnor can cause some difficulties when using Drools technology as a BRMS, especially if it is intended for use by non-technical users. Due to the many options that this software offers, it can be time consuming to become familiar with this software. However, the rewards are definitely a benefit of using this software. Further views on Drools technology and its practical use for enforcing and administering business rules will be provided in the following chapters.

5 Application of the Corporate Rules Engine in the Design of an IT System Business policy technology, which enables the practical implementation of the requirements of the business policy concept, is an undoubted convenience for users. The creators of the IT system and the people who use it as a working tool will certainly appreciate the benefits offered. On the other hand, using the business rules tool in an IT system is not an activity that solves all the problems you may encounter in the software development process. It should be remembered that for the correct and most comfortable operation of the system, it is necessary to properly configure and integrate the technologies used, which is often a non-trivial task. The practical application of business rules technology certainly allows a new entry into the assessment of the whole concept of business rules. The following chapter provides information on the design of a sample IT system that uses a business rule management system in its operation.

5.1 Problem Presentation When analyzing the activities of the various institutions and organizations, it can be noted that many of them are defined under certain regulations. Such institutions include, for example, banks, courier companies, warehouses and many others. In the case of an IT system that supports the activities of such institutions, regulations are often the first source of business rules that should be included in the design of such a system. The IT system project implemented as part of this work and

78

S. Balcerek et al.

representing the technology of business rules is a software called BiblioRule, which allows ordering, borrowing and returning books. Systems of this type are often found in larger libraries and loans. However, in the implemented application, the emphasis is on the use of the business rules mechanism and integration with other technologies. A rental support system seems to be a good choice for presenting business technology for several reasons. The first is the existence of regulations in such credit agencies, which determine, inter alia, the length and price of the loan, delivery times, the amount of fines imposed for possible infringements. The second reason is the possibility of using such software for different leases, as the use of business rules allows the procurement and rental policy to be adapted free of charge to the requirements of the administration. Another reason is the finding that this type of software does not contain complex logic, making it easier to understand and see how the rules of business engine works.

5.2 Requirements for the Proposed System The requirements for the created system were divided into two generally accepted categories of requirements: functional and non-functional (Fig. 23). Fig. 23 Case diagram for the debtor

Application of Business Rules Mechanism in IT System Projects

5.2.1

79

Functional Requirements

The functional requirements that determine the functions that the system should implement are presented using the use case diagrams shown in the drawings 23 and 24. As you can see, these cases were defined for two actors: the creditor and the lending employee. The borrower is any registered rental user. His activities include typical activities that are certainly familiar to people who use some type of rental, such as a library. On the other hand, a lending employee is a person who has the right to rent, receives the return of books and pays fines for violations. Such a person, of course, has the ability to define business rules that define the rules for loans, returns and the like. Among these use cases, the Define Rules rule is particularly important. This is a crucial case because its implementation will allow us to evaluate the use of the business rule management system (Fig. 24). In addition to the functions listed in the diagram, the system should regularly (once a day) check the rental and order dates and send e-mails regarding these deadlines and approaching. Dates book return dates. In addition, rules defined in the DSL format and in the form of decision tables should be supported. Fig. 24 Case diagram for a hired employee

80

5.2.2

S. Balcerek et al.

Broken Requirements

Among the non-functional requirements. Those that do not directly affect the functions of the system are most important to the comfort of use. This is due to the fact that “in a way, the main task of the rules engine is to introduce a little lightness and flexibility into the evolving IT system” and flexibility. The non-functional requirements of the BiblioRule project for the project include: • a simple and intuitive way to add business rules—the assessment of this requirement can be done, for example, on the basis of the time the user needs to train in defining the rules, • enabling dynamic modification of the rule without the need for a “restart” applications, • saving a rule to the business rules archive independently of the application, • performing tests that verify the proper functioning of the system, with particular emphasis on tests that check the results of the operations performed, using the logic contained in the business rules, • providing assistance mechanisms to the debtor during the activities performed in the system.

5.3 Description of the System Architecture BiblioRule is an web application with a graphical interface accessible via a web browser. The architecture of this high-level system is shown in Fig. 25. Users—lenders—communicate with the system through a browser. They can search for books, order orders for selected copies, and perform other operations

Fig. 25 BiblioRule system architecture

Application of Business Rules Mechanism in IT System Projects

81

specified in the use case diagram (Fig. 23). This part of the application, intended for lenders, was called BiblioNet. Library staff also perform their activities (Fig. 24) using a browser. However, the graphical interface intended for them is different from the interface for debtors. It is designed to perform typical tasks in the work of a librarian—borrowing, receiving returns, resolving regulatory sanctions and other activities. The name of this part of the system is BiblioManager. Both parts—BiblioNet and BiblioManager—use the same logical layer and are based on the same data model. The logical layer is located in the servlet container, which provides support for the HTTP request. The data model used in the project is shown in Fig. 26. The significance of the individual elements of this model is as follows: • Lenders—represents a rental user who can perform book search, ordering, and lending operations. Depending on the value of areas such as university, faculty, city, and user type, different restrictions apply, such as loan length, based on business rules. The user type field in the current version of the program can have the values: STUDENT, UNIVERSITY EMPLOYER, LIBRARY EMPLOYEES, EMPLOYEE EMPLOYEES and BALANCES. • Book—represents an object that will appear in the search results. Provides a “label” for copies, of which each book contains at least one. • Copy—represents an object that is directly available for ordering and renting. Each copy has a status indicating its availability. Possible values of this field are ON TOP, ORDER, RENT, LOSS.

Fig. 26 Data model of the BiblioRule project

82

S. Balcerek et al.

• Order—means an object created when a Customer copies a given Copy. Using the DataUplynieciaTerminu field, it is possible to determine whether to grant it to the Lender who will order this criminal order. Each Order always refers to one Copy and each Borrower may have a number of Orders. The number of Orders «is determined by the rental managers in the form of business rules. • Lease—represents an object created at the time of physical lending a copy to the lender. The DataUpplyTermin field has the same function as in the order. The loan can be extended, which means that the date of the Termination will receive a new value that is more distant from the previous one. The pre-accession period and the number of prescribed regulations are defined by the lease managers in the form of business rules. • Category—represents an object to which Books on similar subjects belong. Each book can fall into many categories, and each category can have many books. • Author—defines the creator of the book. For simplicity, it is assumed that each book has only one author. • Catalog—represents a collection of books to which individual copies belong. Each copy belongs to only one catalog. In addition, copies of the same book may belong to different catalogs. Possible values in the current version of the program are: STUDENT, SCIENTIFIC, SHORT, FOR EMPLOYEES, MANUAL. Based on the name of the catalog, you can also “specify the length of the order” and the rent. The BiblioRule system uses the architectural pattern REST (a. Representational State Transfer). REST is a style of web application design. It is based on the exchange of nationality information between the client and the server, which means that each client request is independent of the others. The REST pattern is based on the use of HTTP methods—among other things, GET, POST, PUT, DELETE and formats such as XML or JSON are used to transfer data between the client and the server. The key concept in REST-style projects is the source. A resource is a set of information. In the case of a given project, the sources are, for example, borrowers, copies of books, orders. Each resource has a unique identifier, which means its name, and allows it to perform certain operations using HTTP methods. An example of such an identifier is the URL—http://namedomain.com/wypozyczenia/5. Sending a GET request to this address should return all requests from users with ID 5 [53]. The main idea of the REST pattern is shown in Fig. 27. The detailed architecture of the BiblioRule system is shown in Fig. 28. Customer requests implemented using HTTP methods are handled by the controller layer. Drivers use classes of services that are responsible for implementing the basic logic of the system, which is, among other things, user login, creation of order objects, loans, and storage of this information in a database. These services use the services of business rule engines in their operation. It can be identified using the decision service shown in the figure, which is responsible for creating a session (KnowledgeSession), adding facts, and ordering the engine to activate matching rules. To perform these operations, the service only needs to call the doAction method of the decision service, along with a set of facts about which

Application of Business Rules Mechanism in IT System Projects

83

Fig. 27 Idea of the REST pattern

Fig. 28 Detailed architecture of the BiblioRule system

rule pairing should take place. After service operations, the relevant data is returned to the client in JSON format. An example of the interaction between participants in the book lending process is shown in Fig. 29. In addition to lending, it is a very important task for lending staff to define the rules that define the regulations, as well as the “procurement policy” and “loans”. These rules are stored in the rules depository and are made available by the Guvnor

84

S. Balcerek et al.

Fig. 29 Interaction process between participants in the rental process

utility. They can be changed at any time because they are used when designing an agent to dynamically modify the rule base.

5.4 Identification of Key Processes The operation of the BiblioRule system supports the support of libraries in the implementation of the most important processes. These include, but are not limited to, the following processes: • • • • • • • • •

reservation cancelation, reporting lost items, extension of the rental period, check of late rental or reservation, new user registration. However, the most important processes are: reservation of copies, borrowing a copy, return a copy. They will be discussed in this subsection.

5.4.1

Reservation of Copies

The copy reservation procedure, which is performed using the BiblioRule system, is shown in Fig. 30. Undoubtedly, it is very readable and does not contain complex activities. The reason for this simplification is the use of the corporate rules engine. Used for an action called Restriction Regulation. Here, the business rules are checked on the basis of the existing facts transferred to the engine. Figure 30 shows some

Application of Business Rules Mechanism in IT System Projects

85

Fig. 30 The process of reserving a copy using the business rules tool

examples of business rules that can be activated. It should be noted that adding additional rules does not change the control flows in the process. To better illustrate the rules tool in this process, Fig. 33 shows the same book ordering procedure, but without the use of the rules engine. In this case, all business rules will be replaced by statements of the conditional programming language—if.„ elsewhere.„, It is easy to note that this solution complicates the analysis of the credit process. In addition, if you want to make changes, you must modify the application logic, its source code, and thus resume the process.

5.4.2

Borrow a Copy

The procedure for borrowing a copy performed by BiblioRule is shown in Fig. 32. As you can see, it has a very similar process to the process of ordering copies. In addition, an order removal action is performed in the process to create a credit.

86

S. Balcerek et al.

Figure 33 shows the part of the copy hiring process that should be performed instead of the rules Engine Restrictions Determination action if the business rules technology services are not used.

5.4.3

Return a Copy

The return process begins with the lessor assessing whether the returned item has any damage. This estimate represents a percentage, i.e. it is determined what percentage of the entire copy is damaged. Then all the collected facts (user, loan, damage) get to the rules engine, which performs rule pairing and activation of the selected ones. Figure 34 shows the process using the business rules mechanism. Also marked are examples of rules that the engine can check. On the other hand, Fig. 35 shows the part of the process that should be performed instead of the Check Return Conditions action if the rules motor was not used.

5.5 Technologies and Tools Used in the Project A number of IT technologies and tools have been used to implement the BiblioRule project, enabling the implementation of the above functions and use cases. BiblioRule was implemented in Java version 1.6. Currently, Java is widely used in web application projects, such as BiblioRule. The existence of many tools and technologies that support software development is irrelevant when using Java. The business policy management system used in this project is the JBoss Drools platform discussed in Chapter “Management and Measuring Customer Loyalty in Digital Marketplace—Analysis of KPIs and Influence Factors in CLTV”. The Expert packages as well as » Guvnor were used. The entire BiblioRule application is based on the well-known application framework Spring Framework version 3.0. By using the Spring Framework skeleton pattern called inversion control—IoC, managing dependencies between application components is not a problem. The many mechanisms offered by Spring Framework technology are very impressive and allow you to perform such key tasks in a relatively straightforward way, such as authorization. MySQL software from Oracle version 5.1 was used as a relational database management system. Efficient and fast operation of this system is one of its biggest advantages. Another tool is related to the release of the database—long sleep mode from JBoss version 3.5. It is a technology that implements object-relational mapping, ie the translation of data contained in database tables into the form of objects of the class of the proposed system. The whole application is enabled by the Apache Tomcat servlet container. It is very often used in systems created in the Spring Framework environment, which do not require a complete application server.

Application of Business Rules Mechanism in IT System Projects

87

The presentation layer is responsible for the ExtJS library version 3.2. It is a JavaScript library that allows the use of techniques such as Ajax or DHTML. It allows you to create interesting user interfaces available through a web browser. The last two saunas were used to directly create applications. The first is the Eclipse version 3.5 graphical development environment. Due to the many options offered by the programmer, such as extensions to the Drools platform, Spring Framework and many others, Eclipse is very widespread in the IT industry. The second tool, Apache Maven, is a system that facilitates software development, including compilation, testing, or publishing on a server. The undoubted advantage of this tool is the ease of the programmer to manage the number of dependent libraries used in the created system.

5.6 System Design—Summary The development and integration of many different IT technologies is generally one of the conditions for creating competitive software. The BiblioRule system fits into this trend. From the point of view of expanding the development and conservation of the system, the most advanced technology used is the engine of business regulation. The analysis of the processes presented in Figs. 30, 31, 32 and 33 invited the fact that the technology of business regulation will ensure elastic and even in the implementation of the logic of the business system (Figs. 34 and 35). The BiblioRule architecture project has called for a trouble-free and uncomplicated implementation of activities that have taken place in typical workshops: including contracts, the completion of calculations or the resolution of regulatory cards. For the sake of application in the projection of a package not far from the system of the rules repository (Drools Guvnor), the administrators have a convenient way to keep up to date with the principles in the policy library. The application of the system of business regimes has invited programmers to group networks on the various requirements of the designed system, but there are no problems in this case, as well as the preservation of the regimes, and their response to them will always take place. All these requirements and many others are included in the BRMS system.

6 Analysis of the Application of the Business Regulatory Force in the IT System Business regulatory systems are advertised by manufacturers as tools, enabling a viable way to implement business logic in IT systems. Through the implementation of postulates of the concept of business regulation, non-technical users are defining the definition of their system.

88

S. Balcerek et al.

Fig. 31 Copy reservation process without using the business rules tool

The practical use of the business regulatory force in the BiblioRule system has invited the perfect evaluation of the application of this technology and the analysis of the corrections and wages arising from its teaching. This section provides a description of the established BiblioRule system and the information that will be appreciated by the use of the business regency engine.

Application of Business Rules Mechanism in IT System Projects

89

Fig. 32 The process of borrowing a copy using a rules engine

6.1 Description of the User Interface The BiblioRule system, as presented in the previous section, has two graphical interfaces, enabling the “performing” of real events “. The first of these, called BiblioNet in Polish (Fig. 36), is for renters. Using the BiblioNet interface, they can perform basic actions: searching for books, ordering and the like. To search for a search, go to the Search Panel (Fig. 37) by clicking on the Search button in the menu on the right side of the screen. The committee will be provided with three categories that can be searched for: the author, title or category. It is also possible to select columns that should be used for research results. The results of the search are presented in the Search Results Panel (Fig. 38). In order to complete the selection of a given folder, you can double-click on the selected items in the search results table. This is due to the search for the Search Panel (Fig. 39), which can be used to check the number of the book and which is available for calculation. To select an example to calculate, click on the mark in the

90

S. Balcerek et al.

Fig. 33 A fragment of the copy rental process without using a rules engine

letter V, placed in the order column. If the user is not logged in, we are currently providing information on both the final and final login. In the event of failure, it shall be forwarded to the Confirmation Panel of the order referred to in Sect. 6.5 (Fig. 40). You can check the status of a user’s account by clicking the Account Balance button in the side menu. Then the panel shown in Fig. 41 will appear. It contains three cards: Orders, Rent and Fines. On the Orders tab, you can view the currently booked items and cancel the selected item. To do this, click the arrow icons in the Cancel column. The Loan tab offers, in addition to an overview of borrowed books, the option to expand selected items. Click the V-shaped icon in the before you run column. In addition to the pre-sale option, you can also report the loss of an item on the Loan tab. This is done by right-clicking on the selected row and submitting a copy of the book. A context menu will appear with the only option Report loss, after clicking on which a confirmation window for book loss will be displayed (Fig. 43).

Application of Business Rules Mechanism in IT System Projects

91

Fig. 34 Return process using the rules motor

On the Sanctions tab, the user has the option to review information about sanctions that have been created for violating the rules. Here you will find information about the reason for the sentence (e.g. non-withdrawal of the order, withholding of credit), the date of the sentence and a copy with which the sentence is associated (Figs. 42 and 43). Help panels have been created for all BiblioNet panels that are located on the right side of the screen. Depending on the view the user is currently in, the corresponding help information is displayed. Examples of auxiliary panels are shown in Fig. 44. The second interface—BiblioManager (Fig. 45)—is intended for tenants. It allows you to take actions such as: borrowing and returning books, identifying possible book damage, removing sanctions, and blocking users. To perform the above actions, after clicking the Reader button in the side menu, enter the email address of the selected borrower in the User ID field. Another way to select a user is to double-click on a row in the table in the Users panel (Fig. 46). To open this panel, click the Readers button in the sidebar. After selecting a user, a panel with user account details will appear on the screen (Fig. 47). It contains four cards. In the first, the user information is a summary of the user account. An employee of the rental company may, in accordance with the following tabs of the order, lease and sanctions, duly borrow the ordered copy of the user, accept the return of the copy and financial receivables for existing user fines. These operations are performed by clicking on the icons in the last columns of the tables on the following tabs. It is necessary to add that by clicking on the icon.

92

S. Balcerek et al.

Fig. 35 Fragment of the return process without using the rules motor

A window will appear on the screen (Fig. 48) in which the library employee can determine the extent to which the debtor has damaged the returned copy of the book. A value of “0” means no damage. From the BiblioManager interface level, you can go (by clicking the Manage button) to Guvnor, which allows you to manage business rules. If you want to define new rules or change existing rules, after entering the Guvnor system, you must find the Knowledge Base tab in the side menu and an item called BiblioRule in it. After extending it, options will appear that allow you to modify the rule, define the data model, create test scenarios and much more (Fig. 49). Each entry in a given rule set results in a history entry that can be referenced to restore the previous form of the rule. This is a very convenient feature because it provides the user with convenience and can always return to the most optimal version of the rule.

Application of Business Rules Mechanism in IT System Projects

Fig. 36 BiblioNet play interface in Polish, designed for those who wait

Fig. 37 Book search panel

Fig. 38 BiblioRule book search results panel

93

94

Fig. 39 Search panel for details about the selected book

Fig. 40 Order confirmation panel

Fig. 41 Panel showing the status of the debtor’s account

Fig. 42 Penalty tab in the user account details panel

S. Balcerek et al.

Application of Business Rules Mechanism in IT System Projects

Fig. 43 Book confirmation notification window

Fig. 44 Example of help panels available in the BiblioNet interface

Fig. 45 BiblioManager graphical interface for hired employees

95

96

S. Balcerek et al.

Fig. 46 Panel showing the list of users who borrow in the BiblioManager interface

Fig. 47 User account details panel in BiblioManager Fig. 48 Window for determining the degree of damage «of the returned item

Application of Business Rules Mechanism in IT System Projects

97

Fig. 49 Guvnor menu

To restore a previous version of a rule, in the panel where the rule is being modified, in the menu on the right side of the screen, the Version History tab (Fig. 50). Its extension will list all saved changes that have been made to the rule set. After selecting a version, click the View button below the tab. It will be released then a window representing the previously defined form of rules. To set the current version, click the Restore this version button at the top of this window. For a non-technical user, the first contact with Guvnor may confuse him. The large number of possibilities that this tool offers takes some time to move effectively

Fig. 50 Change history tab and window for restoring an older version in Guvnor

98

S. Balcerek et al.

between the many functions. However, when the user is able to use the Guvnor system, he will certainly appreciate the convenience of hiding behind seemingly complex options.

6.2 Description of the Module for Adding Business Rules The BiblioRule system enables the provision of rental services through the implementation of the most common functions in this type of systems. Another and at the same time the most important functionality in terms of the subject of this work is the ability to define business rules, through which some requirements are expressed. As has been emphasized many times, the Guvnor tool from the JBoss Drools package was used to manage business rules in BiblioRule. Guvnor offers a number of editors that allow you to define rules in DRL, DSLR and decision tables. In order to eliminate any errors that may occur during the creation of rules, it is possible to confirm the created rules. By accurately determining the location of the error, the validation mechanism helps to create the correct form of the rule. The process of creating or editing business rules in Guvnor is not complicated. If you already know about rule formats and rules, creating them in Guvnor is no different from editing a regular text file. To add a new rule or group of business rules, on the Guvnor menu, click the Create New button located on the KnowledgeBases tab and select New Rule from the context menu (Fig. 51). Then a window will appear in which you should enter the

Fig. 51 Window for creating a new business rule

Application of Business Rules Mechanism in IT System Projects

99

Fig. 52 Panels with a list of rule files in various formats

name of the created rule, the category to which it belongs and the format in which the rule will be specified. After confirming the entered information (by clicking the OK button), a panel with one editor (Fig. 21) will be displayed (depending on the selected rule format) or a panel that will allow you to send the table in XLS format to the repository (Fig. 5.19). Modifying existing rules should not be difficult. To do this, double-click one of the following items in the BiblioRule drop-down list on the KnowledgeBases tab (Fig. 5.14): Business Rule Assets, Technical Rule Assets, or DSL Configurations. This displays a panel with a list of rules files in DSLR format and decision tables, rules files in DSL format or the configuration of the map «expression« Java into natural language—DSL format (Fig. 52). Double-clicking on the selected row will bring up a panel on the screen with the appropriate editor, in which you can change the rule. Saving a rule to a given package does not mean that it will be possible to use it immediately in the given IT system (for example BiblioRule). For this purpose, it is necessary to create a so-called A view that reflects the status of a given rules package. This approach is advantageous because it is not always desirable to immediately use rules data in the information system. The reason may be, for example, the desire to test the created rules—until a new version of the rule is published, the IT system will be able to use the previous version without any problems. To create a view of the rules package, click on the KnowledgeBases tab (Fig. 49), the BiblioRule element and on the panel that will be displayed (Fig. 20), find the Build and verify section (Fig. 53). First, click the Build Package button to create and confirm the rules package. When you see the package creation message, click the Create Snapshot to Deploy button. A window will appear in which you should enter a name for the created view and click the Create New Slide button. This will create a new view of the rules package.

Fig. 53 Panel and window for creating a view of the rules package

100

S. Balcerek et al.

Using the authorization mechanism, Guvnor allows you to specify the appropriate levels of access to the rule. This way, you can only create a rule for selected hiring employees, while others can, for example, view the rules without modifying them.

6.3 Analysis of Selected Use Scenarios: Defining Business Rules Use case Defining business rules is a key function implemented by BiblioRule. This makes it possible to dynamically implement business requirements and give the user some control over the system. This subsection provides an analysis of selected scenarios for this use case.

6.3.1

Loan Length Specification

In the BiblioRule system, the period for which the book has been borrowed and the number of possible leases for this loan are determined on the basis of business rules defined in the form of a table of decisions. If you want to deviate from the length of the loan, you must download the archive from the Guvnor repository. In the KnowledgeBase drop-down menu, expand the BiblioRule element and select Business Rule. Then the first of the panels shown in Fig. 52 will be displayed. Double-click the row with the name of the loan length. The panel shown in Fig. 54 is displayed. Click the Download button to save the XLS file to your computer’s local disk. For example, to edit a rule, open the downloaded file in OpenOffice Calc. Determining the length of a book loan can be based on many criteria. On the Fig. 55 shows an example of the contents of a table that includes rules governing the rental period. In this case, the city in which the debtor lives, the university in which he studies, the academic degree he may have or the catalog to which he belongs is taken into account. Copy required. If several rules match, only one rule with the highest priority will apply, for example: a student at the Technical University of Łód, called Sylwester, resides in

Fig. 54 Panel for downloading and adding to the rules archive in the form of a decision table defined in the table

Application of Business Rules Mechanism in IT System Projects

101

Fig. 55 View of a table in which the business rules determine the lengths of borrowed books

Łódži and pays the debtor. The book from the “student” catalog will correspond to the rules in lines 9, 13, 18 and 27. However, the rules in line 27 have the highest priority (equal to 21) and will be activated, which means» The maximum length of New Year’s student lending and a maximum of two provisional charges may be levied. Adding a new rule to a worksheet takes effect by adding a new row and filling in the values of the appropriate columns. It should be borne in mind that one of the fields in the columns specifying the conditions of the rule and each of the fields in the priority columns, the length of the loans and the number of advance payments must be completed. After editing the rules file, you should add it to the repository. To do this, select the path to access the file in the panel in Fig. 54 (in the Upload new version field) and click the Upload button. Once a new view is created, the rules will be ready for use by BiblioRule.

6.3.2

Determining the Value of Sanctions for Violating Regulations

The business rules, which determine the values of sanctions imposed on users for infringements, as well as the length of loans, are determined in the form of a table with decision-making powers, which are located in the table. The procedure for accessing these rules is the same as the procedure described above, except for the sheet name, which in this case is called the kar value. Figure 56 shows an example of a table in which rules have been defined defining the values of regulatory sanctions. Criteria by which it is determined the value of the fine is the reason for the fine and the conditions depend on the reason: the length of the loan retention and the percentage of damage to the book. These conditions are of a very general nature. But it costs nothing which prevent them from being specified by other criteria. On the to illustrate such a scenario, the

102

S. Balcerek et al.

Fig. 56 View of the table showing the values of regulatory sanctions

case in which the employee will be taken into account libraries want to define the following requirements: Cancellation of an order for students at the Technical University of Łódži results in a penalty of 1. The loss of a book will cause professors to pay a fine of 2 for. To meet these requirements, the table in Fig. 56 should be modified by introducing additional columns, which will gradually determine the type of user account, the university at which he is studying, and the academic degree he has. The changes in the table are shown in Fig. 57. It should be noted that in line 7, the relevant instructions for the rule engine were defined, which allowed it to “understand” the entered data. As in the case of “rental” lengths, only one rule is activated when many rules are adapted to the facts. To accommodate the new requirements in BiblioRule, follow the steps above to add a table to the repository and rebuild the view of the rules package.

Fig. 57 The table containing the changes requires requirements regarding the value of regulatory sanctions

Application of Business Rules Mechanism in IT System Projects

6.3.3

103

Determining the Consequences of Maintaining a Loan

In the BiblioRule system, the functionality of an automatic task has been created, which is called every day at a specified time, in order to find loans whose maturity has already expired. In connection with such a collection of loans, the library administrator may impose fines on users who have not met the repayment deadline. The current version of the system implements a rule whose form in DSL format is as follows: the rule "Exceeding the deadline leads to a sanction" when If you did not return the borrowed book on time then A fine is imposed for maintaining the loan, if such a penalty does not exist the end

Of course, it may turn out that creating a fine for the user for keeping the book is not the most accurate way to achieve the consequences, according to the rental manager. For example, the situation where the administrator wants to meet the requirement will be taken into account: If you do not submit the book within the specified time, the user’s account will be suspended. To implement this scenario, the administrator should find a set of rules called DSLR lending, which can be accessed by selecting the following items in order: the KnowledgeBases tab in the Guvnor side menu, the BiblioRule element, the business subgroup of the asset being managed. Then select the row with the above name in the list of rule sets. After opening the DSLR control editor, you need to find the above rule and make changes to some of the consequences. At the beginning, you must remove the existing consequence of the rules. If the credit is withheld, the text A will appear, if such a penalty does not exist, a penalty will be created. Then, in the right part of the editor, click on the bottom icon, which will display a list of terms that can be used as a result of the rule (Fig. 58). You should be on the list select User is blocked and click OK. In this way, the required requirement was defined. The business rule that defines them is shown in Fig. 58. At this point, however, you need to ask what to do if the list of available terms to be included in the RHS section does not have the required term, for example, send the user an email notifying you to return the book. Then you need to create the correct DSL mapping Java to natural. To do this, specify a DSL configuration set called lendingsDSL, which is available in the DSL configuration element, and add a mapping. Its form could be: [effect] [] Send a comment email to the user when returning a book »User u = $ len.getUser (); Book b = $ len.getCopy (). GetBook (); emailService.sendMessage (u.getEmail (), "Feedback", The book "+ b.getTitle ()) should be returned as soon as possible;

104

S. Balcerek et al.

Fig. 58 Change the rules consequences in the DSLR format editor

After saving such an expression, it should be available in the consequence selection list in the DSLR editor. This case reveals two important comments: • Creating the required audio rule in an easy-to-understand format (DSLR) may require the intervention of a programmer, as a non-technical user is likely to encounter a problem in determining the DSL mapping. • The ability to change the behavior of a system without having to modify its source code, and just entering a few instructions into a form on an HTML page is a huge advantage that will undoubtedly save time. 6.3.4

Determining the Ability to Perform Actions by a User with a Status of Blocked

In the previous section, a requirement was introduced that if the book is not delivered within the set deadline, the user’s account will be blocked. This seems like an obvious limitation. However, by more careful analysis, it can be concluded that this does not really contribute to the behavior of the system, because the mere suspension of the user’s account does not mean anything. If you want the penalty for blocking your account to get value, you need to emphasize the consequences associated with it. A situation is considered where blocking a user account involves the following requirements: A blocked user cannot order books. A blocked user cannot borrow more than 3 books. The first of these requirements is quite obvious. However, the second may seem unclear. The point is that when a user’s account is blocked, he or she may currently have a number of orders stored. If he has less than three loans, he will be able to borrow one, two or three books, depending on the current state of the loans. In the case of a larger number of loans, no more loans will be provided.

Application of Business Rules Mechanism in IT System Projects

105

The presentation of the business rules implementing the above requirements will be submitted in the DRL technical format this time rulesa reflecting the first requirement takes the following form: "Blocked user cannot place order" rule protrusion 1000 when r: Reservation (id == null, user.userStatus.name == "BLOCKED") then r.setError (true); r.setErrorMsg ("Blocked user cannot place order"); insertion (r); the end

Two terms are notable in this rule: salience 1000 and retrract® . The first determines the priority of the rules. A value of 1000 means a very high priority, so this rule is executed before all others that are activated. The second term mentioned as a result of the rules means that the fact should be removed from the memory, which in this case is the subject of the Reservation class. Removing this fact from the state memory means that some rules that were previously intended for activation will not be activated. Specifically, these will be rules that, in a given state, matched the object of the Reservation class. This will prevent unnecessary activation that would occur if the user did not have a blocked account. The business rule implementing the second of these requirements shall take the following form: rule # Blocked user cannot borrow more than »3 books # protrusion 1000 when l: Rent (id == null, user.userStatus.name == # BLOCKED #) borrows: java.util.HashSet () from collect (Rent (removed == false) from l.user.lendings) eval (lends.size ()> 3) then l.setError (true); l.setErrorMsg (# You cannot borrow more than 3 books #); insertion (l); the end

An analysis of the complicated state of the above rules confirms the previous remark that creating a rule in the technical DRL format is a task that is too demanding for users who do not have programming skills.

106

S. Balcerek et al.

6.4 Advantages of Using the Rules Motor in the Created System There are some advantages to using a business rule engine in a BiblioRule project. The most important of these, from the programmer’s point of view, is the ability to define requirements in the form of business rules and their dynamic integration into the system. This means that it is not necessary to modify the source code of the application itself, in addition, there is no need to pause the application, because the changes are taken into account dynamically. In the BiblioRule system, an example of the discussed advantage can be the determination of the loan length in a table. In this case, if the rental regulations change in terms of the rental period, it is sufficient to modify the table and create a new view of the rules package so that the application takes the new changes into account. Another example of the dynamic implementation required in BiblioRule is to specify the action to be taken in the event of a breach of any of the rules. If the credit bureau’s rules say that canceling a deposited order will result in a fine, you can easily create a business rule that meets this requirement. If, after a certain time, it turns out that the cancellation of the order should not result in a sanction, but, for example, a blocking of the possibility of giving instructions to users, it is also possible to implement such a problem without any problems. Requirement in the form of commercial rules. Another advantage of using the business rules tool in the proposed term is that it can be done in almost natural language. For this reason, the Drools platform offers the possibility that the rules are in a pair of DSLR/DSL formats. Of course, there are some limitations to the creation of this rule mentioned above, although the developers who created the system have correctly configured the mapping of Java code to natural language expressions, solutions this conclusion should be successful. The undoubted advantage of using a business rules engine and a business rules management system is easier access to the specifications of the proposed system. An overview of defined business rules is definitely a faster way to analyze the logic of the IT system or find time-consuming methods in the source code of the application. This advantage has one more thing—it is the separation of program logic and data layer. Mention of the benefits that apply to the use of the business rules mechanism when designing an IT system cannot be mentioned, nor is the possibility of giving some control over the system to the user. This means that if it is necessary to implement a new request in the system, no intervention of the programmer is required—the user will be able, if possible, to execute the requested request himself, of course, in the form of business rules.

Application of Business Rules Mechanism in IT System Projects

107

6.5 Problems Encountered in Designing System and Application Errors The previous section lists the benefits that can be paid out when using a business policy management tool and a business policy management system in an IT system. However, it should be borne in mind that in order to obtain these benefits, several critical conditions often need to be met. An example of such a phenomenon is the previously discussed definition of business rules in a readable format using natural language. It should be understood that in order to use this feature, you must first map the Java code to the natural language. Of course, there may be a situation where the user will not be able to de-define the rule in a readable format, because the corresponding mappings may not exist. In this situation, you will undoubtedly need the help of a programmer, as a non-technical user can have great difficulty creating business rules in the technical DRL format. There were several issues with creating BiblioRule that should always be considered when using the Business Rules tool. The first, strictly programming problem, can be difficult to find errors in the system. In the event of a program failure, there can be many suspicions that it is caused by incorrect definition of business rules. There can be many reasons, starting with a letter in a text string, which is one of the conditions of a rule, and ending with more serious problems—for example, the mutual exclusion of a rule. To prevent or reduce such situations, the application code should be tested very well in terms of business rules. For testing purposes, you can use the test scenario feature provided by Guvnor. However, this method only gives poetic success. You can use it to check which rule will be activated based on the available facts. On the other hand, other key aspects are not checked, such as the correct implementation of the part of the rule that results from it. To perform these tests, you need to test the code of the application itself. The JUnit library was used for this purpose when creating the BiblioRule system. This eliminated errors, for example in the automatic loan length determination function. A good problem that users may have is a good knowledge of the Guvnor business rules management system. There is no doubt that for non-technical users, it will be necessary to help the programmer in the initial phase of working with this tool, otherwise they may be quickly discouraged for the application of rules business concepts. The introduction to software design of any technology previously unknown to programmers forces them to learn well and use it. When using the business rules tool, some experience with working with business rules also seems to be very useful. This experience avoids a situation where almost all attempts are made to implement most of them with the help of business rules. If a simpler solution can be applied in place, it should be used instead of serious code editing to implement the business rule. When working with the Expert business engine from the Drools package, you can see that it is an already advanced technology and it is easy to integrate it with

108

S. Balcerek et al.

other tools, such as the Spring Framework. However, errors may have occurred at this stage in the development of the Drools platform. During the implementation of the BiblioRule system, the absence of the possibility of using decision tables in conjunction with the agent responsible for the dynamic loading of modified rules proved to be an unexpected error. However, this problem does not exist when Guvnor is used with a rules engine. There may be some issues with Drools’ business policy technology when integrating Guvnor with the proposed application. In order to create and store a package of business rules, it is necessary to define the data model (facts) with which the business rules will be compared. If the model classes contain only simple attributes of type (String), total (int) or floating point (float), then there should be no problem creating the rules package. However, the problem occurs when model classes reference the content of external libraries, for example, when implementing an interface from such a library. Then there is often a long chain of dependence between classes, most of which are not relevant in defining a factual model. In this case, it is often not possible to create a rules package in Guvnor. Then you need to use certain maneuvers aimed at creating a data model that will not contain unnecessary dependencies, but will allow you to build the right rules package ready to use designed applications. Security issues should be taken into account when deciding to grant some control over the system to users who will be able to create their own business rules. A user with some programming knowledge can sometimes deviate from a business rule, the enforcement of which can cause significant damage to the system, for example, if you rent his name is John, then remove all fines of this borrower, it will certainly not comply with the lease. One should remember such extreme situations and protect oneself from their occurrence.

6.6 Analysis of the Use of the Commercial Rules Engine The advantages and disadvantages of using the business rules engine above can undoubtedly provide an answer to the question “is it worth using business rules technology when designing an IT system?”. However, the software development process is too expensive to answer with a simple “yes” or “no” answer. Work on the design and implementation of the BiblioRule system suggests that in this case, the business rules engine is doing its job. With its help, the criteria for dynamic implementation of requirements into the system (in the form of business rules) were met. In addition, the introduction of the rules register has ensured the centralization of knowledge, thus facilitating access for a wider group of users. The use of business policy technology in BiblioRule means that it can be used by many different lenders or libraries. Although each of these institutions has different rules regarding lending policy or sanctions, due to the flexibility offered by the business rules mechanism, BiblioRule could be adapted to the short-term rules of the rental company.

Application of Business Rules Mechanism in IT System Projects

109

However, when taking advantage of the rules offered by the trade rules mechanisms, be sure not to use them if this solution is too difficult to solve the problem. A good example of this phenomenon can be the use of decision tables in forms of tables. If a simple (required by a table) value of certain parameters is required when creating an application, it is worth using a simpler solution, such as a library that allows reading data from XLS files. Using the trade rules mechanism for this purpose can certainly prove too laborious, inefficient and completely unnecessary. We should also be aware of the fact that corporate policy mechanisms, despite their many benefits, are not a tool to solve all the problems that have arisen when designing an IT system. Some difficulties require the use of other concepts or tools. It is often necessary to rework the data model used in the application. It may also be the case that, although the use of business rules in a given part of the system is most expected, the mechanisms implemented in it prevent the use of the motor engine and it is necessary to do a fashionable procedure—programming science. When planning to use the rules motor in your application, you should analyze the balance of profits and costs that result from using the rules motor. The result of such calculations should be a relatively good indicator of the need for a trade rules mechanism.

7 Summary Modern companies are required to conduct their operations quickly and flexibly [54]. Similar requirements apply to software used by these organizations. The carefully implemented business logic of the application may prove to be completely outdated after some time. Then fast and accurate changes are needed to take account of the new requirements. However, if the implementation of the logic is contained in the hinges of the source code, any changes can become slow, laborious, and error-prone. The enterprise engine technology presented in the above work is an answer to the problems of rapid and flexible change of business requirements in information systems. The description and analysis of the use of business rule engines can be a useful source of information in the context of IT systems design. Based on the performed literature studies and analysis of the IT system project implemented as part of the practical part of the work, it is possible to draw several important conclusions, which are listed below: • The use of business rule mechanisms is a good solution in the context of the problem of frequent and rapid changes in business logic in information systems. The basis of such an opinion is, among other things, the fact that a simple expression—business rules—has a user who often does not have IT skills, the possibility of easy fashion system behavior.

110

S. Balcerek et al.

• The use of the trade rules mechanism should be preceded by a thorough analysis of the need to use it. In the case of the implementation of uncomplicated software or software for which there is a certainty that the requirements set for this software will not change during use, it is absolutely necessary to leave the business rules module. The time devoted to the implementation of this technology and the acquisition of expertise in determining rules will certainly prove to be incompatible with the effect achieved. • An introduction to a business rules management IT project, exemplified by Drools Guvnor, makes it easier to work with business rules. The storage of all business rules ensures the centralization of knowledge and allows stakeholders an easy way to find the information that interests them. In addition, the ability to test the business rules created in these tools allows you to fix common mistakes. • Despite the postulates of concepts and information on commercial regulations submitted by rules engine manufacturers, it is not possible to give all control over system requirements to a non-technical user. A programmer or other person who has the knowledge and skills in In software development, you will definitely need to define a decision table structure in a table or create files that map a natural language to a programming language. • Integrating the business rules tool on the example of the Drools platform with other technologies used in information systems is not a difficult task. Currently existing solutions in the world of business rules are such developed tools and cooperation with other popular technologies is not a problem. Business rule engines are not a specialized technology and are used by many users. Any potential error that may occur in these tools is detected and corrected relatively quickly. BiblioRule software created as part of the work, which aims to support the work of lending and libraries, is an example of the application of business rules concepts in the IT system. At present, however, this system is not yet suitable for commercial use. Key aspects that need to be improved in terms of system usage are application security issues. It would therefore be necessary to introduce tasks for rental staff in order to apply the appropriate levels of authorization when drawing up and amending business rules. In addition, mechanisms should be used to protect the system and data against “save” rules. Achieved goals of the above work. Analysis of the use of business rules engines and assessment of the possibilities of integration with other information technologies have been achieved. Given the lack of Polish literature on business policy technology and the fact that the work can be a useful source of information in connection with the decision to use the business policy engine in IT, another goal is to contribute to the popularization of the topic in Polish literature can also be considered completed.

Application of Business Rules Mechanism in IT System Projects

111

References 1. Sacha, K.: Software Engineering. PWN, Warszawa (2010) 2. Gregus, M., Kryvinska, N.: Service Orientation of Enterprises—Aspects, Dimensions, Technologies. Comenius University in Bratislava. (2015). ISBN: 9788022339780 3. Kryvinska, N., Gregus, M.: SOA and its Business Value in Requirements, Features, Practices and Methodologies. Comenius University in Bratislava (2014). ISBN: 9788022337649 4. Molnár, E., Molnár, R., Kryvinska, N., Greguš, M.: Web Intelligence in practice. Soc Serv Sci J Serv Sci Res 6(1), 149–172 (2014) 5. Bali, M.: Drool JBoss Rules 5.0 Developer’s Guide. Pack Publishing, Birmingham (2009) 6. Kryvinska, N., Poniszewska-Maranda, A., Gregus, M.: An Approach towards Service System Building for Road Traffic Signs Detection and Recognition. Elsevier Journal Procedia Computer Science, Special Issue on “The 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2018)”, vol. 141, pp. 64–71. (2018). https:// doi.org/10.1016/j.procs.2018.10.150 7. Pawlak, M., Poniszewska-Maranda, A., Kryvinska, N.: Towards the intelligent agents for blockchain e-voting system. Elsevier Journal Procedia Computer Science, Special Issue on “The 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2018), vol. 141, pp. 239–246, (2018). https://doi.org/10.1016/j.procs.2018.10.177 8. Poniszewska-Maranda, A., Kaczmarek, D., Kryvinska, N., Xhafa, F.: Endowing IoT devices with Intelligent services. Barolli et al., L. (eds.) In: The 6th International Conference on Emerging Internet, Data & Web Technologies (EIDWT-2018), March 15–17, 2018, Polytechnic University of Tirana, Albania, Springer, Lecture Notes on Data Engineering and Communications Technologies (LNDECT), vol. 17. pp. 359–370 (2018) 9. Poniszewska-Maranda, A., Matusiak, R., Kryvinska, N., Yasar, A.-U.-H.: A real-time service system in the cloud. J. Amb. Intell. Human. Comput. https://doi.org/10.1007/s12652-019-012 03-7 10. Poniszewska-Maranda, A., Kaczmarek, D., Kryvinska, N., Xhafa, F.: Studying usability of AI in the IoT systems/paradigm through embedding NN techniques into mobile smart service system. J. Comput. 101(11):1661–1685. https://doi.org/10.1007/s00607-018-0680-z 11. Poniszewska-Mara´nda, A., Vesely, P. Urikova, O., Ivanochko, I.: Building microservices architecture for smart banking. In: Barolli, L., Nishino, H., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems (INCoS 2019), Advances in Intelligent Systems and Computing, vol. 1035, https://doi.org/10.1007/978-3-030-29035-1_52 12. von Halle, B.: Business Rules Applied Buidling Better Systems Using the Business Rules Approach. Wiley, New York (2002) 13. Doorenbos, R.: Production Matching for Large Learning Systems. Computer Science Department Carnegie Mellon University, Pittsburgh (1995) 14. https://www.infoq.com/articles/business-rules-processes/#ftn.id1 15. Lämmel U.: Business Rules make Business more flexible. www.wi.hs-wismar.de/~laemmel/.../ Docs/laemmel_BBSED07.pdf 16. Ross, R.: Principles of the Business Rules Approach, Addison Wesley (2003). http://books. google.pl/books?id=Uyv9hMOt6BsC&printsec=frontcover#v=onepage&q=&f=false 17. Date C.J.: What not how: the business rules approach to application development, dost¦pne na: http://books.google.pl/books?id=IedVFAyu-30C&printsec=frontcover#v=onepage&q=& f=false 18. Nalepa, G., Mach, M.: Business Rules Process Management, Design Method for Business https://ai.ia.agh.edu.pl/wiki/_media/hekate:bib:gjn-bi2009.pdf?id=hekate/%3Abib/%3Ah ekate_bibliography&cache 19. Davenport, T.: Process innovation: reengineering work through information technology, Ernst & Young, TODO - google.books s. 6 (1993) 20. Koornneef, M.: Technology Management and Systems Implementation of Business Rules http://maartenk.nl/dl/Afstudeerscriptie/%20Maarten/%20Koornneef.pdf

112

S. Balcerek et al.

21. Browne, P.: JBoss Drools Business Rules. Pakt Publishing, Birmingham (2009) 22. http://www.cisco.com/en/US/docs/net_mgmt/active_network_abstraction/3.5.1/administr ation/user/guide/admin.html 23. http://news.thomasnet.com/companystory/525431 24. http://www.insidearm.com/index.cfm?objectid=A3E50B18-097B-FF8F/-0A33F5BDAD10 F5E0 25. http://egovasia.enterpriseinnovation.net/content/singapore-manpower-ministry-and-tax-aut hority-recognized-innovative/-it-systems?page=0/%2C0 26. http://www.pressebox.de/pressemeldungen/innovations-software-technology/-gmbh/boxid148542.html 27. Business Rules Group: Defining Business Rules What Are They Really? (2000) http://www. businessrulesgroup.org/first_paper/br01c0.htm 28. von Halle, B., Goldberg, L.: The Business Rule Revolution, Happy About, Silicon Valey (2006) 29. Morgan, T.: Business Wesley, Rules and Information Systems. Addison (2002). http://books. google.pl/books?id=Ro3wI_V5pWMC&printsec=frontcover#v=onepage&q=&f=false 30. Graham, I.: Business Rules Management and Service Oriented Architecture. A Pattern Language. Wiley (2006) 31. http://www.businessrulesgroup.org/brmanifesto/BRManifestoPL.pdf 32. http://www-01.ibm.com/software/websphere/products/business-rule-management/whatis/ 33. http://www.ibm.com/developerworks/architecture/library/ar-busrules1/ 34. Mulawka, J.: Expert Systems, WNT, Warszawa (1996) 35. Cirstea, H., Kirchner, C., Moossen, M., Moreau, P.: Production systems and rete algorithm formalisation (2004). http://hal.inria.fr/docs/00/28/09/38/PDF/rete.formalisation.pdf 36. Friedmann-Hill E.: Jess in Action. Manning (2003) 37. Madden, M.: Optimising RETE for low-memory, multiagent systems (2003). www.cs.nott.ac. uk/~nem/gameon-paper28.pdf 38. http://www-01.ibm.com/software/integration/business-rule-management/jrules/ 39. http://www.bloorresearch.com/analysis/11561/rules-the-ibm-ilog-way.html 40. http://www.fico.com/en/Products/DMTools/Pages/FICO-Blaze-Advisor-System.aspx 41. http://www.it-analysis.com/business/regulation/content.php?cid=11774 42. http://www.fico.com/en/FIResourcesLibrary/ButlerGroupTechnologyAudit.pdf 43. http://www.microsoft.com/biztalk/en/us/business-rule-framework.aspx 44. http://www.oracle.com/technology/products/soa/rules/index.htm 45. http://download.oracle.com/docs/cd/B25221_04/web.1013/b15986/guistart.htm 46. http://www.oracle.com/technology/products/ias/business_rules/pdf/oraclebusinessrulestechn icalwhitepaper.pdf 47. http://java-source.net/open-source/rule-engines 48. http://jackrabbit.apache.org/ 49. Janeczek, B.: Analysis of Data Reliability Using SVM Support Vector Machines, Master’s thesis, Politechnika Warszawska, WEiTI (2008) 50. Dominik, A.: Data Analysis Using Approximate Set Theory, Master’s thesis, Politechnika Warszawska, WEiTI (2004) 51. https://www.downloads.jboss.com/drools/docs/5.0.1.26597.FINAL/drools-guvnor/html/ index.html 52. http://downloads.jboss.com/drools/docs/5.0.1.26597.FINAL/drools-expert/html/index.html 53. Richardson, L., Ruby, S.: Restful Web Services. O’Reilly Media (2007) 54. http://technet.microsoft.com/en-us/library/dd879260/%28BTS.10/%29.aspx

Data-as-a-Service versus Information-as-a-Service: Critical Differences in Theory, Implementation, and Applicability of Two Growing Cloud Services Ilias Wagner and Zuzana Tacacs Abstract By exponential increase of data produced and processed in all industries, data-oriented services are having increasing importance and popularity. Companies are seeking for services related to administration of data, data analysis, preparation of insights, especially when is coming to Big Data. Additionally, to enrich their own data sets are available external data providers. In this chapter, we focus on detailed understanding of both Data-as-a-Service and Information as a service on cloud. As the importance of cloud computing and Big Data continues to rise, service providers, business leaders, consumers, and researchers alike need a clear understanding of these two concepts to be able to distinguish between them. This paper seeks to find a clear definition and specification for DaaS, IaaS, as well as contrast the two concepts, highlighting which service is better in which situation for what kind of company.

1 Introduction 1.1 Relevance In 1997, when Steve Jobs shared his vision of being able to access his data anytime from anywhere in the world, the future of the internet was hard to foresee. A little more than a decade later, cloud computing came about and changed how business was conducted. It allowed companies and individuals to move their data onto centrally stored servers and access data from anywhere using cloud services. The rise of cloud computing brought with it entirely new business models, commonly referred to as Everything-as-a-Service (or Anything-as-a-Service), abbreviated as XaaS [1, 2].

I. Wagner (B) Wirtschaftsuniversität Wien (WU), Vienna, Austria e-mail: [email protected] Z. Tacacs Faculty of Management, Comenius University, Bratislava, Bratislava, Slovakia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Kryvinska and A. Poniszewska-Mara´nda (eds.), Developments in Information & Knowledge Management for Business Applications, Studies in Systems, Decision and Control 376, https://doi.org/10.1007/978-3-030-76632-0_3

113

114

I. Wagner and Z. Tacacs

Now, in 2017, the cloud industry is still growing at an unprecedented 18% rate (according to the [3] that further predicts a total growth of the cloud market of 55.30% between 2017 and 2020) and more and more service options have found their way into the common business realm. While the classic distinction into Software-asa-Service (SaaS), Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service has been around for long [4, 5], there are two more recent additions to the list of cloud services: Data-as-a-Service (DaaS) and Information-as-a-Service (IaaS). DaaS, which is often connected to the trend towards Big Data, allows companies (and in many cases individuals) to use web-based applications to access data stored in the cloud—regardless of where the data consumer is at the time. This can apply to both company-internal data that has been moved to cloud servers, or access to public data such as address data, health data, and consumer data offered by specialized data service companies. IaaS goes a step further. With the use of IaaS options, companies can tap into the data that is stored on cloud servers, and have this data be enriched, contextualized, or transformed into useful information that allows business insights [6]. While both of these services have become more popular in recent years, as evidenced by the growing number of DaaS and IaaS-providers and white Paper publications by companies like Informatica, Intel, Oracle, Microsoft, and others, the terms DaaS and IaaS are often used interchangeably or with different meanings, resulting in potential confusion for consumers, decision-makers and researchers alike.

1.2 Goals and Objectives As the importance of cloud computing and Big Data continues to rise [3, 7], service providers, business leaders, consumers, and researchers alike need a clear understanding of these two concepts to be able to distinguish between them. This paper seeks to find a clear definition and specification for DaaS, IaaS, as well as contrast the two concepts, highlighting which service is better in which situation for what kind of company. To do so, in Sect. 2 the theoretic framework will be presented. It first offers a general introduction into cloud computing, discussing the history of how cloud computing came about and how it evolved over the last 10–15 years. After defining the core concepts, the essential characteristics and different deployment modes of the cloud is discussed. Moreover, a general characterization of cloud services is provided. In following Sect. 3, we describe the difference between Data and Information. Consecutively, the two core concepts of this work, DaaS (Sect. 4) and IaaS (Sect. 5) is introduced and characterized in detail. At first, DaaS is defined, distinguished from similar but different concepts, specifications given, the suitability explained as well as real-world examples mentioned. Moreover, the strengths and downsides of this type of cloud service will then be described. The same is done for IaaS. This is done by utilizing the little scientific research available on the topic, as well as current

Data-as-a-Service versus Information-as-a-Service …

115

white papers by reputed thought leaders and industry-leading companies in cloud computing. In Sect. 6, the two concepts are put together to be contrasted in terms of theory, suitability, and general use in real-world applications. The final analysis is presented in a matrix that clearly outlines the similarities and differences between the two concepts. Finally, guidelines are given as to what company in what context should use what kind of those two services. In Sect. 7, conclusions are drawn from the analysis and recommendations given for business leaders, consumers, and researchers. Moreover, the limitations of the study as well as topics for future research are discussed in the last part of this section.

2 Theoretical and Conceptual Background 2.1 Cloud Computing in General Before various cloud services can be explored in more detail, it is important to have a general understanding of what cloud computing is and how it works. Looking at the scientific literature, it becomes clear that there has been a large confusion around what cloud or cloud computing is. Numerous articles try to develop an integrated, commonly agreed upon definition of the cloud, such as [4] or [8]. One of the most commonly used citations [2] comes from the National Institute of Standards and Technology (NIST) which defines cloud computing as “… a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” [5]. Another alternative definition comes from Gartner in its 2008 report on cloud computing [9]: “a style of computing where massively scalable IT-enabled capabilities are delivered ‘as a service’ to external customers using Internet technologies.” While cloud computing is the technology enabling access to servers hosted by external service providers (as opposed to the company using own servers) using the internet, a company doing so makes use of “cloud services”, or it uses them “as-a-service”. It was in 2008 that Gartner published its special report on cloud computing calling it “an emerging phenomenon” and characterized it as “a way of delivering IT-enabled services in the form of software, infrastructure and more” [9]. It further lists “the use of virtualization technologies […] open source, service-oriented architectures, and widely available computing standards, combined with the pervasiveness of the global Internet” [9]. As reasons for the growing importance of cloud computing. As per (Hashemi) [10], Cloud computing emerges as one of the hottest topics in the field of information technology. Cloud computing is based on several other computing research areas such as HPC, virtualization, utility computing, and grid

116

I. Wagner and Z. Tacacs

computing. In 2009 white paper, IBM cites the “dramatic reduction of IT costs that is achieved with dynamic infrastructure” as the key driver for the cloud [11]. An important distinction needs to be made between grid computing and cloud computing. As IBM explains [12] in a blog post on the very topic, grid computing was about the linkage of “disparate computers to form one large infrastructure, harnessing unused resources.” They explain further, that grid computing enables users to “provision resources as a utility that can be turned on or off.” According to IBM, the difference is that cloud computing takes this to a new level, by delivering provision “on-demand”, which would prevent over-provisioning [12]. For the sake of this paper, the definition of cloud computing as per [5] will be followed. Characteristics of Cloud Computing As the frequently cited paper by NIST [5] points out, there are five essential characteristics of cloud computing: • On-demand self-service: a consumer can by himself initiate the use of elements “such as server time and network storage, as needed automatically without requiring human interaction with each service provider. • Broad network access: because cloud services are provided via the internet and special online interfaces, customers can use these as self-services wherever and whenever they want, from any supported device like mobile phones, tablets, or laptops. It is therefore possible to utilize these services not just when at the office/inside the company, but only an internet connection and a supported device are required. • Resource pooling: before the advent of the cloud, most companies used to store their data in separate repository silos. This led to the frequent problem that within big organizations one department couldn’t access or wasn’t aware of the data/information that other departments had gathered and available [13]. In cloud computing, data is pooled together while the customer typically doesn’t know exactly where the physical location of the storage is [5]. This pooling allows for the serving of multiple clients at the same time. That is also reason why the cloud is especially interesting for start-ups and SMEs that often don’t have the financial resources necessary to set up such infrastructure themselves and can flexibly scale these cloud resources [2]. • Rapid elasticity: Companies that need to scale their cloud computing resources can do so quickly and effortlessly. In case customer demand goes down, they can also release these resources rapidly. As [2] point out, this elasticity allows companies “to react more flexible to changes in customer demands” (p. 214). The National Institute of Standards and Technology (NIST) even goes as far as to say that in the perception of the consumer these capabilities “often appear to be unlimited and can be appropriated in any quantity at any time” [5]. • Measured service: Sometimes also referred to as “metered service”, the measurability describes a “metering capability” [5], depending on the type of service, for example, storage, bandwidth, etc. As a result, cloud services typically use a pay-per-use-pricing model which means that consumers only pay for when they

Data-as-a-Service versus Information-as-a-Service …

117

actively use the service. In [2] is called this benefit “variable, usage-based billing” (p. 218) and this again is especially attractive to start-ups and SMEs as “potential savings” can be realized, both “upfront and ongoing”, [5] point out that as such resource use may be “reported, monitored and controlled”, this characteristic of cloud computing provides transparency for both parties, the service provider and the consumer. Deployment Modes of Cloud Computing. Cloud computing can be provisioned in several different ways, depending on where the servers are hosted and who has access to or may use them. Altogether, according to the NIST, there are four different deployment modes [5]: private cloud, community cloud, public cloud, hybrid cloud. The Indian smart services company Sysfore summarized them in an illustration in one of their blog posts as follows [14] (Fig. 1):

Fig. 1 Types of cloud computing (based on [14])

118

I. Wagner and Z. Tacacs

Private cloud: if access to a cloud infrastructure is exclusive to one single company where multiple users access them, e.g. different departments, a private cloud exists. The location of the server, as well as its hosting and management, do not have to be with the company (on-premises) but may be with a third party [5]. Community cloud: This is in place if access to the cloud infrastructure is shared by, but is exclusive to “a specific community of consumers from organizations that have shared concerns” [5]. It is possible that one of the organizations or a third-party owns, manages and operates the infrastructure, while the server may be on or off-premises. Public cloud: Some cloud infrastructures are made available to and accessible by the general public by organizations, academic or public institutions. Famous examples are cloud-storage services like Dropbox or Google Drive where any user can upload and store files. The servers exist “on the premises of the cloud provider” [5]. Hybrid cloud: This is not a separate form per se, but a term given to solutions that combine two or more of the other deployment modes. A hybrid cloud is created when two cloud infrastructures that “remain unique entities” are “bound together by standardized or proprietary technology that enables data and application portability” [5]. Service-Oriented Architecture and Web Services as enablers of cloud computing and cloud services. Two important technologies lay the framework for the existence of cloud computing: service-oriented architecture (SOA) and web services. The most frequently cited definition of service-oriented architecture (SOA) comes from the OASIS [15] which defines it as a “paradigm for organizing and utilizing distributed capabilities that may be under the control of different ownership domains.” While the focus had for the longest time been on components and their respective needs, this service-oriented paradigm enables that smaller, lower-level individual services can be grouped for a higher-level service. This delivers the benefit of reduced complexity for the service, as with SOAs “capabilities can be used without needing to know all the details” [15]. The advantages of service-oriented architecture most commonly listed are: • Reduced complexity: As [16] highlights, the loose coupling that characterizes SOA makes it easier to adjust services when required. Moreover, application developers no longer need to have an data management expert [16]. • Enhanced interoperability: due to its focus on enabling communication between various services instead of focusing on specific components of a service, SOA allows for enhanced interoperability between services [17]. This means that workflows can be expanded to integrate external business partners [18]. • Rapid scaling: because SOA allows for many smaller level services to be combined (orchestrated) for the delivery of a higher-level service to the end-user, a company can scale faster if the business needs dictate it [15].

Data-as-a-Service versus Information-as-a-Service …

119

• More agility: OASIS underlines that due to the ability to grow and scale as required, the infrastructure supported by SOA “is also more agile and responsive than one built on an exponential number of pair-wise interfaces” [15]. As the mentioned definition implies, SOA offers the required architecture for a complex and integrative environment of cloud services like DaaS and IaaS where resources of various owners [15], including data owner and data provider, are used to deliver end services to clients via a service interface. The end customer is less concerned with whose resources are being used but is interested in having access on-demand. Web Services are the preferred mechanism for the implementation of SOA. As an emerging modeling paradigm for distributed systems SOA is often confused with a wide range of networked information technologies. The Web Services Working group of the World Wide Web Consortium (W3C) defines Web Service [19] as “a software system designed to support interoperable machine-to-machine interaction over a network. It has an interface described in a machine-processable format (specifically WSDL). Other systems interact with the Web Service in a manner prescribed by its description using SOAP [20] messages, typically conveyed using HTTP with an XML serialization in conjunction with other Web-related standards.” As [21] concludes: “The comparison to SOA is, in this case, immediate and natural as SOA was born through the convergence of several web technologies.” The described SOA lays the foundation for the communication of web services. This functioning of web services is illustrated in Fig. 2. As IBM describes on its websites, there are 3 main components to a web service: service provider, service requestor, and service registry. The service provider publishes a service description (using WSDL language) which is stored through a

Fig. 2 How web services work (based on [22])

120

I. Wagner and Z. Tacacs

publish action to the service registry. The service requestor finds the description there and therefore understands what services are available and binds the service [22]. The publish and find actions are done via SOAP (Simple Object Access Protocol), the bind and the communication between service requestor and service provider is typically done via Extensible Markup Language (XML). In this way, web services connect applications and allow for interoperability. They also allow for the realization of cloud services. Cloud Services—Everything-as-a-Service (XaaS): As more and more companies started to adopt cloud computing and moved their data from local servers onto cloudbased servers, entirely new services were developed by cloud-service providers to deal with the needs and demand of their consumers. Because the range of these services was soon so broad “that everything will be delivered as a service mostly over the Internet” [1], the term of “Everything-as-a-Service” (or XaaS) was soon born and coined by companies like Hewlett Packard. In its most basic form, cloud services are a wide range of resources that a service provider delivers to customers via the internet [23]. According to a commonly used distinction, mentioned e.g. by [4, 5], three types of cloud computing differ in the actors addressed and the services (cloud services) offered: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) (Fig. 3). Software-as-a-Service: SaaS describes software that is not hosted on-premises, but on a web server, that can be accessed independently of location and device using an Internet Browser. The customer only uses the software, but all aspects from networks, storage, server, up to the application itself are managed by the vendor. The most common types of SaaS are Customer Relationship Management (CRM) and Enterprise Resource Management (ERP) software. Moreover, collaboration tools like

Fig. 3 Cloud service models (according to [5])

Data-as-a-Service versus Information-as-a-Service …

121

Google Docs or project management software like Asana or Basecamp are all SaaS services that can be integrated to allow for seamless cloud collaboration between teams and companies. Platform-as-a-Service: a less-integrative approach is PaaS where the customer is in charge of managing the application and data whereas the vendor takes care of everything else. As the name suggests, PaaS provides a platform in the cloud in which the customer can upload and run their own applications—whether for exclusive or public use. The most important PaaS providers include Salesforce, Microsoft Azure, Amazon Web Services. Infrastructure-as-a-Service: as the least integrative approach, IaaS leaves the customer in charge of application, data, runtime, middleware, and operation system, while the vendor itself offers networking, storage, servers, and virtualization services. The benefits of freedom of configuration and customization come with the downside of more responsibility and required knowledge to manage the infrastructure. IaaS is most common in big research and public companies that require heavy customization and individual cloud usage. How responsibilities are shared between vendor and customer is summarized in the following illustration (Fig. 4) by Australian cloud provider Crucial [24]. While this three-tiered-distinction has long prevailed, it is not sufficient to capture the entire breadth of cloud services (everything-as-a-service) that have been suggested over the last years. Gartner also summarizes in its 2008 report on cloud computing the future potential as cloud services by stating that “Ultimately, everything potentially becomes a service” [9]. Distinction. Cloud services must differ from a few other terms that are erroneously used as synonyms by some companies and managers. It is important to distinguish cloud services from web services. As TechTarget writes [25], web services “are the tools that allow users to interact with software over the Internet.” The W3C (World Wide Web Consortium) defines them as “a software system designed to

Fig. 4 Responsibilities of vendor and client in on-premise hosting versus the three forms of cloud services from a client’s perspective (according to [24])

122

I. Wagner and Z. Tacacs

support interoperable machine-to-machine interaction over a network” [26]. They allow the integration of web-based applications by the utilization of standards like XML, SOAP, WSDL [27]. Cloud-services on the other hand “are the servers that store the data, security and other infrastructure” that web services require to deliver value to their users [25]. As described in Sect. 2.1, web services are typically realized through a serviceoriented architecture (SOA) and allow for communication between service requestor and service provider as well as the service registry. The connection of SOA to business value is described in [28]. Web services are different from cloud services in that while they may be based on cloud computing, they need not be, as they may simply connect two applications [25]. However, they are also interrelated, especially as they enable one another as the following example shows. When a user opens Google Docs to create a document, the software Google Docs is offered to him as a Software-as-aService via the cloud. He can access the software through his web browser, without having to install the software on his local computer; the software is stored on the cloud servers and provided as a service to him. When the user starts typing and enters content to be put into the document file, the communication is sent from the user as the service consumer to Google as the service provider via a web-service. In other words, the web service sends the user’s input to Google Docs. So, while the cloud service provides and enables the software, the web service allows the use of the cloud service. To summarize, cloud services can be characterized as “any service that is delivered via the internet to a customer on a local device.” The extent of the resources provided and the sharing of responsibility between provider and vendor may differ.

3 Data Versus Information Before DaaS and IaaS can be characterized, a distinction shall be made between the terms “data” and “information”. The most frequently cited model that—amongst others—also distinguishes between data and information is the so-called DIKW pyramid [29], originally developed by [30]. It puts data at the bottom, information at the next higher hierarchical level, to be followed by knowledge and wisdom (Fig. 5). The Open Data Center Alliance (ODCA) offers a definition of data and information that positions information as being derived from data. More specifically, they define data as “The lowest level of abstraction from which information is derived; for example, an address” while information is defined as “A combination of contextualized data that can provide meaningful business value or usage; for example, the postal address of a business client” [31]. Furthermore, the ODCA offers a model that explains how data is turned into information as a prerequisite for effective business insights (see Fig. 6).

Data-as-a-Service versus Information-as-a-Service …

123

Fig. 5 The DIKW Pyramid (according to [29])

Fig. 6 Transforming data into information and insights (based on [31])

The processes therefore by which data is turned into information are collation, standardization, integration, contextualization, value-add/new data derivation, aggregation. This however only produces information in itself; it is not yet guaranteed, that it is in a usable and optimized format for the company, or what [6] call “enterprisefriendly or user-friendly format”. As previously mentioned, analytics has the task to put information together in such a way, that it allows a company to derive clear insights. The tasks mentioned by the ODCA are examined below: • Collation: The collation of data means to sort data according to predefined criteria—referred to as identifiers, such as alphabetical or numerical order. Collation of data in databases can be done automatically once an algorithm has been defined. The algorithm then goes about comparing two sets of data at a time and sorts it, eventually producing a total order amongst all datasets resulting in collated data (ordered items). • Standardization: As Oracle [32] describes “the purpose of data standardization is to make your data consistent and clear. Consistent is ensuring that the output

124









I. Wagner and Z. Tacacs

is reliable so that related data can be identified using common terminology and format. Clear is to ensure that the data can be easily understood by those who are not involved with the data maintenance process.” Integration: When data is integrated, it means that data from varying input sources are combined in such a way, that they appear for end-users in a unified view. The importance of data integration has grown as big data has come around and data from more and more different sources need to be integrated, such as when two companies merge their databases or scientific research has to access data from multiple databases for the same one project. Contextualization: this describes the process of putting input data into a broader context within which it was generated. The ODCA gives the example of data as “the number of customers unsubscribed from a service” [31], while looking at how long these people were subscribed, why they unsubscribed, and other factors would allow this data to be contextualized. Value-add/new data derivation: this is when input data is used to derive further data through a formula/derivation algorithm. New data is created that may be used by the company to derive valuable information and insights. Both the quality of the input data must be high and consistent, and the derivation method is correct as otherwise, the derived data is corrupt. Aggregation: gather and express data in the form of a summary on the maximum required level of detail.

The [31] also states that if information-as-a-service is to be effective, it must “have the ability to provide standardized and secure methods to create, manage, exchange, and extract meaningful information from all available data in the right format at the right time”.

4 Data-as-a-Service (DaaS) As one of the most frequently cited as-a-service modes, DaaS has received a lot of attention in recent years. This section will examine this concept in detail, distinguish it from other services, highlight its benefits and downsides, as well as characteristics and pricing models of DaaS. To conclude, real-world examples and applications as well as drivers of DaaS will be described. DaaS might be classified as a service while having four characteristics: intangibility, heterogeneity, inseparability, and perishability [33]. Before we take a closer look at DaaS, the concept of data first needs to be laid out in more detail. According to the ODCA, the term data generally describes “The lowest level of abstraction from which information is derived; for example, an address” [31]. A commonly used model in cloud computing separates data into three separate types of data: structured, unstructured, and semi-structured data [34].

Data-as-a-Service versus Information-as-a-Service …

125

• Structured data: As implied by the name structured, when data can be brought into order and classified according to certain criteria, data is structured. Such data can easily be stored in databases where it typically resides in rows and columns and can easily be processed automatically and be used for analysis and derivation of information and insights. • Unstructured data: this kind of data cannot typically be stored in databases and therefore not be automatically analyzed by algorithms. This data is in a way useless until it has been analyzed manually and information and insights derived. This type of data is said to be in raw formats, such as audio or video data. No identifiable structure within this kind of data is available” [35]. • Semi-structured data: as a hybrid form between the other two, the semi refers to the fact that there is some structure, but the data is not in a table like structured data. One definition by [36] states it as “data that is (from a particular viewpoint) neither raw data nor strictly typed, i.e., not table-oriented as in a relational model or sorted-graph as in object databases.” The research firm IDC estimates that around 90% of all enterprise data is unstructured [37]. And the increasing use of big data especially leads to more unstructured data being created which is why companies need to focus more on its analysis [38].

4.1 Definition of DaaS As Delen and Demirkan point out “with data-as-a-service, any business process can access data wherever it resides”. In other words, using cloud technology as described earlier, businesses can utilize an online interface (connected via the API— Application Programming Interface) to access data stored on a server in the cloud [39]. A concise definition comes from David Linthicum from InfoWorld who simply calls DaaS “cloud-based databases” [40]. The innovation with DaaS is that companies are no longer storing their data on their own on-premises servers, but upload them to cloud servers. Two examples of data providers are Google Adwords or Google Trends where using an online interface, any person from any device worldwide can enter search queries and find out how many times certain keywords were searched in a certain time frame in a certain region. Based on this information, any SME or larger company can decide how to advertise via the Google Search Platform. Ovum defines DaaS as “the sourcing, management, and provision of data delivered in an immediately consumable format to organization’s business users as a service” [41]. As mentioned in [42] about architecture aspects, DaaS systems should own extremely dynamic characteristics to respond to requirements from different communities of users.

126

I. Wagner and Z. Tacacs

And as [43] highlights in a blog post on the IBM Big Data Hub, offering a taxonomy between storage-as-a-service, data-as-a-service, analytics-platform-as-aservice, and analytics-as-a-service, DaaS also includes data feed and aggregation.

4.2 Distinction Given the wide number of as-a-service-options, it is necessary to provide a clearer understanding of how DaaS is different from other cloud services. From storage-as-a-service. As Fattah points out in his blog post, while storageas-a-service typically only offers a company the storing of its data, a DaaS solution allows “more sophisticated access to the data”, including relational database management systems, as well as data aggregation and data feeds [43]. From software-as-a-service. While DaaS has been called “a cousin of software as a service” [44], it differs from SaaS in that DaaS utilizes SaaS in order to be delivered. When companies utilize DaaS services to access data from anywhere, they do this via an online interface of a SaaS tool. Hence SaaS is a required “deployment model” [5] that serves to deliver DaaS. From platform-as-a-service. As another cloud deployment model, PaaS describes an online environment where a company can host and run its own applications on the platform offered by a cloud provider [5]. In other words, applications are built and deployed in the cloud, whereas with DaaS, a company may initially load its data to the cloud from which it is then accessed through a special online interface. Hence, for the sake of this paper, DaaS is defined as “a cloud service model in which a company can access relevant data, which is stored on cloud servers by a specialized DaaS provider, through the use of a specialized interface. The DaaS service provider may also aggregate the data from various sources and offer it in the form of customized data feeds. The data can be in any one or a combination of structured, unstructured or semi-structured form.”

4.3 Characteristics of DaaS The following DaaS characteristics we have identified: • Multiple sources: DaaS services are frequently used to bring different data sources together. It is important to understand that the DaaS provider and the data source are two different roles, that often do not coincide. Oftentimes DaaS providers are specialized companies that focus on aggregating various data sources into relevant streams for customers [13]. Moreover, companies use DaaS frequently to organize their data and enrich it with third-party data [31].

Data-as-a-Service versus Information-as-a-Service …

127

• Different types of data: DaaS utilizes all three different types of data: structured data, unstructured data, and semi-structured data. • Relevant actors: the DaaS framework typically encapsulates the following actors: data consumer, DaaS provider, and data source(s). • Up-to-date data: DaaS allows companies to access real-time data streams that are aggregated by specialized DaaS providers. This is because the data is put together at the moment the company decides to look up the data. • On-demand access: as with all cloud services, companies make use of the service at the moment they want to access certain data utilizing an online interface, typically through a web browser or a customized software client. • Storage is provided: the data that the company accesses is stored in the cloud, whether it is company-proprietary data that has been transferred to cloud services or third-party and public data that is used to enrich the data sets.

4.4 Suitability of DaaS There are various situations in which companies can benefit from the use of DaaS: For SMEs that don’t have a particularly large set of customer data. DaaS is an attractive option for any company that wants to access relevant data provided by third parties to either enrich, complement, or largely make up for a potential lack of company-internal data. This may especially be relevant for startups or SMEs that do not (yet) have large data sets on their customers. Companies that want to enrich their data with relevant public data. Companies of all sizes can use DaaS to enrich their own data sets with relevant data from third parties. A commonly cited example for the United States is Dun & Bradstreet which offers detailed company records that can enrich customer data sets [38]. Companies with in-house data analytics experts. As the ODCA points out, data itself still needs to be interpreted and analyzed for companies to derive actionable insights [31]. If a company has the required data analysis experts in-house, then it can profit from focusing on the analytics while using a DaaS provider to gain real-time access to the data. Companies without data management expertise. As Pringle et al. state DaaS for many companies data management is rarely “a high priority” and for most of them it would not be a good resource investment to develop these skills “especially where businesses are looking to adopt and experiment with new technologies accelerated timescales” [41]. Companies that need big data, but do not have resources or demand to hire data scientists full time. BrightPlanet points out on its website that especially smaller companies and companies not willing to hire data experts for in-house engagements needing data experts brought about the DaaS market [45]. DaaS has the potential to provide cheaper, faster, and easier access to data. DaaS is an emerging cloud computing service model to making useful data available to users as a service through a network in a timely and cost efficient manner [46].

128

I. Wagner and Z. Tacacs

4.5 Benefits of DaaS DaaS (DaaS) on cloud solutions have multiple benefits: agility, cost-effectiveness, data quality, cloud-like efficiency, high availability, elastic capacity. As they are largely in line with the general characteristics of the cloud described under Sect. 2.1, only agility, cost-effectiveness, and data quality will be further described here. • Agility: As Newman points out when DaaS is provided by specialized DaaS companies that exclusively focus on compiling data into relevant streams so that customers can “access the streams they need, when they need them”, companies no longer have to be so focused on data themselves and benefit from “greater agility, because they can seamlessly and effortlessly get the exact data they need” [13]. • Cost-effectiveness: DaaS allows companies to benefit from actionable insights from big data, without having a lot of the administrative burden, such as maintaining databases, parsing data or requiring internal company specialists for data management that company-internal databases require by “offloading” it “to vendors for whom it is a core competence” [41]. • Data quality: compared to other configurations, DaaS leads to a single point for updates, which ensures that the quality of the data is high and prevents it from being corrupted or manipulated. However, as will be shown in the next point, others see data quality at risk in DaaS.

4.6 Downside/Challenges of DaaS As much potential as DaaS brings for companies, there are also significant downsides to its implementation that must be considered when opting for the usage of DaaS. These include: • Data security: Once data is moved from internal databases to databases in the cloud, security becomes an issue as a company needs to rely on security measures taken by the DaaS providers to secure access to the data. Companies need to carefully analyze how security features and tools by DaaS vendors “work with” a company’s internal system. As Mukherjee and Shaw point out, there are no effective solutions yet that help to restrict access to data to those few that are authorized to do so [47]. • Data quality: As Pringle et al. state, if companies rely on DaaS providers to access data and derive insights from it, “users should be able to implicitly trust that data” [41] However, especially given the many sources from which data can be compiled and the fact that most of them are unstructured (meaning it can’t easily be put into databases), data quality remains a large concern [41, 48]. They recommend that as it is not within the ability of most companies to develop the competence of how to process this data, it is DaaS providers who can position themselves for

Data-as-a-Service versus Information-as-a-Service …





• •

129

success by providing that expertise [41]. If data can’t be processed and structured well, it will be hard to separate which data is valuable and what is just “noise”. Governance: Data governance is defined as “a companywide framework for assigning decision-related rights and duties in order to be able to adequately handle data as a company asset” [49]. This is required, because different file formats, definitions, and standards across departments can lead to a lack of interoperability and high costs unless controlled by governance policies [38]. Sarkar further recommends to “regularly audit the implementation of data governance policies” and Nazemoff points out that such policies can help organizations to “to more easily integrate, synchronize and consolidate data from different departments and to exchange data in a common format allowing for faster decision to occur” [50]. Special attention needs to be paid to handling data by end-users and protect the data and insights from the misuse [51]. Privacy: Another major concern with DaaS is to ensure the privacy of data stored on cloud servers. As Pringle et al. points out, it was easier to manage customer data on-premise inside a company, than for example data derived from public social media posts or web cookies [41]. They suggest that DaaS vendors should free DaaS users of this concern by providing compliance with legal rules and develop experience in “identifying data provenance, user preference and anonymizing where appropriate”. The privacy issue is complicated further by differences in applicable national regulations; there is demand from DaaS vendors to handle this issue, highlighting it could be a valuable competitive advantage for them against other vendors [41]. Expertise: The company only has access to the data, but still needs to perform analysis on it. This can be a downside if the company has not yet developed the required experience in-house to perform data analytics. Integration into current enterprise architecture: Regardless of a selected type of DaaS, the solution needs to fit into current Enterprise architecture. According to [52], architecture can be described as the structure of components, their inter-relationships, and the principles and guidelines governing their design and evolution over time.

IBM found in a global study that surveyed more than 1000 companies that only about 20% of these companies use more complex forms of analytics while 70% of companies state that “they aren’t lacking data” [53]. Therefore, IBM concludes, “the key to driving business value is how enterprises use data and sophisticated analytics” [53]. Companies that use this complex data are labeled by IBM as “data-driven” and “Generation D”. The study found that companies using these complex analytics were outperforming other companies on significant benchmarks, such as penetrating new markets, developing new revenue streams, and efficiency in operations overall.

130

I. Wagner and Z. Tacacs

4.7 Pricing Models As Sarkar reports, there are four general types of pricing models used in the DaaS industry: request-based model, volume-based model, data type based-model, and corporate subscription model [38]. • Request-based model: in this model, every time the customer makes a request for data, it is charged a fixed, predetermined amount. This type is especially useful for small-scale customers that only rarely require DaaS services. • Volume-based model: with this model, the pricing is based on the amount of transaction data (the volume) that the customer consumes. There are typically a few different categories and a limit at which pricing is capped; if the consumers go beyond this, they “get charged at the next tier level”. • Datatype-based model: as a more sophisticated strategy, this pricing model differs between the different data types a customer consumes. Sarkar points out that this complexity can be a downside, but may be an adequate strategy in certain sectors. • Corporate subscription model: as the most commonly used pricing model, a company buys a subscription either for the entire company or for a certain number of users. Once the subscription has been bought, the service can be used without limits during the subscription period. Which type of pricing model may be best for a company to choose depends on the type of industry as well on the preferences of the customer and should be considered in the light of how often and extensively DaaS is used [38].

4.8 Examples of Real-World Applications An example of Data as a Service is the same-called service “Informatica Data as a Service” which e.g. encompasses postal addresses, email addresses and phone numbers verification [54]. When a customer enters the address information into an Informatica customer’s registration form, the data is automatically checked against Informatica’s address database and verified. More public examples are e.g. the portal undata (http://data.un.org) by the United Nations as well as Google Public Data (http://www.google.com/publicdata). Both these portals make public data available to customers all around the world through an online interface with a search function. Undata also offers an API through which SaaS integrations can be made, allowing data to be illustrated on websites or software (Figs. 7 and 8).

Data-as-a-Service versus Information-as-a-Service …

131

Fig. 7 UN data as an example of a DaaS application1

Fig. 8 Google public data2 as another example of a DaaS application

Another frequently cited example in the B2B field is Dun & Bradstreet’s solution Hoovers [38]. According to [55], “D&B Hoovers combines more than 120 million business records from 30,000 sources in 190 countries, updated 5 million times a day with an intuitive, dynamic user interface to help customers put the best data in the hands of sellers at the point of interaction. This unique combination of data, analytics 1 https://data.un.org/DocumentData.aspx?q=intentional+homicide&id=432. 2 https://www.google.com/publicdata/directory?hl=en_US&dl=en_US#!.

132

I. Wagner and Z. Tacacs

Fig. 9 Dun & Bradstreet as leading DaaS provider of business data in the US

and technology increase sales productivity by enabling more strategic targeting and the ability to have more informed conversations, so organizations can accelerate sales and drive global business growth. One of the ways how businesses can use D&B Hoovers’ service is by entering information on desired leads and automatically receiving a list of potential leads that correspond to the desired characteristics [38] (Fig. 9).

4.9 Drivers According to [38], 5 drivers as reasons why more and more providers start offering DaaS options to their customers: • Engaging customers with data-driven choices. More and more companies like Amazon and Alibaba have found ways in which data can be utilized to help customers make better choices, such as recommended products based on similar customers’ past purchases on Amazon. Furthermore, social networks have become more important for customers as platforms of discussion and sharing. Sarkar points out that this has led to customers being more empowered in making decisions, and companies searching for ways to offer customers this data. • Monetization. Some companies have come up with ways to make money by renting the company’s internal data to partner or customer companies that can benefit from the harnessed data. Entirely new business models have evolved as companies are seeking to build new profitable income streams from big data available. • Public sector and government. Also, the public sector and government have started to become data providers [38]. This gives companies the additional benefit of utilizing public data to enrich their data sources. The most famous example is UN

Data-as-a-Service versus Information-as-a-Service …

133

Data organized by the United Nations Statistics Division which gathers knowledge from data-publishing institutions and makes it accessible to consumers worldwide via a separate API. • Technology shift. The increasing number of customers using the internet to inform themselves and purchase products and services creates enormous amounts of data (big data) that can be utilized to better understand customer preferences and behavior. Companies, therefore, are confronted with learning how to leverage and organize that data for increased customer satisfaction and profitability of the company. • Pioneering organizations. Pioneering companies are already offering DaaS services to customers is another reason why more and more businesses have to check if and how becoming a DaaS provider themselves may be relevant for their success. Another important prerequisite that allowed for the development of DaaS is Service-oriented architecture (SOA) (for more details see Sect. 2.1). The reason SOA is an enabler of DaaS is that it “has rendered the actual platform on which the data resides irrelevant” [44].

4.9.1

Structure of Cloud-Based DaaS Providers

There are three important actors in a DaaS network. They are the original data providers, so data-publishing institutions, there is the DaaS provider utilizing cloud computing and cloud infrastructure, as well as data consumers utilizing a web interface to access data in real-time. This is shown in the following illustration [38] (Fig. 10).

Fig. 10 Overview of cloud-based DaaS provider (based on [38])

134

I. Wagner and Z. Tacacs

5 Information-as-a-Service In this section, the recently evolved IaaS service will be characterized. Analogous to Sect. 3, after defining the term, benefits and challenges will be explained, suitability discussed as well as drivers and examples of providers/services given).

5.1 Definition One definition of the term comes from the ODCA that defines Information as a Service as “The ability to provide standardized and secure methods to create, manage, exchange, and extract meaningful information from all available data in the right format at the right time” [31]. A more extensive definition by [6] states that “Information-as-a-Service is a cloud service model that provides cloud customers with data in an enterprise-friendly or user-friendly format, which is a service representation using a standardized schema to generate and present information efficiently.”

5.2 Distinction From analytics-as-a-service. A term that is increasingly being used by companies like Oracle and SAS is analytics-as-a-service. To get a better understanding, it has to be examined what analytics actually means. The ODCA describes analysis as an essential step for actionable insights to be derived from data [31]. With analyticsas-a-service, the service becomes the data analysis itself. The company outsources the analysis of specific datasets to a specialized external vendor and is presented on-demand with the results of the data analysis. The process of how this data is analyzed and made sense of is still individual to the strategic goals and objectives of the company, as well as its industry and activities. The customer, therefore, needs to work together with the specialized vendor to create what [6] termed a “standardized scheme” (p. 28) as a basis for analysis and interpretation of the data. The results of the data analysis are information in a company-usable format that may be used to derive further insights [6]. Therefore, analytics as a service can be understood as a part of IaaS and presents the first step of turning data (e.g. from a DaaS provider) into actionable insights for the customer company. From insights-as-a-service. Providing actionable insights for business becoming more demanded in recent years. Perform analysis and describe the status of a company based on the data collected or by using ML is powerful. To implement changes and improve the situation in the company is often required to obtain as well actionable insights based on the findings. The ODCA also sees that information is being derived

Data-as-a-Service versus Information-as-a-Service …

135

Fig. 11 Information-as-a-Service and its subforms (own interpretation)

from data as one step before actionable insights in its model, it takes analytics and predictions to turn information into “actionable insights” [31]. It is commonly agreed that insights are information in a company-friendly format. Insights-as-a-Service can, therefore, be understood as an advanced form of Information-as-a-Service. Hence, for the sake of this paper, the definition of IaaS is (following the definition by [6]: Information-as-a-Service describes a cloud service model in which a customer outsources the tasks of data analysis and interpretation to a specialized external provider that performs the service using a standardized scheme developed by or in cooperation with the customer company. The result is a service representation that offers the customer with company-friendly and user-friendly information that can be used to derive actionable insights on demand. This definition sees analytics-as-a-service, as in the analysis of data being conducted as a service to the customer company, as well as insights-as-a-service, the delivery of actionable insights as a service to the customer company, as a part of the more general concept of information-as-a-service. The connection between analytics-as-a-service, insights-as-a-service, and information-as-a-service can be then visualized as follows (Fig. 11).

5.3 Characteristics of Information-as-a-Service There are several essential characteristics to IaaS: • Transforms data into useable information: IaaS providers “generate information” from data sources which may be their own or from a third party and they then deliver that information to the client which has to “follow users’ business rules” and this way the company receives “data in enterprise-friendly or user-friendly format” [6]. • Not necessarily but best used with cloud computing: the ODCA writes, describing the “role of cloud in information as a service” that the advantage of using IaaS via

136

I. Wagner and Z. Tacacs

the cloud is that both the “orchestration function” of gathering data from various sources and offering them effectively as information can be combined [31]. • Need not be DaaS provider: the IaaS provider can be a DaaS provider that adds analytics-service or insights-service on top his existing DaaS service, but may also be a separate company that uses data provided by third parties, as the illustration of [6] shows. • Follows company-specific rules—“standardized schema”: what [6] describe is a “standardized schema” which allows data to be put into a form that corresponds to the needs of the user company. The ODCA demands the same when it states that IaaS “should have the ability to provide standardized and secure methods […] from all available data in the right format […] to meet the business goals” [31]. • Enabled by using an API: The realization of IaaS necessitates the implementation of an Application Programming Interface (API) that “generates an open architectural environment in which multiple applications can share data” [6]. This is necessary for data to be transferred effectively between various sources and applications, as [6] put it, this “is helping applications to remove information sharing restrictions”.

5.4 Suitability Because IaaS allows access to analytics experts on demand, it is especially useful for a company that either does not have the financial or personal resources available to develop such skills in-house. Outsourcing the task of data analysis (analytics-asa-service) up to the point of deriving insights (insights-as-a-service) is a strategic decision that while enabling agility and profiting from external competence leads to dependence on external partners and prevents the development of in-house competencies. Generally, IaaS is especially suitable for companies that: • • • •

Are small to medium company size. Have no to low prior experience with data analysis. Have no to low availability of in-house data experts. Want to build a trusted network of strategic partners rather than in-house competencies. • Have a legal framework that allows for storing of data on cloud servers and sharing data for analysis by external partners and do NOT have data analysis as one of their core competencies. A thorough analysis of the following benefits and downsides should enable a company to make a qualified decision about the potential suitability of IaaS.

Data-as-a-Service versus Information-as-a-Service …

137

5.5 Benefits There are four big benefits to IaaS [6]: • Rapid acquisition of meaningful information from service providers • No need for a messy collection of data: this applies to DaaS and IaaS alike. A specialized IaaS provider organizes the collection of data from various data sources either through data warehousing (DWs) or operational data stores (ODSs) [31]. A previously tedious and time-intensive task is outsourced to specialized providers while the need for in-house data experts is reduced or even eliminated. • No need to dig into a large amount of data: once the standardized schema has been created a user can let the IaaS vendor take care of deriving information from the data [6]. A user must no longer perform long manual examination and analysis of the data supplied. • No necessity to wrap up all data for results: as the IaaS vendor already delivers information in a clear and easy-to-understand service representation, the customer can utilize the information without having to put them together itself. Furthermore, services such as Insights-as-a-Service offer advantages like. • Access of expertise when it is needed without fixed costs. Data scientists are generally in short supply. Many companies don’t have or can’t afford to have a data scientist expert in the company full-time. By using an Insights-as-a-Service offering, the company can still benefit from the expertise when required [56]. • Benefiting from the expertise in case of new cases/scenarios the company has not had before. A McKinsey survey revealed that 86% of companies asked stated that at best they were “somewhat effective” when it comes to achieving their set goals for data and analytics [56]. • Operational expense instead of capital-intensive fixed costs. As SAS points out, as Insights are used as a service and only when needed, a company can typically declare its expenses for insights as operational expenses, instead of building longterm and fixed costs through investment in capita-intensive infrastructure [56]. • Increased speed. As data, analytics, and insights can be used as a service and no technical infrastructure has to be implemented on-premise, insights can typically be delivered much faster. As Capgemini describes one case, a process that used to take a retailer 10 h to load “has improved insight creation from 60 min to 60 s” [57].

5.6 Downsides However, utilization of any IaaS vendor, depending on the scope of the services consumed, comes with several downsides, including: • Increased dependency: handling this task to an external partner creates an increased dependence on this vendor and its quality of service. The external

138

I. Wagner and Z. Tacacs

vendor may not have the oversight and understanding of the business required to perform high-quality service; good cooperation and clarification of needs at the outset are therefore required. • No development of internal analytics competencies: due to the analysis being performed and insights delivered by the external partner, the company does not develop these competencies themselves. As Big Data continues to grow in importance [3], this can be a competitive disadvantage to companies that develop these skills in-house. • The potential loss of sensible/confidential data: it can never be guaranteed that confidential information may not either be lost due to security issues or a leakage somewhere in the cooperation with the IaaS vendor. This applies generally to any data hosted in the cloud though.

5.7 Pricing Models Analysis of existing IaaS solutions on the market shows that there are different pricing models available: Deliverable-based pricing. One pricing model is based on the deliverable which the IaaS provider is to send to the end customer; such deliverables can be e.g. data set, presentation, or a report [58]. User-based pricing. A widely adopted pricing model is based on the number of users a customer may want to have. A monthly fee per user subscription is to be paid. For example, Salesforce uses such a model for its Einstein Analytics3 solution, or Tableau Software.4 Corporate subscription (Salesforce) or Seattle-based Tableau Software. Feature-based pricing. Another common pricing model is based on the features the customer has access to. As one example, Tableau offers a different set of features between the Personal Edition and Professional Edition of its service “Tableau Desktop”. Also, Oracle5 offers a Standard and an Enterprise Edition, whereby the latter is expanded by the number of features while offering all the features of the Standard Version as well.

5.8 Examples of Real-World Applications The most common use of IaaS is with the delivery of information between applications using an Application Programming Interface (API) [6].

3 https://www.salesforce.com/products/analytics-cloud/pricing/. 4 https://reviews.financesonline.com/p/tableau-software/. 5 https://cloud.oracle.com/en_US/oac/pricing.

Data-as-a-Service versus Information-as-a-Service …

139

Fig. 12 Capgemini and its Insights-as-a-Service offering (captured from [57])

Two big players in the IaaS field are Capgemini and SAS. Capgemini offers a specialized service called Insights-as-a-Service which it describes on its website as a service that cares for data being “secured, cleaned, sorted and shaped into useful analytical insights” [57] (Fig. 12). SAS, on the other hand, speaks of “analytics-as-a-service” and calls its service “SAS Results”, describing it as a combination of “the expertise of SAS services with its award-winning software to help you develop more powerful analytical insights” [58]. The service can be deployed in the cloud or on-premises. Customers can get direct access to analysts and have the analysis of data be performed by data experts from SAS and have insights or results be delivered in the desired form of deliverable that is agreed before the beginning of the project [58] (Fig. 13).

5.9 Drivers • Shortage of skilled data experts: as SAS points out [56] there is an immense shortage of skilled data experts—demand outweighs their availability. Therefore, even if companies wanted to hire them full-time, many of them would not be able to. This acts as a driver for the establishment of specialized IaaS providers, that perform analytics and deliver insights on demand. • Need for competitive advantage: in a global marketplace where big data is trending and ubiquitous, companies are searching for competitive advantage through superior data and customer insight. The companies’ focus is therefore on gaining competitive advantage through the fast and qualitative derivation of insights from the mountain of available data.

140

I. Wagner and Z. Tacacs

Fig. 13 SAS IaaS offering is called “Results” on website6

• The ever-growing amount of data: due to the trend of big data the amount of data collected skyrocketed in recent years and grew much faster than the ability to process or handle that data. As a result, companies are overwhelmed with the choice of data sources and types and try to separate true value from the noise [41]. This rapidly evolving data landscape has also led to companies developing more complex information architectures, which, as the ODCA is confident, drives the need for IaaS [31]. • Increased need for agility: agility has been claimed to be the key to business success in the twenty-first century [13]. In the aim to realize the full benefit of data and customer insights while remaining agile and maintaining fixed costs to an absolute minimum, it may be better to buy insights on-demand from expert providers than to invest long-term into the development of such skills in-house. 5.9.1

Structure of Cloud-Based IaaS Providers

The following illustration showing the relevant actors in an IaaS network [6]. The integrated parties are the data sources, the IaaS service provider as well as the users (Fig. 14).

6 https://www.sas.com/en_us/solutions/cloud-computing/cloud-analytics/consulting-services/res

ults.html.

Data-as-a-Service versus Information-as-a-Service …

141

Fig. 14 Framework of IaaS (according to [6])

6 Comparison of DaaS and IaaS Along Discussed Dimensions Now that both of the concepts have been discussed individually, it is time to bring them together to compare and contrast the differences between them. Looking at the two definitions of DaaS and IaaS in the context of this paper, their connection can be visualized as follows (Fig. 15). In the DaaS case, it is only the access to data that is provided and turning it into information, by potentially enriching, segmenting, and interpreting the data, is left to the consumer. In the IaaS case, the provider offers additional services such as the creation of customized streams through which the consumer can access the information at any time, at the right place, for the right person, in the right form. More importantly, they offer automated and secure ways of collating, standardizing, integrating, contextualizing, value-adding, and aggregating data to turn it into usable information for customers. This is done by company and IaaS provider sitting down and putting together what [6] call a “standardized schema” for transforming data into “an enterprise-friendly or user-friendly format” (p. 28). It thereby fulfills

Fig. 15 Connection between DaaS and IaaS (own illustration)

142

I. Wagner and Z. Tacacs

what the ODCA demands when it says IaaS “should have the ability to provide standardized and secure methods to create, manage, exchange, and extract meaningful information from all available data” [31]. Whether a company wants to choose DaaS or IaaS for their company is going to depend on various factors.

6.1 Criteria for Suitability of the Respective Format A company’s decision to become a DaaS or IaaS customer should comply with several important considerations, including company size, previous experience with data science and analysis, availability of in-house data experts, strategic considerations, legal framework, core competencies, and long-term growth potential. • Company size: Previous experience with data science and analysis: if a company has performed previous projects in terms of data science and analysis, it might prefer not to use an IaaS offering and instead conduct the analysis in-house. If such experience however is not present yet, then an IaaS provider will be a cost-effective and time-efficient solution. • Availability of in-house data experts: in the situation where a company already has data analysis experts in-house, an IaaS offering may not be meaningful. However, even if a company has data management skills, it may still profit from a respective DaaS offering as third-party data can enrich or complement existing internal data sets. • Strategic considerations: if a company determines in its evaluation, that it may too like to become a DaaS provider in the near future as it sits on data that could be interesting to other customers, it may be best to take a cooperative approach in becoming a DaaS or IaaS customer. This way the company can benefit from using big data, but also gain experience and build in-house expertise for a later-in-time launch as a DaaS provider itself. If the company however plans to grow quickly without having data analysis as a core competence, it may be best to build a deep strategic partnership with an IaaS provider so it can harvest the power of big data and data-driven insights quickly. • Legal framework: given the data quality and data security concerns mentioned for both DaaS and IaaS, the question is whether a company is legally allowed to host and perhaps share its data with other partners which an effective realization of DaaS and IaaS require. For companies or organizations in the public sector, for example, DaaS and IaaS realizations may be interdicted for legal reasons. • Core competencies: as companies seek to become more agile for competing in a global marketplace, if data analysis is a core competence of the business, but data management is not, a DaaS offering may be the best solution. If a company doesn’t have lots of experience in data analysis and deriving insights, it might opt for an IaaS offering.

Data-as-a-Service versus Information-as-a-Service …

143

• Long-term growth potential: if the company intends to grow quickly then it may prefer to simply utilize external partners to provide the required insights to achieve the desired growth. If a company determines however that it wants to develop into a DaaS or IaaS provider itself in the long-term, it may decide to develop the skills in-house (perhaps in combination with consulting from DaaS/IaaS providers). Hence, a company should rather consider choosing DaaS instead of IaaS in case of: • • • • • • •

Big company size, Medium to high prior experience with data analysis, Good availability of in-house data experts, Build a trusted network of strategic partners rather than in-house competencies, The legal framework allows for storing of data on cloud servers, Core competencies include data analysis, Seeking to grow more slowly and develop in-house competencies in data analysis.

6.2 Summary of the Key Findings In the following Table 1 are summarized key findings from our research to compare DaaS and IaaS.

7 Conclusion 7.1 Synopsis In this paper, the concepts of DaaS and IaaS were analyzed in terms of characteristics, drivers, benefits, and downsides, as well as their suitability for companies. They were researched for similarities and differences with the clear result that they are more similar than different. IaaS is an extension of DaaS. IaaS vendors typically offer the same services that DaaS vendors do but complement them by utilizing a scheme to organize data into the enterprise-friendly and user-friendly information for customers. Data is sourced from various providers, built into a hub, and then put into a service representation for final consumers utilizing collation, aggregation, standardization, data derivation, and additional measures. Depending on how far these services of specialized companies go, we can distinguish between analytics-asa-service where analysis of data is handled and insights-as-a-service that goes even further in providing companies with actionable insights on-demand. Both analytics-as-a-service and insights-as-a-service are seen as a subset of IaaS offerings. While DaaS already provides customers the benefits of more agility, higher cost-effectiveness and reduced time to analyze and gather data, IaaS goes beyond

144

I. Wagner and Z. Tacacs

Table 1 Comparison of DaaS and IaaS accross multiple dimensions (own interpretation) Dimension

DaaS

IaaS

Characteristics

Multiple data sources Different types of data Relevant actors Real-time data On-demand access Storage is provided

Transforms data into useable information Not necessarily but best used with cloud computing Need not be DaaS provider Follows company-specific rules—“standardized schema” Enabled by using an API

Suitability

SMEs without large data sets Companies want to enrich their data sets Companies with in-house analytics experts Companies without data management expertise Companies cannot afford a full-time data scientist

Small to medium company size No to low prior experience with data analysis No to low availability of in-house data experts Want to build a trusted network of strategic partners rather than in-house competencies Have a legal framework that allows for “sharing” of data Core competencies do NOT include data analysis

Benefits

Agility Cost-effectiveness Data quality

Rapid acquisition of meaningful information No need for a messy collection of data No need to dig into a large amount of data No necessity to wrap up all data for results Access to expertise when required, e.g. in case of new cases/scenarios Operational expense instead of fixed costs Increased speed

Downsides

Data security Data quality Governance Privacy Expertise

Increased dependence No development of internal analytics competencies A potential loss of sensible/confidential data

Pricing Models Request-based Volume-based Data-type based Corporate subscription

Deliverable-based Consumption-based User-based Corporate subscription Feature-based

Actors

Data source IaaS provider IaaS consumer

Data source DaaS provider Data consumer

(continued)

Data-as-a-Service versus Information-as-a-Service …

145

Table 1 (continued) Dimension

DaaS

IaaS

Drivers

Engaging customers with data-driven choices Monetization Public sector and government Technology shift Pioneering organizations

Shortage of skilled data experts Need for competitive advantage The ever-growing amount of data Increased need for agility

in offering customers freedom from diving into deep data analysis and providing relevant, actionable information and insights on-demand and at adequate costs. As both the DaaS and the IaaS market are predicted to further grow over the next years, the industry is going to see further cooperation between customers and DaaS/IaaS vendors as well as the development of core competencies in data analysis and information presentation on the side of specialized DaaS/IaaS vendors. In return, companies are going to focus more and more on their core competencies and benefit from increased agility as DaaS and IaaS services are outsourced to specialized vendors. This is also necessary because as the importance of DaaS and IaaS grows, not only big companies but also SMEs and start-ups have to be able to tap into the power of big data and DaaS and IaaS if they are to compete in a global marketplace. This is also a strategy to deal with the acute lack of data experts and analysts in this still young industry [56]. It is therefore essential that both customers, vendors, managers, and researchers alike have a clear understanding of DaaS/IaaS and understand that IaaS transfers more tasks from the customer to the vendor. Each company is required to take an in-depth analysis of their own data needs, usage patterns, and preferences and make a qualified decision whether IaaS or DaaS is adequate for them. The main aspects to pay attention to are the core competencies of the business, the type of industry, the size of the company, the availability of data scientists, previous experience with data providers. Specific attention needs to be paid to the legal framework in which the company acts. Concerns of security, privacy, and data quality must be weighed and effectively approached. before a decision to be made. Finally, as the importance of DaaS and IaaS grows further, more and more companies need to examine as well, if they do not potentially have the required data resources that other companies could be interested in. It is possible that a company merely considering to become a customer of DaaS or IaaS vendors may have the potential to become a DaaS or IaaS vendor itself, by licensing and providing relevant data to complementary businesses. New business models will be developed, and new sources of income can be generated.

146

I. Wagner and Z. Tacacs

7.2 Further Research While a lot of important findings have been made in this study, there are various limitations need to be kept in mind. Firstly, due to the dynamic and innovative nature of the topic, there are only a few peer-reviewed sources that serve as the foundation of this comparison study. Most of the information has been gathered and compiled from white papers and articles published by companies that while at the heart of innovating and determining the future of cloud computing, might also represent their commercial interests in their published documents. This was accepted as a given shortcoming of the study and all reported findings have therefore been as much as possible been scrutinized and checked for multiple mentions to prove their accuracy. Naturally, this is a quickly evolving field that has gained traction in the last 2– 3 years. Most of the findings in this work has hardly been covered or documented in scientific research yet. It is research institutions by big companies like IBM or Gartner themselves that provide most of the data and findings to date on this topic. Already, some companies argue that neither DaaS nor IaaS is going far enough, claiming what exactly businesses need is Insights from the derived data and information, hence the term Insights-as-a-Service has been coined by companies like. Secondly, the internet of things (IoT) is generating a tremendous amount of data. According to [59] “The Web is the largest data repository in the world, this implies a big hidden potential for those who will utilize available knowledge to improve their business. The importance of the intelligent Web-based information systems with capabilities to discover useful knowledge through analyzing a huge amount of content and its structures is growing.” As a result, DaaS providers will be challenged to integrate this data created and companies have to learn to be able to utilize this data and integrate it into their decision making by effectively gaining insights from it. Data scientists and researchers have to work together to find new and more effective ways of deriving information and insights from ever-growing amounts of unstructured data that are produced “from social networking sites, sensors and satellites” [47]. There is a lot of future research that has to be conducted. Academic researchers need to catch up with the evolving trends in the cloud landscape and can assist with their research to determine common standards and best practices as well as a reliable framework for the implementation of DaaS and IaaS services. The need of the further research was identified also in [60]: There is a need for research into “Insights-as-a-Service” that operates as an integrated function i.e., it jointly considers function-centric orchestration in data movement, machine learning, data sharing and computing for ‘ubiquitous analytics purposes’. Such an Insights-as-a-Service paradigm can help us handle the fact that data and processing will be increasingly distributed in nature and (sometimes) federated. Moreover, as mentioned in Sect. 6, DaaS and IaaS also need to be approached from the perspective of companies potentially becoming data or service providers themselves.

Data-as-a-Service versus Information-as-a-Service …

147

Researchers can help by providing clear models and definitions of the terms and technologies being used, to avoid customer confusion and provide more transparency for all involved agents.

References 1. Duan, Y., Fu, G., Zhou, N., Sun, X., Narendra, N.C, Hu, B.: Everything as a Service (XaaS) on the cloud: origins, current and future trends. In: 2015 IEEE 8th International Conference on Cloud Computing, New York, pp. 621–628 (2015) 2. Mladenow, A., Kryvinska, N., Strauss, C.: Towards cloud-centric service environments. J. Serv. Sci. Res. 4(2), 213 (2012) 3. Gartner: Gartner Says Worldwide Public Cloud Services Market to Grow 18 Percent in 2017 [Press release]. Retrieved from http://www.gartner.com/newsroom/id/3616417. Accessed 2 August 2020 (2017) 4. Vaquero, L.M., Rodero-Merno, L., Caceres, J., Lindner, M.: A break in the clouds: towards a cloud definition. ACM SIGCOMM Comput. Commun. Rev. 39(1), 50–55 (2008) 5. Mell, P., Grance, T.: The NIST Definition of Cloud Computing, pp. 800–145. National Institute of Standards and Technology, Spec. Pub. (2011) 6. Qiu, M., Gai, K.: Mobile Cloud Computing: Models, Implementation, and Security. CRC Press, FL (2017) 7. IBM Marketing Cloud: 10 Key Marketing Trends for 2017 and Ideas for Exceeding Customer Expectations. Retrieved from https://bizibl.com/marketing/download/10-key-marketing-tre nds-2017-and-ideas-exceeding-customer-expectations. Accessed 2 August 2020 (2017) 8. Grandison, T., Maximilien, E.M., Thorpe, S.S.E., Alba, A.: Towards a formal definition of a computing cloud. In: 6th World Congress on Services, pp. 191–192. IEEE Computer Society (2010) 9. Plummer, D.C., Bittman, T.J., Austin, T., Cearley, D.W., Smith, D.M.: Cloud Computing: Defining and Describing an Emerging Phenomenon, pp. 1–9. Gartner (2008) 10. Hashemi, S.M., Bardsiri, A.K.: Cloud computing versus grid computing. ARPN J. Syst. Softw. 2(5) (2012) 11. International Business Machines Corporation: Capturing the potential of cloud: How cloud drives value in enterprise IT strategy [White paper]. Retrieved from https://www.ibm.com/ibm/ files/K640311W72867H78/12Capturing_the_Potential_of_Cloud_1_5MB.pdf. Accessed on 2 August 2020 (2009b) 12. Myerson, J.: Cloud computing versus grid computing. Retrieved from https://www.ibm.com/ developerworks/library/wa-cloudgrid/. Accessed on 2 August 2020 (2009) 13. Newman, D.: Data as a service: the big opportunity for business. Retrieved from https://www.forbes.com/sites/danielnewman/2017/02/07/data-as-a-service-the-big-opp ortunity-for-business/. Accessed on 2 August 2020 (2009) 14. Sultana, A.: Cloud Computing—How much you know of it (Part-2). Retrieved from https:// blog.sysfore.com/cloud-computing-introduction-part2/. Accesses on 2 August 2020 (2004) 15. MacKenzie, C.M., Laskey, K., McCabe, F., Brown, P.F., Metz, R., Hamilton, B.A.: Reference model for service oriented architecture 1.0. OASIS standard, 12, 18. Retrieved from https:// docs.oasis-open.org/soa-rm/v1.0/soa-rm.html. Accessed 2 August 2020 (2006) 16. Dan, A., Johnson, R., Arsanjani, A.: Information as a service: modeling and realization. In: International Workshop on IEEE Systems Development in SOA Environments, 2007. SDSOA’07: ICSE Workshops 2007, pp. 2–2 (2007) 17. Mahmood, Z.: Service oriented architecture: potential benefits and challenges. In: Proceedings of the 11th WSEAS International Conference on Computers, pp. 497–501. World Scientific and Engineering Academy and Society (WSEAS) (2007)

148

I. Wagner and Z. Tacacs

18. Epicor: The Business Benefits of Service-Oriented Architecture: A Guide for Manufacturing Executives [White paper]. Retrieved from https://static.ziftsolutions.com/files/8a8930614688 a40e01468c1ec3730df8.pdf. Accessed 2 August 2020 (2004) 19. Booth, D., Hass, H., McCabe, F., Newcomer, E., Champion, M., Ferris, C., Orchard, D.: Web services architecture. W3C Working Group Note 11 February 2004 (2004) 20. Box, D., Ehnebuske, D., Kakivaya, G., Layman, A., Mendelsohn, N., Frystyk Nielsen, H., Thatte, S., Winer, D.: Simple Object Access Protocol (SOAP) 1.1. W3C Note 08 May 2000 (2000) 21. Ribeiro L., Barata J., Mendes P.: MAS and SOA: complementary automation paradigms. In: Azevedo, A. (eds.) Innovation in Manufacturing Networks. BASYS 2008. IFIP—The International Federation for Information Processing, vol. 266(28), pp. 259–268. Springer, Boston, MA (2008) 22. Brittenham, P.: An overview of the web services inspection language. Retrieved from https:// www.ibm.com/developerworks/library/ws-wsilover/. Accessed 2 August 2020 (2002) 23. Rouse, M.: Cloud services. Retrieved from http://searchcloudprovider.techtarget.com/defini tion/cloud-services. Accessed 2 August 2020 (2016) 24. Weller, A.: Types of cloud computing [Web blog post]. Retrieved from https://www.crucial. com.au/blog/2013/05/27/types-of-cloud-computing/. Accessed 2 August 2020 (2013) 25. Reichert, A.: Web Services versus Cloud Services: Are They the Same? Retrieved from http://searchmicroservices.techtarget.com/tip/Web-services-vs-cloud-services-Are-theythe-same. Accessed 2 August 2020 (2014) 26. Haas, H., Brown, A.: Web Services Glossary: W3C Working Group Note 11 February 2004. Retrieved from https://www.w3.org/TR/ws-gloss/. Accessed 2 August 2020 (2004) 27. French, J.: Cloud Computing and Web Services. Retrieved from http://www.theiet.org/resour ces/inspec/support/subject-guides/cloud.cfm. Accessed 2 August 2020 (2011) 28. Kryvinska, N., Gregus, M.: SOA and its Business Value in Requirements, Features, Practices and Methodologies. Comenius University in Bratislava (2016) 29. Rowley, J.: The wisdom hierarchy: representations of the DIKW hierarchy. J. Inf. Sci. 33(2), 163–180 (2007) 30. Ackoff, R.L.: From data to wisdom. J. Appl. Syst. Anal. 16(1), 3–9 (1989) 31. Open Data Center Alliance: Open Data Center Alliance™ Master Usage Model: Information as a Service Rev. 1.0. Retrieved from https://opendatacenteralliance.org/docs/Information_as_ a_Service_Master_Usage_Model_Rev1.0.pdf. Accessed 25 July 2020 (2013) 32. Oracle: Oracle® Enterprise Data Quality for Product Data Knowledge Studio Reference Guide Release 11g R1 (11.1.1.6). Retrieved from https://docs.oracle.com/cd/E35636_01/doc.11116/ e29134/stan_data.htm. Accessed 25 July 2020 (2013) 33. Kaczor, S., Kryvinska, N.: It is all about services—fundamentals, drivers, and business models. Soc. Serv. Sci. J. Serv. Sci. Res. 5(2), 125–154 (2013) 34. Li, G., Ooi, B.C., Feng, J., Wang, J., Zhou, L.: EASE: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp 903–914. ACM (2008) 35. Sint, R., Stroka, S., Schaffert, S., Ferstl, R.: Combining unstructured, fully structured and semi-structured information in semantic wikis. In: Proceedings of the Forth Semantic Wiki Workshop (SemWiki 2009), Co-located with 6th European Semantic Web Conference (ESWC 2009), vol. 464(14), pp. 2–6 (2009) 36. Abiteboul, S.: Querying semi-structured data. In: Database Theory—ICDT’97, pp. 1–18 (1997) 37. Gantz, J., Reinsel, D.: The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East. IDC iView. IDC Analyze the Future, pp. 1–16 (2007) 38. Sarkar, P.: Data as a Service: A Framework for Providing Reusable Enterprise Data Services. Wiley, Hoboken, NJ (2015) 39. Delen, D., Demirkan, H.: Data, information and analytics as services. Decis. Supp. Syst. 55(1), 359–363 (2012) 40. Linthicum, D.: Why data-as-a-service has taken off so fast [Web blog post]. Retrieved from http://www.infoworld.com/article/2610427/cloud-computing/why-data-as-aservice-has-taken-off-so-fast.html. Accessed 2 August 2020 (2014)

Data-as-a-Service versus Information-as-a-Service …

149

41. Pringle, T., Baer, T., Brown: Data-as-a-service: the next step in the as-a-service journey. Retrieved from http://www.oracle.com/us/solutions/cloud/analyst-report-ovum-daas2245256.pdf. Accessed 2 August 2020 (2014) 42. Terzo, O., Ruiu, P., Bucci, E., Xhafa, F.: Data as a Service (DaaS) for sharing and processing of large data collections in the cloud. In: 2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems (2013) 43. Fattah, A.: Cloud Analytics: A Taxonomy for Service Offerings [Web blog post]. Retrieved from http://www.ibmbigdatahub.com/blog/cloud-analytics-taxonomy-service-offerings. Accessed 2 August 2020 (2014) 44. Rajesh, S., Swapna, S., Reddy, P.S.: Data as a Service (DaaS) in cloud computing. Glob. J. Comput. Sci. Technol. 12(11-B) (2012) 45. BrightPlanet.: Why you should be using Data-as-a-Service in 2016 [Web blog post]. Retrieved from https://brightplanet.com/2016/01/why-you-should-tap-into-osint-and-data-as-a-servicein-2016/. Accessed 2 August 2020 (2016) 46. Badidi, E., Routaib, H., El Koutbi, M.: Towards Data-as-a-Service provisioning with highquality data. In: El-Azouzi, R., Menasche, D., Sabir, E., De Pellegrini, F., Benjillali, M. (eds.) Advances in Ubiquitous Networking 2. UNet 2016. Lecture Notes in Electrical Engineering, vol. 397, p. 48. Springer, Singapore (2017) 47. Mukherjee, S., Shaw, R.: Big data—concepts, applications, challenges and future scope. Int. J. Adv. Res. Comput. Commun. Eng. 5(2), 66–74 (2016) 48. Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., Khan, S.U.: The rise of “big data” on cloud computing: review and open research issues. Inf. Syst. 47, 98–115 (2015) 49. Otto, B.: Organizing data governance: findings from the telecommunications industry and consequences for large service providers. Commun. Assoc. Inf. Syst. 29(3), 45–66 (2011) 50. Nazemoff, V.: Data Governance Roadmap for IT Executives [White paper]. Retrieved from http://acolyst.com/wp-content/uploads/2013/04/Acolyst-Data-Governance-White-Paper_ Final.pdf. Accessed 2 August 2020 (2013) 51. Gregus, M., Kryvinska, N.: Service Orientation of Enterprises—Aspects, Dimensions, Technologies. Comenius University in Bratislava (2015) 52. Kryvinska, N.: Building consistent formal specification for the service enterprise agility foundation. Soc. Serv. Sci. J. Serv. Sci. Res. 4(2), 235–269 (2012) 53. Franks, D., Gallagher, J., Jarvis, D., Rogers, S.: Inside the mind of Generation D: What it means to be data-rich and analytically driven [White paper]. Retrieved from http://www.ebisol utions.co.uk/wp-content/uploads/2015/09/Inside-the-mind-of-Generation-D.pdf. Accessed: 2 August 2020 (2014) 54. Informatica: Informatica Data as a Service Installation and Configuration Guide. Retrieved from https://docs.informatica.com/master-data-management/mdm-customer-360/10-4/installationand-configuration-guide/before-you-install/informatica-data-as-a-service.html. Accessed 2 August 2020 (2020) 55. Dun & Bradstreet, Inc.: D&B Hoovers. Accelerate the Path from Prospect to Profitable Relationship. Retrieved from https://www.dnb.com/content/dam/english/dnb-solutions/DNB_Hoo vers_Product_Brochure.pdf. Accessed 5 August 2020 (2018) 56. SAS: SAS Results Delivers Value [White Paper]. Retrieved from https://www.sas.com/content/ dam/SAS/en_us/doc/whitepaper1/sas-results-delivers-value-108618. Accessed 5 August 2020 (2017a) 57. Capgemini: Insights-as-a-Service. Retrieved from: https://www.capgemini.com/insights-data/ insights-as-a-service. Accessed 2 August 2020 (2017) 58. SAS SAS Results [Services Brief]. Retrieved from https://www.sas.com/content/dam/SAS/en_ us/doc/servicebrief/sas-results-108617.pdf. Accessed 2 August 2020 (2017b) 59. Molnár, E., Molnár, R., Kryvinska, N., Greguš, M.: Web intelligence in practice. Soc. Serv. Sci. J. Serv. Sci. Res. 6(1), 149–172 (2014) 60. Calyam, P., Ricart, G.: Research and infrastructure challenges for applications and services in the year 2021. ACM SIGCOMM Comput. Commun. Rev. 46(3), 1–5 (2018)

Management and Measuring Customer Loyalty in Digital Marketplace—Analysis of KPIs and Influence Factors in CLTV Wolfgang Neussner

Abstract In a highly competitive digital business environment, the management of customer loyalty becomes particularly important, as new customers can only be won at considerable expense and in manageable numbers. It is essential to strengthen customer loyalty to the company in order to ward off competitive marketing campaigns in the best possible way. Thus, the aim of this research is to analyze and compare the concept of bonus programs in the retail industries of Austria, Germany and Switzerland. The starting point of the loyal customer as the basis of a successful customer value management, in the context of maximizing enterprise value, is the necessity of customer loyalty and emotional bonding. The research is supplemented by the development of purchase decision processes. Besides, the implementation in companies is quantitatively surveyed and compared with the theoretical concepts.

1 Introduction 1.1 Relevance In a saturated and highly competitive environment, the management of customer loyalty must become particularly important, as new customers can only be won at considerable expense and in manageable numbers. The aim is to strengthen customer loyalty to the company in order to ward off competitive marketing campaigns in the best possible way. For example, customer loyalty programs, in which existing customers are rewarded (monetary or non-monetary) for their repurchasing behavior, are used to try to bind existing customers to the company. However, the possibility of unnecessarily reducing profits can also be seen if customer behavior cannot be controlled as desired.

W. Neussner (B) Faculty of Management, Comenius University in Bratislava, Bratislava 25 82005, Slovakia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Kryvinska and A. Poniszewska-Mara´nda (eds.), Developments in Information & Knowledge Management for Business Applications, Studies in Systems, Decision and Control 376, https://doi.org/10.1007/978-3-030-76632-0_4

151

152

W. Neussner

2 Theoretical and Conceptual Background 2.1 Customer Relation Management Loyalty programs can be understood as marketing programs designed to generate customer education by offering benefits to profitable customers [1]. However, Henderson et al. consider it an incentive system which influences customer behavior in the sense that consumer behavior is still improved irrespective of price or assortment changes [2]. Customer loyalty programs can also be seen as the structured set of marketing measures that rewards desired customer behavior [3]. Bonus programs are part of Customer Relationship Management (CRM). The goal is the long-term improvement of the profit potential. Key Performance Indicator is the Customer Lifetime Value (CLTV) [4]. This is calculated from the balance of all revenues and costs of a customer over the duration of the business relationship. Quantitative and qualitative components must be taken into account. The main qualitative factors include recommendation, cross-selling and up-selling potential. Since the duration of the customer relationship is a key input parameter, it makes more sense to calculate CLTV dynamically than statically [5]. Main factors influencing customer value are customer loyalty, cross-selling and up-selling potential, the reduction of customer service costs and other value-enhancing potential, such as recommendation behavior. Customer loyalty is intended to generate loyal customers who deliver greater profits for companies, since acquisition costs are not incurred with every purchase. The increasing purchasing power in a person’s life cycle can also have positive effects. [4, 84, 85]. Hofmann [6] defines (see Fig. 1 [6]) the influencing factors for declining customer loyalty on the one hand on the provider side and on the other on the consumer side. The main reasons on the supplier side include market saturation, increasing price competition, the variety of offerings, a lack of innovative strength, homogenization of offerings due to the concentration of suppliers, price transparency due to new media (addition of the author: in addition to the Internet, also an increasing number Influence factors of offer side

Influence factors of demand side

- Market penetration / Increasing price competition - Experience Society and Hedonism - Variety of offers - Variety Seeking - Lacking innovative power - Emacipation of customer behavior - Homogenisation of the offer - Price awareness / bargain mentality - Price transparency through new media - Online shopping - New operating forms - Increasing mobility - New distribution channels / Increase of online distribution ↓ ↓ Decreasing loyalty in consumer markets ↓ Increasing relevance of customer loyalty as marketing target

Fig. 1 Influence factors on the customer loyalty [6]

Management and Measuring Customer Loyalty in Digital Marketplace …

↓ Formal legal loyalty

Increase of change barriers ↓ ↓ Technical Economic functional Loyalty loyalty

Bondedness

153

↓ Psychological Loyalty

Connectedness

Fig. 2 Increase of change barriers [6]

of social media channels), new business forms and new distribution channels. On the consumer side there are changes concerning the experience society, variety seeking, emancipation of the consumer, price awareness, bargain mentality, online shopping as well as the increasing mobility, which creates more possibilities than before [86–88]. The resulting decline in loyalty in the end consumer markets automatically leads to an increased relevance of customer loyalty as a marketing objective. Motivated by this, the goal is to raise the barriers to change through a customer loyalty program as explained in Fig. 2 [6]. Hofmann [6] distinguishes in this context between the formallegal, technical-functional and the economic bond in the sense of being bound and as a clear demarcation the psychological bond also as a bond in contrast to being bound. Hofmann [6] also subdivides the exogenous factors influencing customer loyalty into situational factors (attractiveness of competing products and lack of time) and personality psychological factors (convenience, need for variety, price awareness, involvement, aspiration level and willingness to relate) as shown in Fig. 3 [6]. As mentioned above, customer loyalty has an impact on profit and thus on the value of the company. Derived from this, the systematic evaluation of customers by their purchasing behavior takes place and is included in the company evaluation. Kumar and Reinartz [7] show a model (Fig. 4 [7]) with the detailed calculation of the measure from the business point of view of customer loyalty, namely the CLTV. Figure 4 [7] presents the calculation of the LTV. Recurring revenues are reduced by recurring costs, which results in the contribution margin, which is discounted in connection with the customer duration and a discount factor to be selected. Afterwards the result is reduced by the acquisition costs to receive the LTV. Customer Lifetime Value Management (hereinafter CLTV-M) has aligned all sales and marketing activities in the company with customer value. This serves to maximize the company’s profit, based on a strategy of customer orientation, which is reached through a conscious non-equal treatment of all customers “the right measure for the right customer”. In the author’s opinion, this should be supplemented by the component “right time”, since this could lead unnecessary costs spent too early or a lost customer spent too late could be the result. Higher expected profits also justify higher or non-standardized acquisition costs. The main task of the CLTV-M is therefore the adequate selection of the right instruments at the right time.

154

W. Neussner Exogen influencing factors of customer loyalty

Situative influencing factors

Personality psychological influencing factors

- Attractivity of the competitor´s offer

- Comfort

- Lack of time

- Need for variety - Price consciousness - Involvement - Demand level - Relationship readiness

Fig. 3 Exogen influence factors of customer loyalty [6]

Recurring revenues Contribution margin Recurring costs Lifetime of a customer

Lifetime profit

Discount rate

Acquistion cost

LTV

Fig. 4 Customer Lifetime Value (CLTV) [7]

Bolton et al. [8] see the use of marketing instruments (service quality, direct mailings, relationship instruments, reward programs as well as advertising and distribution) as a way of perceiving the customer (price, satisfaction, commitment) explained in Fig. 5 [8]. If the instruments are chosen to suit the customer, this will be reflected on the one hand in the customer’s behavior (repetition, additional and crossbuying behavior) and on the other hand in the resulting costs and revenues and thus ultimately in CLTV. Figure 6 [5] shows the demarcation between product, process and value orientation in the company. The starting point is that the company´s organizational structure shows a product management and a sales unit (product orientation). The process orientation complements the product orientation with the levels of customer marketing and customer care/retention, where the value-oriented approach has been expanded to include CLTV-M. The product orientation has been expanded to include CLTV-M.

Management and Measuring Customer Loyalty in Digital Marketplace …

155

Fig. 5 Customer asset management of services [8]

Product Orientation

Product Management

Process Orientation

Product Management

Value Orientation

Product Management

Sales

Sales

Sales

Customer Marketing

Customer Care, Customer Retention

CRM

Customer Marketing

Customer Care, Customer Retention

Value Lifetime Management

Fig. 6 Structure of customer lifetime value management [5]

This also shows (see Fig. 6) that conventional (one-dimensional) strategies (cost leaders or quality leaders) must be developed in a value-oriented approach to multidimensionality in order to be able to live customer value in all dimensions. CLTV-M is therefore not an organizational unit, but a target for the creation and maintenance of profitable, sustainable customer relationships. In estimating the expected CLTV,

156

W. Neussner

valuable customers are treated individually, and less valuable customers standardized. The assessment in valuable and less valuable is carried out by socio-demographic characteristics, the obtained turnover with the help of the analysis of the life cycle of the customer. The monitoring of a customer’s intrinsic value is given very high priority, which is also measured continuously based on the availability of data on individual customers. In this way only those customers are to be bound who are worth it to be bound [5]. In addition to the aspects mentioned above, the relative sales development could also be added as a key figure. The challenge in the conception of such a bonus program is to bind the customer to the company and not to the bonus program. Even if companies regard the bonus program exclusively as part of the marketing mix, customers only see the totality. The consequence of this may be that a bonus program that is not perceived as serious or the products and/or services offered as bonuses are not perceived as qualitatively meeting the requirements, and the products and services may also be perceived as inferior. For example, a bonus program that is perceived negatively can also have a negative impact on a company’s sales [9]. Loyalty expresses the customer’s affiliation with the company or a brand. The following distinctions regarding the degree of brand loyalty can be found in the literature: “Hard-core loyalty” refers to customers who buy only one brand from a company at all times. In terms of customer loyalty, these are the target customers that all companies want. “Spilt loyals” are those which consistently purchase two or three different brands from different dealers. This happens because no reason or obligation is seen to show exclusive loyalty to a brand [10]. “Shifting loyals” include those customers who occasionally change brands from different companies [11], whereas “switchers” have no ties to any brand and very often switch between different brands, different businesses [11, 12]. If customers regularly buy from the same company and have little opportunity to switch to competing companies, this is referred to as “Behavioral loyalty” [13] “Attitudinal loyalty” is the term used when a customer gives a brand a strong preference [14]. “Attitudinal loyalty” leads to positive recommendations through “word-of-mouth propaganda” [13]. Customers with a “cognitive loyalty” have a willingness to pay higher prices for products or services compared to other providers [13]. Uncles et al. [15] define “Monogamous Loyalty” as 100% loyalty to a single brand and “Polygamous Loyalty” as loyalty to a portfolio of brands in a product category. Customers can be either in connection with a company (the net benefit lies in the customer’s perception of the company or a state of connection to the customer loyalty program). In the second case, the condition is covered by a change barrier, which restricts customer freedom [16]. However, it is only possible to speak of customer loyalty if net benefit aspects or barriers to switching are the triggers of repeat purchases [17]. A habitual buying behavior can in no way be understood as customer loyalty [18]. This classification is due to the fact that the customer would have been retained as a customer even without the customer loyalty program.

Management and Measuring Customer Loyalty in Digital Marketplace …

157

2.2 Customer Loyalty Program In order to maximize CLTV, it is possible to bind customers to one’s own company and to increase it through additional purchases and repurchases. Ranzinger [19] differentiates customer loyalty instruments into bonus programs, discount cards, customer cards without regular incentives, point gluing campaigns and couponing. As can be seen in Fig. 7 [19] bonus programs are divided into single and multipartner programs. Thus, advantages can be collected either only with one or several enterprises. Notwithstanding the single bonus program, products or services from other companies can also be offered as benefits. The multi-partner programs usually have only one player from an industry on the market without further competitors from the industry. Discount cards can be issued as single or multi-partner cards. It must also be distinguished whether the discount is an immediate discount at the cash desk or a refund at the end of the year. The refund at the end of the year can be in the form of money or vouchers of the company. The disadvantage of customer cards without regular incentives is that they are not shown at the checkout every time a customer makes a purchase due to the lack of an advantage, which means that data on customer behavior is not recorded even though the customer is aware of it. Irregular coupons are sent and can be earned without showing the customer card. Point gluing actions can be carried out with or without address acquisition. From a predefined purchase value, customers receive adhesive dots. After the collection of a predefined number of adhesive dots, these can be redeemed partly with partly Potential Customer Retention Tools ↓



Bonus Program

Rebate Cards

Single Bonus Program

Multi PartnerBonus Program

↓ Customer Card without continous Incentivation





Points Collections

Couponing

Single Rebate Cards

Discounts without Adress Registration

Coupon (mail)

Mulit Partner Rebate Cards

Discounts with Adress Registration

Coupon (customer brochure)

CheckoutCouponing Mobile Couponing Couponing within Bonus Programs Coupon- /Rebate Platsforms Web

Fig. 7 Potential customer loyalty tools [19]

158

W. Neussner

without additional payment. When redeeming, there is the possibility of collecting personal data in order to be able to use them for marketing purposes. In the case of couponing, a distinction is made according to the distribution channel (mailshot, customer magazine, checkout process, mobile, as part of bonus programs and platforms). Couponing can be implemented by industry funded discounts on special products or by pre-defined discounts on all products [19]. In addition to the Ranzinger categorizations, Minnema et al. [20] see the possibility of the permanent and immediate rewarding of sales through bonuses. For example, the effects of an Instant Reward Program (IRP) with bonus awards on consumer purchasing behavior are investigated. An IRP is a fast-growing form of short-term program that immediately rewards consumers with small rewards per sales made, which are part of a wider range of collectibles. An additional element in many IRPs promotes specific brands with an additional premium known as bonus rewards. Bonus rewards are the additional rewards that consumers can earn by purchasing a particular advertised brand, which is a non-price promotion related to the IRP. Therefore, consumers can earn rewards in two ways: based on the sales made and the purchase of the advertised brands. To test the impact of these marketing tools, Minnema et al. use Dutch household panel data from 23 product categories in four supermarket chains. Consumer buying behavior is analyzed by modeling the number of shopping trips, frequency of purchases at product category level, brand choice, and volume of purchases. The results show that an IRP leads to incremental shopping trips. The promotion of a brand with a bonus and a discount compared to a pure discount leads to higher selection probabilities for the advertised brand. Finally, IRP and bonuses are particularly effective for households that collect the bonuses, but also positive, albeit smaller, effects can be seen for households that do not collect bonuses. In summary, IRP increases the number of purchases, premiums increase the share of the promoted product and product category, but have a lower effect on non-collective consumers [20]. While many studies have investigated consumer purchasing behavior in reward programs, there is a lack of a better understanding of customer solution behavior, especially when promotions influence a core aspect of reward programs—free rewards. Montoya and Flores [21] investigate the impact of a promotion on purchase and reward redemption in a reward program where consumers can partially cover the cost of a free reward with their money. The literature on rewards programs indicates positive reinforcement through redemption of rewards, while the literature on promotion provides different views on purchasing behavior after rewards are redeemed. Data from a major retailer’s reward program was used to determine whether such a promotion attracted customers with less transaction activity and shorter maturities. As a result, consumers using the promotion increased their preference for hedonic rewards compared to their previously observed behavior. This change in preference remained in place even after the termination of the action. Overall, the campaign significantly increased the number of redemption requests, but had a negative impact on subsequent consumer behavior by reducing the frequency and volume of purchases [21].

Management and Measuring Customer Loyalty in Digital Marketplace …

159

2.3 Bonus Schemes One possibility of a customer loyalty program is a bonus program. Bonus programs serve as a means of customer retention. A clear definition is not given in the literature. For example, Lauer [22] defines bonus programs as “when a systematic offer is made by companies to customers to collect specifically created value units (bonus points) for certain behaviors, which can be converted into benefits (bonuses) above a certain size (redemption threshold). Künzel [23] defines the goal of a bonus program as a strategic marketing instrument for customer loyalty, whereby the rewards for the customers [23] in proportion to their purchasing behavior. Diller [24] on the other hand, sees bonus programs as rebate systems for customers who receive rebates in kind or cash bonuses after achieving predefined values (purchase quantities or points). Minnema et al. speaks of an Instant Reward Program and emphasizes the immediacy of such a program [20]. As different as the approaches to the definition of a bonus program may be, they all have four characteristics: • • • •

The bonus program runs over a relatively long period of time. Participants are rewarded for predefined behaviors. The participants of the bonus program collect units of value. The participants of the bonus program will exchange these value units for bonuses or rewards at a later date [4].

A distinction must be made between systems where participants can only collect and redeem points at one or more companies due to their purchasing behavior. There is also the possibility of using the bonus systems Business to Business and Business to Customer [9]. The first bonus program to be mentioned in the literature was that of American Airlines in 1981 [25]. Bonus programs have their origins in the airline business and trade, although today other industries such as banks, insurance companies or energy companies offer bonus programs. Bonus programs are strongly influenced in the retail sector by greater digitization (e-commerce) and supra-regionalization of formerly individual businesses. The rapid progress of e-commerce providers is also leading to the digitalization of bonus programs [4]. According to Lauer [22] the basic mechanics of bonus programs work as shown in the Fig. 8 [22]. For a predefined behavior (e.g. turnover), the customer registered in the bonus program receives bonus points (discounts or status points), which he can exchange for a bonus performance when reaching a redemption threshold (minimum turnover or minimum number of points). From the point of view of the company, the registration of the customer generates more knowledge about the individual customer, which leads to customer-specific offers and ideally ends in cross/upselling. Functions of a bonus program are.

160

W. Neussner

Fig. 8 Mechanism of bonus programs [22]

1.

2. 3. 4.

Identification function: the participants are identified, and their buying habits are recorded. Sociodemographic data are recorded, and a map is usually used for identification purposes. Bonus function: predefined behavior is rewarded. Interaction function: the identification (see 1.) also enables targeted interaction with the participant. Service function: Services for participants, such as free credit card or ticket service [9].

Implementing the design possibilities of bonus programs [9] a combination of the following instruments should be considered. The target group is defined as businessto-business and/or business-to-customer. The extent and quality of the coverage must be determined and possibly also achieved in the form of cooperation. Should repeated shopping behavior be positively rewarded and this be defined by entry barriers, rewarding the behavior, defining point heights and the collection medium (card or app). Which channels should be used for communication with the participants (direct mailing/customer magazine/hotline/posters/TV). Should the redemption of points be made more difficult (bonus threshold/gradations/expiration of points) or easier (co-payment, access). The benefit is offered to repurchasers in the form of money, vouchers, material prizes, experience prizes or a status program.

Management and Measuring Customer Loyalty in Digital Marketplace …

161

For bonus programs, a distinction is made between multi-partner programs and bonus programs without partners. Cross-industry and therefore non-competing companies join to form multi-partner programs. This is usually handled by a specially founded company, which acts as a service provider and is financed by the participating partners. In return, the company takes over the entire processing, such as advertising, customer data management and the provision of premiums. The best-known multipartner programs in Germany include Payback, HappyDigits, which was founded by Deutsche Telekom, DeutschlandCard and the bonus program of Lufthansa, Miles and More. Bonus programs without partners are offered by individual companies that do not have any cooperations or collection partners, which serves the purpose of clarity [9]. Lauer sees only four ways to bonus credit. A distinction must be made between status points and discount points. Collection points may be provided by own products/services or by third parties. If the customers are not motivated by the bonus to the desired behavior, the bonus program loses its meaning. The dimensions of the bonus according to Lauer [4] are shown in Fig. 9 [4]. The first subdivision of bonus benefits distinguishes between economic and rational benefits. Economic benefit is understood to mean those products and services which could be acquired with the help of financial means even without membership, whereby the customer receives a measurable advantage (in monetary units). The (exclusively) emotional benefit, on the other hand, cannot be measured in monetary terms and cannot be acquired with money either. Since the monetary benefit can also lead to feelings, a clear separation is not possible and reasonable and leads Lauer to the following differentiation into four dimensions: Discount, extra services, status and fun and experience. A clear demarcation of these four models is often not given, whereby the best combination should be developed. By definition, the rebate is not granted immediately but only after thresholds have been reached. Since the discount is only paid out afterwards, it is also referred to as cashback. The advantage of the discount as a bonus option lies in the simplicity of the availability and logistics of

Fig. 9 Dimensions of bonification [4]

162

W. Neussner

the bonus (money) and practice over decades. The big disadvantage, however, is that little or no emotions can be aroused [4]. The financial test carried out by Stiftung Warentest in August 2010 quoted the Gesellschaft für Konsumforschung (Gfk) as saying that cardholders of the cash back program provider Payback in Germany made 25% more sales with their Payback card in the third year of their participation than in the first [26]. Another example is ABCO, where the launch of a baby club generated 25% revenue growth in 6 months due to the consolidation of the share of total spending [27]. The meaningfulness of a bonus program for every company must be denied. If a company already has sufficient information about customers and their purchasing behavior or if there are insufficient resources available to analyze the generated data, a bonus program will not contribute to the positive development of the company (company characteristics). Bonus programs are designed to prevent customers from migrating to competitors. If there is a contractual or psychological dependence on the existing supplier or service provider, a bonus program will not increase customer loyalty (market characteristics). A high heterogeneity of the target customers leads to a high cross-selling potential, which can be exploited by a bonus program (customer characteristics). If the relevant competitors all have bonus programs, it must be considered whether the programs will not (must) be aligned in the long term. If a bonus program is not an industry-specific minimum requirement, it should be dispensed with (competitive features) if it is not based on promising, affordable differentiation. The fact that the conception of a bonus program must be thought through is also due to the fact that well-known companies such as Subway, American Airlines (note: reintroduced), eBay America, America Online have discontinued their bonus programs. The art lies in not being so generous that margins erode, but still generating additional sales and profit margins [27]. In 2010, Stiftung Warentest examined 29 bonus programs for their data protection conditions and rated four as acceptable [26]. 2018, the DSGVO [28] tightened the data protection conditions.

2.4 Requirements for the Conception of a Bonus Program The construction of exit barriers is essential. The creation of exit barriers occurs when an unfaithful customer would have to forego a service or bonus if he switched to a competitor because the competitor did not offer it or would not accept it. If a customer buys comparable products/services from several providers, a bonus program aims to increase the customer’s share of total expenditure. Even if a competing supplier offers a comparable system, already acquired points or status should make a change unattractive. A convex bonus (meaning that the more money spent, the higher the percentage refunded) is one way to motivate customers to bundle spending with one or a few vendors. The introduction of different status levels can

Management and Measuring Customer Loyalty in Digital Marketplace …

163

also lead to a concentration of expenditure. The concern for a status and the associated benefits (see airlines) can lead to expenses owed solely to the non-loss of status [9]. Steinhof [29] reports increased customer loyalty following promotion to the status hierarchy of a bonus program. The attempt of some managers to give the company’s customers increased status is viewed critically. This is where emotions, customer gratitude and customer skepticism come together. It would be wrong to assume intuitively that if one “does something good” for the customer, this is also perceived by the customer without restriction, even if the customer is considered with advantages which, strictly speaking, he is not (yet) entitled to within the scope of the bonus program due to his purchasing behavior. The customer does not assume that he can actually expect a “gift” from the company and reacts with skepticism and the expectation of the company’s self-interest. Thus, when designing a bonus program, consideration must be given not only to the expected positive but also to the unexpected negative reactions. If the customer status is designed in such a way that it is not perceived as a “pure gift” by the customer, the skepticism of the customer is reduced. This can be achieved by the fact that the customer is not forced to ascend the status, but must actively give his consent, whereby the customer retains control and can act in a selfdetermined manner and/or this has already had a proximity to the status ascent. This contributes to the reduction of customer skepticism and the gift customer status leads to a loyalty increasing customer gratitude and enables the preferred customer status as a possible measure within the scope of bonus programs. This leads from a past to a future-oriented classification of customers in the sense of CLTV. Thus managers of companies can develop potential customers to the “desired” customers by the given customer status, provided that the status striving of the customers can be used without their concern of overreaching for the company in the sense of re-purchases or additional purchases. As a result, less profitable customers can currently be developed into significantly more profitable customers for the company in the future. Thus, the correct use of the donated status is just as promising for the company as the status the customer has developed himself. [29] The design dimensions of bonus programs according to Hofmann [6] (see Fig. 10 [6]) include the collection mechanism, redemption mechanism and bonus offer. The collection mechanism (process of collecting bonus units) can be granted in the form of bonus units (proportional, staggered or s-shaped), where the awarding rules and the reference value (sales, miles, etc.) as well as time and amount must be clarified. Customer loyalty should also be controlled on the basis of allocation rules and reference values. The awarding rules can be applied on a constant, cyclical or action basis. While the action-related pursuit of the action targets is the result, constant allocation rules reward the continuous behavior of the customer, which is also the most common variant. Cyclical behavior does not reward individual behavior, but behavioral constancy. Examples include basic bonuses independent of consumption, such as birthday or Christmas vouchers. Bonus allocation is usually linked to turnover, sometimes to distances (see airlines) or quantities.

164

W. Neussner

Parameter

Dimensions of bonus programs Collecting mechanism

Redeem mechanism

Bonus offering

Constant award of bonuses (proportional, graduated, s-shaped)

Redemption tresholds

Bonus charakter (utilitaristisch / hedonistisch)

Aktionsbezogene related Vergabe of bonus units

Rules of expiration bonus units

Connection of the bonus to the main business (business oriented / non business oriented)

Cyclical award of bonus units

Possibility of additional payment (voluntarily/required)

Fig. 10 Dimensions of a loyalty program [6]

The redemption threshold must be regulated in the course of the redemption mechanisms. The rules are for the expiry of bonus units as well as the possibility of voluntary or required co-payment. In the area of premiums, these can be sociopsychological (image, exclusivity, experience) or hedonistic (fun, joy). The bonuses those have already been collected and not yet redeemed serve as a protective shield against customers migrating to competitors and can be regarded as a withdrawal barrier. Redemption thresholds are the values that must be reached in order to redeem the units already collected [6]. The psychological effect of redemption [30] was established by Nunes and Dreze in a study in which they were able to clarify that immediately after redemption, the purchasing frequency of customers decreased by almost 40%. The motivational effect of reaching the redemption threshold is thus documented. Furthermore, it could be demonstrated that low redemption thresholds as well as closely related redemption thresholds do not constitute a sufficient incentive for consumers. Point expiry rules make sense from an entrepreneurial point of view, as otherwise provisions must be formed and timely information about the future expiry of the bonus can have a control effect on the customer [6]. Earning the bonus is the core benefit of participating in a bonus program. The proximity of the premium offer to the core benefit can be designed on the one hand to be performance-related or more performance-oriented. The necessity and usefulness of customer-specific offers led Tesco to produce four million different variants of its mailings in 2004. A redemption limit adjusted to the individual sales is essential. This must not be too low (in order not to be easily interchangeable with other providers), but also not prohibitively high, so that potential participants in the bonus program are deterred from participating because of the unrealistic goal. Furthermore, a disproportionate bonus for the registration for the bonus program is recommended, because the “felt” distance to the first bonus seems not unreachable. Studies where bonus programs required the same turnover, but where a start-up bonus was given away, had about twice the participation rate. Analyses of the customer behavior of bonus programs show that luxury or entertainment articles are more popular as bonuses than everyday products [9].

Management and Measuring Customer Loyalty in Digital Marketplace …

165

The effectiveness and efficiency of bonus programs is often questioned in the literature, which is why relevant studies and results are summarized below. A successful customer loyalty program is an investment by companies in longterm relationships with their customers. However, to be effective, customer loyalty programs must be perceived as valuable by customers. So et al. [31] proceeded a study to examine the different types of values that customers derive from loyalty program membership and the relationship between program value, program loyalty, and brand (company) loyalty. In addition, the influence of program and brand loyalty on behavioral responses, including the share of wallet, share of purchase, word of mouth and willingness to pay more, was investigated. The moderating effect of the program adjustment and the duration of the customer loyalty program membership of the customer on the relationship between loyalty to the program and loyalty to the brand was surveyed. Based on 628 respondents from the two largest independent retail loyalty programs in Australia, it was established that the loyalty program value consists of six primary constructs that promote loyalty. These include reward attractiveness, knowledge benefits and effort that affect the perceived experience benefits of customer loyalty program members, which in turn affect program loyalty; while values derived from group membership and disclosure convenience promote brand loyalty. Furthermore, it was surveyed that program loyalty influences brand loyalty; and program and brand loyalty together lead to positive customer behavior. However, program loyalty has a negative impact on a customer’s willingness to pay. The results show the influence of several program value elements on customer loyalty and expand the literature by clarifying the relationships between program value, attitude loyalty and behavioral loyalty. In practice, the findings show how managers can better design and implement their customer loyalty programs to build customer loyalty [31]. Shainesh et al. [32] have asked themselves in the context of their work how customers develop from brand loyal customers to company loyal customers. In the first step, it was established that loyalty to the customer loyalty program on the one hand increases the perceived functional value of the brand and on the other hand also increases commitment to the brand. Furthermore, it was found that the increase in loyalty to the customer loyalty program also increases the effectiveness of brand communication. Kieningham et al. [33] see a consistently critical stance towards the prevailing myths about customer loyalty. In contrast to Levitt [34], author of one of the most quoted articles ever, who sees customer loyalty over products as a management priority, customer loyalty is not regarded as the highest goal because customer loyalty can be purchased. As a second point, the meaningfulness of the investments in retaining customers instead of acquiring new customers is not generally affirmed, but rather made dependent on the phase of the product life cycle. In the introductory phase, product awareness and product acceptance will be in the foreground. In the next phase (growth), brand awareness and market share are the top priorities, whereas in the saturation phase the focus is on defending market shares in combination with profitability. In the downturn phase, however, management must decide whether to equip the product with new features, focus on reducing costs and focusing on a niche or abandon the product. A fundamental concentration of all customers

166

W. Neussner

will not be recommendable due to the different profitability. Kieningham et al. [33] divide customers into preferred customers, break-even customers and customers with whom money is lost. Companies with the highest customer loyalty do not automatically have the highest number of customers. The goal of changing customers into loyal customers is being questioned, as these customers are looking for variety. The danger of excessive marketing efforts to win these customers exists. An exclusive focus on customers is questioned, but the combination of customer and brand orientation is recommended. The assumption that retaining customers is more profitable than winning new customers is just as questioned as the assumption that acquiring new customers causes more costs than retaining a customer as a loyal customer. Using “share of wallet” as the top key figure for price-sensitive customers is not conducive to success, as there may always be competitors who offer their products at lower prices. Furthermore, it must not be assumed that customers will not change their behavior and will show comparable behavior in the future. Many loyalty programs are designed in such a way that members earn points, and these points have practically become a currency with a monetary value. Multiplying the point money value by the number of points required to redeem a premium result in the “point price". Dannaher et al. [35] compare this point price with a corresponding market price in order to determine whether the participants in the customer loyalty program are adequately rewarded for their efforts to earn points. To achieve this, nearly 7000 items from six loyalty programs from three countries were analyzed and it was found that the point price is on average higher than the market price, which is not advantageous for the acceptance of loyalty programs. A survey among the members of the Fly Buys program (author’s note: this is the largest Australian Reward program according to his own information) has shown that point prices are very important. It was tested how important it is to the members that the point prices do not exceed the market prices. It was found that if the point price exceeds the market price by only 10%, that more than half of respondents are slightly or very angry, many to the extent that they want to leave the loyalty program [35]. Despite the strong use in marketing practice, the effectiveness of loyalty programs is still strongly questioned by researchers. In a study by Kreis and Mafael [36], an empirically tested framework is presented that regards customer loyalty programs with their different designs as a moderating instrument in a middle-end relationship between customer motives and value. The results support the argument that customer loyalty programs can be an effective tool and not only increase the value of a product or service, but also create value. However, this is only the case with programs that target the prevailing customer motives and thus offer a higher perceived value [36]. According to Kang et al. [37] loyalty programs lead to the formation of the Customer-Company Identification. The empirical results of the study show that nonfinancial benefits of loyalty programs can promote customer company identification by evoking the customers’ sense of status and belonging to a corporate community. Relationship marketers interested in building customer identification with loyalty programs should therefore design appropriate non-financial rewards to strengthen and confirm customers’ feelings of status and affiliation. As study results it can be

Management and Measuring Customer Loyalty in Digital Marketplace …

167

mentioned that bonus programs are an effective instrument to promote the identification of customers and companies and those non-financial rewards strengthen the customers’ sense of status and identification with the company [37]. In their study, Gomez Garcia et al. aim to determine which personal characteristics of customers can determine the probability of participating in a customer loyalty program for food retailers. Five aspects were taken into account: Price sensitivity, search for variety, shopping pleasure, attitude to loyalty programs and a personality trait (data protection). Gomez Garcia et al. aim to identify the difference between customer profiles attracted by two of the most common types of loyalty programs currently used by food retailers: a reward program and a loyalty card. The two types of programs show differences in the way they are managed and assume that the drivers of the probability of participating in each program are different. The study was conducted using logistic regression with a sample of 600 customers from a Spanish supermarket chain. The results show that a certain type of customer is more likely to participate in these programs: those who have little shopping pleasure, who care a lot about privacy, and who have a positive attitude towards loyalty programs in general. In addition, differences were observed between the factors influencing the probability of participation in reward programs and customer cards [38].

3 Bonus Programs in Austrian, German and Swiss Retail From February to April 2019, 52 bonus programs were recorded and categorized. After some companies offered more than one program, the programs of 44 companies and two multi-partner programs were examined. The company’s homepages and the brochures available in the branches served as sources for the conclusion of a membership in a bonus program. The analysis thus covered bonus programs, six German, three Swiss and 35 Austrian (several dealers have several programs which differ either in status or target group) retailers. Forty-eight bonus programs address private individuals and four companies. Whether the privately addressed bonus programs could also be used by companies is unclear throughout. Some conditions presuppose a minimum age, which would suggest purely private use. The survey covered single or multi-partner programs offered to private individuals or companies, the possibility of cross-border use of the cards, the possibility of reaching a status, the possibility of reaching a status, redemption thresholds, offers, coupons and the availability of an app as demonstrated in Table 1. Of the 52 programs, only two are multi-partner programs, from which it can be concluded that the advantage for merchants of having exclusive access to customer data outweighs the consideration of sharing the costs of the program. Two companies offer their members a differentiation via a status to be reached and another company a differentiation via a free and a paid version of the bonus program. 20 out of 52 programs offer the possibility to collect points, the remaining ones do not. In order to make the customer a returning customer, 28 programs provide for a threshold value (minimum turnover) in order to benefit from advantages, one of which orients

Austria

Austria

Austria

Austria

Austria

Austria

Austria

Conrad

Dehner

Öamtc

Libro

Hartlauer

Bauhaus

Penny

3

4

5

6

7

8

9

10 Leiner

11 Obi

12 Lidl

13 Lutz

14 CCC

15 Marionnaud

16 Esprit

No

Austria

Austria

18 Deichmann

19 Humanic

No

No

No

Austria

No

No

No

No

No

No

No

No

No

No

No

No

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

Yes

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

Yes

Yes

Yes

No

No

No

No

Yes

No

No

Yes

No

No

No

No

No

Yes

No

No

Yes

Yes

Yes

No

No

No

Yes

No

No

No

Yes

Yes

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

No

No

No

No

No

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

Yes

Yes

No

No

No

Yes

Yes

No

Yes

Yes

Yes

No

Yes

Yes

No

No

Yes

No

No

No

No

No

No

No

Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

No

Yes

Yes

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

(continued)

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

Yes

Company/private Mulitpartner Single Status Collecting Ttesshold Coupons Offers App Online Offline poi

17 Esprit Platinum card Austria

Austria

Austria

Austria

Austria

Austria

Austria

Austria

Conrad

2

Austria

dm, BP, Nordsee, Fressnapf, etc

Country

1

Company

Table 1 Loyalty program survey (own illustration)

168 W. Neussner

Austria

Austria

Austria

Austria

Austria

24 Douglas

25 Intersport

26 Billa

27 Astro

28 bständig

No

No

37 Pagro-Pädagoginnen Austria

38 Triumph

Austria

No

Austria

36 Pagro-Company

No

No

Austria

No

Austria

33 Akakiko

No

No

35 Pagro

Austria

32 Salamander

No

No

No

No

No

No

No

No

No

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

Yes

Yes

Yes

No

Yes

No

No

No

No

No

No

Yes

No

No

No

Yes

No

No

Yes

Yes

Yes

Yes

No

Yes

No

Yes

Yes

10th purchase

No

Yes

Yes

No

No

Yes

Yes

Yes

Yes

No

No

No

Yes

No

No

No

Yes

Yes

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

Yes

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

No

Yes

Yes

No

No

No

Yes

Yes

Yes

No

Yes

No

No

Yes

No

No

Yes

No

Yes

No

Yes

Yes

No

No

No

Yes

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

(continued)

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No

Company/private Mulitpartner Single Status Collecting Ttesshold Coupons Offers App Online Offline poi

34 Delka

Austria

Austria

31 Hervis

Austria

Austria

23 Kika

Austria

Austria

22 Bipa

30 Merkur

Austria

21 Palmers Basic Club

29 P&C

Austria

Country

20 Palmers Diamond Club

Company

Table 1 (continued)

Management and Measuring Customer Loyalty in Digital Marketplace … 169

Austria

Austria

Germany No

CH

Germany No

CH

CH

40 Triumph Gold

41 Tchibo

42 Karstadt

43 Migros

44 Lafayette

45 Manor

46 Coop

Germany No

48 Hunkemöller

49 Ikea

Germany Yes

51 Rossmann

52 Edeka

No

Austria

Germany No

50 Bellaflora

No

Germany No

Austria

47 Budni

No

No

No

No

No

No

Austria

39 Triumph Silber

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

No

No

No

No

No

No

No

Yes

Yes

Yes

No

No

No

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

No

No

Yes

No

Yes

No

No

No

No

No

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

No

No

Yes

Yes

Yes

Yes

No

Yes

No

Yes

Yes

Yes

Yes

No

No

No

Yes

No

No

Yes

No

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

No

No

No

No

No

No

No

No

Company/private Mulitpartner Single Status Collecting Ttesshold Coupons Offers App Online Offline poi

Country

Company

Table 1 (continued)

170 W. Neussner

Management and Measuring Customer Loyalty in Digital Marketplace …

171

the threshold value not on turnover but on the number of purchases, whereby the value-based consideration is replaced by a quantity-based one. 43 programs offer special promotions for participants of the bonus program, but only 32 discounts through coupons. This could be due to the reduced effort in communication as well as in handling at the cash desk (collection, devaluation etc.). However, these 32 programs offer both coupons and promotions. With 25 programs, less than half of the information on an app is available, leading many merchants to use mail, email and one merchant even to use SMS messaging. Only one could be identified as a transnational program. Four of the programs offer no monetary bonuses for purchases, another four offer an instant rebate, two of them offer a discount coupon, three cashback, 31 a credit on a purchase, and the remaining discount vouchers or combinations in multiple options. The achievement of the thresholds can be determined either by achieving a turnover or a predefined number of purchases. Of the 31 schemes offering a credit, 21 have linked this to the exceeding of a threshold, whereby not every euro of turnover leads to additional expenditure in the form of a credit if this threshold is not exceeded. Discount coupons lead to an unforeseeable expense for the retailer in the form of the discount, because this is to be regarded as a percentage of the turnover. In the case of discount vouchers, on the other hand, the next purchase could be paid in full with this voucher, depending on the amount. Five programs offer an instant discount on every purchase, which is attractive to the customer, but the retailer does not take any action to motivate the customer to make a new purchase by redeeming credit. Cashback programs automatically do not lead to any further turnover, as the money is refunded to the cardholder in cash and thus the motivation to make a new turnover in order to enjoy an advantage is prevented. Two programs are subject to a charge, one of which provides a voucher in the amount of the charge, thus creating an incentive to make a turnover. The communication with the member of the customer loyalty program takes place via the customer magazine, other companies via email, mail, sms. Nine programs do not express in their promotional materials whether or not they communicate with members of the loyalty program. A transnational system offers only programs, many exclude this, and some program information does not express itself. Crossborder collection and redemption would lead to greater accounting effort, and the members’ credit balances would have to be shown in the balance sheet as liabilities of the program providers, considering a redemption quota. In summary, the results of the study are shown in Fig. 11. Of the 52 singlepartner and two multi-partner programs analyzed, none uses multi-partner discount cards, not even the multi-partner programs, as these are cashback programs. Only few companies offer birthday coupon, free of charge dispatch, birthday voucher, best price guarantee, member get member advertising or a prolonged right of return. A single program does not regularly address customers about incentive opportunities and no company offers point gluing promotions. Couponing, on the other hand, uses 33 programs. Six programs do not offer their members any benefits that are related to the sales made. Thus, no binding of the program to a customer behavior can be expected. It is worth mentioning that not all programs enable the generation

172

W. Neussner

Potential Customer Retention Tools (52)











Bonus Program 52

Rebate Cards 43

Customer Card without continous Incentivation 1

Points Collections 0

Couponing 33

Single Bonus Program 50

Single Rebate Cards 43

Discounts without Adress Registration

Coupon (mail)

Multi PartnerBonus Program 2

Mulit Partner Rebate Cards 0

Discounts with Adress Registration

Coupon (customer brochure)

CheckoutCouponing Mobile Couponing Couponing within Bonus Programs Coupon- /Rebate Platsforms Web

Fig. 11 Survey on 52 bonus programs (own illustration according [19])

and redemption of points online despite webshop, whereby offline customers are not motivated to become online customers.

4 Results Evaluation and Conclusions A comparison of the bonus dimensions offered between literature [4] and the market shows that neither immaterial experiences nor material bonuses are offered. Status symbols in the form of higher classified cards and higher service levels occur selectively. Only a few companies offer different levels of customer cards to their clients. It appears that these companies have realised that not all customers should be treated equally and have therefore made a distinction in their customer retention policies and programs. Cash is offered exclusively in the form of cash back programs. Thus, the discount is in the foreground. There is a substantial discrepancy both in the design and implementation of the programs observed. Also, the ambiguity in the documentation of the programs about whether the cards can be used online and offline shows that the

Management and Measuring Customer Loyalty in Digital Marketplace …

173

merchants omit possibilities to collect customer data. The non-utilization of essential components of bonus programs in the literature shows that there is still potential here or that they are deliberately not used. Elements of programs which are only part of one or few programs are e.g. birthday coupon, free of charge dispatch, birthday voucher, best price guarantee, member get member advertising or a prolonged right of return. Whether cash back or bonus programs are the more attractive form of customer loyalty from the customer and company point of view remains to be seen and, just like the digitalization of bonus programs, the effect on the expansion to offline and online offerings in the sense of Omni Channel Retail would still be an area to be researched in detail, which would have to be extended by the measurability of the results of the individual activities within the framework of a bonus program. Limitation of this paper is the lack of knowledge about the success of the relevant loyalty programs. Basis for the review would be the costs and the benefits over a time period of several years. The question arises as to how long companies can still afford or want to grant discounts without consideration, e.g. customer data. From the management´s point of view it will be important to have the possibility to measure the CLTV at any time and the single sales initiative per single customer card holder. Further research will be necessary to compare scientific concept of CLTV and the realization and the efficiency in retail companies. Further research is needed to explore the reliability of current loyalty programs and the CLTV as measurement tool of marketing activities. From the authors’ point of view, one possibility of customer retention not yet mentioned in the literature is the widespread possibility in Austria of selling pricereduced vouchers from dealers via works councils. This procedure, which has been common in Austria for decades, leads to employees buying reduced-price vouchers from works councils. The works council has agreed reduced prices for these vouchers with dealers in advance. An analysis of this possibility is still pending. In the next step the relevance and the potential of these price-reduced vouchers will be analyzed from the position of the works councils, customers, and retailers.

References 1. Hoseong, J., Youjae, Y.: Effects of loyalty programs on value perception, program loyalty, and brand loyalty (2003) 2. Henderson, C., Beck, J., Palmatier, R.: Review of the theoretical underpinnings of loyalty programs. J. Consum. Psychol. 21(3), 256–276 (2011) 3. Sharp, B., Sharp, A.: Loyalty programs and their impact on repeat-purchase loyalty patterns. Int. J. Res. Mark. 14, 473–486 (1997). https://doi.org/10.1016/S0167-8116(97)00022-0 4. Lauer, T.: Bonusprogramme: Rabattsysteme für Kunden erfolgreich gestalten. Springer, Berlin (2011) 5. Hofmann, M., Mertiens, M.: Customer-Lifetime-Value-Management (2000) 6. Hofmann, A.: Die Akzeptanz kartenbasierter Kundenbindungsprogramme aus Konsumentensicht. Gabler, Wiesbaden. https://doi.org/10.1007/978-3-8349-9644-2 (2008) 7. Kumar, V., Reinartz: Customer Relationship Management: A Databased Approach (2006)

174

W. Neussner

8. Bolton, R., Lemon, K., Verhoef, P.: The theoretical underpinnings of customer asset management: a framework and propositions for future research. J. Acad. Mark. Sci. 32(3), 271–292 (2004). https://doi.org/10.1177/0092070304263341AccesstoDocument 9. Musiol, G., Kühling, C.: Kundenbindung durch Bonusprogramme: erfolgreiche Konzeption und Umsetzung. Springer, Berlin (2009) 10. Maharaj, A.: Awareness, perceptions and effects of customer loyalty programmes within the retail sector of the Durban Metropolitan area (2008) 11. Kotler, P., Keller, K.L.: Marketing Management (2012) 12. Kasai, C., Chauke, M.X.D.: Investigating customer perceptions of loyalty cards and their influence on purchasing behaviour in major retail stores. 5, 23 13. Srivastava, M., Rai, A.Kr.: Mechanics of engendering customer loyalty: A conceptual framework. IIMB Manag. Rev. 30, 207–218. https://doi.org/10.1016/j.iimb.2018.05.002 (2018) 14. Buttle, F.: Customer Relationship Management (2004) 15. Uncles, M.D., Dowling, G.R., Hammond, K.: Customer loyalty and customer loyalty programs. J. Consum. Mark. 20, 294–316 (2003). https://doi.org/10.1108/07363760310483676 16. Eggert, F.W., Bliemel, A.: Kundenbindung—die neue Soll-Strategie? Mark. ZFP. (1998) 17. Eggert, A.: Kundenbindung aus Kundensicht: Konzeptualisierung—Operationalisierung— Verhaltenswirksamkeit, Wiesbaden (1999) 18. Eggert, A.: Die zwei Perspektiven des Kundenwerts: Darstellung und Versuch einer Integration. In: Günter, B., Helm, S. (Hrsg.) Kundenwert: Grundlagen—Innovative Konzepte—Praktische Umsetzungen, 3. Aufl. Wiesbaden (2006) 19. Ranzinger, A.: Praxiswissen Kundenbindungsprogramme: Konzeption und operative Umsetzung. Gabler, Wiesbaden (2011) 20. Minnema, A., Bijmolt, T.H.A., Non, M.C.: The impact of instant reward programs and bonus premiums on consumer purchase behavior. Int. J. Res. Mark. 34, 194–211 (2017). https://doi. org/10.1016/j.ijresmar.2016.08.001 21. Montoya, R., Flores, C.: Buying free rewards: the impact of a points-plus-cash promotion on purchase and reward redemption. Mark. Lett. 30, 107–118 (2019). https://doi.org/10.1007/s11 002-019-09482-y 22. Lauer, T.: Bonusprogramme richtig gestalten Harv. Bus. Manag. 2002(3), 98–106 (2002) 23. Künzel, S.: Das Bonusprogramm als Instrument zur Kundenbindung (2003) 24. Diller, H.: Vahlens großes Marketinglexikon (2001) 25. Glusac, N.: Bonusprogramme—ein wirkungsvolles Kundenbindungsinstrument? Presented at the (2009) 26. Anonymous: Finanztest. Stift. Warentest. (2010) 27. Nunes, J.C., Drèze, X.: Your loyalty program is betraying you. Harv. Bus. Rev. (2006) 28. DSGVO: Datenschutzgrundverordnung 29. Steinhof, L.: Loyalitätswirkung des geschenkten bevorzugten Kundenstatus (2013) 30. Nunes, J., Drèze, X.: Using combined-currency prices to lower consumers’ perceived cost. J. Mark. Res. XLI, 59–72. https://doi.org/10.1509/jmkr.41.1.59.25085 (2004) 31. So, J.T., Danaher, T., Gupta, S.: What do customers get and give in return for loyalty program membership? Australas. Mark. J. AMJ. 23, 196–206 (2015). https://doi.org/10.1016/j.ausmj. 2015.02.002 32. Shainesh, G., Gupta, T., Gupta, S.: Navigating from programme loyalty to company loyalt. 33. Kieningham, T., Vavra, T., Aksoy, L., Wallard, H.: Loyalty Myths. (2006) 34. Levitt, T.: Marketing Mypopia (1960) 35. Danaher, P.J., Sajtos, L., Danaher, T.S.: Does the reward match the effort for loyalty program members? J. Retail. Consum. Serv. 32, 23–31 (2016). https://doi.org/10.1016/j.jretconser.2016. 05.015 36. Kreis, H., Mafael, A.: The influence of customer loyalty program design on the relationship between customer motives and value perception. J. Retail. Consum. Serv. 21, 590–600 (2014). https://doi.org/10.1016/j.jretconser.2014.04.006 37. Kang, J., Alejandro, T.B., Groza, M.D.: Customer–company identification and the effectiveness of loyalty programs. J. Bus. Res. 68, 464–471 (2015). https://doi.org/10.1016/j.jbusres.2014. 06.002

Management and Measuring Customer Loyalty in Digital Marketplace …

175

38. García Gómez, B., Gutiérrez Arranz, A.M., Gutiérrez Cillán, J.: Drivers of customer likelihood to join grocery retail loyalty programs. An analysis of reward programs and loyalty cards. J. Retail. Consum. Serv. 19, 492–500 (2012). https://doi.org/10.1016/j.jretconser.2012.06.004 39. Payback: https://www.payback.at/informieren 40. Conrad: https://www.conrad.at/de/service/kundenkarten.html?ef_id=EAIaIQobChMIlZzb9qI4gIViuJ3Ch1swA5jEAAYAiAAEgJtkfD_BwE:G:s&insert=U3&gclid=EAIaIQobChMI lZzb9-qI4gIViuJ3Ch1swA5jEAAYAiAAEgJtkfD_BwE 41. Dehner: https://www.dehner.at/service/smartcard/ 42. ÖAMTC: https://www.oeamtc.at/vorteilspartner/ 43. Libro: https://www.libro.at/libroclub-vorteile 44. Hartlauer: https://www.hartlauer.at/Loewenclub/Loewenclub/Loewenclub-Vorteile/ 45. Bauhaus: https://www.bauhaus.at/service/leistungen/plus-card 46. Penny: https://www.penny.at/penny-card 47. Leiner: https://www.leiner.at/service/vorteilskarte 48. Obi: https://www.obi.at/BBC/ 49. Lidl: https://www.lidl.at/de/Ueber-Lidl-Plus.htm 50. Lutz: https://www.xxxlutz.at/c/preisepass-vorteile 51. CCC: https://ccc.eu/at/faq 52. Marionnaud: https://www.marionnaud.at/mymarionnaud 53. Esprit: https://www.esprit.at/mein-esprit/external/benefits 54. Deichmann: https://www.deichmann.com/AT/de/shop/content/deichmannplus_light.jsp 55. Humanic: https://www.humanic.net/at/yourclub 56. Palmers: https://www.palmers-shop.com/vorteilswelt.html 57. Bipa: https://www.bipa.at/content/Content-Landing-BIPACard-Vorteile.html?wt_ad=370430 84608_253479450706&wt_kw=e_37043084608_bipa%20kundenkarte&gclid=EAIaIQobC hMIhKbT4-yI4gIVRuN3Ch2n4QzLEAAYAiAAEgLmsPD_BwE 58. Kika: https://www.kika.at/shop/de/kika/einrichtungshaeuser/kundenkarte 59. Douglas: https://www.douglas.at/Promotions/Douglas-Card/index_c0092.html 60. Intersport: https://www.intersport.at/kundenkarte 61. Billa: https://www.billa.at/vorteils-club 62. Astro: https://www.astromarkenhaus.at/kundenkarte 63. Bständig: https://www.bstaendig.at/service/kundenkarte/ 64. Peek & Cloppenburg: https://www.peek-cloppenburg.at/kundenkarte/ 65. Merkur: https://www.merkurmarkt.at/auth/login?reason=requireLoggedIn&url=/fom/ 66. Hervis: https://www.hervis.at/store/club 67. Salamander: https://www.salamanderclub.at/ 68. Akakiko: https://akakiko.at/kundenkarte 69. Delka: https://www.delka.at/kundenkarte/ 70. Pagro: https://www.pagro.at/bonuskarten/bonuskarte/ 71. Triumph: http://at.triumph.com/loyalty_micro_page.html 72. Tchibo: https://www.eduscho.at/-c400004473.html 73. Karstadt: https://www.karstadt.de/customercard-start.html?src=90L100001 74. Migros: https://www.migros.ch/de/cumulus/teilnehmer-werden.html 75. Galeries LaFayette (2018) 76. Manor: https://www.manor.ch/de/u/manorkarte 77. Coop: www.coop.ch 78. Budni: https://www.budni.de/login 79. Hunkemöller: https://www.hunkemoller.de/de_de/passionpoints.html 80. Ikea: https://www.ikea.com/at/de/ikea-family/neuanmeldung-pub34c075c1#/form-account 81. Bellaflora: https://www.bellaflora.at/meine-vorteile/?gclid=EAIaIQobChMIqtjSp_KI4gIV TeR3Ch0BLAtZEAAYAiAAEgKOafD_BwE 82. Rossmann: https://www.rossmann.de/einkaufsportal/service-hilfe/rossmann-app.html 83. Edeka: https://www.edeka.de/services/bonusprogramme/deutschlandcard-bonusprogramm/ deutschlandcard_bonusprogramm.jsp

176

W. Neussner

84. Kryvinska, N.: Building consistent formal specification for the service enterprise agility foundation. Soc. Serv. Sci. J. Serv. Sci.Res. 4(2), 235–269 (2012) 85. Kaczor, S., Kryvinska, N.: It is all about services—fundamentals, drivers, and business models. Soc. Serv. Sci. J. Serv. Sci. Res. 5(2), 125–154 (2013) 86. Gregus, M., Kryvinska, N.: Service Orientation of Enterprises—Aspects, Dimensions, Technologies. Comenius University in Bratislava (2015). ISBN 9788022339780 87. Kryvinska, N., Gregus, M.: SOA and its Business Value in Requirements, Features, Practices and Methodologies. Comenius University in Bratislava (2014). ISBN 9788022337649 88. Molnár, E., Molnár, R., Kryvinska, N., Greguš, M.: Web Intelligence in practice. Soc. Serv. Sci. J. Serv. Sci. Res. 6(1), 149–172 (2014)

Cost-Effective Solutions in Cloud Computing Security Lumbardha Hasimi

Abstract The popularity and advances in technology, recently have created a great deal of interest in cloud computing, especially for enterprises. Although the cloud computing platform offers a cost-efficient solution, there is a big drawback when it comes to security and the real costs behind it. Depending on the type of deployment and service, outsourcing security is a big concern for the provider, as well as for the client regarding the security itself and the impact on the overall performance of the cloud. Most of the security designers propose high-level assurances using cryptography while making sure to maintain the benefits of outsourcing. Anyhow, there are many challenges and issues with respect to privacy and security of the cloud that need to be addressed and analyzed. This work aims to present some of the most efficient existing solutions for security in cloud computing, present a short overview of the progress in literature and research that tackle such issues, and finally investigate the models and methods of calculating the costs of security in cloud computing. While engaging qualitative and quantitative methods, different analyses were carried out to finally present the state-of-art in cloud security research and, present the existing cost calculation methods.

1 Introduction 1.1 Relevance Cloud computing as a framework has become an important option for many enterprises over the last years. The capability and the solutions offered, followed by the cost of outsourcing, performance, scalability, and availability are the main reasons for this. business organizations and different agencies are the sources of the widespread

L. Hasimi (B) Comenius University in Bratislava, Odbojárov 10, 83104 Nové Mesto, Bratislava, Slovakia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Kryvinska and A. Poniszewska-Mara´nda (eds.), Developments in Information & Knowledge Management for Business Applications, Studies in Systems, Decision and Control 376, https://doi.org/10.1007/978-3-030-76632-0_5

177

178

L. Hasimi

interest in cloud storage services, by promoting the effectiveness and low-cost benefits from the cloud [1–3]. The economic benefits are a great motivation to adopting cloud as they have a direct impact on capital and operational expenditures. Anyway, despite all the motivational features, cloud computing in its core bring several specific security issues that are mainly due to its multitenancy characteristics. There are many security and privacy issues in cloud computing that needs to be tackled in order to have an overview of the possible costs and investment needed to overcome such situations. Regardless of cloud service and the nature of the enterprise, there have been presented some of the most common threads in the cloud environment including data breaches, data loss, hijacking, insecure APIs, denial of service, etc. Dealing with such issues means dealing with the diversity of these services [4], which in return increases the vulnerability of two incidents and attacks. Most of cloud providers invest a lot in risk and security issues. However, taking into account the diversity of such concerns in the advancing field of technology, this issue is hard to tackle in terms of expected costs expenditure. In this research, it is addressed the issue of security in the cloud in regard to costs and investment. The work follows a three-layered research approach where the outcomes of the layers are impacting the consecutive layers [5–7]. In the first part, it is explained and discussed the overall framework of cloud computing as well as the structure and the mechanism. The major issues regarding the service in cloud computing the architecture and the models of deployment offered are explored in detail. Secondly, an overview of the issues related to security and risks, the underlying issues, strengths, and the way these issues affect cloud security are investigated thoroughly. It is then followed with a short scientometric analysis, to investigate and explore the literature available in this topic. Finally, by presenting and discussing the issues in security of cloud computing, the security techniques, and solutions, as well as going through the existing methods of course calculations in cloud security-rich amount of knowledge will be attained and therefore give the foundation for further development in developing models that will be useful in evaluating the security costs specifically. This work’s main objective was to find out some of the methods and models available to calculate the cost of security in cloud computing. The questions aiming to be answered encompass on different research sub-questions that deal with interrelated phenomena in cloud computing covering, available solutions, the existing situation in research and literature, and investigate the most common models of security cost calculations. Concerning the structure of the work, the first section of the paper comprises of a general background in the area of cloud computing services and security issues. Followed by the second section that consists of short scientometrics analyses to present the state of art in the field of costs of security in cloud computing, intending to present the available literature and research done previously. The third section consists of the objective and the aim of the work, data collection methods, data analyses, data processing, and the overall engaged methods and strategies. The fourth section presents findings and results regarding the solutions and models available to calculate costs of security in cloud computing as well as an in-depth discussion of the major issues that come alongside in the field of security. Like a sum-up of the analyses conducted in the review of the existing literature, in the conclusion section

Cost-Effective Solutions in Cloud Computing Security

179

is done and overall concluding word accompanied by the possibilities and ideas for further research and work in the field of security in the cloud.

1.2 Goals and Objectives This work addresses cloud security issues and challenges and eventual cost-effective solutions regarding three aspects to conclude the guidelines for the topic. It follows a three-layered research approach [8], while every layers’ outcome is subsequently related an affective to the consecutive layer. In the first part, it is explained and discussed the overall framework of cloud computing as well as the structure and the mechanism. The major issues regarding the service in cloud computing the architecture and the models of deployment offered are explored in detail.Further, it is given an overview of the issues related to security and risks, the underlying issues, strengths, and the way these issues affect cloud security are investigated thoroughly. further, it is also presented and inside on the actual situation in the research productivity relating the cost of security in cloud computing. Scientometric analyses including Co-authorship Co-citation, Relative Rate Growth parameter, and, other indicators in the research situation are presented. Such investigation and exploring in the literature [5, 9] is performed with the purpose to measure and help understand the nature of the problem as well as conceivable deficiencies regarding the methods and approaches in the field. Finally, by presenting and discussing the issues in security of cloud computing, the security techniques, and solutions, as well as going through the existing methods of course calculations in cloud security-rich amount of knowledge will be attained and therefore give the foundation for further development in developing models that will be useful in evaluating the security costs specifically. This work’s main objective was to find out some of the methods and models available to calculate the cost of security in cloud computing. The questions aiming to be answered encompass on different research sub-questions that deal with interrelated phenomena in cloud computing. The other objectives of this work are: • Present the most efficient existing solutions for the security in cloud computing • Present a short overview of the progress in literature and research in this topic • Investigate the models and methods of calculating the costs of security in cloud computing

2 Theoretical and Conceptual Background 2.1 Cloud Computing as a Cost-Effective Solution Cloud computing is the computing model in which computing resources such as software, hardware, and data are accessed as a service usually through a web browser

180

L. Hasimi

or light-weight desktop machine over the internet [10–14]. A normal cloud contains many computing resources, like servers, application platforms, storage and devices, balancers, and the infrastructure and/or virtual machines that cloud clients use. Cloud computing as the technology in development, provides great potential with the upto-date range in IT, especially for businesses and data storage issues. This is a great extent especially for the fact that it offers a great feature of data storing on the remote servers, that can be accessed anytime and from any location, respectively. The provider of the cloud service offers access and service to the user, accordingly to the needs and specifications. The information technology services are delivered through cloud computing by retrieving the resources from the Internet with webbased tools and applications rather than having a connection to a server [15]. Another definition sees cloud computing in two meanings [11]. The most common refers to running workloads remotely over the internet in a commercial provider’s data center, also known as the “public cloud” model [11, 16]. This comes most probably due to the fact that the most popular cloud offerings are well recognized and frequently used—like Amazon Web Services (AWS) and Microsoft Azure, which explains why the “public cloud” model is a known notion. The second meaning [11] describes how cloud computing works. It is seen as a virtualized pool of resources, from raw compute power to application functionality, available on demand. The key advantage is agility: the ability to apply abstracted compute, storage, and network resources to workloads as needed and tap into an abundance of prebuilt services [17]. On the other hand, according to Flexera [18], the State of the Cloud Report shows that multi-cloud continues to be the leading strategy, as resulting from the surveyed enterprises and its number of adopting it. In this context, it is stated that the most common multi-cloud approach among enterprises is a mix of multiple public and multiple private clouds. Nevertheless, the cloud computing concept through the internet offers accessible resources as services [19]. What stands as the main driver for cloud computing, are the economic benefits, which is shown to be mainly due to the reduction in expenditures in the capital and operational activities [20]. However, this benefit fades when we consider issues and challenges yet to be discussed. As the major ones to mention are the security and trust issues, given that the user in the cloud leaves the protection realm of the data owner, it becomes more of a dependent owner of the service certainty. Thus, why is cloud computing a cost-effective solution? It is obvious that with the business growth, the IT needs to grow as well, which leads to choices between investing in new server infrastructure on-premises or invest in in the cloud base infrastructure [21–24]. Therefore, it is very important to know which investment is better for the business and which will have a greater effect on long term profitability [25]. Such a decision is difficult to make as it is not as common to properly acknowledge the benefits and limitations of on-premises and cloud-based infrastructure [26]. Taken into consideration the research done so far, it is proven that investment in the cloud is probably the best option in terms of costs, availability, and, flexibility [27]. There is however a drawback when it comes to the real cost of it, given the security dimension [27]. For most of the big enterprises that deal with sensitive data and

Cost-Effective Solutions in Cloud Computing Security

181

information, the “going cloud” option at some point has its biggest risk in terms of security and trust. So, what are the main benefits, excluding the security issues, that cloud computing brings? Primarily, the costs. Most businesses find the costs the most attractive factor in deciding on an investment. While considering the price of computing, traditional onpremises server infrastructure, and cloud infrastructure are carried out differently, as well as varying factors into the cost of IT, and often cloud computing services are the most cost-effective and efficient [28]. Some studies try to prove that the cloud can provide extensive financial benefits and workplace productivity [29]. According to a study conducted by Rackspace.com in 2013, which surveyed 1300 companies, it was found that 88% of cloud users experienced significant cost savings and 56% of them noted an increase in profits. However, for many others, this is always case-specific. In a survey conducted in 2015 by the European Parliamentary Research Service [20] about the in-depth analysis of cloud computing, the cost-reduction benefits were among the biggest drivers for the enterprises to accept cloud deployment (see Fig. 1). According to the same survey, cloud computing can bring important benefits to both organizations and individuals. Perhaps the most obvious benefit is cost reduction. On-site ICT facilities are generally over-provisioned to allow for future growth or spikes in demand. The result is that four out of every five companies adopting cloud computing were able to reduce costs by 10–20% [20]. On-Premises Servers. This highlights the capacity to do work, by investing in the physical hardware. An on-premises server can offer consumers a great deal of power but usually, this power is underutilized. The business pays for this on-time regardless of how much the system is being used. Whether that is in the form of electricity to keep the server running all night, or in maintenance costs for a server administrator to monitor the system, this is where cloud computing stands ahead [21].

Fig. 1 Benefits of cloud computing according to the survey [30]

182

L. Hasimi

The work performed. Taken into consideration that the cloud provider maintains systems on a massive scale each data center [31] deals with thousands of clients’ data, the efficiency is at its peak. This consequently passes the savings of economies of scale to your business [32], which would not be possible in cases of on-premises or individual server infrastructure. By investing in cloud computing it is seen to invest in the resources that are actually used. Instead of paying for RAM, storage space, processing power, and so on, you pay only for the service with no cost for underutilization, or electricity, repair, upgrade, etc. [20]. Taken into consideration all these elements of IT infrastructure operations and all the overheads related to it, it is obvious that paying only for the service and the resources you use at the time that you use, it’s much more convenient, and thus has much more financial advantages [33]. Overall, by leveraging cloud services and cloud-based infrastructure instead of purchasing their server, most businesses get a high benefit [31]. By using cloud services you are getting included in a monthly fee the management, maintenance, operating costs while paying for the processing power only instead of capacity off the server, which in return saves you money over time [21].

2.2 Cloud Computing Services In cloud computing the service models are divided or categorized into three basic service models accordingly to the business need that is addressed toward the cloud resource. Infrastructure as a Service (IaaS). Is the most basic service from the cloud service models, it offers to the customer raw computing, network transfer, and storage [34]. This service is offered to the customer as an operating system that comes with the server that has storage and network transfer. At this point, there is a possibility to deliver the service as a single server or as a collection of integrated servers into a virtual private data center [35]. The company that is providing the software also provides the infrastructure to run this software. Infrastructure as a Service gives business access to vital web architecture, such as storage space, servers, and connections, without the business need to purchasing and managing this internet infrastructure themselves. Choosing to use an IaaS cloud demands a willingness to put up with complexity, but with that complexity comes flexibility. Amazon EC2 and Rackspace Cloud are examples of IaaS [36]. Platform as a Service (PaaS). The very next service offered by the cloud framework is Platform as a Service, which is basically the application environment purchased by the customer. As known examples of this layer of service in the cloud, are application stacks: Java, Ruby on Rails, LAMP. The developer can buy an entirely functional development environment. “Platform as a Service (PaaS) clouds are created, many times inside IaaS Clouds by specialists to render the scalability and deployment of any application trivial and to help make your expenses scalable and predictable” [36]. Examples of Platform as a Service system might include Google App Engine, Mosso, etc. What comes as a great advantage at this

Cost-Effective Solutions in Cloud Computing Security

183

point with PaaS, is that for quite a small expenditure it is possible to initiate the application without having to deal with basic development issues [34]. Moreover, PaaS permits scalability of a great deal, due to its design and features of cloud computing [37]. Especially in cases of lean operations staff, PaaS can be exceptionally valuable on the off chance that the app will fail [36]. The biggest drawback, however, while using PaaS is that the service in certain situations may employ restrictions that might not go along properly, with the product. Software as a Service (SaaS). It is considered as the highest layer in the cloud with regards to the services of cloud computing. The customer purchases the use of a working application. Examples of this are NetSuite and SalesForce.com [28]. In this offered service third party vendor manages the software and it is delivered to the end customer by web [38]. Through running the application on the computers that actually belong to the vendor, the applications of the cloud enable the leverage on it by reducing the maintenance [39], operations, support, etc. Gmail for example is a SaaS run as clouds, however not all SaaS needs to be cloud-based.

2.3 Deployment Models in the Cloud Cloud deployment is a specific configuration of environmental parameters such as the accessibility and proprietorship of the deployment infrastructure [7]. Thus, the types of deployment in cloud computing variety depending on who controls the infrastructure and where is it located [40]. Usually, the companies should aim for the model that suits past their needs to make the best use out of it. Features in the deployment type should be taken into consideration from the businesses or customers’ features like computing, networking, storage, resources, goals, and, certain specifications of the model [31]. Accordingly, to the kind of activity, there are different requirements in privacy, availability, cost, etc. Though last years, we have witnessed the increase of the businesses adopting the cloud as an option to improve efficiency, quicken time and flexibility, and benefit from the accessibility of the cloud. However, it is quite case-specific, how the business utilizes the cloud that makes a crucial turn to the advantage level or not. As a first step toward going cloud, the business faces the deployment models variety [41, 42]. To this extent, cloud infrastructure and placement of each workload depend on business needs [43]. The existing deployment thereof, are available to the businesses in different offerings and options in the cloud [44]. The deployment models in the cloud are available as public, private, hybrid, and multi-cloud (Fig. 2). According to the latest stats from Flexera [19], the most common multi-cloud approach among enterprises is a mix of multiple public and multiple private clouds. Public Cloud. Public cloud offers to the businesses the option of virtualized computing, networking services, storage through the public internet from a cloud service provider [16, 17, 46]. A public cloud is usually a good option for businesses that need quick access to two computing resources without a lot of costs in advance

184

L. Hasimi

Fig. 2 Cloud computing deployment models [45]

and further investment in infrastructure [27, 47]. This option of the cloud is considered easy to scale since you have the opportunity to buy the capacity in the amount needed and whenever needed. A great benefit for the businesses is that public cloud services do not need a lot of investment start and they are set up is pay-per-use. this helps the customer especially in speeding time, scaling quickly as well as offer the agility on trying new applications and services. “Public cloud services are especially useful for workloads that may run for a short period—for example, a start-up that can’t afford to wait months to prove its viability can get just the right amount of computing it needs, for just as long as it needs it. Plus, your IT team won’t need to maintain the hardware” [7]. Some of the largest public cloud service providers are Google Cloud, Microsoft Azure, IBM, Amazon Web Services, etc. Each of the providers that are available in the market in large and small capacities has its own services utilized and customized. However, regarding certain workloads, and especially for businesses that deal with legacy applications and sensitive data, deployment in the public cloud has another level of difficulty and risk. Therefore, private clouds come of better use to engage. Private Cloud. Private cloud is the best option in cases of businesses they deal with legacy applications for which the security is the greatest concern. This mainly because the data is managed in control of how is shared and stored [48]. The private cloud requires data centers maintained and hosted by the team of the organization, which means the organization has to install the hardware to deal with ongoing management and operational costs. Despite the lower profitability in terms of cost expenditures and maintenance especially when it comes to physical hardware, private cloud is

Cost-Effective Solutions in Cloud Computing Security

185

the ideal solution for the businesses that want to control the resources and data an eventually have a more secure solution in long term [49, 50]. In terms of privacy risk and security again private cloud can bring the most cost-efficient solution considering the cost that the information breaches and other security issues can bring [41, 51]. The private cloud gives on-demand data availability, ensuring reliability and support for mission-critical workloads, and because it is controllable on how resources are used, it is quickly responding to changing workload demands [52]. Hybrid Cloud. A hybrid cloud is the combination of public cloud and private cloud environments end’s enables data and applications to be shared [53] among them. Therefore, such a feature helps businesses level services interchangeably [7] between their private infrastructure and at the same time use the public cloud resources. Multicloud. Multicloud is made of more than a single cloud service from more than one provider whether it is public or private. The strategy of multi-cloud in an essence is the use of two or more cloud computing services, referring to do any implementation of software or platform as a service [17, 40, 54]. Due to the nature of the workload of different businesses each business has its particular requirements, therefore needs specific combined services from different cloud service providers and sometimes combines it with their own private cloud. As an approach, it gives more flexibility over different services capabilities locations, and prize options. In the implementation, it needs careful planning in a strategy carefully analyzed to create consistency for the business and maintain independency of the services that are consumed. It requires a software layer to be able to deliver management as well as synchronize the activity across the cloud environment. It is the fact that this approach gives the best of both the private and public cloud [54] with a great deal of flexibility to run workloads, which makes it the most used approach among all the enterprises (Fig. 3).

Fig. 3 Multi-cloud strategy for most of the enterprises [19]

186

L. Hasimi

A quite challenging decision when it comes to migrating cloud is how to choose the right deployment model for the business. Before choosing the deployment model it is important not to stick to one size fits all approach, whether it is wise to conduct an assessment on the needs and dependencies of the business drivers. Considering that organizations are quite dynamic and throughout the activities, it is quite possible to engage changes in goals as it is important to consider workloads and the eventual need for control and long-term costs while migrating. It is important though, and challenging at the point, when it comes to shifting from on-premises to cloud, as it requires to consider many sides, like a technical requirement, specifications, and certain prerequisites.

3 Concerns Regarding Cloud Computing and Security 3.1 Challenges in Cloud Computing Security Despite the many advantages that cloud computing brings, for most of the businesses that continue reservation toward migration in cloud, security, and privacy problems of the framework remain the biggest drawbacks. Considering the nature of cloud computing which means easily accessible files from computers through the Internet a highly possible malware and virus infection makes it even more suspicious when it comes to sensitive data. In the survey conducted by RightScale [19] in 2018, which questioned 997 technical professionals across a broad cross-section of organizations about their adoption of cloud infrastructure, security was among the top challenges (Fig. 4.). But that’s not the only issue, security drawbacks are also in terms of control. Loss of data, availability, damage of data, or eventual breaches of information are all issues of loss of control [55]. There are many cases and examples of big enterprises

Fig. 4 Cloud computing challenges RightScale survey 2018 [19]

Cost-Effective Solutions in Cloud Computing Security

187

going through data loss issues and not being able to ensure data retrieving. Cloud computing is seen always in terms of security and reliability however compared to other networks, cloud computing is quite secure as usually, the service vendors utilize the security and its management through multiple systems [56]. Some studies state that cloud computing is not insecure and that security issues can be dealt with easily if the cloud is managed and accessed securely [57]. Up to date, there are available cloud computing security architectures and solutions from different service providers that address this concern, as discussed further in this work. However, conform technical specifications and evolution, the IT governance standards play a significant role [58]. Taken into account that information security has a very important role in supporting the activities of the organization in the cloud, it is tremendously necessary to have a standard or benchmark which regulates governance over security, these standards should function as fundamental guidelines for corporate secure electronic commerce on the global scale [59]. Several of the standards for righty governance that contain information security awareness are PMMM, ISO27001, BS7799, PCIDSS, COSO, SOA, PRINCE2, etc. Most of the studies conducted, classify the security risks into groups, accordingly to the intensity of the issues they can cause. For example, according to the report by ENISA1 [32], the security risks are classified as loss of governance, lock-in, isolation failure, management interface compromise, data protection, insecure of incomplete data deletion, malicious insider, customer security expectation, and the availability chain. Whereas according to CSA there are top nine threats insecure APIs, denial of service, malicious insiders, abuse, insufficient due diligence, shared technology issues, which will be discussed further below. While mentioning the security issues, it is evident that the security issues should be tackled in all the service models [48, 60]. Hence, it is important to also analyse the security issues accordingly to the model of the service, in this way to clarify the issues and eventually present countermeasures accordingly.

3.2 Some Major Challenges Cloud computing has brought resources and services that have changed once and forever the industry of computing. Bringing a new era of computing by developing service models that deliver business supporting technology more efficiently in the first place, the cloud has also brought a new stage of data security challenge. Further classifications have been done, categorized in bigger groups that share eventual roots on the issues of security. In Fig. 5, the classification of the issues of security has been done by first grouping into three main factions such as communication issues, architectural issues, and client management issues [38]. Though to date, different research and organizations have classified the clouds’ security issues in different groups, CSA [61] has narrowed the grouping to nine fundamental threats. Below 1 ENISA-European

Network and Information Security Agency.

188

L. Hasimi

Fig. 5 Classification of security issues in cloud computing [38]

listed accordingly to the severity level as analyzed by CSA, the top nine threats of the cloud environment: Data Breaches. Data breaching concepts refer to any malicious or unauthorized retrieval of sensitive or confidential, taken data from the corporate network. Data Loss. Data is the most valuable asset foreign entity in the cloud therefore its prevention from loss is crucial. A big threat for a company is the data loss due to potential incapacity to prevent it [61, 62]. It can happen for different reasons such as computer freezing, server crash, human error, etc. Account Hijacking. Account hijacking happens when a malicious intruder uses the stolen credentials to enter transactions, insert false information call mom divert abusive sites, which leads to legal issues for the service providers. Insecure APIs. This happens in cases when application programming interfaces that are used for communication with the cloud services are weak or not sufficiently secured [61]. In such cases accidental or malicious attempt to violate them exposes the data to eventual security threats which leads to inflexible access control, scalability, monitoring limitations and, many other issues. Denial of Service. This is a very serious threat especially for organizations and businesses that are fully dependent on 24/7 services. as an initial face of the issues with the DoS, it is the denial of the excessive data the car storage, even by their authorized users, which attacks the server by sending thousands of requests [33, 61] and in return makes it unable to respond to the regular clients. Malicious Insiders. It usually refers to a person that enters a cloud network intending to harm the organization’s confidential data assets by penalizing financial damage, call stagnation in the productivity, install malware for their own purposes, sabotage the system, still information, etc. These are people who have legitimate access do the systems and data information. Abuse of Cloud Services. This refers to the issues for cloud service providers and it raises a number of serious implications for the vendors. To crack the encryption

Cost-Effective Solutions in Cloud Computing Security

189

key usually takes yours for the attacker using limited hardware however in cases of using an array of cloud servers this crack can be completed in minutes. Insufficient Due Diligence. It is very important for the organization and businesses to perform extensive and regular due diligence before deciding to go cloud [61]. It refers to the care from the staff that should be taken before entering an agreement especially in the cases of transactions with other parties. Shared Technology Issues As the main feature of cloud computing, sharing technology is difficult in terms of obtaining a strong insulation property for the multi-tenant architecture. It is the responsibility of the service provider to offer scalable service to the customer without interfering with the other client’s system [63]. However, this does not mean that the responsibility in the cloud environment is only of the provider, the security is a responsibility of both parties. It is evident that the number of threats in the cloud environment is quite diverse and complex therefore the analyses of the risk are very important. The security and privacy of both parties not only in certain vulnerabilities but also in the enter exchangeable activities [64]. Thus, many of the cloud computing providers and analysts try to come up with solutions that minimize the threats and challenges concerning security and privacy. Some of these solutions are discussed further in the next section.

3.3 Security Issues in Cloud Services Considering the cloud computing framework is build containing three service models on top of each other, the security issues, as well as risks, are inherited from one to another [57, 65]. For this reason, the system is seen as a whole and the security remedies are provided for the whole secure environment. The main issues and challenges and there are three layers of the cloud are presented below. Security issues in SaaS. As this is considered is the highest layer in the architecture of cloud computing, the providers of the service try to replicate the data in various places to ensure efficiency and availability. At this point, the customer is dependent on the security mechanism offered by the provider to protect the data and the applications that are being stored along with other companies and individuals [66]. In this layer, there are basic security issues that should be considered to avoid threats in data manipulation and data storage. During the SaaS deployment and development, the provider should consider matters such as data security, network security data confidentiality data integrity, availability, data locality, access control [56, 67]. “Data control over cloud services makes it difficult to protect and enforce identity theft and cybercrime security. Sharing resources across multiple domains and failures of data backup also arise some data leakage [57]. In SaaS data flow security is very important especially when it comes to potential intruders over the network security configuration. Due to the fact that at these layers there are present remote access mechanisms an injection there are present also vulnerabilities through which attackers can gain access through the network. At this extent multi-tenancy

190

L. Hasimi

and multitasking is a reason for confidentiality threats [42, 68] therefore it is important to prevent the unauthorized use of data. The integration of the data on the other hand should be done only by an authorized entity with adequate encryption. SaaS providers should offer flexibility to go along with the companies’ policies to avoid intrusion [65]. Security issues in PaaS. Even though the control is given to the client integrator extends security issues such as networking has intrusion should be tackled carefully from the side of the provider. It is crucial for the provider to do ensure the data continues to be inaccessible from eventual applications. Very important to highlight that the vulnerabilities in the cloud computing environment are related also to the machine to machine service-oriented architecture applications [57]) which are considered as increasingly more deployed in the cloud. Security issues in IaaS. Various security issues occur with the deployment models in IaaS. It is evident that private cloud environments deal with fewer security issues compared to public ones. However, since cloud computing offers the feature of virtualization technology, it brings issues of security such as control of data [34]. Because the cloud is a concept coexists with the Internet all the security issues that the Internet faces should be a concern for the cloud environment at some point. When we talk about the infrastructure and service it is important to reconsider that infrastructure does not refer only to the hardware resources, it also refers to the data being transmitted over the media through Internet [65]. Consequently, the data through network or infrastructure can easily be prone to intruders’ activity.

4 Perspective on Available Solutions 4.1 Security Solutions Available Among the biggest issues in cloud computing is the security in-network, for what is considered no complete available security solution [57]. However, there are available options in management that can reduce this level of risk. In the research conducted for the mitigation of security risks [57], there were explained policies, procedures, and eventual tools to deal with the risk of data an application in any of the models of cloud computing. These recommendations were given for cases such as data security and control, network security, data confidentiality and integrity, data and service availability, and access control. For data security and control it is suggested to identify and classify the data accordingly, for the provider to have a knowledge of the types of the various security breach and to be able to prevent, detect, and eventually react properly. For the cases of malicious data in the cloud such as cross-site scripting, insecure configuration, SQL infections it is needed to have regular validity tests [69, 70]. Therefore, for transparent service, it is necessary to have regular control, security, operations, which are the least costly and eventually preventive for bigger issues.

Cost-Effective Solutions in Cloud Computing Security

191

For the network security to avoid unauthorized modification eventual unauthorized access, it is important to use adequate setup and configuration or firewall as well as auditable access. It is important to test and validate network security with recommended tools as SSL, packet analyses, session management. For data confidentiality and integrity it is crucial to have proper authentication and authorization mechanism for secure key transfer it is recommended RIS and key encryptions [57]. On the other hand, data and service, availability should always have internet bandwidth and connectivity addressed, Furthermore, data replication and backup issues need to be tackled accordingly with the audible proof for data restore procedures and completeness. As for access control the access or changes and services should always be provided in out the table report and properly reviewed and monitored regularly [57]. Cloud computing has an enormous computing power which unfortunately can be misused for cyber-attacks, henceforth, it is important to have a mechanism that will act responsibly for the benefit of the provider and the client. This issue can be challenged using the Service Level Agreement (SLA), which is treated as a contract between the cloud vendor and user [70]. Multitenancy for instance is the future of cloud computing that creates serious data risks in cases, given that the same resources are being provided to different users. In such a case for a secure and reliable multi-tenant model, it is important to use insulation as well as segmentation and restriction [71]. A strong two-factor authentication it is an extra security cheque for the identification of real customer in cases of account hijacking risks. For the companies that deal with highly sensitive data, a great recommendation would be an ideal system that is used to trap the attackers’ activity. This preventive method is a deception trap and if deployed properly, it is a useful tool for warning before a great deal of damage is caused. The challenges in cloud computing security are categorized into three models, in its deployment model, service delivery model end network issues [68, 70]. For this reason, the challenge is in the cloud can be seen in two different perspectives, challenges in architecture and challenges in network infrastructure. Further discussed as presented below are some of the solutions for security challenges contemplating the architecture and network perspective.

4.2 Solutions to Privacy and Security Using Cryptography A continuing research challenge in cloud computing is security and privacypreserving authentication [72, 73]. For companies and enterprises that deal with sensitive information and store information like financial information, health records, and so on, privacy is a fundamental issue. there are many cryptographic tools and schemes available such as anonymous authentication schemes, group signatures, zero-knowledge protocols, that at some point offer identity hiding and authentication. The service providers need to control the authentication process to enter revoke malicious clients [74] by revealing their identity properly. There are a few schemes in cryptographical solutions.

192

L. Hasimi

An example of a share creation scheme was made by XOR visual cryptography [74] using AES (Advanced Encryption Standard) algorithms. Visual cryptography (VC) is a modern cryptographic technique which is applied to the secret image shared securely while maintaining the information with great confidentiality [75]. The generated shares by this scheme and the algorithm are known as encapsulated shares. This technique gives better security and reduces fraud shares. It is widely known to be used to overcome the complex issues of the share of maintenance and identify how to encapsulate these shares in visual cryptography by using encryption for high-security level [74]. Another innovative secure portable document format was proposed by Yang et al. [73] which uses extended visual cryptography, intending to ensure efficient storage and reduce computational time and storage space. The search technique ensures data integrity and tackles significantly the confidentiality aspect. it can hide a large amount of data with minimal afford space and complexity in time. Further, in the visual cryptography method, there’s available another method that has been proposed by Jaya [76], with the idea of encrypting or decrypting the data with last time and applicable in any field of cloud aiming for improved security. The use of this technique could have a great application and improving authentication, access control, encryption of data, and lead so on to a better security parameter.

4.3 Security and Privacy Issues Tackled with Cryptography Some of the cryptographic algorithms used in the security of cloud computing or symmetric key algorithm are symmetric key algorithms as well as a combination of these algorithms. Encryption makes the data more secure in the cloud network, the algorithms as well run on the cloud network [72]. Some of the widely used algorithms in security are AES, MD5, DES, RSA describe below. AES or the Advanced Encryption Standard is the symmetric key encryption standard, that as each of the ciphers with the 128-bit block size, key sizes of 128, 192, and 256 bits, in that order [77]. MD5 commonly used hash function with a 128-bit hash value that process is a variable-length message to a fixed-length output of 128 bits, as input the message is sliding it up into chunks of 512-bit blocks and then it is padded to be a divisible length by 512 [77]. DES (Data Encryption Standard) probably the most commonly used algorithm for encryption. As an algorithm, it operates on plane tax blocks given of size 64 bits and returns ciphertext blocks in the same size. This one as well as the asymmetric key algorithm, which means that the same key is used for encrypting and decrypting data [72]. RSA stands for the algorithm for public-key cryptography, it contains a public key and a private key. The public key is open to everyone and available to be used for message Encryption. However, the decryption of messages with the public key can be done only using the private key. It protected user data include encryption prior

Cost-Effective Solutions in Cloud Computing Security

193

Table 1 Comparison of mean processing time of the algorithms on the local system as well as on cloud network [77] Input

AES

AES Cloud

BLOWFISH

BF Cloud

RSA

RSA Cloud

MD5

MD5 Cloud

10 kb

11.5

1.5

4

2

238

13 kb

14.7

2

4.7

2

328.2

274.2

1

1

331.5

1

1

39 kb

21

3

8.25

2.75

56 kb

245

3.75

15.7

3

358.5

351.7

1

1

496.2

415.2

1

0.5

Table 2 Speed-up ratio of the algorithms for different input sizes [77] Input (KB)

AES

DES

BLOWFISH

RSA

MD5

10

7.6

3.62

2

0.86

1

13

7.2

4

2.3

0.99

1

39

7

4.8

3

1.01

1

56

6.6

5.43

5.25

1.19

0.5

to storage, user authentication procedures prior to storage or retrieval and building secure channels for data transmission [77]. Following the results of the research conducted by [72], on which the used eclipse runs variable input sizes for the local and Google app engine as well. From the results that they provided as seen in Tables 1 and 2, it was found out that the most timeconsuming encryption algorithm was RSA, where is MD5 is found to be the least time-consuming hashing algorithm. Concerning the speedup ratio, the symmetric encryption algorithm AES was found to be the highest in the scoring speedup ratio. Another important feature that was found during this research was that in MD5 and AES algorithms the speedup ratio was decreasing while increasing in size. For the rest of the algorithm, there is slight to not change. It was recommended from the findings of this article that while looking for a performance of algorithm the best solution would be MD5, AES, DES. Whereas for security, the most efficient one was resulting MD5, AES, with the MD5 among the best for security algorithm and last time consuming [78].

5 Models and Methods of Cost Calculations of Security in Cloud 5.1 Encryption Method of Cost Calculation One of the most basic uses of the cloud is the outsourcing of client data for storage purposes. In this method for the elaboration of cost calculation, in the case of

194

L. Hasimi

encrypted data storage with integrity in the S → L scenario, it was evaluated that the cost of storing a bit would be under 9 picocents/month and network transfer approximately would reach 900 picocents per bit. According to [47] rom the technological cost point of view, it is not effective to store data remotely [47]. However, not all of the benefits of outsourcing can be precisely ‘costified’ and for as long as the clients depend on the benefits like easy access, multi-client settings, pay-per-use, and it is most obvious that the need for outsourcing will keep increasing. As the curve of demand increases, also the need for better security and privacy rises, but how much of these benefits will not be considered a drawback considering the costs of security? Several existing systems encrypt data before storing it on potentially data-curious servers some others perform online real-time integrity verification [66]. In the integrity perspective, the cheapest integrity constructs use of hash-based MACs [33]. In the case of publicly verifiable constructs, crypto-hash chains can amortize their costs over multiple blocks, and in the extreme cases, a single signature could authenticate an entire file system, at the expense of increased I/O overheads for verification [47]. In the study conducted about the costs of security [39], it is found out that for a chain secured by a single hash-chain signed using 1024-bit RSA, it yields an amortized cost of approximately 1 M picocents per 4,096-byte block (30 + picocents/bit) for client read verification, whereas 180 + picocents/bit for write/signatures. Which is considered to be more expensive than MAC-based. In the case of confidentiality, as it can be achieved by encrypting the outsourced content before outsourcing it, consequently it is considered rather hard to be processed by servers. To illustrate the cost case for confidentiality in the study by Chen and Sion [47] it is taken a 32-bit search key example and a 1 TB database. Counting the CPU cycles, for performing a search, to outsource it saves 2500–8000 picocents for access. However, the outsourced searching becomes more expensive for any results upwards of 36 bytes per query according to it. For a secure query processing example, [47] illustrated the case, assuming that the operators would behave linearly and be highly selective by incurring two 32 it transfers. Evaluated, for the network cost, it would reach 900 × 32 × 2 = 57,600 picocents traversing database of 105 and up, which would lead to CPU cost savings per cycle. What in return shows that with very selective queries over large enough databases, outsourcing can break even. Another example of such calculation would be the case of outsourcing applying crypto-hash and linear operation [79]. For a database of 109 tuples of size 64 bits, the hash tree nodes would need to be at least 240 bits. For 3 CPU cycles that are needed per data item, the limit condition results in selectivity s ≤ 0.00037 before outsourcing starts to be more economical. In the case of signature aggregation, it is supposed that the break-even selectivity would be even lower due to the higher computation overheads [47].

Cost-Effective Solutions in Cloud Computing Security

195

5.2 The Economic Model of Security Threats A very important step towards dealing with the security issues in Cloud Computing security is first to have metrics in order to be able to measure the risks and effects of eventual issues. According to the majority of the existing literature, the dependability of a system can be measured accordingly to the reliability, availability, usability, and security metrics. Such security metrics include security metrics such as the meantime to failure (MTTF), the mean time between failures (MTBF), the meantime to discovery (MTTD), the mean failure cost (MFC), The mean time to exploit (MTTE), and, Average Uptime Availability [80]. The mean failure cost was first introduced as a concept by Ben Alissa [81], to measure the dependability of the system. In one of the studies proposing the economic model of security threats [82], it is proposed the model of cost calculations, defining the security metrics, and proposing three different matrixes as explained below. The Stakes Matrix—By quantifying the MFCi (Mean Failure Cost), as a random variable that represents the cost to stakeholder Hi, in terms of financial loss per unit of operation time (e.g. $/hour) it is assumed that the Mean Failure Cost for stakeholder Hi is defined as:  STi, j × P R j M FCi = 1≤ j≤n

Equation 3 The Stakes Matrix according to Rabai et al. or given as a product, MFC = ST ◦ DP◦ IM◦ PT In this model, there were considered seven generic security requirements according to [80]: • • • • • • •

AVC: Availability of Critical Data. AVA: Availability of Archival Data. INC: Integrity of Critical Data. INA: Integrity of Archival Data. CC: Confidentiality of Classified Data. CP: Confidentiality of Proprietary Data. CB: Confidentiality of Public Data.

To illustrate the example, the model had considered a fictitious running example, having a cloud computing provider (PR), and a sample of three subscribers: • A corporate subscriber (CS), • A governmental subscriber (GS), • An individual subscriber (IS).

196

L. Hasimi

Table 3 Stakes matrix: cost of failing a security requirement stakes [80] Stakeholders

AVC

AVA

INC

INA

CC

CP

CB

PR

500

90

800

150

1500

1200

120

CS

150

40

220

80

250

180

60

GS

60

20

120

50

2500

30

12

IS

0.05

0.015

0.30

0.20

0.30

0.10

0.01

The stakes matrix, therefore, was modeled based in stakes, in terms of thousands of dollars ($K) per hour of operation, as presented in Table 3.

6 Conclusion 6.1 Synopsis Cloud computing is a relatively new and favorable concept that delivers IT services to cloud computing. Some of the major beneficial effects of the cloud computing implementation, that are expected to have a great impact on the overall operational costs are the reduction in IT expenses, business competitiveness and, further investment in the development of the company. Despite the many advantages that cloud computing brings, many enterprises remain reserved toward migration in the cloud, considering the security and privacy problems of the framework as the biggest drawbacks. Considering the nature of cloud computing which means easily accessible files from computers through the internet a highly sensitive data operation carries a lot of risks [83–86]. In this work, it was discussed the problem of security, the need for security, and the approaches needed for the application and data security. Thoroughly, as an objective was to investigate the security solutions in cloud computing and eventually present the methods of calculation for the available costefficient solutions. It addressed cloud security issues and challenges and eventual cost-effective solutions regarding financial perspective. The work itself followed a three-layered research approach. In the first part, it was explained and discussed the overall framework of cloud computing as well as the structure and the mechanism. The major issues regarding the service in cloud computing the architecture and the models of deployment offered are explored in detail. Consequently, the work methodology was separated in two parts of the qualitative and quantitative nature. In this manner, the observing and describing in-depth concepts of services and security in cloud computing as well as cost-effective concept in it was qualitative. Whereas, to obtain empirical evidence for supporting the metascientific findings for the part of the short scientometrics analyses the supporting methodology was of quantitative nature. Text analysis is, on the other hand, made it possible to process the data sources from articles published, papers, books, reports

Cost-Effective Solutions in Cloud Computing Security

197

on security, manuals, online databases, etc. Moreover, secondary data analyses as a great source of information played a large part in understanding the problem and forming the eventual concepts. A major part of this work was based on secondary data analyses except for the scientometrics parts that had data retrieved from the Scopus database which were later on processed through different tools in the software. The conceptual background in the first section gave an overall picture of the benefits, advantages, and disadvantages of outsourcing. Further in the second section to give a clear perspective of what is the state of the topic, it was conducted a short scientometrics analysis, presenting this way the actual situation. It resulted that even among the top three countries in publishing, it was a decreasing rate of papers, which highlights the need and the cruciality of future work in this topic. Added, in the third section, it was given what methods were involved throughout the work and analysis. The fourth section was separated into two sub-sections giving so the investigation results in the most cost-effective solutions available for the cloud computing security issues such as solutions using secret sharing schemes, cryptography, cryptographic algorithms, public key-based framework, etc. Considered the available solutions, that tackle some of the major issues in security in the cloud, the investigated methods of calculation of costs of security in the cloud were presented in the second part of the fourth section. Described and analyzed there were presented the methods of costs calculations for encryption, the economic model of security threats that proposed different matrixes covering different aspects of security cost-calculations in cloud computing.

6.2 Further Research To conclude, this research has been an opportunity to investigate a wide variety of concepts, models, and technologies in the cloud computing security field. The main objective was to investigate cloud data security concerns while focusing on costeffective solutions and security cost-calculations. Cloud computing security is a field full of ongoing challenges and of paramount importance, henceforth, many research problems remain to be identified and investigated especially when it comes to the monetizing of the certain cost-effective solutions in security. Most of the methods of cost-calculations in cloud security remain oriented in specific issues and cannot be generalized, due to the diverse nature of security and the eventual architecture and models of deployment in different organizations. However, the need for research in the costs of security as separate from cloud computing is quite crucial. The efficiency in the cryptography levels to support outsourcing and eventually secure outsourcing for different range queries is a ground that needs to be undertaken.

198

L. Hasimi

References 1. Ennajjar, I., Tabii, Y., Benkaddour, A.: Security in cloud computing approaches and solutions. 2015, 57–61 (2015). https://doi.org/10.1109/CIST.2014.7016594 2. Da Silva, C.A., De Geus, P.L.: Return on security investment for cloud computing: a customer perspective. pp. 156–160. https://doi.org/10.1145/2857218.2857254 3. Alkhanak, E.N., Lee, S.P., Khan, S.U.R.: Cost-aware challenges for workflow scheduling approaches in cloud computing environments: taxonomy and opportunities. Future Gener. Comput. Syst. 50, 3–21 (2015). https://doi.org/10.1016/j.future.2015.01.007 4. Khairnar, P.P., Ubale, V.S.: In: Cloud Computing Security Issues and Challenges. pp. 13. (2013) 5. Dulock, H.: In: Research Design: Descriptive Research. (1993). https://journals.sagepub.com/ https://doi.org/10.1177/104345429301000406 (Accessed 27 June 2020). 6. Faniyi, F., Bahsoon, R.: A systematic review of service level management in the cloud. ACM Comput. Surv. CSUR 48(3), 1–43 (2015). https://doi.org/10.1145/2843890 7. Shaptunova, Y.: 4 Best cloud deployment models [An Overview] | SaM Solutions (2018). https://www.sam-solutions.com:443/blog/four-best-cloud-deployment-modelsyou-need-to-know/ (Accessed 23 June 2020) 8. Shuttelworth, M.: Descriptive research design—observing a phenomenon (2020). https://exp lorable.com/descriptive-research-design (Accessed 27 June 2020) 9. Goundar, S.: Chapter 3—Research methodology and research method, (2012). https://www.res earchgate.net/publication/333015026_Chapter_3_-_Research_Methodology_and_Research_ Method (Accessed 27 June 2020) 10. ‘What Is Cloud Computing? A Beginner’s Guide | Microsoft Azure’ (2020). https://azure.mic rosoft.com/en-us/overview/what-is-cloud-computing/ (Accessed 09 September 2020) 11. Knorr, E.: ‘What is cloud computing? Everything you need to know now’. InfoWorld (2018). https://www.infoworld.com/article/2683784/what-is-cloud-computing.html (Accessed 22 June 2020) 12. Ranger, S.: What is cloud computing? everything you need to know about the cloud explained. ZDNet (2018). https://www.zdnet.com/article/what-is-cloud-computing-everyt hing-you-need-to-know-about-the-cloud/ (Accessed 09 August 2020). 13. Gregus, M., Kryvinska, N.: Service Orientation of Enterprises—Aspects, Dimensions, Technologies. Comenius University in Bratislava (2015). ISBN: 9788022339780 14. Kryvinska, N., Gregus, M.: SOA and its business value in requirements, features, practices and methodologies. Comenius University in Bratislava, (2014). ISBN: 9788022337649 15. Venkata, P.K.: In: Emerging Technologies and Applications for Cloud-Based Gaming. IGI Global (2016) 16. Dixon, H.B.J.: Cloud computing technology 51 judges. J (2012). https://heinonline.org/HOL/ LandingPage?handle=hein.journals/judgej51&div=25&id=&page= (Accessed 27 June 2020) 17. Knorr, E.: The shift to cloud computing persists as organizations use multiple public clouds. GlobeNewswire News Room (2020). http://www.globenewswire.com/news-release/2020/06/ 18/2050275/0/en/The-Shift-to-Cloud-Computing-Persists-as-Organizations-Use-MultiplePublic-Clouds.html (Accessed 10 September 2020) 18. Cloud Computing Trends: 2020 State of the Cloud Report. Flexera Blog (2020). https://www. flexera.com/blog/industry-trends/trend-of-cloud-computing-2020/ (Accessed 23 June 2020) 19. Flexera.: Cloud computing trends: 2020 state of the cloud report . Flexera Blog (2020). https://www.flexera.com/blog/industry-trends/trend-of-cloud-computing-2020/ (Accessed 22 June 2020) 20. Davies, R.: European parliament, and directorate-general for parliamentary research services. In: Cloud Computing: An Overview of Economic and Policy Issues : In-Depth Analysis. Brussels, European Parliament (2016) 21. ‘Why the cloud is more cost effective than your servers: Syvantis Technologies (2017). https://www.syvantis.com/blog/why-the-cloud-is-more-cost-effective-than-yourservers (Accessed 22 June 2020)

Cost-Effective Solutions in Cloud Computing Security

199

22. Molnár, E., Molnár, R., Kryvinska, N., Greguš, M.: Web intelligence in practice. Soc. Serv. Sci. J. Serv. Sci. Res. Springer 6(1), 149–172 (2014) 23. Kryvinska, N., Poniszewska-Maranda, A., Gregus, M.:An approach towards service system building for road traffic signs detection and recognition. Elsevier J. Proc. Comput. Sci. Special Issue on The 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2018). vol. 141. pp. 64–71. (2018). https://doi.org/10.1016/j.procs.2018. 10.150 24. Pawlak, M., Poniszewska-Maranda, A., Kryvinska, N.: Towards the intelligent agents for blockchain e-voting system. Elsevier J. Proc. Comput. Sci. Special Issue on The 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2018), vol. 141. pp. 239–246. (2018). https://doi.org/10.1016/j.procs.2018.10.177 25. Penzel, D., Kryvinska, N., Strauss, C., Gregu, M.: The future of cloud computing: a SWOT analysis and predictions of development. In 2015 3rd International Conference on Future Internet of Things and Cloud, August, pp. 391–397. (2015). https://doi.org/10.1109/FiCloud. 2015.102 26. Cassimiro Nascimento, D., Santos Pires, C.E., Brasileiro Araújo, T.: A theoretical model for estimating entity resolution costs in cloud computing environments. Discrete Math. Theor. Comput. Sci. 1, (2018). Accessed 09 June 2020. [Online]. Available https://hal.archives-ouv ertes.fr/hal-01809639 27. Kryvinska, N., Bauer, C., Christine, S., Michal, G.: A swot analysis of the soft-computing paradigms SOA/SOC/cloud combination (C-SOA) in software development. In: MCIS 2014 Proceedings September (2014). [Online]. Available https://aisel.aisnet.org/mcis2014/27 28. Durkee, D.: Why cloud computing will never be free. Commun. ACM 53(5), 62–69 (2010). https://doi.org/10.1145/1735223.1735242 29. Costs of Cloud Computing.: Imagine IT, Inc. https://www.imagineiti.com/the-cloud/costscloud/ (Accessed 07 June 2020) 30. European Commission.: Final Report of the study SMART 2013/0043—Uptake of Cloud in Europe. Shaping Europe’s digital future—European Commission June 10, (2015). https://ec.europa.eu/digital-single-market/en/news/final-report-study-smart-201 30043-uptake-cloud-europe (Accessed 24 June 2020) 31. Pearson, S., Yee, G. (eds.): Privacy and Security for Cloud Computing. Springer, London (2013) 32. Catteddu, D.: Cloud computing: benefits, risks and recommendations for information security. In: Serrão, C., Aguilera Díaz, V., Cerullo, F. (eds.) Web Application Security, vol. 72, Berlin, Heidelberg, Springer, pp. 17–17. (2010) 33. Leite, C.W.S., Lacerda, L.L., Naves, F.L.F., Rodrigues, C.S.C.: Aspects related to security in clouds for IoT-benefits and computational costs’. vol. 2018. pp. 1–6. (2018). https://doi.org/ 10.23919/CISTI.2018.8399387 34. Garg, S.K., Versteeg, S., Buyya, R.: A framework for ranking of cloud computing services. Future Gener. Comput. Syst. 29(4), 1012–1023 (2013) 35. Koehler, P., Anandasivam, A., MA, D.: In: Cloud Services From a Consumer Perspective (2010) 36. Avoyan, H.: ‘3 Types of cloud computing services. Monitis Blog (2009). https://www.monitis. com/blog/3-types-of-cloud-computing-services/ (Accessed 23 June 2020) 37. Martens, B., Walterbusch, M., Teuteberg, F.: Costing of cloud computing services: a total cost of ownership approach. In: 2012 45th Hawaii International Conference on System Sciences, January 2012, pp. 1563–1572. (2012) https://doi.org/10.1109/HICSS.2012.186 38. Fatima, S., Ahmad, S.: An exhaustive review on security issues in cloud computing. KSII Trans. Internet Inf. Syst. TIIS 13(6), 3219–3237 (2019). https://doi.org/10.3837/tiis.2019.06.025 39. Chen, Y., Sion, R.: Costs and security in clouds. In: Jajodia, S., Kant, K., Samarati, P., Singhal, A., Swarup, V., Wang, C. (eds.) Secure Cloud Computing, pp. 31–56. New York, NY, Springer (2014) 40. Elio, G., Dahman, K., Gateau, B., Godart, C.: A broker framework for secure and cost-effective business process deployment on multiple clouds. ResearchGate (2014). https://www.resear chgate.net/publication/279258635_A_Broker_Framework_for_Secure_and_Cost-Effective_ Business_Process_Deployment_on_Multiple_Clouds (Accessed 25 June 2020)

200

L. Hasimi

41. Seema: Cloud computing effect on enterprises in terms of cost and security. 3(12), 4 (2012) 42. Martens, B., Teuteberg, F.: Decision-making in cloud computing environments: a cost and risk based approach. Inf. Syst. Front. 14(4), 871–893 (2012) 43. Khajeh-Hosseini, A., Sommerville, I., Sriram, I.: Research challenges for enterprise cloud computing. ArXiv Prepr. ArXiv10013257 (2010) 44. Bisong, A., Rahman, S.: [1101.5613] an overview of the security concerns in enterprise cloud computing (2020). https://arxiv.org/abs/1101.5613 (Accessed 30 April 2020) 45. Types of Cloud Computing: Cloudiofy, April 24 (2020). https://cloudiofy.com/types-of-cloudcomputing/ (Accessed 13 September 2020) 46. Mell, P., Grance, T.: Effectively and securely using the cloud computing paradigm. NIST Inf. Technol. Lab. 2(8), 304–311 (2009) 47. Chen, Y., Sion, R.: Costs and security in clouds. In: Jajodia, S., Kant, K., Samarati, P., Singhal, A., Swarup, V., Wang, C. (eds.) Secure Cloud Computing, pp. 31–56. Springer, New York, NY (2014) 48. Hendre, A., Joshi, K.P.: A semantic approach to cloud security and compliance. In 2015 IEEE 8th International Conference on Cloud Computing, pp. 1081–1084. (2015) 49. Dimitrov, M., Osman, I.: The Impact of Cloud Computing on Organizations in Regard to Cost and Security. Umea University, Sweden (2014) 50. Heilig, L., Voß, S.: Managing cloud-based big data platforms: a reference architecture and cost perspective. In: Big Data Management, pp. 29–45. Springer International Publishing (2017) 51. Opala, O.J.: An analysis of security, cost-effectiveness, and it compliance factors influencing cloud adoption by it managers. (2012). https://www.semanticscholar.org/paper/An-analysisof-security%2C-cost-effectiveness%2C-and-it-Opala/7e6633adbaa8f1a175a906c493615f911 2db9d5c (Accessed 01 May 2020) 52. Jansen, W.A.: Cloud hooks: security and privacy issues in cloud computing. In: 2011 44th Hawaii International Conference on System Sciences, pp. 1–10. (2011) 53. Luo, F., Dong, Z.Y., Chen, Y., Xu, Y., Meng, K., Wong, K.P.: Hybrid cloud computing platform: the next generation IT backbone for smart grid. In: 2012 IEEE Power and Energy Society General Meeting, July 2012, pp. 1–7. (2012). https://doi.org/10.1109/PESGM.2012.6345178 54. AlZain, M.A., Pardede, E., Soh, B., Thom, J.A.: Cloud computing security: from single to multi-clouds. In: 2012 45th Hawaii International Conference on System Sciences, January 2012, pp. 5490–5499. (2012). https://doi.org/10.1109/HICSS.2012.153 55. Carroll, M., Kotzé, P., van der Merwe, A.: Secure cloud computing: benefits, risks and controls. In: 2011 Information Security of South Africa (2011). https://doi.org/10.1109/ISSA.2011.602 7519 56. Vines, R.L.K.R.D., Krutz, R.L.: In: Cloud security: A comprehensive guide to secure cloud computing. Wiley Publishing, Inc (2010) 57. Chowdhury, R.R.: Security in cloud computing. Int. J. Comput. Appl. 96(15), (2014) 58. Hamdi, M.: Security of cloud computing, storage and networking. In: 2012 International Conference on Collaboration Technologies and Systems (CTS), May 2012, pp. 1–5. (2012). https://doi.org/10.1109/CTS.2012.6261019 59. Susanto, H., Almunawar, M.N., Kang, C.: Toward cloud computing evolution: efficiency vs trendy vs security. Social Science Research Network, Rochester, NY, SSRN Scholarly Paper ID 2039739, April (2012). https://doi.org/10.2139/ssrn.2039739 60. Ali, M., Khan, S.U., Vasilakos, A.V.: Security in cloud computing: opportunities and challenges. Inf. Sci. 305, 357–383 (2015). https://doi.org/10.1016/j.ins.2015.01.025 61. ‘Cloud Security Alliance’: Cloud Security Alliance (2013). https://cloudsecurityalliance. org/articles/ca-warns-providers-of-the-notorious-nine-cloud-computing-top-threats-in-2013/ (Accessed 24 June 2020) 62. Chou, T.-S.: Security threats on cloud computing vulnerabilities. Int. J. Comput. Sci. Inf. Technol. 5(3), 79–88 (2013). https://doi.org/10.5121/ijcsit.2013.5306 63. Palanisamy, B., Singh, A., Liu, L.: Cost-effective resource provisioning for MapReduce in a cloud. IEEE Trans. Parallel Distrib. Syst. 26(5), 1265–1279 (2015). https://doi.org/10.1109/ TPDS.2014.2320498

Cost-Effective Solutions in Cloud Computing Security

201

64. Barron, C., Yu, H., Zhan, J.: Cloud computing security case studies and research. pp. 5. (2013) 65. Chowdhury, B.N.: A critical analysis of customer loyalty and customer satisfaction—a case study on Tesco Club Card’, masters. University of East London (2016) 66. Luo, S., Lin, Z., Chen, X., Yang, Z., Chen, J.: Virtualization security for cloud computing service. In: 2011 International Conference on Cloud and Service Computing, pp. 174–179. (2011) 67. Almorsy, M., Grundy, J., Müller, I.: [1609.01107] An Analysis of the Cloud Computing Security Problem. (2020). https://arxiv.org/abs/1609.01107 (Accessed 30 April 2020) 68. Kar, J., Mishra, M.R.: Mitigating threats and security metrics in cloud computing. JIPS (2016). https://doi.org/10.3745/JIPS.03.0049 69. Zunnurhain, K., Vrbsky, S.V.: In: Security Attacks and Solutions in Clouds. pp. 4. (2014) 70. Prakash, C., Dasgupta, S.: Cloud computing security analysis: challenges and possible solutions’. pp. 54–57. (2016). https://doi.org/10.1109/ICEEOT.2016.7755626 71. Almulla, S.A., Yeun, C.Y.: Cloud computing security management. In: 2010 Second International Conference on Engineering System Management and Applications, pp. 1–7. (2010) 72. Kaur, G., Mahajan, M.:Analyzing data security for cloud computing using cryptographic algorithms. 3(5), 5 (2013) 73. Yang, C.-Y., Lee, C.-C., Sun, T.-H., Hwang, M.-S.: Cryptanalysis of a hierarchical data access and key management in cloud computing. IOP Conf. Ser. Mater. Sci. Eng. 466, 012010 (2018). https://doi.org/10.1088/1757-899X/466/1/012010 74. Shankar, K., Eswaran, P.: Sharing a secret image with encapsulated shares in visual cryptography. Procedia Comput. Sci. 70, 462–468 (2015). https://doi.org/10.1016/j.procs.2015. 10.080 75. Othman, A., Ross, A.: Visual cryptography. In: Li, S.Z., Jain, A.K. (eds.) Encyclopedia of Biometrics, pp. 1–11. Boston, MA, Springer US 76. Jaya, J.: Securing cloud data and cheque truncation system with visual cryptography. Int. J. Comput. Appl. 70(2), 16–21 (2013). https://doi.org/10.5120/11934-7721 77. Arora, P., Singh, A., Tyagi, H., Goel, R.K.: Evaluation and comparison of security issues on cloud computing environment. (2012). /paper/Evaluation-and-Comparison-of-Security-Issueson-Arora-Singh/c57b81e6784a99e040f9c5e573668eda28c60c11 (Accessed 29 June 2020) 78. Kaur, G., Mahajan, M.: In: Analyzing Data Security for Cloud Computing Using Cryptographic Algorithms (2013) 79. Wang, J., Huang, C., Xiong, N.N., Wang, J.: Blocked linear secret sharing scheme for scalable attribute based encryption in manageable cloud storage system. Inf. Sci. 424, 1–26 (2018). https://doi.org/10.1016/j.ins.2017.09.032 80. Rabai, L.B.A., Jouini, M., Nafati, M., Aissa, A.B., Mili, A.: An economic model of security threats for cloud computing systems. In: Proceedings Title: 2012 International Conference on Cyber Security, Cyber Warfare and Digital Forensic (CyberSec), June 2012, pp. 100–105. (2012). https://doi.org/10.1109/CyberSec.2012.6246112 81. Aissa, A.B., Abercrombie, R.K., Sheldon, F.T., Mili, A.: Quantifying security threats and their potential impacts: a case study. Innov. Syst. Softw. Eng. 6(4), 269–281 (2010). https://doi.org/ 10.1007/s11334-010-0123-2 82. Rabai, L.B.A., Jouini, M., Nafati, M., Aissa, A.B., Mili, A.: An economic model of security threats for cloud computing systems. In: Proceedings Title 2012 International Conference Cyber Security Cyber Warfare Digital Forensic CyberSecurity. Accessed 15 Jun 2020. [Online] Available https://www.academia.edu/4039987/An_Economic_model_of_sec urity_threats_for_cloud_computing_system 83. Poniszewska-Maranda, A., Kaczmarek, D., Kryvinska, N., Xhafa, F.: Endowing IoT devices with intelligent services. In: Barolli, L. et al. (eds.) The 6th International Conference on Emerging Internet, Data & Web Technologies (EIDWT-2018), March 15–17, Polytechnic University of Tirana, Albania, Springer, Lecture Notes on Data Engineering and Communications Technologies (LNDECT), vol. 17. pp. 359–370. (2018) 84. Poniszewska-Maranda, A., Matusiak, R., Kryvinska, N., Yasar, AUH: A real-time service system in the cloud. J. Ambient Intell. Humanized Comput. Springer (2020). https://doi.org/ 10.1007/s12652-019-01203-7.

202

L. Hasimi

85. Poniszewska-Maranda, A., Kaczmarek, D., Kryvinska, N., Xhafa, F.: Studying usability of AI in the IoT systems/paradigm through embedding NN techniques into mobile smart service system. J. Comput. 101(11), 1661–1685 (2019) Springer . https://doi.org/10.1007/s00607-0180680-z 86. Poniszewska-Mara´nda, A., Vesely, P., Urikova, O., Ivanochko, I.: Building microservices architecture for smart banking. In: Barolli, L., Nishino, H., Miwa, H. (eds.) Advances in Intelligent Networking and Collaborative Systems (INCoS 2019), Advances in Intelligent Systems and Computing, vol 1035. (2019). https://doi.org/10.1007/978-3-030-29035-1_52

Marketing Communication and Its Role in the Process of Creating Rational Awareness of Generation Z Representatives Katarína Gubíniová, Peter Štarchon, ˇ Lucia Vilˇceková, Gabriela Pajtinková Bartáková, and Jarmila Brtková Abstract It is possible to observe significantly negative responses to the principles of marketing in the form as described so far. Phrases such as “marketing tricks” and “marketing lies” are used more and more frequently in both domestic and foreign professional literature. Consumer market representatives themselves characterize many of today’s marketing activities as “intrusive”, “disturbing”, “misleading” and “deceptive”. Based on such associations, marketing both as a scientific discipline and as a functional area of management often becomes despised. This is also documented by many management practices and experiences. The aim of the paper is to evaluate perception of marketing and marketing communication on the basis of quantitative and qualitative analysis on the representative sample of 1248 Generation Z respondents (i.e. age range 18–29 years) in terms of how positive/negative they perceive the current activities of organizations in marketing communication assessing their impact on creating rational awareness about products.

K. Gubíniová (B) · P. Štarchoˇn · L. Vilˇceková · G. P. Bartáková · J. Brtková Faculty of Management, Comenius University in Bratislava, Bratislava, Slovakia e-mail: [email protected] P. Štarchoˇn e-mail: [email protected] L. Vilˇceková e-mail: [email protected] G. P. Bartáková e-mail: [email protected] J. Brtková e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Kryvinska and A. Poniszewska-Mara´nda (eds.), Developments in Information & Knowledge Management for Business Applications, Studies in Systems, Decision and Control 376, https://doi.org/10.1007/978-3-030-76632-0_6

203

204

K. Gubíniová et al.

1 Introduction The hypercompetitive market, which operates today at the level of many national and global economies, is characterized by the increasing number of unfair, unethical and misleading practices in the efforts of market managers to serve markets [1]. With market development and increasing competition, more and more professionals and even marketing managers themselves, are also aware of this “dark side of marketing”, its disturbing, superfluous and often inadequate approach to servicing customer markets [2]. Misleading, deceptive and unethical approaches to marketing management have become the subject of massive social criticism of marketing [3]. Marketing communication through a combination of communication mix tools used up to now currently plays a significant role in the processes of influencing consumer purchasing decisions [4, 5]. However, it is necessary to emphasize the fact that the role of marketing communication in relation to sustainable strategies is often incompatible with the principles of sustainability. On the one hand, it notably promotes consumption of end customers and participates in creating unsustainable patterns of consumer behaviour, but on the other hand, marketing communication (or its tools) can be crucial in the process of spreading the ideas of sustainability, sustainable lifestyle [6, 7]. Marketing communication constitutes an important element of an organization’s marketing strategy. Marketing communication tools are used by organizations taking into account time and place to achieve different goals [8]: 1. 2. 3. 4. 5. 6. 7.

Build product awareness, Educate or provide information about products, a brand, or an organization, Warn (remind) or reassure customers of a brand, Persuade a potential customer to try a product or brand, Reward customers who bought a product, Improve the brand or organization image, Improve or maintain staff morale.

In spite of the given wide range of objectives that can be achieved applying individual marketing communication tools, the subject of social criticism of marketing is to a large extent marketing communication. Representatives of social criticism of marketing are of the opinion that marketing communication is extensively involved in the following deceptive practices [9, 10]: 1. 2. 3. 4. 5. 6.

Deceiving that stems from a careful selection of words and sentences, while thus declared statements are not true, Significant digital editing of photographs, videos and other visual elements, Deceiving by means of figures, calculations, statistical information and results of various surveys, Sophisticated omission, obscuration or confusion of information, Deceiving through a large amount of (dispersed) information, Activities that give the impression that through them relationships with a customer are created,

Marketing Communication and Its Role in the Process …

7. 8. 9. 10. 11. 12. 13. 14.

205

Displaying false emotions in selling and providing services, Incomplete or misleading framework product comparison, Inadequate requirements for information retrieval and instructions on how to use products, Copying products or brands and sophisticated advertisements using confusion of such products with their originals, Concocted customer characteristics through the brand image, Reverse product placement in films, television broadcasts and on websites, Exaggeration, exaggerated advertising and meaningless contents of messages of marketing communication tools, Obvious and open misleading regarding product attributes, product properties and consequences resulting from product use.

It should be emphasized that the issue of deceptive and misleading practices is not exclusively today’s topic, which can be documented by the work of D. M. Gardner [3], who in 1975 created a comprehensive conceptual approach to misleading practices in the important marketing mix element—marketing communication. The author builds on the behavioral approach, while the criteria for evaluating deceptiveness of marketing communication are still valid today (measurement focused on consumer reaction; allows the advertiser to be creative; allows the advertiser to use truthful product claims; is flexible enough to be adaptable to special situations in the marketplace). The issues of product purchases and trust are presented in the research The Reader’s Digest European Trusted Brands 2018, in which 32% of respondents (or customers) trust organizations with a global reach and only 13% of respondents trust advertising [11]. On the other hand, almost half of the respondents (48%) trust their circle of acquaintances. Similar results are presented in the research Nielsen Global Online Consumer Survey 2017 [12], in which up to 90% of respondents indicated that in purchasing behaviour processes they make decisions based on recommendations of their acquaintances. A relatively high percentage of respondents (70%) also reported that they trust consumer opinions posted online [13–15] (for example in social media and networks, blogs, Internet discussions)—see Fig. 1. In the context of the declining degree of trust in “traditional” channels and marketing communication tools and the increasing degree of trust in customer recommendations and testimonials [16–18], expenditure data on individual forms of advertising thus seem paradoxical.

2 Materials and Methods The aim of the paper is on the representative sample of Generation Z respondents on the basis of quantitative and qualitative analysis to evaluate their perception of marketing and marketing communication in terms of how positive/negative they perceive the current activities of the organizations in marketing communication.

206

K. Gubíniová et al.

Fig. 1 Degree of trust in various forms of marketing communication. (Adapted Nielsen Global Online Consumer Survey 2017)

The reliability of the results of the conducted research was at the level of 95% with precision of 3%, while the sample size was represented by 1248 respondents. Quota sampling was used in the selection of respondents, which is a method in which data are collected from a homogeneous group. Quota sampling is a simple but effective way to carry out research in the initial stages. The researcher selects from the population on the basis of quotas, for the purposes of that research they were used gender, education, regional representation and size of the seat. With quota sampling the selection of respondents is not random, which some researchers say may be unreliable. Scientists risk bias. Interviewers may be tempted to interview those individuals on the street who appear to be most useful in completing the form. Quota sampling has its own advantages. Once sampling is complete, the information is easy to perform and decrypt. It also improves the representation of any particular group in the population, thus ensuring that these groups are not over-represented. Based on the chosen sampling method we can say the sample used for this research was representative for the population of the Slovak Republic aged 18–29 years in terms of gender, education, regional representation and size of seat. The questionnaire used in the representative research contained a set of questions comprehensively covering the issue of deceptive practices used in marketing management from the perspective of end customers.

Marketing Communication and Its Role in the Process …

207

In order to achieve the objective of the paper, we were evaluating the part in which marketing communication was central. Factor analysis was used to reduce the number of scale variables within the perception of deceptive marketing practices into a smaller number of factors. This technique extracts the maximum common variance from all variables and puts them into a common score. Before conducting the analysis, the data set was tested for outlier and they were removed from the analysis. The data were interval and the analysis is based on linearity assumption and it does not assume the homoscedasticity between the variables. More conditions of the analysis are no multicollinearity and no singularity. For some questions, respondents were given the opportunity to express their subjective attitudes and give examples, and therefore the analysis of those questions will represent the qualitative part of the research. The scientific methods that were used at various stages of completing the paper are as follows: 1.

2.

3.

4.

Abstraction: the thought process in which we did not take into account those features, properties and relationships in marketing communication that are not related to misleading practices, Analysis and synthesis as paired methods in dividing the complex marketing reality into individual parts (elements, characteristics of marketing communication) and subsequent uniting of individual parts selected through an analysis into a whole, Induction and deduction as other paired methods, while induction was used in drawing conclusions and recommendations from empirical material that was at our disposal and deduction was used in inferring from the general to the specific, Comparison of primary representative research with available, relevant secondary research. The results of the research are presented in three main areas of focus:

1. 2. 3.

Perception of contemporary marketing, Attitudes toward sustainable product strategies, Opinions on marketing communication as a basis for creating customer awareness.

3 Results The aim of the paper is on the representative sample of Generation Z respondents on the basis of quantitative and qualitative analysis to evaluate their perception of marketing and marketing communication in terms of how positive/negative they perceive the current activities of the organizations in marketing communication.

208

K. Gubíniová et al.

3.1 Perception of Contemporary Marketing Given the existence of several research surveys that dealt with how positive the term marketing is perceived [19], we asked respondents which adjectives they would use to describe contemporary marketing. They had a choice of two positive adjectives (honest; true), six negative adjectives (misleading; deceptive; intrusive; superfluous; disturbing; a term that lost credibility and trustworthiness) and one answer that reflects that they do not have a strong opinion on marketing. Respondents could report several options. 264 respondents (more than 20% of respondents) reported only one option, the others responded in a combination of multiple answers. As can be seen from Fig. 2, the option that marketing (or marketing activities of organizations) is honest nowadays was indicated by 16 respondents, the second positive option, namely that marketing is true, was marked by 12 respondents, which is only marginal results given the total number of 1248 respondents. In terms of negative perception of marketing, the results are significantly more embedded in the minds of respondents. From the point of view of frequency, in the first place respondents marked the option that marketing is misleading (816 responses), in the second place respondents indicated the option that marketing is intrusive (672 responses) and in the following place there was the option to view marketing as the term that lost credibility and trustworthiness (548 responses). In negative perception of marketing, we do not deliberately mention the number of respondents, but the number of responses, since in a very small percentage they were reported separately or respondents reported several negative adjectives associated with perception of contemporary marketing. Less than 10% of respondents (108) do not have a strong opinion on contemporary marketing. Within comparison we will use the secondary research of the portal Statista, one of the largest statistical and market data platforms in the world, with access to over 1.5 million statistics, forecasts, documentations and reports. The platform combines economic data, consumer perspective, opinion polls and demographic trends [20].

Fig. 2 Attitudes towards contemporary marketing (own representation)

Marketing Communication and Its Role in the Process …

209

Fig. 3 Consumer attitudes towards advertising in the United States. (Adapted 2017)

With respect to the data in Fig. 3, it is necessary to mention at least size and maturity differences between American and European or Slovak consumer market, but the common characteristics of both markets is the constant trend of increasing expenditure on “traditional” marketing communication tools [21]. It follows from the secondary research in the United States that nearly 60% of respondents indicated advertising as ubiquitous.

3.2 Attitudes Toward Sustainable Product Strategies Another subject of the analysis was a thorough examination of the factors that discourage respondents from purchasing a product. Within these, we offered respondents several options: 1. 2. 3. 4. 5.

So-called abstract factors: distrust; recommendations; lack of information, Factor related to pricing strategy: high prices, Factor related to distribution strategy: poor availability, Factor related to marketing communication strategy: exaggerated advertising, An open answer, in which respondents had the opportunity to indicate a subjective, not mentioned factor which determines their negative attitude to product purchase.

In terms of frequency of occurrence of individual options, we are presenting a frequency table of individual answers (we are talking about answers, not respondents, because they had the opportunity to mark multiple answers) (Table 1). Respondents identified the option “high prices” as the most significant factor that has the potential to discourage them from purchasing a product (this answer occurred 824 times). In terms of microeconomic theory, it is generally true that the price of a product is an important factor on the basis of which a product is evaluated and it is expected to be as low as possible [22–24]. However, there are also exceptions to that relationship where consumers identify the (high) price level of a product with

210

K. Gubíniová et al.

Table 1 Frequency table—factors influencing negative attitudes to product purchase (own research) Valid

Frequency

Percentage

Valid percentage

Distrust

644

51.6

51.6

Lack of information

660

52.9

52.9

Poor availability

356

28.5

28.5

High prices

824

66.0

66.0

Recommendations

288

23.1

23.1

Exaggerated advertising

436

34.9

34.9

quality of a product or with its exclusivity (category of luxury products) [25, 26]. The answer “lack of information” ranked second. It is quite surprising how Generation Z representatives view the present time in which there are numerous websites, applications providing comparison services for product parameters, advantageousness of product price, and others in real time via the Internet, mobile phones [27, 28]. The third most frequent answer was the option “distrust”. We included this factor among non-material factors. Trust is at the center of attention in many areas of social life—whether in academic sphere, business or the media [29, 30]. At the most general level, mostly in the post-crisis period the decline of customer trust in organizations can be observed in many industries [31]. The option “recommendations” did not rank among the top three, but it still needs to be addressed—especially in the context of the Nielsen research mentioned in the literature review. Exaggerated advertising in the hierarchy of factors influencing respondents’ attitudes towards product purchase was identified by a total of 436 respondents. This answer needs to be viewed in the context of (unsustainable) marketing communication that uses deceptive practices [32–34] and will be analyzed in the following text. The global Nielsen research (Graph 1) has shown that recommendations ranked top in trust with a significant lead in product purchasing behavior. 288 respondents identified recommendations as a factor that would discourage them from a potential product purchase. To confront this factor, we used the data from the largest online store in the Slovak Republic—Alza.sk and we selected product categories that are likely to be searched for by Generation Z. These product categories are as follows: 1.

2.

Mobile Phones—smartphones. The best-selling product is a mobile phone Xiaomi Redmi Note 7 LTE 64 GB blue with more than 10,000 + purchases. There are 155 verbal reviews of this product. We assume that in the competitive fight Alza.sk does not disclose the total number of sold pieces of a product, taking into account the marketability at the level of 10 thousand, there is the review ratio at the level of 1.55 customers, i.e. a negligible percentage of customers shared their reviews. Laptops—for general use. The best-selling product is Dell Inspiron 15,3000 (3583) black with 500 + units sold. There is one verbal review of this product and thus the same applies as in the previous product category.

Marketing Communication and Its Role in the Process …

3. 4.

211

Headphones. The best-selling product is Xiaomi Mi True Wireless Earbuds Basic with more than 5000 units sold and 28 verbal reviews. Video game consoles. The best-selling product is a video game console PlayStation 4 Pro 1 TB + FIFA 20 with more than 2000 units sold and a relatively low number of verbal reviews (160, i.e. less than 8% of customers).

The indicated numbers of customers [35] who in real space of the largest online store were willing to share their (positive) experiences with purchased products are very low, so we can reasonably assume that respondents for that reason do not consider other customers’ recommendations as a meaningful factor.

3.3 Opinion on Marketing Communication as a Basis for Creating Customer Awareness In the quantitative analysis of evaluating attitudes to contemporary marketing it can be concluded that respondents perceive it relatively negatively. The analysis of evaluating respondents’ attitudes to advertising (Fig. 4) is a natural follow-up to examining associations with marketing as such. We gave respondents a choice of three categories: 1. 2. 3.

Positive associations linked to advertising: creative; humorous; imaginative; eye-catching, Negative associations linked to advertising: boring; annoying; full of clichés; unrealistic, Another option, with enough space for giving an answer.

The results achieved correspond to the quantitative analysis of evaluating marketing as such. Fig. 4 Attitudes towards contemporary marketing communication (own representation)

212

K. Gubíniová et al.

Among 1248 respondents we noticed 128 responses to the statement that contemporary advertising has a positive rate of creativity. We noticed 88 responses to the statement that advertising is humorous, somewhat less (80 responses) to the statement that advertising is imaginative. The option “advertising is eye-catching” ranked second in positive associations. A significantly higher frequency of occurrence was seen in only negative associations with which respondents associate contemporary advertising. The answer that advertising is annoying ranked first (352 responses), then the second-ranked was advertising full of clichés (276 responses), and the third-ranked was unrealistic advertising (204 responses). At first glance the values may appear to be low compared to the sample size, but this is due to the fact that we dealt either with only positive or with only negative associations with advertising. We received several hundred responses in which respondents indicated a combination of positive and negative associations, saying that in an open option it is quite difficult for them to report only positive or only negative associations, since there are both positive and negative examples in advertising practice (see, for example, various competitions in which creative rendering of advertising is evaluated, or prejudices that have been present in advertising for decades). More than 100 respondents (less than 9% of respondents) took the opportunity to express their own perception of advertising. The most frequent responses among these were: 1. 2. 3. 4. 5. 6.

Misleading, Frustrating, Naive, Without an idea, Deceptive, Repetitive.

We can say that almost all the answers were negative, only 12 were positive (but in the sense that there are also examples of flawed advertising practice). When examining attitudes to deceptive practices currently applied, from the point of view of respondents, we provide the percentage of respondents who indicated that they agree with the (deceptive) marketing practice or have been confronted with it. Table 2 provides a list of the given practices with respondents’ attitudes expressed as an absolute number and percentage (there was the possibility to mark more answers). Based on the results of the quantitative analysis of deceptive practices we identified that from the perspective of respondents the most frequent are the following (deceptive practices): deceiving that stems from a careful selection of words and sentences, while thus declared statements are not true (63% of respondents); significant digital editing of photographs, videos and other visual elements (59% of respondents), and exaggeration, exaggerated advertising and meaningless contents of messages of marketing communication tools (57% of respondents). Based on the percentage differences among these three practices, it can be concluded that the differences among them are not very significant. The gap between respondents’ perception of

Marketing Communication and Its Role in the Process …

213

Table 2 Perception of deceptive practices used in marketing communication from the perspective of respondents (own research) Deceptive practice

Absolute number Percentage (%) Ranking

Deceiving that stems from a careful selection of 786 words and sentences, while thus declared statements are not true

63

1.

Significant digital editing of photographs, videos and other visual elements

736

59

2.

Deceiving by means of figures, calculations, statistical information and results of various surveys

524

42

4.

Sophisticated omission, obscuration or confusion of information

424

34

6.

Displaying false emotions in selling and providing services

449

36

5.

Incomplete or misleading framework product comparison

412

33

7.

Inadequate requirements for information 137 retrieval and instructions on how to use products

11 11.

Copying products or brands and sophisticated 312 advertisements using confusion of such products with their originals

25

8.

Concocted customer characteristics through the brand image

237

19

9.

Reverse product placement in films, television broadcasts and on websites

449

36

5.

Exaggeration, exaggerated advertising and meaningless contents of messages of marketing communication tools

711

57

3.

Obvious and open misleading regarding product 187 attributes, product properties and consequences resulting from product use

15 10.

practices in the first place and twelfth place is 52 percentage points, which is quite a significant difference (the practice “inadequate requirements for information retrieval and instructions on how to use products” was ranked last—it was marked by 11% of respondents) (Table 3). Based on the results of the factors analysis it is clear that we can identify two main factors of deceptive practices used in marketing communication. The first factor is product deception. Respondents feel deceived by significant digital editing of photographs, videos, and other visual elements. They complain about incomplete or misleading framework, product comparison and find the requirements for information retrieval and instructions on how to use products incomplete. Copying products or brands and sophisticated advertisements using confusion of such products with their originals is also an important issue as well as reverse

214

K. Gubíniová et al.

Table 3 Perception of deceptive practices used in marketing communication from the perspective of respondents (own research) Rotated component matrix

Factor 1

Factor 2

Deceiving that stems from a careful selection of words and sentences, 0.418 while thus declared statements are not true Significant digital editing of photographs, videos and other visual elements

0.554

Deceiving by means of figures, calculations, statistical information and results of various surveys

0.502

Sophisticated omission, obscuration or confusion of information

0.748

Displaying false emotions in selling and providing services

0.641

0.496

Incomplete or misleading framework product comparison

0.566

Inadequate requirements for information retrieval and instructions on how to use products

0.639

Copying products or brands and sophisticated advertisements using confusion of such products with their originals

0.742

Concocted customer characteristics through the brand image

0.419

Reverse product placement in films, television broadcasts and on websites Exaggeration, exaggerated advertising and meaningless contents of messages of marketing communication tools Obvious and open misleading regarding product attributes, product properties and consequences resulting from product use

0.659 0.711 0.558

product placement in films, television broadcasts and on websites. The last characteristic within this factor is obvious and open misleading regarding product attributes, product properties and consequences resulting from product use. The second factor of deceptive practices is communication. It covers deceiving that stems from a careful selection of words and sentences, while thus declared statements are not true as well as significant digital editing of photographs, videos and other visual elements. Respondents also stated the feel deceived by means of figures, calculations, statistical information and the results of various surveys or sophisticated omission, obscuration, or confusion of information. Furthermore, they complained about displaying false emotions in selling and providing services. An important factor also is concocted customer characteristics through the brand image and exaggeration, exaggerated advertising, and meaningless contents of messages of marketing communication tools.

4 Discussion and Conclusions In the concept of sustainable marketing management, marketing communication is just as an important element of the marketing mix as in the traditional concept of

Marketing Communication and Its Role in the Process …

215

marketing management. Without effective marketing communication, it is difficult to build awareness of sustainable solutions that organizations offer to the customers [36, 37]. Effective marketing communication establishes long-term relationships with customers (which are further strengthened in different stages—for example in the stage of product use, in the stage after the product life cycle). The challenge for marketing managers who manage an organization’s communication strategy in accordance with the concept of sustainable marketing management is to create marketing communication campaigns that respect characteristics of customers and use the power of individual marketing communication tools so that they would not be associated with negative social and environmental aspects of communication [38]. In view of our suggestions for the above results, the following recommendations can be formulated. Regarding the question in which we examined the strength of associations (positive versus negative) towards contemporary marketing, in the context of modern marketing management, there is a need for a qualitative shift towards these areas and activities. Perception of contemporary marketing. Marketing as a concept has often been identified with sales [39], what has been at least as many times refused as such a view of marketing is too limited and narrowed. Organizations need to adopt a much broader perspective aimed at improving life quality of their customers. Marketing managers need to develop new marketing models that focus on addressing long-term issues that customers are truly interested in. To eliminate emerging pressures and resistance to products that are harmful to individual customers or the company itself, organizations must even through their marketing activities take responsibility for educating their customers in ways that will have a positive impact on the company. In customer behaviour there is the main focus on customer education (emphasizing sustainability in their purchasing decisions) and sustainable consumption. Regaining customer trust is an important issue. A new paradigm in marketing should reconsider the attitude towards customers. Thus, instead of producing products and services that customers require to meet their needs, marketing managers should actively support their customers throughout the organization. Nowadays there are several driving forces in the marketspace that determine a customer’s position in the market, such as: Internet enabling too, or particularly dissatisfied customers to communicate with other customers; decreasing influence of (traditional) media; a large number of nearly identical products and services; stricter consumer protection legislation [40–42]. More and more customers are aware of this “power and authority” and use it actively. The answer to this situation must be at the organizational level a choice between traditional marketing (the so-called offensive marketing model that in the world characterizes marketing from 1950 to 2000 [43]) or modern, trust-based marketing, in which organizations cooperate with a customer and thus contribute to mutual satisfaction. Despite an increasing number of markets driving forces in the world and the fact that the market environment has become hypercompetitive [44–46], marketing is overlooked and “sidelined” in many cases. One of the causes can be the fact that “side effects” of marketing have prevailed over the determined main effects. Can this situation be improved? Several variables in the customer—organization— society relationship need to be rethought in order to compensate. Marketing used

216

K. Gubíniová et al.

to be declared a representative of customers in an organization [47, 48], however, in fact it represented (also using unfair practices in many cases) an organization of customers by means of endless innovations, aggressive marketing communication, etc. Marketing does not work in its traditional concepts anymore, and therefore it is necessary to identify with a new view of it. According to Kotler [49], the benefits of marketing include the facts that it has improved the quality of life, played an important role in creating markets and products, increasing comfort and enriching life in general. However, it is questionable where the boundary is between a healthy, rational, responsible, and sustainable approach to marketing and a moment when this approach becomes harmful. Product strategy. In a product strategy, sustainability building concepts include [50–52]: sustainable aspects from sourcing necessary for manufacturing a product to beyond the end of the product life cycle (from cradle to cradle), product life cycle assessment, certification of sustainable products, recycling and product reusability, sustainable product design, product development for the lowest income groups. Redefinition of product strategy is needed. It is enviable for companies to emphasize the way in which products are produced, distributed and sold. An innovation strategy should focus on responsible and ethical production methods (e.g. child labor ban). Based on the research results companies should promote benefits of using a product instead of possessing a product. Communication strategy. If marketing communication is to be prepared with a sense of responsibility for a customer, it should use the following concepts: publishing sustainability reports, labelling and certifying sustainable products, marketing communication that focuses on rejecting and reducing unsustainable principles and vice versa that emphasizes recycling, reusing, repairability and implementing the principles of demarketing [53, 54]. Marketing communication is at the center of attention of social critics of marketing [55–57], because in many cases it remarkably promotes consumption of end customers and contributes to the development of unsustainable patterns of consumer behaviour. That is why it is quite complicated to manage marketing communication of (sustainable) products. Advertising as one of the most frequent tools of marketing communication has attracted a great deal of criticism caused by the social and environmental impacts of advertising. Many social critics ask (often reasonably) whether advertising faithfully reflects experiences that a customer might have or has. A wide range of marketing communication activities raises questions about how individual tools are used in terms of efficiency and effectiveness (resources invested in tools versus their return in various forms, such as a completed transaction, memorizing message content)—it particularly refers to direct marketing tools, sales support and (television, print) advertising. The social consequences of marketing communication are constantly in the focus of many subjects—both critics and the academic community [58, 59]. Some argue that tools of marketing communication (or advertising) are ubiquitous and so disturbing that they are capable of significantly influencing and shaping the values and desires of society, and thereby co-create materialistic, cynical, egoistic and shallow people. Ultimately, such individuals will become a homogeneous global culture, represented by customers with unrealistic stereotypes [60, 61]. Another, relatively

Marketing Communication and Its Role in the Process …

217

frequent argument is that while marketing as such seeks to ensure and create customer satisfaction, marketing communication often unintentionally creates considerable customer dissatisfaction [62]. The content of messages of various marketing communication tools directed at certain segments or target groups is aimed at evoking a desire for a certain product. However, many individuals who share the same desire, but who, for various reasons (predominantly objective), cannot afford the product or satisfy the desire, will also be affected by these tools. The effect of “dissatisfaction” often remains ignored, because such a part of the target audience is not taken into account in the “original” target group and therefore their response to the content of the message (passive or negative) is not considered or evaluated. It is also necessary to draw attention to other opinions that advocate the content of marketing communication messages and statements in such a way that they “only” reflect the existing values of society without affecting and creating them. Supporters of this approach further argue, that although marketing communication tools are able to influence customers in the sense that based on them, customers choose from a large number of brands and product categories, they are not able to generate demand on the end customer side. Based on the results of the primary research, we identified a need that for implementing sustainable marketing management it is necessary to rethink many existing marketing activities related to customer needs, individual elements of the marketing mix, and responsibility at the organizational level. A very significant change is extending the time for return on investments related to building sustainable marketing management, because this concept emphasizes the long-term aspect or continuity in the long term. Another examples of changes in marketing activities are shown in Table 4. The table above shows that the changes towards sustainable marketing management are mainly related to traditional marketing activities in strategies of the marketing mix elements, but also to customer behaviour (only strategies of product and communication strategy are formulated, as these were subject to the examination for the purposes of the paper). Changes within the academic sphere need to be made as well. Marketing has an unquestionable role and responsibility in building its position in the organization, but also in society (in the sociological sense). However, there is a demand for a greater degree of relevance of marketing as an academic scientific discipline, as well as addressing the challenges of marketing research (rigidity versus practical relevance of marketing research). In marketing, it is necessary to choose the right combination of learning as art versus as science, as the creative dimension of marketing is nowadays still at the forefront.

218

K. Gubíniová et al.

Table 4 Rethinking of traditional marketing activities towards sustainable activities (own research) Change in a marketing activity

Description and examples of change

Redefinition of product strategy

Emphasizing the way in which products are produced, distributed and sold. An innovation strategy should focus on responsible and ethical production methods (e.g. child labor ban)

Willingness to change the market

Focus on new types of markets in which material flows become cyclical due to recycling and reusability Take into account alternative production and consumption patterns (e.g. farm shops)

Emphasizing benefits of using a product (instead of possessing a product)

Possession of a tangible product is replaced by consuming a service

Marketing communication which aim is to inform (instead of emphasizing and persuading)

Marketing communication should educate customers and explain to them what makes a product sustainable Declaring sustainability using standards and labels developed by independent entities

Focusing also outside the current customer needs (view of the future)

Production and consumption affect not only current but also potential customers and future generations of customers

Willingness to manage changes in demand (downwards)

Applying demarketing (motivate to responsible consumption)

Importance placed on costs (instead of final product price)

Competition based on total production costs, costs resulting from possession, use and depreciation of a product Determining the amount of costs taking into account the environmental and social costs of marketing activities

Demanding a higher level of responsibility, education

Prosperity of customers and society Take responsibility and educate customers, employees, suppliers towards their sustainable behaviour

Acknowledgments Funding This research was funded by the Scientific Grant Agency of the Ministry of Education, Science, Research, and Sport of the Slovak Republic, grant registration number 1/0737/20—Consumer Literacy and Intergenerational Changes in Consumer Preferences when Purchasing Slovak Products.

References 1. Shen, G.C.-C., Chiou, J.-S., Hsiao, C.-H., Wang, C.-H., Li, H.-N.: Effective marketing communication via social networking site: the moderating role of the social tie. J. Business Res. 69(6), (2016). https://doi.org/10.1016/j.jbusres.2015.12.040

Marketing Communication and Its Role in the Process …

219

2. Finne, A., Gronroos, C.: Communication-in-use: customer-integrated marketing communication. Europ. J. Market. 51(3), (2017). https://doi.org/10.1108/ejm-08-2015-0553 3. Gardner, D.M.: Deception in advertising: a conceptual approach. J. Market. 39, (1975) 4. Tadajewski, M., Brownlie, D.: In: Critical Marketing. Contemporary Issues in Marketing. Wiley (2008) 5. Santor, D.A., Fethi, I., McIntee, S.-E.: Restricting our consumption of material goods: an application of the theory of planned behavior. Sustainability 12, (2020) https://doi.org/10. 3390/su12030800 6. Risser, R., Sucha, M.: Start walking! how to boost sustainable mode choice—psychological measures to support a shift from individual car use to more sustainable traffic modes. Sustainability 12 (2020). https://doi.org/10.3390/su12020554 7. Petranova, D., Magal, S.: Marketing communications and generation Z in the context of business management. In: Megatrends and Media: Critique in Media, Critique of Media (2016) 8. Weinacht, S.: Marketing communication of media brands: a literature review. In: Sieger, G., Förster, K., Chan-Olmsted, S., Ots, M. (eds.) Handbook of Media Brandig. Springer, Cham, (2015). https://doi.org/10.1007/978-3-319-18236-0_16 9. Batka, R., Keller, K.L.: Integrating marketing communications: new findings, new lessons, and new ideas. J. Market. 80(6), (2016). https://doi.org/10.1509/jm.15.0419 10. Bruhn, M., Schnebelen, S.: Integrated marketing communication—from instrumental to a customer-centric perspective. Europ. J. Market. 51(3) (2017). https://doi.org/10.1108/ejm-082015-0591 11. https://www.rankingthebrands.com/The-Brand-Rankings.aspx?rankingID=125&nav=cat egory 12. https://www.nielsen.com/mena/en/solutions/measurement/global-consumer-confidence/ 13. Porcu, L., Del Barrio-Garcia, S., Kitchen, P.J.: Measuring integrated marketing communication by taking a broad organisational approach. the firm-wide IMC Scale. Europ. J. Market. 51(3), (2017). https://doi.org/10.1108/ejm-08-2015-0587 14. Hesková, M., Štarchoˇn, P.: Marketingová komunikace a moderní trendy v marketingu. Praha: Oeconomica 180 (2009). ISBN 978-80-24515-20-5 15. Lenhard, T.H., Greguš, M.: In: An Unusual Approach to Basic Challenges of Data Mining. pp. 105-109. Piscataway, IEEE (2015). http://dx.doi.org/10.1109/INCoS.2015.40 16. Payne, E.M., Peltier, J.W., Barger, V.A.: Omni-chanel marketing, integrated marketing communications and consumer engagement. a research agenda. J. Res. Interact. Market. 11(2), (2017). https://doi.org/10.1108/jrim-08-2016-0091 17. Swani, K., Milne, G.R., Brown, B.P., Assaf, A.G., Donthu, N.: What messages to post? evaluating the popularity of social media communications in business versus consumer markets. Indus. Market. Manage. 62 (2017). https://doi.org/10.1016/j.indmarman.2016.07.006 18. Hanninen, N., Karjaluoto, H.: The effect of marketing communication on business relationship loyalty. Market. Intelligence Plann. 35(4), (2017). https://doi.org/10.1108/mip-01-2016-0006 19. Dalsace, F., Markovitch, D.G.: Is marketing becoming a dirty word? a longitudinal study of public perceptions of marketing. In: HEC Research Paper Series, Issue 923 (2009) 20. https://www.statista.com/study/51596/advertising-consumption-and-perception/ 21. Chamberlin, L., Boks, C.: Marketing approaches for a circular economy: using design frameworks to interpret online communications. Sustainability 10(6) (2018). https://doi.org/10.3390/ su10062070 22. Berezan, O., Krishen, A.S., Tanford, S., Raab, C.: Style before substance? builing loyalty through marketing communication congruity. Europ. J. Market. 51(7–8) (2017). https://doi. org/10.1108/ejm-06-2015-0314 23. Fang, Y., Wang, X., Yan, J.: Green product pricing and order strategies in a supply chain under demand forecasting. Sustainability 12, (2020). https://doi.org/10.3390/su12020713 24. Schmidt, P.: Market failure versus. system failure as a rationale for ecomomic policy? a critique from an evolutionary perspective. J. Evolution. Econ. 28(4) (2018). https://doi.org/10.1007/s00 191-018-0564-6

220

K. Gubíniová et al.

25. Amatulli, C., Guido Carli, L., Costabile, M., Guido, G.: In: Sustainable Luxury Brands. Evidence from Research and Implications from Managers. Palgrave Macmillan (2017). https:// doi.org/10.1057/978-1-137-60159-9 26. Parguel, B., Delécolle, T., Chaabane, Am. M.: Does fashionization impede luxury brands´CSR image?. Sustainability 12 (2020). https://doi.org/10.3390/su12010428 27. Godes, D.: Product policy in markets with word-of-mouth communications. Manage. Sci. 63(1) (2017). https://doi.org/10.1287/mnsc.2015.2330 28. Bridger, E.K., Wood, A.: Gratitude mediates consumer responses to marketing communications. Europ. J. Market. 51(1) (2017). https://doi.org/10.1108/ejm-11-2015-0810 29. Blašková, M.: Creative proactive-concluding theory of motivating. Business: Theory Practice 11(1), 39–48 (2010) 30. Linhartová, L., Urbancová, H.: Results of analysis of employee mobility: factors affecting knowledge continuity. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60(4), 235–244 (2012). http://dx.doi.org/10.11118/actaun201260040235 31. Leon, U.M.M.: Is the marketing always right? criticism of the ethical, political, and social ideas of neo-liberalism in hayek. Pensamiento 72(274), (2016) 32. Slaba, M., Martiskova, P., Svec, R.: Usage of urgency variable in marketing communication of colleges. In: Innovation Management and Education Excellence through Vision 2020, vol. 1–11. (2018) 33. Boush, D., Friestad, M., Wright, P.: Deception in the marketplace. In: The Psychology of Deceptive Persuasion and Consumer Self Protection. pp. 264. New York, Routledge (2009). ISBN 978-08-05860-87-0 34. Cohen, D.: The concept of unfairness as it relates to advertising legislation. J. Market. 38(3), 8–13 (1974). https://doi.org/10.2307/1249844 35. https://www.alza.sk/ 36. Lazarevi´c, D., Švadlenka, L., Radojiˇci´c, V., Dobrodolac, M.: New express delivery service and its impact on CO2 emissions. Sustainability 12, (2020). https://doi.org/10.3390/su12020456 37. Haase, M., Becker, I., Pick, D.: Alternative economies as marketing systems? the role of value creation and the criticism of economic growth. J. Macromarket. 38(1), (2018). https://doi.org/ 10.1177/0276146717728776 38. Crawford, V.P.: Lying for strategic advantage: rational and boundedly rational misrepresentation of intentions. Am. Econ. Rev. 93(1), 133–149 (2003). https://doi.org/10.1257/000282803 321455197 39. Dannenberg, H., Zupancic, D.: The interrelationship of marketing and sales strategies. In: Gabler (ed.) Excellence in Sales. (2009). https://doi.org/10.1007/978-3-8349-8782-2_4 40. Kaczor, S., Kryvinska, N.: It is all about services—fundamentals, drivers, and business models. Soc. Serv. Sci. J. Serv. Sci. Res. 5(2), 125–154 (2013) 41. Kryvinska, N., Greguš, M.: SOA and its Business Value in Requirements, Features, Practices and Methodologies. Comenius University in Bratislava, Bratislava (2014) 42. Kryvinska N.: Building consistent formal specification for the service enterprise agility foundation. Soc. Serv. Sci. J. Serv. Sci. Res. 4(2), 235–269 (2012) 43. Gneezy, U.: Deception: the role of consequences. Am. Econ. Rev. 95(1), 384–394 (2005). https://doi.org/10.1257/0002828053828662 44. Papula, J., Papulová, Z., Papula, J.: In: Competitive Strategies: Traditional Approaches versus New Perspectives and Techniques. pp. 174. Bratislava, Wolters Kluwer (2014) ISBN 978-808168-011-3 45. Stacho, Z., Stachová, K., Cagáˇnová, D.: Potential of human resources as key factor of success of innovation in organisations. In: Smart Technology Trends in Industrial and Business Management. EAI/Springer Innovations in Communication and Computing. Springer (2019). https:// doi.org/10.1007/978-3-319-76998-1_16 46. Krošlák, D., Nevolná, Z., Olšovská, A.: In: Entrepreneurial Law. pp. 290. Bratislava, Wolters Kluwer (2014). ISBN 978-80-81680-45-8 47. Martiskova, P., Svec, R., Slaba, M.: Online shopping and reading E-Shop´s terms and conditions. In: Education Excellence and Innovation Management through Vision (2020)

Marketing Communication and Its Role in the Process …

221

48. Vilˇceková, L.: Ethnocentrism of slovak consumers. Market. Sci. Inspirations. 9(3), 53-59 (2014) 49. Kotler, P.: Philip Kotler: some of my adventures in marketing. J. Historical Res. Market. 9(2), (2017). https://doi.org/10.1108/jhrm-11-2016-0027 50. Kubiˇcková, V.: In: Innovation Activites of the Services Companies. pp. 162. Bratislava, Ekonóm (2009). ISBN 978-80-225-2850-4 51. Sheth, J.N., Sisodia, R.S.: Does marketing need reform?. J. Market. 69(4), 1-25 (2005) 52. Pajtinková Bartáková, G., Gubíniová K.: In: Sustainable Marketing Managementu. pp. 241. Bratislava: IAM Press (2012). ISBN 978-80-89600-08-3 53. Parsons, E., Maclaran, P.: In: Contemporary Issues in Marketing and Consumer Behaviour. pp. 219. New York, Routledge (2011). ISBN 978-0-7506-8739-3 54. Walker, J.R., Tyler Eastman, S.: On air promotion effectiveness for programs of different genres, familiarity, and audience demographics. J. Broadcast. Electron. Media 47(4), (2003). https:// doi.org/10.1207/s15506878jobem4704_8 55. Pollay, R.W.: The distorted mirrors: reflections on the unintended consequences of advertising. J. Market. 50(2), 18–36 (1986). https://doi.org/10.2307/1251597 56. Southerton, D., Warde, A., Hand, M.: In: Sustainable Consumption: The Implication of Changing Infrastructure of Provision. pp. 192. London, Edward Elgar Publishing, (2004). ISBN 978-18-43763-30-7 57. Solarová, P.: Consumer engagement in retailing—obtaining of suggestions. Market. Sci. Inspirations 9(1), 16–25 (2014) 58. Saraite-Sariene, L., Alonso-Cañadas, J., Galán-Valdivieso, F., Caba-Pérez, C.: Non-Financial information versus financial as a key to the stakeholder engagement: a higher education perspective. Sustainability 12, (2020). https://doi.org/10.3390/su12010331 59. Škrinár, A., Nevolná, Z., Kvokaˇcka, L.: In: Fundamentals of Slovak Commercial Law. pp. 143. ˇ ek, (2009). ISBN 978-80-7380-222-6 Plzeˇn, Vydavatelství a nakladatelství Aleš Cenˇ 60. Hitka, M., Balážová, Ž.: The impact of age, education and seniority on motivation of employees. J. Business: Theory Practice. 15(1), (2015). http://dx.doi.org/10.3846/btp.2015.433 61. Kampf, R., Lorincová, S., Hitka, M., Stopka, O.: Generational differences in the perception of corporate culture in European transport enterprises. Sustainability 9, (2017). https://doi.org/ 10.3390/su9091561 62. Samáková, J., Šujanová, J., Koltnerová, K.: Project communication management in industrial enterprises. In: 7th European Conference on Information Management and Evaluation, ECIME, pp. 155–163. (2013)

How is Data Visualization Shaping Our Life? The Application of Analytics from Google Trends During the Epidemic of COVID-19 Yuanxin Li

Abstract In this article, we have discussed about how has the diverse information acquisition methods greatly enriched user behavior habits, and give a case study of the application of the methods of big data in China, to fight against the epidemic. During the epidemic, users tended to search actively from passive information viewing. The real demands of people’s livelihood behind searching numbers also make search and public opinion show a kind of “resonance” phenomenon. This article uses data from Google Trends to apply for analysis of Big Data Visualization, and analyzes issues worthy of research such as guiding epidemic control and people’s livelihood. The strategic significance of Big Data technology is not to grasp huge data information, but to professionally process these meaningful data. This article establishes hypotheses, studies how to achieve “value-added” data in Big Data search, and guides government decision-making through data visualization. Keywords COVID-19 · Data visualization · GDPR · Google related search · Health code

1 Introduction The outbreak of COVID-19 pneumonia virus in China from the end of 2019, then raged around the world. From the perspective of the Information Technology, the data of searching volume in the middle of an outbreak is very valuable. The data has been accompanied by the development and changes of human society and playing more and more significant role in modern era. It has carried the efforts and tremendous progress made by mankind to understand the world based on data and information systems. However, it was only since the emergence of modern information technology, represented by computers that provided automatic methods and means for data processing, and the ability of humans to master and process data achieved a qualitative leap. Information technology and its application in all aspects of economic and Y. Li (B) Comenius University of Bratislava, Odbojárov 10, Bratislava, Slovakia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Kryvinska and A. Poniszewska-Mara´nda (eds.), Developments in Information & Knowledge Management for Business Applications, Studies in Systems, Decision and Control 376, https://doi.org/10.1007/978-3-030-76632-0_7

223

224

Y. Li

social development (i.e., informatization), promote data (information), has become another important strategic resource after material and energy. The “Big data” as a concept and trend of thought originated in the field of computing, and then gradually extended to the fields of science and business. Most scholars believe that the concept of “big data” first appeared publicly in 1998, when the chief scientist of the US high-performance computing company SGI, John Mashey, pointed out in an international conference report: “With growth, there will be four problems that are difficult to understand, difficult to obtain, difficult to handle, and difficult to organize.” The challenge is described as “Big Data”, which will trigger thinking in the field of computing. After years of development and precipitation, people have formed a basic consensus on big data: the phenomenon of big data stems from the ubiquitous application of information technology brought by the Internet and its extensions and the continuous low cost of information technology. Big data generally refers to a huge collection of data that cannot be acquired, managed, and processed with traditional information technology and software and hardware tools within a tolerable time. Li et al. [1] have conducted a retrospective analysis of the possibility of predicting the COVID-19 outbreak, and thus have found that internet searches and social media data had high correlation with daily incidences, with the maximum r > 0.89 in all correlations. It has the characteristics of massiveness, diversity, timeliness, and variability. Scalable computing architecture to support its storage, processing and analysis. At present, in the practice of big data applications, there are many applications of descriptive and predictive analysis, and fewer applications of deeper analysis such as decision-making guidance. People’s decision-making process usually includes three basic steps: understanding the current situation, predicting the future, and choosing strategies. These steps also correspond to the three different types of big data analysis applications mentioned above, which indicate different division of labor and collaboration between humans and computers in the decision-making process. For instance, in the descriptive analysis, the computer is only responsible for presenting information and knowledge related to the current situation to human experts, while the judgment of the future situation and the selection of the optimal strategy are still done by human experts. The deeper the application level, the more and more complex tasks the computer undertakes, the greater the efficiency improvement, and the greater the value. However, with the deepening of research and application, people have gradually realized that the deep neural networks that shined in big data analysis applications in the early stage still have problems such as imperfect basic theory, unexplainable models, and poor robustness. Therefore, although the decision-making guidance application with the deepest application level has achieved good application effects in non-critical fields such as human–machine games, it has higher application value in autonomous driving, government decision-making, military command, and medical health. The fields closely related to human life, property, development and safety still face a series of major fundamental theoretical and core technical challenges to be solved if they are to be effectively applied. Before this, people have not dared and cannot let go of more tasks to the computer big data analysis system to complete. This also

How is Data Visualization Shaping Our Life? The Application …

225

means that although there have been many successful big data application cases, they are still far from our expectations, and big data applications are still in their infancy. In the future, with the expansion of application fields, the improvement of technology, the improvement of data sharing and open mechanisms, and the maturity of the industrial ecology, predictive and guiding applications with greater potential value will be the focus of development. The new coronavirus pneumonia epidemic has escalating all over the world since the end of 2019, the scientific control of the epidemic has become a top issue for all humankind. Lin et al. [2] have argued the outbreak in Wuhan, and the control measures impleted by the Chinese government, such as the city lockdown to mitigate the impact of the epidemic. The first positive case with the coronavirus was reported in Wuhan, China at the beginning of December 2019, In fact, for example China has established a complete epidemic monitoring and control system since the “SARS” in 2003. The epidemic notifications that people see every day are based on strict mechanisms that are reported, collected, and summarized at various levels. Also the virus has been later named as “SARS-CoV-2”, the disease it causes has been named in abbreviation “COVID-19”. The epidemic has been developing even until February, when the government has decided to lockdown the city Wuhan, just the day closed to the traditional Spring festival, when the Chinese usually get reunion with their family. Local governments also have the most and most important Big Data. At the same time, people use mobile phones daily to apply various softwares in the field of communication, social networking, search, maps, short videos, and e-commerce to generate a large amount of behavioral data. Operators and various apps have users’ ID information, which can associate activities with their identities. Raghav et al. [3] have explained the major prerequisites and challenges that should be addressed by the recent exploration and visualization systems. Also analyzed the different techniques and tools currently used for the visualization of large sets of data and their capabilities to support massive volume of data from variety of data sources. Although it is a complex task for the companies to explore and visualize very large datasets. Every company should follow some protocol to have accurate insight from analysis of large volume of data. This strategy helps organizations to enhance their process and to find the new product and service opportunities that they may have otherwise missed [4, 5]. McAfee and Brynjolfsson [6] illustrated the connection between big data and management. They concluded that, because of big data, managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance. Gorodov and Gubarev [7] have mentioned in their research, that there are some specific problems in Big Data visualization, so there are definitions for these problems and a set of approaches to avoid them. Also, they gave a review of existing methods for data visualization in application to Big Data and taking into account the described problems. Summarizing the result, they have provided a classification of visualization methods in application to Big Data. Gandomi and Haider [8] have analyzed the case for new statistical

226

Y. Li

techniques for big data, and highlighted the expected future developments in big data analytics. Lidong et al. [9] emphasized, that Big Data analytics and visualization should be integrated seamlessly so that they work best in Big Data applications. Rajeev et al. [10] have also identified the challenges and opportunities in big data visualization and review some current approaches and visualization tools. The contradiction between privacy, security and shared use is particularly prominent. On the one hand, the need for data sharing and openness is very urgent. The important progress made in artificial intelligence applications in recent years mainly stems from the analysis and mining of massive, high-quality data resources [11]. For a single organization, it is often difficult to gather enough high-quality data by relying on its own accumulation. In addition, the power of big data applications, in many cases, stems from the comprehensive integration and in-depth analysis of multi-source data, so as to obtain a comprehensive view of things from different angles. The data of a single system or organization often only contains one-sided and partial information of a thing. Therefore, only through sharing and openness and cross-domain circulation of data can a complete data set of information be established. However, on the other hand, the disorderly circulation and sharing of data may lead to major risks in privacy protection and data security, which must be regulated and restricted. In view of the frequent privacy and security issues caused by the improper use of personal data by Internet companies, the EU has formulated the “most stringent” data security management regulation in history, the General Data Protection Regulation (General Data Protection Regulation, GDPR), and officially came into effect on May 25, 2018. Li and Saxunová [12, 13] have argued that, after the “Regulations” came into effect, Internet companies such as Facebook and Google were accused of forcing users to agree to share personal data and faced huge fines and were pushed to the forefront of public opinion. On January 1, 2020, the “California Consumer Privacy Act” (CCPA), known as the “most stringent and comprehensive personal privacy protection act” in the United States, will come into effect. CCPA stipulates new consumer rights aimed at strengthening consumer privacy and data security protection, involving the access, deletion and sharing of personal information collected by enterprises. Enterprises have the responsibility to protect personal information. Consumers control and own their personal information. Information, this is currently the most typical state privacy legislation in the United States, which has raised the standards of privacy protection in the United States. In this case, the typical Internet business model that used Internet platforms to collect user data centrally to achieve platform-based precision marketing will face major challenges [14, 15].

How is Data Visualization Shaping Our Life? The Application …

227

2 Data Visualization and the COVID-19 Epidemic 2.1 What Can Big Data Do for Epidemic Control? During the epidemic situation, Google search also shows a trend of keyword search related to the epidemic situation. As of March 2020, the ten most popular keywords of Google currently are “Coronavirus”, “Lysol”, “Perishable Food (Perishable foods)”, “Social distancing”, “Trip cancellation”, “Solano County”, “Carnivorous”, “Dog coronavirus”, “Beards” and “Movie ‘Infectious Disease ‘". To some extend, behind the user’s search behavior, search data corroborates the value of public opinion and insights into changes in public opinion. How to use advanced tools and methods to help fight the epidemic is one of the key issues in advancing epidemic control. Zhou et al. [16] have analyzed method of Geographic Information Systems (GIS), together with big data provides geospatial information to fight COVID-19. As a modern method, Big Data has the advantages of multisource, massive, wide openness, and strong integration. Making full use of Big Data technology can effectively strengthen epidemic control efforts. Hereby in Table 1 there are some actual applications of users’ data collected by websites and mobile applications listed, which gives a perspective of how Big Data is shaping our daily life. Listed by author Big Data has the advantages of multi-source and wide openness, thus could play a key role in dynamic monitoring through data mining. On the one hand, real-time broadcast of epidemic data can be realized. For example, platforms such as “Doctor Lilac” launch “real-time dynamics of pneumonia epidemics” based on publicly available epidemic data. On the other hand, it can accurately grasp the traffic control and other conditions adopted in various regions since the spread of the epidemic. For example, apps such as Google Map can realize real-time broadcast of road conditions and help the public plan their trips.

2.2 Big Data of Search Volume Could Guide the Economy and People’s Livelihood With the help of Big Data technology can accurately track the source of virus infection. Figure 1 illustrates the typical factors of Big Data. The more the large-scale population movement and the high incidence of infections, the more it is necessary to accurately track pneumonia diagnosed and suspected patient groups as the main source of infection. Only by quickly and accurately grasping the flow direction of patients can we determine the infected area and scope as soon as possible, and provide important clues and effective information for the next step of epidemic prevention and control. Through Big Data technology, the relevant information of all infection

228

Y. Li

Table 1 Examples of applications of data collected by users during COVID-19 No

Applications

1

Follow up cases or suspected cases and set Screening of people who use real-name up checklists applications that are commonly used to obtain user positioning and action trajectory will allow anyone who comes from, or is exposed to, or through an infected area, to appear in a public place at the same time as a confirmed case, or Contact (indirect or direct contact) is included in the corresponding screening list; analyze the comments and information posted online platforms to investigate as far as possible whether there is potential for disease or virus carry. Simultaneously update in time (When apply tracking methods, care about the current policies of protect self data information.)

Details

2

Provide health education and accurate management for high-risk groups

Members on the list can be targeted to push the relevant knowledge about new coronary pneumonia protection through the online platform, improve their awareness of the disease, and personal protection knowledge, so as to enhance the sense of responsibility to protect themselves and other close contacts around them. Protective skills. The grassroots staff at the place where the person on the list implements grid management can actively contact the person on the list one by one by means of positioning, communication number, etc., carry out short-term monitoring, follow-up measurement of body temperature, and advise on the specific conditions of the list, Urge self-isolation or medical observation

3

Establish and improve a public infectious disease network investigation platform

For those who are accurately monitored, if abnormal conditions are found, the grass-roots staff responsible for the inspection should report in a timely manner and take corresponding prevention and control measures

sources can be effectively integrated, and the basic flow of the disease source can be grasped based on comprehensive statistical analysis, which is conducive to identifying key prevention and control areas and achieving grid-based management and prevention and control. Therefore, Big Data technology should be fully utilized in epidemic prevention and control. By using Big Data can also make basic predictions of the epidemic situation. The epidemic situation has become the focus of attention and discussion from all walks of life. The use of Big Data technology can provide technical support to a certain extent

How is Data Visualization Shaping Our Life? The Application …

229

Fig. 1 The factors of big data Person

Model Technique

Data

against the epidemic. Through data mining, the epidemic law hidden behind the data can be effectively analyzed, and then the development of the epidemic situation can be analyzed and grasped to provide a scientific reference for the formulation and implementation of relevant decisions.

2.3 Case Study:Multiple Applications of Epidemic Control Supported by Big Data in China 1. Queries of close contact supported by big data • People who are in close contact with patients who have epidemic infectious diseases have a higher risk of being infected themselves and may further infect others. Therefore, tracking and observing these close contacts will help these high-risk groups receive care and treatment in the first time, and prevent the virus from spreading further, which is also the key to epidemic control. • From the data essence, contact identification is essentially looking for people who overlap with the infected person in time and space. In the current information society, a big data integration platform can be established through base station data (operators), payment data (UnionPay & third-party payment institutions), travel data (railroads & airplanes & accommodation), urban public security video surveillance systems, etc. Fusion and deep mining further realize personnel tracking. • Some countries use smart city platform related data, combined with mobile phone signaling data, to help track and confirm the patient’s movement trajectory and population contact history, promote the establishment of individual relationship maps, help accurately locate the spread of the epidemic, locate the source of infection and close contact with people, Provide valuable information for epidemic prevention and control.

230

Y. Li

Fig. 2 The application of the “Close Contact Measurer” mobile APP

• The E-Government Office of the General Office of the State Council, the National Health Commission and China Electric Power Technology Co., Ltd. jointly developed the “Close Contact Measurer” APP, which provides accurate data through in-depth sharing of authoritative data resources from the Health Commission, the Ministry of Transport, the Railway Corporation, and the Civil Aviation Administration of China. • By scanning the QR code to download the close contact inquiry software to the mobile phone, the general public can inquire whether they are close contacts of a new coronary pneumonia patient by entering their personal information as Fig. 2 illustrates. • As of February 20, the cumulative number of inquiries has exceeded 150 million, and the self-discovered close contacts of patients with new coronary pneumonia have exceeded 90,000. • The official railway tickets booking software–12,306 takes advantage of the big data of real-name ticket sales to timely cooperate with local governments and prevention and control agencies at all levels to provide hundreds of batches of information on close contacts of confirmed patients on vehicles. If there are confirmed or suspected passengers on the train, passenger-related information will be retrieved, including train numbers, carriages, etc., and then provided to the relevant epidemic prevention department for follow-up processing. • Through trajectory tracking, telecom operators in many places identify people who have been in the range of confirmed patients and help accurately locate close contacts. • A number of Internet platform companies have produced the “Confirmed Patients Same Trip Query Tool". Users can enter information such as date, train number, and region to confirm whether they have traveled with a confirmed patient who has been disclosed. 2. Decision-making on epidemic prevention and control assisted by big data

How is Data Visualization Shaping Our Life? The Application …

231

• Based on historical and existing population movements, data on the occurrence of infected people and behavioral trajectories, some governments and institutions have established epidemic spread and spread models to predict the development trend and controlled probability of the epidemic in real time, and promptly promote early warning information to provide relevant decision-making reference. • Beijing’s decision to completely stop inter-provincial highway traffic in February is based on big data analysis. • The Guizhou Provincial Department of Science and Technology has established an epidemic research and judgment expert group, which organizes experts in epidemiological statistics, pathogenic organisms, and data analysis every day to concentrate research. As of February 20, the expert group has submitted 38 analysis and judgment reports to the provincial government leaders in charge. • At the beginning, Guizhou showed a “little pink” of less than 100 confirmed cases on the epidemic map. However, the expert group gave a “divine prediction” five days in advance: On February 9, there will be more than 100 confirmed cases in Guizhou. Sure enough, the “little pink” changed color after 5 days. • "God judgment” is based on the prediction of the epidemic situation and the study of the transmission route. The expert group also put forward targeted prevention and control suggestions, such as comprehensive investigation of key populations, and strict prevention of communities and towns. 3. Promote the construction of prevention and control system with big data. • On February 8, the Haidian District Government’s Urban Brain Epidemic Prevention and Control Platform was officially launched for trial operation at the Haidian District Urban Service Management Command Center. Relevant government units in Haidian District only need to log in to the “Urban Brain Epidemic Prevention and Control Platform” to be the source of the returnee Information such as whether it has passed through the epidemic area, whether it has passed the vehicle with the confirmed case, and the length of stay can be displayed on the big screen in real time. • The platform consists of four parts: an information management system for epidemic prevention and control personnel, an epidemic big data analysis system, an epidemic early warning system, and a community prevention and control early warning system, which integrates personalized data analysis, analysis of returning populations, population investigation analysis, and key population dynamic monitoring With important functions such as, tracking, and early warning services, a three-dimensional epidemic tracking and prevention system has been constructed that integrates industry management departments at all levels, territorial management departments, social units, and the public, which can effectively help relevant units to prevent and control the epidemic. • On February 3, the epidemic prevention and control combat system in Lucheng District, Wenzhou City was launched. On the basis of physical geographic information, the system enters relevant epidemic data information according to the

232

Y. Li

actual situation, and uses big data analysis to support the service epidemic situation study and judgment, which fully reflects the current situation of the epidemic situation in Lucheng, grid management, bayonet duty, and crowded places, Distribution of medical resources, construction sites, and property communities. • On the basis of protecting personal privacy, the system can also track the spatial distribution of persons diagnosed, suspected, observing, home isolation, close contact, reflux, etc., as well as information on the basic situation of individuals, establish a relationship map, and build personnel The network system accurately locates communities, communities, and buildings, and accurately monitors related persons responsible for epidemic prevention. • The system also grasps the medical resources of the whole district, collects service information such as medical staff, and all types of on-site control personnel information for key checkpoints and property communities are also shown as images, further improving the efficiency of resource allocation. 4. Use big data to help restore production and life. • Epidemic prevention health code (referred to as “health code”) is a new type of application based on technologies such as the Internet and big data that emerged in the context of the prevention and control of the new crown epidemic. The health code includes multiple functions such as data statistical analysis and personnel flow monitoring, and has played a huge role in balancing the relationship between epidemic prevention and control and the resumption of work. • Since the launch of “Yuhang Green Code” on February 7 in Hangzhou City, more than 200 cities have launched “Health Code” in just two weeks. Under the guidance of the Electronic Government Affairs Office of the General Office of the State Council, based on the national integrated government service platform, the national version of the health code has also been announced. • In general, the “health code” is a vivid application of big data thinking and big data technology in the field of emergency management. It is an original and innovative new digital governance mechanism, which has an important role in promoting the modernization of the national governance system and governance capabilities effectively. Figure 3 shows the exact implication when different color of the code shown in your mobile. • According to the basic needs of epidemic prevention and control, the three biggest telecommunications companies of China provide users with the service of “visiting places within 14 days” based on the analysis of telecommunications big data with user authorization. • This service can help relevant departments improve the efficiency of the itinerary inspections of migrants, conduct inspections on key groups, and implement precise prevention and control, especially helping to resume work and production under the current situation. • With the help of inquiry text messages, mobile phone users can prove their itinerary to the employer, community management and other departments when they are asked about the itinerary.

How is Data Visualization Shaping Our Life? The Application …

233

Fig. 3 The application of the “Health code” in China

• Shanghai Big Data Center, Shanghai Small and Medium-sized Enterprise Policy Financing Guarantee Fund Management Center signed agreements with 15 banks including China Construction Bank Shanghai Branch and Bank of Shanghai to jointly release the “Big Data Inclusive Financial Guarantee Cooperation Plan”, which passed the “The big data + guarantee + bank” model supports banks in expanding credit allocation and helping small and medium enterprises resume work and production. • After the enterprise is authorized, the bank obtains public data through the big data inclusive finance application of the city big data center, and forms a list of prospective credit customers after comprehensive evaluation by using financial technology, and then the city guarantee fund guarantees and supports the customers in the list Banks have increased credit allocation, especially focusing on supporting small, medium and micro enterprises that had normal operations in the early stage but encountered temporary difficulties affected by the epidemic. • The data covers social security, provident fund, tax payment, high-tech enterprise identification, etc. that banks are more concerned about. As long as the enterprise is authorized, the bank can obtain these data. After the bank obtains the data, the company will make a portrait of the enterprise. The enterprise can obtain financing without mortgage service. • At the beginning of March, the State Grid Hangzhou Power Supply Company completed the upgrade of power big data and Hangzhou City Brain to “hand in hand”, increasing the power data cruise from once a day to 96 times, allowing relevant government departments to read in real time every 15 min The latest residential mobile power index and enterprise return to work index.

3 Methods and Hypothesis Remuzzi and Remuzzi [17] have suggested several possibilities to help political leaders and health authorities to allocate enough resources, including personnel, beds, and intensive care facilities, to manage the situation in the short time. However, how

234

Y. Li

to fully apply the value of Big Data to public opinion detection or topics worthy of research, during the epidemic situation, especially in the longer period, still remains a priority for policy makers. And its value in guiding people’s livelihood has mainly two points: • Filter the public opinion and the trends that are of concern to the whole society, and provide to local governments or institutions as decision-making references, and feasible observation directions and perspectives for public opinion polls that could take control of public sentiments. • Provide data samples to help more Middle and Small Enterprises to develop their local solutions tailored to actual conditions. Hossain, [18] has applied a quantitative evaluation was conducted to assess the characteristics of the current studies and create visualizations of knowledge areas in COVID-19 research by statistical and text-mining approaches using bibliometric tools and R software. This paper uses induction, observation, simulation and derived methods, analyzes the data collected from Google Trends and the statistics from the China National Bureau of Statistics to apply for analysis of Big Data Visualization, and analyzes issues worthy of research such as guiding epidemic control and people’s livelihood. Dingtao et al. [19] have retrieved public query data, using Google Trends, for terms of “2019-nCoV + SARS-CoV-2 + novel coronavirus + new coronavirus + COVID-19 + Corona Virus Disease 2019” between the 31st December 2019 and the 24th February 2020 in six major English-speaking countries, including the USA, the UK, Canada, Ireland, Australia, and New Zealand. They also suggested that public response time to COVID-19 was different across countries, and the overall duration of public attention was short. The current study reminds us that governments should strengthen the publicity of COVID-19 nationally, strengthen the public’s vigilance and sensitivity to COVID-19, inform public the importance of protecting themselves with enough precautionary measures, and finally control the spread of COVID-19 globally. Based on our previous research, we set up these two hypothesis: H1 When China has preliminary control of epidemic, the search for facial masks categorization would turn down. H2 The more is the prosperity of a region, the more is the search for medical products.

4 Results of Evaluation As governments and businesses respond to COVID-19, we have seen significant changes in our daily lives. Even on Google, the way people search has changed dramatically. Because the coronavirus pandemic dominates news, politics, and economics, it maintains a majority of Google searches. In fact, searches related to COVID-19 have become the most popular searches on Google since the outbreak

How is Data Visualization Shaping Our Life? The Application …

235

100 95 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0

Surgical mask

Respirator

Facial mask

Fig. 4 Google related search volume for „Surgical mask “, „Respirator “ and „Facial mask “ during the whole pandemic in China

of the epidemy, far surpassing other news, weather, politics, Google, Facebook, and Amazon searches. In response to this rapidly evolving trend in new searches, Google company released the Google Trends Coronavirus Center to help advertisers track these emerging searches. Companies could check these search trends regularly and prepare to add new negative keywords to our campaigns to prevent wasting budget. From Fig. 4 we realize that during the period from around 20th January to 10th February in 2020, the search volume has been rising, almost 20 times than before, especially the „Respirator“, has reached its highest on the date of 26th January, which was also the time when chinese people gathered together and celebrated the Chinese New Year holiday, which was totally different situation due to the epideimc, though. Thus, hypothesis 1 accepted. However, we found that the search for „Meltblown cloth“ and „Polypropylene “ as shown in Fig. 5 has risen up from the end of January in 2020, which are t essential materials for producing facial masks. This reflects that the corresponding shortfall in demand for some medical material manufacturers is easing after the resumption of work. From Fig. 6 we could read that at least in the year 2018, the yearly disposable income per capacita of Chinese citizens who live in the eastern and northeastern part of China was definitively higher than people who live in the central and western part, indicates the same level as livelihood. In other word, the most significant economic part in China is the northern, which alongs the seas. Thus, when we take into account the facts shown in this figure, we could find the relationship between the economic developed area and the search for medical products.

236

Y. Li 120 100 80 60 40 20 0

Meltblown cloth

Polypropylene

Fig. 5 Google related search volume for „Meltblown cloth“ and „Polypropylene “ during the whole pandemic in China

The central part

The western part

20%

The eastern part

The northeastern part

34% 58%

22% 24%

Fig. 6 Yearly disposable income of Residents Per Capita by categorizing in Eastern, Central, Western and Northeastern part of China in 2018 (show in percentage of the amount of the whole country, calculated by US dollars)

From Fig. 7 we could clearly notice that the most search area for “respirator” in China are almost the northern part, which is also the area has the most level of economic development. Hypothesis 2 accepted.

5 Conclusion Accelerating the application of Big Data methods will undoubtedly effectively help the battle against epidemics. At present, Big Data technology has been well used in some aspects of epidemic prevention and control, but it needs to be strengthened in

How is Data Visualization Shaping Our Life? The Application …

237

Fig. 7 Search volume for “respirator” in mainland China during March, 2020

terms of sufficiency of application. In this regard, the implementation of Big Data technologies should be accelerated to allow Big Data to play a greater role. Firstly, we must accelerate the pooling of data resources. Only “running against time, against viruses” can run out of confidence and victory. At present, it is necessary to speed up the establishment of a data analysis team and carry out close cooperation with various departments. At the same time, Big Data application companies should be urged to actively disclose to the society data resources related to epidemic analysis, such as “closing villages”, “road closures” and crowd movements. Further strengthen the system integration, aggregation and classification of data in various fields and channels. On the one hand, on the basis of effectively coordinating various types of data resources, the dynamic depiction of the migration path of the population is further realized, and dynamic monitoring and prevention and control of the risk population are maximized; on the other hand, the accurate data of the epidemic situation are thoroughly grasped The next step is to carry out further research and judgment on the epidemic situation, and then lay a solid foundation for the next step of epidemic prevention and control. Secondly, strengthen the control of data and information resources, and severely crack down on malicious infringements and expose personal privacy. Information sharing in the era of Big Data should be principled and conditional. The leakage of personal information of passengers during the epidemic and even malicious attacks on Hubei and Wuhan people, seriously violated personal privacy, and violated the principle of information sharing advocated in the era of Big Data. As the epidemic continues to spread, we need to transparently trace the source of infection, but we cannot violate personal privacy in the name of “tracking the source of infection”. In this regard, on the one hand, we should give full play to the media effect and strengthen the guidance of the public; on the other hand, we should accelerate the introduction of relevant rules and regulations, accelerate the implementation of legislation in the field of Big Data, strictly define the data use rights, and realize the privacy of individuals. Effective protection.

238

Y. Li

Thirdly, accelerate the construction, opening and sharing of public information platforms. The patient travel query tool has been launched recently. Through this platform, the public can accurately grasp whether they have been on the same trip with patients with new coronary pneumonia in the near future, and then determine whether they need to be observed in isolation, which plays an important role in reducing the infection rate. This is also one of the cases where Big Data has been successfully applied in epidemic prevention and control. However, the construction of a Big Data unified information platform is still insufficient. To a certain extent, there have been great deficiencies in terms of resources and personnel deployment in various places in the early stages of the outbreak. In this regard, on the one hand, we should strengthen technological leadership, increase capital investment, accelerate the integration of technical resources, and accelerate the construction of public information platforms for Big Data in medical and other fields; on the other hand, we should pay attention to the openness and sharing of information platforms to achieve The rational allocation of resources further improves the efficiency of resource allocation.

References 1. Al-Najjar, B., Kilincarslan, E.: The effect of ownership structure on dividend policy: evidence from Turkey. Corporate Governance: Int. J. Bus. Soc. 16, 135–161 (2016) 2. Cinelli, M. et al.: The COVID-19 Social Media Infodemic. arXiv:2003.05004 [nlin, physics:physics] (2020) 3. Dingtao, H., Xiaoqi, L., Zhiwei, X., Nana, M., Xie, Q., Zh. M., Yanfeng, Z., Jiatao, L., GuoPing, S., Fang, W.: More effective strategies are required to strengthen public awareness of COVID-19: evidence from google trends.: SSRN. https://papers.ssrn.com/sol3/papers.cfm? abstract_id=3550008. 4. Kaczor, S., Kryvinska, N.: It is all about services—fundamentals, drivers, and business models. Soc. Serv. Sci. J. Serv. Sci. Res. 5(2), 125–154 (2013) 5. Kryvinska, N.: Building consistent formal specification for the service enterprise agility foundation. The society of service science. J. Serv. Sci. Res. 4(2):235–269 (2012) 6. Fiaz, A.S.S., Asha, N., Sumathi, D., Navaz, A.S.S.: Data visualization: enhancing big data more adaptable and valuable. 11(4) (2016). Author, F., Author, S., Author, T.: Book title. 2nd edn. Publisher, Location (1999). 7. Gorodov, E.Y., Gubarev, V.: Analytical review of data visualization methods in application to big data. J. Electr. Comput. Eng. 2013 e969458 https://www.hindawi.com/journals/jece/2013/ 969458/ 8. Gandomi, A., Haider, M.: Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manage. 35, 137–144 (2015) 9. Hossain, M.: Current status of global research on novel coronavirus disease (COVID-19): a bibliometric analysis and knowledge mapping. https://papers.ssrn.com/abstract=3547824 (2020) https://doi.org/10.2139/ssrn.3547824 10. Li, C., et al.: Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020. Eurosurveillance 25, 2000199 (2020) 11. Molnár, E., Molnár, R., Kryvinska, N., Greguš, M.: Web Intelligence in practice. Soc. Serv. Sci. J. Serv. Sci. Res. 6(1), 149–172 (2014)

How is Data Visualization Shaping Our Life? The Application …

239

12. Lidong, W., Guanghui, W., Cheryl Ann, A.: Big data and visualization: methods, challenges and technology progress.Semantic Scholar. https://www.semanticscholar.org/paper/Big-Dataand-Visualization%3A-Methods%2C-Challenges-and-Wang-Wang/e46bc3f36b68ef6cb2a2a 35ffdb6e4a62b4864b3 13. Li, Y., Saxunová, D.: A perspective on categorizing personal and sensitive data and the analysis of practical protection regulations. Procedia Comput Sci 170, 1110–1115 (2020). https://doi. org/10.1016/j.procs.2020.03.060 14. Kryvinska, N., Greguš, M.: SOA and its Business Value in Requirements, Features, Practices and Methodologies. Comenius University in Bratislava, Bratislava (2014) 15. Greguš, M., Kryvinska, N.: Service Orientation of Enterprises—Aspects, Dimensions, Technologies. Comenius University in Bratislava, Bratislava (2015) 16. Lin, Q., et al.: A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action. Int. J. Infect. Dis. 93, 211–216 (2020) 17. McAfee, A., Brynjolfsson, E.: Big data: the management revolution. Big data viewer: visualization and processing for large image data sets. Nature Methods. https://www.nature.com/art icles/nmeth.3392.Raghav. R. S., Pothula, S., Vengattaraman, T., Ponnurangam, D.: A survey of data visualization tools for analyzing large volume of data in big data platform. In: 2016 International Conference on Communication and Electronics Systems (ICCES), pp. 1–6 (2016). https://doi.org/10.1109/CESYS.2016.7889976. 18. Nathaniel, S., Michael, F.: Straining the system: novel coronavirus (COVID-19) and preparedness for concomitant disasters AJPH https://doi.org/10.2105/AJPH.2020.305618 19. Rajeev, A., Anirudh, K., Xiangfeng, D., Frederic, A. Challenges and opportunities with big data visualization. In: Proceedings of the 7th International Conference on Management of computational and collective international Elligence in Digital EcoSystems. https://doi.org/10. 1145/2857218.2857256.

Analysis of the Practices of Financial Intelligence Units (FIUs) and Other Anti-money Laundering Agencies Within EU Darko Panevski, Tomáš Peráˇcek, and Katarína Rentková

Abstract Increasingly, financial data is considered an important resource in the fight against money laundering and terrorism. In the name of pursuing terrorism financing, banks are required to report suspicious transactions, where Financial Intelligence Units create new databases and produce ‘typologies reports. This work focuses on the chain of suspicious transactions analysis in Europe, from banks, to Financial Intelligence Units, to Courts and examine the way in which financial data are interpreted and shared across the security chain, leading to security decisions like frozen assets, closed accounts or criminal convictions. At present, there is NO comparative research on the work of European FIUs. Our ultimate goal is to make an independent conceptual contribution to the understanding of financial security by analyzing the implementation of different lows mechanisms and information systems models, in order to find better solutions that will ensure efficiency, effectiveness and easy access to the information required.

1 Introduction The beginning of the twenty-first century will be remembered by the globalization phenomena, the rapid development of technology, but also by the profound changes in the style and way of life of people. Primarily by disruptions in the value system. Basic human values like honesty, humanity, respect for others and morality are losing the battle with the materiality. Many people lose these values in the chase for material goods, and they don’t choose ways and means to reach profit. These profits arising D. Panevski (B) Swiss Re Reinsurance, Vienna, Austria T. Peráˇcek · K. Rentková Faculty of Management, Comenius University in Bratislava, Odbojárov 10, Bratislava, Slovak Republic e-mail: [email protected] K. Rentková e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Kryvinska and A. Poniszewska-Mara´nda (eds.), Developments in Information & Knowledge Management for Business Applications, Studies in Systems, Decision and Control 376, https://doi.org/10.1007/978-3-030-76632-0_8

241

242

D. Panevski et al.

from illegal activities, globally yield to hundreds of billions of dollars, euros … these are money derived from serious crimes that destroy human lives, fates, (trafficking with drugs, people, weapons, fraud, forgery, tax evasion etc) and these profits year after year grow rapidly, and the same money buy a greater influence in society [49]. This group of activities is complex, often create confusion and there is a high level of ignorance and lack of knowledge for the same. Due to negligence or ignorance anyone can be involved in working with crime groups, who do not choose means and methods how to get to profit. With this work, we want to raise awareness and get closer to our most important partners—entities (financial institutions, notaries, lawyers, accountants, real estate agents and most importantly FIUs). The problem of money laundering is present on a national and international scale and is significantly expressed in those societies where organized crime seeks to legalize the proceeds of money laundering and to infiltrate financial and economic flows, in order to control certain economic and political processes. Criminal activity that precedes the acquisition of illegal income, ways of money laundering and methods of counteraction, contain certain specifics without whose research it is not possible to see all aspects of money laundering, especially in terms of choosing an appropriate strategy to combat it. All this points to the importance of research on this issue, especially money laundering in the function of financing terrorism [48]. Money laundering, as a global phenomenon, causes a number of problems that every country, or every national economy, must face. Characteristic of money laundering is that it not only affects the institutions of the system and business entities, but also affects the daily lives of ordinary citizens. This problem has led to the spread of deviant phenomena in society. The most common sources of money for money launderers are known to be drug trafficking, weapons, white slavery, and so on. If the criminal groups involved in these illegal activities manage to launder money through financial institutions, they will have legitimate financial resources. Given that these criminal groups are involved in various types of crime, it can be expected that these funds will be redirected to illegal activities. If a country’s system, due to its weaknesses, makes money laundering easily feasible, the spread and massing of deviant phenomena is inevitable. Moreover, the country may attract criminals from other countries where more attention is paid to preventing money laundering. In this way, in addition to the spread of deviant phenomena, there can be complete paralysis of the institutions of the system in one country. Money laundering and financing of terrorism are recognized as a global threat, which has its negative consequences on the economy and the democracy in one country. In order to prevent them, numerous governmental institutions work together and are in continuous collaboration with other nongovernmental and private institutions within one system. Their job is to follow the money trail and to detect and prevent crime [41]. This system is defined, we only need to respect the rules of the same, work on finding new better ways to improve it, and each in its domain to give the maximum for a better tomorrow. Guided by the famous Japanese proverb that says, “The chain is strong as its weakest link,” building an effective system for combating money laundering and

Analysis of the Practices of Financial Intelligence Units …

243

terrorist financing in EU implies continuous improvement and development of the capacity of all institutions involved in the system. Within our research we have ambition to analyses and find what kind of challenges will financial institution face in the future, in regard to anti money laundering, with a final goal of providing guideline and recommendation of how they can prepare accordantly. Finally, by implementing empirical research within the financial sectors of Austria, Slovak Republic and N. Macedonia we strive to examine all shortcomings of the current process within all three FIUs of the respective countries, that have been researched, with ambition to provide a comprehensive solution, model that can improve the process and be used globally. We will try to reach out to the possibility of scientifically basing the procedure of choosing methods for reducing the risk of money laundering and gray economy by the state [18].

2 Defining the Phenomenon of Money Laundering and Explanation on its Characteristics International experience regarding the already discovered ways and methods used by criminals, designed to legalize yields realized by committing crimes, imposed the need for organizing a global i.e. international action to fight against this scourge. These efforts of the international community, resulted in the formation of a task force to combat money laundering, called FATF (Financial Action Task Force on Money Laundering). This international body by following and exposing typologies of money laundering (and more recently, the financing of terrorism), set a number of standards and an array of measures to be taken by states, or different institutions within countries (e.g. financial institutions, government bodies, regulatory and supervisory bodies, etc). The role of entities in identifying possible activities (transactions) related to money laundering or terrorist financing is crucial in the fight against these phenomena, because as a first pillar in the system, more or less, from their success to detect suspicious activity depends the success of the other participants in the system, to prove and corroborate them [4]. By the end of the century, most countries established their FIU. Setting up an FIU is not a luxury but a very useful tool against international crime Small states became more reliant on foreign investment and now are more exposed on money laundering which threatens financial viability and integrity of their country. A “financial intelligence unit” (FIU) is a central, national agency responsible for receiving (and as permitted, requesting), analyzing and disseminating to the competent authorities, disclosures of financial information. Concerning suspected proceeds of crime and potential financing of terrorism or required by national legislation or regulation in order to combat money laundering and terrorism financing.

244

D. Panevski et al.

Law Enforcement: Most countries have implemented anti-money laundering measures alongside already existing law enforcement systems. Certain countries, due to their size and perhaps the inherent difficulty in investigating money laundering, felt the need to provide a “clearinghouse” for financial information [47]. Agencies created under this impetus were designed, first and foremost, to support the efforts of multiple law enforcement or judicial authorities with concurrent or sometimes competing jurisdictional authority to investigate money laundering. Detection: Through the Financial Action Task Force 40 Recommendations and other regional initiatives (European Union and the Council of Europe in Europe; CFATF and OAS/CICAD in the Western Hemisphere), the concept of suspicious transaction disclosures has become a standard part of money laundering detection efforts. In creating transaction disclosure systems, some countries saw the logic in centralizing this effort in a single office for receiving, assessing and processing these reports. According to Duthell, FIUs established in this way often also play the role of a “buffer” between the private financial sector and law enforcement and judicial/prosecutorial authorities. With the FIU serving as the honest broker between the Information Paper on Financial Intelligence Units and the Egmont Group 3 private and government sectors, this arrangement has in many cases, fostered a greater amount of trust in the anti-money laundering system as a whole [11, 12, 50, 53]. FIUs process great amounts of information that potentially lead to corruption— yet too little has been accomplished to turn this intelligence into evidence and to allow for a detection and confiscation of proceeds of corruption [14]. Money laundering takes place in three phases: placement, concealment and integration.

2.1 Phases of the Money Laundering Process The trend of money laundering is so common in the world, that many discussions take place of this criminal activity, which is not referring only to individuals but also to organized Money laundering process varies from country to country depending on its legal framework and the possibility of tax evasion [24]. Although there is a diversity of this process, the literature is dominated by the opinion that the money laundering process consists of three basic stages: 1. 2. 3.

Placement, Concealment and Integration.

The placement phase is the first stage in the money laundering process. Since money laundering is essentially an activity that is based on ready-made assets, it generates a large amount of cash that originates from illegal activities (for example: drug sale where payment is made in cash, smuggling and etc.). This money then flow inside in the system of financial institutions or small businesses, or smuggled out of the country [2]. The purpose of these so-called money launderers is to convey cash

Analysis of the Practices of Financial Intelligence Units …

245

away from the place of execution of criminal activity and to avoid detection by the authorities. Here there is the possibility that cash will be also converted into other types of assets (traveler’s checks, postal items, etc.). In the second phase, a concealing of the source of funds is implemented by creating a system of financial transactions aimed to prevent the tracking of the suspicious money trail. The purpose of the concealment is the separation of illegal money from their criminal source [16]. Most often, the concealment is carried out by transferring assets to offshore banks’ accounts or investing in bearer shares of so-called bank accounts of shell companies. Electronic transfer does the transfer of money from illegal origin. Taking into account that over 500,000 transfers of 1 trillion US dollars per day, are carried out worldwide and that most of these transfers are legitimate, it is really difficult to determine whether such a transfer of money is money laundering or not. In addition to the electronic money transfer, criminals in the cover-up phase are dealing with transactions on the securities market. Taking into account the volume of transactions in this market and the anonymity of the process, which is most often guaranteed, it is very unlikely, that the eventual money laundering will be revealed [5]. Integration is the final stage in the money laundering process. At this stage, money is integrated into the legitimate economic and financial system and forms an integral part of the total assets in that system [3]. The integration of money laundering into economic flows is achieved in such a way that a criminal individual or group claims or proves that money is acquired in a legal way. In this phase, it is almost impossible to distinguish between legal and illegal property. In the integration phase, moneylaunderers most often use the following methods: • Establishing anonymous companies in countries where secrecy is guaranteed. The newly-established companies can legally qualify for a loan from a bank and to expand its field of action. Moreover, these companies require tax exemptions for repayment annuities after the loan [19]. • Sending false import or export invoices with an overvalued value of the goods or service that enables the entity to transfer funds from one country to another [51, 52]. • The simplest method is to transfer funds from a bank owned by criminals to a legitimate bank in another country. It is known that in countries that are socalled. A tax haven, money laundering agents easily become owners of banks. The simplest method is to transfer funds from a bank owned by criminals to a legitimate bank in another country. It is known that in countries that are so-called a tax haven, money laundering agents easily become owners of banks [7].

246

D. Panevski et al.

2.2 Multicriteria Decision Making Model Within Banks as a Successful Criterion for Risk Elimination of Money Laundering Within the Financial Sector The aim of this empirical part of the dissertation is to use a rational and scientifically based approach to solve the problem of decision-making, by testing and applying an appropriate decision support system, in the financial sectors, tested within the three countries in which we did the research (financial systems of Austria, Slovakia and N. Macedonia). This research should point out the possibility of scientifically basing the procedure of choosing methods for reducing the risk of money laundering and gray economy by the state, using the method of multi-criteria decision-making. Precisely the applicative aspect of this part of the research is the basic contribution that should result from the elaborated example [23]. Multicriteria decision making plays a key role in many real-life problems. This has been confirmed in practice, whether it is about state bodies, managers in the company or any other activity, since everyone is faced with situations to choose the best among some alternatives, based on the existing criteria. This research provides an empirical analysis of the decision support system for money laundering and the gray economy in the three states tested, with a recommendation for the creation and implementation of a model that will improve the decision-making process within financial institutions [10]. The structure or definition of the problem are the most important characteristics, relevant for the selection of methods and procedures to support decision-making in that particular problem. Simply put, the degree of structure is the answer to the question of whether the problem is known and whether it is reliably known what needs to be done to solve the problem. Based on the fact that a large number of semi-structured problems are encountered in the financial sector, the main motive for making this part of the paper was to present a decision support system that would significantly facilitate decision-making in this sector. Securing legal cash flows and financial transactions is a major issue that arises when we talk about money laundering and the gray economy [15]. One of the basic challenges we face is how to make the right decision for the observed problem. The notion of decision-making exists as much as civilization itself. Science tries to help decision makers in choosing the optimal decision by developing many theories. One of them is multi-criteria decision-making, which offers representative methods for making the right decision. The method of analytical hierarchical processes (AHP) is one of the most well-known and highly applied methods for multi-criteria decision-making, when the decision is made on the basis of a number of criteria and in multiple time periods. The conceptual and mathematical setting of this method was set by Thomas SAATY with the aim of assisting decision makers in solving complex decision-making problems. This method will be used in this part of the paper to determine the relative weights of the criteria, while the solution to the problem, the selection of an appropriate system to reduce the risk of money laundering and the gray economy, will be obtained by applying the TOPSIS method.

Analysis of the Practices of Financial Intelligence Units …

247

This method is also very often used in situations of multi-criteria decision-making and is very grateful in its application [45].

2.3 Decision Support Systems Decisions and decision-making are everyday human activities, which are performed continuously during the execution of tasks related to the management of activities and systems. Modern information technologies enable the collection and storage of data that focus on usable data for analysis and provide assistance in making decisions based on these analyzes. Every knowledge-based decision must have the exact consequences that result from that decision [9]. Decision support systems were used as initial forms to support decision-making in business organizations. These systems primarily meant the entire information technology infrastructure that the company, i.e. a certain organization uses to provide informed decision-making. The main advantages of using a decision support system: 1.

2.

3.

4.

Influences the efficiency of the assessment teams in terms of better understanding of the non-compliance management process, and shortens the time required to verify corrective actions. The result of automatic decision support is an increase in consistency and accuracy of the decision made as well as time savings; Effectiveness in solving problems in such a way that management and evaluators can directly get answers to non-routine questions and consider several alternatives at the same time; Facilitating mutual communication so that users of decision support systems are provided with tools to better understand the problem on which an analysis is based; Promoting learning and reasoning based on the experience of other decision support systems provides a better understanding of the mismatch management process and the environment in which decisions are made [29]. Each decision support system consists of at least three subsystems:

(a) (b)

(c)

A database that is part of a decision support system that stores the organization’s input and output data; Model databases that represent a component of a decision support system consisting of business decision models. Each model solves a specific problem in a specific business process. Their task is to generate output based on input data and decision-making models on the basis of which the decision maker can make decisions; The user interface that should enable, in the simplest possible way, communication between the decision support system and the user [26].

The application of an appropriate system for decision support on investment security of banks for the needs of long-term placements of clients will be presented in

248

D. Panevski et al.

this paper. This system will also consist of three basic parts of each decision support system, which will: (a)

(b)

(c)

The database subsystem contains the relevant data obtained from the analysis of the financial statements of ten banks whose performance is discussed in this paper; The model base subsystem will involve the application of a combination of two methods of multicriteria analysis AHP and TOPSIS, whereby a hybrid decision-making model characteristic of the observed problem is obtained; The user interface subsystem would include a program created in Microsoft Excel that would allow automatic calculation and display of analysis results, which will significantly facilitate the use of this system by users [28].

2.4 Principles of Banking and Financial Operations, Including Anti Money Laundering Principles as a Part of Modern Private Banking Like every market player, every bank aims to achieve the best possible rate of return per unit of share capital, i.e. to maximize its market value. To achieve this goal, a bank must cover its expenses with income and make a profit in order to ensure market expansion. This is the basis of the logic used in doing business in market-oriented economies. In addition, the bank must recognize its competitive advantages over other banks and other economic entities, in order to survive in a fierce competitive struggle in the market [4]. In their operations, banks must adhere to certain principles, i.e. principles. Basically, these are economic and security business principles that are important for all economic entities, not just banks. If these principles are not respected by banks, there are far-reaching consequences for their work and survival. In the event of noncompliance with the economic principles of business by banks, the consequences affect not only the bank but also the business of its customers, as well as the economic system of the country as a whole [8]. That is why there is an intensified control of banks operations by the central bank in order to minimize unwanted cases in banks operations or breach in anti-money laundering principles. In the practice of modern banking operations, certain principles of banking operations have been differentiated, which are especially taken into account when making a decision of clients on long-term secured placements in a particular bank, having in mind the reputation and transparency of the bank, and among which the most important are: (a) (b) (c) (d)

liquidity and solvency principles; principle of efficiency; principle of profitability and most important for this research anti-money laundering principles (i.e. prevention of criminal use of the banking institution) [27].

Analysis of the Practices of Financial Intelligence Units …

249

2.5 Liquidity and Solvency Principles Simply put, a bank’s liquidity is its ability to meet its obligations on maturities, with the bank’s obligations relating to the bank’s depositors and creditors. Respect for the principle of liquidity is a prerequisite for successful and stable operations of every bank. The principle of liquidity derives from the harmonization of placement deadlines and bank debts. This means that the bank must have the means to meet its obligations at all times [30]. The bank’s balance sheet structure determines the position of its liquidity. On the assets side are financial instruments with varying degrees of liquidity, so that at one end are the most liquid and at the other the most illiquid assets. On the liabilities side of the bank’s balance sheet, some liabilities are liquid, which means that creditors can at any time or at short notice seek money in connection with deposits or loans. Banking management can influence the balance sheet to be more or less liquid. According to Malloney the most important factors that affect the bank’s liquidity are: 1. 2. 3. 4. 5. 6.

Speed of turnover of placed bank funds; Creditworthiness of total bank placements and the degree of their individual liquidity; Consistency of maturity structure of placements for sources of bank funds; Increase in cash deposits; Possibility to obtain loans on the money market or from other banking institutions; Level of collection of overdue loans and related interest [25].

Liquidity indicators and instruments are the basic indicators used for liquidity measurement. The basic indicators, i.e. liquidity ratios in use are: 1. 2. 3. 4. 5.

cash and unsecured marketable securities/total assets; total deposits/borrowed funds; variable assets/liquid assets; total loans/total deposits; liquid assets/total assets.

On the other hand, the term solvency implies the ability of the debtor to settle its due obligations in full. Insolvency is the inability to settle due obligations. In other words, the insolvency of a bank occurs in a situation when the amount of its liabilities exceeds its assets, i.e. when the realized losses exceed the share capital of the bank. The size of the bank’s losses is compared with the bank’s nominal capital, i.e. share capital. If the loss is less than the nominal value of the capital, then the bank is still solvent, because it can cover the loss and other liabilities on other bases. If the loss is higher than the nominal value of capital, then the bank is insolvent, because the nominal value of capital is not sufficient to cover losses and the bank cannot fulfill its obligations to customers [6, 28].

250

D. Panevski et al.

In connection with the above, one of the solvency indicators is capital adequacy, which indicates the need for the bank to have adequate capital according to its obligations. The amount of capital required depends on the insolvency risk, which is considered appropriate for a given economy, which in turn depends on the combination and structure of assets, liabilities and capital in the bank’s portfolios. The capital ratio is a key financial ratio for measuring a bank’s capital adequacy. The higher this rate, the healthier and better the bank is. A bank with a high capital ratio in terms of assets is more protected from current (operating) operating loss than a bank with a lower rate. A number of indicators are used to measure capital adequacy: 1. 2. 3. 4.

share capital/total assets; share capital/risk assets; total liabilities/equity; equity/total loans.

2.6 Principle of Efficiency One of the most important principles that a bank must adhere to in order to achieve the best possible business results is the principle of efficiency, which is focused on the costs of the bank’s operations. Efficiency affects the security and strength of the bank, the quality and prices of its products and services. All indicators of bank efficiency can be classified into measures of the effectiveness of securing resources with the lowest possible costs and measures of the effectiveness of the use of funds with the highest possible return. The most commonly used indicators from these two groups are: 1. 2. 3.

interest expenses/interest income; provisioning costs (loss costs)/net interest income; interest income/total number of employees.

In order for a bank to operate efficiently, it means to achieve as many effects as possible with the lowest possible costs or to achieve the highest possible effects or results with the same costs [32]. When it is reduced to a monetary unit, then the effectiveness is measured by the ratio of realized costs to one monetary unit of income. To predict the optimal volume of the bank’s economy, the Cobb-Douglas function of the volume of the bank’s economy is most often used, which has the following form: BOC = a + b(BO) + c(POL) + d(POB) + RET; where: a. b.

BOC—operating costs of the bank; a, b, c, d—statistical parameters;

Analysis of the Practices of Financial Intelligence Units …

c. d. e. f.

251

BO—the bank’s outputs; POL—labor costs; POB—building price; RET—random error term. Measuring the economy comes down to:

Economies of scale (SCE) = percentage change in costs/percentage change in output [23].

2.7 Profitability Principle The basic motive of each bank’s business is to make as much profit as possible, in order to increase the dividend to its shareholders through profits, and by reinvesting in the bank’s capital (shares) to create conditions for increasing credit and financial potential. Financial policy should be set in such a way that the interest rates that banks pay to their depositors are a motivation for new savings. A profitable bank increases its share capital either by retaining part of the realized profit or by issuing additional quantities of shares on the primary market. Starting from the basic principle of profitability: to make as much profit as possible as the difference between income and expenses of the bank, it can be concluded that the goal is to place the collected funds on the market with as little cost as possible, in order to achieve biggest profit [39]. Given its specificity, the bank as a company transforms its goal function from profit maximization to providing a sufficient rate of return per unit of share capital. A higher rate of return per unit of share capital over a longer period of time results in an increase in the market value of the bank [36]. Therefore, it can be said that the essential function of the goal is to increase the market value of the bank, while maximizing the rate of return per unit of share capital is a manifestation of the bank’s target function. The point is that the growing flows of profit rates from previous periods are used as a basis for the projection of the future characteristics of the bank, which is of interest to potential investors in the banking sector. The profitability of the bank is influenced by a number of factors, among which the most important are: the quality of management, the quality of assets, economies of scale, off-balance sheet operations, the bank’s operating costs and the bank’s environment. The main indicators of bank profitability are as follows: 1. 2. 3.

ROI = net income/operating income; ROA = net income/total assets; ROE = net income/share capital.

252

D. Panevski et al.

2.8 Anti-money Laundering Principles (I.E. Prevention of Criminal Use of the Banking Institution) It is well known that banks and multi-government financial institutions tend to be relatively abused as intermediaries to transfer or deposit dirty money. As reported by Refrigeri et al. in this way the true origin and ownership of money is masked. Widespread abuse of the financial system in such an unfair way directly affects the designated law enforcement authorities. This is of concern not only for European but also for national banking supervision [35]. The primary objective of this Statement of Principles is to strictly specify the direction of banking policy and the procedures that banks will provide at their own expense. It is about working together to help combat money laundering through, in particular, the European banking system. This declaration therefore aims to effectively strengthen existing practices between several banks. In particular, it is about supporting banks’ fight against money laundering [1, 44]. 1.

Proper customer identification:

All banks are required to put in place effective procedures to obtain identification from their new clients. There must be an explicit rule that significant commercial transactions will not take place with untrustworthy transactions [13]. 2.

Strict application of legislation:

Banks ensure trading in accordance with all ethical standards and legislation relating to financial transactions. Banks must not offer services or provide active assistance in questionable banking operations [21]. 3.

Cooperation with law enforcement agencies:

Banks must cooperate fully and without exception with all law enforcement authorities in accordance with specific local regulations regarding client confidentiality. We are of the opinion that they must take care to avoid providing support or assistance to unsuspecting clients. If banks have information and that the money at their disposal comes from crime, they report this fact and break the contact with the suspicious client [40]. 4.

Compliance with the declaration:

All banks must pursue a single policy in accordance with these binding principles and ensure that all employees are demonstrably informed of this bank’s policy. Emphasis is placed on the necessary training and education of employees in this area.

Analysis of the Practices of Financial Intelligence Units …

253

2.9 Harmonization of Banking Principles Each bank strives to harmonize the operation of the stated principles, whereby we must not give priority to one and neglect the others. All these principles are interconnected and are in a certain degree of interdependence and conditionality, so they cannot exist and act individually. Their action can be described as functional solidarity, i.e. on the principle of feedback. Coordination between the principles should ensure overcoming the conflict that arises due to different views on the place and role of the bank in the economy, as well as the priority of certain principles that contribute to the growth and development of the bank itself [20]. It is very important to accept the fact that changes in the economy and society are something that must be expected and that the relationship between the bank’s potential and market demands must be constantly re-examined if survival, growth and development are to be ensured. Due to the specifics of banking operations and the fact that it operates mainly with other people’s funds, liquidity must be taken into account. On the other hand, because of the profit it makes, profitability must be taken into account, and in addition, macroeconomic interests must be satisfied, so business security must also be taken into account. The management team should not place special emphasis on one principle, it should find the optimal combination in meeting all business principles. If the management forces the principle of liquidity, then it will have enough funds to settle depositors and all other obligations, but it will make a minimum profit (or it will not be at all), which affects profitability. If profitability is forced, it will jeopardize liquidity and even its own security, because there is a possibility of illiquid credit placements (increased credit risk can cause bankruptcy of the bank) [38].

3 Multicriteria Decision Making The idea of the way people make decisions is as old as civilization itself and it is presented through a variety of theories. These theories are not characterized as rigorous scientific approaches found in the literature. Therefore, it is not surprising that the literature in the field of decision-making is in constant progress. What is common to all authors who deal with the issue of decision-making is the view that decision-making is the most important task of every manager and that the decisionmaking process occupies most of their working time [42]. Having in mind the fact that the decision implies the choice of one of the considered alternatives, they can be made immediately or after the analysis of the problem under consideration. There are a large number of methods and techniques that have been developed over time and used to solve decision-making problems in real situations, and we can divide them all into two large groups: single-criteria decision-making methods and multi-criteria decision-making methods [46]. The first group of methods is often

254

D. Panevski et al.

called operational research methods and includes: linear programming, transport methods, nonlinear, dynamic programming, network planning, game theory, etc. Another large group, methods of multi-criteria decision-making “refer to decisionmaking situations when there are a large number of, most often, conflicting criteria. Precisely this fact represents a significant step towards the reality of problems that can be solved by multicriteria decision-making methods. All classical optimization methods use only one criterion when deciding, i.e. solving, which drastically reduces the reality of the problems that can be solved [22]. The problems that are solved by applying the methods of multi-criteria decision-making have the following common characteristics: • A number of criteria, that is, attributes that must be created by the decision maker; • Conflict between criteria, as by far the most common case with real problems; • Incomparable (incomparable) units of measure, because as a rule, each criterion or attribute has different units of measure; • Design or selection. Solutions to this type of problem are either designing the best action (alternative) or selecting the best action from a set of pre-defined final actions. Taking into account these characteristics, and especially the last multi-criteria decision-making, it can be divided into multi-attribute decision-making (multicriteria analysis) and multi-purpose decision-making. Multi-attribute decisionmaking takes into account problems where there is no continuity in the decisionmaking process [43]. In these problems, a set of alternatives is predetermined, while multi-purpose decision-making deals with decision-making problems in which the decision-making process is continuous. The differences in the properties of the two statements of the mentioned group can be most easily seen from Table 1. Table 1 Features of multi-attribute and multi-purpose decision-making Multi-attribute decision making

Multi-purpose decision making

Attributes

Goals

Goal

Implicit (poorly defined)

Explicit

Attribute

Explicit

Implicit

Limitations

Inactive (included in attributes)

Active

Actions (alternatives)

Final number (discrete)

Infinite number (continuous)

Interaction with the decision maker

It is not distinct

Distinctive

Application

Choice

Designing

Criterion (defined)

Source Made by the author according to data provided in “Critical and descriptive goals in discourse analysis” by FAIRCLOUGH, Norman

Analysis of the Practices of Financial Intelligence Units …

255

Formulation of a mathematical model of multicriteria decision making The multicriteria decision model has the following mathematical formulation: // STAT//   max f1(x), f2(x), . . . , fp(x) , p ≥ 2 under restrictions: gi(x) ≤ 0, i = 1, m xj ≥ 0, j = 1, n where: • • • • • •

n—number of variables; p—number of criterion functions; m—number of restrictions; X—n-dimensional vector of variables xj, j = 1, n; fk—function (goal) of the criterion, k = 1, p; gi (x)—set of constraints, i = 1, m.

It should be emphasized that the vector of the objective function is maximized at given constraints, since the minimization criteria can be translated into the maximization criteria, as follows: maxfr (x) = −min[−fr (x)], r ⊂ (1, p) By solving the above model, a set of admissible solutions is obtained, the vector X belonging to the set of natural numbers X ∈ Rn , for which applies:   X = x|gi(x) ≤ 0, i = 1, m, xj ≥ 0, j = 1, n The set of solutions of the criterion obtained in this way corresponds to the set of values of the criterion function, i.e. the vector f(x), so that the set of admissible solutions X can be mapped to the criterion set S: f(x) = [f1 (x)f2 (x), . . . , fP (x)] S = [f(x)|x ⊂ X]

256

D. Panevski et al.

3.1 Defining Terms in the Problem of Decision Making The problem observed in this paper is the selection of an appropriate strategy for reducing the risk of money laundering and the gray economy through banks and the inclusion of several terms that need to be defined at the beginning. Choosing the safest banking strategy is the goal of this problem. Starting from the definition of decision-making, that decision-making is the choice of one of a set of possible alternatives, whereby there must be at least two alternatives in the set, it can be concluded that the application of decision-making theory in the procedure itself is possible. The notion of criteria occupies an important place in the process of deciding on the most favorable bid. Criterion as a term refers to the attributes that are related to the alternatives from which we make a choice. They can be divided into qualitative and quantitative criteria depending on the degree of measurability. Quantitative criteria are those that can be precisely measured and expressed in different units of measurement. Qualitative criteria are those that cannot be expressed numerically. They can be classified into two subgroups: attributes whose values cannot be precisely measured but can still be ragged by “intensity” and attributes on the basis of which no quantitative comparison of alternatives can be made. There are many ways to translate qualitative values of criteria into quantitative ones. The most commonly used scales are: ordinal scale, interval scale and ratio scale. Another criterion that is also used to divide the decision criteria is the direction of correlation of their values and the usefulness they provide. According to the direction of stacking, they differ: (a) (b) (c)

Revenue criteria; Expenditure criteria and Non-monotonic criteria.

In the process of observed selection, a number of criteria are available, which are more or less important in the observed case. The criteria must be precisely defined at the beginning, and in our case, they are indicators of the success of the observed banks: liquidity, efficiency, profitability and solvency. Alternatives are solutions that appear as a choice and from which the best one is chosen. Alternatives represent ten banks, from which the most favorable one is chosen, i.e. in our case the one that shows the highest value of priorities. They have characteristics that meet pre-defined criteria.

3.2 Methods of Multicriteria Analysis There are numerous methods for solving the model of multi-criteria decision-making that can be divided on the basis of several criteria, and the best ones today are: (a) (b)

ELECTRE method; PROMETHEE method;

Analysis of the Practices of Financial Intelligence Units …

(c) (d) (e)

257

AHP method (analytical hierarchical processes); TOPSIS method; SAW method, etc.

Special attention in this empirical research will be paid to the AHP method and the TORSIS method, which are methods of multi-criteria decision-making, designed to assist decision makers in solving complex decision-making problems involving a large number of decision makers, multiple criteria and in multiple periods. Methodologically, AHP is a multicriteria technique based on the decomposition of a complex problem into a hierarchy and will be used in this paper to determine the relative weights of the criteria. The TORSIS method will be used to rank alternatives based on the obtained criteria, by comparing the distance of alternatives from ideal solutions. The goal is at the top of the hierarchy, while the criteria, sub-criteria and alternatives are at lower levels. AHP keeps all parts of the hierarchy in a relationship, so it’s easy to see how changing one factor affects other factors. AHP has found its application in various areas of strategic management, and the importance of the scientific contribution of AHP is evidenced by the fact that this method has been studied and developed in detail through numerous doctoral dissertations, scientific papers and conferences dedicated to this method [34]. The decision-making process is often very complex due to the presence of conflicting goals among the available criteria or alternatives. The problem is to choose the alternative that will best meet the set goals. The area of application of this method is multi-criteria decision-making where, based on a defined set of criteria and attribute values, the most acceptable choice is made for each alternative. To facilitate the application of this method, a software tool from the Expert Choice decision support system was developed. The process of realization of the AHP method includes four basic phases: (1)

(2)

(3)

(4)

Structuring a problem that consists of decomposing any complex decision problem into a series of hierarchies, where each level represents a smaller number of manageable attributes. They are then decomposed into another set of elements corresponding to the next level. Such a hierarchical structuring of any decision problem in this way is an effective way of dealing with the complexity of real problems and identifying significant attributes in order to achieve the overall goal of the problem. Data collection is the beginning of the second phase of the AHP method. The decision maker assigns relative grades in attribute pairs, one hierarchical level, for all levels of the entire hierarchy. The most famous scale used to assign weights is the Saaty nine-point scale. Estimation of relative weights implies that the comparison matrix, in pairs, is translated into problems of determining eigenvalues, in order to obtain normalized and unique eigenvectors, with weights for all attributes at each level of the hierarchy. Determining the solution to the problem is the last phase, which involves finding the so-called composite normalized vector. After determining the vector of the order of the values of the criteria in the model, in the next round it is necessary

258

D. Panevski et al.

to determine within each observed criterion, the order of importance of the alternatives in the model with respect to the same procedure [33, 45]. The TOPSIS method (Technique for Order Preference by Symilarity to Ideal Solution) ranks alternatives according to the distance from the so-called ideal solution and the ideal negative solution to be determined first. The optimal alternative is the one that is geometrically closest to the ideal solution, i.e. the furthest from the ideal negative solution. The ranking of alternatives is based on the “relative with the ideal solution” thus avoiding the situation that the alternative has the same similarity with the ideal and with the negative ideal solution. The ideal solution is defined by the best rating values of the alternatives for each individual criterion; conversely, the negative ideal solution is represented by the worst value rating alternatives. TOPSIS consists of 6 steps: (1) (2) (3) (4) (5) (6)

Normalization of decision matrix values; Multiplication of normalized matrix values by weight coefficients of criteria; Determining ideal solutions; Determining the distance of alternatives from ideal solutions; Determining the relative proximity of alternatives to the ideal solution and Ranking alternatives.

The advantages of using the TOPSIS method are that the user can express his preferences by assigning weight coefficients to the criteria (through determining the relative weights to the criteria), along with easy application of the method in practice and clearly defined range of alternatives. The disadvantages of using the TOPSIS method are that the solution directly depends on the input values (evaluation of alternatives by criteria) and the criteria are linear in this method.

3.3 Combining AHP and TOPSIS Methods in Multi-criteria Evaluation Optimization Investment Benefits of Banks The development of an appropriate decision support system for bank security in order to reduce the risk of money laundering and the gray economy is a problem that will be solved by a combination of AHP and TOPSIS methods. A mitigating circumstance when using any method of multi-criteria decision-making is the fact that all of them are software-supported [31]. The purpose of this research is to show how in practice, with using a combination of the mentioned methods, an optimal solution can be reached in order to facilitate the work as much as possible when making a decision on choosing a strategy for reducing money laundering risk in the banking sector. Also, an important requirement that will be satisfied in this way is the scientific basis of the conducted decision-making procedure. The criteria on the basis of which the evaluation of alternatives in this case will be performed are:

Analysis of the Practices of Financial Intelligence Units …

259

• K1—The bank’s liquidity, which is its ability to meet its obligations on deadlines maturities, where the bank’s liabilities relate to depositors and creditors of the bank. As well as the bank’s solvency i.e. the ability to settle its due obligations within the prescribed deadlines. Respect for the principle of liquidity and solvency is a prerequisite for success and stability operations of each bank; • K2—Efficiency that is directly focused on the bank’s operating costs. Efficiency affects the security and strength of the bank, the quality and prices of its products and services; • K3—Profitability of the bank, as an aspiration to make as much profit as possible as the difference between the income and expenses of the bank, aims to place the collected funds on the market with the lowest possible cost at the highest possible price, in order to make a profit; • K4—Prevention of criminal use of the banking institution with strong and standardized anti-money laundering programs and mechanisms. Aspiration and ability of the bank to successfully fight financial crime and bank fraud driven by demands to protect the bank’s assets, reputation as well as by regulatory compliance. These criteria will be considered on the basis of collected data on indicators of financial operations of banks and financial institutions in the Austria, Slovakia and N. Macedonia in the period between 2017 and 2019. Taking into account the final sample for operations of ten banks from each of the three countries tested in this research [37]. The first subsystem of the decision support system is a database which in our case consists of the data collected and they are shown in Table 2. Next step is estimation of relative weights of criteria. At the beginning of the processing of the problem, it is necessary to start from determining the relative weights criteria, i.e. the significance of the criteria. The Table 2 Decision matrix (database) Criteria Alternatives

K1

K2

K3

K4

Bank 01

1.679

0.369

0.339

16.850

Bank 02

1.550

0.269

0.339

24.820

Bank 03

2.909

0.430

0.453

17.239

Bank 04

1.769

0.420

0.131

18.850

Bank 05

1.250

0.389

0.062

17.759

Bank 06

2.390

0.409

0.061

17.468

Bank 07

1.469

0.420

0.149

26.612

Bank 08

2.120

0.370

0.230

15.611

Bank 09

1.809

0.321

0.130

24.362

Bank 10

2.301

0.318

0.120

26.471

Source Produced by the Author

260

D. Panevski et al.

AHP method will be used here for determining the relative weights of the criteria. To estimate the relative weights of the criteria the Saaty’s 9 scale of relative importance will be used (Tables 3 and 4). Application of TOPSIS method for determining the optimal solution: The second part of the model involves the application of the TOPSIS method to find the optimal solution to the observed problem (Tables 5, 6 and 7). Bearing in mind the fact that all criteria belong to the maximization criteria, it follows that: Ideal solution : A∗ = {0.278, 0.036, 0.119, 0.04} Table 3 Saaty’s 9 scale of relative importance Intensity of relative importance

Definition

Elaboration

1

Equal importance

Two activities contribute equally to the objective

3

Moderate importance of one over Experience and judgment slightly another favor one activity over another

5

Essential or strong

Experience or judgment considerably favors one activity over another

7

Very strong importance

An activity is strongly favored, and its dominance is demonstrated in practice

9

Extreme importance

The evidence favoring one activity over another is of the highest possible order of affirmation

2, 4, 6, 8

Intermediate values between two adjacent judgments

When compromise is needed between two judgments [17]

Source Produced by the Author

Table 4 Estimation of relative weights of criteria Liquidity\solvency

Efficiency

Profitability

Anti-money laundering mechanisms



W(/4)

0.596

0.536

0.663

0.438

2.231

0.558

Efficiency

0.118

0.106

0.074

0.188

0.485

0.123

Profitability

0.199

0.322

0.221

0.313

1.053

0.261

Anti-money laundering mechanisms

0.088

0.034

0.043

0.063

0.227

0.058

Liquidity\ Solvency

Source Produced by the Author

Analysis of the Practices of Financial Intelligence Units …

261

Table 5 Decision-making matrix that needs to be normalized Criteria

Liquidity\solvency

Efficiency

Profitability

Anti-money laundering mechanisms

Alternatives

w1 = 0.6

w2 = 0.1

w3 = 0.2

w4 = 0.1

Bank 01

1.681

0.371

0.341

16.861

Bank 02

1.549

0.271

0.339

24.934

Bank 03

2.909

0.433

0.451

17.251

Bank 04

1.772

0.421

0.132

18.852

Bank 05

1.253

0.389

0.058

17.762

Bank 06

2.392

0.409

0.059

17.471

Bank 07

1.468

0.421

0.151

26.613

Bank 08

2.119

0.374

0.231

15.61

Bank 09

1.810

0.333

0.132

24.362

Bank 10

2.088

0.323

0.121

26.471

Source Produced by the Author

Table 6 Normalized decision matrix Criteria

Liquidity\solvency

Efficiency

Profitability

Anti-money laundering mechanisms

Alternatives

w1 = 0.6

w2 = 0.1

w3 = 0.2

w4 = 0.1

Bank 1

0.268

0.311

0.453

0.253

Bank 2

0.247

0.227

0.453

0.375

Bank 3

0.464

0.361

0.599

0.259

Bank 4

0.282

0.353

0.173

0.283

Bank 5

0.199

0.328

0.079

0.267

Bank 6

0.381

0.345

0.078

0.263

Bank 7

0.234

0.353

0.199

0.4

Bank 8

0.338

0.311

0.306

0.234

Bank 9

0.288

0.277

0.173

0.366

Bank 10

0.366

0.269

0.159

0.398

Negative ideal solution : A− = {0.119, 0.023, 0.015, 0.023} If we denote the distances from ideal solutions with Si* and Si− , we get the results shown in Table 8. Determining the relative proximity of alternatives to the ideal solution is the next stage, which involves calculating using the following formula: Qi∗ = Si− /Si∗ + Si− , i = 1, . . . , n

262

D. Panevski et al.

Table 7 Multiplication of normalized matrix values by weighting coefficients Criteria

Liquidity\solvency

Efficiency

Profitability

Anti-money laundering mechanisms

Alternatives

w1 = 0.6

w2 = 0.1

w3 = 0.2

w4 = 0.1

Bank 01

0.162

0.032

0.091

0.024

Bank 02

0.148

0.023

0.09

0.037

Bank 03

0.278

0.036

0.119

0.025

Bank 04

0.169

0.035

0.034

0.028

Bank 05

0.119

0.032

0.016

0.027

Bank 06

0.228

0.034

0.015

0.026

Bank 07

0.14

0.035

0.039

0.04

Bank 08

0.202

0.031

0.061

0.023

Bank 09

0.173

0.027

0.034

0.036

Bank 10

0.219

0.027

0.031

0.039

Table 8 Determining the distance of alternatives from ideal solutions

Distance from ideal solutions Alternatives

Si*

Si−

Bank 01

0.376

0.086

Bank 02

0.134

0.081

Bank 03

0.015

0.187

Bank 04

0.139

0.055

Bank 05

0.19

0.009

Bank 06

0.118

0.105

Bank 07

0.159

0.038

Bank 08

0.097

0.095

Bank 09

0.135

0.058

Bank 10

0.106

0.195

Source Produced by the Author

Based on the obtained solutions, the ranking of alternatives can be performed (Table 9). Based on the implemented TOPSIS method, it was decided that the most suitable bank for investment in regard to security of investment, stability of the bank and anti-money principles implemented to prevent criminal use of that institution, is bank number 5. It has the best combination of elements, scoring high in our model, harmonizing all 4 principles in great manner, and implementing methods for reducing the risk of money laundering and gray economy with parallel positive effect on profitability, efficiency and liquidity and achieves the highest rank among all alternative.

Analysis of the Practices of Financial Intelligence Units … Table 9 Ranking alternatives

263

Alternatives

Relative proximity

Rank

Bank 01

0.383

5.

Bank 02

0.353

6.

Bank 03

0.933

1.

Bank 04

0.249

8.

Bank 05

0.048

10.

Bank 06

0.456

2.

Bank 07

0.171

9.

Bank 08

0.444

4.

Bank 09

0.266

7.

Bank 10

0.454

3.

Source Produced by the Author

The third part of the decision support system solution, that strave to point out the possibility of scientifically basing the procedure of choosing methods for reducing the risk of money laundering and gray economy by the state, is the user interface which provides communication between users and the system in the simplest way. For the purposes of this research, in the Visual Basic for Applications program, we have created a program that provides solutions, i.e. a range of alternatives, in a simple way after entering the database After starting the program, a note appears informing you that you have started the program for determining the strategy for reducing the risk of money laundering in the payment system of banks. By clicking the OK button, you continue with the work and further calculation (Fig. 1).

Fig. 1 User interface. Source Produced by the author

264

D. Panevski et al.

Fig. 2 The final rank of bank. Source Produced by the author

Clicking on the calculate button gives a ranking alternative. However, if any of data in the database is missing, the program warns you about it. The program also offers graphical representation of the solution, as an option after the ranking is completed (Fig. 2).

4 Conclusion From the obtained chart in Fig. 3, it can be seen that the largest changes in value are achieved by alternative 3 where due to the reduction of the weight of the criterion “liquidity”, the increase in the weight of other criteria only further proves the conclusion that this bank showed the lowest risk of money laundering in the payment system. Also, in general, with other alternatives, the conclusion is that due to the growing weight of the criteria “efficiency”, “profitability” and “anti-money laundering mechanisms” their value is growing too, but still insufficient to achieve higher value compared to alternative 3. Everyone—individuals, politicians, experts, business people, are considering and making small and large decisions on a daily basis-decisions concerning individuals, families, business systems or social communities—regions, countries and the world as a whole. For most cases, i.e. problems that are solved, there are several solutions. But the question that arises is, which solution to choose? The one who considers and makes a decision takes into account several aspects of the problem he is solving: some reasons speak in favor of making a decision in one way, but other reasons say that such a decision is reconsidered and often changed.

Analysis of the Practices of Financial Intelligence Units …

265

Fig. 3 Influence of weight change of all criteria on alternatives. Source Produced by the author

Thus, the practice of solving problems in the financial sector shows that they can be solved in different ways, respecting the appropriate criteria. In recent years, when it comes to the financial sector of the Republic of N. Macedonia and Slovak Republic, positive changes have taken place, which is confirmed by parameters that indicate increased efficiency of the banking system, growth of loans and deposits with banks, growth of capital market turnover, stimulating household savings, etc. which has a positive effect on reducing the risk of money laundering and the gray economy. The Austrian financial sector as leading from these three have seen these changes in very early stages and currently they are functioning flawlessly. In order to achieve even better results, it is necessary to provide additional investment funds by customers, as well as the establishment of a system of networked monitoring of cash inflows and outflows, which will make decisions based on appropriate indicators obtained by applying the best ways to measure bank performance, is certainly under the jurisdiction of the NBS. The application of decision support systems combines the results of economic theory with the data provided by economic statistics. The key characteristics of high-performance banks are reflected in providing high liquidity, maximizing profits, cost control, etc. There are several approaches to measuring the performance of commercial banks. They all come down to a greater or lesser choice of certain coefficients or ratios. Financial ratios or indicators represent relative relations that should determine in advance the degree of certainty that the bank will be able to monitor the state of payment operations of deposits and placements. The obtained sizes can be compared with other banks, planned sizes or with the sizes of the bank realized in the previous period, as well as with international payment operations, i.e. foreign banks.

266

D. Panevski et al.

We have proven the possibility of using a number of representative methods available to the decision-making process with goal of reducing the risk of money laundering for each bank separately, which facilitates the work and raises the quality of the decision making within the institutions, to a higher level. Precisely on the example of using the combination of AHP and TOPSIS method, it is shown how a decision can be made by a precise procedure, while respecting all the set criteria on the basis of which the selection is made. Also, in this way, it has been shown that there are significant arguments for the selection procedure to be based on scientific grounds. The development of the decision support system was performed with data collected from 10 most successful banks from the financial sectors of Austria, Slovakia, and North Macedonia. Data on their operations refer to 2017–2019 year and were collected from annual financial reports with help of National Banks from the three countries which were partners for conducting this research along with FIUs of the respective countries. By applying the appropriate system, the banks were ranked, and it was determined that the bank under number 3, bank coming from Austrian financial system with its indicators has the best characteristics. After our research on this topic and on tree independent FIUs from Austria, Slovakia and North Macedonia, we can conclude that preventing money laundering is an extremely complex issue that needs to be addressed appropriately. Given that money laundering itself is a “living matter”, i.e. a process that is constantly evolving and seeking new forms of manifestation, based on our research will provide some guidelines and recommendations for applying the process of preventing money laundering in the future. With the help of the empirical part of this research we have shown that in practice, solving problems in the financial sector, can be done in different ways, respecting the appropriate criteria, and appropriate model solution. Technology can be very helpful in detecting suspicious transactions. Money laundering software can be found in a variety of shapes and sizes on the market, but most often their price is high. For that reason, the person responsible for preventing money laundering must justify investing in modern technology. It will only do so if it changes its philosophy—money laundering is not only detected by monitoring certain activities or transactions, but also by a complex and dynamic risk assessment. The technology, i.e. the software solution to enable efficient fight against money launderers. Namely, these criminal entities know that they can be detected while laundering money and try to cover up their activities. However, certain features of money laundering cannot be obscured by a simple change in the frequency, amount or type of transaction. That technology, which allows for a more comprehensive analysis of the risks of money laundering, will help avoid costly investigations into legitimate transactions. We hope that this research will help point out the possibility of scientifically basing the procedure of choosing methods for reducing the risk of money laundering and gray economy by the state, using the method of multi-criteria decision-making, implementing the findings and solutions used in our model.

Analysis of the Practices of Financial Intelligence Units …

267

References 1. Basel Committee: Prevention of criminal use of the banking system for the purpose of moneylaundering 1988. Available at https://www.bis.org/publ/bcbsc137.pdf 2. Bernstein, J.: Secrecy World (Now the Major Motion Picture THE LAUNDROMAT): Inside the Panama Papers, Illicit Money Networks, and the Global Elite. SP Press (2017) 3. Buchanan, L., O’Connell, A.: A Brief History of Decision Making. Available at https://hbr. org/2006/01/a-brief-history-of-decision-making 4. Cassara, A.J: Trade-Based Money Laundering: The Next Frontier in International Money Laundering Enforcement. Wiley, London (2015) 5. Cerny, P.: Globalization and the Changing Logic of Collective Action. University of York, Department of Politics, York (1995) 6. Commonwealth Secretariat: Combating Money Laundering and Terrorist Financing: A Model of Best Practices for the Financial Sector, the Professions (2016) 7. Development Research Group, World Bank: Financial Inclusion and Inclusive Growth [online]. Accessed 15 Feb 2019. Available on http://documents.worldbank.org/curated/en/403611493 134249446/pdf/WPS8040.pdf 8. Diekman, P.: Protecting Financial Market Integrity: Roles and Responsibilities of Auditors. Kluwer, UK (2018) 9. Doncev, D.: Ctpancki dipektni invecticii. [Online]. Skopje: Utrinski Newspaper 2010, Date of citation [16.03.2019]. Available on http://www.utrinski.mk/?ItemID=BB4232F78272 1E479DC17799606B6B9D 10. Dudic, B., Dudic, Z., Smolen, J., Mirkovic, V.: Support for foreign direct investment inflows in Serbia. Econ. Annals-XXI 169(1–2), 4–11 (2018). https://doi.org/10.21003/ea.V169-01 11. Dudic, Z., Dudic, B., Gregus, M., Novackova, D., Djakovic, I.: The Innovativeness and usage of the balanced scorecard model in SMEs, Sustainability 12(8) (2020). Article Number 3221. https://doi.org/10.3390/su12083221 12. Duthel, H.: The Profesionals Politic an Crime International Money Laundering. Aster Press, London (2016) 13. European Commission: Questions and Answers—Commission steps up fight against money laundering and terrorist financing. Cited 21 June 2020. Available at https://ec.europa.eu/com mission/presscorner/detail/en/qanda_20_821 14. European Union: Directive (EU) 2015/849 of the European Parliament and of the Council of 20 May 2015 on the prevention of the use of the financial system for the purposes of money laundering or terrorist financing, amending Regulation (EU) No 648/2012 of the European Parliament and of the Council, and repealing Directive 2005/60/EC of the European Parliament and of the Council and Commission Directive 2006/70/E. Available at https://eur-lex.europa. eu/legal-content/EN/TXT/HTML/?uri=CELEX:32015L0849&from=FR 15. FATF: Anti-money laundering and counter terrorist financing for judges and prosecutors. FATF, Paris (2018) 16. Gruevski, N.: Kon izlezot, ctpanckite dipektni invecticii, ekonomckiot pazvoj i vpabotenocta. Evropa, Kocani, Mac (2007) 17. Horecký, J.: Operation and action of a trade union (in terms of Czech Republic labour law). Central Eur. J. Labour Law Personnel Manag. 1(1), 17–27 (2018). https://doi.org/10.33382/cej llpm.2018.01.02 18. Hsiao, S.-W.: Concurrent design method for developing a new product. Int. J. Ind. Ergon. 29(1), 41–55 (2002). https://doi.org/10.1016/S0169-8141(01)00048-8 19. Jurik, P., Panevski, D.: Anti-money Laundering Institutions and Their Practices Within EU. Comenius University in Bratislava, Bratislava (2018) 20. Kesner-Skreb, M.: Employment policy in the European union. Finan. Theor. Prac. 34(3), 315– 317 (2018). Institute of Public Finance 21. Korauš, A., Kašˇcáková, Z., Felcan, M.: The impact of ability-enhancing HRM practices on perceived individual performance in IT industry in Slovakia. Central Eur. J. Labour Law Personnel Manag. 3(1), 33–45 (2020). https://doi.org/10.33382/cejllpm.2020.04.03

268

D. Panevski et al.

22. Law on Foreign Exchange, Republic of Macedonia 2011. (Cl.vecnik na PM 34/01,49/01,103/01,51/03 i 81/08) 23. Lobo-Guerro, L.: ‘Archives’. In: Salter, M.B., Mutlu, C.E. (eds.) Research Methods in Critical Security Studies. Routledge, NY (2013) 24. Madinger, J.: Money Laundering: A Guide for Criminal Investigators, 3rd edn. CBC Print, Rome (2018) 25. Maloney, C.B.: Understanding the Economy. Riviere Press, New York (2011) 26. Management study HQ. What is Decision Support System? Available at https://www.manage mentstudyhq.com/components-of-decision-support-systems.html 27. Mankinw, G.: Principles of Macroeconomics. Cengage Learning, New York (2014) 28. McConnell, C., Brue, S., Flynn, S.: Macroeconomics (McGraw-Hill Series Economics). McGraw-Hill/Irwin, New York (2011) 29. Meyers, L.S., Gamst, G.C., Guarino, A.J.: Performing Data Analysis Using IBM SPSS. Wiley, Hoboken (2013) 30. Money Laundering Forum Slovakia: Money laundering guidance for lawyers (for example, Law Society or Bar Association Guidelines) currently in place. [online] [cit. 10.02.2019]. Available from https://www.anti-moneylaundering.org/europe/slovakia.aspx 31. Mucha, B.: Tools to increase the effectiveness of comprehensive management of emergencies affected by climate change in the Slovak republic. Int. Multidisc. Sci. GeoConf. Surv. Geol. Min. Ecol. Manag. SGEM 19(5.4), 573–580. https://doi.org/10.5593/sgem2019/5.4/s23.075 32. Mucha, B.: Vykon vladneho auditu medzinárodnych zdrojov. Comenius university in Bratislava, Bratislava (2020) 33. Mura, L., Buleca, J., Hajduová, Z., Andrejkoviˇc, M.: Quantitative financial analysis of small and medium food enterprises in a developing country. Transform. Bus. Econ. 14(34), 212–224 (2015) 34. Mura, L., Sleziak, J.: Innovation and entrepreneurship network. In: CERS 2014: 5th Central European Conference in Regional Science, International Conference Proceedings, pp. 643–651 (2015) 35. Nastišin, L.: The dependency of online reputation and financial performance of companies in selected industry. Acta Oeconomica Universitatis Selye 5(2), 127–133 (2016) 36. National Bank of Slovakia: Methodological Guidelineof the Financial Market Supervision Unit of Národná banka Slovenska, prevention of money laundering and terrorist financing at banks and branches of foreign banks. [online] [cit. 18.04.2019]. Available from https://www.nbs.sk/_ img/Documents/_Dohlad/ORM/AML/MU_3-2019_AML_EN.pdf 37. NBRM: Godixen izvextaj za pabotenjeto na HBPM. [online]. Skopje 30.12.2010, 36 p. Cited [25.02.2018] Available on http://www.nbrm.mk/WBStorage/Files/WebBuilder_Godi sen_izvestaj_2010.pdf 38. OECD: Money Laundering Awareness Handbook for Tax Examiners and Tax Auditors. [online]. [cit. 15.05.2018]. Available from http://www.oecd.org/ctp/crime/money-launderingawareness-handbook-for-tax-examiners-and-tax-auditors.pdf 39. Okanazu, O.O.: Financial management decision practices for ensuring business solvency by small and medium scale enterprises. Acta Oeconomica Universitatis Selye 7(2), 109–121 (2018) 40. Okanazu, O.O., Madu, M.A., Igboke, S.A.: A recipe for efficient and corrupt free public sector. Central Eur. J. Labour Law Personnel Manag. 2(1), 29–46 (2018). https://doi.org/10.33382/cej llpm.2019.02.03 41. Refrigeri, L., Alfadri, G.: Research methods. Procedia Soc. Behav. Sci. 93, 1263–1268 (2013) 42. Saaty. L.T.: Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1(1), 83–98 (2008). https://doi.org/10.1504/ijssci.2008.017590 43. Sararu, C.S.: European administrative convergences. In: European Administrative Space— Recent Challenges and Evolution, Prospects, pp. 15–48 (2017) 44. Sararu, C.S.: Administrative litigation systems in Europe. Juridical Tribune-Tribuna Juridica 7(1), 227–235 (2017)

Analysis of the Practices of Financial Intelligence Units …

269

45. Sararu, C.: S: The State and the separation of powers. Juridical Tribune-Tribuna Juridica 5(2), 274–280 (2015) 46. STAT: Kpatkopoqni ctatictiqki podatoci za ctopanckite dvienja vo Pepyblika Makedonija. [online]. Skopje 29.12.2017, 27 p. Cited [25.02.2018]. ISSN 1857-7512 Available on http://www.stat.gov.mk/Publikacii/1.3.15.02.pdf 47. Sullivan, K.: Anti-Money Laundering in a Nutshell: Awareness and Compliance for Financial Personnel and Business Managers. Apress (2015) 48. The World Bank: Doing Business 2018. [Online]. Washington DC. Corporate Visions, (2013) 270p Date of citation [16.03.2018]. Available on http://www.investinmacedonia.com/images/ resources/Doing%20Business%202013.pdf 49. United Nations: The United Nations Conventions and Resolutions. [online]. Accessed 20 December 2019. Available on https://www.unodc.org/unodc/en/money-laundering/Instru ments-Standards.html?ref=menuside#UN-Conventions 50. Molnár, E., Molnár, R., Kryvinska, N., Greguš, M.: Web intelligence in practice. Soc. Serv. Sci. J. Serv. Sci. Res. 6(1), 149–172 (2014) 51. Kryvinska N.: Building Consistent formal specification for the service enterprise agility foundation. Soc. Serv. Sci. J. Serv. Sci. Res. 4(2), 235–269 (2012) 52. Kaczor, S., Kryvinska, N.: It is all about services—fundamentals, drivers, and business models. Soc. Serv. Sci. J. Serv. Sci. Res. 5(2), 125–154 (2013) 53. Greguš, M., Kryvinska, N.: Service Orientation of Enterprises—Aspects, Dimensions, Technologies. Comenius University in Bratislava, Bratislava (2015)

Modern Approaches to Leadership Development—An Overview Helena Kiß and Rozália Sulíková

Abstract In today’s economy, organizations are distinguishing themselves by qualified leaders. These decisively determine the success or failure of an organization and thus are an elementary part of personnel development. But before leaders can be effectively deployed, they need be identified as potentials and further developed. Hereby, it is of importance to identify the professional and interdisciplinary qualifications of leaders and to prepare them to success-fully meet future organizational requirements and thus to contribute to its success. The present work examines which modern approaches to leadership development predominate in both theory and practice. The result shows that there is an enormous wealth of opportunities that organizations can embrace. It would therefore make sense for further research to empirically investigate whether and which measures are known to the organizations and, above all, if they are actually being used effectively. This article is an excerpt from the author’s dissertation, with the subject “Modern Leadership Development as a Competitive Advantage of Organizations—Analysis of Approaches to Developing Leaders in Selected Financial Institutions”, published in 2020 at the Comenius University in Bratislava.

1 Introduction For all organizations, personnel are one of the most important sources of sustainable competitive advantage. Particularly in the case of strongly changing market situations or in times of increasing competitive pressure and advancing digitization in the financial industry, it is necessary for these organizations to identify themselves with qualified personnel. Leaders play a crucial role here because they can decide on the success of an organization through their actions and their influence on core processes. H. Kiß (B) · R. Sulíková Faculty of Management, Comenius University in Bratislava, Odbojárov 10, 820 05 Bratislava 25, Slovakia R. Sulíková e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Kryvinska and A. Poniszewska-Mara´nda (eds.), Developments in Information & Knowledge Management for Business Applications, Studies in Systems, Decision and Control 376, https://doi.org/10.1007/978-3-030-76632-0_9

271

272

H. Kiß and R. Sulíková

For this reason, organizations are increasingly relying on leaders with emotional and cultural intelligence as well as personal characteristics—i.e. According to investigations in this field, for example, it is primarily networked thinking, openness, willingness to learn and role model function that are expected of leaders in the digital age—which Liebermeister, head of the Institute for Leadership Culture in the Digital Age, has shown [1]. Digitization promotes and demands courage to change and move something and serves as a driver of leadership behavior. It is in the interests of both the organization and the leader to continuously develop in order to run the organization successfully. Leadership development is therefore an important success factor in practice in order to attract and retain high potentials and to develop them into “ideal” leaders. Most organizations have recognized that the acquisition and retention of high potentials has become a real “war for talent”. In addition, due to the demographic development, more and more leadership positions will be vacant in the next few years that have to be filled with qualified employees [2, 3]. In addition, only 13% of millennials are striving to assume a leadership role, as a survey by the personnel service provider Manpower reveals [4–6]. A widespread method of filling vacancies is to cultivate your own candidates in a talent pool or with the help of your own succession plans. Employer branding is becoming more and more important for securing your own staff and attracting new qualified candidates. In addition to remuneration or the receipt of benefits in kind, other features such as flexible working hours, mobile/home office and a good working atmosphere for today’s candidates come closer to the fore when it comes to the attractiveness of an organization [7, 8]. As the demands on today’s leaders have changed and the pressure to develop a recipe for success for leadership is increasing, this task is becoming more and more difficult for organizations. In addition, leaders repeatedly receive new knowledge requirements from their organization. In leadership roles in particular, the trick is to balance between the massive pressure to implement “from above” and the interests of employees of all generations (here Generation X, Y and Z). The leaders must have not only technical, but also general qualifications [9, 10]. Only a small part of these general key features can be learned through further training measures or training positions. Many of the most critical characteristics of executives for success depend on psychological-curricular developments and in various ways cause other, much more tangible characteristics. Not least because of global networking and the resulting increasing digitalization, processes are becoming more abstract for leaders, results are less tangible, and communication is more complex. In recent years, the ongoing cooperation in international competition has been increasingly expanded. Sales markets in China and other parts of Asia no longer necessarily require a pure command of the national language, but are based on your own local business network as well as knowledge and sensitivity for region-specific, cultural differences, as these determine the success or failure of upcoming negotiations. As Lou Gerstner, the former Chief Executive Officer of IBM once said: “Culture isn’t just one aspect of the game—it is the game” [11, 12].

Modern Approaches to Leadership Development—An Overview

273

For this reason, organizations at all management levels place great value on modern access to their development programs, in particular their content, methods, transfer into practice and evaluation of the programs. They stand by their leaders as companions and advisors. However, the development alone does not lead to the hoped-for increase in the organization’s performance. Leaders need a work environment that enables them to perform at their best and in which they can develop. At the same time, it is their job to ensure that the employees in the area of responsibility are managed in such a way that they remain healthy and motivated. Particularly in view of the increasing intensification of work, topics such as “healthy” leadership are therefore becoming socially relevant topics and keywords such as burnout and other psychological complaints are present in the media on a daily basis, which is also in line with the needs of today’s Generation Y [13–16]. It is therefore of particular interest to organizations whether a recipe for success or even a roadmap for “ideal” leadership can be derived from current practical experience and how success can be comprehensively defined by leaders? However, organizations need to know that there is not only one effective style of leadership. Leadership behavior varies depending on the situation and environment. Therefore, this work presents what the literature on this topic says as possible measures, so that a foundation of options can be summarized, which should serve for further research, especially empirically. This is worked out in the following chapters. The summary and the further outlook round off this work.

2 Modern Approaches to Leadership Development 2.1 Personnel and Leadership Development One of the main reasons that organizations invest in the education and training of employees and leaders is to improve and preserve their human capital [17–20], whereby the development of individual leaders is the result of a targeted investment in human capital. Leadership development typically aims at individual knowledge, skills and abilities that are related to formal, i.e. H. hierarchically defined leadership roles are linked [21]. In this context, the question arises in particular which factors are relevant for the development of successful leadership skills and for leadership potential. Lord and Hall recommend that leadership development should begin at the skill level and progressively align [22]. With their approach, the authors pursue a general theory of learning and expertise that is still valid today. Associated with this are changes in information processing and the underlying knowledge structures, which are gradually being redefined as skills. In the course of development, a leader makes progress in his/her skills at the various levels (from beginner to advance to expert level). With each advancement or with each organization level, a leader is faced with

274

H. Kiß and R. Sulíková

increasingly difficult tasks, while at the same time increasingly demanding knowledge structures and information processing skills are required within the broadly defined task [23–27]. These tasks in turn differ from area to area and not only represent an additional difficulty for leaders in their advancement, but they also require individual development approaches in order to optimally meet the requirements and changes that arise. In requirement areas with a high degree of variability, it can happen that the development of standard concepts for problem solving is prevented because new problems in the area arise so quickly and require individual solutions that the leader is not able to analyze the underlying basic structure and to solve (economic complexity reduction through targeted learning). Lord and Hall identified a specific structure in developing a leader’s identity; the findings are still used today in the literature. A distinction was made between the following three levels: In the first level, the individual level, the leader defines himself through his/her uniqueness and the differentiation of the self from others. In the next level, the relational level, the leader expands his/her self-image through roles and relationships with others. On the third level, the collective level, the self-image flows into group or organization affiliation. In parallel to the identity, meta-cognitive abilities develop which enable the leader to gain better access to knowledge, more critical goal setting, more reflective action and a more appropriate social reaction [22, 28–30]. The cognitive resources released in the process can help the leader to more effective self-regulation, which strengthens the leader’s control over himself, for example through good time management. Self-regulation also includes the control and communication of emotions to others and thus directly influences the behavior of the leader. In order to be able to lead effectively, the leader must now be able to question himself and be aware of his abilities [29, 31]. The process of meta-cognition is therefore very important in the maturation of executives and contributes—as well as the understanding of the situation and the cooperation with others—to the development of their identity and thus their behavior. In addition, further possibilities for social interaction can be derived from the amount of skills that a leader develops over time, so that this also influences their behavior. Since the executive’s development path is limited by his/her personality, researchers have looked at further possibilities for executive self-development. Boyce, Zaccaro and Wisecarver have created a model for this, which predicts leadership development activities on the basis of dispositional properties. This model was later tested and confirmed in an empirical study with younger military executives. The identified characteristics that could be associated with development activities were work orientation (e.g. work mentality/involvement and organizational commitment), control orientation (more self-efficacy, conscientiousness, openness to experience and intellectual maturity) and career growth orientation (greater growth within career/career research and behavior seeking feedback). Depending on the strength of their mastery and work orientation, people were more or less motivated to engage in self-development activities. Those who were more career-growth oriented were found to be better qualified in carrying out self-development activities [32, 33]. Overall, the results show that work orientation, control orientation and career growth orientation

Modern Approaches to Leadership Development—An Overview

275

play a key role in the self-development of leaders. Reichard and Johnson further claim that self-development is an inexpensive way for organizations to develop leaders [34].

2.2 Systematic Approach to Development Programs The systematic approach to leadership development enables the management of the qualification programs and ensures that the leader is prepared for his/her job by having the knowledge, skills, and abilities to do his/her job in the best possible way. As part of the systematic approach, it can be ensured within the organization units that important topics/content and skills are identified, and suitable learning methods are used and applied. The final evaluation of the development measure enables the effectiveness to be examined and improvements to be shown. This is why this is discussed in more detail in the following two sections.

2.2.1

Content Focus and Method of Development Programs

The core element of any training is the content. With their study, Bates and Khasawneh have shown that the training content and the training goals as well as the associated materials must be relevant to the topic and, above all, must have a strong connection to the participants’ everyday work [35]. To this day, nothing has changed [36, 37]. Other important factors to consider are the motivation of the participants, their reactions, a positive work environment and the methods chosen. The trainer must therefore ensure that the participants can work in and with the training environment and that they can relate to the transfer to the real world of work. This underlines the usefulness of a needs analysis prior to the training so that the training content is relevant [38, 39]. In a qualitative study that examined the various types of support provided by the organization, Hawley and Barnard found that regular networking and exchanging new ideas about the training content with the other training participants positively increases the transfer of what has been learned into everyday work. This knowledge is still used in today’s literature. Although support from work colleagues and acquaintances has the strongest influence on learning success, this effect fizzles out if there is no support from management. This can be avoided through an effective training and target agreement plan and direct implementation [40–43]. According to Lim and Johnson, the lack of practicability of newly learned content in the workplace represents the greatest obstacle to transfer, i.e. it hinders the transfer of the newly learned content to different task situations and thus its maintenance. More recent studies still refer to this finding today, which underlines the continued relevance [44–47]. Furthermore, the results of qualitative studies show that a number of factors from the work environment have a negative effect on training transfer. These factors mainly include a high workload, time pressure, the lack of reinforcement of the trained behaviors, the lack of performance feedback and the perception that the

276

H. Kiß and R. Sulíková

time of training needs to be made up. The organization must minimize all of the above-mentioned obstacles as much as possible [48–51]. In order to be able to better capture the experience of executives as employees and to guarantee an easier and more sustainable transfer into everyday work, it is now important to optimally coordinate the content, i.e. theory and practice. In a detailed meta-analysis with 8980 samples, Donovan and Radosevich researched decades ago that the combination of theory and practice as a learning method leads to greater learning success (participatory method). However, the impact of this on the actual transfer into everyday work was minimal. Nevertheless, this investigation is still justified in today’s literature [52–56]. In contrast to passive participation in training, such as B. a lecture, the participant is involved in an active learning approach in the training process through carefully constructed activities [57]. This can be explained by the fact that active learning extends the attention span of adults [58–62]. The level of research in learning situations in which a trainer leads the training has so far been insufficient for further observations. An interesting addition at this point is a current working paper from the Harvard Business School, which goes one level deeper in terms of personality behavior in terms of openness to new content among colleagues. The researchers found that women update their opinions 13% of the time more often than men and that so-called superstars do this less often than everyone else in 24% of the cases [63]. Casciaro, Edmondson, and Jang also determine that collaborative knowledge transfer is the essential success factor of organizations since value-destroying silos are minimized through cross-departmental collaboration [64]. Burke, Sarpy, Smith-Crowe, Chan-Serafin, Salvador and Islam found in their empirical research on the relative effectiveness of occupational safety training and health that active learning, e.g. B. the inclusion of dialogues and role play (“visual learning”), the learning process positively strengthened and negative side effects reduced, as in this example the reduction of injuries in the workplace [65]. However, the lack of empirical data on active learning methods continues to leave a large void. While there are numerous defenses of learning success, the claim that what has been learned is successfully transferred to everyday work remains unfounded. In order to be able to design and enable agility of structures, processes and behavior of employees, leaders themselves have to be agile. For this they need the strength of self-reflection, but also regular feedback from their colleagues and employees, which should serve as the basis for their own change processes [66, 67]. Agile learning is best done in real everyday work, “on demand” and at short notice, whenever the leader recognizes his/her learning needs (spontaneous, situational learning) and this self-learning is enabled or promoted [68]. The procedure is usually characterized by stages with direct application relevance: learning, acting, and improving—in selforganization and in a collegial, small learning unit/team [69]. The learners take on a decisive active role and are accompanied by so-called “agile instructors” who offer them assistance in organizing their learning themselves, e. B. by submitting learning offers or taking on a moderator role. Agile learning formats can be [70]:

Modern Approaches to Leadership Development—An Overview

277

• Brown Bag Meeting—collegial exchange to promote knowledge transfer in the team and in the organization. An employee either reports on his/her work or gives a short lecture, which is then discussed in the panel by a maximum of 30 participants. The format takes place over lunchtime and does not last longer than an hour. You can bring your own lunch (known from the USA, where lunch is usually packed in a brown paper bag). • Lean Coffee—open, collegial exchange of knowledge in smaller groups on specific topics and times, which are agreed upon in the group at the beginning. • Ted Talks—thinkers, doers and artists come together to personally present approaches and new ideas/visions to a selected audience. The talks last no longer than 18 min and aim to arouse inspiration and enthusiasm in the audience. The talks are subsequently available as a video on the network. • Working out Loud (WOL)—learning-oriented exchange in a network of three to five people. The exchange takes place regularly over 12 weeks in so-called circles. The participants work together—personally or virtually—on their individual goals in order to make their own work transparent for everyone. “Circle guides” help to structure the meetings. The 360° feedback has gained relevance in the relevant literature over the past two decades and is still one of the most important and effective feedback methods today [71–74]. In addition to other practices (e.g. individual coaching, mentoring, networking, active vs passive learning and classic seminars), it offers an opportunity to further develop leadership skills. The 360° feedback provides the feedback recipient with valuable information on how his behavior is perceived, understood, and experienced by others. With this assessment (external image) the leader can check whether his/her self-perception (self-image) corresponds to it. Without such feedback, she cannot understand whether her behavior is targeted, efficient or assessed as acceptable by the community. In addition, there is the complexity of the different perspectives on the work, as employees, team members, customers, colleagues, and superiors give an assessment of the behavior, skills, and performance of the leader. This assessment is then contrasted by the self-assessment [75]. However, it would be too easy to assume that a leader changes his/her behavior just because they have received feedback. Shipper, Hoffman and Rotondo found that feedback in the form of employee appraisals is not valued or given voluntarily in some cultures. For example, in some Asian cultures, criticism should be avoided. Furthermore, colleagues in collectivist cultures are reluctant to be critical of a group member (whether directly or indirectly) for fear of disrupting group dynamics and harmony [76, 77]. Any direct or negative feedback to a particular person would be discouraged, since social interactions in collectivist cultures often occur in groups [78]. Additionally, it may be possible for individuals to receive information that sets them apart from the community, and individual feedback can be avoided. Sully de Luque and Sommer suggest that individualistic cultures would provide more individual feedback, while collectivist cultures would provide more group-oriented feedback [79]. Research also suggests that culturally adapted leaders are better able to cope with the cognitive demands of

278

H. Kiß and R. Sulíková

working in a multicultural environment. Greater cultural exposure is associated with greater transnational social capital [80], cultural intelligence [81] and a better ability to cognitively cope with demands from multiple cultures [82–84]. The actual contribution of the four sources of assessment (supervisors, colleagues, employees of the leader and self-assessment of the leader) to the evaluation of the leader remains questionable, since until today the general problems of the organization form the core of the derivation, instead of, for example, a focus on the level of the quality of the statements to have [85]. In addition, the question arises at which point 360 ° feedback has the greatest influence in a management career and to what extent these feedback initiatives impair organization performance. In particular annual development meetings help leaders plan their careers, whereby the next development steps in a management career are recorded. It is important for organizations to know what potential their employees have and how they can be further developed over time so that there is enough management to achieve the strategic corporate goals [86]. The focus is therefore on the need to identify the workforce, for example in the form of talent matrices/nine-box grids, ideally to be allocated in the organization and enriched with knowledge [87, 88]. Knowledge can be academic and practical in nature. Academic means here that additional qualifications, e.g. B. at business schools (MBA, executive programs, others) a foundation is created “off the job”, which makes it easier to master the practical challenges within the organization. In contrast, the procedure of a purely practical nature consists in fulfilling real-life tasks in the organization according to the principle of “on the job” learning [89]. The practical topics are more focused on the execution of individual steps, while the relining with the academic, e.g. B. by an MBA degree, more prepared for management tasks [90, 91]. The fact that organizations cannot be satisfied with individual measures to expand their employees’ know-how is further exacerbated by increasing technological progress. The current pressure to innovate forces organizations to constantly and, above all, to develop extremely quickly. Academic or purely practical development options can be too rigid to achieve the goals. Because of this, e-learning offers, and blogs are becoming more and more attractive. They allow employees “near the job” not only to acquire academic knowledge quickly and in a concentrated manner, but also to be at the cutting edge, as the offers are thematically precisely designed and always claim to be up to date [92–94]. “EdTech” (short for Education Technology) [95] is no longer an unknown term in the startup world—the range of innovative, digital learning services [96, 97] and learning products for organizations. Global investments in learning technologies in 2017 totaled more than 9.5 billion US dollars, as the results from the market research institute Metaari show [98–100].

2.3 Evaluation of Development Programs For years, organizations have invested time and money in improving the skills of executives and developing new executives. US organizations alone spent around

Modern Approaches to Leadership Development—An Overview

279

15.5 billion US dollars on executive development programs in 2013 [101]. Colleges and universities offer a variety of courses in the field of leadership and the costs for individual leadership development offers of a top business school, such as B. London Business School, MIT, Stanford, and Harvard can quickly run to $50,000 or more per person [102]. Despite this high investment in executive training, studies show that most organizations are not sure whether their (young) executives can meet the organization’s future requirements [103, 104]. In the subjective feeling of everyone, the management training seems successful at first glance—satisfied training participants, very good ratings in the “Happy Sheets”, all good. At second glance, however, when the training becomes a topic again a little later and the inevitable question arises: What did the development measure actually bring—for the leader, for the organization? Where are the concrete results? the hoped-for answers are usually missing. Typically, this can be attributed to the lack of framework conditions in practice, which allow a pre- and post-test as well as a follow-up to be carried out with a comparison group [105]. IBM shows an example of good practice for evaluating development offers. The measurement is carried out in several stages and uses the Kirkpatrick model [106]. Figure 1 illustrates the model. Following Kirkpatrick’s four-level model, management training is evaluated on the levels of reaction (acceptance of the measure), learning (leadership skills of the participant), behavior (transfer of skills into everyday work) and results (benefit for the organization) [105, 107]. Here, level three “behavior” comes into play: Has the leadership training led to changes in behavior? Has the leader changed to approach tasks? Is she applying the new knowledge and skills she has acquired in training? [107, 108]

Fig. 1 Kirkpatrick’s four-level model. Source: Based on Witt, 2017, p. 7

280

H. Kiß and R. Sulíková

By analyzing changes in behavior after the training, organizations learn valuable information about which content-related and methodological aspects of the training had an impact and which did not. Using tests and surveys, organizations can assess whether the leader has drawn important insights from the training or whether skills need to be strengthened. A disadvantage of developing effective competencies is that skills enhanced in training are often not necessarily relevant to managerial development [109]. It is therefore important to ensure that the leadership training not only achieves the training goals, but also the organization goals and increases the impact on business operations. Organizations need to know exactly what results they want to achieve after training and record them precisely. Training goals are often broad, and organizations do not set clear benchmarks. After defining result-oriented goals, measurable reinforcement goals must be determined [108, 110, 111]. A popular approach in practice is the 3 × 4 approach, which includes the 3-time measurements weeks, months, and quarters. What does the organization want to achieve in 4 weeks, 4 months and 4 quarters? These measurable training and learning goals should be built into the training programs and discussed with the management participant. When leaders know what they are being assessed for and why it is important, they will more easily understand and implement training goals and reinforcement goals [108]. The extension of the four-level model from Kirkpatrick by a fifth level specifically for measuring the return on investment (ROI) opens up a further possibility for evaluating development measures. The so-called Philips methodology, based on the researcher’s name, uses a cost–benefit analysis to determine the value of the development measure or the specific net profit (as ROI = profit/investment) for the organization. This methodology helps organizations measure whether the money they invested in the action has produced measurable results. The question that needs to be answered in advance is what is the minimum return that I, as an organization and manager, would like to get from the development measure? [112–114] The actual lever for a benefit-oriented view of the development measure is finally provided by level 6 for measuring the value of investment—the question of the added value of the development measure [115, 116]. As part of a management training course, the added value could e.g. B. in an increased employee satisfaction and loyalty and would thus limit the (unwanted) employee turnover. Just one departure of qualified specialists and executives can quickly cost organizations five-digit sums per position and must be multiplied by the number of necessary replacements, according to the study results from a current Deloitte analysis. According to Deloitte, the departure of a qualified key worker could even threaten the very existence of the organization [117]. The study by the Federal Institute for Vocational Education and Training (BIBB) in 2008 when it came to measuring the success of training measures was a surprising result. Only about a third of the larger organizations with over 500 employees have extensive training controlling in use. In contrast, in only less than 14% of the organizations a benefit assessment of further training is carried out, which presupposes that level 5 (quantitative benefit) and 6 (qualitative benefit) of the model described above must be fulfilled [118].

Modern Approaches to Leadership Development—An Overview

281

With its two extensions, the Kirkpatrick model offers a good systematic approach for organizations to measure the success or benefit of a further training measure.

3 Conclusion 3.1 Synopsis The underlying work makes it clear that organizations have a whole arsenal of freely accessible opportunities to develop their leaders and thereby contribute as much as possible to achieving the organization’s success. The design can be varied individually and adapted to the respective specifics. This is all the more important in the current times, which are mainly driven by globalization and digitization across generations. It underlines that leadership activities have become indispensable, as they can generate the knowledge and experience necessary for organizations through constant change. Theoretical measures versus effectively implemented and implemented measures make the difference and are the responsibility of the respective management.

3.2 Further Research This article offers a compiled summary of opportunities in leadership development. It would be interesting to conduct further empirical research into which measures are used how often in corporate practice and, above all, whether they are used effectively.

References 1. Liebermeister, B.: Offenheit und vernetztes Denken – darauf kommt’s bei Führungskräften an, in: Computerwoche (online). Job & Karriere, vol. 9/5, 46 (2016). Accessed 9 Sept 2019 2. Sorge, N.V.: Fachkräftemangel erreicht Führungsetagen: Forscher warnen vor TopmanagerKnappheit (online). Hamburg: Manager Magazin Verlagsgesellschaft mbH (2017). Accessed 15 Sept 2018 3. VBW: Arbeitslandschaft 2040 (online). München, Vereinigung der Bayerischen Wirtschaft e. V. (2015). Accessed 15 Sept 2018 4. Manpower: Millennials im Karriere-Marathon: Junge Arbeitnehmer erwarten deutlich mehr und länger zu arbeiten als die Eltern-Generationen (online). Frankfurt, Manpower GmbH & Co. KG (2016). Accessed 14 Sept 2018 5. Kaczor, S., Kryvinska, N.: It is all about services - fundamentals, drivers, and business models. Soc. Serv. Sci. J. Serv. Sci. Res. 5(2), 125–154 (2013) 6. Kryvinska, N.: Building consistent formal specification for the service enterprise agility foundation. Soc. Serv. Sci. J. Serv. Sci. Res. 4(2), 235–269 (2012)

282

H. Kiß and R. Sulíková

7. Weitzel, T., Maier, C., Oehlhorn, C., Weinert, C., Wirth, J., Laumer, S.: Centre of human resources information systems (CHRIS). Themen-Special 2018: Employer Branding (online) (2018) 8. Monster Worldwide: Ausgewählte Ergebnisse der Recruiting Trends 2018, einer empirischen Unternehmens-Studie mit den Top-1.000-Unternehmen aus Deutschland sowie den Top300-Unternehmen aus der Branche IT und der Bewerbungspraxis 2018, einer empirischen Kandidaten-Studie mit Antworten von über 2.800 Kandidaten. Eschborn: Monster Worldwide Deutschland GmbH (2018). Accessed 14 Sept 2018 9. Liebermeister, B.: Offenheit und vernetztes Denken – darauf kommt’s bei Führungskräften an. In: Computerwoche (online). Job & Karriere, vol. 5/9, 46 (2016). Accessed 9 Sept 2019 10. Schwarzmüller, T., Brosi, P., Welpe, I.M.: Führung 4.0 - Wie die Digitalisierung Führung verändert. In: Hildebrandt, A., Landhäuser, W.: CSR und Digitalwirtschaft: Der digitale Wandel als Chance und Herausforderung für Wirtschaft und Gesellschaft, Berlin, Heidelberg, Springer Gabler (2017) 11. Petry, T.: Digital leadership: Erfolgreiches Führen in Zeiten der Digital. Freiburg, Haufe (2016) 12. Von Ameln, F., Wimmer, R.: Neue Arbeitswelt, Führung und organisationaler Wandel. Gruppe. Interaktion. Organisation. In: Gruppe. Interaktion. Organisation, Zeitschrift für Angewandte Organisationspsychologie, vol. 47(1), pp. 11–21 (2016) 13. Kraus, M.: Comparing generation X and generation Y on their preferred emotional leadership style. J. Appl. Leadership Manage. 5, 62–75 (2017) 14. Sa’aban, S., Ismail, N., Mansor, M.F.: A study of generation Y behavior at workplace. In: International Conference on Business Innovation, Entrepreneurship and Engineering, pp. 549– 554 (2013) 15. Seppälä, E., Moeller, J.: 1 in 5 employees is highly engaged and at risk of burnout. Harvard Business Review (online) (2 Feb 2018). Accessed 13 Nov 2019 16. Valentine, D.B., Powers, T.L.: Generation Y values and lifestyle segments. J. Consum. Mark. 30(7), 597–606 (2013) 17. Aryee, S., Walumba, F.O., Seidu, E.Y.M., Otaye, L.E.: Developing and leveraging human capital resource to promote service quality: testing a theory of performance. J. Manag. 42(2), 480–499 (2016) 18. Becker, B.E., Huselid, M.A.: Strategic human resources management: where do we go from here? J. Manag. 32(6), 898–925 (2006) 19. Crook, T.R., Todd, S.Y., Combs, J.G., Woehr, D.J., Ketchen, D.J., Jr.: Does human capital matter? A meta-analysis of the relationship between human capital and firm performance. J. Appl. Psychol. 96(3), 443–456 (2011) 20. Riley, S.M., Michael, S.C., Mahoney, J.T.: Human capital matters: market valuation of firm investments in training and the role of complementary assets. Strateg. Manage. J. 38(9), 1895–1914 (2017) 21. Day, D.V., Dragoni, L.: Leadership development: an outcome-oriented review based on time and levels of analyses. Annu. Rev. Organ. Psych. Organ. Behav. 2, 133–156 (2015) 22. Lord, R.G., Hall, R.L.: Identity, deep structure and the development of leadership skill. Leadersh. Q. 16(4), 591–615 (2005) 23. Hammond, M.M., Lester, G., Clapp-Smith, R., Palanski, M.: Age diversity and leadership: enacting and developing leadership for all ages. In: McCarthy, P.E. (ed.) The Palgrave handbook of age diversity and work, pp. 737–759. Palgrave Macmillan, London, UK (2017) 24. Lord, R.G., Gatti, P., Chui, S.L.M.: Social-cognitive, relational, and identity-based approaches to leadership. Organ. Behav. Hum. Decis. Process. 136, 119–134 (2016) 25. Lord, R.G., Hall, R.J.: Identity, deep structure and the development of leadership skill. Leadersh. Quarterlym 16(4), 591–615 (2005) 26. Lord, R.G., Hall, R.J., Halpin, S.M.: Leadership skill development and divergence: a model for the early effects of gender and race on leadership development. In: Murphy, S.E., Reichard, R.J. (eds.) Early Development and Leadership: Building the Next Generation of Leaders, pp. 229–252. New York, NY: Routledge (2011)

Modern Approaches to Leadership Development—An Overview

283

27. Reichard, R.J., Walker, D.O.: In Pursuit: Mastering leadership through leader developmental readiness. In: Reichard, R.J., Thompson, S.E. (eds.) Leader Developmental Readiness: Pursuit of Leadership Excellence. New Dir. Stu. Leadersh. vol. 149, pp. 15–25. San Francisco, CA, Wiley (2016) 28. Epitropaki, O., Kark, R., Mainemelis, C., Lord, R.G.: Leadership and followership identity processes: a multilevel review. Leadersh. Q. 28(1), 104–129 (2017) 29. Ibarra, H., Snook, S., Ramo, L.R.: Identity-based leader development. In: Nohria, N., Khurana, R. (eds.) Handbook of Leadership Theory and Practice: An HBS Centennial Colloquium on Advancing Leadership, pp. 657–678. MA: Harvard Business Press, Boston (2010) 30. Lord, R.G., Hall, R.J, Halpin, S.M.: Leadership skill development and divergence: a model for the early effects of gender and race on leadership development. In: Murphy, S.E., Reichard, R.J. (eds.) Early Development and Leadership: Building the Next Generation of Leaders, pp. 229–252. New York, NY: Routledge (2011) 31. Reichard, R.J., Walker, D.O.: In Pursuit: Mastering leadership through leader developmental readiness. In: Reichard, R.J., Thompson, S.E. (eds.) Leader Developmental Readiness: Pursuit of leadership excellence. New Dir. Stud. Leadersh. vol. 149, pp. 15–25. Wiley, San Francisco, CA (2016) 32. Boyce, L.A., Zaccaro, S.J., Wisecarver, S.Z.: Propensity for self-development of leadership attributes: understanding, predicting, and supporting performance of leader self-development. Leadersh. Q. 21(1), 159–178 (2010) 33. Day, D.V.: The Oxford handbook of leadership and organizations. Oxford University Press, Oxford, UK (2014) 34. Reichard, R.J., Johnson, S.K.: Leader self-development as organizational strategy. Leadersh. Q. 22(1), 33–42 (2011) 35. Bates, R., Khasawneh, S.: Organizational learning culture, learning transfer climate and perceived innovation in Jordanian organizations. Int. J. Train. Dev. 9(2), 96–109 (2005) 36. Banerjee, P., Gupta, R., Bates, R.: Influence of organizational learning culture on knowledge worker’s motivation to transfer training: testing moderating effects of learning transfer climate. Curr. Psychol. 36(3), 606–617 (2017) 37. Chatterjee, A., Pereira, A., Bates, R.: Impact of individual perception of organizational culture on the learning transfer environment. Int. J. Train. Dev. 22(1), 15–33 (2018) 38. Barnett, S.M., Ceci, S.J.: When and where do we apply what we learn?: a taxonomy for far transfer. Psychol. Bull. 128(4), 612–637 (2002) 39. Thorndike, E.L.: The principles of teaching: Based on psychology. Routledge, London, UK; New York, NY (2014) 40. Chauhan, R., Ghosh, P., Rai, A., Shukla, D.: The impact of support at the workplace on transfer of training: a study of an Indian manufacturing unit. Int. J. Train. Dev. 20(3), 200–213 (2016) 41. Govaerts, N.: Transfer of training in corporate settings: Toward an understanding of the multidimensional role of the supervisor (Doctoral dissertation). Katholieke Universiteit Leuven, Leuven, Belgium (2017) 42. Hawley, J.D., Barnard, J.K.: Work environment characteristics and implications for training transfer: a case study of the nuclear power industry. Hum. Resour. Dev. Int. 8(1), 65–80 (2005) 43. Ng, K.H., Ahmad, R.: Personality traits, social support, and training transfer: the mediating mechanism of motivation to improve work through learning. Pers. Rev. 47(1), 39–59 (2018) 44. Dewayani, J., Ferdinand, A.: Motivation to transfer, supervisor support, proactive learning, and training transfer: testing interaction effects. Int. J. Econ. Bus. Adm. 7(3), 141–150 (2019) 45. Lim, D.H., Johnson, S.D.: Trainee perceptions of factors that influence learning transfer. Int. J. Train. Dev. 6(1), 36–48 (2002) 46. Sandmeier, A., Hanke, U., Gubler, M.: Die Bedeutung der Gestaltung des Lernfelds und des Funktionsfelds für den subjektiven Erfolg betrieblicher Weiterbildung. Zeitschrift für Weiterbildungsforschung 41, 41–55 (2018) 47. Weinbauer-Heidel, I.: Transferförderung in der betrieblichen Weiterbildungspraxis: Warum transferfördernde Maßnahmen (nicht) implementiert werden. Springer Gabler, Wiesbaden (2016)

284

H. Kiß and R. Sulíková

48. Blume, B.D., Ford, J.K., Surface, E.A., Olenick, J.: A dynamic model of training transfer. Hum. Resour. Manag. Rev. 29(2), 270–283 (2019) 49. Botke, J.A., Jansen, P.G.W., Khapova, S.N., Tims, M.: Work factors influencing the transfer stages of soft skills training: a literature review. Educ. Res. Rev. 24, 130–147 (2018) 50. Briar-Lawson, K., Zlotnik, J.L.: Evaluation research in child welfare: improving outcomes through university-public agency partnerships. New edn. Routledge, London, UK (2018) 51. Na-Nan, K., Chaiprasit, K., Pukkeeree, P.: Influences of workplace environment factors on employees’ training transfer. Ind. Commer. Train. 49(6), 303–314 (2017) 52. Cepeda, N.J., Pashler, H., Vul, E., Wixted, J.T., Rohrer, D.: Distributed practice in verbal recall tasks: a review and quantitative synthesis. Psychol. Bull. 132(3), 354–380 (2006) 53. Donovan, J.J., Radosevich, D.J.: A meta-analytic review of the distribution of practice effect: now you see it, now you don’t. J. Appl. Psychol. 84(5), 795–805 (1999) 54. Hewitt, J.: Commentary on distributed revisiting: an analytic for retention of coherent science learning. J. Learn. Analytics 2(2), 102–106 (2015) 55. Nemeth, C., Fosha, R., Veinott, E.S.: Training and transfer: exploring issues of embedded training in complex systems. Proc. Hum. Factors Ergonomics Soc. Annu. Meet. 63(1), 2139– 2141 (2019) 56. Rogers, J.: The spacing effect and its relevance to second language acquisition. Appl. Linguis. 38(6), 906–911 (2017) 57. Dobler, E.: E-textbooks: a personalized learning experience or a digital distraction? J. Adolesc. Health. 58(6), 482–491 (2015) 58. Bradbury, N.A.: Attention span during lectures: 8 seconds, 10 minutes, or more? Adv. Physiol. Educ. 40(4), 509–513 (2016) 59. Risko, E.F., Anderson, N., Sarwal, A., Engelhardt, M., Kingstone, A.: Everyday attention: variation in mind wandering and memory in a lecture. Appl. Cogn. Psychol. 26(2), 234–242 (2012) 60. Steinert, Y., Snell, L.S.: Interactive lecturing: strategies for increasing participation in large group presentations. Med. Teach. 21(1), 37–42 (1999) 61. Stuart, J., Rutherford, R.J.D.: Medical student concentration during lectures. The Lancet 312(8088), 514–516 (1978) 62. Wilson, K., Korn, J.H.: Attention during lectures: beyond ten minutes. Teach. Psychol. 34(2), 85–89 (2007) 63. Teplitskiy, M., Ranu, H., Gray, G., Menietti, M, Guinan, E., Lakhani, K.R.: Do experts listen to other experts? Field experimental evidence from scientific peer review. Harvard Business School, Working Paper, vol. 19–107 (2019) 64. Casciaro, T., Edmondson, A.C., Jang, S.: Collaboration: cross-silo leadership. Harvard Business Review (online) (May–June 2019). Accessed 6 Jan 2020 65. Burke, M.J., Sarpy, S.A., Smith-Crowe, K., Chan-Serafin, S., Salvador, R.O., Islam, G.: Relative effectiveness of worker safety and health training methods. Am. J. Public Health 96(2), 315–324 (2006) 66. Franken, S.: Dynamische Führung. In: Franken, S. (ed.) Führen in der Arbeitswelt der Zukunft: Instrumente, Techniken und Best-Practice-Beispiele, pp. 145–171. Springer Gabler, Wiesbaden (2016) 67. Rigby, D.K., Sutherland, J., Noble, A.: Agile at scale. Harvard Business Review (online) (May–June 2018). Accessed 10 Oct 2019 68. Trost, A.: Neue Personalstrategien zwischen Stabilität und Agilität. Heidelberg, Springer Gabler, Berlin (2018) 69. Höhne, B.P., Bräutigam, S., Longmuss, J., Schindler, F.: Agiles Lernen am Arbeitsplatz – Eine neue Lernkultur in Zeiten der Digitalisierung. Zeitschrift für Arbeitswissenschaft 71(2), 110–119 (2017) 70. Walther, P.: Agiles Lernen: Wissen prosumieren statt konsumieren. Weiterbildung: Special Personalwirtschaft (online), vol. 12, pp. 1–28 (2018). Accessed 19 Dec 2019 71. Fleenor, J.W.: Delivering 360-degree feedback. In: Steelman, L.A., William, J.R. (eds.) Feedback at work, pp. 227–247. Springer International Publishing, Cham, CH (2019)

Modern Approaches to Leadership Development—An Overview

285

72. Gregory, P.J., Robbins, B., Schwaitzberg, S.D., Harmon, L.: Leadership development in a professional medical society using 360-degree survey feedback to assess emotional intelligence. Surg. Endosc. 31(9), 3565–3573 (2017) 73. Lepsinger, R., Lucia, A.D.: The art and science of 360° feedback, 2nd edn. CA. Jossey-Bass, San Francisco (2009) 74. Lundberg, A., Westerman, G.: The Transformer CLO. Harvard Business Review (online) (Jan–Feb 2020). Accessed 18 April 2020 75. Pelz, W.: Das 360-Grad-Feedback zur Erkennung und Entwicklung von Potentialträgern. In: Sauer, J., Cisik, A.J. (eds.) In Deutschland führen die Falschen: Wie sich Unternehmen ändern müssen, pp. 250–284. Helios Media, Berlin (2014) 76. Hofstede, G.: Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations, 2nd edn. Thousand Oaks, CA, Sage (2001) 77. Shipper, F., Hoffman, R.C., Rotondo, D.M.: Does the 360 degree feedback process create actionable knowledge equally across cultures? Acad. Manag. Learn. Educ. 6(1), 33–50 (2007) 78. Aycan, Z., Kanungo, R.N.: Cross-cultural industrial and organizational psychology: a critical appraisal of the field and future directions. In: Anderson, N., Ones, D.S., Sinangil, H.K., Viswesvaran, C. (eds.) Handbook of industrial, work, and organizational psychology–Vol 1: Personnel Psychology, pp. 385–408. Sage, Thousand Oaks (2002) 79. Sully de Luque, M.F., Sommer, S.M.: The impact of culture on feedback-seeking behavior: an integrated model and propositions. Acad. Manag. Rev. 25(4), 829–849 (2000) 80. Levy, O., Peiperl, M., Bouquet, C.: Transnational social capital: a conceptualization and research instrument. Int. J. Cross Cult. Manage. 13(3), 319–338 (2013) 81. Crowne, K.A.: Cultural exposure, emotional intelligence, and cultural intelligence: an exploratory study. Int. J. Cross Cult. Manage. 13(1), 5–22 (2013) 82. Dragoni, L., McAlpine, K.: Leading the business: the criticality of global leaders’ cognitive complexity in setting strategic directions. Ind. Organ. Psychol. 5(2), 237–240 (2012) 83. Fee, A., Gray, S.J., Lu, S.: Developing cognitive complexity from the expatriate experience: Evidence from a longitudinal field study. Int. J. Cross Cult. Manage. 13(3), 299–318 (2013) 84. Molnár E., Molnár R., Kryvinska N., Greguš M. Web Intelligence in practice. The Society of Service Science Journal of Service Science Research, Springer, vol. 6(1), pp. 149–172 (2014) 85. Puckett, S.: Die Führungskraft aus unterschiedlichen Blickwinkeln. Führungsbeurteilung über quellenspezifische 360°-Skalen, Wiesbaden, Springer Gabler (2016). ISBN 978–3–658– 13798–4 86. Sosik, J.J., Jung, D.: Full range leadership development: Pathways for people, profit, and planet, 2nd edn. NY; Abingdon, UK, Routledge, New York (2018) 87. Martin, A.: Talent management: preparing a “ready” agile workforce. Int. J. Pediatr. Adolesc. Med. 2(3–4), 112–116 (2015) 88. Swailes, S., Blackburn, M.: Employee reactions to talent pool membership. Empl. Relat. 38(1), 112–128 (2016) 89. Ahadi, S., Jacobs, R.L.: A review of the literature on structured on-the-job training and directions for future research. Hum. Resour. Dev. Rev. 16(4), 323–349 (2017) 90. Lunt, D., Chonko, L., Burke-Smalley, L.A.: Creating a culture of engagement in business schools. Organ. Manage. J. 15(3), 95–109 (2018) 91. Seijts, G., Gandz, J., Crossnan, M., Reno, M.: Character matters: character dimensions’ impact on leader performance and outcomes. Organ. Dyn. 44(1), 65–74 (2015) 92. Dückert, S.: Leitbild der digitalen Führungskraft. In: Petry, T. (ed.) Digital Leadership: Erfolgreiches Führen in Zeiten der Digital Economy, pp. 115–125. Freiburg, Haufe (2016) 93. Meier, C., Schuchmann, D., Seufert, S.: Corporate (E-)Learning in Zeiten der digitalen Transformation: Ausgangspunkte und Handlungsfelder einer Transformationsstrategie. In: Wilbers, K. (ed.) Handbuch E-Learning: Expertenwissen aus Wissenschaft und Praxis – Strategien, Instrumente, Fallstudien, pp. 1–23. Fachverlag Deutsche Wirtschaft, Köln (2018) 94. Siemens, G., Gasevic, D., Dawson, S.: Preparing for the digital university (online): a review of the history and current state of distance, blended, and online learning. Newburyport, MA, Online Learning Consortium (2015). Accessed 5 Aug 2018

286

H. Kiß and R. Sulíková

95. Gruenderfreunde: EdTech – die digitale Bildung bekommt eine Lobby (online). Berlin, Gruenderfreunde UG (2017). Accessed 10 March 2019 96. Kryvinska, N., Greguš, M.: SOA and its Business Value in Requirements, Features, Practices and Methodologies. Comenius University in Bratislava, Bratislava (2014) 97. Greguš, M., Kryvinska, N.: Service Orientation of Enterprises - Aspects, Dimensions, Technologies. Comenius University in Bratislava, Bratislava (2015) 98. Adkins, S.S.: The 2017 global learning technology investment patterns (online). Metaari Advanced Learning Technology Research, Monroe, WA (2018). Accessed 03 March 2019 99. Goertz, L.: EdTech: Junge Unternehmen in den Startlöchern (online). Bonn, Deutsches Institut für Erwachsenenbildung – Leibniz-Zentrum für Lebenslanges Lernen e.V (2018). Accessed 2 March 2019 100. Heuberger, S.: Mit Edtech wird Lernen zur Startup-Sache (online). Lernen wird jetzt StartupSache. Der Bildungsmarkt gilt als komplex – und bietet gerade im Bereich Edtech (Education Tech) viel Potential für findige Gründer. Berlin, Berlin Valley – NKF Media GmbH (2018). Accessed 3 March 2019 101. O’Leonard, K., Krider, J.: Leadership development factbook 2014 (online). Benchmarks and trends in U.S. leadership development. San Francisco, CA, Bersin by Deloitte, Deloitte Consulting LLP (2014) Accessed 2 Feb 2019. Available at: www.bersin.com/library 102. Harvard Business School, Program for leadership development: Accelerating the careers of high-potential leaders (online). Harvard Business School, Boston (2020) Accessed 13 March 2020 103. Bersin, J., McDowell, T., Rahnema, A., van Durme, Y.: The organization of the future: arriving now. Rewriting the rules for the digital age: 2017 Global Human Capital Trends, pp. 19–27. In: Deloitte University Press (online) (2017). Accessed 19 Sept 2019 104. Leslie, J.B.: The leadership gap: What you need, and still don’t have, when it comes to leadership talent (online), pp. 1–16. Center for Creative Leadership, White Paper, Bruxelles (2015). Accessed 02 Feb 2019 105. Popper, V., Spiel, C., Von Eye, A.: Evaluation von Führungskräfteentwicklung: Lösungsansätze zur Sicherung methodischer Standards an einem Fallbeispiel. Z. Eval. 11(1), 39–59 (2012) 106. Team Training: Best- und Good-Practice-Beispiele (online). Praxis und Anwendung von Bildungscontrolling. Tübingen, ttg team training GmbH (2013). Accessed 3 Feb 2019 107. Kirkpatrick, J.D., Kirkpatrick, W.K.: Kirkpatrick’s four levels of training evaluation. VA, ATD Press, Alexandria (2016) 108. Wurth, A.: How to measure leadership training effectiveness (online). Mindmarker, Boston, MA (2015). Accessed 3 Feb 2019 109. Wurth, A.: How to measure leadership training effectiveness (online). Mindmarker, Boston, MA (2015) Accessed 3 Feb 2019 110. Clark, L.: Measuring the impact of leadership development: getting back to basics (online). Harvard Business Publishing, Brighton, MA (2018). Accessed 3 Feb 2019 111. Nielson, B.: Measuring leadership effectiveness (online). Your Training Edge (2017). Accessed 3 Feb 2019 112. Müller, J.: Kommunikative Begleitung als Erfolgsfaktor in der Nutzendarstellung von Investitionen in die Personalentwicklung. Corp. Commun. J. vol. 5 (2017) 113. Philipps, J.J., Schirmer, F.C.: Return on Investment in der Personalentwicklung: Der 5-StufenEvaluationsprozess. Springer, Berlin, Heidelberg (2008) 114. Schuld, M., Keuper, F., Brüggemann, C.: Talententwicklung durch betriebliche Weiterbildung - Konzeption eines Evaluationsmodells. In: Keuper, F., Hiebeler, M.M. (eds.) Leadership and Talent Management in a digital world, pp. 23–52. Berlin, Logos Verlag (2013) 115. Braun, B.: Curriculare Planungsphasen von Lehr-/Lernprozessen in der Aus- und Weiterbildung, in: Negri, C.: Angewandte Psychologie für die Personalentwicklung. Konzepte und Methoden für Bildungsmanagement, betriebliche Aus- und Weiterbildung, pp. 131–156. Berlin, Heidelberg, Springer (2011)

Modern Approaches to Leadership Development—An Overview

287

116. Kellner, H.: Value of Investment: Neue Evaluierungsmethoden für Personalentwicklung und Bildungscontrolling. Offenbach, GABAL (2006) 117. Deloitte: Fluktuation und deren Auswirkung auf Unternehmen - Eine Studie von Deloitte Österreich (online). Deloitte Consulting GmbH (2019). Accessed 28 Jan 2020 118. BIBB Report: Bildungscontrolling - Vor allem in Großbetrieben ein Thema (online), vol. 13/09. BIBB – Bundesinstitut für Berufsbildung (2009). Accessed 1 Feb 2020 119. Clarke, N.: Job/work environment factors influencing training transfer within a human service agency: some indicative support for Baldwin and Fords’ transfer climate construct. Int. J. Train. Dev. 6(3), 146–162 (2002)

Crowdfunding and Uncertain Decision Problems—Applying Shannon Entropy to Support Entrepreneurs Valerie Busse and Christine Strauss

Abstract Crowdfunding has become an interesting and widely accepted alternative to finance start-ups and projects. This paper presents an introductory motivation by making transparent recent developments in crowdfunding; furthermore, it provides insights into the decision-making of the involved crowdfunding actors in their triadic relationship. Based on empirical data collected from five in-depth expert interviews we identified several factors, which influence the behavioral decision process of an entrepreneur when making a choice on a crowdfunding platform for their crowdfunding campaign. On the example of two of those identified factors we formalize the entrepreneur’s decision process using Shannon Entropy and give a numerical example. Results are interpreted and directions for further research are given.

1 Introduction 1.1 Relevance Crowdfunding has gained an enormous growth within the last years. In this context several new platforms were established, and platform providers together with entrepreneurs and the crowd showed a tremendous engagement in the relatively new alternative funding method of crowdfunding. By analyzing the current and future (e)market development of crowdfunding it can be noticed that in literature only rudimentary and vague statistics, data, and facts about the global market volume of the crowdfunding industry and its growth can be found. This is shown in the following V. Busse (B) · C. Strauss University of Vienna, Oskar Morgenstern Platz 1, 1090 Vienna, Austria e-mail: [email protected] C. Strauss e-mail: [email protected] V. Busse Comenius University, Odbojárov 10, Bratislava, Slovakia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Kryvinska and A. Poniszewska-Mara´nda (eds.), Developments in Information & Knowledge Management for Business Applications, Studies in Systems, Decision and Control 376, https://doi.org/10.1007/978-3-030-76632-0_10

289

290

V. Busse and C. Strauss

example: The latest report of global crowdfunding market analysis [1] shows a crowdfunding industry volume in 2022e of 78.81 billion EUR [1]. Whereas another report on global crowdfunding market development [2] determines a global market volume of the crowdfunding industry of 27.8 billion EUR, which is about half of the market development [2].

1.2 Development and Future Expectations in Crowdfunding As a result of several sources (Technavio, Cision, Bloomberg, Statista, Bundesverband Crowdfunding, University of Cambridge, Crowdfunding Monitor 2018) we provide aggregated values for the crowdfunding market volume worldwide, the geographically segmented crowdfunding market volume, average transaction values, and number of campaigns [1–7]. The worldwide market volume of crowdfunding shows a clear upsurge (cf. Fig. 1). The compound average growth rate (CAGR) rose particularly raising in the years from 2017 to 2018. All sources assume that the CAGR will be about 18% from 2020 to 2022e. The global market segmentation shows a large increase in Asian Pacific States (APAC). The reason for this is the high expansion of crowdfunding in China (cf. Fig. 2). Besides an increase in market volumes another increase on an averaged individual level can be observed. Figure 3 shows the average transaction value per entrepreneur in the crowdfunding market over the recent seven years, i.e. since 2014. This indicator undergoes a strong increase, and is expected to continually rise in the future [1–6]. Another indicator for the development of the crowdfunding trend over time is the number of campaigns worldwide. The following Fig. 4 provides an overview of the rise in numbers of campaigns from 2017 to 2020e.

Fig. 1 Market volume of crowdfunding in billion EUR worldwide

Crowdfunding and Uncertain Decision Problems—Applying Shannon …

291

Market volume of crowdfunding worldwide

The Americans

EMEA (Europe, Middle East, Africa)

Asian Pacific St ates (APAC)

Fig. 2 Market volume of crowdfunding, geographical segmentation measured on basis of transaction volume worldwide (2019) in %

Average transac on value in EUR worldwide 200 180 160 140 120 100 80 60 40 20 0 2014

2015

2016

2017

2019

2020e

Fig. 3 Average transaction value per entrepreneur in the crowdfunding market in EUR

By analysing the value of raised funds of the leading crowdfunding platforms in 2019, Kickstarter stands out with a sum of raised funds of 4.06 billion EUR as the leading platform in the global market followed by Indiegogo and crowdfunder. Kickstarter with 17 million customers in the year 2019 is by far the biggest and most used service provider platform for crowdfunding. The success rate expresses the ratio of successfully funded projects versus the entire number of projects; in the case of Kickstarter projects the success rate makes up 36.98% [7]. Over three million customers have a high amount of customer loyalty, and they have used the platform more than one time. Not only the FinTech Sector is on a

292

V. Busse and C. Strauss

Fig. 4 Number of campaigns worldwide (in million)

giant growth but also the scientific interest in crowdfunding is rapidly growing. Nevertheless, the topic of crowdfunding is still rather unexplored. Particularly mathematical expressions ad modelling approaches of the complex decision-making structures within the three main actors in crowdfunding, i.e. the entrepreneur, the intermediary and the crowd, is still a rather unexplored field in scientific research. Thus, this paper develops a mathematical model to formalize the triadic relationship of the three generic roles (i.e. entrepreneur, intermediary and crowd). The model is fostered and completed by semi-structured interviews with five entrepreneurs, which supported the identification of decision attributes from the entrepreneurial perspective in order to provide data for a first mathematical base model. Additionally, the contribution discusses first implications on how to structure and formalize complex decision-making behavior in crowdfunding-processes in a mathematical way.

2 Theoretical and Conceptual Background 2.1 Triadic Relationship in Crowdfunding The crowdfunding process consists of three different actors as depicted in Fig. 5. The Entrepreneur who uploads the project idea, the intermediary who serves as service provider and the crowd who provides funding. Busse [8] and Peisl et al. [9] propose the different intentions of the three actors in the triadic relationship [8, 9]. According to the authors, the entrepreneur aims to generate money, feedback and the proof of competence by uploading his/her idea. The intermediary aim is to create trust, reputation, integrity and benevolence. The crowd, however, seeks to generate rewards, either socially or financially. This paper will not focus on any special type of

Crowdfunding and Uncertain Decision Problems—Applying Shannon …

293

Fig. 5 Triadic relationship

crowdfunding such as donation-bases, reward-based, equity-based or lending-based, by focusing on the general main factors. This paper addresses in a first step the behavioral components of the entrepreneur towards the intermediary. The goal is to identify factors which influence the entrepreneur to act towards a crowdfunding platform in terms of motivation and behavior. Subsequently, these factors will provide a base for the analysis.

2.1.1

Financing Alternatives

Raising capital is an important step to start-up and run a business. Many start-ups have to choose one or several of the many existing alternatives to finance their crucial initial phase. In the following we portray which types of funding already exist, and how crowdfunding became such a rapidly accepted and popular funding alternative for entrepreneurs in recent times. Entrepreneurs are defined as “persons who set up a business, taking on financial risks in the hope of profit” followed by definitions of other authors which characterize entrepreneurs as people who invent innovative performances, initiative takers or strategic thinkers [10]. Summarizing, the term “entrepreneur” in our paper denotes a person, who wishes to realize his/her business idea and puts it into the market. In order to do so, the entrepreneur needs a certain amount of capital to get his/her business started and to the next stage. There are several routes available to entrepreneurs to raise capital in order to fund their business, which is a crucial step. One of the most common ways is raising capital through a business angel, a person who provides a certain start-up capital for new companies. The amount of the requested start-up capital depends on the start-up, the product of the start-up, the ownership structure, and the market as well as the willingness and ability of the business angel to contribute. In return, very often the business angel gets the opportunity to contribute in terms of management and to influence important company decisions [11]. This funding form, however, does not necessarily lead to success as McKaskill [12] points out by stating that around fifty percent of business angel’s investments are in ventures, which turn out to be unsuccessful [12]. Other authors underline this argument by mentioning the high dependency on one person [13]. Another way of raising capital is through friends and family, which are often willing to support the new business. According to McKaskill [12], family and friends

294

V. Busse and C. Strauss

are very often highly risk averse and are aware of the relatively high probability of not getting their money back or any other reward in return [12]. However, authors such as Busse [11] claim the low accountability in terms of liquidity of family and friends [11]. Alternatively, entrepreneurs can make use of venture capital firms, which also refers to early stage and seed capital [12]. The concept of venture capital was introduced in 1946 and is known as a funding form of many successful companies including Tesla, Facebook, Starbucks, Apple, Google and many more [14]. Venture capital firms are investors, which provide seed capital to start-ups and are eager to get a high return in case the start-up is successful. Several venture capitalists experience major losses [11]. One of the newest funding forms, however, is called “crowdfunding” and became quickly a popular method during the last few years [15–17]. This form will be explained in further detail in the following subsections.

2.1.2

Decision-Making in Crowdfunding

In order to provide the basis for an analysis of the complexity and nature of the decision processes in between the three ideal–typical actors, that is, the entrepreneur, the crowd, and the platform as intermediary, and their triadic relationship, it is essential to know, that each actor has different decisions to make within the crowdfunding process. Some examples are listed below [18].: • Decisions from the viewpoint of the entrepreneur: general access to capital through four different channels such as crowdfunding, venture capital, family and friends or business angel. Subsequently, the decision of using equity-, reward-, donationor lending form. In any of these cases the entrepreneur can decide to use a crowdfunding platform, special fairs, TV-Shows or Social Media. By considering venture capital, the entrepreneur can either use venture funds or mergers and acquisitions. By choosing friends and family as the funding method, the entrepreneur has the opportunity to do this directly or indirectly through social media. When the entrepreneur is choosing a business angel, he/she has further the opportunity to go through an investment company or an entrepreneurial circle. • Decisions from the viewpoint of the crowd: the crowd has, amongst others, also several opportunities when choosing to invest in an entrepreneurial idea. First, the crowd chooses its drivers to invest. The most common driver is mostly the return driver in the form of return on equity (ROE) or gaining a special interest rate if the crowd grants a loan. Another factor is the interest in a special organization or product. The crowd could be interested in innovation, environmental advantage or social parts. By being interested in the product, the crowd can be interested in one of the four crowdfunding forms (lending, reward, donation, equity). If the crowd wants to generate social help, it can do it either with cash or by providing know how, coaching, time or any other form of tangible or intangible support. • Decisions from the viewpoint of the intermediary: the intermediary, which is the platform provider, has to decide which approach to provide (equity-, reward-,

Crowdfunding and Uncertain Decision Problems—Applying Shannon …

295

lending, donation or mixed-method approach). Additionally, a decision of which visual appearance and structure it might offer, the time and cost of realization as well as how to select the entrepreneur and the crowd [18].

3 Research Method The data forming the basis of our analysis was collected through in-depth interviews with five entrepreneurs from different sectors, who have used crowdfunding platforms to finance their start-up. The interviews were conducted face to face. The interviewees were selected and recruited from a circle of entrepreneurs and members of the entrepreneurial association (WU Gründungszentrum) of the Vienna University of Economics and Business. With the permission of the participants the interviews were voice-recorded. Each interview was scheduled for approximately twenty minutes per interview. In order to enable the participants to go in depth in their interviews, there was no time fixing given. All questions were established on the theoretical background of crowdfunding platforms in terms of decision-making attributes for using a platform. Semi-structured interviews were used due to several advantages. The advantage of semi-structured interviews compared to structured ones is that the interviews follow a relative loose level of standardization to enable the entrepreneur to answer in his/her own manner. Generally, semi-structured interviews are less formal and follow more an open conversation about a specific topic [19]. A further advantage is that these interviews follow a certain “road-map” in which the interviewer covers the same topics in every interview. The interviewee has therefore the possibility to answer all topics and furthermore pose new questions within the interview [19]. Two main types of interview questions were applied: open questions and closed ones [20]. The participants is able to describe specific scenarios in detail with open questions and can give personal statements, backgrounds and experience to the questioned topics. If closed questions are used within the interview, the interviewer is able to gain a specific amount of data [20]. In order to allow the researcher to choose a sample, which indicates a suitable amount of professionals in order to get the research question answered properly the so-called purposive sampling method was used [20]. In this empirical pilot study it seems necessary to interview entrepreneurs, who have at least certain experiences in crowdfunding or experience in using crowdfunding service provider platforms in order to gain useful responses. Five entrepreneurs were considered as sufficient for the sample size as this limited number allows the researcher to go into more depth [21]. Three days before conducting the interview, participants were requested to take 15 min to visit the main two crowdfunding platforms: Kickstarter.com and Indiegogo.com [22, 23]. In order to spread the given information of the interviews entrepreneur with different backgrounds were searched. To be specific different genders, years of experience a start-up business and crowdfunding campaigns were selected as depicted in Table 1.

296

V. Busse and C. Strauss

Table 1 Participants of qualitative analysis and their data structure Participators

Gender

Years of entrepreneurial experience

Crowdfunding project finalized

Crowdfundmg campaign pending

Intention to start a ciowdfunding campaign

P1

Male

1

No

No

Yes

P2

Male

3

Yes

No

Yes

P3

Female

2

No

Yes

Yes

P4

Male

6

Yes

No

Yes

P5

Male

4

No

Yes

Yes

Male Participant 1 (P1) has one year experience in the start-up business and focused on technical projects, the male Participant 2 (P2) has three years’ experience with entrepreneurial business in consumer fashion. Participant 3 (P3) has two years´ experience in an Austrian online wine trading company. Participant (P4) with six years’ experience is successful in online trading of perfumes. Participant (P5) has four years’ experience in financial services (FinTech). Before conducting the interviews, the interviewer signed a data privacy statement in order to guarantee anonymity of participants. The participating entrepreneurs were asked the following four questions (Q1) to (Q4) with sample answers to provide support for the interviewees: (Q1): What are the main decision drivers of entrepreneur to crowdfunding? (Guideline: Choice between business angel, venture capital, family and friends). (Q2): Did the intention to use a platform influence the active use? (Guideline: How long did you have the plan to conduct a crowdfunding campaign, and how concrete are/were these plans). (Q3): Did your environment influence you in your decision to use a certain crowdfunding platform? (Guideline: Influenced by family and friends, business environment, advertising, political environment). (Q4): Which attributes mostly affected your decision to use a specific platform by considering Kickstarter.com and Indiegogo.com? (Guideline: Platform design, cost of use, market.

3.1 Answers of Participants The answers of the participants were audio recorded and later on transcribed. The most important and most significant answers are presented as follows. Q1: What are the main decision drivers of entrepreneur to crowdfunding? By asking for reasons why participants chose the crowdfunding approach rather than other funding methods, different reasons were pointed out.

Crowdfunding and Uncertain Decision Problems—Applying Shannon …

297

P1 stated “There is no other possibility for me to raise capital because I have been in this entrepreneurial situation only for a short time”. Whereas P2 mentioned that “For one reason I only need a small amount of money per product; for the second reason I reach a high number of potential customers through the crowd, also I have a risk spread and a relatively simple access to the market”. The female participant P3 answered that “… because online wine trading and online crowdfunding have similarities and I am online-minded because this is the future of businesses today”. P4 stated that he already had a positive success rate in crowdfunding with one campaign being so successful that he intends to do another one. P5 answered “I chose crowdfunding as it is a part of the Fintech market, and financial services are traded on this market as well”. Q2: Did the intention to use a platform influence the active use? By asking the second question on the intention to use a platform and its influence on the actual use, P1 to P5 agreed that their intention to use a crowdfunding platform will result in an effective use. Even P1 who had not yet used a crowdfunding platform had a strong wish to use it in the near future. Whereas P2 proposes “Due to my business concept I had already a successful campaign, and that showed me that my next project with a new design and package deal will be realized by a new campaign”. Q3: Did your environment influence you in your decision to use a certain crowdfunding platform? P1 stated that “I have some friends who successfully realized crowdfunding campaigns, and this strongly influenced my own wish to raise capital via crowdfunding in the near future”. P2 mentioned that he”… saw an advertisement of a German crowdfunding platform “Starnext” with a similar business concept like mine … which inspired me to use it”. P3 said: “My parents are viniculturists, I am influenced by my family and the colleagues of my parents”. P4 stated that he was influenced by his client, a leading perfume company to use crowdfunding. P5 said that “I was impressed and therefore somehow influenced by the market position of the leading crowdfunding pages”. Q4: Which attributes mostly affected your decision to use a specific platform by considering Kickstarter.com and Indiegogo.com? P1 mentioned that “… after examining Kickstarter.com and Indiegogo.com, in my opinion providing a high degree of security as well as low cost of use are the most important attributes for me to make a decision on which platform I should use”. P2 rated factors such as “usability and the size of the market” as most important. P3 said: “Design of the platform is the first thing I noticed, as it is necessary for my products, and therefore I rate it as most important for me”. She also mentioned that “… in my brand name for the products I am using the quality feature “trusted shop” therefore trustworthiness on the webpage is also an essential factor for me”. P4 stated, that due to his experience in crowdfunding “platform dimension and reputation” as well as the “time of realization of the overall crowdfunding process” as the major decisiondriving attributes when choosing the most appropriate platform. P5 said: “For me the

298

V. Busse and C. Strauss

most important attribute is a perfect and a straightforward usability of the platform as well as unrestriced access for my products”.

3.2 First Results From the answers it can be concluded, that entrepreneurs prefer using crowdfunding over other common financial methods due to factors such as easy access to the market, risk spread, successfully tested,adequate for the own product. The sample of five entrepreneurs indicates that attributes such as security, low cost of use, time of realization, design, trustworthiness, platform dimension and reputation, unrestricted access as well as the market position are important for them to decide which crowdfunding platform to choose (cf. Table 2). Another result of the study refers to the stimulus to use crowdfunding as a financing alternative: all participants admit that they were influenced by their environment through friends and family, advertisements or other factors.

4 Mathematical Decision Function to Analyse the Complex Structures of the Decision Process in Crowdfunding In a next step of analysis, we use the findings and attributes described in Subsection 4.2. The following part will explain how attributes can be ranked by importance from the viewpoint of the entrepreneur. In a first step, two of the attributes, namely “time of realization” and “cost of realization”, which were identified in Q4 (cf. Table 2) will be further used in order to show how factors could be quantified and ranked based on importance and weights according to the principles of Shannon Entropy. Table 2 Questions and findings Questions

Findings

(Ql): What are the main decision drivers of entrepreneur to crowdfunding?

Easy access to market, risk split, successfully tested, adequacy to own product

(Q2): Did the intention to use a platform influence the active use?

All participatants state that the intention to use leads to an actual behavior to use

(Q3): Did your environment influence you in All participatants are influenced by the your decision to use a certain crowdfunding environment (Subjective norm TRA) platform? (Q4): Which attributes mostly affected your decision to use a specific platform by considering Kickstarter.com and Indiegogo.com?

High security, low cost of use, usability, size of the market, design, trustworthiness, platform dimension and reputation, unrestricted access

Crowdfunding and Uncertain Decision Problems—Applying Shannon …

299

4.1 Decision Drivers and Decision Aims Specifying the decision-making process demands a clear distinction between decision drivers and decision goal. Decision drivers are determinants for actors to make a decision towards a decision goal. For example, two main determinants of decision are in a first step “time of realization of a decision goal” and “costs of realization”.

4.2 Example of Two Decision Drivers (Determinants for Actors): Time of Realization and Costs of Realization and Mathematical Approach of Their Intensity The question of intensity and ranking of the determinants as time and costs of realization can be solved by using probability theory and Shannon Entropy.In order to express the decisions of the actors (entrepreneur, intermediary and crowd), general decision functions can be formulated: DE = Decision Entrepreneur DI = Decision Intermediary DC = Decision Crowd The aims of a decision can be various. In addition to this, determinants of decisions (decision drivers) can be added such as security, platform design, trustworthiness, platform dimension, and reputation. These determinants vary on different viewpoints, namely the viewpoint of the entrepreneur, the viewpoint of the intermediary, and the viewpoint of the crowd. x iE … x nE = determinants of decision from the viewpoint of the entrepreneur x iI … x nI = determinants of decision from the viewpoint of the intermediary x iC … x nC = determinants of decision from the viewpoint of the crowd ajE … anE = aims of decision from the viewpoint of the entrepreneur ajI … anI = aims of decision from the viewpoint of the intermediary ajC … anC = aims of decision from the viewpoint of the crowd. These decision functions depend on the individual target functions of E, I and C. DE = f DI = f DC = f

n  i=1 n  i=1 n  i=1

xi E . . . xn E + xi I . . . xn I + xiC . . . xnc +

n 

a j E . . . an E

j=1 n 

a j I . . . an I

j=1 n 

a jc . . . anc

j=1

Example of DE: The decision of the entrepreneur for a financial method such as crowdfunding a1E is a function of the aims, for x 1E and x 2E two determinants

300

V. Busse and C. Strauss

are defined, which are the time and the cost of realization of a funding campaign. Therefore, x 1E denotes the time of realization, and x 2E denotes the cost of realization.

4.3 Example to Explain the Intensity and Ranking of Two Decision Drivers in One Decision Aim Based on Probability Theory and Shannon Entropy In order to simplify the complex decision setting, the following example considers the first decision of the entrepreneur, namely, where the entrepreneur has the opportunity to choose between crowdfunding, venture capital, family and friends or business angel. The values are  assumptions based on actual market conditions. For the function of decision aims nj=1 a j E . . . an E , it is assumed that a j E . . . an E are the funding opportunities from the entrepreneurial perspective. Hence, a1E stands for crowdfunding, a2E for venture capital, a3E for family and friends, and a4E for the business angel alternative. nDue to simplification aspects, two determinants from the decision function i=1 x i E . . . x n E of the entrepreneur are chosen from the previous example in order to explain the complexity, specifically, the time and the cost of realization of a funding generation. Therefore, x 1E denotes time of realization, and x 2E denotes cost of realization. In order to make a decision, it is essential to consider the different preference decisions in terms of selection, evaluation and prioritization according to the multiple attribute decision making (MADM) [24]. The determination of appropriate weights for each attribute (decision driver) is one of the major efforts in MADM problems. Due to different meanings of the decision drivers, it cannot be assumed that they are weighted equally. In the following example the concept of Shannon’s Entropy is used, as it is one of the well-known methods of determining adequate weights. There exist various other methods of defining weights such as analytic hierarchy AHP method, Delphi method or weighted least square method [25]. Shannon Entropy refers to a measure of uncertainty and plays an important role in information theory [24]. Numerous scholars used Shannon Entropy in different scientific research fields and applications to model uncertainty by means of weights. It is important to identify which of the decision determinants time of realization x 1E or cost of realization x 2E is weighted higher, and therefore more important for the entrepreneur when choosing one of the four funding alternatives. Shannon Entropy is applied to the following in order to identify a ranking sequence of the determinants. H(X) =

n  i=1

 pi log2

1 pl



Legend: H denotes the Entropy of X; X denotes the Information Source, whereas x1 and x2 are variable data, which can have different information, n denotes the

Crowdfunding and Uncertain Decision Problems—Applying Shannon …

301

Fig. 6 Estimated probability values based on actual market conditions

numbers of symbols that information source contains, and pi stands for the probability of symbol 1. All terms pi are added:  p1 log2

1 p1



 + . . . + pn log2

  n 1 ; pi = 1 pn i=1

(sum of all probabilities equals 1) (Fig. 6). Probabilities of occurrence p1 …p2 are basic assumptions of market segmentation in the form of probabilities: crowdfunding 50%, venture capital 25%, family and friends 12,5%, and business angel 12.5%. Row z11 , a1 is explained as follows: with 100% probability interviewees are convinced that crowdfunding can be realized in a period of less than 100 days. Row z13 , a3 with a 50% probability interviewees are convinced that family and friends investments will not be realized in a time frame of less than 50 days. Both matrixes are now used to identify which of the factors, time of realization or costs of realization, is weighted as more important from the viewpoint of the entrepreneur.  H = 0.5 log2

1 0.5



 + 0.25 log2

1 0.25



 + 0.125 log2

1 0.125



 + 0.125 log2

1 0.125

= 1 * 0.5 + 2 * 0.25 + 3 * 0.125 + 3 * 0.125 = 1.75 information units Entropy of information source x 1E and x 2E . In a next step, we assume that information on the preferences is provided. p11 − p31 and p12 − p32 , values are assumed as 0.25 for each preference.



302

V. Busse and C. Strauss

 H(x1E ) =

     1 1 + 0.5 log2 = 0.25 p11 ∗ 0 + p21 ∗ 0 + p31 ∗ 0.5 log2 0.5 0.5  H(x2E ) = p12 + 0 + p22 ∗ 0 + p32 ∗      1 1 0.25 log2 + 0.25 log2 0.25 0.25     1 1 +0.25 log2 + 0.25 log2 = 0.5 0.25 0.25

Hence, the information gain can now be calculated as:  H − H(x1E ) = 1.75 − p11 ∗ 0 + p21 ∗ 0 + p31 ∗      1 1 0.5 log2 + 0.5 log2 = 1.5 0.5 0.5  H − H(x2E ) = 1.75 − p12 ∗ 0 + p22 ∗ 0 + p32 ∗      1 1 0.25 log2 + 0.25 log2 0.25 0.25     1 1 + 0.25 log2 = 1.5 + 0.25 log2 0.25 0.25 The maximum of information gain is 1.5 under the described assumptions and shows that from the entrepreneur‘s perspective realization time is weighted as more important than costs of realization. In a further step it would be necessary to identify more decision drivers, and give empirical evidence on the importance as well as on the weights of these drivers by collecting quantitative empirical data.

5 Conclusion and Further Research Due to a lack of analysis of decision-making structures in crowdfunding research and particularly within the triadic relationship of the players involved, several researchers state the need for a more systematic exploration of decision-making components within crowdfunding [18, 26, 27]. However, to analyse complex decisions among the actors in their different roles in a crowdfunding setting, it is essential to clarify in a first step which decisions are necessary for every single component. The contribution at hand presented recent data on the current development of crowdfunding and the dynamics between the three main actors, i.e. entrepreneur, intermediary and crowd, in their triadic relationship. As a result from five in-depth expert interviews with entrepreneurs from different backgrounds and sectors have shown that factors such as degree of security, cost of use, time of realization, usability, size of the market,

Crowdfunding and Uncertain Decision Problems—Applying Shannon …

303

design, trustworthiness, platform dimension and reputation, and unrestricted access but also easy access to market, risk spread, thoroughly tested mechanisms, adequacy to own product, and the environment influenced their behavioral decision to choose a certain crowdfunding platform for their campaign. With focusing of two of these factors, time and cost of realization, the Shannon Entropy was applied in order to provide an example and demonstrate how the decision determinants can be mathematically defined, measured and weighted to operationalize how decision determinants can be ranked. Further research could collect and analyze empirical data to underpin the results and possibly reveal general patterns or system-inherent interdependencies. Furthermore, the following issues could be used for further research approaches: (i) influence and ranking of other decision-drivers such as platform design and functionality, (ii) impact factor(s) of cognitive style and subconscious factors for decision-making of crowdsourcees, and (iii) attitude and behavioral components influencing the decisionmaking process from other perspectives of the triadic relationship, i.e. from the viewpoint of the intermediary and the viewpoint of crowdsourcees. our study we performed five in-depth interviews with entrepreneurs engaged in crowdfunding as a financing alternative for their start-up. As a result from the empirical work we were able to identify and derive (i) decision aims of entrepreneurs who use crowdfunding, and (ii) several of their decision drivers. We used two of those decision drivers to demonstrate in a final step on an example how Shannon Entropy can be applied to quantify decision drivers and generate their ranking, which indicates what decision drivers are considered more important over others by a decision-maker in a certain role and crowdfunding-context. Applying such a classic approach exploiting empirically collected data may provide a starting point and/or may stimulate further decision-making research in crowdfunding.

References 1. Technavio (2018). https://www.technavio.com/report/global-crowdfunding-market-analysisshare. (seen on April 20th 2019, 10 pm). 2. Cision: (2019). https://www.prnewswire.com/news-releases/the-global-crowdfunding-mar ket-was-valued-at-10-2-billion-us-in-2018-and-is-expected-to-reach-28-8-billion-us-with-acagr-of-16-by-2025---valuates-reports-300869509.html. (seen on April 20th 2019, 10 pm). 3. Bloomberg: (2019). https://www.bloomberg.com/press-releases/2019-06-26/crowdfundingmarket. (seen on April 20th 2019, 11 pm). 4. Statista: (2019). https://de.statista.com/outlook/335/100/crowdfunding/weltweit. (seen on April 20th 2019, 9 pm) 5. Bundesverband Crowdfunding: (2019). https://www.bundesverband-crowdfunding.de. (seen on April 20th 2019, 10 pm) 6. University of Cambridge: (2019). https://www.crowdfundinsider.com/2019/04/146445cambridge-centre-for-alternative-finance-publishes-4th-EURpean-alternative-finance-reporttotal-online-alternative-finance-grows-36-topping-e10-billion/ ( seen on April 20th 2019, 10 pm) 7. Galkiewicz, D., Galkiewicz, M.: An overview of European projects financed on startnext and kickstarter platforms between 2010 and mid- 2017. J. Crowdfunding Monitor (2018)

304

V. Busse and C. Strauss

8. Busse, V.: Crowdfunding—an empirical study on the entrepreneurial viewpoint. In: Fatos, X., Barolli, L., Gregus, M. (eds.) Advances in Intelligent Networking and Collaborative Systems, the 10th International Conference on Intelligent Networking and Collaborative Systems , Bratislava, 306–318 (2018) 9. Peisl, T., Raeside, R., Busse, V.: Predictive crowding: the role of trust in crowd selection. In: Proceedings 3E Conference Ireland, pp. 1–19 (2017) 10. Carlsson, B., Braunerhjelm, P., McKelvey, M., Olofsson, L., Persson, L., Ylinenpää, H.: The evolving domain of entrepreneurship research: are they different from independent entrepreneurs? Working Paper 12, Swedish Entrepreneurship Forum, pp.1–24 (2011) 11. Busse, F.: In: Grundlagen der betrieblichen Finanzwirtschaft. 5th edn. München, (2009) 12. McKaskill, T.: An introduction to angel investing: a guide to investing in early stage entrepreneurial ventures, Melbourne (2009) 13. Brettel, M.: Business angels in Germany: a research note. Venture Capital: An Int. J. Entrepreneurial Finance 5(3), 251–268 (2010) 14. Vinturella, J., Erickson, S.: In: Raising Entrepreneurial Capital, 2nd edn. Gainesville USA (2013) 15. Kryvinska, N., Strauss, C.: Conceptual model of business services availability vs. interoperability on collaborative IoT-enabled eBusiness platforms. In: Bessis, N. et al (eds.) Internet of Things and Inter-cooperative Computational Technologies for Collective Intelligence, Studies in Computational Intelligence, vol. 460. pp. 167–187 (2013) 16. Kryvinska, N., Strauss, C., Zinterhof, P.: Next generation service delivery network as enabler of applicable intelligence in decision and management support systems. In: Bessis, N. et al (eds.) Next Generation Data Technologies for Collective Computational Intelligence, Studies in Computational Intelligence, vol. 352, pp. 473–502 (2011) 17. Kaczor, S., Kryvinska, N.: It is all about services-fundamentals drivers and business models. J. Serv. Sci. Res. ed. The Society of Service Science 5(2), 125–154 (2013) 18. Busse V., Gregus M.: Crowdfunding—An innovative corporate finance method and its decisionmaking steps. In: Barolli, L., Nishino, H., Miwa H. (eds.). Advances in Intelligent Systems and Computing, vol. 1035 (2019) 19. Baggozzi, R., Wong, N., Abe, S., Bergami, M.: Cultural and situational contingencies and the theory of reasoned action: application to fast food restaurant consumption. J. Consum. Psychol. 9(2), (2000) 20. Park, H.: Relationships among attitudes and subjective norms: testing the theory of reasoned action across cultures. J. Commun. Stud. 51(3), 162–175 (2000) 21. Gallois, C., McCamish, M., Terry, D.: The theory of reasoned action: its application to aidspreventive behavior. In: International Series in Social Psychology, Oxford et al (1993) 22. Ogden, J.: Some problems with social cognition models: a pragmatic and conceptual analysis. J. Health Psychol. 22(4), 424–428 (2003) 23. Godin, G., Gravel, A., Eccles, M., Grimshaw, J.: Healthcare professionals’ intentions and behaviors: a systematic review of studies based on social cognitive theories. J. Implementation Sci. 3(36), 1–12 (2008) 24. Lofti, F., Fallahnejad, R.: Imprecise Shannon’s entropy and multi attribute decision making. J. Entropy 12, 53–62 (2010) 25. Saaty, T.: Decision making. In: The Analytic Hierarchy Process: Planning Setting Priorities Resource Allocation. New York (1980) 26. Kryvinska, N.: Building consistent formal specification for the service enterprise agility foundation. Soc. Serv. Sci. J. Serv. Sci. Res. Springer 4(2), 235–269 (2012) 27. Bauer, C., Mladenow, A., Strauss, C.: Fostering collaboration by location-based crowdsourcing. In: Luo, Y. (ed.) 11th International Conference on Cooperative Design, Visualization and Engineering (CDVE) 14–17 September 2014, Seattle, Lecture Notes in Computer Science (LNCS), vol. 8683, pp. 88–95. (2014)

The Impact of Electronic Services on Traditional Services Dragana Saric and Marian Mikolasik

Abstract In the last years, companies have seen that the quality of the services they provided is becoming more and more and more important. They try to reach as many clients as possible and try to improve their services. New technologies (internet, computers, smartphones) are something that companies are taking advantage of, and there has been a huge change in the way services are provided. We have less physical contact between companies and clients and even less contact by phone (already being exceeded). Companies like Amazon, Netflix, or Uber are good examples of how the way companies provide services is changing, taking advantage of new technologies, making everyone’s life easier.

1 Introduction In the past decades, we have witnessed major changes in the way services are being delivered to consumers as well as changes in services themselves. With the rapid development of technology, it was expected that this would lead to changes in everyday life of people and the way they manage their tasks. The transformation in the economy began in the middle of the twentieth century, and it became evident that manufacturing was no longer the dominant source of output creation and customers have new expectations and wishes. As firms started to shift towards creating services, the competition grew, and there was a need to create as good customer services as possible. Firms saw this shift as the opportunity to brand themselves and beat their competitors. This also had a significant impact on developing electronic services because the technology has enabled providing services of a better quality which has given firms the possibility to differentiate themselves from competitors. Services are defined as an economic activity where an D. Saric Wirtschaftsuniversität Wien (WU), Vienna, Austria M. Mikolasik (B) Faculty of Management, Comenius University in Bratislava, Bratislava, Slovakia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Kryvinska and A. Poniszewska-Mara´nda (eds.), Developments in Information & Knowledge Management for Business Applications, Studies in Systems, Decision and Control 376, https://doi.org/10.1007/978-3-030-76632-0_11

305

306

D. Saric and M. Mikolasik

immaterial exchange of value occurs, and they are delivered at the point of sale; [1] the main difference between traditional services and electronic services is the usage of information and communications technology for delivering e-Services [2]. In the beginning, customers were not opened to accepting this change, but as the technology developed and the customers started to experience the improvement in service quality, e-Services started to take over traditional services and change the way the services are being delivered and consumed. They enabled access to services anytime, at any place, which was not possible with traditional services that are restricted in the market area that they can cover [3]. This appears to draw consumers because it offers them the convenience and the possibility to choose from a wider spectrum of products and enables them to make the best decisions. A large number of studies and reports have been conducted in the past years about this thesis because of its rapid growth and importance of services [4–6]. It is essential to acknowledge the changes that are occurring and to realize that the structure of the economy is changing and the consumers’ expectations are rising. Businesses are either adapting to this change or closing down, and many old-fashioned business retailers are expressing their concerns regarding cannibalizing old ways of delivery and services, which is the point that will be addressed [7]. Many firms are now starting to deliver services in addition to goods that they have been providing and add new ways of delivery—electronic channels. The importance of services is visible in the fact that most of the revenue of many firms comes from delivering services. From an economic perspective, e-Services are different from traditional services when it comes to cost structure, the rapid development of new services, the availability of transparent service feedback, the degree of outsourcing, and the improvement of services [8]. With the help of theory and practical examples, I will try to compare the way providing services has changed and the impact the electronic way of delivery had on the way people think, buy, and the quality they expect. The focus will be on the retail branch, banking, and entertainment industry, as these are industries where major changes have occurred, and many researchers have contributed to the understanding of the matter with their work, which will be used as a template for further discussion. From the economic point of view, the additional cost that has been caused as well as cost savings will be discussed and the impact it had on firms and businesses themselves. The work will be based on the studies conducted by now and the analysis of the market changes. This will be done with the help of real examples, the comparison of revenue and cost structure of the firms, the change in service and product offerings in time that occurred, and analysis of the changes in the market which firms have experienced. The firms which will be discussed are Amazon as one of the biggest online retailers, Netflix-representing the entertainment industry, Wal-mart as a traditional retailer, and Uber as a new means of transporting people.

The Impact of Electronic Services on Traditional Services

307

2 Traditional Services 2.1 Shift in the Economy: The Importance of Services As firms started to realize they could fulfill their customers’ needs better and raise their satisfaction by offering different kinds of services, they started to shift towards providing more services [9]. The transformation included both changes in what is produced and how it is produced [10]. As Zeithaml et al. claim, shifts in technology, consumer markets, and labor markets had led to changes in how it is produced and in the way services are being delivered. The time needed for production has decreased, and with the new ways and channels, the delivery time has also reduced significantly. Firms realized they could achieve the better identification of the product in the mind of consumers by changing the consumption and offering services together with goods. This enabled firms to sell both goods and services as a bundle and make additional profit. The increase in income and demand changes had led to steady growth of freestanding consumer services such as hotels, restaurants, travels, etc. The importance of services is visible in the fact that most of the revenue of many firms comes from delivering services. For example, the IBM Canada project has 70,000 service encounters daily, and PSC Health System, a pharmacy benefits provider more than 1 million daily. IBM generates more than $30 billion in revenue, and most of this profit is from service offerings [6]. In an economy, services are defined as an economic activity where an immaterial exchange of value occurs [1]. In this transaction, no physical goods are exchanged, the value is intangible, and it is consumed at the point of sale. Four main characteristics of services as suggested by Zeithaml et al. (1985) are intangibility, inseparability, heterogeneity, and perishability. Services cannot be stored, which means they cannot be saved and consumed later. Services are perishable in the ways that they have to be used in a particular period of time, and if they are not used then, they are lost and cannot be stored for later. This represents a loss for the service provider and can be seen as lost profit. Additionally, services cannot be reused, once they are delivered, they vanish entirely, which means if you have not used services once it has been delivered there is no way for customer or provider to use that exact same service again. Another characteristic of services is inseparability, which means that services are delivered and consumed on the point of sale. Both service provider and customer have to be present at the same time because there is no physical good that can be produced and stored for later for the consumer [11]. Services are heterogeneous and variable, which is notable in the fact that once delivered, service cannot be repeated, as the circumstances are different, even if the consumer asks for the same service from the same provider. Some of the examples of services are cleaning, banking, shopping, hair styling, education, medical care, or transportation. In order to deliver services, it is important to observe six factors: • The service provider (workers and managers)

308

D. Saric and M. Mikolasik

• Equipment used to provide the service (e.g., cash registers, vehicles, computer and technology systems) • Customer contact • Service consumer • Physical facilities (e.g., buildings, parking, waiting rooms) • Other customers at the service delivery location activities. The focus of this work will mostly be at the equipment used to provide the services because this factor makes the most difference between traditional and electronic delivery of services.

2.2 Internal Communication Tools Traditional services are the services delivered to the customer without the help of information and communication technologies or with little interference of technology, and they are characterized as low tech and high face-to-face contact [6]. In traditional services, most of the encounters were face to face in an actual service setting without much of interference of technology. Most of the time the only way technology was included in service delivery was if the service was delivered over the phone, which is today already considered as the old-fashioned way. Each encounter is an opportunity for a firm to sell itself, to reinforce its offerings, and to satisfy the customer, which leads to stronger consumer-based and loyal customers, which are the biggest advantage one firm can develop. High-touch service means that the customer is highly involved in the sales process, and it has to be guided via human involvement. This includes negotiations, meetings, and the presence of the customer. Even though in traditional services technology was present, as for example, telecommunications, most of the services were delivered with the presence of the customer and with their involvement [12, 13]. With the rapid development of the Internet, this has changed, and now we have the opportunity to get service delivered without our presence, even at the time and place consumer chooses. Sometimes services are simple one time encounter with the firm, as buying groceries at the supermarket, and sometimes the services are made from a sequence of encounters in order to satisfy consumer needs [6]. In most of the industries, for example, healthcare, retail, travel, education, and in business to business services such as consulting, maintenance, administrative work, consumer services have been delivered by human contact, but even in these industries, technology is now taking over, and delivery of services is changing [8].

The Impact of Electronic Services on Traditional Services

309

2.3 Service Quality In most services, quality occurs during service delivery, usually in an interaction between the customer and contact personnel of the service firm [14].

Service quality is one of the main points that differentiate a firm from competitors, and it is very important to understand what service quality is for customers and what they hold as valuable [15]. There are many definitions and dimensions of service quality. Seven service attributes are used to define service quality—security, consistency, attitude, completeness, condition, availability, and training of service providers [16]. Lehtinen and Lehtinen (1982) defines service quality dimensions as physical quality, interactive quality, and corporate quality. Corporate quality can be seen as the image that the service provider portrays, communication between the service provider and the consumer is interactive quality, and the tangible appearance of the service is considered as physical quality. One of the advanced models to define service quality is SERVQUAL, which defines tangibles, reliability, responsiveness, assurance, and empathy as the five most important dimensions [14]. Leading companies are focused on improving service quality, and they use it to differentiate themselves, to increase productivity, to earn customers’ loyalty, or to fan positive word-of-mouth advertising. Competitors can seek to increase their performance with the help of quality in two ways described below [17]. In the short run, they can increase profit by offering premium prices. Frank Perdue, the well-known chicken grower, said: “Customers will go out their way to buy a superior product and you can charge them a toll for the trip.” [17, p. 9]. The truth of these words we can see in the case of PIMS businesses, which ranked the top third on relative quality, and sold their product or services, on average, at a price 5–6% higher relative to their competitors also stated by Zeithaml et al. Quality and excellence pay off because it creates customers that are loyal and will use services again. Stew Leonard, a food store retailer, said: “We should never let a customer leave the store unhappy because we look at each customer as a potential $50,000 asset. An average customer spends $100 a week on food shopping. That is more than $5000 a year, and more than $50,000 over ten years. Customer service is big business if you look at the long run picture.” [17, p. 10]. Service quality is much harder to evaluate than goods quality. Customer does not only value the outcome of the service but it puts a great deal of importance on the way service is delivered, the process of service delivery, are the workers friendly, responsive, helpful etc. and therefore service quality can be defined as “the extent of discrepancy between customers’ expectations or desires and their perceptions” [17]. Customers’ expectations are being shaped under the influence of several factors defined by Zeithaml et al. (1990). Word-of-mouth advertising is one of them because customers shape their expectations under the influence of what they have heard about a particular service provider from their friends, family, neighbors, etc. Past experiences are also important, as well as the personal needs of the customer, his

310

D. Saric and M. Mikolasik

individual characteristics, and circumstances that he is experiencing at the moment of need. Many firms lay a great deal of meaning to ways of external communications, advertising their services, which is important in order to make customers aware of their existence and the services they offer. This also forms expectations by customers because they require service quality as it has been advertised [18]. What customers find important is responsiveness—the willingness to help the customer and provide prompt service, competence—is the provider competent and has skills to provide service, reliability—possibility to perform service accurately and dependably, credibility, security, approachability and ease of contact, communication, and understanding of the customer [14]. Most of these dimensions can be improved with the help of technology, the fact which has led to such a rapid and significant growth of usage of technology in every day’s life. For the retailer, this brings the greater possibility to make a profit and increase its revenue. In Germany, we have witnessed a steady growth of revenue of the IT services industry, and it is predicted that it will continue to grow in next few years (Fig. 1).

Fig. 1 Projected revenue of IT services in Germany

The Impact of Electronic Services on Traditional Services

311

3 Electronic Services 3.1 Definition of Electronic Services E-services can be defined as “deeds, effort or performances, whose delivery is mediated by information technology, including the web, information kiosk, and mobile devices. Such e-service includes the service element of e-tailing, customer support and service, and service delivery” [3, p. 341]. They are interactive software-based information systems, and information and communication technology is used for their delivery, which opens the possibility to increase customer satisfaction, increase efficiency and improve customer-provider relations [19]. “Providing an alternative delivery channel, here electronic, can generate competitive advantage which can be seen as an increase of customer base and improvement of customer loyalty to the firm” [20, p. 3]. Other studies have shown that retailers are adding an online channel to traditional one risk possible cannibalization in sales of one channel which will be discussed later in this work [21]. Even now, but especially at the beginning, it was very hard for consumers to shift towards e-Services, primarily because they had no trust in delivering services this way. The perceived risk they experience has an impact on the acceptance of e-Services, and some claim it is dependent on the importance of the situation [8]. However, now more and more people are shifting toward using technology, and we can see a significant increase in total worldwide Internet users from the year 2005 to the year 2016 [22]. Access to the Internet enables collecting and exchanging more information about the content of consumer’s interest, and it gives them access to content that might not be available in brick-and-mortar stores [23]. This gives an opportunity to the service providers to gain more customers and increase their revenue. On the other hand, other authors argue that digital networks can have a negative impact on media companies economically because the copies of media content can be obtained illegally for free [24]. For example, in the United States, in 2005 only, Hollywood studios lost $1.3 billion due to consumers’ illegally obtaining media copies and Internet piracy [25].

3.2 Service Quality Same as electronic services can be distinguished from services, e-service quality can be distinguished from traditional service quality. As already mentioned, e-services have developed because of the need to have better delivery of services and because of the need to differentiate from the competitors. Because of the difference between the two, measuring the quality is also different. E-Services are different from traditional services when it comes to cost structure, the rapid development of new services, the availability of transparent service feedback, the degree of outsourcing, and the improvement of services [8].

312

D. Saric and M. Mikolasik

When it comes to electronic services, they are provided through web portals and electronic channels, which is the reason why providers focus on developing userfriendly websites that will attract customers. A successful website should be easy to use, have useful information, be attractive, provide a search button on the web page itself, display the site and provide links to other sites that can be useful [26]. There are many more things that electronic service providers should be aware of as for example, ease of navigation, security, product delivery, personalization, and communication over the website [26]. These are all the factors that have an impact on the user’s perception of the firm and service quality. As there is a SERVQUAL model for traditional services in 2002, Zeithaml et al. developed an e-SERVQUAL model for measuring electronic service quality. The dimensions that it holds as valuable and important are divided into two parts. First, there is a core e-service quality scale with four dimensions: efficiency, reliability, fulfillment, and privacy and recovery e-service quality scale with dimensions such as responsiveness, compensation, and contact [17]. The user’s major positive themes in the online environment are flexibility, convenience, enjoyment, efficiency, speed, responsiveness, etc. The reason why online service has become so important for the customers is the fact that is much easier for them to find the product or the service that they need, compare the prices, compare the quality and technical features as well as rate the products [6]. Technology has enabled to increase satisfaction for customers but also for employees, and Bitner et al. [6] defined this with the help of a model created by Bitner et al. [27], and they claim that “technology can be used by contact employees to improve the efficiency and effectiveness of service encounters by enabling customization, improving service recovery and spontaneously delighting customers and it can be used independently by customers to improve efficiency and effectiveness of their own service encounter experience also by enabling customization, improving service recovery and providing spontaneous delight.”

3.3 The Long Tail The Long Tail is a term presented by Chris Anderson, and he argued that “products low in sale volume or with low demand can collectively make up a market share that can even exceed the relatively few current bestsellers but only if the distribution channel is large enough” [28 p. 63] (Fig. 2). As retail stores are physically restricted and can display only a particular number of products, they opt for displaying bestsellers as they want to maximize revenue [29]. They are also restricted in the space they can cover as they can serve only a smaller area for a typical movie theater and even less than that for music and bookstores [28]. The Pareto principle, also known as the 80/20 Rule, states that around 20% of products in the market often generate a large proportion of sales, e.g., 80%. Therefore, the rule is there to help managers decide which factors or products are most important to generate the most revenue and receive the most attention. However, in the digital

The Impact of Electronic Services on Traditional Services

313

Fig. 2 The long tail title distribution [28]

content business, this rule does not apply, and Anderson states that now with niche products, we can talk about a 98% rule. This rule predicts that 98% of the items for sale in large commerce sites like Amazon or Netflix will be sold each quarter, at least a handful of times [28]. The 98% rule almost turned out to be universal in digital industries; Netflix estimated that 98% of its DVDs rented at least once a quarter, Apple said that every one of their iTunes tracks sold at least once, and independent academic research suggested that 98% of Amazon’s top 100,000 books sold at least once a quarter, too [28]. The Long Tail phenomena were notable even before Anderson defined it, as Frank Urbanowski, Director of MIT Press, stated that in 1999 the accessibility to niche products or backlist titles through Internet led to a 12% increase in sales of these titles, even if the increase in overall book sales was flat [30]. Two main explanations can be derived in order to define The Long Tail [31]. Firstly, we can examine the supply side. Internet supply channels can carry and deliver much more products than the traditional retailer. The niche products in online stores represent just bits in the database, and the costs of storing them are minimal if not nothing [28]. Physical stores are limited with self-space and the costs of storing the items are much larger than on the Internet. It is estimated that a CD copy needs to sell at least four copies in order to be worth displaying- these are the cost of half an inch of self-space [28]. The online bookstores, for example, allow retailers to offer over 2 million book titles, whereas traditional books store can carry between 40,000 and 100,000 titles, which are incomparably lower [31]. Next, we can observe the demand side, as the second important factor defined by Anderson and extended by Brynjolfsson et al. [32]. The increased demand for

314

D. Saric and M. Mikolasik

niche products can be seen as the consequence of increased convenience and lower cost. IT technology enables customers to search through titles and products much faster using discovery tools and recommendation systems, as opposed to the retail store where many shoppers do not search deeply because of the inconvenience of searching for a product in a store with thousands of items. There are three forces behind the Long Tail [28]. First, there is democratizing the tools of production, which is represented through possibility, with the help of technology, to do everything professionals can do. Now, everyone with access to the Internet and technology can make content and upload it online. This leads to the growth of content, and this increase in available goods extends Tail to the right. The second force is cutting costs of consumption by democratizing distribution. The Internet has lowered costs of reaching customers, even for physical goods, and it is much cheaper to reach more people. The third and last force is connecting supply and demand by connecting consumers with newly available goods and increasing the demand down the Tail. The use of technology enabled to lower “search costs” of finding niche content. Search costs are seen as all costs that occur for the consumer while trying to find what he wants [28]. This includes both monetary costs, for example, paying the higher price for a product because there was no possibility to find a cheaper one, and non-monetary costs as wasted time, confusion, and wrong turns. All this combined resulted in increasing demand for niches and flattening the curve, shifting its center to the right. In conclusion, it can be derived that The Long Tail is one of the important concepts in understanding the changes that have occurred and the difference between the delivery of electronic and delivery of traditional services. It is also important because it contributes to the understanding of the economic aspect of these changes, and it helps to evaluate the consequences that we have witnessed in the economy.

4 Examples in the Industries 4.1 Traditional Banking Service Versus E-Banking There are some fundamental differences between traditional and e-Banking. In this part, it will be discussed how traditional and e-banking services are delivered and their advantages and disadvantages. Banking services are going through major changes, and the main force behind all these changes is technology, which enables creating new products, delivering services faster and more convenient for the user [33]. With the expansion of the Internet and computer usage, electronic banking has become an ideal way for banks to meet customers’ expectations. Online banking can be seen as Internet portal which enables fulfilling different banking actions e.g., transaction, payments, etc. [34]. In ebanking, as in many kind of e-services, ease of use, delivery speed, control, reliability, security, costs, etc. have the crucial role [26]. Financial institutions that offer services

The Impact of Electronic Services on Traditional Services

315

online are perceived as leaders in technology implementation by the customers, and they provide better response to the market, which leads to enjoying better brand image which institutions build [35]. The main positive traits of internet banking are cost savings and improved operational efficiency, additional revenues from transaction and user fees for online bill payment, e-commerce portal offerings, and cash management [36]. It also offers opportunities to acquire new customers as well as offer better services to the old ones. The costs are lower than in the traditional banking service and include the costs of outsourced e-banking, the cost of one-time implementation or set-up fees, and ongoing monthly fees, which are calculated based on the total asset size, the total number of customers, and a number of e-banking users [36]. Usually, these fees are paid by customers. The costs that financial institutions can expect when setting electronic banking include the purchase of hardware and software, website development, quality assurance testing, user interface development, Web hosting services and ongoing operation expenses [36]. An estimated cost of providing the routine business of a full-service branch in the USA was $1.07 per transaction, in comparison to 54 cents for telephone banking, 27 cents for ATM banking, and only 15 cents for internet banking [33]. The share of individuals using e-banking is increasing every year, and expectedly it is higher among the individuals that have used the Internet in the last three months [37] (Fig. 3).

Fig. 3 Online banking penetration in EU [37]

316

D. Saric and M. Mikolasik

Some of the additional features that internet banking offers and that attract the attention of the user are the provision of interactive loan calculators, exchange rate converters, mortgage calculators on the websites, etc. One study on consumer acceptance of Internet banking obtained results that the perceived ease of use, security and privacy, the amount of information available on the platform, perceived enjoyment, and perceived usefulness are the main factors consumers find important [34]. Perceived usefulness and value includes transaction speed, user-friendliness, accuracy, security, user experience, user involvement, and convenience [38]. This kind of service delivery saves a lot of delivery time. It has been shown that user would rather opt for self-service in order to save time than getting served by professionals if it meant slower delivery time [39]. With e-banking, delivery time is brought to a minimum because it does not require a user to travel to the bank, wait in the line, and spent time being served by the worker, and it does not require the physical presence of the consumer. For people who often travel abroad, it enables better control of their finances and gives them a possibility to manage their money transfer and people who find it more convenient to manage their accounts from home, as it is available 24/7 [40]. Research conducted by Marenzi et al. has shown that online customers are more likely to become captive users of multiple services as a result of the “stickiness value” of the institution’s e-banking website. Customers using Internet banking, especially electronic bill payment, are more likely to retain their bank customers [36]. When it comes to cost, it is cost-saving both for the bank and for the consumers [6]. For banks, it means eliminating costs of staff, facilities and administration and decline in marketing costs and for consumers, it eliminates traveling cost, and some possible bank charges that may occur for certain transactions or when user pay bills electronically—directly from their account to the merchant and it also helps to save money on postal charges [41]. It offers institutions the opportunity to conduct surveys among customers that can measure customers’ satisfaction and bank’s performance and respond quickly and effectively to consumers’ needs [26]. The main disadvantages are the lack of personal contact because the user does everything online using electronic channels and security issues [40]. Also, what can be viewed as a problem is the socioeconomically disadvantaged consumer would less likely be ready to pay a monthly fee to subscribe to internet service and would be less likely to have a home computer [42]. The authentication and capsulation of private data are critical, and this is the main risk that users bear when they opt for internet banking [38]. The adoption of e-banking may be affected by security and privacy issues, and for this way of service delivery to be successful, banks have to provide privacy methods, such as unique identifiers, for instance, a password, important date, name, or a few minutes of inactivity automatically logs the user out. When it comes to cost, Internet banking services are typically profitable for banks, and the main reason for this is increased customer retention, improved cross-selling opportunities, reduced transaction costs [36]. Traditional banking is based on human contact and the physical presence of customers by providing services [38]. In the conventional banking system, a customer

The Impact of Electronic Services on Traditional Services

317

can open any bank account in any bank and manage his money transactions, and as one of the primary traditional services encounters traits, he can meet with the bank manager and have one-on-one, face-to-face consulting. The main advantage of this way of banking, as in all traditional services, is face-to-face contact and the possibility to talk with your banker and have a better understanding and insight in managing your money. As already mentioned, there are many security problems that occur when it comes to electronic banking that can be avoided in traditional banking, which can be seen as one of the main advantages of the traditional way of delivery of banking services. It can be unsafe for the customer to share confidential information online, especially over public and open networks. When it comes to traditional services, this is not a problem, and it is safer for the user [43]. When it comes to the disadvantages of traditional banking- it is time-consuming because it requires the user to be physically present at every service, which means that user has to come to the bank in order to fulfill his task. The consumer has also to pay attention to the working hours of the institution and is unable to manage something when banks are closed. It is also cost consuming for the consumer if we consider the costs of traveling to the bank, as well as for banks because in order to provide good service quality, they have to be able to provide quick and effective service and be always available. All of this requires high operating and fixed costs, and the quality of service can be affected because the employees and clerical staff of the bank can attend only a few customers at the same time. There is also a problem for users abroad that are unable to pay attention and manage their finances. Even though all of these disadvantages are present, there are still users that highly value physical contact and consulting face-to-face with professionals and that do not have trust in electronic service providers.

4.2 Traditional Shopping Service Versus Online Shopping The shopping industry is one of the sectors that have experienced the most of the changes that have occurred with technology development. Traditional shopping can be replaced by online shopping to the extent where it is possible to order groceries online and have them delivered to your house at the time you select [6]. There are some basic types of traditional retail stores like department store which offers a broad type of goods in one place, and customers can get almost every product they aspire. Discount stores also offer a wide range of products but at a discount rate and the quality can be a little inferior in comparison to the department store. Supermarkets are the type of retail store which sells food products and household items. It offers a wider range of products than the small grocery stores. Malls are many retail stores operating in one place, and it consists of several retail outlets, each selling its own merchandise. With traditional shopping, the customer has no or minimal contact with technology, and in some ways, this can be very limiting for the buyer. The customer can

318

D. Saric and M. Mikolasik

only buy products that are on display in the store and that are available to him at that particular time in that particular store, which is one of the main problems of traditional shopping. Usually, if you are looking for a particular item, you will find only limited choices of it. Having a limitation of shelf space, brick and mortar shops simply cannot afford to provide large varieties of products, and for them, it makes economic sense to fill the limited space on their shelves with items that sell most [28]. These products may not necessarily be the best, but probably the ones that are marketed as the best and the most popular [29]. The other problem related to this matter is if the retailer increases the number of products carried in the store the costs of carrying are increasing due to increased shipping costs, inventory costs, and shelf space costs [44]. The customer does not have all the information about the other products and possible replacements because, for example, there are not in the store. When it comes to costs, there is a possibility that the product customer needs costs less at some other store, but the buyer has no choice but to buy it at a higher price in this store. It also includes costs of traveling to the store, and it is not available at any time because the customer has to find free time and physically go to the shop. This is a particularly important problem when it comes to the people that live in small cities or rural areas, and there is a great chance that there is no store that they need and therefore no product and the choice and the variety of product are highly dependent on the place where the buyer lives [19]. As there are disadvantages there are also many advantages. One of them is a possibility to see the product, touch and feel it and see the quality of the productcustomer can use all his senses [3]. Even though one of the disadvantages is the unavailability of products anytime, anywhere, still if a customer needs the product right away he can go and buy it and does not have to wait until the product is delivered. As in most of the traditional services, this way of delivering services requires social contact and face-to-face communication between people. Also, the buyer has the opportunity to ask sellers questions about the product and get the better understanding about it. Returning the product can also be easier it only requires a customer to go shopping as opposed to online shopping where he has to contact seller, go to the post office and sent the product back, and then wait for the money return [45]. Online shopping, on the other hand, allows customers to purchase items or services online, directly from a seller using a web browser. This enables purchase without the need to physically go to the store and wait in the line. Customers have to use some of the methods of payment that are accepted, like PayPal, credit card or debit card. When the transaction is completed, if it is a physical product e-retailer ships the product to the buyer, if it is about a digital product, the retailer sends it over the Internet. Three main factors that drive customers to online purchasing based on Macquarie Bank [46] are: • Price—Price is very transparent in an online world, and with the use of the internet customers are able to quickly and easily compare prices. Some websites

The Impact of Electronic Services on Traditional Services

319

as Shopbot or Static Ice collect prices from different retailers and display them from the lowest to the highest depending on customers’ wishes. • The convenience of the shopping experience—consumers find this type of shopping convenient because they are able to do all of their research and order goods and get them delivered to their house without the need for them to leave their home. • The range of products available online—the product offered online is much larger as one in the traditional stores because online retailers are not constricted by physical space, and they can display all products they have. Plus, there are certain products that are unavailable offline as some media and entertainment content. The process of ordering and buying online is relatively simple. First, the customer finds a product of interest directly on the retailer’s site or by using shopping search engines. Most of the retailers use shopping cart software that enables users to store their products and adjust quantities before checkout. In the checkout process, payment and delivery information is collected. Often buyer receives an email confirming their purchase and the process is finished [6]. Many of the online stores offer the possibility to create a permanent online account so that customers do not have to enter its information by every purchase. Online shopping offers a broader selection and more information about the product, which is very attractive to the customers. It is required from the seller to write the description of the product and in some cases even a how-to-use guide. Buyers have a better understanding of the product or service, and they have a greater variety of products to choose from and select the best one, the cheapest one, etc. It is possible to personalize and customize the shopping experience, which is very attractive to the users. Many sites include a review section where users can rate the item or the service and write their own experience [6]. What users find important when it comes to websites is the ease of use and userfriendliness, and in order to create an advantage, an online retailer has to be able to provide the good speed of the website, easy to use and understandable site, and it has to provide good security measures [8]. Online stores are available 24 h a day, which makes them very convenient for the user if we take into account that the average user has internet access a whole day [6]. It does not require the buyer to travel to the shop, pay attention to opening hours, and spend their time shopping. Although the costs of the product are typically lower because of the broader offer, the shipping cost can be very high. Usually, they are not listed up until the last step of the purchase, which can be an issue for the buyer because he does not know the full price of the product until the checkout. When it comes to the costs for the retailer setting up an online business is much cheaper than the traditional store. It excludes the costs for the rent; there are no other utility costs related to the store, and it is often required to hire much less working force as it would normally be. The costs that are related to the online business include the cost of the web hosting and technical support, the retailer is required to pay for its domain name usage and may pay someone to develop their website and e-commerce platform, which can be expensive but it still cheaper than opening a real store.

320

D. Saric and M. Mikolasik

E-commerce estimated share in the total value of United States manufacturing shipments was around 18% in the year 2000, and in the year 2014, it grew to 60.9% (Bureau). Based on statistics done in Canada, cross-border shopping reduces money circulating in local economy by up to 32% and the higher the volume of online purchasing a consumer does, the more likely is that they purchase with chains versus local business [47]. According to the Commerce Department data, e-commerce doubled its share of the retail pie from the end of 2009 to the middle of 2016, and while overall sales have risen a cumulative 31%, department store sales have plunged 17%. On top of that, Howard Davidowitz, the chairman of Davidowitz & Associates Inc., claims that half the 1100 US regional malls will close over the next decade [48]. As shown in the graph Fig. 4, e-commerce sales in the United States have been growing and are expected to grow in the next years [49]. In the economic view, e-commerce is expected to have an impact on traditional store business. In combination with other factors such as stagnating retail sales, modesty growing space and an increase in fix costs, traditional retailers are forced to invest and increase their cost in order to compete with an online business. All of this combined is making retailers aware that their margins are likely to decrease in the future. The most affected group is and will be small local businesses as they already see consequences. The most affected stores are toys, electronics and books, and clothing. The customers are more opposed to buying groceries and luxury goods online, but still,

Fig. 4 Retail e-commerce sales in the US from 2015 to 2021

The Impact of Electronic Services on Traditional Services

321

Fig. 5 Retail revenue [50]

even in these industries, there have been changes in the way customers shop. In order to preserve their position, traditional stores are now focusing more on branding and creating a special character among the mix of the store. They believe that customers will feel that they are in a special place with the best products and that they are going to experience something they cannot get online. One study has shown that turnover and profit margin of the retailers has significantly decreased in the past few years and therefore retail stores are now more engaged in services related to customer satisfaction and lately they have started to deliver services of their products at the doorstep of their customers. In the graph on Fig. 5 we can see that from 2010 to 2015 Amazon’s revenue, one of the biggest e-commerce representatives, grow almost 200%, while Walmart, one of the biggest traditional store representatives, revenue grow only around 25% [50].

4.2.1

Amazon.com Inc

When we talk about e-commerce, one of the main representatives is Amazon, which is a great example for discussion because it offers many e-services, and up until now, it is one of the e-retailers which had the largest impact on traditional businesses. Amazon is the largest Internet-based retailer based on total sales and market capitalization. It was founded in 1994, and it started as an online bookstore that later started to sell DVDs, CDs, electronics, clothes, food, toys, music, etc. It also produces consumer electronics—Kindle, e-readers, Fire tablets, Fire TV, and Echo.

322

D. Saric and M. Mikolasik

They focus on providing high value using technology in order to offer low-cost solutions to the customers. Offering “lowest possible prices”, sometimes free shipping, selling e-reader Kindle at the lowest price among competitors have led to Amazon being the cost leader in the e-commerce branch and customers choosing this way of purchase. Amazon had a strategy that included steady growth and focusing on longterm success, and this had, for a consequence, lower profit in the first five years. The real growth it had experienced in the year 2002 and later when it started to dominate the market. Amazon is one of the most prominent representatives of online shopping platforms. It offers customers added value, which is represented through the possibility to search for products online, compare product prices, choose the best one and have it delivered to the customer’s home address. It also offers the possibility of Amazon Prime, where customers pay the membership fee and they enjoy the faster delivery time, no delivery cost and the possibility to stream and watch movies and music for free. The revenue of Amazon is steadily growing as shown in the graphic Fig. 6 [51]. In addition to offering its own products, Amazon derives around 40% of its sales from third-party sellers who sell their products over this platform [52]. Amazon was and still is focusing on selling books, especially e-books. The first time e-books outsold the printed books was in 2011 when Amazon sold 105 copies of e-books for every 100 copies of printed books, including books without Kindle version and excluding free e-books [53]. At this point, Amazon has been selling print

Fig. 6 Net sales revenues of Amazon [51]

The Impact of Electronic Services on Traditional Services

323

books for more than 15 years and e-books for only four years. In 2011 when this trend started, the e-book sales at the beginning of the year were $69 million, which is an increase of 146% from the year before, and sales of adult hardcover books grew 6%, while paperback sales decreased nearly 8% [53]. Still, Amazon argues that e-books will not cannibalize sales of printed versions but rather represent incremental sales. Some consumers have strong preferences when it comes to the distribution channel and between physical and digital products. Jeff Bezos, CEO of Amazon, commented on the matter: “When people buy Kindle, they actually continue to buy the same number of physical books going forward as they did before they owned a Kindle. And then incrementally they buy about 1.6–1.7 electronic books, Kindle books, for every physical book that they buy” [54]. But still, authors worry that sales of the printed versions will suffer because of the electronic channels that provide e-versions of the books, and they argue that book buyers do not buy format but the content [55]. A study was done by Corintas et al. [56] suggest that possibility to offer delivery through the online channel can actually lead to increase in profitability rather than cannibalization and complement traditional channel by enabling firms to retain some of the customers of the items withdrawn from traditional stores. Retailers should offer only popular products in stores and less popular online, which leads to an increase in customers welfare [23]. The bookstore sales in the United States are steadily decreasing from the year 2007, and it is even more significant when we take into account that book sales were increasing 15 years prior to 2007. (Bureau) Barnes & Noble, one of the biggest U.S. brick and mortar bookstores, has been experiencing a significant revenue decrease from 2012 when the revenue was $5.39 billion, and in 2016 it was reported that revenue decreased to $4.16 billion [57]. This decreasing trend is estimated to continue [58]. The study conducted by Nielsen Scarborough illustrated the number of people who shopped at Barnes & Noble from spring 2008 to spring 2016 and the results showed the decrease in the customer number from 67.88 million as of 2008 to 52.57 million in spring 2016. (Scarborough) All this resulted in closing down many bookstores which can be seen in a graph Fig. 7. (IBISWorld) The forecast for 2018 also shows that this trend is estimated to continue. In order to minimize the effect on sales, some publishers delay releasing the ebooks couple of weeks after the printed book is published. Some studies have shown that this has no impact on sales of hardcover books but it just minimizes the sales of e-books once they are released [55]. One of the reasons why e-books may not affect sales of hardcover books is the fact that there are customers that highly value the experience of reading a book and having a real copy in their collection and the problem that may occur with this strategy of delaying e-book release or not releasing them at all is a possibility that customers that wanted to read the book, but did not want to buy the hardcover copy, will be denied possibility to read it [55].

324

D. Saric and M. Mikolasik

Fig. 7 Number of bookstores in the US [59]

If we observe the United States, as one of the biggest markets, we can see that overall the printed books had higher sales than electronic books, but as shown in the graphic Fig. 8, it is expected that in 2017 the e-books will surpass hardcover ones. In 2012 hardcover book sales were $11.9 billion and were expected to fall down to $7.9 billion in 2017, and eBooks excluding education publications were expected to reach $8.2 billion [60].

4.2.2

Wal-Mart Stores, Inc

In order to obtain a better understanding of the difference between e-commerce and traditional commerce, the comparison between Amazon and Walmart will be made, as Walmart is one of the biggest traditional retailers. Walmart is an American retail chain that operates as hypermarkets, grocery stores, and discount department stores. As of January 31, 2017, Walmart had a total of 11,695 units, 4672 in the United States and 6363 units internationally [61]. Although Walmart does offer a possibility of online shopping, it is still not even close to competing with its retail stores, considering the fact that only 3% of its annual revenue comes from online shopping [61]. And even with this possibility, Walmart encourages its customers when ordering online to collect their orders at physical stores, at service areas designed for this purpose. This can lower the convenience of online shopping because, as already mentioned, one of the biggest reasons why

The Impact of Electronic Services on Traditional Services

325

Fig. 8 eBook sales against printed books sales [60]

consumers choose online shopping is the home delivery of the products and no need for them to go to the retail store [41]. Walmart is encouraging Omni-channel model, which is defined as “the synergic management of the numerous available channels and customer touchpoints, in such a way that the customer experience across channels and the performance over channels are optimized” [62, p. 176]. Many customers use both physical and online channels when making a purchasing decision, and they combine the information they obtain using one channel and make a purchase using another channel [62]. Some authors claim that this strategy is the winning one for Walmart to beat the competitors and to differentiate itself because although online purchasing is very important in today’s society, brick-and-mortar retail still counts for over 90% of retail revenue [61]. On the other hand, some claim that one of the reasons Amazon may have the advantage over Walmart is the use of the algorithmic approach to present particular products to its customers and the usage of real-time data to fuel dynamic pricing [63]. Walmart has yet to develop a strong online platform that can collect a large volume of quality consumer data that can be used in decision making. Studies have shown that customers highly value the price of the product, and in most cases, they will choose the cheaper product, not taking into consideration the delivery channel [64]. According to William Blair & Co.’s study, there is a significant difference between Amazon and Walmart prices. Amazon processes are estimated to be roughly 9% lower than Walmart’s when sales taxes are excluded for Amazon and shipping is calculated for both. This represents one of the major reasons why online shopping has gained importance in the last years. In order to gain a competitive

326

D. Saric and M. Mikolasik

Fig. 9 Walmart’s net sales [66]

advantage over Amazon, as of February 2017, Walmart offers free two-day shipping on more than 2 million items without a membership program [65]. One of the main problems of physical retail stores is space restriction. (Anderson 2006) Walmart is unable to display all of the products that can be of customer’s interest, so, like many retailers, it opts for the most popular and looking for at the time. It is hard to satisfy all customers because of this restriction, but online retail offers the possibility of surpassing this obstacle, as it offers “endless” storage space. Walmart’s revenue has been steadily growing for the last decade, but the first time from 2006, it has decreased from the year 2015 to the year 2016 [66] (Fig. 9). It cannot be claimed that this decrease is because of the impact of electronic services, but it is important to take into consideration that Walmart has opened 74 new stores worldwide from the year 2015 to the year 2016, but still, the sales decreased [63]. At the same time, as already analyzed, Amazon’s revenue has been steadily growing, and it is estimated to continue increasing in the future [67]. The comparison matrix below shows some selected company metrics in order to provide a better understanding of the differences between the two (Table 1). When we observe all the facts and statistic that is obtained it is clear that online providers are taking the very important position in the retail industry and their market share is increasing rapidly over the time. On the other hand, traditional retailers are experiencing low revenue increase or in some cases even decrease. As shown above, many brick-and-mortar stores are closing down but it cannot be claimed that this is caused by this major development of electronic delivery channels and online retailers.

The Impact of Electronic Services on Traditional Services Table 1 Amazon and Walmart comparison matrix

327 Amazon

Walmart

Employees

341,400

2.3 million

Market cap

$433 billion

$228 billion

Revenue

$135 billion

$485 billion

Share price

$909.29

$75.43

Net income

$2.37 billion

13.64 billion

Income per employee

$6945

$5.932

2017 sales growth (%)

27.1

0.6

4.3 Netflix Another example when it comes to online services is Netflix. It operates in the entertainment industry and it provides customers with media streaming services and video-on-demand services. Netflix was founded at 1999, and at the time it offered DVD rental and selling, but slowly it shifted to the media content streaming, which is now one of its most important services. In the beginning, Netflix’s strategy was to create an efficient system for distributing physical DVDs through the mail, but its long-term strategy was to provide consumers video content over the Internet and PCs and to enable them to download videos directly to TV sets [68]. With Watch Instantly feature, Netflix enabled their costumes to play selected movies and shows on their computer immediately and with time their online streaming feature gained on the importance and it is now their major source of revenue, serving more than 93 million subscribers in over 190 countries and providing more than 125 million of streaming hours per day [69]. With this major success and growth, Netflix started to produce its own shows as “House of Cards” and “Orange is New Black”, which are now also important sources of revenue [69]. As Anderson (2006) argued, besides popular programming content from major media companies, niche content plays a significant role in the success of digital content providers. Netflix gives an opportunity to its users to watch thousands of titles for the price of $7.99 per month, including a one-month free trial [69]. The providing of added value, which is visible in more convenient delivery, lower prices and better quality of content, led to Netflix becoming the leader in the industry and gaining a competitive advantage over its competition. The revenue growth in the last decade showed in the graphic Fig. 10 is one of the indicators of Netflix’s expansion [70]. Even though the number of consumers renting DVDs by mail service is decreasing over time and in the last quarter of 2016, it lost 159,000 subscribers, it is still making a profit with 4.1 million people that are renting DVDs per mail [69]. At the beginning of the 2000s when Netflix and other digital media channels started to gain on importance, DVDs and other media sales went down 46% ($14.6 billion) in 2002, which led to the concern regarding the future of the brick-and-mortar stores in this industry [71]. The concerns proved to be reasonable as the number of

328

D. Saric and M. Mikolasik

Fig. 10 Netflix’s annual revenue [70]

video rental outlets in Europe decreased more than double from 2008 to 2012—in 2008 the number of video rental outlets was 23,608 and in 2012 it was 10,870, which represents a large and worrisome decrease (IVF). All this can contribute to the theory that the electronic way of delivery cannibalized the traditional sales, as the video and music rental outlets are closing down and the sales are steadily decreasing, as shown in the research and statistic cited above. The entertainment industry has lived through many changes, and the structure of the industry and economy has changed, and service providers are either adapting to this change or closing down their businesses.

4.4 Uber Uber is a tech company that provides a service similar to a taxi. In Uber, you can sign in as a driver if you have the conditions that uber requires, or you can ask for a private car that is giving you a ride and taking you where you want to go. Uber has a mobile app that has access to your location, has your credit car number, and if you need a ride, it will tell you how much is it and the route you are taking. The Uber service (and other companies like Taxify for example) is very similar to the services that regular taxis provide. However, uber took advantage of the new information systems and created a simple way for the common citizen that has a

The Impact of Electronic Services on Traditional Services

329

smartphone to move around the city he is in. A Taxi is a standard service that you find almost everywhere where you have a center that you can call to ask for a taxi that is picking you up in the place you are. Or, in the alternative, you can go to a taxi station where the taxis will take you where you want to go. The advantages that Uber has over normal taxi service are many: first of all, uber is very intuitive, it has access to your location, so you only need to tell them where do you want to go and if you want a particular type of car (more or less luxury, with more or less sits). All of the above mentioned are available in the app where you only have to download to your smartphone, create an account and associate your credit card number. It is more comfortable and faster than having to search for the taxi central phone number, waiting for them to answer the phone, and asking for the taxi for a location that sometimes might not be easy to explain for the taxi driver if you don’t know the place you are. With Uber, the driver knows exactly your location, so if you have your smartphone with you, it is not likely to fail to find you, and the app searches immediately for an available driver that will start going for your location right away. Also, if you are a foreigner or you are simply in a city that you don’t know very well, you can be tricked by a normal taxi driver. In Uber, you have access to the way the driver is taking you to reach your destination. Still talking about a foreign client, you don’t need to speak the language of the country you are at the moment to call a taxi. With Uber, you don’t need to talk on the phone, you have the app in English and you do everything to the online platform, from asking for the car to paying. Another factor and not less important is that Uber is less expensive than taxis in most of the countries. Uber is already available in 65 countries, in more than 600 cities, has more than 3 million drivers and approximately 75 million users around the world [72]. Maybe the biggest disadvantage in uber is that they are missing older potential customers that are the older people that don’t use smartphones, but in a few years, everyone will know how to use a smartphone. Also, if you are in a city where Uber doesn’t have many drivers, you might have to wait longer than in a big city. Besides this, uber is progressively gaining market share against taxis mainly because of the competitive advantage they got by using new technological platforms. However, Uber’s revenue growth slowed from Q1, when its top line expanded 70% YoY [73] (Fig. 11).

5 Conclusion The main purpose of this review was to show the difference between traditional and electronic services, the changes that occurred in service delivery and in the economy, and the changes in customer behavior and their expectations. This thesis is significant because the technology keeps developing, and it enables new ways of

330

D. Saric and M. Mikolasik

Fig. 11 Uber’s global quarterly gross bookings [73]

providing services, which cause concern when it comes to cannibalizing old-fashion providers. Many types of research and studies have been conducted on this matter, and most of them agree in one- the technology has changed the world we live in as well as our expectations, and it enabled new services and new delivery channels, which caused fear of the disappearance of traditional delivery channels. Some of the researchers showed that fear of cannibalization is unsupported [7, 55], others on the other hand, claim differently [6, 20]. The comparison of traditional and electronic retailers and service providers showed that the online providers gained on the importance and they experienced significant growth in revenue, whereas traditional brick-and-mortar stores revenue either saturated or decreased. The significant impact on traditional services could have been expected as most of the researches had been conducted in highly developed countries where the Internet is available to most of the citizens. Even in these countries, the poor and undeveloped rural areas were not taken into account when the research has been conducted, which may have had an impact on acceptance and the frequency of usage of electronic delivery channels and may have delivered different results. As the technology is developing daily, it is important to track these phenomena and changes that occur regularly and conduct more of up to date research and studies, particularly when it comes to this matter, as the older studies lose their significance with time.

The Impact of Electronic Services on Traditional Services

331

References 1. Eatwell, J., Milgate, M., Newman, P.: The World of Economics. Springer, Berlin (1991) 2. Saha, A.: A study on “The impact of online shopping upon retail trade business.” IOSR J. Bus. Manag. (IOSR-JBM) National Conf. Adv. Eng. Technol. Manag. 74–78 (2017) 3. Rowley, J.: An analysis of the e-service literature: towards a research agenda. Internet Res. (2006). https://doi.org/10.1108/10662240610673736 4. Brynjolfsson, E., Smith, M.D.: Frictionless commerce? A comparison of internet and conventional retailers. Manage. Sci. 46, 563–585 (2000). https://doi.org/10.1287/mnsc.46.4.563. 12061 5. Yarimoglu, E.K.: A review of service and E-service quality measurements: previous literature and extension. J. Econ. Soc. Stud. (JECOSS) 5, 169–200 (2015) 6. Bitner, M.J., Brown, S.W., Meuter, M.L.: Technology infusion in service encounters. J. Acad. Mark. Sci. 28, 138–149 (2000). https://doi.org/10.1177/0092070300281013 7. Kollmann, T., Kuckertz, A., Kayser, I.: Cannibalization or synergy? Consumers’ channel selection in online–offline multichannel systems. J. Retail. Consum. Serv. 19, 186–194 (2012). https://doi.org/10.1016/j.jretconser.2011.11.008 8. Riedl, C., Leimeister, J.M., Krcmar, H.: New service development for electronic services—a literature review (2009) 9. Meier, H., Roy, R., Seliger, G.: Industrial product-service systems—IPS2 . CIRP Ann. 59, 607–627 (2010). https://doi.org/10.1016/j.cirp.2010.05.004 10. Parasuraman-Berry, V.A.Z.-A., Berry, L.L.: Delivering Quality Service, Balancing Customer Perceptions and Expectations. The Free Press, A Division of Macmillan, Inc, New York (1990) 11. Veselý, P.: Definice kybernetické bezpeˇcnosti, kybernetických útok˚u a kybernetické kriminality. Bezpeˇcnostní vˇedy: úvod do teorie, metodologie a bezpeˇcnostní terminologie, pp. 160–175. ˇ ek, Plzeˇn (2019) Vydavatelství a nakladatelství Aleš Cenˇ 12. Poniszewska-Maranda, A., Matusiak, R., Kryvinska, N., Yasar, A.-U.-H.: A real-time service system in the cloud. J. Ambient Intell. Humaniz. Comput. 11, 961–977 (2020). https://doi.org/ 10.1007/s12652-019-01203-7 13. Poniszewska-Maranda, A., Kaczmarek, D., Kryvinska, N., Xhafa, F.: Studying usability of AI in the IoT systems/paradigm through embedding NN techniques into mobile smart service system. J. Comput. 101(11), 1661–1685. https://doi.org/10.1007/s00607-018-0680-z 14. Parasuraman, A., Zeithaml, V.A., Berry, L.L.: A conceptual model of service quality and its implications for future research. J. Mark. 49, 41–50 (1985). https://doi.org/10.1177/002224 298504900403 15. Lehtinen, U., Lehtinen, J.R.: Service quality: a study of quality dimensions. Serv. Manage. Inst. (1982) 16. Sasser, W.E., Olsen, R.P., Wyckoff, D.D.: Management of Service Operations: Text, Cases, and Readings. Allyn & Bacon, Boston (1978) 17. Zeithaml, V.A., Parasuraman, A., Malhotra, A.: Service quality delivery through web sites: a critical review of extant knowledge. J. Acad. Mark. Sci. 30, 362–375 (2002). https://doi.org/ 10.1177/009207002236911 18. Veselý, P.: Technické normy a další metodiky pro bezpeˇcnost ICT. Bezpeˇcnostní vˇedy: úvod do teorie, metodologie a bezpeˇcnostní terminologie, p. 175. Vydavatelství a nakladatelství Aleš ˇ ek, Plzeˇn (2019) Cenˇ 19. de Ruyter, K., Wetzels, M., Kleijnen, M.: Customer adoption of e-service: an experimental study. Int. J. Serv. Ind. Mgmt. 12, 184–207 (2001). https://doi.org/10.1108/095642301103 87542 20. Boehm, M.: Determining the impact of internet channel use on a customer’s lifetime. J. Interact. Mark. 22, 2–22 (2008). https://doi.org/10.1002/dir.20114 21. Montoya-Weiss, M.M., Voss, G.B., Grewal, D.: Determinants of online channel use and overall satisfaction with a relational, multichannel service provider. J. Acad. Mark. Sci. 31, 448–458 (2003). https://doi.org/10.1177/0092070303254408

332

D. Saric and M. Mikolasik

22. Clement, J.: Number of internet users worldwide. In: Statista (2019). https://www.statista.com/ statistics/273018/number-of-internet-users-worldwide/. Accessed 23 Jan 2021 23. Brynjolfsson, E., Hu, Y.J., Smith, M.D.: Consumer surplus in the digital economy: estimating the value of increased product variety at online booksellers. Manage. Sci. 49, 1580–1596 (2003). https://doi.org/10.1287/mnsc.49.11.1580.20580 24. Smith, M.D., Telang, R.: Piracy or promotion? The impact of broadband Internet penetration on DVD sales. Inf. Econ. Policy 22, 289–298 (2010). https://doi.org/10.1016/j.infoecopol.2010. 02.001 25. McBride, S., Fowler, G.: Studios see big rise in estimates of losses to movie piracy. Wall Street J. (2006) 26. Agrawal, V., Tripathi, V., Seth, N.: A conceptual framework on review of E-service quality in banking industry. 8:5 (2014) 27. Bitner, M.J., Booms, B.H., Tetreault, M.S.: The service encounter: diagnosing favorable and unfavorable incidents. J. Mark. 54, 71–84 (1990). https://doi.org/10.1177/002224299005 400105 28. Anderson, C.: Long tail : why the future of business is selling less of more (2006) 29. Dukes, A.J., Geylani, T., Srinivasan, K.: Strategic assortment reduction by a dominant retailer. Mark. Sci. 28, 309–319 (2008). https://doi.org/10.1287/mksc.1080.0399 30. University Presses Credit Internet for Increased Sales. Cambrige MIT Press (1999) 31. Brynjolfsson, E., Hu, Y.J., Smith, M.D.: From Niches to Riches: Anatomy of the Long Tail. Social Science Research Network, Rochester, NY (2006) 32. Brynjolfsson, E., Hu, Y.J., Simester, D.: Goodbye pareto principle, hello long tail: the effect of search costs on the concentration of product sales. Manage. Sci. 57, 1373–1386 (2011). https:// doi.org/10.1287/mnsc.1110.1371 33. Allen, O., Hamilton, I.: Internet banking: a survey of current and future development. Emerald Internet Research (2002) 34. Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., Pahnila, S.: Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Res. 14, 224–235 (2004). https://doi.org/10.1108/10662240410542652 35. Korankye, A.: The impact of e-banking on customer service and profitability of banks in Ghana. Glob. J. Commer. Manage. Perspect. 3, 61–65 (2014) 36. Marenzi, O., Hickman, M., Dehler, L.: Is internet banking profitable. A study of digital insight’s offering 1–28 (2000) 37. Online banking: reach in the European Union (EU28). In: Statista (2017). https://www.statista. com/statistics/380803/online-banking-penetration-in-the-eu/. Accessed 23 Jan 2021 38. Liao, Z., Cheung, M.T.: Internet-based e-banking and consumer attitudes: an empirical study. Inf. Manag. 39, 283–295 (2002). https://doi.org/10.1016/S0378-7206(01)00097-0 39. Lovelock, C.H., Young, R.F.: Look to consumers to increase productivity. Harvard Bus. Rev. 57, 168–178 (1979) 40. Mattila, M., Karjaluoto, H., Pento, T.: Internet banking adoption among mature customers: early majority or laggards? J. Serv. Mark. 17, 514–528 (2003). https://doi.org/10.1108/088760 40310486294 41. Black, N., Lockett, A., Ennew, C., Winklhofer, H., Mckechnie, S.: Modelling consumer choice of distribution channels: an illustration from financial services. Int. J. Bank Mark. 20, 161–173 (2002). https://doi.org/10.1108/02652320210432945 42. Wilson, E.: Closing the Digital Divide: An Initial Review: Briefing the President. The Internet Policy Institute, Washington, DC (2000) 43. Porada, V., Smejkal, V., Veselý, P.: Kybernetická bezpeˇcnost. 6. In: Bezpeˇcnostní vˇedy : úvod ˇ ek, do teorie, metodologie a bezpeˇcnostní terminologie. Vydavatelství a nakladatelství Aleš Cenˇ Plzeˇn, pp 160–182 (2019) 44. Smith, S.A., Agrawal, N.: Management of multi-item retail inventory systems with demand substitution. Oper. Res. 48, 50–64 (2000). https://doi.org/10.1287/opre.48.1.50.12443 45. Jurˇcák, V., Klimek, L., Porada, V., Veselý, P., Pawera, R.: Evropská bezpeˇcnost. 4. In: Bezpeˇcnostní vˇedy : úvod do teorie, metodologie a bezpeˇcnostní terminologie. Vydavatelství ˇ ek, Plzeˇn, pp 89–140 (2019) a nakladatelství Aleš Cenˇ

The Impact of Electronic Services on Traditional Services

333

46. Capital, A.M.P.: The impact of online shopping on retail property (2013) 47. LOCO BC|Blog. In: LOCO BC. http://www.locobc.com:443/blogs?searchable=date:2015-11. Accessed 5 Jan 2021 48. Lash, H., Wintermuth, J.: Mall investors are about to feel the impact of online shopping. In: Business Insider (2016). https://www.businessinsider.com/impact-on-mall-investors-of-shiftto-online-shopping-2016-10. Accessed 5 Jan 2021 49. Coppola, D.: U.S. e-commerce market size 2017–2024. In: Statista (2020). https://www.sta tista.com/statistics/272391/us-retail-e-commerce-sales-forecast/. Accessed 23 Jan 2021 50. Retail Revenue. The Economist (2016) 51. Sabanoglu, T.: Amazon: annual revenue. In: Statista (2018). https://www.statista.com/statis tics/266282/annual-net-revenue-of-amazoncom/. Accessed 23 Jan 2021 52. Papandimitriou, G., Siegel, M., Gibbons, F.: Amazon Enters the Cloud Computing Business. 33 53. Miller, C.C., Bosman, J.: E-Books Outsell Print Books at Amazon. The New York Times (2011) 54. Amazon.com, Inc. Q4 2008 earnings call transcript. In: SeekingAlpha. https://seekingalpha. com/article/117508-amazon-com-inc-q4-2008-earnings-call-transcript. Accessed 4 Jan 2021 55. Chen, H., Hu, Y.J., Smith, M.D.: The impact of ebook distribution on print sales: analysis of a natural experiment. Social Science Research Network, Rochester, NY (2017) 56. Cortiñas, M., Chocarro, R., Villanueva, M.L.: Understanding multi-channel banking customers. J. Bus. Res. 63, 1215–1221 (2010). https://doi.org/10.1016/j.jbusres.2009.10.020 57. Barnes & Noble—revenue 2019. In: Statista. https://www.statista.com/statistics/273460/rev enue-of-barnes-und-noble/. Accessed 4 Jan 2021 58. Share of Americans who shopped for books at Barnes & Noble in the last 12 months in 2018, by age. In: Statista (2020). https://www.statista.com/statistics/231201/people-who-shoppedfor-books-at-barnes-and-noble-in-last-12-months-usa/. Accessed 23 Jan 2021 59. Number of bookstores in the U.S. 2017. In: Statista (2017). https://www.statista.com/statistics/ 249027/number-of-bookstores-in-the-us/. Accessed 5 Jan 2021 60. Richter, F.: Infographic: U.S. eBook sales to surpass printed book sales in 2017. In: Statista Infographics. https://www.statista.com/chart/1159/ebook-sales-to-surpass-printed-book-sales-in2017/. Accessed 23 Jan 2021 61. Buckley, C., DeFina, P., Root, L.: Wallmart ws Amazon. Economist 2016 Investment Case Competition sponsored by RealVision 17 (2016) 62. Verhoef, P.C., Kannan, P.K., Inman, J.J.: From multi-channel retailing to omni-channel retailing: introduction to the special issue on multi-channel retailing. J. Retail. 91, 174–181 (2015). https://doi.org/10.1016/j.jretai.2015.02.005 63. Petro, G.: Amazon Vs. walmart: clash of the titans. In: Forbes. https://www.forbes.com/sites/ gregpetro/2016/08/25/amazon-vs-walmart-clash-of-the-titans/. Accessed 23 Jan 2021 64. Zimmerman, A.: Can retailers halt ‘showrooming’. Wall Street J. 259, B1–B8 (2012) 65. Garcia, T.: Wal-Mart ends ShippingPass, launches free two-day shipping. In: MarketWatch (2017). https://www.marketwatch.com/story/wal-mart-ends-shippingpass-pilot-launchesfree-two-day-shipping-2017-01-31. Accessed 5 Jan 2021 66. Ahrens, S.: Umsatz von Walmart weltweit bis 2020. In: Statista (2020). https://de.statista.com/ statistik/daten/studie/282764/umfrage/umsatz-von-walmart-weltweit/. Accessed 23 Jan 2021 67. Amazon.com, Inc. (AMZN) Income Statement—Yahoo Finance. https://finance.yahoo.com/ quote/AMZN/financials/. Accessed 4 Jan 2021 68. Mithas, S., Lucas, H.C.: What is your digital business strategy? IT Prof. 12, 4–6 (2010). https:// doi.org/10.1109/MITP.2010.154 69. Netflix Annual Report (2017) 70. Netflix: revenue in 2018. In: Statista. https://www.statista.com/statistics/272545/annual-rev enue-of-netflix/. Accessed 5 Jan 2021

334

D. Saric and M. Mikolasik

71. McMurtry, L., Epstein, E.J.: The big picture: the new logic of money and power in Hollywood (2005). (New York Review 250 West 57TH ST, New York, NY 10107 USA) 72. Uber revenue and usage statistics (2020). In: Business of Apps (2017). https://www.businesso fapps.com/data/uber-statistics/. Accessed 24 Jan 2021 73. Shields, N.: Uber’s revenue growth slows as competition heats up. In: Business Insider. https:// www.businessinsider.com/ubers-revenue-growth-slowed-in-q4-2018-8. Accessed 24 Jan 2021

Use of Digital Technologies in Business in Slovakia Daniela Nováˇcková and Jarmila Wefersová

Abstract Business activities performed by economic entities are influenced by several factors. These factors include the regulation of business activities and the effects of the external environment, which undoubtedly include the introduction of innovation and digital technologies. Digitization, informatisation, automation, and robotisation have their impact on the improvement of competitiveness and are beneficial not only for industry but also for consumers. This scientific study describes by descriptive analysis phenomena and processes related to the use of digital technologies within the framework of business in some areas in Slovakia. The study opens a discourse on digital economy that changes the system of business and represents the challenge for new jobs. With the help of scientific methods and data obtained from the websites of major entities, while applying the principles of legal logic and systematics, accuracy, and the possibility of generalising, we have concluded that digital technologies are widely used in Slovakia in business, with the banking sector at the forefront. Due to the increasing use of digital technologies, we also addressed in this study the issue of personal data protection. The article presents an analysis of impacts from laws of the European Union on the formation of a digital market in Slovakia.

1 Introduction Digitization of industrial production and service providing changes the business models, the production, products, process, and the production of values. There are several business entities operating on the Slovak market, which are affected by digitization and innovation. Part of this process also represents the rise of a new branch of D. Nováˇcková (B) · J. Wefersová Faculty of Management, Department of International Management, Comenius University Bratislava, Odbojárov 10, Bratislava 25, 820 05 Bratislava, Slovakia e-mail: [email protected] J. Wefersová e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Kryvinska and A. Poniszewska-Mara´nda (eds.), Developments in Information & Knowledge Management for Business Applications, Studies in Systems, Decision and Control 376, https://doi.org/10.1007/978-3-030-76632-0_12

335

336

D. Nováˇcková and J. Wefersová

the economy, namely the digital economy. New in the New Economy is the proliferation of the use of the internet, a new level and form of connectivity among multiple heterogeneous ideas and actors, giving rise to a vast new range of combinations [1]. Internet has become the communication tool, it connects millions of people in more than 160 countries, while participants are from various social groups and it replaced telegraph, fax telephone, broadcasting, TV. Alexander Bain, the inventor of fax, came as first with remote data transmission, faxing his first messages already in 1843. The revolutionary change that is currently taking place in form of digitization affects all forms of business, because today we cannot imagine the functioning of a company or a service provider without internet and digital technologies. Interconnected networks have been created thanks to the internationally recognised computer expert Vinton Cerf. By introduction of the TCP/IP protocol the ARPAnet became internet used around the world. In 1991 the Swiss programmer Tim Berners-Lee introduced the World Wide Web, which is considered as the improvement of internet [2]. The first World Wide Web Internet was launched in the Swiss CERN, that caused a fundamental revolution in the communication and it is one of the mostly used tool not only in commerce, services, and business, but also among people. Digitization enables people to communicate publicly and accessibly through the web, while the price of this service is accessible [3]. With regard to the goal of this study in a broader context, we analyse the tools enabling communication between natural and legal persons in the field of financial services. Technologically modern, sophisticated equipment enables daily communication between entrepreneurs and state authorities. It is on a specific example that we point out the obligations of natural and legal persons in the field of tax policy in the context of using digital technologies. Respecting the increase of personal data being processed through the use of digital media, we also studied data protection as part of the Charter of Fundamental Rights in the European Union and the Treaty on the Functioning of the European Union. The scientific study also provides the results of DESI as it concerns Slovakia, which are processed by the European Commission.

2 Goal and Methodology The primary aim of this scientific study is to examine in what way the business environment in Slovakia has changed and how information technologies are used within business activities of companies. With regard to the expansive increase of digitization that has an impact on everyday life of citizens as well as on the business environment, we have decided to point out the use of digital technologies in practice with regard to the economic activities performed by companies, through the descriptive secondary analysis. We have focused on the use of digital technologies in the field of tax policies and in the area of providing financial services. At the same time, we examined the issue of data protection in the context of Slovak legal regulation, which is to a large extent influenced by the law of the European Union. With regard to the stipulated outcome, we have built on the scientific and expert literature, from

Use of Digital Technologies in Business in Slovakia

337

the Slovak applicable legislation, from the primary and secondary EU law, judgements of the Court of Justice of the EU, and from the accessible websites of national authorities and institutions. With regard to the descriptive character of this study, we have used mainly classical scientific methods in processing its subject matter. We have used the method of structured observation, while we have determined in advance the category of Slovak applicable legislation in the field of taxes, financial services, and personal data protection. Through systematic analysis of laws, regulations and measures we have examined concrete tendencies related to digital technologies. Through deduction we interpreted their use in the business environment. Using the method of generalisation, we pointed out the impacts of introducing the digital technologies on the business environment. The method of specification, or concretisation, respectively, has served in studying the impact on some areas of business where digital technologies are being introduced. We used abstraction to define and give general and essential information on some tendencies in the Slovak legislation. The synthesis enabled us to connect the relations between the acquired knowledge in the field of taxes and financial services. In this context, we relied mainly on past and current Slovak legislation regulating the issue, as well as on professional published discussions of various domestic and, last but not least, foreign authors. As a method of information processing, we used the collection and subsequent analysis of relevant sources of literature. The method of induction and the method of deduction are also largely represented. By using the comparative method, we have highlighted the regulated digital money and the unregulated one. With regard to the set goal, we have identified two research questions: 1. 2.

How are digital technologies used in business in Slovakia? How is the personal data protection ensured in using the digital technologies in Slovakia?

To verify our first research question, we examined the applicable legislation and obligations established by law addressed directly to business entities. The analysis itself is based on the rules of legal logic, systematics, accuracy, and the possibility to generalise conclusions. Analysis and interpretation of the obtained results prove that the business system is changing and new models are merging as well as new types of jobs. To verify the second research question we used the method of legal comparison to compare the rights of persons from the point of view of personal data protection regulated by the Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) and the Law on Protection of Personal Data. The system of protection of personal data as applied in Slovakia has a transnational dimension and represents one of the tools for the protection of fundamental rights.

338

D. Nováˇcková and J. Wefersová

3 Use of Digital Technologies in Doing Business In this part of the thesis, we focus on the use of digital technologies within the framework of doing business by companies. We will try to find the answer to the first research question: How are digital technologies used in business in Slovakia? In the interest of searching the correct answer we have analysed the important initiatives taken at the level of the European Union having significant influence on forming the digital economy. The notions such as digitization, informatisation, Industry 4.0, hands-free solutions form part of our lives and influence all social processes starting from the industry, through services, culture, and education [4, 5]. New possibilities arise, nonetheless accompanied with new problems. The basis for the functioning of the digital economy is formed by initiatives taken at the level of the European Union aimed at the creation of a digital market without frontiers. According to Miˇcátek [6]: In developing the digital economy, we must not forget digital-adapted regulations that promote fair competition, solve the problems of digital monopolies, and support innovative business models. In the period of building the common digital market it is necessary that the new common rules are introduced at the EU level. The digital market ranks among priorities of the European Union since 2010, when the initiative Strategy Europe 2020 was adopted. Legal basis for building the digital market are the Articles 4(2)(a), 26, 27, 114 and 115 of the Treaty on the Functioning of the European Union (TFEU). The digital single market boosts the economy and improves the quality of life through e-commerce and e-government [7]. The basis for building the digital market is removing the obstacles and creating suitable conditions. Ever evolving online markets and online software application stores bring benefits to consumers and save time. Digital integration, combining research areas, personnel, processes, users, and data will create conditions for scientific and technological achievements and breakthroughs, providing scientific and economic developments in related industries and, above all, in the global mineral and raw materials market [8]. The coordination at the EU level in the process of building the digital market is necessary because the digital transformation and financial instruments of the EU help to solve the current crisis of COVID-19 pandemic. Due to the COVID-19 pandemic many activities related to business can be ensured through digital technologies. Due to this unwanted era the digital technologies showed that it is possible that employees work from home, schools provided online training, and the banking sector performed most payment operations through online applications. Intelligent industry and inclusive economy will ensure the high employment rate and productivity, while the energy consumption should be reduced. Thanks to the development of technologies more and more human work is replaced by machines and automats. Simply put, many activities can be made better by technology than by humans. Innovations of internet platforms make the market access simpler, which has a positive influence on consumer policy and the boost of e-commerce. The modern procedures used by online platforms have big potential for the future and will advance further. The advance in technologies changes significantly various business sectors as well as professions. IT specialists, programmers and robot operating experts come to the

Use of Digital Technologies in Business in Slovakia

339

forefront. Building cross-border services has a big perspective. Today’s digital technologies affect the organisation outside and in, enabling the creation of new business models and transforming the customer experience. The incumbents are acutely aware that they need to transform strategically—to build new networks and value chains [9]. One of the current priorities of the European Union is Shaping Europe’s digital future which includes connectivity, digital value chains, eHealth, data economy, artificial intelligence, digital platforms [10]. The digital economy and society index DESI contains tools for measuring the performance of digital economy. The DESI index includes the following indicators: • • • •

Connectivity: how widespread, fast, and affordable broadband is, Human Capital/Digital Skills: the digital skills of the population and workforce, Use of Internet: the use of online activities from news to banking or shopping, Integration of Digital Technology: how businesses integrate key digital technologies, such as e-invoices, cloud services, e-commerce, etc., and • Digital Public Services: such as e-government and e-health [11]. The European Commission started to monitor the DESI Index in its Member States. The Report DESI/2020 writes about Slovakia, that “Slovakia fell from 17 to 20th in the EU ranking with scores for some indicators well below the EU average. 27% of Slovaks have above basic digital skills, which is the best score in the Visegrad 4 region but it remains below the EU average (33%). Slovakia continues to improve the fast and ultrafast broadband coverage. The proportion of people who have never used the internet has decreased to 12% but remains above the EU average (9%). More Slovaks are using the internet (82%, up from 78% in 2018) and banking online (66%, up from 62% in 2018)” [12]. The process of digitization is most visible in the management of corporate data, finance, document management, protection and security, internal communication in companies or interaction with customers, but also in providing services. Part of the digital economy are online platforms, which are of particular benefit to the consumer as a recipient of the service. The example is UBER, an entity providing taxi services without owning a single car. The strong position of online platforms on the market discriminates other service providers who are unable to compete with innovative solutions. Slovak entities are unable to resist strong competitive pressure and therefore their only competitive advantage is price. The economic development of all countries directly depends on the use of intelligent digitization, which helps to automate and simplify several processes, while increasing both labour productivity and profits. Digitization and automation do not affect only production processes, but also the storage of documents that is necessary in business activities. Key benefits of business digitization include: • • • •

Reduction of overheads and other costs related to economic activity Saving time and human labour Increasing of energy efficiency Increasing the competitiveness of the company and thus its position in the market

340

• • • •

D. Nováˇcková and J. Wefersová

Streamlining workflows and eliminating duplication Deploying Cloud applications provides flexibility, speed, and clarity Improving the communication with consumers Supporting new business models.

The current pandemic caused by the spread of COVID-19 has helped to accelerate digitization and intelligent automation. It is obvious mainly on the use of internet banking in financial institutions and commercial networks, where most payments are realized through payment cards. Digital technologies transform all sectors of industries and all segments of services. New business models are formed, as well as the structure of jobs or professions. Digital technologies are used in every company, at minimum in the form of basic in-house digital system related to the financial and administrative processes. The individual units and departments in the company are interconnected with the aim of transferring and processing information based on the joint digital platform [13, 14]. Planning and management of production in real time, consolidation, and enhanced effectiveness of processes, as well as the increase of flexibility of operations are mostly ensured by modern technologies. Digital automatization of companies and factories is one of the strategies aimed at achieving of effectiveness, reducing error rate, saving time and costs as well as removing the manual work.

3.1 Digitization in the Area of Taxes Digital technologies are a set of all kinds of hardware and software devices that facilitate communication and access, transmission and storage of information and knowledge in a digital environment [15]. Electronic communication is constantly expanding, not only in business relations, but also in relations with national authorities competent in tax and other relevant agenda. An example is the Financial Administration of the Slovak Republic, which prefers electronic communication that has numerous advantages and in particular enables natural and legal persons to communicate with national authorities. The obligation of electronic communication for VAT payers was introduced in Slovakia in 2014 within the framework of the programme Internetization of Public Administration. According to the Law no. 563/2009 Coll. On the Administration of Taxes, the duty to file applications electronically applies to every. • Taxable entity who is a payer of value added tax, • Tax adviser on behalf of a taxable entity represented in the tax administration, • Advocate on behalf of a taxable entity represented in the tax administration. As of 1st January 2018, the mandatory electronic communication is extended to all legal persons registered in the Commercial Register and as of 1st July 2018 to every natural person—entrepreneur. Business entities are allowed to use electronic

Use of Digital Technologies in Business in Slovakia

341

communications in the field of customs and the international exchange of information in the field of excise duties. Natural persons—entrepreneurs are obliged to communicate exclusively electronically with the Financial Administration as of 1st July 2018. In practice it means, that in addition to tax declaration all other documents will be delivered electronically. The Financial Administration of the Slovak Republic introduced a project of online connection for all entrepreneurs using the cashiers to the Financial Administration. The system of E-Cash (E-Kasa) represents the new electronic system for recording sales and cash receipts in real time, which will enable the integration of online cashiers and virtual registration cashiers within the central database of Financial Administration, real time data transmission as well as off-line mode. It represents one of the measures aimed at fighting tax fraud and removing unfair practices in business. As of 1st July 2019, all entrepreneurs must be fully connected to the ECash register system. The new online fiscalisation of E-Cash allows the tax authority to receive important data on sales and taxes directly after they are spent on their servers, which significantly simplifies the processing and control of the receipt. For entrepreneurs, the system of E-Cash will mean reducing the administrative burden when procuring hardware, a reduction in operating costs, a continuous transition between accounting days, and the availability of data during archiving. The system will also allow customers to verify the authenticity of cash documents in real time.

3.2 Use of Digital Technologies in Financial Services Nowadays there are many new trends in the development of digital technologies of banks as well as changes in business processes, banking products and services, service models and development of banks’ own ecosystems occurring under their influence [16]. Business activity is linked to the banking sector. Entrepreneurs as market entities use the products of financial institutions on a daily basis. The following table shows that the main Slovak financial institutions are already fully integrated into the zone Open the EU payment market (Table 1). For many banks and insurance companies the digital transformation enables to focus on products and services with high added value. The structure of new business models is following: • • • •

Alternative payment methods; Crypto actives; Crowdfunding; Tech insurance.

The Electronic payment system is used within the payment system, using a payment card, internet banking or other payment applications used in e-banking. Traditional means of payment, banknotes and coins are used in payments to a lesser extent and are replaced by electronic solutions, i.e., payments by cards, or, most recently, by phone or watch, even if the relationships among businesspeople have

342

D. Nováˇcková and J. Wefersová

Table 1 Slovak financial institutions providing services Open the EU payment market (based on [17]) Financial institutions

Payment initiation service

Account information service

Card-based payment instrument issuer service

single SEPA credit transfer

single foreign payment

ˇ CSOB, a.s











Poštová banka, a.s











Prima banka, a.s











Privatbanka, a.s











Slovenská sporiteˇlˇna, a.s











Tatrabanka, a.s











Všeobecná úverová banka, a.s











so far been based more on paper and metal. Electronic money represents monetary value stored electronically, including magnetic recording, depending on the used technology they are placed on hardware or software systems [18]. Software systems and products require online connection. There are several new innovative types of payments used at present. Another significant change in the area of financial services was brought by the Directive (EU) 2015/2366 of the European Parliament and of the Council of 25 November 2015 on payment services in the internal market (PSD2) [19]. The directive was transposed in Slovakia by the Law No. 281/2017 Coll. amending the Law No. 492/2009 Z.z. on payment services [20], through which the payment system is liberalized and thus enables third parties to provide payment services. The aim of the Directive is to increase European competitiveness and participation of non-banking actors in payment services, harmonisation of consumer protection and rights and obligations of payment services providers and users by creating the equality of treatment throughout the Union between the different categories of authorised payment service providers. The following table compares the development in the field of payment services since 2007 due to the formation of the digital EU market (Table 2). Within this table we compared the legal basis for the creation of an EU-wide single market for payment services and innovative payment services. The digital technology has changed over a period of 11 years the payment services in the EU and in the world. Innovations in the area of financial services concern following new services:

Use of Digital Technologies in Business in Slovakia

343

Table 2 Comparison PSD1 and PSD2 (based on [21]) The first payment services directive (PSD 1) in 2007

The second payment services directive (PSD 2) in 2018

Established the same set of rules on payments It includes provisions to: across the whole European – make it easier and safer to use internet payment services Economic area (European Union, Iceland, Norway and Liechtenstein)

– better protect consumers against fraud, abuse, and payment problems

To enable the legal foundation of a single

– promote innovative mobile and internet

Euro payments area (SEPA). was created: – direct debits – card payments – mobile and online payments technical standards

Payment services – strengthen consumer rights – strengthen the role of the European Banking Authority (EBA) to coordinate supervisory authorities and draft

• Account Information Services, AIS, • Payment Initiation Services, PIS, • Payment Instrument Issuer Service Provider, PIISP. The AIS payment account information service is an online service that provides consolidated information on one or more payment accounts that a payment service user has with another or more payment service providers. This service allows the client to obtain, through the account information service provider (third party) to which the client has given consent, information on the client’s account balance and the history of transactions on the client’s account. According to the Register of service providers, which is kept by the National Bank of the Slovak Republic, there are 20 foreign providers of information on payment accounts in Slovakia as of 1st November 2020. The Payment initiation service PIS allows clients to enter a payment order in the bank through a payment initiation service provider (third party) from the client’s payment account maintained by the bank, while the client consents to the execution of this payment order (the client authorizes the payment order). This service can be implemented in an online environment. Such services offer a low-cost solution for both merchants and consumers and provide consumers with a possibility to shop online even if they do not possess payment cards (recital 29 Directive (EU) 2015/2366). The legislator sought to respond to the ever-increasing rate of innovation in payment services, to increase competition in the market and thus achieve simplification of payments for clients. These services are provided on the basis of the Law No. 492/2009 on Payment Services (mainly its Articles 2 paragraphs 43 and 44, article 3 lit. (a) and (b), Article 28 lit. (b). In general, the influence of globalisation and European economic integration is evident also in the financial sector, which has a transnational dimension at present.

344

D. Nováˇcková and J. Wefersová

3.3 Electronic Payment System The electronic payment system also serves to prevent money laundering. The Law No. 394/2012 Coll. on Limitations of Cash Payments limits the possibility to make cash payments exceeding the amount of 15.000 Euro in case of payments between natural persons who are not entrepreneurs. A natural person performing business activities or other self-employed activity or a legal person may make cash payments only up to 5.000 Euro. At the same time, with the adoption of the Law on Limitations of Cash Payments the tax authorities have a better overview of the activities of entrepreneurs and are able to better focus their attention on suspicious transactions. This legal regulation introducing the mandatory cashless communication above certain limit creates legal preconditions for the fight against money laundering, for the fight against corruption and crime and for the protection against terrorist financing. New digital technologies open up new possibilities and change the range of products and services of commercial bank services. Investments into innovations and changing priorities are too expensive and procedurally demanding especially for big traditional banks, nonetheless at the same time they all introduce the system of innovations and the most modern technologies in providing services to consumers. The Slovak financial market tis fully liberalized and many financial institutions are active therein. Each institution provides many products of electronic banking. As part of monitoring the provision of electronic banking services, we focused on a part of products and services. Banks are also trying to facilitate access to finance through biometric data, which is an alternative to the use of passwords. Technologies to recognize fingerprint, voice, face, or signature biometrics have been applied for a long time. Biometric solutions quickly find their application. The main reason is the rapidly growing use of mobile banking applications, customer comfort and security. In Slovakia almost all banks operating on the market are using biometrics.

3.4 Transactions with Virtual Cryptocurrency Transactions with virtual cryptocurrencies are also performed at present between entities, providing certain benefits. Virtual cryptocurrencies, such as Bitcoin or Litecoin were not recognized in the Slovak Republic as official domestic or foreign currency, and thus do not represent electronic money under the Law on Payment Services and have no physical consideration in the form of legal tender. (Explanatory Report to the draft Law No. 297/2008 Coll. [22]). In defining the notion of virtual cryptocurrency, the legislator was inspired by article 3 points 18 and 19 of the Directive (EU) 2018/843 of the European Parliament and of the Council of 30 May 2018 amending Directive (EU) 2015/849 on the prevention of the use of the financial system for the purposes of money laundering or terrorist financing and amending

Use of Digital Technologies in Business in Slovakia

345

Directives 2009/138/EC and 2013/36/EU (5th AML Directive) [23]. Secondary legislation introduces new obligations for exchange platforms of virtual currencies and providers of e-wallet services, who will have to carry out checks and report suspicious illegal transactions with cryptocurrencies as obligated persons in the framework of due diligence in relation to the customer, thus ending the anonymity associated with such exchange offices. Nonetheless, the Directive only covers situations of exchanges of so-called flat currencies for cryptocurrencies and vice versa, and not the exchange of individual cryptocurrencies for other cryptocurrencies. In connection with the sale of virtual currency, the Methodological Guidelines of the Ministry of Finance of the Slovak Republic No. MF/10386/2018–721 to the Procedure of Taxing the Virtual Currency were also adopted, which defines the digital currency as a digital bearer of value that is not issued nor guaranteed by a central bank or a public authority, nor it is necessarily linked to legal tender, does not have the legal status of currency or money, but is accepted by some natural or legal persons as a means of payment that may be transferred, stored or traded electronically. The national legislation further defines what can be regarded as the sale of virtual currency for tax purposes under the Law on Income Tax. As the sale of virtual currency is regarded “any exchange, for example exchange of virtual currency for assets or exchange of virtual currency for the provision of a service or its transfer for consideration, including exchange for another virtual currency”. It is evident from the above that the notion virtual currency began to be used as early as 2018. The amendment of the Law on Protection Against Money Laundering of 2020 introduced the notion of virtual currency (cryptocurrency), which was absent in the Slovak legal system. This legal regulation extends the list of specific business services to “the provision of virtual currency wallet services” and “the provision of virtual currency exchange services”. The condition for the operation of these services is the competition of the secondary general or vocational education. Article 9 lit. l) to lit. o) of the Law on the Protection Against Money Laundering defines. • “l) virtual currency as the digital bearer of value that is not issued nor guaranteed by a central bank or a public authority, nor it is necessarily linked to legal tender, does not have the legal status of currency or money, but is accepted by some natural or legal persons as a means of payment that may be transferred, stored or traded electronically, • n) provider of e-wallet services as a person providing services aimed at protection of private cryptographic keys on behalf of his/her clients, possession, holding, storing, and transferring virtual currency, • o) virtual currency exchange service provider as a person who, in the course of his/her business, offers or executes transactions with a virtual currency, the object of which is the purchase of a virtual currency for euros or for a foreign currency or the sale of a virtual currency for euros or for a foreign currency”. Legal definitions of these notions will increase the credibility of the market with cryptocurrencies for financial institutions. Following its membership in the European Union the Slovak Republic is obliged to transpose the EU directives into its national

346

D. Nováˇcková and J. Wefersová

Table 3 Comparison: regulated digital money and unregulated digital money (based on [25, 26]) • Regulated digital money

• Non-regulated digital money

• Electronic money, digital cash, or e-money, • Virtual currencies that are not legal tenders is monetary value stored in a pre-paid card or (bitcoin) that exist without a central point of smartphone, for example means control like a central bank electronically, including magnetically, stored • They are an alternative to legal tender and they have no purpose other than the purpose monetary value as represented by a claim on of currency, they represent financial services the issuer which is issued on receipt of funds • These are not regarded as money from a legal for the purpose of making payment perspective. (ECB, 2015) transactions (Directive 2009/110)

legislation. These definitions are based on Article 3 points 18 and 19 of the 5thAML Directive of the EU 2018/843. Entities providing services related to virtual currencies cannot perform this activity on the basis of business license. According to Malata and Martaus [24] cryptocurrencies (virtual currencies) together with the blockchain are gradually creating a new social reality and it is only a matter of time before these changes are fully reflected in the legal reality too. For the time being, legal regulation in this area is limited to seeking appropriate legal definitions and the most appropriate application of the current legal framework to the trade in cryptocurrencies (especially in the area of tax and financial law). Due to trends in global financial markets, the system of providing financial services is also changing. Innovative financial services are mainly focused on the consumer, they are affordable and save time. Innovative financial services are therefore also subject of the EU legislation, which has a significant impact on Slovak laws. On the basis of the mentioned facts, the digital money can be divided into two categories (Table 3). If we consider criteria for determining the function of money. • as a medium of exchange—a means of payment with a value that everyone trusts, • as a unit of account allowing goods and services to be priced, • and as a store of value [25]. then our answer is that virtual currencies cannot be regarded as legal tender. Nonetheless, virtual currencies can be electronically transferred, stored, or traded. As the Court of Justice stated, in so far as, unlike that electronic money, for virtual currencies the funds are not expressed in traditional accounting units, such as in euro, but in virtual accounting units, such as the ‘bitcoin’ (Recital 12 Case C-264/14). The ‘bitcoin’ virtual currency was the question in the judgment of the Court of Justice of 22 October 2015 in Case C-264/14 [27]. The subject of the dispute was the exchange of traditional currencies for the virtual currency bitcoin and its exemption from VAT. Swedish national David Hedqvist wanted to provide services consisting of exchanging traditional names for the virtual currency bitcoin and vice versa. Before commencing such transactions, Mr. Hedqvist asked the Swedish Revenue Law Commission for a preliminary opinion on whether it be liable to pay VAT on the purchase and sale of bitcoin units. The Revenue Law Commission in this matter

Use of Digital Technologies in Business in Slovakia

347

hold the view in 2013 that virtual currency bitcoin is a mean of payment used in a similar way to legal means of payment, and the exchange service was covered by the exemption under Chap 3, Paragraph 9, of the Law on VAT. The Skatteverket (Swedish tax authority) referred this opinion of the Revenue Law Commission to the Högsta förvaltningsdomstolen (Supreme Administrative Court). The Högsta förvaltningsdomstolen referred the questions on the basis of Article 267 TFEU to the Court of Justice, namely, whether such transactions constitute the supply of a service effected for consideration, and if so, whether they are tax exempt. According to the Revenue Law Commission, the bitcoin virtual currency is a means of payment used in a similar way to legal means of payment. Furthermore, the term ‘legal tender’ referred to in Article 135(1)(e) of the VAT Directive is used in order to restrict the scope of the exemption as regards bank notes and coins. It follows, according to the Revenue Law Commission, that term must be taken to mean that it relates only to bank notes and coins and not to currencies. That interpretation is also consistent with the objective of the exemptions laid down in Article 135(1) (b) to (g) of the VAT Directive, namely, to avoid the difficulties involved in making financial services subject to VAT. The Court of Justice in this case hold the view that “Article 135(1)(e) of Directive 2006/112 must be interpreted as meaning that the supply of services such as those at issue in the main proceedings, which consist of the exchange of traditional currencies for units of the ‘bitcoin’ virtual currency and vice versa, performed in return for payment of a sum equal to the difference between, on the one hand, the price paid by the operator to purchase the currency and, on the other hand, the price at which he sells that currency to his clients, are transactions exempt from VAT, within the meaning of that provision.“

3.5 Crowdfunding One of the modern business models is also crowdfunding or “collective financing”, respectively. Crowdfunding platforms operate through web technology and online payment systems as alternative financing for innovative projects. Scholz [28] shows that crowdfunding potentially shortens the development cycle of new products, thus enabling an earlier market entry. Hence, crowdfunding serves as a multifaceted early-stage support instrument for innovation implementation facilitated by the crowd’s resources. Her opinion is correct, it represents the alternative financing that forms part of the sharing economy. Business entities are directly connected by the investors or peer-to-peer lending platforms. The advantage of sharing economy is that through digital technologies it allows a wide group of natural and legal persons to participate in the sharing of own resources, goods or even by offering services. The rapid development of a shared economy is made possible by digitization. Collaborative financing enables investors to enter the company—acquiring a share in the assets (equity crowdfunding). At the level of the European Union and in Slovakia, a regulatory tool for the regulation of crowdfunding relations has not yet been adopted.

348

D. Nováˇcková and J. Wefersová

In this context, we have to consider the need to distinguish crowdfunding from the concept of “collective investment”, which is regulated by the Law on Collective Investment Schemes. Crowdfunding ranks among the newly created alternative forms of financing directly connecting those who have funds at their disposal and want to lend or invest them with those who need funds to finance a specific innovative project. It represents a simple and relatively cheap way of raising funds from a crowd of people who contribute different amounts for rewards, starting with a thank you and ending with a share in the company. Start-ups gain a number of benefits through crowdfunding. It is not only a simple and cheap way of obtaining share capital, but also a way of obtaining important and significant information about the target market at a relatively low price. Through crowdfunding, the entrepreneur is able to determine the expected interest in his product, the target group, and also s/he can more accurately determine the selling price of the product when entering the market. It can also increase the awareness of the product and gain potential investors or partners. Crowdfunding in Slovakia is still at its outset. The essence of crowdfunding is that the applicant for funding, i.e., the author of the project, who is also referred to as founder in this area, registers on the crowdfunding platform, where s/he sets a goal—how many euros s/he wants to collect and also the date by which s/he wants to collect this amount as well as the duration campaigns. Subsequently, it is also necessary to present the project by a specific text, picture, or video so that the contributors, who are also referred to backers or investors, have the motivation to invest into the project [29]. We know various models of crowdfunding which differ in whether the donor—backer will receive a reward for his/her contribution and what kind of reward it will be. It often depends on the on the amount of the contribution submitted by an individual and on the crowdfunding model that the project used to finance it. The reward various from thank you email, through gaining the product prototype, to the participation in the profit through a share in the company. If the project does not reach the target amount in the specified time period, it depends on the model chosen by the funder at the beginning of the campaign, whether s/he will keep the money or it will be returned to the backers’ accounts [29]. The amount that backers can contribute to projects was not initially limited in any way. However, now when States are starting to regulate crowdfunding, rules have been created according to which investors—backers are also limited in the amount to which they contribute. In most countries, it mostly involves the regulation of the capital model of crowdfunding. According to Klembara [30] there are several models of crowdfunding campaigns: • Crowdfunding based on donorship—individuals provide money to the company or project on a voluntary basis. • Crowdfunding based on remuneration—the contributor obtains goods or services in exchange for provided funds, this model is often linked to the product pre-sale model. The reward also depends on the size of the contribution and can

Use of Digital Technologies in Business in Slovakia



• • •

349

vary from a thank-you email to, for example, the naming the production hall of the future factory after the investor. Presale of products—When pre-selling products, the campaign announcer raises funds for the development and production of his or her future product, with contributors pre-ordering with their contributions, however, usually at a lower price than the expected market sale price. Crowdfunding based on the profit sharing/capital – individuals invest for a share in the company, while the reward is the income generated by the company / project. It is mainly used in big projects. Crowdfunding loan model – the individuals lend money to the company or project for a loan and interest on the investment. In this model, the campaign owner has the position of a debtor and the contributor acts as a creditor. Securities (shares and bonds) – investment into securities represents another form of crowdfunding. However, it is the slowest growing model, as there are several obstacles involved. The entrepreneur has to give up a part of the capital and the investors still risk losing all their money. Another problem may involve the difficult administration of a high number of shareholders [30].

Participants in crowdfunding relationships are a group of natural or legal persons who have raised funds and an entity that brings a project that will be funded from the funds raised. The group of persons will financially support human creativity and inspiring business ideas that can be of economic as well as social significance. In its study of crowdfunding’s potential for the developing world, the World Bank considered recommendations from governments, NGOs and the private sector for four areas. Several aspects should definitely be considered and promoted in Central Europe as well (Table 4).

3.6 Insurance Services InsurTech There is no legal definition of the notion hereinafter referred to as “InsurTech” at present. And there is no European nor Slovak legislation applicable for insurance technologies at present, either defining InsurTech or laying down rules for the application and use of technologies in insurance. However, it is the duty of financial market participants to comply with all existing legislative provisions in the areas in which InsurTech intervenes. These apply not only in cases where various software applications are used to set up the insurance contract directly for the client when concluding insurance in a personal meeting with the client, but also when insurance at a distance is concluded via internet or by telephone. The insurance industry is constantly evolving as regards the use of digital technologies, and in recent years the technologies have been applied mainly in the distribution and management of insurance products, i.e., they are mainly used for mediation of insurance. These are mainly various applications searching for insurance according to the parameters entered by the client, internet portals comparing similar types of

350

D. Nováˇcková and J. Wefersová

Table 4 World Bank: Recommendations for crowdfunding (based on [31]) Economic

Social

One should One should • craft exceptions to securities regulations that • harness top social media experts/bloggers/ allow easy registration for equity offerings tastemakers to communicate with local • strategically tie crowdfunding to cultural audiences messages • hold media and educational events to build awareness and understanding • form a crowdfunding market alliance • hold regular crowdfunding events with trusted third parties to teach successful techniques Technology

Cultural

One should • where appropriate, apply lessons learned from more developed countries • consider buy, build, or white label • determine gaps in existing technology for online financial transactions

One should • leverage existing incubator/accelerator/structured co-working spaces as hubs for innovation in funding • foster professional investor and consumer confidence in crowdfunding through education and communication • encourage the participation of women and girls

insurance products of various insurance companies, but also applications used in concluding insurance contracts, which determine the price of insurance directly to a specific client and can also include internet mediation of insurance contracts at a distance without a personal meeting with the client. A concrete example of online services are for example signature free contracts that are used in some types of nonlife insurance, such as travel insurance. This has helped to speed up the approval of contracts and, last but not least, this step is environmentally friendly and also contributes to saving financial resources.

4 Personal Data Protection Informatization and digitization will further continue, new equipment and technologies will arise offering enhanced possibilities. Nonetheless, digitization brings some risks, mainly the processing of personal data can be demanding. The security of data processing related to company data or even personal data of employees is, of course, crucial, because within the public as well as the private sector, data are transmitted through information and communication technologies for various purposes. The expansion of global business, the presence of parent companies and their subsidiaries with registered offices and establishments in various countries of the world and the associated global processing and transfer of personal data cannot be neglected either. Any leakage of such data can be very unpleasant for the company, in extreme cases even liquidation is the consequence. The company is responsible for security of data

Use of Digital Technologies in Business in Slovakia

351

processing, and therefore must have a system of internal protection and especially the protection of personal data of employees. This duty follows from the national legislation as well as from EU law. There are high financial penalties at stake when these rules are infringed. The protection of natural persons in relation to the processing of personal data is a fundamental right. Article 8(1) of the Charter of Fundamental Rights of the European Union (the ‘Charter’) [32] and Article 16(1) of the Treaty on the Functioning of the European Union (TFEU) [33] provide that everyone has the right to the protection of personal data concerning him or her (Recital 1 Regulation 2016/679). The Treaty on the European Union in its Articles 6 and 39 regulate the issue of processing of personal data and their protection. The primary legislation was supplemented by the secondary legislation. However, the major breakthrough was brought by the GDPR. Data protection relates to any information related to identified or identifiable natural persons. In principle, it is the protection of rights of a natural person who is identifiable according to certain identifications (as a name, an identification number, genetic, mental, economic, cultural, or social identity of such a natural person). New EU data protection rules strengthening citizens’ rights and simplifying rules for companies in the digital age took effect in May 2018. At the same time, we have to point out the fact that increased use of new technologies and means of electronic communication in the employment leads to concerns related to the privacy of employees and new possibilities of monitoring them during their presence at the workplace. At present the protection of personal data is guaranteed at national level by the Slovak Constitution (constitutional law no. 460/1992 coll. The Constitution of the Slovak republic as amended1 and Law No. 18/2018 Coll. on Personal Data Protection and amending and supplementing certain Acts, as well as the Decree of the Office for Personal Data Protection of the Slovak Republic No. 158/2018 Coll. on procedure for data protection impact assessment. The reason for the adoption of the Law on Personal Data Protection is primarily the European reform of the rules on the protection of personal data, implemented in particular by the Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) [34]. New legislation expressly states that the consent of the involved person has to be clearly separable from other requirements and arrangements, and, that the consent of involved person can be withdrawn anytime without any reason. The catalogue of rights of personal data protection in the territory of the Slovak Republic has the following structure:

1 The

Constitution of the Slovak Republic in its article 19 guarantees that everyone shall have the right to be protected against unjustified collection, disclosure and other misuse of his or her personal data.

352

D. Nováˇcková and J. Wefersová

• When processing personal data, the consent of an involved person is required. This consent should be provided by express and clear expression of the will, which is a free, concrete, informed and clear expression of the will of the involved person with processing of personal data. • Any processing of personal data has to be legal and based on the law. • Processed personal data have to be adequate, relevant, and limited to the list or extent of personal data necessary with regard to the purpose, for which they are processed. • The involved person has the right to correct his or her personal data in the information systems of the provider. • Personal data should be processed in such a way, that their adequate security and confidentiality, including preventing the unlawful access to personal data and means of processing the personal data. • The involved person has the right for the copy of his or her personal data that are processed. • The operator is liable for the processing the personal data. • The involved person has the right to object, if her personal data are processed for the purpose of direct marketing carried out by the profiling. • The involved person has the right to have his or her personal data erased when the purpose of the processing was reached. The Law stipulates that an area accessible to public may be monitored by video or audio recording only for the purposes of public order and security, detection of crime or breach of state security, and only if the area is clearly marked as monitored. The operator and intermediary are obliged to maintain the confidentiality of the personal data they process. The confidentiality and secrecy obligation are regulated in Article 79 of the Law on Personal Data Protection, and it lasts also after the termination of the processing of personal data. The law enforcement authorities are not bound by the obligation of secrecy when fulfilling their tasks according to the special legislation. The Office for Personal Data Protection of the Slovak Republic (Bratislava) is an independent state authority which performs the supervision of data protection and contributes to the protection of your fundamental rights and freedoms with regard to the processing of your personal data (Article 80 of the Law on Personal Data Protection). The Office monitors the implementation of the Law on Personal Data Protection, cooperates with the European Data Protection Board in the area of personal data protection. According to Article 104 of the Law on Personal Data Protection the Office for Personal Data Protection of the Slovak Republic is authorized to impose penalties for non-fulfilment or infringement of duties as stipulated by the Law. As an example, we have chosen the processing of personal data by the Financial Administration of the Slovak Republic, which in accordance with the applicable Slovak legislation and the GDPR Regulation, as well as in harmony with good manners obtains and processes the personal data of natural persons who claim their rights in fulfilment of tasks of Financial Administration. The Financial Administration receives the personal data exclusively for the defined or stipulated purpose, at the same time it has to ensure that only those personal data

Use of Digital Technologies in Business in Slovakia

353

are processed that correspond by their extent and content to the purpose of their processing and are necessary to achieve this purpose [35].

5 Conclusion If we think at a global level, we will not avoid the process of digitization and robotization. New economic theories, practical knowledge and people’s thinking immediately raise the question of the natural development of information technologies. The process itself is to a large extent influenced by the achievements of science and technology and has international dimensions. The process of digitization is unstoppable and will move even faster in the light of new innovations. With regard to the first research question: How are digital technologies used in business in Slovakia? we can unequivocally state that in Slovakia the digitization is involved in almost every company or entity that provides services, because based on the facts we have shown that they use a digital system in administrative and financial processes. The clear answer to the first research question is that the digitization is used in the area of public administration, where the relationship between the financial administration and tax entity (natural or legal person) is involved. To a large extent the digitization is used in the accountancy. The Slovak legislation explicitly imposes the obligation for business entities to use digital technologies within their business activities and their communication. Our second research question was aimed at the system of protection of personal data. We have shown that under the influence of the EU law, a harmonized system of personal data protection has been adopted, which aims to ensure respect for fundamental rights and freedoms, in particular the right to the protection of personal data in the environment of new and increasingly used digital technologies. The involved persons can claim their right of access by the data subject (in accordance with Article 15 Regulation GDPR and with Article 21 of the Law on Personal Data Protection, right to rectification of inaccurate personal data (in accordance with Article 16 of the Regulation GDPR and with Article 22 of the Law on Personal Data Protection), right to erasure (‘right to be forgotten’) (Article 17 Regulation GDPR and Article 23 of the Law on Personal Data Protection, right to restriction of processing (Article 18 Regulation GDPR and Article 24 of the Law on Personal Data Protection,) right to data portability (Article 20 Regulation GDPR and Article 26 of the Law on Personal Data Protection), right to lodge a complaint with a supervisory authority, which is the Office for the Protection of Personal Data (Article 77 Regulation GDPR and Article 100 of the Law on Personal Data Protection). The operator is obliged on the basis of the law to eliminate the risks of misuse of personal data as much as possible. The Law enables the penalties to be imposed for the infringement of its provisions by the Office for the Protection of Personal Data. Further to this, the Slovak legislation meets European standards of personal data protection, which means stability in the international context and, above all, it has a positive effect on the business environment.

354

D. Nováˇcková and J. Wefersová

References 1. Carlsson, B.: The Digital Economy: what is new and what is not? In: Structural Change and Economic Dynamics. Elsevier. Accessible at: https://doi.org/10.1016/ j.strueco.2004.02.001. Retrieved from: http://cs.wikipedia.org/wiki/Mobiln%C3%AD_tel efon / http://www.bezpecne-online.cz/prorodice-a-ucitele/teenageri-a-komunikace-na-intern etu/co-je-to-kybersikana-a-jak-se-projevuje.html (2004). Accessed 15 May 2018 2. Liˇcko, O.: Návrat do minulosti. Accesible at: https://www.mojandroid.sk/navrat-do-minulostiako-vznikol-internet-tema/ (2016). Accessed 23 October 2020 3. Molnár, E., Molnár, R., Kryvinska, N., Greguš, M.: Web intelligence in practice. Soc. Serv. Sci. J. Serv. Sci. Res. 6(1), 149–172 (2014) 4. Kaczor, S., Kryvinska, N.: It is all about services-fundamentals, drivers, and business models. Soc. Serv. Sci. J. Serv. Sci. Res. 5(2), 125–154 (2013) 5. Kryvinska, N.: Building consistent formal specification for the service enterprise agility foundation. Soc. Serv. Sci. J. Serv. Sci. Res. 4(2), 235–269 (2012) 6. Miˇcátek, V.: New rules to adress to business users of online intermediation services. Manažment podnikania a vecí verejných (online), vol. 4/2019, Bratislava (2019). ISSN 2453–8167 7. Maciejewski, M., Ratcliff, C.H., Næss, K.: The ubiquitous digital single market. Retrieved from: https://www.europarl.europa.eu/factsheets/en/sheet/43/digitalny-jednotny-trh-je-vsade (2020). Accessed 23 Nov 2020 8. Litvinenko, V.S.: Digital economy as a factor in the technological development of the mineral sector. Nat. Resour. Res. 29, 1521–1541 (2020). Retrieved from: https://doi.org/10.1007/s11 053-019-09568-4 9. Zaki, M.: Digital transformation: harnessing digital technologies for the next generation of services. J. Serv. Mark. 33(4), 429–435 (2019). Retrieved from: https://doi.org/10.1108/JSM01-2019-0034 10. Council of the European Union: Shaping Europe’s digital future. Retrieved from: https://www. consilium.europa.eu/en/policies/digital-single-market/ (2020). Accessed 3 Nov 2020 11. European Commission: What is the digital economy and society index? Retrieved from: https:// ec.europa.eu/commission/presscorner/detail/en/MEMO_16_385. (2016) 12. European Commission: Digital Economy and Society Index 2020—Country Reporting. The DESI 2020 reports are based on 2019 data. Retrieved from: https://ec.europa.eu/digital-singlemarket/en/desi (2020). Accessed 23 Nov 23 2020 13. Kryvinska, N., Greguš, M.: SOA and its Business Value in Requirements, Features, Practices and Methodologies. Comenius University in Bratislava, Bratislava (2014) 14. Greguš, M., Kryvinska, N.: Service Orientation of Enterprises—Aspects, Dimensions, Technologies. Comenius University in Bratislava, Bratislava (2015) 15. Mercader, C., Gairín, J.: University teachers’ perception of barriers to the use of digital technologies: the importance of the academic discipline. Int. J. Educ. Technol. High Educ. 17(4) (2020). Retrieved from: https://doi.org/10.1186/s41239-020-0182-x 16. Petrova, L. A., Kuznetsova, T.E., 2020: Digitalization in the banking Industry: digital transformation of environment and business processes. Finansovyj žhurnal—Finan. J. Finan. Res. Inst. Moscow 127006 Russia (3), 91–101 (2020) 17. Retrieved from: www.financial institutions 2021 18. Národná Banka Slovenska: Alternatívne spôsoby platieb. Retrieved from: https://www.nbs.sk/ sk/dohlad-nad-financnym-trhom/fintech/alternativne-sposoby-platieb#o. (2020) 19. European Union: Directive (EU) 2015/2366 of the European parliament and of the Council of 25 November 2015 on payment services in the internal market, amending directives 2002/65/EC, 2009/110/EC and 2013/36/EU and regulation (EU) no 1093/2010, and repealing directive 2007/64/EC (text with EEA relevance) OJ L 337, pp. 35–127 (23 Dec 2015) 20. National Council of the Slovak Republic: Act No 492/2009 on payment services (and amending certain laws), as amended by Act No 130/2011, Act No 394/2011, Act No 520/2011 (2009)

Use of Digital Technologies in Business in Slovakia

355

21. European Commission: Payment services EU. Retrieved from: https://ec.europa.eu/info/ business-economy-euro/banking-and-finance/consumer-finance-and-payments/payment-ser vices/payment-services_en. (2021). Accessed 22 Jan 2021 22. National Council of the Slovak Republic: Act amending Act no. 297/2008 Coll. on protection against money laundering and on protection against terrorist financing and amending certain laws (2018) 23. European Union: Directive (EU) 2018/843 of the European Parliament and of the Council of 30 May 2018 amending Directive (EU) 2015/849 on the prevention of the use of the financial system for the purposes of money laundering or terrorist financing, and amending directives 2009/138/EC and 2013/36/EU (Text with EEA relevance) OJ L 156, pp. 43–74 19 (June 2018) 24. Malata, M., Martaus, J.: Kryptomeny a rodiaci sa právny rámec. Retrieved from: https:// www.epravo.sk/top/clanky/kryptomeny-a-rodiaci-sa-pravny-ramec-4099.html?mail. (2018). Accessed at 23 Nov 2020 25. European Central Bank: What is money? Retrieved from: https://www.ecb.europa.eu/explai ners/tell-me-more/html/what_is_money.en.html. (2015). Accessed 23 Nov 2020 26. Directive 2009/110/EC of the European Parliament and of the Council of 16 September 2009 on the taking up, pursuit and prudential supervision of the business of electronic money institutions amending Directives 2005/60/EC and 2006/48/EC and repealing Directive 2000/46/EC (Text with EEA relevance) OJ L 267 (10 Oct 2009) 27. Judgement of the Court (Fifth Chamber) of 22 October 2015 (request for a preliminary ruling from the Högsta förvaltningsdomstolen—Sweden)—Skatteverket v David Hedqvist (Case C-264/14) (2015). Retrieved from: https://eur-lex.europa.eu/legal-content/EN/TXT/? uri=CELEX:62014CA0264 28. Scholz, N.: The Relevance of Crowdfunding. Springer eBook (2015). ISBN 978-3-658-09837-7 29. Zheng, H., Li, D., Wu, J., Xu, Y.: The role of multidimensional social capital in crowdfunding: a comparative study. In: China and Us, pp. 488–496 (2014) 30. Klembara, M.: Ako na crowdfunding. Creativeindustry.sk. Retrieved from: http://www.creati veindustry.sk/ako-na-crowdfunding/s. (2012). Accessed 1 Oct 2018 31. Information for development program (infoDev)/the world bank. Washington DC (2013). Retrieved from: www.infoDev.org 32. European Union: The EU charter of fundamental rights. OJ C 326 (26 Oct 2012) 33. European Union: The treaty on the functioning of the European Union. OJ C 326 (26 Oct 2012) 34. European Union: Regulation (EU) 2016/679 of the European Parliament and of the council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA relevance) OJ L 119, p. 1–88 (4 May 2016) 35. Financial Administration of the Slovak Republic: Ochrana osobných údajov. Retrieved from: https://www.financnasprava.sk/sk/financna-sprava/ochrana-osobnych-udajov. (2019). Accessed 23 Nov 2020

Business Information Through Choice-Based Conjoint Analysis: The Case of Electric Vehicle Home Charging Marvin Klein, Christine Strauss, and Christian Stummer

Abstract Information and data fuel businesses and markets; thus, provision, generation, and interpretation of information and data are crucial to support managerial decisions. We demonstrate the generation of information through choice-based conjoint analysis using the example of electric vehicle charging. There are several alternatives for electric vehicle charging, with home charging being the main charging point for most of today’s electric and plug-in hybrid electric vehicles. Therefore, a large number of consumers consider home charging as mandatory when buying a car. Before blindly investing in the construction of charging stations close to citizens’ homes, decision makers (e.g., policy makers) need to learn about the impact of possible measures. This paper examines whether performance improvements in alternative vehicles (e.g., in terms of range or charging time) or governmental incentives (e.g., price subsidies) could compensate consumers for not having home charging stations. Findings reveal that, in general, both electric and plug-in hybrid electric vehicles profit from the construction of home charging stations, but its perceived benefit decreases continuously with faster charging times at public charging stations. However, at the present time, when the technological progress of electric vehicles remains low, monetary subsidies for environmentally friendly vehicles appear to mainly support only sales of plug-in hybrid electric vehicles.

M. Klein · C. Stummer Bielefeld University, Universitaetsstr. 25, 33615 Bielefeld, Germany e-mail: [email protected] C. Stummer e-mail: [email protected] C. Strauss (B) University of Vienna, Oskar Morgenstern Platz 1, 1090 Vienna, Austria e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Kryvinska and A. Poniszewska-Mara´nda (eds.), Developments in Information & Knowledge Management for Business Applications, Studies in Systems, Decision and Control 376, https://doi.org/10.1007/978-3-030-76632-0_13

357

358

M. Klein et al.

1 Introduction In order to fight climate change, the European Union (EU) introduced a long-term strategy in 2019—commonly known as ‘The European Green Deal’—with the aim of achieving net zero greenhouse gas (GHG) emissions by 2050. The transport sector accounts for approximately 25% of all GHG emissions in the EU and is of special concern, as its carbon emissions have been rising more than those of most other sectors (e.g., energy, industry) [1]. While more investments in local transport might reduce the strong desire for individual mobility and digitization may improve traffic efficiency via autonomous driving or smart parking methods in the future, one of the key elements of a prompt energy transition may be a successful market introduction of innovative green modes of transportation, such as electric vehicles (EV) or plug-in hybrid electric vehicles (PHEV). With the aim of supporting the market diffusion of both EV and PHEV, the EU has undertaken such measures as penalizing environmentally unfriendly cars more severely. From 2021 onwards, the permissible emission limits for the average new passenger car in the EU will be only 95 g CO2 per km (previously this was 130 g CO2 per km). The penalty for exceeding this limit is e95 per vehicle for each gram per km [2]. To illustrate this, in 2019, only approximately 1 million out of 270 million passenger cars in the EU were either an EV or a PHEV [3]. Thus, according to a study by PA Consulting, Europe’s top 13 original equipment manufacturers (OEM) would have to pay e14.5 billion of penalties in 2021 with the same CO2 emission statistics for their fleet as those in 2019 [4]. However, in order to further incentivise the development of environmentally friendly engines, cars that emit less than 50 g CO2 per km are counted twice for a fleet in 2020, 1.67 times in 2021, and 1.33 times in 2022 [2]. The most frequently cited reasons for the currently low popularity of EVs among consumers are short driving range, a high purchase price, and lack of a charging infrastructure [5–7]. According to several studies, home charging in particular is considered one of the main adoption barriers for consumers [8–12] and is often termed a ‘knockout criterion’, as it is currently the main charging point for most EV drivers [13–17]. However, consumer demand for a home charging station might become less relevant once public charging infrastructure expands or next generation batteries help to increase driving ranges. In order to simulate the impact of technological progress on the market shares of alternative fuel vehicles, several authors have already examined consumer preferences towards specific vehicle attributes and their contribution to the overall utility of a vehicle via discrete choice experiments (e.g., [18–33]). However, none of them have explicitly studied the attribute of home charging. This is somewhat surprising, since it represents a beneficial differentiation from EVs/PHEVs to conventional vehicles (CV). Thus far, only [27, 28] considered home charging in their discrete choice studies; however, they did not study it as a standalone attribute, which is necessary for examining its impact uncoupled from other factors, such as charging station density or charging time.

Business Information Through Choice-Based Conjoint Analysis…

359

This work contributes to existing research in two ways. First, building on similar research that has already been conducted in this field, we present a choice-based conjoint analysis (CBC) to analyse consumer preferences regarding new passenger cars with combustion engines, electric motors, or plug-in hybrid drives. Second, we explicitly consider the promising attribute of home charging, which has thus far been disregarded in discrete choice experiments. This is our major contribution to the extant literature, as it enables decision makers to examine its impact separate from other factors, as well as showing interaction effects with other attributes. In doing so, we focus on new cars in the compact class and the (future) adoption behaviour of young potential German vehicle buyers. The remainder of this paper is structured as follows. In Sect. 2, a brief explanation of the methodology of CBC and a review of related work is provided. In Sect. 3, the design of the CBC study is described. In Sect. 4, the results of the parameter estimation are presented. In Sect. 5, different market scenarios are simulated to illustrate possible consequences of the findings. In Sect. 6, results from the CBC, managerial implications, limitations, and promising areas for further research are discussed.

2 Background 2.1 Choice-Based Conjoint Analysis CBC belongs to the family of discrete choice experiments. It is a purchase decision simulation in which participants must repeatedly choose between different products with varying attributes [34]; in this study, these products are passenger cars with combustion engines, electric motors, or plug-in hybrid drives. Discrete choice experiments have been developed independently in different fields of research, such as psychology, econometrics, or marketing. Therefore, the nomenclature is occasionally confusing or misleading. Discrete choice experiments, unlike CBCs, do not necessarily decompose utilities into an attribute- and level-based structure. In this paper, we utilise the term discrete choice experiment for the general concept and CBC for the special subcategory. A unique feature of conjoint analyses is that the objects/products are initially jointly considered based on the given choice tasks and then hashed into single components called ‘part-worth utilities’, which represent preferences for objects and their elements [35]. Due to its ability to closely represent reality, CBC has gained popularity since the 1990s and is now ‘the most widely used flavour of conjoint analysis’ [36]. It is advantageous because it enables researchers to analyse both main and interaction effects. Interaction effects occur when the net utility of two different attribute levels is significantly lower or higher than predicted based on the sum of their main effect parts. For example, red is usually a more popular colour for Ferrari cars than for other vehicle brands [36]. Further, CBC enables researchers to create alternative

360

M. Klein et al.

specific designs to avoid unrealistic product combinations (e.g., the possibility of charging a CV at home). However, CBC comes with some disadvantages. Although it replicates purchase decisions realistically, participants must process a large amount of information during a choice task. Therefore, it is recommended to consider a maximum of 10 product attributes [36]. Further, to avoid overstraining the respondents, it is recommended that respondents be presented with a limited number of choice tasks [35]. Originally, the information offered by discrete choice models was only sufficient to analyse aggregated preferences. However, with the higher computational power achieved in the mid-1990s, latent class analysis enabled clustering of participants with homogenous response behaviour. Subsequently, even individual part-worth utilities can be estimated using the hierarchical Bayesian estimation introduced by [37, 38].

2.2 Previous Studies The first attempts to study the market potential of EVs via choice experiments were a reaction to the oil crisis, with studies conducted by [39, 40] being the most cited. Interestingly, already then, the authors had identified range as a key barrier to adoption. The efforts of numerous countries to reduce carbon emissions in the transport sector led to a new wave of studies in the 2010s. Table 1 summarises the design of recent choice studies that were reviewed before designing the CBC of this study. The attributes engine type, price, consumption costs, charging/fuel station density (hereafter: station density), charging time, and range were identified as the most commonly used attributes in comparable studies. It is noteworthy that none of the reviewed choice experiments explicitly considers the attribute of home charging, although the findings of several questionnaire survey studies reveal that it appears to be a knockout criterion for numerous consumers [13, 14, 16, 17, 41]. Moreover, as of today, home charging is typically the most convenient and, thus, the main charging point for numerous EV drivers [13–17]. The only comparable studies that use CBC to consider home charging at all are those by [27, 28]. However, home charging is not considered a standalone attribute in either of the studies, as recommended by [43]. For example, [27] includes home charging in the attribute ‘fuel availability’ and combines it with the general filling station density (fuel availability: 10% of all existing gas stations; 50% of all existing gas stations; home; home and supermarkets). However, this merging only allows the simulation of charging either at home or at the station, which reflects reality to a limited extent. The study by [28] places the charging location indirectly in the attribute ‘refuel or charging time’ [refuel or charging time: Never; 5 min (station); 10 min (station); 2 h (home) and 10 min (station); 8 h (home) and 5 min (station); 8 h (home) and 30 min (station); 8 h (home)]. However, the complexity of this approach is reflected by an illogical or paradoxical output. For example, the possibility to charge in five minutes at a station had a higher benefit than the possibility of charging in five minutes at a station and to additionally charge in eight hours at home [44]. It is

Business Information Through Choice-Based Conjoint Analysis…

361

Table 1 Literature on choice-based conjoint experiments and EVs since 2011 (based on [42]) Engine Price Consumption Station Charging Range Home Other type costs density time charging attributes Achtnicht [18]

X

X

X

X







Performance

Bauer et al. [19]

X

X

X





X



Performance, design, equipment, brand

Byun et al. [20]

X

X

X

X

X



Local emissions

Daziano and Achtnicht [21]

X

X

X

X







Performance

Götz et al. [22]

X

X

X



X

X



Vehicle class, performance, political incentives

Hackbarth and Madlener [23]

X

X

X

X

X

X



Refuelling time, political incentives

Helveston et al. [24]

X

X

X





O



Brand, acceleration, fast charging capability

Hidrue et al. [25]

X

X

X



X

X



Acceleration

Hoen and Koetse [26]

X

X





X

X



Monthly costs, detour time, brands, political incentives

Ito et al. [27]

X

X

X

X

X

X

O

Vehicle class, brand

Lebeau et al. [28]



X

X

X

X

X

O

Fixed costs (per year), max. speed, brand

Qian and X Soopramanien [29]

X



X



X



Political incentives, annual running costs

Rijnsoever et al. [30]



X

X

X

X

X



Local emissions

Tanaka et al. [31]

X

X

X

X



X



– (continued)

362

M. Klein et al.

Table 1 (continued) Engine Price Consumption Station Charging Range Home Other type costs density time charging attributes Zhang et al. [32]

X

X

X





X



Vehicle class

Ziegler [33]

X

X

X

X







Performance

This study

X

X

X

X

X

X

X



Legend: X attribute considered, O attribute partially considered, − attribute not considered

possible that the chosen attribute levels have confused participants and they were not aware in which case they could charge at home and in which case they could not. For this reason, we consider home charging—in addition to engine type, price, consumption costs, station density, charging, time and range home charging—as a standalone attribute to investigate its impact uncoupled from station density or charging time (for a review of consumer preferences towards EV charging infrastructure, see [45]).

3 Study Design 3.1 Attributes and Levels Designing the study required a specification of attributes and associated levels. The attribute selection was discussed in a focus group which consisted of a total of six participants and we ultimately opted for four levels for each of the five ratio-scaled attributes: price, consumption costs, station density, station charging time, and range. The identical number of levels per attribute prevents the so-called ‘number-of-levels effect’, in which one or more attributes are erroneously assigned greater significance only because of the large number of levels [43]. In our study, we focus on new compact cars, which are the most popular in terms of sales in Germany [46]. Therefore, the selected values of the attribute levels encompass typical performances of new compact cars. Table 2 summarises the attributes and levels that were employed in this study, whereas the detailed selection process is discussed below. Engine type. The most important attribute characterising a vehicle is the engine type. In addition to a conventional combustion engine, the vehicles can have a plugin hybrid electric and an electric engine. We also consider PHEVs, which has often been ignored in comparable studies thus far, as this transitional technology has a high potential for market shares [47, 48]. Moreover, in Germany PHEVs are considered EVs by law and, therefo