The European Digital Economy: Drivers of Digital Transition and Economic Recovery

Lubacha, Judyta (Ed.); Mäihäniemi, Beata (Ed.); Wisła, Rafał (Ed.) Book The European Digital Economy: Drivers of Digital Transition and Economic Recovery Routledge Open Business and Economics Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Lubacha, Judyta (Ed.); Mäihäniemi, Beata (Ed.); Wisła, Rafał (Ed.) (2024) : The European Digital Economy: Drivers of Digital Transition and Economic Recovery, Routledge Open Business and Economics, ISBN 978-1-003-84539-3, Routledge, London, https://doi.org/10.4324/9781003450160 This Version is available at: https://hdl.handle.net/10419/290623 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ The European Digital Economy The “digital economy” is a conceptual umbrella referring to markets, organizations and their networks that are based on digital technologies, communication, data processing and e-commerce. It is multidimensional and its dynamic structure must be analysed from various dimensions, such as economic – changes in the nature of resources, production factors and economic processes; technological – technological progress viewed from a macroeconomic perspective vs. technological innovation viewed from a microeconomic perspective; regulatory – challenges facing regulators, new risks affecting the institutional order; and sociological – changes in society’s functioning principles, attitudes towards work and human relations. The purpose of this book is to analyse the effectiveness of digital technologies as well as the fundamental factors that contribute to technological progress in the long run. It also examines structural and qualitative shifts in economies and societies. It investigates many research questions, such as the gap between the level of digital economic development in European Union countries; digital transformation and its impact on workplace skills development patterns; and also the legal framework for data as resource. The book approaches these issues from a multidisciplinary perspective, from law to economics and sociology. It focuses on definitional discussions, the measurement challenges, drivers for digital transition, the impact on labour relations, digital skills and education, data reuse and data extractivism. This is a comprehensive introduction to the different contexts from which the digital economy can be addressed, offering an innovative method for studying this complex phenomenon, and as such, it will be a valuable resource for students, scholars and researchers across a range of disciplines. Judyta Lubacha is an Assistant Professor in the Department of Economics and Innovation of the Jagiellonian University, Krakow, Poland. Beata Mäihäniemi is a University Researcher, Faculty of Law, University of Lapland, Finland. Rafał Wisła is a Professor of Economics in the Department of Economics and Innovation of the Jagiellonian University, Krakow, Poland. Routledge Open Business and Economics provides a platform for the open access publication of monographs and edited collections across the full breadth of these disciplines including accounting, finance, management, marketing and political economy. Reflecting our commitment to supporting open access publishing, this series provides a key repository for academic research in business and economics. Books in the series are published via the Gold Open Access model and are therefore available for free download and re-use according to the terms of Creative Commons licence. They can be accessed via the Routledge and Taylor & Francis website, as well as third party discovery sites such as the Directory of OAPEN Library, Open Access Books, PMC Bookshelf, and Google Books. Note that the other Business and Economics series at Routledge also all accept open access books for publication. Managing Generation Z Motivation, Engagement and Loyalty Edited by Joanna Nieżurawska-Zajac, Radosław Antoni Kycia and Agnieszka Niemczynowicz Higher Education Institutions and Digital Transformation Building University-Enterprise Collaborative Relationships Marcin Lis Organizing Sustainable Development Edited by Aneta Kuźniarska, Karolina Mania and Monika Jedynak The European Digital Economy Drivers of Digital Transition and Economic Recovery Edited by Judyta Lubacha, Beata Mäihäniemi and Rafał Wisła Routledge Open Business and Economics For more information about this series, please visit: Routledge Open Business and Economics – Book Series – Routledge & CRC Press LONDON AND NEW YORK The European Digital Economy Drivers of Digital Transition and EconomicRecovery Edited by Judyta Lubacha, Beata Mäihäniemi and Rafał Wisła First published 2024 by Routledge 4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2024 selection and editorial matter, Judyta Lubacha, Beata Mäihäniemi and Rafał Wisła; individual chapters, the contributors The right of Judyta Lubacha, Beata Mäihäniemi and Rafał Wisła to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. The Open Access version of this book, available at www.taylorfrancis. com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license. The open access license of the publication was funded by the Priority Research Area Society of the Future under the programme “Excellence Initiative – Research University” at the Jagiellonian University in Krakow. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-032-58459-1 (hbk) ISBN: 978-1-032-58458-4 (pbk) ISBN: 978-1-003-45016-0 (ebk) DOI: 10.4324/9781003450160 Typeset in Sabon by codeMantra List of Figures vii List of Tables ix List of Contributors xi Introduction 1 JUDYTA LUBACHA, BEATA MÄIHÄNIEMI AND RAFAŁ WISŁA PART I Measuring the digital economy 7 1 The dimensions of the digital economy and society 9 JUDYTA LUBACHA, RAFAŁ WISŁA, MICHAŁ WŁODARCZYK AND ANNA ZACHOROWSKA-MAZURKIEWICZ 2 Measuring the digital economy with “digital economy” tools 27 ALEKSANDER ŻOŁNIERSKI 3 Differentiation of the digital economic development inEurope 45 MATEUSZ BIERNACKI, AGATA LUŚTYK AND RAFAŁWISŁA PART II Sources for developing the digital economy 61 4 Digital innovation hubs as drivers for digital transition andeconomic recovery: the case of the Arctic Development Environments Cluster in Lapland 63 SILVIA GAIANI AND URSZULA ALA-KARVIA Contents vi Contents 5 Digitalization and the impact on the labour relations 83 ALEJANDRO DÍAZ MORENO, Mª DEL MILAGRO MARTÍN LÓPEZ, MYRIAM GONZÁLEZ LIMÓN AND MANUEL RIVERA FERNÁNDEZ 6 Digitalization and digital skills development patterns. Evidence for European countries 101 HELENA ANACKA AND EWA LECHMAN 7 Virtual reality in legal education. Challenges and possibilities to transform normative knowledge 120 AMALIA VERDU SANMARTIN AND JOHANNA NIEMI 8 The patent system and the problem of innovation diffusion in the digital economy 141 MAŁGORZATA NIKLEWICZ-PIJACZYŃSKA PART III Nature of resources 159 9 Behind the transparency of ‘data reuse’ 161 BEATA MÄIHÄNIEMI 10 Data extractivism: social pollution and real-world costs 186 CHRISTOPHER W. CHAGNON AND SOPHIA E. HAGOLANI-ALBOV 11 FinTech future trends: secondary data review 204 YEVHENIIA POLISHCHUK Index 221 Figures 1.1 Number of publications for “digital economy” query from 1990 11 1.2 Share of research areas represented in the articles for “digital economy” query 12 1.3 Map of the keywords occurrence for “digital economy” query in 1990–1999 13 1.4 Map of the keywords occurrence for “digital economy” query in 2000–2009 13 1.5 Map of the keywords occurrence for “digital economy” query in 2010–2019 14 1.6 Map of the keywords occurrence for “digital economy” query in 2020–2022 15 1.7 Key digital technologies 17 2.1 Data processing procedure: refining unstructured data online 33 4.1 Digital Economy and Society Index 2022 by the main dimensions 65 4.2 Services delivered by DIHs for all EU countries 67 4.3 DIHs supporting selected technologies for all small-and medium-sized enterprises 68 4.4 Fully operational DIHs by selected technologies they provide 70 4.5 Five thematic areas for clusters in East and North Finland 75 6.1 ICT diffusion trajectories. Mobile cellular telephony, active mobile subscribers and IU. 1980–2021 109 6.2 Changes in ICT-related inequalities. Gini indices values. Mobile cellular telephony and IU. 1980–2021 112 6.3 Individuals with basic or above basic information and data literacy skills; individuals with online information and communication skills, and individuals with basic or above basic overall digital skills. Year 2021 114 6.A1 Digital skills cross-country distribution. Year 2021 119 xiv Contributors of journals indexed in Journal Citation Report and reviewer of papers for international conferences and scientific journals. She is a member of the Asociación Libre de Economía (AldE) and of the Asociación de Mujeres Investigadoras y Tecnólogas (AMIT). Mª del Milagro Martín López is a Professor at the University of Seville, PhD of Business Administration and Management, Vice Dean of Teaching Innovation at the Faculty of Labour Sciences, University of Seville (2004– 2008), Dean of the Faculty of Labour Sciences from 2009 to 2017, expert of the Economic and Social City Council of Seville from 2015 to date, President of the Economic and Social Council of the City Council of Seville from 2016 to date, main coordinator of Project No. VP / 2012/001/0068. Department: DS EMPL.B.1. European Commission and entitled “Les Relations professionnelles dans le contexte du développement de la SousTritance” and national coordinator of the project VP/2018/004/0006 (Call for proposals “Improving expertise in the field of industrial relations”. European Commission). “The impact of the digitalisation of the economy on professional competences and qualifications, and its impact on working and working conditions” with financial support from the European Union. She is Master Program Coordinator in Management and Development of Human Resources. Judyta Lubacha is a researcher and works as an Assistant Professor in the Department of Economics and Innovation of the Jagiellonian University in Krakow (Poland). Her research is focused on innovative activities and regional development. She received many national and international scholarships and research grants: PRELUDIUM grant financed by the National Science Center; DAAD Research Grant for PhD students and young scientists; Polish National Bank Research scholarship for PhD students; and Special Award of the Minister of Regional Development in the competition of master’s theses “Now Poland Promotion”. Currently, she works in the field of sustainable development and circular economy. In 2023, she received three-year grant for the project: Economic and institutional factors influencing the implementation of the circular economy model in enterprises of the manufacturing sector. Agata Luśtyk is a PhD candidate in Economics and Finance at the Doctoral School in the Social Sciences, Jagiellonian University in Krakow (Poland). She is author and co-author of academic publications in the field of innovative potential and labour productivity, such as Assessment of the innovative potential of Polish voivodeships and the probability of its change (2022) and Determinants of spatial differentiation of labor productivity in Poland (2022). Beata Mäihäniemi is a University Researcher, Faculty of Law, University of Lapland and is also the author of the book Competition Law and BigData. Contributors xv Imposing Access to Information in Digital Markets published in 2020. She is an international expert in competition law and has authored over 20 publications in this area. She has been teaching competition law, European law and Internet Regulation for over ten years. She has been involved in two national projects on AI that involve elements of competition law. She has authored few national reports on the recent developments, and she actively participates as an expert in competition law and data governance in national workshops and conferences. Alejandro Díaz Moreno is a Professor of the Department of Civil and Private International Law at the University of Seville. He is the author of more than 50 scientific articles and more than 150 case law reviews. He earned his PhD in Private Law from the University of Seville. He is the Dean of the Faculty of Labor Sciences. He is the President of the Association of Centers and Faculties of Labor Relations and Human Resources (ARELCIT). The issues that he deals with are in the field of credit protection and the satisfaction of the creditor’s interest, jurisdictional competence as well as problems arising in practice like the delay in commercial operations and its impact on the guarantees and traditional mechanisms of commercial credit and the treatment of some of the multiple problems raised by the recent insolvency reforms. He is a permanent collaborator of the Journal of Patrimonial Civil Law. He is a coordinator and Lead Researcher of the EC project, The impact of digitization of the economy on the skills and professional qualifications and labor relations, Call for proposals: Improving expertise in the field of industrial relations (GRANT_NUMBER: VP/2018/004/0006). Johanna Niemi (Niemi-Kiesiläinen) is a Professor and the Dean, Faculty of Law, University of Helsinki. Niemi has worked as a professor at the Universities of Turku and Umeå and as a visiting professor at Lund University, Sweden. She is a Doctor Honoris Causa at Uppsala University and has been Fulbright Scholar at the University of Wisconsin, Madison Law School. She was the member of the Scientific Committee of the EU Fundamental Rights Agency 2013–2018 and Academy of Finland Research Council for Culture and Society 2016–2018. Her research interests include criminal procedure, consumer insolvency, human rights and the construction of gender in legal discourses. Małgorzata Niklewicz-Pijaczyńska is a researcher and works as an Assistant Professor in the Department of General Theory of Economics, Faculty of Law, Administration and Economics, University of Wrocław. She is the author of numerous publications in the field of economic and legal problems of industrial and intellectual property, the functioning of patent systems, inventiveness and innovation in economic development and the tasks of the knowledge-based economy. In 2020, she received the Award of the Polish Minister of Education and Science. The award was granted within the xvi Contributors competition held by the Polish Patent Office for the monograph Patent system in knowledge management: economics of codified technical knowledge. Yevheniia Polishchuk is a Professor in the Corporate Finance and Controlling Department of Kyiv National Economic University named after Vadym Hetman (Ukraine) and is the author and co-author of over 100 academic publications including Government early policy responses on COVID-19 challenges in central and eastern Europe: SME support (2022), SMEs debt financing in the EU: On the eve of the coronacrisis (2020), Smart-contracts via blockchain as the innovation tool for SMEs development (2019), Fintech platforms in SME’s financing: EU experience and ways of their application in Ukraine (2018) and Barriers and opportunities for hi-tech innovative small and medium enterprises development in the 4th industrial revolution era (2017). Her research interests are FinTech development, economic shocks and crisis, regional development and smart specialization. In 2015, she was rewarded with President Prize for Young Scholars in Ukraine. She is the Bachelor Program Coordinator in Investment Management (2019–2022) and United Nation Development Program expert in SMEs development and COVID-19 impact. Amalia Verdu Sanmartin is a Postdoctoral Researcher at the Turku Institution for Advance Studies (TIAS). She does interdisciplinary research with a focus on discrimination, teaching methods, legal knowledge and legal subjects. She is currently working in a project called The Person on the Edge: Disrupting Normative Legal Knowledge in the Digital Age with a focus in the intraactions between the digital and physical space through blended education. Rafał Wisła is a Professor of Economics in the Department of Economics and Innovation of the Jagiellonian University in Krakow (Poland) and is also the author and co-author of over 80 academic publications such as Innovation in the Pharmaceutical and Medical Technology Industries of Poland (2018), Developmental Diversification of Contemporary Europe (2016) and Regional Patterns of Technology Accumulation in Central and Eastern Europe Countries (2014). Recently, he has co-edited and contributed to Economic Transformation in Poland and Ukraine (Routledge, 2020), The Socioeconomic Impact of COVID-19 on Eastern European Countries (Routledge, 2022) and The Solow Model of Economic Growth (2023). He is the Head of the Department of Economics and Innovation at the Institute of Economics, Finance and Management, Jagiellonian University in Krakow and also Doctoral Program Coordinator in Economics and Finance at Jagiellonian University (2019–2023). The issues that he deals with are the differentiation of spatial economic development in Europe and innovation activity from a regional perspective. Contributors xvii Michał Włodarczyk is a doctoral student at the Faculty of Management and Social Communication, Jagiellonian University (Krakow, Poland). He is the author and co-author of academic publications in the field of financial innovations, new technologies and the fintech sector, such as Between Social Responsibility and Potential Profit. The Technological Giants’ Dilemma (2018) and Financial Clusters and Fintech Agglomerations – Location Factors (2020). He is also a co-founder of StaćMnie, a scientific YouTube channel focused on financial education and popularizing economics among young people. Anna Zachorowska-Mazurkiewicz is an Associate Professor at the Institute of Economics, Finance and Management and a Director of the Doctoral School of Social Sciences at Jagiellonian University in Krakow, Poland. She holds a PhD in Economics. Her research interests focus on heterodox economics. She is the author and co-author of more than 80 academic publications, including the most recent Women’s work and its conceptualization in Post-Keyensian Institutionalism, [in:] Ch. J. Whalen (ed.) (2022) A Modern Guide to PostKeynesian Institutional Economics, London: Edward Elgar, pp. 339–358. In years 2013–2016, she was a leading researcher in the project Innogend – Innovative Gender as a New Source of Progress. Aleksander Żołnierski is an Assistant Professor in the Department of Microeconomics, Institute of Economics of the Polish Academy of Sciences. He is the author and co-author of over 50 publications on innovation, information management, organizational culture and SMEs sector. Next to academic and research activity, he is involved in advisory projects, using in practice the knowledge from research on innovation, communication and social capital. He is also engaged in business-related organizations and successfully applied academic and R&D knowledge in economic practice. DOI: 10.4324/9781003450160-1 Societies and economies are not digitally neutral. Technological progress is a disruptive process that stimulates the emergence of a new status quo. Technology and technological change enrich and reshape socio-economic systems, raising their responsiveness and adaptability to further technological development. The “digital economy” is a multidisciplinary conceptual “umbrella” referring to markets, organizations and their networks that are based on digital technologies, communication, data processing and e-commerce. The digital economy is multidimensional, and its dynamic structure must be analysed considering its various aspects: economic (changes in the nature of resources, production factors and economic processes), technological (technological progress viewed from a macroeconomic perspective vs. technological innovation viewed from a microeconomic perspective), regulatory (challenges facing regulators and new risks affecting the institutional order) and sociological (changes in society functioning principles, attitudes towards work and human relations). The purpose of this book is to analyse the effectiveness of the implemented digital technologies as well as fundamental factors that contribute to technological progress in the long run. It also analyses structural and qualitative shifts in economies and societies. The following research topics are investigated and discussed: the gap between the level of digital economic development in the EU countries, digital transformation and its impact on the development patterns of labour skills and the legal framework for using data as a resource. The book is the result of interdisciplinary workshops, namely, (1) “Digital Economy”, organized at the Jagiellonian University in Kraków, 23 June 2022, and (2) The Interdisciplinary Insights into Digital Economy, organized at the University of Helsinki on 1 December 2022. During the workshops, we had the chance to present our research to scholars representing other disciplines in a way understandable to specialists from outside our own research orbit. This approach is also visible in the result, that is, the monograph itself: the editors, who conduct research in two different disciplines, prepared the volume in the spirit of interdisciplinarity. Selected chapters are peer-reviewed Introduction Judyta Lubacha, Beata Mäihäniemi and Rafał Wisła This chapter has been made available under a CC-BY-NC-ND license 2 Judyta Lubacha et al. by scholars from other disciplines. This interdisciplinary cooperation has been one of the most rewarding endeavours we have recently undertaken. The book aims at approaching the topics discussed from a multidisciplinary perspective, ranging from law to economy and sociology. The monograph confirms that, on the one hand, digitalization is a complex phenomenon which alters the economy and society while law does not always keep up with these changes. On the other hand, changes in legislation shape the environment in which companies operate, and new or amended laws may either stimulate or inhibit the development of an economic sector. In our monograph, we focus on definitional discussions, the problems of measurement, drivers of digital transition, changing labour relations, digital skills and education, data reuse and data extractivism. We closely consider selected aspects of the digital economy, many of which are hot topics. The strength of the monograph also lies in the rich background of the team of authors that consists of researchers from eight European academic centres. The book is divided into three parts: Measuring the Digital Economy, Sources for Developing the Digital Economy and Nature of Resources. In Chapter 1 “The dimensions of the digital economy and society”, Judyta Lubacha, Rafał Wisła, Michał Włodarczyk and Anna ZachorowskaMazurkiewicz present and discuss different dimensions and the extent of the impact of digital transformation on the economy. The authors also look into opportunities and risks arising from the digitalization of economic and social processes. Chapter 2 “Measuring the digital economy with ‘digital economy’ tools” by Aleksander Żołnierski presents emerging methods of monitoring the use of digital economy that include not only artificial intelligence or big data analysis but also a wide range of technologies of the digital economy itself. The described methodology is increasingly employed in a number of research projects but has not been used on a large scale to date. It can eliminate many imperfections of commonly used quantitative methods. The chapter analyses the potential offered by three of them: (1) big data analysis of unstructured data, (2) analysis based on Google Trends used in many scientific studies and (3) beacon technology which has new applications, e.g., in monitoring the work environment in Industry 4.0. The main objective of Chapter 3 “Differentiation of the digital economic development in Europe” (Mateusz Biernacki, Agata Luśtyk and Rafał Wisła) is to examine the variation in the digital economic development in Europe. The first section of this chapter contains a review of proposals aimed to measure the digital economy, considering various approaches to its definition. The second section discusses two methods designed to identify changes in the digital economy from a macro perspective and gives the characteristics of data used in the following sections. The third section presents research results with a discussion of their limitations and downsides. In Chapter 4 “Digital innovation hubs as drivers for digital transition and economic recovery: the case of the Arctic Development Environments Introduction 3 Cluster in Lapland ”, Silvia Gaiani and Urszula Ala-Karvia describe the increasingly important role that Digital Innovation Hubs (DIHs) play in the European digital economy where supply chains are systematically digitalized, traditional business models are transforming, companies work in an integrated way and smart distributed production has become a new standard. This chapter first adds to the general discussion on DIHs as supportive ecosystems and underlines their role as drivers of regional competitiveness, innovation capacity and digital transition. Second, it focuses on Finland, the country with the highest IT skills in the world, and specifically on the Arctic Development Environments Cluster which has recently been approved by the European Commission as the first official DIH in Lapland. The analysis conducted in Chapter 5 by Alejandro Díaz Moreno, Mª del Milagro Martín López, Myriam González Limón and Manuel Rivera Fernández concerns digital transformation and its impact on labour relations. Digital transformation is of such magnitude and is happening so fast in recent years that it is having a major effect on the competitiveness and growth of companies. Digital transformation is changing the nature of work and the structure of the labour market. Digital technologies, on the one hand, minimize production costs by replacing workers with computers and robots and, on the other hand, are related to the balance in the labour market. Digitalization of the economy is a social process that is still under construction and has accelerated in recent years as a result of the pandemic. It involves a new way of understanding the forms of working and the organization of work itself, and therefore has an impact on the complex world of labour relations. The study contained in Chapter 6 by Helena Anacka and Ewa Lechman concerns the digitalization and digital skills development patterns. It aims to shed light on digitalization and digital skills dynamics in Europe between 1980 and 2022. The authors have identified three research goals: (1) to identify digitalization trajectories in European countries, (2) to identify digital skills development patterns in European countries and (3) to examine digitalization and digital skills inequalities across countries in Europe. Their empirical sample comprises 27 European economies, and the time span of the analysis is set for the period between 1980 and 2022. Statistical data on digitalization and digital skills are extracted from the ITU and Eurostat databases. In Chapter 7 “Virtual reality in legal education. Challenges and possibilities to transform normative knowledge”, Amalia Verdu Sanmartin and Johanna Niemi explore the intersection between digital education and law, explaining how they challenge each other while coming together in a continuous becoming process affecting the substance of the law, the legal profession and education. The chapter is organized so that Part 2 introduces virtual reality, and Part 3 discusses how VR is transforming the classroom into a smart learning environment. Part 4 explores the possibilities of using virtual reality in legal education. 4 Judyta Lubacha et al. The aim of Chapter 8 “The patent system and the problem of innovation diffusion in the digital economy” (Małgorzata Niklewicz-Pijaczyńska) is to indicate the most problematic, from the perspective of digital management, areas that determine the functioning of patent systems. It also analyses a new function which, once implemented, will make patent regulations an important player in the global process of innovation diffusion. The chapter is based on a critical analysis of the source literature in the field of economics and law. The main conclusions of the chapter state that with the information function implemented, patent systems show a significant potential for a wide spectrum of applications in the process of innovation diffusion. However, in order for this role to be performed in an optimal way, it is necessary to urgently verify the applicable patent rules and thoroughly improve the IT infrastructure so that they respond to the challenges of the digital economy to a greater extent than before. In Chapter 9 “Behind the transparency of ‘data reuse’”, Beata Mäihäniemi assesses the framework for the reuse of personal data by gatekeepers, currently being shaped in the EU. The starting point is provided by the question whether data should be seen as property or commons. Current EU-wide regulations such as the General Data Protection Regulation do not create a property right as regards data, although some, such as competition law, are based on the idea that data is an economic good that can be re-materialized and commodified. Moreover, how does the abundance of data affect possible data sharing? It seems that information on the origin of datasets must only be provided when sharing sensitive data. However, the recently introduced EU-wide proposals of the Data Act and the Digital Markets Act are rooted in the idea of “data altruism”. The Data Act also aims at empowering users, while the Digital Markets Act imposes several obligations on gatekeepers. The chapter analyses in-force and upcoming regulations in the light of the data as property, commodity/commons divide. What is the legal framework for facilitating the reuse of personal data by gatekeepers? Which pieces of the puzzle are missing? Chapter 10 “Data extractivism: social pollution and real-world costs” (Christopher W. Chagnon and Sophia E. Hagolani-Albov) utilizes the concept of extractivism to highlight the socio-cultural damage done by data extractive systems in Europe and around the world. Just as previous industrial revolutions relied on resources like coal and oil, the digital revolution has sparked an insatiable demand for its own resource—personal data. Rather than using open-pit mines, data extraction depends on proliferating devices that do their digging by embedding themselves ever deeper into our lives and societies. This desire for data has led to modes of extraction that cause environmental pollution and what could be termed “social pollution”, which causes damage to societies and individual lives. In Chapter 11 “FinTech future trends: secondary data review”, Yevheniia Polishchuk analyses how the phenomenon of digitalization has also affected the financial sector, how the emergence of such an industry as FinTech has Introduction 5 forced financial intermediaries to adopt the changes we are witnessing now. Currently, investments in FinTech are an integral part of the development strategy of banking institutions and large companies operating outside the market of financial services. Despite the rapid development of the FinTech industry, it faces challenges such as the COVID-19 shock, innovations in FinTech regulation, competition from banks, as well as a lack of specialists with the skills that are required in the FinTech industry. In addition, the image of consumers of financial services is changing, and the role of socially significant projects is growing. The need to identify signals that indicate future developments arises on the part of businesses from the FinTech industry when formulating their strategies. The secondary data review method is used to summarize the reports from various reliable organizations, the main trends in future development of the FinTech industry, providing useful evidence for the decision-making process. The DEEP software has become the main methodological tool for identifying and studying various sectors related to the FinTech industry, factors determining forecast development trends that bring both opportunities and risks. Acknowledgements The publication has been supported by a grant from the Faculty of Management and Social Communication under the Strategic Programme Excellence Initiative at Jagiellonian University. We would like to thank the Jagiellonian University for funding open access and proofreading to the monograph. We also appreciate UNA Europa Network for facilitating the exchange of researchers among universities in network. We would like to express our thanks to Mr. Alexander MöreliusWulff from the Legal Tech Lab, University of Helsinki, for his assistance in editing the book and to Mr. Wojciech Rynduch-Walecki for copy editing. Our warm thanks go to Ms. Kristina Abbotts from Routledge for ensuring smooth cooperation on the publisher’s part, to the Jagiellonian University for sponsoring our workshop in Kraków and partly that in Helsinki and to the University of Helsinki Legal Tech Lab for funding the Helsinki workshop. 12 Judyta Lubacha et al. Figure 1.2 Share of research areas represented in the articles for “digital economy” query. Source: Own calculations based on Web of Science (2022). this topic has increased by 30%–60% year by year. In 2022, until September of that year, as many as 674 papers on the digital economy were published. From 1990 to September 2022, the Web of Science database collected 3,950 papers with “digital economy” as their keyword. The largest set includes papers dedicated to the areas of Business and Economics (27%) and Communication (12%). Such research fields are found within the range between 2% and 4% as Government and Law, Environmental Science and Ecology and Sociology and Education. This indicates the interdisciplinary nature of the subject discussed (Figure 1.2). In the first two decades of research on the digital economy (Figures 1.3 and 1.4), the studies focused mainly on technology topics and the use of the Internet and ICT. Such concepts as e-commerce, e-government or information society were introduced in 2000–2009. An analysis of keywords occurring jointly in 2010–2019 (Figure 1.5) compared to prior years shows a significant increase in associating the concept of digital economy with other areas of the social system, not limited to its economic aspects. Several fields of research developed in 2010–2019: (1) the digital economy combined with collaborative economy and sharing economy; (2) information society, the use of digital tools in teaching and digital skills; (3) property rights, intellectual property and piracy; (4) social media The dimensions of the digital economy and society 13 Figure 1.3 Map of the keywords occurrence for “digital economy” query in 1990–1999. Source: Own elaboration based on Web of Science (2022), prepared in VOSviewer. Figure 1.4 Map of the keywords occurrence for “digital economy” query in 2000–2009. Source: Own elaboration based on Web of Science (2022), prepared in VOSviewer. and their use in advertising, and the question of personal data protection; and (5) big data and digital tools used in regulation. The years after 2020 were affected by the COVID-19 pandemic as the virus characteristics limited society’s activity in the real world and caused a transfer of numerous tasks into the virtual world. Therefore, many studies on the digital economy also discussed the COVID-19 pandemic 14 Judyta Lubacha et al. Figure 1.5 Map of the keywords occurrence for “digital economy” query in 2010–2019. Source: Own elaboration based on Web of Science (2022), prepared in VOSviewer. (Figure 1.6). The COVID-19 pandemic caused accelerated implementation of numerous digital solutions in the economy, management, finance and education. Other important fields of research appeared in 2020–2022: (1) changes to the labour market – platform economy, gig economy, crowdsourcing and precarity; (2) changes in digital products and services – digital trade, fintech, mobile money, e-government and associated emotions; (3) necessary amendments to legislation on personal data and privacy protection, on algorithm use and on improving security in using Internet applications; (4) the rise of cryptocurrencies; and (5) the development of Economy 4.0 combined with the topics of a circular economy and a sustainable business model. The dimensions of the digital economy and society 15 1.4 A comparison of approaches to defining the digital economy The conducted review of the main research topics in the area of digital economy gives its picture as a multi-dimensional and dynamic structure that should be analysed considering its economic, technological, regulatory and social aspects. All those aspects are discussed in the further chapters of the book. From an economic perspective, the digital economy is founded on a digital form of trade (Teo, 2001). Digital information transfers have dramatically changed the nature of management processes in enterprises (D’Ippolito et al., 2019). Given this observation, Cheon and Kim (2003) define the sphere of digital economy as an economy in which the production, sales, and consumption of goods and services depend on the network of electronic means based on an intermediary information flow. Thus, digital technologies and broadband network access can be identified as the core of the digital economy (Banning, 2016). In this approach, the digital economy is an economic form of production in the digital technology sector. It is driven by the development Figure 1.6 Map of the keywords occurrence for “digital economy” query in 2020–2022. Source: Own elaboration based on Web of Science (2022), prepared in VOSviewer. 16 Judyta Lubacha et al. of the information and communication industry, which directly translates into growth in electronic commerce (Lane, 1999). The digital economy can be understood as a kind of umbrella concept describing digital markets, technologies and communication, data processing and e-commerce (Nathan et al., 2013). The growing number of interrelations between the traditional (offline) and the digital economy makes the boundary between them increasingly difficult to define. The difficulty becomes greater as the influence of the digital economy grows beyond business, to include the area of lifestyles in society (e.g., the sharing economy, algorithms and big data) (Capobianco & Nyeso, 2018). The digital economy with its multiple dimensions and internal dynamics requires a flexible approach to its definition (Barefoot et al., 2018). However, an excessively general and easy-tomodify description of the digital economy can present problems in analysis and observation due to the boundaries of the research subject being blurred and changing in time. The material basis for the digital economy is provided by the processes and products offered by the ICT sector that pervade all areas of the economy and society in a majority of developed countries (Lazanyuk & Revinova, 2019): the banking system – mobile and online banking and electronic payment; trade– auction and sales platforms; energy – coordinating fuel supplies, energy purchase and remote reading of consumption meters; transport– advanced logistics, real-time vehicle tracking and autonomous vehicles; education – remote teaching/learning; health – teleconsultation, teleoperations, surgical robots and patient records (e.g., Internet patient account in health service); offices – online access to data, documents and requests. The structure of the digital economy can be analysed at its three levels (UNCTAD, 2017). The core refers to the ICT and IT sectors. It includes telecommunications, software development, computer hardware manufacture and offering IT services. This level is considered from the perspective focusing on specific technologies, such as 3D printing, blockchain, 5G or the Internet of Things. The narrow scope includes digital platforms, the sharing economy and digital services (e.g., Facebook and Google). This level employs specific technologies to create innovative processes, new methods of distribution or to change the approach to fundamental concepts in economics, like utility and ownership. The broad scope of the concept of digital economy extends beyond advanced technology industries (e-agriculture, e-administration, ebusiness and Industry 4.0). It includes the sphere of finance (fintech and open banking), e-commerce and the labour market (gig economy) (Figure 1.7). The digital economy is distinguished from the traditional economy by Valenduc and Vendramin (2016) as the diminishing role of geographical location, no longer providing a competitive advantage; the key role played by digital platforms; the great importance of network effects; and the use of big data. Digital transformation also initiated the fourth industrial revolution, conceptualized by Klaus Schwab (2017). It is based on digital data, the combination of sensors and data warehouse analysis made by artificial The dimensions of the digital economy and society 17 intelligence (Industry 4.0). Adaptation of digital technologies in production and services causes changes in the production process. The digital economy determines the quality of economic growth and development. Digital transformation of a traditional economy is sufficient to produce desirable effects (Zhao et al., 2020). ICTs provide foundations for the digital economy. Therefore, it is important to identify various types of digital technologies (Nathan et al., 2019), namely, IT hardware (e.g., drones, industrial robots and wearables) and digital content (software, online advertising, design, online media and online business). The digital economy is now replacing the economy based on natural resources. An enormous challenge is posed by establishing adequate formal institutions (a regulatory infrastructure) designed to lay down rules for market play, a new deal, e.g., for the digital data market. Wiebe (2017) emphasizes the need for regulating the right to trade in industrial data and its protection in the digital age. Considering that big data, which involves collecting and processing large data sets, represents an essential component of the digital economy, immediate decisive actions are indispensable. During the following years, the rise of industrial robots, autonomous vehicles the growing automation of numerous processes will cause an increase in the number of data producers. The OECD (2020) identifies many areas where digitalization and innovation based on digital data will affect competitiveness. The digital economy was selected as a strategic theme for the OECD Competition Committee, with a focus on four sub-streams (Capobianco & Nyeso, 2018): (1) the relationship between the digital economy, law and innovation; (2) challenges posed to antitrust tools and approaches; (3) practical challenges to competition enforcement; and (4) development and evolution of specific industries. By using ICT and virtual resources (software and algorithms), businesses can easily expand their operations (this is termed flexible scalability of Figure 1.7 Key digital technologies. Source: UNCTAD (2017). 18 Judyta Lubacha et al. activity). This capability is described as a cross-jurisdictional scale without mass. The technology companies actively participate in the economies of numerous countries, influencing social processes and decisions made by people. They ignore constraints imposed by local (national) laws (Śledziewska & Włoch, 2020). This gives reasons for introducing a digital tax payable by the “digital giants” in the member states of the EU. Not waiting for a joint initiative to introduce a “digital tax” and aiming to protect their domestic markets from being monopolized by American and Chinese corporations, Spain imposed a 3% tax on “digital revenue” generated in the Spanish market, and Great Britain imposed a similar 2% tax. Such measures are taken to achieve the community objective of creating a single digital market in the European Economic Area and thus facilitating the free movement of digital goods, increasing productivity and improving access to information. The solutions used to regulate the digital economy include regulatory test environments known as regulatory sandboxes. Their concept consists of creating special and isolated areas for testing the potential consequences of a new technical solution to be introduced. Such sandboxes facilitate performing an “experiment” in a safe manner, rapid verification by the regulator of the consequences of an innovation, reducing the barriers to entry faced by innovators and accelerating the pace of implementation of a solution. This is done in the spirit of mitigating risks to consumers of digital services. Currently, regulatory sandboxes are operating in more than 20 countries. A regulatory sandbox has been operated by the Financial Supervision Authority in Poland since 2018. The European leader and pioneer is the British regulatory body the Financial Conduct Authority (FCA) that has improved the regulation process, making Great Britain an inspiring example of fintech development. On average, one-third of the applications for participating in the FCA sandbox are approved and admitted to testing (MarchewkaBartkowiak, 2019). The development of the digital economy is also characterized by significant changes in work organization. A new global division of labour across value chains, the new business model of online platforms, reflecting the increasing capacity to extract value from big data, and the digital renewal of the informal economy are fostering new forms of work and employment (Valenduc, 2019, p. 79). There is ICT-based nomadic work with digital nomads characterized by two specific work practices: they make extensive use of computers, smartphones, cloud services and the Internet, and their working time is not spent solely on the premises of the employer (p. 68). Another change is linked to online platforms that have enabled on-demand work. Such work relies on the continued employment relationship with an employer but without continuity of job, pre-defined working hours or level of remuneration. The employer calls on the worker only when needed (p. 70). There is crowd working that refers to work carried out through online The dimensions of the digital economy and society 19 platforms which allow organizations or individuals prepared to solve specific problems or supply specific services or products in exchange for payment (Green et al., 2013); in other words, work is “externalized to the crowd” (Valenduc, 2019, p. 71). And finally prosumers – individuals who both produce and consume digitized information – carry out work by supplying data and services without being paid for it, but for which salaried employers were previously partly responsible (p. 73). Participation in the digital economy appears to be characterized by social stratification. According to Eichhorn et al. (2020, p. 396), digital inequality research has shown that individuals cannot simply be categorized as users and non-users of online services – or haves and have-nots. Rather, individuals can be distinguished along various dimensions of access. The “digital divide” describes not only the difference between those who are connected to the digital world and those who are not, or those with “digital readiness skills” and those without them, but also widening inequality within groups and places that are connected (Sturgeon, 2021, p. 50). Notwithstanding the elimination of the classic elements of the digital divide, such as barriers to ICT adaptation, use of social media or the uptake of current e-government services, new chasms have appeared, e.g., regarding privacy, cybersecurity or the major challenge of how to deal with fake news and other forms of cyber manipulation (Bánhidi et al., 2020, p. 43). The idea that the digital economy will advance with great rapidity creates worry about dislocations, especially from rapid reductions in demand for labour-intensive and routine jobs from automation, autonomy and artificial intelligence (Sturgeon, 2021, p. 35). According to Heikki Hiilamo (2022, p.2), with economic globalization, technological change will have an impact across the globe with potential political repercussions. An increase in precariousness, unemployment and inequality may lead to widespread discontent which is a breeding ground for xenophobia, populism and political violence (Hiilamo, 2022). Thus, the role of policy is crucial. Policy makers have an obligation to shape digital technologies in ways to protect citizens and key institutions from abuse or damage and mitigate market concentration (Sturgeon, 2021, p. 50). Knowledge, skills and competencies desired in the labour market change over time. Today, the following competencies are indicated as particularly important: collaboration, communication, digital literacy, citizenship, openness, capability of problem solving and critical thinking (Voogt & Roblin, 2012). The development of knowledge society has led to an accelerated growth in the importance of soft skills. However, ITC literacy has become equally essential (Lewin & McNicol, 2015). The ability to effectively function in a technology-rich society has become crucial (Eshet-Alkalai, 2004). Aiming to classify the desired 21st-century skills, Claro et al. (2012) indicate (1) the mastery of ICT applications to solve cognitive tasks at work, (2) skills supporting higher order thinking processes and (3) skills related to cognitive processes favouring continuous learning. 20 Judyta Lubacha et al. 1.5 The sphere of influence of an economy’s digital transformation The fourth industrial revolution was triggered mainly by the development of the Internet. It enabled global and instant communication between people, and between people and machines, using cyber-physical systems. The transformation process of the industry is triggered by social, economic and political changes (Lasi et al., 2014), in particular, by pressure on shortening consecutive phases of the innovation development process. High innovation capability is becoming an essential success factor for many enterprises, enabling them to shorten “time to market”. Individualization on demand and a change from a seller’s into a buyer’s market have been observed for decades, due to market saturation. Buyers wish to define the conditions of transactions, and this requires that individualized products be offered. Flexibility means growing flexibility in product development and manufacturing processes. Decentralization means that organizational structures are reduced to introduce faster decision-making procedures in response to sharp market fluctuations. Resource efficiency (increase in prices for resources) is caused by their shortage. Ecological aspects grow in importance, entailing a transition of manufacturing processes towards a sustainable industrial model. Digital transformation is based on the development of the Internet and ICT. They enable developing new products in a digital form, their virtual distribution, and the emergence of new enterprise models and industries. ICT, generation by generation, offers an increasing range of functionalities, also reducing the cost of their purchase which leads to their growing accessibility. The development of the Internet has made it possible to provide services through digital channels (Table 1.2). Automated services can be provided remotely and with a minimum participation of humans. The time of day and geographical location are irrelevant. The Internet, mobile devices (smartphones and tablets) or satellite television are used for the purposes of entertainment (music, films and games), education (remote teaching, websites, magazines and ebooks), communication (video conferencing, chats, forums and social media), physical exercise (online training sessions with a coach) and even telework (call centres and hotlines, consultancy, freelancing and financial services). Investment in advanced distribution networks of digital services became crucial, especially in the times of the COVID-19 pandemic and lockdowns. The development of ICT in agriculture has led to an improvement in the standard of living in rural areas, more efficient plant growing and animal breeding methods. Due to technological progress, farmers are provided with precise and current information or dedicated services opening opportunities for more profitable digital agriculture (e-agriculture). The term e-agriculture refers to the conceptualization, design, development, evaluation and application of innovative ways to use ICT in rural areas (Mahant et al., 2012). As a result, digital technologies make it possible to conduct precision and The dimensions of the digital economy and society 21 computer-aided farming. The data collected (from agricultural machinery, e.g., on machine locations indicated by the Global Positioning System, analysis of weather and soil conditions) facilitate precise planning of soil fertilization and plant protection from pests, storms or droughts. This finally results in getting larger volumes of quality crops while managing the costs of agricultural produce (Gozdowski et al., 2007). Another industry that has undergone a revolution due to technological progress is finance. Through the digital transformation of financial services, a new sector emerged, known as fintech (a portmanteau of “financial technology”). The term fintech, as regards market players, refers to the entities coming from the technology industry. They possess necessary know-how and technical resources useful in offering innovative financial products. In this narrow definition, the fintech industry includes only new technology companies characterized by a considerable degree of flexibility, innovation and their focus on a competitive advantage over traditional banks, gained from technology. In a broader definition, fintech may also include the digital giants that offer financial services and even the traditional banks that invest in digital solutions (Harasim & Mitręga-Niestrój, 2018). Table 1.2 The most important areas of digital services Service Directions of further development E-health The possibility of remote medical consultation, arranging online a visit to a health centre or receiving an e-prescription E-work Using the Internet to conduct remote recruitment of workers, to cooperate, complete projects and access corporate data resources E-learning Language courses, professional training, tertiary education, remote classes at schools, private tutoring and electronic textbooks E-logistics Services that support the supply chain, coordination of drivers and business partners and real-time tracking on a map of current locations of specific shipments and parcels E-finance The possibility of completing all tasks in the areas of finance, banking, investment and insurance with the use of dedicated software and Internet access E-commerce Buying and selling products over the Internet, discussed in more detail in a dedicated section below E-administration The provision of public services using ICT. This includes the possibility of filing applications with authorities and submitting requests by email, and even of taking popular vote over the Internet E-culture Access to scanned paintings and to other works of art in a digital format (also using augmented and virtual reality). This gives people with disabilities or those living in the provinces the opportunity to experience culture Source: Flis et al. (2009). 28 Aleksander Żołnierski ostensive definition. In a word, it examines those areas of the socio-economic spectrum where the expected impact of digitalization is discernible. Presumably, these issues stem from the lack of a rigorous definition of the field of research and the vague description of the digital economy itself. The digital economy is not a new concept. Some years ago we observed the phenomena related to economic applications of digital technologies, termed a “new economy”. Technology-based economies emerged, wherein new digital technologies facilitating more extensive and efficient business were referred to as the digital economy, the knowledge economy or the data economy. The term itself, as used in the book “The Digital Economy: Promise and Peril in the Age of Networked Intelligence” and popularized by Tapscott in 1995, was still far from being unambiguous (Tapscott, 2015). Tapscott wrote about it in the context of the new information medium: A new medium of human communications is emerging, one that may prove to surpass all previous revolutions […] in its impact on our economic and social life. The computer is expanding from a tool for information management to a tool for communications. The internet and World Wide Web are enabling a new economy based on the networking of human intelligence. In this digital economy, individuals and enterprises create wealth by applying knowledge, networked human intelligence, and effort to manufacturing, agriculture, and services. In the digital frontier of this economy, the players, dynamics, rules and requirements for survival and success are all changing. (Tapscott, 2015, XXIII) Tapscott argues that the spread of new practices used by industries to ensure economic growth is becoming noticeable (Tapscott, 2015, 362). However, all these concepts refer to the knowledge-based economy or, for short, the knowledge economy, where information becomes the basic factor in creating a modern and competitive product. Initially, the conceptualization of characteristics describing the digital economy principally focused on its social and economic aspects (knowledge management, ICT applications in enterprises, education of workers in the economy or the degree of internationalization), to evolve in time, with the widespread use of the Internet of Things (IoT) technology and analysis of big data sets, with the development of data processing technologies, into an approach based rather on technical sciences, computer science and mathematics. Despite the increasing use of the term “digital economy”, a precise definition of this phenomenon still leaves room for discussion among scientists and theorists, as well as business practitioners. Thus, with the development of technology, including data collection techniques and big data analysis, the approach to conceptualizing the digital economy has changed – beginning with knowledge management, to structures and networks, ending with security issues and all types of crypto and tokenization applications. Measuring the digital economy with “digital economy” tools 29 Three primary components of the concept of the digital economy were distinguished by Mesenbourg (2001, 2), namely, the supporting infrastructure, electronic business processes (how business is conducted) and electronic commerce transactions (selling of goods and services online). Contemporary concepts describe the digital economy as a type of economy focused on the flow of intangible goods with zero marginal cost (Rifkin, 2014). The digital economy is also closely related to the applications of digital technologies, known for nearly a decade as Industry 4.0. Bukht and Heeks (2017) emphasize, that “definitions are always a reflection of the times and trends from which they emerge”, and define the digital economy as an economy of digital services and platforms, with its core formed by the digital (IT/ICT) sector (that consists of four components: hardware manufacturing, software and IT consulting, information services and telecommunications). Bukht and Heeks (2017) also distinguish a broad scope (digitalized economy) as a sum of the above-mentioned items and e-business, e-commerce, Industry 4.0, precision agriculture and algorithmic economy, with the presence of sharing economy and gig economy. The broad scope is related to the development of the Industry 4.0 concept. It seems that the concept of the digital economy also evolved from the earlier concepts of the knowledge economy and the knowledgebased economy, following the development of digital technologies, especially the applications of artificial intelligence (AI) in economic practice. Industry 4.0 is a concept describing value creation with the use of digital technologies (Schwab, 2018). Industry 4.0 is based on cyber-physical systems enabling real-time connection of the physical and the virtual world, supported by intelligent data analysis systems. Cyber-physical production systems enable such functions as condition monitoring, preventive diagnostics and maintenance and autonomous machine control. On the one hand, Industry 4.0 is used for management in dynamically changing environmental conditions; on the other hand, it is based on technological changes, such as automation, digitization and networking within machine and human and product environments along the entire value chain. The main trends of development are big data and analytics, AI, augmented reality, digitalization of supply chains, cybersecurity and the IoT (Sweeney, 2018). As mentioned above, the research and analysis of the digital economy is mostly based on quantitative studies covering the scope of dissemination of ICT in economic entities, households and among individual users. This approach is “boosted” by social research methods (e.g., surveys) to analyse the effects of ICT applications. It covers the whole spectrum, from social and economic issues to social psychology, education and employment. Measurement tools are used both to create an aid policy and to monitor the condition of the economy, including the potential demand for high-skilled workforce (jobs) from the high-tech and IT industries in the near and somewhat more distant future. The described tools are reliable measuring instruments and show a clear, although not multidimensional reality. However, in view of the dynamically 30 Aleksander Żołnierski changing socio-technological aspects of the digital economy, they leave much to be desired. First of all, as tools that rely on a specific method of collecting data, on the one hand, they generate problems resulting from time delay; therefore, they are not ideal for assessing the current situation of the digital economy. On the other hand, the data used for statistics is collected as part of social research (i.e., surveys) and thus may be distorted by the interviewer error. The most serious issue, however, is that in each case, they reflect the mindset concerning the digital economy of planners and politicians, and even if researchers and practitioners are involved in the tool design process– measurement tools are not appropriate for an accurate identification of new phenomena related to the digital economy. In this respect, information about both new, fast-growing technologies and competencies being developed is lost. The tools commonly used measure what in the digital economy was already visible a year or several years ago and are unfit for the identification and measurement of new phenomena and contemporary changes within the economy. An example is provided by many high-end technology issues related to Industry 4.0. Despite the fact that the existing tools allow to identify the scope of use of technologies based on AI, blockchain, or robotics and automation, and on implementing cyber-physical systems, they do not support reliable measurement of phenomena caused by the use of specific types of these technologies (e.g., the scope of application of tools related to access protection, monitoring or even ecotechnology). Not only do surveys or statistical research provide a measurement tool but the data used for analysis also comes from the internet or sensors and devices of the IoT. An example is provided by lighting systems that use various sensors to optimize power consumption by office and manufacturing floor lighting.1 An important point is that the dynamic digitalization processes taking place today make it difficult to research and analyse the subject of digital economy using the existing and most common research methods. It seems that the applied measurement tools and concepts ignore the wide spectrum of possibilities offered by digital technologies themselves and by the methods using behavioural analysis that are increasingly popular in the field of commercial research. The emerging new methods for monitoring the technological and socioeconomic environment of digital economies, in particular the methods using AI, business intelligence systems, big data and information refining, are increasingly being used in many research projects, but their widespread use will take time. On the one hand, official statistics grapple with a number of problems which make the task of monitoring a dynamically changing environment particularly difficult; the complexity of economic processes increases the wealth of data to be analysed, and the analysis process itself becomes more and more complicated. On the other hand, at each stage of operation, organizations generate information whose bulk is placed in various forms in Measuring the digital economy with “digital economy” tools 31 the virtual space. Identifying this information, refining and analysing it appears to be the key to effective monitoring of changes in the economy. Internet data resources provide material for qualitative research. The use of big data tools, in which information from several or over a dozen million sources is analysed, allows to combine the advantages of quantitative and qualitative methods. So far, in research focusing on economic topics, in particular on the subjects of innovation, economy and ecology, or in the analysis of stock exchange trends where the collected information is qualitative, extensive social research was used: surveys, interviews etc. This also applies to issues (not only of a qualitative nature) relating to the digital economy. The method based on the “digital economy” approach, including the tools for refining information and big data, eliminates any imperfections of the above-mentioned representative methods – in practice, analysing a million or 10 million records does not affect the cost of the study. It turns out that systematic scanning of available unstructured data in the environment may become a strategically important activity of economic process researchers. This is especially so due to the growing complexity of available information and business data, primarily related to new technologies. The process underlying the analyses with the use of big data refining and analysis tools is a multi-stage process. The first step is to identify offline and online digital resources, including internet sources, object databases and streaming media. The next stage is collecting the identified resources by an online bot, which is a specialized ICT system for targeted monitoring and data collection from indicated websites or other data sources. Identified and collected resources are subject to a query in search of selected keywords– cores and sentiments that define a given keyword. The next step is to transform cores or words into matrices (to transform them as elements of an “algebraic structure”). Matrices are used to compute statistics. The frequency of occurrence of individual cores and sentiments (or words) in the analysed corpus is calculated. The columns constitute a statistically verified reference point for the selection of sentiments (words and phrases) of an evaluative nature that accompany the topics under study. The new approach is useful in analysing multiple characteristics of the digital economy. It includes the analysis of keywords referring to new and emerging technologies, the analysis of the content (discussions) on social networks related to the economic and social aspect of digitalization, of discussion among both specialists and scientists, and managers and users – information technology experts etc. It is important that big data analytical tools are already used by many institutions (e.g., in the United States) to predict, inter alia, the financial condition of stock market companies or even the likelihood of crimes.2 What can be achieved by implementing “digital economy” tools to measure digital economy issues is getting easy access to current data on research and development and innovation activity within digital economy areas (in a geographic, regional, technological and industrial sense). 32 Aleksander Żołnierski Further possibilities include monitoring the situation in terms of human resources involved in digital economy processes and the situation in terms of the competitiveness of the digital economy entities and industries. 2.2 Big data analysis and the data refining method As regards unstructured data sets analysis, a number of useful methods already exist, like rough set theory, theory of approximation and fuzzy rough sets theory (Tran & Huh, 2022). In many cases, the information analysing system allows to integrate unstructured data with structured one (Balabin etal., 2022). To monitor and analyse the digital economy environment of high complexity, a knowledge management system must be built. Such systems are increasingly based on AI, big data analysis and information refining as well as business intelligence technologies (Cetera et al., 2022). Unstructured data analysis requires effective data management methods also in the case of databases derived from IoT sources (Azad et al., 2020). The challenge is to “create, transfer, pool, integrate and exploit knowledge resources” (Frishammar & Richtnér, 2008). Methods based on digital economy tools principally employ the data acquisition technology. The examples of methods of refined data and unstructured data analysis found in the literature are focused on unsupervised text processing (Jain et al., 2021). The text mining technology is widely used, from management and social sciences to medicine and biomedical data analysis. The first attempts to explore the online data and big data acquired from structured scientific data repositories resulted from the need to shorten the time of big data processing. The information collected from the online “behavioural” sources is characterized by independence from the observer and its growing volume. One of the first steps in exploring the online sources is to distinguish valuable sources of information for identifying the issues to be analysed. A data processing system is needed to facilitate automatic collection of source data as well as statistical processing. Collected and analysed data is used for quantitative analysis and visualized to obtain a better description of results. A modular design of the data processing system is created to meet the requirements of scalability. Scalability applies to both the infrastructure of the system itself and the use of distributed task processing. The application of a specific tool results from the needs of the research area and the range of tests – each particular search is heuristic in its nature and is practically a separate, unique study. Identifying valuable information sources relies on using methods fitting the analysed theory and is always dictated by the research problem. The key issues are related to both the research area and the types of data sources. Finally, a practical method must be worked out for collecting and exploring data and selecting tools used for information refining (see Figure 2.1). The use of big data analysis begins with encoding plain text in the UTF-8 standard. Data converted to this form and input information provided in the Measuring the digital economy with “digital economy” tools 33 form of URL links directing to internet sites with data needed are collected by automated online tools. The data processing system contains an analytical warehouse accepting data sources as RSS feeds. The utility program performs data extraction and converts the non-text file to a text file (in a UTF-8 format). A research project generates a database of sources both from many different services and in different formats. This creates a need to check the sources for completeness before starting computations. The most common method is based on comparing individual items from the list of data sources intended for analysis to the sources appearing in the database (see Cetera etal., 2022). The next step is to launch analysis, and this is usually associated with using the R scripting language (R Core Team, 2021). It is important to create an automatic data classifier, enabling the necessary use of supervised learning and natural language processing algorithms as part of the analysis being carried out. To guarantee the high quality of analysis results, the script is strengthened by integrating measures of its effectiveness. Finally, a confusion matrix is created to evaluate the results of the model. The trained machine learning model accuracy is about 0.84. Structured data is subject to a statistical analysis. This includes the following sequence of operations: tokenization, quantitative analysis, TF-IDF statistics, bigram analysis, correlation analysis and cluster analysis. The required high confidence level is Websites, documents, ebooks, e-journals, online forms, stream, PDFetc. Webscraper /keywoeds, phrasesetc./ Data refined Data ProcessingSystem start end „Raw”data prepared forfuture reference Results PreparatoryPhase Analztical Phase Data determiningthe scopeof the study input parameters topic of the study time interval of the research key words defining the subject of the study proposed data sources Selection and algorithmisation of the data collecting procedure Unstructured data collected Lemmatisation Data refining process Definitionof categories of data (categoriesprocessedby AI algorithms) Selectionand algorithmisation of the analysis procedure Configurationforthe test data selection Statisticalanalysisofeconometric models Figure 2.1 Data processing procedure: refining unstructured data online. Source: Own research. 34 Aleksander Żołnierski determined not only by choosing adequate statistical tools but also indirectly by the machine learning model. As mentioned above, the accuracy of the data processing system is 0.84. A good application example of the model described above is a system for identifying technology trends (Cetera et al., 2022). The system is designed to identify technological development within specific areas and to evaluate the level of technological development. The tools identifying technology trends represent one of the first applications of data refining and big data analysis in a country that is not among the world’s most innovative economies. This shows that modern analytical tools of the digital economy can also be successfully used in places where the level of advancement and use of Industry 4.0 technology is not particularly high. 2.3 Google Trends as a trend prediction tool The use of predictive capabilities provided by data from the internet is growing in popularity. Some studies indicate that Google Trends can be used as an effective source of data indicating the interest of stock investors (Huang etal., 2020). Google Trends is a service by Google that allows users to obtain information on the number of all queries in the Google search engine for a specific term or phrase in a selected period and for a selected geographic area. The selected period can be freely defined and thus the data obtained in time series (since 2004). Google Trends in a specific, selected period of time returns information about the relative number of queries. For example, researchers analysing data from Google Trends use the KaplanMeier estimate to gauge trends in S&P 500 in a term’s Granger causality. A correlation is observed between S&P 500 trends and dynamics of queries (indicated in Google Trends). This depends on the specific term searched and, above all, the sentiment related to the term being searched. Google Trends itself is an indicator of investor interest, but the dynamics of changes in queries about individual values depends on the evaluative sentiment appearing around the term or phrase searched (specific securities, shares of stock, bonds or other financial instruments; e.g., Bayer, SAP, Apple and Microsoft Corporation). Google Trends can also be used to analyse the response to environmental crises. Researches in this area reflect the ever wider and more complex possibilities of analysing the communication ecosystem. The analysis of Google Trends in this context indicates the consequences of interactions that occur between social media, “traditional” mass media and queries in the Google search engine (Matei et al., 2021). The analysis of queries in the Google search engine, as “powered” in a kind of secondary way by media users’ responses to press and TV news and activity on Twitter, indicates that Google Trends may also be a valuable source of data for predicting trends in the natural environment. Internet users, both individual and collective, increasingly create data within new information channels outside of mainstream media. Measuring the digital economy with “digital economy” tools 35 This is especially true in crisis situations, when social media becomes one of the main information channels (due to the up-to-date information published there by users). For example, by analysing the time series, the correlation between each pair of the following variables was investigated – soil moisture, Twitter activity, search engine trends and media reports on drought. Based on, inter alia, Autoregressive Moving Average models, a statistically significant correlation was established between geological conditions and activity in the Google search engine, media coverage and the number of tweets. Researchers also use Google Trends to predict price trends in grain futures (Gómez Martínez et al., 2021). Gómez Martínez et al. (2021) used data from soybean and corn futures contracts (at the Chicago Mercantile Exchange) to evaluate the potential of a tool based on data from a search engine. The researchers proved that there was a potential in terms of price forecasting and proposed that the analysed possibilities be used by individual traders as well as investors and trading companies. Over the years, multiple investment strategies have been developed for the analysis of raw materials prices in the agricultural sector, mainly based on the methods of fundamental analysis and on selected indicators of technical analysis. Gómez Martínez etal. (2021) propose methods based on behavioural finance, primarily analysing investor sentiment and indicators based on big data and social media. Behavioural finance focuses on the analysis of individual investor behaviour and on the psychological aspects of behaviour that influence investment decisions in capital markets. Accurate predictions of the price level of soybeans and corn based on online data – primarily Google Trends – have become possible. Gómez Martínez et al. (2021) demonstrate how individual investors and traders can predict market price fluctuations by using the available data of Google Trends as a tool to work out their own strategy. The use of online data on investor and consumer sentiments instead of information “traditionally” obtained through surveys and personal interviews has become a common tool for marketers and market researchers. It is also useful for more sophisticated analyses of economic and social issues. As Google Trends contains data based on queries, entered practically from the beginning of this tool’s existence, researchers have used it as a kind of substitute tool for opinion polls or sentiment surveys. Google Trends has proved to have considerable potential for forecasting many economic and financial variables. Researchers have used it to explore such issues as unemployment, inflation and fluctuations in the stock market, as well as the value of cryptocurrencies, the dynamics of exchange rates or – more than a decade ago – consumer sentiment and consumption rates. Wilcoxson et al. (2020) examine the possibility of using Google Trends to analyse exchange rates and forecast rates for the US dollar and ten other currencies. The study covers the period from January 2004 to August 2018 and shows that Google Trends can also be an important prediction tool in this case. There are studies showing that analyses based on Google Trends data can produce more accurate forecasts than traditional (and reliable) surveys (Zhu 36 Aleksander Żołnierski et al., 2012). But researchers indicate that the data from Google Trends has a certain limitation, namely, accessibility of the internet. Not everyone who has access to the internet and uses the global web will use the Google search engine. When using Google Trends, it should therefore be remembered that the data contained therein defines “only” the active population of internet users who use the Google search engine. Despite these limitations, data from Google Trends makes it possible to accurately predict investor behaviour and provides good material for analysing the psychological aspect of currency markets. Some researchers, including Zhu et al. (2012), find no systematic difference in the opinions expressed in standard polls between internet users and non-users. It turns out that public opinion – and therefore also investors’ preferences – can be studied based on the analysis of search queries in the search engine; however, it should be remembered that the strength of the correlation between “opinion” and “query” probably depends on the analysed problem. Another research team analysed the potential of using Google Trends for sales forecasts (Fritzsch et al., 2020). Fritzsch et al. (2020) combined standard time series models with Google Trends data. Sales forecasts, from the producer’s point of view, form a basis for strategic planning. If the data used is imprecise or false, the forecasts can cause shortages of supply (the forecast demand falls below its actual volume) on the one hand; on the other hand, they can increase inventory costs (the forecast demand exceeds its actual volume). Traditional time series models aimed at predicting sales tend to rely on historical data. Fritzsch et al. (2020) prove that this problem can be solved by using (up-to-date) data from search engines. This data also describes businessrelated issues and is available online in real time. It should be noted that Google Trends data was first used in econometrics in 2009 (Choi& Varian, 2009; Choi & Varian, 2012). The available Google Trends data that can be used for sales forecasts (at a product level) mainly covers online products. This is because customers enter the product they are looking for in the search engine and then go to the websites of online stores or manufacturers offering this product for online sale. Fritzsch et al. (2020) focused on the data for two (Sennheiser) products that are offered for sale in a traditional market. Therefore, the researchers had to demonstrate a link between sales data and Google Trends data. The results indicate that the data from Google Trends may be helpful in obtaining a more precise sales forecast. The development of internet technologies contributes to the rapid increase in the availability of online data. For over a decade, researchers have had access to new and rich data sets which for some time have been supplemented by analyses of economic and social issues, made using “traditional” survey tools (Woo & Owen, 2019). Woo and Owen (2019) augmented the set of data from the Michigan Consumer Sentiment Index (MCSI) and the Conference Board Consumer Confidence Index (CCI) with data from Google Trends analyses. Their research focused on the potential of online data for economic forecasts. Unlike surveys, Google Trends data indicates economic consumer Measuring the digital economy with “digital economy” tools 37 behaviour (e.g., pre-purchase activity). They studied data on consumption of both durable and fast-moving consumer goods and services. The research proved the high usefulness of the data from Google Trends and the high quality (accuracy) of the trends, exceeding the results of the MCSI and CCI research on private consumption forecasts. Moreover, the use of data from Google Trends reduces the cost of analyses – this data is available to virtually every internet user, and also has another very desirable value – it is possible to obtain data updated on a daily basis in a relatively long time series. Woo and Owen (2019) proved that surveys (of consumer sentiment) as a basis for predicting actual economic behaviour are not the best tool for consumption forecasts. Such surveys give relatively trivial results and are also criticized for their opacity in terms of cause-and-effect relationship (consumer sentimentconsumption decisions) and high correlation with other macroeconomic indicators (it turns out that macroeconomic indicators explain 70% of MCSI variation). In the case of Google Trends, a direct correlation between search engine queries and individual consumption was demonstrated. The development of analytical methods based on big data and advanced IT tools results, on the one hand, from the increase in computing power of IT systems, and on the other hand, it is related to the generation of massive data sets by organizations and individuals in many economic sectors. Big data is generated by telecommunications, IT producers, organizations operating in the field of healthcare, pharmaceutical companies and the financial sector. In addition to these sectors, data is generated by internet users and amassed by institutions interested in (and able to use) big data related to user behaviour. In recent years, technologies have been developed that allow the analysis of big data “extracted” from streaming media. Already in 2015, it was estimated (Tsui & Zhao, 2017) that the average network user generates 2.5 trillion bytes of data per day (behavioural data, such as tweets, likes, comments, blogs and media streams, e.g., on YouTube). Behavioural big data (BBD) contains a large amount of information revealing individual behaviour, sentiment and multiple interactions. Researchers define BBD as very large and rich multidimensional data sets on human behaviour, actions and interactions that have become available to companies, governments and scientists. BBDs are generated (and often shared) in processes related to management in public health institutions, marketing and market research, business monitoring or in Recency, Frequency and Monetary Value analysis and Customer Relationship Management methods etc. In the United States, BBD is used by government agencies to improve the decision-making process. In large cities around the world, cameras and sensors are used to record traffic and human activity – the data is used not only to manage urban traffic but also to detect and prevent crime. In some countries, for example, in China, extreme monitoring measures are adopted to identify people who may pose a threat in the eyes of a totalitarian regime, mainly due to their actual or potential initiatives for democracy and human rights. For several years, this type of data has been used to identify and monitor the spread of various types of epidemics. 44 Aleksander Żołnierski Tsui, K.L. & Zhao, Y. (2017). Discussion of ‘analyzing behavioral big data: Methodological, practical, ethical, and moral issues’. Quality Engineering 29(1), 79–83. https://doi.org/10.1080/08982112.2016.1210981. UNCTAD. (2021). Digital Economy Report 2021. United Nations Publications. https://doi.org/10.18356/9789210058254. van de Sanden, S., Willems, K. & Brengman, M. (2019). In-store location-based marketing with beacons: From inflated expectations to smart use in retailing. Journal of Marketing Management 35(15/16), 1514–1541. https://doi.org/10.1080/02672 57X.2019.1689154. Wilcoxson, J., Follett, L. & Severe, S. (2020). Forecasting foreign exchange markets using Google Trends: Prediction performance of competing models. Journal of Behavioral Finance 21(4), 412–422. https://doi.org/10.1080/15427560.2020. 1716233. Woo, J. & Owen, A.L. (2019). Forecasting private consumption with Google Trends data. Journal of Forecasting 38(2), 81–91. https://doi.org/10.1002/for.2559. Zhu, J.J.H., Wang, X., Qin, J. & Wu, L. (2012). Assessing Public Opinion Trends Based on User Search Queries: Validity, Reliability, and Practicality [Conference Paper]. 65th Annual Conference of the World Association for Public Opinion Research, China. https://scholars.cityu.edu.hk/en/publications/publication (54cc3372-239d-40cd-8e3f-1a6e875ecfd7).html. Electronic sources Big Data Framework (2021, 18 March). A Short History of Big Data. https://www. bigdataframework.org/knowledge/a-short-history-of-big-data/. Chakravorti, B. & Chaturvedi, R.S. (2019, 5 September). Ranking 42 Countries by Ease of Doing Digital Business. Harvard Business Review. https://hbr.org/2019/09/ ranking-42-countries-by-ease-of-doing-digital-business. Dataversity (2021, 20 September). A Brief History of Analytics. https://www. dataversity.net/brief-history-analytics/. Fraunhofer Institute (2021, 6 May). German Index of Digitization 2021. https:// www.fokus.fraunhofer.de/en/news/fokus/dps/D-Index_2021_05. Maxin, J. 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DOI: 10.4324/9781003450160-5 3.1 Introduction The digital economy is a conceptual “umbrella” referring to markets, organizations and their networks that are based on digital technologies, communication, data processing and e-commerce (see Nathan et al., 2013). The term denotes a multidimensional, dynamic structure that must be analysed considering its various dimensions, such as economic aspects (changes in the nature of resources, production factors and economic processes), the area of technology (technological progress viewed from a macroeconomic perspective vs. technological innovation viewed from a microeconomic perspective), regulatory measures (challenges facing regulators, new risks affecting the institutional order) and sociological phenomena (changes in society functioning principles, attitudes towards work and human relations). The Organisation for Economic Co-operation and Development (OECD, 2020) defines the digital economy as an economic system wherein data is used as a factor of production. Businesses operating in this type of economy use or process digital information aiming to increase its value (create value added). The enterprises adopt new business models enabled by new market conditions (digital services, digital distribution channels and digital networks) (OECD, 2020). The digital components (digital resources and IT infrastructure) drive the digital economy value chain; new industry sectors and new business models emerge. The new value chain opens new spaces and promotes the consolidation of growth areas and job creation (Zhenlong, 2021). Digitalization dramatically modifies the very nature of products, the value creation process and the competitive environment in business. Based on the network-centric view, the firms may achieve a competitive advantage by actively shaping the digital environment and by interconnecting in the digital environment (Koch& Windsperger, 2017). Carlsson (2004) emphasizes that the digital economy is more about new activities and products than about higher productivity. Its key resources include information and a series of economic and social activities that people carry out using the Internet and related technologies (Turcan et al., 2014). Continual 3 Differentiation of the digital economic development inEurope Mateusz Biernacki, Agata Luśtyk and RafałWisła This chapter has been made available under a CC-BY-NC-ND license 46 Mateusz Biernacki et al. technological progress and growing data repositories and flows can be indicated as the key trends shaping digital transformation on a global scale. The terminological context outlined above and in Chapter 1 gives rise to a fundamental question about a method suitable for quantifying the dynamics of these changes at various levels of economic analysis. The International Monetary Fund (IMF, 2018, pp. 2–6) indicates the lack of generally agreed understanding and definition of the digital economy as a major hurdle to reliable measurements of changes associated with that economy. The IMF (2018) distinguishes the concept of the digital sector and that of the digital economy. The concept of the digital sector is limited to the core activities of digitalization, such as ICT goods and services, online platforms and platform-enabled digital services, including the sharing economy. Considering enormous difficulties in quantifying the dynamics of changes in the digital economy environment, interdependencies observed in that economy and their characteristics, the IMF (2018) proposes to focus measurement efforts on a concrete range of economic activities at the core of digitalization. In this chapter, we will not follow this recommendation. Instead, we propose an original method for a quantitative description of changes in the digital economy from a macroeconomic perspective. For this purpose, the taxonomic analysis will be used. The first section of this chapter contains a review of proposals aimed to measure the digital economy, considering various approaches to its definitions. The second section discusses two methods designed to identify changes in the digital economy from a macro perspective and gives the characteristics of data used in the following sections. The third section presents research results with a discussion of their limitations and downsides. The last section contains a summary. 3.2 A review of proposed methods for measuring the digitaleconomy The current approach to measuring the digital economy, adopted by international organizations (G20, 2018; IMF, 2018; OECD, 2020), is broad and addresses its various aspects: infrastructure, employment, applications, social change and innovation. The infrastructural context consists of physical, service and security infrastructure. The measurement methods use such indicators as access to mobile and landline telephone networks, the development of next-generation access, the number of broadband service subscribers and the number of active mobile Internet subscribers. But in addition to accessibility and affordability, such factors as the connection quality and Internet transmission speed (both in mobile and DSL technologies) play an important role in measuring the individuals’ and enterprises’ capability of participating in the development of the digital economy. The OECD (2020) also discusses the concept of Internet of Things (IoT), i.e., an ecosystem of applications and devices that connect and exchange data with their environment and with each other without Differentiation of the digital economic development in Europe 47 human intervention. The OECD (2020, p. 21) expects that the IoT will become a central element of the digital economy in G-20 countries. The aspect of employment, digital competencies and labour market is operationalized using the following indicators (OECD, 2020, pp. 72–73): • the number of jobs in the ICT sector, • the proportion of enterprises that employ ICT specialists, • the number of individuals teleworking from home, • Eurostat Digital Skills Indicator, • ICT usage in schools, • the number of tertiary graduates in natural sciences and engineering, • value added by information industries, • information industry-related domestic value added, • labour productivity in information industries, • ICT contribution to labour productivity growth, • ICT goods exports and imports, • ICT services exports and imports. The category of emerging applications, i.e., technological innovation, is quantified in terms of e-commerce or robotization in manufacturing (robot intensity) (OECD, 2020, pp. 28–34). The social dimension of the digital economy is understood as using digital technologies to improve general well-being and the quality of life and to enhance communication capabilities. The principal quantifiers of changes include here the percentage of Internet users and the percentage of individuals using the Internet to interact with public authorities. The digitalization and automation of procedures are aimed to simplify those interactions and provide easy access to various official forms and means of efficient completion of government procedures (OECD, 2020, pp. 23–26). The International Telecommunication Union (ITU, 2020, p. 4) describes access by individuals and households to ICT infrastructure as a factor accelerating social development and stimulating economic changes. The concept of the digital economy is understood as available digital infrastructure, digital products, their accessibility and society’s digital skills. The ITU (2020, pp.47–49) proposes a list of ICT household equipment indicators. These indicators include, e.g., the proportion of households with multichannel television, the proportion of households with Internet, household expenditure on ICT, the proportion of individuals using the Internet, the proportion of individuals who purchased goods or services online, the number of individuals with ICT skills and total household expenditure on ICT. The ITU adopts the following principal indicators of ICT infrastructure development and access to that infrastructure (2020, p. 235) • fixed-telephone subscriptions per 100 inhabitants, • mobile cellular telephone subscriptions per 100 inhabitants, 48 Mateusz Biernacki et al. • fixed broadband Internet subscriptions per 100 inhabitants (broken down by speed), • active broadband Internet subscriptions per 100 inhabitants, • Internet throughput per inhabitant (bits/second/inhabitant), • fixed broadband Internet prices per month, • mobile cellular telephone prices and TV broadcasting subscriptions per 100 inhabitants. Bukht and Heeks (2017) indicate temporal changes in conceptualizing the digital economy. They result from the development of infrastructure and its use (the Internet as a leading technology, mobile networks and cloud computing). Kling and Lamb (2000, pp. 295–324) identify four areas of the digital economy: highly digital goods and services (e.g., online education), mixed digital goods and services (e.g., books), IT-intensive services or goods production (e.g., accounting) and the segments of the IT industry that support these three segments of the digital economy (e.g., the computer networking industry). Bukht and Heeks (2017) also emphasize the importance of measuring the digital economy. They propose such measures as value added by the ICT sector, employment in the IT/ICT sector and comparing labour productivity in highly digital sectors with that in the traditional economy. Similarly, ITU (2020, pp. 236–237) uses a macro perspective to propose the proportion of ICT specialists in total employment, ICT sector share of gross value added, ICT goods imports as a percentage of total imports and ICT goods exports as a percentage of total exports. The G20 DETF (2018) indicates that sound measurement is crucial for policymaking, as it helps to produce precise diagnostics, assess the potential impact, monitor progress and evaluate the efficiency and efficacy of implemented actions. The digital economy is believed to have a great potential for transforming jobs, hence the rapidly growing demand for measurement tools and indicators. The G-20 member states are encouraged to disclose measurements characterizing the digital economy in their national statistics, using various methods for monitoring the digitalization level. 30 key indicators are recommended, divided into four main thematic areas: (1) infrastructure, (2) empowering society, (3) innovation and technology adoption and (4) jobs and growth. In addition to these four areas, the report authors emphasize the importance of measuring such indicators as expenditure on research and development (R&D), machine learning, AI-related technologies and cloud computing services used by enterprises (G20 DETF, 2018, pp. 37–41, 48). Currently (G20 DETF, 2018, pp. 4–8), multiple hurdles are identified for the systematic collection of comparable statistical data in the discussed area. Main obstacles include differences in data collection methodologies and approaches and a limited range of surveys. The methodological differences are evident in the currently used indicators aimed Differentiation of the digital economic development in Europe 49 to measure the digital economy. It is not enough to improve the existing indicators; new measures and data collection methods must be identified. There are areas with internationally recognized standards for statistical data collection, but states have insufficient capabilities and resources to systematically implement those standards and then distribute the figures obtained. The recommendations proposed by the authors of the G20 DETF report (2018) include: • experimenting with concepts and data gathering within existing measurement frameworks, • exploiting the potential of existing survey and administrative data, • adding questions to existing surveys, • augmenting existing surveys with topic-specific modules, • developing short turnaround surveys to meet specific needs, • defining policy needs and, in cooperation with other stakeholders, setting priorities for internationally comparable measurement, • using the potential of big data for developing indicators to measure the digital economy. The authors of the above recommendations (G20 DETF, 2018, p. 10) indicate a series of crucial actions aimed to improve the quality of presented measurements. The International Standard Industrial Classification, like the Central Product Classification, adopts a definition of the digital economy understood as the ICT, media and entertainment sectors. In general, typically for an initial phase in defining new categories, numerous approaches are observed to the conceptualization of the digital economy. However, even the impressive number of proposed definitions and their variations are insufficient to embrace the dynamic growth in digital products and services. Those definitions frequently fail to include new categories, leading to an underestimated value of economic activities based on digital products. The variation and elasticity of definitions pose an obstacle in research, which requires accurate measurements or temporal and spatial comparisons. The challenges include (1) capturing the fast-changing quality of digital services, (2) distinguishing between revolutionary and evolutionary developed digital products, (3) measuring e-commerce and (4) measuring the sharing economy (IMF, 2018, pp. 7, 17). 3.3 Data Most of the cited authors agree that in measuring the global economy, the most useful information is provided by ICT1 and IC2 sector data, being both globally applicable and comparable. Consequently, seven variables are 50 Mateusz Biernacki et al. proposed to construct a taxonomic indicator of the development of states’ digital economy. These include: X1 – percentage of the ICT personnel on total employment in the country X2 – percentage of value added (at factor cost) in the ICT sector on GDP, X3 – the value of the import stream of IC sector products, X4 – the value of the export stream of IC sector products, X5 – percentage of enterprises that employ ICT specialists, X6 – business expenditure on R&D (BERD) in ICT sector as percentage of total R&D expenditure, X7 – percentage of enterprises’ total turnover from e-commerce sales; without financial sector. The ICT and IC sectors are distinguished in line with the currently applicable Statistical classification of economic activities in the European Community (Eurostat, 2008, pp. 164–170, 224, 252–255, 308). Imports and exports of IC products are calculated as the value of foreign trade in products manufactured by the information and communication sector (IC: 58–63). The figures are collected from the databases published by the European Statistical Office (Eurostat) and cover the years 2012–2019. This is the largest time interval for which figures are available in all of the selected categories. The following method was employed in imputing missing data for individual years: • if the value is unavailable at a period endpoint, i.e., for the year 2012 or 2019, it is replaced with the value for the nearest year, • if the value is unavailable in between the endpoints, it is replaced with the mean from adjacent years, • if more than one value are missing in a sequence, all subsequent replacement values are equal and imputed as above. Separate taxonomic indicators are constructed in four selected groups of states. These include: • EU15+1 – member states of the European Union prior to its enlargement in 2004 plus Norway, • EU15 – member states of the European Union prior to its enlargement in 2004, • EU13 – the states that joined the European Union after 2003, • EU28 – all member states of the European Union in 2019. Certain states are excluded from the EU15+1 and EU15 groups, due to the absence of figures, namely, Ireland, Luxembourg, Portugal and Sweden (hence, the same omissions in the EU28 group). Cyprus was excluded from the EU13 index for the same reason. The EU28 index covering the entire European Union does not include the five indicated states but includes the United Kingdom (that did not withdraw from the European Union until 2020). Differentiation of the digital economic development in Europe 51 3.4 Presentation of analysis results 3.4.1 Descriptive statistics The first proposed variable describes the proportion of employment in the ICT sector in the total state’s employment level. The economies that are characterized by a high indicator of technological and digital development should report a high percentage of employment in that sector. In all countries covered by the study, the proportion ranged on average3 from 1.4% in Greece to 4.4% in Malta. Almost all countries disclosed in the analysed period an increase in the indicator, ranging from about 0–0.1 percentage points (hereinafter: pp) (the Netherlands, Finland and Hungary) to 1.3–1.6pp (Estonia and Latvia). The only exception is provided by Denmark, with a drop in ICT personnel ratio by 0.5pp. A substantial majority of the EU states were characterized by a moderate but stable increase in ICT personnel, reaching an annual average of 0.06pp and 0.5pp over the analysed period of eight years. No correlation was observed between the rate of increase in employment ratio and its value for the first year analysed, i.e., a high or low base level had no effect on future rises in employment. The second discussed variable describes the proportion of ICT value added in the state’s GDP. Variation in this variable is considerably greater than that in ICT personnel percentage and ranges on average from 2.1% in Greece to 7.4% in Malta. The remaining countries mostly fall within the interval 3%– 5%, disclosing average annual growth of about 0.1pp. Falls in that proportion were observed in five countries over the period of eight years: Spain, Italy, Denmark, Slovakia and Malta (from −0.1pp to −0.8pp). The remaining states achieved a growth reaching on average 0.5pp, with its largest value observed in Bulgaria and Latvia (2pp). The percentage of ICT sector employment and ICT value added on GDP are correlated. A substantial majority of countries characterized by top ICT personnel proportions also belong to the group of leaders in creating ICT value added on GDP, and the countries characterized by the lowest ICT personnel indicators disclose a small ICT value added on GDP. However, exceptions are identified, such as Bulgaria. At an impressively high ICT value added on GDP, reaching 5.4% on average (the fourth highest result), the country is characterized by one of the lowest percentages of ICT personnel (2.5%, the eighth worst result). This may indicate an enormous difference between productivity in the ICT sector and in other industries. Table 3.1 contains mean values of ICT value added on GDP, mean values of the percentage of ICT sector employment on total employment and the quotients of those indicators. The third column is described as productivity of the ICT sector in an economy. If that indicator is greater than 1, productivity in the sector is higher than in other economy sectors. Values less than 1 would indicate lower productivity in the ICT sector than in the remaining economy. Over the analysed years, the ICT productivity indicator in all countries surveyed was greater than the average in their economies, exceeding 2 in Bulgaria where only 2.5% of employees generated more than 5% of GDP. 52 Mateusz Biernacki et al. A study into the percentage of imports and exports of IC products on the total country’s imports and exports leads to similar conclusions regarding the dynamics and direction of changes over time. In imports, the proportion of IC products equalled on average 1.17% of the total imports value. The highest average proportions were observed in the Netherlands and the United Kingdom (3% and 2.5%, respectively), and the lowest – in Malta and Czechia (0.43% each). An increase in the discussed proportion was observed only in eight states, the remaining economies disclosed falls. In exports, the proportion of IC products equalled on average 0.4% of the total export value. The countries characterized by top average proportions included the United Kingdom and Netherlands (1.32% and 0.98%, respectively) while Malta and Italy disclosed the lowest proportions (0.06% and 0.14%, respectively). Like in nominal values, more countries disclosed an increase in the proportion of IC product exports in total exports; IC imports compared to total imports increased in 12 states, representing one-half of the analysed group. The largest increase between 2012 and 2019 was observed in Slovenia: by 0.69pp. Table 3.1 Values of variables X1 – percentages of employment in the ICT sector on total country’s employment, X2 – percentages of value added in the ICT sector on GDP and ICT sector productivity indicators in the national economies Country Percentage of value added in the ICT sector on GDP Percentage of the ICT personnel on total employment ICT sector productivity indicator Bulgaria 5.36 2.48 2.16 Croatia 4.17 2.36 1.77 United Kingdom 5.85 3.42 1.71 Malta 7.37 4.42 1.67 Hungary 5.80 3.55 1.63 Netherlands 4.90 3.06 1.60 Romania 3.45 2.17 1.59 Germany 4.19 2.74 1.53 Greece 2.14 1.42 1.50 Czechia 4.39 2.95 1.49 Poland 3.27 2.24 1.46 Belgium 3.94 2.72 1.45 Slovakia 4.30 3.03 1.42 Spain 3.27 2.33 1.41 Slovenia 3.61 2.61 1.38 Italy 3.30 2.40 1.38 France 4.09 2.99 1.37 Austria 3.41 2.53 1.35 Estonia 5.04 3.86 1.30 Latvia 4.33 3.39 1.28 Finland 4.63 3.75 1.24 Denmark 4.57 3.80 1.20 Lithuania 2.87 2.39 1.20 Norway 3.39 3.00 1.13 Source: Eurostat and own calculations. Differentiation of the digital economic development in Europe 53 The fifth proposed variable is the percentage of enterprises that employ ICT specialists. The value of this indicator is comparable in most states and equals about 20% on average. The countries characterized by top values are Belgium and Finland (27% each), and the lowest values are observed in Romania and Poland (10% and 13%, respectively). Importantly, this proportion has dropped in almost all analysed countries for years. The mean indicator value in all those countries equalled 23.2% in 2012 and 20.6% in 2019, showing an average annual drop by −0.4pp. An increase was observed over the analysed period only in seven countries (Romania, Poland, Italy, France, Bulgaria, Malta and Denmark) – between 1pp and 9pp. Considering the discussed increase in the percentage of ICT personnel on total employment, a hypothesis can be proposed: the reduction was not caused by dismissing ICT specialists, but rather by a large number of newly established businesses that could not afford hiring this type of personnel in their initial phase of operation. However, this cannot be confirmed due to the absence of data. Another variable is the BERD in ICT sector as a percentage of total R&D expenditure. This indicator dramatically varied not only from one country to another but also in individual countries over the analysed eight years. The lowest average proportions were characteristic of Slovenia and the Netherlands (10% each), and the highest – of Malta and Estonia (50% and 44%, respectively). The mean value for all countries equalled 18% in the first and 21% in the last analysed year, showing an average annual increase by 0.5pp. A drop in the proportion of expenditure was observed in eight countries and ranged from −1.5pp to −6.5pp. The increase rates were higher, reaching even 18pp in Estonia and 33pp in Bulgaria. The last proposed variable is the percentage of enterprises’ turnover from e-commerce sales on their total turnover, without the financial sector. This indicator reflects, in addition to the development level of the digital economy, such aspects as Internet access or computer use in society. The mean indicator value ranges from a modest 3% to an impressive 29%. The lowest values were observed in Greece (3.3%), Bulgaria (4%) and Romania (6.9%), the highest – in Czechia (29%), Belgium (24.7%) and Norway (21.5%). An increase in this indicator was observed in all states, except Germany, over the analysed years – the greatest in Belgium (an increase from 14% in 2012 to 33% in 2019). The indicator rose annually in all discussed states by 0.6pp on average (a total increase in the mean value from 13% to 17.4%). 3.4.2 Taxonomic analysis results The first method used to assess the development of the digital economy in the European countries consists of the determination of a taxonomic indicator. All variables proposed above are understood as measures (but also stimulants) of the digital economic development. Following their normalization, the taxonomic approach was adopted, based on the maximum value of the total of Pearson correlation coefficients between the taxonomic indicator Skt i and standardized variables Xi,j . 60 Mateusz Biernacki et al. 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Available at: https://www.imf.org/en/Publications/Policy-Papers/Issues/2018/04/ 03/022818-measuring-the-digital-economy. ITU (2020). Manual for Measuring ICT Access and Use by Households and Individuals. ISBN: 978-92-61-30861-2. Available at: https://www.itu.int/en/ITU-D/ Statistics/Pages/publications/manual.aspx. Kling, R. & Lamb, R. (2000). IT and organizational change in digital economies. In E. Brynjolfsson & B. Kahin (Eds.), Understanding the Digital Economy (pp. 295–324). MIT Press. https://doi.org/10.7551/mitpress/6986.001.0001. Koch, T. & Windsperger, J. (2017). Seeing through the network: Competitive advantage in the digital economy. Journal of Organization Design, 6(1), 1–30. https:// doi.org/10.1186/s41469-017-0016-z. Nathan, M., Rosso, A., Gatten, T., Majmudar, P. & Mitchell, A. (2013). Measuring the UK’s Digital Economy with Big Data. National Institute of Economic and Social Research. Available at: https://www.niesr.ac.uk/wp-content/uploads/2021/10/ SI024_GI_NIESR_Google_Report12.pdf. OECD (2020). A Roadmap toward a Common Framework for Measuring the Digital Economy: Report for the G20 Digital Economy Task Force. OECD Publishing. Available at: https://www.oecd.org/sti/roadmap-toward-a-common-frameworkfor-measuring-the-digital-economy.pdf. Turcan, V., Gribincea, A. & Birca, I. (2014). Digital economy – A premise for economic development in the 20th century. Economy & Sociology: Theoretical & Scientifical Journal, 2, 109–115. Available at: https://ince.md/uploads/files/1455540156_14. turcan109_115.pdf. Zhenlong, M. (2021). Digital economy value chain: Concept, model structure, and mechanism. Applied Economics, 53(37), 4342–4357. https://doi.org/10.1080/ 00036846.2021.1899121. Part II Sources for developing the digital economy DOI: 10.4324/9781003450160-7 4.1 Introduction The chapter aims to detect the increasingly important role that Digital Innovation Hubs (DIHs) play in the European digital economy where supply chains are increasingly digitalised, traditional business models are transforming, companies work in an integrated way, and smart distributed production are the new norm. It starts by depicting the current European framework where DIHs are fundamentally changing the paradigm of industrial development by blurring the boundaries among companies, sectors, and regions, by facilitating the transfer of knowledge and the development of cooperation between science and business. It argues that DIHs can be both subjects of a single digital space and at the same time objects of the use of digital tools: their aim is to accelerate the digitalisation of small-and medium-sized enterprises (SMEs) and the public sector – mostly through training, testing and trial services, financial advice, and support for networking – and to bring about substantial economic benefits. They are pillars in the European Commission’s Digitising European Industry (DEI) strategy which aims to promote the competitiveness of European industry and the continent’s carbon neutrality goals. The chapter then focuses on Finland, a long-time forerunner in the development and uptake of digitalisation and specifically on the Lapland region where in 2021 the Arctic Development Environments Cluster was approved by the European Commission as the first official DIH in Lapland. The Artic Development Environments Cluster is presented by explaining its functioning, structure, aims, and activities. Data for the case study were collected through a systematic mapping of secondary material, primarily from the Regional Innovation Monitor, the European Innovation Scoreboard, and a wide range of policy documents, such as smart specialisation (S3) strategies, Digital Innovation Scoreboard, and Digital Economy and Societal Index. 4 Digital innovation hubs as drivers for digital transition andeconomic recovery The case of the Arctic Development Environments Cluster in Lapland Silvia Gaiani and Urszula Ala-Karvia This chapter has been made available under a CC-BY-NC-ND license 64 Silvia Gaiani and Urszula Ala-Karvia Primary data were collected through cooperation with the Artic Development Environments Cluster manager, Mr. Raimo Pyyny from Lapland University of Applied Sciences. The process was semi-structured; alongside quantitative data, the aim was to collect qualitative data based on personal reflections in terms of the role of the cluster in the region. 4.2 The European dimension of the digital economy The term ‘digital economy’ has been used extensively in recent years to describe the functioning of that part of the economy which is linked to information and communication technologies (ICTs). The most important aspect of the current trend is not the shift to high-tech industries, but the way that IT can improve the efficiency of all parts of the economy, especially old-economy firms. The digital economy may be characterised by three main factors (OECD, 2020): • etwork effects’ that lead to considerable spillovers, and these contribute to higher economic growth. The more participants use a network, the greater is its value to all who use it. The power of a network increases in proportion to the square of the number of access points to the network. • a change in the business cycle since ICT in combination with globalisation may lower the non-accelerating inflation rate of unemployment and change the short-run trade-off between inflation and unemployment. As a result, the economy can expand for a longer period of time accompanied by low inflation rates. • more efficient business methods linked to the use of new technologies that lead to higher trend growth. Digitalisation has become widespread in the second half of the 1990s and it has not yet realised its full impact on aggregate productivity. The European Commission has recently published the results of the 2022 Digital Economy and Society Index (DESI) (European Commission, 2022d), which tracks the progress made in EU Member States in digitalisation. DESI measures the progress of EU Member States towards a digital economy and society, on the basis of both Eurostat data and specialised studies and collection methods. It supports EU Member States by identifying priority areas requiring targeted investment and action. Based on DESI Index, it seems that during the COVID pandemic, Member States have been advancing in their digitalisation efforts but still struggle to close the gaps in digital skills, the digital transformation of SMEs, and the establishment of advanced 5G networks. The findings, also presented in Figure 4.1, show that most of the Member States are making progress in their digital transformation, but the adoption of key digital technologies by businesses, such as Artificial Intelligence (AI) and Big Data remains low – at 8% and 14% respectively. Digital innovation hubs as drivers 65 The positive trend is that the EU continues to improve its level of digitalisation, and Member States that started from lower levels are gradually catching up, by growing at a faster rate. In particular, Italy, Poland, and Greece are substantially improving their DESI scores over the past five years, implementing sustained investments with a reinforced political focus on digitalisation. Three Scandinavian countries (Finland, Denmark, Sweden) and the Netherlands remain the EU frontrunners. However, they are faced with gaps in key areas: the uptake of advanced digital technologies such as AI and Big Data remains below 30% and there is a widespread skill shortage, which are slowing down overall progress and lead to digital exclusion. Regarding the uptake of key technologies, during the COVID-19 pandemic, businesses have pushed the use of digital solutions, but only 55% of EU SMEs have a basic knowledge of digital tools indicating that almost half of SMEs are not availing of the opportunities created by digitalisation. In 2021, in Europe, the gigabit connectivity grew further (European Commission, 2022b). The coverage of networks connecting buildings with glass fibre reached 50% of households, driving overall fixed very high capacity network (VHCN) coverage up to 70%. 5G coverage also went up last year 01020304050607 080 Finland Denmark Netherlands Sweden Ireland Malta Spain Luxembourg Estonia Austria Slovenia France Germany Lithuania Eu ropean Union Portugal Belgium Latvia Italy Czechia Cyprus Croaa Hungary Slovakia Poland Greece Bulgaria Romania DigitalEconomy and Society Index 2022, by Main Dimensions of theDESI HumanCapital ConnecvityIntegraon of Digital Technology DigitalPublicServices Figure 4.1 Digital Economy and Society Index 2022 by the main dimensions. Source: European Commission (2022d). 66 Silvia Gaiani and Urszula Ala-Karvia to 66% of populated areas in the EU. However, an important precondition for the commercial launch of 5G is said as not complete by EC (2022a): only 56% of the total 5G harmonised spectrum has been assigned in the vast majority of Member States (Estonia and Poland are the exceptions). In order to increase the level of digitalisation, the Digital Decade Policy Programme (European Commission, 2021), which will enter into force in Europe by the end of 2022, will set out targets organised under four cardinal points: a digitally skilled population and highly skilled digital professionals, secure and sustainable digital infrastructures, the digital transformation of businesses, and the digitalisation of public services. According to the Digital Decade Policy Programme, by 2030, at least 80% of the European population aged between 16 and 74 should have basic digital skills (currently we are at 54%) and 20 million of ICT specialists should enter the labour market. The current shortages in filling ICT specialist vacancies represent a significant obstacle for the recovery and competitiveness of EU enterprises. To fill in the gaps, the EU has put on the table significant resources to support the digital transformation. €127 billion are dedicated to digital related reforms and investments in the 25 national Recovery and Resilience Plans (RRPs) (European Union, 2022) that have so far been approved by the Council. Member States dedicate on average 26% of their Recovery and Resilience Facility (RRF) allocation to the digital transformation, above the compulsory 20% threshold. Member States that chose to invest more than 30% of their RRF allocation to digital are Austria, Germany, Luxembourg, Ireland, and Lithuania. 4.3 The role of digital innovation hubs in the European digitaleconomy Currently the main EU programme on digitalisation is the Digital Single Market package (https://eufordigital.eu/discover-eu/eu-digital-singlemarket/), launched on 19 April 2016. Building on and complementing the various national initiatives for digitizing industry, the Commission aims through it to create better framework conditions for the digital industrial revolution. One of the most important pillars of the Digital Single Market package is the activity to develop a network of DIHs. DIHs are said to be ‘one-stop-shops’ that support companies to become more competitive with regard to their business, processes, products, or services using digital technologies (European Commission, 2022a). DIHs are based upon technology infrastructure (Competence Centre – CC) and provide access to the latest knowledge, expertise, and technology to support customers with numerous processes including piloting, testing, and experimenting with digital innovations. DIHs may also provide business and financing support to implement innovations and IT solutions, if needed across the value chain. Their aim is to facilitate the experimentation and uptake Digital innovation hubs as drivers 67 of technologies coming from six main areas: Big Data and AI, Internet of Things, Manufacturing/Industry 4.0, Robotics, HPC, and Photonics. As proximity is considered crucial, it acts as a doorway and strengthens the innovation ecosystem. A DIH is a regional multi-partner cooperation (including organisations like universities, industry associations, chambers of commerce, incubators/accelerators, regional development agencies and even governments) and the organisational form is usually adapted to regional conditions and contexts. A DIH can be formed from existing organisations taking on the title and/ or rebranding themselves, from existing projects under Horizon 2020 programme, by bringing together several existing actors in a (new) virtual organisation or by creating an entirely new organisation from scratch. It should have or develop a dedicated expertise, based on the available local strengths and the current and emerging needs of the local industry or public sector. The geographical scale of a DIH’s focus is also a varying factor. Most DIHs are clearly regional in their original scope but recognise the need to attract expertise and experience from outside the region. A successful implementation of the DIH concept could lead in some cases to the DIH playing a prominent role for digitalisation at a national level. Exceptionally there are cases where the high level of competences allows internationalisation and success in a global scale but the impact on the regional scale remains mostly the norm. Depending on the structure and needs of the region, this may mean specialising in one technology and one sector, but often a combination of different topics is more the case. In addition to specialists with sound knowledge of a technology, generalists and change managers may also be required to provide digital transformation expertise. This means that in the subsequent advice following a digital maturity assessment, the expert can evaluate the technological possibilities, knowing the current trends and market developments and provide 0% 10%20% 30%40% 50%60% 70%80% 90%100% Voice of the customer / product consorti Commercial infrastructure Pre-competitive series production Digital maturity assessment Market intelligence Access to funding and investor readiness, etc. Mentoring Visioning and strategy development, etc. Incubator/accelerator support Testing and validation Concept validation and prototyping Education and skills development Awareness creation Collaborative researches Ec osystem building, scouting, brokerag, networking Figure 4.2 Services delivered by DIHs for all EU countries. Source: European Investment Bank (2020). 68 Silvia Gaiani and Urszula Ala-Karvia access to the appropriate technical experts. The focus should always be on how best to serve the regional economy with an appropriate matrix of sectors and technologies. Figure 4.2 lists the services currently being delivered by the fully operational DIHs for all EU countries, according to the EU catalogue at the time of writing. Figure 4.3 illustrates the percentage of DIHs which support the most promising technologies for all smalland medium-sized enterprises. Only between 2016 and 2020, more than 150 DIHs have taken part in 370 different innovation trials testing digital innovations in collaboration with DIHs. In 2020, approximately 2,000 innovative SMEs across Europe have received the EU support through the DIHs to complete their digital transformation. 0% 10%20% 30%40% 50%60% 70%80% 90% Cybersecurity (including biometrics) Cloud computing Augmented reality / Virt ual reality / Visualisation Artificial intelligence & cognitive systems Internet of things Figure 4.3 DIHs supporting selected technologies for all small-and medium-sized enterprises. Source: European Investment Bank (2020). Box 4.1 Digital innovation hubs, clusters, research and technology organisations (RTOs) DIHs, clusters, and ecosystems are more than buzz words in economic development, they are engines of growth for cities and regions that accelerate innovation but there are some differences among them. – Digital Innovation Hubs focus on developing innovative digital products, services, and training in a specific area of their community, taking targeted actions to help overcome key challenges in that field. Each hub operates with its own management, legal structure, and business plan and has clear, measurable objectives to deliver value to its partners. – RTOs are often mentioned as DIHs. In many DIHs, the RTOs are one of the critical partners, but they usually do not have the capacity Digital innovation hubs as drivers 69 to offer all the needed services to SMEs. Even though many RTOs also have network/business service capacities, their main function/ mission is related to technology development (CC). Also, they are mostly networks, with departments that have a specific industry/ technology orientation with related technological infrastructures/ expertise. So, it can be said that an RTO is a key partner and can support many DIHs, acting as the CCs within the DIH organisation. RTOs are often an initiator of a DIH. – Clusters are market-driven phenomena. Clusters emerge without the help of any specific policy, as a result either of the spontaneous accumulation of competitive advantage or by chance. Cluster/ network organisations are often suggested as DIHs. As the mission of these organisations is to create (industrial) innovation networks it is not a surprise that they are highly related to the DIHs. In many cases, these types of organisations act as the orchestrator of a DIH, as they have high-quality capacities to organise the innovation ecosystem community. However, typically these clusters/ networks do not have the technological infrastructure, as well as (some of the) business service expertise. Therefore, they need to partner with Competence Centres (RTOs, Universities, etc.) and in some cases other stakeholders to provide a mixed portfolio of services. This cooperation in a multi-partner entity often forms the DIH concept. Source: Adapted from: Butter et al. (2020). The high interest of the European Commission in DIHs has been seen over the last years in large investments for their developments. Only from Horizon 2020 programme, 500M€ has been devoted to support their development (European Commission, 2022a). It is the Commission’s aim that all companies have access to a regional DIH, allowing them to access competences and to digitise their organisations, products, and services. Furthermore, in 2021, the Commission decided to create European DIH network with over 300 candidate DIHs, pan-European network of DIHs with designated DIHs from all the member states. In August 2022, 416 fully operational DIHs, 218 in preparation, and 70 potential DIHs from H2020 were registered and listed at the EC’s S3 Platform (European Commission, 2023). As presented in Figure 4.4, DIHs differ strongly in the provided technologies. The highest number of DIHs 353 being 85% of all fully operating DIHs provide the Internet of Things support, following by the AI, Big Data, and robotics (77%, 69% and 67% respectively). Cloud computing is a focus for half of the DIHs, and cybersecurity technology is supported by 44% of the DIHs. 76 Silvia Gaiani and Urszula Ala-Karvia EU. These regions have been chosen as one of European Commission’s pilot areas to develop new approaches and a S3 strategy for the period 2019–2023 has been developed. A mapping was carried out in the ENF area during 2019 in the priority areas of the strategy in order to identify the existing competencies and networks in the regions. As a result of the mapping, five thematic areas were identified as presented in Figure 4.5: clean technologies and low-carbon solutions, industrial circular economy, ICT and digitalisation, innovative technologies, and production processes. The industries of the ENF area are strongly focused on the utilisation of natural resources and conditions and the ENF area is already a pioneer in the development of solutions for an industrial circular economy, one of the most crucial growth sectors in the region. A series of networking events and training in the ENF were conducted during 2020 to facilitate cluster development and to enable cross-regional cluster networking. One of the main goals was to support better utilisation of the research, development and innovation (RDI) services offered by innovation platforms that have been systematically financed and created in the region in recent years. Creating new businesses and supporting SMEs in producing new or improved products, processes and services have been common goals. 4.7 A case study: Arctic Development Environments Cluster – the heart of Arctic Smartness Clusters Looking closer to the ENF, Lapland is the northernmost region (NUTS3) of the country and European Union. In 2021, the regional population was a bit over 175 thousand citizens living in a sparsely populated rural area with density of less than two persons per square kilometre. At the same time, Lapland’s rich natural resources have made it a favourable industrial destination blooming in forestry, mining, metallurgy, and tourism. Lapland is one of the first Finnish regions that adopted a S3 that is centred on innovation-driven socio-economic development of territories, through innovative multi-level and multi-stakeholder governance. An interactive process of public–private cooperation is defined as an entrepreneurial discovery process that helps to identify investment priorities – i.e., entrepreneurs with scientific, technological, and engineering expertise and market knowledge jointly produce and share information on new economic activity domains in which the region excels or has the potential to excel in the future. Lapland’s S3 focuses on the sustainable utilisation and commercialisation of Arctic natural resources and conditions started in 2013 through a strategic step-by-step implementation approach. In the approach, the Regional Council of Lapland clarified the strengths, value chains, and new forms of cooperation in the Lapland region and launched the Arctic Specialisation Implementation Project. As a part of the project, 650 projects were analysed, and such analysis was used as the basis for the construction of five Digital innovation hubs as drivers 77 clusters. Clustering started in 2015 with the Arctic Smartness portfolio project (Jokelainen & Jänkälä, 2017), which works like an ecosystem, where the actors share common goals to develop Lapland. Arctic Smartness Clusters act as engines for the regional development and are implementing new local and European initiatives and projects creating a breeding ground for growth in the regional economy. As of 2022, there are six established Arctic Smartness Clusters – Arctic Smart Rural Communities, Arctic Development Environments, Arctic Design, Arctic Safety, Smart and Sustainable Arctic Tourism, and Arctic Industry and Circular Economy. According to the data provided by Lapland University of Applied Sciences, the funding in-flow to Lapland in years 2020 and 2021 from national and international projects via the Arctic Smart Clusters exceeded EUR 22.3 Million (Table 4.2). Table 4.2 Arctic Smartness Clusters and their aims Arctic Smartness Clusters In a nutshell The Arctic Smart Rural Community The cluster’s main role is to prevent capital outflow from rural Lapland and to promote the region as prosperous as it offers a surplus of raw materials to a wide range of smart resource-intensive businesses. Another focus is on further processing of food and the promotion of renewable energy. This cluster, managed by ProAgria Lapland, is built upon network of 100 entrepreneurs and 200 developers: municipalities, financiers, politicians, projects (including international ones), research institutes, and business advisors The Arctic Development Environments Cluster The Arctic Development Environments Cluster serves as a supporting network to all Arctic Smartness Clusters by, e.g., enabling technologies to all industries and especially SMEs. This cluster, managed by Lapland University of Applied Sciences, is thoughtfully addressed further in 4.7 The Arctic Design Cluster The cluster aims at making Lapland’s businesses, products, and services nationally and internationally recognisable and competitive by utilising smart specialisation focusing on research, art, and design. The core processes are based on the knowledge and research of arctic designing including service design, product design, interaction design, and applied visual arts. The cluster is managed by the faculty of art at the University of Lapland The Arctic Safety Cluster The cluster aims at strengthening interregional networks and safety, both for the citizens and for the business. It is composed of the safety of tourism and everyday life and its beneficiaries are local businesses, residents, travellers, industries, and the environment. Lapland University of Applied Sciences is managing this cluster (Continued) 78 Silvia Gaiani and Urszula Ala-Karvia The Smart and Sustainable Arctic Tourism Cluster The cluster aims at Lapland’s tourism growing smartly: by 2030, the cluster wishes to increase tourism income up to EUR 1.5 billion (e.g., by developing year-round tourism). The biggest challenge and aim is to reach the growth responsibly, without compromising the safety and quality of the industry. The cluster includes a network of entrepreneurs, Destination Management Organizations, research and education institutes, development organisations, municipalities, and tourism projects managed by the Lapland Regional Council I. The Arctic Industry and Circular Economy Cluster The cluster supports Lapland as a frontrunner in sustainable utilisation of natural resources, sustainable industry, and circular economy. A mix of industrial expertise and commitment to sustainable development are at the core of refining natural resources in the Lapland region. The process industry actively searches for new, eco-innovative ways to modernise its processes while the management of by-product processes of industries and process optimisation is also a prioritised issue. The cluster is being managed by Digipolis Source: arcticsmartness.eu. The Arctic Development Environments Cluster was approved by the EC in February 2021 as Lapland’s first official DIH (Arctic Smartness, 2018) with its objective to bring together the RDI environments and expert services operating separately in the region. Yet, the collaboration as part of Lapland’s S3 strategy started in 2013. The aim was to form a uniform body to serve and boost Lapland’s business life and business investments in product development as well as internationalisation. In contrast to other clusters from the Arctic Smartness, the Arctic Development Environments Cluster is not a thematic cluster and acts as an umbrella support to the other clusters. The cluster produces services for the region’s businesses via its 50 environments and 700 specialists. Arctic Development Environments are both physical and virtual environments providing learning opportunities and triggering innovation, such as laboratories, studios, workshops, and simulation environments. The funding is preliminary public – via the Regional Council of Lapland, Business Finland, and the EU. The main partners in this cluster are multidisciplinary research communities from University of Lapland, Lapland University of Applied Sciences (the manager), Natural Resources Institute Finland, Geological Survey of Finland, Vocational College Lappia, and Lapland Vocational College. Yet, worth mentioning are also Artic Power – Cold climate testing, Arctic Steel, and Mining as the strong industry partners that have benefited by increased capacity and maturity of their actions and processes. The development work has been enabled via different projects, primarily financed from the European Regional Development Fund. Table 4.2 (Continued) Digital innovation hubs as drivers 79 Box 4.3 Arctic Development Environments Cluster – service model, competences, and services Service model in steps • The business (client) contacts the cluster. • The cluster prepares a requirement specification based on the client’s need. • The client receives a tender presenting the fee and schedule of the service. • If the tender is accepted, a service contract is drawn between the client and the cluster. The fee is based on the actual costs of the service. • A group of experts to be involved is selected, and the cooperation is coordinated by the cluster. • The cluster assists the client with identifying and applying for suitable R&D funding. • The cluster reports the final results to the company. Sectors to support • Agriculture and food • Community, social, and personal service activities • Construction • Education • Energy and utilities • Life sciences and healthcare • Manufacture of basic metals and fabricated metal products • Manufacture of food products, beverages, and tobacco • Manufacture of textiles and textile products • Maritime and fishery • Mining and quarrying • Other Manufacturing • Tourism (including restaurants and hospitality) • Transport and logistic Technical competences • Additive manufacturing • Artificial intelligence • Cyber-physical systems • Gamification • Interaction technologies • Internet of things • Internet services 80 Silvia Gaiani and Urszula Ala-Karvia During the COVID-19 pandemic, the demand for the cluster’s services increased. On one hand, local businesses have tried to adapt to pandemicrelated restrictions or develop brand new products or services supporting distant working and remote living. An additional challenge has been to uptake the pre-pandemic level of international partners due to closure of the borders and travelling restrictions. The evaluation of the Arctic Development Environments Cluster is ongoing. Among the challenges, the cluster faces are the identification of innovation gaps and the development of better monitoring and evaluation practises for the next programming period. The COVID-19 pandemic has had an impact on the clusters activities and has created new prioritisation. The low levels of hierarchy and low borders between organisations have helped Lapland • Logistics • Micro/nano electronics • New media technologies • Organic and large-area electronics • Sensory systems • Simulation, modelling, and digital twins • Software as a service and service architectures • Virtual, augmented, and extended reality Services provided • Awareness creation • Collaborative Research • Concept validation and prototyping • Ecosystem building, scouting, brokerage, and networking • Testing and validation • Visioning and Strategy Development for Businesses Source: Smart Specialisation Platform (European Commission, 2023). The Arctic Development Environments Cluster is one of only two Finnish DIHs that offer the services up to the highest technology readiness levels (TRL9 – Actual system proven through successful mission operations). The cluster, operating on non-profit terms, offers several solutions and services to the clients (that represent already existing companies, mostly SMEs or micro businesses). Depending on the speed of the services and the level of expert engagement, the cluster is able to offer services free of charge provided by local students. Payment in innovation vouchers by Business Finland (accounting for EUR 5,000 of 100% aid) are one of the most common payment options by SMEs. Digital innovation hubs as drivers 81 to gain a competitive advantage on other EU clusters and have allowed the ongoing activities to be persistent and effective. 4.8 Conclusion DIHs comprise represent a set of ecosystems characterised by high digitisation capacity building, wide advanced digital service offering, and strong linkages to European counterparts. Their importance in post-pandemic economic recovery is unquestionable. Finland has traditionally a strong cooperation culture across public and private organisations and strong digital innovation initiatives operate in the various regions across the country, including sparsely populated northern regions. In addition, Finland has Finnish strengths – numerous vibrant innovation and business ecosystems of national economic importance in which the public sector plays an important role. Digital transformation is not only about technology but requires also a deep context-specific understanding of how digital technologies can create benefit. Arctic Smartness Clusters, and especially the Arctic Development Environments Cluster, are excellent examples of how to endorse technology development but also the local competencies and know-how to solve significant economic or societal challenges in which digitisation plays a crucial role. References Arctic Smartness (2018). Arctic Development Clusters, Foundations of Cooperation and Innovations. 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DOI: 10.4324/9781003450160-8 5.1 Introduction Progress in the digital transformation process, which is largely inseparable from globalization and changes traditional economic and social organization patterns and balances, is reaching such magnitude and speed that it is currently taking a leading role in the discussions on competitiveness and economic growth in the European Union (EU). Such digital transformation has been enhanced by the impacts of the crisis caused by the COVID19. Digital skills are not only leading to new qualification and competence requirements in all occupations but are also creating new jobs. One of the most relevant sectors is the ICT industry, where data reveal a significant increase in employment over the last decade. In spite of such increase, there is already an excess demand, with a number of vacancies in the EU28 that would rise to almost 800,000 by 2020. The OECD (2016) estimates that on average, around 25% of the jobs will experience significant changes in the competences they presently require, and some 9% will be displaced by automation of workplaces. Concerning the challenges and opportunities arising from the digital shift in employment, it is necessary to address changes in the specific characteristics of the different jobs, and ways to adjust the competences acquired by workers to the future supply and demand of labour. Digital technologies minimize the cost of transferring ideas, knowledge, know-how and technology to anywhere in the world and reduce the cost of coordinating geographically separated complex activities. In a context of international economic liberalization and reduction of transportation costs, they allow companies to further fragment productive processes and relocate the different stages down to the task level and get the most out of international differences in costs and wages. Thus, the global economy tends to rest on long global value chains of large multinational companies, where firms and workers from all over the world compete with each other for integration (Baldwin, 2016). This structural transformation has substantial distributive implications and poses major challenges to the economic policies of different countries. The digital economy goes beyond national jurisdictions and intensifies 5 Digitalization and the impact on the labour relations1 Alejandro Díaz Moreno, Mª del Milagro Martín López, Myriam González Limón and Manuel Rivera Fernández This chapter has been made available under a CC-BY-NC-ND license 84 Alejandro Díaz Moreno et al. economic interdependence among various territories, which leads to the need to reformulating and reinforcing supranational governance structures, if protectionist responses are to be avoided. The EU is well aware of the fact that the digital shift is changing the nature of work and the structure of the labour market. EU institutions have launched various employment initiatives in the field of e-skills, education and training, but there is clear evidence of a deficit in digital skills or abilities. Moreover, the process is slow and it must be realized that there are significant differences among the member states. In 2015, the European Commission approved the Digital Single Market strategy with the aim of overcoming the fragmentation of the European Digital market, and providing a common approach to guide national strategies. However, in practice, this strategy has not been very successful, since the European Commission has proposed just a few initiatives, and they have not resulted in agreements by the Council and the Parliament to draft regulations allowing for its effective implementation (Council of the European Union, 2015). The Commission has developed the European Digital Competence Framework for Citizens (DigComp). Digitalization of the economy has multiple implications and effects on the forms of working and organizing work, and therefore, on employment relations and on the conditions in which work is performed. Digitalization of the production of goods and services may affect, among other relevant aspects: labour relations of employees; application of employment contracts; forms of employment; terms of employment provision; exercise of management’s direction and control powers; time and place where work is performed; onthe-job training; occupational health and safety; or a collective level, collective representation and bargaining instruments. Digitalization and its impact on employment relations generate new challenges resulting from changes in business models, the emergence of new forms of employment based on the online economy, and the increase in capacities brought about by the increasing connectivity. Experts believe that there will be changes in employment relations, resulting from the changes in the uniformity that was typical of the provision of subordinated work, and materialized in the fragmentation of production processes and their growing decentralization, among others. Digital technologies allow specifically, in some cases, for the replacement of employees by computers or robots in all kinds of works and tasks, either manual or intellectual, that no matter how complex they are can be expressed by programmable rules (algorithms), i.e. that are routinized, which may affect horizontally, to a greater or lesser extent, all production sectors (European Commission, 2020). This has led to proposing the idea of a new division of labour (Levy & Murnane, 2004), between digital work and human work, where the latter would focus on performing works or tasks requiring problem-solving, intuition, creativity, persuasion, adaptation to new situations, improvisation in changing environments, sensitivity, affection and empathy, skills that are difficult to replicate by machines. Digitalization and the impact on the labour relations 85 Successive industrial revolutions have caused movements in the opposite direction; although they have eliminated jobs through the destruction of certain forms of employment, they have also generated new types of employment. The digital revolution, also called the fourth industrial revolution, will produce similar effects, although it may generate imbalances leading to pay gaps between qualified and non-qualified employees, and even gender pay gaps. Digitalization is also generating major challenges in terms of quality of employment. It reinforces the trend towards the proliferation of atypical employment relations and new forms of self-employment, which are associated with more insecure and less promising professional careers, because such workers have fewer opportunities to access training programmes, the social protection system, forms of Trade Union representation and collective bargaining processes. In addition to fostering technological innovation and its positive impacts, national digitalization strategies should include policies to minimize and balance out its negative impacts, as well as the trends towards market power concentration and increase of inequality. Governance of these policies should also focus on participation and involvement of the social partners. In terms of organization of work, digitalization will lead to greater flexibility, which will affect many aspects of the organization of work; the times and places where tasks are performed are more and more flexible, like the types of work. This may create an advantage for both employers and employees, in the form of more autonomy and productivity, a better balance between work and personal life, and cost reduction. In turn, it may also entail risks, for example, in terms of certainty of income. Moreover, flexibility requires and results in new forms of management and new types of skills. Therefore, legislation and collective agreements must take into account the need for flexibility concerning working times and workplaces. 5.2 Basic aspects of digitalization. The digital transformationprocess Manufacturing processes are undergoing a digital transformation process, triggered by the advances in digital information technologies and, mainly, by the development of computers and software. The fourth industrial revolution has come about through the application of digital technologies to the industry’s business models – in other words, to the production models – leading to a new concept of factory, termed “intelligent”, which is dominated by the digital aspects applied to its production. “The intelligence of the new factory is the result of the convergence of information technologies; their union in a ‘digital ecosystem’ with other industrial technologies, and the development of new organizational processes” (Del Val, 2016, p. 4). This digital transformation may benefit the localization process, favouring national production and industries, and open the possibility that these will recover all the value processes (“botsourcing”), which will lead to an increase 92 Alejandro Díaz Moreno et al. to the concept of teleworking, which is already well established among us, a corresponding term should be coined: tele-unionism. Individualization, fragmentation and the absence of employment and/or contractual relations imply a reduction in the capacity of trade unions to represent and protect workers. In fact, not only is it difficult to determine bargaining units, but working for different platforms would, in principle, make it difficult for workers to be covered by a collective agreement (Todolí Signes, 2015). Individualism, disaffection and desertion from protecting collective rights: we observe a strongly “volatile” workforce, with distinct interests, difficult to reconcile with unitary and shared objectives. It is an indisputable fact that the relocation of a worker that involves virtual work, or work outside the workplace, causes him or her to become detached, and at the same time isolated, from other colleagues; and in addition, where appropriate, from the company’s trade union representation. How can we organize workers in the company when the company itself is diluted, as a result of the processes of fragmentation that they carry out? How can we organize nomadic workers who change workplaces? How can we organize workers on digital platforms who have no workplace or company? (Gutiérrez & Pueyo, 2017, p. 231) These forms of work (casual work, crowdworking, collaborative work, etc.) are deficient from the point of view of collective representation and negotiation (Eurofound, 2015). As there are only particular interests, it is logical that membership of trade union organizations in Spain should be even lower, as well as the interest in holding positions in employee representation. In this situation, it is difficult to convince workers that collective bargaining is a suitable method for solving their specific problems, let alone the possibility of collective action to exert pressure for maintaining or exercising certain rights. For these reasons, major problems are encountered in adopting/introducing regulations, unified to some degree, for workers who lack precisely the qualities or characteristics of employees. The diversity and fragmentation of this group makes it difficult to provide regulation that is unspecific, but at least adequate to their needs. The diversity we are talking about translates not only into the plurality of activities or jobs they carry out but also into the objectives or purposes pursued. Thus, there are workers who combine self-employment with another salaried job they already have, to supplement their income or simply to hedge against growing unemployment in Spain (Molina & Pastor, 2018). Representation of collective interests is also under threat, as structures of worker representation and social dialogue are largely absent in the world of on-demand work and collective labour. However, some proposals and experiments are creating new internal structures within the trade unions, aimed Digitalization and the impact on the labour relations 93 at attracting economically dependent self-employed workers, as they are called in Spain, and providing this formula of collective assistance, i.e. treating them collectively as a class of workers with the same rights as employed workers (Degryse, 2016). An example of this is platform work which not only leads to a reduction in bargaining power but practically excludes this possibility. All these cases when the worker is alone – in other words, when the worker is unprotected, isolated from collective support – generate an imbalance between the powers of the worker and the employer. Today, we can see how, despite a very unfavourable environment, in some cases, these workers and trade unions have organized themselves to encourage collective action. Although the courts have finally recognized many platform workers as salaried employees, the specific nature of this work requires that existing labour legislation be updated. The specificity of platform work requires new special labour regulations. In Spain, the government has chosen to regulate teleworking through legislation, specifically through the Law 10/2021 of 9 July on remote work, that seeks to mitigate abuse or inconveniences that may arise from deregulation. In particular, the aim is to prevent companies from passing on production costs to workers without offering any kind of compensation. The aim is to ensure that teleworking does not lead to a decrease in wage benefits, or a loss or weakening of labour rights. This is based on the basic premise that teleworking should be an option that is accepted by workers voluntarily, and under no circumstances can it be imposed by the company. It would therefore be a working modality which would depend on the worker’s own wishes, and which would be reversible; in other words, the possibility would always remain open for the worker to revoke his or her consent and return to a face-to-face activity. Among the most important issues arising from teleworking are those relating to the exercise of collective rights. This brings us precisely to the role of trade unions in the future framework of labour relations. In addition, we will address the question of equal treatment, not only with regard to gender and wage or salary gaps but also very especially with regard to promotion and professional training. Digitalization linked to the performance of work outside the company raises enormous doubts regarding employer control of workers’ private lives, either in a direct and invasive way, or in an indirect way, depriving the worker of privacy even during hours outside the workplace. 5.7 Digitalization and quality of employment. New forms of employment linkage The incorporation of new technologies into the world of work, or the digitalization of employment, requires a dynamic and flexible labour law, capable of being adapted to successive innovations of this type. This labour law 94 Alejandro Díaz Moreno et al. should permit and guarantee, as established in art. 38 of the Spanish Constitution (1978), the company’s planning and defence of productivity. At the same time, the law carries out its traditional function of protecting the worker. In this sense, our rules must allow the integration of new technologies into the company with the aim of increasing and optimizing its productivity. However, the implementation of new technologies brings with it the responsibility of the employer towards his staff. This is what has become known as a “technologically responsible company” (Mercader Uguina, 2017). Thus, our Spanish legislature recognizes the right of the worker “to promotion and vocational training at work, including training aimed at adapting to changes in the workplace, as well as the development of training plans intended to promote increased employability” [art. 4.2(b), Worker’s Statute Law]. Furthermore, Article 23.1(d) of the Worker’s Statute Law provides for the specific right of workers to training necessary for their adaptation to changes in the workplace. On the one hand, it stresses that training is the company’s responsibility, “without prejudice to the possibility of obtaining financing intended for the training purposes”; and, on the other hand, that “the time allocated to training shall in all cases be considered effective working time”. In short, in today’s global world, workers need the competence of learning to learn or lifelong learning, understood as the continuous development of knowledge and skills that people are offered, subsequent to formal education, throughout their lives. The legislature is making giant strides in the case of Spain, with such statutes as the recent Law 10/2021 of July 9 on remote work which has established the latest legal regulations for distance working in Spain. In addition, Organic law 3/2018 on the protection of personal data and the guarantee of digital rights responds to the processing of personal data and, more importantly, to the use of certain electronic or computer-based devices in the workplace, and contains a relatively detailed regulation directly applicable to the employment contract, deployed under the general heading of “guarantee of digital rights” (Articles 87 to 91, Organic Law 3/2018). Digitalization and working time: The impact of new technologies is evident in a model of labour relations in which it is increasingly difficult to determine when the working day begins, and when it ends. The world of work, in this new era of digitalization, is immersed in the search for flexibility policies concerning the working day, called “flexiworking”. This is a new way of working in which each employee can manage his or her schedule and work according to his or her needs. The aim is to find a simpler, more efficient, and more flexible way of working. The situation described would undoubtedly allow the worker to make work compatible with other types of personal or family occupations or aspirations. But, at the same time, new technologies can generate new ties and servitudes in the performance of wage-earning work. We should consider, above all, the worker’s connection through digital means with the company’s management and decision-making bodies. There is only one step from flexibility of working time according to production Digitalization and the impact on the labour relations 95 to the permanent availability of the worker, as the main tool of distance work. Technology eliminates the coordinates of time and place and blurs the boundaries between work and rest, to the point of perpetual connection. However, the use of electronic media in the workplace is undoubtedly linked to greater availability of the worker for company business. Within this framework, we run the risk of identifying ourselves with the permanently connected professional who consequently constantly works. The presence of new technologies in the field of labour, expanding the possibilities of permanent and uncontrolled connectivity, also leads to legal uncertainty about which regulations are applicable to these new situations. Therefore, to maintain an unchanged number of hours worked, the existence of effective regulations is necessary, establishing the right to digital disconnection (in Spain, the Organic Law 3/2018 of 5 December on the protection of personal data and the guarantee of digital rights and the Law 10/2021 of 9 July on remote work introduce for the first time the right to digital disconnection in the sphere of employment). The new digital platforms: The issue that has undoubtedly received the most attention is the situation of workers who provide their professional services through digital platforms. The platforms do not consider themselves as employers, nor do they consider the professionals who provide their services as employees. This situation does not fit in with either self-employment or paid employment. Therefore, authoritative voices have proposed the creation of a new intermediate figure called the “independent worker”. This type of worker would have the majority of the employed worker’s recognized labour rights, since the organization of work through digital platforms is considered, in most cases, a de facto relationship of subordination and labour dependence. The various legal rulings lacking a unitary solution do not help to clarify the issue.2 But, apart from the legal recognition that employment relationships generated on digital platforms deserve, the working conditions of the professionals who provide their services through these platforms are undoubtedly characterized by (1) low remuneration which depends on the number of services provided; (2) confusion about access to social security benefits; (3) risks to occupational health and safety, identified, for example, with permanent availability; and (4) the unilateral establishment of working conditions by the platform, which may even make remuneration, or stability of the service itself, dependent on surveys, in which the work carried out by the professional is rated; these are prepared by the platform itself. For all these reasons, measures need to be introduced that promote decent work, in all the aspects mentioned (salary, social security, working conditions, occupational health), for professionals who work in this field. Teleworking, A new path in labour relations in Spain: The pandemic that we have suffered has decisively promoted a specific form of work: teleworking. Telework is one of the facets of companies’ digitalization (the most visible one at present), and even though all physical work cannot be 96 Alejandro Díaz Moreno et al. converted to teleworking via telematics, certain characteristics, modes and ways of working online are going to be transferred to the majority of services provided, merging (which was already happening previously) physical and remote work. Telework is currently not covered by any international statistical standards. Countries have used slightly different operational definitions, which are typically based on two different components (Eurofound & ILO, 2017, p. 5): I The work is fully or partly carried out at an alternative location other than the default place of work. This criterion is based on the previous definition of remote work. II The use of personal electronic devices such as a computer, tablet or telephone (mobile or landline) to perform the work: The use of personal electronic devices needs to be an essential part of carrying out specific jobrelated tasks without being directly in contact with other persons. This phenomenon historically carried little weight in the organization and work culture of most companies in Spain but has achieved a sudden and intense growth due to the need to maintain economic activity and guarantee social distance during the pandemic. As of 9 July 2021, the Law 10/2021 on remote work has introduced new regulations on telework in Spain, applicable to any work performed in the worker’s home, for at least 30% of the working day during a reference period of three months, or an equivalent percentage depending on the duration of the employment contract (Article 13 of the Workers’ Statute). The Spanish law addresses many essential questions. (1) The statute introduces payment and compensation by the company of expenses generated by this type of work as a specific right of workers, as well as the company’s obligation to provide its employees with all the means, equipment and tools necessary for performing their work. This is a matter for collective bargaining. (2) The requirement for a printed form of the distance working contract, and its minimum contents (Article 6 of the Law 10/2021 of 9 July on remote work). (3) The possibility of unilateral withdrawal of the worker from remote working model, in accordance with the voluntary nature of remote work (Article 5 of the Law 10/2021 of 9 July on remote work). (4) The refusal of the worker to work remotely may not be a cause for the termination of the employment relationship, nor for the modification of his working conditions. (5) The new regulations on remote work bring the rights of the workers affected into line with those of workers in an ordinary employment relationship. (6) The right to digital disconnection regulated in Article 18 of the Law 10/2021 of 9 July on remote work, and the business duty to guarantee such disconnection. This right is also associated with the right of workers to have the company keep an appropriate record of their working hours. The right to disconnect: In Spain, the right to digital disconnection arises from the Organic Law 3/2018 of 5 December on the protection of personal Digitalization and the impact on the labour relations 97 data and the guarantee of digital rights, which introduced for the first time the right to digital disconnection in the sphere of employment. More recently, Article 18 of the Law 10/2021 of 9 July on remote work emphasizes special relevance of the right in the case of distance workers. Specifically, the first paragraph of Article 88 of the Organic Law 3/2018 indicates that workers have the right to digital disconnection in order to guarantee, outside the legally or conventionally established working time, respect for their rest time, leave and holidays, as well as their personal and family privacy (this is also confirmed by Article 18 of the Law 10/2021 of 9 July on remote work). The legislature intends to control misuse of electronic media outside working hours, and provides, in the second point of the aforementioned Article 88 of the Organic Law 3/2018, that the modalities of exercising this right will take into account the nature and purpose of the labour relationship, promoting the right to work-life balance, referring to collective bargaining or, where appropriate, to the individual contract, the power to regulate the scope and conditions of exercising this right to disconnection. The third point of the article, and this is relevant, includes the obligation of the company to develop an internal policy (after consulting employee representatives, if they exist in the company in question, including those who hold management positions) which regulates the modalities of exercising the right to disconnection and training in the use of technological means, preventing the risk of “computer fatigue”, with a special reference to those cases in which work is carried out remotely. This business obligation is strict, and its breach will be sanctioned. Any company that provides a mobile device, or that simply demands in one way or another availability by email or telephone from its employees, will have to carry out this control policy. The employer’s duty is to … ensure that the disconnection is limited to the use of technological means of business communication and work during rest periods, as well as to respect the maximum duration of the working day and any limits and precautions regarding the working day that are provided for in the applicable legal or conventional regulations. (Article 18 of the Law 10/2021 of 9 July on remote work) In any case, there must be a working schedule established by the parties, so that, outside this schedule, the worker has the right to interrupt communication with the company and co-workers, regardless of the form of remote working that has been agreed. Thus, the right of the remote worker to disconnect implies a duty of the employer to limit his or her ability to send communications to workers during rest periods. However, this right is not absolute and there is room for extraordinary “re-connection”. Thus, the 1st Additional Provision of the Law 10/2021 of 9 July on remote work states in its 2nd section that “Collective agreements or arrangements may regulate (…) possible extraordinary circumstances modifying the right to disconnection”. 98 Alejandro Díaz Moreno et al. Notes 1 “This work is the result of a Project carried with the title “The impact of digitalization of the economy on the skills and professional qualifications and labour relations” (Call for Proposals: Improving expertise in the field of industrial relations (VP/2018/004), Sub-programme II, within the budget heading 04.03.01.08). The Project has been financed by the European Commission DG Employment, Social, Affairs & Inclusion. 2 According to the European Commission, a digital platform is the provider of the underlying service, that is, with active and direct intervention in the organization and provision of the service, and not a simple technology company, when it is the one that: (1) determines the final price what the customer must pay; (2) sets the conditions and terms that determine the contractual relationship between the provider and the client; and (3) possesses the key assets or resources for the provision of the service. 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Available at SSRN: https://ssrn.com/abstract=2705538. Soete, L. & ter Well, B. (2005). The Economy of the Digital Society. Edward Elgar Publishing. ISBN: 978-1-84376-774-9. DOI: 10.4324/9781003450160-9 6.1 Introduction Societies and economies are not digitally neutral. Technological progress is a disruptive process that alters social and economic structures, stimulating the emergence of a new status quo. Technology and technological change do not just bring change or inventions to the economy and society; they enrich and shape socio-economic systems, raising their responsiveness and adaptability to further technological development. This demonstrates the interrelatedness of society, economy and technology, driving home the point that none of these elements exists in isolation. Digital technologies are claimed General Purpose Technologies; hence, they generate path-breaking innovations and are recognized as fundamental factors in long-run technological progress and deep-going structural and qualitative shifts in economies and societies (Sahal, 1981; Bresnahan, 2010; Coccia, 2017). As argued by Rosenberg and Trajtenberg (2004), digital technologies are ‘epochal innovations’ as they demonstrate the capacity to radically reshape the contours of the world economies, ways of doing business, and enforce the emergence of new industries, services et alia. Digital technologies (ICT) are widely acknowledged as the critical drivers for knowledge and information acquiring, labour and capital productivity, social, political and economic empowerment (Graham, 2019). Digital technologies, due to strong network effects (Katz & Shapiro, 1985) that they generate, enable the emergence of various networks reshaping the way businesses are run, as well as trading and consumption patterns, economic and social behaviours, social norms and attitudes (Graham & Dutton, 2019). The network economy emerges in the economy that is driven by digital technologies. Henceforth, tracing and understanding of relationships existing between society-wide adoption and usage of digital technologies and economic development deserve special attention. Not only because this deepens our knowledge on how economies work but also from a perspective of state policy that shapes the institutional and economic environment and is of seminal importance in this case (Gilbert, 2020). Moreover, digital technologies (information and communication technologies, ICT) greatly affect socio-economic transformation (van Deursen 6 Digitalization and digital skills development patterns. Evidence for European countries Helena Anacka and Ewa Lechman This chapter has been made available under a CC-BY-NC-ND license 108 Helena Anacka and Ewa Lechman where k(u) stands for the kernel function satisfying the condition of 1kudu ∫ ) ( = −∞ ∞ . In our study, we adopt an Epanechnikov kernel: 3 411 2 uu ) ( ) ( −< (6.9) Finally, to determine cross-country inequalities and divides, we use the Gini coefficient (Dorfman, 1979; Milanovic, 1997) that represents the inequality of, e.g., income distribution or any other variable. For a given population attributed to values yi, I = 1, …, n, if 1 yy i i ) ( ≤+ , the general formula of Gini coefficient is as follows: ∑ ∑ () +− +−               = = nn niy y i i n i i n 112 1 1 1 (6.10) The values of Gini coefficient range from 0 to 1, where 0 reflects perfect equality and 1 – perfect inequality. 6.4 Digital technologies and digital skills development trajectories and inequalities The empirical evidence summarized below was collected to draw a general picture of the development of digital technologies across 28 European economies between 1980 and 2021. The picture telling about the ICT diffusion trajectories complements a brief analysis of digital gaps evolution, and digital skills state of development. Figure 6.1 shows country-wise ICT diffusion patterns, for three macro-ICT indicators: mobile cellular telephony subscribers, active mobilebroadband subscribers and IU. These selected indicators perfectly show changes in access to and use of digital technologies in Europe, since the very initial years that certain technological solutions have started being absorbed by societies. The visualization of digital technologies diffusion patterns is then enriched by the logistic growth estimates – see Table 6.1 that shows specific features of the ICT diffusion process, including the most significant intrinsic growth rate.4 Next, Figures 6.2 and 6.3 demonstrate changes regarding digital gap and digital skills development, respectively. Considering jointly the country-wise graphs summarized in Figure 6.1 and logistic growth estimates in Table 6.1, several interesting conclusions can be drawn. Apparently, from the 1980 and the first introduction of mobile telephony to the general public, the assimilation of this digital means of communication started to spread fast across countries in Europe. A brief analysis of the country-wise diffusion pattern with regard to MCS shows Digitalization and digital skills development patterns 109 050 100150 1980 1990 2000 2010 2020 Austria 050 100150 1980 1990 2000 2010 2020 Belgium 050 100150 1980 1990 2000 2010 2020 Croatia 050 100150 1980 1990 2000 2010 2020 Cyprus 050 100150 1980 1990 2000 2010 2020 Czech Rep. 050 100150 1980 1990 2000 2010 2020 Denmark 050 100150200 1980 1990 2000 2010 2020 Estonia 050 100150200 1980 1990 2000 2010 2020 Finland 050 100 1980 1990 2000 2010 2020 France 050 100150 1980 1990 2000 2010 2020 Germany 050 100150 1980 1990 2000 2010 2020 Hungary 050 100150 1980 1990 2000 2010 2020 Iceland 050 100150 1980 1990 2000 2010 2020 Ireland 050 100150 1980 1990 2000 2010 2020 Italy 050 100150 1980 1990 2000 2010 2020 Latvia 050 100150200 1980 1990 2000 2010 2020 Lithuania 050 100150 1980 1990 2000 2010 2020 Malta 050 100150 1980 1990 2000 2010 2020 Netherlands 050 100150 1980 1990 2000 2010 2020 Norway 050 100150200 1980 1990 2000 2010 2020 Poland 050 100150 1980 1990 2000 20102020 Portugal 050 100150 1980 1990 2000 2010 2020 Romania 050 100150 1980 1990 2000 2010 2020 Slovakia 050 100150 1980 1990 2000 20102020 Slovenia 050 100150 1980 1990 2000 2010 2020 Spain 050 100150 1980 1990 2000 2010 2020 Sweden 050 100150 1980 1990 2000 20102020 Switzerland 050 100150 1980 1990 2000 2010 2020 United Kingdom Figure 6.1 ICT diffusion trajectories. Mobile cellular telephony, active mobile subscribers and IU. 1980–2021. 110 Helena Anacka and Ewa Lechman Table 6.1 ICT development pattern estimates. Mobile cellular telephony and IU. European countries. 1980–2021 Mobile cellular telephony Internet users Country Upper ceiling (κ) Intrinsic growth rate (α) Midpoint (β) Root MSE Upper ceiling (κ) Intrinsic growth rate (α) Midpoint (β) Root MSE Austria 137.5 0.41 2,000.8 10.9 86.4 0.31 2,002.6 3.25 Belgium 105.8 0.57 2,000.3 4.99 87.9 0.31 2,003.2 4.46 Croatia 108.9 0.54 2,002.6 4.54 77.9 0.28 2,006.6 2.91 Cyprus 134.9 0.45 2,002.5 3.98 98.1 0.19 2,008.9 3.63 Czech Republic 124.7 0.71 2,001 3.56 79.9 0.35 2,004.9 2.48 Denmark 125.7 0.35 2,000.3 2.62 94.3 0.48 2,000.7 3.47 Estonia 144.5 0.41 2,002.6 4.65 87.01 0.32 2,002.9 3.54 Finland 140.3 0.31 2,000.3 10.5 89.3 0.38 2,000.4 2.66 France 103.5 0.38 2,001.1 5.01 83.7 0.36 2,004.2 3.08 Germany 124.1 0.43 2,001.3 6.03 85.6 0.44 2,001.8 2.67 Hungary 110.6 0.62 2,001.5 5.29 79.9 0.37 2,005.4 2.81 Iceland 111.4 0.49 1,998.9 4.34 97.5 0.41 1,999.8 3.77 Ireland 106.6 0.61 1,999.6 3.4 87.1 0.31 2,004.8 2.35 Italy 147.1 0.45 2,000.5 8.12 69.7 0.23 2,005.1 4.51 Latvia 118.9 0.52 2,003.4 5.3 82.3 0.39 2,004.8 4.12 Lithuania 147.1 0.82 2,003.1 7.78 79.5 0.33 2,005.8 3.62 Malta 123.4 0.35 2,003.1 6.98 86.3 0.25 2,006.1 3.29 Netherlands 120.5 0.45 2,000.6 5.27 92.2 0.41 2,000.6 2.39 Norway 111.3 0.38 1,998.7 2.22 95.2 0.44 1,999.8 3.1 Poland 135.6 0.47 2,004.3 4.77 77.1 0.31 2,005.6 3.87 Portugal 116.5 0.57 1,999.9 4.45 81.6 0.22 2,006.9 2.61 Romania 117.7 0.62 2,004.5 3.78 89 0.21 2,011.6 3.22 Slovakia 125.5 0.39 2,003.2 6.01 81 0.42 2,003.4 5 Slovenia 109.2 0.61 2,000.4 6.97 81.5 0.28 2,004.3 3.59 Spain 111.4 0.55 2,000.3 3.93 87.5 0.28 2,005.3 4.87 Sweden 123.7 0.32 1,999.6 3.03 91.7 0.47 1,999.7 3.19 Switzerland 130.8 0.34 2,001.4 5.56 88.2 0.39 2,000.4 4.17 United Kingdom 120.9 0.49 2,000.1 3.5 90.1 0.41 2,001.8 3.99 Source: Authors’ estimates. Note: Logistic growth model applied; nonlinear least square estimator adopted; raw data used. Digitalization and digital skills development patterns 111 that this process follows a fairly similar trajectory in each country. Initially, the process of assimilation is slow since this path-breaking invention is not really broadly recognized. However, strong network effects emerge then and the number of users grows exponentially, inevitably leading to the stabilization phase during which societies are fully saturated with this type of technological solution. What also attracts attention are extremely high intrinsic growth rates (see Table 6.1) estimated for the process of diffusion of mobile telephony. According to logistic growth model estimates, the intrinsic growth rate – in our sample – ranges from 0.31 in Finland to 0.82 in Lithuania, which suggests average 31% and 82% annual rates of increase in the number of mobile telephony subscribers in respective countries. Interestingly, Finland, along with Sweden and Norway, belongs to the group of core innovators where the mobile technologies were first invented and implemented among society members. The specific feature of these core innovating countries is that the process of assimilation of technological innovations is relatively slow there, compared to other economies that simply imitate and introduce ready-made technological solutions. If we look at our sample of countries, we see that approximately twice as high intrinsic growth rates (compared to Finland) are reported for, e.g., Czech Republic (0.71), Hungary (0.62), Portugal (0.57) or Romania (0.62). Another interesting feature of the MCS diffusion process noted among our 28 countries is the relatively short time span for achieving the midpoint along the diffusion trajectory. The midpoint (β) shows the specific period (here – the year) during which saturation reaches 50% in a certain environment. In Table 6.1, we see summarized estimated β-parameters for each country in a time span from the year 1998 in Norway and Iceland to 2004 in Poland and Romania. These results show high homogeneity of MCS diffusion paths in European economies, as the assimilation of mobile telephony proceeds quite analogously and simultaneously across countries. In the case of mobile telephony, the process of rapid diffusion and hence growing number of its users gave birth to another interesting process – technological substitution, which in this instance led to gradually diminishing role of fixed-line telephony infrastructure in societal communications. Mobile telephony fast gained a massive part of the telecommunication market, and today the role of traditional (fixed-line) telephony is absolutely marginal. Quite similar concluding remarks can be made, if we consider the diffusion of active mobile-broadband networks and – consequently – changes in the percentage of society using internet applications and services. For the AMS variable, data is not available until 2007 as this is the initial year during which that type of technological solutions began to be widely accessible. Hence, the time series are relatively short in this case, but what can be seen in country-wise charts (see Figure 6.1) is that the increase in its usage is extremely fast in Europe. In only 12 years, in almost all 28 European countries, the saturation rates exceeded 100%, meaning that almost all society 112 Helena Anacka and Ewa Lechman members have access to this type of digital solution. The rapid diffusion and fast-growing access to mobile-broadband resulted in universal access to internet applications. As for the IU indicator, our data covers the period beginning in 1990; we need to note that during early years, access to internet services was facilitated mostly by the fixed-line – not mobile – infrastructure. Initially, societies could access internet services using fixed-line narrowband networks that were then gradually substituted by fixed-line broadband, and today – a huge proportion of connection is facilitated by mobile networks (although their throughput is still lower than that of fixed-line broadband). Logistic growth estimates for IU (see Table 6.1) – analogously to what we have concluded for MCS – support the hypothesis on fast increasing number of IU in Europe. Estimated – for the period 1990–2021 – intrinsic growth rates (α) show how fast the number of IU was growing annually. Starting from 19% per annum in Cyprus, to 47% per annum in Sweden and 48% in Denmark. As for the estimated country-specific midpoints (β), they cover the period from the year 1999 in Iceland, Norway and Sweden, to 2008 in Cyprus and even 2011 in Romania. Here, in the case of IU indicator, we see relatively greater differences in achieving midpoints, which might be a direct consequence of poor development of hardware infrastructure in some countries that was the necessary condition for internet access during early years of its implementation. 0.2 .4 .6 .8 1 1980 1990 2000 2010 2020 Gini_MCS Gini_IU Figure 6.2 Changes in ICT-related inequalities. Gini indices values. Mobile cellular telephony and IU. 1980–2021. Digitalization and digital skills development patterns 113 Such fast and unequivocal diffusion of digital technologies inevitably leads to gradual eradication of digital gaps and inequalities. Figure 6.2 illustrates changes in cross-country inequalities regarding mobile telephony adoption and IU. The evidence clearly demonstrates how massive drops – both in terms of MCS and IU – in cross-country inequalities took place between 1980 and 2021. In the case of MCS, the 1980 Gini coefficient was 0.96 which suggested ‘perfect’ inequality among examined economies. However, this extreme inequality was rapidly and steadily dropping over subsequent years, reaching 0.51 in 1996, then 0.11 in 2003, and from 2004, the Gini coefficient was below 0.1, to drop to 0.049 in 2021. Analogous trends in cross-country inequalities are observable for the IU variable. During the period 1990–2021, the gaps in this regard greatly diminished. In 1990, the Gini coefficient was 0.785, indicating massive inequalities among European economies; however, in 2021, it dropped to 0.035 indicating hardly any inequalities in this respect. Extensive diffusion and society-wide assimilation of digital solutions result– after all – in the emergence of digital skills. Those skills, as stated above, constitute unique attributes of individuals that make them able – or not – to effectively use ICT tools and software, both for professional and for personal purposes. Still, digital skills development is not automatically associated with the technological distribution, which is visible in the data related to basic and above basic information and data literacy skills, overall digital skills and online information and communication skills (see Figures 6.3 and 1A). For example, online communication skills most frequently fall within the range from 0 to 5 on the 0–10 scale (Figure 6.3), suggesting that the overall level of communication e-skills still crawls in Europe and those competencies are to be further developed in the coming years. On the other hand, results for the informational data literacy skills range between 75% and 99% in the absolute majority of European countries. Similarly, overall digital skills range from 50% to 80% in almost all European economies in 2021. This leads to a conclusion that a majority of the European countries have improved statistics related to the basic digital skills and increased their overall competitiveness while gradually catching up with the leading economies. To summarize, the distribution of digital skills across European economies (Figure 1A of the Appendix part) demonstrates significant disparities when it comes to individual e-skills levels. These results bring evidence of the strong variation in different digital competencies, and rather mixed outcomes regarding the distribution of advanced digital skills across Europe. Additional evidence comes from Figure 6.3 results ranking European countries in order of computer skills, where mean values and whiskers demonstrate significant runaways and disparities in maximum and minimum distribution across European countries. According to the analysed data, Romania, Bulgaria and Greece are lagging behind when it comes to overall digital skills, while Denmark, the Netherlands and Luxembourg are the leaders of the studied sample. These results confirm previous studies of Bejaković and Mrnjavac (2020) or Bontadini et al. (2022) on diverse distribution of digital skills across European economies. 114 Helena Anacka and Ewa Lechman 6.5 Final remarks This research has identified core digital trajectories in the European economies related to the digital development patterns. In the examined sample, digital technologies penetration and usage significantly expanded, leading to European market saturation over the tested period of 1980–2021. The diffusion rate of mobile cellular telephony and IU numbers demonstrate high intrinsic growth rates reaching the midpoint around 2002. Due to strong network effects, European economies managed to reach technological saturation and an impressive decrease in technological penetration disproportions, getting to a symbolic 0.05 inequality rate in 2021. While less technologically developed European countries catch up with Europe’s digital leaders by achieving an impressive growth rate, differences are still evident when it comes to the online communication and advanced digital skills. Therefore, digital skills development patterns are rather diverse, and they do not automatically arise from the technological penetration itself. In order to address those inequalities, European countries need further investment, e.g., in research and development, education, as well as policies on continuous upskilling and re-skilling (Jagannathan et al., 2019). Relative advantage in terms of technological innovations and digital lead is only possible when all three levels of the digital divide are appropriately addressed. 11 3 111 44 3 1111 2 1 2 0 1 23 4 Frequency 60 70 80 90 100 Inform_datLiteracySkills 5 2 1 33 1 22 1 22 11 1 0 1 2 3 4 5 Frequency 0 2 4 6 8 10 OnlineCommunSkills 111 5 2 1 2 11 22 3 1 1 1 1 2 012345 Frequency 30 40 50 60 70 80 OverallDigitalSkills Figure 6.3 Individuals with basic or above basic information and data literacy skills; individuals with online information and communication skills, and individuals with basic or above basic overall digital skills. Year 2021. Digitalization and digital skills development patterns 115 To conclude, we identified digital skills development patterns and examined digital skills inequalities across European economies. 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Development and validation of the Internet Skills Scale (ISS). Information, Communication & Society, 19(6), 804–823. https://doi.org/10.1080/1369118X.2015.1078834. Van Deursen, A.J., & van Dijk, J.A. (2018). The first-level digital divide shifts from inequalities in physical access to inequalities in material access. New Media and Society, 21(2), 354–375. https://doi.org/10.1177/1461444818797. 124 Amalia Verdu Sanmartin and Johanna Niemi AR does not replace the world entirely; it supplements computer-generated perceptual items of information and interacts with them. AR requires the use of special glasses like VR, and it can be combined with VR to create mixed reality (MR). The difference between this and other digital interconnections lies in the emotions experienced by the user (Dieck et al., 2021). 7.3 VR as a tool of Smart Education: the relationship between education and the digital Now, the digital realm has become an active content creator and is causing educational methods to move from the first timid steps into online education with tools, such as Moodle, to interactive digital tools and gaming. The emergence of smart education compels us to rethink our approaches to learning and knowledge creation, as it engages universities and research institutes in new paradigms of teaching and learning by intertwining teaching/learning with digital technologies. New concepts pop up in relation to smart education, such as smart pedagogy, smart environment, and smart learning (Meng et al., 2020). Zhu, Sun and Riezebos (2016) suggest that smart education is a shift from traditional teacher-centred pedagogies towards more learner-centred methods, making use of adaptive and interactive technologies. Coccoli et al (2014). say that it is “education in a smart environment supported by smart technologies, making use of smart tools and smart devices” (2014, p. 1008). Additionally, Lee et al. (2014) underline the potential applications of intelligent technologies aimed to support online collaborative activities and create an active learning environment in which emerging technology tools promote knowledge sharing between learners. Research has shown that the relationship between devices and technology requires the development of smart pedagogies, that create a smart learning environment for smart learners (Zhu et al., 2016), and education adaptive to students’ needs (Bajaj & Sharma, 2018). Smart education appears as a novel approach in which digital intertwines with education and knowledge. The research shows that smart education in combination with smart pedagogies improves high-order thinking skills (Julius et al., 2018). However, this integration of technology and learning is not sufficiently implemented in legal education (Rabadi & Salem, 2018). Smart education is now incorporating VR, as shown by the EDUCAUSE Horizon Report (2020), with very positive results. Kavanagh et al. (2017) conducted a systematic review that revealed four main applications of VR when used in the educational setting: simulation, training, accessing limited resources, and distance learning. By utilizing immersive VR, students are able to interact more effectively with knowledge, and those living in remote locations with limited access to education can also benefit from its use. However, the relationship between technology and education requires specific frameworks due to potential ethical concerns (Zhu et al., 2016; Meng et al., 2020; Virtual reality in legal education 125 Fischer et al., 2021). Cai et al. (2021) propose a blended approach which would maintain self-efficacy and individuality while addressing pedagogical aims of high-order skills and deep learning alongside community knowledge building and transformation in smart education contexts (Cai et al., 2021). 7.4 Technology, law, and education: from a linear one-to-one connection to becoming together 7.4.1 One-to-one relationships In the last 20 years, digital technologies have rapidly progressed and spurred a lively interdisciplinary debate. Richard Susskind (1996) predicted that lawyers would communicate via email which, though revolutionary at the time, has since become commonplace. On top of all these changes, the production of knowledge and the acquisition of information are leaving traditional sites; the Internet and digital technologies are enabling self-learning and new knowledge. The relation between law and technology is shaped by a one-to-one binary discourse about the constraining and fostering role of each other in this mutual relation. The result is usually the production of norms, principles, standards, and new legislation, ignoring the nature of knowledge technology. The legal response to technologies was first focusing on the problems of privacy and data, such as the implications of Big Data and AI for privacy, anti-discrimination, due processing, and the rule of law, and resulted in the adoption of “digital” regulation which emphasizes processes of writing that resemble drafting a legal text (Lezaun, 2012, p. 38). The latest growing use of AI shifts the focus towards its ethical implications and the question how it may alter the practice of law. In the legal realm, digital tools have a poor image and are blamed due to their negative effects. However, the effects of using digital tools may merely be reflections of the existing discriminatory and biased practices in society. Since technology deals with real data, the outcome is merely a reflection of the society we live in and only results in exposing the normative flaws that sustain discriminatory and biased legal practices (Whittaker, 2019). The ethical implications of digital technologies led to the emergence of trendy AI ethics which is a rebirth of the concerns and critiques already expressed by feminist, gender, critical, race, colonial, and decolonial theories, among many others, over the last decades. But still, the solution offered at the EU level is more new legislation (European Commission, 2022). Richard and Daniel Susskind (2022) explored the one-to-one relation between the legal sector and technologies, forecasting an increase in the demand for digital dispute resolution, replacing traditional courts with online resolution systems. LegalTech tools are currently being implemented to scrutinize court verdicts,3 and to review case material (e.g. Westlaw Edge). Fears are voiced that algorithms and machines will replace the work of legal 126 Amalia Verdu Sanmartin and Johanna Niemi professionals such as lawyers, juries, and judges; however, a general change and transformation of legal jobs and services reveals interesting prospects for individuals working in the legal sector rather than a decline in jobs. Therefore, law faculties must begin a curricular transformation to prepare law students to thrive in 21st-century society. With the millennial generation of digital native students engaging in new ways of learning (Manuel, 2002), different skills are required than those prior to digitalization. Consequently, legal professionals must learn new tools and skills in order to adapt to evolving legal problems and gain the knowledge needed for success. In 1936, Fred Rodell said “There are two things wrong with legal writing. One is style; the other is content” (Rodell, 1936), asserting that the traditional style and content of legal writing were not in sync with society and professional needs. Despite these issues, legal education continued to rely upon the written text as indubitable truth and focus on the legal style. With the emergence of online education offering online courses, intensified by the Covid-19 pandemic, technology has been introduced into law faculties in the form of Mass Online Courses, Moodle courses, and teaching with Zoom, Teams, Skype, etc., yet this has not necessarily resulted in pedagogical innovation. Alimisis states in his comments on robotics education that current uses of technology are simply reinforcing old ways of teaching and learning. She critically argues, “most uses of technologies in schools today do not support 21st-century learning skills” (2013, p. 66). Interactive teaching methods such as flipped classrooms and simulations have been left to stagnate. Amidst this digital knowledge era, much remains unchanged within law faculties, predominantly due to an ongoing reliance on “legal writing style”. Legal education in the European context has been characterized as banking education, according to Paulo Freire (1974). Deleuze (1994) further explains that this banking education fails to promote critical and creative thinking and reinforces dogmatic thinking. Banking education relies on the belief that thinking means representational repeating, reinforcing, and reifying dogmatic thought. However, as Gandorfer and Ayub (2021) argue, “Thought is relational, non-representational, and collaborative” (2021, p. 2). There is urgency for integrating the ethics of thought in the co-creation and transmission of knowledge to achieve “both sense-making and sensing in the making” (Gandorfer & Ayub, 2021, p. 1). Higher education has a political dimension (Barrier et al., 2019), and Diana Laurillard described teaching in higher education as fundamentally being “a rhetorical activity, persuading students to change the way they experience the world through an understanding of the insights of others” (Laurillard, 2013, p. 23). The problem of the rhetorical teaching/learning method in law, as revealed by pedagogical approaches, is the difficulty in achieving deep learning, which prevents the acquisition of higher order skills such as critically examining texts or making connections with other ideas and knowledge (van Dongen & Kirschner, 2020; Wang et al., 2022). A power relationship between academic, experiential, and everyday knowledge underlies banking education. Virtual reality in legal education 127 Written texts and norms “impose” this hierarchy, curbing thoughtful enquiry and supressing the importance of experience-based insight and relations in producing knowledge. Alfred Whitehead recognized this hierarchical method of legal thought when he said: “In all systematic thought, there is a tinge of pedantry. There is a putting aside of notions, of experiences, and of suggestions, with the prim excuse that of course we are not thinking of such things” (1938, p. 2). With no interaction between such modes of thought like sensing or feeling (Manning, 2009), a dogmatic image remains fixed. The dogmatic image of thought should be replaced in order to recognize the violence implicit in representational thinking. Legal systems are presented as independent from social, cultural, political, economic, and especially digital systems. Additionally, law is traditionally viewed as an established and authoritative symbolic representation of truth. Students during legal studies learn to refer to the world using this legal representation which leads to the reification of symbols. Students are still largely experiencing legal education as “learning by heart”, despite the implementation of several projects aimed to introduce new methods such as flipped classrooms, game-based learning (GBL), and problem-based learning (PBL) (Knight & Wood, 2005; Kapralos et al., 2015). These methods have produced positive results in integrating experiential knowledge and stimulating active student involvement, yet they are usually restricted within traditional environments not encouraging to transgress the disciplinary boundaries of thought. Therefore, alongside lectures, the modern Socratic method is still a preferred teaching style in legal education. This method enables a dialogue among the students, advancing critical and creative thinking aimed to discover solutions for the world’s legal problems. However, there is the risk of reifying legal symbols and norms, since dialogues are often limited to the definition of concepts from a positivist perspective. In this way, students become familiar with predetermined court-made interpretations and are not encouraged to search for creative alternative solutions. Hence, the application of knowledge becomes a narrow exercise of discerning a pre-settled matter. The modern Socratic method as used in current legal education ends up in a right and normative reconstruction of the normative truth and, thus, teaching students to think like a lawyer, which entails learning how the world is and should be experienced. The modern Socratic method is infused with the very same pedantic truth earlier exposed by Whitehead (1938), promoting normative representational thought. Recognizing the relationship between education, technology, and society, as well as understanding their relational rather than representational nature, is critical for improving students’ understanding of the role and effects of law (Maharg, 2016). There is an inner relational quality within the system and in-between the systems. Investigations into the “Knowledge Practices” that are present within and between these systems can help to achieve this aim by highlighting their co-production through contingent entanglements (Weidemaier & Gulati, 2013). Despite the relational nature of thinking/acting, the 128 Amalia Verdu Sanmartin and Johanna Niemi digital influence on education is limited to a one-to-one relation, thus placing emphasis on the acquisition of IT skills and the sourcing and handling of information now freely available in a vast amount online. Through incorporation into blended teaching curricula, VR can become a transformative technology capable of heightening attention to non-representational knowledge involving situated learning (Maharg, 2001, 2016). Many studies indicate the effectiveness of VR in education across a myriad of disciplines including medicine (surgery training) (Nassar et al., 2021) and law, in the form of legal case management simulations and visualizations of legal actions (Baksi, 2016). VR has the potential for providing a new form of access to knowledge previously disconnected from education. It can foster collaboration across disciplines and represent complex connections, aiding in the connective understanding of laws and the legal process. VR enables students to experience law concepts in a way that is both realistic and interactive. This allows them to better understand the way the law works and its context. Additionally, robots can be used to create online tutorials for students – making learning easier than ever before. Students’ experience of living in VR as well as in the physical world necessitates a re-evaluation of the relationship between materiality, relationality, and representation in both worlds. Knowledge acts as the catalyst for investigating this entanglement between technology, education, law, and VR which enables us to go beyond a mere simulation and explore the hidden interactions influencing knowledge. The growing ubiquity of digital aspects in other parts of life brings up further questions with regard to law, VR, and humanity. 7.4.2 Becoming together Mobile learning and e-learning are the precursors of crossing the temporal and spatial boundaries in teaching/learning (Looi et al., 2009). Previous research on smart education revealed that effective teaching/learning strategies can improve thinking skills (Julius et al., 2018). Zhou’s smart education framework (Zhu et al., 2016) includes such vital elements as teaching presence, technological presence, and the learner’s presence. Within this framework, a focus on the construction of knowledge enables us to understand how knowledge reproduces, and what are the possibilities for transformation (Berry & Fagerjord, 2017). Rethinking this relationality from a new materialist perspective exposes the power relations embedded in the binaries pervading the transmission of normative knowledge, like: teaching vs learning, teacher vs student, nature vs culture, physical vs non-physical, and human vs non-human. The becoming-together approach highlights the role of what is sensible but not represented or, as Deleuze words it, what is between the actual and the virtual (Deleuze, 1994). A diffractive reading (Barad, 2014; Bozalek & Zembylas, 2017; Merten, 2021) of pedagogical and critical approaches in education, alongside digital Virtual reality in legal education 129 technologies and law, allows us to analyse and understand the becoming together of all these dimensions. This task stems from realizing that, when thinking as a lawyer, representational and critical thinking alone are insufficient for future legal professionals in the technology-mediated 21st century. The modern approach to the relationship between these elements leads to the transmission of legal knowledge within a modern framework with some touches of the postmodern, which in a blended world seems inadequate. The postmodern element contracts to fit within the boundaries set by the modern framework. Modern boundaries are challenged by the non-human elements and their entanglement with human subjects. However, the modern framework rejects these entanglements and reproduces itself through text-based academic and legal knowledge and through the fixity characteristic of the written text. This fixity attaches to written texts, privileging them in legal education, and marginalizing messages that are not written. Nonetheless, the actual/virtual becomes materialized when the digital comes in. The non-human is entangled with the human, producing new phenomena; however, the transformative possibilities brought in by the non-human evaporate due to the insistence on complying with the modern principles embedded in the deeper layers of law and education. The erosion of the boundaries between law and other practices requires engaging with the materiality of meaning in the transmission of knowledge. This would entail openness to reimagining knowledge ontologies to understand the doing of theory and the effect of the presupposed (Barad, 2007). Reimagining law and visualizing the power/knowledge co-constitution, the entanglement with the digital creates the opportunity to explore the embeddedness of bodies, nature, space, and time in the materialsemiotic entanglement of law before entering the process of thinking as a lawyer. The digital realms reconfigure the modern boundaries, offering a crack from which we can reimagine and rethink knowledge while experiencing the entanglement between the physical and non-physical. Post-human, postmodern, and new materialist pedagogies of inclusion and collaboration offer a breakthrough, replacing representational thinking and binaries. Gilbert (2005) further solidified this opinion by verifying that traditional locations for gaining knowledge are expanding further onto such platforms as the Internet. Laurillard (2013, p. 27) has explained that academic knowledge is reliant on symbolic representation “or any symbol system that can represent a description of the world and requires interpretation”: legal knowledge relies on the text and language for interpretation within a specified legal framework delimited by inherited implicit assumptions. Successful learning is possible only if it is related to the given context of action. Through VR, the distinction between knowledge and object is questioned, as well as that of written representation and law. VR leads to a new mentality concerning the body, emotions, and how they go hand in hand with theory, while maintaining a focus on the words used in legal practice – uncovering any unseen undefined issues 130 Amalia Verdu Sanmartin and Johanna Niemi within language. The body and emotions are entangled when putting theory, the text into practice, revealing the silences and absences of the text alongside the materiality of the words. The experiential turn is entangled with the linguistic turn, opening the possibility to understand the agency of the intraactions, the “other” matter in the construction of knowledge. Knowledge as the intersection point of law, technology, and education is, from a new materialist perspective, embedded and becomes another intra-acting element. This understanding creates another node where technology, law, and education meet which consequently changes academic views of everyday knowledge. Constructivist theories of learning seem to underpin Bloom’s taxonomy of knowledge and learning in university pedagogics, where students are viewed as active participants in the learning process. Educators recognize that the transfer of knowledge is not solely responsible for educating the student; rather, students actively seek, accumulate, critique, and construct knowledge (Anderson, 2005). Anderson (1991, 2010) also notes that higher levels of learning require an ability to reflectively critique both subject matter and the process applied. Nonetheless, it is believed that in legal education, students must initially learn the content of law before being able to critically assess it. Constructivist elements tend to be found at the Master’s level, at which courses with a critical approach are often elective additions to the mandatory curriculum. However, courses framed as critical approaches to law form part of wider shifts in education, where learning outcomes and goals focus on skills, and teachers are seen as facilitators rather than lecturers (Lemaître, 2018). Notwithstanding these shifts, the teacher is the primary active agent leading the learning process and defining the learning outcomes, as regards both content and skills, yet forgetting the constructive elements. Constructivist pedagogy and VR advocate a radical shift towards a system in which students are seen as active participants in the construction of knowledge and as actors responsible for their own learning. In a smart education framework, constructivist pedagogies are explored in VR to overcome the limitations encountered by traditional teaching and promote deeper learning. The goal of research on smart education is to develop methodologies and frameworks assisting in purposeful planning of courses that include the effective use of technology from the beginning of study. To utilize the full potential of VR, the students should be seen as active participants in knowledge production early on. It is important to acknowledge their agency within the world (Lemley & Volokh, 2018; Jian et al., 2019; Mohamad et al., 2020; Cho et al., 2021). Post-human and new materialisms pedagogies seem to deconstruct the power hierarchies implicit in the Western binary thinking (Baofu, 2011; Gough, 2013; Kosofsky Sedgwick, 2003; Sherbine, 2015; Revelles Benavente & Cielemecka, 2016; Carstens, 2019; Egea et al., 2020). Thus, the first binary to break is that of teacher/student. Students are actively encouraged to work together in the metaverse, with the aim of facilitating peer interaction and ultimately producing a collaborative environment in which both Virtual reality in legal education 131 academic and experiential knowledge is shared by all parties involved. Teachers’ hierarchical position diminishes as students share their digital skills and situated knowledge with their teacher. Informal knowledge is entangled with academic knowledge (Prensky, 2007). The role of the teacher shifts towards that of facilitator, while students practice and transform academic knowledge as they engage with each other. Passive reception of information is transformed into learning by being, rather than doing, and this shows how academic and experiential learning intra-act in creating and transforming knowledge. Students provide their individual experiential knowledge integrating it with others’ and academic knowledge (Lee& Reeves, 2017). VR can activate silent voices and perspectives, encouraging visual learners or shy students to become more participative and motivated (Herrera et al., 2018).4 Research indicates that immersive and non-immersive VR can improve student focus, engagement, and interest in the subject of study. Simulations have shown that students become bolder when given a role, often overcoming their shyness. However, the lack of authenticity minimizes the effectiveness of simulation learning (Daly & Higgins, 2011). Through immersive VR, not only are students provided with an engaging learning experience but also with an environment that is highly interactive and realistic. This lends itself to enabling a deeper understanding of complex concepts and providing learners with more comprehensive knowledge. These simulations are often controlled and interactive, allowing students to interact with the environment and experience the relevant scientific concepts in a simulated environment. Furthermore, VR can be used to provide students with a virtual tour of a location or process, immersing them in a realistic environment. The integrated use of technology is a not just a mediated tool; but it also allows us to reflect on how the virtual, the non-human in general, is embedded in and transforms law-making (Lezaun, 2012; Cloatre, 2015). The deployment of power/knowledge of law through legal education is a journey from imparting knowledge to enabling student’s learning. In both stages, there is an ontological transmission of academic knowledge. However, the disembodied nature of thinking in, of, and about law prevents individuals from visualizing material entanglements and the ontologicalepistemological nature of this knowledge (Barad, 2007). In teaching law, it is essential to comprehend how the meaning behind law shapes laws and how such laws will in turn continue to shape society. Thus, questioning what law does and will do becomes vital when making sense of why certain laws are presupposed in certain ways. Reflection on such questions with law students may sound utopian, but utopias are possible in a digital environment giving the opportunity to understand becoming processes and invisible relational entanglements. The Research Handbook on the Law of Virtual and Augmented Reality (Barfield & Blitz, 2018) elucidates the immanent obstacle that accompanies the confluence of the physical and the virtual world. Current legal theory belongs to the physical world and needs reinterpretation or rethinking to 132 Amalia Verdu Sanmartin and Johanna Niemi address the virtual world. The union of the online universe and the tangible plane equivalates to the amalgamation of the human and the non-human; we can be ourselves or our avatars. Therefore, the established legislation on injuries, amenities, crimes, responsibilities, adjudication, etc. may encounter many future challenges in the virtual world. When engineering a VR environment for legal education, we can develop principles that will support attaining an intuitiveness of the intrinsic mechanics of the law and “what the law does”. For this purpose, the design principles of a VR law course may focus on: 1 Knowledge production and transformation: experiential and academic knowledge entanglement. This allows us to understand the shift in knowledge production and the future challenges. 2 Interdisciplinarity of knowledge: The simulated environment enables practicing how to solve problems and situations with others and in collaboration with students with different experiential and academic knowledge. 3 Theoretical perspective entanglement with practice: to play and understand intersectionality, situated knowledges, experiences, and identities. These principles help to achieve deep learning and heightened attention to the ethics of thought using the matterphoric (Gandorfer, 2020) possibilities offered by the virtual realm. The entanglement of the digital/physical elements in a simulated environment enables an understanding of the links between experiential and academic knowledge from a multidisciplinary perspective. This results from context-based learning (CBL)5 and crowd learning6 that promote deeper learning through exposure to other users, students, and learners that goes beyond GBL due to its realistic challenges experienced through body, senses, emotions, and feelings (Rose, 2012; Kalisz, 2016; Plass et al., 2020). The role of the body in the act of thinking is widely acknowledged, yet rarely considered in the academic world. Embodiment and situatedness rarely cross the line that divides academic and experiential knowledge. “Thinking like a lawyer” requires detaching oneself from personal experiences, making neutral analysis and interpretations of various situations. Avatar technology enables students to explore different identities, backgrounds, and layers of intersectionality, and their effects on others. Through engaging education, technology, and students’ multiplicity, tools are provided to promote intrinsic motivation while highlighting individuals’ connection with the group. A 3D simulated environment creates a space where to comprehend the interconnectedness of the world. Students integrate themselves with this non-physical domain in order to solve interdisciplinary problems by experiencing rather than speculating or reading an authoritative text. Furthermore, digital entanglement with law and education highlights embodiment by bringing into focus the act of thinking that is often neglected. The digital entanglement with law and education allows bringing in embodiment and situatedness while experiencing its implications in the very act of Virtual reality in legal education 133 thinking. Simulation is a key factor within VR which enables all senses to become engaged in an experiential learning event, thus opening diverse ways of learning and implementing practical experiences. This demonstrates the role that the body plays regarding understanding, interpreting, and deciding issues at hand; emotions and feelings are also employed in order to deepen the understanding of the knowledge-production processes associated with bodily engagement. The incorporation of digital technologies allows us to work with tools that can be adapted to different learning experiences while promoting collaboration.7 VR offers experience-based teaching/learning that encourages engaging with the issue in question rather than assimilating and memorizing: it helps to experience it. 7.5 Conclusion With technology advancements, the legal world has begun to embrace the concept of smart education. Smart education has opened up new possibilities for the transformation of knowledge and the building of a better future for society. VR usage in education has both positive and negative implications. Nevertheless, digital technology is an ever-increasing presence in modern society, and it is important to research and experience its role at the intersection of education and knowledge. If students wish to stay ahead of the challenges posed by a quickly changing society, it is essential for them to become familiar with advances in technology as well as comprehend how societies evolve. Furthermore, understanding the collaborative creation of knowledge can help students to recognize potential issues, before a legal recourse becomes necessary. Analysis of the potential implications carried by VR technology suggests that it may help to teach legal reasoning and develop problem-solving skills, enabling students to explore theory in practical terms and recognize the interrelation between different legal fields. Further study into this technology is necessary in order to predict potential policy changes, new laws, and educational benefits: to anticipate problems rather than merely react by drafting new legislation which is usually outdated by the time it is enacted. VR encourages students to explore theoretical materiality of the intertwining physical and non-physical, human and non-human realms, which present not only new legal challenges but also transformative opportunities. VR can be used to assist in teaching students to re-create the physical and legal world as well as challenge the foundations of law, to promote diverse ways of thinking and of understanding digital landscapes from an onto-epistemological perspective. Encouraging them to analyse how virtual worlds shape their experiences, identities, and bodies is undoubtedly a good starting point for understanding, learning, practicing, and experimenting with embodied thinking within the realm of law. Simulations in a VR environment focus on the role of matter and its importance in introducing embodied teaching/learning in a non-human environment. The real and virtual entanglement creates a utopian setting in which