Data-Centric Business and Applications: Advancements in Information and Knowledge Management [208] 9783031608148, 9783031608155

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Lecture Notes on Data Engineering and Communications Technologies 212

Peter Štarchoň Solomiia Fedushko Katarína Gubíniová   Editors

Data-Centric Business and Applications Advancements in Information and Knowledge Management, Volume 3

Lecture Notes on Data Engineering and Communications Technologies Volume 212

Series Editor Fatos Xhafa, Technical University of Catalonia, Barcelona, Spain

The aim of the book series is to present cutting edge engineering approaches to data technologies and communications. It will publish latest advances on the engineering task of building and deploying distributed, scalable and reliable data infrastructures and communication systems. The series will have a prominent applied focus on data technologies and communications with aim to promote the bridging from fundamental research on data science and networking to data engineering and communications that lead to industry products, business knowledge and standardisation. Indexed by SCOPUS, INSPEC, EI Compendex. All books published in the series are submitted for consideration in Web of Science.

Peter Štarchoň · Solomiia Fedushko · Katarína Gubíniová Editors

Data-Centric Business and Applications Advancements in Information and Knowledge Management, Volume 3

Editors Peter Štarchoň Faculty of Management Comenius University Bratislava Bratislava, Slovakia

Solomiia Fedushko Department of Social Communication and Information Activities Lviv Polytechnic National University Lviv, Ukraine

Katarína Gubíniová Department of Marketing, Faculty of Management Comenius University Bratislava Bratislava, Slovakia

ISSN 2367-4512 ISSN 2367-4520 (electronic) Lecture Notes on Data Engineering and Communications Technologies ISBN 978-3-031-60814-8 ISBN 978-3-031-60815-5 (eBook) https://doi.org/10.1007/978-3-031-60815-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 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 If disposing of this product, please recycle the paper.

Preface

The digital age has made data the lifeblood of businesses and a cornerstone for innovative applications in various domains. Data-Centric Business and Applications: Advancements in Information and Knowledge Management, Volume 3 is a book that explores the transformative power of data by delving into the latest advancements in information and knowledge management. It aims to provide readers with a comprehensive understanding of data’s crucial role in modern business environments. The book covers the ongoing paradigm shift toward data-centric approaches and their profound impact on business strategies, decision-making processes, and application development. Exploring this book begins with the latest concepts and methodologies in information and knowledge management. It covers everything from data governance and analytics to knowledge discovery and artificial intelligence. This book provides an in-depth understanding of managing, analyzing, and deriving meaningful insights from massive and complex datasets. As readers progress through the chapters, they will encounter a wide range of topics. These include the integration of data-centric approaches into business models, the challenges and opportunities that arise from emerging technologies, and the ethical considerations in data-driven decision-making. Each chapter has been written by an expert in their respective field, offering unique perspectives and insights to understand the data-centric landscape comprehensively. Authors Lumbardha Hasimi and Daniel Penzel delve into cloud computing from a provider’s perspective in their chapter titled “Cloud Market—Possibilities, Potentials and Challenges of Cloud Computing from a Provider’s Perspective.” The chapter explores the various facets of the cloud market, discussing possibilities, potentials, and challenges that providers may encounter. It provides valuable insights into the evolving landscape of cloud computing and is essential for those interested in understanding this dynamic industry. Innovation Regional Policy and Smart Specialization take center stage in the second chapter, authored by Maryna Nehrey, Larysa Zomchak, and Myroslav Havryliuk. The chapter “Innovation Regional Policy and Smart Specialization: European Countries and Ukraine” comprehensively examines regional policies fostering innovation, particularly in European countries and Ukraine. Readers will gain v

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insights into the strategic approaches to smart specialization, making it a valuable resource for those interested in regional innovation dynamics. Torsten Huschbeck, Oliver Haas, and Peter Markovič explore the critical theme of risk management in the non-profit public sector in their chapter titled “Risk Management in the Non-profit Public Sector—Hazards of a Lack of Reflection Within the Police Force.” The chapter sheds light on the hazards stemming from a lack of reflection within the police force and emphasizes the importance of effective risk management strategies in the non-profit sector. Petro Pukach, Bohdan Liubinskyi, Volodymyr Hladun, and Vladyslav Holdovanskyi collaborate on a chapter titled “The Classifier Models Usage for the Recruitment Process Forecasting for Applicants of Higher Education to Universities of Ukraine.” The chapter explores the application of classifier models in forecasting the recruitment process for higher education in Ukrainian universities, providing valuable insights into the intersection of technology and education. Olena Shlyakhetko and Michal Greguš sr. contribute a chapter titled “Virtual Museum Design in Sustainable Cultural Heritage: A Literature Review.” This chapter conducts a comprehensive literature review on virtual museum design within the context of sustainable cultural heritage. It serves as a valuable resource for those interested in the intersection of virtual technology and cultural preservation. Lea Saal, Torsten Huschbeck, and Christian Horres present a quantitativeempirical study embedded in risk management in their chapter titled “Reputation of a Non-profit Organisation—A Quantitative-Empirical Study Embedded in Risk Management on Police Reputation and Reputation Loss.” The chapter explores the reputation of non-profit organizations, focusing on the police sector, and provides valuable insights into reputation management strategies. Olesya Slavchanyk, Solomiia Fedushko, Vladyslav Mykhailyshyn, Nataliya Shakhovska, and Yuriy Syerov delve into the application of artificial intelligence for customer behavior and churn prediction in their chapter titled “Artificial Intelligence Application for Customer Behavior and Churn Prediction,” the chapter discusses the role of AI in predicting customer behavior and churn, making it a valuable resource for those interested in customer relationship management. Tibor Zsigmond, Ladislav Mura, Renáta Machová, and Diana Ignácová present a pilot study on workplace discrimination from the perspective of leaders of Slovak enterprises in their chapter titled “Workplace Discrimination from the Perspective of Leaders of Slovak Enterprises—pilot Study.” The chapter sheds light on the issue of workplace discrimination, providing insights from the perspective of organizational leaders in Slovak enterprises. Salem Lepaja evaluates the performance of the IEEE802.11ax amendment in his chapter titled “Performance Evaluation of the IEEE802.11ax Amendment.” This chapter thoroughly examines the IEEE802.11ax amendment, providing insights into its performance and relevance in the context of wireless communication. Monika Pikus and Michal Greguš explore the dynamics of virtual agile collaboration during a lockdown in their case study chapter titled “Virtual Agile Collaboration During a Lockdown: Case Study.” The chapter provides a real-world case

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study, offering insights into the challenges and opportunities of virtual collaboration during lockdown situations. Reza Shahin tackles the concept of flex-route transit in his chapter. “Flex-Route Transit: Problem Definition, Case Studies and Development over Years in Optimization Literature” defines the problem, presents case studies, and traces the development of flex-route transit over the years in optimization literature, offering a comprehensive overview of this transit concept. Mariya Kirzhetska, Ihor Novakivskyi, Olena Zahoretska, Yuriy Kirzhetskyy, and Anatolii Havryliak contribute to the understanding of the development of regional IT clusters in conditions of permanent socio-economic threats. “Development of Regional IT Clusters in Conditions of Permanent Socio-Economic Threats” offers insights into strategies for fostering IT clusters in challenging socio-economic environments. Oleh Kuzmin, Yevhen Tsikalo, Lesya Say, Rostyslav Bala, and Oleksandra Vivchar explore information and economic mechanisms for developing system integration in managing enterprises’ business processes in their chapter. “Information and Economic Mechanisms for the Development of System Integration in the Management of Enterprises’ Business Processes” provides valuable insights into integrating information and economic mechanisms for effective business process management. Lubica Bajzikova and Tetiana Smerdova collaborate on aggregating resumes and extracting information in their chapter. “Improving the Recruitment Process in Multinational Organizations Using Robotic Process Automation and Artificial Intelligence,” explores techniques and methods for aggregating and extracting relevant information, offering practical insights for recruitment processes. Andrii Dumyn, Solomiia Fedushko, and Yuriy Syerov conducted a comprehensive review of automatic speech recognition systems for Ukrainian and English languages in their chapter titled “Review of Automatic Speech Recognition Systems for Ukrainian and English Language.” The chapter provides an overview of ASR systems, contributing to the understanding of their capabilities and limitations in the context of Ukrainian and English languages. Katarina Vavrova and Igor Šarlina delve into the strategic direction of financial activities of EU states in digital business models in their chapter titled “Strategic Direction of Financial Activities of EU States in Digital Business Models.” The chapter explores the strategic aspects of financial activities in the context of evolving digital business models, providing insights into the challenges and opportunities EU states face. Akshay Bajpai, Denys Nevinskyi, and Yaroslav Vyklyuk focus on Alzheimer’s Disease diagnosis using a machine learning approach in their chapter titled “Alzheimer’s Disease Diagnosis Using Machine Learning Approach.” The chapter explores the application of machine learning techniques for Alzheimer’s diagnosis, providing insights into the potential of technology in healthcare. The book is suitable for various audiences, including researchers, practitioners, academicians, and business professionals who want to keep up with the latest advancements in information and knowledge management. Whether you are a data

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scientist, a business executive, or an academic researcher, the various topics covered in this book can provide valuable insights into the role of data in shaping the future of businesses and applications. We would like to express our appreciation to the contributing authors who have shared their expertise and experiences, making this book a collaborative effort that reflects the multidimensional nature of the data-centric revolution. As editors, we hope this compilation serves as a valuable resource, inspiring further exploration and innovation in the dynamic realm of data-centric business and applications. Many companies face the challenge of dealing with high levels of risk and uncertainty and need sustainable solutions. These solutions are often based on digitalinfluenced techniques. This volume of our subseries encourages further research by highlighting ongoing progress in structural management. Previously understood knowledge, technologies, and data can provide a significant boost in achieving this goal. Bratislava, Slovakia Lviv, Ukraine Bratislava, Slovakia

Peter Štarchoň [email protected] Solomiia Fedushko [email protected] Katarína Gubíniová [email protected]

Contents

Cloud Market—Possibilities, Potentials and Challenges of Cloud Computing from a Provider’s Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lumbardha Hasimi and Daniel Penzel

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Innovation Regional Policy and Smart Specialization: European Countries and Ukraine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maryna Nehrey, Larysa Zomchak, and Myroslav Havryliuk

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Risk Management in the Non-profit Public Sector—Hazards of a Lack of Reflection Within the Police Force . . . . . . . . . . . . . . . . . . . . . . . Torsten Huschbeck, Oliver Haas, and Peter Markovič

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The Classifier Models Usage for the Recruitment Process Forecasting for Applicants of Higher Education to Universities of Ukraine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Petro Pukach, Bohdan Liubinskyi, Volodymyr Hladun, and Vladyslav Holdovanskyi

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Virtual Museum Design in Sustainable Cultural Heritage: A Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Olena Shlyakhetko and Michal Greguš Reputation of a Non-profit Organisation—A Quantitative-Empirical Study Embedded in Risk Management on Police Reputation and Reputation Loss . . . . . . . . . . . . . . 117 Lea Saal, Torsten Huschbeck, and Christian Horres Artificial Intelligence Application for Customer Behavior and Churn Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Olesya Slavchanyk, Solomiia Fedushko, Vladyslav Mykhailyshyn, Nataliya Shakhovska, and Yuriy Syerov

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Workplace Discrimination from the Perspective of Leaders of Slovak Enterprises—pilot Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Tibor Zsigmond, Ladislav Mura, Renáta Machová, and Diana Ignácová Performance Evaluation of the IEEE802.11ax Amendment . . . . . . . . . . . . 185 Salem Lepaja Virtual Agile Collaboration During a Lockdown: Case Study . . . . . . . . . . 201 Monika Pikus and Michal Greguš Flex-Route Transit: Problem Definition, Case Studies and Development over Years in Optimization Literature . . . . . . . . . . . . . . 221 Reza Shahin Development of Regional IT Clusters in Conditions of Permanent Socio-Economic Threats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 Mariya Kirzhetska, Ihor Novakivskyi, Olena Zahoretska, Yuriy Kirzhetskyy, and Anatolii Havryliak Information and Economic Mechanisms for the Development of System Integration in the Management of Enterprises’ Business Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Oleh Kuzmin, Yevhen Tsikalo, Lesya Say, Rostyslav Bala, and Oleksandra Vivchar Improving the Recruitment Process in Multinational Organizations Using Robotic Process Automation and Artificial Intelligence . . . . . . . . . . 287 Lubica Bajzikova and Tetiana Smerdova Review of Automatic Speech Recognition Systems for Ukrainian and English Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Andrii Dumyn, Solomiia Fedushko, and Yuriy Syerov Strategic Direction of Financial Activities of EU States in Digital Business Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Katarina Vavrova and Alexander Šarlina Alzheimer’s Disease Diagnosis Using Machine Learning Approach . . . . . 367 Akshay Bajpai, Denys Nevinskyi, and Yaroslav Vyklyuk

Cloud Market—Possibilities, Potentials and Challenges of Cloud Computing from a Provider’s Perspective Lumbardha Hasimi and Daniel Penzel

Abstract This chapter concentrates on cloud computing challenges, possibilities and potentials analyzed from provider’s point of view. The main focus lies on analyzing the implemented enterprise value of cloud computing as well as obstacles and challenges towards the development of its full potential and the status-quo of the market size and share. An overview of the biggest players in the market, the regional analysis, market segmentation, value drivers and overall market conditions. The chapter ends with insights on unique value proposition, the challenges towards it and the competition drivers. The businesses focus on becoming agile to counter hostile environmental changes and global reachability, as selling proposition for cloud computing while the actual state, potential and expectations are fairly high. Keywords Computing · Cloud market · Challenges · Cloud paradigm · Enterprise value

1 Cloud Computing from Customer’s Perspective 1.1 A Brief Overview of the Costumer’s Perspective in the Recent Years Exceeding the predictions and bringing a new definition of the potential of cloud, the post-covid era presented a new scenario for the cloud market. In 2022 only, a total of $490.3 billion were invested in cloud computing, while according to Gartner, Inc. in 2023, it is predicted a worldwide public cloud spending of $591.8 billion, which L. Hasimi (B) Comenius University, Bratislava, Slovakia e-mail: [email protected] D. Penzel University of Vienna, Vienna, Austria e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_1

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represents a growth of 20.7% from the last year’s data. This is a massive boost, which for further analysis needs the figures need to be set in context. Back in 2010 cloud computing as a new trend, was growing rapidly alongside traditional hosting. However, last decade was at some point a contrast to the high expectations of the IT industry—the development seemed more organic than exploding. Furthermore, now same as a decade ago, the cloud market continues to be dominated by the US [1]. One reason for that is the fragmentation of the European hosting market. Local differences like the size of the market, the cultural background and the IT buying behavior as important marks, are viable when analyzing the European hosting market [2]. Figure 1 summarizes the European hosting market, divided into the categories market size by segment. Figure 2 visualizes a highly divided European market. Despite of service providers having grown cloud revenues by 167 percent, the market share declined from 27 to 13 percent as the growth rate was much behind the overall cloud market growth [1]. Amazon, Microsoft, and Google account for 72% of the regional market and their market share continues to rise steadily, while being the main beneficiaries of this market growth. SAP and Deutsche Telekom account for two percent of the European market, being the leading European cloud providers. With the recent blast on the digital transformation, the companies have started to focus more towards shifting the internal strategies and including the adaption of cloud applications. As the figures present, cloud computing is developing and progressing, but there is a long way to go and

Fig. 1 The European Hosting Market. Retreived from statista.com

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Fig. 2 European cloud provider share of local market

traditional hosting remains the leading technology. Through creating trust, transparency and security cloud providers could take the next step in the future, and make a move towards unlimited potential and establishment of cloud services. The classification of cloud computing recently, gives the impulse to take a closer look at the markets of cloud computing and their segmentation.

1.2 Market Segmentation This subsection provides a small overview on the segments cloud computing is dealing with. It should give an idea how to classify cloud computing in today’s business environment. The framework to create Table 1 relates to Wedel and Kamakura [3]. As cloud computing has different requirements than traditional products, an own framework to evaluate the segments of cloud computing has been developed. Table 1 Market Segmentation [3] Product

Service

Customers

Infrastructure-as-a-service Private cloud Small Platform-as-a-service Public cloud Medium Software-as-a-service Hybrid cloud Large Global player

Behavioral

Geography

Pay-per-use In-house Service level agreements

USA Europe Asia

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1.3 Product Section The product section differentiates three major offerings: Infrastructure-as-aService (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). Infrastructure-as-a-Service offers infrastructure to its clients. Over the Internet access is provided to mainly processing power and storage. An enterprise using Infrastructure-as-a-Service can deploy its own software and has control about the operations executed in the cloud environment. The platform-as-a-Service providers do not only offer infrastructure, but a platform for the user to develop own services [4]. In the Software-as-a-Service, the user can utilize a provider’s proposal of cloud applications that are offered via a cloud infrastructure. The user gets access to specific services over a web browser or an interface and does not manage or control the underlying cloud infrastructure [5]. The first decision for a customer at market segmentation is getting aware of the product one wants to get through the integration of cloud computing. The market offers mainly the three described solutions to enter cloud computing. The decision for a product is essential, as nearly every segment of cloud computing is directly connected to the product section.

1.4 Service Section The next part of the segmentation is the service section. Cloud providers offer different deployment models to integrate cloud computing. In combination with the product section, the service section builds the most important part of the integration of cloud computing and therefore also the most important part of the market segmentation [6]. Three deployment models are offered to divide the market: private cloud, public cloud and hybrid cloud. Private clouds are used for the internal operations of enterprises and they are only handled by one enterprise. The structures of the cloud can be provided in-house or offered by a third party. In contrast to private clouds, public clouds house the data sets of various users. On an outsourced system, enterprises access to third party structures to run their business [7]. A hybrid cloud combines separate private and public cloud environments. The hybrid cloud proposes the best parts of private and public clouds, but also pools their negative aspects [8]. Studies [9, 10] directly connect the service segment with the customer segment, as they worked out that large businesses own private clouds in contrast to small businesses and individual consumers, which integrate public cloud structures. This outcome relates to the behavioral segment. Private clouds are more expensive to integrate and run, so small enterprises and individual consumers often do not have the possibility to afford a private cloud. For this reason, the decision to whether to enter the private or public cloud segment is only up to large enterprises.

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1.5 Customer Section The customer section for cloud computing differentiates between small businesses, medium businesses, large businesses and global players. This is a result of the product and service offerings, already presented. Cloud computing tries to attract every company in need for IT infrastructure, customers are segmented to analyze what kind of product and service they need or in the mind of providers, which kind of service they can afford. Private or hybrid clouds are affordable for large businesses and global players [10, 11]. Small and medium businesses have to optimize their needs through the adoption of public clouds.

1.6 Behavioral Section The behavioral section differs between three types of payment: the pay-per-use model, the in-house model and the service level agreement (SLA) model. The payper-use model is a model, where the exact costs of usage are determined and the customer pays for what he gets [12]. The pay-per-use system offers more flexibility and the loss of long-term fixation for both, the customer and the provider. The system makes a step forward in contrast to traditional hosting, where fixed resources are offered. Large enterprises and global players have the possibility to develop an in-house cloud computing data center, where employees use their devices to enter a web interface hosted by the IT department. The private cloud offered through this system is limited because many advantages of a real cloud are wiped out through still running own systems and regretting on-demand structures. For large enterprises and global players the specification of services are provided in terms of metrics agreed upon by all parties, and penalties for violating the expectations are written down in service level agreements [13]. A service level agreement offers warranty to the customer, it defines the level of maintenance and administration by the cloud provider, bringing the service level agreement as a negotiated contract between two parties, the customer and the service provider [14–16]. The agreement can be legally binding or informal and specifies the services that the customer receives rather than how the service provider delivers the services. Small and medium businesses in most cases do not have the opportunity to get that type of service level agreement. Public cloud vendors often deliver their service based and own pre-assembled contracts adapted to their type of cloud service. However, it is important to consider that not only pricing costs have to be included, when integrating cloud services. There are costs for migration, implementation, integration, training and redesigning [15, 17]. Especially for small and medium enterprises, but even for large enterprises and global players this argument delivers a connection to the service and product segment. The choice of product and service directly influences the behavioral segment through the price factor.

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1.7 Geography Section The last important market segment is the geographical position. The biggest market for cloud services is the US market (Fig. 3), while the European market is deeply divided as presented in the first section. An important issue is the physical location of data centers, since clouds have to face geographic and political borders. A cloud is best located, where law coincides with the law the enterprise has to operate with [18]. Another point to be mentioned, when talking about the location is the cost. To optimize the costs related to the locaiton, most cloud computing data centers are located outside urban districts and their location is tied to the low tax states, for instance in the North American case [19]. According to the data released by GlobalData [20], the forecasted investments in cloud computing divided into regions, once again North America is projected to surpass the Asia Pacific by 2026 accounting for over 25 percent of the global cloud computing market value, with a forecasted growth rate exceeding 15 percent from 2022 to 2026. The North American region is characterized by a strong inclination towards rapid digital transformation and implementation of next-gen technologies coupled with advanced telecommunication technologies is anticipated to drive regional growth over the forecast period [20]. The North American market has already been the biggest market for cloud computing and will retain its position according to the forecasts.

Fig. 3 Market Growth in Cloud Computing 2018–2023 [20]

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This segment is least connected to the other market segments of cloud computing. The geographical section delivers an insight, where cloud providers should be interested to gain market share. The regional distribution of cloud computing growth only offers data about the investment, not about the potential to conquer a market or stretch market share. The African and Latin American market remains behind in instating the real potential of cloud computing.

2 Market Segmentation Despite of being a paradigm in the IT industry, the segmentation of cloud computing markets is still not mature enough. There are divided market segments, but in contrast to the objective of offering strict borders between the different market segments, there are still a lot of connections in between. A positive point is that nearly every segment already has strict and comparable attributes. As cloud computing develops, the market segmentation also will. Enterprises already enlarged their spectrum of offerings and this will continue in the future [21]. A more differentiated market segmentation would have a positive effect on small and medium enterprises, as the services will get cheaper and the selection of software would further improve. Not only the enterprises and the strategy seekers, shall focus more on the maturity of the cloud, a better understanding of the product is also important from all instances in order to enable easier segmentation. The segmentation of cloud computing will continue as the product itself quests for maturity. Enterprises will develop new service offerings to divide the market and to acclimatize with customer needs. Product evolution facilitates better-detailed future market segmentation.

2.1 Area of Operations This section explains the research projections of the paper and sets the third chapter into context. A short characterization is offered to arrange cloud computing’s operations. The section carves out challenges and their relation to the goals of the paper. Some studies elude the following five major characteristics of cloud computing: The characteristics mentioned in Table 2 define the possibilities and abilities of cloud computing. As the paper will focus later on, the characteristics coin the advantages of cloud computing. One research implication is that the implemented enterprise value of cloud computing is far behind the expectations of the IT industry. To prove or neglect the hypothesis the conversion of characteristics and advantages in enterprises has to be checked and the impact of cloud offerings has to be examined. The definition of customer expectations is an important issue when integrating cloud computing. Cloud providers need to define accurately, what the customers do expect from the cloud and allow easy adaption. In [22] authors state “collaboration

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Table 2 Characteristics of cloud computing [22] On demand self-service

Access and fulfillment of requested data and processes are fully automated. Available resources are offered on-demand, which provides quick and easy access

Elasticity

“Computing is provided in the amount required and disposed off when no longer needed” (Durkee 2010, p. 1). The user only has to pay the amount of computing provided, not a fixed amount for a fixed bulk of resources

Resource pooling

A provider has a pool of resources every customer is able to access. Dynamic provision of resources to every customer using the cloud is the goal. Through scalability of resources the provider enables the offering of the same computing services and service layers to different customers without straining the structures

Measured service

The service cloud computing systems are offering is automatic. The system controls and optimizes the resources of the user adapted to the service Rountree and Castrillo (2013, p. 5) add that the measured service is taken to bill the customer. The usage can be quantified “using various metrics such as time used, bandwidth used, and data used”

Broad network access

Cloud services are typically accessed over the network. Every type of device licensed to use the cloud is able to connect to the service. Because of the network access no client or a lightweight client would be perfect to enable a stable connection

for employees and transparency in pricing and cost, a fast change and more agility in supplying application development platforms [and] a decrease in the energy use of the company” as a solution for rising customer expectations. Cloud providers have to develop their offerings while facing a price battle. As technology develops, it gets cheaper at the same time. This is a chance for providers, but at the same time a huge threat to the cloud operations. Rising expectations and a better understanding of the cloud computing system by customers puts pressure on the providers. Even though cloud computing emerged over the last years, there are still challenges to be faced. Majority of the entities, although being in front of the necessitity of digital trnsformation, still have the fear that comes with uncertainty. In otherhand, security remains the most important challenge of the deployement [23, 24]. Control, is another issue closely connected to security. Many studies notice enterprise’s unwillingness to trust third party staff in control and design of the platform [25, 26]. Another challenge is the interoperability, portability and migration of IT systems. A challenge towards a unified system would benefit customers, as they would no longer be bound to one single provider [27, 28]. The speed of clouds is limited to the speed of the Internet. The industry presents scalability as one of cloud computing’s most intriguing advantages. However, not always can problems be solved through adding additional capacities.

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3 Models for the Integration of Cloud Computing This section focuses on the objectives providers want to achieve through offering cloud structures or services. The case study compares the most successful cloud providers and wants to find a solution for whether there is an enterprise with a unique selling proposition that leads the market.

3.1 Value Drivers The first subsection analyzes the advantages a cloud provider is able to realize through offering cloud services. As the advantages of cloud computing are usually described from a users point of view, we go back to the user’s advantages to describe the value drivers and the intentions of cloud providers. A study published by [30] opens up the possibility to look at value creation in e-business from a cloud provider’s point of view. The study defined four different sources of value creation in e-business, presented in Fig. 4. Figure 4 illustrates the circumstances of value creation without looking on either customer or provider side. Efficiency increases through the decrease of transaction costs. Cloud computing decreases transaction costs for providers through a standardized system and service offering. Cloud providers that broaden the range, simplify their systems and scale the services. Loyalty and trust are important points for cloud providers, as the cloud adopter will not decide imprudently when getting locked-in to a customized service offering [2, 29, 30].

Fig. 4 Sources of value creation in E-business [30]

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The complementarity features one of the major opportunities in cloud providing. A cloud provider is able to offer the whole spectrum of services over his cloud providing system. Infrastructure, services and software can be connected to create additional value. Cloud computing has not presented a new technical dawn, but the transaction methods and structures have been optimized, opening the hosting market for new segments of customers [2]. In the business sector cost presents the most important factor. As already described, the potential of cloud services is enormous and enterprises no longer only want but also need to benefit from this development. For a decade, cloud computing has been on the way of being the biggest market in IT. Starting from $40.7 billion in 2011 the cloud computing market is forcasted to $591.8 billion in 2023 [31]. Enterprises with the ability to implement a cloud providing structure have an incentive to do so, to enter an afferent market, even if there is an innovative player as Amazon that led the way in the first years of cloud integration. Fast pace and rapid growth create huge competition in cloud computing markets. The problem to start cloud computing offerings is high up-front investment for cloud providers. But many statistics ensure high costs for the multi-tenant architecture will be outweighed by the long-term income [19, 32, 33]. In contrast to cloud users, which benefit from the pay-per-use system, cloud providers have to offer the underlying hardware, platform or service. Because of that, cloud providing is in contrast to cloud using bound to enterprises with the ability to invest up-front. Despite a high investment to establish a cloud providing system, providers are able to save money through lower continuing investments. Once established, cloud providers have the possibility to move IT systems forward at high pace, because they can directly hit the market and do not have to wait for investment cycles. Significant cost savings occur in selected situations, notably when the scale of an enterprise’s computing resources is relatively small compared with that of cloud providers [34, 35]. Transformed to a cloud provider’s point of view, this means that in contrast to fixed hosting services with high investments, providers are now able to access a new category of customers. Services are paid by the amount of consumption and no longer based on fixed contracts. Small and medium enterprises are able to integrate powerful IT systems by paying for the real use of services and not for a hosted server. The additional use for providers is the flexibility of the services and the new customer segment, which could be added to the portfolio [36, 37]. Providers can now offer unbound access to their services. Small and medium enterprises benefit heavily through making their investment more calculable and providers again have a bargain through achieving new customer segments. Although the market segmentation had shown that Eastern Europe, Africa and the Middle East will not feature a drastic increase, with the development of structures and a widespread integration, cloud computing has become the most important factor for business outsourcing. The possibility of a development in at least a few third-world countries in combination with the possibility to deliver service from all over the world could open up big markets in the future and some providers will at least have thought about that possibility. The real value of that development is unpredictable, because

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third-world countries in normal case do not even have proper Internet connections, to be in line with the latest advancements [38]. Access to a new group of customers can open up possibilities for diversification and growth. Through the scalability of services, a cloud provider is able to benefit from economies of scale, just as cloud users do [39]. The high up-front investment can be balanced with lower investments into existing systems. Cloud providers offer standardized systems when offering public clouds, so the maintenance and service is standardized in this type of cloud, too. Therefore the costs can be reduced. Generating profit and attracting new groups of customers are the most intriguing arguments for providers to access the cloud computing market [11]. So, there is additional value cloud computing can generate for providers. Economies of scale do not only reduce the costs for cloud providers and cloud users as well, they also facilitate the distribution of standardized service offerings for example. The service provider is able to offer his software solution to all of his customers at the same time. The customers access through the structures of the provider and share the resources. Economies of scale can, as already mentioned, reduce capital expenditure, as well as reduce operational expenditures and time-tomarket. The deployment of standardized services is fast over a cloud computing platform and easy access is granted [36, 40, 75]. Cloud computing simplifies interaction between provider and customer through a standardized platform and standardized applications. IT service management can be standardized as well as products. Another important factor is the on-demand service. An upscale or the addition of resources is easy to handle for a provider in contrast to a contract, where resources are fixed. The on-demand service works automatically, so there are no employees needed to enable new resources. The computing power and storage can be delivered rapidly in contrast to traditional hosting, where interaction is needed to offer accessory resources [2]. Application providers can offer new software to their customers without changes in infrastructure or a change of existing software offerings for every customer at the same time, if standardized. Service providers do not have to optimize software for every single customer. Cloud providers can benefit from customer satisfaction and feasibility and therefore on-demand self-service and rapid elasticity is expedient for both, cloud providers and cloud users. Additional resources generate additional income for providers and flexibility for customers [41]. There are many reasons, why customer relationships as an important issue for cloud providers. Through customer service and technical support, providers have the possibility to be in direct contact with the customer and improve services to customer needs. The customer can be better integrated into structures and it is easier for providers to offer support in standardized systems. Employees can be trained directly to work with the specific structures the provider developed. This opportunity is offered by standardized systems. An enterprise with a private cloud, which is adjusted to its own structures will not benefit in the same way as an enterprise using a standardized system. For providers it is the same way around. A non-standardized system cannot be trained as effective to employees, as a standardized one implemented by a cloud computing solution. For the future, a value driver for cloud providers will be to reduce private clouds and elate enterprises to integrate public

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clouds. The motivation behind is the relationship between customer and provider, the maintenance, security and interoperability and once again the reduced cost of standardized systems [42, 43]. As worked out in the market segmentation and in the cost section, geography can play a major role for cloud providers. Despite problems with law and trust, the distribution without borders will continue growing. For the providers the role of the geographical destination of servers is declining and the opportunity to sell across a broader geography without investing in infrastructure is evident [44]. Cloud computing improve the service of providers through quality, fastness while communication takes place with customers, suppliers and all stakeholders irrespective of their location [2]. Cloud providers are attracted by the vision of offering their whole range of services to the customer over one single platform and at top with a customer lock-in. A value driver for cloud providers is this lock-in opportunity. Customers are not tied to their platform, but most customers stick to the platform because switching data to a different platform is difficult and sometimes disconcerting. So despite the fact that customers are not fixed to a cloud computing system, the incentive to stick to their provider is relevant. The goals of cloud providers are highly connected to the advantages and the use customers want to see in cloud computing. Figure 5, illustrates what cloud providers are aiming for when creating and assembling a cloud computing structure [45]. Cloud computing systems are very expensive to create and therefore implementation costs are the main factor. Every objective a provider wants to achieve with the cloud is connected to the costs as first consideration. As presented in the Fig. 5, the efficiency, operational security and the overall security are driven by costs. Cloud computing’s major advantage is that high investments for the establishment of a

Fig. 5 Construction of a cloud [22]

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cloud providing structure are followed by low investment into maintenance, service, communication and distribution [45]. Cloud computing systems offer a lot of advantages for cloud providers, most of them are directly connected to the benefits of cloud users, which makes cloud computing in theory even more useful. But again most of the points mentioned are predictions for the future. After analyzing why enterprises find interest in serving cloud computing structures the next subsection will focus in the cloud computing market from a provider’s perspective.

3.2 Market Conditions Cloud providers have a concrete vision of what cloud computing should deliver. It is all about the efficient management of IT to provide services rapidly. Cloud computing markets are highly contested. There are a lot of established players in the cloud computing market and as building of cloud structures is a very cost intensive business, some of the players are the biggest enterprises in the IT market. Smaller cloud providers struggle on finding the niche in the market, as the global players operate through a price war and growth strategies [46]. Differentiation should be the key for smaller providers to stay in a competitive market and establish themselves. Customer knowledge, flexible solutions and premium quality of services are the issues to focus on when trying to refrain from a price war. Depending on the service offering cloud providers are arranged into a category of customers. A broader service offering with different deployment models accompanies with a broader segment of potential customers. Private cloud structures are offered for huge enterprises by more diversified cloud providers, they are not as easy to handle and not as easy to implement. The focus of the big players in the cloud computing market is the offering of the best services available at lowest costs and therefore adjustments in private cloud structures do not fit into the service portfolio. Trust is not the only issue cloud customers have to accept when adopting cloud systems. Cloud infrastructure, platforms and services are developed by providers customers have to approve their terms or search for a different provider. Cloud providers develop the conditions, customers have to accept conditions in terms of securing costs or move on. A major factor considering market conditions is globalization. The businesses have to focus on becoming agile to counter hostile environmental changes and global reachability, as selling proposition for cloud computing [2, 38]. As market shares and profitability vary enterprises have to shift, too. The market conditions change very fast in the world of IT, a successful enterprise has to keep up with the changes and constantly adapt its structures to new requirements. For cloud providers unless they are part of the big players it is the only alternative to run their service. The cloud providing market is a very competitive market, but at the same time a market optimized for the cloud provider and not for the customer. In other words, it is

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designed for maintenance, scalability and lowest common denominator functionality: Cloud computing as a system limits the abilities of the customer through creating an own self-managed offering, which is not created to offer best customer service [47]. Thus the market is hard to enter, but once established, the potential is enormous. Because of big players dominating the market it is reasonable to find a niche to avoid price wars and capability fights. The requirements will be shifting within the next years. Providers are investing on being flexible for a switch or adaption of their structures to stay competitive.

3.3 Service Offerings Cloud computing presents different methods to satisfy customer needs. Cloud providers decide first, what spectrum of service offerings they want to render possible for their customers. In between three service models cloud providers develop their own kind of service in context to their strengths. An Infrastructure-as-a-Service offering can include the capability to provision resources and allow the consumer to deploy and run (arbitrary) software [4, 25]. Computing power is another service offering providers can serve to their customers. The customer is enabled to use the underlying hardware, but has to deploy his own software and system. The consumer is able to control the software and operating system. Depending on the contract with the service provider, the customer has the ability to control limited parts of the networking components [48]. Platform-as-a-Service defines the next step in the possibility range of cloud computing. Platform-as-a-Service supports the development of applications in the cloud, with design, implementation, debugging, testing, deployment, operation and support as the most important services a cloud provider proposes to the customer. The customer is able to deploy own services, services created by the service provider and optimized by the enterprise, or services developed by a third-party and ordered by the customer of the cloud service [49]. Software-as-a-Service is described as the most commonly used application of cloud computing [50]. Software-as-a-Service represents a one-to-many model. The provider enables the use of his own software and applications to the customer on his cloud infrastructure. The application is offered to a bunch of customers, which share the use of the application and run it on the Internet [51]. The software is hosted and operated by the provider, whereas the underlying infrastructure is not managed or controlled by the user [5]. The provider cannot only generate service offerings out of Software-as-a-Service, but is also able to increase the speed of software development, ensure a faster adoption of services, decrease support requirements and ease implementation updates. There are different models to offer services in cloud computing (Fig. 6) and in between the different services there are also various possibilities to create service offerings for cloud providers. The cloud provider detects the type of service to offer to his customers and build an efficient structure to support the system.

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Fig. 6 The development stages of cloud [52]

Different types of cloud providers have variable goals they want to accomplish by offering cloud computing services. Dependent on the business archetype, the type of service is designed and presented to the customer. Based on service models, business archetype and personal specifications cloud providers develop their spectrum of service offerings. The development of cloud computing service start with the virtualization of hardware, which through years evolved to extensive service offerings. The objective of the cloud providing industry is to transform enterprises into virtual enterprises where every service is offered through the cloud. For instance, in [52], authors distinguish four stages of the cloud technology development for their case. These stages as seen in the Fig. 6, start with the cloud computing based on virtualization, with focus on use of resources. Followed by second phase which deals with the service of infrastructure resources. The integration of infrastructure services, platform services and data services, combining public and private clouds to form hybrid cloud capabilities presents the third stage. Finally, the fourth stage as a comprehensive multi-cloud phase. The multi-cloud phase is based on a hybrid and heterogeneous public cloud and private cloud [52]. Section 4.4 presents the different pricing systems in cloud computing. As a small overview on pricing was already served in the market segmentation, this section leads towards the real pricing design of cloud providers to guide towards the showcase.

3.4 Price or Differentiation Cloud computing is advertised as a system of low commitment and customer-oriented solutions in the pricing sector. In an industry focused on revenue and profit and in a segment with heavy price wars, it is not customer-orientation, which leads towards decreasing prices and the development of better services [38]. There are two paths to follow when developing a cloud computing strategy, like in most other business sectors, too: price or differentiation.

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Price of Cloud Computing

The big players in cloud computing fight a heavy price war. In 2020 experts predict a new escalation of the cloud war price. Although the maturity of the cloud has overcome the ascertained belief that prices in cloud do not overcome the hardware prices, the new definition of price and cost has been take to another point. Figure 7 carves out three steps of cloud cost audition. There are many researchers that argue on the complexity of the pricing in cloud computing. Even if cloud computing is cheap, the cost of hardware drops at faster pace. According to [53] cloud computing is not always cheaper than traditional onpremises systems. There is still a lot of reduction potential, to make the cloud cheaper for its customers. Cloud providers have a leverage to conform prices and reduce them according to the trade-off between capital expense and operational expense and not according to sinking hardware prices. Cloud providers, which force a cost-leadership, reduce prices. But it is not about the customer who should get a cheaper service. The sinking prices are influenced by the price war between the big players. Competition drives prices down and customers can benefit from the price war, just as providers do from the sinking hardware prices [36, 54]. There many different pricing models used in cloud. For instance, in the pay-peruse model, the customer pays for the service that he needs without any additional expenditures. The pay-per-use model is the most common model in the pricing section

Fig. 7 Price of the cloud computing [53]

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Table 3 Cloud Pricing [55] Tiered pricing

Different tiers each with different specifications are provided at a cost per unit time

Per-unit pricing

The user pays for the exact resource usage

Subscription pricing Common at Software-as-a-Service products, the user pays a fixed monthly fee

of cloud service models. Table 3, shows three models of pay-per-use introduced by cloud providers Simple pricing models are the future of cloud computing because of their transparency and understandability. Researchers argue, that transparency is the core value of the service. Hence, focusing on the applications, and minimizing the risk of wasted resources on complex pricing models, is the best solution.

3.4.2

Differentiation

Smaller providers cannot fight a price war against the global players to stay in the cloud market. However in [55] authors carved out that cost reduction and cost savings are not always the main factors, when small or medium enterprises decide to move their operations to the cloud. In their study the ease of use and convenience and security and privacy are the most important factors for small and medium businesses when deciding to integrate a cloud provider into their structures. Smaller cloud providers have to tread a different path. They have to focus on different customer needs than price and cost reduction. A well-defined service offering can often beat the low-price offering, especially in the IT sector where security and operability play a major role. Additional services, better transparency and customer relationship can lead smaller providers to their goals, without being in competition with a global player [56]. The most important issue for small cloud providers is to find the niche where they can operate with the highest profitability. There are three categories (Table 4) that can affect customer satisfaction and need to be recognized when enterprises are differentiating their cloud providing structures. Cloud providers are the leverage for an organization’s boost towards better structures. Through designing valuable services, cloud providers can push themselves Table 4 Focus of differentiation [58] Strategic success

Renewed focus on core business activities that can accompany a move to cloud computing when its IT functions are hosted and/or managed by a cloud vendor

Economic success

Ability to tap the cloud vendor’s expertise and technological resources to reduce in-house IT expenses

Technological success

Access to state-of-the-art technology and skilled personnel, eliminating the risk and cost of in-house technological obsolescence

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into a better position by offering additional value to the customers and furthermore create a better relationship of trust and efficiency and reacting to customer needs.

3.4.3

Pricing Strategies

An understanding of the cloud is the most important point when deciding for a cloud computing service. Not only the amount of savings but also the type of savings plays a major role [24]. The pricing strategy for global players can lead to three different decisions, operating in the low-cost sector, operating by offering differentiated products or find a combination of both. Smaller providers often do not have many options to choose from. They differentiate their products to be competitive in a high-frequented market.

4 Analysis—Enterprise Value 4.1 Focus of the Offerings in Cloud Software as a Service (SaaS) is the largest segment of the Public Cloud market, comprising more than half of the total market size [57]. As SaaS rises, accounting for 75 percent of the total workload, with forcasts of growth at 17.5% compound annual growth rate (CAGR) over the forecast period (until 2025) [58], the global players have to unlock a new market segment. The Software-as-a-Service segment is dominated by Salesforce, which could be seen as both, a niche and a global player. Among the global players in the Software-as-a-Service, Microsoft continues to lead, while Amazon and Google focus also on Infrastructure-as-a-Service and Platform-as-a-Service [57]. Amazon leads the Platform-as-a-Service market and the Infrastructure-as-a-Service market. The challenge for the enterprises is to keep up with the development of the industry and conform the service offering. As the market developed towards Software-as-aService offerings the global players have to adapt their model to stay competitive in the short run. Development into other kinds of services in the long run is already ongoing. Global players can stick to the forecasts and therefore ignore Software-asa-Service or adapt their services to a changing environment. Niche players do not have the financial possibilities to do so. They have to develop the product with the best value for their forced range of customers. Amazon already broadened the range by adopting Platform-as-a-Service offerings after having had a start as one of the first Infrastructure-as-a-Service providers. Google and Microsoft entered the market later and had provided Software-as-a-Service offerings initially in a range and quality that needed extensive developmenet. Google has to broaden its focus, as they mostly

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design cloud services and neglect on-premise service offerings, which prevents mainstream companies from an adoption. A focus on web innovators prevented Google’s services from becoming an enterprise-class application platform [2]. The niche players do not rely as much on the Software-as-a-Service market as the global players. Salesforce is already an established player in that market, while Rackspace and VMware have a completely different niche and attract their customers through additional services in Infrastructure-as-a-Service. Salesforce’s main areas of operations are cloud application and platform markets, in which they have shown a successful presence for some time. VMWare’s focus is on infrastructure. A valuable service can tie customers to an enterprise in contrast to the global players, where price and computing power are the most important services. The global players have not created a niche they could develop, however the gap to keep up with the changes of technology is narrowing compared to the previous years. As the focus develops and the enterprises have to fight against hard competition. There is a development towards full potential and maturity, but it is still a long way to go and the markets will change and new obstacles will be on the way. The focus of the cloud providing section leads to an additional definition and additional value. Cloud providers pursuit towards full potential of cloud computing in their regard.

4.2 Service Type The service type is a major tool for enterprises to differentiate their services. Global players normally offer a standardized variety of services, while niche players try to add additional benefit to avoid price wars. The enterprise resource management (ERM) and customer relationship management (CRM) are the top secondary markets. The enterprise applications market is comprised of the following secondary markets: enterprise resource management (ERM), customer relationship management (CRM), engineering applications, supply chain management (SCM), and production applications. Each of these secondary markets consists of multiple functional market [59]. The niche players like Salesforce, Rackspace and VMware focus on secondary markets and align their strategy to them. Salesforce has been especially active in ERM and CRM and also uses collaborative applications, while Rackspace and VMware focus on CRM [60]. Amazon and Microsoft focus on standardized products to cut prices. Google is the global player with the highest interest in conquering niche markets through integrating direct sales and support investments around their cloud platform [60]. New Platform-as-a-Service offerings boosted Google’s standing in the competition, as they eliminated obstacles and made the Google App Engine more attractive for enterprise integration (Gartner). Whereas, the Salesforce is the pioneer in facilitation of CRM on-demand-software and extends its offer very fast. They offer an enormous portfolio of services, combined with innovative products. In addition, the Softwareas-a-Service segment opens up possibilities for the Platform-as-a-Service segment by connecting the services. But the successful Software-as-a-Service section creates a

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problem for the Platform-as-a-Service segment, as some customers view the platform as an additional but low-developed service to complement the enterprise’s premium product [61]. Rackspace offers a system of managed cloud to avoid competition with the global players. However, the pace of innovation of Rackspace’s service offering has been a struggle for a while. As the enterprise could not keep up with the big players and bring in managed service and innovation, it was not able to compete. VMware known as the biggest player in server virtualization, focuses on customer relationships through supporting the customers. The aim same as Rackspace, lies on avoiding direct rivalry to the global players by broadening the market and creating a valuable service offering. The pressure of staying in the market and the necessity to break into well-defined niches presents a fast developing style of cloud providing. Cloud providers have to optimize their services through innovation or adding value to stay competitive. This leads to a rapid development of cloud computing services and creates possibilities to overcome obstacles. It is to say that cloud providers are on the way to differentiate their products, create new services, new service types and lead cloud providing towards higher potential. There may be obstacles occurring in the future development of the service types, which are not discovered yet. The services can already boost an enterprise’s structure through the offering of unique niche services or through high power infrastructure solutions.

4.3 Customers Amazon focused on start-ups and small companies, but they started attracting bigger players and developed their customer range. The enterprise offers powerful systems and additional tools to bigger enterprises and global players [62]. Amazon focuses on making use of the customer base to push the services. Their service portfolio had to change through expanded AWS infrastructure, enterprise and public sales capabilities and commitment to reach an additional range of customers. Microsoft Azure on the other hand concentrates on the Fortune 500. More than 78 percent of the Fortune 500 companies now use Microsoft hybrid cloud offerings [63]. Google has not provided a lot of information on its customers. The customer study on their website presents a focus on start-ups, as well as small and medium companies. Gartner [61] mentions Google as a company, which is still looking on ways of engaging with enterprise and midmarket customers. Salesforce concentrate on specific sectors to optimize their niches [64]. Through the creation of new services Salesforce tries to attract every customer segment. With its operations small and medium sized enterprises were added to the customer range. Salesforce is keen on tying customers to its services through delivering a lot of customer interaction [43]. Rackspace focuses on managed services, as already mentioned. They cannot compete in the never ending price reduction game and maintain the margins required

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to run a profitable business [65]. Rackspace uses its established Infrastructure-asa-Service niche with managed services to attract a different customer group: those who are willing to pay for additional managed service to keep their own business easier. Rackspace already has a lot of customers in the managed hosting section and focused on small and medium enterprises [65]. VMware benefits from its brand, and focuses on potential customers who have already accepted VMware as the virtualization provider for the core of the enterprise data center. VMware has a lot of potential customers in mind, as their customer profile differs between small enterprises and global players [66]. The enterprises already defined their customer segment very well. Niche Players have very specific customers in mind and even Google tries to break into niches. While Microsoft and Amazon—as global players—want to conquer the whole market. The expectations of cloud providers in the customer section are outlined clearly but some enterprises in the showcase like VMware and Google lag behind their expectations and it will be hard to achieve them as enterprises like Amazon try to broaden their customer range. So it is the global players and well-suited niche players, who perform within their expectations, other enterprises are attracted by the possibilities of cloud providing. The main obstacle is competition, which can force providers to leave the market, if their product does not attract the customer group the enterprise had in mind.

4.4 Financial Situation Cloud computing revenues are hard to specify. Most enterprises do not offer separate revenue data for cloud computing. This possibility exists if a revenue stream does not represent more than 10 percent of a company’s total revenue [2, 36]. Regarding the financial situation cloud computing performs well. Every enterprise listed in the case study improved its revenue over the last year. Google’s and VMware’s performance in cloud computing was not applicable because both enterprises do not release separate data. Salesforce’s data refers to the complete enterprise, but as Salesforce nearly gets its whole income from cloud computing the data can be compared to the other players [2, 67]. Microsoft and Amazon do not offer financial data on the cloud computing segment either, as cloud computing is pooled with other segments [62]. Some cloud providers act in a really aggressive manner to secure their market. Enterprises in the cloud computing sectors are forced to undertake heavy investments to expand and optimize their services in order to stay competitive. AWS was named as a Leader in the 2022 Gartner Cloud Infrastructure & Platform Services (CIPS) Magic Quadrant for the twelfth consecutive year [62]. However, the problems that Amazon faced lies mainly on very low margins and the system that is designed to keep operative costs low to create cheap prices for service offerings. Salesforce constitutes the biggest niche player, with the highest revenue coming directly from cloud computing services. Rackspace is small in comparison to the enterprises mentioned before, but has a constant growth and represents the major

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opponent of Amazon in Infrastructure-as-a-Service offerings through handling a different kind of service than the other providers [68]. The acquisition of cloudrelated enterprises could create a financial problem, as they expanded the number of developers but did not add any value by now, not in financial terms, nor in service terms. VMware is not as competitive as the other players in the cloud computing market. In contrast to the other enterprises mentioned, VMware was not able to establish its cloud operations successfully, yet. The enterprise is still preparing the ground and in need for customers, which move to their cloud computing offerings. The revenues for cloud computing are not applicable, but server virtualization remains and will remain the dominating sector in the next years at VMware [69]. According to the Azure Active Directory, 85 percent of Fortune 500 companies use Microsoft Azure Cloud, which shows the development in the potential of Microsoft. Furthermore, Azure’s market share in 2021 reached 22 percent in the cloud computing industry according to Statista.com. In contrast to other evolutions Microsoft did not miss a potential paradigm, like often performed over the last decade, by starting its operations too late. Keeping costs at a minimum in the development of services could be counterproductive and therefore cloud computing could be prevented from developing its full potential. Another threat, which could prevent from achieving full potential and lead to financial losses, is a breakdown of the cloud. Amazon had to suffer one in 2010, when the Amazon network host service did not work for four hours. Amazon lost confidence from customers and because of that also revenue in the end [33]. A stable system is essential for developing the full potential of cloud computing, as customers still need to be convinced to switch. This is just an example of what prevents providers from receiving their full potential. From a provider’s point of view the development of cloud computing is very promising at this time and the predictions for the future are bright, even if there are obstacles in the way of their development. According to [70] Amazon’s cloud segment is growing much faster than the overall company, but expansion is slowing. Based on figures from 2020, over a billion in sales a year comes just from the top 10 customers, while raking in $62 billion of revenue overall. What makes AWS the leader in the competitive cloud market. While Amazon still counts on retail for the bulk of its revenue, AWS as the company’s profit engine provides a significant source of diversity as the economy slows [62, 70]. However, some of the lags in the expectations of the company in the first two quarters of the 2022 were seen as a change in customer-purchasing behavior [74], which effected a great deal of market. Salesforce had to adjust their profit and revenue forecasts for 2022. The company raised its annual profit forecast, signaling that demand for business software is holding up despite a broader downturn for major tech firms [71]. Microsoft developed its revenue from $1 billion in 2012 to $27 billion in 2022. However, this numbers are much lower than the forecasted revenue for 2022, which was expected to hit $37 billion according to the data of Bank of America [63]. The pandemic has introduced considerable challenges for companies trying to execute key processes. However, the situation of the post-pandemic period alongside

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integration of Big Data, AI and ML with cloud have been the main factors driving market growth and higher potential execusion.

4.5 Regional Analysis By region, the global public cloud services market is segmented into APAC, North America, Europe, South America, and Middle East and Africa. Factors such as the increased adoption of cloud computing services and a rise in data center investments are driving the growth of the public cloud services market in North America [17]. North America dominated the global market with a revenue share of over 40 percent in 2021, whereas in 2022 this percentage was slightly lower [60]. Companies are putting strong emphasis on digital transformation and are frequently regarded as early adopters of next-generation technologies, which is boding well for the growth. However, compared to previous years, the drop in the market share percentage is quite evident, despite of the presence of numerous vendors which help this region maintain its position in the market [18]. The Asia Pacific is expected to emerge as the fastest-growing region. The rapid rise of China and India, as well as the emergence of regional players like Alibaba Group, boosted the regional market growth [72]. Furthermore, the initiatives such as Make in India have made significant investments in the manufacturing and IT sector, while hyper-scale cloud providers such as Microsoft Corporation, Amazon, and Google are developing data centers in markets such as Indonesia and Thailand. This in return is serving as a great fuel, to rapidly boost the numbers in market share. Figure 8 presents market size and annual growth rate of the different geographic regions for 2022.

Fig. 8 Cloud Computing Market: Market Share (%) by Region, 2022 [73]

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All other regions are covered with data centers by every global or niche player, except of VMware, which does not run a data center in Asia. Recently, AsiaPac is partnering in the Southeast Asia and Korea region to adopt VMware operations [73]. A disassembling of additional regions leads towards new potentials and additional value for cloud providers.

4.6 Enterprise Value In some points cloud providing lags behind the expectations of the IT industry. The analyzed companies are global or niche players and at that time they are performing well in a very competitive area. Their expectations are mostly driving them to new innovations, new services and unlocking new potentials for the industry. Cloud computing already boosted enterprise structures in the provider section, like the example of Salesforce, which developed into a global player through offering a specific cloud computing service. Other players fight a price war and because of that their margins rest low. Through developing the cloud computing system the own infrastructure can profit, too. The global players are IT giants and therefore in need for the best infrastructure available for operations. Amazon first offered parts of their unused infrastructure as a start into cloud providing. Later to enlarge their infrastructure, but using same infrastructure offered to the customers [33]. Cloud providing already has proven an unlimited potential. A final statement if cloud providers can develop their full potential is not possible right now. There are still a lot of obstacles to remove mainly regarding the security and maturity, but cloud providing is showing desirable growth the recent years. The main issues the analyzed providers have been facing are rapid innovation and competition. The price war which can lead global players into difficulties is present mainly because of low margins and the constant development into new types of services. Software-as-a– Service remains the dominating cloud service, but Infrastructure-as-a-Service and Platform-as-a-Service are on the rise. There are six strategies listed to compete in the cloud providing market as presented in[38]: . . . . . .

Geographic expansion—geographically distributed hosting service Partner channel through creating a service platform for resellers Offer cloud-based applications—the value chain Develop services using local advantage Hybrid clouds—integrating and upgrading the multicloud infrastructures Offer customization—the most efficient and flexible cloud model

The development of cloud providing will continue and enterprises have to be innovative and resistant to stay in the market. The future presents many possibilities and obstacles. Although the last years, have shown a blast in the cloud development curve, achieving the expectations of the IT industry in contrast will be very difficult, as cloud computing is presented as the new dawn in IT and expectations are high.

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5 Conclusion Niche players are trying to develop a unique selling proposition in the cloud providing market, as global players force growth strategies. The niche players focus on specific value-adding services, but do not deliver anything unique. The niche players are the best operating companies in a specialized spectrum of cloud providing, but there is no offering which cannot be copied, just like VMware offered a model related to Rackspace’s one. As there is no unique selling proposition the niche players have to be innovative and develop their services. As mentioned above it is hard for an enterprise like Rackspace to keep up with the pace of innovation, a global player can offer. The enterprise needs to develop its niche without a unique selling proposition. The global players do not have a unique selling proposition. To keep costs at a minimum the global players do not intend to create a unique selling proposition. Mostly trying to operate at low margins and offer the best service possible for the lowest price. Google tries to create a business sector outside the global player’s price war, by trying to adapt to what niche players do. But that approach does not create a unique selling proposition, either. The market is all about competition as there is no unique selling proposition, but different niches. The global players will keep their low-price strategy, while the niche players try to continue developing their niches. Both, global and niche players are under constant pressure to keep their position in the fast developing cloud computing industry. In contrast to cloud providing, cloud computing itself can create a unique selling proposition. The businesses have to focus on becoming agile to counter hostile environmental changes and global reachability, as selling proposition for cloud computing. Cloud providers are interested in cloud computing because of its possibilities, although not foucused to developing a unique selling proposition in the cloud computing market. The providers want to get big players in a market that can one day be a unique selling proposition by itself. By now cloud computing has broken the lin of competition with other hosting systems, but in a fast developing world it looks like cloud computing can realize its advantage and exploit its unique attributes.

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Innovation Regional Policy and Smart Specialization: European Countries and Ukraine Maryna Nehrey, Larysa Zomchak, and Myroslav Havryliuk

Abstract The successful implementation of smart specialization strategies in the EU regions (more than 120 strategies) has been the key to their popularity in Europe and outside. The advantages and disadvantages of implementing smart specialization strategies in new conditions are investigated. The main obstacles to the effective implementation of RIS3 policy have been identified. Studies have shown that the innovation policy of the regions should be more focused on the heterogeneity of the regions, taking into account not only their specialization but also the level of development. The RIS3 experience is being adopted by countries around the world. The advantages and disadvantages of using smart specialization strategies in countries with different levels of economic development are shown. The process of implementation of smart specialization strategies in Ukraine is studied. The main challenges and opportunities for further transformation of the RIS3 concept into Smart Specialization Strategies for Sustainability have been identified. Keywords Innovations · EU regions · Smart specialization · RIS3 · European innovation scoreboard · sustainability

M. Nehrey Department of Economic Cybernetics, National University of Life and Environment Science of Ukraine, Kyiv 03041, Ukraine L. Zomchak Department of Economic Cybernetics, University Ivan Franko National University of Lviv, Lviv 79001, Ukraine M. Havryliuk (B) Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv 79013, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_2

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1 Introduction The speed, progress and qualitative characteristics of the development of regions, organizations and enterprises are largely determined by the level of management efficiency and innovation in the organization of the functioning of the socio-economic system itself. In industrially developed countries, the state stimulates innovation and in-novation, determines the strategic directions for the development of the national economy. At the same time, regulation of the innovation sphere is closely related to the implementation of organizational measures to update existing approaches both in management and in the modernization of the technological potential of territorial entities based on the implementation of fundamental and applied scientific research, as well as innovative projects. The innovative development of the region presupposes the integration of the scientific and technical sphere into the socio-economic processes of the development of the region, which means the formation of a system of institutions capable of generating innovations and creating new markets for science-intensive products and services, as a result of which a regional innovation system is formed. Innovation is a broad concept that does not always lend itself to positive analysis directly. It is not enough to list government initiatives or count the number of companies that position themselves as innovative to form a comprehensive picture of the situation in the region, and most importantly, to design further solutions to stimulate innovative activity. This can be achieved by identifying the strengths and weaknesses of each region, the factors contributing to the implementation of the innovative scenario or, conversely, constraining it. It is the correct information support that makes it possible to adequately assess the effectiveness of certain efforts of the state, taking into account the existing regional context. At the present stage of development of the global economy under the influence of the information technology revolution radical changes in innovation policy occur simultaneously with the evolution of innovation theories designed to reflect and explain changes in innovation at the national and regional levels of the system hierarchy. Substantiate various policies of spatial development management, taking into account the location of economic entities, their interaction with each other and with the external environment, the provision of production factors, human capital development, including regional management of regional competitiveness. The substantiation of economic development strategies of the regions of developed countries is based on different methodological approaches specific to specific countries and depends on the level of differentiation of regional development. The regional level of government is important in economic growth based on the dynamic competitiveness of developed countries. The analysis of world experience shows insufficient rationality and efficiency of centralized formation of innovation policy in contrast to the positive results of the functioning of decentralized regional innovation systems. The research is aimed at substantiating the theoretical and methodological bases, determining priorities and developing a system of means to increase the competitiveness of the regions of Ukraine on an innovative basis in the context of European integration.

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This involves the development of conceptual foundations for strategic programming and institutional design of rational spatial and sectoral specialization of Ukraine’s regions based on European experience and smart specialization approach. European experience proves the possibility of applying and disseminating smart specialization for regional development strategy, which determines competitive advantages, sets strategic priorities and implements intellectual policies to maximize the potential of knowledge-based development. Smart specialization contributes to the transformation of regions into regional clusters of robotics and innovation agencies in the EU. This is also important for Ukraine, as the regions of Ukraine are integrated into world trade flows with broad cooperation and a competitive market, where innovative transformations are constantly taking place. The implementation of smart specializations can be quite an effective tool for future development, in particular for the possibility of integration into higher stages of European value chains. Equally important is that if we succeed in implementing SMART specializations in Ukraine as it operates in Europe, it will provide the transformational effect needed to modernize the industry under the influence of Industry 4.0.

2 Materials and Methods The Smart specialization includes concentration and specification of knowledge resources of regions. RIS3 is a two-way dynamic process that combines government policy and entrepreneurial innovation. In the process of Smart specialization, entrepreneurial innovations are accompanied by targeted support for public policy. The smart specialization policy process includes the next three phases: identification and reinforcement of entrepreneurial discovery, monitoring and assessment, coordination, and complementary investment [1]. Strategies for smart specialization contribute to the development of opportunities to take advantage of individual economies of the regions and are less vulnerable to change supply and demand of the global market. The formation and implementation of a smart specialization strategy is a process that must be coordinated with other policy agendas. The coordination of Research and Innovation Smart Specialization Strategy (RIS3) and macro-regional strategies (MRSs) is discussed in [2]. Seven years of experience in the formation and implementation of smart specialization strategies have shown positive results, but also revealed some shortcomings, the discussion of which is quite active in scientific and political circles. The authors in [3] suggest that the problems of implementing smart specialization arise from the lack of risk-taking by politicians, lobbying, and the lack of appropriate institutional and administrative national and regional capacity. Problems with the implementation of smart strategy in the regions, according to Marques and Morgan [4], arise due to the uncritical perception of RIS3 in politics and inflated expectations for policy implementation.

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The authors in [5] studied the implementation of RIS3 in 28 regions from 5 countries in Central and Eastern Europe. The results showed that the innovative activity of the regions does not correspond to the specialization that was chosen for them. In addition, the specialization of regions is quite general and insufficiently differentiated. The authors offer their approach to the formation and improvement of strategies for smart specialization, taking into account the innovative capabilities of the regions. Cluster analysis of the results of RIS3 implementation was performed in [6]. 9 clusters were identified, which are similar in priorities and socio-economic characteristics, and recommendations were given to improve the implementation of smart specialization strategies in individual clusters. Gianelle et al. [7] note that the implementation of smart specialization policy was not without challenges and opportunities, and give recommendations for improving the smart specialization strategy. In [8] analized Regional Innovation Scoreboard 2019 and researched the main characteristics of innovation processes, identified weaknesses and made proposals for the management of innovation policy. Analysis of the performance of regional innovation in the EU in 2019 showed that the development of the region itself has a significant impact on the success of the strategy [9]. The implementation of smart specialization strategies has been more successful in developed regions. A similar conclusion was made in [10]. The authors believe that less developed regions should specialize in low and medium sectors to modernize them to more valuable activities. Szopik-Depczyńska et al. [11] determine that knowledge and innovation are concentrated in several regions. They emphasize the need to take this fact into account when developing region innovation policy. To solve the problem of regional differentiation, Iammarino et al. [12] proposes to implement a place-sensitive distributed development policy. McCann and OrtegaArgilés [13] consider it necessary to harmonize the interregional innovation system and regional innovation systems, taking into account the flow of knowledge between regions. Rusu [14] notes that to form and effectively implement a smart strategy, it is necessary to improve the connection between research and innovation and to form public–private research partnerships. The authors in [15] emphasizes that globalization creates conditions and challenges for the economic development of the regions. The formation of a specialization strategy requires the division of powers between the levels of decision-making and implementation. Cooke [16] notes that in addition to RIS, it is advisable to implement “enterprise eco-systems” that can further unleash the development potential not only of enterprises but also of regions in general. The conclusions of research on the implementation of smart specialization strategies in some regions are presented in Table 1. Among the numerous approaches to modeling efficiency, Data Envelopment Analysis takes a leading position. One of the reasons is the possibility of comparative modeling of the efficiency of functioning of various sectors of the economy in different countries, which has become especially important in connection with the development of information technologies.

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Table 1 The results on the implementation of smart specialization strategies in some regions Country/Region

Smart specialization strategies

Austria, Italy, Slovenia, Croatia

The implementation of the smart specialization [17] approach is easier in highly coordinated economies, although economies with low coordination are more in need of its implementation. Entrepreneurial discovery processes are beneficial to regional economies not only because of their outcome but also because of its institutional by-products, which include new approaches to public–private governance, agents’ deeper understanding of the institutional context of the economy, increased sensitivity of institutions to regional policy and behavioral change

Bulgaria

The formation and implementation of Smart [18] specialization are accompanied by many limitations and shortcomings. The study showed that even in the best case, Smart specialization alone is not enough to ensure a high level of competitiveness in the region. To maintain a certain level and increase competitive advantages, it is necessary to introduce entrepreneurial activity in combination with a flexible policy at the regional level. Therefore, less developed countries should focus their efforts on long-term dynamic goals of increasing competitiveness

Czech Republic, Hungary, Less developed regions need significant investment in Poland, Slovakia innovation but invest much less than well-developed regions. Long-term growth requires investment primarily in human resources and research infrastructure

References

[19]

Denmark, Sweden, Norway

When formulating a specialization strategy, regions [20] use a detailed analysis of their innovative capacity and are based on the use of their unique assets. The analysis was performed to reaffirms the need to apply a broad innovation policy, ensuring the diversification of its own economy, using its strengths

Finland

The main drivers of regional development change are [21] the workforce and higher education. Instead, innovation does not have such a strong impact on the development of regions, which is explained by the lag between innovation and economic results. Empirical analysis has shown that urban areas have a higher level of R&D and innovative production compared to peripheral regions. Accordingly, the implementation of Smart specialization is more effective in urban areas

Finland, Ostrobotni

To increase the efficiency of Smart Specialization implementation, it is necessary to look for local partners, cooperate with universities and research institutes, and support medium and small companies

[22]

(continued)

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Table 1 (continued) Country/Region

Smart specialization strategies

References

Germany

Effective implementation of RIS3 requires a balance between local autonomy and coordination at higher levels of government. The study found significant differences in East–West and North–South, which separates subnational innovation systems for manufacturing industries. When developing the RIS3 strategy, it is necessary to focus on the Sustainable Development Goals, the achievement of which is possible only through intersectoral cooperation of researchers, politicians, civil society, and industry at various levels, including regional

[23]

Italy

It is proposed to redistribute funds to sectors that will [24] have a positive impact on innovation. It is determined that one of the drivers of RIS3 is to increase the institutional quality and quantitative efficiency with the subsequent redistribution of funds to relevant issues

Italy, Sicily and Apulia

RIS3 analysis showed that the bottom-up approach does not give the expected result. Differentiation of approaches is necessary. Smart specialization is an open process that should consolidate permanent opening procedures. Transformation of the concept into political innovation requires support from organizational mechanisms and skills. RIS3 creates opportunities for regional capacity development but requires significant efforts from regional institutions

[25]

Poland

A comparative analysis of the innovative activity of Polish companies with other EU countries with the level of innovation similar to Poland. Conclusions are drawn on the significant distance of Polish firms from companies from other moderate countries-innovators, from the point of view of SMEs that implement their services at home

[26]

Poland, Mazovia

Mazovia region has a general low degree of system [27] differentiation, which creates difficulties for building a regional innovation system. There are great potentials in the agricultural sector. The strategy of the learning region approach of building broad local or regional development coalitions was proposed as a way of implementing the entrepreneurial discovery process in peripheral parts of Eastern Europe

Portugal

It is proposed to use the Barney’s VRIO model with modifications to assess the perception of regional competitiveness of stakeholders, taking into account intellectual specialization. This model can be used both at the stage of developing a smart specialization strategy and when assessing the perception of RIS3 implementation by stakeholders

[28]

(continued)

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37

Table 1 (continued) Country/Region

Smart specialization strategies

Portugal, Alentejo

The core infrastructures influence the results of [29] implementing smart strategies for regions. In particular, they can increase the innovation potential of rural areas, create positive experiences of the impact of RIS3 on the regions and promote the implementation of smart strategies in less favorable territorial conditions

References

Romania

The implementation of RIS3 in Romania has shown that it is a complex process that requires significant time, resources and political will at the regional level to identify regional structures and allocate the necessary resources for their operation. There is no standard algorithm for the development of regional innovation systems, the implementation process must be adapted to each country and region in particular

[30]

Slovenia

An approach to the operationalization of the concept of smart specialization, which provides for the profiling of regions and countries, based on data from national statistical offices, which form indicators for assessing the potential of regions and countries. This approach allows efficient use of time to develop a strategy of smart specialization, save resources and prevent the excessive influence of lobbyists

[31]

Spain, Extremadura

Smart specialization has some limitations for [32] implementation in poor and backward regions. The benefits of RIS3 ensure effective implementation in large, developed areas. Accordingly, Smart Specialization is not always suitable for regional development policy aimed at convergence. Poor regions should focus more on moving closer to the EU average to achieve significant convergence results

Turkey

The analysis of R&D effectiveness in 12 Turkish regions revealed the problem of low commercialization of R&D results. To solve this problem, it is proposed to improve university-industry cooperation, focusing on less efficient regions

[33]

Currently, DEA is a developed methodology for assessing the comparative efficiency of the functioning of a set of homogeneous economic, industrial or other objects using various models of mathematical programming. The facilities whose performance is assessed in the DEA are usually called production units or production facilities and perform the same production function, transforming many of some inputs into many of some outputs. The advantage of DEA is the ability to work with software without any assumptions about the type of functional relationship between inputs and outputs. Basically, DEA is used to assess the efficiency of the budget

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system, regional authorities, banking structures, agriculture, energy, finance, etc. [34, 35]. In the classical DEA model, denoted by CCR (according to the first letters of the developers’ names—Charnes, Cooper, Rhodes [36]), a linear fractional problem is solved for each object mathematical programming, where is maximized the ratio of the linear combination of weighted outputs to linear combination of weighted inputs: ∑ i ui yij ∑ max h = , u,v k vk xkj

(1)

This ratio is called the coefficient of efficiency, its values are in the range from zero to one. Objects, for which the efficiency coefficient is equal to one, are recognized as effective, and the rest—as ineffective. If the efficiency factor is given in the formula (1), then the problem itself is called input-oriented. Another form of setting the efficiency factor is also possible: as the ratio of a linear combination of weighted inputs to the linear combination of weighted outputs. In this case, the task will be called output-oriented. Calculation of the projections of ineffective objects in the space of inputs/outputs to the efficiency frontier allows you to determine the targets for reducing inputs/ increasing outputs, the achievement of which allows the object to become effective.

3 Results 3.1 Assess the Implementation of Smart Specialization Strategy in European Countries The strategy of smart policy specialization has been used in Europe for quite some time. Therefore, it is possible to assess what changes are taking place and how the implementation of this policy affects. The analysis is based on the European Innovation Scoreboards (EIS) for 2014–2021 [37]. According to the EIS 2021 methodology, 32 indicators are evaluated, which are grouped as follows (Fig. 1). The study of the dynamics of the framework conditions (Fig. 2) showed an overall increase with the exception of New doctorate graduates, whose indicators in 2020 and 2021 have a declining trend. The reason for this may actually be a change in the methodology of evaluation of the indicator, because from 2020 it includes only graduates in science, technology, engineering and mathematics. Analysis of investment dynamics allows us to draw conclusions about the predominant growth trend of indicators (Fig. 3). However, in 2021 there was a sharp decline in Non-R & D innovation expenditures. This situation is possible due to the crisis caused by COVID-19, the consequences of which have a long-term impact. Enterprises providing ICT training also decreased by almost 10% in 2021. This may also

Innovation Regional Policy and Smart Specialization: European …

Fig. 1 EIS 2021 measurement framework

39

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M. Nehrey et al. -10%

-5%

0%

5%

10%

15%

20%

25%

30%

1.1.1 New doctorate graduates

1.1.2 Population with tertiary education

1.1.3 Population involved in lifelong learning

1.2.1 International scientific co-publications

1.2.2 Scientific publications among the top 10% most cited

1.2.3 Foreign doctorate students as a % of all doctorate students

1.3.1 Broadband penetration

1.3.2 Individuals with above basic overall digital skills

2015

2016

2017

2018

2019

2020

2021

Fig. 2 Framework conditions dynamics

be evidence of the impact of COVID-19 and the transformation of many processes online. The results of the evaluation of innovation activity showed different dynamics (Fig. 4). Indicators of innovators of both products and business processes are increasing, and in 2021 the increase compared to 2020 was 17.11% and 7.43%, respectively. Note the peculiarities of the interpretation of the indicator job-to-job mobility of HRST. Mobility here is interpreted as the transfer of persons from one job to another from one year to the next. By 2020, this figure has been growing at an accelerated pace, starting from 2020, the growth rate of mobility has slowed down. This situation is understandable given the quarantine restrictions and the crisis caused by COVID-19. Indicators of intellectual assets are characterized by a steady downward trend, especially a high level of decline is characterized by the indicator design applications.

Innovation Regional Policy and Smart Specialization: European … -30%

-20%

-10%

0%

10%

20%

41 30%

40%

50%

2.1.1 R&D expenditure in the public sector

2.1.2 Venture capital expenditures

2.1.3 Direct and indirect government support of business R&D

2.2.1 R&D expenditure in the business sector

2.2.2 Non-R&D innovation expenditures

2.2.3 Innovation expenditures per person employed

2.3.1 Enteprises providing ICT training

2.3.2 Employed ICT specialists

2015

2016

2017

2018

2019

2020

2021

Fig. 3 Investment’s dynamics

Impacts dynamics research showed generally positive dynamics with the exception of Sales of new-to-market and new-to-firm innovations and Environment-related technologies (Fig. 5). Moreover, the larg-est decline in Sales of new-to-market and new-to-firm innovations − 14.1% occurred in 2017 with further growth and redecline in 2021. The indicator Environment-related technologies since 2018 has a declining dynamics every year. The DEA model analyzes the efficiency of innovation in European countries using VRS technology (variable return on scale) and efficiency focused on input resources, where input is the indicators shown in Fig. 1, output—Summary Innovation Index. The results are shown in Table 2. The obtained results allow us to conclude that 17 countries out of 35 (48.6%) use their resources most efficiently. The least effective is Portugal, as well as Spain, Denmark, Switzerland. The average efficiency is 0.903. This shows that Europe has

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-5%

0%

5%

10%

15%

20%

3.1.1 SMEs introducing product innovations

3.1.2 SMEs introducing business process innovations

3.2.1 Innovative SMEs collaborating with others

3.2.2 Public-private co-publications

3.2.3 Job-to-job mobility of HRST

3.3.1 PCT patent applications

3.3.2 Trademark applications

3.3.3 Design applications

2015

2016

2017

2018

2019

2020

2021

Fig. 4 Innovation activities dynamics

the potential to increase resource efficiency. How this can be achieved is discussed below.

3.2 Smart Specialization in Ukraine Over the last few years, several important events have taken place in the regional policy in Ukraine. The Ministry of Development of Communities and Territories of Ukraine has developed the State Strategy for Regional Development of Ukraine for the period up to 2027. This Strategy presents the new vision of regional development aims and strategic goals. The next stage goal of regional policy is improving the living

Innovation Regional Policy and Smart Specialization: European … -20%

-15%

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-5%

0%

43

5%

10%

15%

4.1.1 Employment in knowledge-intensive activities

4.1.2 Employment in innovative enterprises

4.2.1 Exports of medium and high technology products

4.2.2 Knowledge-intensive services exports

4.2.3 Sales of new-to-market and new-to-firm innovations

4.3.1 Resource productivity

4.3.2 Air emissions by fine particulates

2015

2016

2017

2018

2019

2020

2021

Fig. 5 Impacts dynamics

standards of the population regardless of the place, where they live, decentralized, competitive and democratic Ukraine. Achieving the goal is provided through the implementation of tasks within three goals: 1. the country development in social, economic, environmental and spatial dimensions; 2. the level of regions competitiveness increasing; 3. effective human-centered multilevel governance.

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Table 2 DEA results for innovation Eff range 0.5 ≤ E < 0.6

Number of countries 1

% 2.9

Countries Portugal

0.6 ≤ E < 0.7

3

8.6

Spain, Denmark, Switzerland

0.7 ≤ E < 0.8

3

8.6

Luxembourg, Sweden, Ireland

0.8 ≤ E < 0.9

9

25.7

Netherlands, Latvia, Slovakia, Belgium, Finland, Romania, Slovenia, United Kingdom, Norway

0.9 ≤ E < 1 E=1

2

5.7

17

48.6

Iceland, Hungary Bulgaria, Czechia, Germany, Estonia, Greece, France, Croatia, Italy, Cyprus, Lithuania, Malta, Austria, Poland, North Macedonia, Montenegro, Serbia, Turkey

The strategy declares a departure from an approach of the depressed areas supporting and the differentiation of the regional development tools depending on the functional type of territory. The strategy also declares increasing attention to “soft” development projects, increasing role of regional development agencies, regional development on the basis of inclusion of all economic entities, allocation of 1/3 of the State Fund for Regional Development for the implementation of regional development programs, effectively managing resources for development, regional policy entities capable of strategically planning development and. The Strategy is developed according to the principles of the European regional development model and on the base of the best examples of European experience. The implementation of the European strategic planning principles of regional development is an integral part of the approximation of domestic legislation to the EU regulatory framework, which takes place within the framework of the Association Agreement between Ukraine and the EU. The main principle of smart specialization in Ukraine is knowledge focusing and combining it with a limited number of priority economic activity so that the country and regions become more competitive in the global economy. This approach allows regions to take advantage of the knowledge transfer and use new knowledge, which is an important factor in productivity. In other words, smart specialization is the creation of unique resources and opportunities based on unique industrial industry structures and a corresponding knowledge base. A national/regional research and innovation strategy for smart specialization should be based on four general principles: • choice and critical mass: a limited number of priorities defined with regional capacity and international cooperation • competitive advantage: the process of entrepreneurial discovery • interconnection and clusters: synchronizes between regions and the rest of the world

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45

• joint management: public–private partnership. According to the documents, there are the following practical steps defining the design and method-ology of regional strategies for smart specialization: 1. 2. 3. 4. 5. 6.

The regional context and innovation potential analysis An inclusive governance structure establishing A common vision for the future of the region development A limited number of priorities for regional development selection A coherent policy, roadmap and action plan establishment The monitoring and evaluation system implementation.

A particularly important source of information and guidance on how to validate strategies for smart specialization is peer review, an in-depth review by the partner region. This practice allows gaining experience from regions that have already solved similar problems and establish direct contact with potential partners for future cooperation. Peer review can provide regional leadership with a new and important “insight” about the strategy from the perspective of other regions. The implementation of the expert assessment in Ukraine was organized within the framework of the “Smart-specialization” platform to ensure the exchange of experience between the regions. In general, the implementation of the peer review took place in three stages. The application of the smart specialization approach has become a powerful impetus for the innovative development of the European Union. Now, this approach is being implemented in Ukraine with the expectation of bringing Ukraine closer to the European community. In November 2018, the Government of Ukraine approved amendments to the legislation providing for strategic planning of regional development according to the smart specialization European approach. This solution identifies the unique advantages of each region and sector of the economy that have innovative potential and increase the competitiveness of the region. Over the past two years, significant work has been carried out in Ukraine to prepare regional development strategies. So far, 21 regional development strategies, developed on the basis of smart specialization, have been approved. For the first time, specific sectors of economic activity are identified in the regional development strategies for 2021–2027. In 2016, there were only 3 pilot regions (Zaporizhzhya, Kharkiv, Odessa) where the smart specialization approach was implemented, and in 2019 their number increased to 12. The uniqueness of the example of Ukraine is that, as part of the implementation of the approach, it was possible to build an effective dialogue with the European Commission regarding the potential of smart specialization in Ukraine, as a result of which European experts met Ukraine halfway and changed the conditions in such a way that, in addition to 3 pilot regions, each region received the ability to develop a regional development strategy based on a smart specialization approach. The development of smart specialization strategies is relevant and important for the Ukrainian industry [38, 39]. A threefold drop in such sectors as mechanical engineering, as well as most of the medium and high-tech industries since 2014, turned the country’s economy into a commodity economy in just 5 years. One of the

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main reasons for this decline is the weak competitiveness of manufacturers, which in turn is directly related to the innovativeness of their organizations and products. The processes of lowering the innovative level of industrialists, their separation from science continues throughout the entire period of Ukraine’s independence. In most regions of Ukraine, there is weak project management [40] based on a smart specialization approach, as a result of which important stakeholders leave these processes, and the results of these projects of regional strategies are little known or insufficient. The lack of leadership at the national and regional levels [41] is the most obvious and critical flaw in the implementation process. Today, in accordance with the Strategy for the development of innovation for the period up to 2030, Ukraine has created and operates: • • • • • • • • •

47 industrial parks, which are included in the Register of Industrial Parks; 16 technology parks; 24 centers of innovation and technology transfer; 22 innovation centers; 38 commercialization centers; 24 innovative business incubators, one investment and technology cluster; more than 30 clusters; one innovation and production association; other startup schools (business entities that provide theoretical knowledge and practical skills in the field of creation and operation of startups); • incubation programs (programs for start-ups aimed at developing a startup); • intellectual property centers (business entities that ensure the implementation of educational-professional, educational-scientific and scientific programs, as well as advanced training of employees in the field of intellectual property); • venture and investment funds; centers of scientific, technical and economic activity, etc. In 2020, Ukraine ranked 45th in the overall ranking of the Global Innovation Index 2020 and scored 37.4 points out of 100. Some components of the rating: • • • • • • • • •

education—23rd place (+20 steps); R&D—44th place (+10 steps); creation of knowledge (patents and inventions) 23rd place (− 6 steps), according to the registration of utility models we No 1 !; political and operational stability—123 place (+2 steps); government efficiency—93rd place (+2 steps); rule of law—109th place (− 2 steps); regulatory policy—88th place (+6 steps); ease of starting a business—52 place (− 4 steps).

In 2020, Ukraine entered the top 30 countries in the global ranking of startups— StartupBlink, which evaluates the startup ecosystem among 100 countries and 1000 cities and ranked 29th in the ranking. In 2019, 782 enterprises carried out innovative activities in industry. At the same time, the share of the number of industrial enterprises that implemented innovations

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(products and/or technological processes) in the total number of industrial enterprises was 13.8%. Of the total number of innovatively active enterprises carried out: internal and external research—24.4%; purchase of machinery, equipment and software—64.7%; acquisition of external knowledge—4.5%; other works—20.6% of enterprises. By types of economic activity, the largest shares of innovatively active enterprises account for food production—16.8%, production of machinery and equipment, n.v.i.u. (not included in other groups)—10.2%. In 2019, the company spent UAH 14,420.90 million on innovations, including UAH 10,185.11 million for the purchase of machinery, equipment and software, UAH 2918.85 million for internal and external research and development, other external knowledge (acquisition of new technologies)—UAH 37.49 million and for other works related to the creation and implementation of innovations (other costs)—UAH 1079.45 million. The share of expenditures for the purchase of machinery, equipment and software compared to 2018 increased from 68.1 to 71.6%. At the same time, the share of expenditures on research and development (R&D) decreased from 26.3% in 2018 to 20.5% in 2019, on the acquisition of other external knowledge—from 0.4 to 0.3% At the same time, the share of expenses on other expenses, including for marketing and advertising from 5.2 to 7.6% The share of expenditures for scientific and technical work in GDP in 2019 was 0.43%. The share of sold innovative products in the volume of industry in 2019 was 1.3%. According to the State Statistics Service of Ukraine (Fig. 6), the most developed industrially regions of Ukraine demonstrate the largest research and development expenditure for R&D, in particular Kharkiv and Dnipropetrovsk regions. Zaporizhzhya and Khmelnytskiy data are not published in order to ensure compliance with the requirements of the Law of Ukraine On the State Statistics regarding confidentiality of statistical information (primary and secondary blocking of vulnerable values). As one can see on Fig. 7 innovations expenditures are distributed by the type of economic activity in such way: • • • • • • •

Mining and quarrying: 1640.7 Manufacturing: 13346.7 Electricity, gas, steam: 374.5 Water supply; sewerage: 69.9 Transportation and storage: 2364.2 Information and communication: 840.3 Financial and insurance activities: 24.9

The largest innovation expenditures in Ukraine were in 2016 (Fig. 8) but soon in 2017 it has fallen more then twice, and it is growing since 2000. The main financing source of innovation activities of industrial enterprises in 2020 (Fig. 9) are own funds of enterprises (85%), other sources are not so important.

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3000.0 2500.0 2000.0 1500.0 1000.0 500.0

Volyn Rivne Kirovohrad Donetsk Zhytomyr Luhansk Ternopil Poltava Vinnytsya Ivano-Frankivsk Chernihiv Cherkasy Kherson Zakarpattya Chernivtsi Sumy Mikolayiv Odesa Kyiv Lviv Dnipropetrovsk Kharkiv

0.0

Fig. 6 Research and development expenditure by R&D type by region in 2020

Mining and quarrying Manufacturing 13%

4%0% 9%

Electricity, gas, steam

0% 2% Water supply; sewerage

72%

Transportation and storage Information and communication Financial and insurance activities

Fig. 7 Innovation expenditure by type of economic activity, 2020

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25000.0

20000.0

15000.0

10000.0

5000.0

0.0 1995

2000

2005

2010

2015

2020

2025

Fig. 8 Innovation expenditure, million UAH funds of non- funds of other resident investors sources 1% 12%

own funds of enterprises state budget funds 2%

state budget funds funds of non-resident investors own funds of enterprises 85%

funds of other sources

Fig. 9 Financing sources of innovation activities of industrial enterprises, 2020

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4 Discussion COVID-19 has identified many problems in many areas of society, including innovation policy in the regions. To overcome the crisis caused by COVID-19, rapid policy decisions and the creation of new policy initiatives have become urgent. Governments have gone to great lengths to maintain the well-being of the population, curb rising unemployment and reduce incomes. The European Union has overcome the crisis fairly quickly and needs a steady recovery from the pandemic. Innovation Regional Policy, aimed at opening and implementing innovative activities that have rethought the needs of modern society, can be the basis for sustainable recovery. The focus of innovation policy should be environmental sustainability, promoting innovation in the context of globalization, using the prospects of digitalization, and strengthening territorial and social cohesion. With this in mind, a smart specialization strategy aimed at achieving sustainable development should become the basis for future regional innovation policy. The formation of Sustainable Smart Specialization Strategies requires a critical rethinking of the RIS3 experience, elimination of shortcomings, consideration of new conditions, and possible challenges. In particular, it is necessary to form an analytical framework to support policy decisions in the field of innovation. It is important to assess the priority and select innovative solutions that will contribute to the development of the region. Such an assessment must take into account a significant number of factors and take into account both national and regional interests. It is also important to take into account the institutional factors that are decisive in the management of political processes. When designing Sustainable Smart Specialization Strategies, the design of business resource generation processes is also important. A feature of Sustainable Smart Specialization Strategies should be to support the green economy and use the potential for green growth. The topic of S4 implementation and implementation is actively discussed in scientific and political circles in Europe. In some regions, a pilot initiative of the Smart Specialization Strategy for Sustainability has already been launched. It is expected that the implementation of S4 will contribute to the achievement of the Green Agreement. This is made possible by paradigm shifts in innovation. In particular, innovation becomes a platform for finding solutions to environmental and social problems. The use of innovation in the green economy creates additional competitive advantages in the markets through the use of new technological solutions. In the field of politics, shifts are taking place from the bottom up. Policy decisions need to be consistent, based on data analysis using Data Science, characterized by increasing inclusiveness and have a holistic forward-looking approach.

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5 Conclusions The introduction of smart specialization strategies has given impetus to the development of innovation in the regions. Seven years of practical implementation of the strategies have shown both positive developments and some shortcomings of RIS3. There is now much criticism and doubt as to whether the policy will continue to yield positive results and be implemented with the same care that has been put into developing them. This article contributes to the existing literature by providing new insight into the understanding of issues related to the development and implementation of smart specialization strategies. Studies have shown that the innovation policy of the regions should be more focused on the heterogeneity of the regions, taking into account not only their specialization but also the level of development. The analysis showed that one of the obstacles to the implementation of RIS3 was the crisis COVID-19. On the other hand, this situation is a point of a paradigm shift, when it becomes clear that innovation should focus not only on ensuring economic development, but also take into account the problems of sustainable development. The experience of implementing the concept of smart specialization strategies in non-European countries is studied in the example of Ukraine. Ukraine has significant innovation potential, however, many problems still need to be addressed. In particular, the Strategy for the Development of Innovation for the period up to 2030 declares program measures in the following areas: adoption of legislation to simplify innovation; development of innovation infrastructure and expansion of connections of domestic and foreign scientists and innovators; raising the level of innovation culture, including through educational activities. The introduction of the smart specialization model in Ukraine would allow unlocking its regional innovation potential and moving to structural and technological changes on an innovative basis. Smart specialization in Ukraine would allow to obtain much broader results than just the modernization of industry in the regions, but this requires identifying it as the main tool of the new regional policy.

References 1. Meyer, C., Howe, T.O., Stollberg, C., Gerlitz, L.: Cross-border cooperation concept in multifunctional agriculture under RIS3. Environ. Climate Technol. 25, 537–550 (2021). https://doi. org/10.2478/rtuect-2021-0039 2. Russo, M.: RIS3 in macro-regional strategies: tools to design and monitor integrated territorial development paths, https://iris.unimore.it/handle/11380/1182621, last accessed 2022/08/19. 3. Gianelle, C., Guzzo, F., Mieszkowski, K.: Smart Specialisation: what gets lost in translation from concept to practice? Reg. Stud. 54, 1377–1388 (2020). https://doi.org/10.1080/00343404. 2019.1607970 4. Marques, P., Morgan, K.: The heroic assumptions of smart specialisation: a sympathetic critique of regional innovation policy. In: Isaksen, A., Martin, R., Trippl, M. (eds.) New Avenues for Regional Innovation Systems—Theoretical Advances, Empirical Cases and Policy Lessons.

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23. Ruhrmann, H., Fritsch, M., Leydesdorff, L.: Synergy and policy-making in German innovation systems: smart specialisation strategies at national, regional, local levels? Regional Studies. 0, pp. 1–12 (2021). https://doi.org/10.1080/00343404.2021.1872780 24. Arbolino, R., Boffardi, R., De Simone, L.: Which are the factors influencing innovation performances? Evidence from Italian cohesion policy. Soc. Indic. Res. 146, 221–247 (2019). https:// doi.org/10.1007/s11205-018-1904-5 25. Bellini, N., Lazzeri, G., Rovai, S.: Patterns of policy learning in the RIS3 processes of less developed regions. Reg. Stud. 55, 414–426 (2021). https://doi.org/10.1080/00343404.2020. 1762855 26. Zygmunt, A.: Innovation activities of polish firms. Multivariate analysis of the moderate innovator countries. Oecon. Copernicana. 8, 505–521 (2017). https://doi.org/10.24136/oc.v8i 4.31 27. Asheim, B.T.: Smart specialisation, innovation policy and regional innovation systems: what about new path development in less innovative regions? Innov. Eur. J. Soc. Sci. Res. 32, 8–25 (2019). https://doi.org/10.1080/13511610.2018.1491001 28. Farinha, L., Lopes, J., Sebastião, J.R., Ferreira, J.J., Oliveira, J., Silveira, P.: How do stakeholders evaluate smart specialization policies defined for their regions? Compet. Rev. Int. Bus. J. 31, 594–624 (2020). https://doi.org/10.1108/CR-12-2019-0149 29. Sarkar, S., Bilau, J.J., Basílio, M.: Do anchor infrastructures matter for regional smart specialisation Strategy? The case of Alentejo. Reg. Stud. 55, 453–464 (2021). https://doi.org/10.1080/ 00343404.2020.1722804 30. Ranga, M.: Smart specialization as a strategy to develop early-stage regional innovation systems. Eur. Plan. Stud. 26, 2125–2146 (2018). https://doi.org/10.1080/09654313.2018.153 0149 31. Kotnik, P., Petrin, T.: Implementing a smart specialisation strategy: an evidence-based approach. Int. Rev. Adm. Sci. 83, 85–105 (2017). https://doi.org/10.1177/0020852315574994 32. Madeira, P.M., Vale, M., Mora-Aliseda, J.: Smart specialisation strategies and regional convergence: Spanish extremadura after a period of divergence. Economies. 9, 138 (2021). https:// doi.org/10.3390/economies9040138 33. Belgin, O.: Analysing R&D efficiency of Turkish regions using data envelopment analysis. Technol. Anal. Strat. Manag. 31, 1341–1352 (2019). https://doi.org/10.1080/09537325.2019. 1613521 34. Izadikhah, M.: Improving the banks shareholder long term values by using data envelopment analysis model. Adv. Math. Financ. Appl. 3, 27–41 (2018). https://doi.org/10.22034/amfa. 2018.540829 35. Mahmoudabadi, M.Z., Emrouznejad, A.: Comprehensive performance evaluation of banking branches: a three-stage slacks-based measure (SBM) data envelopment analysis. Int. Rev. Econ. Financ. 64, 359–376 (2019). https://doi.org/10.1016/j.iref.2019.08.001 36. Data envelopment analysis: theory, methodology, and applications 37. European innovation scoreboard, https://research-and-innovation.ec.europa.eu/statistics/per formance-indicators/european-innovation-scoreboard_en, Last accessed 19 Aug 2022 38. Oleksiv, I., Lema, H., Kharchuk, V., Lisovych, T., Dluhopolskyi, O., Dluhopolska, T.: Identification of stakeholders importance for the company’s social responsibility using the analytic hierarchy process. ACIT-2020, (2020), pp. 1–4, https://doi.org/10.1109/ACIT49673.2020.920 8897 39. Kharchuk, V., Shulyar, R., Dluhopolskyi, O.: Quality of student support at IT educational programmes: case of Lviv polytechnic national university, ACIT-2021, (2021), pp. 270–275, https://doi.org/10.1109/ACIT52158.2021.9548648 40. Fedushko, S., Mastykash, O., Syerov, Y., Peracek, T.: Model of user data analysis complex for the management of diverse web projects during crises. Appl. Sci. 10, 9122 (2020). https://doi. org/10.3390/app10249122 41. Oleksiv, I.B.: Selection of important company stakeholders: theory and practice. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 1, 128–134 (2013)

Risk Management in the Non-profit Public Sector—Hazards of a Lack of Reflection Within the Police Force Torsten Huschbeck, Oliver Haas, and Peter Markovič

Abstract The considaration of legal restrictions and contraints on police officers and others outside the non-profit sector of public administration is examined and evaluated in this article. There is always the possibility that a particular police officer would act inappropriately, even in cases where threats or criminal offences are avoided in order to end up in court. Public criticism of the police as a public institution harms endangers their reputation and democratic legitimacy. A specifically designed quantitative-empirical study appears to demonstrate that varied opinions are held about using force against young people in particular. Keywords Risk and crisis management · Scenario technique · Police force

1 Introduction Numerous new recruits to the police force in North Rhine-Westphalia (NRW) and elsewhere not only contribute to an enormous appreciation, but also effect future courses of events. In principle, it is not possible to predict beyond doubt at the present time what risks are associated with police measures of a strongly rejuvenating age structure in the police. In particular, the use of reckless police violence can have undesirable consequences. In particular, the police use of inappropriate force and more or less sophisticated means of intervention may have considerable effects on T. Huschbeck (B) · O. Haas Faculty of Management of the Comenius, University of Bratislava, Odbojárov 10, 82005 Bratislava 25, Slovakia e-mail: [email protected] O. Haas e-mail: [email protected] P. Markovič Department of Corporate Finance, Faculty of Business Management, University of Economics in Dolnozemská, Cesta 1/b, 85235 Bratislava, Slovakia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_3

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the health of the police users as well as on the persons directly and indirectly affected by them [1]. The organisation of the police, as well as police scientists and economists, can knowledge of the links between such avoidable uses and their economic impact. After then, a empirical study was carried out. The daily newspaper in the Ruhr area with the highest readership had its letters to the editor examined for this study. This was done in the context of right-wing extremist propaganda spread within the police. When a police agency is then openly criticised, there are more physical assaults on emergency personnel, which raises the cost of their medical care. Furthermore, a decline in the reputation of the police is noticeable [2]. A risk of this magnitude and its long-term implications, which pose a threat to the organisation’s existence, led to the classification of risk management (rm) at the strategic level of corporate management. In order to determine the extent to which police rm uses crisis management and scenario technique tools, a thorough investigation is required. In this regard, rm is significant for all businesses, not just profit-driven ones. The police also carry out a number of jobs that need for sophisticated approaches. It is well known that the reputation of the police suffers when they are criticised. This crucial risk factor should be supported by the democratically legitimate police and then skillfully “managed” so that they can carry out their mission (security/law enforcement) in accordance with the laws and the constitution. In this situation, it is essential that the economic impact of the police and their use is adequately examined from a scientific point of view [3].

2 Methods As a result, the paper’s main focus is the intellectual penetration of knowledge decisions regarding the use of force by the police. The decisions that are made in this context regarding how to handle existing and suspected hazards are a component of a bigger strategic decision that must be taken in principle, according to the first author, who has already called attention to a research gap in an earlier work [1]. In this context, the external effects of police use of supposedly non-lethal intervention measures and the current scientific foundation of the topic are first subjected to a qualitative content analysis on the basis of a systematic literature review. Although the relationship between the public and the police is largely based on trust, this issue has only been empirically researched once. For this purpose, test participants were devidet into three groups and shown a control scenario, a crime series, and a fictionalised police documentary with a real setting. The question of whether and how legal belief and enforcement have evolved was then answered. No group difference could be found using a “implicit association test” on the conscious level of the test subjects. However, when the actual information and the situation under control are taken into account, a decline in trust in the police can be observed. The respondents’ unconscious assessments of police behaviour also clearly differed from one another [4]. Against this background, a second (quantitative) empirical method is used in this

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article that explores a hazard awareness in both the official police guiding culture and the police subculture with regard to rm. In the light of these special economic considerations, the following research questions are explored: . To what extent can links between theory-based variables and inappropriate use of police force be demonstrated? . To what extent does an appropriate police rm system exist to counteract these risks?

2.1 Literature Review In order to get a better overview of the state of research on rm in the police, a literature study, which has already been indicated, will first be conducted. The preceding literature review also serves to verify or falsify a research gap that currently exists or has been closed in the meantime and to compile the scientific findings obtained. Therefore, all literature sources, especially empirical data and reports, on the management of hazards and risks concerning the police and the actions of its employees are now subjected to a systematic literature review. In addition, the study question contains confidential information about ongoing criminal and disciplinary proceedings involving police officers. On this reason, only freely available papers were used for the systematic literature search. In this context, a common phase model is used to maintain scientific quality standards, be devided into the following consecutive parts, taking into account the results [5].

2.1.1

Identification and Delimitation of the Problem

The danger to directly affected persons as well as to police officers on duty increase when violence is used. The use of force also has adverse effects on uninvolved third parties, as the example of the introduction shows. Therefore, as was already indicated in the introduction, there are many recommendations that deal explicitly with the usage and areas of application of various forms of intervention. On occasion, the reasons and repercussions of the use of police force are not clearly stated. However, these data are required in order for the operational police forces and the general public to evaluate the acceptability and dependability of the use of reasonable force. Furthermore, it has to be noted that only asymmetric information is provided by the police leadership. The above-mentioned problem is the current topic of the systematic literature review, from which the research question for the study will eventually emerge.

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2.1.2

Choosing the Data Sources

The following databases are utilised as a data base to compile a quantitative overview of the literature sources on rm in the police: . . . . . . . . . . .

Beck eLibrary EMBASE (Elsevier) Google Google Books Google Scholar JSTOR Microsoft Academic ScienceDirect (Elsevier) Scopus (Elsevier) SpringerLink Web of Science

2.1.3

A Glossary of Keywords

In addition to the search term “rm”, further common search terms are derived from the scientific question in the course of the study. These additional search criteria are also defined in relation to an open and supported literature search: . . . . . . .

Organisation, administration Organisational culture Risk culture Corruption prevention Crisis management Scenario technique Police force, military, civil protection

2.1.4

Grouping of the Inclusion Criteria

In the current literature study, only papers that are not touch the following exclusion criterias are analyzed. In Table 1, the relevant generic term (type) is also given the associated exclusion condition. Table 1 Comparison of the associated kind and the exclusion criterias (based on [6])

Exclusion criterias

Type

Publication relates not to the topic

Content

Publication before 2019

Publication date

Publication is not peer-reviewed

Publication quality

Publication with no empirical method

Research design

Publication not in German or Englisch

Publication language

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Establishing Exclusion Standards

By default, publications that are exclusively evaluations, working papers and/or socalled abstracts are also excluded.

2.1.6

Analysis in Its More Limited Form

The chosen search terms are linked using the Boolean operators “AND” and “OR” or the asterisk (*) in the designated information systems to link the terms with one another and expand their final syllables, in order to identify the pertinent literature. This makes it possible to locate 112 sources, of which 18 duplicates are eliminated. The identified literature is then reviewed, and another 22 sources are excluded because they do not meet the quality and level of detail required by generally accepted scientific criteria. 12 of the remaining sources are eliminated after a review. 34 more sources that were published before 2019 will be removed from this list. This search method yielded 26 sources for the analysis. Using “Qualitative Data Analysis Software” (QDA software), a hierarchically structured framework with main and subcodes is established for analysis and display, as shown in Fig. 1.

2.2 Quantitative Empirical Method The presentation of a quantitative empirical study design, which deals specifically with the forms of police intervention to be evaluated individually and their supposed non-lethal effects, are the focus of this subchapter. The conclusions of this study are

Fig. 1 Own code representation

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analyzed in terms of content in order to make their implementation transparent and understandable. However, this requires first a description of the empirical research techniques [7]. The study design makes an investigation’s methodological approach clear and aids in making the research process more vivid and better penetrating its underlying logic. Additionally, it gives the research process a unifying theme and a constant point of reference. Finally, the study design makes it easier to get the necessary resources to carry out a planned research project [8]. The sample is then discussed, taking care to ensure that the research findings are as objective as possible after outlining the study objectives. Using the available technique, a cross-sectional online study is carried out for this objective. Two movies are integrated into an online poll that are made available to an unnamed set of individuals via the internet platform “Youtube” in order to explore the internal attitudes of people both inside and outside the police. This relates to a 15-year-old teenager who is physically abused by eight police officers who are using a baton and a pepper spray because the teenager is riding an e-scooter on the pavement and did not want to identify himself to punish the offence [9]. The choice of whether all or only some characteristics of the population is crucial in the context of developing the design for a quantitative survey. A survey is referred to as a total survey if it includes every component of the population. The comprehensive survey has the advantage of excluding some inaccuracies, i.e. is always done with sampling, which makes the results more trustworthy and accurate. Comprehensive surveys are not recommended due to financial constraints, labour demands, and other factors [10]. The goal is therefore to generate a valid and representative set of participants (sample) with an survey (https://www.soscisurvey.de/massnahme n2022/). Regarding the evaluation of the facts, in which police officers used simple physical force (intervention techniques), an aid to physical force (pepper spray), and a weapon (baton) as a means of coercion, the responses will be compared with one another as part of the research process. By using the mentioned methods of coercion, the police could potentially harm their reputation and (sustainably) lose the trust of the public. Furthermore, it needs to be made clear how much these dangers are factored into the police’s estimates. These findings can be utilised to inform future police training and deployment policies, allowing for more informed choices. The survey begins with a 4 min, 12 s video that includes part of the incident mentioned at the beginning against the euphemistic background of “PLEASE, I CAN’T BREATHE” (Fig. 2). The 15-year-old teenager who is the object of coercive police tactics explains why he was unable to identify himself in a second (entrance) video (Fig. 3). Ten mostly scaled questions are asked after a mention of the generally applicable ethical research guidelines and the submission of a statement of consent for voluntary and anonymous participation in the online survey. These survey-based, observational qualities are supported by evidence and can therefore be directly correlated with a scale value [13]. In this instance, a so-called Likert scaling is used to provide five different replies. The closed-ended questions serve to categorise the characteristics identified by respondents perhaps have been noticed. The main objective is to evaluate

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Fig. 2 Screenshot from Youtube video at 0 min and 49 s [11]

Fig. 3 Screenshot from Youtube video at 0 min and 10 s [12]

the video’s actor behaviour in light of the interviewees’ perceptions. Finding out the respondents’ ideas and attitudes is of great importance. When responding to the online survey, multiple responses are also permitted in other places. After the survey is complete, enough anonymised personal information is collected about the respondents so that one of the two groups can receive the answers. The following two inquiries deal with how much the respondents were swayed or sidetracked while filling out the online survey. By identifying unwelcome influences, inaccuracies, and/or bias effects, these two questions ensure objective, trustworthy, and accurate measurement by taking into account the three quality requirements for scientific work.

3 Results and Discussion Different empirical research methodologies might be used, depending on the subject of study in question. One the one hand, it appears that a logical and hypothesis-testing approach to the search for causal linkages is particularly well suited for quantitative

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methods [14]. However, qualitative methods, with their inductive and hypothesisgenerating approach, are equally as effective as quantitative methods for examining causal mechanisms in novel application areas [15]. Additionally, it is not always possible to continually maintain the clear division between qualitative research that generates hypotheses and quantitative research that tests them. For empirical studies, several research methodologies (mixed methods) and data sets are frequently combined (referred to as “triangulations”) [16].

3.1 Qualitative Analysis of the Sources In this subchapter, several sources are used as data material for an empirical social research. The practical implementation of the research project requires a theoretical framework. For this purpose, the approach follows a elaborated concept (Fig. 4).

3.1.1

Culture in Organisations and Administrations

The author team Bauer, Kryvinska and Dorn have demonstrated that trust has a favourable impact on professional outcomes [18]. This holds true for both institutional trust in the police as a whole and for supervisors’ personal faith in their subordinates. A living culture of trust has been proven to typically boost performance in the police departments, evaluated in a study from 2021 [19]. Additionally, a necessary condition for long-term employee retention is trust. The direction of the investigation by Andersen et al. is the same [20]. The focus of this study is employee motivation in the public sector. This study shows that individual motivations at the highest level is possible, if a leadership culture based on shared values and trust is lived. The topic of trust in and mistrust of the police is covered by Schultz in another study by the Federal Agency for Civic Education. The substantial majority of the populace, it is believed, essentially has trust in the police. However, due to extremely critical reporting or media misinterpretation of specific unfavourable instances, this trust is diminishing more and more. In order for the police to continue earning the public’s trust, Schultz contends that their exclusive right to use force must be accompanied by dialogue and governed by the law [21].

Hypothesis

Content analysis

Media selection

Study period

Unit of analysis

Type of survey

Fig. 4 Content analysis of literature (based on [17])

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The premise advanced by the writers’ collective Kyprianides et al. is that even though the range of police intervention methods is expanding, a public discussion regarding the “proper” use of force is never far behind. The writers looked into the relationship between public trust and acceptability and police use of force. They interviewed people in Great Britain about the use of police force in two online trials. According to the survey, people are more willing to accept handcuffs than situations involving a baton, pepper spray, taser, or handgun [22]. Shannon’s book illuminates police commanders’ (self-) understanding, particularly their view of their legal authority to use force. According to his research, police narratives that defend use of force can be loosely categorised into three kinds. The first is the use of force to defend individuals, especially the weak. The second is how consent to surveillance is socially constructed. Third, following the rules of the law or the orders of the political authority [23]. In an empirical research, Constantinou uses the Cyprus police to analyse the impact of politics on policing. His research focuses on political interactions as well as police techniques and procedures. In order to achieve this, interviews with patrol service police officers are conducted and analysed from both quantitative and qualitative angles. In addition, the unique features of Cypriot law enforcement are presented in a global context [24], taking into account the political division of the nation. The authors Huschbeck et al. focus on presenting and discussing the economically based methods to strategic management and how they apply to police organisations in their study on strategic management [25]. The question of how strategic expertise can be incorporated into the police organisation and working culture serves as the beginning point for the discussions. Important decision-making processes are referred to as the selection and purchase of resources by the police as an organisation as well as their utilisation by specific police personnel [26]. Additionally important are the ethical considerations. As there are no independent empirical investigations on the risk potential of the resources, economic considerations appear to have dominated in their acquisition. However, it can be noted that a principle that could be considered a moral law seems to govern all police officers. The behaviour of police officers may be influenced by such a code of ethics if good role models are seen as moral and reliable. Another study by Huschbeck et al. only provides contradictory answers to the linked topic of whether police leadership and their claim to leadership truly take ethical considerations into account. The role model function, on the other hand, sets an emphasis on the outward manifestation of ethical leadership as well as its perception and reputation. However, it is claimed that the employer and its managers don’t always comply with these requirements. The role model function is comparable to the concept of “doing the right thing,” but it does not take the place of professional ethics as a regular subject of study at state and federal police colleges. Additionally, the ethical training material is included into the various curricula in such a variety of ways, that the topic of professional ethics is anchored in a heterogeneous way across the country [27]. The police in NRW provide a successful illustration of how ethical considerations can be incorporated into training. There, mandatory “border walks” teach police recruits how to manoeuvre “in the area of friction between demands and professional

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Border walk Police in the area of tension between demands and professional practice

Power room We cannot change the wind, but we can set the sails differently

Fig. 5 Own represantation with insert translations

practise” from an early age. It strikes me as particularly cunning that this offer is presented to an age group that is more interested in exercise as a “weight room”. The offer also fosters ethical thinking on the part of people entering the profession and is associated to positive reactions. Additionally, it aims to make this broad base’s moral compass stronger (Fig. 5). This appears to have no impact on police management’s economic considerations. However, it is a significant act of will with strong symbolic significance that conveys the huge significance of ethics in the police profession [27]. On the other hand, the authors Piotrowski et al. main’s concern centres on the issue of the impact that police commanders have. 170 Polish police officers were systematically questioned to provide an answer to this question. The findings demonstrate that job satisfaction is positively impacted by both the support offered by the corresponding organisational structure and procedures as well as the cooperation and support of subordinates and superiors. The concept of “organisational fairness” is given equal weight because it is the only means through which difficult management decisions may be accepted [28]. The preliminary finding from the aforementioned factors is that a positive organisational culture in which a culture of trust and a professional ethic are established, along with an implementation idea, are essential for the successful implementation of an rm system in the police (Change management).

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Risk Culture in Organisations and Administrations

The components of an authority-specific rm are covered by Hirsch et al. Although they are aware of the need for it, they do not investigate why the actual implementation is so sluggish. The authors contend that a systematic rm requires the existence of legislative requirements. It is up to the management of each authority to decide whether to use a rm as there is no mandate to do so. The writers persuasively show why there won’t be development without legislative restrictions, even though they caution against an overreaction of administrative action. In essence, it pertains to creating a risk culture with precise reporting requirements in public bodies. The necessity of taking action can only be recognised through the methodical recording and evaluation of particular official hazards, it is noted, and not as an end in itself [29]. A 4-stage strategy for tracking and managing risks is suggested in the literature [30]. The framework conditions are established at the first phase (pre-assessment). Alleged and actual dangers are documented and distinguished in the second phase (risk-appraisal). The chance of occurrence is evaluated in the third step. The right countermeasures are chosen or a counterstrategy is developed in the fourth and final phase. An intense communication process is necessary at every stage, as has already been suggested in other investigations. There is a chance of power abuse whenever police officers interact with outside parties in a subordinate position. The contradictory culture of the police is one reason for such wrongdoing. The police culture, on the other hand, is rather lived by a deviant subculture at the base of the organisational units, whereas the official guiding culture (police culture) conveys a sufficiently determined model in terms of content through police laws, administrative regulations, police service regulations, guidelines, decrees, and relevant court rulings. The police culture, on the other hand, calls for open and cordial dialogue between officers and the general public. As a result, a “police ethic” is created, which is exemplified by the NRW police’s corporate design motto, “Citizen-oriented, professional, constitutional” [2]. Additionally, police officers create their own approaches to dealing with police operations, which are frequently at odds with their policies [31]. The individuals who are impacted incur the hazard of disobeying already-existing standards, laws, and regulations. This is viewed as morally acceptable, particularly in light of police investigations and the natural behaviour of people involved that frequently goes along with them. However, one fundamental aspect of police investigations is complexity. Operational police forces conduct preliminary work to begin a police investigation, which is then followed by tasks requiring specialised investigative techniques and tactics. To better portray the case, comprehend what actually occurred, and identify which elements and specific actions are connected to other crimes and crime scenes, these can include, for example, extensive and intensive study to acquire a vast amount of data. Numerous updates must be given as time passes and the investigation proceeds in order to control and monitor the findings and the precise course of the inquiry. Additional resources, time, money, and quality must all be wisely allocated and kept

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at a high standard throughout the investigation. Additionally, some risk and communication factors need to be considered. Reporting and communications with the media and the general public must always be handled. The cops are under constant scrutiny from all perspectives and are compelled to complete their tasks efficiently and on schedule. Politicians, the media, and society look to the police for professional and efficient administrative actions. In order to accomplish police goals, it is apparent that the level of complexity necessitates a comprehensive idea into which contemporary management practises should be implemented. As a result, the primary duties of the police (law enforcement and risk prevention) ought to be viewed as projects that require the use of proper project-managements methods and tools. The use of suitable project management for the completion of police tasks is determined and predetermined by all the aforementioned criteria and traits. The transfer of specific police work, such as data collecting, profiling, data review, data assessment, investigative work, linkage, and case review, to project management tools and methodologies is a crucial component for task performance success. Project management strategies aid in efficiently visualising, controlling, monitoring, documenting, and tying intermediate outcomes to the case’s larger investigation goals because police necessitates a variety of concerns. Despite this, the information gathered during the police inquiry may also be added to databases, including those that are accessible at the federal level. Saal et al. provide the following equation for this purpose in order to more accurately calculate the time needed for the rm and the likelihood of danger [2]: Number of communication channels =

3.1.3

Number of persons × (Number of persons − 1) 2

Corruption Prevention in Organisations and Administrations

The activity of the investigative authorities in corruption cases is looked at in a research project by the GermaPolice University. Using the specific example of corruption offences, the rm of the actors concerned (authorities, municipalities, business entities) is the subject of the examination. The number of reports is where the analysis begins. In terms of the benefits and drawbacks of each reporting channel, the numerous opportunities for gathering evidence of corruption are methodically presented and further investigated. Also covered are the structural requirements and legal foundation for obtaining corruption indicators. The collaboration of various governmental actors (investigative bodies, municipalities, and administration) with private sector businesses is covered in the next portion of the project. The focus is on information sharing and communication amongst the many actors. Finally, “best practise examples” will be utilised to highlight the areas in which the processing of corruption reports still has to be optimised. The project’s goal is to draw attention to the dangers of incomplete information recording and still-inadequate coordination between the various actors. Finally, suggestions for the organisational and legal aspects of the system’s future evolution will be developed [32].

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Connections Between Risk Management and Crisis Management

An paper by the authors Glesmann et al. on the international (civilian) police operation in Myanmar serves as the beginning point for the discussion of the issue “crisis management.“ The theoretical superstructure for international civilian missions is initially given based on this case study. The implications of police operations on the target state are also looked at, in addition to the broad legal foundation for a civilian assistance mission by German police forces. The elements that can be utilised as benchmarks for an international police mission’s success are also developed during this procedure. The work of Glesmann et al. devotes a significant portion of its time to interviews with practitioners (experts) of the civilian mission in Myanmar. The authors describe the specific requirements for a successful police assignment overseas based on the evaluated interview results. A proper preparation and planning of the mission is also important, in addition to having a clear mandate and a task with enough time to complete it. In order to prevent risks to the participants’ personal safety during the mission itself, thorough training and preparation of the mission participants is also required. This can be viewed as a real-world application of successful rm. However, there is also a clear explanation of the political ramifications for the sending state in relation to international police missions. It is noted that (civilian) police missions have so far attracted little public attention in comparison to military missions abroad [33]. On the other hand, according to a different study, the objectives of each mission must be in line with the conflict framework, which must be established first [34]. The tight relationship between rm and crisis management in the business world is highlighted for the domain of compliance. Moosmayer urges against “silo risk thinking” in favour of a comprehensive approach to risk. Strategic, operational, and “awareness themes” are the categories into which the risks—particularly those related to geopolitical developments—are broken down. The rm ought to make it possible for the business to carry on operating during a crisis [35]. An earlier study from the USA that looked at how the relevant authorities responded to Hurricane “Katrina” in 2005 defined several crisis patterns and described them as a cyclical process [36]. Risk assessment, planning, preparation, mitigation, emergency response, recovery, and final assessment are the steps that can be used to categorise crises. Cancian investigates the relationships between strategic planning and budget in a research for the US military. According to him, there is frequently a significant disparity between the strategic needs for efficient administration of strategic resources and the available (budgetary) resources [37]. However, the operational capacity and interoperability of the (armed) forces weaken without an adequate budgetary foundation.

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Risk Management in the Context of Scenario Technique

In their article, Kryvinska and Bickel deal with the fourth industrial revolution. They describe scenarios of the transition from the analogue to the digital world of work, which poses great challenges for traditional companies and the IT industry [38]. Czech contends in a contribution that strategic thinking is mostly needed for rm. The complexity of potential future developments necessitates the use of methodologically competent scientific approaches to ascertain such developments. The “glass ball” frequently discussed in this context is unrelated to the scenario technique, which the author describes in detail. Additionally, a number of examples are used to illustrate the additional utility of the scenario technique for economic decision-makers. Different visions of the future must be created due to the complexity and ambiguity of potential events. The so-called “scenario funnel” analyzes the future in six categories (politics, economy, social affairs, technology, ecology and law). To begin the process, it is necessary to identify the influencing elements that will have an effect on the future circumstance, whether it be favourable or negative. It is interesting to notice that when the temporal projection (prediction) is extended, the images of the potential future get fuzzier [39]. The business sector is the main audience for the contribution. The scenario technique, however, can also be a useful tool for police supervisors to handle potential dangers. Zhu and Wang provide in their paper a similar model for the scenario technique [40]. This American crime simulation model is scalable. It investigates the connection between population statistics and police everyday routines and is based on routine activity theory.

3.1.6

Risk Management in the Area of Police, Military and Civil Protection

The applicability of rm to state actors’ planning and operations is now being investigated. The author specifically focuses on how the economically based models might be used to police organisations. The topic of whether “just” a further development of the rm models modified from the economy is recommended or whether separate models should be created for police reasons is raised in the course of the study. This raises the question of what, if any, impact new rm models might have on how police organisations operate, particularly in terms of their structural and procedural structures and decision-making processes [41]. The identification and management of risks are essential preconditions for managing crises, according to a different study from the USA. Communication, interoperability, leadership, and regulatory responsibility are the issues that must constantly be addressed. This is illustrated in detail using the situation management scenario related to Hurricane “Katrina” [36]. The value of knowledge during or after a crisis cannot be overstated, according to the field of several military study. The use of social media and the World Wide Web (better known as the internet) expand the possibilities of access to information.

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McCarty and Laakso emphasise that it is always an issue of the same fundamental principles in information management. Information is crucial to reducing current uncertainty, on the one hand. Contrarily, information is always shared within a social setting or process [42]. As a result, decision-makers (in the army) would be wise to use the knowledge that is currently accessible in the social environment to prepare for the next crisis.

3.2 Quantitative Analysis of the Survey Results The elements of a sample are chosen from the population. In order to be able to draw a valid conclusion about the underlying totality, this selection should ideally be made in accordance with specific mathematical-statistical standards. Different sampling techniques are employed in quantitative empirical research. The importance of random picks cannot be overstated. They can be identified by the fact that each component of the population has a calculable probability of becoming a component of the sample that is larger than zero. The concept of sampling error is only valid with random samples, which enables claims to be made regarding the accuracy of the estimate. The dispersion of the sample characteristic values around the overall value of the sought-after parameter is referred to as the sampling error. The greater the sampling error, the wider the confidence interval that determines the parameter you are looking for with a certain probability. The size of the sampling error depends on the dispersion of the measurements in the population and the sample size compared to the population. The following Fig. 6 can be used to determine the required sample size.

Fig. 6 Comparison of the associated kind and the inclusion criteria (based on [43])

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Therefore, it is necessary to first consider the total size (N) to be taken into account. The total population in Germany is 84.3 million inhabitants. Of these, 42.8 million inhabitants are female and 41.5 million inhabitants are male [44]. However, because only people over the age of 18 are to be taken into account, the overall population that must be taken into account is reduced. The number is far the highest value listed in Table 2 (100,000). The next step is to choose the size of the error that can be tolerated, which is given as 5%. To meet this maxim, n = 383 randomly selected population members are needed. Since it is assumed that the proportions to be estimated are 50%, this estimation variant is very reliable [10]. More than 383 people (532) took the poll. They were made up of 40.1% female participants and 59.5% male individuals. Table 2 below shows that there were only 0.4% of the participants identified as being diverse. There is a skewed age distribution to the left relative to the normal distribution. Nevertheless, the age composition is heterogeneous and seems to be almost regularly distributed. With the exception of two statistical outliers, the participants’ (known) ages essentially range from 18 to 74. However, as shown in Fig. 7 below, the majority of participants are about 20 years old. The fact, that many of the participants were selected from the students of the institution, where the first author teaches can be used to explain this. In addition, it seems that this age group is very internet-savvy and likely to participate in online surveys. With regard to the age distribution, it should also be noted that the mean value is 34.82 years and the deviation from the normal distribution is 13.241 years. As already indicated, many students from the University of Applied Sciences for Police and Public Administration in NRW took part in the survey. With regard to the total size (N) to be considered and its representativeness, it is therefore also necessary to take a closer look at the work status of the other survey participants and their distribution. The Work Status of the survey participants is not normally distributed. A proportionate 7.9% of police officers participated in the survey. However, the number of full-time employees in the jurisdiction of the federal and state police (295,000) does not come close to the German population of persons over 18 years of age. The same applies to the number of students, who are also overrepresented with a proportional share of 19.4%. Both can be attributed to the proximity of the authors to higher education as well as the internet affinity of these age and employment groups already Table 2 Presentation of the gender composition Frequency Valid

Missing Total

Percent

Valid percent

Female

213

40.0

40.1

Male

316

59.4

59.5

Diverse

2

0.4

0.4 100.0

Total

531

99.8

Unanswered

1

0.2

532

100.0

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Fig. 7 Histogram of the age composition

mentioned. In view of the limited representativeness of the work status, it should finally be noted that only 4% of the survey participants did not make any statements in this regard. The remaining information on the employment relationships can be seen in Fig. 8 below.

Fig. 8 Presentation of the work status

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Fig. 9 Distribution of votes for question 1

After the administrative part of the survey, the team of authors now turns to the descripitve statistics. Surveys are usually developed on the basis of expert interviews or other qualitative-empirical methods in order to test theory-based hypoteses. In the present case, this is done on the basis of numerous expert interviews [1, 45, 46]. These dealt with the use of force and police resources by operational police forces. From the knowledge gained from this, the following hypothesis are formed: . Age an gender have an impact on recognizing whether a particular police restraint is necessary and proportionate. . The greater a person’s understanding of appropriateness of a particular coercive police measure and its proportionality, the more likely the coercive police measure taken is recognised as lawful from this person. Seven categories of questions are used for hypothesis testing (Q1–Q7). The questions and their results are first presented on the following pages and then discussed. After this, the variables are examined for correlations (see Figs. 9, 10, 11, 12, 13, 14 and 15). Q1. I am aware of the goal of the police officers who were deployed. Q2. I am aware of why proportionality is so important in police intervention measures. Q3. I am aware that the selection of the respective police means of coercion must also be made under the aspect of proportionality. Q4. The measures taken by the intervening police officers in video 2 are lawful. Q5. The use of force by the intervening police officers in video 2 is proportionate. Q6. As a supervisor, I would follow up on the conduct of the intervening police officers in a subsequent meeting. Q7. As a superior, I would initiate an investigation based on the behavior of the intervening police officers.

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Fig. 10 Distribution of votes for question 2

Fig. 11 Distribution of votes for question 3

Fig. 12 Distribution of votes for question 4

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Fig. 13 Distribution of votes for question 5

Fig. 14 Distribution of votes for question 6

The answer to Q1 shows that the goals of the police officers were not clearly identifiable to all respondents. This may be due to the fact that it is a rather shaky video that offered only a short excerpt and not all respondents are legally knowledgeable. The proportionality of police measures is an outflow of our constitutional state and a valuable asset. It is therefore not surprising that the majority of respondents in their answer to Q2 agree that proportionality in police measures is so important. Correspondingly, the same picture emerges with regard to Q3 with regard to its answer. Here, too, the majority of respondents consider that police measures must be taken from the point of view of proportionality. Consequently, the majority of respondents do not agree that the use of eight police officers, using a baton and a pepper spray, against a 15-year-old teenager for an administrative offense is lawful (Q4). Here, however, the picture is not so clear. Since an almost equal number of respondents also vote for the other answer options. However, a clearer picture of the mood emerges

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Fig. 15 Distribution of votes for question 7

in the answer to Q5 on proportionality. Here the consent is clearly diminished that the described use was proportionate. The follow-up of such an operation is hampered by the enormous majority of respondents support, as Q6 responses show. The majority is still voting for the initiation of preliminary proceedings against the intervening police officers, but less clearly (Q7). The aim of determining correlation is to examine two variables that are interdependent. This works much like a crosstab. For this purpose, variables (Q1–Q7) are formed on the vertical axis and the same variables (Q1–Q7) on the horizontal axis. The variable with itself logically correlates perfectly with the value 1. A correlation can be seen when the value of one variable increases as well as the value of another variable. One could also say that there is perhaps a causal connection. However, the causal relationship is not always 100%, because bogus correlations must also be taken into account methodically. When looking at the correlation table (cf. Annex 1), there are apparently slight statistical correlations that significantly indicate a verification of the hypotheses to be tested. Therefore, in two cases, a regression calculation is performed for hypothesis testing. Regression analyses are based on correlations. Correlations are always two variables that oscillate with each other upwards or downwards or not. With regressions, on the other hand, the model becomes somewhat more complex because a third variable is usually added. At this point, a first regression analysis looks at whether there is a correlation between the independent variables (age/gender) and a dependent variable. The latter is the possible assumption of the survey participants that the coercive measures of the intervening police officers are proportionate. These are therefore three variables that are examined to see whether they are related to each other. Here, however, the R-Square is 0.056, which is far below the value of 1. Accordingly, it

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can be assumed with a probability of only 0.056% that there is an effect relationship between the age and gender of the survey participants on the one hand and the assumption that the measures taken by the police officers are proportionate on the other. This basically means that in this case the correlation of effects does not exist. The value of < 0.001 is significant, but the regression analyses show too low a statistical correlation. In a next step, effect associations between the dependent variable from Q4, i.e. whether the measures taken by the intervening police officers in video 2 are lawful, were crossed with the following constant influence variables from Q1 (I am aware of the goal of the police officers who were deployed) and Q2 (I am aware of why proportionality is so important in police intervention measures). According to this, it would be the case that the survey participants who say that the actions of the police officers depicted in the video are lawful (Q4) and who were also aware of what goal the police were pursuing with their intervention measures (Q1) were also aware of why proportionality is so important in police intervention measures (Q2). In this case, the R-Square is 0.33. That is, 33% of the results of the dependent variable are generated by the two independent variables mentioned above. Basically, this only confirms what was already known through correlation analysis. However, a value of 33% is not very high (Table 3). The regression must also be significant in this case. For this purpose, a significance calculation (ANOVA) is carried out, which results in an equally high significance value of < 0.001. The probability that the values have come about by chance is therefore very low (Table 4). This also confirms the assumption from the correlation analysis that the better the police communicate with the citizen about police measures, the more likely the citizen is able to judge whether the intervention measures of the police are lawful. In this way, the police presumably also gain the support of citizens. This is a strong call for the police to communicate even more with citizens and not to slacken in their efforts to explain their measures. This is certainly something that the police do as part of their rm, but has never been statistically proven. Table 3 Presentation of the Model Summaryb Model

R

R square

Adjusted R square

Std. error of the estimate

1

0.574a

0.330

0.326

0.145

Note: Quality of the result that predicts the dependent variable

Table 4 Presentation of the ANOVAa 1

Model

Sum of squares

df

Mean square

F

Sig

Regression

270,771

2

135,385

103,266

0.001b

1311

Residual

550,634

420

Total

821,404

422

Note: The probability that the values have come about by chance

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Calculating the distribution of the different values (residuals) is the penultimate stage (Table 5). Finally, the P-P Plot below illustrates how close the residuals are to the normal distribution at points (Fig. 16). The values are known to be (about) regularly distributed based on the histogram displayed considerably further above. Random values would happen by random rather than as a result of an effect relationship. The residuals and significance level of 0.001 indicate that the values were not generated randomly. Thus, it is now impossible to challenge the interpretation of the residuals and the effect correlations. Table 5 Presentation of the residuals statisticsa Minimum

Maximum

Mean

Std. deviation

N

1.10

3.58

2.57

0.801

423

− 2.581

2.787

0.000

1.142

423

Predicted value Residual Std. predicted value

− 1.842

1.257

0.000

1.000

423

Std. residual

− 2.254

2.434

0.000

0.998

423

Fig. 16 Normal P-P Plot of regression standardized residual

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4 Conclusions Risks at large were ever part of human sapiens history, and the exposure of consequences which comes with risks will be unavoidable in the future. In principle, risks are unavoidable and are defined as the effect of uncertainty on objectives [47]. The rm and the structured application of it, in particular for police and law enforcement, helps to identify and mitigate the effects and negative consequences for the police and the society at the same time. The rm within the police force is not an” fastselling item “: it can only get by with cultural change, open mind set up, engaged departments, leadership, individual and departmental lessons learnt approach, accurate budget and resource planning, and permanent adjustment of rm methodology, roles and responsibilities, stakeholder involvement, proper reporting strategies and continuous tracking methods within the police and law enforcement. External and internal litigation respectively lawsuits against members of the police forces can not only lead to spent enormously time and money, but also undermine police and law services morale and negatively effect reputation on the long term. This paper reveals that rm has to become an integrated part of the police and law enforcement. Threats, opportunities, the level of uncertainty, particular risk factors, risk appetite, tolerances, thresholds, and present risk aversion are just a few of the aspects that affect how rm is used [48]. The absence of introspection inside the police is the basis for this paper’s discussion of all current threats. Without doubt police forces need to handle permanently and instantly all kind of high-risk critical tasks that effects the society, e.g. domestic violence, searching and seizure of different subjects, secure lives and property, produce and record evidence immediately of „what happened “, conduct and ensure safely emergency and rescue operations etc. At the same time, the police forces and its individual members also seeks to uphold the law in any aspects. With that being said, the police office in charge has to recognize and assess the risks of his specific activity, structure and prioritize those risks according to severity, likelihood and impact and finally take the right action to deal or mitigate those risks. That process of individual rm requires permanent training, research and provision of tools of and for the police officers and adoption associated with recent and current risk response strategy of local and federal governments rm goals. That being the case, police and law enforcement executives should take the leadership. Consequently, for the implementation of proper rm, the onboarding of the right staff for all hierarchy level of personal is mandatory and prerequisite being successful. The involvement of professional staff agencies to recruit and hire skilled staff might help to ensure the correct long term application of the right rm process within police and law enforcement departments. In particular, new recruited staff being able to work collaboratively and open-minded together with other departments and authorities increases the likelihood of being accepted of all parts of the society. Finally, it shall be emphasized that communication and collaboration between the individual police officers, the police and law enforcement, but also between the local and federal governments, labor unions, the risk managers and the society is crucial for a successful rm. Part of successful communication and collaboration is the use of professional tools and data

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available at the right time when its needed, e.g. categorizied case studies, integrated database and website technologies, pre-warn systems in place, in-situ adjustments tools etc. The structure and implementation of such communication and collaboration tool and platform has to be developed and requires budget and resources.

5 Limitation This paper were mainly based on the assessment of a case and survey study and certain literature review listed. However, a deep analysis of the aforementioned outcome compared of nature of existing internal standards, e.g. ISO 31000:2018 and ONR 49,000–49,003:2010 [47] were skipped. It shall be noted that evaluation against the most common international standards might come to similar outcomes or, in the event of substantial omissions, to recommendations to adjust those standards accordingly. The paper mainly focuses on German research results. By widen the approach and database to other international jurisdiction and police and law areas the results could be compared and proofed, cultural and national-depending specifics could be revealed. In addition, a survey and further investigation whether special compiled and tailor made certification program can be integrated in the daily police and law enforcement works has not yet started, e.g. PMI [48]. The quality standards within such police and law organization might be permanently improved and based on a qualitative assessment system of measures. That would help all stakeholders inclusive the society to compare within a federal government system, but also makes different standards in countries recognizable. This paper did not strive recent and current litigation and lawsuits where police and law enforcement has been involved. As numerous civil and criminal litigation and lawsuits are still in process and awaiting verdicts or settlements, it shall be noted, that some of the recent conclusions are only preliminary only and therefore partially limited linked to the aforementioned matters. Dependent on the outcome of those lawsuits and settlement, new information and/ or status will be revealed and brought up. Based on those new aspects and findings, new research shall be conducted.

Annex Attached are one correlation table (Annex 1). Additionally, the annex includes the survey’s SPSS raw data, which is made available to researchers and the general public who are interested (Annex 2). The correlation (Annex 1) and the survey’s SPSS raw data (Annex 2) are available at the following https://doi.org/10.17605/OSF.IO/KCWY4. The correlation (Annex 1) and the survey’s SPSS raw data (Annex 2) can also be accessed via the following https://doi.org/10.17605/OSF.IO/KCWY4.

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The Classifier Models Usage for the Recruitment Process Forecasting for Applicants of Higher Education to Universities of Ukraine Petro Pukach , Bohdan Liubinskyi , Volodymyr Hladun , and Vladyslav Holdovanskyi

Abstract The paper considers the task of classifying students entering the budget form of education using machine learning methods. Previous studies have been analyzed, which made it possible to develop the solving the problem strategy. Data for 2017–2020 Entrants has been collected thanks to open data. The data have been cleansed out of outliers, scaled using MinMaxScaler. Eliminated class imbalance using SMOTETomek methods. The specifics of various classifier models have been analyzed with the aim of better model selection and more detailed results analysis. Several classifier models have been trained, among them the utmost effective machine learning model was selected, which has reached out the Accuracy Level of 96% on the test sample, and 90% on the initial data. Hyperparameters for the final model have been reconciled. Its output values are calibrated for the purpose of more informative output of the model. The data processing algorithms and the model have been collected in the pipeline for convenient further use. The prototype of the program has been developed to demonstrate the model’s operability in conditions, which are close to the real ones. The anticipated input/output have been demonstrated while functioning, along with developing the scheme of the program’s operation in order to simplify its implementation into a real product. The conclusions have been made, thanks to which the context of the similar problems is being better understood, and their solutions can be used in a wider variety range of tasks. Keywords Data science · Machine learning · Classification problem · Confidence calibration · Introductory campaign

P. Pukach · B. Liubinskyi (B) · V. Hladun · V. Holdovanskyi Lviv Polytechnic National University, 12 Bandera Str., Lviv 79013, Ukraine e-mail: [email protected] P. Pukach e-mail: [email protected] V. Hladun e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_4

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1 Introduction Machine learning models are a technology that combines mathematical methods (mostly statistics), programming, and databases to create models that can learn from data and make the future predictions. The main advantages of this technology are: (1). Computers perform calculations incomparably faster than humans and can consider much more information simultaneously; (2). Having access to databases and being armed with processing speed, they are able to process huge arrays of data; (3). In the Internet era, most processes are digitized, which makes it much easier to collect data for these models’ research and training. The machine learning classification task is the issue of predicting a target variable (class) from the values, given from the independent variables (features). The target variable is treated as either 0 or 1 (binary classification); There’s aslo a fact that sometimes multiclass (when an object relates to a single class from the set of classes) or multilabel (when a chosen object can relate to many classes from the set) classification problems are considered. The most typical models are the Linear models (logistic regression, SVC— Support Vector Classifier). They have many modifications such as regularization (l1, l2, elastic) for logistic regression, kernels for SVC, non-linearity, which is achieved by considering not the features, but their mapping by a corresponding non-linear function. Although linear models are easy to interpret, they have a significant drawback—they require linearity of the data. In case of nonlinearity, it must be eliminated, while there is no single algorithm, the search for transformations is carried out during the data analysis and the models’ result. Typical models also include Naive Bayes (based on the Bayes theorem on conditional probability) and models based on the Nearest Neighbors algorithm (for example, K-Nearest Neighbors Classifier). The latter is based on the hypothesis of compactness—the object has the same label as its nearest neighbors. These methods learn quickly and guarantee some non-linearity, but they have significant drawbacks. KNN requires the selection of K, also due to the use of distance metrics, requirements are imposed on the data: they must have the same range of values, not being categories data, etc. Naive Bayes assumes that all factors have equal statistical significance, which is quite rarely in practice. Also an important class of classifiers are models based on decision trees. They have a specific non-linearity. If we talk about the transformation of features when using logistic regression or in SVC kernels, we mean the consideration of a nonlinear function from this feature. In decision trees, the non-linearity is manifested in the fact that they can recognize atypical cases and process them separately. They’re also easy to interpret and don’t require the same processing as other models: they don’t need scaling, the outliers are insignificant. However, they are highly suitble to retrain. Quite often the decision trees learning boils down to reducing overtraining. Combining many models into one to improve metrics is called ensembles. Ensemble methods, in fact, perform two tasks: (1). They’re a regularizations for models, due to which their outputs are more stable, and retraining is reduced; (2).

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They achieve greater non-linearity (mainly Boosting models). There’re three types of ensemble methods: Bagging, Boosting, and Stacking. Bagging is a combination of many identical models that are trained on different subsets, and the final prediction is made by voting—e.g., the prediction that made the most models becomes chosen. Boosting is also a combination of many models of the same type, but every each subsequent model considers the errors of the previous one either through the formation of subsets or through weights for observations. The Stacking algorithm trains different types of models, while the so-called meta-algorithm is also trained based on their predictions. Problem statement. The admission campaign period every year brings stress to applicants and takes a lot of resources from higher education institutions (HEIs). For entrants, this time is difficult, because their future life and career depends on their choice. Future students need to choose a university and an educational program (specialty) that would fit their desires, skills and capabilities to obtain an education and further work in the desired field. Accordingly, the wrong choice can cause at least a year of time lost. For universities, this campaign is both time and resourceconsuming. Employees of educational institutions provision the future students selection, implement analytical and information support, etc. Hence, this process optimization could free up the resources for other needs. A model that could predict an applicant’s admission to a certain specialty of a certain university could easy the admissions campaign for both applicants and HEIs. For the former, it could show their chances of admission to the desired educational program and reduce uncertainty respectively, which in turn would reduce their stress and allow them to better plan for the future. For the university, such a model would make it possible to speed up and ease up the process of enrolling applicants and work with future students until the admissions campaign finish. The article’s objectives. The purpose of the research is to create a machine learning model that will predict the success of an applicant’s application for admission. Such a model will streamline the applicants’ behavior, allow them to better plan their future, and accordingly reduce the number of students entering unwished educational programs. For higher education institutions, similar models would help to highly likely identify future students even before the admissions campaign finishes. Usually, applicants estimate the probability of admission roughly, focusing on the scores of previous years, the number of applications with higher scores, etc. All these interdependencies in the data used by entrants can be learned by machine learning models and then—to predict admissions with much greater accuracy respectively. Another advantage of machine learning models is their speed. Unlike the applicants, who need to spend a lot of time to review and evaluate the ranking scores of previous years’ applications, the applications of their competitors, the models are able to return the result in a split second, which in turn allows applicants to consider many more potential universities and educational programs. Another feature of the models, which in this case is one more advantage, is the solution of the Confidence Calibration problem. This’s one of the tasks in machine learning, which lies in approximating the output of the model to the probability. After all, the output of the model is a value that reflects the confidence of the model, but not the probability of the object

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belonging to a certain class. Thus, the entrant can get much more information for risk management. So they would be able, for example, to submit applications according to the decreasing probability of admission, placing the most desirable specialties at the top. To achieve all the advantages described above, the following tasks have been formulated. 1. To collect data from the applicants’ applications (they should be sufficient to train models and assess their accuracy). 2. To clean the data (from missing or anomalous values, also remove data that may be unusable for various reasons). 3. To carry out feature processing (feature engineering—generation of new features, based on the ones we have). This can tremendously simplify the training of models and increase their accuracy. 4. To select the best model—classifier. 5. To select the parameters and features used for training the model. 6. To calibrate the output of the model (values in the model output should reflect the real entry probability). 7. To create a prototype to demonstrate the operation of the model.

2 Analysis of Recent Research In recent years, scientific works from various countries and universities of the world have begun to appear, in which the authors try to analyze and predict the students’ success: whether they will successfully complete the course, whether they will complete the entire educational program, etc. This is important, because students are future employees, and possibly researchers, who will move progress forward. Accordingly, the ability to find insights to improve education, increase the number of graduates, and improve the quality of their knowledge is an extremely important task. Eliot Colin Ploutz tried to predict whether a student would graduate from the university at the beginning of his studies [1]. According to his data, the share of graduates reaches 60%, and in part-time studies it does not even reach 20% (the specifics of the university). To train the models, the author uses about 30 selected features. Among them there’s information about the gender, age, first-year student school rating, academic arrears, etc. Based on this data, he built several models. The Decision Tree gave the best result—the model achieved an f1-score of 0.9177, which is actually a good result for complex social data and certifies the possibility of analysis and prediction in this area. A similar study was carried out in California using data from colleges [2]. The researchers tried to predict whether a student would complete the course or not. 8690 records were collected for model training and 2172 for testing—the accuracy level of 65.7% has been achieved. Such indicators as grades for secondary school and grade point average (average of all subjects, average of mathematical disciplines and of all disciplines excluding mathematics) were used.

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These results suggest that a student’s grades are not sufficient to accurately predict his or her study program completion and don’t reflect all the interdependencies in the data. In the article [3], student’s success is predicted by dividing them into “weak” and “strong”. As a criterion for this division, the average grade from all courses was used—if it’s been greater than or equal to 4.50, then such a student was considered as “strong”. Models on this data showed the accuracy level in the range from 67.46 to 73.59%. A similar division into “success classes” was used in another study [4]. It provided an assessment according to the American grading system (A, B, C, D, F). At the same time, it is worth noting that in this model the grade B was best predicted, the other classes had significantly worse accuracy,—despite this, an overall accuracy of 82.58% has been achieved across all classes. Shudong Yang was predicting students who would drop out [5]. At the same time, the emphasis in his work is on reducing the risk for the university and the interpretability of machine learning models to analyze the causes of these risks. In the paper he performed the in-depth meta-analysis of other studies and pointed out the main problems of analysis in this area, such as data imbalance, heteroskedasticity, difficulty of data collection and also a high dimensionality, which is typical for them. He used more than 36 features to train the model, including grades in various subjects, whether or not the student was a visitor encounter, academic awards, library debt, the presence of tattoos, and even the zodiac constellation affiliation. The LightGBM model has been trained on this data, which showed a result of 0.96—accuracy, 0.95— f1-score and 0.89—ROC AUC. Also, this model, as stated in the article’s objectives, made it possible to interpret the result as global (for all students) and local (for each individual student). From this article and previous studies, it can be concluded that various social factors (the atmosphere in the group, the absence of problems in the family, etc.) have a more significant influence than the student’s previous evaluations. It’s also important to remark that the results of machine learning can be interpreted both to improve the success of the students themselves and to reduce the university’s risks. Obviously, the problem of risks taken by both first-year students and the university is of interest to both parties,—such as many other studies on this topic [6–9]. In the next study [10], the topic and purpose of which echoes ours, the applicant’s admission was predicted based on such data as academic performance in grades 10–12, exam grade, whether the student opened an e-mail from the college or not, whether the applicant visited the college’s website, etc. In this work, the decision tree achieved the highest accuracy, which was also the most accurate model for almost all data sets. For two sets, the tree achieved 73% accuracy (in one of them the Naive Bayes model had 0.09% better accuracy). For all others—in the range of 80–82%. In another article, the authors built a model for predicting an applicant’s chance of admission [11]. For this, the UCLA Graduate Dataset has been used. About this, a regression analysis of the data was performed, which gave a list of the most influential factors for successful admission. The authors also tested their model on the same data set and obtained an accuracy of 87.5%. It is also worth mentioning other papers [12–14].

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Ther’re also papers on predicting the admission chances to the desired universities [15]. To train the machine learning models, the authors used their GRE and TOEFL scores, as well as the ranking of the university, which the applicant wanted to enter to, whether he did research, and whether he had a LOR (letter of recommendation). To test the models, the data were divided into training and test samples in the ratio of 80 to 20. RMSE was used as a metric and the smallest error was 7.2% for logistic regression, linear regression had approximately the same error. The decision tree had an error of 11%. Machine learning models are also used to analyze the minimum necessary achievements of an applicant for admission [16, 17]. In one of these papers, the authors took data from the SIS (University Student Information System) from fall 2008 to spring 2012. In total, the database contained 52,717 records from 454 fields. From the analysis, they defined an admission as fulfilling the condition: “The applicant has a GPA of at least 2.75 points and completed 24 h of college-level study OR completed 72 h of college-level education”. Next, a decision tree was trained on this data, which has correctly classified 70.8% of students. After analyzing the decision tree, the authors obtained the criteria, thanks to which the applicant can analyze and increase his admission chances. Usually, the data of applicants have a high disparity, because there’re more applicants who didn’t enter the desired university than those who passed the competition. In the article [18], the researchers tried to determine which method of combating the disparity and according to which model would be the best. Four data sets have been generated (for each method of combating disparity). The most effective combination was: SVM model and aggregating ENN and borderline SVM-based SMOTE. The second place gets AdaBoost and the borderline SVM-based SMOTE. Some articles describe the creation of programs for entrants that would allow assessing their chances of admission and analyzing each feature [19, 20]. Thus, the authors of one of them achieved an accuracy of 80% and have developed an application where the entrant could enter his data and receive predictions from the model. Based on the forecast, he could further plan his future. After analyzing other studies, the following conclusions can be depicted. 1. The success of students and the probability of admission by applicants can be predicted with sufficiently high accuracy [1, 4, 5, 10, 11, 15, 16, 21–23]. In particular, in [21] they note that 70% of the articles reviewed by them successfully solve this problem. 2. Entrant data is usually highly disparited [5, 18], and models are often overtrained. 3. Although there’re many results that indicate that predictions based only on the applicant’s performance (grades on entrance exams, in school, etc.) can give a good result, this is not always the case. In cases where grades are not decisive the main role is played by social and personal characteristics. Often, such trends are seen in the tasks of predicting the success of a student after admission, or the risks that he wouldn’t cope with the educational program [2, 5].

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4. Usually, the problem posed in the article is better solved by non-linear simple models—decision trees, ensemble models, fully connected neural networks [1, 5, 10, 16]. 5. Analysis results and interpretation of machine learning models can be used to provide more detailed analysis to entrants, which can be used to plan their entry [16, 17, 19, 20]. Also, such information may interest universities to reduce risks or automate processes [5–9].

3 Results 3.1 Data Acquisition and Preparation The data has been obtained by scraping technology from the site—abit-poisk.org, which provides information about applicants’ applications, whether they are enrolled in studies or not (see Table 1). Data for the years 2017–2020 were chosen because data before 2017 would be too difficult to cleanse and bring to a single format. The data also included statements from five universities of Ukraine, namely Lviv Polytechnic National University, Ivan Franko Lviv National University, V.N. Karazin Kharkiv National University, Ihor Sikorsky Kyiv Polytechnic Institute and Sumy State University. The data has been obtained without first and last name in order to preserve anonymity. As a result, the following characteristics were obtained: id, priority, rating score, status, availability of quotas (state-guaranteed benefits for certain categories of applicants), applications, year of admission campaign, specialty (educational program), number of budgetary and contract places and the total number of places on educational program (specialty), the university to which the application had been submitted. Table 1 Example data

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The data contained no missing values, but contained abnormal values. So, for example, there were statements with scores below 100, given that statements with a rating of 110 and below didn’t fall into the interquartile range. They bring no value for training models, since the applications with such points would obviously not pass the budget, moreover, they can interfere with the models’ “learning”, so for these reasons they were rejected. Applications with the following statuses: Rejection, Canceled application, etc., were also rejected. Such applications were small in number (for example, for 2020, their total number did not exceed 6000, considering that there were more than 110,000 applications with other statuses). Thus in order not to confuse the models and not to introduce statements into the training data that a priori would not pass the competition, they were filtered. As the target variable, the status of the applications was selected, which indicated the admission to the budgetary form of education (or recommended for it), all others are considered unsuccessful. Next, new ones were obtained from the existing features, which can significantly improve the accuracy of the model. Such characteristics were: average, maximum and minimum grades for budget places and in general for those educational programs, university and year in which the application was submitted. Given that we cannot know in advance what the values will be, the values of previous years were used to predict the current one. This should at least partially take into account the trends in the distribution of admission points each year. Also, new ones were formed on the basis of our features. What is the ratio of the entrant’s score to the average, minimum, maximum among the budget ones for the specialty to which he/she applies. This should make it easier for models to train and improve metrics. One variable denoting the ratio by which it is possible to determine how much the entrant’s score differs from, for example, the average score for last year’s budget places. By design, this should work for the following reasons: (1). It considers two variables in one,—this is useful, for example, for decision trees, where only one variable can be processed in one leaf; (2). The general ratio is considered, rather than exact values, which makes it easier for models to account for trends. After all, for each specialty, each university, the passing score will be different, but the new ratio will be similar in general. All values of numerical features were scaled to the interval [0, 1] (using MinMaxScaler—that is, through the minimum and maximum values). This is done due to the fact that the linear models work better with such data. Next, it’s been necessary to eliminate the too great disparity of classes, because the statements that did not pass the budget form were 10.14 times more, 186,590 against 18,389 on the training sample. The disparity is eliminated by two methods: randomly discarding entries of the majority class (getting a sample of 35,000 entries for models that would take too long to train on more data) and SMOTETomek (a method that generates new entries from existing SMOTE method and then discards those that are similar to others or are anomalous—by the method of Tomek’s links). 340,650 records were obtained from SMOTETomek, which were used for models that could quickly learn on them.

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3.2 Training and Selection of Models Consider the machine learning models we’ve used. And let’s start with the baseline—logistic regression. The model is simple, quick-to-learn and easy-to-interpret. It achieved accuracy of 0.8446 and 0.8445 for the training and test samples respectively, on the initial data set—0.8071. The first conclusion that can be drawn is that the machine learning models can predict with good accuracy whether an applicant would enter the budget form or not. The second is the good, but far from ideal,—accuracy of logistic regression. That’s why other algorithms need to be tried to understand the specifics of the data and where to move on in this research. The next model is a variation of Naïve Bayes, which is available in the sklearn library for the Python programming language. There were no high hopes for these models, because they work better with categorical data, when most of our features are numerical. However, as mentioned, there are variations of them, so we wanted to check them out as well. MultinomialNV had the best accuracy—0.7691/0.7711/ 0.7117—on the training, test and seed samples. It can be seen that the model learned much worse than logistic regression. The last of the linear models is SVC (Support vector classification). Despite the fact that the logistic regression threshold is 0.85. SVC pleasantly surprised. It outperformed logistic regression with an accuracy of 0.8502/0.8620/0.8256 on the training, test, and unaltered samples, respectively. It seems that the increase is insignificant and this is the case for the training and test samples: 0.0056 and 0.0175, that is, 0.5 and 1.75% in both cases. However, the accuracy threshold for the initial data increased from 80.71 to 82.56%—almost 2%. Which is quite significant. It is worth considering the non-linear machine learning models. The most typical model is a decision tree. Without regularization, using the default model parameters, it achieved an accuracy of 0.9931/0.9535/0.9087. You can immediately notice an increasing in accuracy by 10–15%, depending on the data set. For machine learning, this is a significant difference, which tells us the following: (1). The machine learning models can predict an entry with high accuracy; (2). Decision trees perform significantly better than linear and simple models. From the last conclusion, it can be assumed that the coherence between the data is non-linear, and accordingly,—it’s necessary to pay attention to non-linear models: ensemble, neural networks, etc.; (3). Decision trees are being highly overtrained, which suggests that in theory the accuracy on the test sample can be greatly improved, although in practice this is not often the case. Let’s consider the ensemble models. Given that the ensemble methods are designed primarily to reduce the bias and scatter of models, the favorites will be models based on decision trees (because they’ve performed best). We will also consider the Stacking model based on previous models. The Stacking model has been built on the basis of the previously considered models—logistic regression, MNB and SVC, which were trained on data, and a decision tree, as a model, which was trained on the predictions of these models and on the data. The general scheme looks as follows (see Fig. 1).

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Fig. 1 The StackingClassifier scheme

This model has achieved an accuracy level of 0.8871/0.8877/0.8490. We see that although the accuracy is better than that of logistic regression, SVC and MNB, the accuracy is much lower than that of decision trees. And accordingly, the models from the lower layer, in addition to not bringing better final accuracy for the decision tree, also have worsened its accuracy. Accordingly, such models should be abandoned and considered only: decision trees, ensemble and neural models. Although it’s worth noting that using Feature Engineering, they can be improved. For example, by using signs based on the leaves of the decision tree,—because each node from the decision tree can essentially be considered as a boolean sign. Next, we consider GradientBoosting, RandomForest, and AdaBoost models based on decision trees. After training them as a decision tree, with the default parameters we’ve gotten: GradientBoosting—0.8913/0.8934/0.8564; RandomForest—0.9931/ 0.9605/0.9084; AdaBoost—0.9931/0.9565/0.9068. It can be seen that GradientBoosting is worse than decision trees. RandomForest and AdaBoost are about the same, except for the test set, where RandomForest performs best among all models. As a result, you can look at the graph of model accuracies on different data sets (see Fig. 2). The blue horizontal line indicates the average accuracy, which is 0.8802. It is clear from the graph that logistic regression, MNB and SVC perform significantly worse than other models. They did not achieve average accuracy in any data set. StackingClassifier and GradientBoosting reached the middle, but performed significantly worse on the initial data. And accordingly, decision tree, RandomForest and AdaBoost are favorites. They show accuracy at almost the same level. They’re significantly overtrained. Accordingly, it’s worth asking whether it makes sense to use RandomForest or AdaBoost since they take longer to learn than a decision tree and the result is almost

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Fig. 2 Comparison of accuracy of models on different data sets

the same. In my opinion, the answer is YES, because considering the fact there’re hundreds of decision trees in ensemble models, the answer of such a model will be more stable, while the decision tree with uncertain input data will more often shift the answer. So, the best models are decision tree, RandomForest and AdaBoost (see Table 2). They’re significantly overtrained. We’ve tried to reduce overtraining by: (1). Feature selection via RFECV; (2) Selection of hyperparameters. Both methods failed. The decision tree with parameter selection had accuracy: 0.9932/0.9566/0.9009. As we can see, the accuracy on the test sample increased slightly, while the accuracy has remained the same for the training and initial data. Moreover, after the selection of hyperparameters, the accuracy also almost hasn’t changed: 0.9932/0.9566/ 0.9009. We see that the accuracy has not changed and these methods cannot reduce overtraining.

3.3 Model Output Calibration For an applicant, it is important to know not only the prediction of the model, but also the probability of his admission, because knowing the chances of admission significantly increases the informativeness of such a model. However, models usually do not return classification probabilities, but only a certain value that indicates the model’s confidence in the object’s belonging to one or another class.

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Table 2 Table of accuracy of models on different data sets Model

Method of combating disparity in the classroom

Accuracy over Accuracy over Accuracy over the training set the test set the initial set

Logistic regression

SMOTETomek

0.846

0.844

0.807

Naive bayes (типу SMOTETomek MultinomialNB)

0.757

0.762

0.712

SVC (Support vector Classification)

RandomUnderSampling 0.850

0.862

0.826

Decision tree

SMOTETomek

0.993

0.954

0.909

StackingClassifier SMOTETomek

0.887

0.888

0.849

RandomForest

SMOTETomek

0.993

0.961

0.908

GradientBoosting

SMOTETomek

0.891

0.893

0.856

AdaBoost (based on the decision tree)

SMOTETomek

0.993

0.956

0.907

It can be seen from the calibration curve that the model strongly underestimates its confidence before the value of 0.5(50%), and after—overestimates it. If the model returned probabilities, then this curve would be like a dashed line—absolutely straight (see Fig. 3). Also, let’s take a look at the expected calibration error, it’s 0.0565(5.65%), which is too much. We will eliminate this problem by training Isotonic Regression, based on the predictions of our model. In other words, we train another model based on the results of the previous model (see Fig. 4). As it can be seen from the graph, it looks like the model is perfectly calibrated, the same is indicated by the expected calibration error—2.93e−14.

3.4 Program Prototype The application, which is to predict for the applicant’s entry, should have the following functionality: (1) To receive data entered by the user, about the desired educational program (specialty) and university, as well as its rating score, application priority and availability of quotas; (2) To get data from the local database for the selected educational program of the university for the past years (average, minimum, maximum grades for budget places and in general); (3). To transfer data to the model, obtain and output the result. An example of the interface is shown on the screenshot (see Fig. 5). The general scheme of the application will accordingly look like this (see Fig. 6). The user enters his data, and data on the distribution of points for a given specialty and

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Fig. 3 Calibration curve for RandomForest

Fig. 4 Calibration curve for RandomForest after calibration via Isotonic regression

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Fig. 5 Program prototype

university are being pulled from the database of applicants’ applications for previous years. They are combined into one record, scaled. The model makes a prediction that is calibrated through Isotonic regression. At the output, the algorithm returns a class label of either 1 or 0 (would enter the budget form or not), as well as the chance of admission estimated by the model.

4 Conclusions The paper examines the problem of the admission uncertainty for applicants to the desired educational programs (specialties). The relevance of solving this problem is substantiated. The data has been collected, a number of models have been built and the best one has been chosen eventually. As the study showed, the best models are decision tree, RandomForest and AdaBoost based on decision tree. They achieved over 99% accuracy on the training sample, over 95% on the test sample, and over 90% on the original, unaltered data. StackingClassifier and GradientBoosting performed averagely, as can be seen from the model comparison graph. The worst models you suffer from are logistic regression, Naive Bayes, and SVC. At the same time, it’s worth noting that the models are significantly overtrained, due to which, theoretically, it is possible to achieve better accuracy. The feature selection and hyperparameter selection test showed no significant changes.

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Fig. 6 The Application concept

Probability calibration has been performed for a more informative model result. With the help of training on the output of the main model and class labels, an isotonic regression has been trained. A prototype of the program has been developed to demonstrate the work and the likely further UI design. The results of the study guarantee high accuracy of predictions in the field of student admission success. Such models are better for people to predict an applicant’s admission, their success in studies, and whether they would complete their studies, which allows to reveal the factors of student success and to develop applications that would allow applicants and students to plan their future.

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Virtual Museum Design in Sustainable Cultural Heritage: A Literature Review Olena Shlyakhetko and Michal Greguš

Abstract Cultural heritage virtualization management is becoming increasingly relevant every year in science, construction, and management. The aim of the research is to develop a baseline design for a set of technologies and the use of virtual and augmented reality to find recommendations for the sustainability of the national identity of countries via the prism of cultural heritage. The results of an extensive literature review of the term Virtual museum—the keywords used to identify research relevant to the study were Smart Heritage, Smart Cities, 3D Digitization Methodologies for Cultural Heritage, 3-D Data Post-processing, Cultural Heritage Fruition, Photogrammetry, Laser scanning Maritime cultural heritage 3D modeling, Reverse engineering. The analysis used various databases, including ScienceDirect-Elsevier, SpringerLink, JSTOR, Emerald, IEEE Xplore, Scopus, Willey, Taylor & Francis, and Google Scholar. As a part of Google’s trends, we can determine the number of reviews, categories, and types of searches where keywords were used. In addition, cross-reference methods were used to find relevant research related to the topic. Keywords Cultural heritage · Virtualization management · Virtual and augmented reality · Virtual museum · Smart heritage · 3D modeling · Reverse engineering · Databases

1 Introduction The creation of 3D models and the development of virtualization determine the possibility of creating and promoting a culture based on the interaction with not real objects but with their specially designed models, graphics (including landscape), and virtual images. The Virtualization of cultural heritage opens new opportunities for O. Shlyakhetko (B) · M. Greguš Comenius University in Bratislava, 820 05 Bratislava, Slovakia e-mail: [email protected] M. Greguš e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_5

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travelers. Even a wheelchair-bound person can plunge into the boundless world of virtual travel. There are different interpretations of the concept of virtual tourism. One of them: “a virtual tour,” is a way to display three-dimensional multi-element space on the screen realistically. It creates a “presence effect” in the viewer—vivid, memorable visual images [1]. Virtual museums are an integrative cultural form characterized by structural and functional diversity. It is a new technology of contactless information interaction of the user with the museum environment, a computer system that provides certain visual and sound effects [2]. The application of new technology for digital conservation has increased in recent years. Three-dimensional computer models of various sizes, ranging from the smallest museum exhibits to the grandest cathedrals and castles, have been constructed [3]. There is no denying the advantages of computer modeling. Significant exhibitions are created through prototyping and reverse engineering, offering opportunities for not only seeing but also holding in one’s hands and interacting, allowing for a greater understanding of historical events and their significance. Virtual tours in many locations and at various times are offered using three-dimensional visualization [4]. Technologies are now being developed for a successful immersion level. This holds true for the standards of content reliability, accessibility, and interactivity. Understanding what components to virtualize and how it relates to the provision of national identity is equally crucial [3]. Technology growth is obviously fueled by factors like competition and conditions for tourism promotion, but from the perspective of identity sustainability, it’s crucial that these factors work in tandem [3]. Thus, the aim of our research is to develop a baseline design for a set of technologies and the use of virtual and augmented reality [5, 6] to find recommendations for the sustainability of the national identity of countries via the prism of cultural heritage. Cultural heritage virtualization management is still developing in Slovakia and may have potential due to its novelty. Starting with this research, we focus on the non-empirical research, e.g., literature survey. The literature review cultural heritage virtualization management (and similar terms) provides a powerful tool for reflecting the current state of topics and finding space for further research.

2 Popularity of the Topic in Slovakia and Worldwide The topic of the study was to examine the popularity of the Cultural heritage 3d modelling aspect using Google Scholar. Popularity in Google Scholar is calculated based on the number of searches. As a part of Google’s trends, we can determine the amount of review, categories, and type of search where keywords were used (3D Printing, Cultural Heritage Preservation, Accessibility Education, Models, Reliefs Museums, Rapid Prototyping Additive Manufacturing). The first magazine on “3D

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Fig. 1 Popularity aspects in Google Scholar. Source Popularity aspects in (Google Scholar 2022)

digitizing of cultural heritage” which was created in 2001 (Publisher of ELSEVIER magazine). The most in search of Cultural heritage 3d modelling are conferences (Fig. 1). Interest in Cultural heritage 3d modelling confirms the novelty of the topic and its growing popularity in recent years [7]. This part of the thesis could be otherwise called “the literature overview”. The share of this part of the work should be about 30% of the work. The topic of Cultural Heritage Virtualization Management is becoming more and more relevant every year in the field of science, construction, and management. In the most common queries of the term Virtual Museum, we could see how its popularity is growing every year and becomes relevant for all countries. For example, in the Table 1 we can observe that the Virtual Museum aspect is the most popular among others. The results of an extensive literature review of the term Virtual museum—the key words used to identify research relevant to the study were: Smart Heritage, Smart Cities, 3D Digitization Methodologies for Cultural Heritage, 3D Data PostProcessing, Cultural Heritage Fruition, Photogrammetry, Laser scanning Maritime cultural heritage 3D modeling, Reverse engineering. The analysis used a wide variety of databases, including ScienceDirect (Elsevier), SpringerLink, JSTOR, Emerald, IEEE Xplore, Scopus, Willey, Taylor & Francis, and Google Scholar. In addition, cross-reference methods were used to find relevant research related to the topic [2]. IT technologies play an essential role in the preservation and promotion of contemporary cultural heritage as well as virtual tourism. Thus, we perform an analysis of different digital techniques [42], such as documentation, photogrammetry, 3D modeling and virtual reality, for detailed examination of the research performed in this direction.

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Table 1 Aspect—Cultural heritage 3d modelling Autor

Keywords

2013 Archaeologizing’ Heritage and Transcultural Entanglements [8]

Michael Falser, Monica Juneja

Cultural Heritage, Virtual Model

2019 3D digitizing of cultural heritage Built-Heritage Multi-temporal Monitoring through Photogrammetry and 2D/3D Change Detection Algorithms. [9]

GabrieleGuidi, CarloAtzeni, Massimiliano Pieraccini

Cultural heritage, 3D digitizing imaging, digital archive

2019 3D modelling of geological and anthropogenic deposits at the World Heritage Site of Bryggen in Bergen, Norway [9]

Johannes de Beer, Simon J., Jonathan R

Cultural heritage, 3D modelling

2014 3D Printing for Cultural Heritage: Preservation, Accessibility, Research and Education [10]

Moritz Neumüller, Andreas Reichinger, Florian Rist, Christian Kern

3D Printing,Cultural Heritage Preservation, Accessibility Education, Models, Reliefs Museums, Rapid Prototyping Additive Manufacturing

2013 3D Reconstruction & Traditional Illustrations, a Non-Invasive Resource for the Practice and Teaching of Conservation and Restoration of Cultural Heritage. [11]

Ovidia Soto-Martín

Cultural heritage; 3D modelling; Illustration; Augmented reality; Teaching

2014 3D reconstruction methods for digital preservation of cultural heritage: A survey [12]

Leonardo Gomes, Olga Regina Pereira, Bellon Luciano Silva

3D reconstruction, Depth image Digital preservation, Cultural heritage, Survey

2018 4D Modelling in Cultural Heritage [13]

Anastasios Doulamis, Nikolaos Doulamis, Eftychios Protopapadakis, Athanasios Voulodimos, Marinos Ioannides

4D modelling, Tangible cultural heritage, 3D reconstruction, tweets analysis and recommendation systems

2015 A flexible platform for the creation of 3D semi-immersive environments to teach Cultural Heritage

Andres Bustilloa, Virtual Reality,3D Mario Alaguero, Ines modelling Teaching, Miguel, Jose M.Saiza, Cultural Heritage Lena S.Iglesias

2018 A Route Selection Scheme for Supporting Virtual Tours in Sites with Cultural Interest Using Drones [14]

Emmanouil Skondras; Konstantina Siountri; Angelos Michalas; Dimitrios D. Vergados

Year

Title

Drones, Streaming media, Real-time systems, Virtual reality, Cultural differences, Three-dimensional displays (continued)

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Table 1 (continued) Year

Title

2018 A Semantic Web-based Approach for generating parametric models using RDF [15]

Autor

Keywords

Farhad Sadeghineko, Building Information Bimal Kumar, Warren Modelling (BIM), Chan Semantic Web, Resource Description Framework

2013 Advanced Use of Historic-Archival D. Calisi; M. G. Cultural heritage, 3D Resources in the Management of Cianci; F. Geremia digitizing imaging Built Heritage: The District of Monti [16] 2020 Algorithm for Creating Massive Amounts of Unique Three-Dimensional Models and Materials from Rocks [17]

Egor Budlov, Ekaterina Bainova, Olga Sedova

Modelling, Archaeology Photogrammetry, Object reconstruction

2019 An Ongoing Experimentation for VR, AR and MAR of a Design Museum [18]

Cecilia Bolognesi

Low-cost technologies, 3d survey Virtual, Museums, Storytelling

2019 Automated Creation of Unique Editable Textures for Three-Dimensional Models of Archaeological Artefacts [19]

Egor Budlov, Alexei Uteshev, Valery Grishkin

Modelling, Archaeology, Photogrammetry, Object reconstruction

2019 Built-Heritage Multi-temporal Monitoring through Photogrammetry and 2D/3D Change Detection Algorithms [9]

Dante Abate

Built heritage, change detection, remote sensing, photogrammetry, MAD/ MAF

2014 Cross media integration of 3D contents for cultural Communication [20]

Antonella Guidazzoli; Cultural Heritage, Virtual Antonio Baglivo; Model Daniele De Luca; Silvano Imboden; Maria Chiara Liguori; Alessandro Rivalta

2019 Early History of Hungarian Ballet in Virtual Reality [21]

Attila Gil´nyi; Anna R´cz; Anna M´ria B´lya; Katarzyna Chmielewska

Buildings,nSolid modeling, Footwear, Virtual reality, Visualization, History

2010 Cultural heritage interactive 3D Alberto Guarnieri, models on the web: An approach Francesco Pirotti, using open source and free software Antonio Vettore [22]

Open source, 3D models Cultural Heritage, Web documentation

2015 Development of High-Definition Hirokazu Ban, Virtual Reality for Historical Katsuhito Yagi, Junro Architectural and Urban Digital Nishiie Reconstruction: A Case Study of Azuchi Castle and Old Castle Town in 1581 [23]

Cultural heritage, Digital reconstruction Virtual reality, Visualization 3D modeling Presentation

(continued)

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Table 1 (continued) Autor

Keywords

2007 Documentation of cultural heritage using digital photogrammetry and laser scanning [24]

Naci Yastikli

Cultural heritage, Documentation Digital photogrammetry, Laser scanning Ortho-image

2019 Documentation of paintings restoration through photogrammetry and change detection algorithms [25]

Dante Abate

Restoration, Documentation, Icon, Painting, Change detection

Year

Title

2021 Documentation, Ali Ulvi Three-Dimensional (3D) Modelling and visualization of cultural heritage by using Unmanned Aerial Vehicle (UAV) photogrammetry and terrestrial laser scanners [26]

Imaging, related sensors Image processing

2019 Early History of Hungarian Ballet in Virtual Reality [21]

Cultural heritage, 3D digitizing imaging

Attila Gil´nyi; Anna R´cz; Anna M´ria B´lya; Katarzyna Chmielewska

2018 Geomatics for structural assessment Grazia Tucci, and surface diagnostic of CH [27] Alessandro Conti, Lidia Fiorini

Virtual Reality,3D modelling Teaching, Cultural Heritage

2004 High-accuracy 3D modeling of cultural heritage: the digitizing of Donatello’s “Maddalena” [28]

Modelling, Archaeology, Object reconstruction

G. Guidi; J.-A. Beraldin; C. Atzeni

2014 Hybrid survey method for 3D Erica Nocerino digital recording and documentation of maritime heritage [29]

Photogrammetry, Laser scanning Maritime cultural heritage 3D modelling, Reverse engineering

2012 Integration of Historic Building C. Dore, M. Murphy Information Modeling (HBIM) and 3D GIS for Recording and Managing Cultural Heritage Sites [30]

Laser Scanning; Parametric Modeling; BIM; Cultural Heritage

2021 Interactive Tools for the J. Sebastián, J. Visualization of Tangible and Sevilla, et al Intangible Silk Heritage Emerging from an Interdisciplinary Work [31]

Silk fabrics, Cultural heritage, 3D representation, Spatio-temporal maps Ontologies Interdisciplinarity

2017 Modelling Life Through Time: Cultural Heritage Case Studies [32]

Cultural heritage,Digital documentation 3D reconstruction,Virtual humans

N. Cadi, M. Arévalo, Nadia MagnenatThalmann

(continued)

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Table 1 (continued) Autor

Keywords

2009 Multi-scale cultural heritage survey: Quick digital photogrammetric systems [33]

P. Salonia, S. Scolastico, A. Pozzi, A. Marcolongo, T. LetiMessina

Documentation, Survey, Multi-scale, modelling, Archaeology, Cultural heritage

2021 Optimization and Trajectory Analysis of Drone’s Flying and Environmental Variables for 3D Modelling the Construction Progress Monitoring (Keyvanfar [34]

Muhammad Akmal Awanghamat, Arezou Shafaghat, Ali Keyvanfar

Unmanned aerial vehicle, Dronography, Toolbox, Trajectory analysis

2013 Reality-Based Virtual Models in Cultural Heritage [8]

Armin Gruen

Reality-based virtual model, Cultural heritage, 3D modelling, Archaeology

2018 Recent trends in cultural heritage 3D survey: The photogrammetric computer vision approach [35]

Irene Aicardia, Filiberto Chiabrandob, Andrea Linguaa, F. Noardoa

Computer vision, Algorithms, 3D modelling, Orthophoto

2019 Review of Surveying Devices for Structural Health Monitoring of Cultural Heritage Buildings [36]

J. Markiewicz, A. Cultural Heritage, Sensor, Tobiasz, P. Kot, M. Surveying Device, Health Muradov, Andy Monitoring Shaw

2021 Sacred Architecture and Fashion Drawing. The Late Antique Decorations in Cimitile as Ideational [37]

Alessandra Avella

Year

Title

Geometry, Traditional surveying, Textures, Textile design, Cultural heritage

2014 Smart Cultural Heritage and Open Alessandro De Masi Source as Regeneration of Historical Centers: Fruition, Conservation and Preservation in Current Methods of 2D/3D Digitization and 3D Modelling [38]

Smart Heritage, Smart Cities, 3D Digitisation Methodologies for Cultural Heritage, 3D Data Post-Processing, Cultural Heritage Fruition

2016 The Use of Terrestrial Laser Scanning in Surveying Historic Buildings [39]

Three-dimensional displays, Measurement by laser beam, Software, Buildings

B. Kwoczynska; Urszula Litwin; Izabela Piech; Piotr Obirek; Jakub Sledz

(continued)

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Table 1 (continued) Autor

Keywords

2020 Urban History Research and Discovery in the Age of Digital Repositories. A Report About Users and Requirements [40]

Sander Münster, et al

Urban history, Photogrammetry Augmented reality, 3D User

2019 Vox AR: 3D Modelling Editor Using Real Hands Gesture for Augmented Reality [41]

Fazliaty Edora Fadzli; Solid modeling, Ajune Wanis Ismail Three-dimensional displays, Computational modeling, Target tracking, Color, Prototypes

Year

Title

Source Own interpretation

The Cultural heritage 3d modeling aspect is also popular. Many journals and conferences have been written on this topic. About 18 conferences have been held since 2001. For example, on the topic of the museum virtualization of 24 conferences. The least number of conferences was on the topic Cultural heritage management— only 4. The aspect Cultural heritage management is the latest topic among other aspects, so far, the least number of conferences. The first conference according to the Google Scholar on the topic of Cultural heritage management was held in 2011. This is a new topic for the world and very relevant. Because new technologies are evolving, and many new professions are emerging in this field. Creation of 3D models and development of virtualization determine the possibility of creating and promoting a culture based on the interaction not with real objects and feelings of life, but with their specially designed models, graphics (including landscape) and virtual images. There are different interpretations of the concept of virtual tourism. One of them: “a virtual tour” is a way to realistically display three-dimensional multi-element space on the screen. It creates a “presence effect” in the viewer—vivid, memorable visual images. A virtual model can provide/support a new concept of architectural representation. This allows to learn an evolution and a transformation as well as an entire life cycle of architectural artifacts. Virtualization helps also to examine a documentary base of an entire process of an architectural monument development [43]. Architectural monument virtualization is a system that uses software [44–46] and creates 3D models with high-quality rendering (Fig. 2). For the past three decades, the art market has been taking slow and uncertain steps toward virtualization, while remaining more committed to the idea that it is best to interact live with cultural artifacts, whether museum exhibits or paintings. In the 1990s, most institutions considered digitalization too slow and unreasonably expensive, which also did not bring the desired results: because until recently the technology of virtual exhibitions was not sufficiently developed, the navigation and overall quality of such exhibitions left much to be desired. Another reason why museums and galleries were in no hurry to create virtual analogues of their exhibitions

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Fig. 2 Cultural heritage site. Source [43]

was the fear that the possibility of viewing the exhibition online will deprive people of motivation to visit it physically. Although some saw the potential in virtual exhibitions long before they became mainstream. For example, one of the first attempts to digitalize the exhibition dates to 1993, when Microsoft Art Gallery was created on CD. It became a kind of platform where galleries could place digital versions of their collections, and the first such institution was the National Gallery in London [47]. Two decades later, when virtual exhibits no longer seemed like space, but had not yet become widespread, Google launched the large-scale Google Arts and Culture initiative, an online platform for hosting digital artifacts stored in cultural institutions. Today, the platform contains virtual collections of about 2000 galleries and museums of world importance and offers not only high-quality digital exhibitions, but also a variety of interactive experiences (such as visual crosswords and art selfies) and AR and VR functionality [48] (Fig. 3). Among the recent important events: the first in the history of the creation of virtual viewing rooms by a commercial gallery. This was done by David Zwirner in 2017 [49]. Looking at the statistics of each country in Google trends showed that the virtualization of cultural heritage is most popular in Cyprus, United States, Canada, United Kingdom, and Singapore. The most common requests of each of these countries are virtual museums [50] and virtualization of cultural heritage. It can be concluded that these countries are popular among online tourism. Looking only at Slovakia, on all topics of Google trends, from 2004 to date, enough traffic has been detected so that any of the terms can be shown in the chart (Fig. 4). Regarding online tourism in

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Fig. 3 Virtual museums in the world in Google trends. Source (Google Trends, 2022)

Fig. 4 The popularity of virtual museums in Slovakia. Source (Google Trends, 2022)

Fig. 5 Since 2004, interest in relevant search queries virtualization of cultural heritage and virtual museums in Slovakia. Source (Google Trends, 2022)

neighboring countries, such as the Czech Republic, Poland or Ukraine, there was also enough search for Google trends. In addition, we have separately considered aspects of “Cultural heritage management”, “Cultural heritage 3d modeling” and “Digital cultural heritage” to get a proportional sense of the search context (Fig. 5). We have been looking for the last 5 years to see if there have been specific peaks in those years, more seems to be seasonal throughout the year in research trends.

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Reputation of a Non-profit Organisation—A Quantitative-Empirical Study Embedded in Risk Management on Police Reputation and Reputation Loss Lea Saal, Torsten Huschbeck, and Christian Horres

Abstract This paper analyses and assesses the internal and external operations of the police and how the public perceives them. There are risks that specific police officers will act improperly in situations where threats are stopped, or crimes are investigated and prosecuted. If the public criticises the police as an authority, their reputation suffers. The damage to the reputation of the police and police departments has been hardly considered and must be covered by a risk management strategy in the future. Keywords Cop culture · Reputation · Police · Risk management

1 Introduction “On New Year’s Eve, emergency and rescue workers were attacked with firecrackers and rockets in several German cities while they were working. Police, firefighters and paramedics experienced a particularly large number of attacks in Berlin” [1]. According to the European Commission’s Standard Eurobarometer, 78% of respondents in Germany said they trust the police last winter [2]. Despite this positive balance, there were numerous attacks on rescue workers and police officers on New Year’s Eve 2022/2023. Furthermore, the police have received much criticism in the context of the large-scale police operation in the village of Lützerath in recent months. During the two-week clearance operation, there were riots between demonstrators L. Saal (B) · T. Huschbeck · C. Horres Comenius University of Bratislava, Odbojárov 10, 820 05 Bratislava, Slovakia e-mail: [email protected] T. Huschbeck e-mail: [email protected] C. Horres e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_6

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and police officers, in which several activists and police officers were injured. The police were accused of disproportionate police violence, which they rejected [3]. Furthermore, there is a shortage of junior staff in the police, so the application deadline for the 2023 training year at the North Rhine-Westphalia Police (NRW) has been extended to fill the required number of commissioner candidates for this year [4]. On the one hand, it is questionable what the current reputation of the police in society looks like. On the other hand, the question arises as to the status of the police in the following generations. The paper, therefore, deals with the reputation of the police and the attractiveness of the police for career entrants. The following scientific questions are to be answered: 1. What is the current reputation of the police? 2. To what extent is the police still attractive to young professionals? To enable a suitable examination of the topic, the current knowledge of the literature is first analysed using a literary review and summarised in a literary-theoretical framework. Based on the findings from the literature review, a questionnaire was generated in which 590 participants were interviewed in the form of a quantitative survey on the two research priorities described. Section 2.1 explains the principles of qualitative content analysis and the literature’s filtering process. In contrast, Sect. 2.2 presents the principles of quantitative survey design with Microsoft Forms and the quality criteria of scientific work. Section 3.1 presents the results of the structured literature review, which sheds light on the reputation of the police and its influencing factors, distinguishes between the different generations and examines the employer branding of the police in detail. From this, the hypotheses can be generated, tested using the survey and presented in Sect. 3.2. This is followed by the documentation of the results and their evaluation in Sect. 3.3 and an explanation of the evaluation methods. The survey’s main findings are summarised in Sect. 3.3 in the overall context. Subsequently, the results of the quantitative survey are controversially discussed. Finally, the scientific questions are answered in Sect. 4. The end of this paper is formed by the limitation in Sect. 5, in which the limitations of this study are shown.

2 Methods In the introduction, the scientific questions have already been named, which will be answered in the present article. The current state of scientific knowledge must first be researched and controversially discussed to address the topic and the questions appropriately. For this purpose, a structured literature review of the existing literature on this topic was carried out. Furthermore, a quantitative survey was carried out to answer the questions. The methods of the structured literature review and the quantitative survey are explained in the course of this Chapter.

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2.1 Structured Literature Review The present article’s structured literature review was conducted to write a literarytheoretical construct for quantitative empirical work. Therefore, the principles of qualitative content analysis and the filtering process in literature research are presented. Qualitative content analysis is a method of evaluating literary texts that are to be examined in scientific research. It enables a qualitatively oriented text analysis and can analyse extensive amounts of text with the expertise of quantitative content analysis. Characteristics of qualitative content analysis include the characteristic of “category-drivenness” [5]. This way, a category system is designed in advance, with which the texts are processed, and suitable text passages are assigned to different categories. The evaluation aspects and rules are defined so strictly that the literature can be processed intersubjectively and systematically. For this purpose, the texts are first classified into a communication model, and the analysis units are divided and defined into coding, context and evaluation units. The basic process then consists of the rule-based assignment of the categories to specific text passages. The texts are not classified into a communication model in the present content analysis, and no analysis units have been formed. Furthermore, due to time constraints, neither the intercoder nor the intracoder match can be measured, so the present content analysis is not carried out systematically. Instead, the selected texts are analysed using the MaxQDA software program, assigning suitable text passages to predefined codes. Figure 1 shows an overview of the codes used: By assigning the different codes, it is thus possible to determine which literature appears particularly suitable for this research work’s literary-theoretical framework. Furthermore, it can generate the hypotheses to be tested using the quantitative survey (Sect. 3.2). The literature relevant to the research process must first be researched in order to conduct the qualitative content analysis. For this purpose, it is based on the guidelines for preparing a literature review by Paré and Templier [6]. Accordingly, the research topic and the scientific questions must be formulated as the first step.

Fig. 1 Own representation with MaxQDA

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This has already been done in Sect. 1. This is followed by the literature search, which is carried out in the following databases and catalogues: . . . . .

Web of Science (Elsevier) Scopus (Elsevier) Bonnus (University and State Library Bonn) SpringerLink Google Scholar. The following keywords are used for the search:

. . . . . . . . . . . . . .

Reputation of the police Reputation of the police AND trust in the police Image of the Police Study Image of the Police Study AND Trust in the Police Personnel recruitment AND Police Young professionals AND Police Employer Branding Employer Branding AND Polizei Generation Z Human Ressource Management Police NRW Police AND trust Recruiting Recruiting AND police.

The third step was to search only for literature published from 2017 to 2023 to further narrow the results. Furthermore, the Bonnus search portal results are narrowed down to full texts. The search is limited by the parameters “Open access” and “Reviewed Articles” in the Web of Science and Scopus databases. Subsequently, relevant texts are filtered out based on their headings and table of contents. This research resulted in 17 book parts, seven books, six articles and a website as potentially usable sources. The qualitative content analysis of these sources, as described in Sect. 2.1, is carried out. Five books, two parts of the book and one article were sorted out since their contents are not relevant to the literary-theoretical framework of this research. The snowball methodology, on the other hand, makes it possible to find 11 other relevant sources during processing. Still, they are not filtered according to their year of publication, as they are older than the sources. In addition, during the writing process, three other Internet sources were found on Google Scholar by the keywords “Police NRW”, “Employer Branding”, and “Human Resources Management” and used in the work. Thus, a total of 37 sources are used for the creation of the literacy theoretical framework.

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2.2 The quantitative survey The quantitative survey was carried out using the Microsoft Forms program. The following explains the principles of quantitative survey, the survey design and the quality criteria of scientific work. The quantitative survey is probably the most frequently used methodology in empirical research [7]. In the present paper, the expression of several characteristics will be measured by interviewing a representative sample. These are properties, so-called variables, to be investigated using a survey. For this purpose, a standardised and structured questionnaire with Microsoft Forms is created, in which each respondent receives the same requirements when answering the questions. This makes it possible for the responses of the subjects to be comparable with each other. Each question in the questionnaire corresponds to a variable with several characteristics that can be selected when answering the respective questions. If the measured values of the characteristics are related to other variables or each other, it makes it possible, among other things, to test different hypotheses. A hypothesis is a statement or an assertion made regarding a fact, typically one that relates at least two features. The hypotheses to be tested by the quantitative survey are presented in Sect. 3.2 after the qualitative content analysis. The results of hypothesis testing are related to an empirical-deductive approach. In the quantitative survey, several characteristics are queried that are not used to test the hypotheses made. Instead, they are intended to describe existing conditions in society, which is why the survey also undertakes descriptive investigations [8]. Thus, in the following survey and evaluation of the results, both a deductive and a descriptive approach is carried out. As a result, a mix of methods, a so-called “method triangulation” is applied in order to obtain a uniform understanding of the object of investigation [9]. The quantitative survey was conducted using a structured questionnaire generated with the Microsoft Forms application. Microsoft Forms is a web-based application from the hardware and software developer Microsoft that enables the creation of surveys and quizzes and integrated analyses for evaluating the results and transferring the results to an Excel spreadsheet [10]. Access to this application is available to anyone with an active Microsoft account. The questionnaire has eleven quantitative and qualitative questions and can be divided into three sections (Annex 1). The first section contains four questions about gender, age, level of education and professional or academic qualifications. In contrast, the second section covers questions five to eight and deals with the public image of the police. In question five, the frequency of contact with the police is queried and in question six, the form of contact. For this question, it is possible to select several possible answers, and further forms of contact can be specified via the “Other” answer field. In the following question, a Likert scale was used to assess the reputation of the police, broken down into the items of professionalism, satisfaction, trust, respect and presence of the police in public. Participants can rate the statements with the answer options “disagree”, “tend to disagree”, “neutral” “tend to agree”, or “agree”. Subsequently, the subjects in question eight were asked about their feelings towards the police. Here, selecting several answers and entering further feelings is possible via the “Other” field. The

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third section of the questionnaire focuses on the attractiveness of the police as an employer for young professionals. In question nine, the first question is whether the participant is a young professional. If the answer to the question is negative, the questionnaire ends at this point. If the answer to the question is in the affirmative, the subjects are asked to use the Likert scale to indicate their relevance about a secure job, flexibility in everyday work, flat hierarchical structures, as well as an open error culture and the attractiveness of the employer for career choice. Furthermore, the attractiveness of the police as an employer and the probability of choosing them as an employer are queried. Finally, an open-ended question determines which factors would make the police more attractive for young professionals. Since the questionnaire is a web-based form, the quantitative survey is sent and shared using a URL. No active Microsoft account is required to participate in the survey, only an Internet-enabled device will change. In order to obtain reliable measurement results in quantitative research and to be able to draw conclusions from them, the measurement process must meet three quality criteria: objectivity, reliability and validity. In the case of objectivity, there must be an independence between the user and the measurement results. The standardised online questionnaire avoids direct communication with the participants and the author and thus prevents possible direct influence. Since the survey is shared exclusively by e-mail, via the messenger service WhatsApp and the social media platforms Instagram and Facebook, some participants come from the researchers personal environment. However, since the answers would be collected anonymously and the survey would also be shared beyond the personal environment of the researchers, no influence on the research results is to be expected here. Furthermore, the questionnaire mainly uses closed-ended questions with predefined answer options, which enables an increased objectivity of the measurement results. It is true that the participants in the quantitative survey also have the opportunity to answer open-ended questions, which leads to a lower level of evaluation objectivity. However, since the results are relevant for answering the research question, the reduction of the evaluation quality with the relevance of the results must be legitimised. In order to maintain the highest possible objectivity of interpretation, the quantitative survey results are transferred to an Excel spreadsheet and evaluated using the Jamovi computer program. In quantitative social research, reliability is the degree of reproducibility of measurement results. Whether quantitative research is reliable can be checked, among other things, by repeating the study or conducting a parallel study [11]. However, since a limited period is specified for the preparation of the present paper and the investigation to be carried out, the above-mentioned measurement methods cannot be carried out from a time point of view. The reliability of a survey can also be calculated using internal consistency. For this purpose, the agreement between several survey questions is calculated using the “Cronbach’s Alpha” measurement method. For this purpose, a survey pretest would be sent to a total of 13 people, and the internal consistency of the results would be calculated. It can be seen that the value of Cronbach’s alpha in the calculation of the five items “professionalism”, “trust”, “satisfaction”, “respect”, and “presence” is 0.856. The internal consistency is considered reliable for values between 0.7 and 1.0 [12]. Thus, the reliability of the items in the question about the reputation of the

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police is high. Furthermore, the reliability of the study cannot be tested due to the time frame. However, since the survey has a large sample of 590 participants, it can be assumed that similar measurement results would be achieved if the research were repeated. Finally, quantitative research must also serve the last quality criterion of scientific work, validity. Research is valid if it measures what it claims to measure. This is measured, among other things, by content validity and design validity. In the case of content validity, it is important to prove that the research takes into account all aspects of the topic to be investigated. In the quantitative survey, questions were asked to check the reputation of the police. Furthermore, the relevance of several factors that influence the attractiveness of an employer and, based on this, the attractiveness of the police as an employer is queried. Thus, the research questions asked can be answered with the results of the survey and the validity of the content can be confirmed. Construct validity is given when the research design is comparable to similar research. In a similar study carried out on the reputation of the police [13]. The participants were differentiated according to their gender, age, level of education and state, among other things, and these characteristics were examined in connection with the stated trust. The questionnaire was collected online and the questions were offered in a scaled form. Based on this study, the first two parts of this quantitative survey are structured. The scaled questions are also used in the last part of the questionnaire. The survey construct is thus similar to research that has already been carried out and accordingly the construct validity is also verifiable. It can be stated that the research design meets the requirements of the quality criteria of scientific work.

3 Results and Discussion To be able to carry out a suitable discussion of the results of the quantitative survey, the results of the structured literature review and the survey must first be documented and evaluated in this Chapter.

3.1 Results of the Structured Literature Review In the following, the results of the literature review are presented by critically analysing and discussing the current state of research in the literature. In order to be able to discuss the research results controversially with regard to the research questions, the following chapter examines the reputation of the police and its influencing factors, makes a distinction between the different generations and examines the employer branding of the police in detail. In the literature, the synonyms image, prestige or reputation are often used for the term reputation. In the present work, the term image is thus to be understood as synonymous with the term reputation. First of all, it can be stated that the image can be divided into two components,

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namely the affective and the cognitive component. In this context, verbal and nonverbal communication during police contact have a particular effect on the perceived quality of service [14]. Trust in the police is multifaceted and cannot be measured one-dimensionally and is differentiated in some studies into legitimacy and interaction trust [15–17]. Another study by Morrell et al. divides the public’s trust in the police into three categories: people’s basic attitude towards the institution, the assessment of the legitimacy of police action, and one’s own experience during a police contact [18]. According to Nägel and Nivette, the media, outstanding events such as terrorist attacks, mass shootings or pandemics can have a negative or positive impact on the trust and image of the police [19]. Furthermore, studies have shown that disproportionate police violence has a negative effect on the above-mentioned aspects. Unlawful behaviour by police officers can be explained, among other things, by the so-called “cop culture”. This is to be understood as a subculture deviant from the official dominant culture, which is lived in the service groups among the police officers. A study by Saal et al., which has dealt with unlawful, deviant behaviour by police officers, found that such behaviour can have a negative impact on the trust and image of the police [20]. In order to counteract such behaviour and to meet the requirements of society, government and politics, a “corporate identity” has been developed to convey a certain image of the police and to create security and trust. This image is made visible, among other things, by the “corporate design”, which is conveyed by the appearance of the police officers [21]. Thus, the external appearance, especially a neat uniform, creates the impression of professionalism and thus also has a positive effect on the image of the police. It is striking that most research deals with trust in the police, but not with their reputation among the population. Another study indicates that trust in the police is very high. According to the “2017—Global Trust Report”, society’s trust has risen from 81% in 2013 to 85% in 2017 [22]. The “2017—Global Trust Report” and the 2022 study by the Center for Criminological Research Saxony (ZKFS) show that institutions such as the Bundeswehr, the judiciary, the administration and the police place the greatest trust. Furthermore, it turns out that trust increases with age and level of education and is influenced by political attitudes or migration backgrounds. Furthermore, almost half of those surveyed in a study by Pricewaterhouse Coopers GmbH (pwc) would like to see more police presence in public [23]. It can be observed that the demands for more presence are particularly evident in the older age groups. This study focused on the impact of the coronavirus pandemic on society’s trust in the police. It was found that the pandemic, especially among the younger generations, led to a loss of trust and a decrease in the sense of security. For the research topic of the present scientific work, it is of particular interest to take a closer look at the studies that examine the reputation of the police. While the above-mentioned studies only examined trust or the feeling of security, the “Bochum IV” study by Schwind deals with the reputation of the police [24]. This longitudinal study was preceded by the “Bochum I–III” surveys, which were carried out in 1975, 1986 and 1998. The last survey took place in 2015/2016 and was published in 2018. The result of the study is that more than half of the respondents assume that the police still have a good reputation. Furthermore, in contrast to the other studies, the assessment of various characteristics of the police was queried

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here. About 80% of the participants stated that they found the police to be helpful and polite. However, it should be noted that just under 13% of respondents rate the police as rude. Approximately 76% consider police officers to be competent. Compared to 1998, however, it should be noted that the assessments of helpfulness as well as politeness and competence fell by 2016. One possible reason for this decreasing trend is educational deficits and training deficiencies [25]. In contrast, the assessment of police officers as respected persons rose from just under 66% in 1998 to 88% in 2016. In summary, it can be stated that the reputation of the police is controlled on the one hand by personal contact with police officers, but on the other hand also by the media or major events. Furthermore, it is influenced by various factors, such as trust in the police, perceived professionalism, satisfaction, respect and perceived presence. Based on this finding, the quantitative survey is used to query the reputation of the police in the population, broken down into the factors mentioned. The second part of the quantitative research deals with the attractiveness of the police as an employer for young professionals. In the present research, career starters are defined as employees who have not previously worked in a profession. This primarily refers to representatives of generations Y and Z who are currently or will soon establish themselves in professional life and represent future employees [26]. Since the employee market is characterised by different generations, it is relevant to take a closer look at them and to differentiate them from each other. Currently, it is mainly representatives of the Baby Boomer Generation and Generation X who are in professional life. Baby boomers are those born between the post-war period and the early sixties. They were strongly influenced by the values of the previous generation, the so-called veterans, and recognise hierarchy and authority, as well as the desire for material values and professional success [27]. Representatives of this generation are about to retire or have already retired. The next generation of baby boomers is described as Generation X, which includes the birth years between the sixties and 1979 [28]. This generation was also exposed to authoritarian structures and grew up with political and material insecurity. They are described as skeptical and prefer independent work, as well as structures and direct contact [29]. They are followed by generations Y and Z, who are also referred to as “digital natives” and are important for the present research. There are different views in the literature on the chronological division of two generations [30]. However, it can be assumed that Generation Y was born between 1980 and 1995 and that representatives of Generation Z were born from 1995 onwards. Rather, it is crucial to deal with the values of the two generations. Generation Y grew up in a restructured world due to the change from an industrial to a knowledge-based society and was treated with a lot of appreciation and attention. This change has created a diverse educational landscape, which means that this generation has a high level of knowledge and training. Furthermore, there was increasing prosperity within the families, whereby the representatives of the generation received monetary support. Thanks to the financial support, they were able to leave their parents’ home early and build their own lives, which is why the importance of the circle of friends and the need for community also increased. Furthermore, self-realisation is important to representatives of Generation Y. They want to take the optimal perspective for themselves and thus often put themselves under pressure. Since work is no longer seen as

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a means of securing one’s livelihood, intrinsic motivators in particular are becoming more important in addition to extrinsic motivators, such as salary. It is questionable what this means for the employer world and what demands Generation Y has on the professional world. To this end, Kochhan et al. conducted research in the form of qualitative interviews by interviewing, among others, twelve Generation Y young professionals about their expectations and requirements of their future employer. As a result, the participants stated that they consider the performance and values of a company to be important, with environmental sustainability playing a particularly important role. Furthermore, the company should reflect the values of tolerance and diversity and the field of activity should be fun, meaningful and attractive in the long term. In addition, a good atmosphere at the workplace and team understanding are important when choosing a job, while job security is not considered so important in the entry-level profession. In turn, the learning factor, the opportunity to make a career and further one’s education are considered important. As already mentioned at the beginning, Generation Y attaches great importance to the circle of friends and thus also to leisure time, so that they attach importance to regular and manageable working hours. However, they see flexible working hours as less important, where as the possibilities for flextime or home office are perceived as positive [31]. Even though Generations Y and Z are collectively referred to as “digital natives”, Generation Z has some differences in their values and their attitude towards life and work compared to Generation Y. Generation Z, which provides the future workforce, includes about 13.5% of the German population, i.e. around eleven million people. Her childhood and adolescence were marked by uncertainties and crises, such as terrorist attacks, the refugee, economic, climate and corona crises and, last but not least, the Ukraine war, which has an impact on her private and professional everyday life and on her plans for the future. Furthermore, representatives of this generation have grown up in a digital environment from an early age and have spent their school or university years homeschooling and distance learning during the corona pandemic, which is why digitalisation can be seen as the most striking feature of this generation. According to the study, Generation Z spends a large part of their free time on the Internet and is permanently digitally networked. As a result, the digitalisation of companies plays a major role in the choice of employers of generations Z and Y for the labor market. Furthermore, they also received monetary support from their parents, which is why they had the opportunity to train and educate themselves extensively. The parents were often seen as sparring partners or coaches. In contrast to Generation Y, which became independent at an early age, Generation Z is permanently cared for by their parents, which is why they are also called the “sheltered generation” [32, 33]. According to the Trendence Institute, the police rank first among the 100 most attractive employers among young people [34]. Despite this good rating, the police have problems with young people and challenges in recruiting employees. This is facilitated, among other things, by demographic change and the associated decline in birth rates, which has transformed the labour market from an employer’s to an employee’s market. Furthermore, the advancing digitalisation of work processes is leading to a reduction in jobs in the economy, which on the one hand leads to a stagnation in training needs and on the other hand to an increasing shortage of skilled

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workers. Although the police can benefit from increasing digitalisation, the shortage of skilled workers is also making itself felt here in the recruitment of young people [35]. For this reason, this chapter will take a closer look at the employer branding of the police, with reference to the requirements of generations Y and Z mentioned in Sect. 3.1. Employer branding is a sub-area of human resources management, which includes the personnel management and personnel administration of a company [36]. Employer branding is therefore understood to mean the development and maintenance of a company as an employer brand in order to increase the attractiveness of the company to employees and future employees [37]. Thus, internal employer branding is aimed at employee retention, while external employer branding deals with personnel acquisition. This is closely linked to personnel marketing, i.e. communication with and enthusiasm of the applicant target group and recruiting, which has the goal of filling vacancies. Since the present work deals with the attractiveness of the police among young professionals, external employer branding will be discussed in the following. Due to demographic change and the changing value patterns of digital natives, some challenges have arisen in personnel recruitment. According to Stierle and Lakner, on the employee side, these are the declining number of people entering the workforce, the changed value patterns of the following generations and an increasing ethnically heterogeneous population [38]. On the employer side, on the other hand, budget cuts, a high retirement rate, as well as a growing range of tasks of the police pose challenges for employer branding. In addition, there is the ageing of the workforce and, in relation to an increasingly heterogeneous society, a low level of diversity within the workforce. Especially with regard to the values of generations Y and Z, it is therefore important to deal with diversity management in the police. The diversity of aspects, such as gender, nationality, religion and ethnicity within an organisation, is examined in more detail. A diverse workforce thus corresponds to the values of generations Y and Z and can thus have a positive effect on the attractiveness of the police for young professionals. Nevertheless, a study by the Media Service Integration has shown that although the proportion of newly hired police officers with a migration background has increased over the years, people with a migration background are still significantly underrepresented in the police [39]. It can be stated that the police in almost all federal states carry out advertising measures for people with a migration background. However, not only cultural diversity, but also gender diversity is relevant [40]. One of the reasons for this is that many mothers go on maternity leave or work part-time after family planning. With generations Y and Z attitudes towards work and family are changing. Increasingly, a partnership-based division of roles is becoming a central trend in family planning. However, police shift work, irregular working hours and on-call duty stand in the way of reconciling work and family life. In order to remain attractive to potential employees, the police as an employer must face up to these challenges. To this end, working groups have been set up in some federal states to deal with the topic and have already implemented measures not only to maintain the attractiveness as an employer, but also to increase employee satisfaction and motivation [41]. This shows that internal and external employer branding often go hand in hand. This is especially true when employees act as brand ambassadors via social media and communicate the values prevailing in

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the company to the outside world. In general, the police use social media for various purposes, including employee recruitment and training [42]. For example, vacancies are offered on Twitter, advertising and information about the profession are shared on Facebook, and video campaigns are uploaded to YouTube. A study by ARD/ZDF found that 100% of 14 to 19-year-olds use the Internet every day and stream more than two and a half hours of media content per day over the Internet [43]. From this it can be seen that the use of social media can primarily address the younger population. Furthermore, social media campaigns make the police more approachable for potential career starters by sharing insights into everyday practice and training and thus giving interested parties a better opportunity to decide in advance whether the profession suits them or not. The qualitative study by Kochan et al. also took a closer look at the process of looking for an employer and job among Generation Y. It turns out that online sources are particularly important when choosing a career. Typically, Generation Y’s job search begins with a keyword-by-keyword search for a job title, especially on job boards. In addition, the selection is also based on the recommendations of friends, identification with the values of the company and on the image. In order to obtain further information about the potential employer, career sites, networks and social media channels are mainly viewed. Generation Y wants the company to have a credible image, especially by sharing information from employees about their everyday work as well as about their field of activity. In this context, “take-over” formats, especially on Instagram, are perceived positively [44]. It is questionable how the police will implement the demands of digital natives in the recruitment of young people. When looking at the social media use of the NRW police, one finds that all 50 police authorities operate their own Facebook channel. Almost all police authorities are present on Twitter and on Instagram. Furthermore, there are channels on Xing, LinkedIn and YouTube, where interested parties can gain insights into everyday working life and training. Outside the Internet, representatives of the police take part in various career fairs and provide information about the various fields of activity. Furthermore, future employees perceive an application option without registration with an application portal, as well as a mobile application process, among other things, as positive. Registration is still required to access the application portal of the NRW police. Currently, the instant messaging service WhatsApp is also being tested in order to attract police applicants via a mobile application procedure [45]. The NRW police have largely adapted their recruitment to the needs of the next generations. It is relevant to look at the various aspects and values with which the police advertise their attractiveness. According to the survey by the Trendence Institute, students are particularly interested in the cohesion of colleagues, a meaningful job, salary, enough time besides work and career prospects when choosing an employer. The previous chapter also shows that a secure job, work-life balance, team understanding, flat hierarchical structures, an open error culture, as well as tolerance and diversity in a company are important to future generations. The profession of police officer gives you civil servant status, which basically advertises job security and a fixed salary. Furthermore, the professional field offers many different meaningful and interesting fields of activity and is characterised by teamwork, which meets the

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requirements of the next generation. Nevertheless, the police organisation is characterised by hierarchical structures. Furthermore, there is a culture of error within the police, in which mistakes are often covered up or concealed, among other things for fear of damaging the image or trust in the police [46]. This, on the other hand, contradicts the views of potential future employees. Accordingly, the quantitative survey is intended to investigate the effects of the hierarchical structure of the police, the error culture prevailing there, the lack of flexibility in everyday working life and the importance of a secure job on the attractiveness of the police as an employer for career starters.

3.2 Forming Hypotheses From the results of the literature review, several hypotheses can be derived, which are to be tested with the quantitative survey. While a qualitative approach was chosen for the content analysis, a quantitative research method was used for the standardised questionnaire. The aim of quantitative research is to explain the scientific model, the correlation or the numerical characteristics. The quantitative methodology, due to a large sample and the standardised procedure, is suitable, among other things, for testing hypotheses. These are differentiated into contextual, differentiated, change and individual case hypotheses: H01 H02 H03a H03b H04a H04b H05 H06 H07 H08

The form of contact with the police influences the satisfaction of the population. The presence of the police in public has a positive effect on the reputation of the police. Age affects the reputation of the police. Age influences trust in the police. The higher the level of education, the higher the rank in the police. The higher the level of education, the higher the level of trust in the police. A high level of professionalism has a positive effect on the reputation of the police. The relevance of flexibility in everyday working life influences the attractiveness of the police for young professionals. The relevance of job security influences the attractiveness of the police for young professionals. The relevance of flat hierarchical structures and an open error culture influences the attractiveness of the police for young professionals.

The hypotheses were generated based on the results of the qualitative content analysis and are evaluated in the following chapter. Crucially, an opposing null hypothesis is formed for each of the ten hypotheses, which assumes that a certain relationship does not exist. This assumption will then be discussed in Sect. 3 and rejected by hypothesis testing so that the alternative hypothesis can be accepted.

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3.3 Evaluation of the Survey In this subchapter, the documentation and evaluation of the quantitative survey results are discussed and summarised in the following in the overall context. As explained in Sect. 2.2, the quantitative survey was conducted using a structured questionnaire generated and designed with the Microsoft Forms application. Using a generated URL, which was shared by e-mail, via the messenger service WhatsApp and the social media platforms Instagram and Facebook, it is possible to participate in the survey with an Internet-enabled device. A weak point here is that participation in the questionnaire has not been restricted, so that participants can theoretically vote several times via the URL. Since a restriction on participation in the questionnaire would mean that only holders of a Microsoft account would be able to participate in the survey, this was waived and the theoretical falsification of the results by multiple participation was accepted. First, it is necessary to calculate how many participants are needed to obtain representative survey results. Since the questionnaire was shared on social media channels and forwarded several times, the number of people who had access to the URL cannot be determined. Thus, there is an unknown population. Since each of these persons has the same opportunity to participate in the survey and thus to be included in the sample, there is a simple random selection. Furthermore, the probability of error and the confidence level must be determined to calculate the sample size. For this purpose, a probability of error of 5% was chosen, which is to be regarded as common in the economic sciences and a confidence level of 95%. A table from Borg shows that out of a population of 100,000 people, a sample of 383 participants is needed. Since the present research has a sample size of 590 subjects and it can be assumed that the population is less than 100,000 people, the survey is to be regarded as representative. The survey results of the 590 participants were collected in the period from 04.04.2023 to 19.04.2023, collected via the Microsoft Forms application and transferred to an Excel spreadsheet [47]. As mentioned in the previous chapter, the results of the quantitative survey are evaluated and presented using the statistical evaluation program Jamovi. The question about gender has a nominal-polytomic scale level since the answer values (male, female, diverse) do not have a directly usable scale value. On the other hand, questions two to five of the survey have an ordinal scale level since the answer values can be assigned to a rank scale, but the values have different scale sections. Questions seven a to e are also ordinal since the answer values were collected using a Likert scale. For questions six to eight, multiple answers were possible, and the scale level of question nine is nominal-dichotomous, as these can only be answered with “yes” or “no”. Questions ten a to f are also ordinally scaled using the Likert scale. The questionnaire was concluded with an open and, therefore, qualitative question. Since no numerical values can be assigned to the various answers, the results of this question and those of question eight are not evaluated by Jamovi but manually. To evaluate the results, the collected answers are encoded in the Excel spreadsheet in predefined numerical values so that they can be processed with the statistics program Jamovi. This is followed by a descriptive evaluation of the survey results and hypothesis

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testing. The results are presented and explained below. Annex 2 shows the descriptive results of the quantitative survey. It should be noted that 590 subjects took part in the survey, of which 346 people are female, 242 are male, and 2 are diverse. By a closer look at the age distribution of the participants, it can be seen that the average age is around 41 years, as more younger than older people took part in the survey. Of all participants, 373 subjects completed the (technical) Abitur, 135 received the Realschule diploma and 79 received a Secondary school diploma, while three subjects did not achieve a school-leaving diploma. Furthermore, 263 people have completed vocational training, 133 have completed a business administrator/master craftsman/ technician or bachelor’s degree, 83 subjects have achieved a business economist/ diploma/master’s or master’s degree and nine people have completed a doctorate. When asked about the number of contacts with the police, 41 people said they had not had any contact with the police so far. 203 subjects claimed to have had contact with the police once or twice, while 170 people had three to five contacts. A total of 70 subjects had contact with the police six to ten times and the remaining 106 people had contact more than ten times. It also asks about the form of contact. Multiple answers are possible, so that the sum of the answers exceeds the number of survey participants. A total of 400 people stated that they were involved in a traffic or person check. In addition, 292 people had contact with the police because of a traffic accident and 149 because they were injured or injured in criminal proceedings. 217 subjects were already witnesses and 43 were accused of a crime. In the end, 108 people were reported to have contacted the police for security or for a request for help. Furthermore, it is possible to specify further forms of contact by means of an open text field. A summary of these results can be found in Annex 3. Almost 40 people said they had already had contact with the police through their work in the public order office, in the hospital, in the rescue service, in disaster control or the fire brigade, among other things. Other answers are, for example, contact through prevention counselling, due to an internship, training or an administrative offence, and private contact with police officers. Subsequently, the reputation of the police, broken down into the items “professionalism”, “trust”, “satisfaction”, “attention/respect”, and “presence in public”, was queried using a five-point Likert scale. For the statistical analysis, the answer options are also assigned to a fixed number scale. For example, the answer option “fully agree” has the value + 2 and the answer option “strongly disagree” has the value − 2. From this, an average value can be calculated that represents the view of the participants. Since the scale was chosen bipolar, positive means are more likely to indicate agreement, while negative means are more likely to indicate disagreement. It can be seen that with an average value of + 1.04, the esteem and respect for the police is particularly high, while the police presence in public is rated rather low at + 0.04. Both the professionalism of the police (+ 0.7) and the trust in the police (+ 0.74) were also rated rather high, while satisfaction with the way the police work is in the positive midfield with + 0.41. Afterwards, the participants were asked about their feelings towards the police. The manual evaluation shows that 458 subjects have respect and esteem towards the police, 335 subjects feel trust and protection towards them and 215 participants have sympathy for the police. On the other hand, 78 subjects felt anger, 52 feared and 16 participants aggression towards

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the police. The full interpretation of question eight, including the answers from the open box ‘Other’, can be found in Annex 4. Now the descriptive evaluation of the results of question ten, which is aimed at young professionals and asks about the importance of several factors in the choice of employer (Annex 5), takes place. The answer options are assigned to the number scale from − 2 to + 2 and the mean value of the results is determined. A total of 83 test subjects claimed to be young professionals and therefore answered this question. The results visualised in tabular form in the annex show that job security, with an average value of + 1.34, is very important for those entering the workforce. Flexibility in everyday working life (+ 0.92), the attractiveness of the employer (+ 0.88) and flat hierarchical structures and an open error culture (+ 0.76) are also considered important by the test subjects. The attractiveness of the police, on the other hand, is rated as low with an average value of − 0.32. Furthermore, the statement that the police should be chosen as an employer was strongly rejected (− 1.3). The column charts in Annex 5 illustrates these results. For example, most young professionals tend to agree that a secure job, flexibility in everyday working life, flat hierarchical structures, an open error culture, and an attractive employer are relevant to their career choice. On the other hand, most young professionals do not agree with the statement that the police are an attractive employer, and almost all of them say that they would not choose the police as an employer. A more detailed analysis of these results is carried out to evaluate the hypotheses. Finally, the manual evaluation of the results of question eleven shows that, among other things, an increase in salary, a reduction in admission criteria, more family-friendly working hours, increased internal police studies on racial profiling and racism, as well as a higher reputation of the police in society would increase the attractiveness of the police as an employer. A summary of all replies can be found in Annex 6. A total of ten hypotheses are tested, which have been presented in Sect. 2.1. The first hypothesis is intended to test the effect of the form of contact on satisfaction with the police (Annex 7). Since multiple answers are possible with the contact form, and this results in countless answer combinations, no hypothesis test can be carried out here, and the evaluation is descriptive in this case. It can be seen from Annex 7 that, in particular, subjects who have contacted the police to avert danger or for a request for help (+ 0.54), victims of a crime (+ 0.42) and those involved in a traffic or person check (+ 0.41) are satisfied with the way the police work. The satisfaction of witnesses to a crime (+ 0.34) and those involved in a traffic accident (+ 0.37) is in the positive midfield. On the other hand, defendants in criminal proceedings are not so satisfied with an average value of + 0.14. Hypothesis 02 is intended to test whether the presence of the police in public has a positive effect on their reputation (Annex 8). Since the variable “view” is composed of several items and they have the same scale sections on a scale, this variable is to be considered metric. For this reason, the hypothesis is calculated using linear regression. This calculates the influence of one or more variables on a dependent variable by reducing the characteristics of the dependent variable to those of the independent variable. The p-value and the r-value are particularly important. The p-value refers to the probability of error of the hypothesis, i.e. the probability that the results of hypothesis testing are erroneous and depend on chance. If the p-value is < 0.05, the null hypothesis can be rejected

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and the credibility of the n hypothesis can be assumed. The r-value describes the correlation coefficient that checks the effect of the linear relationship between the variables [48]. For the calculation of hypothesis 02, the item “presence” is removed from the composition of the variable “reputation”, since the relationship between the items “presence” and “reputation” is to be checked. The calculation results in < 0.01 and a correlation coefficient of 0.41. Thus, the presence of the police in public has a highly significant effect on the reputation of the police and the effect is to be regarded as strong (Annex 8). Hypothesis H03a is intended to test whether the reputation of the police depends on the age of the persons interviewed (Annex 9). The linear regression calculation gives a probability of error of 29% and a very low correlation coefficient of 0.04. The null hypothesis cannot be refuted and the reputation of the police is independent of the age of the interviewees. In the calculation of the following hypothesis 03b, ordinal logistic regression analysis is chosen because the variable “trust” is ordinal scaled (Annex 10). Again, the probability of error p indicates whether the null hypothesis is credible or can be rejected. Since p = 0.24, the null hypothesis must be assumed so that trust in the police does not depend on the age of the participants. Hypothesis 04a is used to check whether there is a correlation between the professional or university degree and the reputation of the police (Annex 11). A linear regression calculation results in a probability of error of 0.09, which is why the relationship is not considered significant. Furthermore, hypothesis 04b is intended to calculate whether the professional or university degree influences trust in the police (Annex 12). The ordinal logistic regression results in a p-value of more than 0.05 (p = 0.29), which is why this hypothesis cannot be confirmed. Consequently, a linear regression calculation is used to examine the effect of police professionalism on trust in them (Annex 13). Here, the item “Professionalism” is removed from the variable “Reputation” composition and considered separately for the calculation. The probability of error calculated here is p < 0.01, and the correlation coefficient r = 0.89, which means that professionalism significantly affects the reputation of the police. Finally, hypotheses 06 to 08 were used to test whether the relevance of flexibility in everyday working life, job security and the relevance of flat hierarchical structures and an open error culture influence the attractiveness of the police for young professionals (Annexes 14–17). When testing the individual hypotheses, it is calculated that none of the assumptions is statistically significant. However, since the values of ordinal logistic regression are difficult to interpret due to the smaller sample, a common model of the three influences on the attractiveness of the police as an employer was tested. In the model, which takes into account the simultaneous influence of the relevance of flat hierarchies, an open error culture, job security and flexibility in everyday work, the relevance of job security becomes statistically significant (p = 0.03), and flexibility is statistically significant (p = 0.06). As a result, the relevance of job security influences the attractiveness of the police, while the relevance of flexibility does not significantly influence attractiveness but affects it. As a result, the police are less attractive to those who value flexibility in their day-to-day work. Ultimately, the relevance of flat hierarchical structures and an open error culture does not affect the attractiveness of the police. The quantitative survey

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results show that the 590 subjects have very high esteem and respect for the police (+ 1.04). The trust in the police (+ 0.74) and their professionalism (+ 0.7) are highly rated. For this purpose, the testing of hypothesis 05 shows that professionalism has a highly significant effect on the reputation of the police. The survey shows satisfaction with how the police work is in the positive midfield, with a value of + 0.41. The police presence in public was considered low at + 0.04. The testing of hypothesis 02 shows that the presence of the police in public has a highly significant effect on their reputation. Furthermore, it shows that participants who have contacted the police for a request for help or to avert danger (+ 0.54), as well as victims of a crime and participants in a person or traffic control, are satisfied with the police work. Nevertheless, the satisfaction of witnesses or participants in a traffic accident is moderately high, while those accused of a crime are not so satisfied with the police work, with an average value of + 0.14. Although the two hypotheses mentioned above can be confirmed, hypotheses 03a/b and 04a/b have too high a probability of error. Thus, an influence on the reputation of the police, or the trust in the institution, by age cannot be proven. Furthermore, no dependencies can be established between the professional or academic qualification and the reputation of the police or the trust in them. In the second part of the questionnaire, 83 participants were asked to be young professionals. They argue that job security (+ 1.34) is relevant to their career choice. They rate the relevance of flexibility in everyday work (+ 0.92), employer attractiveness (+ 0.88), flat hierarchies, and an open error culture as important for their choice of employer. On the other hand, young people rate the attractiveness of the police as low, with an average value of − 0.32 and strongly reject the statement that they choose the police as an employer (− 1.3). Looking at the corresponding bar charts (Annex 5), it is noticeable that the columns increase with the increasing relevance of the above-mentioned factors, while they decrease in the attractiveness of the police and the choice of the police as an employer. This tendency is also reflected in the testing of hypotheses 06–08. None of these three hypotheses can prove that the relevance of job security, flexibility in everyday working life or flat hierarchical structures and an open error culture affects the attractiveness of the police as an employer. The testing of the three influences in a Community model shows that the relevance of job security has a significant impact on the attractiveness of the police, while the relevance of flexibility, although not statistically significant, also tends to affect attractiveness. The influence on the attractiveness of the police through the relevance of flat hierarchical structures and an open error culture can be ruled out.

3.4 Discussion In the previous chapter, the results of the literature review and the quantitative survey were presented and explained. Now, it is necessary to discuss the results of the survey that were evaluated controversially in the context of the literary-theoretical framework. With the help of the first scientific question, the current reputation of the police in society is to be analysed. The literature review shows that the reputation of the

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police can be divided into an affective and a cognitive component and can thus be influenced on the one hand by personal experiences from a police contact and the news. In this regard, the evaluation of the quantitative survey shows that people who have contacted the police in the context of a request for help or to avert danger are quite satisfied with the work of the police. Likewise, victims of a crime and those involved in a traffic or person control, are satisfied with the police, while devastated in criminal proceedings are rather dissatisfied. Some participants state that their feelings towards the police depend on the person and the situation. In addition, four subjects expressed that they felt anger or powerlessness because the police officers behaved snooty or arbitrarily and that they were “bean counters” and had an “excessive duty of order” (Annex 4). Overall, the participants rated their satisfaction with the way the police work at an average of + 0.41, which puts them in the middle of the pack. To counteract the negative feelings, police officers should choose respectful and understanding, as well as factual and explanatory verbal communication, which in turn has an impact on the quality of service and, thus, on the satisfaction of citizens and the image of the police. Furthermore, this behaviour and a well-groomed and orderly external appearance create a professional effect on the police counterpart. Despite the negative feelings of some participants, however, the professionalism of the police was rated at an average of + 0.71 and is therefore considered high. Furthermore, the survey can be used to prove that a high level of professionalism has a positive effect on the reputation of the police. Another component of the reputation of the police is trust in them. Various studies examined in the literature review showed that the police have been trusted over the years and are the most trusted among the institutions, according to the “2017-Global Trust Report” and the ZKFS study. This high confidence level can be confirmed by the quantitative survey, with an average value of + 0.74. Furthermore, 335 participants stated that they would feel trust in the police representing 57% of the participants (Annex 4). The study examined the influence of trust on age and level of education, as well as by political attitudes or migration backgrounds. Links between political attitudes or a migration background and trust are not checked with the quantitative survey. There is no correlation between age, professional or university degree and trust in the police (p = 0.24; p = 0.29). It was also stated here that the correlations examined above were not significant. Since trust is a component of the reputation of the police, the above variables are also examined to determine whether they affect the reputation of the police. Again, no correlation between the variables can be established. Another component that affects the reputation of the police is the police presence in the public sphere. A study by the PWC shows that the population does not feel that the police presence in public is sufficient. The quantitative survey can confirm this statement. According to the survey, participants rated the police presence as + 0.04 on average. Since hypothesis 02 can confirm that the police presence has a positive effect on their reputation, increasing the presence would be a possible measure to increase the prestige of the police. Nevertheless, 57% of those surveyed said they were protected by the police, and 78% had respect for the police. Accordingly, the item “Attention/Respect for the Police” achieves a very high mean value of + 1.04 and confirms the results of the “Bochum IV” study. On the other hand, 13% of respondents say they feel anger towards the police, 9%

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are afraid of the police and 3% are afraid of aggression against the police. Looking at other feelings that participants entered in the “Other” field (Annex 4), two other subjects mention that they would feel rejection by the police. Three participants feel uncomfortable with the police, partly because of the firearm, and one respondent says that the police should reconsider their own actions. In addition, it was said that there would be too little control within the police, as well as unequal treatment in identity checks. One participant states that he or she would feel contempt “if police officers beat up old people and women at freedom demonstrations in Berlin” (Annex 4). Even if these are exceptions, it would damage the reputation of the police. These are individual statements and it cannot be determined whether the police violence mentioned in the quote was legitimate or illegitimate. The statements also show that deviant behavior of police officers and police violence can have a negative impact on the image of the police and thus reinforce the results of the study by Saal et al. Since unlawful behaviour can also be caused by the “cop culture”, one possible solution would be to improve the way police officers interact with each other, as well as to revise the service regulations and training of staff. Furthermore, in the second part of the survey, it was stated that a “supernatant supervisory authority” (Annex 6), as well as more internal police studies on racial profiling and racist behaviour, would increase the attractiveness of the police as an employer. Nevertheless, 36% of the participants said that they liked the police. The second question is to be used to investigate the extent to which the police are still attractive as an employer for career starters. By means of the literature review, the characteristics of the different generations and the requirements of generations Y and Z on the employer market were worked out. As a result, Generation Z is very concerned about job security, while Generation Y does not see it as essential. The quantitative survey shows that job security is very important to the 83 new entrants, with a score of + 1.34. It should be noted, however, that no distinction was made between generations Y and Z when evaluating the results. The above-mentioned job security is given by the police through preserving civil servant status, which means that the police as an employer fulfils an important demand of the next generations. Furthermore, the qualitative study by Kochhan et al. shows that Generation Y does not consider flexible working hours to be so important but perceives flextime and home office as positive. On the other hand, Generation Z is unsure in the literature whether they perceive the possibilities for home office and flextime positively. In the survey, the item “flexibility”, which includes the factors of home office and flextime, receives a value of + 0.92, from which it can be deduced that these aspects are also considered important by those entering the profession. When looking at the evaluation of the attractiveness of the police, it can be seen that the police are seen as a rather unattractive employer with an average value of − 0.33 and are strongly rejected as an employer with a value of − 1.3. It can be stated that the relevance of flexibility affects the attractiveness of the police as an employer. The evaluation of the last question shows that some participants believe that homework, a work-life balance, more family-friendly working hours and no-shift work would increase the attractiveness of the police (Annex 5). It is impossible to abolish shift work, for example, in the area of the guard and alternating services; otherwise, the police would no longer be able to pursue their legal task of averting danger and

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prosecution. Above all, it would be crucial to reconcile work and family life, change work structures to reduce working hours and make part-time work compatible. This would also increase the attractiveness of the police for single mothers and counteract the prevailing shortage of skilled workers. The survey conducted by the Trendence Institute shows that the salary factor is important to future generations. Also, in the open question at the end, it was stated that more salary would increase the attractiveness of the police (Annex 5). The problem is that public budgets and administrative breakdown plans hinder the creation of monetary stimuli. Furthermore, it was stated that changing the admission criteria and lowering the standards would increase the attractiveness. A study by the Hamburg Police and the Berlin School of Economics and Law dealt with what lowering the hurdles in the selection process would mean and whether they can still identify future police junior police officers. However, due to the time and writing limitations of this article, this study will not be direcognised in more detail. The police must adapt to the new value patterns and role understandings of the younger generations. While the baby boomers and Generation X were still shaped by authority and recognise hierarchies, the digital natives demand flat hierarchical structures and an open error culture. Accordingly, the relevance of these factors is rated at + 0.76 in the survey. Since the police organisation is characterised by hierarchical structures and, in some parts, mistakes are covered up or concealed, the hypothesis is that this could have an impact on the attractiveness of the police. The survey cannot be confirmed. Nevertheless, the structure of the police must adapt to the values of the next generations to successfully attract young people and retain employees; two participants argued that the attractiveness of the police as an employer would increase if they had a better reputation in society. This underlines the survey results that the employer’s attractiveness has a high relevance in the employer’s choice (+ 0.88) and, on the other hand, shows that the reputation of the police and the recruitment of personnel are closely linked and interact with each other.

4 Conclusion In the end, the reputation of the police comprises the components of professionalism, trust, satisfaction, esteem and respect, as well as the presence in public. Furthermore, it can be divided into an affective and a cognitive component and thus be influenced by personal experiences, such as police contacts and the news. Accordingly, through the formation of cognitive opinions, even outstanding events such as terrorist attacks, rampages or pandemics can have both a negative and positive effect on the reputation of the police. Looking at the affective component, the quantitative survey showed that participants who have contacted the police for a request for help or to avert danger, as well as victims of a crime and those involved in a traffic or person check, are particularly satisfied with the police work. Defendants in criminal proceedings, on the other hand, tend to be dissatisfied. Overall, satisfaction with the police is in a positive midfield and can be improved, among other things, by respectful and objectively

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explanatory verbal communication by police officers. Furthermore, this can also impact professionalism, which is rated highly by the subjects. This, in turn, has a positive effect on the reputation of the police. Trust in the police is also rated as high, although there is no evidence of correlations between age, professional or university degree and trust. Similarly, no correlation between these variables and reputation can be confirmed. However, it has been proven that the presence of police has a positive effect on the reputation of the police. Since this is not considered sufficient in the survey, increasing the police presence would be a measure to increase the prestige of the police. Furthermore, it can be stated that the population very much respects the police. However, some participants are angry or aggressive against the police, as some of them exhibit deviant and unlawful behaviour, which would thus damage the reputation of the police. To counteract this, one proposed solution is to revise the service regulations, provide further training, and increase the implementation of internal police studies. After examining the reputation of the police in society, the last section of the quantitative survey analysed the attractiveness of the police to young professionals. The importance of various factors in the choice of employer will be investigated. As a result, it follows that job security and flexible working hours, especially home office and flextime, are decisive for career starters when choosing an employer. While civil servants ensure job security in the police, flexible working hours due to shift work and on-call duty are less represented in the police professional world. Furthermore, the trend in the following generations is towards a partnershipbased division of roles in family planning, so the compatibility of work and family must not be disregarded. In the survey, the police were seen as unattractive and strongly rejected as a potential employer. Since it can be stated that the relevance of flexibility does not have a significant correlation with the attractiveness of the police but has an effect on it, the conversion of work structures to shorter working hours and part-time compatibility would be one way to increase the attractiveness of the police. This would also increase the attractiveness of the police for single mothers and could counteract the prevailing shortage of skilled workers. In addition, young professionals consider the relevance of flat hierarchical structures and an open error culture important. Since the police have a hierarchical structure and there is a culture of error in which mistakes are covered up or concealed, the hypothesis is that this could influence the attractiveness of the police. However, this hypothesis cannot be confirmed. Nevertheless, it is important for the recruitment of young people and employee retention that the structure of the police adapts to the values and role understandings of the next generations. In the end, the attractiveness of the police would increase if they had a better reputation in society. This statement underlines the survey results, in which the participants stated that the employer’s attractiveness is highly relevant to the choice of employer. It also shows that the reputation of the police and the recruitment of personnel are closely linked.

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5 Limitation In evaluating the results, the mean values of the various items were first collected, and the relationships between different variables and their influences on each other were checked using different hypothesis tests. To obtain more specific results for the different federal states or even the responsible police authorities, this survey would have to be carried out again, with the additional question about the federal state or place of residence. Furthermore, the query of political attitudes, migration background or employment would provide further risk-relevant results, which would also be important for police work. Since no parallel investigation or repetition of the survey could be carried out, the reliability of this research can only be assumed due to its high sample. Because more young than older people participated in the survey, the age structure has shifted to the left and does not correspond to the age distribution of the German population. If this study were to be carried out again, a quota sample could be generated instead of a random sample to obtain a representative sample in terms of age. Furthermore, only 83 young professionals took part in the survey. Re-conducting this quantitative research with a larger sample of young professionals would, therefore, increase the reliability of the results.

Annex Attached are 17 digitised documents. The data corpus contains the following contents: . . . . . . . . . . . . . . . . .

Questionnaire (Annex 1) Descriptive evaluation of the quantitative survey (Annex 2) Descriptive results of the quantitative survey (Annex 3) Interpretation of question eight and answers from an open box (Annex 4) Descriptive evaluation of the results of question ten (Annex 5) Summary off all replies (Annex 6) Calculation of the hypothesis 01 (Annex 7) Calculation of the hypothesis 02 (Annex 8) Calculation of the hypothesis 03a (Annex 9) Calculation of the hypothesis 03b (Annex 10) Calculation of the hypothesis 04a (Annex 11) Calculation of the hypothesis 04b (Annex 12) Calculation of the hypothesis 05 (Annex 13) Calculation of the hypothesis 06 (Annex 14) Calculation of the hypothesis 07 (Annex 15) Calculation of the hypothesis 08 (Annex 16) Calculation of the hypothesis 06–08 (Annex 17) The annex is available at the following link:

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https://doi.org/10.17605/OSF.IO/92D4S The appendix can also be accessed via the following https://doi.org/10.17605/ OSF.IO/92D4S

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Artificial Intelligence Application for Customer Behavior and Churn Prediction Olesya Slavchanyk, Solomiia Fedushko, Vladyslav Mykhailyshyn, Nataliya Shakhovska, and Yuriy Syerov

Abstract Customer churn is one of the most common problems in a company’s business. In this paper, a machine learning algorithm is used to predict customer behavior and the occurrence of this problem. The study compared and analyzed the performance of different algorithms, including decision tree, random forest, and gradient boosting, in predicting customer churn based on historical data on customer behavior. The results showed that random forest and gradient boosting had good values of evaluation metrics, while the decision tree algorithm performed worse. The study also provided details on the software implementation and data analysis tools, as well as the necessary libraries and versions for running the program. The findings of this study suggest that the use of machine learning algorithms effectively predicts customer behavior and churn, which is crucial for companies to retain existing customers and improve their marketing campaigns. Keywords Artificial intelligence · Customer behavior · Churn prediction · Machine learning algorithms · Customer retention

O. Slavchanyk · V. Mykhailyshyn · N. Shakhovska · Y. Syerov Lviv Polytechnic National University, Lviv 79000, Ukraine e-mail: [email protected] N. Shakhovska e-mail: [email protected] Y. Syerov e-mail: [email protected] S. Fedushko (B) · Y. Syerov Comenius University Bratislava, 820 05 Bratislava, Slovakia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_7

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1 Introduction Customer churn prediction is a crucial aspect of customer relationship management. In recent years, the application of machine learning algorithms for customer churn prediction has gained significant attention. The task of this work is to compare different machine learning algorithms for predicting customer churn. For this, several artificial intelligence algorithms will be trained on the same data sample. The results of different algorithms will be compared and described in this paper. The main goal is to get a trained machine learning model that can be used to predict customer churn and market analysis. Many people now use different types of subscriptions for different products, and it is important for companies that customers stay with them and not go to competitors, so with the help of predicting customer churn, you can better adjust the work of marketing campaigns to retain as many existing users as possible. The data set for tasks of this type is collected based on historical data on customer behavior. Important in this is what customers buy and how they react to different types of campaigns (discounts, advertising). Currently, many companies use simple statistics to understand whether a customer will leave or not. But this is often not enough, because human behavior can be quite unpredictable and quite complex. That is why it is important to take into account even the most insignificant characteristics of the customer’s behavior to most accurately determine the possibility that he will stop using a certain product. The purpose of the work is to develop an information system for identifying the possibility of customer churn using machine learning algorithms. To solve this problem, we analyzed known solutions for predicting customer churn, searched for a data sample and processed input data, and used machine learning algorithms to predict customer behavior and churn. This research provides significant contributions to the field of customer churn prediction using machine learning algorithms. The main contributions of this work are summarized as follows: . The article provides a comparative analysis of several machine learning algorithms to predict customer churn. The algorithms used in this study include decision tree, random forests, and gradient boosting. By comparing the performance of different algorithms on the same data sample, this study offers valuable insights into the effectiveness of different machine learning approaches. . The research article describes the development of an information system for identifying the possibility of customer churn using machine learning algorithms. This system can help companies to better adjust their marketing campaigns to retain as many existing users as possible. . To analyze the data, profiling reports are used, which are simple and intuitive to use. These reports allow the user to quickly and deeply understand the data in the sample and how to work with it in the future. . The article presents three trained classifiers with their evaluation metrics, including precision, recall, f-score, and roc-auc. The classifiers used in this study

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are decision tree, random forest, and gradient boosting. The evaluation metrics provide valuable insights into the performance of these classifiers in predicting customer churn. . The developed software is easy to use and extendible. The software is easy to extend, and the user can add another algorithm for classification or change the data processing as needed. This research provides significant contributions to the field of customer churn prediction and demonstrates the effectiveness of machine learning algorithms in predicting customer behavior and churn. The results and insights presented in this article can be used to improve the marketing strategies of companies and retain existing customers. The study highlights the relevance of the problem of customer churn, especially in the current business environment where attracting new customers is more expensive than retaining existing ones.

2 Materials and Methods In today’s commercial world, competition is high, and every customer is valuable. Understanding the customer is extremely important, including being able to understand that customer’s behavior patterns [1]. Customer churn is the rate at which a commercial customer leaves a commercial business and takes their money elsewhere. Understanding customer churn is vital to a company’s success, and churn analysis is the first step to understanding the customer. Subscriptions by renewal type are mostly divided into two main types: . auto-renewable subscription (charged from the account after the subscription period expires); . a subscription that needs to be renewed manually (monitor when the subscription ends and pay yourself using the details or any other method offered by the company providing the product). Customer churn is divided into two main types: . voluntary; . involuntary. Voluntary churn is when a customer takes some action to unsubscribe (for example, calls customer support to request a cancellation, or uses a site or app to unsubscribe from a specific product). Involuntary churn is when the customer does not pay for the next period for certain reasons (for example, the card has run out of funds if it is a subscription that renews automatically, or the user simply did not pay for the next subscription period). According to statistics of the churn rates by industry (health, consumer goods, box of month, consumer services, education, media, entertainment, SaaS), most

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customers leave the company voluntarily, not involuntarily. And the percentage of customers who leave involuntarily does not differ greatly depending on the service sector to which a particular product belongs. This paper uses the Telco Customer Churn dataset https://www.kaggle.com/dat asets/blastchar/telco-customer-churn (accessed Apr. 12, 2023) [2]. In this dataset, each row represents a customer, and each column contains customer attributes. The dataset contains information about customers who left or stayed with the company during the last month—the column is called ‘Churn’ and corresponds to the column that will be used as a label for the training dataset. Services that each client has signed up for [2]: . . . . . . . .

telephone; several lines; Internet; online security; online backup; device protection; technical support; streaming TV and movies. Customer account information [2]:

. . . . . .

how long they have been a client; the contract; payment method; paperless payment; monthly payments; total costs. Demographic information about customers [2]:

. gender . age; . whether they have partners or dependents. Figure 1 shows the distribution of customers who churned and those who continued to use the product. From the information shown in the figure, we can conclude that the data sample is unbalanced, as only 26.5% of customers stop using the product, while the remaining 73.5% continue their subscriptions. We can also see that the label in this data set has only two possible values: . No—indicates that the user has stopped using the subscription; . Yes—indicates that the user has continued to use the subscription. Before starting this work, we reviewed and analyzed existing analogs. In [3], a new model for predicting customer churn was proposed, namely the AOA-SBLSTM model. This model is aimed at effectively predicting the occurrence of customer churn. This model is initially used not only to train the machine learning algorithm,

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Fig. 1 Breakdown of customers who churned and those who continued to use the product

but also includes pre-processing of the input data, and then trains the classifier. This paper also develops an optimal process for tuning hyperparameters, which significantly affects the results obtained from algorithm training. The following results were obtained using the AOA-SBLSTM model: . . . .

accuracy: 0.9643; accuracy: 0.9718; completeness: 0.8828; f-measure: 0.9206.

Paper [4] describes a solution to the problem that tree-based models, which are most often used to predict customer churn, do not use the time characteristics of customer behavior. The authors propose a solution to this problem by combining a new Multivariate Behavior Sequence Transformer model with a simple tree-based classifier. The results of training models using this approach returned the following metrics: . f-measure: 0.8272; . the area under the curve (AUC): 0.9375. In the following paper [5], the authors propose a new approach to collecting data that will be used to predict customer churn. The paper notes that information about customer interaction with the contact center is important and can be used to estimate churn along with all other information. The authors argue that by using information about customer interaction with the contact center, it was possible to improve the metrics of the model for predicting customer churn.

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The authors of [6] compared the use of different algorithms to predict customer churn: . decision tree; . support vector machine; . rough set theory. Metrics for the decision tree are obtained: . accuracy: 0.797; . sensitivity: 0.938; . specificity: 0.406. The metrics for the support vector machine are obtained: . accuracy: 0.693; . sensitivity: 0.667; . specificity: 0.765. Metrics for rough set theory are obtained: . accuracy: 0.772; . sensitivity: 0.967; . specificity: 0.249. In [7], the authors propose using an ensemble of classification algorithms and clustering methods to predict customer churn. The clustering algorithms used are: . k-means; . k-medoids; . Random. The following machine learning algorithms were chosen for the classification models: . . . . .

gradient boosting; decision tree; random forest; deep learning; naive Bayes.

The author claims that the best results are obtained by stacking the following algorithms: . . . .

k-medoids; gradient boosting; decision tree; deep learning.

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The accuracy obtained as a result of the stacking described above is 0.96 and 0.936 for different datasets. In [8], the authors raise the problem of scalability of approaches for predicting customer churn, which is quite important at the moment, as the number of users and information about them is constantly growing. The paper investigates the effectiveness of the following models: . logistic tree; . random forest; . functional trees. The highest training results were returned by the following evaluation metrics: . accuracy: 0.9656 . f-measure: 0.965; . area under the curve (AUC): 0.988. Predicting customer churn is a binary classification problem that should return a value between 0 and 1 and indicate the probability that a customer will stop using a particular product, and thereby help retain customers through certain discounts or other marketing campaigns. Binary classification is the task of classifying elements of a set into two groups (each called a class) based on a classification rule. Typical binary classification problems include: . medical examination to determine whether a patient has a certain disease or not; . quality control in the industry, determining compliance with specifications; . when searching for information, deciding whether a page should be in the set of search results or not [9]. At this point in time, quite a lot of algorithms have been developed that can be used for the problem of binary classification. The most popular are: . . . . .

logistic regression; decision tree [10, 11]; methods of gradient amplification; neural networks [12, 13]; random forest.

To compare the work of different methods, it is necessary to use metrics for assessing the accuracy of the results of the trained classifier, of which there are already quite a few. Most often, the binary classification uses the accuracy metric, which reflects the percentage of records that the trained classifier predicted correctly. But this metric is not suitable for the unbalanced data set used in this work. Therefore, the following evaluation metrics were chosen to compare the results of this work: . recision;

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. recal; . f-score; . roc-auc. To implement this work, the following tasks were set: . . . . . .

analysis of analogs; search for a sample of data; analysis of the found data; processing of the found data; building a classification model to predict customer churn; model evaluation.

The subject environment, which includes the dataset that will be used to train the classifier, is described, and the main terms related to the definition of customer churn are defined. The selected dataset is described, and the customer data in the existing sample is divided into the following main categories: . services to which each client has subscribed; . information about the client’s account; . demographic information. Considering the existing analogs, which are quite numerous and offer completely different approaches to solving this problem, is equally important. The works use different data sets, so it is quite difficult to compare the results with each other. Before you start training a model, you need to determine what data the model should expect as input. In this paper, the input data will be the processed values from the selected dataset (see Table 1). In addition to correctly passing input data to the model, it is equally important to understand what the model returns. Since this paper solves the problem of binary classification, the model output is expected to be a value from 0 to 1, which will indicate the probability that the user will stop using this product. Based on the model result, you can assign a user to a specific group. Each group can use different methods of customer retention. In this study, we will analyze the behavior and present the results of customer retention forecasting for two groups: . the user is likely to continue using the product; . the user is likely to stop using the product. To more correctly distribute customers into two groups, a threshold value of 0.5 was determined, which means that if the model results are between 0 and 0.5 inclusive, the customer belongs to the group of users who are likely to continue using the product. If the value is between 0.5 and 1, then the customer belongs to the group of users who are likely to stop using the product. Therefore, the following values will be returned as a result of the prediction: . 0—if the customer is in the group of those who are likely to continue using the product;

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Table 1 Table for collecting the input data set Question

Answer

gender;

(a) 0—female (b) 1—male

whether a person is a pensioner;

(a) 0—no (b) 1—yes

presence of partners;

(a) 0—absent (b) 1—available

presence of dependents;

(a) 0—absent (b) 1—available

time of product use;

(a) 0—no (b) 1—yes

whether the phone service is selected;

(a) 0—no (b) 1—yes

whether the service of several lines is selected;

(a) 0—no (b) 1—yes

whether Internet service “Fiber optic” is selected;

(a) 0—no (b) 1—yes

whether DSL Internet service is selected;

(a) 0—no (b) 1—yes

whether the Internet service is selected;

(a) 0—no (b) 1—yes

whether the online security service is selected;

a) 0—no b) 1—yes

whether the online backup service is selected;

(a) 0—no (b) 1—yes

whether the device protection service is selected;

(a) 0—no (b) 1—yes

whether the technical support service is selected;

(a) 0—no (b) 1—yes

whether the TV streaming service is selected;

(a) 0—no (b) 1—yes

whether a movie streaming service is selected;

(a) 0—no b) 1—yes

whether the contract is monthly;

a) 0—no b) 1—yes

whether the contract is annual;

(a) 0—no (b) 1—yes

whether the contract is for two years;

(a) 0—no (b) 1—yes

whether the paperless payment is used;

a) 0—no b) 1—yes

whether the payment method “Electronic check” is selected;

(a) 0—no (b) 1—yes (continued)

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Table 1 (continued) Question

Answer

whether the payment method “Mailed check” is selected;

(a) 0—no (b) 1—yes

whether the payment method “Bank transfer” is selected;

(a) 0—no (b) 1—yes

whether the payment method “Credit card” is selected;

(a) 0—no (b) 1—yes

cost of monthly payments; general expenses

. 1—if the user is in the group of those who are likely to stop using the product. To choose data processing methods and training models, you first need to analyze the data in the selected sample. In the selected dataset, most of the columns are ribbons, so for their processing, we used number coding if there are only two unique values in a column, or unitary coding if there are more than two unique values in a column. As a result of this processing, a sample of 27 columns was obtained, where 26 of them are potential features and one is a label. The Spearman correlation coefficient is used to calculate the linear relationship between the features and the label. Spearman’s correlation coefficient measures the strength and direction of the relationship between two ranked variables [14]. The results of calculating the correlation between the features and the label are shown in Fig. 2. The figure shows that the tenure feature has a high correlation coefficient with the other features: . TotalCharges; . Contract_is_Month_to_month; . Contract_is_Two_year. The MultipleLines feature has a high correlation coefficient with MonthlyCharges. The Device Protection feature has a high correlation coefficient with TotalCharges. The StreamingTV feature has a high correlation coefficient with the other features: . MonthlyCharges; . StreamingMovies. And the StreamingMovies feature also has a high correlation coefficient with MonthlyCharges. The feature MonthlyCharges, in turn, has a high correlation coefficient with the features: . InternetService_is_Fiber_optic; . InternetService_is_No; . TotalCharges.

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Fig. 2 Spearman correlation matrix between features and label

The InternetService_is_Fiber_optic and InternetService_is_DSL attributes also have a high correlation coefficient with each other. And the Contract_is_Month_to_month feature correlates with other types of contracts: . Contract_is_Two_year; . Contract_is_One_year. All correlations between features are quite expected and understandable, so none of them need to be removed and can be used for further training. Equally important is the evaluation of the dependencies between the features and the label. Figure 3 shows the features and their absolute value of the Spearman correlation coefficient with the label.

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Fig. 3 Features and their absolute value of the Spearman correlation coefficient with the label

After analyzing the correlation values, we can conclude that the following signs are the most informative: . . . . .

Contract_is_Month_to_month; tenure; InternetService_is_Fiber_optic; Contract_is_Two_year; PaymentMethod_is_Electronic_check. The least informative signs are:

. . . . . .

gender_is_male; PhoneService; MultipleLines; StreamingMovies; StreamingTV; DeviceProtection.

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To train machine learning algorithms, it is also important to analyze the label. Figure 4 shows the unique values stored in the label column and their frequency of occurrence. The figure shows that the dataset is unbalanced, which means that not all the metrics for evaluating algorithm performance will be informative in this case. In the course of this work, 3 algorithms were independently trained. Each of them consists of the following steps: . data preprocessing: processing categorical data, filling in missing values; . model training; . search for optimal hyperparameters. To train the decision tree, we developed the scheme shown in Fig. 5. To train a random forest, we developed the scheme shown in Fig. 6. To train gradient boosting, we developed the scheme shown in Fig. 7. In this paper, two types of data encoding and one type of filling in missing values are used for data processing. The following methods were used for data coding: . Ordinal Encoding, which encodes string data into numbers by replacing the strings with numbers in a certain order, using a dictionary with the value of the string and the number corresponding to it; . One Hot Encoding, which creates a separate column for each unique string value and fills it with 0 if the value does not match the string, or 1 if it does.

Fig. 4 Unique values stored in the label column and their frequency

Fig. 5 Scheme for training a decision tree

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Fig. 6 A scheme for training a random forest

Fig. 7 Scheme for training a decision tree

To fill in the missing values, the median filling algorithm was used, since the missing values are only in the numerical characteristic. The next step was to choose a cross-validation method. One of the methods considered was K Fold validation. In K Fold cross-validation, the data is divided into k subsets, so that each time one of the k subsets is used as a validation set and the other k-1 subsets as a training set. The error is averaged over all k trials to get the overall performance of our model [15]. However, the K Fold cross-validation technique will not work properly for an unbalanced dataset, in which case we need to slightly modify the K Fold crossvalidation technique so that each group contains approximately the same percentage of samples of each original class as the full group. This variant of K Fold crossvalidation is called Stratified K Fold, and it was used in [16]. Two methods were considered to find the most optimal hyperparameters: . Grid Search is an exhaustive search that is performed on specific values of model parameters;

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. Random Search is a search that selects only random values of model parameters. . In this paper, we chose Grid Search because it is a more accurate method for finding optimal values of hyperparameters, and since the models are simple, the search does not take too long. Three tree-based algorithms were chosen to train the models. One of them is a simple decision tree, one is a boosting algorithm, and one is a bugging algorithm. Bugging and boosting are subtypes of ensemble algorithms, that is, those that build more than one model. Bugging is the training of several iterations of algorithms independently of each other. To obtain the final result, it is necessary to average the results of all untrained algorithms according to certain rules, which may be different for different algorithms. Boosting is the training of several iterations of algorithms in such a way that each subsequent iteration learns from the mistakes of the previous one. And the final result is the output value from the last iteration. In order to build a decision tree, you must perform the following actions: . . . . .

calculate entropy in the classroom; calculate entropy in classes for possible divisions; calculate information gained for each division; make a division according to the value that has the greatest gain of information; repeat the steps above for the following sheets until the information gain is small enough.

In this paper, a random forest is used as a bugging algorithm. A random forest is a bagging algorithm that randomly selects a subset of features and uses them to build different decision trees. This algorithm uses voting to average the results. Gradient boosting [17, 18] is chosen as a boosting algorithm, which builds weak decision trees that predict residuals, and each subsequent tree improves the residuals of the previous one so as to reduce them to a value close to zero. The output values are calculated using the derivative of the loss function. The following metrics are used to evaluate the performance of the algorithms: . accuracy, which determines the proportion of relevant samples among those found; . completeness, which determines the proportion of the total number of positive samples that were actually found [18]; . f-measure, which is calculated using the previous two; . roc-auc, which is calculated as the area under the curve of the dependence of true positive values and false positive values. This step describes in detail all the input data for the job. The output data is also described, and how the results will be divided into two different classes. The data is analyzed and certain dependencies are identified that exist in the selected data set. After reviewing the results of the analysis, you can see that all dependencies are expected and are not so large that they need to be removed. That is why no data was removed from the sample. The system diagrams for each algorithm used to train the model were also shown.

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3 Results and Discussion To implement this work, we chose the Python programming language. This language is most often used to work with machine learning, as it has many libraries that have implemented methods, which greatly facilitate and speed up the work. Python is an interpreted, object-oriented, high-level programming language. High-level built-in data structures combined with dynamic typing and dynamic binding make it very attractive for rapid development as well as for use as a scripting language. Python’s simple and lightweight syntax emphasizes readability and thus reduces program maintenance costs. Python [19] supports modules and packages, which encourages program modularity and code reuse [20]. Python libraries used in this work: . sklearn is a free machine learning software library for the Python programming language that provides functionality for creating and training a variety of classification, regression, and clustering algorithms such as linear regression, random forest, and gradient boosting, and works in conjunction with the NumPy and SciPy libraries. Scikit-learn is one of the most popular machine learning libraries. [21] . numpy is a core package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and a set of procedures for fast array operations, including mathematical, logical, shape manipulation, sorting, selection, input/ output, discrete Fourier transform, basic linear algebra, basic statistical operations, random modeling, and much more. [22] . pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. pandas is free software released under the BSD three-point license. The name comes from the term panel data, which in econometrics refers to multivariate structured data sets. [23] . pandas_profiling—a software library that helps in creating a report for a dataset with many functions and settings for the generated report. The sklearn library is used the most in this work. The packages used are from this library: . . . . . . . .

compose; pipeline; impute; preprocessing; model_selection; metrics; tree; ensemble.

To run the developed program, it is recommended to have Python version 3.7 installed. Also recommended are library versions no older than:

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sklearn: 1.0.2; numpy: 1.21.6; pandas: 1.3.5; pandas_profiling: 3.1.0.

We also recommend using a notebook to run the program, as it was developed in the Kaggle environment. There are no requirements for the operating system. The system requirements are not high either. The software implementation of this work was developed in a notebook. This notebook consists of two main parts: . data analysis; . preprocessing and training. To analyze the data, we first read the dataset using the pandas library, and then created a profiling report of this sample using the pandas_profiling library. A profiling report consists of the following parts: . . . . . .

an overview of the dataset, which is shown in Fig. 8; a review by features, an example of such a review is shown in Fig. 9; interaction of features with each other, an example is shown in Fig. 10; correlation, an example of which is shown in Fig. 11; the number of missing values for the features shown in Fig. 12; an example of the data in the sample is shown in Fig. 13.

Afterward, a certain transformation of the data was performed, and the correlation matrix (see Fig. 2) was built on the transformed data. As a result of the analysis of the previous steps, data processing methods and models were selected for further training. Therefore, the next step was to build a

Fig. 8 General overview of the dataset

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Fig. 9 Review by signs

Fig. 10 Interaction of features with each other

pipeline for processing, training, and finding optimal hyperparameters using the sklearn library. The data were divided into training and test groups as follows: . 80%—training data; . 20%—for testing. The training data were divided into 5 groups for cross-validation. To search for optimal hyperparameters, the following values are used: . for the decision tree: (a) criterion: gini, entropy, log_loss;

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Fig. 11 Correlation matrix

Fig. 12 Number of missing values by features

(b) maximum depth: 500, 700, 900, 1100, 1300, 1500; . for random forests: (a) criterion: gini, entropy, log_loss; (b) number of classifiers: 500, 700, 900, 1100, 1300, 1500;

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Fig. 13 An example of the data in the sample

. for gradient boosting: (a) loss function: log_loss, deviance, exponential; (b) number of classifiers: 500, 700, 900, 1100, 1300, 1500. The parameters that were not optimized are shown in Table 2. This program is quite easy to use, all the code is written in a single notebook that can be run locally or remotely. All the data for analysis and results are displayed after the program is launched, so this should not be a problem either. The program is easy to extend, if the user needs to add another algorithm for classification or change the data processing, this can be passed to the method in which the training pipeline is created, and the training results will be obtained with these changes. To analyze the data, we used profiling reports, which are also simple and intuitive to use, allowing the user to quickly and deeply understand the data in the sample and how to work with it in the future. The result of the notebook is: . trained model; . the values of the parameters, which are selected using the search for optimal hyperparameters; . the value of the evaluation metrics on the test data set. At this stage, we describe the libraries that were used to develop the software. Important packages of these libraries that played a significant role in the development are also listed. The versions of the libraries that must be installed to run the program are described. An example of an environment where this software can be executed is also given. A detailed description of the software implementation and how to use certain tools for data analysis is provided. The figures also show examples of what these analysis tools look like and what parts they consist of. The user manual contains a list of what to expect when running the program.

Artificial Intelligence Application for Customer Behavior and Churn … Table 2 Table of hyperparameters

Algorithm name

Hyperparameters

Decision tree

splitter: best min_samples_split: 2 min_samples_leaf: 1 min_weight_fraction_leaf: 0 min_impurity_decrease: 0 ccp_alpha: 0

Random forest

min_samples_split: 2 min_samples_leaf: 1 min_weight_fraction_leaf: 0 max_features: sqrt min_impurity_decrease: 0 bootstrap: True oob_score: False verbose: 0 warm_start: False ccp_alpha: 0

Gradient boost

learning_rate: 0.1 subsample: 1 criterion: friedman_mse min_samples_split: 2 min_samples_leaf: 1 min_weight_fraction_leaf: 0 max_depth: 3 min_impurity_decrease: 0 verbose: 0 warm_start: False validation_fraction: 0.1 tol: 0.0001 ccp_alpha: 0

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4 Conclusions The problem of customer churn is quite relevant at the moment because more and more new services are appearing, and it is much more expensive to attract new customers than to retain existing ones. As a result of the thesis, three trained classifiers were obtained with the following metrics for evaluating the results: . decision tree: (a) (b) (c) (d)

precision: 0.73; recall: 0.73; f-score: 0.73; roc-auc: 0.66;

. random forest: (e) precision: 0.78; (f) recall: 0.79;

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(g) f-score: 0.78; (h) roc-auc: 0.83; . gradient boosting: (i) (j) (k) (l)

precision: 0.78; recall: 0.79; f-score: 0.78; roc-auc: 0.83.

From the results above, it can be seen that random forest and gradient boosting have good values of the evaluation metrics, while the decision tree algorithm performed much worse. Also, during the search for optimal hyperparameter values, the following values were obtained: . decision tree: (a) criterion: entropy; (b) maximum depth: 1300; . random forest: (a) criterion: entropy; (b) number of classifiers: 700; . gradient boosting: (a) loss function: exponential; (b) number of classifiers: 500. The problem of customer churn is relevant, and it is more expensive to attract new customers than to retain existing ones. The application of machine learning algorithms for customer churn prediction is an effective way to retain customers and improve market analysis. The results showed that random forest and gradient boosting algorithms are better suited for customer churn prediction than the decision tree algorithm. The developed program can be easily extended and used for future analysis. Acknowledgements This work was supported by the Shota Rustaveli National Foundation of Georgia (SRNSFG) (NFR-22-14060), the National Scholarship Programme of the Slovak Republic and EU Next Generation EU through the Recovery and Resilience Plan for Slovakia under project No. 09I03-03-V01-000153.

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Workplace Discrimination from the Perspective of Leaders of Slovak Enterprises—pilot Study Tibor Zsigmond, Ladislav Mura, Renáta Machová, and Diana Ignácová

Abstract The aim of the research is to examine workplace discrimination at the Slovak enterprises. A questionnaire survey was applied to assess the attitude of leader towards discrimination in Slovak enterprises. A total of 71 responses were collected from leaders, working at different enterprises. 83 leaders did not finish the questionnaire, some of which expressed “subject sensitivity” or even describe the topic as “taboo”. Two hypotheses were formulated, which were tested by Fisher’s exact test and Binary logistic regression. SPSS software was used to evaluate the formulated hypotheses. The negative attitude of some leaders indicates, that the topic is important to deal with and there is a need for addressing attention to the issue. Based on the results of the hypotheses testing, top and middle managers consider the issue of discrimination to be important in a similar proportion. Furthermore, the perception of the importance of discrimination contributes significantly to whether organizations act carefully during job interviews. This study can be considered a pilot study, which contributes to addressing more attention to discrimination and achieve a significant change in viewpoints. Keywords Workplace discrimination · Slovak enterprises · Leaders · Pilot study · Fisher’s exact test · Binary logistic regression

T. Zsigmond · R. Machová · D. Ignácová Faculty of Economics and Informatics, J. Selye University, Bratislavská Cesta 3322, 945 01 Komárno, Slovakia e-mail: [email protected] R. Machová e-mail: [email protected] D. Ignácová e-mail: [email protected] L. Mura (B) University of Economics in Bratislava, Faculty of Commerce, Dolnozemská cesta 1, 852 35 Bratislava, Slovakia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_8

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1 Introduction The research is addressing the topic of workplace discrimination in enterprises operating in Slovakia. The concept of equality and equal treatment is considered a fundamental value in modern societies of the XXI century [1]. One of the basic principles of modern societies has to be the respect for diversity. Despite of this, there are individuals, who encounter different form of discrimination in certain areas of their life (school, workplace, etc.). We believe that empathic attitude is the basic pillar of building modern societies. Several examples of political, religious and even social discrimination had been recorded throughout the history. Since the emergence of private ownership, people started to focus on economic and social development. Rationality lost its significance and individuals became differentiated based on their abilities and qualities. An improvement in terms of discrimination was detected when politicians of the world implemented decisions and introduced law related to equality and equal treatment of individuals. As a result, discrimination against the individual was prohibited by the law, however unfair discrimination between the individuals could not be influenced, especially if the discriminated person did not recognize being a victim of discrimination. Discrimination can be defined as a “pathological social phenomenon”, which has become so widespread, up-to-date and controversial topic that it was necessary to create a legal framework for [2]. In May 2004, Slovakia introduced the Act No. 365/2004 on equal treatment in certain areas and protection against discrimination [3]. The comprehensive legislation against discrimination prohibits discrimination in education, healthcare, employment, etc. The Constitution of the Slovak Republic already established the basis of the principle of equal treatment in Act No. 460/ 1992 12 (1) [4]. In addition to the principle laid down in the Constitution of the Slovak Republic and the Anti-Discrimination Act, the prohibition of discrimination in workplaces is defined in the Labour Code, which provides a platform for maintaining equality during the entire duration of the employment relationship that exists between the employee and the employer [5, 6, 48]. This is included in Act No.311/2001Coll of Labour Code [7].

2 Theoretical and Conceptual Background 2.1 Workplace Discrimination The concept of discrimination comes from the Latin word of “discriminare”, which means “to differentiate, to separate” [8–10]. It is described as a form of unfair discrimination, based on which a group or an individual receives a treatment different from treatment exercised with other groups or individuals based on ethnic or racial origin, sexual orientation, age, religious orientation, health condition or mental state.

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Discrimination can be practiced against a group, individual, company or state [11– 13]. Discriminating entity can be an individual, company or the state [11–13]. Discrimination is often interpreted incorrectly. It is often interpreted as negative discrimination, however the word has a broader meaning, including the concept of positive discrimination [14, 15]. In economic terms, labour market discrimination is detected, when members of two groups with the same productivity, but characterized by different demographic features receive different treatment. It means that the market is interested in personal qualities of individuals that are not associated with workplace productivity of the individual [16–18]. According to Kaufman [19], the society discriminates against individuals with lower status in the society, however they would be able to utilize their skills as effectively as possible in their workplace. They are experiencing workplace discrimination and wage discrimination compared to other employees of the company, despite obtaining the same skills. This type of discrimination primarily affected the members of minority groups, where negative discrimination was based on color of the skin, religious background or national identity as well as discriminated were women, who were denied certain market opportunities, which were open only for men. [20]. Workplace plays a significant role in forming the living conditions of individuals. Income from work is the main source of income for the majority of people. It provides an access to a wide range of consumer goods, which not only affects the quality of life but also the access to opportunities. If discrimination hinders work opportunities or leads to income loss, it does not only affect the quality of life spent with work, but also affects different areas of life. If certain groups of employees regularly face workplace discrimination, this can strengthen further inequalities in the economy resulting in a spill over effect, which has an impact on healthcare, housing, access to quality education and equality rights [21].

2.2 Workplace Harassment In publication issued by the International Labour Organization [22], we obtain a broader definition of workplace harassment closely related to conducting work. Workplace harassment is a one-time or a repeated action, which is characterized by unacceptable behaviour, practice or threat, the most likely purpose of which is to cause physical, psychological, sexual or economic harm. The actors of workplace harassment are the victims, witnesses and the perpetrator. “Harassment” refers to all forms of discrimination and inappropriate behaviour, which is not limited to particular age group. Different forms of workplace abuse can be generally classified as a form of emotional and physical abuse [23]. Harassment detected in an improperly managed employment relationship can have a toxic effect on organizational culture [24, 46, 50] and will increase the fluctuation of employees [47, 51, 52]. Harassment is intentional in nature, aimed at violating the employee’s dignity, and creating a workplace environment that can be characterized

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as intimidating, hostile, humiliating and offensive. There can be many reasons of harassment as well as creating a hostile environment, among which may be the fear of authority, resignation of the employee experiencing workplace harassment or termination of the employment contract from the side of the employer. If we take into account the circumstances of harassment, it can last minimum of 6 months even a shorter period is enough [25].

2.3 Forms of Discrimination in Labour Relations Mobbing at work refers to a group of people engaged in different types of harassment and bullying behaviour they practice against co-workers. Heinz Leymann (1980), a Swedish resercher mentioned first that the phenomenon of mobbing also appeared on the labour market. Workplace mobbing can be linked to organizational factors, e.g. organization of work in the company or inadequate leadership. According to Leymann [26], the personality of the victim does not play role in mobbing. The synonym for mobbing is the psychological terror in the workplace. Psychoterror in the workplace is detected when the victim is forced to face the attack for at least half a year and at least once a week [27]. Bossing: The phenomenon of bossing differs from mobbing in terms that the pressure on individual is practiced by the employer. The main reason for bossing is that the employee in higher position feels threatened by the employee in lower position. Intelligent and educated subordinates often become the victims of bossing. The employer feels lonely and isolated in his position, and because of his fear of losing the status, begins to attack the subordinates by creating uncertainty and fear [28].

2.4 Models of Negative Discrimination In economic terms, discrimination falls into two basic groups. These two groups are designed to examine the discriminatory behaviour resulting from lack of information. These two groups are referred to as taste-based and statistical discrimination models. The taste-based discrimination model was first introduced by Becker [29]. The basic principle of this model is that the majority of people on the labour market do not intend to work with individuals from minority groups based on their own preferences and prejudices. This approach has no economic basis, it is also present, when the productivity of working groups is the same. Based on the discriminator, the model distinguishes thee types [30]. A discriminative employer is characterized by discriminating preferences, and will hire employees from minority group if their wage is significantly lower than the wage of majority employees in the company. In this model, we assume that

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productivity of the mentioned groups of employees is the same, and they conduct an equally good job [31]. A type of discriminative co-worker refers to those who state that wage discrimination might occur between the two groups of employees. This type of discrimination can be detected when preferences and skills are distributed differently between the groups in terms of employment. In the case these facts are not present, the employees would have to do the work in separate group and we could not talk about wage difference. If we talk about the shortage of highly qualified workforce in connection to minority groups, and the employees with high qualification practice prejudice, it happens that the employees of the low qualified minority group have to work together with the employees of majority group, who claim higher wages. Thus, the wage difference will appear between the representatives of the mentioned groups. [32]. Buyers with prejudice will prefer those products that they can buy cheaper [33]. They also choose products produced by minority workers during their purchase. The model of Becker [29] says that the value and demand for employees belonging to minority groups is significantly lower in those professions where contact with the customer is required [34]. Phelps [35] and Arrow [36] explained in their work that statistical discrimination compared to taste-based discrimination is not based on the taste of those active on the labour market or the preferences of the majority workers [37]. Statistical discrimination is due to discrepancy and inadequate amount of information. According to Samuelson and Nordhaus [38], statistical discrimination is determining the individual based on the behaviour of group he/she belongs to. This model has a realistic assumption that during job interviews the employer can assess the performance and productivity of the candidate up to certain limits. If not the appropriate individual is employed, it means additional costs, as the employee will later be fired and new employee has to be hired and trained. In order to avoid additional costs, the employer will take into account those aspects during the hiring process that may be closely related to productivity. These aspects are not described in details, e.g. gender or race [37, 38]. Since the statistical discrimination model is strengthening the prejudice as well as decreasing the motivation of individual [49], it will lead to declining efficiency of the economy. Samuelson and Nordhaus [38] provided an example, when the employers select the employee based on the institution the candidate graduated from. Thus a graduate, who graduated from a less prestigious institution, is less likely to find a job than a candidate completing his/her studies in a prestigious institution. It is important to emphasize that it is very difficult to recognize the different types of discrimination. We might not think that we have become a victim of discrimination when we happen to be involved in it. Negative discrimination is difficult to measure, since the victim is afraid to speak about it. The vast majority of violations committed against the employee will remain unknown, cases of discrimination are not public, and the victims prefer and try to overcome these problems. It does not mean that the problem does not exist or does not have to be addressed attention [39].

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Employees are an important asset for the company, as their skills, knowledge and experience cannot be replaced. Therefore, they have to be dealt with care and responsibility. This is the reason why the company management has to focus on measures related to human resources. This also points to the fact that employees of the company should not be valued based on their perceived qualities, but based on their skills and knowledge. Employees contribute to the growth of the company [40]. Workplace discrimination is associated with many passive consequences, since it undermines the human value and dignity. Society that tolerates inequal treatment and discrimination on workplaces, will risk the proper functioning of the labour market [41–43].

3 Methodology The main goal of the research is to assess the attitude of company managers to workplace discrimination in Slovakia. We believe that in a modern society, respect for diversity should be a basic principle. Despite of this, it still happens in many workplaces that employees are discriminated against by their colleagues or leaders. That is why we feel important to address this issue. In this research we used a questionnaire survey as a quantitative method. Using this method helped us to contact the managers of companies operating in Slovakia and obtain information how leaders of these companies approach the issue of workplace discrimination. The questionnaire consists of 27 questions: 6 demographic questions, while the remaining 21 questions are examining the behaviour of entrepreneurs related to the issue of workplace discrimination. When compiling the questionnaire, we selected structured, closed types of questions, where the possible answers were provided. In case of some closed questions, the respondents were given an opportunity to chose “Other” option. Our research sample consisted of companies operating in Slovakia. The printed and online questionnaires were sent to 153 companies. 83 managers did not finish completing the questionnaire, so the number of completed and processed questionnaires was 71. We used MS Excel and SPSS to evaluate the questionnaires. We formulated 2 research hypotheses, which were examined using the Fisher’s exact test and binary logistic regression. (Table 1) The formula for calculating the logistic regression was the following: P=

(a + b)!(c + d )!(a + c)!(b + d )! (a!b!c!d !n!)

where, n = population size. a + c = sample size. a = sample “successes” =

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Table 1 Statistical methods applied in hypotheses testing Hypothesis

Dependent variable

H1: Discrimination is Importance of more important for top discrimination (Likert, managers than middle ordinal) managers

Independent variable

Statistical method applied

Position (ordinal)

Fisher’s exact test

H2: Managers and Discrimination-free job Importance of middle managers who interview (nominal) discrimination (Likert, ordinal) consider the issue of discrimination to be important, act carefully on job interviews

Binary logistic regression

a + b = population “successes”. The formula for calculating the logistic regression was the following: P=

ea+bX 1 + ea+bX

where e = the base of the natural logarithm (2.718). P = the probability of a “one”. a, b = the parameters of the model.

3.1 Research Sample This research examined the responses provided by managers of 71 companies operating in Slovakia. A table presenting demographic data is shown below (Table 2). Since we examined the companies in this research, we indicated only the position of the respondent filling in the questionnaire. 64.8% of the respondents were company owners and 35.2% were middle-managers. Most of the respondents (56.3%) represented the age group between 36 and 55. The age group between 18 and 25 accounted for 5.6% of the respondents. This age group has the smallest number of representatives in the survey. The reason is that most of them are representatives of Generation Z, who are students and/or do not yet work in the company management. In terms of general characteristics of companies, we can assume that most of the companies represented the service sector (52.1%), while 23.9% were operating in industry. In terms of employee number, the majority of the responding companies represented the SME sector (95.8%). Companies with more than 250 employees accounted for 4.2% of enterprises participating in the survey. 78.9% of the respondents were represented by leaders who have been operating their companies for at least 40 years.

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Table 2 Demographic data of research participants Age of survey participants

Position of survey participants Sector

Number of employees

Length of business operation (years)

18–25

5.6%

26–35

19.7%

26–35

19.7%

46–55

28.2%

55 +

18.3%

Leader/owner

64.8%

Middle manager

35.2%

Industry

23.9%

Services

52.1%

Agriculture

12.7%

Other

11.3%

0–9

24.0%

10–49

50.7%

50–249

21.1%

250 +

4.2%

0–20

42.3%

21–40

36.6%

41–60

11.3%

61–80

9.8%

4 Results Before presenting the results of hypotheses testing, we will present the questions that are integral part of our hypotheses. The first is a Likert-scale question, which examined the extent to which the company leaders and middle-managers consider the issue of equal treatment and discrimination important in the company. The respondents were provided to chose from 4 options in order to avoid answers that can be interpreted both yes and no. (Figs. 1, 2 and 3) The results obtained show that 91.6% of the respondents consider the issue of discrimination important. Only 8.4% of the respondents did not feel it important. This figure shows 2 questions included in our questionnaire, which were merged in our analysis. The first question is addressing the issue, whether the organizations themselves act carefully on job interviews, while the second question asked whether it has happened on job interview that a young female candidate was not hired because wanted to establish a family in near future. 80.3% of the top managers and middle managers reacted that they act carefully during the job interview and no discriminatory comments have place during the process. 21.1% of 71 respondents answered that there has already been a case when a female candidate applying for a position was rejected because she was planning to establish a family after getting the job. However, it is also surprising that almost

Workplace Discrimination from the Perspective of Leaders of Slovak …

I do not consider it important at all

4.2%

I do not consider it important

4.2%

177

12.7%

I consider it important

78.9%

I consider it very important 0%

20%

40%

60%

80%

Fig. 1 Importance of discrimination

80.3% 100% 80% 60% 40% 20% 0%

78.9% 21.1%

19.7%

Careful treatment in job Rejecting a candidate interviews planning to establish a family Yes

No

Fig. 2 Discrimination in job interviews

20% of our respondents reported discrimination based on first impression, and it can also happen that the applicant is discriminated against because of his or her existing or perceived qualities. The next question, which is not an integral part of our hypotheses, but an important element of the survey is about the opinion of the respondents. They had to evaluate on a scale from 1 to 4 to what extent they think discrimination is present on the Slovak labour market. 80.3% of the top managers and middle-managers responded that discrimination is present on the Slovak labour market. Workplace discrimination is a sensitive issue. This research also proved that the issue has to be addressed not only in social life, but also in the workplace. Employees should be aware of their rights, and every employer should inform the employees what steps can be done if workplace discrimination is detected.

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45.1% 35.2%

50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0%

15.5% 4.2%

There is no Discrimination Discrimination Discrimination is present on is significantly discrimination is less characteristic the Slovak present on the on the Slovak labour market Slovak labour market labour market Fig. 3 Discrimination on the Slovak labour market

4.1 Hypotheses Testing Our first hypothesis stated that the topic of discrimination is more important for top managers than middle-managers. Position in the company became the independent variable, while the importance of detecting discrimination became the dependent variable. Both of the variables are of ordinal type. In order to prove our first hypothesis, we used the Fisher’s exact test. The cross-table shows that the issue of discrimination is important both for the top and middle-managers of the company. Elaboration of cross-table was followed by the Chi-square test. (Tables 3 and 4) The Fisher’s exact test is used in 2*2 contingency table. The values evaluated by performing the Fisher’s exact test were marked in a table. The value is 0.175, which is significantly higher than the 5% margin of error. The results show no relationship between the nominal variables. According to the test, the issue of discrimination is equally important both for the top and middle-managers. Table 3 Cross table—H1 Importance of discrimination

Position of respondents Top-manager (%)

Total (%)

Middle-manager (%)

2.8

5.6

Important

62.0

29.6

91.5

Total

64.8

35.2

100.0

Not important

*Position of respondents Cross-tab

8.5

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Table 4 Chi-square results—H1

Pearson chi-square

Value

df

Asymptotic significance (2-sided)

2.843a

1

0.092

Fisher’s exact test a2

Exact Sig. (2-sided)

Exact Sig. (1-sided)

0.175

0.110

cells (50.0%) have expected count less than 5. The minimum count is 2.11

Based on the results, we can state that the importance of job title and discrimination are completely independent from each other. According to the evaluation of the final results, hypothesis H1 can be rejected. The second hypothesis, H2 says that those top and middle-managers who found the issue of discrimination important, act carefully on job interviews. We stated that there is no significant relationship between the importance of discrimination (independent, metric variable) and the job interview free from discrimination aspect (dependent, metric variable). We used binary logistic regression to prove this hypothesis. (Table 5) The cross table shows the extent to which the managers who consider the issue of discrimination and equal treatment to be important, avoid any act related to discrimination during job interviews. If we look at both variables, this is achieved in 76.1% of the cases. The next table will present the relationship between the variables. We used the SPSS program to examine the binear logistic regression. The table shows the contribution of independent variables to the binary logistic regression and its statistical significance. The Wald test is used to determine the level of statistical significance of independent variables. In Table 6, column “Sig” indicates the statistical significance of the test. The results show that the importance of discrimination (p = 0.014) significantly contributes to the fact that organizations act carefully during the job interviews. Based on the results obtained, we can assume that there is a significant relationship between the determined variables, so hypothesis H2 is approved.

Table 5 Cross table—H2 Careful approach during job interview

Importance of discrimination

Total

Not important

Important

Yes

2.8%

76.1%

78.9%

No

5.6%

15.5%

21.1%

Total

8.5%

91.5%

100%

* Importance

of discrimination Cross-table

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Table 6 Results of binary logistic regression analysis—H2 B

S.E

Wald

df Sig

Exp(B) 95% C.I.for EXP (B) Lower Upper

Importance of discrimination − 2.284 0.927 6.071 1 Constant

0.693

0.866 0.641 1

0.14

0.102

0.017

0.627

0.423 2.000

5 Conclusion The purpose of the research was to examine workplace discrimination in Slovak enterprises from the perspective of enterprise leaders. Before evaluating the results, it became clear that the phenomenom“workplace discrimination” can be still considered a taboo topic in Slovak enterprises. A large number of respondents (who got the request to participate in the study) refused to participate in the research. Some of them straightly declared that the topic is sensitive and do not want to participate in the research. According to Clingan [44], courage is needed to discuss the sensitive issue of workplace discrimination. Lämsä et al. [45] studied the “appearance-based discrimination against young women in the workplace” . They also noticed “that it was difficult to find people who were willing to speak of the sensitive topic” [45, p. 130]. In line with this, the present research contributes to the topic receiving more attention and hopefully helps in steering social viewpoints in the right direction. Based on the results, there is no significant difference between middle and top managers, since both of the groups consider the issue of discrimination to be equally important. Since the authors of the research did not find previous research papers that discussed the issue from the perspective of middle and top managers, this research field offers a further opportunity to deal with. The perception of the importance of discrimination contributes significantly whether organizations act carefully during job interviews. If a manager considers discrimination as top issue, the processes around the job interview tends to be transparent and careful. As an obstacle to research, the main problem were the self-administered questionnaires. Since the questionnaire was available online, the respondents were filling in them alone. At the same time, the questionnaire was sent by e-mail and the researchers indicated their personal contacts at the beginning and at the end of the questionnaire, so the potential respondents had opportunity for asking questions in case of something was not clear for them. Furtheremore, as explained earlier, some of the adressed persons explained their negative attitude to the problem. The latter one (negative attitude to the topic of discrimination) can be considered as the main constraint of this study. At the same time, this confirmed the importance of the topic and made it clear that it needs to be addressed in the future. Regarding the future directions, it can be declared that it would be worth to try another research methods. It should help to minimize the level of rejecting participation in the research. The authors also aim to assess the opinion and perspective of

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the employees. The research method should be also crucial in achieving this goal. There is a room for assessing gender differences regarding the topic, as well as a cross-cultural study is also among the future possibilities.

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Performance Evaluation of the IEEE802.11ax Amendment Salem Lepaja

Abstract Since the introduction of the first IEEE 802.11 standard in 1997, WLANs have continued to dominate the wireless communication market. With each new amendment to the standard, data transmission rates have increased, and coverage has improved. IEEE 802.11ax-based WLANs are a promising technology for meeting the demands of high-throughput applications in dense communication environments. This chapter evaluates the performance of IEEE 802.11ax amendment in terms of throughput, considering the impact of channel bandwidth, modulation and coding scheme (MCS), and guard time interval. The performance evaluation is conducted through both simulation and theoretical calculation. The simulation results demonstrate that IEEE 802.11ax significantly improves throughput compared to previous IEEE 802.11 standards, while the theoretical calculation yields even better throughput values than the simulation, as expeted. Keywords IEEE 802.11ax · WLAN performance · Throughput evaluation · Channel bandwidth · Modulation and coding scheme (MCS) · Guard time interval

1 Introduction 1.1 Relevance Wireless local area networks (WLANs) data rates have failed to keep pace with increasing Internet data rates. To address this issue, there has been a swift transition from 2.4 GHz IEEE802.11n WLAN to IEEE802.11ac WLAN, and more recently to IEEE802.11ax WLAN, also known as Wi-Fi 6 and HEW (High-Efficiency Wireless), to match the Internet data rates.

S. Lepaja (B) Faculty of Electrical Engineering and Computing, University of Prishtina, Prishtina, Kosovo e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_9

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IEEE802.11ax based WLANs are in particularly promising technology in meeting the requirements for bandwidth-hungry applications in high-density communication environments such as stadiums, airports, railway stations, campuses, and auditoriums. Several key novelties have been introduced in IEEE802.11ax amendment to achieve high throughput values, including AP supervision of uplink and downlink, flexible-sized resource units, targeted wake time, BSS colour method, MU-MIMO technology in both links, and multiuser OFDMA enhancement [1–8]. This research provides insights into how various parameters impact the performance of IEEE 802.11ax networks. The findings have practical implications for optimizing the settings of these parameters to achieve better performance and efficiency in Wi-Fi networks based on the IEEE 802.11ax standard. Overall, this chapter contributes towards advancing the understanding and optimization of IEEE 802.11ax networks, which can lead to improved performance in real-world communication.

1.2 Goals and Objectives There have been numerous contributions to analysis of IEEE802.11ax amendment from various perspectives. However, only a few studies [5] have explored the impact of three crucial parameters: channel bandwidth, MCS (modulation and coding scheme), and the guard time interval on the physical layer throughput. The primary goal of this chapter is to provide a comprehensive analysis that enhances our understanding of the interaction between the above-mentioned parameters and throughput. Therefore, our objective is to evaluate the performance of IEEE802.11ax amendment in terms of throughput while considering the impact of the channel bandwidth, MCS and the guard time interval. To achieve our objective we carried out performance evaluation by means of simulation, using the NS3 simulator, and theoretical calculation, using a formula. The chapter’s structure is as follows: in Sect. 2, we present a brief overview of the development of the IEEE802.11 standard and its amendments. In Sect. 3, we provide an explanation of the IEEE802.11ax amendment. In Sect. 4 we discuss performance evaluation of IEEE802.11ax, including theoretical throughput calculation and simulation results and analysis. Finally, the chapter concludes with a summary and suggestions for further research.

2 IEEE802.11 Brief Overview In 1997 IEEE (Institute of Electrical and Electronic Engineering) published the first specification for IEEE802.11 standard [9]. Whereas, in 1999 specifications for two new amendments to the original IEEE802.11 standard were published: IEEE802.11a, operating in the 5 GHZ band offering 54 Mbit/s raw data rate, for Enterprise market and IEEE802.11b for consumer market, operating in the 2.4 GHZ band offering

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11 Mbit/s raw data rate. With the development of the new applications user demands for higher data rate WLANs were growing [10, 11]. In response to the demands, in 2003 IEEE released a new standard amendment IEEE802.11 g, operating at 2.4 GHz band and offering 54 Mbit/s. The year 2009 was a breakthrough point for IEEE802.11 amendments development. The IEEE released 802.11n amendment operating in both 2.4 GHz and 5 GHz bands offering 300 Mbit/s using 20 MHz channel and 600 Mbit/s with 40 MHz channel. This was the first standard to compete with the wired Ethernet. Moreover, at this time most of user devices, laptops and smartphones came with wireless adapters build in. As the number of users with more bandwidth hungry applications and sophisticated mobile computing devices such as smartphones, tablets, PDAs, laptops, continued to grow rapidly, the need for a new amendment was obvious. Hence in 2013, IEEE ratified IEEE802.11ac amendment to offer raw data rate of 1.3 Gbit/s in the 5 GHz band. The latest implemented amendment is IEEE802.11ax, ratified on 1.02.2021 [12], designed to meet the requirements in dense user areas, targeting higher data rates than previous amendment. Intensive activity is going on a new IEEE802.11be amendment [13].

3 IEEE802.11ax Amendment The need for higher data rates and improved coverage has led to a massive and uncoordinated deployments of IEEE802.11 based WLAN Access Points in geographically limited areas. This has resulted in increased interference and reduced throughput. To address this problem, a Task Group IEEE802.11ax was created to produce specifications for a new amendment aiming four times increase of the average throughput than previous IEEE802.11ac amendment in the user dense communication environments. The IEEE802.11ax amendment defines modifications to IEEE 802.11 physical layer (PHY) and the medium access control (MAC) sublayer for high efficiency operation in both 2.4 GHz and 5 GHz bands, resulting in more channels. The following key novelties are introduced in IEEE802.11ax amendment to achieve targeted high throughput values: . . . . . . .

MU-MIMO for both downlink and uplink OFDMA and RU (resource units) The new Modulation and Coding Scheme, MCS10 and MCS11 Longer OFDMA symbol duration i.e., narrower subcarrier spacing Spectrum reuse or BSS coloring TWT (Target Wake Time) Backwards compatibility with IEEE802.11a/g/n/ac in both frequency bands: 2.4 GHz and 5 GHz

MU-MIMO Allows for multiple-user data transmission by using different spatial streams (SS), therefore the physical distance between the clients and between the clients and the AP is necessary. Moreover, most present-day enterprise deployments

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of WLANs involve a high density of users, which is not conducive for MU-MIMO implementation. Hence, the 802.11ax does allow combined use of multiuser OFDMA and MU-MIMO at the same time, but this is not expected to be widely implemented. The IEEE802.11ax is designed to support 8xTx and 8xRx antennas and variable channel bandwidths: 20, 40, 80, and 160 MHz. The raw data rate (PHY speed) of 4.8 Gbps is targeted on an 80 MHz channel using 8 × 8 MIMO. However, in practice, we will not see this maximum potential speed. In real world testing, IEEE802.11ax single stream raw data rate has been raised to 1.2 Gbps, 20% faster than connecting via Gigabit Ethernet. Note that what really matters is realistic data rates achieved by Wi-Fi enabled client devices, that exist today. Nevertheless, IEEE802.11ax still aims to offer multiple times the data rates of IEEE802.11ac and will meet better requirements for video with many clients streaming or transferring large files at once. OFDMA and RU The IEEE802.11ax amendment specifies the use of the multiuser technology OFDMA and RU. OFDMA, like OFDM, subdivides channel into several subcarriers. However, different to OFDM, which uses all data subcarriers within a channel for one user, OFDMA organizes subcarriers into resource units (RU) or subchannels and it can allocate a RU to different clients in the same physical channel. In the Fig. 1 [4] it is shown the case of 20 MHz channel with the smallest RU being 26 subcarriers (2 MHz), serving 9 users, and the largest one being 242 subcarriers (20 MHz), serving 1 user. The number of resource units used within a channel as well as their possible combinations is determined by the AP. The access point can allocate the entire channel to a single client (one RU) or it can split the channel to serve several clients simultaneously (several RUs). The same scenario is applied with 40, 80 and 160 MHz channels, as shown in Table 1.

Fig. 1 Resource units in the 20 MHZ channel [4]

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Table 1 Size of resource units and number of clients for different channel bandwidths Size of resource units

Channel 20 MHz

Channel 40 MHz

Channel 80 MHz

Channel 160 MHz

Channel 80 + 80 MHz

996 (2x) subcarriers

n/a

n/a

n/a

1 client

1 client

996 subcarriers

n/a

n/a

1 client

2 clients

2 clients

484 subcarriers

n/a

1 client

2 clients

4 clients

4 clients

242 subcarriers

1 client

2 clients

4 clients

8 clients

8 clients

106 subcarriers

2 clients

4 clients

8 clients

16 clients

16 clients

52 subcarriers

4 clients

8 clients

16 clients

32 clients

32 clients

26 subcarriers

9 clients

18 clients

37 clients

74 clients

74 clients

MCS10 and MCS11 Another novelty of IEEE802.11ax are the two new Modulation and Coding Schemes (MCS10 and MCS11). This is achieved by using QAM-1024 (Quadratic Amplitude Modulation-1024) in combination with 3/4 and 5/6 coding rates. With the QAM-1024 the raw data rate gain over the QAM-256 is 10/8 or twentyfive percent, making IEEE802.11ax the first commercial wireless technology capable of gigabit data rates with a single antenna. However, QAM-1024 modulation can only be used on resource units with 242 subcarriers i.e., the entire 20 MHz transmit channel must be used for a single user. In addition, for IEEE802.11ax radios to be able to use this modulation it is necessary that the signal to noise ratio (SNR) is at least 36 dB [14]. Hence only users closer to AP benefit from QAM-1024. Longer OFDMA Symbol Duration IEEE802.11ax uses a symbol duration of 12.8 µs which is four times longer than the symbol duration in IEEE802.11ac. The four time increase in the duration of OFDM symbols results in reducing the space between subcarriers from 312.5 to 78.125 kHz, which affects the increase in the number of subcarriers to four times more. Furthermore IEEE802.11ax draft amendment proposes three different security time-bands for use in indoor and outdoor communication environments [15]. BSS Color The IEEE802.11ax amendment also defines a so-called BSS color capability for identifying home WLAN network. The BSS color represents a numerical identifier through which IEEE802.11ax devices can distinguish when different BSSs are using the same channel. The goal is to increase the channel reuse by a factor of eight. This is achieved by addressing medium access contention overhead due to an overlapping basic service set (OBSS). Each BSS (i.e., access point) uses a different “color” (6 bits in the signal preamble) to differentiation between access points and their clients on the same channel. Target Wake Time Is another new feature [14] introduced by IEEE802.11ax with the aim to improve wake and sleep efficiency (power savings) on smartphones and other wireless devices. Flexible wake-up time scheduling lets client devices sleep much longer than for example with IEEE802.11ac, and wake up to less contention,

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resulting in better power management for longer battery life of smart phones, IoT, and other devices. Compatibility IEEE802.11ax is carefully designed to be maximally backward compatible with IEEE802.11a/g/n/ac amendments. However, the IEEE802.11ax OFDMA is not backward compatible with any prior version of IEEE 802.11. Hence, IEEE802.11ax clients need to take advantage of IEEE802.11ax router features on the one hand and on the other hand any IEEE802.11ax router will be able to revert to WLAN IEEE802.11ac and WLAN IEEE802.11n to support older devices with no data rate advantage over WLAN IEEE802.11ac. AP Function Another significant novelty that IEEE80211ax brings is that AP supervises uplink and downlink transmission to multiple clients while AP has the control of the transmission medium. Hence, differing from previous standards, clients do not access the medium independently. On the contrary, client access depends solely on the AP’s schedule. High Data Rate Wireless raw data rate is dictated by the following four factors: channel bandwidth, constellation density, number of spatial streams, and per-symbol overhead [3]. Firstly, the impact of the channel bandwidth to data rate is defined with the wellknown Shannon formula: C = W log (1 + S/N). In addition, the IEEE802.11ax uses 980 OFDMA sub-carriers per 13.6 µs (Ts + minimum GI) over 80 MHz channel bandwidth. This increased subcarrier density results in peak throughput gain of 10 percent with respect to the previous standard IEEE802.11ac. Secondly, IEEE802.ax pushes on constellation density from QAM-256 to QAM1024, resulting in an increase of peak data rates by 10/8 = 1.25 times. Being closer together, the constellation points are more sensitive to noise, so QAM-1024 helps most at shorter range. However, QAM-256 is more reliable, but QAM-1024 does not require any more spectrum or more antennas than QAM-256. Hence, it can be implemented easily with existing physical systems. Thirdly, the data rate is directly proportional to the number of spatial streams. More spatial streams require more antennas, RF connectors, and RF chains at transmitter and receiver. The physical separation of antenna should be at least 1/3 wavelength (at 5.25 GHz). This requirement is not a problem for access points design, so it is expected that IEEE802.11ax access points to support up to 8 spatial streams. However, majority of mobile devices have to limit the number of antennas to one, or two. Fourthly, going from a fixed symbol duration (Ts) of 3.2 microseconds (µs) and only two Guard Intervals (GI) of 400 or 800 ns to a longer Ts (12.8 µs) and three guard-interval options (0.8, 1.6, or 3.2 µs) improves the per-symbol overhead, which allows both higher data rate and more reliability.

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4 Performance Evaluation Performance evaluation of the IEEE802.11ax in terms of the throughput, considering the impact of, channel bandwidth, MCS, and the guard time-interval, is carried out by means of simulation, using NS3 simulator, version ns-3.28.1 [15] and theoretically using a formula [4–6].

4.1 Theoretical Throughput We calculated the maximum theoretical throughput using the following formula: CxMTT =

Tsym 1 NFFT · bps · CR Tsym + TGI Tsym

(1)

where CxMTT is the maximum theoretical throughput, x indicates type of modulation and coding (MCS) Tsym Symbol duration TGI guard interval NFFT number of subcarriers bps number of bits per symbol CR coding rate. The modulation type, coding rate and the values of the OFDMA parameters used for calculation of the theoretical throughput in the 5 GHz band are given in Table 2 [4]. The results of the theoretical throughput calculation using formula (1) are depicted graphically in Fig. 2. It is apparent that the wider the channel bandwidth, the higher the throughput. The use of the 40 MHz channel has resulted in a doubling of throughput in comparison to 20 MHz channel, keeping the rest of the parameters the same. The use of the 80 MHz channel resulted in a 109.4% increase in throughput compared to the 40 MHz channel due to the increase of the number of data subcarriers from 468 in the 40 MHz channel to 980 (for 109.4%) in the 80 MHz channel. Whereas for 160 MHz channel the throughput is doubled in comparison to 80 MHz channel due to doubling the number of the data subcarriers. The above results apply for all MCS values supports by IEEE802.11ax. Figure 2 indicates that the guard time interval between symbols also impact the throughput. The shorter the guard interval, the higher the throughput. The guard interval of 800 ns results in the increase of the throughput by 5.9% in comparison to the 1600 ns guard interval. Furthermore, the guard interval of 1600 ns results in the increase of the throughput by 11%, in comparison to 3200 ns guard interval, whereas

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Table 2 Modulation, Coding and OFDMA parameters for throughput calculation Coding

Modulation Type

Bps

BPSK

1

½

QPSK

2

½

16-QAM

4

½

Tsym µs

NFFT 20

40

80

160

234

468

980

1960

12.8

TGI Long µs

Middle µs

Short µs

3.2

1.6

0.8

¾ ¾ 64-QAM

6

½ 2/3 ¾

256-QAM

8

2/3

1024-QAM

10

¾

5/6 5/6

Fig. 2 Theoretical throughput variation for IEEE80.11ax as a function of MCS values

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the use of the 800 ns guard interval results in the higher throughput by 17.7% in comparison to 3200 ns guard interval, for all MCS value and channel bandwidths.

4.2 Simulation As it can be seen from formula (1), theoretical throughput does not account for packet error rate (PER) and efficiency ratio of the throughput [5]. It is well known that throughput decreases due to retransmission of the erroneous packets. The throughput efficiency ratio includes type of transmission protocol, management and control time frames, and different media access control parameters. WLAN module of NS3 simulator offers the possibility to calculate more realistic throughput considering above mentioned factors. Hence, for this goal we used simulations, presented in the following paragraphs. Simulation Environment We used a simple network topology consisting of a single AP and one client. The UDP protocol is used in the transport layer, whereas IP protocol in the network layer. The values of the simulation parameters we experimented with are given in the following table. Simulation Results and Analysis Figure 3 shows throughput values obtained from simulation of the IEEE 802.11ax in 5 GHz band for 20, 40, 80 and 160 MHz channels; for 3.2 µs, 1.6 µs, 0.8 µs guard time intervals; and for all MCS values (0–11) supported by this amendment (Table 3) Impact of Channel Bandwidth As it can be seen from Fig. 3, using the 40 MHz channel with MCS0 has resulted in a doubling of throughput in comparison to 20 MHz channel; using 80 MHz channel resulted in a 109.4% increase in throughput compared to the 40 MHz channel. This increase is a consequence of the increase of data subcarriers from 468 in the 40 MHz channel to 980 (109.4%) in the 80 MHz channel. The 160 MHz channel, regardless of whether it is a contiguous or not, is considered as two 80 MHz + 80 MHz channels i.e., subcarriers between channels cannot be used. Therefore, the number of data subcarriers only doubles, hence the throughput doubles. However, for higher MCS values the throughput does not increase linearly with the number of subcarriers (channel bandwidth). For example, for MCS4 with 80 MHz channel throughput increases for 100% compared to the 40 MHz channel (note that the number of subcarriers is increased for 109.4%); using 160 MHz channel throughput increases for 84.5% compared to the 80 MHz channel. Furthermore, for MCS10, impact on throughput efficiency decrease is even higher. With 40 MHz channel throughput increases for 91% compared to 20 MHz channel (9% lower increase of the throughput compared to increase with MCS4); the use of the 80 MHz channel resulted to 89.5% increase in throughput compared to the 40 MHz channel (10.5% lower throughput

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Fig.3 Simulation throughput for 5 GHz IEEE 802.11ax as a function of MCS

Table 3 Values of the simulation parameters Simulation time

10 s

Mobility model

Constant

Packet size

1472 byte

Standard

IEEE802.11ax (2.4 GHz)

IEEE802.11ax (5 GHz)

Channel width (MHz)

20, 40

20, 40, 80, 160

Guard intervals (ns)

3200, 1600, 800

3200, 1600, 800

MCS

MCS (0–11)

MCS (0–11)

increase compared to MCS 4); using 160 MHz channel throughput increases for 68% compared to the 80 MHz channel (16.5% lower throughput increase compared to MCS4). Hence, the wider is the channel bandwidth and the higher is the MCS value the lower is the throughput efficiency. An explanation for the throughput efficiency decrease is the well-known impact of SINR to packet error rate with the higher modulation schemes. Impact of Guard Time-Interval Figure 3 also shows the impact of the guard intervals (Gi) between symbols on the throughput. One may notice that with the increase of channel bandwidth and MCS values the impact of the shorter Gi intervals to the throughput increase is smaller. For example, for 40 MHz channel and MCS4, with the reduction of the guard time interval

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from 3.2 µs to 0.8 µs throughput increases for 17.5%, whereas for MCS10 throughput increases for 16%. For 80 MHz channel and MCS4, decreasing the guard time interval to 0.8 µs, throughput increases for 16%. whereas for MCS10, throughput increases for 14.5%. For 160 MHZ channel and MCS4, throughput increases for 15%, whereas for MCS10 for 12.4%. Note that, to avoid inter-symbol interference, the guard interval should be two to four times larger than the value of signal propagation delays [6]. Common values for indoors signal propagation delay range from 50 to 100 ns, while the maximum values are up to 200 ns. Therefore, in communication environments where large signal propagation delays are very common there is a risk of throughput drop if guard intervals are not appropriate. From this point of view the selection of the size of the guard intervals is very important for throughput optimization. Comparing Theoretical Throughput with Simulation Throughput Maximum theoretical throughput and simulation throughput for 20 MHz channel in 5 GHz band, for all three guard time intervals and for all supported MCS values, is shown in Fig. 4. Our findings reveal that, in comparison to theoretical throughput, simulation throughput is lower for all MCS values, guard time intervals and channel bandwidths, as expected. We carried out further analyses on impact of the channel bandwidth to the difference between simulation and theoretical throughput. For 40 MHz channel, TGI

Fig. 4 Maximum theoretical throughput and simulation throughput with TGI = 3200 ns, TGI = 1600 ns and TGI = 800 ns, as a function of MCS

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= 0.8 µs and MCS8, simulation throughput is for 12.4% smaller than theoretical throughput, whereas for MCS10 simulation throughput is smaller for 14%. Difference between TGI = 0.8 µs and TGI = 3.2 µs is only 1.2%. For 80 MHz channel, TGI = 0.8, difference between simulation and theoretical throughput increases, 19.5% for MCS8 and 22% for MCS10. Comparing 80 to 40 MHz channel the difference is 8%. Whereas the TGI = 3.2 µs reduces the difference between two throughputs for 2.5%. For 160 MHz channel difference between simulation and theoretical throughput is even higher, 30% for MCS8 and 34.5% for MCS10. Comparing 160 to 80 MHz channel the difference is 14.5%. Whereas the TGI = 3.2 µs reduces the difference between two throughputs for 4.5%. From the above analyses it is obvious that the channel bandwidth has the most significant impact to the difference between simulation throughput and theoretical throughput with a range of values between 10% for 20 MHz channel and 34.5% for 160 MHz channel. The wider the channel the smaller the simulation throughput compared to theoretical throughput (i.e. throughput difference is greater). Impact of the MCS values is 1 to 4.5%, the higher the MCS value, the greater the difference. Whereas impact of TGI is 1 to 3%, the shorter the guard interval, the greater the difference between the two throughputs. One possible explanation for the difference between simulation and theoretical throughput lies in the MAC sublayer implementation, which is not included into the theoretical throughput calculation. Additionally, theoretical throughput does not account for packet error rate (PER), which can result in the retransmission of erroneous packets and lower simulation throughput than the theoretical counterpart. It is obvious that in scenarios with denser subcarriers, more favorable transmission conditions will be required for successful data transmission. Moreover, the higher the value of MCS the higher the probability of retransmissions, leading to a decrease in simulation throughput.

5 Conclusion 5.1 Synopsis Wireless local area networks (WLANs) that are based on the IEEE802.11ax amendment are a highly promising technology in meeting the demands of the bandwidth-hungry applications in communication environment with high user density. These environments include stadiums, airports, railway stations, campuses, and auditoriums, among others. The IEEE802.11ax amendment has introduced several key innovations to achieve high throughput values, including AP supervision of uplink and downlink transmission to multiple clients, flexible-sized resource units, targeted wake time, BSS colour method, MU-MIMO technology in both links, and multiuser OFDMA enhancement.

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The main task of this chapter was the performance evaluation of the IEEE802.11ax amendment in terms of throughput considering the channel bandwidth, MCS value, and the guard time interval. Performance evaluation was carried out through simulation and theoretical calculation. Theoretical throughput values are derived directly from the formula. The wider channel bandwidth leads to linear increase of the throughput. For instance, employing a 40 MHz channel has resulted in twice the throughput of a 20 MHz channel. The use of the 80 MHz channel resulted in a 109.4% increase in throughput compared to the 40 MHz channel because of the increase of the number of data subcarriers for 109.4% in the 80 MHz channel. Whereas for 160 MHz channel the throughput is doubled in comparison to 80 MHz channel due to doubling the number of the data subcarriers. The above results apply for all MCS values. In regarding the guard time intervals between symbols, the shorter the guard intervals are the higher is the throughput. Thus, the guard interval of 800 ns results in the increase of the throughput by 17.7% in comparison to the 3200 ns guard interval, for all MCS value and channel bandwidths. The simulation results show that for MCS0 value, increasing the number of subcarriers (channel bandwidth), throughput increases linearly. However, for higher MCS values the throughput does not increase linearly. For MCS4 with 80 MHz channel bandwidth throughput increases for 100% compared to the 40 MHz channel (number of subcarriers increased for 109.4%); using 160 MHz channel throughput increases for 84.5% compared to the 80 MHz channel. For MCS10, impact on throughput efficiency decrease is even higher. Using 80 MHz channel and MCS10 resulted in 89.5% increase in throughput compared to the 40 MHz channel (10.5% lower throughput efficiency compared to MCS4). Whereas, with 160 MHz channel throughput increases for 68% compared to the 80 MHz channel (16.5% lower throughput increase compared to MCS4). Hence, the wider the channel bandwidth and the higher the MCS value the lower the throughput efficiency. An explanation for the throughput efficiency decrease is the well-known impact of SINR to packet error rate with the higher modulation schemes. It is obvious that with the denser subcarriers it will be necessary to increase conditions of successful transmission. With regarding to the impact of guard intervals on throughput, simulation results indicate that, as channel bandwidth and MCS values increase, the effect of shorter guard intervals on throughput increases is smaller. In the case of a 40 MHz channel and MCS4, reducing the guard time interval from 3.2 to 0.8 µs, resulted in a throughput increases of 17.5%, while for MCS10 and 40 MHz channel the increase of the throughput is 16%. Similarly, for an 80 MHz channel and MCS4, a guard time interval of 0.8 µs led to a throughput increases for 16%, whereas for MCS10 and 80 MHz channel, the throughput increase is 14.5%. Finally, for a 160 MHz channel and MCS4, a reduction in the guard interval led to a 15% increase in throughput, while for MCS10, the increase was 12.4%. Our findings reveal that simulation throughput is lower than theoretical throughput for all MCS values, guard time intervals and channel bandwidths, as expected. However, the channel bandwidth has the most significant impact on this difference, with a range of values between 10% for 20 MHz channel and 34.5% for 160 MHz

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channel. The impact of the MCS values is between 1 and 4.5%, with greater differences observed at higher MCS value. Whereas the impact of guard interval ranges from 1 to 3%, with greater difference observed at shorter guard intervals. Thus, analyses for the impact of the guard interval and MCS values on throughput can be simplified by using only theoretical model. The distinction between simulation and theoretical throughput is closely tied to the implementation of the MAC sublayer and the packet error rate, neither of which are accounted for in the theoretical throughput calculation. Overall, the outcomes of this study can potentially be used as a valuable reference for network designers and operators aiming to enhance the performance of IEEE 802.11ax networks under specific usage scenarios and communication environments.

5.2 Further Research Although, this study provides a detailed analysis of the throughput performance of the IEEE 802.11ax amendment, specifically considering the impact of channel bandwidth, MCS values, and guard time interval, it is necessary to conduct further research and experimentation to include additional parameters and scenarios. Firstly, to validate findings of simulation results and provide avenues for further research, we recommend performance evaluation of IEEE02.11ax through measurements or mathematical models. Secondly, the simulations conducted in this study utilized a simple network topology, consisting of a single access point and one client. Thus, further research should include various scenarios and more clients. Thirdly, the impact of other crucial parameters such as distance of clients and frequency band on throughput should be thoroughly investigated. Finally, it is important to compare the performance evaluation of IEEE 802.11ax to the performance of the IEEE 802.11ac and IEEE 802.11be amendments [16, 17].

References 1. National Instruments: Introduction to 802.11ax High-Efficiency Wireless. http://www.ni.com/ white-paper/53150/en/ Accessed (2018) 2. L, Wand.: IEEE802.11ax Technology introduction, Rohde & Schwarz, White paper (2020) 3. Cisco Public: IEEE 802.11ax: the sixth generation of Wi-Fi technical white paper, Cisco (2020) 4. Bicaj, Sh.: Performance comparison of WLAN IEEE802.ax and IEEE 802.ac, Master Degree thesis, University of Prishtina, Prishtina (2020) 5. Masiukiewicz, A.: Throughput comparison between the new HEW 802.11ax standard and 802.11n/ac standards in selected distance windows. Int. J. Electron. Telecomun. 65(1), 79–84 (2019) 6. Rathor, R.G., Joshi, R.D.: Performance analysis of IEEE802.11ax Wi-Fi 6 technology u sing Multi-user MIMO and Up-Link OFDMA for dense environment. In: International conference AESPC, (2021)

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7. Der-Jiung Deng, et al.: On quality of-service-provisioning in IEEE 802.11ax WLANs, special section on green communications and networking for 5G Wireless, 4 (2016) 8. Sharon, O., Alpet, Y.: Single user MAC level throughput comparision: IEEE 802.11ax vs. IEEE 802.11ac. Wirel. Sens. Netw. (2017) 9. Coleman D, Westcott, D.: Certified wireless network administrator, 5th edn. Sybex (2018) 10. Rasheed, O., Soh, PJ., Jamlos, MF., Ajulo, EB., Eludire, A.A., Enosegbe, DL.: A circularly polarized planar dipole with L-Shaped metamaterial radome for 5.8 GHz WLAN application. Int. J. Wirel. Microw. Technol. (IJWMT) 13(6), 15-22 (2023). https://doi.org/10.5815/ijwmt. 2023.06.02 11. Alfred-Abam, F.E., Gyang, P.P.: Design of the E-Patch dual-band microstrip antenna with low reflections for WLAN application. Int. J. Wireless Microw. Technol. (IJWMT) 3(1), 14–26 (2023). https://doi.org/10.5815/ijwmt.2023.01.02 12. Official IEEE 802.11 Working Group Project Timelines-2022-09-02 http://www.ieee802.org/ 11/Reports/802.11_Timelines.htm 13. Chauhan, S., Sharma, A., Pandey, S., Rao, K.N, Kumar, P.:IEEE 802.11be: a review on Wi-Fi 7 use cases. In: 2021 9th International Conference on reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, pp. 1–7, (2021). https:// doi.org/10.1109/ICRITO51393.2021.9596344 14. Urban, D.: Application of 4K QAM, LDPC and OFDM for Gbps data rates over HFC plant, Comcast (2013) 15. Azizi,Sh., Perahia, E., et al OFDMA Numerology and structure. IEEE 802.11–15/0330r5 (2015) 16. Ali, R., Kim, S., Kim, B., Park, Y.: Design of MAC layer resource allocation schemes for IEEE 802.11ax: future directions. IETE Tech. Rev. (2016) 17. NS-3 Network Simulator NS-3 Model Library, Release ns-3 dev, NS-3 Project (2017). www. nsnam.org Accessed Nov 2017

Virtual Agile Collaboration During a Lockdown: Case Study Monika Pikus and Michal Greguš

Abstract There is a great interest in research and organization of work in virtual teams based on agile principles. There are many publications on the collaboration challenges in distributed teams. In our study, we examined the key factors for the successful transformation of physical, collocated collaboration into purely virtual. Using the method of case study, we described how a selected company using agile principles dealt with the unexpected situation of a forced transition to an online collaboration space during the lockdown caused by the COVID-19 pandemic. We conducted 8 interviews, analyzed the use of the communication application Slack and the results of a satisfaction survey with the current organization of work. We have identified the key success factors for the almost seamless transition from physical agile collaboration to virtual agile collaboration: Maturity of agile, Digital habits and the use of online collaboration tools, Open corporate culture, Strong trust—existing teams. Keywords Agile collaboration · Virtual teams · Online collaboration · COVID-19 impact · Case study · Communication tools

1 Introduction Agile project management has gained popularity due to its ability to adapt to changing customer requirements and altering environments. The core of its principles is based on intensive personal contact of team members. It inevitably includes, for example, daily morning “stand-up” meetings with specific team dynamics. Agile development M. Pikus · M. Greguš (B) Faculty of Management, Comenius University in Bratislava, Odbojárov 10, 82005 Bratislava, Slovakia e-mail: [email protected] M. Pikus e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_10

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methods have close face-to-face communication anchored in their basic principles in Agile Manifesto [1]. In the spring of 2020, the COVID-19 pandemic and related measures forced many people to leave the workplace. Companies and organizations had to reorganize their work almost overnight so that it could be performed without physical proximity as much as possible. Agile management [2, 3] is one of those types of collaboration methods which are strictly tied to tight personal collaboration. The forced transition to the world of purely online cooperation affected agile teams existentially. The principles of the Agile Manifesto of 2001 explicitly state that “The most efficient and effective method of conveying information to and within a development team is face-to-face conversation.” and “Business people and developers must work together daily throughout the project.”. It is this basic requirement of close and personal cooperation that suffers the most from the transfer of all activities to the virtual environment. Is it possible to transfer the essence of an agile way of working to an exclusive online space without suffering not only the efficiency but also the full spirit of this type of cooperation? In this article, we will look for answers to this very question. In the case study we will show how a selected company following agile principles coped with an unexpected and non-standard situation of forced transition to an online collaboration space, how they transformed agile ceremonies and principles into a virtual space, what difficulties and benefits they identified and what recommendations and lessons can be concluded for general organization of agile virtual collaboration. We are aware of the limits of a single case study, so at the beginning of the article we will offer a broader context of the topic of virtual collaboration with an emphasis on research in the field of agile collaboration in distributed teams. This is followed by a description of the methodology used, an introduction of the studied company, and the background and reasons for its historical transformation into an agile organization. In chapter 6, we summarize the results of individual interviews with company members in comparison with internal research of employee satisfaction. We also use the basic usage data of the Slack communication tool as supporting data. We categorize the findings into a clear table of the methodological framework used. The conclusions are presented in chapter 7.

2 Virtual Collaboration The area of virtual collaboration in the world of software development is not an unexplored field for research. Technological progress in recent decades has made it possible to overcome spatial and temporal distance and create the opportunity to work together through information and communication technologies (ICT) across the globe. Especially in the field of software development and support services,

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geographical boundaries have gradually disappeared, and large companies have established their business centers anywhere in the world. This created the concept of virtual collaboration in geographically distributed teams. The concept of team virtuality and its definition have changed over time, we can compare to [4]. Many traditional definitions have been based on the assumption that a virtual team is a grouping that is geographically separated, and its members are not in physical contact, but connected by technological tools, which has led to dichotomous definitions: collocated team vs virtual team. An example of classic definition of virtual teams: “groups of geographically and/or organizationally dispersed coworkers that are assembled using a combination of telecommunications and information technologies to accomplish a variety of critical tasks” [5, 6]. The results of a survey conducted by the consultancy-research institution RW3 CultureWizard from 2016 [7] indicate that up to 85% of workers work in a virtual team, which means that a certain degree of virtuality is found in almost every working group. Consistent with this finding, a multidimensional definition of virtuality began to appear in the definitions of virtual teams. According to this understanding, colocated teams also use modern ICT tools for communication, so it is better to perceive the concept of virtuality in broader dimensions. Studies suggest considering (in addition to traditional factors—geographical dispersion and use of ICT tools) the degree and frequency of the use of ICT tools, the level of formality of the communication and how synchronous the virtual communication is. At one extreme end of the multidimensional spectrum there will be personal, face-to-face cooperation and at the other a pure virtual one without any personal contact [8]. For the purposes of this article (as face-to-face contact was practically banned for team members during a hard lockdown) we will define the term virtual team as follows: A group of people personally knowing each other who are currently collaborating on a joint assignment or project almost exclusively using ICT tools through which they exchange formal and informal information to complete the assigned tasks.

3 Agile Collaboration in Distributed Teams The main topic of this article is the challenges and barriers of virtual cooperation with the requirement to maintain agility. The definitions of agility are derived from the Agile Manifesto of 2001 [1] and generally refer to the ability to quickly adapt to the changing requirements of the environment through the close cooperation of the team on the assigned task without unnecessary processes and documentation, compare with e.g. [9]. In recent decades, attention has also intensified to explore agile collaboration in a purely virtual space. Research has focused mainly on the problems of adapting agile practices in the global software industry (referred to as GSD—Global software development). A recent literature review dealing with the challenges of distributed agile software development distinguishes types of distributed teams into [10]:

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a. one team—distributed members, b. more distributed teams with co-located members, c. more distributed teams with distributed members. It analyzes articles in this area and divides the identified challenges into 5 categories (Control, Collaboration, Cooperation, Coordination, Communication) caused by distance factors as physical, temporal, socio-cultural and knowledge/experience based. This categorization is an extension of the classic concept of Agerfalk et al. [11], originally using 3 process categories Communication, Control, Coordination, and 3 distance factors—temporal, physical and socio-cultural. In a later work Agerfalk even recommends agile practices to bridge distances in the GSD [12]. The study [13], led by M. Paasivara, examines the problems of large-scale agile transformation and its key finding is that despite the urgency of the issue, research lags behind business experience and there are no objective recommendations but rather subjective experiences of practitioners. M. Paasivara also coordinated several empirical studies examining various aspects of agile collaboration in distributed teams. Based on case studies, the article [14] gathers practical advice on implementing Scrum practices and ceremonies in distributed software development, and in a recent large case study on agile transformation of the Ericsson company, recommends choosing an experimental approach when dealing with transformation of large companies, and a clear common guidance and coaching of implemented agile methodologies [15]. An interesting case study [16] has recently emerged from the small business environment (where implementing agile techniques seems easier) [17], describing the agile operation of a smaller company in distributed teams and arguing that appropriate choice of ICT tools and continuous assessment and improvement of used agile practices avoids the known pitfalls of virtual collaboration. The basic support tools for virtual work teams are modern information and communication technologies, enabling mutual cooperation without physical proximity. A wide range of 132 ICT tools used in global software development was mapped for example in [18]. A brief comparative summary of tools to support agile project management is presented in a recent review [19] that was published just before the outbreak of the pandemic, so it does not capture tools that became popular during lockdowns.

4 Research Methodology In this article, we will examine the effects of a forced transition to an exclusively online space within a selected company with agile practice. Due to the exploratory nature of our article, we chose a case study method based primarily on empirical findings: we collected data on the studied subject (company) through interviews with its owners and employees, we also used the results of a company-wide satisfaction survey and available usage data of the communication tool Slack (See

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https://slack.com/). This application can be included in the category of corporate social networks supporting synchronous and asynchronous communication, exchange of knowledge and files, mutual visibility, and feedback. The core business of the researched company (LB*) is the design of user interface (UX) of web-based services. It is based in Bratislava. At the time of the research (January—March 2021), it had 36 employees with an average age of approximately 30 years, consisting of 15 women and 21 men, with operations primarily on the Slovak market. The choice of the company was influenced by several factors: the company operated on the principles of agile work organization well before the forced transition to online, it had a mature agile framework of cooperation (based on “Lean UX”) not only internally but also with its customers. The company’s core competency is not software development (where agile methodologies are more domesticated) but creative activity (design). The main sources of the study’s data are interviews with members of agile teams at the company and its customers, which took place in January to March 2021 in the form of online discussions via Google Meet lasting approximately 1 h each. There were 8 semi-structured interviews with randomly selected employees of the company and 2 interviews with representatives of its customers. We chose the form of semi-structured interviews to cover the topics we were interested in, but at the same time to ensure a certain flexibility of the respondent statements. We also used the relevant part of the traditional year-end in-house satisfaction survey, as well as usage data from the use of the online collaboration tool Slack. We formulated the research question as follows: RQ: How did the agile company manage the transition to exclusive online collaboration? Sub-questions that help find the answer to the main question: RQ1: What advantages / disadvantages of online collaboration have team members encountered in practice and how did they solve them? RQ2: What impact did the forced transformation into the online space have on the performance and cohesion of agile teams? RQ3: What online ceremonies and artifacts have worked well in organizing work? RQ4: What will be the effects of this experience on the future organization of work (when life returns to “normal”?)

4.1 Conceptual Research Framework After a thorough review of the literature, we decided to use the concept of Agerfalk et al. from 2005 [11] as a starting point for research, which breaks down the challenges in distributed software development as follows in Table 1: Temporal distance: Indicates the distance caused by time shifts or different work shifts.

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Table 1 Overview of the theoretical framework of virtual collaboration challenges [11] Process

Dimension Temporal distance

Geographical distance

Socio-cultural distance

Communication Reduced opportunities for synchronous communication, introducing delayed feedback. Improved record of communications

Potential for closer proximity to market, and utilization of remote skilled workers. Increased cost and logistics of holding face to face meetings

Potential for simulating innovation and sharing best practice, but also for misunderstandings

Coordination

With appropriate division of work, coordination needs can be minimized. However, coordination cost typically increases with distance

Increase in size and skills of labor pool can offer more flexible coordination planning. Reduce informal contact can lead to reduce trust and lack of critical task awareness

Potential for learning and access to richer skill set Inconsistency in work practices can impinge on effective coordination, as can reduced cooperation through misunderstandings

Control

Time zone effectiveness can be utilized for gaining efficient 24 × 7 working Management of project artefacts may be subject to delays

Difficult to convey vision and strategy Communication channels often leave an audit trail but can be threatened at key times

Perceived threat training low-cost ‘rivals’ Different perceptions of authority/hierarchy can undermine morale Managers must adapt to local regulations

Geographical distance: Indicates the physical distance between team members (team members are located in different locations without personal contact). Socio-cultural distance: Points to the social and cultural differences of team members (e.g. language, work methods, ethical and moral values, etc.) Control—Coordination—Communication are the three basic processes performed in any team-project cooperation. We are aware that this concept originally reflects the problems of global software development (which is not entirely the case of our company), but the categories are generally accepted in the literature [20] when examining the challenges of virtual team collaboration. LB* de facto became a virtual company after the strict countrywide lockdown. For the needs of our research, we have modified these basic dimensions and processes.

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Based on the proposal of Ghani et.al [10], we expanded the basic processes from three to five: Communication, Control, Cooperation/Collaboration, Coordination. We mainly lacked the aspect of cooperation / collaboration, as autonomous cooperation is among the basic principles of agile teams. Furthermore, we removed the time and socio-cultural distance from the examined dimensions, as in our research the employees were in one time zone and were from one socio-cultural circle. We replaced them with dimensions that revealed certain specific elements of working in a lockdown environment. We were inspired by an empirical study from the Australian research agency PaperGiant (commissioned by the global software manufacturer Atlassian) to examine the impact of the COVID-19 pandemic on the nature of work and collaboration during the lockdown on a sample of 5000 respondents from five countries [21, 22]. From this study, we applied the factors of home environment complexity and the nature of the work/role. As a last dimension, we added the chosen information and communication tools, which in the specific setting of the lockdown had to completely replace the physical contact and so the suitability of their selection affected every process of virtual cooperation. We used this matrix (Table 2) when compiling a semi-structured interview with selected employees of the company.

5 Agile Transformation of the Company The company LB* defines itself as a “partner for people-centered design”. It designs the user interface of services and applications mainly in the online space, offers digital services and consulting, and is an expert in designing user-friendly UX / UI design. Based on research, it also suggests improvements to the overall brand customer experience (CX). Since 2018, the company has undergone an agile transformation process and currently its degree of agility is very mature. According to the company’s management, the driving force behind this rebirth was the frustration from failures of classic planning and project management practices: resource planning and scheduling was inaccurate, people worked on too many projects at once, specifications didn’t work, and project closures often resulted in endless negotiations with customers. They saw the main problem in defining effective communication and cooperation with customers, aligning mutual expectations and feedback. They were looking for a systematic way to improve these areas and the book “Lean UX”[23] came into their hands. Based on this publication, they began to apply agile practices in projects in a learning-by-doing style until it spread to the entire company and all major projects. The company describes the Lean UX methodology, which they follow: https://www.LBstudio.sk/sk/blog/lean-ux-our-recipe-for-agile-design. Since in the following text we will refer to some special terms and expressions of agile rituals from the company’s daily work, we present their basic explanation in Table 3. The

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Table 2 Factors influencing agile virtual cooperation during lockdown (based on [11]) Process

Dimension Physical distance

Nature of work/ Home environment role

Chosen ICT

Communication Lack of informal communication Low spontaneity Needed planning Various communication channels and forms

The need and degree of communication depends on the role (eg the project manager spends most of the day communicating, the developer does not)

In the case of families with children or a larger household, the need to set up more flexible work communication and to plan meetings Disturbing household noises while communicating with colleagues Stretching communication to non-working hours (early morning, late evening)

Chosen ICT tools for day-to-day communication Availability of the other visible through ICT Greater psychological barrier to reach someone via mobile / computer than in person (“what if I interrupt him?”)

Explicit management vs greater autonomy of individuals and teams Adapting people’s leadership style to the new situation More difficult coordination of mutual tasks

Lack of immediate feedback The need for proactive communication about outputs and assigned tasks

The need for household reorganization—higher demands on work time management Home time can flow into one mass, more self-motivation and self-control is needed

Selected ICT tools to coordinate work and outputs

Coordination

(continued)

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Table 2 (continued) Process

Dimension Physical distance

Nature of work/ Home environment role

Chosen ICT

Control

Strengthened control mechanisms vs greater confidence in independence

More difficult visibility of work results productivity demonstration (“labor evidence”) Roles with non-measurable (soft) outputs (people manager, project coordinator) may feel more insecure about what is expected of them

Difficulties in balancing between household care duties and work Support from the management in managing the psychological burden of lockdown

Selected ICT tools to control work and outputs

Cooperation collaboration

More difficult to build trust without personal contact Declining team spirit

Work outputs may be more or less dependent on the outputs of others (some people must meet)

Lack of “office vibe” and a motivating work environment Disruptive home environment for focused collaboration

Selected ICT tools for collaboration / co-development Tools to support instant communication without unnecessary barriers

table is processed on the basis of interviews with employees using an internal blog post of the company. Based on the subjective evaluation of the management, the new approach brought immediate results: communication issues with customers changed radically—it was no longer a project delivery for which “we” or “they” were responsible, but rather a joint teamwork of supplier and customer towards a common goal. One of the customers described the cooperation from this period as follows: “The key to a smooth and trouble-free cooperation was clearly the mediation and communication skills of LB*. If a problem arose on the project, everything was immediately honestly communicated and no culprits or excuses were sought, rather common constructive solutions. LB* did not see the project just as a commercial contract for which they were paid, but focused on the deeper meaning of the work, which had a common goa—to improve the service. A radically open and cohesive atmosphere has been created, where no one has played the hierarchy, and no one tried to get rid of responsibility by “throwing the ball in their court”.

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Table 3 Terminology of agile terms used in the LB* company Explanation Demo

Demonstration of the functional part of the solution / prototype at the end of the sprint

Retrospective

Retrospective and evaluation of the past sprint in order to improve the next sprint

Planning

The first ritual at the beginning of the sprint, the team plans the upcoming sprint: tasks are selected from the backlog, and it is agreed what will be done in the next sprint

Standup

A short morning meeting of the team, where each member reports on what they are working on, what they plan for the day and where they see any obstacles

Sprint

Basic iterative development cycle lasting 1–2 weeks. The result is a functional part of the solution or a functional prototype

Sparring

Joint focused work of the team with the customer on a specific task. It usually has a minimum duration of 2–3 h (more often half a day), where all important people meet and actively solve a task assigned to them

Academy, teambuilding

The Academy is a regular (usually occurring every Friday) training event in which employees pass on knowledge and experience in a selected professional field in an informal spirit. Teambuilding is an informal meeting that also includes structured activities aimed at strengthening

According to the management, this has radically shortened the conceptual and planning phase of projects as well as the time of introducing the product to production. Endless arguments disappeared when handing over the solution, all of a sudden they were all “in the same boat”. Through constant communication and continuous feedback, the solution was born through joint efforts and to mutual satisfaction. Of course, this also meant a radical opening of supplier-customer relationships: instead of a one-time supplier work that has a predefined assignment and output, they needed to be transformed into a partner who continuously improves the customer’s services or services design. The work organization evolved such that the basic organizational units of the company are autonomous, agile teams—Value Teams. Each team is cross-functional, self-sufficient and has all the necessary roles to ensure the entire delivery cycle within its projects. Through the position of Team Lead, the connection of Value Teams with the strategic-operational management of the company (the so-called Production Team) is ensured. Bridge Team brings together leads from competency areas and provides center of excellence and quality assurance to individual Value Teams shown in Fig. 1.

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Fig. 1 Work organization in LB*

6 Discussion 6.1 Virtual Agile Cooperation During the COVID-19 Pandemic After a successful transformation into an agile work organization, LB*, just like the rest of the world, was struck with the COVID-19 pandemic in the spring of 2020. The government of the Slovak Republic responded with restrictive measures, on 12 March 2020 the state of emergency was declared and school facilities, shopping centers, leisure establishments, etc. were gradually closed. In the new situation, LB* also had to introduce the possibility of working from home almost overnight and adapt its work organization accordingly. As agile is about close physical collaboration, there was also concern in LB*—they expected that efficiency, quality of work would suffer and that, overall, close collaboration could not be transferred to virtual space. The reality was different. Each respondent during the interviews replied that the transition to virtual collaboration was smooth, everyone moved into virtual space overnight and the efficiency of work did not suffer, it even increased in many respects. As an explanation, the following facts were most often mentioned during the interviews. Thanks to the established UX Lean methodology, the company had a firm rhythm and rituals of cooperation, both internally and with customers. Personal meetings

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were replaced by virtual ones, but they did not have to introduce new tools or meetings, they were already in place. This subjective opinion is also confirmed by the year-end survey, where several questions concerning the current effectiveness of work with regard to agile rituals received mostly positive responses. Shown in Table 4, where employees could indicate the strength of their attitude to the given statement on a six-point Likert scale, where there was clearly a negative attitude at the left end and a positive one at the right. The numbers show how many people have chosen a given degree of agreement / disagreement. The year-end survey was completed by 33 employees. The company used online cloud tools to support collaboration even before the pandemic outbreak (Table 5), so physical collaboration already had elements of virtuality in it. “We didn’t have to think quickly about what tools to use and how to use them, because we already had certain digital habits”—sounded one of the responses. The use of virtual tools has just simply strengthened. Personal morning standup meetings became virtual and mostly (except the big Monday ones) shrank to simple written statuses on Slack. In this particular company it was sufficient to have morning standups as written text statuses. However, the researcher’s recommendation is to mimic the morning standups as close as possible to personal meetings, i.e. use video calls when all team members are available [14]. Table 4 Responses on agile rituals in year-end survey I consider Monday morning standup as _________

Useless 0 1 1 5 10 16 useful

I consider the demo & retrospective for me as ______

Useless 0 1 0 4 9 19 useful

I consider planning for me as ________

Useless 0 0 1 1 9 22 useful

I think that rituals help me to deliver results

Disagree 0 0 0 7 7 19 agree

Table 5 Tools used in the company Tools used in the company environment Name

Description

Slack

Application supporting synchronous and asynchronous communication, exchange of files and knowledge, mutual visibility, and feedback. It is used for peer-to-peer communication (chat), written group communication (closed and open group channels), file exchange and ad-hoc online calls

Asana

General tool for planning and managing projects, tasks, customer, and product backlog. It has been used in the company for over 10 years

Toggl

A time tracking tool. It is used to monitor the utilization of team members

Google suite

Google Meet is used for video calls among team members and with external customers, Google Drive for file saving and sharing

Figma

Cloud based collaborative design tool. It is the main basic tool for developing and collaborating on design proposals. It is also used with external customers

Miro

Used for virtual workshops, brainstorming or virtual teambuilding activities. It is also utilized as a virtual, always visible notice board in projects

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Employees perceived the biggest problems in online environment during rituals requiring intensive creative cooperation (e.g. Sparring, Workshops, Research)— despite efforts to find tools that most accurately mediate and complement holistic communication. There was a lack of personal contact, nonverbal signs of communication, spontaneous discussions. LB* uses the application Slack for regular online communication and file exchange. This application can be included in the category of corporate social networks supporting synchronous and asynchronous communication, exchange of knowledge and files, mutual visibility and feedback. “Slack can be seen as a large virtual chat room divided into smaller communication channels for group communication. Channels are created around common projects or themes and create smaller group virtual chat rooms. These can be public, to which the whole team has access, or private, to which members will be given access by invitation.” [24]. From the available basic usage statistics of this tool, it was possible to show and compare the intensity of online communication before and after the first COVID-19 lockdown (Figs. 2, and 3). The data source was the export of basic usage statistics of Slack in.csv format for 2019 and 2020. We did not take earlier years into account in the analysis, as we wanted to examine the impact of the COVID-19 pandemic. Further the data was visualized by Microsoft Power BI. The intensity of communication via Slack rose immediately after the lockdown date. The graphs clearly show the loosening of lockdown measures in the months of July–September 2020, when the intensity of chat use via Slack dropped back to the level before the lockdown (people were allowed to come back to the offices). Employees stated certain immediate benefits of working online: the time required for meetings was significantly reduced, travel to physical meetings was eliminated, small-talks, i.e. informal interactions before the actual work discussion, were reduced. People tried to be as effective as possible in online meetings and not to waste time. This trend is in line with the results of the DeFilippis study, which examined people’s digital communication patterns during lockdowns and found that people spent less time in meetings and their average length decreased [25]. The company tried to move various socialization activities to the online: they organized informal meetings, games, and even teambuildings online. A brief summary

Fig. 2 Number of messages sent via Slack on a monthly basis

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Fig. 3 Number of files uploaded via Slack on a monthly basis. The dashed line indicates the average

of the company’s online way of work is to be found on its blog: https://www.LBs tudio.sk/blog/keeping-team-sanity-in-the-times-of-remote-work. The need to create a virtual platform for informal meetings is also confirmed by research [26], as virtual communication is by its nature much more formal, planned and focused on workrelated topics. In this empirical research on the use of time by developers, D.E.Perry concluded that “One of the most significant observation impressions was the huge amount of time each developer spent on informal communication.“ The developers in the study spent an average of 75 min each day on “unplanned interpersonal interaction.”. So, it is true that the less communication is personal, and the more interaction is formal, the greater the level of misunderstanding and tension, as was already mentioned in [20]. This can be most effectively prevented by looking for opportunities for informal interactions through virtual channels as well, which is confirmed by the case of LB*. An interesting proof of finding a way for informal communication into virtual space is usage data from Slack shown in Fig. 4. The distribution of messages between different types of channels changed significantly in 2020. A large increase in communication on the so-called private channels can be seen. As the volume of direct messages increased in 2020 by 37,21% the number of messages sent in private channels increased by 343,28%! The explanation may be that due to the loss of physical proximity, groups of colleagues had to find an alternative channel through which they could communicate more personal topics inside their groups and lead non-working, informal discussions. Many respondents mentioned a corporate culture based on trust, radical openness, and a high degree of team autonomy as an important element of the seamless transformation. As one respondent said, “it’s not so much about whether we work together offline or online, but how close our relationships are.” Even according to relevant research, trust is an absolutely key element of every aspect of cooperation [27]. It is the invisible glue of every team that encourages open communication, mutual support and drives the team forward to common goals. Trust is not easy to build and maintain at a distance without personal contact, as it is acquired through informal personal interactions, spontaneous non-work conversations and frequent close-up meetings, which are difficult to conduct in the virtual world [20].

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Fig. 4 Distribution of messages among different Slack channels. Comparison of 2019 and 2020

As mentioned above, LB* has also intuitively sought ways to move its social, informal activities into the online space from the beginning of the forced transition, which may explain how they have managed to maintain trust and corporate culture in a virtual environment. Neither managers nor employees succumbed to the temptation to strengthen explicit management—the opposite has happened; the autonomy of teams has been strengthened. This phenomenon seems to contradict the general recommendations for managing virtual teams where the need for strong management and effective leadership is usually emphasized in compare with the recommendations of the consulting company Deloitte [28] or with research [29]. It contradict but it fully fits into the agile thinking that highlights and supports such autonomy and self-organization of teams. In the words of J. Sutherland, author of Scrum: “Just as critical (…) is the freedom to do your job in the way that you think best—to have autonomy. On all great teams, it’s left to the members to decide how to carry out the goals.” [30]. One respondent even formulated this observation saying: “my team is my company”. However, over time, this resulted in the autonomous teams becoming too focused on themselves and beginning to lose touch with other groups and events in the company. This challenge was often mentioned as a disadvantage of a purely online operation—employees began to feel that mutual visibility and awareness is being lost: “I don’t even know what LB* is doing as a company”, mutual information was exchanged almost exclusively on Monday morning company-wide stand-ups and throughout the rest of the week people were in contact almost exclusively only with their teams. Already at the time of field research, the management was aware of this problem and was looking for ways to reconnect people with internal company activities (mainly through Academies and online Teambuildings). When life returns to “normal”, the new standard way of working will look different for LB* as well. They all assume a more flexible setting of which activities and meetings will remain in the virtual space and which are the ones that will require a personal presence at the workplace or with customers. Many would for instance welcome the preservation of the home office institute for more personal, focused work. It is not a surprising finding that there is a great desire at LB* to return to in-person social activities and rituals that work better in face-to-face contact (Brainstorming,

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Table 6 Attitudes of employees to agile rituals once the COVID-19 crises is over from year-end survey After a vaccine is available to me or COVID-19 crisis ends I would Online 2 3 1 11 6 10 offline rather spend my day working __________ Socialization and camaraderie

Online 0 0 1 1 7 24 offline

Brainstorming and workshops

Online 2 2 2 6 5 16 offline

Sparring

Online 4 3 7 9 3 7 offline

Planning

Online 6 5 5 6 5 6 offline

Demo & Retro

Online 10 0 4 10 4 5 offline

Monday morning standup

Online 11 7 5 2 1 7 offline

Workshops), but there seems to be no strong preference to return basic agile rituals (Demo, Retro, Standups) back to the physical world in Table 6. Concerning Sparring and Planning, the answers are neutral (by Sparring, a slight predominance of offline). A summary of the perceived positive and negative aspects of cooperation in the forced virtual mode is presented in Table 7. The sign (+) indicates a positive phenomenon, the sign (−) a negative one. Table 7 Positive (+) and negative (−) experiences mentioned by employees during forced virtual cooperation Process

Dimension Physical distance Nature of work/ role

Home environment

Chosen ICT

Communication

(+) the company has already worked with online tools and had set up communication processes—easy transition to a virtual environment

(+) set processes and roles even before the lockdown—only the online communication channel has changed and strengthened

(−) in the case of families with children in a small space, more difficult reconciliation of family and working time, which merged into one mass during the lockdown

(+) Slack and Google Meets for communication used before the lockdown—a smooth transition (−) annoying technical issues while using online tools

Coordination

(+) enhanced team autonomy (−) lack of physical contact (rather mentally, work efficiency did not suffer)

(−) more difficult strategic meetings and decisions

(−) stretching working hours (working early in the morning or late in the evening so that parents can take care of the children during the day)

(+) Slack used for communication before the lockdown—smooth transition

(continued)

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Table 7 (continued) Process

Dimension Physical distance Nature of work/ role

Home environment

Chosen ICT

Control

(+) greater confidence in the independence of individuals and teams

(+) everyone knows what is expected of him, what his role is in the team (+) corporate culture of radical openness and trust

(−) work mode and boundaries settings required (−) at the beginning, there was a tendency to work harder and more, later it normalized

(+) Asana for timeand backlog tracking used before the lockdown—a smooth transition

Cooperation collaboration

(−) loss of an overview of what is happening in the company (−) decline of team spirit (−) lack of coffee / beer / lunch, informal chats, informal face-to-face meetings, intensive personal sprints

(−) it is more difficult to do research, creative work in a clean online environment (−) it is more difficult to verify concepts and designs

(−) lack of social life with colleagues to disengage from the home environment (+) more time for self-focused work, less distraction compared to the workplace (in low density households)

(+) Figma for collaborative design used before the lockdown—a smooth transition (+) Miro—a tool for brainstorming and workshops implemented

7 Conclusion Following the outbreak of the COVID-19 pandemic, measures were taken that forced companies to change the rules regarding work from home and move employee collaboration to a virtual space. The organization of LB*’s work is based on agile principles emphasizing the close physical cooperation of the teams, so there were concerns about how to handle the forced transition to an exclusively online space. However, the company’s initial concerns and the general expectation that cooperation and the quality of outputs would suffer as a result of the transfer did not come true at all. Agile collaboration seemed to be intuitively transferred to the online space, but some changes or choices of ICT tools had to be well thought of, and continuously evaluated and modified. The key factors that have contributed to the rather smooth transition to the virtual space are the following: Maturity of agility. The agile approach within internal cooperation as well as towards external stakeholders have had a very strong base long before the lockdown. Everybody knew and understood agile ceremonies and roles, had a regular rhythm of work and made sure that this rhythm was not significantly disrupted by the lockdown.

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As the company itself writes on its blog: “There is a reason why agile companies incorporate rituals into their way of working. Security and regularity keep people motivated and attuned to a common goal… Each team should follow a regime that is as similar as possible to its normal regime. The fact that you are not physically in the same room should have no effect on that. It’s business as usual, with a little extra fun.“ [31]. Digital habits and the use of online collaboration tools. Most cloud collaborative tools were in use before the lockdown and presented a natural part of everyday work. After moving to a purely virtual space, the company did not have to search for new ones, only the use of existing ones intensified. If the company was missing a tool for some activity (e.g. teambuilding, brainstorming), it was not afraid to try new ones, constantly evaluating their suitability. This is in line with Berczuk’s recommendation: “agile is about people, but distributed agile requires good tools to help people communicate effectively over distances” [32]. Open corporate culture. The company believes in radically open relationships. In line with the spirit of the agile manifesto, “the team evaluates itself at regular intervals in order to be more effective and adapts its behavior accordingly” [1]. Team members have regular retrospectives looking for the strengths and weaknesses of a past sprint, analyzing them, and constantly proposing new and new practices. This is only possible if everyone involved is open and honest with each other. The principles of open corporate culture are explicitly named in the basic corporate values: Growth, Reason, Empathy, Authenticity, Transparency. Every year, the company evaluates how they manage to translate these principles into real everyday company life. Existing teams—built trust. LB* employees knew each other well and in person, the teams already existed before the lockdown. The main task was to strengthen this trust and team spirit in a virtual environment. Teams introduced virtual morning “stand-up coffees”, met many times at a virtual lunch or organized various online social activities (online games, team building, virtual celebrations, etc.) Based on this research, we can state that the lived agility and the associated open corporate culture were the enablers of easy and almost painless transfer to online space. Agility (is above all) a mindset, as one respondent put it: “Agility is a willingness to accept change. For us, the forced online work was just another change, which we had to adapt to.” We are aware that no general conclusions and recommendations can be drawn from one case study, but we are convinced that such analyzes can contribute to a better understanding of the success of agile companies. We would like to emphasize that these results describe the atmosphere within the company at a certain point of time (January—March 2021). Attitudes and perceived challenges might have changed since and should be the subject of further research.

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Flex-Route Transit: Problem Definition, Case Studies and Development over Years in Optimization Literature Reza Shahin

Abstract Transport regulatory bodies are actively exploring strategies to minimize expenditures while maintaining a high standard of service for their customers. One innovative transportation method gaining prominence is Flex-Route Transit, which integrates impromptu stops into public transportation systems, ensuring personalized pick-up and drop-off services. This research delves into a comprehensive analysis of the problem at hand, examining existing operational examples. Additionally, we serve the foundation for proposing potential avenues for future research. Keywords Flex-route transit · Vehicle routing problem · Literature review · Optimization

1 Introduction Faced with environmental concerns, increasing needs for mobility, and traffic congestion, transportation authorities are actively working to reduce the use of private vehicles [12]. Currently, these organizations are investigating ways to enhance the flexibility and financial sustainability of public transportation systems. In modern contexts, Conventional Public Transport (CPT) operates based on fixed schedules or frequencies, using shuttles for passenger boarding and alighting at predetermined stops. On the other hand, Demand Responsive Transit (DRT) systems provide highly flexible transportation options to users [5]. Users of these systems can request ad hoc stops, selecting specific pick-up and drop-off points, along with preferred timings for these stops. A prime example of DRT is the Dial-a-Ride Problem (DARP) transport model [4]. It’s clear that the operational cost of TPT is lower than that of DRT [5, 9]. However, users often find TPT less accommodating due to three main reasons: R. Shahin (B) COSYS-ESTAS, Universite Gustave Eiffel, F-59650 Villeneuve d’Ascq, France IFSTTAR, Universite de Lille, F-59650 Villeneuve d’Ascq, France e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoˇn et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_11

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1. The fixed boarding and alighting locations don’t always meet their individual needs; 2. The rigidity of the service schedule; 3. The total travel time often surpasses what is possible with personal vehicles. While DRT systems can address these issues, their implementation comes with additional expenses. The need to balance financial costs with service flexibility has led to the development of Demand-Adaptive Systems (DAS) [3], and within this, the introduction of Flex-Route Transit (FRT). FRT combines the adaptability of DRT systems with the cost-effectiveness of TPT. In this mixed model, shuttles typically follow set routes and schedules similar to TPT. However, they have the ability to deviate from these routes to accommodate ad hoc stops, serving passengers at their specified locations within a limited service area. This is made possible by utilizing the buffer times at fixed stops.

1.1 Applications Advancements in Flexible Route Transit (FRT) models have evolved from conceptual frameworks to tangible implementations, albeit in more elementary and constrained versions. For example, in the early 2000s, Los Angeles County’s Metropolitan Transit Authority (MTA) adopted the FRT paradigm for one of its bus services [7]. This service maintained a fixed itinerary and timetable during daytime operations. Conversely, at night, while adhering to established stops, it permitted passengers to request additional stops within a vicinity of half a mile from the standard path. This approach was viable owing to the reduced number of passengers during night hours, facilitating efficient route modifications by drivers. Nevertheless, as the demand for route diversions increased, the necessity for an optimization-driven support system became imperative. Academic inquiries have extensively utilized real-life examples for research, drawing on either existing bus routes with their specific demand matrices and infrastructure, or on operational FRT frameworks, some of which may now be defunct. A case in point is MTA’s feeder-line 646 in Los Angeles County, USA, which functioned as an FRT model in the early 2000s. This system was characterized by a rectangular service zone, extending 12 miles in length and half a mile in breadth. The predefined route incorporated three checkpoints, including two end points and a central checkpoint, operating for 4.5 h nightly. A solitary shuttle completed nine circuits each night, accommodating approximately 4–5 passengers per hour. Diverse studies have tweaked parameters from this empirical instance for sensitivity assessment, examining the resultant impacts on system efficacy. Alterations included modifications to the service area’s dimensions (6–16 miles in length and 0.5–2 miles in width), and variations in the number of shuttle journeys (ranging from 1 to 6). Some analyses also contemplated the effects of integrating a second shuttle. The passenger makeup typically comprises 40.

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Subsequent research initiatives have employed data from traditional bus routes to conduct comparative analyses between Conventional Public Transport (CPT) and FRT systems. Numerous studies adopt a rectangular service region akin to the Los Angeles example, although the dimensions are tailored to each specific case. For instance, Sun and Liu [13] analyzed a service area measuring 27 km by 1 km. Certain studies have concentrated on pre-existing routes from conventional bus services that do not conform to a linear layout, instead using a simplified rectangular format to encapsulate the route. The number of checkpoints in these analyses varies significantly, ranging from 3 to as many as 21, depending on the study. These investigations often select a portion of the CPT stops as checkpoints, accommodating the operational limitations inherent in FRT systems. The customer composition often reflects that of the Los Angeles scenario, with minor discrepancies in some cases. While feeder-line 646 in Los Angeles stands as a prominent case study, there are multiple instances of route-deviation services implemented globally over the past quarter-century. The technical report by [6] enumerates several such systems across the United States, catering to varied population densities and operating distinct numbers of route-deviation lines. The permitted deviation “half-width” varies among these services, and passengers typically need to pre-arrange stops beyond the designated checkpoints. Technological progress has significantly refined these systems, exemplified by the introduction of mobile applications for reservations, such as the Late Night Bus service in Belleville, USA, which enables passengers to conveniently arrange transport between any two bus stations within the city’s boundaries [14]. These advancements have markedly augmented operational efficiency, as well as enhanced user accessibility and convenience. The structure of this study is as follows: We begin by defining the problem at hand in Sect. 2. Next, we provide an overview of the problem classification in Sect. 3. We then highlight and detail the current research gaps in Sect. 4. Finally, we offer a summary and conclusion in Sect. 5.

2 Problem Description A FRT system is predicated on a foundational structure, referred to herein as the base route, which bears a notable resemblance to conventional bus transportation systems. This base route comprises an origin and a destination terminal, serving as the extremities between which shuttles execute reciprocating journeys. Intermediary to these terminal points lie pre-established locations denominated as checkpoints. The shuttles adhere to a predetermined timetable for departures from these checkpoints. The temporal span required for a complete journey between terminals is termed the service time interval, while the aggregate of these intervals over multiple trips constitutes the time horizon. Within this framework, shuttles are assumed to traverse at a uniform speed. However, diverging from standard bus systems, the timetable for FRT is ingeniously structured to incorporate supplementary slack time-time intervals over and

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Fig. 1 Schematic of a FRT system, illustrating the fixed route and service area

above the transit duration between successive checkpoints. This slack time empowers shuttles to momentarily digress from the base route in order to accommodate ondemand passenger requests at their preferred locations, granted that such deviations do not transgress the checkpoint timetable constraints. The concept of longitudinal speed is introduced to quantify the mean speed sustained between the terminals. In the absence of any route deviations, this speed is tantamount to the shuttle’s constant speed; however, it diminishes proportionally with the distance covered for ad hoc stops. Customers have the option to utilize checkpoints for fixed-route journeys, or they can opt for ad hoc stops within the predefined service area. A diagrammatic representation of this system is delineated in Fig. 1. Furthermore, the FRT system is designed to cater to a diverse customer base, characterized by varying pick-up and drop-off preferences [8]: 1. PD (Regular): Both pick-up and drop-off occur at checkpoints. 2. PND (Hybrid): Pick-up is facilitated at a checkpoint, whereas drop-off is executed at an ad hoc location. 3. NPD (Hybrid): Pick-up takes place at an ad hoc location, while drop-off is at a checkpoint. 4. NPND (Random): Both pick-up and drop-off are facilitated at ad hoc locations. This multi-faceted approach ensures a greater degree of adaptability and customer convenience, thereby rendering FRT systems a compelling alternative to traditional public transport modalities. In optimizing the FRT problem, the objectives typically center on a composite aim: to minimize (1) the aggregate travel time incurred by the shuttle(s); (2) the total ride time experienced by the customers; and (3) the cumulative waiting time for customer pick-ups. FRT operations can be delineated within either a static or a dynamic environment. The static framework presupposes that all customer requests are known a priori, before the onset of the service. Conversely, in a dynamic setup, customer requests can be introduced spontaneously during the service period, dynamically altering the shuttle schedule. In this latter context, the strategy of backtracking could be employed, where shuttles may revisit locations they have already passed in order to accommodate new ad hoc stops. This strategy often has a spatial limitation, referred

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Fig. 2 Diagrammatic elucidation of dynamic scheduling in FRT without backtracking

to herein as the backtracking threshold, which designates the maximum distance a shuttle is allowed to backtrack. Figure 2 serves as an illustrative example of a shuttle’s decision-making process in a dynamic FRT environment wherein backtracking is disallowed. The representation focuses on the journey segment from the origin to the first checkpoint, as per Fig. 1. Following standard assumptions in the literature, the shuttle is restricted to horizontal and vertical movements within the service area. Here, requests.a and.b are made prior to the shuttle’s departure. During the shuttle’s journey to serve request.a, an additional request, .c, is initiated. Owing to the constraint against backtracking, the shuttle continues towards .a, forgoing request .c, which lies on its longitudinal trajectory. Subsequently, when request .d emerges immediately after the shuttle has served .a, the shuttle opts to modify its planned route. Rather than proceeding directly to serve request .b-as would be indicated by the dashed trajectory-the shuttle follows the solid line to accommodate the newly issued request .d. This adaptability in a dynamic environment, albeit within defined limitations, underscores the FRT system’s capability to balance efficiency and customer service. Nevertheless, the constraints against backtracking warrant further investigation, particularly to understand their implications for service quality and operational efficiency.

2.1 Graph Although the FRT problem may bear a superficial resemblance to the (multi-trip) Vehicle Routing Problem (VRP)-especially in terms of the mathematical programming models employed for their resolution-the distinct characteristics of FRT necessitate the consideration of a highly specialized, non-trivial transport graph. Intriguingly, some operational constraints that are pivotal to the problem formulation are inherently encoded within the architecture of this graph, rather than being explic-

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itly stipulated within the constraints of the mathematical programming models commonly referenced in scholarly literature. An example of such an embedded constraint is the prohibition against backtracking. To elucidate this matter, we propose an examination centered on the construction of the transport graph, employing a simplified FRT instance for illustrative purposes. This discussion aims to shed light on the complexities that set FRT apart from classical VRP problems, and to clarify how these unique attributes are integrated into the transport graph. Moreover, this exploration aspires to provide an augmented understanding that would be instrumental for future research and practical applications in this domain. By addressing these intricacies head-on, we intend to enrich the existing body of knowledge, offering new perspectives that could facilitate the development of more nuanced and effective optimization models for FRT systems. This, in turn, has the potential to drive innovations in the deployment of FRT systems, enhancing both their efficiency and adaptability to serve diverse transport needs.

2.2 Graph for PND-NPD Customers In the scenario described, a single FRT system with a no-backtracking policy is considered, where one shuttle executes two round-trip journeys. The base route comprises three discrete physical checkpoints: the base depot (. D), an intermediate depot (. I )-at which point the shuttle reverses direction-and an additional intermediate checkpoint. Given that the shuttle completes multiple round-trips, it will halt at these physical checkpoints multiple times. Consequently, in the corresponding transport graph, these physical checkpoints are represented as multiple nodes. These nodes, depicted in green in Fig. 3, total up to 10 checkpoint stops across the two round-trips (indexed as .r = 1, 2). It is noteworthy that nodes 2, 4, 6, and 8 in the graph represent the same physical checkpoint. Additionally, there is one non-checkpoint stop, represented as node number 11 and highlighted in red. The shuttle’s no-backtracking policy, along with its mandate to continuously move forward along the longitudinal axis, restricts the feasible arcs within the graph. Given the nature of the demand linked to node 11, this node can only be serviced between the starting depot. D and the intermediate checkpoint. As a result, permissible arcs in the graph are limited to the following: Arcs connecting two adjacent checkpoint stops, which are obligatory due to the fixed timetable. Arcs between nodes 1 and 11 as well as 11 and 2. These arcs would be applicable if the non-checkpoint stop is integrated into the first round-trip (.r = 1). Arcs between nodes 5 and 11 as well as 11 and 6. These arcs would be applicable if the non-checkpoint stop is incorporated into the second round-trip (.r = 2). This level of specificity in the transport graph underscores the complexities involved in optimizing an FRT system. The unique structural nuances of the graph encapsulate crucial operational constraints that are not explicitly present in the mathematical programming formulations commonly employed. Thus, understanding the construction of the transport graph is pivotal not only for elucidating the challenges associated

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Fig. 3 Example of an FRT graph for a simple instance

with FRT optimization but also for crafting reliable decision-support systems for its practical application.

2.3 Graph for NPND Customers In the FRT system, the treatment of NPND (pick-up and drop-off not at the checkpoints) customer requests introduces additional layers of complexity to the transport graph. This complexity manifests in two general scenarios for NPND customers: First Scenario: At least one checkpoint node exists between the pick-up and drop-off nodes. An illustrative example is provided in Fig. 4, where nodes 11 and 12 serve as the pick-up and drop-off points for an NPND customer, respectively. While the permissible arcs for node 11 remain consistent with previous examples, the arc structure for node 12 requires careful consideration. Specifically, arcs may exist from nodes 2 and 6 to node 12, and from node 12 to nodes 3 and 7. Importantly, a direct arc from node 11 to node 12 is not permitted as it would breach one of the core operational principles of the FRT system-namely, that no checkpoint node can be bypassed. Consequently, should an NPND customer be served during the first round-trip, the shuttle would need to visit node 2 before proceeding to node 12. Second Scenario: The pick-up and drop-off nodes are situated between two consecutive checkpoints. As depicted in Fig. 5, this situation requires a more nuanced approach to the construction of the transport graph. In this specific case, a direct arc from node 11 to node 12 is allowed, thereby diverging from the constraints applicable to the first scenario. The remainder of the arcs would be consistent with those in the previous example.

Fig. 4 Example of an FRT graph for a simple instance

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Fig. 5 Example of an FRT graph for a simple instance

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This bifurcated approach to dealing with NPND customers accentuates the intricacies involved in modeling and optimizing an FRT system. The addition of these more complicated customer scenarios imposes a higher level of granularity on the transport graph, influencing the mathematical formulations and algorithms employed for optimization. Each case demands distinct sets of constraints and permissible arcs, thereby enriching the problem space and dictating the need for tailored optimization approaches.

2.4 Graph for All Customers In an illustrative example featuring a diverse customer base, comprising one PD (Pick-up and Drop-off at Checkpoints), one PND (Pick-up at Checkpoint, Drop-off Not at Checkpoint), one NPD (Pick-up Not at Checkpoint, Drop-off at Checkpoint), and two NPND (Pick-up and Drop-off Not at Checkpoints) customers, the structure of the transport graph becomes significantly more nuanced. The example is visually represented in Fig. 6, where a subset of arcs is shown for clarity. PD Customer: If a PD customer wishes to be picked up at node 4 and dropped off at node 5, no additional arcs are needed in the transport graph; the pre-existing arcs between these checkpoint nodes suffice. PND Customer: For a PND customer, let us assume a pick-up at node 1 and a dropoff at node 11. Corresponding arcs would include one from node 1 to 11 and another from node 11 to 2. If the customer is to be served during the second trip, arcs from node 5 to 11 and from node 11 to 6 should also be included. NPD Customer: Assuming the customer wants to be picked up at node 12 and dropped off at node 4, the relevant arcs would be from node 2 to 12 and from node 12 to 3. Note that a direct arc from node 12 to 4 is not allowed, adhering to the fundamental rules of the FRT system. For the second trip, arcs from node 6 to 12 and from node 12 to 7 are also necessary. Complexity with Additional Customers: Introducing another customer to be picked up or dropped off between node 12 and node 3 adds another layer of complexity to the arc structure. Let us call this hypothetical customer’s node .h h. Additional arcs would be needed: from node 12 to .h, from .h to 3, from 6 to .h and from .h to 7. This rich example illuminates the intricacies involved in constructing the transport graph for a diverse set of customer types in an FRT system. As more customer types and scenarios are considered, the complexity increases exponentially, affecting not only the graph’s structure but also the corresponding optimization models. The variety in customer types results in multiple cases that each require unique considerations for the permissible arcs in the transport graph, underlining the necessity for specialized optimization methodologies tailored to address this multifaceted problem.

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Fig. 6 Example of an FRT graph for a simple instance

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The introduction of the “blue customer,” who desires to be picked up at node 13 and dropped off at node 15, adds another layer of complexity to the transport graph, particularly in the configuration of permissible arcs. The scenario you presented underscores the need for a meticulous representation of arc choices to correctly model the FRT system’s constraints and opportunities. First Trip Arcs for Blue Customer: The arcs in the first trip that are specific to this customer include the following: From node 3 to 13 for pick-up; From node 13 to 14, an auxiliary arc; From node 13 to 15 for drop-off; From node 14 to 15, another auxiliary arc; From node 15 to 4, to continue the regular route; From node 4 to 16, an auxiliary arc; From node 16 to 5, to complete the route. Second Trip Arcs for Blue Customer: For the second trip, the relevant arcs encompass: From node 7 to 13 for pick-up; From node 7 to 14, another auxiliary arc; From node 14 to 8, to continue the regular route; From node 15 to 8, another option to continue the regular route; From node 8 to 16, an auxiliary arc; From node 16 to 10, to complete the route. Incorporating these arcs into the transport graph accommodates the specific needs of the blue customer without violating the foundational principles of the FRT system. The inclusion of auxiliary arcs is particularly notable; they allow for greater flexibility in servicing the customer while adhering to the no-backtracking policy and other operational constraints. It should be observed that as each new customer type with unique requirements is added, the transport graph’s structure becomes increasingly intricate, necessitating more refined and comprehensive optimization techniques. This further accentuates the need for specialized algorithms or Mixed-Integer Linear Programming (MILP) models capable of addressing such complexities efficiently. Observations shed light on important logistical considerations that need to be accounted for in modeling a FRT system. Specifically, there are temporal and operational constraints that must be integrated into the structure of the transport graph, which include: Temporal Boundaries: Customers issuing requests from the second trip onward should not have arcs built from nodes associated with previous trips. This ensures that the optimization model respects the temporal limits and sequencing of the FRT system. Terminal Trip Constraints: As rightly pointed out, the last trip presents a unique constraint: the inability to push customers to subsequent trips due to the absence of such trips. This implies that capacity considerations become paramount. The system could either ensure sufficient capacity in the last trip to accommodate these requests or allow for the possibility of rejecting customers, especially when the system is approaching saturation. Complexity Scaling: The relatively straightforward example provided with just five customers and a single shuttle over two trips underscores the complexity even a simple instance can introduce. When the system scales to include more customers, multiple shuttles, and additional trips, the complexity will magnify exponentially. This accentuates the need for advanced optimization methodologies that can effec-

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tively deal with such intricate scenarios. Timetable Design: The timetable design should be sufficiently flexible to accommodate unforeseen demand or fluctuating service requirements while adhering to operational constraints like no-backtracking or maximum allowed travel time. To handle the rising complexity, particularly when scaling up the system, specialized optimization algorithms or advanced MILP models might be necessary. These can more effectively manage the multiple constraints and variables involved in complex FRT systems. In light of these considerations, meticulous planning and rigorous model validation become critical to ensuring that the system functions both efficiently and in alignment with logistical and operational requirements.

3 Problem Classification 3.1 Taxonomy Classification elucidates the key dimensions along which FRT systems can vary, specifically in relation to the timing of decision-making and the certainty of the information involved. This taxonomy-comprising static-deterministic, static-stochastic, dynamic-deterministic, and dynamic-stochastic categories-offers a useful framework for both academic research and practical applications. Static-Deterministic: In this scenario, a fixed plan is developed based on known information, and no deviations are permitted after operations begin. This model is useful in environments where the factors affecting the FRT are constant or can be accurately predicted. However, it might be less suitable for scenarios where there is high variability in demand or other operational parameters. Static-Stochastic: In this version, planners develop a fixed set of routes and schedules based on probabilistic information. While the plan itself is static and cannot be changed, its development accounts for a range of possible scenarios. Techniques like scenario analysis or robust optimization might be employed to handle the uncertainties involved. Dynamic-Deterministic: Here, the decision maker has the latitude to adjust the plan as new information is acquired. Even though the data may be completely known at each decision point, its evolution over time allows for on-the-fly adjustments. This flexibility can be particularly beneficial when the system experiences unforeseen changes in demand or operational constraints. Dynamic-Stochastic: In the most complex category, not only does information evolve over time, but there is also uncertainty associated with the newly arriving

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information. Here, real-time analytics and stochastic optimization techniques would be of great utility in dynamically adapting the system’s operations to evolving, uncertain conditions. By partitioning FRT systems into these four categories, you allow for a nuanced understanding that can guide the design of optimization models appropriate for each case. This taxonomy also informs the kind of data collection and analytical tools that would be most pertinent for managing a given FRT system. It lays a strong foundation for further academic investigation into each category and could significantly aid practitioners in choosing the most suitable optimization models and algorithms for their specific circumstances. Elaboration on the distinctions between deterministic and stochastic FRT systems, as well as between static and dynamic versions of each, adds substantial depth to the initial classification. Here, the focus is on the quality of the information available to the decision maker and how it influences the operational capabilities and constraints. Deterministic FRT: The hallmark of a deterministic FRT is that decisions are made in an environment of perfect information. In the static version, all variables related to users and operations are known in advance, making it possible to formulate an optimal strategy at time 0. The dynamic variant of deterministic FRT accommodates the appearance of new users and cancellations, which are the only unknowns; all other variables continue to be known perfectly. In both cases, the assumption of perfect information might be a reasonable approximation in stable and well-controlled environments but could be a limitation in more volatile settings. Stochastic FRT: Here, the decision maker must work with incomplete or uncertain information, but probabilistic models of this uncertainty may be available. In contrast to deterministic FRT, decisions here are made in the context of imperfect information. This category often involves using stochastic optimization techniques that incorporate the known probability distributions of the uncertain variables into the decision-making process. Dynamic Versus Static: The dynamic versions of both deterministic and stochastic FRTs allow for real-time adjustments based on evolving circumstances. The static versions, on the other hand, lock in a plan at time 0, with no possibility for modification thereafter. Perfect Versus Imperfect Information: This can also be viewed as a spectrum where deterministic FRT with perfect information sits at one extreme, and stochastic FRT with imperfect information sits at the other. The quality of information available to the decision maker directly impacts the complexity of the optimization models that need to be developed. Implications for Decision-Making: In deterministic settings, especially the static kind, standard linear or MILP models may suffice. In stochastic settings, more com-

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plex modeling techniques, such as stochastic programming or robust optimization, would likely be required to capture the inherent uncertainties effectively. Detailed articulation serves as an important guide for understanding the nuances involved in classifying FRT systems, a step that is crucial for both academic studies and practical applications. By tailoring the optimization and operational strategies to the specific characteristics identified through this classification, one can aim for a more efficient and resilient FRT system. Further exposition on the FRT problem by diving into stochastic variants elucidates the complex nature of decision-making under uncertainty. You astutely point out the predominance of stochasticity in real-world FRT, an attribute largely attributed to human unpredictability, external circumstances, and various operational uncertainties. Static-Stochastic FRT: In this variant, the decision maker is endowed with imperfect information and must finalize routing plans at time 0. This is a complex challenge as the optimization model would have to incorporate various forms of uncertainties like the exact time of arrival, the specific needs of the users, and even the feasibility of certain routes. Since the plans, once made, are immutable, the role of robust optimization techniques becomes crucial here to guard against the worst-case scenarios. Dynamic-Stochastic FRT: This represents perhaps the most complex case where the decision-maker must continually adapt to new and imperfect information. While the flexibility to change routes in real-time is an advantage, it also requires sophisticated decision-support systems capable of real-time optimization under uncertainty. The use of methods like model predictive control, machine learning, or real-time simulation could be relevant in this context. Comprehensive analysis offers an intellectual scaffold for both academics and practitioners in the FRT domain. The clarity with which you delineate between different types of FRTs, especially focusing on stochastic variants, can serve as a benchmark for future research efforts aimed at advancing our understanding and solving these complex optimization problems.

4 Future Challenges and Opportunities Upon conducting a thorough analysis of the extant academic literature, this paper proposes several potential research trajectories, building upon insights gleaned from our comprehensive literature review. The ensuing discourse elaborates on these proposed future research directions in a more detailed manner. A prevailing tendency in academic investigations is to represent vehicle fleets as homogenous units, not only in terms of quantity but also concerning their types. A more nuanced approach would involve considering variations in fleet sizes and capacities, particularly in the context of round-the-clock services where demand evidently shifts between day and night operations. Specifically, it is advisable to

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differentiate fleet sizes during peak and off-peak hours to more accurately reflect real-world scenarios. Another significant research aspect involves the transition towards environmentally sustainable fleets. The existing body of literature predominantly focuses on fleets that utilize traditional fossil fuels, thereby overlooking the advantages of integrating electric or hybrid vehicles. This aspect is of paramount importance in urban settings, where certain areas restrict access to only environmentally friendly vehicles to reduce pollution. Transportation authorities planning to operate in such zones should seriously consider the adoption of these alternative fleet types. Shifting our attention to the perspective of management, the issue under discussion can similarly be approached through the application of methodologies derived from management science [1, 2]. These approaches offer valuable insights into various facets of the problem. For example, by utilizing Multi-Criteria Decision Making (MCDM) techniques, it is possible to integrate the viewpoints of transportation authorities regarding the determination of weights for the objective function [10]. Incorporating their perspectives through MCDM techniques would render the system more aligned with the practical considerations of transportation authorities An additional consideration is the undertaking of a more thorough sensitivity analysis on the system. The prevailing approach in the literature for conducting sensitivity analysis is the one-factor-at-a-time method, with the exception of the work by Shahin et al. [11]. Shahin et al. [11] implement a full factorial experimental design, examining the effects of altering three parameters simultaneously. While this approach offers a comprehensive understanding of how system outputs may vary with simultaneous changes in multiple parameters, it is notably resource-intensive, necessitating extensive testing configurations and repetitions to ensure more reliable outcomes. Consequently, the exploration of a more sophisticated sensitivity analysis technique could be advantageous for transportation authorities in optimizing their systems. Such a method could leverage the principles of experimental design to evaluate the model with the minimal necessary configurations. Moreover, the objective functions typically used in FRT systems are somewhat uniform across existing studies. It is recommended that this area be diversified, particularly by incorporating two novel categories of objective functions: (1) the capital costs associated with fleet procurement, commonly examined in vehicle routing problems; and (2) environmental objectives, extensively studied within the realm of pollution routing problem research. In the context of adopting green fleets, a compelling research area pertains to the strategic placement of battery charging or exchange stations. Specifically, models could integrate multiple objectives, such as minimizing both the fleet procurement costs and the expenses related to setting up battery stations, while maintaining minimum service level requirements. This multifaceted approach would enable a balance between customer service quality and environmental sustainability. Presently, the literature appears to lack exploration of large-scale instances of this problem, particularly those involving over 500 customers. This absence signifies a lack of meta-heuristic algorithms tailored to scenarios with high customer volumes

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during peak times, highlighting an area ripe for further research to improve the system’s practical applicability. Finally, the domains of static-stochastic and dynamic-stochastic systems remain relatively uncharted in academic discourse. These complex yet realistic scenarios pose significant challenges. Employing various stochastic methodologies, such as chance constraints and sample average approximation, could provide effective means to model uncertainties. Additionally, incorporating dynamic elements, such as realtime customer requests or unforeseen shuttle breakdowns, would add a layer of realism to these models.

5 Conclusion In the present study, we commence by elucidating the FRT problem and delineating the underlying imperatives that have propelled its emergence within scholarly discourse. Subsequent to this introductory exposition, we furnish an exhaustive account of the problem’s architecture, detailing not only the system’s definition but also explicating the intricacies involved in constructing the FRT’s complex graph. Following this, we undertake a critical problem classifications, categorizing them according to the taxonomical frameworks. Thereafter, we articulate prospective avenues for future research in this domain.

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Development of Regional IT Clusters in Conditions of Permanent Socio-Economic Threats Mariya Kirzhetska, Ihor Novakivskyi, Olena Zahoretska, Yuriy Kirzhetskyy, and Anatolii Havryliak

Abstract This paper is concerned with analyzing the development of regional it clusters in conditions of permanent socio-economic threats. In our example, the impact of the war on the development of Lviv and Polish IT clusters. Research topic novelty consists in researching the possibilities of restoring a high level of orderliness and stability under the condition of transformation of the used adaptive mechanisms, by expanding regional IT clusters, which were located in the border regions into cross-border formations: cross-border clusters. The results showed positive effects from the transformation of regional border clusters into a cross-border cluster for both participants, despite the fact that they develop along different trajectories. Keywords Cross-border cluster · Regional IT cluster · IT sector · Cluster development · Intellectual capital

1 Introduction Topicality. The development of the IT sector in Ukraine is the result of the rapid growth of the IT market in recent years. Thus, at the beginning of the first decade of the XXI century. IT was a niche industry both in Ukraine and in the Lviv region. At that time, approximately 50 IT companies and less than 4000 IT specialists worked in Lviv. In ten years (as of the end of 2021), the number of companies in the IT market of the region has increased 10 times, and the number of IT specialists has increased eight times [1]. The sector’s development took place through institutional transformation, which can be described as a transition from a chaotic state to an orderly development. Like any organized system, the IT sector gravitates towards entropy, therefore, in such systems, there is a constant search for a new, more effective management paradigm based on the universal principles of the evolution of natural complex M. Kirzhetska (B) · I. Novakivskyi · O. Zahoretska · Y. Kirzhetskyy · A. Havryliak Lviv Polytechnic National University, Bandera Street, 12, Lviv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_12

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systems, described by the theory of chaos and synergy. According to this paradigm, the search for effective management at each stage of development [24] is carried out using a combination of self-organization processes and the growth of internal entropy, where the variety of managerial reactions is consistent with the variety of actions and disturbances of the external environment in the market. The process of self-organization of enterprises of the same sector operating in the same region (and functioning in the conditions of: intensive market development, expansion of the range of end consumers and the range of demanded services, the growth of ever deeper integration of enterprise products into the activities of organizations and end consumers) transforms market competition from an aggressive to soft power competition, coordinating the activities of these participants in certain regions. Establishing and maintaining a high level of orderliness and stability is possible under the condition of using institutional or inter-organizational associations. The concept of a cluster as an inter-organizational association in the scientific literature was one of the first to be used by Porter, M.E., who defined Clusters are geographic concentrations of interconnected companies and institutions in a particular field. Clusters encompass an array of linked industries and other entities important to competition. They include, for example, suppliers of specialized inputs such as components, machinery, and services, and providers of specialized infrastructure. Clusters also often extend downstream to channels and customers and laterally to manufacturers of complementary products and to companies in industries related by skills, technologies, or common inputs. Finally, many clusters include governmental and other institutions—such as universities, standards-setting agencies, think tanks, vocational training providers, and trade associations—that provide specialized training, education, information, research, and technical support [2]. An IT cluster (as one of the forms of institutional and/or inter-organizational association between legal entities and/or individuals based on common interests or the presence of identical problems) is created with the aim of ensuring fruitful, constructive and transparent cooperation of business, government and educational institutions in in the field of ICT, using intellectual capital. The main task of the cluster involves the creation of connections and contractual relations between its participants in order to ensure: (1) maximum communication and coordination among the main areas of activity of the IT business organization and educational institutions and authorities; (2) popularization and development of the IT sector in a certain regional business agglomeration, thanks to the implementation of various business and social projects. IT clusters are evolving self-organized systems. Rivalry between IT companies that operate in the middle of the cluster moves into the sphere of mild healthy competition, and relations between companies are built on a solid foundation of key rules of business ethics. The war disrupted the existing orderliness and stability in the IT market of Ukraine and the Lviv region, causing a backlash from the level of entropy achieved between market participants to chaos. That is why, the Research topic novelty consists in researching the possibilities of restoring a high level of orderliness and stability under the condition of transformation of the used adaptive mechanisms (which have

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Fig. 1 Five phases, twelve steps model for cluster development. Source [17]

shown their practical value in the conditions of the development of the IT sector of the Lviv region), by expanding regional IT clusters, which were located in the border regions into cross-border formations: cross-border clusters (Fig. 1).

2 Literature Review Positive effects and increased resilience from sectoral externalities arising from the co-location of firms due to the scaling of the regional economy have been theoretically proven [2–6]. Empirical assessments of the impact of regional business agglomeration on economic growth are actively researched, despite the fact that the conclusions drawn by researchers can sometimes be contradictory, because some researchers consider them drivers of innovative development and recipients of regional agglomeration effects, while others assume that firms within the cluster do not support the same benefit due to uneven distribution of resources used by business, increased level of industry competition, etc. [7–11] The multiplier effect of geographical concentration of entity is not clearly defined and depend on different factors, including: the sector in which the business operates, the region, the resources used and other factors [12, 13]. The positive impact on the regional economy due to the increase in employment or the increase in the amount of innovation in the region of concentration of enterprises, which is the result of a general increase in regional productivity, is undeniable [2, 14–16]. In the context of the research being carried out, the scientific position of Williams [17] regarding the identification of five phases of cluster development is important for us: mastering support; building the base, creating momentum, extending the base, sustaining momentum. Having analyzed the activity of IT clusters in Ukraine, we can conclude that only the Lviv IT cluster, the Kharkiv IT cluster and the Kyiv IT cluster are at the stage of sustainable dynamics and have a significant impact on the regional economy due to the functions they perform. In the conditions of the priority development of the economy, which is based on the knowledge and values of the information society, the development of the information space and information technologies acts as a powerful factor in stimulating

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economic growth, higher quality integration of social processes, and increasing the level of competitiveness of the national economy in general. That is why the intellectual capital of an enterprise becomes a strategic resource and a key competitive advantage of enterprises, individuals, and society. Effectively organized and properly managed processes of creation, accumulation, storage, distribution and use of intellectual capital form the basis for creating long-term competitive advantages of the enterprise and increasing its long-term sustainability. Structurally, intellectual capital as the driving force of an IT cluster can be considered from the position of combining two main components [18], of the human capital and organizational capital (Fig. 2). And it is the organic combination of these components that enables the IT business to develop harmoniously. In this study, we partially agree with the opinion of K.-E. Sweby [19], who proved that the only source of profit is human capital, whose efforts are reflected in the structure of internal and external resources of the organization. We believe that the determining (but not the only) source of creating additional value

Fig. 2 Structure of intellectual capital

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and competitive advantages in the process of innovative activity in the IT business is human capital itself.

2.1 Analysis of the State of Development of the Lviv IT Cluster In the IT sector of Ukraine over the past ten years, we have observed the active formation of IT clusters, the purpose of which was to balance and coordinate the activities of IT companies in certain regions, which are connected not only by territorial location, but also by the specifics of business organization. This intra-sectoral orderliness enabled IT companies of Ukraine to demonstrate a high level of stability and adaptability, despite the high sensitivity of the national economy to Russian aggression. Until February 2022, the development of the IT landscape was implemented through the creation of regional IT clusters in 20 cities of Ukraine, mainly regional centers, where there was a sufficient number of IT companies to consider that an IT cluster was operating in the region. However, it is worth noting that only Lviv IT-cluster, Kharkiv IT-cluster and Kyiv IT-cluster have become the most developed forms of associations of IT companies in regional IT markets. In general, IT companies, unlike traditional enterprises, are relatively mobile. And one of the most dynamic and leading areas of the IT sector in Ukraine is IT outsourcing. Moreover, Ukraine’s competitive advantages are actually a large number of specialists, a high level of their qualifications and one of the lowest levels of labor costs in the global IT market. Considering the fact that the Ukrainian IT sector is mostly an outsourcing development industry, it is quite logical that the leading outsourcing IT companies have become the core of IT clusters. The described characteristics of IT companies and the structure allow us to assert that the main production components of the modern IT sector include personnel and capital, and the level of their concentration is a determining factor for the localization and further development of the IT business in a certain city or region. The staff in IT companies is highly mobile and, in the conditions, (compared to more economically developed countries) of lower salaries, there was no mass emigration of IT specialists from Ukraine. We explain this by the fact that the inherent voluntariness of the behavior of IT specialists was mainly regulated by the development of the infrastructure of IT clusters (Fig. 3). Military aggression against Ukraine became one of the external factors of vulnerability that changed the development of the IT sector. These changes were reflected in the change of strategic priorities from development priorities to security priorities and deformed the territorial location of IT business in Ukraine. The first and most important factor that influenced this process was the rapid, significant increase in the supply in the labor market of the Lviv region. Thus, as a result of the negative security situation, an additional 11% of all specialists in the IT market of Ukraine migrated to the Lviv region, mainly internal migration took place in the direction

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Fig. 3 Forecast of the number of graduates of bachelors in IT—specialties [20]

of Kharkiv-Lviv. In January 2023 demand exceeded supply on the regional IT labor market by 5 times. The second important factor was the reduction in the hiring of junior specialists in the IT company. The total number of vacancies for junior specialists on the regional IT market during 2022–2023 has halved. However, the number of graduates of this specialty grows by 12% every year. Another factor that will have a strategic impact on the functioning of the IT sector of Ukraine and the Lviv region is the readiness of IT specialists for foreign emigration (Fig. 4). Thus, according to survey data [1], approximately 30,000 IT specialists are determined to emigrate, and the same number has not yet been determined. That is, under any conditions, after the removal of travel restrictions, the IT industry will lose 1/8 of specialists. That is, already today, under the influence of an unfavorable external environment, deterministic factors are forming that disrupt established sectoral relationships, increasing the chaotic nature of strategic management of IT companies. This increases the weak forecasting of sectoral trends and the weak predictability of life cycles, etc. Given the fact that the products of the IT sector have a close connection

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Fig. 4 Decisions to stay or migrate from Ukraine in IT industry, 2022 [21]

with the broad masses of consumers, uncertainty will be transferred not only to the economic, but also to the socio-political spheres of society.

2.2 Formation of the Task of Developing a Cross-Border IT Cluster One of the models of development of regional clusters located on border territories is their natural expansion to cross-border clusters. The peculiarity of cross-border clusters is that the participants of such a cluster operate in different tax, customs, legislative environments of neighboring countries, can have joint enterprises and organizations, use common infrastructure and operate in cross-border markets. In the work of the Institute of Regional Studies of the National Academy of Sciences of Ukraine [22] distinguish two models of transformation of regional clusters into cross-border ones (Fig. 5): (1) monopolar (asymmetric) cross-border cluster, which is formed on one side of the border with the involvement of certain individual participants from the adjacent region of the neighboring country; (2) bipolar (polypolar) cross-border cluster is based on the existence of regional networks as constituent elements on both sides of the border. We believe that in conditions of a negative security situation or other conditions of vulnerability of one of the border territories, it is more likely to use monopolar cross-border clusters with the expansion of the regional cluster in a safer environment. The formation of a cross-border Ukrainian-Polish IT cluster should be based on the principles of the self-organization process [23]:

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Fig. 5 Models of transformation of regional clusters into cross-border cluster

• complicating the system and increasing the level of organization is possible only in an open system; • only under the condition of disequilibrium about the existing properties of the system, which are capable of structural changes; • self-organization can begin in systems formed by a large number of interacting elements, in which case a more ordered structure is formed by the cooperative concerted action of individual parts. So, in simple words, the system must be open, brought out of balance (as you know, a closed system tends to internal balance, which is equivalent to oblivion). Another important feature is the presence of a large number of interacting elements. Within this study, we focus on the evolution of the system as a process of continuous directed irreversible development, qualitative and quantitative changes. We use the term “self-organization”, which is the essence of evolution and reflects the process by which the structure of the system is restructured in the direction of improving its qualities.

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The choice of a method for determining the development opportunities of a crossborder IT cluster (within the scope of this study: the Lviv IT cluster), in addition to the security component of the functioning of the participants of this cluster, should be based on the analysis of a group of indicators that would make it possible to assess its economic efficiency based on data on the combination of initial resources (labor and capital) that are spent on the production of a conventional unit of IT products. When evaluating, it should be taken into account that the cross-border cluster will cover two regional border IT markets, so the evaluation indicators should be formed for two different regions.

3 Analysis of the Peculiarities of the Development of IT Clusters in Poland and Lviv Lviv region belongs to the Ukrainian-Polish cross-border region. That is why, in conditions of vulnerability, it is quite logical to transform the Lviv regional cluster into a Ukrainian-Polish cross-border monopolar IT cluster.

3.1 Analysis of the Main Absolute Indicators of Development The theoretical concept of our research is based on the choice of a classic model, which should take into account at least the following three basic factors that reflect the activity of the IT cluster: volumes of sold products, capital, labor. Monitoring of selected absolute indicators showed that in the Lviv region, the rate of increase in sales in the period from 2017 to 2020 was higher than in Poland. The rate of growth of labor costs exceeded the rate of growth of the volume of sold products, both in the Lviv region and in Poland, which confirms our hypothesis that personnel is a key resource for the development of the IT sector. In general, it can be stated that both in Poland and in Ukraine IT clusters are developing. This is confirmed by the growth of absolute indicators of the activity of IT clusters in the analyzed countries (Figs. 6 and 7). In general, we can see that all the main factors characterizing the development of the IT cluster of Lviv are developing proportionally, although it should be noted that the capital growth is ahead of schedule and the salary growth is late. In recent years, the IT cluster of Poland shows the stability of capital, relatively slower growth of wages, and during the analyzed period there is a rapid increase in the volume of IT products sold.

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Fig. 6 The main relative indicators of the development of the Lviv IT cluster (2017 is taken as a base—as in Poland)

Fig. 7 The main relative indicators of the development of the IT cluster in Poland

3.2 Analysis of Trends in the Effectiveness of Development Activities More information in the analysis can be obtained by examining the use of capital and labor costs for the production of IT products in comparison with different clusters. In Fig. 8 shows the ratio of the volume of IT products sold to labor costs in Ukraine (Lviv region) and in Poland.

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Fig. 8 Comparison of the efficiency indicator of labor organization (product/labor ratio)

From Fig. 8, we can conclude that the COVID epidemic significantly affected the efficiency of the IT cluster in Poland. The indicator of labor efficiency: the ratio of the volume of sold products to the payment of labor has decreased by more than half. In Ukraine, the effectiveness of IT product sales fell not so significantly, by approximately 40%. Changes in the capital provision of the IT clusters of Poland and Ukraine (Fig. 9) make it possible to conclude that the capital provision of the Polish IT cluster (the ratio of capital/volume of sales) is several times higher than the similar indicator of the Lviv IT cluster. During the observation period, this indicator (the ratio of capital/ volume of sold products) fell somewhat in Poland (the fall was about 7%). In Ukraine, it remained relatively stable. Which may indicate that the COVID epidemic had a stronger impact on the results of the IT sector in Poland than in Ukraine.

Fig. 9 Comparison of the efficiency of the indicator of providing capital for the production of IT products (capital/production ratio)

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Fig. 10 The efficiency of the use of capital and labor based on the averaged model of the organization of the activities of the Lviv IT cluster

Fig. 11 The efficiency of the use of capital and labor based on the averaged model of the organization of the activities of the polish IT cluster

Although the presented results of the analysis give a qualitative picture of changes based on a set of indicators of the activity of IT clusters, they do not allow us to understand the essence of the development of the mechanisms of their functioning (Figs. 10 and 11).

3.3 Construction of Development Models and Their Analysis The current state of the economy creates certain problems in the analysis of essential changes in the development of the IT industry, both in Poland and in Ukraine.

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To assess the effectiveness of the IT cluster functioning mechanisms, we will use mathematical modeling methods that will allow not only to analyze the current state, but also to make predictions. The assessment of the effectiveness of the creation of IT clusters in Poland and Lviv will be carried out using the Cobb–Douglas production formula. P = k × Kα × Lβ α+β=1 where P is the volume of manufactured IT products, K—amount of capital employed, L—payroll, α, β—coefficients of the Cobb–Douglas model, which reflect the intensity of the use of capital and labor for the production of IT products. To find the unknowns k, α, β, we will use the gradient method of the fastest descent for the functional  F= Jj = 1(Pj−k × Kαj × Lj(1 − α))2 → min where J—the number of observations by observation years from Table 1. {Pj; Kjα; Lj(1 − a)}j = 1, . . . , J As a result, the following representation of the production function for the Lviv IT cluster was obtained. P = 0.518061859611047 × K 0.389777503 × L0.61022249 No less important is the study of deviations from the calculated model during the studied period in order to evaluate the effectiveness of management both in terms of the use of capital and labor (Table 2). Table 1 Indicators that reflect the activity of the IT sector in the Lviv region (in million UAH) Years

Indexes Product

% to 2017

Capital

% to 2017

Labor

% to 2017

2015

3759.2

n/a

775,988.4

n/a

501,1

n/a

2016

4932.6

n/a

863,977

n/a

558,4

n/a

2017

5539.9

n/a

1,329,662

n/a

588,4

n/a

2018

7685.5

38,73

1,602,692

20,53

723,4

22,94

2019

9881.8

78,38

1,520,903

14,38

928,4

57,78

2020

10,806.2

95,06

1,745,983

31,31

1297,2

120,46

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Table 2 Indicators reflecting the activity of the IT sector in Poland (in million zlotys) Years

Indexes Product

% to 2017

Capital

% to 2017

Labor

% to 2017

2015

n/a

n/a

n/a

n/a

n/a

n/a

2016

n/a

n/a

n/a

n/a

n/a

n/a

2017

150,515.4

n/a

175,890.96

n/a

12,416.55

n/a

2018

158,339.2

5,20

175,910.21

0,01

11,447.65

-7,80

2019

170,335.5

13,17

175,930.26

0,02

21,225.74

70,95

2020

189,123.8

25,65

175,952.06

0,03

32,419.4

161,10

Changes in the economic, socio-political landscape are determined by the following features: • inertia; • submission to the economic laws of development; • classical methods of analysis have strong limitations on application and do not give sufficiently clear results; • short interval for research; • complexity of using analytical methods. Therefore, the need to develop effective analysis tools is more urgent today than ever. We will analyze the intensity/efficiency of the use of capital and labor during this period Let’s build the functionality F=



(Pj−k × (ζ j × Kj)α × (ηj × Lj)β)2 + χ   × (ζ j + 1 − ζ j)2 + (ηj + 1 − ηj)2 → min

ζ j—coefficients of the intensity of capital use ηj—labor intensity coefficients To find the unknowns ζ j, ηj, j = 1, . . . , J we will use the gradient method of the fastest descent The efficiency of the use of capital and labor based on the averaged model of the organization of the activities of the Lviv IT cluster (Ukraine). In general, the following conclusions can be drawn for the Lviv IT cluster (Table 3). A similar analysis was carried out for the IT cluster of Poland. The Cobb–Douglas model will have the following representation: P = 0, 010364026 × K 0,737253098 × L0,262745

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Table 3 Factors affecting the formation process of the Lviv IT cluster Factors contributing to the formation of an IT cluster

Factors hindering the development of an IT cluster

1. High level of IT services 2. Availability of IT personnel 3. Availability of potential for the formation of implementation of large IT projects 4. Availability of institutions for training IT specialists 5. Development of technological culture

1. Weak traditions of cooperation 2. Low efficiency of intellectual capital provision systems 3. Low demand for IT products 4. Weak integration of the IT cluster into industrial production 5. Low efficiency of industry and professional associations 6. Low competitiveness of IT companies and a short time horizon of business strategies

In general, based on this model, it can be concluded that the production rate of IT products in the Lviv cluster is 22.75 times lower, compared to Poland. It can also be seen from the model that the capital utilization rate α in Poland is 0.737253098, while in the Lviv IT cluster it is 0.389777503. Regarding the use of human resources, in the Lviv IT cluster, the β indicator is 0.610222497 compared to 0.26274 in Poland (Table 4). Table 4 Factors affecting the formation process of IT cluster in Poland Factors contributing to the formation of an IT cluster

Factors hindering the development of the IT cluster [25]

1. Low efficiency of industry and professional 1. High dynamics of growth associations and dependence on financing 2. Partner contacts within the EU and from EU structural funds internationalization of Polish IT clusters 2. Insufficient involvement of regional 3. Intensive transfer of technologies, stakeholders in the implementation of knowledge and dissemination of innovations cluster regional policy and impossibility of 4. Availability of IT personne attracting regional funds for investment and 5. Availability of potential for the formation of administration of large projects implementation of large IT projects 3. Lack of information on the exploitation of 6. Availability of IT specialist training clusters and cluster initiatives at the level of facilities and development of technological socio-economic diagnostics and tools culture dedicated to clusters 7. Lower mobility of labor resources 4. Insignificant use of clusters to implement the concept of smart specialization 5. Insufficient cooperation with research organizations and coordination of cluster policy between the national and regional levels 6. Lack of tools for supra-regional clusters that have not received the status of a Key National Cluster

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In general, it can be stated that structural and client capital are used more intensively in Poland, while in Lviv the emphasis is on personnel. This is the main weakness of the IT cluster of Lviv—insufficient provision of structural capital and means of intellectual property. In general, it should be noted that Ukrainian IT companies are characterized in comparison to Polish IT companies: • relatively lower incomes, since most of them are oriented towards work in offshoring or outsourcing; • higher personnel mobility; • relatively higher training potential of young specialists; • a lower level of capital provision when carrying out production activities; • difficulty of initiating and implementing large innovative projects; • a higher level of external instability, which has a negative impact on business development. A number of these problems can be solved through the formation of a cross-border Ukrainian-Polish IT cluster. It should be emphasized that the IT cluster of Poland will also receive significant preferences. This includes the expansion of the offer of highly qualified specialists in the IT labor market of Poland, the development of cross-border cooperation, the development of innovative infrastructure and the sector of creative industries, the improvement of the institutional environment of cross-border convergence between Ukraine and Poland, the improvement of the quality of the intellectual capital of the Ukrainian-Polish border, primarily through the implementation of joint cross-border business initiatives.

3.4 Recommendations for the Formation of a Cross-Border Ukrainian-Polish IT Cluster The development of a cross-border IT cluster should be adjusted taking into account such stages, which echo those proposed by Williams [18]: 1. The IT cluster will not develop in unfavorable macroeconomic conditions. A favorable macroeconomic landscape includes: low level of corruption, law enforcement, protection of intellectual property, high quality of education, reasonable/moderate regulation, access to finance, availability of appropriate IT infrastructure. In addition, the ability to cooperate, the constant search for innovative technological solutions, and the ability to conduct a constructive dialogue with partners are important. Currently, Ukraine and Poland have different macroeconomic conditions and level of economic development. That is why it is worth emphasizing that the Lviv IT cluster (relative to today’s conditions) can develop as a monopolar cross-border IT cluster: where Ukraine will be the donor of resources (mainly labor), and Poland will be the recipient.

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2. The formation of clusters should take place “from below”, i.e. exclusively at the initiative of the enterprises themselves. Establishing cooperation between IT companies that share the same market niche. From the point of view of evolution, the system is in an unbalanced state, because certain external circumstances force IT companies to cooperate in the areas of cost optimization, labor productivity improvement, and joint training of personnel. The creation of cross-border network infrastructure, specifically in the IT cluster, will not require significant efforts because the organization of IT business in Ukraine is carried out in accordance with Western practices, which will not cause significant differences in the general rules, standards and procedures for managing network interaction at the cross-border level. 3. Proactive IT companies are starting to form teams for cooperation with educational institutions, other non-profit organizations, having the opportunity to work with participants from both sides of the border. In this way, the framework of the territorial cross-border IT infrastructure is formed. Effective state support cannot be universal in nature, but must be adapted to specific conditions. The priority direction of the state is the stimulation of convergent processes in cross-border regions through the improvement of the institutional environment with the aim of bringing together formal and informal institutions operating on different sides of the border. In this context, in the Ukrainian-Polish cross-border region, institutional changes aimed at deepening socio-economic convergence should develop in three key directions: the formation of a common institutional and legal field, the activation of the activities of cross-border institutions, and the improvement of the quality of institutional support for cross-border markets. 4. In the IT infrastructure, the level of consistency increases, for which specific goals are formed, interaction mechanisms are created and formalized. The IT cluster begins to develop intensively through the transfer of information, attracting new tangible, financial and intangible assets. The general effect of the formation of a cross-border IT cluster will be manifested in the following directions: • budgetary—increase in revenues due to increased volumes of production of IT products; • investment—due to the inflow of foreign and domestic investments; • production—due to the modernization of the production capital base; • social—due to the increase in employment during the expansion of exportoriented production, the opening of joint ventures. 5. The IT cluster becomes attractive for investment, which contributes to the establishment of effective business processes, for which investments are attracted in promising startups, measures are taken to support innovation, marketing and brand support, to create incubators and research centers. 6. Expansion to other sales markets is expected, cooperative ties with other IT clusters are emerging, which makes it possible to expand the cross-border cluster into mega-level clusters.

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Information and Economic Mechanisms for the Development of System Integration in the Management of Enterprises’ Business Processes Oleh Kuzmin, Yevhen Tsikalo, Lesya Say, Rostyslav Bala, and Oleksandra Vivchar

Abstract In this article, we have developed an integration framework (platform) for managing the business processes of enterprises, on which the mechanism for making and implementing decisions in this area will function. Such a mechanism will ensure the formation of integrated business processes at the instrumental level by acquiring integration features in the information and economic context of integration. The article also presents the concept of structural positioning of the information and economic mechanism for decision-making and implementation in the integrated system of management of business processes of enterprises. According to this concept, the information and economic components of the mechanism are integrated according to the process approach to management through the decisionmaking and implementation system (a subsystem of the management system). This system interacts with other subsystems (in a structural and functional format) on the integration basis of the management system. We propose to ensure the integration of components through the process and procedural integration of decisions by subsystems. These measures make it possible to technologically support the parameters of integration features and performance indicators within the framework of solving the functional tasks of managing enterprises’ business processes and maintaining the system’s integration status. Also, the article considers methodological bases O. Kuzmin (B) · L. Say · R. Bala · O. Vivchar Lviv Polytechnic National University, Bandera Street, 12, Lviv, Ukraine e-mail: [email protected] L. Say e-mail: [email protected] R. Bala e-mail: [email protected] O. Vivchar e-mail: [email protected] Y. Tsikalo Ivan Franko National University of Lviv, 1 Universytetska Street, 1, Lviv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_13

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for building and functioning enterprises’ integrated business process management systems by introducing an integration basis as a platform for combining subsystems. Such measures provide methodologically verified acquisition of integration qualities by the system and are manifested in the methodologically ordered establishment of integration features. Keywords Business process · Development · System integration · Enterprise · System approach · Management

1 Introduction In today’s environment, the activities of enterprises involve a significant amount of multifaceted information that affects both the management system and the processes of making management decisions in it. At the same time, we must form an integrated information and management environment within the organisation, achieving a synergistic management effect in managing business processes due to the emergent property of the system in the spatial and temporal dimensions. Such a motivational benchmark shows the need for research to improve the information and economic mechanism for the development of system integration in the management of business processes of enterprises. It should be noted that the integrative property of such management should be considered an integration process that is continuous, regardless of the direction and level of managerial influence. The such emphasis in management will lead to the improvement of strategic and tactical economic parameters of the value manifestation of business processes from the perspective of different groups of stakeholders.

2 Methodology We consider the problems of decomposition of goals and development of the functional structure of system integration in the management of business processes of enterprises in conjunction with the problem of setting the management task in general. Based on the decomposition tree of goals by levels of management, we distribute the functions of managing business processes of enterprises and the corresponding sets of tasks. A decision-making task will cover the entire management cycle with interrelated indicators necessary to achieve the values of target indicators. The task becomes an integration unit of the hierarchical tree structure of goals and management functions by setting integration criteria for groups of interrelated goals. The many integration features identified and ordered within the management of enterprises’ business processes require classification structuring. Such a classification formation should be integrated by nature since it should consider the interrelationships of features (through compatibility and consistency) within and between the

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classification group. Applying this approach involves identifying common characteristics of groups of features that will consider the implementation of the standards’ provisions for managing enterprises’ business processes. We have used a combination of deductive and inductive methods of acquiringestablishing the integration features of the basis to reveal the content of implementation of the integration basis of the system of business process management of enterprises on cooperative principles and standardised elements of reporting and management systems. We present the information and economic context in the development of system integration in the management of business processes of enterprises as a result of the implementation of the appropriate mechanism for managing business processes. This mechanism is based on applying a process-oriented approach to management in the information context and on filling the said process with economic content by the value-oriented approach. At the same time, we propose to reproduce the information and economic tools for managing business processes of enterprises in the instrumental elements of the processes of making and implementing specific decisions.

3 Paper Preparation 3.1 Directions of Development of System Integration in the Management of Business Processes of Enterprises The problem of improving the information and economic mechanism for the development of system integration in the management of business processes of enterprises is related to the efficiency, quality and scientific and technical level of the management system, which is developing as an integrated system. Therefore, implementing any measures in this area should be aimed at achieving a synergistic effect of integration. Furthermore, it means that the business process management system should acquire a higher level of integration during the phases of its life cycle. In the context of reengineering business processes of enterprises, we must consider the problem of adaptability in the context of the characteristics of the transition to effective functioning in the face of changing goals and resources to achieve them. It is necessary to strengthen the business process management system’s adaptation properties by introducing new elements. Introducing these elements into the system should enhance its adaptation properties [1–4]. At the same time, we should evaluate the costs of integrated adaptive reengineering of the system compared with the change in the level of integration. To ensure the interaction of system components, we must solve several tasks, namely [5–8]:

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• Coordinated selection of parameters of accuracy, reliability and performance of system components that ensure the achievement of goals; • Organisation of inter- and intra-level interconnection and interaction of hierarchical parts of the integrated system; • Intra-component and inter-component compatibility of the supporting parts of the system; • Selection of rational methods and regulations for intra-component and intercomponent interaction; • Standardisation of interfaces to ensure various types of compatibility and interaction. To solve these problems, the arguments for ensuring system integration should be considered. The components must be aligned not only in terms of technical but also in terms of technical, economic and financial characteristics, considering internal and external influence factors. Integration costs, including reengineering costs, should be assessed by the criteria of compatibility, consistency, communication and interaction, considering the management process’s functional and cost characteristics. The openness of modern systems poses new challenges to implementing integrated links with the external environment and ensuring the security of the integrated functioning of business process management systems of enterprises. The management standards included in the integrated management system only set the direction of management work. The development of appropriate tools in the management process area is necessary to achieve the integration of management standards at the level of daily operations within business processes. Implementing management system standards includes the diversity of system structures and documentation. In turn, standardisation of reporting also provides an information space for creating the necessary reports for business entities and stakeholders. These circumstances necessitate a theoretical generalisation and definition of the conceptual prerequisites for developing system integration in business process management. Such conceptual prerequisites allow entities to create practical integrated management tools. This problem determines the search directions of research in the integration plane of business process management of enterprises (Fig. 1). We will consider these two types of tools as a single integrated management and reporting tool that will ensure the orderliness and integrity of information disclosure of ESG activities. However, first, let us define the conceptual markers of such a toolkit. The toolkit should reflect the principles and approaches to business process management and reporting. At the same time, the principles laid down in the management standards should interact with the principles of integrated reporting and sustainability reporting, creating an informative picture of activities at each management step (action) during the reporting period. The principles of the standards of enterprise business process management systems are focused on the subject matter of a particular standard. However, the information reflection of the effect of these principles should be based on the reporting

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Standards of management systems (subsystems) Management system Integrated management system Institutional (national and international) guidelines for the implementation of integration solutions in management

Conceptual and instrumental support of integration solutions (projects) in management

Theoretical and methodological provisions: - formation of the information and economic basis for the integration of activities and its management; - formation of the integration basis of management; - combination of integration and self-organisation in management.

Information for stakeholders Integrated information presentation of complex activities (ESG activities)

Financial and non-financial information for stakeholders (expanded and supplemented reports; information messages upon request)

Management report

Disclosure of information Integrated reporting

Fig. 1 Directions for the development of system integration in the management of business processes of enterprises. Note Suggested by the authors

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principles. On the other hand, the information structure that meets the reporting principles should respect the functioning of the management systems. It means that it should fully inform the operation of its principles. The convergence of principles will methodologically determine the combination of two approaches: (1) a process-oriented approach to management based on the plan-do-check-act cycle in ISO standards; (2) a value-based approach to reporting (according to integrated reporting standards and sustainability reporting) is implemented as a value-creation process. It considers the interdependencies of a holistic nature in ESG activities. Thus, as a form of implementation of management tools in the context of achieving ESG integration, the process will act as a process of managing value creation in the course of sustainable development (“integrated value creation management tools”). The development of system integration in business process management should reproduce the results of activities in three recursively nested in the space–time dimension information areas, which will reflect: • The level of implementation of the entity’s strategy at certain stages (at the points of trajectories towards achieving strategic goals); • The level of implementation of tactical plans directly at a particular strategic stage (for the reporting period); • The course of activities in real-time (within the reporting period) with the possibility of its self-organisational verification and analysis, with simultaneous reproduction of tactical and strategic results at specific time points of management. These information spheres will illustrate the creation of a single taxonomically defined “information portrait” of the subject, which will gradually acquire an appearance-image (with a possible fractal property) associated with the achievement of the strategy. Therefore, together they will act as a coherently created integrated information and content reporting set that will reflect the continuity of the management process. Risk-oriented thinking is an intellectual element of the management toolkit declared in the management and reporting standards. This thinking is viewed from the perspective of preventing (anticipating) changes in activities that may cause significant negative disturbances in the homeostatic equilibrium of the management system. Such changes can manifest themselves as the system’s disintegration concerning its resistance or as ineffective integration, which leads to an improvement in the integrative property of the system. Therefore, the toolkit should identify such situations’ risks and develop actions to implement the possibilities of an adequate response based on risk and opportunity assessments. The complexity of decision-making in the management of business processes of enterprises is due to the information ambiguity of situations and the logic of decision algorithms in a fuzzy environment. The business process management process should implement procedures for the self-organisational settlement of such situations in the system, which localise the points of integration of business processes

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(intra-process and inter-process points). Point-based self-organisational solutions should fully use the integration effect, neutralising disintegration threats as risks and integrating opportunities to overcome stressful situations. Taking into account the iteration of the PDSA cycle to achieve the best result, managing the value creation of a business process becomes a reflexive convergence of proposals and intentions to meet the stakeholder’s interests both from outside and inside the business entity. Coordinated interaction of the subject and the stakeholder (as a fundamental condition for system integration in the management of business processes of enterprises) will be projected (transferred) to all management objects. The synergistic quality of the business process management process will be manifested (formed) due to the acquisition of integration features (signs of integration) by the objects of management (i.e. business processes). The principles and requirements of integrated functioning and information support of management should determine these integration features. We will call the process of acquiring features the integration process (“overlay”, “covering”, “background”, auxiliary process). It should also follow the rules of the PDSA cycle within the framework of the abovementioned unified approach. Thus, the integrated process will relate to management and reporting and will result from the integration process’s application to the implementation of the above features. Thus, the management of a business process, which will involve the acquisition of integration features, will turn it into an integrated process. In turn, integration features in the reporting give it integration status. The integration process with a networked set of interrelated integration features will constitute, in the information and semantic context, intellectual content in the management of business processes of enterprises. Moreover, the instrumental implementation of such content will make it possible to respond adequately at the integration points (localised processing of integration features) to mutual influences, thus obtaining the effect of integrated management decisions.

3.2 Information and Economic Context in the Development of System Integration in the Management of Business Processes of Enterprises An enterprise is traditionally an open system that tries to pragmatically integrate with the external environment, benefiting from integrated interaction with stakeholders [9–13]. Intra-system integration subordinates to external integration. As a result, the system-wide synergistic effect of management forms from the emergent combination of these types of integration. Therefore, a business entity should apply a systematic approach to study the prerequisites and the fact of achieving holistic integration. The entity will generally manage business processes and activities on the integration platform—a basis that is a set of system-forming management elements (structured elements of the management system with the corresponding categorical

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management content) with integrative (emergent) quality. Figure 2 illustrates the formation and use of the basis in the management of business processes of enterprises. Management’s information and economic “vector” plays a leading role in forming the integration framework. Enterprise business process management’s information and economic context must be more substantiated. It is considered in connection with other management aspects, taking into account both the direct impact of the business process on the environmental and social spheres and the reverse impact. The economic component of all integrated activities will be assessed and reassessed with an integrated consideration of these impacts. The pragmatic content of economic decisions is complemented by the categories of responsibility and harmonisation, which are necessary conditions for the viability and development of the subject. Let us explore the prerequisites for forming an integration framework in the information and economic context in the development of system integration in the

Systematic basis of the integration management framework

Information and economic basis of the integration management framework

The concept of an information and economic oriented integration management framework

Information and economic "catalysts" for the impact of self-organisational management

Design of an integration framework for the information and economic mechanism and management tools

Instrumental application of the integration management framework

Making and implementing integrated management decisions

Integrated reporting of activities and their management

Building and managing integrated activities

Economic activity / Social activity / Environmental activity

Fig. 2 Formation and use of an integration framework for managing business processes of enterprises. Note Suggested by the authors

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management of business processes of enterprises in terms of external and internal integration. External relations of an entity manifest at the information and communication level of interaction with stakeholders. Information contact with the external environment is most often carried out through reporting as a tool for informing about the results and status of the entity. On the other hand, it is necessary to argue for the requirements to internal management to ensure internal integration in combination with external integration (and, therefore, to establish internal integration processes). In general, internal and external integration processes should be aligned within the framework of coordinating business process management tactics and strategy and be considered a single holistic process. The critical information and economic markers are the following essential categorical elements (these markers are associated with the usefulness of the information and will be the focus of interest in achieving system integration in the management of business processes of enterprises): • Conceptual basis of activity management and orientation of decisions in the management environment (management conceptualisation); • Strategic orientation of activities (business strategy); • Abstract (formalised) representation of activities (business model); • Spatial and temporal parameterisation of the activity (business plan); • Business process structuring of the activity to interpret its business model at the operational level of management; • Performance results (final and at the stage of formation; efficiency); • The state of the entity (fixed in time, both final and situational; sustainability); • Impact of risks on activities and opportunities for an adequate response to them; • Prospects for operations and development and proposals for the future. We have identified these essential elements, considering the guidelines (principles) of directives and recommendations on the purpose of financial and non-financial information reporting and disclosure tasks and the provisions of management system standards. Signs of information and economic integration will be manifested on this elementary basis. These signs will determine the use of appropriate methods and tools for managing the business processes of enterprises. These actions will give such management the status of integration. We illustrated the information and logical links between the essential categorical elements in Fig. 3 and the substantive characteristics of the elements present in Table 1. The hierarchical arrangement (orderliness) of the essential categorical elements, in combination with their network connectivity, allows for information about each element, comprehensively reflecting the links with other elements and providing the information and economic basis for integrated management actions and decisions. Adherence to the principles of integration of financial and non-financial information (integrated reporting) (as the fulfilment of integration requirements on their basis by implementing integration requests for essential categorical elements (key

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Economic activity

Informational (epistemological) content of the complex content of the activity and its management Conceptualisation of management

Business model

Business strategy

Business plan

Business processes

State of the enterprise

Performance results

Economic justification of management decisions

Performance of the business

Management information technology

Information and economic analytical support (processing) of management actions

State stability

Prospects for activity and development (strategic and tactical proposals)

Risks and opportunities

Social activities

Environmental activities

Fig. 3 Basic categorical elements in the research of the information and economic context of tools for managing business processes of enterprises. Note Suggested by the authors

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Table 1 Characteristics of the basic categorical elements in the research of the information and economic context of the tools for management of business processes of enterprises Basic category elements Ek (k = 1, 2, . . . , 10) Content characteristics of basic category elements Ekl (l ∈ Lk ; Lk ⊆ N ; N ={1 − 11}) E1 . Management conceptualisation (concept and management environment) E11 . The conceptual approach to management is a combination of the process-oriented approach used in management system standards and the value-oriented approach used in integrated reporting and sustainability reporting standards. It is transferred to the management report as part of the symbiosis of integration and self-organisational approaches to managing the business entity’s activities E12 . Strategic orientation in making and implementing management decisions, driven by the needs of stakeholders, with a projection for the short, medium and long term E13 . Model abstraction in the formalisation of ideas about complex activities in accordance with the conceptual approach to management E14 . Information and parametric representation of complex activity with reproduction of the dimensionality of the characteristics of the components of the conceptual approach to management on the spatial and temporal plane of activity E15 . Process and resource mobility of activities in the dynamics of enterprise functioning in accordance with management tasks for certain periods of time E16 . Uncertainty (information insufficiency, vagueness, randomness) and risk in actions and events, their causes (factors) and consequences, and possibilities of adequate response E17 . The permanence of the effect in achieving performance results, which is incorporated into the decision to manage the resulting factors E18 . Egocentricity of sustainability management in ensuring subjectivity and business continuity E19 . A vision of the future of the business: a balance of desires and capabilities (intentions and expectations with potential consequences and the corresponding response capacity) E110 . Structuring of the operating environment and management system and contextual stratification of the management process (space for implementation of conceptual provisions) E111 . Maintaining the relevance and quality of management information technology E2. Business strategy E21 . Strategic goals of the entity and their structural and time decomposition at the functional and organisational levels of value creation management in interaction with the external environment E22 . Strategically oriented types and methods of conducting business activities (business processes) E23 . Resource opportunities for doing business and achieving strategic goals E24 . Economic justification of strategic decisions (strategic planning), taking into account the interests of stakeholders, business risks, social and environmental responsibility of the business, targeted assessment of performance and the achieved state, consequences and development prospects E3 . Business model (based on [14, 15]) E31 . Types of activities and sectoral positioning of the business E32 . Strategic focus on consumer segments with attractive value propositions E33 . Customer-oriented sales system (sales channels, marketing tools for sales in customer relations) (continued)

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Table 1 (continued) E34 . Resource provision and resource use (capital raising) and partnership in business activities; innovative business solutions (projects) E35 . Performance: generation of revenue streams; resource costs E36 . Identification of the state and trajectory of business activities in cooperation with stakeholders in financial and non-financial indicators E4 . Business plan (corresponding to the business model, as its parametric reproduction and economic tool for implementation) E41 . Targeted focus of the business plan and its stakeholders E42 . Comprehensive content of the planned business activity (of a certain type or combined types) and its process and resource support (taking into account external and internal factors of influence, risks and opportunities for implementation) E43 . Key indicators of business activity (value created) and its economic efficiency E44 . Organisational and technological specification of the plan in the management system (at the level of actions) and informing (based on the results of monitoring and analysis) about its implementation E5 . Business processes (the substance of conducting business activities according to the business model and implementing the business plan) E51 . Specific alignment between business processes and activities E52 . Structure and innovation of business processes E53 . Process landscape (a model representation of business processes as a structural and spatial arrangement and functioning in the business environment) E54 . Operational and resource parameterisation of business processes E55 . Impact factors and risks in business processes: identification and assessment E56 . Opportunities (economically and technologically feasible) to neutralise failures in business processes and reengineer them E57 . The impact of the effectiveness (efficiency) of business processes on the state of the entity (taking into account the materiality and criticality of the impact) and the feasibility of using business processes in the future E58 . Applied information technology tools for operational management engineering and business process reengineering E6 . Risks and opportunities: relevance and effectiveness E61 . Identification of risks (sources, circumstances, conditions, causes of uncertainty) in business planning and business process E62 . Assessment of possible consequences of risks (likelihood of risks occurring and response to them; scale and materiality of consequences in terms of impact on the success and prospects of the business E63 . Counteraction to the emergence and realisation of risks based on actions to mobilise resource capabilities that are crucial (key-essential) in the strategic aspect and rationally managed in the process of value creation to achieve the required result and a safe state E64 . Monitoring risks and exploiting opportunities E65 . Information technology support for managing risks and opportunities and accumulating knowledge to respond adequately to challenges (continued)

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Table 1 (continued) E7 . Результати діяльності та її ефект E71 . Target performance results (financial and non-financial, quantitative and qualitative information on results) E72 . Factors, risks and opportunities that affected the outcome in the course of value creation (in the value creation process) E73 . Contribution (participation) of internal and external stakeholders to the results E74 . The resultant effect of activities (efficiency) in financial and non-financial terms and its impact on the entity’s position and development prospects E75 . Information and technological application support for decision-making and implementation processes to manage the formation of results and generate the effect E8 . The state of the entity and its sustainability E81 . Identification (financial and non-financial) of the state and factors influencing it E82 . Impact of the results of the use of resources on the state of the entity E83 . Sustainability in the strategic reflection of the dynamics of sustainable development (self-critical assessment and objective information of stakeholders) E84 . Opportunities and risks to the ability to achieve a position acceptable for continued value creation in the next timeframe (in relative current or strategic perspectives) E85 . Information and technology support for condition monitoring and analytical support for effective actions in condition management E9 . Business and development prospects E91 . Time stages and horizon (temporal discreteness and continuity) of activities within the strategic perspective and their corresponding goals and a factor coordinate system for moving towards achieving the goals in the future E92 . Projection of changes in activities for the future, taking into account the current state and the correlation between expectations, opportunities (including investment and innovation) and risks, and a vision of potential consequences (benefits and threats) in the current and strategic dimensions E93 . Innovation and investment prospects for development E94 . Information and technological innovation support for the development of promising solutions and ensuring their implementation in development management E10 . Management information technology (Information technology system for preparing, processing and providing information on the progress of activities and their management) E101 . Identification of information on business and management processes and formation of databases E102 . Intelligent processing of information to develop and implement management decisions based on knowledge bases E103 . Real-time communication and up-to-date information exchange and data and knowledge protection E104 . Ability to expand and develop the information technology system in management as an open system Note Developed by the authors using [14, 15]

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labels-markers in the information and economic context) of activity and its management) will lead to the acquisition of integration features by the management system, processes in it and information reproduction (“picture”) of the subject’s life. Such features will be formed from the system-wide integration features of the information and economic management tools (allocated by the critical type of information and economic integration of the management system) with the integration status. The system-wide integration features interpreted through the essential categorical elements will be called category-oriented (systematised by essential categorical elements). For example, the fundamental categorical element “Business Processes” will include the following category-oriented integration features: The information structure of the management system represented by the system of economic indicators is such that: • Ensures transparency and end-to-end concentration and localisation of information about business processes in elements of target, functional, and organisational structures (according to the substantive characteristic “process landscape”); • Ensures identification of business process parameters for structurally coordinated management of economic performance (by the substantive characteristic “operational and resource parameterisation”) and the state of the business entity (by the substantive characteristic “impact of performance on the state”). The process of value creation will be considered through the prism of business processes within the framework of a combination of process-oriented and valueoriented approaches to business management. This approach will allow the introduction of procedures for establishing integration features into the processes of making and implementing decisions on business processes. Thus, making and implementing management decisions on a particular business process will also involve integration. The decision, like the business process itself, becomes integrated. The established integration features should be justified in decisions based on the analysis of integration benefits and costs. Currently, when developing and implementing economic plans, a business entity enters a business environment with two related narratives: • To integrate into it, which allows pragmatic rationalisation of its processes (external and internal processes, sub-processes) with economic benefits from commonality, unification, coherence and coordination of links; • Constantly be in a self-organisational “tone” to stay and develop in its segment or flexibly adapt to changes, taking into account the uncertainty lurking in the information space of a dynamically changing environment. Therefore, taking into account management’s self-organisational and integration factors with an emphasis on its information and economic context becomes a prerequisite for effective decisions on business processes. It is necessary to consider the effect of integration’s impact on self-organisation and, at the same time, ensure that integration achieves the effect of self-organisation. As a management phenomenon, integration may have its antithesis—disintegration, the possible consequences of which should be accepted as challenges

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to self-organisation. Economically sound self-organisational decisions should be consistent with economically sound decisions on integration. Therefore, integration features will be combined with self-organisational features that are established (similarly to integration features) according to the fundamental requirements of selforganisational management. Such a combination will be based on common essential categorical elements for economic decisions in the information and economic context. These decisions contain assessments of the mutual influence of integration and self-organisation processes and are based on information from these elements. Information support for the management of business processes of enterprises and communication (such as filling management decisions with information and providing information to stakeholders) will be carried out through the prism of essential categorical elements. The latter will be used to structure the needs for input and output information represented by information blocks. An information block is an integral structured set of systematised information intended for a business entity’s external and internal management needs. The concept of an information block will be further used as a generalised term for a set of information for a specific purpose as understood by the business entity and the stakeholder. Information blocks will include: (1) external regulatory and statutory reporting information (financial, tax and fee, statistical, and additional reporting with informative expansion and supplementation (e.g., information on integration and self-organisation factors of management, including analytical assessments (through the degree of integration and self-organisation) of their impact on changes in reporting indicators, etc.); (2) external regulatory and variable reporting information (mandatory management report with the proposed content from the entity or prototype reporting (similar management report developed at the initiative of the entity, but not mandatory). This reporting documentation is created as a result of the use and processing of information from the previous block with possible (if necessary) additional expansion or addition of additionally processed information (which was not in the “field of interest” of the previous information block); (3) particular additional external reporting information (unique information messages) at the request of a stakeholder that was not contained in the previous two information blocks; note that the information in this block and the previous two is provided to stakeholders with restrictions, given the potential damage to the entity as a result of leakage of material information; (4) internal reporting information generated during the reporting period (interim and fragmentary reporting; management accounting reports prepared for internal management needs and may be provided to stakeholders to a limited extent if it facilitates their cooperation with the entity); (5) internal operational and situationally episodic information that is within the control (monitoring) area of the entity (for example, key criticality indicators). This information may be provided in a limited format to stakeholders as participants in business processes;

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(6) information from the external environment, which regulates the legislative and regulatory framework of the entity’s activities and is used for processing within the framework of links with other information blocks. The relationship of information blocks in the context of meeting the enterprise’s and stakeholders’ information needs is shown in Fig. 4. These information blocks will represent the following hierarchical levels of information formations: (1) “macro-blocks”, for example, financial statements, non-financial statements, and combined statements with financial and non-financial information; (2) “mini-blocks”, e.g., income statement in financial statements, labour statistics report in non-financial statements, management report (integrated report)

Information about the company's activities (indicative interpretation of the activity taking into account integration and self-organisation aspects)

Extended systematic information

External reporting information

External regulatory and statutory reporting information

Regulated information from the external environment

Internal reporting information

Integration and selforganisational processing of information

External regulatory, variation or countervailing reporting information

External supplementary reporting information

Internal operational and situational information in real time

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Setting up and receiving information on stakeholder requests through basic category elements

Requests for information about the business entity's activities (indicative interpretation of stakeholder requests)

Basic category elements

Initiation of management decisions and implementati on of management decisions and fulfilment of stakeholder requests

Limited provision of material information

Notation: - information flow; - information and semantic (content) affiliation (relationship) of information; - mutual compliance through the exchange of information.

Fig. 4 Information field of application of integration and self-organisation tools for managing the activities of a business entity. Note Suggested by the authors

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Self-organising features

Basic category elements

Management system standards

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Information blocks of management systems

Information and economic integration and selforganisational processing of information

Integrated solutions for business and management system management

Integrated presentation and management of business information

Fig. 5 Supporting elements of the integrated information and economic mechanism and management tools. Note Suggested by the authors

in combined statements, which may contain sub-blocks with expanded and supplemented information, if necessary; (3) “micro-blocks”, e.g., structural elements—sections, followed by information aggregates—substantive characteristics of structural elements (e.g., indicators in report sections); further breakdown can be made in terms of sources of substantive characteristics (indicators). Integration and self-organisational information processing involve acquiring information blocks of combined integration and self-organisational features (composite integration and self-organisational features). The value of these attributes results from economically justified decisions to implementing integrated business processes within the framework of self-organisational management and management of integration processes (as illustrated in Fig. 5). The task is to ensure the features are assigned to the information blocks. Such an initiative will make it possible to track the formation of information (by “operational traffic”) while implementing management decisions. These decisions involve integration and self-organisation actions to ensure the continuity of information for the tactical and strategic management of business processes.

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3.3 Prerequisites for Achieving Integration Within the Information and Economic Mechanism for the Development of Systemic Integration in the Management of Business Processes of Enterprises We present the information and economic context in the development of system integration in the management of business processes of enterprises as a result of the implementation of the corresponding mechanism of business process management. The latter is based on two systemic conceptual “pillars”: (1) application of a process-oriented approach to business process management as a fundamental one in the information context of management (i.e., with the formation of an information management process); (2) filling the said process with managerial and economic content by the valuebased approach to informing about the value creation process, the economic characteristics of which reflect the complex value quality of the business entity’s activities. At the same time, the information and economic management toolkit, an active component of the mechanism, will be reproduced in the instrumental elements of the processes of making and implementing specific decisions. The tools are specified in the business process management mechanism and identified in the decision-making and implementation processes. Thus, the information and economic mechanism and tools for decision-making and implementation will be based on theoretically and methodologically defined and conceptually oriented principles, methods and means of management. The integration platform of the information and economic mechanism for managing enterprises’ business processes will be the management system’s integration framework. This basis should be used and maintained in a state that will provide process-effective solutions. Acquiring the status of an integrated system with the degree of integration necessary and sufficient to produce effective solutions will depend on how well the basis is theoretically substantiated and practically implemented. Therefore, a study of its content is necessary for building the framework. Let’s explore the content of the framework. We consider the integration framework as a generalised category that combines partial categories: system properties, management attributes, integration factors, requirements and features, principles and types of integration, which are schematically arranged in Fig. 6. The article defines the categories’ essential substantive features, connectivity and qualitative integrative perfection, which will determine the constructive completeness and parameterisation of the basis. The property of emergence (integrative) becomes the basic (dominant) in enterprises’ business process management system with integrative quality. According to this property, the system’s integrative quality is that the set (sum) of properties of individual elements is not equated to the properties of the system as an integral entity

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Properties of a system with integrative qualities (acquisition of qualities due to emergence)

Management attributes

Integration factors (action of factors as a manifestation of integrative qualities of system properties)

Signs of integration (acquisition of signs by the system: system elements, links between elements)

Integration requirements (requirements for each type of integration, in accordance with the integration principles)

Integration types (selection of types according to management attributes)

System-wide integration principles

Fig. 6 Theoretical prerequisites for achieving integration in the management of business processes of enterprises. Note Suggested by the authors

[16–22]. Systemic integral properties do not belong to individual elements but are unifying for them. It is impossible to know the properties of the system as a whole by studying each element separately. Therefore, we must study the elements and the system as a holistic (integrated) union of elements. The property of emergence (integrity) of the enterprise business process management system is a unifying or “super-property” for other properties. These properties include: • Integrity of the system and its division into elements; • Connectivity—the structured nature of the system based on the ordering in space and time of a set of elements and links between them; • Organisation of the system, which is manifested in the reduction of current information uncertainty by accumulating information on the elements and the system as a whole based on targeted activities; • In the system’s life cycle, its development occurs, accompanied by restructuring processes, reaching a new qualitative level based on association, restoration, the emergence of new connections, and thus the formation of the system as an integrated one [23–28].

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The combination of all properties is due to emergence. This results in a synergistic effect, manifesting in a new resultant quality of the system’s functioning. This synergistic effect, as an increase in the efficiency and benefits of the system, will confirm the effectiveness of integration. On the other hand, the negative effect will indicate the inconsistency of the integrated environment of enterprise business process management with management decisions. The task of enterprise business process management is to ensure the growth of the positive effect and the reduction of the negative effect while maintaining the appropriate level of system integration to achieve higher performance results. Therefore, we should study system properties in the information and economic mechanism of enterprise business process management through integration factors. Emergence affects other system properties and “dissolves” in them, providing integrative qualities as additional features. This position is fundamental and determines the approach to the formation of theoretical prerequisites for achieving system integration in the management of business processes of enterprises. The peculiarities of the properties are determined by the division of the business process management system (as a cybernetic system) into management and managed subsystems and the links between them (reflecting the interaction between the subject and object of management) and within them. Management is traditionally understood as the development and implementation by a subject of targeted effects on an object [29–31]. The systemic approach to management involves considering the subject and the object as separate parts of the management system: the managing and the managed, respectively. These parts should interact with each other as a whole, ensuring the viability and development of their “union” in a particular subject area. Decisions in the management system should be made and implemented optimally according to its structure. The structural elements of the system should be fine with the efficiency of management. Management processes are technologically rationalised to ensure the quality of management influences. Under such conditions, perfect business process management systems of enterprises become integrated. Information exchange between the management and managed parts is necessary to develop management influences to ensure the dynamism of integrated business process management and the total activity and effectiveness. Creating closed management loops based on feedback allows us to concentrate information in the system elements that will reflect the dynamics of processes. This way, we can make developing and implementing business process management decisions efficient and effective. Implementation of the above principles of cybernetic laws of construction and functioning of management systems, with an emphasis on their emergence, allows to obtain a natural derivative effect—reduction of uncertainty (entropy) in the data on the structure and behaviour of the managed system and growth of awareness and “skill” of the management system in the development of management influences.

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Organisational integration

Targeted integration

Instrumentation and application integration

Information and economic integration Fig. 7 Links between types of integration of the enterprise business process management system. Note Suggested by the authors

The information and economic types of integration of the enterprise business process management system will be the integrative basis of the information and economic mechanism for developing system integration in such management. Their symbiosis will form derivative information and economical type. The interconnectedness of the types of integration illustrates in Fig. 7. The information and economic integration will achieve the business entity’s best systemic economic effect and sustainable state. These actions will be ensured by an integrated information and economic management mechanism (tools) in the decisionmaking and implementation processes. Acquisition of integrative qualities by the information and economic mechanism (tools) will involve the application of the principles of management system integration. Each type of integration will be based on system-wide integration principles. The principles correlate with the types of integration as “one to many”. The requirements are formulated based on the principles for each type of integration; therefore, their correlation with the features will be “one-to-one”. Therefore, the correspondence of the attributes to the requirements can be typified (standardised), as illustrated in Table 2. We will define integration requirements and features as a projection of the requirements and features (given in Table 2) of information integration through the prism of economic integration (as well as information and economic integration) to other types of management system integration. Such an approach will allow taking into account the requirements and features of target, functional, organisational, instrumental and

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Table 2 Compliance of integration features with the requirements by type of integration in the business process management system Integration requirements

Integration features

1. Targeted integration All-factor certainty of goals Consistency and coherence of goals

Goal setting takes into account internal and external factors influencing their definition Goals are structured as a result of decomposition

2. Functional integration Alignment of functions with management objectives Eliminate duplication of management functions

Functional structuring of the system corresponds to the target structuring based on decomposition Management functions have been collapsed based on the allocation of functions with the same tasks

3. Organisational integration Coherence of the organisational structure of management with the target and functional structures Regulation and inter-element coherence of actions in the management process Ensuring the throughput of the management system elements The resilience of the management system to destructive influences

The organisational elements of the system have goals, localised management functions and defined targets and functional tasks Distribution and assignment (taking into account coordination) of actions performed in the processes of decision-making and implementation in the management system to organisational elements are performed Load distribution on system elements is optimised Control over the system integration parameters is established, and measures to counteract disintegration are developed

Updating changes in the course of system operation Professional and cognitive qualities of developers and implementers of integrated solutions

Procedures for system autoregulation and re-engineering have been developed, taking into account-controlled integration parameters Professional compliance and cognitive compatibility of the personnel are achieved, which allows them to make and implement integrated decisions

4. Information integration (continued)

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Table 2 (continued) Integration requirements

Integration features

Coherence of the information structure with the target, functional, and organisational structures (“infrastructure positioning”) Sharing of information in the management system Elimination of inappropriate duplication of information in the management system Compatibility of information in the elements of the management system Aggregation of information on the progress and results of activities, the state of the management system

The information structure ensures the concentration and localisation of information in the elements of the target, functional and organisational structures Availability of a single information database that meets the functional needs of management Minimum required level of information duplication Conversion of heterogeneous information into homogeneous information for data comparability is ensured Availability of accounting methods and tools for synthesising and summarising data on the system’s operation

5. Economic integration Coherence of the management system structures and indicators of the economic performance of the entity Ensuring the economic feasibility of actions in the management process to achieve the cost-effective performance of the entity

Economic effect and effect-forming indicators are structured by the structural structure of the management system and the applied management concept and are representative of determining the entity’s state The system of economic indicators is hierarchically ordered, vertically and horizontally positioned and acceptable for coordinated economic performance management Methods and means of intellectualising actions (“knowledge economy”) for economic justification of management decisions on the impact on the economic effect and the state of the business entity are available

6. Instrumentation and application integration End-to-end management process by management levels based on unification and standardisation of information transformation processes Security of information storage and transformation processes Compatibility of decision-making and implementation processes

Note Suggested by the authors

We have not achieved standardisation, modularity and non-duplication of information technologies Means of control and protection of information and its transformation processes are in place Information technology tools are available for designing solutions and tracking their implementation on a single base of design and implementation (planning and actual) information using unified (universal) information and process technologies

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applied integration in information and economic integration as a synthesised type. Thus, this approach will ensure the integrative nature of the information and economic mechanism (tools) for managing business processes. We have provided a list of such requirements and features in Table 3. Table 3 System-wide synthesised integration requirements and features of the integration basis for the information and economic mechanism in the management of business processes of enterprises Integration requirements

Integration features (ІОj )

Conceptual agreement (coherence) in the management system of the information and economic structure of the mechanism with the target, functional, organisational structures and, accordingly, the content of applied tools to achieve a synergistic economic effect and identify and assess the state of the business entity

The information and economic structural basis of the mechanism (toolkit) ensure the concentration and localisation of information on target, functional, and organisational nature and its substantive relevance through a system of knowledge and indicators, metadata and data, communicative information about the course of activities and the formation of an “effect state” on the based on the information and economic management model (ІО1 )

Compatibility of the elements of the information and economic structure and establishment of inter-element (communication) links to ensure correct formation, holistic, complete and unambiguous assessment of the “effect state”

We have achieved homogeneity, commensurability and comparability of indicators (data) of structural elements, taking into account the semantic content of information (knowledge-metadata) connectivity and its symmetry based on the selection of methods and means of information processing and the implementation of the rules for the formation and management of the “effect state” (ІО2 )

Consolidation of information on the progress With the help of methods and means of of activities and formation of the “effect state” synthesis and synthesis of data that have usefulness, minimum necessary duplication and redundancy, the level of aggregation and interpretation of information sufficient for the application of methods and means of decision-making and implementation of decisions for the purposeful achievement of the “effect state” is achieved Optimisation of managerial decisions on the information and economic “infrastructure” of the management system in planning and achieving the “effect state”

The economic “effect state” corresponds to the level of information support of the applied methods and means of decision-making and implementation; decision algorithms “accept” the criteria and restrictions in the information and economic management model and ensure the coordinated formation of the “effect state” and achievement of its target parameters (ІО4 ) (continued)

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Table 3 (continued) Integration requirements

Integration features (ІОj )

Ensuring hierarchical network (vertical-horizontal) end-to-end and security protection of the processes of information and economical identification and processing of business actions or events in the management system based on technological standardisation and unification of processes in the context of individualisation of economic and digital intellectual design and implementation actions in decision-making and implementation procedures

Processes of identification of economic activity and development of its information and economic image: standardised and unified through maximum rationalisation based on typicality, modularity, universality, and uniqueness of technological actions; provided with instrumental applications of technological personalisation (authorisation of access to information and performance of technological actions) of decision-making and implementation procedures, as well as means of transaction protection; synchronised with the course of economic activity (business processes) within its (their) information and economic support through management decisions

Information and technological combination of decision-making and implementation processes, coordinated with the technological model of the management system of the business entity

Available tools and applications for designing, dispatching the implementation of management decisions based on the use of a single common database of design and implementation (planning and actual) information and the application of universal information and technology models for managing business processes (ІО6 )

The information and economic management model results from implementing a business model in the management process (supplementing the business model with management content) in the information and economic context of management (providing information and economic characteristics of the business model). The above requirements and features will be specified for specific management systems with their quantitative estimates based on parametric analysis of the system. Quantitative estimates of integration features can be attributed to partial parameters of the integrated management system, and their “convolution” will provide a generalised evaluation parameter.

4 Conclusion The value created by a business entity is an integrated economic expression (in the combined financial and non-financial information display) of the synergy of managing complex activities that consider social and environmental factors (ESG activities). In this context, the integration features that will be distinguished as system-wide will constitute information and economic integration as the leading type of integration related to other types: target, functional, organisational, etc. The

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mechanism for managing business processes of enterprises (as well as the process of making and implementing decisions in this area) will be characterised as information and economics. The integration platform on which the mechanism will be “installed” will be the integration basis of the management system. We propose to include the following elements in the integration framework of a business entity’s business process management system: properties with integrative qualities; management attributes; integration factors, requirements and attributes; principles and types of system integration. The composition of the framework’s elements represents an integral constructive formation that methodologically fills the systematology of integrated systems. The framework’s integrity is determined by the content of the links between its elements. Thus, integration factors manifest the integration qualities of properties and affect the management attributes. The attributes include goals, functions, organisation, information, economic semantics of management (decisions), and instrumental and applied means of developing and implementing management decisions. Signs of integration arise due to the influence of factors on the attributes. Each attribute indicates the corresponding (similarly named) type of system integration. For example, attribute information support of economically meaningful decisions in management will represent the above-synthesised information and economical type of integration. Attributes localise the effect of integration factors and identify the emergence of integration features. The signs are identified using attribute values. Accordingly, the values of attributes are formed under the influence of integration factors concerning the system integration requirements. Integration requirements are set for each attribute type of integration. Types of integration are based on principles correlated with the system’s integrative qualities. Therefore, a relationship is established between integration attributes and requirements, according to which requirements and attributes can be systematised in pairs by types of system integration. Thus, the built-up logical scheme of the integration framework makes it possible to determine the conditions of integration that must be observed when making and implementing management decisions. Implementation of the information-economic mechanism for managing business processes of enterprises will take place through integration features of the framework. These integration features are coherently allocated within the framework of the links of information and economic integration with its other types. The integration features allocated to the mechanism should be bilaterally linked to the provisions of the management and reporting systems (subsystems) standards. Such links between integration features and standards are proposed to be established through essential categorical elements acting as unified information and economic elements. These elements will directly correspond to the structural elements of reporting and indirectly to the management subsystems in which information for reporting will be generated. Essential categorical elements will be presented covering ESG activities. Thus, these elements include management concept and structured environment; business strategy; business model; business plan; business processes; performance; state of the entity; risks and opportunities; prospects for operations and development; and outlook for the future. The system-wide integration features will be systematised

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and interpreted through the substantive characteristics of the essential categorical elements, becoming category-oriented, which will be reproduced (reflected) in the content of reporting and management subsystems. Given the interconnection between management’s integration and selforganisational factors, we must do the same with self-organisational features in terms of using essential categorical elements. By joining the integration and selforganisational features through the substantive characteristics of the essential categorical elements, we will get pairs of categorically composed integration and self-organisational features. Decision-making and implementation in managing activities, and a specific sense, business processes, will involve integration and self-organisation of operational, situational, episodic, and reporting information in management subsystems. Such information will be structured as information blocks with hierarchically ordered data aggregation in the time and space dimensions. The information blocks will focus on meeting the entity’s information needs and stakeholders’ requests. They will be organisationally positioned concerning the management subsystems (through their affiliation with subsystems), in which decisions will be made and implemented.

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Improving the Recruitment Process in Multinational Organizations Using Robotic Process Automation and Artificial Intelligence Lubica Bajzikova and Tetiana Smerdova

Abstract In the ever-evolving business landscape, firms seek to improve efficiency and cut costs, often through workforce reduction and automation using technologies like Robotic Process Automation (RPA) and Artificial Intelligence (AI). This paper explores RPA and AI in-depth, discussing their significance, applications, and potential to replace human tasks. Artificial Intelligence covers a spectrum of intelligent machine behaviors, encompassing Fuzzy Logic, Neural Networks, and Robotics. It aims to create machines capable of human-like cognitive tasks, such as playing games and logical reasoning. RPA, on the other hand, automates business processes using computer programs with the capability to perform repetitive tasks typically done by human employees. The paper provides real-world examples of RPA and AI implementation in various sectors, including finance, telecommunications, and Human Resources, where automation can significantly streamline the recruitment process. Ethical and legal considerations, like GDPR compliance, are vital in these implementations. The paper concludes by emphasizing the potential for cost and time savings through automation while also suggesting using tools like Ansible and future enhancements like intelligent chatbots. Keywords RPA · AI · Automation · Efficiency · Recruitment · Cost reduction · Implementation

L. Bajzikova (B) Univerzita Komenského v Bratislave, Šafárikovo Námestie 6, P.O.BOX 440, 814 99 Bratislava 1, Slovakia e-mail: [email protected] T. Smerdova Lviv Polytechnic National University, 12 Bandera Street, Lviv 79000, Ukraine © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_14

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1 Introduction In the times of new technologies, methods of development, and willingness to be more efficient than competing companies, firms are looking for more advanced technologies. Despite their main goal, which is to have better results than their rivals in the market, they try to reduce costs by releasing some employees or replacing them with machines. Most people, when hearing that, are shocked and ask themselves if it is possible to do so, but here comes the answer, which is Robotic Process Automation (RPA) [1–3] and Artificial Intelligence (AI) [4]. Those two technologies are gaining popularity and being the most developed in the modern world. In the following paragraphs, those two methods will be explained in order to clarify their meaning, application, and spheres of life in which they could totally replace people.

1.1 Artificial Intelligence Artificial Intelligence (AI) is intelligence demonstrated by machines or programs which has been previously implemented by programmers [5, 6]. In the domain of AI, we include Fuzzy Logic, Evolutionary algorithms, Neural Networks, Artificial life, and Robotics. It may also be taken into consideration as a part of Computer Science, which researches intelligence and simulation of behavior in made programs. The main purpose for its development is to create machines able to perform operations similar to the human mind and senses, like computer games, knowledge management, expert systems, and logical reasoning [7]. The first test concerning AI was held in the 1950s and is called the Touring Test. Its primary role was to carry out a speech between a human and a machine and check if a person was able to recognize whether a computer or another person made the responses. Also, at the same time, the first AI laboratories were created, and until today, they are the leading ones. During the delves, it turned out that the progress in that field of study is very slow and hard to discover, but on the other hand, some failures in this area were the beginning of new technologies useful for other purposes.

1.2 Robotic Process Automation Robotic process automation (RPA) is an advanced technology of automatisation of business process models [8–10] with the use of computer programmes or robots which may operate using artificial intelligence in order to simulate human’s work [11]. The main feature or advantage of that technology is performing operations which are normally made by employees [11]. The proper way of programming the robots allows the to accomplish operations with different difficulty level which main feature is their recurrence and limited number of exception [12]. RPA may be used in

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every place where one robot can replace the process of integration of several systems. The main advantage is that robots do not need any workstations with physical interface because all their operations take place in virtual environment. Another worth mentioning feature is the ability to work between several systems without need to move so our invention may be called a virtual employee which proceeds the same process like a person to which it has been assigned before. The history of RPA begins with the first processes of automation [13]. Still, the breaking point was the evolution of programming and creating simple algorithms used for testing applications and catching the mistakes. It also allowed the employees to get better motivation for their work because properly written programme simplified the creation of documentation and allowed people to save their time, rise the efficiency and reduce amount of mistakes. Lots of effort is put to develop those technologies and make them useful in every sphere of life but for now, their implementation is in the phase of testing and first projects are being introduced to our life to check their abilities whether they will be able to fulfill their role or no [14]. The aforementioned methods are considered as the future of development and alternative for people as employees [15, 16]. The machines may save lots of time because the virtual workers will not move when there will be a need to make some operation in another place opposite to employees who would have to change place and even go between the buildings to press only some key on the other computer.

2 Examples of Implementations Although RPA & AI is relatively new topic, there are already some implementations in the real world.

2.1 Multinational Hospitality Company with PwC Another example of implementation of Robotic Process with automation and Artificial Intelligence was done by a multinational company call PwC (also known as PricewaterhouseCoopers). It has a headquarters in United Kingdom and deals with all kind of professional services in fields such as Assurances, Taxes, Analytics and Financial Advisory. It estimates that 45% of the work activities can be automated, which would results in global saving worth around 2 trillion dollars [17]. One of their implementations concerned a multinational hospitality company, which possessed and managed huge variety of hotels and hostels around the whole world. Finance and Accounting department of this company wanted to reduce costs of all repetitive manual tasks performed daily by the employees. Second goal of this case was to create more automated activity reporting. The company PwC believes that an RPA can be used in Financial field to process various kind of documents

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such as invoices, generate journal entries and for synchronization and integration of accounts. Firstly their approach was to identify process that could be automated: (a) Employees regularly rewrite data about revenue from one application to an Excel spreadsheet and another application simultaneously creating reports. This task was identified as relatively simple and tedious, but very time-consuming, consequently huge number of so called FTEs (Full Time equivalent) were associated with it, which resulted in lack of time for the rest of assignments, usually more important ones. (b) Daily reporting that concerned cover information, which had to be feed into application, which on the other hand created out of it a daily journal, which should be send to defined list of emails. Secondly, they have targeted exact processes yet to be automated to describe the problem above and created programs that fulfilled the requirements. Unfortunately, a technical description of solution is not available to public information. After the implementation they have measured they have measured feedback and listed results: . After automatization of processes 5 FTEs were saved and a gain of 50–60% efficiently was obtained while performing the given tasks. . Clients can now add more hotels/resorts to existing workbook in the Finance and Accounting Department. . Further 50+ processes were identified as potentially optimizable (PwC n.d., Source: https://www.pwc.be/en/documents/20171123-rethinking-ret ail-artificial-intelligence-and-robotic-process-automation.pdf).

2.2 Robotic Process Automation for Corporate Functions The next example of utilization of RPA and AI instead of a Traditional Automation is the case of internal implementation of RPA program on key Corporate Functions, such as finance, compliance, treasury and marketing, in a company called Capgemini. The main assumption of such a move is to reduce the time spent on performing tedious tasks and allow the workforce to perform more valuable assignments, consequently lowering the costs and susceptibility to errors. The illustration of an automated process can be seen in Fig. 1. The main difference between the RPA and TA is the need of human interaction with the system during the process. Whereas in the latter category, the activities are still performed by people, only being aided by technology, in the first group the human activities are unnecessary since the decisions are made automatically. This therefore results in a much greater performance and mitigation of the human error. Studies suggest that office workers on average spend up to 80% of their time at work performing repetitive tasks which could easily be “robotized”. Delegating these chores to robots would liberate the employees to dedicate more time to operations

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Fig. 1 Schema illustrating the process (https://www.capgemini.com/consulting-fr/wp-content/upl oads/sites/31/2017/08/robotic_process_automation_the_next_revolution_of_corporate_functions_ 0.pdf)

involving creativity, judgement or soft skills, such as customer service. This in turn would result in increase of customer satisfaction. Since its launch in 2014 the internal RPA program has automated over 200 processes on 50 robots in their delivery centers around the world. This caused the investment to pay back within just few months and in total decreased the running costs by 7 times. Those results however varied depending on the type of processes executed and seemed to have the highest effectiveness when a number of them could be run without interruption and on those not requiring human decisions In those best case scenarios, the cost reduction reached over 80%. Finally, since the robots are able to operate continuously throughout the year, their total number of working hours in comparison with a standard employee is 8 times higher. What’s more, the predictability of the operation of robots and their unconditional compliance to standards lead to easy record keeping of the activity’s history, allowing for full transparency during audits [12].

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2.3 UiPath—Finance and Banking Robotic Process Automation and Artificial Intelligence can also be applied to financial services. Banking organization and institutions are already using intelligent software to lower the costs of hiring staff and paying them salaries. This solution saves valuable time, boosts the efficiency of company’s prosperity and increases its profits. However, there are still some inconvenients concerning that topic. In this situation UiPath company comes with the best solution offering an intuitive and crucial platform. It has many advantages such as: . Scalability—multiple robots can work independently and on multiple machines. Moreover, they can connect to a single platform, anywhere from the whole world. . Reduced repeatability and redundancy—no repeatable data entry tasks, best skilled employees focuses on high-value activities. . No paperwork—platform provides tool for interpreting scans, transferring documents into computers, sending emails and detecting language to automate boring office work. . Untouched system—robot does not affect the source code, it can be implemented with many desktop applications. This feature provides much faster automation of projects and lowers the expenses. . Data analysis—robot can be applied to perform data analytics, create performance reports and identify transaction patterns. Aforementioned features may be applied to many solutions in the banking area. Two of them Retail Credit Assessment and Retail Fraud detection will be described in the next paragraphs. Retail Credit Assessment is a repeatable task which can be performed by UiPath robot [18]. This machine would check the income, expenses and many different financial aspects of the client. Then the analysis report would be created containing the creditworthiness. Robot can also be applied to Retail Fraud Detection. Every banking organization is obliged to monitor the clients activities and find any suspicious actions. Gathering such information is a wearisome and time consuming process for employees. Robot can find retrieve all the data, find patterns and potentially suspicious activities. Then everything would be included into a report. To sum up, UiPath solutions improve many different activities like: office work and data analysis. Its RPA platform is used widely in the world with successful results. (UiPath, n.d., Source: https://www.uipath.com/rpa-in-banking).

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2.4 Robotic Process Automation in the Telecom Industry Another field in which Robotic Process Automation can be utilized is the Telecom sector. In modern world the need for being constantly connected with the globalized network is constantly increasing, and providers must compete with one another to provide the fastest, most affordable, and cutting edge services. The biggest problem is that most of them are slowed down with large amount of operational process such as administering data and costs. Those problems are of the of such kind that could benefit significantly from use of the modern technology. With the process automation provided by RPA, telecom providers will be able to more easily manage their back office and deal with large volumes of repetitive and rules-based operational processes. The main focus could be centred on the customer experience, since their satisfaction is the main source of constant income. But even the telecom industry right from the start has the nature that should be fitting the solutions suitable for automatic solutions, there are still problems impeding their implementations: . Low levels of operational agility—the software layers used by the companies run simultaneously for support operation and their communication is very restricted and specific, making it hard for any possible modifications. Also many of them still require manual input, what absorbs a lot of resources, that could be spend on interaction with the clients. . Inefficient flow of information—in telecom industry providers on a daily basis must manage a big amount of data, which includes documents for clients, workers and intermediary companies. Large amount of that is still in physical format and paperwork. Because of this inconsistency of used formats providers are unable to follow the course of their services. . High operating cost and capital expenditures—telecom providers need to control the costs from multiple sources from maintaining data integrity and employees salary to software and hardware. Because of such high costs, not enough is spend on improving the customer satisfaction. With investing into RPA, those problems could be solved and those factors could improve the work of the companies: . Higher levels agility and scalability—RPA allows in the easy way to manage the software’s workflow. By connecting it with the different departments, it could eliminate the need for manual input from the employees and let them focus more on satisfying customer needs. . Improved data communication and transmission—RPA systems are noninvasive and could coexist with already used legacy systems. This would allow for removing the data gaps between different information sources and monitor them easily.

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. Significant cost reduction—even though RPA requires investing the initial cost, later it is profitable in a long run with all its benefits allowing to focus on customer experience and making them stay with the current provider. The applications of RPA that are planned to be implemented in telecommunication industry can be seen in Fig. 2. The example of a company, which implemented Robotic Process Automation is AT&T, which trained over 2000 employees to be able to create robots to automate the tedious, mind-numbing portions of their jobs. Those employees analyse data compiled from software bots. Starting from 2015 created were 1000 software bots automating mundane, repetitive tasks for employees. They perform tasks ranging from helping technicians activate equipment for customers to aggregating data for service orders and customer service reports. The result of the implementation of this system is not only that robots have taken the repetitive tasks from the workers, giving them the opportunity to focus on the more important jobs, but also bots are doing it much faster and more efficiently. Previously, employees spent hours aggregating this data every week and could only compile reports weekly. Now, they can see more data and make more insightful decisions.

Fig. 2 Applications of RPA in telecom industries (http://www.prodapt.com/transforming-businessusing-rpa/)

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In conclusion the telecom industry is the great field for introducing the Robotic Process Automation, because of the amount of repetitive jobs, that are necessary, but very simple. The other benefits of it would be higher levels agility and scalability, improved data communication and transmission and significant cost reduction [19].

3 Problem Description In the times of modern development, changes in the world, and finding new branches of business [20], people started becoming mobile, changing their university courses, trying to study different subjects to have a wider range of possibilities on the job market. If not, they try to develop new skills and gain knowledge on paid courses after finishing their studies and finding a job because the course they graduated from is not for them. This is the purpose of many changes, huge rotation on job market and thousands of CVs (Curriculum vitae) and motivational letters that human resources management has to deal with and due to such actions people have to wait weeks or sometimes months until they receive answer telling that they are suitable people for that place. This is the cause created by the time needed to check if the person is suitable for the place and fulfills all the requirements. It is nearly impossible to remember all the candidates so statistics have to be done and among those people several are chosen, calls are made and then we can really start the recruitment process [21, 22]. This could be fastened due to implementation of new technologies, that would be helping people from HR (Human resources) department confirm their duties faster and allow them to take care of other exercises waiting for them. Thanks to the development of information technology, artificial intelligence and robotic process automation, recruiters could be relieved from some of their duties. New technologies, programmes or even machines may be implemented and introduced to the daily life of those employees. At the beginning, the system would be taking care of preliminary phase of recruitment which would be the candidates selection by information given in their papers. This could shorten the waiting time for about a half and allow recruiters to just check if the correct CVs have been taken and not spend our in front of the wrongly made ones. In the presented system all of the requirements must be implemented, all the content to which HR management people pay attention will be included in order to check if the programme has been correctly made. At first the system will be in testing phase so all the CVs would be checked by the computer along with the recruitment service [23] which will consume same amount of time but after implementing all of the solutions, correcting mistakes and getting on the right track this process would make a significant difference for the companies. When this phase will be made, the presented system should independently accomplish all the operations and give the manager a list of all people suitable for the position. In the next phase the system may be developed in such a way that an email would be send automatically to the candidates who have successfully gone through the qualification process to let them know that they are taken into consideration in the next phase and should wait for a

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contact from a consultant or they will receive a mail with ready date and time of the meeting they should go to if they want to get that job. What is obvious, if a person does not qualify for the place, an email with such information will be also send back. Aforementioned features are not the only ones that may be implemented for the programme. The presented ones are just the beginning and they will be created with the prototype. If the prototype will succeed, then further features may be added to increase programme efficiency, lower the costs of having employees and allow HR department to take care of other tasks not considering looking through CVs anymore. The exemplary features will be monitoring the employees, their work and awarding them with benefits if they do everything according to the plan or warning them if their job is all the time missing something but those features will be described lated when we will speak about further development of our system. That innovation could lead people working human resources department to many simplification and save of time so they can take care of other activities instead of wasting time on looking through the CV in order to separate and select the proper candidates.

4 General Description of Solution Our solution is going to provide the company with an efficient automatic filtration of CVs which will be an irreplaceable tool helping during recruitment process [24, 25]. Our project will be replacing people at their places and raise the efficiency of the company. The number of duties of one system could be easily separated between several employees but considering the fact that AI robots will appear in our company this process would be omitted and several employees could be fired and lose their workplaces. Main advantage would be that CVs will be separated to the groups of CVs of people who qualified for the job and the refused ones but in the future the lists may be developed in such a way that instead of dividing CVs on the useful and useless ones there will be an option of sorting CVs to several different profession at the same point and make the employees save more time then it was predicted at the very beginning. The selection of the CVs will be very precise to give the HR specialists clear lists of candidates fulfilling all the requirements. The system must be properly implemented to not give any false information and prepare reports after scanning all the data. The prepared reports will consider data about the employees who could be possibly hired for the place and with whom the company should contact in order to check the skills of future employee and hire a proper person. Apart from the names and skills required for the position, from the report employees will be able to take information about the forms of contacting the candidate, his availabilities, working expectations and information whether an email considering information about recruitment meeting has been sent or delivered or not. This means that the reports will consider also the preliminary information about the time and date where the recruitment speech will take place. The whole recruitment process can be seen in Figs. 3 and 4. In the next chapters it will be described in details.

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Fig. 3 Flowchart of recruitment process—part I

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5 Phases of Recruitment Process 5.1 Phase 0—Setting Up Offers for Job Positions First of all, before setting up the application, people from HR group has to analyse, which types of jobs are needed at the given company. Depending on the field of company’s operation one can create job positions that can should be available for recruitment process (Fig. 5). While having the needed jobs positions one should determine the required skills for each of them and start writing the description of the position which will be placed in the future job offer. The next step is to point out clearly defined skills required from the candidate. Our application will automatically analyse the description of the position and will make a list, in which there will be skills such English, Java, SQL and being opened for challenges. An employee from the HR group can modify these lists by adding or removing new skills. Please note that the entire list will be used by our application in the future to verify the employee. The technical skills will be checked during tests in the third phase, while the soft skills will be checked in the following one, therefore the list of requirements should be succinct and not too vast. It is also preferred to provide the expected level of proficiency in knowledge of listed languages and technologies on a scale of 1–5 stars, where 1 means basic knowledge, and 5 a full understanding of the subject. Example can be seen in Fig. 6. On the next page: Moreover, sometimes in companies there is for instance demand for a given position, and then after a right person is found is becomes obsolete. However, after few months it is common that the position is needed again, since in big companies there is high turnover rate. In order to simplify this our application will offer option to set the current job offer to “inactive”, so it will disappear from the website until it is activated again. In the future one can also think about automatic propagation of the job offers to locally popular web services for job searching such as LinkedIn, however one has Fig. 5 Diagram of Phase 0

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Fig. 6 Choice and ratings of required skills for job position

to remember that if the offer in on foreign service it is still required for an future employee to send their CV, since it will be automatically analysed by the application, what is further described in the next phase.

5.2 Phase 1—Filtration of CVs After the deployment of jobs offers on the company’s website, one can expect many cover letters and CV to be sent. Even today, many companies already have an automatic CV scanning procedure, which search the key information and skills required for the given position. Our application will also use such a function. A virtual account associated with the applicant will be created and filled in with data from CV such as address, name, surname and acquired technical skills. The computer will then decide whether the applicant is suitable for the applied position or related position. One has to remember that in large companies there are often many similar positions, so the system should provide information to employee from HR which jobs positions are appropriate in the given case. The acquired skills and features will be then used in the next phase of recruitment in order to prepare a skill test before the interview (Fig. 7). If the system decides that the applicant does not have the required skills for any company, then an automatic message will be sent back to the email address that will look like in Fig. 8. In the case when it turns out that the future employee has the appropriate features for other job, which he did not applied at first, he will be also informed about that to encourage him/her (Fig. 9). There is also a possibility for the situation in which the person submitted the application, but during its processing or even before sending the CV, the position has become inactive or one has took a stand of it. In this case, an appropriate email will be sent which will tell that the applicant has indeed the required skills (or not), but

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Fig. 7 Diagram of Phase 1—part I

the position is currently unavailable and that the company will automatically sent in the future e-mail when it will be interested in employing him/her, which can be read in Fig. 10. If the application will decide that the applicant has required skill and the job position is still opened it will try to arrange a meeting for the test. At this point there are two possible scenarios. (1) Person from HR group will formerly define in which hours he/she will have free time at work and the system will automatically assign the time in this interval. For instance: A given employee has free time on Wednesday from 10:00 to 14:00, consequently the system will automatically sent the email with invitation for the test that will be held from 10:00 to 11:00. (2) A special notification will pop up at the computer of HR group with the request to choose meeting time manually. Such a prepared e-mail will contain all needed information such as time, data and place for meeting. It will also ask if the applicant is available at that time and offer

Fig. 8 Exemplary email for applicant without necessary skills

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Fig. 9 Diagram of Phase 1—part II

Fig. 10 Exemplary email in case of free job positions

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him a possibility to change the date. One can also call the company if they want to arrange the meeting personally.

5.3 Phase 2a—Recruitment Tests The next phase of the recruitment process is dedicated to checking the real skills of the candidate. After CV is checked and the data included in it, shows that the applicant is meeting the requirements for the position, that is currently open, system sends him the invitation on the interview. In fact the actual interview is only the part of the process of determining if the person can take the position. The procedure begins with the test. This test is from the fields important for the given job, for example if it is connected to programming, questions would be about appropriate technologies or languages. In order to make this process automatic, required is the database of questions and tasks categorized and marked in the difficulty level. Everything will start from determining, what fields of knowledge are required to be checked. This will be assigned of what are the requirement for the positions fitted for investigated person’s CV and the guidelines for opened jobs. With this information random questions from appropriate categories are pulled from the database and the test is prepared by the program (Fig. 11). This test may be taken in the paper version or the digital one solved on the computer in the company. Today many test for the companies are solved online. It is very convenient for the applicant, however it also causes, that exam questions can easily be copied and spread in the internet and other sources. In such situation, no matter how big is the question pull, the new applicants may have contact with them already, what makes the test result to be unreliable. The other aspect, that helps in preventing the escalation of test outside the company, is the fact, that the questions on the tests are not in order of the categories, they are in random order, what makes it more difficult to remember after the test. The test consist of certain amount of tasks, from different fields and of different types. There are closed question with given four possible answers and goal of choosing the correct one and open questions in which the applicant will have to write his own answer. In terms of grading those questions the checking of the closed tasks can be automated if the test is performed on computer, simply by the program, by just finding which answer was marked and if the test was performed on paper, by scanning it and later performing similar action. The open question unfortunately cannot be automated, at least for now. Because of that there is the need for the person which will be evaluation open task and grading it with previously established scale and later introducing it to the system. As previously mentioned the topic of the tasks is strictly connected to the character of the open positions. For that the questions from those fields are taken from the database and put in random order. The other element of the test are the questions for soft skills. Those types of questions have a purpose to test the analytical thinking of the candidate, for example, sorting algorithms or general knowledge from Information

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Fig. 11 Diagram of Phase 2a

Technology. Also they can take a role similar as in the interview to collect more knowledge about the candidate from himself as “Have you ever experienced hard situation in your life and how did you deal with it?” In this way it is possible to get more initial impressions about the applicant and decide it is worth to invite him to the next phase. Also the good aspect of those questions typically reserved for the interview is the fact, that some people find it easier when they have some time and write the answer, instead of for the answers in quick moment. After candidates come to the company’s headquarters to write the test, it needs to be evaluated. Closed questions are checked by the application, open questions are checked by the recruiter, and the score is put in the system. This process should not take more than a few days, and after it’s finished, the mail is sent to each with information on whether they passed the test. If they did, the date and time of the interview will also be given in Fig. 12.

5.4 Phase 2b—Job Interview After successful part of sorting CV and being chosen, the process of recruitment will begin. Candidates will be firstly asked to take part in an interview. Here they will have

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Fig. 12 Exemplary email with test’s results

ability to make a choice which would be more convenient for them. Such options will be skype conversation or physical appearance in the company and holding the speech there. Both cases will be equally suitable for the company because the candidates will be observed by a camera analysing the behaviour of the candidate so in each case the conversation will be almost the same. The algorithm implemented will check the look of the face of the candidate and gestures to give output if the candidate is lying, cheating or doing other unpleasant things (Fig. 13). Skype interview will be one of the possibilities to choose for a recruitment speech. This would be an innovation for a recruitment speech allowing many people to fit of better to their daily schedule. During a speech, a camera will be precisely observing the candidate in order to read his face. His feelings will be read from the mimicry, and all the confusion, lies, and others will be discovered immediately. The system will also focus on the eyes of the speaker. During Skype speech, the candidates may be trying to cheat and put some papers invisibly around the computer, but if their eyes are moving, looking for the answer, the system will inform us about it. To prevent candidates from using the Internet, they will be asked to share the screen while the speech is held. To record facial expressions, and emotions and monitor the general behavior of a potential worker, Affectiva software [26] can be used, which provides high-level solutions and ready-to-use tools. This platform has a built-in module which allows connecting with machine learning programs [27–29]. Due to this solution, after several recruitment processes, our platform will provide better results in finding skilled workers and managing teams.

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Fig. 13 Diagram of Phase 2b

Local interviews are preferred by the companies. It gives HR people more possibilities to check candidates, take control of them, and monitor their behavior. The candidates will be observed constantly, their gestures, face mimicry or eyesight will be under constant monitoring of a recruiter. Of course, the cameras with our implemented programs will also be used to compare the results from the program with those from a recruiter. After such analysis of results, the right decision may be made considering the candidate and his competencies considering the place he is applying for. It will also be easier to give candidates some tests to solve and then quickly mark them in order to grade the level of references. During the recruitment phase the candidate will have firstly technical and language knowledge checked. Those two factors have the biggest impact on the final result and of recrutation because of those factors are not fulfilled then the candidate has no chance to get place in our company. Those skills will be checked during the speech by tests and talk on the date estimated by HR specialists and candidates. Character is another worth mentioning point according to which the expectations, position and clients will be assigned. Social networks will be manually checked by recruiters in order to check photos, posts and groups people are assigned to in order to hire suitable new employees whose will be suitable for the politics of our company. Candidates by the end of recruitment will be asked to tell their expectations considering job. Their expectations may consider health care, free days, benefit cards,

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Fig. 14 Diagram of Phase 2b—final step

money earned, bonuses, and the possibility of getting a better position in the company. All of those aforementioned will also be taken into consideration during the speech; the machine will also make some recognition on the market to see if the expectations are not too high and suitable for the applied position (Fig. 14). After finishing the recrutation, results will be saved in the database of the company. Candidates whose skill were not enough or there were some doubts concerning them, will have special adaptation next to their profile so in some case like one of the accepted candidates refuses to take our offer or after selecting the candidates there will be still free spaces so we could faster get to people who could be taken one more time for testing and then may be taken as an employee to our company. The reports from the camera will be also added to candidates profiles in order to be familiar with their behaviour. This will definitely help with dividing tasks between employees. If the employee gets easily confused, stressed or is disorganised then such a person will be assigned to office work and not have any contact with clients in order not to scary the client and lose the potential source of money and tasks. Opposite to workers who have significant contact with customers, know what they want, and can convince clients to invest more, widen the project in order to gain more benefits and money at all.

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Fig. 15 Diagram of Phase 3

5.5 Phase 3—Future Development The future of the system may be crucial for the development of the company and its employees. The way in which it will all go will also have great impact on the future of the companies and HR departments. Instead of only selecting the proper candidates for employees and sending them information about the recruitation and checking candidates skills the programme may have additional features added. The first change would be monitoring employees work, their results and deciding whether they should be awarded a pay rise or warning. The system will be connected with employees accounts, then monitoring their working time will be possible. The data, progress and project created will be visible for our robot as well as the schedule of the employee. If all the tasks have been done according to the plan, the results are satisfactory and there are no delay, the system will send an information to the HR department about suggesting the pay rise to the employee to reward his skills, dedication and motivate for further development. What is more if the employee will not be sticking to the projects, do prohibited things with their computers at work or miss all the deadlines the system may suggest to fire that employee and look for someone else for his place (Fig. 15). The next change will be monitoring the work of the groups of employees. Every team would have a project to complete which must be divided fairly between all of the group members. Unfortunately not all of the members may feel good in the tasks assigned to them, so the system will be used to analyse the progress of the groups and targets and goal and try to define whether a change in that team is necessary or not. The change may consider, team leader change, project change or just change of one or two employees in order to raise efficiency and fasten the processes in the company. In special cases there will be a possibility the assign already started projects to other groups but only in case that the group will totally not be able to do anything with it.

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6 Advantages of RPA and AI in HR The newly developed systems which consist of artificial intelligence and robotic process automation are the future of companies, machines and processes in HR. This may simplify life of people working in that department, shorten waiting time of candidates and allow the company to move to another level of services but this are not the only advantages. The rest of them will be mentioned in following points. 1. The introduction of RPA and AI in HR will definitely cut the costs and allow companies to save money. First of all, list of employees will be definitely shortened. This will be the main occurring change causing the whole chain of changes to appear. Created system will replace people in some tasks which means that some of the employees will not have anything to do at the place and some of the duties of the other employees will also disappear. Employees will be slowly fired because computers will replace them and employee who does not have any activities is useless because nobody will be paid for doing nothing but this will bring lost of financial benefits to the company which is one of the main reasons of opening a business. In that case that process will look in such way that system will replace people, less people will mean less workplaces and less computers, just several people will be needed to analyse reports generated by the robot. 2. The other benefit for company owners will be the reduction of working time of HR employees. Despite firing lots of people, the ones that will stay may have shortened working hours or change their agreement from full-time to part time job because of lacking duties. 3. The next worth mentioning point is higher efficiency provided by the machines. Many responsibilities will be taken from employees and given to the robots so employees could take care of other activities while robots will be making their previous time-consuming job. Employees instead of spending many hours, reading the CVs, selecting the proper ones and analysing all the data will be able to do other tasks while those will be made by robots. This will help the companies to have everything finished on time and do not wait for some results longer than expected. Worth mentioning point is also giving HR managers new tasks to fulfill all their working time if they will stay with full-time job to increase efficiency and pay them money for the job, not for doing nothing. 4. Moving on to the robots and system, we should remember about the better choice of employees for the place. Robots will stick strictly to the requirements given and implemented functions. Their selection will be more accurate because they will look only for the clearly given points, without looking at others or trying to cover some points with other which may not be directly connected. That point gives our machine a great advantage over traditional candidates selected by the HR department. 5. During the recreation process, some well-skilled candidates who were perfect for someplace faced a problem that could not be overcome easily, and it was being a friend or family of a recruiter. The truth is that many well-skilled employees cannot find a job despite great qualifications, technical background,

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and knowledge possessed just because one of the candidates has great contact with a recruiter, and that is the only reason for hiring unskilled employees. Our product will prevent the formation of such a situation. Only professional skills and working experience will be taken into consideration in order to hire the best possible employee for the development of a company. 6. In the next phases of recrutation our robot will have one of the hardest tasks to do which will be the controlling the interviewed person. All of the recruitment speeches can be hold by Internet via Skype or other free to use programme in order to ensure everyone with some comfort. All of those conversations will be recorded and checked by our product in order to know where was the client looking if it was direct to the camera of was the sight going somewhere else in order to look for some information or read some hints to make good impression on the interviewer. Some face or feelings recognition could be also included in the project, that would tell us if the candidate lies or tells the truth. That could show the lack of confidence or some sort of knowledge of the interviewed person in order to know some character features and check how this person reacts for such situation. 7. The last but not least point is making an advertisement for our company using the AI. That is the newly created system not widely spread, not known for many people so using such a system for the purpose of advertisement would guarantee an immediate success and show the development and advanced technologies used in the company which will be attracting new candidates to come to our company and work in such developed environment. This will be one of the keys to success. All of the mentioned points are the main advantages of RPA and AI usage in the company but it is just a beginning. More solutions will be implemented later after the first point which is AI in HR brings benefits and people will convince themselves to it. Below one can see a brief summary of the advantages given by usage of RPA&AI in form of Table 1 Table 1 Summary of the advantages of using RPA&AI Main advantages

With RPA&AI

Without RPA&AI

Cutting costs on employees

+

−

Shorter working time

+

−

Higher efficiency provided by machines

+

−

Focusing on particular skills

+

+

No hiring unskilled people who are friends of recruiters

+

−

High level of observation of candidates

+

+

Good advertisement to showing progress of the company

+

−

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7 Legal Aspects While considering each of the steps included in the recruitment process [30] it is essential to take into consideration their ethical and legal repercussions. While the former aspect could be debated over and its abidance is not always necessary and frequently depends on subjective and specific decision of a committee, the latter one is strictly regulated in numerous legislations and has to be followed regardless of the situation. One of the most notable laws is called General Data Protection Regulation (GDPR). It was created on 14 April 2016 and comes into effect on 25 May 2018. Since this date, every organization which collects and processes data of citizens in European Union countries will have to comply with the rules prescribed in the Regulation. Any infringement of them may result in severe administrative fines either to the company or the natural person committing the offence. However, the main idea of the Regulation is the unification and simplification of laws of all European countries concerning the privacy of their citizens. This has been done by inclusion of number of articles regarding the subjects such as data pseudonymisation, designing the information systems with privacy in mind and enabled by default, easier access to the data being processed or the usage of plain language understandable for everyone in the policies of the companies [31]. The article, which is most relevant to the previously described algorithm of recruitment process, talks about profiling and in particular the techniques allowed for its execution. According to the given definition, profiling includes any means of utilizing personal data to perform automatic evaluation of natural person’s status, such as their performance at work, economic situation, health, personal preferences or interests which produces legal or similarly significant effects concerning him or her. These groups also include recruiting practices without any human intervention. In this article it is stated that any person has the right not to be subject to such an automated system, unless it is used under the pre-specified conditions. One of these exceptions is its utilization to attest the correctness and efficiency of execution of a contract between the employee and the employer. It is also worth mentioning that for attaining and maintaining the fairest and most transparent rules during the automated evaluation appropriate mathematical or statistical procedures [32] should be adopted and any inaccuracies or errors resulting from these operations should be minimised. The other category of personal data, which has to be mentioned when dealing with decision making about the person’s future are the sensitive data, such as their racial or ethnic origin, political opinion, religion or beliefs, genetic or health status or sexual orientation. It is forbidden for the company and especially their automated systems to classify or discriminate against a person because of it and as a result put them in a disadvantageous position in comparison with the other candidates during the recruitment process. The same category also includes. Finally, each of the stages of recruitment, which deals with personal data of the candidate has to be accompanied by a separate, freely given and specific consent in

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a clear form, preferable written. For each of them, the purpose of data processing, and in particular profiling, its methods and consequences have to be openly stated to the applicant and must be accepted with their affirmative action. The lack of such activity is equivalent with their disagreement. Additionally, every person has the right to audit the personal data which have been collected about them without obstacles or additional fees from the employee.

8 Time Savings and Optimization Assessment In order to evaluate optimization that comes with deploying our application, one need to analyze time spent on each individual task. There are 5 steps that can be separated. 1. Firstly, one can assume that in an enormous company there are about 30 CV sent weekly and average employee from HR group has to spend about 5 min to read each one. 2. Secondly, one can assume that about 25 CVs will pass the first step, so that the employee has to create a account in database with all necessary information about each person what can take around 30 min. 3. Such a HR employee has also write an email in which he will invite an applicant for a test or say the position is currently filled in or person lacks some of the skills required for the position. Such a task can be estimated for no more than 10 min. 4. After the tests, one has to firstly grade the answers (what can be also optimized by the machine), write them into database and generate a report. In case of usage of our application one will save here around 15 min for each person. 5. Lastly, one has to send an email with results and information about further recruitment process what will also take around 10 min. All the time optimization can be seen in Table 2. It shows time spend for each task, per person, weekly and monthly. As one can see, with the use of our application a company can save up to 6300 min per month, what is also equal to 105 h. Therefore it can be said that the application saves around 0.66 of Full Time Worker (FTW), assuming that a typical employee works 160 h in a month. (Also called FTE—Full Time equivalent) One can also analyse the percentage share of each task in Fig. 16. Mostly the time is saved during the writing the data into database and checking if applicant is suitable for various positions.

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Table 2 Evaluation of resources required to spend in the recruitment process Task to be done Number of people/ Repetitions of the task weekly

Time spent per repetition (min)

Time spent weekly (min)

Time spent monthly (min)

Read CV and evaluate

30

5

150

600

Write info into DB and check other positions

25

30

750

3000

Write email response (invitation or turndown)

25

10

250

1000

Generate report 25 of test’s results

15

375

1500

5

10

Send 2nd email

50

200

Sum

6300

Fig. 16 Percentage share of each task

9 Future Development of Our Project The first possibility is the usage of Ansible. It is an automation tool used particularly in IT that automates cloud processing, configuration management, and application installation. It is lightweight software that does not need any additional configuration or programs. It uses a very simple language (YAML) that allows to automate many

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different tasks. Our platform could be installed on many computers and servers with just one program. There is no need to install software physically on every machine. Our project focuses on recruiting people, generating tests, controlling the cooperation of hired teams, and managing payroll and benefits. However, there could be expanded functionalities such as intelligent and automatic chatbots. Their purpose could be supportive for employees as well as people who would like to get know some information about the company or have questions about a job they apply for.

10 Conclusions In the modern world global development allowed society to become much more mobile and change their lives much more often. People move from place to place because of education, work, or just personal preferences. That situation created the biggest rotation in the job market in history. This also puts pressure on the Human Resources systems of all the companies since they receive more applications than ever before and need to go through them in a reasonable time and choose the most suitable candidates for open positions. This sort of task today is still mostly performed by humans, who have limited capabilities in this sort of task. The recruiter needs to read all the applications, make statistics out of them, and decide which ones should be considered. This process takes a lot of time, from the moment of receiving the CV to the call inviting the interview and, finally, after that, the information if the company is interested in hiring the person. This is all caused by the inability to process such a large amount of information with standard methods. The use of modern information technology, robotic process automation [33] and artificial intelligence [34–36] can improve this process. The new technologies can make it faster and more accurate allowing to free the time of recruiters and allowing them to focus on other important tasks. The goal of this project was to create a system that would allow us to do that. Such a solution, at the beginning, should collect the CVs sent with the applications to the company and analyze them with the use of keywords, determining what skills are included in the document. Later, this information is saved in the database. The system holds the list of actual open positions in the company with requirements for them and compares if any candidate could be suitable for it. If such a case arises, automatic mail is sent inviting the test to the company’s headquarters. The test cannot be performed remotely, because of security reasons and to prevent the spreading of tasks outside of the company. The purpose of this test is to check the real skills of the candidate required for suitable solutions. After the test is written, it is graded by the system and recruiters. If the result comes out positive, another mail is sent inviting on the interview, which can be performed in the headquarters or remotely. The system also has the function used for this, where it evaluates the behavior of the candidate during the conversation. If the candidate, after all the stages, seems to be suitable for any open position, system informs the Human Resources about that and recommends the candidate.

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The described system uses Robotic Process Automation and Artificial Intelligence, which, in consequence, creates a lot of advantages for HR. After the initial investment, the costs decrease by decreasing the required amount of employees in the department since less amount of recruiters are required to analyze the system outcomes with proposed workers, and their time is better spent not wasting on unnecessary tasks. The system also, with time, will improve its performance, making better recommendations for candidates due to its AI element, which is constantly learning. The whole outcome of the project shows that the new technologies implemented in the Human Resources department can significantly improve the functioning of the company.

References 1. Hofmann, P., Samp, C., Urbach, N.: Robotic process automation. Electron. Mark. 30(1), 99–106 (2020) 2. Van der Aalst, W.M., Bichler, M., Heinzl, A.: Robotic process automation. Bus. Inf. Syst. Eng. 60, 269–272 (2018) 3. Ivančić, L., Suša Vugec, D., & Bosilj Vukšić, V.: Robotic process automation: systematic literature review. In: Business Process Management: Blockchain and Central and Eastern Europe Forum: BPM 2019 Blockchain and CEE Forum, Vienna, Austria, 1–6 Sept 2019, Proceedings 17, pp. 280–295. Springer International Publishing (2019) 4. Artificial Intelligence and Robotics: Separating reality from the hype—Opinion— outsourcing—sourcingfocus.com [WWW Document], n.d. http://www.sourcingfocus.com/ site/opinionscomments/artificial_intelligence_and_robotics_separating_reality_from_the_ hype/. Accessed 4.8.18 5. Ariely, D.: Predictably Irrational, Revised and Expanded Edition: The Hidden Forces that Shape Our Decisions, Revised and Expanded ed. edition. ed. Harper Perennial, New York, NY (2010) 6. Artificial Intelligence: Definition, Examples, and Applications [WWW Document]. Encyclopedia Britannica. https://www.britannica.com/technology/artificial-intelligence. Accessed 5.21.18 7. Benefits & Risks of Artificial Intelligence [WWW Document], n.d.: Future of Life Institute. https://futureoflife.org/background/benefits-risks-of-artificial-intelligence/. Accessed 4.9.18 8. Syed, R., Suriadi, S., Adams, M., Bandara, W., Leemans, S.J., Ouyang, C., Reijers, H.A.: Robotic process automation: contemporary themes and challenges. Comput. Ind. 115, 103162 (2020) 9. Aguirre, S., Rodriguez, A.: Automation of a business process using robotic process automation (RPA): a case study. In: Applied Computer Sciences in Engineering: 4th Workshop on Engineering Applications, WEA 2017, Cartagena, Colombia, 27–29 Sept 2017, Proceedings 4, pp. 65–71. Springer International Publishing (2017) 10. Cooper, L.A., Holderness, D.K., Jr., Sorensen, T.L., Wood, D.A.: Robotic process automation in public accounting. Account. Horiz. 33(4), 15–35 (2019) 11. Boulton, C.: What is RPA? A revolution in business process automation [WWW Document]. CIO (2017). https://www.cio.com/article/3236451/business-process-management/ what-is-rpa-robotic-process-automation-explained.html. Accessed 4.10.18 12. Capgemini Consulting: Robotic Process Automation (RPA) The Next Revolution of Corporate Functions [WWW Document] (2016). https://www.capgemini.com/consulting-fr/wp-content/ uploads/sites/31/2017/08/robotic_process_automation_the_next_revolution_of_corporate_ functions_0.pdf. Accessed 4.9.18

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Review of Automatic Speech Recognition Systems for Ukrainian and English Language Andrii Dumyn, Solomiia Fedushko, and Yuriy Syerov

Abstract Automatic speech recognition systems are highly regarded today since they can improve inclusivity, streamline business communications, etc. This page overviews the most recent scientific research and a quick description of the automatic speech recognition system’s overall structure. The paper evaluates popular speech recognition products in English and Ukrainian based on model parameters, error frequency, and processing time. Studying information dynamics during internet warfare has led to developing technologies that enhance human–machine interaction, including speech recognition. Speech recognition systems convert spoken language into readable text, enabling convenient and fast communication in various business applications. The accuracy and capabilities of speech recognition systems depend on the underlying algorithms and technologies employed. This paper provides an overview of the leading technologies in speech recognition, including hidden Markov models, natural language processing, N-grams, and artificial intelligence. The traditional hybrid approach to automatic speech recognition (ASR) has been widely used, but it has limitations regarding accuracy and training time. End-to-end deep learning has emerged as a promising alternative for speech recognition, directly converting audio input to text. This paper overviews commercial and open-source ASR tools, highlighting popular options like DeepSpeech, Whisper, and Facebook Wav2Vec 2.0. Open-source ASR systems offer flexibility and integration possibilities, making them an attractive option for developers. This paper offers insights into the advancements and challenges in automatic speech recognition, which covers various technologies,

A. Dumyn · S. Fedushko (B) · Y. Syerov Lviv Polytechnic National University, Lviv 79000, Ukraine e-mail: [email protected] A. Dumyn e-mail: [email protected] Y. Syerov e-mail: [email protected] S. Fedushko · Y. Syerov Comenius University in Bratislava, 820 05 Bratislava, Slovakia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_15

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research studies, and software tools that contribute to improving the accuracy and functionality of speech recognition systems. Keywords ASR · AI · Speech recognition · Automatic speech recognition · Speech-to-text

1 Introduction Due to the development of technologies, the amount of ways to implement human– machine interaction also increases. Speech recognition is one of the many ways people communicate with computers with little to no typing. Various business communication applications take advantage of the convenience and speed of oral communication. Speech recognition or speech-to-text systems allow the identification of spoken language and convert it into readable text. Simple natural language recognition software only detects words and phrases that are spoken clearly and have a small repertoire. More advanced software deals with background noise, accents, multiple languages, real speech, etc. The power of speech recognition functions depends on algorithms and technologies. The leading speech recognition technologies are: . . . .

Hidden Markov model (HMM); Natural language processing (NLP); N-grams; Artificial intelligence (AI).

1.1 The Subject Environment Description Automatic Speech Recognition (ASR) [1]—uses machine learning or artificial intelligence (AI) technology to convert human speech into readable text. Over the past decade, this field has expanded exponentially, with ASR systems appearing in popular applications that people use every day, such as real-time captioning, podcast transcriptions, meeting transcriptions, and more. The main popular approaches to automatic speech recognition are the traditional hybrid approach and the end-to-end deep learning approach [2]. The traditional hybrid approach is general and has been dominant in this field for years. Many companies use this approach because there is more knowledge about building a reliable model, thanks to extensive research and training data, despite plateaus in accuracy. Traditional HMM [3] and GMM [4] require forced data alignment. Force alignment is the process of recording a textual transcription of a segment of audio speech and determining where certain words occur in time within the segment of speech.

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Fig. 1 The general structure of the traditional hybrid approach of automatic speech recognition system

Figure 1 shows the system of the traditional hybrid approach consisting of a vocabulary model, an acoustic model, and a speech model for transcription prediction [1]. A lexical model describes how words are pronounced phonetically. Usually, each language requires a particular set of phonemes created by phonetic experts. The task of an acoustic model (AM) is to predict which sound or phoneme is pronounced in each speech segment based on force-aligned data. An acoustic model is usually of the HMM or GMM variant. A language model (LM) simulates a language’s statistical properties. It establishes which word combinations are most comparable to be uttered. Its job is to forecast which words, and with what probability, will come after the present words. Decoding uses a lexicon, an acoustic and linguistic model, to create decoding. Although the traditional hybrid approach to speech recognition is still widely used, it has several drawbacks. This approach is characterized by lower accuracy of results. Each model must be trained independently, which is time-consuming. Another drawback is the difficulty of obtaining forcibly aligned data. An equally important drawback is the mandatory presence of the necessary experts to create a unique phonetic set to increase the model’s accuracy. While the data does not require forced alignment, end-to-end deep learning [5] enables users to directly transfer auditory input features into a series of words. Depending on the architecture, a Deep Learning system may be trained to generate correct transcriptions without vocabulary and language models. But language models could be able to provide more precise outcomes. CTC [6], LAS [7], and RNN [8] are popular end-to-end deep learning architectures for speech recognition. These systems is trained to produce highly accurate results without forcefully aligning data, lexical, and language models. End-to-end deep learning models use less human resources and are simpler to train than conventional methods. Additionally, they exhibit more accurate outcomes than conventional models. Scientists working with end-to-end deep learning models continuously look for ways to improve these models using the latest research. Such models are expected to reach human-level accuracy in a few years.

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Modern ASR systems recognize entirely spontaneous speech that is natural, unrehearsed and contains minor errors or hesitation markers. Certain speech recognition programs offer the possibility of customization to meet the specific requirements of users. This customization is made possible through several features, including language weighting, which prioritizes certain words, particularly those that are commonly used or unique to a particular discussion or topic. The software is trained to recognize links to specific products as well. Additionally, acoustic training enables the program to adjust the ambient noise that affects the vocal sound. By learning the nuances of conversational style, pace, and volume, the software differentiates between various forms of ambient noise, including the speech of multiple individuals and that of children. The speaker marking feature enables the program to identify individual participants and their specific contributions to the conversation. Finally, profanity filtering allows the software to filter out unwanted language and words.

1.2 Literary Sources Overview As mentioned above, the scientific community is actively working on automatic speech recognition. Below is an overview of scientific works on the selected topic. The authors of [9] described the actual voice recognition and speech-to-text conversion procedure and reviewed relevant machine learning methods. As the analysis result, the authors found that the Hidden Markov Model (HMM) works better in speech-to-text conversion with only the disadvantage of computing power. The article [10] showcases the development of an Automatic Speech Recognition (ASR) system for the Romanian language. The system leverages a multilevel neural network architecture to transcribe input speech and utilizes a statistical language model to enhance transcription accuracy. The authors’ language model successfully achieved a 9.91%-word error rate and a 2.81%-character error rate. The study also evaluated the system’s response time, with an average delay of 70 ms when executed on a GPU, making it potentially suitable for near-real-time speech recognition applications. The scientists [11] examine the challenge of operating Automatic Speech Recognition (ASR) systems for users with diverse language profiles. To address this issue, the authors propose personalized ASR systems tailored to each individual user’s needs. The authors investigate the feasibility of training customized end-to-end speech recognition models securely on mobile devices. This approach ensures that user data and models never leave the device or get stored on a server. The study’s findings reveal that personalization leads to a 63.7% reduction in relative Word Error Rate (WER) during server-based training and 58.1% in the mobile environment. The authors also observe that fine-tuning the encoder levels of the RNN-T model could produce significant performance gains for speech-impaired users. The authors [12] propose a system that converts speech into text by adding information about the speaker’s turns. Since the absence of punctuation makes it difficult

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for humans to read and degrades the performance of many subsequent machine processing tasks, the authors investigated different models of punctuation restoration based on Deep Learning of the Ukrainian language. The authors presented a developed transcription system supporting punctuation recovery that uses a bidirectional recurrent neural network to generate probabilities for hypothetically placed punctuation marks. The authors used a word embedding program to improve the accuracy of punctuation generation. The paper [13] presents such a level of speech signal recognition, which speeds transcription and effective search in vast volumes of oral information during broadcasting. The multilingual speech recognition model implemented by the authors does not require prior language recognition and has a specific potential for modeling vocabulary complementarity. The paper’s authors propose a speech conversion scheme that allows for topic selection, tracking of actual values (proper names, numbers, dates, etc.), punctuation marks, and reduces manual editing of the resulting text. The paper compares the WER for models trained in one (monolingual mode) and two (combined language mode) languages, allowing to estimate of the language error rate (LER) of the system based on the assumption that the WER degradation is caused by incorrect language recognition. The authors note that the average LER for the Ukrainian language is about 5%. The article [14] covers acoustic phoneme model-based implementation techniques and algorithms for automated speech recognition. The typical and most effective speech-to-text decoder architecture is for languages spoken in the West. Working with the Ukrainian language, which is distinguished by a somewhat open word order, the authors investigate this strategy. The process of changing graphemes into phonemes takes into account the issue of word stress as well as the characteristics of spontaneous continuous speech. With 100,000 dictionaries available, the main speechto-text conversion engine operates in real-time. Discussed are potential avenues for language and domain expansion, enhanced parameter estimation, and ergonomic concerns. The authors of the report suggest doing a detailed investigation of the issue of LM integration in the ASR model using the coder-decoder approach [15]. On the largescale Google voice search and dictation datasets as well as the medium and public Switchboard datasets, the study compares some of the most well-known past and many suggested approaches. The comparative findings demonstrate that for the firstpass estimate, the straightforward fine-merging strategy performs well. After several iterations of Google voice search, cold fusion works better than shallow fusion and has a reduced mistake rate. The authors show that the suggested strategy of employing a pre-trained language model as a lower-level decoder on the Switchboard performs better than cold and deep synthesis, which forms the basis for further study. Ambient noise adversely affects the operation of ASR systems. The authors of [16] propose to solve this problem by using multiple learning of acoustic models based on a neural network. The authors propose an approach based on noise vectors obtained by combining the means of speech frames and silence frames in an utterance, where the speech/silence labels are derived from a GMM-HMM model trained for ASR alignment in a way that does not require additional computation. Except for the

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averaging of feature vectors. Experiments show that this adaptation technique leads to a relative WER improvement of 6–7%. In [17], a deep learning-based intelligent system is suggested for filtering movies utilizing a quick and precise profanity detection method based on convolutional neural networks (CNN). The results, which show numerous detections of offensive spoken phrases, support the efficacy of the suggested techniques. The suggested system surpassed the most recent base algorithms in terms of macro-average AUC (93.85%), weighted average AUC (94.58%), and all other metrics, including F1score, on the new profanity dataset assessment measures. The suggested method also exhibits improved profanity detection results based on assessment criteria, such as AUC, accuracy, and F1 score, and is demonstrated to be faster than manual human inspection. The authors of [18] propose an end-to-end unified ASR-LID architecture based on a recurrent neural network to provide seamless multilingual interaction. The authors’ experiments show that multitasking architectures perform better. The best collaborative architecture outperforms monolingual ASR with a 6.4–9.2% reduction in word errors and acoustic LID with a 53.9–56.1% reduction in error rates. In [19] the authors propose an improved speech (SE) based on DNN aimed at increasing the performance of the automatic speech recognition system. The authors build a CER-oriented SE model that does not have additional computational costs and changes in network architecture because the proposed method is simply a training scheme for existing DNN-based methods. Experimental results show a CER improvement of 8.8%. The concept of reinforcement learning (RL) is used in [20] to provide an alternate method for training ASR models sequentially. The method suggested by the authors uses the gradient algorithm as opposed to the typical learning scheme using maximum probability estimation. The whole transcription was retrieved by the authors based on the projected model during the learning phase, and as a reward, the model was immediately optimized with a low Levenshtein distance. The outcomes demonstrate that the authors outperformed a model trained only using maximum probability estimation. The well-known Conformer model has become the base model for various speech tasks thanks to its hybrid architecture support. However, through a series of studies, the authors [21] found that the Conformer architecture design could be more optimal. After a detailed Conformer macro- and micro-architecture analysis, the authors proposed a Squeezeformer architecture that outperforms state-of-the-art ASR models using the same training schemes. In particular, the authors used the Temporal U-Net structure instead of the Macaron structure proposed in Conformer. Squeezeformer achieves 7.5%, 6.5%, and 6.0% better WER results on LibriSpeech without external speech models, which is 3.1%, 1.4%, and 0.6% better than Conformer-CTC. Among the tasks of artificial intelligence, an important area of research is the recognition of linguistic emotions. Work [22] addresses the problem of language emotion recognition by using a genetic algorithm to optimize SVM for language emotion recognition. To highlight the features and compare them with the formal recognition of speech emotions, the analysis of the main components based on

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wavelet packets was carried out in the work. Experimental results show that the optimal recognition rate using SVM is 95%.

2 Existing Software Tools Overview There are many ASR systems on the market. At the same time, they are still too expensive to develop from scratch. Commercial systems offer limited access to detailed model output and limited integration into other software. Therefore, ASR systems such as Google Speech API, Amazon Transcribe, Microsoft Azure Speech Service, Nuance Recognizer, and others are not very flexible. With these limitations in mind, more open-source ASR systems and frameworks are emerging. However, knowing which systems best suit the project and which is used with little effort and with satisfactory results is not easy. The variety of opensource ASR systems makes it challenging to find one that combines flexibility with an acceptable word error rate. An overview of the most popular open-source ASR systems is provided below.

2.1 Project DeepSpeech DeepSpeech [23] is a free and open-source speech-to-text library [24] created by the Mozilla organization. The library also provides machine learning techniques using the TensorFlow framework to accomplish its mission. Users use the product to build their training models to improve the underlying speech-to-text technology for better results or port it to other languages as needed. The project currently supports only English by default. It is also available in many programming languages. A welloptimized neural network-based machine learning system eliminates the need to develop separate components to model different anomalies such as noise, echo, and speech characteristics [25]. Since DeepSpeech was last updated in 2020, it is not appropriate to compare it with the latest current versions of other systems.

2.2 Whisper by OpenAI Whisper [26] is an open-source ASR system [27] trained on 680,000 h of multilanguage and multi-task controlled data collected from the Internet. The authors [28] show that such a large and diverse data set improves robustness to accents, background noise, and technical language. In addition, the system allows for transcription in several languages and translation from these languages to English. Whisper models are trained to perform speech recognition and translation tasks, capable of transcribing audio speech into text in the language spoken (ASR) and performing

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speech translation. Researchers at OpenAI have developed models to study the reliability of language processing systems trained under massively weak supervision. Nine models of different sizes and capabilities have been developed.

2.3 Facebook Wav2Vec 2.0 Facebook Wav2Vec 2.0 [29] is one of the most advanced models for automatic speech recognition [30] thanks to self-supervised learning, a relatively new concept in the field. This training method allows pre-training the model on unlabeled data, which is always more accessible. The model is configured on a specific data set for a specific purpose. The model proposed by the authors [31] trained in two stages. The first stage takes place in a self-monitored mode, performed using unlabeled data to achieve the best speech representation. The second training phase is supervised fine-tuning using labeled data for model training to predict specific words or phonemes.

2.4 Nvidia Conformer-CTC Large The Nvidia model [32, 33] was created to transcribe speech using lowercase English alphabet letters, spaces, and apostrophes. It was trained using audio recordings from several thousand hours of English speech. With almost 120 million parameters, this model is a non-autoregressive “big” variation of Conformer. The feature of this product is the so-called transformer models, which well capture global interactions based on content, while CNNs effectively use local features. Convolutional neural networks and transformer models are combined by the authors [34] to effectively model the local and global dependencies of an audio sequence. Transformer and CNN are greatly outperformed by Conformer, which also achieves the best level of accuracy.

2.5 SpeechT5 The self-learning encoder-decoder pre-training is learned by the Microsoft [36] SpeechT5 [35] unified modal framework. Six modal pre- and post-networks are included in SpeechT5, along with a conventional encoder-decoder network. To assure alignment of the text and voice information, the developers suggest a cross-modal vector quantization method that randomly combines speech/text states with latent units as an encoder-decoder interface. The duties of automatic speech recognition, synthesis, translation, voice transformation, augmentation, and identification are all covered by this product [37]. Table 1 provides general information about the ASRs selected for analysis.

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Table 1 General data on selected ASRs ASR

License

Github starts

Contributors

DeepSpeech

Mozilla Public License 2.0

20,542

136

Whisper

MIT License

15,004

28

Wave2.0Vec2.0

MIT License

19,818

297

Conformer

Apache License 2.0

5046

189

SpeechT5

MIT License

490

9

3 Results 3.1 Comparison Criteria When choosing a ready-made ASR system for further integration into their software, developers are guided by various parameters of these ASRs. First, whether the selected ASR supports the language used in the developed tool is worth considering. Several metrics are used to evaluate ASR performance, such as word error rate (WER), match error rate (MER), character error rate (СER), optimal input/output alignment, relative lost information (RIL), the word lost information (WIL) [38]. For this study, it was decided to perform a comparison based on WER and CER indicators since already trained models are being investigated. WER [39] is a standard for measuring how accurate an ASR transcription is compared to a transcription made by a human. In other words, it is a general indicator of the automatic speech recognition system performance. A general difficulty in measuring performance is that the recognized word sequence may have a different length from the (presumably correct) reference word sequence. WER is derived from the Levenshtein distance, which operates at the word level rather than the phoneme level. WER is useful for comparing different systems and evaluating improvements within a single system. To determine the primary source of mistake, additional work is required because this type of assessment must provide information on the nature of translation problems. The frequency of errors in words is calculated as follows: WER = (S + D + I) / N = (S + D + I) / (S + D + C),

(1)

where S is the substitution number, D is the deletion number, I is the insertion number, C is the correct word number, and N is the word number. The better the ASR system performance, the lower the WER value, with a WER of 0 as the best result. CER [40] is a general indicator of the performance of an automatic speech recognition system. CER is similar to WER but works on a character rather than a word. The character error rate is calculated as follows:

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CER = (S + D + I) / N = (S + D + I) / (S + D + C),

(2)

where S is the substitutions number, D is the deletions number, I is the number of the insertion, C is the correct characters number, N is the characters number. When there are numerous inserts, the CER output occasionally range between 0 and 1. This number is frequently linked to the proportion of characters that were mistakenly guessed. The ASR system performs better the lower the CER value is, with a CER of 0 being the optimal outcome. Another essential characteristic is the model’s size and the number of parameters used for recognition, which affects the system’s accuracy. However, the larger the model’s size, the more computing power is required to operate the system. Since work with the Ukrainian language is planned in further studies, comparing the selected ASR in work with both English and Ukrainian languages is appropriate. Two datasets for comparing the results of the selected ASR were decided to be used: . The LibriSpeech corpus[41]—is a collection of read English speech created from audiobooks. This corpus is part of the LibriVox project and contains 1000 h of speech at a sampling frequency of 16 kHz[42]. . The Common Voice corpus[43]—is a multilingual collection of transcribed speech. Mozilla developed this corpus containing 2500 h of audio in 38 languages [44], including Ukrainian. The performance comparison of the selected ASRs was performed based on the 11th Gen Intel® Core™ i7-11800H @ 2.30 GHz processor using the NVIDIA GeForce RTX 3060 Laptop GPU 6 Gb Ubuntu 20.04 LTS video card. Dataset for English language LibriSpeech test-clean (2620 samples) for the Ukrainian language Common Voice corpus (7095 samples).

3.2 Comparison Results The measurements presented in the work are the average value of several measurements. Table 2 shows the performance of selected ASRs for the LibriSpeech dataset. Below is a diagram to illustrate the execution time of the analyzed ASR models (Fig. 2). Figure 3 shows the results of the analysis of ASR by WER and CER indicators. Table 3 shows the performance results of selected ASRs for the Common Voice dataset. Figures 4 and 5 show a diagram to illustrate the execution time of the analyzed ASR models and the results of their analysis by WER and CER indicators.

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Table 2 The performance of selected ASRs for the LibriSpeech dataset Model

Language

Number of model parameters

WER, %

CER, %

Dataset processing time, seconds

Openai Whisper tiny

En

37 184 256

5.6574

2.1917

155.6391

Openai Whisper base

En

71 825 408

4.2695

1.6826

238.3150

Openai Whisper small

En

240 582 144 3.0569

1.1507

507.4517

Openai Whisper medium

En

762 320 896 3.0192

1.4169

1283.7428

Facebook’s wav2vec2-base-960 h

En

94 396 320

3.3837

0.9551

70.9348

Facebook’s wav2vec2-large-960 h

En

315 461 792 2.7674

0.7608

171.5118

Facebook’s wav2vec2-large-960 h-lv60

En

315 471 520 2.1588

0.5708

185.3550

Nvidia Citrinet Large

En

141 224 337 3.3704

1.0943

61.764950

Nvidia Conformer ctc large

En

121 501 313 2.1968

0.6014

84.9243

Microsoft Speecht5 ast

En

154 588 800 6.3698

6.3699

901.41772

Fig. 2 Diagram to illustrate the processing time of the dataset in English (sec.)

4 Discussion Improving the level of recognition accuracy is one of the primary goals of ASR. Although both the end-to-end Deep Learning ASR approach and the conventional hybrid approach are fairly accurate, neither claim 100% accuracy. This is caused by a variety of speech inflections, including accents, slang, and tone of voice. Without

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Fig. 3 Visual presentation of the analysis results for the dataset in English (%)

Table 3 Results of ASR work with the dataset in Ukrainian Model

Language

Number of model parameters

WER, %

CER, %

Openai Whisper tiny

Multi

37 184 256

68.8096

27.2190

412.2983

Openai Whisper base

Multi

71 825 408

55.5651

20.8864

600.7720

Openai Whisper small

Multi

240 582 144

34.1833

13.7738

1232.4181

Openai Whisper medium

Multi

762 320 896

22.7521

10.7898

3110.1124

Nvidia Citrinet Large

Ua

141 224 337

11.7691

8.1250

1593.3079

Dataset processing time, seconds

considerable effort, even the greatest deep-learning models cannot be trained to cover the whole spectrum of speech features. Some researchers think that by using personalized speech-to-text algorithms, they resolve the accuracy issue. However, user models are less accurate, more difficult to train, and more expensive to employ in reality than a great end-to-end deep learning model without a specific use case, such as child speech. While analyzing the operation of the selected ASR systems, Facebook’s wav2vec2, Nvidia Conformer CTC large, and Microsoft Speecht5 could not work

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Fig. 4 The diagram to illustrate the processing time of the dataset in Ukrainian (sec.)

Fig. 5 Visual presentation of the analysis results for the dataset in Ukrainian (%)

with the Ukrainian language dataset. The study shows that the selected ASR systems work better with the English language, while with the Ukrainian language, they show worse results. In particular, the best results of working with the Ukrainian language were shown by the Nvidia Citrinet Large model with WER and CER values of 11.7691% and 8.1250%, respectively. As for working with the English language, the best values of WER 2.1588% and CER 0.5708% were obtained using Facebook’s wav2vec2-large-960 h-lv60 model. Regarding data processing speed, Nvidia

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Citrinet Large showed the best results for the English language data set and Openai Whisper Tiny for the Ukrainian data set. It is also worth noting that, on average, the Ukrainian-language dataset takes longer to process.

5 Conclusions As a result of this analysis, it is concluded that there are many tools for implementing automatic voice recognition systems on the market. However, most show better results in working with the English language. There are also tools for work on speech recognition in the Ukrainian language, but their recognition accuracy metrics show worse results. Therefore, work on developing models for speech recognition in the Ukrainian language with greater accuracy is an urgent task for further work. Acknowledgements This work was supported by the National Scholarship Programme of the Slovak Republic and EU Next Generation EU through the Recovery and Resilience Plan for Slovakia under project No. 09I03-03-V01-000153.

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10. Avram, A.-M., Vasile, P.A.I.S., Tufis, D.: towards a Romanian end-to-end automatic speech recognition based on deepspeech2. Proceedings of Romanian Academy Series A. vol. 21, pp.395–402. (2020) Access mode: https://academiaromana.ro/sectii2002/proceedings/doc 2020-4/11-Avram_Tufis.pdf 11. Sim, K.C., Zadrazil, P., Beaufays, F.: An investigation into on-device personalization of endto-end automatic speech recognition models. arXiv preprint arXiv:1909.06678. (2019). https:// doi.org/10.48550/arXiv.1909.06678 12. Sazhok, M.M., et al.: Punctuation restoration for Ukrainian broadcast speech recognition system based on bidirectional recurrent neural network and word embeddings. In: COLINS, p. 300–310. (2021). Access mode: https://ceur-ws.org/Vol-2870/paper25.pdf 13. Sazhok, M.M., et al.: Automatic speech recognition for Ukrainian broadcast media transcribing. Control Syst. Comput. 6, 46–57 (2019). https://doi.org/10.15407/usim.2019.06.046 14. Robeiko, V., Sazhok, M.: Real-time spontaneous Ukrainian speech recognition system based on word acoustic composite models. (2012). Access mode: http://cybermova.com/publicati ons/2012--sazhok-robeiko--speech-recognition--ukrobraz.pdf 15. Toshniwal, S., Kannan, A., Chiu, C.-C., Wu, Y., Sainath, T.N., Livescu, K.: A comparison of techniques for language model integration in encoder-decoder speech recognition. In: 2018 IEEE Spoken Language Technology Workshop (SLT), pp. 369-375. (2018). https://doi.org/10. 1109/SLT.2018.8639038. 16. Raj, D., et al.: Frustratingly easy noise-aware training of acoustic models. arXiv preprint arXiv: 2011.02090 (2020). https://arxiv.org/pdf/2011.02090.pdf 17. Wazir, A.S.B., Karim, H.A., Lyn, H.S., Ahmad Fauzi, M.F., Mansor, S., Lye, M.H.: Deep learning-based detection of inappropriate speech content for film censorship. IEEE Access 10, 101697–101715 (2022). https://doi.org/10.1109/ACCESS.2022.3208921 18. Punjabi, S., et al.: Joint ASR and language identification using RNN-T: an efficient approach to dynamic language switching. In: ICASSP 2021—2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7218–7222. (2021). https://doi.org/ 10.1109/ICASSP39728.2021.9413734 19. Sawata, R., Kashiwagi, Y., Takahashi, S.: Improving character error rate is not equal to having clean speech: speech enhancement for ASR systems with black-box acoustic models. In: ICASSP 2022—2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 991–995. (2022). https://doi.org/10.1109/ICASSP43922.2022.974 6398 20. Tjandra, A., Sakti, S., Nakamura, S.: Sequence-to-sequence Asr optimization via reinforcement learning, In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5829–5833. (2018). https://doi.org/10.1109/ICASSP.2018.8461705. 21. Kim, S., Gholami, A., Shaw, A., Lee, N., Mangalam, K., Malik, J., Keutzer, K.: Squeezeformer: an efficient transformer for automatic speech recognition. arXiv preprint arXiv:2206.00888. (2022). Access mode: https://arxiv.org/pdf/2206.00888.pdf 22. Wang, Y., Huo, H.: Speech recognition based on genetic algorithm optimized support vector machine. In: 2019 6th International Conference on Systems and Informatics (ICSAI), pp. 439– 444. (2019). https://doi.org/10.1109/ICSAI48974.2019.9010502. 23. Official documentation DeepSpeech. Режим доступу: https://deepspeech.readthedocs.io/ 24. Офіційний репозиторій DeepSpeech Режим доступу: https://github.com/mozilla/DeepSp eech 25. Pratap, V., Xu, Q., Kahn, J., Avidov, G., Likhomanenko, T., Hannun, A., Collobert, R.: Scaling up online speech recognition using convnets. (2020). arXiv preprint arXiv:2001.09727 26. Official documentation OpenAI’s Whisper. Access mode: https://beta.openai.com/docs/introd uction/overview 27. Official repository OpenAI’s Whisper. Access mode: https://github.com/openai/whisper 28. Radford, A., et al.: Robust speech recognition via large-scale weak supervision. Tech. Rep., OpenAI. (2022). Access mode: https://cdn.openai.com/papers/whisper.pdf 29. Official documentation Facebook’s Wav2Vec 2.0. Access mode: https://ai.facebook.com/blog/ wav2vec-20-learning-the-structure-of-speech-from-raw-audio/

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Strategic Direction of Financial Activities of EU States in Digital Business Models Katarina Vavrova and Alexander Šarlina

Abstract The Digital Finance Strategy is to ensure that the EU regulatory framework for financial services is fit for the digital age. Embrace digital finance for the benefit of consumers and businesses. Businesses need to strengthen their digital business models. Likewise, most tax laws predated the digital economy. For this reason, taxation of the digital economy is on the list of priorities of the European Commission. The goal of taxation is to ensure fair and efficient taxation of the income of all companies operating in the EU. The aim of the contribution is to comprehensively characterize the possibilities of the digital economy in selected EU states and to evaluate the already submitted proposals for solving the given problem. Subsequently, compare the proposals for solving the taxation of the digital economy. The paper contains a summary view of the current topic of the digital economy, taxes, description of individual proposals and identification of differences between the described proposals. Keywords Digital finance strategy · Taxation of digital economy · EU regulatory framework · Digital business models · Base erosion and profit shifting · Financial activities strategic direction

K. Vavrova (B) · A. Šarlina University of Bratislava, Dolnozemská Cesta 1, 852 35 Bratislava, Katarina, Slovakia e-mail: [email protected] A. Šarlina e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_16

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1 Introduction 1.1 Relevance The future of finance is digital. Consumers and businesses are increasingly accessing financial services digitally, innovative market participants are introducing new technologies and existing business models are changing. Digital finance has helped citizens and companies cope with the unprecedented situation created by the COVID-19 pandemic. Online identity verification, for example, has allowed consumers to open accounts and access multiple financial services remotely. Fintech solutions have helped expand and accelerate access to loans, including government-backed loans, in response to the COVID-19 crisis. The phenomenon of digitization is considered the most important economic development since the industrial revolution and one of the main driving forces of growth and innovation [1, 14]. The digital economy is also associated with major challenges for the international tax system. Traditional tax laws regulate new ways of doing business, but current international tax law and its underlying principles may not keep pace with changes in global business practices [1]. In the digital economy, the boundaries of innovation have significantly broadened. Traditionally, innovation has referred primarily to product or service innovation, and to a lesser extent process innovation. Along the same lines, most scholarly research has examined product and service innovation [2]. Or a more generic form of innovation as captured by the patent activity of firms, or even by their R&D expenditures [3]. Regarding digital business models, the main tax challenges in the digital economy arise from the decreasing importance of physical presence in the customer market, the increasing importance and mobility of intangible assets and the high degree of value chain integration. Although this development is not entirely new and does not have a high-quality detection of appropriate sources, it has sparked a political and academic debate on how international taxation can be reformed to create a “reasonable and stable system of taxation of the profits of multinational companies in the twenty-first century”. In this spirit, academic scholars and transnational political institutions are calling for comprehensive and systematic changes in international tax principles [4]. The relevant literature on the digital economy examines individual aspects such as the concept of a permanent establishment, the characteristics of income, the determination of transfer prices and the application of withholding tax or transaction taxes. There is general agreement that the digital economy cannot be “bounded” primarily for tax purposes only. At the same time, proposals for taxation of companies that operate in the digital economy differ greatly in terms of the basic goals and methods of solving challenges and opportunities. The lack of consensus also results from the fact that there is no common definition and measurement of the relevant elements in digital value chains, which are characterized by recent technological developments and the prevalence of online networks [6]. As part of the Base Erosion and Profit Shifting base erosion and profit shifting (BEPS) project, the OECD has identified the tax challenges of the digital economy

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as the highest priority measure. It is concluded that digital business models bring significant opportunities for (aggressive) tax planning, but also raise wider challenges for the tax system. Although the OECD depicts some of the technological underpinnings and innovative business models of the digital era, the relevant implications and potential problems for taxation are not thoroughly discussed. Rather, the latest report highlights how the business models of multinational companies in the IT or e-commerce sector can mitigate the undesirable outcomes of low or no taxation. The options for solving the tax issue presented by the OECD are characterized by mitigating BEPS risks and creating awareness of tax challenges, rather than solving long-term tax problems caused by digital business models [5]. In the digital economy, information is digitized and transmitted through digital networks, creating a new world of opportunities for business development. More and more people and businesses are getting into the new information space. Huge amounts of information can be compressed and transmitted at high speed anywhere in the world. Information and information technology are used in all economic sectors, to a lesser extent, but ultimately, they are used everywhere. Their effective use enables companies to be competitive [13]. The general concern in this area is lack of base rather than base erosion. This is not a new area of concern. In the late 1990s, the digital economy, formerly known as e-commerce, was considered by the OECD [15] (Graph 1). According to the chart, the estimated size of the European digital economy is 24.5% of the European Union’s GDP, which corresponds to 3.6 trillion euros, and according to estimates, by 2020 it will represent 27.3% of the European Union’s GDP, which means 4.4 trillion euros. Although all this money is unimaginably large, it does not reach the budgets of the respective states, but ends up in tax havens, where it is incorrectly taxed or not taxed at all. Taking the results in Chart 1 as a reference point, it is very important to think about the digital toll, because increasing household

Graph 1 Size of the digital economy in the world. Source own processing according to: https:// digital-strategy.ec.europa.eu/sk

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access to the Internet also increases the amount of data that the population provides to digital companies, and then this data should be rewarded in different ways [18].

1.2 Goals and Objectives The initial work procedure was the preparation of a research and the subsequent study of the literature, which was part of the investigated issue and helped us to understand all the connections and problems related to the taxation of the digital economy. During the entire time of processing the contribution, we relied on the legislative directive, based on which we were able to divide the work into the following chapters. In the first chapter, the goal was to present the area in which the object of our investigation falls. We drew data from various professional articles and sources, laws, guidelines, and books, whether from domestic or foreign authors. We divided this chapter into six parts, while in the first we focused on the description of the digital economy, then we moved on to the strategic and tactical direction of the financial activities of the states in the digital economy, which we characterized, and ended by describing the transformation of traditional subjects of taxation in the digital economy. In the second part, we focused on the tax absence in connection with the taxation of digital services in the EU, which represents one of the main parts of the object of our investigation in the practical part as well. Other parts relate to the digitization of tax processes in EU countries and digital business models. In the end, we paid attention to individual adjustments to income taxation, namely to the adjustment to the taxation of corporate incomes with a significant digital presence at the European Union level and to the adjustment to the taxation of income from digital transactions at the OECD level. Since this topic requires current and updated information, it was necessary to draw it from Internet sources, respectively. From various research and guidelines, it was necessary to determine the main goal and sub-goals that led to its achievement. Subsequently, we described the methodology of the work and the research methodology, which led to a more transparent paper. The fourth chapter specifically covers several parts that we have analyzed in more detail. In the first part, we specifically looked at the taxation of digital income in selected countries, which were Italy, Belgium, France and the like. Subsequently, we examined and characterized in detail the action plan for fair and efficient taxation of legal entities, we examined the directives related to the action plan and the impact of the CCTB on Romania. Next, we looked at the tax profiles of the digital economy in Italy, legislation to address tax challenges at the EU level, and specific solutions to the challenges that value added tax brings. In the last part, we analyzed the latest OECD statement from October 2021, which deals with the questions of the first and second pillars and a view of the future of the digital economy as such.

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2 Theoretical and Conceptual Background 2.1 Strategic and Tactical Direction of Financial Activities of States in the Digital Economy: The Foundation for Service Science Digitalization of the economy is characterized by the emergence of new digital tools and their active implementation in various spheres. Scientists recognize that the digital economy is characterized primarily by the dependence on intangible assets, the extensive use of data (personal data), the extensive use of multilateral business models with income from the sale of “free” products, and the complexity of determining the jurisdiction in which such income is generated. The need to develop legal solutions in the field of taxation of the digital economy is determined by the focus on legal support for the stability of financial and legal regulation and the principles of tax certainty. In these conditions, it is necessary to talk not only about financial and legal regulations, but also about financial policy, which is aimed at setting goals and defining tasks for the creation, distribution, and use of public funds. In this context, it seems possible to talk about two aspects of financial policy—strategy and tactics. Financial strategy is a long-term line of behavior based on the development of long-term goals, the achievement of which should lead to the financial stability of the state. Financial tactics are aimed at solving specific tasks that arise at certain stages of state development [5, 7, 8]. Due to the absence of a thorough scientific study of the problem of not securing support, the economic and legal challenges caused by the activities of actors within the EU in the conditions of the ne digital reality will remain unanswered. Such consequences threaten the fiscal interests of the EU states, the essence of which also requires a reassessment, considering the “digital transformation” [9, 10, 13]. In the current conditions, it is important to focus on the implementation of the control function of financial law. Financial activity is intrinsically linked to financial control because finance objectively implements the control function. Financial control is carried out at all levels and in all phases of financial activity and is manifested in the control of the distribution of the gross domestic product between the relevant funds and their expenditure for the selected purpose. Feedback is the most important link in management relationships. In financial law, the principle of direct and indirect relationships has many manifestations. Especially in tax law, one of the most important areas of this principle of direct and indirect relations is ensuring the balance of public and private interests by preserving the principle of tax certainty in conditions of legal instability [3, 11, 12]. In the theory of tax law, there are elements of the legal structure of the tax, the summary of which determines the obligation of the payer to pay the tax. The main elements of the tax include, for example: . The subject of taxation, which is important because the presence of the subject of taxation is directly the basis for the taxpayer’s obligation to pay the tax established

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Table 1 Axioms and foundational premises of digital economy [52–54] Axiom 1

Axiom 2 Axiom 3

FP 1

Digitization of financial processes is the basis of changes

FP 2

Goods and services are the basis for digitization

FP 3

The need for legal support

FP 4

Transformation of traditional subjects of taxation

FP 7

Digitization of tax processes

FP 9

Digital business models

FP 10

Transforming the taxation of income from digital transactions

FP 11

Tax profiles of the digital economy in EU countries

by law. The subject of taxation are those legal facts (activities, events, states) that determine the subject’s obligation to pay tax. . Subject (subject); legal person or a physical person, which is obliged to pay tax, is obliged by law to enter a tax-legal relationship, to suffer a specified property damage, i.e., to pay the specified amount of tax to the state. Designation of a tax subject as a taxpayer is possible in the case of an obligated or obligated subject of tax liability. . Basis (foundation); defines the scope of tax liability within tax legal relations. It is the measure according to which the amount of tax is calculated from the tax base, it is the real culmination of the tax-legal relationship in its phase of preparation and fulfilment of the tax obligation. . Rate (limit); the tax rate is the share of tax per tax unit. It is a measure of tax liability. Each tax has a separate subject of taxation. This is one of the guarantees of avoiding multiple taxation, when the same object is subject to several taxes at the same time. For example, the subject of taxation according to the income tax is traditionally defined as profit—the financial result of economic activity, which arises from the difference between income and expenses [15] (Table 1).

2.2 Tax Absence in Connection with the Taxation of Digital Services The aim of the introduction of the digital tax is, among other issues, to supplement the missing funds in the state budgets, it is necessary that it be properly regulated by the laws of individual jurisdictions. In addition, since it is often an international element that is an important factor in potential tax evasion, it is necessary to achieve a certain standard of harmonization also at the level of international organizations. That should be a priority.

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The need to introduce a digital tax is one of the consequences of the advanced phase of globalization, which was achieved mainly thanks to digital means of communication. It therefore almost goes without saying that an international element will be one of the hallmarks of a taxable transaction. The Organization for Economic Cooperation and Development (OECD) is dealing with this issue in cooperation with the G20 countries. The main goal of the OECD is to solve the problem of low transparency in the taxation of digital companies and the exploitation of loopholes in national tax systems. The European Union has come up with a solution that is currently being prepared: it is the mentioned directive on digital tax, the aim of which is to harmonize the legal regulations of this tax in individual member states. Cooperation between the two organizations is quite problematic, as some member states fear that if the digital tax is adjusted only for the territory of the European Union, it will lead to the relocation of companies such as Google or Facebook, which would mean great economic losses. Therefore, a global solution is preferred [11, 16]. Achieving an agreement at the global level is a big challenge, both in terms of discussions between states and in terms of time. While some member states of the European Union (e.g., Austria or Spain) have been operating with digital taxation for several years, in other countries (Czech Republic, Slovakia) its introduction is still being prepared. In a group such as the European Union, it is of course undesirable for such significant differences in tax systems to arise. This was probably the main motivation for the Union to act in this direction despite the risks. Another attractive factor is the overall increase in the economic level of the entire group thanks to greater transparency and better competitiveness of smaller businesses. In the current situation, large companies with many users pay significantly lower taxes than digital companies with a user base in one or two countries. On the other hand, some states fear that this effect will not occur, as companies will have to charge for some of their services for the public, which are normally free for now, to compensate for the shortfall in funds transferred to the state coffers. The rapid variability of business in the digital environment can also be risky. According to the Commission, a tax on digital services, proposed in 2018 as a short-term solution, would bring the Union annual revenues of around EUR 5 billion and reduce the fragmentation of the single market [3, 17]. A certain overlap between the approach of the OECD and the European Union can be assumed in the directive on the fight against tax evasion (hereinafter referred to as ATAD- Anti-Tax Avoidance Guidelines). It was adopted because of long-term negotiations at the OECD level and is based on them. It therefore has a much wider international impact on entities operating not only in the European Union, but also outside it. The ATAD directive is a directive aimed at coordinating the procedures of national governments and tax administrations so that companies operating in the territory of two or more EU member states or non-EU countries do not use different tax codes for tax evasion in the country where they should be taxed, although for these businesses may not be the most advantageous. Two of its pillars will then be directly relevant to the effectiveness of the Digital Taxation Directive:

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. Rules for solving hybrid structures (irregularities). . Rules for controlled foreign companies” [7, 19]. So-called hybrid contradictions occur in situations where income and expenses resulting from the foreign activities of an entrepreneur are qualified differently by different relevant legal systems. The functioning of the digital tax in only some countries serves as a good example. While in one of the countries concerned, the revenue from the provision of digital services may be considered as an item of the company’s total revenue for the sale of its services, in the other country it may be considered, for example, as the rental of digital space for advertising, license fees for code generation, etc. Since there are no common rules determining which types of services are subject to which tax and under which conditions, and especially rules for determining the place of creation of the subject value, the obliged entity has a choice. Therefore, he adjusts the taxable performance to the legal arrangement that is more advantageous for him, even if he has a legal relationship with a state with a different legal arrangement. In the case of companies where there is a hierarchy of subsidiaries between units located in different countries, there is another kind of tax optimization. This is usually a relationship between the controlling company and the controlled company, which does not appear to be a single company. As a result of the subsidiary relationship, they should be considered as one company. Values move between these entities in such a way that they appear to be transactions between completely different entities. This again facilitates the search for tax rules that are the most advantageous for the subject, and thus also the outflow of financial resources that would belong to the budget of the state on whose territory the value in question was created. This is most often done through intra-company loans, investments of the controlling company in the controlled company, handling of receivables, etc. [5, 12]. Both issues are expected to be addressed in the renewed Common Consolidated Corporate Tax Base (CCCTB) Directive. Although it was already introduced in the past, the business environment in the European Union and the national regulations were not ready for it at that time. After the adoption of the ATAD directive, there was room for its revision and adaptation to current market conditions. The objective of the CCCTB is to consolidate the tax base of companies that have branches in two or more Member States. It intends to achieve this by several methods, depending on the nature of the relationships between these units: Full consolidation method: . The consolidating entity excludes financial investments in the consolidated entity. . Intra-company receivables and other property liabilities are excluded. Proportional consolidation method: . A consolidated entity is controlled by two or more consolidating entities together. . Consolidating units show a proportional share of assets and liabilities. . Intra-company receivables and other property liabilities are excluded.

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. capital method of consolidation: . The consolidating accounting unit does not control the consolidated accounting unit but has significant influence in it. . The consolidating accounting unit reports the following items against securities: – profit or loss in equivalence, which represents a share of the profit or loss of the consolidated accounting unit, – consolidation reserve fund, which represents a share of the undistributed profit of previous years of the consolidated accounting unit, – receivables and other property obligations within the company are not excluded. The consolidated tax base is shared only in case of a positive result, the negative consolidated tax base is included in future consolidated profits. Finally, a rather complex calculation formula is used. However, a prerequisite for these procedures is sufficient previous tax harmonization. Because the calculations for the sharing of the consolidated tax base are carried out at the end of the accounting period of the entire group, it will be necessary for these periods to overlap in different countries. The European Commission intends to implement this directive in two steps. In the first phase, the calculation of the tax base will be harmonized in such a way that it is treated approximately the same in all countries and that undesirable differences do not arise. Important criteria are neutrality, fairness, simplicity, enforceability, scale, revenue stability, public policy and costs associated with reform. In the second phase, the consolidation itself is to be carried out using the above-mentioned methods.

3 Digitization of Tax Processes in EU Countries as a Stabilization Factor from Global Financial Shock The issue of taxation of the digital economy within the EU is wide-ranging. The emergence of the EU’s digital agenda does not solve the problems of digitization of taxation. The problems that exist within the EU also concern USA. One of the most important issues concerns the taxation of digital products and online services in (denoted as) B2C (consumer-to-business) and (denoted as) C2C (consumer-toconsumer) transactions. E-commerce, due to its size and diversity, is very difficult to control. There are some difficulties in determining the identity and location of the buyer, currently this can only be done using bank card data. When paying through an anonymous payment system, it is impossible to determine the buyer. This problem for tax authorities remains unsolved in all EU countries. One of the identification mechanisms is an electronic cloud signature, with the help of which people could register all their activities on the Internet, and above all perform and confirm transactions. It is also possible to develop a blockchain technology smart contract that

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provides guarantees for transactions and fixes all changes in the process of their execution [7]. The second huge problem is the insufficient level of tax legislation of the EU member states when it comes to the regulation of e-commerce taxation. The laws of the member states of the EU countries do not regulate the concept of electronic commerce, online commerce, or other relevant definitions. At the same time, the terms themselves were regularly used in official documents. In addition, there is still no classification of digital products that divides them into goods and services. Many countries are developing taxation of online trading, trying to go it alone, but it does not bring visible results. The Internet is a worldwide network in which international business transactions take place, and relevant international rules are necessary for effective regulation. The solution to this problem is the unification of taxation legislation [22, 40, 41]. It seems that further transformations of tax legislation will take place based on the principles established at the Ottawa conference in 1998, which have not lost their relevance even today: . Similar taxation rules. . Suppression of cases of tax evasion. . Certainty and simplicity (presentation of tax rules in a clear and comprehensible form). . Flexibility and dynamism of the tax system corresponding to the level of development in the field of taxes and levies and in the field of technology and trade. . Possible ways to solve these problems are: . Creation of new technologies that will enable tax authorities to identify and track transactions made in cyberspace. . Ensuring quality collection of statistical data in the field of electronic commerce. . Improving the provisions of the current legislation in the field of taxes: expanding the definition of services by including the term “electronically provided services”, supplementing the definition of the term “services” with the term “services and place of delivery” for electronically provided services”. To digitize tax processes in EU countries, it is necessary to develop issues in the field of full interaction with remote taxpayers. Currently, many questions still require a mandatory visit to the tax office and paper support. Electronic management of documents will allow states to monitor all activities, financial and commodity flows of economic entities, which will ensure honesty and transparency and contribute to the development of the economy [42, 46]. Based on the above information, we can unequivocally state that digitization, and especially the digitization of the tax system of the EU countries, will become a key tool for the economy to get out of recession.

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3.1 Digital Business Models and the OECD Perspective on the Challenges of the Digital Economy Digital business models define how a business creates, delivers, and captures value, and we can "divide them into the following categories: . A subscription model where users pay a subscription to access a service or content on a website, such as Amazon or Netflix. . An advertising model where end users generate revenue by being exposed. . On platforms provided by companies such as YouTube or Yahoo. . An access model where content and application developers (ISPS–International Ship and Port Facility Security Code, data brokers and data analysts) pay for access to end-user data such as business applications” [14]. Online marketplaces such as eBay, dating sites such as Tinder. Com, online job postings from agencies such as Upwork, price comparison sites, online travel sites such as Skyscanner or Expedia, or accommodation sites such as Booking. Com, peer-to-peer services such as Airbnb, and transportation services such as Uber are the equivalent of offline businesses. Payments, media, auctions, logistics solutions all have their online versions today. Profit is generated by charging usage fees or commissions to service providers, service users and advertisers [5, 33, 34]. New business models created by the digital sector are e-commerce, app stores, online advertising, cloud computing, payment services, high-frequency trading and participatory network platforms. The digital economy enables a highly mobile division of tasks between different sectors of one company in different countries (report, 2015). The discussion points to the challenges posed by the current international tax regime. While the complete abolition of corporate taxation is unlikely in practice, existing tax principles and concepts are outdated, having been developed before the invention of the Internet and struggling to cope with the tax avoidance motive of modern multinationals. This problem is exacerbated by the rise of digital business models, as the high mobility of intangible assets increases the ability of multinationals to engage in tax avoidance structures. One of the main problems of the current international tax regime is its default attitude towards separate treatment of entities. As pointed out by paper [31], the general principle of respecting intra-group transactions has this undesirable effect: The practical result is a tax planning free-for-all that has allowed MNCs (multinational corporation-MNC) to undermine the arm’s length principle by using the transaction to allocate functions, assets, and risks instead of they were looking at basic economic substance. Tax administrations must deal with transfer pricing based on economic analysis with one hand tied behind their back because of the transaction requirement. While the OECD acknowledges the challenges of reaching an international consensus on a long-term solution to the taxation of the digital economy, it appears that some countries may become more open to exploring the possibility of allocating more tax rights to source countries where sales occur [31, 42, 43]. A reform

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option in line with this line of thinking is the allocation of worldwide consolidated profits of an MNE (multinational enterprise-MNE) based on sales, as suggested by paper [35]. Under this model, the MNE’s (multinational enterprise-MNE) consolidated worldwide profits are allocated to countries based on the proportion of sales to customers in those jurisdictions (“Sales Countries”). This distribution forms the tax base in the selling countries, which are subject to corporate income tax at national rates. Corporate income tax paid by other group companies in the GVC (Global Value Chains) leading to the final products will be eligible as a tax credit in the country of sale. For example, suppose that goods are manufactured by company X in country A and sold to another group company in country B, where they are sold to end customers. Country B imposes a corporation tax based on the distribution of the group’s worldwide profits and provides a tax credit for the corporation tax paid (if any) by company X in country A. This model ensures that the total tax paid by the multinational is the same. On corporate income tax payable on the group’s consolidated profit from country sales, with this tax revenue being effectively split between sales and production countries through a system of tax credits. The implementation of this sales-based allocation model will be facilitated by one of the most successful action points in the OECD’s BEPS project, namely the introduction of country-by-country breakdown [44, 45]. Reporting CbCR (Country by country reporting) under (OECD, 2015b). MNCs as a single enterprise have complete information about their tax affairs, while taxing administrations often struggle to obtain the necessary information to make informed decisions about taxpayers’ tax positions. This problem of information asymmetry has been a major obstacle to effective tax investigation of tax avoidance structures. Voluntary Country Disclosures in MNE Financial Statements and Taxation Reassessments Antony Ting and Sidney J Gray 1664 Journal of International Business Studies reports often lack sufficient detail to be meaningful for tax reporting [36, 46, 47]. In contrast, the recently introduced CbCR regime requires large multinational companies to submit reports to tax authorities providing key information by country, including revenue, profits, income tax and headcount. This improvement in transparency provides a global view of the tax position of multinational companies and is invaluable to tax administrations in identifying potential targets of tax audits. In addition, the CbCR regime ensures that sales data for multinational companies in each country is readily available, facilitating implementing a sales-based profit allocation model. This model has many advantages.

3.2 Technological Features of Digital Business Models Instead of developing direct tax policy measures, the OECD is expected to examine the characteristics of the digital economy to provide insight into tax challenges. The OECD considers the sharp increase in the expansion and development of information and communication technologies (ICT) as a factor that enables the digitization of

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businesses in several areas. This development represents the integration of hardware and software in the form of computing devices that are increasingly connected by Internet access (OECD/G20, 2014). Software business models in the digital economy are further characterized by a reliance on the Internet, as well as open-source approaches and on-demand implementations at the end-customer level. In addition, the OECD notes that the main players in the digital economy rely on different ways of creating, using, and generating revenue from online content, as well as collecting and analyzing data. In conclusion, the OECD lists cloud computing as the main result of ICT trends. Businesses can provide traditional, on-premises resources as services over the Internet, such as computing power, data warehouses, or software applications. Overall, the OECD states that this development creates “new opportunities at another level of the value chain”. This shift (innovation) to another phase of the value chain caused by the onset and use of modern ICT is not further investigated, which to some extent can be considered a situation that brings opportunities, but also threats for the EU states in the future [48]. BEPS (Base Erosion and Profit Shifting) defines a project that was born in 2013 and is promoted by the OECD and the G20. Known as the Base Erosion and Profit Shifting plan, it aims to create a coherent set of international tax rules and ensure that large companies pay taxes in the countries where they create value. “The description of the digital economy and the emergence of new business models remains superficial in the sense that it only mentions new forms of user experience and revenue generation.” The focus on BEPS is justified by the general view that the specifics of the digital economy make BEPS worse, although tax planning strategies may be like traditional businesses [8, 34]. The OECD has identified four areas of BEPS opportunities that are of particular importance in the digital economy. The first of these involves the elimination or reduction of tax in the market country because of either avoiding taxable presence or minimizing income in the market country. In the case of cross-border online transactions that do not require a physical presence, the tax liability is usually not defined in domestic law. If the country of residence does not assume its right to tax, the relevant income is in fact untaxed. In the case of a taxable presence, income can be minimized by allocating only minimal functions, assets, and risks, or by maximizing deductions in the country of the market. This is considered problematic because the allocation of functions and assets is often tax-motivated, and the functions and risks are not actually performed. This concept also applies to the second BEPS opportunity, which is to reduce taxes in the home country, especially if valuable (intangible) assets are transferred to affiliates in a low-tax regime. Two other BEPS opportunities are the avoidance of withholding taxes and the elimination or reduction of tax in the intermediary country with specific contractual payments and the establishment of holding companies.

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3.3 BEPS Affecting Taxation in the Digital Economy What is BEPS—profit shifting (BEPS) refers to tax planning strategies used by multinational enterprises that exploit gaps and mismatches in tax rules to avoid paying tax. Developing countries’ higher reliance on corporate income tax means they suffer from BEPS disproportionately. BEPS practices cost countries USD 100– 240 billion in lost revenue annually. Working together within OECD/G20 Inclusive Framework on BEPS, over 135 countries and jurisdictions are collaborating on the implementation of 15 measures to tackle tax avoidance, improve the coherence of international tax rules and ensure a more transparent tax environment. Domestic tax base erosion and profit shifting (BEPS) due to multinational enterprises exploiting gaps and mismatches between different countries’ tax systems affects all countries. Developing countries’ higher reliance on corporate income tax means they suffer from BEPS disproportionately. Business operates internationally, so governments must act together to tackle BEPS and restore trust in domestic and international tax systems. BEPS practices cost countries 100–240 billion USD in lost revenue annually, which is the equivalent to 4–10% of the global corporate income tax revenue. Working together in the OECD/G20 Inclusive Framework on BEPS, over 135 countries and jurisdictions are implementing 15 Actions to tackle tax avoidance, improve the coherence of international tax rules, ensure a more transparent tax environment, and address the tax challenges arising from the digitalization of the economy. The aim of the work on the BEPS project is to combat artificial structures and the shifting of profits to low-tax or no-tax jurisdictions, thereby aligning taxation with the location of economic activities [32]. The BEPS project and other recent multilateral initiatives focus on tax avoidance rather than what is arguably an even bigger problem: tax competition. Such competition is most evident in the trends of statutory corporate income tax rates, which can be seen on Graph 2 (including average rates at a lower than national level), however, it also has other forms (for example, special tax incentives). The subsequent revenue losses may exceed those resulting from tax avoidance. For example, the OECD estimates the total revenue lost from tax evasion at up to 10% of corporate income tax revenue. In addition to the 15 measures that make up the BEPS plan, a monitoring process for four minimum standards (measures 5, 6, 13 and 14) has been introduced. Digitalization has accelerated the spread of the global value chain, which integrates the worldwide operations and profits of MNEs. The BEPS Action Plan, through its Action 1, identifies the key elements of digitization, namely phenomena such as the collection and use of data, the emergence of new business models and network effects, multilateral digital interfaces that complicate the challenges of already existing tax rules. According to the OECD, digitization does not pose any obstacle to BEPS, but specific business models may exacerbate BEPS concerns. The interim report of Action1 from 2018 identified three characteristic features of highly digitized business models, namely:

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Fig. 1 Well-being metrics. Source https://hbr.org/2019/11/how-should-we-measure-the-digitaleconomy

. Cross-border scale without matter—Businesses that form part of the economy of a particular jurisdiction without their physical presence, and thus are characterized by a large scale without "matter." Thanks to digitalization, such enterprises can allocate individual production processes in different countries, while at the same time reaching large number of subscribers. . Intangible assets—Companies are characterized by an increasing emphasis on investing in intangible assets, especially IP assets, regardless of whether they are owned by the company or leased to a third party. . The role of data and users, including network effects—Data, user participation and their synergy with IP are conceptual features that come together in the business models of some companies. By collecting data, they increase the amount of information and directly contribute to the acquisition of customers". The mentioned signs represent: . The rule of allocation of the right of individual jurisdictions to tax the income in question (nexus rule), which regulates the extent of the authority of the jurisdiction concerned to tax the income of a non-resident. . The profit allocation rule, which determines the appropriate share of MNE (national enterprises) profits to be taxed in the assigned jurisdiction. This rule is based on the arm’s length principle and is directly linked to the Transfer Pricing Directive. Based on BEPS Action 1, the Inclusive Framework responded to the request of the G20 group by issuing a "Preliminary Report on Tax Challenges Arising from Digitization", which presents an in-depth analysis of value creation in digital business models while describing the essential features of digital markets" and presents potential future solutions. Due to the inconsistent opinions of individual jurisdictions regarding the relevance of the proposed measures and their preliminary nature, the preliminary report has so far received only contradictory acceptance.

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4 Tax Profiles of the Digital Economy in EU Countries Currently, digital taxation focuses primarily on the following two main aspects: . How to ensure that tax policy remains neutral when targeting traditional and digital businesses: Digital businesses benefit from preferential tax regimes, e. g. tax benefits for income from intellectual property, shorter depreciation of intangible assets, tax credits for research and development. The risk is that preferences for digitized businesses can create windfall tax benefits that can be used in a way that distorts investment rather than focusing on innovation. . Digital businesses can operate without a physical presence in countries where digital businesses have customers, as they can reach customers through remote sales and service platforms: The ability of digitalized businesses to generate profits through cross-border sales without a physical presence challenges the traditional rule of corporate income taxes. Until now, digital businesses paid corporate income taxes only in those countries where they had a permanent establishment, i.e., either headquarters, or a factory, or a store. This means that the countries where sales are made or where online users are located do not have the right to tax the company’s income. . Several tools can be used and implemented to tax digital profits. The first option consists in expanding the existing rules. For example, a country can expand its Value Added Tax (VAT) and Goods and Services Tax (GST) to include digital services or expand the tax base to include income from the provision of digital goods and services. The second option is to impose a Digital Services Tax (DST). . In recent years, many countries have implemented DSTs and VATs on digital goods and services unilaterally, highlighting that a lack of coordination and harmonization of standards can be harmful to the global economy and potentially lead to economically damaging trade wars. The lack of international coordination in recent years has pointed to some fundamental steps that need to be taken urgently. First, the VAT and GST rules need to be revised to ensure that foreign suppliers are responsible for the collection and remittance of these taxes in the countries where they sell their goods and services, even without being physically present. A lack of coordination can also lead to confusion and hamper economic activity, as digital businesses that sell in different countries where they do not have a permanent establishment must adapt to the wide variety of requirements in each of the countries where they have customers. In addition, lack of coordination can also facilitate tax avoidance, as MNEs can take advantage of differences in corporate tax rates. Finally, the risk of double taxation can easily arise as digital businesses can be taxed twice in the host country under the national CIT (corporate income tax) regime and in the countries where they have customers under the DST (Digital Services Tax) [20–22].

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4.1 Digital Income in Selected Countries Countries and international organizations are developing various initiatives at the national level and, more recently, at the international level. When it comes to VAT and GST (Goods and Services Tax), in most OECD countries, VAT or GST primarily applies to a large set of goods and services. Goods and Services Tax (GST) is a Value Added Tax (VAT) levied on most goods and services sold for domestic consumption. GST is paid by consumers but paid to the government by businesses that sell goods and services. However, critics point out that the GST may place a disproportionate burden on self-reported incomes in the lowest and middle-income brackets, making the GST a regressive tax. Most countries with GST have a uniform GST system, which means that one tax rate is applied across the country. A country with a unified GST platform combines central taxes (e.g., sales tax, excise tax and service tax) with state level taxes (egg entertainment tax, entry tax, transfer tax and luxury tax) and collects them as one tax. These countries tax virtually everything at one rate. France was the first country to introduce GST in 1954 and since then around 140 countries have adopted this tax system [26, 28, 29]. On the other hand, the situation around DST is more complex. Until now, digital businesses paid corporate income tax in the country where they had a permanent establishment, not in the country where consumers or users are located. In practice, a digital business can provide services abroad through digital means without having a physical presence abroad and generate profits without being subject to corporate income tax abroad. Several countries have decided to tax digital goods and services in recent years and unilaterally introduced DST, the rate of which varies from country to country. As of May 2020, Austria, France, Hungary, Italy, Turkey, and the United Kingdom have introduced a digital services tax, while a proposal for a digital services tax has been submitted in Spain, the Czech Republic, Slovakia, and Poland. Some timid steps in this direction have been taken by Latvia, Norway, and Slovenia (OECD/G20 Base Erosion and Profit Shifting Project, 2021). We describe some cases in more detail in the following text: France . The country decided to levy tolls on tech giants in December 2018 after talks on digital taxation across the European Union stalled that same month. The companies affected by this measure are those with an annual digital revenue of 750 million euros worldwide and 25 million euros in France. It is estimated that this tax will bring revenues to the budget of 500 million euros per year. Spain . The tax proposed by Spain for digital taxation represents three percent of the digital income achieved in the territory of the state. This measure affects companies with an annual digital revenue of 750 million euros worldwide and 3 million

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euros in Spain. The Spanish government predicts that the tax on financial transactions (DFT) and digital services (DST), which came into effect on January 16, 2021, will bring in an additional 1.818 billion. EUR (2.1 billion USD) per year, while the Independent Office for Fiscal Responsibility (IAFR) identified a lower pre-pandemic forecast of 966 million. euro United Kingdom . It proposed a 2% digital income tax rate for companies with around £500 million (around e580 million) of digital revenue worldwide and £25 million (around e29 million) of digital revenue in the UK. The law entered into force in January 2020. Belgium . It is a European Union country that has published a law on digital service tax. The country has imposed a temporary tax of 3% on digital revenue, such as the sale of user data to companies with a total revenue of 750 million euros worldwide and 50 million euros in the European Union. Italy . Italy proposed a digital tax since 2017 and the parliament finally approved it in 2018. Unlike the EU tax, the digital tax affects buyers of services rather than sellers. Any business that makes more than 3000 digital business-to-business transactions in Italy per calendar year will be subject to the tax. Businesses cannot use this tax to offset Italian income tax. . Outside of Europe, other countries (e.g., India, Indonesia, and Tunisia) have adopted a tax on digital services” [11].

4.2 Parent Subsidiary Directive and Interest and Royalties Directive As the principle of free movement of capital is important under the four fundamental freedoms of movement within the EU, the Parent Subsidiary Directive and the Interest and Royalties Directive were the instruments that enabled the removal of withholding tax on cross-border flows within the EU. Since the free flow of capital posed a potential risk of abuse by multinational companies, an anti-abuse clause was added to the Parent Subsidiary Directive in January 2015.

4.3 Common Consolidated Corporate Tax Base (CCCTB) The CCTB represents and covers a single set of rules for calculating the taxable profits of companies in the EU. Thanks to this set of rules, cross-border companies

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now follow only one single EU system for calculating taxable income, rather than many different sets of national rules, which is a significant improvement over the past. A leaked document published by the Financial Times in January 2016 revealed the Commission’s proposal for the CCCTB to be introduced in two steps, with the second step being the consolidation part. “In June 2015, the Commission announced its plans to renew the CCCTB, which were based on the principle of two important changes: a mandatory CCCTB to combat BEPS risks and a two-step approach to harmonize the tax base within the EU and consolidate”. The European Commission explained that the consolidation element in the CCCTB would also allow companies to offset losses in one-member state with profits in another member state [17, 18]. Thus, if a subsidiary in one-member state suffers losses, the parent company in another member state would be entitled to a temporary tax relief. “It is widely believed that the CCCTB would help expand business activities and support start-ups in the single market by treating cross-border activities in the same way as domestic activities in terms of offsetting losses. Once the subsidiary starts making a profit, the Member State where the parent company is located can reclaim the taxes that were previously used to offset the losses. This would mean eliminating the risk of any member state bearing the burden of non-profit companies in another member state. The first step involving cross-border compensation of losses would bring many benefits to businesses, but the consolidation step is a much more important issue, which would fundamentally change the way profits and losses are distributed between Member States, with a real impact on Member States’ revenues. Nevertheless, this issue is controversial among member states and is therefore expected to be reached at a later stage. The non-governmental organization demands guarantees that corporate incomes that have already been taxed in a source country outside the EU cannot benefit from tax reliefs and exemptions in the EU. This directive contains the principles of tax consolidation as well as the formula for dividing the tax base of a multinational company between the Member States. In short: . the tax base of the consolidated group is determined as if it were a single entity: profits or losses between two or more entities within the group are consolidated; if the consolidated tax base is negative, losses can be carried forward for a maximum of five years, . four factors are used for the formula for apportioning the consolidated tax base, which consider: labor, assets, sales, and data collected and used by digital users of digital content, each of these factors being given equal weight, . stipulates that the EC will adopt acts that will establish the rules for the electronic submission of the consolidated tax return and other forms, . stipulates that the EC will introduce a special compensation mechanism, financed from the budget surplus of the member states that will receive tax revenues because of the introduction of this regime. . The tax harmonization measures adopted by the European Union, although they are beneficial in theory, will certainly have negative effects for some states in

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practice. Since in practice it is possible that member states with less developed economies will be adversely affected [10]. Advantages and disadvantages arising from the new tax system: Advantages: – all companies in the European Union will be subject to the same set of corporate tax rules with respect to the tax base, – the possibility of consolidating the result at the group level and implicitly compensating losses recorded by subsidiaries in other member states, – reduction of administrative costs caused by the loss of significance of documentation on transfer prices in the case of operations carried out in Member States, – expansion of the possibility to turn to the Community Court in case of incorrect interpretation of Community law in the field of corporate income tax. Disadvantages: – the formula for the proportional distribution of the tax base is based on four factors, the values of which are not comparable between member states, therefore it can be concluded that more developed member states that export capital (Germany, France, the Netherlands, etc.) and import industrial goods will have a mathematical an advantage from the formula for dividing the tax base at the expense of smaller and less developed member states, – the allocation formula will determine the emergence of new tax planning mechanisms, probably even more aggressive, to reduce the tax burden, – cancels the effects of the BEPS project at the level of the European Union, – the percentage of profit tax remains the only element of tax competition between member states.

5 Impact and Impacts of CCTB on Romania and Italy 5.1 Impacts of CCTB on Romania There are several reasons why I chose Romania as the focus country in this subsection: . Romania is a member of the European Union, which proposed the CCTB as a potential solution to address tax evasion and tax competition among EU member states, . Romania has a relatively low corporate tax rate compared to other EU member states, which can make it an attractive location for multinational companies seeking to reduce their tax burden. The introduction of the CCTB could potentially affect Romania’s competitiveness in terms of attracting foreign investment,

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. Since joining the EU in 2007, Romania has undergone significant economic and political changes, including reforms aimed at improving tax collection and fighting corruption, . Romania is the seventh most populous country in the EU and has a growing economy, making it an important player in the European market. . In the case of Romania, the tax harmonization measures adopted by the European Union bring, in addition to the previous advantages that cover the entire EU, the following advantages: increasing the stability and clarity of the legal provisions regarding the profit tax (it will not be possible to change the rules frequently). . Disadvantages can be identified in the form of: . the high dependence of the Romanian economy on foreign capital of companies with foreign capital, which is the target and will be the first to be affected, . the provision of tax reliefs will no longer be a tax lever at the national level, . the deductibility of excessive costs with debt will be stricter than at present [30–32]. . “Most companies with foreign capital operating in Romania perform “lohn” type of activity. This is probably the most significant and most frequently occurring model of companies with foreign capital in the Romanian economy. According to PIAROM (Romanian Employers’ Union), in 2018 only 50 companies out of the 500 largest Romanian exporters were companies with Romanian private capital, which shows how dependent the Romanian economy is on foreign capital. Romanian companies that perform activities for the group provide services or they supply goods mainly to related persons outside Romania from the European Union. They have a low degree of independence or are heavily controlled. The companies do not have significant assets, do not own production know-how, or raw materials, and all the elements necessary for the performance of the activity are stored and controlled by the group. The market for the sale of products/services generally represents the market of the European Union (without Romania)”. . The criterion in the formula for dividing the tax base with respect to employees considers the value of wages and the number of employees. The results can therefore be influenced by two factors: the number of employees and the number of wages. The distribution is not favorable for the countries of Eastern and SouthEastern Europe, where the level of wages in subsidiaries in these countries is much lower than in the more developed countries of Western Europe. . The difference is obvious because the cost of wages in Romania reached only 36% of GDP, in contrast to the member states of the European Union, where the average according to Eurostat is 47.3% of GDP. Payroll costs include all net wages, levies, taxes and social security contributions related to wages paid by employers for work performed by employees. Macroeconomic indicators can be precisely measured, but they tell only part of the story. Well-being metrics convey a truer picture of how consumers are doing, but they are more subjective. By considering an array of measures, including our GDP-B metric, policy makers, regulators, and investors can establish a better foundation for decision making (Fig. 1).

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The method shown has two important limitations. First, our GDP-B estimates are still far from comprehensive and not as precise as the traditional GDP measure. We will need to include many more goods and conduct more online choice experiments for each to get a more accurate assessment of the full contribution of free goods to the economy. Second, like traditional GDP, our measure does not capture some potentially negative externalities associated with goods and services, including online platforms. The study suggests that platforms can lead to addictive behavior. So far, our HDP-B metric only captures the personal benefits and costs associated with the good, as judged (perhaps imperfectly) by participants in online choice experiments, not the social costs and benefits. Although a survey of leading macroeconomists suggests that such metrics are not yet as accurate as GDP, it is a step in the right direction. Our research pointed to the situation in Romania and Italy. As the following Table 2 shows, the lowest share of costs with wages in GDP was recorded in 2017 in Ireland (29.4%), Greece (33.6%), Romania (36%), Poland (38.2%) and Italy (39.8%), and the highest share of the wage bill to GDP was in France (52.2%), Denmark (51.8%), Germany (50.9%) and Luxembourg (50.2%). However, we can say that in Romania the wage bill has increased in 10 years by more than 14 billion euros (from 53.3 billion euros in 2008 to 67.7 billion euros in 2017), due to the continuous increase in wages and economic growth; of course, Romania’s GDP also increased significantly during this period, which caused the wage bill’s share of GDP to continue to decrease.

5.2 The Impact of NAFA Control After the Application of Transfer Pricing Rules in Romania NAFA (Net Acquisition of Financial Assets) represents the net accumulation of financial assets, i.e., accumulation cleared of deaccumulation. Transfer documentation must be kept by all business entity whose commercial and financial transactions are carried out between dependent (economically, personnel, capital or otherwise connected) persons. From January 2015, the preparation of transfer documentation is mandatory not only for foreign dependents, but also for domestic dependents. The tax office may at any time request the taxpayer to submit transfer documentation in view of the transfer valuation method used, and not only during the tax audit. Graph 2 with the decreasing loss shows how much the fiscal loss has decreased due to the National Financial Administration Agency (NAFA) controls after the application of the transfer pricing rules. From the graph, we can clearly identify that in 2018, 2020 the loss reduction was 157,398,566 EUR (calculated from LEI, where 1 Romanian LEI = 0.2039 EUR), which represents the highest value recorded between 2015 and 2018.

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Table 2 15 measures that make up the BEPS plan Measure 1: Digital economy

Risks of the digital economy Complements the rest of the actions

Measure 2: Hybrid agreements

Rules against hybrid agreements National legislation (hybrid instruments) + Model Convention (hybrid instruments)

Measure 3: Controlling the Foreign Company CFC definition rules (including control (CFC) definition) Exemptions and CFC thresholds Definition of CFC income Income calculation rules Income Allocation Rules Rules for the avoidance or elimination of double taxation Measure 4: Limitation of interest deductions

Main recommendation/standard rule—deductibility according to a fixed indicator. Side rule—indicator for the whole group (optional) for each country Identify preferential tax regimes

Measure 5: Unsafe tax practices

It introduces the mandatory automatic exchange of information on tax rulings regarding preferential tax regimes It requires the existence of economic substance for each preferential tax regime

Measure 6: Abuse of CEDI

Preferred access Inclusion of both a benefit limitation clause and a general clause in contracts of the anti-abuse rule as a primary endpoint test

Measure 7: Permanent establishment

Changes in the definition of a permanent establishment (online commissions/sales, e-commerce)

Measure 8–10: Transfer pricing, Intangibles, risk and capital, high risk transactions

Transfer of intangible assets between group members Transfer of risk/allocation of capital between group members

Measure 11: Data

It does not propose any changes in the local laws of these countries. It points to several procedures in data collection and analysis It provides some concrete recommendations for more effective measurement in the future

Measure 12: Disclosure of aggressive tax planning

Mandatory reporting rules for tax planning that is considered aggressive or abusive

Measure 13: Reporting “Country after country”

Increasing tax transparency Reporting of appropriate information for the purposes of conducting risk assessments regarding transfer prices (continued)

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Table 2 (continued) Measure 1: Digital economy

Risks of the digital economy Complements the rest of the actions

Measure 14: Dispute Resolution

Improving the efficiency of the conciliation procedure for the settlement of disputes arising from post-transfer double taxation treaties price adjustments by local tax authorities. Request to introduce mandatory arbitration

Measure 15: Multilateral instrument

Improve the implementation of BEPS measures related to double taxation treaties by multilateral instrument for the modification of existing bilateral agreements

Source own processing according to. https://www.oecd.org/tax/beps/beps-actions/

Graph 2 Reduction of loss in Romania due to tax audits for the period 2015–2020 in EUR. Source own processing according to: https://www.Developmentaid.org/donors/view/43403/

The development of loss reduction had a significant increase in 2015, followed by a decline in 2016. The loss recorded in 2015 was EUR 307,068, which we consider to be the lowest value of the period under review. Graph 3 with additional profit tax shows the additional amounts of profit tax determined by NAFA after applying the transfer pricing rules. This chart personifies the following idea: the company is either at a loss or making a profit. The additional income tax imposed because of transfer pricing adjustments in Romania started to increase from 2016, when it decreased, while in 2017 its value doubled compared to the previous year and followed an increasing trend in the following period. In 2018, the increase was significant, more than four times compared to 2017. Ensuring that tax systems in Romania are ready to respond to the changes brought about by the digital transformation, take advantage of its opportunities and provide protection against its potential risks is a key challenge. The review of international tax

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Graph 3 Additional income tax determined because of the development of transfer pricing adjustment in Romania in EUR. Source own processing according to: https://www.Developmentaid.org/ donors/view/43403/

rules in the light of the impact of digitization will be a significant part of this activity and will have significant implications for multinational companies and governments, as well as the future of tax systems. An update of the OECD’s work in these areas will be part of the Tax Policy and Digitization conference, which will be prepared as part of the inclusive framework that will be handed over to the G20 in 2020. The report also acknowledges that it would be difficult, if not impossible, to separate the digital economy from the rest of the economy for tax purposes, given the increasingly pervasive nature of digitization (OECD 2015). Although the banking system has grown in terms of net assets reported by banks and their activities, the profit tax paid by banks has been at a very low level. In 2015, the first bank-level audits began, and on May 9, 2016, the tax office begins the first tax audit in a bank in Romania. The audit focuses on several categories of taxes, but the result is focused on the profit tax [35–37].

5.3 Tax Profiles of the Digital Economy in Italy The computer science and technological development of the last decades have significantly influenced the forms and methods of production and circulation of wealth, thereby promoting the proliferation of new activities that are completely dematerialized in a social and economic context characterized by the turbulent circulation of knowledge and information available at the click of a button and production, distribution and consumption goods that are increasingly virtual and intangible. As activities that could acquire economic value from the point of view of taxation, we were interested in whether, to face the extraordinary situations caused by

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the digital economy, it is enough to adapt existing tax instruments, or rather it is necessary to develop new forms of taxation, creating a taxation of the virtual world. Special emphasis must be placed on the tax profiles of activities carried out by large multinational companies, a digital compny with subsidiaries in several countries, which can produce very high incomes that are hardly taxed in the country of origin or are otherwise often taxed to a lesser extent than in the normal tax regime. The Italian legal system tried to correct this widespread phenomenon with timid measures that aimed to introduce first the Google tax, then the digital tax, before arriving at the web tax, recently adopted in two different subsequent versions (before the “digital transaction tax” and after “ taxes on digital services”) [32].

6 Tax Google Operators of the digital economy, taking advantage of the regulatory shortcomings of various legal systems that are not in line with rapid technological development, and moving their activities to privileged tax states, are taking a series of steps aimed at limiting the tax burden. It seems that this behavior, which would be difficult to implement in the old economy and which in the new economy would lead to tax sanctions in the form of tax evasion, cannot be attributed to a clean and defined framework, given the high degree of given dematerialization and relocation that characterizes performed economic activity. In this case, the Italian legislator, repeating the experience of other member states of the European Union and citizens of non-EU countries, tried to remedy it by introducing a web tax. Although the Italian Google tax never entered into force, it did not actually escape criticism, which, while recognizing the common goals of the discussed statement, emphasized its inadequacy due to the existing differences compared to the principles of the EU—the establishment of the single European market and the BEPS project. An intervention supported by the OECD during the G20 summit in Moscow with an action plan of 19 July 2013 to combat the activities carried out by digital multinationals, which aimed to minimize the tax burden through the erosion of the tax base and the shifting of profits between different tax jurisdictions [1, 24, 25]. For this reason, the idea of introducing restrictions of a subjective and territorial nature in VAT on Internet advertising caused quite a lot of doubts and did not lead to the implementation of this tax. After the failure brought by the so-called tax “Google” On April 27, 2015, a draft law was presented which, following studies carried out by the OECD in order to combat transactions that avoid paying taxes, amended the definition of a permanent establishment and supported the introduction of a digital tax consisting of a withholding tax of 25% of payments made by persons residing in Italy at the time of purchase of digital products or services from a digital operator (e-commerce) based abroad. Within the digital economy, it is possible to operate in a local market without having to maintain a physical presence there, configurable as a permanent establishment, which results in the obligation to tax the profits of an intangible company in

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such a state. “However, consumers cannot be qualified as a substitute for files, the only way to apply the withholding tax is to involve the financial institutions responsible for regulating payments for online purchases, except in cases where digital multinationals do not have a fixed establishment in Italian territory or have concluded an agreement with the financial administration”, so that only income from activities carried out in Italy is subject to taxation. However, this bill stagnated and remained deadlocked as it was not approved [1, 2, 26]. This tax profile of the digital economy, which is currently used in Italy, consists of two parts, which are: . the first version of the Italian web tax: digital transaction tax: following the results of the Ecofin informal summit held on 15 and 16 September 2017 in Tallinn, a digital transaction tax (the so-called web tax) was introduced, which applies to the provision services by electronic means for the benefit of persons resident in Italy who have not joined the flat-rate regime and taxation of benefits and for the benefit of permanent establishments of non-residents established in Italy. This instrument of taxation, whose meaning is much closer to indirect taxation, represents the Italian answer to the debate on the taxation practices of the digital economy; however, many of them profile critical issues arising from current legislation and given the differences compared to similar initiatives taken in other legal systems. The domestic tax net appears to be an emergency solution that becomes almost a sales tax that could become definitive, due to the difficulty of achieving wider structural financing at an international level prone to change and conventional forecasts, anchoring taxation even considering place of significant presence as well as determining appropriate revenue allocation policies to challenge digital value creation activities” [2, 33, 34]. . second version of the Italian web tax: digital services tax: “law of 30 December 2018, n. 145 in article 1, paragraphs 35–50, like before the “Google” tax, abolished the tax on digital transactions before it came into force and introduced a new version of the web tax, called the tax on digital services” [2, 23]. This tax is currently subject to entities that suffer a rate equal to 3%—entities carrying out business activities with or without residence in Italian territory, providing either individual or group digital services and having total revenues anywhere equal to or exceeding 750 million euros, of which at least 5.5 million were reached on Italian territory, in connection with the provision of digital services.

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7 Conclusion 7.1 Synopsis Another tax issue, apart from the digital economy, is the competition between tax jurisdictions to attract foreign investment. Competition between tax jurisdictions has a major impact on the sustainability of a country’s budget in the case of developed economies and economies that rely heavily on direct taxes [5, 6] (Graph 4). The last decade shows that taxes cannot be reduced below a certain threshold, they are unsustainable for medium- and long-term development and financial stability, which is clearly stated in graph No. 6. Due to the unsustainability of competition between tax jurisdictions, we can agree that a minimum global corporate tax of 15% seems to be the solution for the moment. There will be no financial impact on companies’ investment budgets if taxes are lower than 15%. We can also note that over the last 40 years, direct taxes have decreased from more than 40% to less than 25%. This dynamic is also affected by the increase in indirect tax policies, but nevertheless, this decrease is largely due to tax competition.

Graph 4 Worldwide average statutory corporate income tax rates. Source own processing according to: https://taxfoundation.org/2021-international-tax-competitiveness-index/

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7.2 Further Research How Should We Measure the Digital Economy? We state that digital media consumes a large and growing part of our lives, but services are largely not counted in GDP. This is because the measure is based on what people pay for goods and services. If something has a zero price, then it usually contributes zero to GDP. Policymakers use GDP data to decide how to invest in everything from infrastructure and research and development to education and cyber defense, and regulators use economic data to set policy. Because the benefits of digitization are underestimated, these decisions and policies are made with little understanding of reality. GDP is an alternative metric that complements the traditional GDP framework by quantifying the contributions of new and free goods to consumer welfare. Measures within the framework of the digital economy also include the introduction of a more efficient, modern and digital approach to capital markets. The digital economy brings a whole range of measures to modernize and make capital markets more efficient. One of these measures includes strengthening the regulated market within the capital market as a viable alternative to increasingly expensive and inaccessible bank financing, thus supporting the growth of small and medium-sized enterprises. This transparent method of financing helps stem the flow of domestic savings and capital across borders, while potentially offering higher appreciation. However, for the regulated market to function more efficiently, it is necessary to digitize and modernize the systems and processes that are its basis. This will simplify access to the Slovak capital market for members of the stock exchange, issuers (for example companies) and investors, which will lead to a better allocation of local capital, the appreciation and growth of small and medium-sized enterprises and possibly also to the financing of local governments. In conclusion, we would state what factors force companies with digital platforms to set or adjust their boundaries. Strategic enterprise management generally suggests that digital platforms make strategic decisions within three different types of interrelated boundaries: (1) the scope of the platform firm (what assets are owned, what workforce is employed, and what activities the firm performs), (2) the configuration and the composition of the platform sides (which distinct groups of customers have access to the platform) and (3) digital interfaces (which specify the two-way exchange of data between the fixed platform and each of its sides). Scholarly enthusiasm for platforms stems from the ability of platforms to generate value by reducing transaction costs or “economic friction” [50, 51]. The platform phenomenon has stimulated a rapidly growing body of academic research on platform competition [51, 53–55]. Platform leadership and innovation [54, 55], and platform ecosystems. And digital platform firms “adopt platform business models that use information and communication technologies to facilitate interactions (including commercial transactions) between users, collection and use of data about these interactions, and [are subject to] network effects which make the use of the platforms with most users most valuable to other users” (European Commission, 2018).

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Alzheimer’s Disease Diagnosis Using Machine Learning Approach Akshay Bajpai, Denys Nevinskyi, and Yaroslav Vyklyuk

Abstract Alzheimer’s disease is the most common form of neurodegenerative disease found in the world today and it has shown no signs of curability to this day. Not only that, the causes of this disease are fairly unclear which makes the diagnosis and treatment to Alzheimer’s disease a very difficult task to accomplish. The aim of the research is to assist medical professionals in the early diagnosis of Alzheimer’s disease before it has fully metastasized and medical practices become useless. In the research a total of nine machine learning models were used which include standalone models as well as ensemble machine learning models to automate the process of diagnosis of this illness and compare the efficiency of each model. Each model uses the best parameters to make predictions which revealed that the employed classification model using random forest performed the best among all the other models. The best parameters for each model were automatically set by employing loops and conditional statements. The results revealed that the accuracy of the random forest classification model was same as AdaBoost ensemble model however, its overall performance was better than all the other models employed, with the highest accuracy percentage of 84.2105% and an AUC score of 84.4444%. Keywords Alzheimer’s disease · Dementia · Machine learning · Artificial intelligence · Magnetic resonance imaging · Exploratory data analysis

1 Introduction Alzheimer’s disease [1] is one among the many forms of neurodegenerative diseases, and is found more commonly in older individuals [2]. Alzheimer’s disease is also the leading cause of dementia when it comes to older and ageing adults. Alzheimer’s essentially shrinks the brain tissue and brain cells die due to this shrinkage [3]. A. Bajpai (B) · D. Nevinskyi · Y. Vyklyuk Lviv Polytechnic National University, Lviv, Ukraine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 P. Štarchoň et al. (eds.), Data-Centric Business and Applications, Lecture Notes on Data Engineering and Communications Technologies 212, https://doi.org/10.1007/978-3-031-60815-5_17

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This disease causes depreciation in thinking capacity, behavior and social skill impairments that does not allow a person to function as a normal human being. Since there are currently no cures available for Alzheimer’s disease, the earliest possible [4] diagnosis of [2] Alzheimer’s is necessary for the proper care and early deflection of Alzheimer’s disease. This Disease is manifested in the earliest stages as memory impairment of selective nature. There are currently no cures available as stated above but some treatments are still available to soothe and suppress some symptoms of Alzheimer’s disease. Magnetic Resonance Imaging [5] (MRI) is the most widely used form of evaluation method for Alzheimer’s disease in suspected patients of the disease. The longitudinal MRI data [6] from The Open Access Series of Imaging Studies or OASIS was used in the research and the features of the dataset were used as benchmarks for evaluating the models. According to previous Studies [5] Magnetic Resonance Imaging may be helpful in predicting the rate of decline in Alzheimer’s disease and it may help in guiding the appropriate therapy for the patients in the future. However, if sufficient and impactful stages of these therapies are to be reached then the researchers and scientists have to make use of [4, 7] Artificial Intelligence and Machine Learning methods and techniques to be accurate in their predictions of progress in the patients from mild [8] cognitive impairment to dementia. Artificial Intelligence has been used in medicinal fields for solving various problems by using medical [6] data to produce insights and help to create healthcare better. And for this reason, [9] Artificial Intelligence can also detect very minute nuances in normal and degenerated brain tissue images which may help in diagnosing [9] Alzheimer’s disease in the early stages so that appropriate actions and measures can be undertaken. With the help of Artificial Intelligence doctors and medical professionals can start treating the disease earlier than it has the time to manifest completely in the patient and diagnose it in the earliest stage possible. Artificial Intelligence models [8] have also predicted that highly intelligent people can show signs of Alzheimer’s further later in their lives than normal people but once they do show the signs, their decline in cognitive abilities become much faster than normal Alzheimer’s Patients. This research planned to develop such a model which will help doctors and medical professionals to diagnose Alzheimer’s disease. The research has gone through various Artificial Intelligence models [7, 10] from Logistic Regression, Decision Tree, Random Forest Classifier, [4] Adaboost and Support Vector Machine (SVM) [11] with 4 different kernels starting with rbf to linear kernel to poly to sigmoid to see which type works the best.

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2 Methods 2.1 Overview of the Methods The course of the research followed a number of machine learning models for the diagnosis of Alzheimer’s. However, to draw valuable insights, Exploratory Data Analysis or EDA was used. The machine learning models employed in the research include logistic regression model, Support Vector Machine (SVM) models with 4 different kernel types, random forest classification model, decision tree model and AdaBoost ensemble model.

2.2 Exploratory Data Analysis EDA or Exploratory Data Analysis was used to draw valuable insights from the dataset used. Multiple relationships between different dataset features and the susceptibility of patients towards Alzheimer’s were visualized to create a better understanding of the OASIS dataset. The relationships visualized in this method include the relation of age, years of education, Socioeconomic Status (SES), Mini Mental State Examination (MMSE), Clinical Dementia Rating (CDR), estimated Total Intracranial Volume (eTIV), normalize Whole Brain Volume (nWBV), Atlas Scaling Factor (ASF) with the manifestation of Alzheimer’s in patients.

2.3 Logistic Regression Two variations of logistic regression models have been used where one model uses a dataset in which the missing values have been corrected using data imputation and the next logistic regression model uses a dataset where the rows containing missing values have been dropped. Both the models are then compared against each other as well as all the other models. The best parameter are choses automatically by the model to reduce overfitting.

2.4 Support Vector Machine (SVM) Models A total of 4 SVM models have been created with different kernel types which are then compared against each other and then compared will all the models used. The first model uses RBF or Radial Basis Function kernel, linear kernel, polynomial kernel and sigmoid kernel. RBF kernel performed the best among all other SVM kernels.

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2.5 Decision Tree Decision tree with the maximum depth range of 0–8 was used out of which the model chose the depth of 1 as the optimal maximum depth and 8 features were considered. This model gave the second-best AUC score and performed extremely well considering the tree depth was 1.

2.6 Random Forest Classification Model This model used in the research was the most efficient and accurate as it had the best AUC score. This model used 14 trees in the forest and the maximum number of features considered when looking for the best split were 5 along with 7 as the maximum depth of the tree.

2.7 AdaBoost Ensemble Model This model was built on top of a combination of 2 decision trees and all the best parameters such as learning rate and n_estimator, were chosen automatically by the model from a specified range. It had one of the best AUC scores just after random forest classification model.

3 Results 3.1 Overview of the Results In the research two methods were used for the production of results and I have employed multiple machine learning algorithms as the major comparison and result production method. In addition to that I have used extensive exploratory data analysis or EDA to gather very insightful findings and results. Several methods have been employed in the machine learning section of the research to gather the best parameters for the diagnosis of Alzheimer’s disease, such as decision trees, random forests, support vector machines (SVM), boosting, and classifiers. I have measured the machine learning model’s peak performance using parameters such as AUC, accuracy, recall, FPR, TPR, and TH. It is safe to believe that this research system can help medical professionals to diagnose and detect these diseases.

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3.2 Exploratory Data Analysis (EDA) The EDA section of the research focused on the relationship of every feature of the MRI scan and the corresponding dementia of the patients. The correlations revealed some very insightful results and Fig. 1. is a graph which shows the correlation of gender to the manifestation of Alzheimer’s Disease in the individuals who underwent the study. In all the charts group 0 indicates non-demented patients and group 1 indicates demented patients. This graph clearly states that men are at a higher risk of dementia than women. However, it is also found in previous studies that after the age of 90 women are at a higher risk of dementia than men. In the next stage of EDA, I plotted a chart depicted in Fig. 2. Which shows that non demented group had a much higher MMSE score than any of the demented patients MMSE is an abbreviation for Mini-Mental State Examination and The Mini Mental State Test (MMSE) is a collection of a total of 11 questions commonly used by doctors and other healthcare professionals to test for cognitive impairment in aged and older adults which comprise of thinking, communication, understanding, and memory problems. MMSE score in the dataset was found to be significantly higher for non-demented individuals than demented individuals The next section of EDA showed that non-demented patients have a higher brain volume and have better neural volume than demented patients and this is because the brain shrinks due to Alzheimer’s disease. Fig. 3. Shows Atlas Scaling Factor

Fig. 1 Graph indicates that men are more likely to have dementia

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Fig. 2 Non demented patients had higher MMSE score than demented patients

parameter for the loss of brain tissue and brain shrinkage in non-demented patients is much lower than demented patients. According to Estimated Total Intracranial Volume parameter we find the same results as the non-demented group has higher brain volume compared to the demented patient group. Fig. 4. Depicts the finding in a charted form to give a better understanding in the form of visualized data. When we look at Normalize Whole Brain Volume parameter, we are reminded of the fact again that brain volume is higher in non-demented individuals than demented individuals and this seems to be the common finding with respect to any parameter I have compared. The (Fig. 5.) Is a chart showing the results in a charted manner to get a better visual understanding of the comparison.

Fig. 3 Brain volume in demented and non-demented group according to ASF parameter

Fig. 4 Non demented group having higher brain volume than demented group with eTIV parameter

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Fig. 5 Chart comparing demented and non-demented subject’s brain volume

In the following stage of EDA, we find out that the higher volume of individuals ranges from ages 70–80 in the demented patient pool than in the non-demented pool of patients Fig. 6. Depicts that non demented patients were higher in ages as they had better survivability than demented patients and they lived to older ages. Demented patients on the other hand had higher concentrations in the 70–80-year-old range as they were likely to die earlier. The final stage of EDA revealed that the patients who were less educated had more chances of dementia and the non-demented group consisted of better educated individuals Fig. 7. Depicts the graph showing the relevance between the education and the contrast groups of demented and non-demented patients. The years of education directly correspond to the lack of demented patients in the resulting chart.

Fig. 6 Concentration of demented patients in 70–80-year age range where group 0 is non-demented and group 1 is demented

Fig. 7 Chart showing demented patients were less educated

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From this EDA we can summarize the results of the EDA section that gender plays a role in the manifestation of dementia as men are likely to be more demented which is an Alzheimer’s disease but women on the other hand have less chances of contracting Alzheimer’s according to the analysis I performed. The patients who were better educated were found to be less in the demented patient pool while people who were less educated had higher chances of dementia and it was found that in this dataset the less educated individuals were concentrated in the demented patient pool. The brain volume of the non-demented group was significantly higher than demented group of patients which is indicative of the fact that brain tissues shrink due to Alzheimer’s disease. The decrease in brain tissue volume also gives us the inference that the neurological connectivity and cognitive ability decrement. I found in EDA that the higher cluster of demented patients resided in the 7080 age range but it was not the case with non-demented patients as non-demented patients could be found in 90 years age range since they had a better chance of surviving than demented patients and reaching higher ages. Demented patients were in very low numbers in the 90 years age range since they reached early death due to the manifestation of Alzheimer’s disease as it is a fatal disease and significantly reduces life expectancy.

3.3 Machine Learning Methods Now we look at the results from our main area of focus which is machine learning and ensemble methods. In this method I used 9 machine learning models which are Linear Regression (With Imputation), Linear Regression (With Dropped Row), SVM (RBF), SVM (linear), SVM (Sigmoid), SVM (Poly), Decision Tree, Random Forest and AdaBoost ensemble. The first model which is Logistic Regression (with imputation) and Table 1. Shows the description of results and the corresponding values of the results. This model gave us the best accuracy on validation set which was 0.754 or 75.4%. This value is higher when we use imputation since we are not removing any useful data to get rid of empty values. The best parameter for regularization chosen by the model is 10 from a tuning range of [0.001, 0.1, 1, 10, 100] to minimize overfitting of the model data. Best test accuracy with the defined c parameter is 0.789 or 78.9%. Best test recall with best c parameter chosen by the model is 0.75 or 75% which is the summarization of threshold value score. Test AUC with the best c parameter was reached by the logistic regression model with the vale 0.791 or 79.1%. In the next variant of Logistic Regression Model, I did not use imputation and in place of that I removed the empty value rows by dropping them and the Table 2. Shows the results from the logistic regression model with dropped rows hence the results are slightly less accurate since some data is missing from the dataset used in this model.

Alzheimer’s Disease Diagnosis Using Machine Learning Approach Table 1 Results from logistic regression (With Imputation) model

Table 2 Results from logistic regression (Without Imputation) model

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Description

Value

Best accuracy on validation set

0.754112554

Best parameter for regularization

10

Test accuracy with best C parameter

0.789473684

Test recall with the best C parameter

0.75

Test AUC with the best C parameter

0.791666667

Description

Value

Best accuracy on validation set

0.700909091

Best parameter for regularization

10

Test accuracy with best C parameter

0.75

Test recall with the best C parameter

0.7

Test AUC with the best C parameter

0.794444444

This model gave the best accuracy on validation set of 0.700 or 70.0%. Since this is a variant of the same logistic regression model it used the same parameter for regularization as it did with the previous model which is 10. Test accuracy received from this model with best c parameters automatically chosen by the model was 0.75 or 75%. The test recall I received with the best c parameters was 0.7 or 70%. However, we can see that the test AUC was higher than the previous model with the score of 0.794 or 79.4%. Now we come to the Support Vector Machine Model and I have created 4 SVM models, each with a different kernel type so as to differentiate and distinguish which model works best and with which kernel type. First SVM model uses RBF or Radial Basis Function as the kernel type. Table 3. Shows the detailed results from the SVM model using RBF kernel and this model gives us the best accuracy on cross validation set of 0.771 or 77.1%. It chooses the best parameter for c as 100 where c is the penalty parameter, from an array of gamma coefficient or kernel coefficient provided as [0.001, 0.01, 0.1, 1, 10, 100, 1000]. The Best parameter for gamma is 0.1 as chosen by the model and the parameter for kernel is RBF which I have specified. Test accuracy with the best parameters is 0.815 or 81.5%. The Test recall we get with the best parameters is 0.7 or 70%. Test AUC with the best parameter is 0.82 or 82%. In the next model we use linear kernel in SVM as the kernel type and all the penalty parameter array (c) and gamma coefficient remain same as the previous model. Table 4. Shows the detailed results from SVM (Linear) model. This model gives us the best accuracy on cross validation set of 0.769 or 76.9%. It chooses the best parameter for c as 1 and the best parameter for gamma as 0.001. The kernel used here is linear and the test accuracy with the best parameters is shown as

376 Table 3 Results from SVM (RBF) Model

Table 4 Results from SVM (Linear) model

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Description

Value

Best accuracy on cross validation set

0.771503858

Best parameter for c

100

Best parameter for gamma

0.1

Parameter for kernel

rbf

Test accuracy with the best parameters

0.815789474

Test recall with the best parameters

0.7

Test AUC with the best parameter

0.822222222

Description

Value

Best accuracy on cross validation set

0.769452286844

Best parameter for c

1

Best parameter for gamma

0.001

Parameter for kernel

Linear

Test accuracy with the best parameters

0.763157894737

Test recall with the best parameters

0.65

Test AUC with the best parameter

0.769444444444

0.763 or 76.3%. Test recall is 0.65 or 65% with the best parameters and test AUC is 0.769 or 76.9%. This model performed slightly lower than RBF kernel SVM model. In the following SVM model we use polynomial kernel as the kernel type and the penalty parameter array (c) and gamma coefficient stay the same as the original SVM model. Table 5. Shows the detailed results from SVM (Polynomial) model. This model gives us the best accuracy on cross validation set of 0.744 or 74.4%. It chooses the best parameter for c as 0.001 and the best parameter for gamma as 10. The kernel used here is polynomial and the test accuracy with the best parameters is shown as 0.789 or 78.9%. Test recall is 0.7 or 70% with the best parameters and test AUC is 0.794 or 79.4%. This model performed even lower than previous SVM models. Table 5 Results from SVM (Polynomial) model

Description

Value

Best accuracy on cross validation set

0.0.744193487672

Best parameter for c

0.001

Best parameter for gamma

10

Parameter for kernel

Poly

Test accuracy with the best parameters

0.789473684211

Test recall with the best parameters

0.7

Test AUC with the best parameter

0.794444444444

Alzheimer’s Disease Diagnosis Using Machine Learning Approach Table 6 Results from SVM (Sigmoid) model

Description

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Value

Best accuracy on cross validation set

0.744193487672

Best parameter for c

0.001

Best parameter for gamma

10

Parameter for kernel

Sigmoid

Test accuracy with the best parameters

0.789473684211

Test recall with the best parameters

0.7

Test AUC with the best parameter

0.794444444444

In this last SVM model we use sigmoid kernel as the final kernel type and here the penalty parameter array (c) and gamma coefficient also stay the same as the previous SVM models. Table 6. Shows the detailed results from SVM (Sigmoid) model. This final SVM model provides us with the best accuracy on cross validation set of 0.769 or 76.9%. It chooses the best parameter for c as 100 and the best parameter for gamma as 0.01. The kernel used here is sigmoid and the test accuracy with the best parameters is shown as 0.763 or 76.3%. Test recall is 0.65 or 65% with the best parameters and test AUC is 0.769 or 76.9%. This model performed similar to linear SVM model. The next machine learning model used is decision tree with the maximum depth of 8 layers which takes in 8 as the number of features. Table 7. Shows the detailed results from the decision tree model. The results from this model turned out to be significantly satisfactory. The results we gathered from this model showed that the best accuracy on validation set is 0.778 or 77.8% and the best parameter chosen for the maximum depth by the model is 1. Also, the Test accuracy with best parameter chosen by the model is 0.815 or 81.5%. The Test recall with best parameters is 0.65 or 65% and lastly the Test AUC with the best parameter is 0.825 or 82.5%. Decision tree model performed just way it was expected to however the same could not be said for SVM. Random Forest Classifier is also used in the research to gather fruitful results. In this model I have specified M as the number of trees in the forest, d as the number of features the model needs to consider when it is looking for the best split and m as the maximum depth of the tree. Table 7 Results from decision tree model

Description

Value

Best accuracy on validation set

0.778543195935

Best parameter for the maximum depth

1

Test accuracy with the best parameters

0.815789473684

Test recall with the best parameters

0.65

Test AUC with the best parameter

0.825

378 Table 8 Results from random forest classifier model

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Description

Value

Best accuracy on validation set

0.796329757199

Best parameter for the M, d, m

14, 5, 7

Test accuracy with the best parameters

0.842105263158

Test recall with the best parameters

0.8

Test AUC with the best parameter

0.844444444444

Table 8. Shows the detailed results from the Random Forest Classifier Model. In this model we found out that the best accuracy on validation set is 0.796 or 79.6% and the best parameters of M, d, m chosen by the model are 14, 5 and 7 which means that the number of trees in the forest are 14, number of features considered by the model are 5 and the depth of the tree is 7. With all these selections we arrive at the result of Test accuracy with the best parameters is 0.842 or 84.2%. Test recall with the best parameters is found to be 0.8 or 80% and lastly Test AUC with the best parameters is 0.844 or 84.4%. The random forest model provided the best results among any other machine learning model and hence it is sufficed to say that the best performing model in the entire research turned out to be random forest classifier model which exceeded the expectations set by SVM in past researches. The last machine learning model used to draw out results is an ensemble method called AdaBoost which works on top of decision trees. In the model I created M is the number of trees and lr signifies the Learning Rate of the model. Table 9. Shows the detailed results from the AdaBoost Model. In this model we find out that the best accuracy on validation set is 0.778 or 77.8% where the best parameter of M is 2 as chosen by the model itself and best parameter of LR or learning rate is 0.0001. Test accuracy with the best parameter is found to be 0.842 or 84.2% while Test recall with the best parameters is shown as 0.65 or 65%and Test AUC with the best parameters is shown as 0.825 or 82.5%. Before comparing the results of all the models against each other I first compared the results of similar models or same models with different parameters. The first comparison was made within Logistic regression models with and without imputation. Since with imputation I had more data to work with and the model was being fed more relevant data it performed better than the model where I dropped the rows with Table 9 Results from AdaBoost Model

Description

Value

Best accuracy on validation set

0.778543195935

Best parameter for M, lr (learning rate)

2, 0.0001

Test accuracy with the best parameters

0.842105263158

Test recall with the best parameters

0.65

Test AUC with the best parameter

0.825

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Fig. 8 Accuracy, Recall and AUC scores for all Machine Learning Models Used

empty values in them. The results show that Logistic Regression Model with imputation had an accuracy of 78.9% whereas Logistic Regression Model with dropped rows had an accuracy of 75%. The recall for imputation LR model was 75% but for non-imputation LR model it was 70%. Also, the AUC for the former is 79.1% whereas for the latter it was 70%. A total of 4 different SVM models were used in the research with different kernel types. The first model uses RBF kernel type, the second model uses linear kernel type, the third uses polynomial kernel type and the last uses sigmoid kernel type and have accuracies 81.5%, 76.3%, 78.9% and 76.3% respectively. The corresponding Recall scores for these models are 70% for RBF, 65% for linear, 70% for polynomial and 65% for sigmoid SVM models. We also see the AUC of RBF as 82.2%, linear as 76.9%, polynomial as 79.4% and lastly sigmoid as 76.9%. The RBF SVM model has the best accuracy and AUC among all other SVM models while sigmoid SVM model and linear SVM model have all the same metric scores. Polynomial lands in between RBF and Linear/Sigmoid. Visualizing the results received from all the machine learning models together provides a strong visual understanding of the resulting scores we have gathered from these models and it creates a better grasp of the capabilities of each model in our case. In the bar chart shown in (Fig. 8) we see the resulting score differences of each machine learning model.

4 Conclusions Alzheimer’s disease is a bane for humanity and this research plans to unburden the medical professionals in diagnosing Alzheimer’s disease in the stages where it can be suppressed and the suffering of the affected individuals can be reduced. The results provided by the research first and foremost conclude that this disease is a

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severe medical and socioeconomic issue in today’s world. During the course of the research, it was realized that medical professionals are facing a grave threat to human lives as Alzheimer’s disease in incurable and the final stage of Alzheimer’s is death.

4.1 Conclusions from EDA As we saw in the EDA section of the research that most demented patients have very little chance of making it to the age range of 90 years. The EDA done in the research provided some important insights and helped me conclude that factors even which seemed insignificant to me previously when thinking about Alzheimer’s disease such as gender, years of education and socioeconomic status played a major role in the manifestation of Alzheimer’s disease in aged and old individuals. Looking at chart created by EDA techniques depicting the correlation of MMSE score to demented and non-demented groups gave clear results that non-demented individuals gathered higher MMSE scores than demented individuals which brought me to a clear conclusion that coherence during answering MMSE questions is a key factor in determining whether an individual is susceptible to contracting Alzheimer’s disease. The charts depicting the correlation between brain volume ratio and both the groups of individuals revealed that the non-demented group of individuals had greater brain volume then those in the demented group and conclusions such as the non-demented group of individuals have better cognitive functioning since their brain shows lesser shrinkage compared to demented individuals. The shrunk brain tissues of demented patients impaired their ability for better decision making and over all functioning. Another graph depicting the concentration of demented individuals and non-demented individuals in an age range graph showed that the demented individuals were found mostly in the 70–80 year age range as most patients under the demented umbrella succumbed to the illness and passed away and most of the concentration of individuals in the 90 year age range was from non-demented individuals and hence I concluded that Alzheimer’s disease should be diagnosed at an earlier stage because once it manifests itself completely and reaches the later stage nothing can be done for the affected individuals as growing age only becomes more fatal for those suffering with Alzheimer’s disease. The correlation of educational level and dementia manifestation shown in the relevant charts revealed that individuals with better education were less likely to be demented and hence we can infer that maybe better education and prolonged use of the brain for mental exercises could help in reducing the manifestation of Alzheimer’s disease.

4.2 Conclusions from Machine Learning Methods If we talk about the conclusions I’ve reached from the machine learning model evaluation, we can see that Random Forest Classification Model provided the best

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results and the highest accuracy for the diagnosis of the individuals suffering from Alzheimer’s disease with an accuracy of 84.2%. This accuracy is also shared by AdaBoost Model but its AUC score was lower than Random Forest Classifier Model and it was closely followed by Decision Tree and RBF SVM Model which both had the accuracy of 81.5% while also having the similar score for AUC. With this knowledge it can also be concluded that while Random Forest Classification Model is best suited for our chosen dataset. If we take just accuracy into account then AdaBoost also a good choice for such machine learning diagnosis of Alzheimer’s disease. SVM with RBF kernel type and Decision Tree models can also be used to fairly accurately diagnose Alzheimer’s in a dataset of this caliber. Experimenting with different kernel types in SVM models and then comparing them concluded that same SVM models work better than the others while their differences in accuracies were not huge but still significant enough that before choosing a model, we should give a trail to all kernel type if it is feasible and while the most commonly used kernel type which is RBF performed the best it should be taken into consideration that polynomial kernel type performed on par with RBF kernel type. If we look at the results provided by Logistic Regression, we conclude that if there is a scope for imputation in the dataset then it should be done as it can provide much better results and help in effective diagnosis. However, if we drop empty value columns or rows during logistic regression, we rid ourselves of useful data which can result in reduced accuracy as we see in our model evaluation. While most research papers studied for the research pointed towards SVM being the superior choice, I found out that the Random Forest Classifier produced better results and gave the best accuracy and AUC score. This finding however, does not mean that Random Forest Classifiers will work best with every dataset pertaining to Alzheimer’s disease as there is no one-size-fits-all when it comes to machine learning methodologies. However, it can be concluded that with the data of similar nature we may be able to get better results by not following the norm and experimenting with different machine learning models.

4.3 Limitations As is the case with any research, limitations are bound to occur and in the case of this research the limitations were that the complexity of a dataset calls for a more complex machine learning model and it is clear that the dataset I used had clearly labelled features however the values used for testing pertaining to every group had loosely classified ranges. Since the highest performing model can be scaled further, I believe that better results can be obtained if data is more processed and analyzed.

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4.4 Future Scope For the future scope of the research it is believed that datasets with higher density and better labelling should be taken into account and better clarity is to be obtained with specialized data processing methods. The model functioning and efficiency can be improved drastically if multiple parameters are used simultaneously to better optimize the machine learning models.

4.5 Final Thoughts As a final thought I believe that machine learning methods can help and even improve the diagnosis process of Alzheimer’s disease and in turn help medical professionals in the early identification and diagnosis of this terrible illness, possibly even find a cure for Alzheimer’s disease if extensive research and efforts are put towards this issue.

4.6 Availability of Dataset The dataset used in the research is available at: https://www.oasis-brains.org/

4.7 Contributions The data collection along with preliminary analysis was done by Denys Nevinskyi and Akshay Bajpai. Modeling was accomplished by Akshay Bajpai and conceptual modeling combined with research guidance was done by Yaroslav Vyklyuk.

References 1. What is Alzheimer’s disease? https://www.nia.nih.gov/health/what-alzheimers-disease. 2. Assessing cognitive impairment in older patients. https://www.nia.nih.gov/health/assessingcognitive-impairment-older-patients. 3. Causes of Dementia. https://www.dementia.org/causes. 4. Fulton, L., Dolezel, D., Harrop, J., Yan, Y., Fulton, C.: Classification of Alzheimer’s disease with and without Imagery using gradient boosted machines and ResNet-50. Brain Sci. 9, 212 (2019). https://doi.org/10.3390/brainsci9090212 5. Moradi E. et. al.: Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects, NeuroImage, 104: 398–412, (2015) ISSN 1053–8119, doi.org/ https://doi.org/10.1016/j.neuroimage.2014.10.002

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6. Marcus, D.S., et al.: Open access series of imaging studies (OASIS): longitudinal MRI data in nondemented and demented older adults. J. Cogn. Neurosci. 22(12), 2677–2684 (2010). https://doi.org/10.1162/jocn.2009.21407 7. Tanveer, M.: Machine learning techniques for the diagnosis of Alzheimer’s disease: A review. ACM Trans. Multimed. Comput. Commun. Appl. 16: 35 https://doi.org/10.1145/3344998 8. Trambaiolli, L.R., et al.: Improving Alzheimer’s disease diagnosis with machine learning techniques. Clin. EEG Neurosci. 42(3), 160–165 (2011). https://doi.org/10.1177/155005941104 200304 9. Mirzaei, G., et al.: Imaging and machine learning techniques for diagnosis of Alzheimer’s disease. Rev. Neurosci. 27(8), 857–870 (2016). https://doi.org/10.1515/revneuro-2016-0029 10. P. Lodha, et. al. Diagnosis of Alzheimer’s disease using machine learning. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), (2018) pp. 1–4, https://doi.org/10.1109/ICCUBEA.2018.8697386 11. Alam, S. et. al. Alzheimer disease classification using KPCA, LDA, and multi-kernel learning SVM. Int. J. Imag. Syst. Technol. 27, 2 (2017)