Society and technology: Opportunities and Challenges 0367232510, 9780367232511

This book offers broad evidence on how new information and communication technologies (ICT) impact social development an

560 106 4MB

English Pages 248 [249] Year 2020

Report DMCA / Copyright

DOWNLOAD FILE

Polecaj historie

Society and technology: Opportunities and Challenges
 0367232510, 9780367232511

Table of contents :
Cover
Half Title
Series Information
Title Page
Copyright Page
Table of contents
List of Contributors
Acknowledgments
Preface
1 ICT and social development: Conceptual considerations
1.1 Society and technology
1.2 The Digital Revolution
1.3 Linking ICT to social development: opportunities and threats
Acknowledgement
References
2 I, Robot: Between angel and evil
2.1 Introduction
2.2 Artificial intelligence’s path to independent decision-making
2.3 Recent applications of AI and their possible impact on our lives
2.4 Responsible and ethical artificial intelligence
2.5 Some existing answers to the challenge of ethical/responsible AI
2.6 Concluding remarks
Notes
References
3 Next-generation networks as general-purpose technologies: The impact on economic development
3.1 Introduction: background and purpose
3.2 Data and descriptive statistics
3.3 Econometric methodology and identification strategy
3.4 Estimation results
3.5 Concluding remarks
Appendix
4 After access: The importance of social capital and Internet use for human development
4.1 Introduction
4.2 Theoretical framework
4.2.1 People-centered development: the Capability Approach
4.2.2 It is not what you know, but who you know: social capital as a determinant of Internet use
4.3 Methodology
4.3.1 Internet use for specific activities (within capabilities model)
4.3.2 Internet use for diverse activities (cross capabilities model)
4.4 Data
4.5 Results
4.5.1 Within capabilities model
4.5.2 Cross capabilities model
4.5 Conclusions
Acknowledgments
Appendix
1583258488865_45
Notes
References
5 The regional shaping of ICT: Exploring the links between ICT research, innovation and diffusion
5.1 Introduction
5.2 Conceptual framework and literature review
5.3 Methodology
5.4 Data
5.5 Results
5.6 Conclusions
Notes
References
6 Forecasting business software piracy rates: A machine-learning approach
6.1 Introduction
6.2 Literature review
6.3 Some general considerations for machine learning
6.3.1 Introduction to support vector regression models
6.3.2 Introduction to regression models
6.3.3 Applications of machine-learning techniques in economics and finance
6.4 Key variables and data
6.4.1 Dependent variable
6.4.2 Independent variables
6.5 Empirical results
6.5.1 Specific methods: imputation of missing values
6.6 Concluding remarks
Acknowledgments
Notes
References
7 ICT for education and health care systems: Potentialities and discrepancies in low- and high-income countries
7.1 Introduction
7.2 ICT for education: effectiveness and impact
7.3 ICT for better health care systems: opportunities and challenges
7.4 Low- and high-income countries: are they equally ICT-endowed?
7.5 Conclusions and policy recommendations
References
8 Role of social media and digital skills in adoption of online shopping in selected EU countries
8.1 Introduction
8.2 Literature review
8.3 Research strategy
8.4 Results and discussion
8.5 Conclusions
Notes
References
9 ICT as challenging driver for social transformations in Serbia
9.1 Introduction
9.1.1 ICT and employment
9.1.2 The IT industry in Serbia
9.2 Potential of ICT sector in Serbia
9.2.1 ICT in Serbia: business sector facing challenges
9.3 The data and empirical settings
9.3.1 ICT case study design
9.3.1.1 Data collection and methodology
9.3.2 Network analysis in the field of computer science
9.3.2.1 Data collection and methodology
9.3.3 Current and potential human resources in ICT in Serbia in the business and R&D sector
9.4 Results and discussion
9.4.1 Research cooperation in the field of computer science
9.4.2 Network density
9.4.3 Measures of centralization
9.5 Conclusions
Acknowledgement
References
10 The impact of ICT and digitalization on consumer purchase behaviour of Millennials as emerging economic and social force: Th
10.1 Introduction
10.2 Literature review
10.3 Methodology and materials
10.3.1 Empirical sample and data used
10.3.2 Empirical settings/methods
10.4 Results and discussion
10.4.1 Characteristics of Serbian Millennials
10.4.2 Digital behaviour segments model
10.4.3 Digital activities
10.4.4 E-commerce habits
10.4.5 Attitude towards local products
10.4.6 Foods and drinks and free time
10.4.7 Media consumption
10.5 Conclusion
References
11 Forecasting risks and challenges of digital innovations: Towards a Socially Responsible Design agenda
11.1 Introduction
11.2 Related research
11.3 Methodology
Stage A
Stage B
Stage C
Stage D
Stage E
11.4 Results
11.4.1 Catalogues of DI-related threats, damages and policies
11.4.2 AHP analysis: impact of TRIGGERS on DI_FAILURE
11.4.3 AHP analysis: contribution of POLICIES to DI_SECURITY
11.4.4 Relationship matrix for profiling POLICIES
11.5 Discussion and conclusions
Acknowledgement
References
12 Internationalization of an educational software as a service (EduSaaS) company
12.1 Introduction
12.2 Internationalization in a born global setting
12.3 Case study: educational software as a service (SaaS) company
12.4 Conclusions
References
13 Evaluation of a company’s image on social media using the Net Sentiment Rate
13.1 Introduction
13.2 Related work
13.2.1 Measurement of sentiment on social media
13.3 Methodology
13.3.1 Data collection
13.3.2 Software
13.3.3 The sentiment analysis procedure
13.3.4 Measurement
13.4 Results and discussion
13.5 Conclusions
References
Index

Citation preview



Society and Technology

This book offers broad evidence on how new information and communication technologies (ICT) impact social development and contribute to social welfare. Its aim is to show how new technological solutions may contribute to society’s welfare by encouraging new ‘socially responsible’ initiatives and practices as the broad adoption of new technologies becomes an integral component of organizations, and of the overall economy. Society and Technology: Opportunities and Challenges is designed to provide deep insight into theoretical and empirical evidence on ICT as socially responsible technologies. More specifically, it puts special focus on examining the following:

• how channels of ICT impact on social progress, environmental sustainability and instability

• the role of ICT in creating social networks, with positive and negative consequences of networking

• how ICT encourages education, skills development, institutional development, etc.

• the ethical aspects of technological progress, and • technology management for social corporate responsibility. The book is written primarily for scholars and academic professionals from a wide variety of disciplines that are addressing issues of economic development and growth, social development, and the role of technology progress in broadly defined socioeconomic progress. It is also an invaluable source of knowledge for graduate and postgraduate students, particularly within economic and social development, information and technology, worldwide studies, social policy or comparative economics. Ewa Lechman is Professor of Economics at the Faculty of Management and Economics at Gdańsk University of Technology, Poland. Since 2017 she has been Vice-​Dean for Development and a PhD Programme Director. Magdalena Popowska is a researcher and lecturer of Organization Science and Entrepreneurship at the Faculty of Management and Economics of Gdańsk University of Technology, Poland.

Routledge Studies in Innovation, Organizations and Technology

Service Innovation Esam Mustafa Innovation Finance and Technology Transfer Funding Proof of Concept Andrea Alunni Finance, Innovation and Geography Harnessing Knowledge Dynamics in German Biotechnology Felix C. Müller Business and Development Studies Issues and Perspectives Edited by Peter Lund-​Thomsen, Michael Wendelboe Hansen and Adam Lindgreen Frugal Innovation A Global Research Companion Edited by Adela J. McMurray and Gerrit A. de Waal Digital Work and the Platform Economy Understanding Tasks, Skills and Capabilities in the New Era Edited by Seppo Poutanen, Anne Kovalainen, and Petri Rouvinen The Future of Work in Asia and Beyond A Technological Revolution or Evolution? Edited by Alan R. Nankervis, Julia Connell and John Burgess Society and Technology Opportunities and Challenges Edited by Ewa Lechman and Magdalena Popowska For more information about this series, please visit www.routledge.com/​ Routledge-​Studies-​in-​Innovation-​Organizations-​and-​Technology/​book-​ series/​RIOT



Society and Technology Opportunities and Challenges

Edited by Ewa Lechman and Magdalena Popowska

First published 2020 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 52 Vanderbilt Avenue, New York, NY 10017 Routledge is an imprint of the Taylor & Francis Group, an Informa business © 2020 selection and editorial matter, Ewa Lechman and Magdalena Popowska; individual chapters, the contributors The right of Ewa Lechman and Magdalena Popowska 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. All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. 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 Library of Congress Cataloging-​in-​Publication Data A catalog record has been requested for this book ISBN: 978-​0-​367-​23251-​1  (hbk) ISBN: 978-​0-​429-​27894-​5  (ebk) Typeset in Bembo Std by Newgen Publishing UK



Contents

List of contributors  Acknowledgments  Preface  1 ICT and social development: conceptual considerations 

vii xvii xix 1

E WA L E C H M AN, FRANCI SZE K K UTRZE BA AND KA I M U STA KOSKI

2 I, Robot: between angel and evil 

13

M AGDA LE N A P O POWSK A

3 Next-​generation networks as general-​purpose technologies: the impact on economic development 

29

A N G E L O CASTALD O, ALE SSAND RO FI O RI NI AND FIL IP P O R EGA N AT I

4 After access: the importance of social capital and Internet use for human development 

49

ROX A N A M A RÍ A I RMA BARRANTE S CÁCE RE S, A I LE E N M I LAGRO S AGÜE RO GARCÍ A AND D I E GO ALO N SO AGUI LAR LLUNCO R

5 The regional shaping of ICT: exploring the links between ICT research, innovation and diffusion 

66

M A RÍ A RO SALÍ A VI CE NTE , ANA SALO MÉ GARCÍA -M UÑ IZ A N D M A RGARI TA  BI LLO N

6 Forecasting business software piracy rates: a machine-​learning approach  A N TO N I O RO D RÍ GUE Z AND RÉ S AND HARLE E N   KAUR

79

vi Contents

7 ICT for education and health care systems: potentialities and discrepancies in low-​and high-​income countries 

95

M O H A M E D SAMI B E N ALI AND NAD I A  SE LMI

8 Role of social media and digital skills in adoption of online shopping in selected EU countries 

108

LJI LJA N A B O ŽI Ć AND VALE RI JA   B OTRIĆ

9 ICT as challenging driver for social transformations in Serbia  124 D J U RO K U TLAČ A, D UŠI CA SE ME NČ E NKO AN D L A Z A R Ž IV KOV IĆ

10 The impact of ICT and digitalization on consumer purchase behaviour of Millennials as emerging economic and social force: the case of Serbia 

151

TATJA N A M A MULA NI KO LI Ć , SANJA PO POVI Ć-​PA N T IĆ A N D I VAN A   M Ü L LE R

11 Forecasting risks and challenges of digital innovations: towards a Socially Responsible Design agenda 

169

M ARC I N SI KO RSK I

12 Internationalization of an educational software as a service (EduSaaS) company 

192

M I N T TU L AMPI NE N AND MI K KO MÄNTY NE VA

13 Evaluation of a company’s image on social media using the Net Sentiment Rate 

202

AN N A BA J-​RO GOWSK A

Index 

219



Contributors

Aileen Milagros Agüero García holds a Master’s degree in Rural Development through the Erasmus Mundus Programme (EU) and a BA in Economics. Currently, she is a senior researcher at the Instituto de Estudios Peruanos (Institute of Peruvian Studies), a private non-​profit organization based in Lima, Pbehavu. She is also a researcher and regional Coordinator for the Andean Region and Southern Cone of Centro Latam Digital, a think tank that generates knowledge, strengthens technical capacities and promotes dialogue around the effective use of information and communication technologies as vehicles for development in Latin America. Aileen has been involved in several research projects in the areas of broadband, telecommunications universal service, ICT and productivity, ICT and poverty, mobile banking, competition and regulation, and women in rural areas, among others. Her work pays special attention to the integration of quantitative and qualitative approaches. As for her experience in the public sector, she has worked at the Ministry of Development and Social Inclusion, in the monitoring and evaluation department, focusing on social programmes providing assistance to poor elderly people and to the poorest people working in agricultural activities. In addition, she has been a consultant for the Ministry of Economics and Finance, the Peruvian telecommunications regulator and the Ministry of Transport and Communications. Diego Alonso Aguilar Lluncor holds a BA in Economics from the Pontificia Universidad Católica del Perú. Currently, he is a research assistant at the Instituto de Estudios Peruanos (Institute of Peruvian Studies) and teaching assistant at the Economics Department of Pontificia Universidad Católica del Perú. His main interests are economic development, the information society, project evaluation and public policy topics, from both a theoretical and an empirical perspective. Antonio Rodríguez Andrés, PhD, has held the position of Associate Professor of Economics at universities in Denmark, Colombia, Spain, Chile, Morocco, and Cyprus. He is currently an associate professor of Economics at VŠB Technical University of Ostrava. He is also a professor at the Faculty of Business and Communication at the International University of La Rioja

viii Contributors (UNIR). His research focuses on quantitative methods, applied health economics and law and economics, with projects in the fields of globalization, trade, crime, gun control, copyright law, mental health and income inequality. Anna Baj-​Rogowska is an assistant professor at the Department of Applied Informatics in Management at Gdańsk University of Technology, Poland. She holds a PhD in Economic Sciences in the field of Management from the University of Gdańsk, Poland. She deals primarily with research regarding the technological and economic aspects of IT in organizations as well as innovations and IT-​ based organizational creativity. Her current research interests focus on issues of extracting information from web content using text-​ mining algorithms and sentiment analysis technique. She examines the impact of knowledge acquired from textual data on contemporary organizations. Roxana María Irma Barrantes Cáceres holds a PhD (1992) and Master’s degree (1989) in Economics from the University of Illinois at Urbana-​ Champaign, and a BS (1984) from Pontificia Universidad Católica del Perú (PUCP). Currently, she is an associate researcher at the Instituto de Estudios Peruanos (Institute of Peruvian Studies), Co-​director of the Centro Latam Digital, and a principal professor at the Economics Department and Director of the Economics Masters programme at PUCP. In addition, she assists the Peruvian Transport Regulatory Commission (OSITRAN) and Peruvian authority for Defense of Competition and Protection of Intellectual Property (INDECOPI). Finally, she is president of the Continental board of advisors of Red Solidaridad and member of its International board of advisors. She served in the Steering Committee of Regional Dialogue on the Information Society (DIRSI), Board President of the Consortium of Economic and Social Research (CIES), Board Member of the Agency for Environmental Assessment and Control (OEFA) and Consultant for the Advisers Cabinet of the Ministry of Agriculture and Irrigation of Peru. Her main professional activities focus on applied microeconomics, regulation and natural resources. Barrantes has also served as staff and member of the board of directors of the Peruvian Telecommunications Regulatory Authority (OSIPTEL), consultant to the Transport Regulatory Commission in Peru (OSITRAN), the National Superintendent of Sanitation (SUNASS), the Peruvian Ministry of Transport and Communication and the Inter-​ American Development Bank. Mohamed Sami Ben Ali, PhD, is an associate professor of Economics at Qatar University. Previously, he was an assistant professor of Economics and International Finance, head of the economics department and member of the scientific board at HEC Business School,Tunisia. Since June 2013 Dr Ben Ali has held an HDR Certificate, the highest European academic qualification for research. Previously, he received a PhD in Economics with high honours from the University of Lille, an MPhil (DEA) in International Finance and



Contributors ix International Trade and a BA in Business Economics. He has been teaching in recent years at graduate and undergraduate levels in Tunisia, Qatar and France where he was a visiting professor. He has published numerous articles in French and in English in international refereed academic journals. His research and publications focus on issues involving economic growth, capital flows, Exchange regimes, workers’ remittances, international trade, inflation and corruption. He is currently a research associate of the Economic Research Forum of the Middle East and North Africa. He is also serving as peer reviewer and guest editor for international journals. Dr Ben Ali is actively participating in and chairing numerous international conferences in the United States. Among others publishing activities, he actively contributes to Pearson Education, especially the 2015 Acemoglu Microeconomics first international edition, and edited a Springer book on economic development for the MENA region. Margarita Billon is Associate Professor at the School of Economics and Business at the Autonomous University of Madrid (UAM) where she also obtained her PhD in Economics. Her current research focuses primarily on the economic impacts of information and communication technologies (ICT) and the digital divide, digital innovation, and ICT and economic development. Her work has been published in international refereed journals, such as Telecommunications Policy, Review of World Economics, Growth and Change, International Journal of Manpower, European Planning Studies, Empirical Economics, Information Technology and People and Journal of Global Information Technology Management, among others. She has been a visiting professor at several universities and institutions, such as the University of Essex, the University of Amsterdam and Dickinson College, Pennsylvania (USA). She received a Fulbright scholarship for university teachers from the US State Department and has also enjoyed many other research grants. She was the Director of the Research Group CONOCYTEC at UAM until 2015. She is currently Academic Advisor and board member for the PhD and MBA programmes in Economics and Management of the UAM-​Accenture Chair in Economics and Innovation Management. She participates in several international programmes and collaborates with the Jean Monnet network ‘The European Union, Mediterranean and Africa: Integration in the Global Age’ (AMENET). Valerija Botrić is a senior research fellow employed by the Institute of Economics, Zagreb. She earned her PhD degree from the Faculty of Economics, University of Zagreb, in 2005. Labour markets in transition economies, with special focus on Croatia, is one of the research areas in which she has published a number of journal articles. She has also participated in projects focused on the analysis of labour market policies, evaluation of economic policies and analysis of labour markets. She uses standard statistical and econometric methods in both her research and project assignments. In addition to labour markets, she has published articles in the areas of

x Contributors macroeconomics, international economics and innovation. Recently, her research interests also include labour market aspects of migration. Ljiljana Božić is a senior research fellow at the Institute of Economics, Zagreb. She received a PhD from the Faculty of Economics, University of Zagreb, in 2009. Her main fields of interest include innovation and R&D, environmental innovations and e-​commerce. She gained valuable experience working on a number of national and international research projects in the field of innovation. Her competences are also attested by papers published in international peer-​reviewed journals. Angelo Castaldo is an aggregate professor in Public Finance and in Law and Economics II at Sapienza University of Rome, Department of Juridical and Economic Studies (DSGE), Faculty of Law. Has obtained a PhD in Law and Economics and a Masters in Law and Economics, both at the Faculty of Economics, University of Siena (Italy). Furthermore, he received an MSc in Economics from the University of York (UK). He has published in several national and international journals in the area of public economics, broadband infrastructure, innovation and competition policies. He is a referee of international journals. Alessandro Fiorini is a research fellow at the Institute for Competitiveness (I-​Com) in Rome. He completed his PhD in Economics at the Sapienza University of Rome and his postgraduate studies at the TorVergata University of Rome. His research interests lie in the area of economics of innovation, industrial economics and policy evaluation. He worked as a researcher and policy analyst for the Joint Research Centre of the European Commission and the Sapienza University of Rome, Department of Law and Economic Studies. He is author and co-​author of several national and international peer-​reviewed publications. Harleen Kaur, PhD, is a distinguished scientist and faculty member at the School of Engineering Sciences and Technology at Jamia Hamdard, New Delhi, India. She recently worked as a research fellow at the United Nations University (UNU) in the IIGH-​ International Centre for Excellence, Malaysia, to conduct research on funded projects from Southeast Asian Nations (SEAN). She is currently working on an Indo-​Poland bilateral international project funded by the Ministry of Science and Technology, India, and the Ministry of Science and Higher Education in Poland, Poland. In addition, she is working on a national project, catalysed and supported by the National Council for Science and Technology Communication (NCSTC), the Ministry of Science and Technology, India. Her key research areas include data analytics, big data, applied machine learning and predictive modelling. She is the author of various publications and has authored/​edited several reputed books. She is a member of various international bodies and is a member of the editorial board of international journals on data analytics and machine learning. She is the recipient of an



Contributors xi Ambassador for Peace Award (UN Agency) and other honours and is a researcher funded by external groups. Djuro Kutlača is head of the Science and Technology Policy Research Centre and Scientific Advisor at the Mihajlo Pupin Institute, University of Belgrade. He is a full professor at the University of Belgrade’s Department of History and Philosophy of Natural Sciences and Technology, where he teaches Science Policy and the National Innovation System, Technology and Transition, and Methodology of Scientific Research. He was a visiting researcher at the FhG Institut für Systemtechnik und Innovationsforschung, Karlsruhe, Germany (1987; 1991–​1992) and at the Science Policy Research Unit, University of Sussex, Brighton, UK (1996, 1997, July 2001 to October 2002). He is a former member of NESTI (National Experts for S&T Indicators) group at the OECD (1988–​1992). During 37 years he has been a member of research teams in 48 large R&D projects, published 38 scientific journal articles, and presented 149 papers at scientific conferences, and is the author of four and co-​author of 27 books. He is an active business consultant in the field of enhancing the innovation capacity of enterprises. Specific areas of his research interests are S&T and industry development and policy, metrics in S&T and innovation, and innovation theory and practice. Franciszek Kutrzeba was born in Poland and grew up in Finland where he obtained primary and secondary education. After finishing his A-​levels he moved to Örebro in Sweden to study business, economics and social sciences. He graduated from the University of Örebro in 2008 with a degree of Master of Science in Business and Economics and fulfils the requirements for a Master’s degree in Social Sciences. In addition, he has completed two internships: at the Karl-​Franzens-​Universität in Graz and the second at the Freie Universität in Berlin. Later on he also studied theology, philosophy and logic at Uppsala University. He is currently employed full-​time as an academic teacher and researcher at the Gdańsk University of Technology while finishing his PhD dissertation. His main field of research interest is competence management in the context of technological change. He is fluent in five languages and occupies his spare time with art. Minttu Lampinen holds a PhD in marketing from the University of Tampere. Her doctoral thesis ‘Users of New Technology  –​Discourse Analysis of a New Technology User’ was published in 2005. She works as a principal lecturer at Häme University of Applied Sciences where her focus is on teaching marketing strategies and market research and leading national and international research projects. In her working career, Minttu’s responsibilities have included, e.g., leading international expert teams, concepting and launching new services globally and setting up innovative digital marketing processes. She has done marketing and communications management for several European countries, the US, Russia, Asia and India. Minttu Lampinen has over 40 publications and she has been a reviewer for the journal

xii Contributors Management Decision (Emerald) since 2011. Her list of publications can be found at ResearchGate: www.researchgate.net/​profile/​Minttu_​Lampinen. Ewa Lechman has been Professor of Economics since 2002 working at the Faculty of Management and Economics at Gdańsk University of Technology. Since 2017 she has been Vice-​Dean for Development and a PhD Programme Director. Her extensive research interests concentrate on economic development, ICT and technological progress, and its role in reshaping social and economic systems and various aspects of poverty and economics in developing countries. In 2013 she won the Emerald Literati Network Award for Excellence. She serves as permanent referee for Policy & Internet (Oxford University Press), Technological Forecasting and Social Change (Elsevier), World Development (Elsevier), Technology in Society (Elsevier), Journal of Applied Research and Technology (Elsevier), Social Indicators Research (Springer Nature), Journal of Business Cycle Research (Springer Nature), Journal of Development Studies (Taylor & Francis), Netnomics (Springer Nature), Neural Computing and Applications (Springer Nature), Eurasian Economic Review (Springer Nature), inter alia. In 2017–​2019 she was nominated by Elsevier as outstanding reviewer. She currently coordinates and/​or is the main investigator in three research grants on ICT diffusion trajectories and technological take-​off (National Science Centre), Exchange-​traded funds development (National Science Centre) and technological development for financial markets (CERGE-​GDN). Tatjana Mamula Nikolić has been working as an assistant professor at the Metropolitan University, Belgrade, where she lectures on the following subjects:  marketing research, consumer behaviour, brand management and leadership and decision-​making. She has over 25 years of professional experience in marketing, research and management. She has managed a large number of quantitative and qualitative research projects as a director of the marketing research company MASMI. She has participated in numerous conferences in the areas of marketing, management and branding. Her research interests are focused on new generations’ leadership behaviour. She has published more than eight papers nationally and three internationally on this topic. She is a member of several professional associations: ESOMAR (global association of marketing researchers), the Association of Business Women, the Serbian Association of Managers and the International Coaching Federation. In November 2018 she gained a PCC certificate (Professional Certified Coach) from the International Coach Federation. When working with students and clients (both individually and with teams), she combines her business management experience with her coaching skills, resulting in the highest outcome of knowledge transfer. Mikko Mäntyneva holds a PhD in Strategic Management from the Tampere University of Technology. Currently he is working as a principal lecturer in business-​ management-​ related Master’s degree programmes at Häme



Contributors xiii University of Applied Sciences. Also, he serves as a researcher at Häme University of Applied Sciences’ Smart Services research centre. His teaching activities focus currently on foresight methods, digitalization and innovation management. His research is currently focused on smart services, innovation management, knowledge management and customer relationship management. He has authored several scientific articles as well as six books on various management topics. Ivana Müller is a statistical consultant, researcher and lecturer, with almost 20 years of experience in delivering statistical analysis and research, and providing statistical training, advice and support. She holds a diploma in Mathematics  –​ Probability and Statistics from the University of Belgrade, giving her a strong quantitative background and the flexibility to apply her expertise to a wide variety of projects.These have entailed the planning and execution of research, from designing and implementing data management and statistical analysis, to writing scientific and technical reports. For more than ten years Ivana has managed and coordinated the data management and statistical analysis of several international research agencies. Since 2016, she has served as a scientific associate and lecturer at the University of Applied Sciences, Berlin, where she teaches statistics (in scientific research) and SPSS. She has co-​authored several articles and reports published in the fields of media and communication, the food industry, biotechnology and humanities, and health care. Ana Salomé García-Muñiz has a PhD in Applied Economics (University of Oviedo, 2006). She is a professor in the Applied Economics Department in the University of Oviedo. She was a visiting professor in the Regional Economics Applications Laboratory (REAL) of the University of Illinois (USA). She specializes in urban and regional economics. Her field of research has been developed around economic modelling, input-​output analysis, network theory and innovation and technological diffusion. She is the author of more than 40 academic publications. In 2007, she founded with other researchers the Hispanic-​American Input-​Output Society (SHAIO) and has been in its directive since then. Kai Mustakoski was born and raised in Finland, where he graduated from high school. After secondary education he moved to Malmö, Sweden, to study English language. He graduated from the University of Malmö in 2013 with a BA degree in English Studies. Furthermore, he has now handed in his MA thesis with a title of The Paradox of Work at the University of Turku, Finland. Moreover, he has also studied political science, literature, critical theory and philosophy at the Turku University. At the moment, Kai is working in his family business and applying to study for a PhD at the University of Turku. His main interests are the genealogy of (moral) values and how work functions as a socially constructed belief system. Sanja Popović-​Pantić’s professional area is entrepreneurship, innovation management, and research and development. She obtained a PhD in

xiv Contributors female entrepreneurship in 2013 at the Faculty of Economics, University of Belgrade. She has been chairing the Woman’s Entrepreneurship Group within the Enterprise Europe Network since May 2015. She also has a strong academic background, as she is employed as a scientific associate at the Science and Technology Policy Research Center of the Mihajlo Pupin Institute. Sanja Popović-​Pantić is leading the biggest national association of female entrepreneurs in Serbia since 1998, completing nearly 200 projects. She is the author of two books in entrepreneurship and a number of scientific papers published in the national and international journals and books of papers. Her greatest passion is enabling businesses environment women’s friendly with a “can do” attitude. Mrs. Popović-​Pantić is the most respected national consultant in female entrepreneurship, specializing in innovation in SMEs.The US Embassy in Serbia nominated her for The World of Difference 100 Award, which has been delivered to Sanja by the International Alliance for Women in 2012. Also, she has won the highly respected national award “Planeta Biznis” and a regional award for contribution to the development of entrepreneurship in the Western Balkans. Magdalena Popowska, PhD, is a researcher and lecturer of Organization Science and Entrepreneurship at the Faculty of Management and Economics of Gdańsk University of Technology. Her research is mainly focused on entrepreneurship, corporate social responsibility, sustainability and corporate governance. She has been a member and leader of several EU projects. Filippo Reganati is a full professor of Economics at the Department of Juridical and Economic Studies, the Sapienza University of Rome. He received an MA in International Economics and a PhD in Economics from the University of Reading (UK). His research has focused on foreign direct investment and multinational enterprises; international trade in imperfect competitive markets; applied industrial organization, and economics of crime. He has published several books and articles in national and international journals. Nadia Selmi, PhD, is an assistant professor of Economics and full-​time faculty member at Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. She has been teaching economics, maths and statistics since 2006. Dr Selmi is also a member of the Statistics Book Committee at Imam Abdulrahman Bin Faisal University and member of numerous committees. Her research deals mainly with technology transfer and economic integration. Dušica Semenčenko’s broadest professional interest relates to the research of the internal laws of the development of science and technology and their impact on the development of society. His most significant contribution to the development of science in Serbia has been a theoretically developed model of action and interaction of investigated factors influencing the state and development of the National Innovation System of Serbia. The national innovation system, as a theoretical concept, remains the basic subject of his



Contributors xv work, with a particular emphasis on the historical and cultural conditions of technological development. One of his narrower fields of specialization is research and support for the development of women’s entrepreneurship and the education of women entrepreneurs. He has professional specializations in the field of national technology foresight organization and the role of governments in science-​ technology-​ innovation policies design. He is a lecturer at the University of Belgrade, in the Department of the History and Philosophy of Natural Science and Technology, teaching the course Technology and transition since 2013. He is an innovation consultant in basic methods for innovation introduction to various types of organization. He is also co-​founder and secretary general of the association Technology and society. He has published more than 100 scientific and professional papers and four books. Marcin Sikorski is a full professor in the Department of Applied Business Informatics at the Faculty of Management and Economics, Gdańsk University of Technology, Poland. His research interests include usability of interactive systems for computer-​supported cooperative work (CSCW) and user experience in interactive services. His research is focused on quality of online services and user-​centred methods for digital innovations, including design, development and deployment. His most recent research work concerns value-​based design of digital services and mobile apps affecting quality of life for individual customers and citizens. He frequently serves as a programme committee member for IT-​related international conferences and journals. His teaching activity covers human–​ computer interaction, cognitive ergonomics and computer-​ supported cooperative work. He has also participated in research fellowships and study visits in numerous research institutions, including the University of Heidelberg (Germany), the Eindhoven University of Technology (Netherlands), the Swiss Federal University of Technology ETH in Zurich (Switzerland), and the Harvard University School of Engineering and Applied Sciences. María Rosalía Vicente is Associate Professor at the Department of Applied Economics in the University of Oviedo (Spain). She has a PhD in Economics. Her research interests focus on information and communication technologies (ICT), their socioeconomic effects and the use of big data for economic forecasting. She has published in several scientific journals, such as Applied Economic Letters, Economic Letters, Computers in Human Behaviour, Government Information Quarterly, Information & Management, The Information Society, Technological Forecasting and Social Change and Telecommunications Policy, among others. She has been a visiting researcher at the Massachusetts Institute of Technology, the Organization for Economic Cooperation and Development (OECD) and Curtin University. She has been involved in Cisco-​Oxford Saïd Business School’s project for the development of a broadband quality index. The results of this project were cited by The Economist, The Financial Times, The New  York Times, The International Herald Tribune, and the BBC,

xvi Contributors among others. She also participated in the Seventh European Framework ICTNET: European Network for the Research on the Economic Impact of ICT, coordinated by the OECD and with the participation of the ZEW, Imperial College and the University of Parma. Lazar Živković graduated in 2009 from the Faculty of Organizational Sciences, University of Belgrade, in the field of Management. In 2011, he obtained his Master’s degree from the Faculty of Organizational Sciences, University of Belgrade, in the field of Financial Management. In 2014, he enrolled for PhD studies at the University of Belgrade, Department of History and Philosophy of Natural Sciences and Technology. Since January 2012, he has been employed at the Mihajlo Pupin Institute as a research associate at the Science and Technology Policy Research Center. Over the last six years, he has been a member of research teams in several national projects and more than ten international R&D projects. He has published numerous scientific papers in both international and national scientific journals and has taken part in a number of scientific and professional conferences. His specific professional fields of interest are quantitative research methods, S&T and innovation policy, the National Innovation System, and scientometrics.



Acknowledgments

This research is part of Project no.  2015/​19/​B/​HS4/​03220 financed by the National Science Centre, Poland.



Preface

The context It all started in the early 1970s in Santa Clara when the first microprocessor developed by Intel saw the light of day. Since then everything has changed: the way that people communicate and interact, how societies learn, store and transmit knowledge, how companies run their businesses and operate in global markets. In early 1990, ICT started to diffuse worldwide  –​slowly at the begging, then speeding up since 2000, and reaching stabilization in the late 2010s. Today, in 2019, almost 90 per cent of the world’s population enjoys access to various ICT tools, gaining unbounded opportunities to benefit from what these offer. Suddenly, all societies in the world, even those materially deprived, poorly educated or living in remote and underdeveloped regions, have gained cheap and unbounded access to any type of information, and knowledge started to be available for all society members. This is an industrial revolution that human history has never witnessed up to this time. Never before has any type of technology diffused at so astonishingly high a pace, reaching even those societies that were ‘traditionally’ excluded from access to technological advances. The ICT (Digital) Revolution, also named the Age of Information and Communications, fundamentally changed the way that people communicate and interact, and in that sense we may argue that it reshaped societies themselves. The changes that information and communication technologies brought to societies are profound, pervasive and undoubtedly revolutionary. The impact of ICT on social life, social activities and interactions, and social network creation is neither automatic nor homogeneous across different countries and regions. It may be enhanced or hindered by a bundle of factors which are often hard even to encapsulate in one single equation. These might be religion, social structures, traditions and many others. ICT seems to be of special importance, for various reasons, not only for economic but also for social development. As ICT is easily deployable and adaptable in all countries and societies, its impact on social spheres of life is pervasive and overwhelming. ICT brings new opportunities for social development in different life spheres like, inter alia, e-​education, e-​government or e-​health. It

xx Preface supports and boosts virtual and real social networks. New technologies also present a transformational opportunity to solve the grand public challenges of our time, from the rising cost of health care, stalling productivity, managing growing cities, and cybersecurity risks. They also offer potential for new dimensions of performance in research and development. Technology deployment may drive the emergence of new, more socially responsible business models and innovations, capital–​labour substitution and skills and education improvements, and may foster the social and economic participation of disadvantaged societal groups. ICT can reach all social agents, and thus it is widely acknowledged that it should become socially responsible technology and its adoption should not only drive shifts in material wealth but also enhance social welfare. Undoubtedly, ICT should meet societal expectations and be employed in a socially responsible manner. As key social actors get unlimited access to information and knowledge, this also allows them to engage in better management and use of resources to deliver corporate social responses. ICT has become an integral component of social and economic reality, and thus its ‘social responsiveness’ should rise. But not all that glitters is gold. At the same time, this imperative of social responsiveness should not uncritically cover the other, darker side of ICT or, more generally, of technology. Technology itself does not harm people but the irresponsible use of it may destroy individuals or even entire social groups, through its extensive influence on the human environment, both natural and institutional. As Beck (1999) mentions, we live in the age of ‘organized irresponsibility’, where environmental risk and occasional accidents involve factors that can be uncertain, complex and far-​reaching. In addition, assessing environmental risk can involve high costs, and require even more advanced technology. In consequence we all live in a ‘risk society’, where ‘nobody really is responsible for those consequences … and this system has to be changed’. Therefore, technology requires us to develop an expanded concept of responsibility. Jonas (2014) argues that we have to take responsibility for the new powers (modern technologies) and ‘develop ethics appropriate for them –​global ethics, environmental ethics, future generation ethics, bioethics, as well as an ethic of technology more generally’. Social expectations towards responsible technology are constantly growing and find more and more common ground with political decision-​makers, especially in the ICT sector (e.g. Facebook). We believe that there is a need to understand the evolution in both the social inclusion and progress possible thanks to modern technology solutions and, on the other side, the social exclusion and other issues connected with fast technological change.

The aims The existence of dynamic links between technological progress and social development should not be neglected; however, we still lack broad evidence of how and why ICTs are becoming socially involved technologies that enhance social development. The gap in valuable and conclusive evidence in the field is



Preface xxi significant; thus, a body of literature awaits elaboration that ideally would concern the role of ICT in fostering social progress and creating social networks, and how these new technologies may become socially responsible. The volume is designed to provide deep insight into the novel evidence on ICTs as socially responsible technologies, and identify how these two are interrelated. More specifically, we aim to put special focus on examining the following:

• how ICTs as general purpose technologies impact social and economic

development • the importance of ICT in societal transformations • the emergence of digital innovations that shape the functioning of societies and trading markets, and • how ICT shapes companies functioning in the context of growing digitalization of social networks.

The contents This volume encompasses 13 chapters.The first two chapters are, to a large extent, conceptual ones, indicating, from a broad perspective, the links between ICT and society. The first chapter, ‘ICT and social development: conceptual considerations’, is intended to provide basic ideas and concepts related to technology, technological progress and society. It introduces the terms Information (Digital) Revolution and information and communication technologies (ICT), showing elementary features of new technologies. It explains how and why technology and society are interrelated, forming a dynamic, complex and interdepending evolutionary system, and briefly discusses the potential channels through which ICT affects society. Chapter 2, ‘I, ROBOT: between angel and evil’, deals with issues of artificial intelligence (AI). It claims that increasing research activity may be observed in the domain of security challenges, and therefore also on the issue of responsibility related to the controlled or hypothetically uncontrolled or autonomous emergence of AI solutions. It raises several considerations on responsibility and ethics in the creation of AI-​based systems. Chapters  3 to 7 empirically explore the emerging relationships between ICT adoption and various aspect of social development, analysing them from a macro perspective. Chapter 3, ‘Next generation networks as general purpose technologies: the impact on economic development’, presents ICT as General purpose technologies having seminal impact on the evolution of societies, transformation of industries and economic development. The authors build upon a growth model with endogenous spillovers and explore the impact on economic growth exerted by the diffusion of broadband connections in two different samples drawn from the OECD countries. They find that broadband diffusion significantly boosts economic growth and deeply affects social and economic interactions. In a similar vein, Chapter 4, ‘After access: the importance of social capital and Internet use for human development’, intends to assess

xxii Preface ICT’s impacts on social relationships. Using the Capability Approach proposed by Sen, it evaluates whether the individual’s social circle influences people’s decisions for using the Internet in specific and/​or diverse ways that expand their capabilities. Major findings are that belonging to a civic association positively enhances not only specific capabilities, but also the diversification of capabilities, simultaneously. Chapters 5 and 6, direct our attention to various aspects of the process of ICT diffusion in the context of emerging innovations, ICT research and ICT-​based research methods. Chapter 5, ‘The regional shaping of ICT: exploring the links between ICT research, innovation and diffusion’, using European countries as examples, examines the links between R&D collaborative networks in the ICT field, the regional technological context and the related innovative outputs.The authors consider the links established in these networks to check for the existence of bridging regions and explore the relationships between the regional level of network connectedness and its technological and economic context. Chapter 6, ‘Forecasting business software piracy rates: a machine-​learning approach’, using a sample of 96 countries between 2000 and 2014, examines how effective and accurate machine-​learning algorithms are in predicting business software piracy rates. Finally, Chapter 7, ‘ICT for education and health care systems: potentialities and discrepancies in low-​and high-​ income countries’, assesses the effects of ICT adoption on education and health care systems in low-​income and high-​income countries.This study reports that ICT adoption improves education effectiveness at different levels and across all disciplines. In addition, health care systems gain from ICT by delivering better, cost-​efficient and error-​free medical services, making people’s lives safer and easier. Discrepancies related to access to ICT’s facilities and infrastructure in low-​income and high-​income countries show different health and education outcomes in these two particular groups The remaining parts of the book –​Chapters 8 to 13 –​concentrate mainly on mezzo and micro analyses. Chapters 8, 9 and 10 deal with issues of societal behaviour and transformation. Chapters 8 and 9 deal with the issues of the role of ICT in the development and enhancement of various types of social network. Chapter 8, ‘Role of social media and digital skills in adoption of online shopping in selected EU countries’, explores whether social network users are more likely to buy online and if the use of social networks affects purchase of specific product categories over the Internet (in particular, durable and non-​ durable household goods, electronics, products and services for leisure and learning and education materials). The authors rely on Eurostat Community Statistics on the Information Society (CSIS) data for the year 2016, for Romania, Bulgaria, Austria and France.They have identified that for consumers with previous online shopping experience, social media serve as a positive online purchase predictor, regardless of the type of product, and that poor digital skills are more relevant than social networks for making a decision to purchase online. Two consecutive chapters use the example of Serbia to show ICT’s impact on social, and especially consumers’, behaviour. The chapter ‘ICT as challenging driver for social transformations in Serbia’ investigates the potential of ICT



Preface xxiii and its ecosystem, with the assumption that the ICT sector can be a driving force for the economic and social transition of Serbia. The authors analyse the potential of the ICT sector in Serbia, the human resources involved, the new graduates who will potentially join the ICT sector, and the level of cooperation between business and the R&D sector in the field of ICT. In concluding remarks they claim that all parts of the quadruple helix regarding the development of ICT in Serbia are an important driver of economic and social transformation. In Chapter 10, ‘The impact of ICT and digitalization on consumer purchase behaviour of Millennials as emerging economic and social force: the case of Serbia’, the authors present the influence of ICT and digitalization on the values, lifestyles and consumer behaviour of the Serbian Millennial population. Their findings are based on the representative survey about Millennials in Serbia conducted in 2018, and find that digital natives as ‘online shoppers’ have the biggest capacity to reshape the traditional form of consumption, followed by  –​as they are termed here  –​smart immigrants, who represent a significant influencing force. Chapter 11, ‘Forecasting risks and challenges of digital innovations: towards a socially responsible design agenda’, presents early results of testing a new approach(RADI methodology) for forecasting and prioritizing societal risks associated with deploying digital innovations. The adapted RADI methodology has been shown to be useful in guiding expert panels in profiling arrays of interventions to be undertaken by specific institutions against potential damages caused by non-​validated and non-​certified digital innovations. In Chapter  12, ‘Internationalization of an educational software as a service (EduSaaS) company’, the authors focus on a case study covering an educational software company and its internationalization. It identifies characteristics that are required for a firm to be considered a SaaS provider.The results indicate that the demand for SaaS is on the rise globally and is influenced by conditions such as the possibility of testing an application, its flexibility and cost-​effectiveness as benefits, easiness of use and pricing as top requirements, and an AI-​base to bring personalization. Finally, Chapter  13, ‘Evaluation of a company’s image on social media using the Net Sentiment Rate’, introduces a novel approach to the quantitative measurement of sentiment by means of a created indicator called the Net Sentiment Rate (NSR). The proposed NSR expresses the net sentiment extracted from text data and provides values on a scale from -​1 to +1. The Net Sentiment Rate implementation has been verified on large datasets crawled from Facebook, in the period from 1 October 2014 to 31 December 2018, for three international companies from the same sector. The creation of the Net Sentiment Rate, together with the classification of its strength, and its verification, constitutes the contribution of this study.

Finally … We need to stress that all intentions to encapsulate the huge stock of ideas, contexts and knowledge that stands behind the ‘socio-​technological system’ in a single ‘box’ are barely achievable. The complexities of this system, with

xxiv Preface its two-​way causalities and connections, are hard to capture in one single equation, and hard to quantify and even describe, although we know they exist. Technology does not just bring changes to society, but even more importantly technology shapes and enriches society, it brings new ideas and innovations. The interconnectedness between society and technology shifts our attention to the fact that these two should not be analysed separately. That idea also stands behind the concept of the techno-​economic paradigm, which claims that technological progress is a perpetuum of radical changes in society; it induces macrosocial transformations, builds networks, reshapes social attitudes and forces the emergence of social actions and movements. Therefore, remembering this two-​sided relationship and willing to fully use this potential, we should ensure the sustainability of ICT tools and solutions, in order to avoid possible disasters and crises resulting from their irresponsible deployment. Ewa Lechman and Magdalena Popowska

References Beck, U. (1999). World Risk Society. Polity Press in association with Blackwell Publishers Ltd. Jonas, H. (2014). Technology and Responsibility:  Reflections on the New Tasks of Ethics. In: Sandler R.L. (eds) Ethics and Emerging Technologies. Palgrave Macmillan, London.



1  ICT and social development Conceptual considerations Ewa Lechman, Franciszek Kutrzeba and Kai Mustakoski 1.1  Society and technology Technology, technological change and society are fundamentally inseparable. For ages people have observed the continuous, profound technological shifts that disruptively and intensively impact, both positively and negatively, multiple aspects of human life. The time dynamics of technological change and social development are closely related (Nelson and Phelps, 1966; Inglehart and Welzel, 2005); needless to say, introduction and then gradual implementation of technological novelties enhance social developments and may even drive changes to social structures (Galor and Tsiddon, 1997). There exists a vast body of theoretical and empirical literature explaining how and why technology and technological change can reshape social life and structures, mainly due to the process of intensification of people’s communication modes and interpersonal interactions. The latter is mainly due to the fact that different technologies usher various types of networks that then become driving forces of extensive social and also economic changes, as underlined in works of, inter alia, Rosenberg (1982), Hakansson (2015) and Saviotti and Metcalfe (2018). As may be traced in Castells and Cardoso (2006), new technologies often become seminal driving forces in societal transformation, but still it has to be borne in mind that technology alone is not a sufficient condition for it. These are institutions, societies with their norms and attitudes, culture and values that precondition effective acquisition and wide usage of technological novelties. These are individuals that need to accept new technologies, that need to assess potential risks and benefits that new technologies may bring. It is obvious that technological novelty adoption does not happen automatically or unconditionally. A bundle of factors determines whether new technologies may diffuse freely and then penetrate social life, and hence whether societies assimilate and know how to effectively use knowledge embedded in newly arriving technologies. In the work of Ames and Rosenberg (1963), or Fagerberg and Srholec (2008), we read that technology assimilation is heavily preconditioned by ‘complementary conditions’ that may be institutional, organizational, economic or even political or cultural. Abramovitz (1986), Cimoli and Dosi (1995) and Fagerberg, Feldman and Srholec (2013) emphasize the unique role of the so-​called ‘social capabilities’ and people’s individual propensity for learning, acquiring technological novelties and risk-​taking, but also social norms, beliefs, habits and attitudes.

2  Ewa Lechman et al. It is interesting to note that in the late 1980s, the concept of ‘social shaping of technology’ emerged, initially developed by MacKenzie and Wajcman (1985, 1999). The social shaping of technology approach offers the reader broader ‘understanding of the relationship between scientific excellence, technological innovation and economic and social well-​being’ (Williams and Edge, 1996, p.865). The social shaping of technology concept stands in clear opposition to ‘technological determinism’, which claims that ‘technological development is autonomous with respect to society; it shapes society, but is not reciprocally influenced’ (Mackay and Gillespie, 1992, p.686). This rather naïve concept of ‘technological determinism’ claims that technology, innovation development and technological progress are entirely deterministic, which would stand in support of the supposition that technologies cause different changes of a social type and that these changes inevitably arrive as technologies diffuse (Mattsson, 2007). Such an approach certainly lacks deeper understanding of the nature of technological progress and how and why it impacts society, whereas the concept of social shaping of technology directs our attention to the fact that it is society and its forces which determine the emergence of new technologies. Dicken (2007) argues that technological changes are socially, economically and institutionally embedded processes. Arguably, when looking at things from a macro perspective, new technologies emerge as they are driven and shaped by socioeconomic forces, hence technology is created and afterwards implemented for particular socioeconomic objectives (see also Molina, 1989; Bijker and Law, 1992; Bijker, 1995; Bijker et  al., 2012). This highly convergent view of how the complex relationship between society and technology is shaped is presented in, inter alia, Mowery and Rosenberg (1991) who provide arguments that disruptive technological changes are enhanced by socioeconomic forces, but also by the stock of past knowledge embodied in already existing technologies. Clearly this is a rather two-​way process; it is technology that reshapes society, and society, with it needs, attitudes and habits, that drives and enhances new technology development. The two-​way causal relationship is evident. Regarding various social aspects of how and why different technologies and technological progress as such affect society, the seminal issue is associated with the fact that technology is transmitted instantaneously to all individuals through the networks created by societies themselves. Technologies, and hence knowledge, information, etc., spread over societies mainly due to the unique phenomenon called ‘network effects’. ‘Network effects’ explains the fast-​g rowing number of users of a given technology, which then attracts and multiplies further links. Katz and Shapiro (1985) claim that ‘network effects’ demonstrate the increasing utility from usage of a given good or service by all society members, when accompanied by an increasing number of users of analogous goods or services. The latter obviously creates certain gains but also certain threats and challenges. The concept of ‘network effects’ was also emphasized by Baumol (1986), Perez and Soete (1988), and Verspagen (1991), who argued that society assesses and assimilates new technological solutions relying upon its ‘intellectual’



ICT and social development 3 capital (Soete and Verspagen, 1993), its formal and informal institutions, governance models and cultural conditions. Some empirical evidence shows that the education and skills of the labour force play a seminal role in adoption and proper usage of technologies (Baumol, 1986). Countries experiencing significant lacks in these will probably never be able to exploit fully the potential that technological change may generate, while  –​on the other hand  –​they may be highly exposed to different risks and threats that inadequate usage of new technologies may bring. These ‘network effect’ have never been better demonstrated than in the Digital Era we live in.

1.2 The Digital Revolution The Digital (ICT) Revolution emerged in the early1970s. The first microprocessor was introduced to the public in 1971, the first personal computer in 1973 and mobile telephony by Motorola in the same year. Since then, new advanced technologies have spread worldwide, profoundly reshaping societies and the way they function. ICTs may be labelled ‘pioneering technologies’ that generate technological breakthrough. ICTs are disruptive technologies; they bring transformational change, ‘shake up and modify’ economy and society. ICTs are pervasive (ubiquitous) technologies; they are permanently available, they are network-​connected, they enrich interactions among entities and they provide effectiveness, efficiency and empowerment; henceforth they produce cross-​ cutting societal and economic effects. Due to their disruptiveness and pervasiveness, ICTs are general purpose technologies (Bresnahan and Trajtenberg, 1995), as they present huge potential for economy-​and society-​wide improvement (Jovanovic and Rousseau, 2005). ICTs are ‘enabling technologies’ that offer unbounded opportunities through their adoption and implementation on multiple social grounds. The impact of ICTs on reshaping social systems is mainly to be demonstrated in the long-​term perspective; Hanna (2010) claims that the information and communication revolution is probably the most pervasive in recent human history. ICTs are unique technologies.They are easily installable and quickly distributable; they spread across countries and societies even in remote, underserved and backward regions. ICT can ‘go beyond geography’ as Cairncross (2001) writes; these technologies have enabled the emergence of distance-​free contacts and transactions. ‘Wireless communication … is killing location, putting the world in our pocket’ (Cairncross, 2001, p.2). ICTs are cheap, easily imitable and adaptable (Lechman, 2015); these technologies may be used even by poorly educated, low-​skilled or even illiterate people. ICTs are labelled ‘technologies for all’ because they diffuse regardless of various barriers –​financial, societal, linguistic, educational and geographical. Undoubtedly there exists a causal relation between level of ICT penetration and society’s ability to develop in a sustainable manner. This unlimited inter-​societal connectivity, freed from spatial and temporal context, that is enabled by ICT adoption and usage, is one of the main forces

4  Ewa Lechman et al. driving such profound and fundamental changes in societies; changes which may be both ‘good and bad’.

1.3  Linking ICT to social development: opportunities and threats The process of both assimilating and putting into operation new technologies generates wide-​spectrum effects. ICTs do not exist nor function in isolation, but rather they diffuse and then are used in specific social contexts. ICTs are a major determinant of social and economic development; ICTs may fundamentally reshape and restructure industries, ways of running businesses, working and organization methods. ICTs offer cheap and timely delivery of information and knowledge-​sharing, and many other generic advantages. ICTs may work in support of networking, connecting isolated agents, building people’s interactivity, but they also increase the interdependency of actors. The near-​ubiquitous spread of information and communication technologies offers unprecedented opportunities to all society members, which may result in empowerment shifts facilitating individuals to become more involved in different life spheres. ICTs significantly reduce existing information asymmetries, enabling previously disadvantaged societal groups to act. The nexus between ICT and social development goals is based on mutually shared objectives, which should include efficient, scalable, affordable and pervasive delivery of goods, services and information flows between people, governments and firms. More dynamic and effective creation of various links between market agents provides new opportunities but, on the other hand, may generate other forms of exclusion, threats or dangers. When the full potential of new technologies is unleashed, multiple advantages and disadvantages may be encountered. By definition, we tend to see the ‘bright sides of the story’, but undoubtedly a variety of dark ones also appear, enforcing new preventative action or regulation. Still, the interrelation between technology, whole societies and individuals is not limited to the effects visible from a wide socioeconomic perspective. A massive body of literature explores different types of such interaction; they are complex and involve different spheres of human activity. For instance, experimental psychological studies demonstrate striking similarity between human interaction within society and human interaction with technology. There is a significant amount of evidence that people treat computers as if they were other social agents (Reeves and Nass, 1996). Yet, the unification is not ready and technologies are still not accepted as equal social partners, especially as they lack advanced cognitive capabilities (Cofta, 2007). Others, such as Heidegger (1977) or Giddens (1984; 2013) and Giddens and Pierson (1998) to name just a few, claim that deployment of technology causes disembodiment and erosion of reality. For Giddens (2013), technologies serve as disembedding mechanisms consisting of symbolic tokens and expert systems. Giddens (2013) maintains that especially digital communication technologies, but also such traditional technologies as pen and paper and literacy in general, can be blamed



ICT and social development 5 for disembodiment. However, more than the content or ‘messages’, Giddens (ibid.) argues that altering space–​time relations depends primarily on the form and reproducibility of a given technological medium rather than the content it carries. Money is a good example of a symbolic token; the recent blockchain technology (first applied in the design of Bitcoin in 2008) has given rise to a lot of hope for altering social arrangements. Apart from the possibility to issue extremely reproducible and intangible money, blockchains also ‘offer scope for rethinking political organization, including, enabling novel ways of creating, managing and maintaining systems of voting rights, property rights and other legal agreements’ (Reijers, O’Brolchain and Haynes, 2016, p.134). There have been both demonizing and criticizing antagonists of technologies in the post-​industrial epoch as much as favouring advocates.Technophobic and dystopic essentialism can be contrasted with technophilism in which technology is seen as a synonym for progress that enhances our control over nature (Kahn and Kellner, 2004), adding transparency to communication and increasing both personal and institutional trust (Cofta, 2007). There is a lot of research and discussion around humanizing ICTs both aesthetically (physical and interface design) and technically. Cofta (2007) proposes the concept of Trust-​Enhancing-​Technologies (TET) that embraces the essentials in the assessment of confidence  –​a set of technical properties that are valid in the digital space only. The principles of TET are (1) amplification of evidence of trust and control, (2) transparency and (3) better assessment of confidence, such as reputation-​based systems. The importance of human-​centred ICT design as a prerequisite for successful ICT deployment in organizations is generally recognized. Socio-​technical approaches to systems design focus on integration of software engineering with human, social and organizational factors. Baxter and Sommerville (2011) maintain that technocentric ICT design overlooks the complexity of human relationships within and between organizations and thus fails to support business processes. A common accusation against technologies in general is that they sustain power to alienate, dehumanize or instrumentalize nature and human existence and threaten individual freedom. Although, inter alia, Weber, Heidegger and the Frankfurt School saw modern technology primarily as an instrument of domination, it is important to point out that Heidegger was explicitly not against modern technology, but he was concerned by what he called Ge-​stell (Enframing) (Heidegger, 1977). For him the core purpose of technology is to reveal and it is a feature that all technology has in common regardless of its technical or organizational complexity, no matter whether ancient or modern. Technology must be examined in both instrumental and anthropological aspects. The former is to be understood as a way of using or arranging things and people or the arrangement itself, while the latter refers to the human activity: setting goals, acquiring and using means to achieve goals.The essence of modern technology is to reveal nature in a certain way in accordance with adequate science, which abstracts the world into ‘a calculable coherence of forces’ (Heidegger 1977, p. 21). He argues that modern science has clandestinely paved the way

6  Ewa Lechman et al. for the essence of modern technology by making it appear in a particular way (namely in the context of Enframing). In other words, in modern times humans are born into this mode of viewing the world; for instance, forests can be seen as standing reserve for paper, rivers for producing electricity, pristine landscapes as oil-​sand; human beings as human resources, labour force, human capital –​this is the way Ge-​stell reveals the violent side of technology. Through Enframing, the world appears only as valuable in so far as it can be exploited, controlled and turned into commercializable value. Enframing is principally a way of thinking about the world which we might not necessarily be aware of and can hence end up rendering ourselves into exploitable objects too. Consequently, technology becomes a mode of human existence rather than a means to an end. Heidegger (1962, 1977) warns that the more we treat technology as neutral, the more we become controlled by it, arguing that ‘everywhere we remain unfree and chained to technology, whether we passionately affirm or deny it. But we are delivered over to it in the worst possible way when we regard it as something neutral’ (Heidegger, 1977, p. 4), and this, he claims, makes us blind to its essence. In other words, we do not actively realize what technology is making the world and us in it appear as. As stated before, the essence of technology is not its technicality as such, but that it essentially creates a way of revealing the world in a new manner. An insightful example of this process is provided in the following quote about a power plant, and how it transforms the way people see the river it is located in and ultimately the whole of nature along with us human beings: The hydroelectric plant is set into the current of the Rhine. It sets the Rhine to supplying its hydraulic pressure, which then sets the turbines turning. This turning sets those machines in motion whose thrust sets going the electric current for which the long-​distance power station and its network of cables are set up to dispatch electricity. In the context of the interlocking processes pertaining to the orderly disposition of electrical energy, even the Rhine itself appears as something at our command. The hydroelectric plant is not built into the Rhine River as was the old wooden bridge that joined bank with bank for hundreds of years. Rather the river is dammed up into the power plant. What the river is now, namely a water power supplier, derives from out of the essence of the power station. (Heidegger, 1977, p.16) For Heidegger, the essence of modern technology comes into plain view here:  it is not its constituent parts, or its previously mentioned technicality, but a way in which we can now perceive the world as a standing reserve to be used with ever-​increasing efficiency. In the context of Ge-​stell, people are perceived as energy sources rather than human beings with complex psychological needs. Critical sociology and work research, especially, analyse the changes that work can cause to human labour. It perceives work primarily as a source of impoverishment and distress which depreciates life and eventually causes alienation (Siltala, 2007). It is hence natural and profitable for workers to



ICT and social development 7 explore opportunities for cultivation and self-​development outside of work. In the context of Heidegger’s Ge-​stell, the afore-​mentioned term, namely alienation (Entfremdung in German), must be discussed here briefly. Probably the most noteworthy theories of alienation were created by Hegel (1977 [1807]) and Marx (1981), although with certain differences. Theory of alienation has been widely discussed by numerous scholars and we shall not go deeper into the phenomenon of that concept in this chapter; to put it briefly: alienation is associated with separation, disintegration or isolation of oneself –​the subject and its innate human precondition for self-​development and self-​realization –​ from a relevant objective entity such as family, community or society. Marx perceives alienation as a result of the capitalistic mode of production which is based upon private property and labour division, where labour is seen as a mere material fact. Marx argued that advanced technology affected people and societies and transformed them more quickly than ever before by erasing previous modes of production, living and working, consequently forcing people to adjust and battle the forces of the new situations created by, essentially, technological developments alongside with the capitalist mode of thinking about the world as a standing reserve. Alienation for Hegel, on the other hand, has an ontological and social rather than economic dimension and is not necessarily associated with the capitalistic mode of production, as, e.g. for Marx. He sees work as a contradictory and dialectical process that involves alienation but also provides a possibility of transcending it. Alienation through work for Hegel is a process of self-​discovery and self-​creativity; however, the central role in self-​ discovery is creativity and reflection. Similarly to Marx, a meaningful work that can possibly hinder a man from being alienated should involve self-​realization and be appreciated by others (Mészáros, 1970; Avineri, 1971; Dupré, 1972; Miettinen, 2016). Paradoxically, computer technologies have enhanced our understanding of social development in terms of both social interaction and human cognition, as these rely highly on research methods requiring new technologies far more advanced than the human mind. Numerous modern technologies such as functional magnetic resonance imaging (fMRI), event-​related potentials (ERP) and other medical diagnostic systems have a basis of non-​destructive or non-​invasive research on the cognitive function of a human brain, which is enabled thanks to advanced ICTs. With the advent of modern computing, new tomographic imaging technologies are used not only in medicine but also in, e.g. geology, archaeology, meteorology and by the military. New volume visualization technologies provide unprecedented precision (Stergiopoulos, 2009). Deng (2019) suggests that building an ideal social development model is inseparable from a multidisciplinary approach that includes cognitive science, social psychology, anthropology, computer science, neuroscience and philosophy. Such research should be supported by collection and processing of cognitive data information on people’s social behaviour, in which ICTs become a great help. Apart from the afore-​mentioned, social phenomena are evaluated with the help of economic indicators such as gross national income (GNI), gross domestic product

8  Ewa Lechman et al. (GDP), the Gini coefficient that measures economic inequality, income and wealth distribution among a population, or more composite indicators such as the human development index (HDI) which is meant to capture and rank the level of socioeconomic development of countries. Finally, a common measurement tool of social development is the ICT Development Index that evaluates and compares the level of ICT use and access to information and communication technologies. Are high levels of ICT use a synonym for high social development? Technological advances in the Western economies have favoured educated and high-​skilled workers and induced creation of knowledge-​intensive societies seemingly at least since the late 1960s. ICTs enhance dynamic flow of services, goods and people, being thus a single major driver behind globalization. Simultaneously, economies have become ICT-​conditioned, not least in terms of financial innovations but also as new technologies lead to structural socioeconomic changes that prompt new norms and ways of doing business (Bresnahan and Trajtenberg, 1995; Marszk, Lechman and Kato, 2019). It is widely recognized that good ICT access favours education (Behar, 2016) which in turn, e.g. by improving literacy skills, enhances social development. May and Diga (2015) claim that modern science acknowledges unanimously that ICTs can be used as tools for both poverty reduction and economic development but very few studies have been able to make a causal inference between poverty and ICT deployment on a micro level. Behar (2016) argues that deployment of ICT automation and robotization has induced skill-​biased technical change (SBTC) and routine-​biased technical change (RBTC). One of the main theses of SBTC is that technological change favours more educated and skilled labour, causing in turn job polarization  –​ diminishing middle-​paid jobs in favour of high-​and low-​paid jobs respectively (Violante, 2008). On the other hand, especially in the Asia-​Pacific region, rising middle classes have benefited from increased ICT deployment as this has provided new business possibilities (in terms of e-​commerce) and cheap wealth management. Arner, Barberis and Buckley (2016) postulate that both low ICT investment on the part of the traditional financial institutions and public distrust in the state-​owned banks have given boost to the private fintech sector in Asia. Taking into consideration that Asian (especially Chinese and Indian) financial markets are heavily regulated and dominated by state-​owned banks, the potential of ICTs is overwhelming (Marszk, Lechman and Kato, 2019). In the year 1930, John Maynard Keynes predicted that, by century’s end, technology would have advanced sufficiently that countries like Great Britain or the United States would have achieved a fifteen-​hour work week. There’s every reason to believe he should have been right. In technological terms, we are quite capable of this. And yet it didn’t happen. Instead, technology has been marshalled, if anything, to figure out ways to make us all work more efficiently. By doing this humans have been replaced, especially in rote jobs thanks to automation, and simultaneously new jobs and whole branches are created thanks to developments in ICT. ‘New technologies have quickly spread in all



ICT and social development 9 economies regardless of their economic performance, institutional framework or dominant religion or social norms’ (Lechman, 2017, p.135).Yet, adoption of new technologies has brought about a high risk of emergence of a technology divide –​socio-​economic inequality in terms of accessibility of ICT –​which is particularly apparent in poor and developing countries or regions; the larger the income inequality and lower the educational levels in a society, the greater the barriers to technology adoption for the low-​income segments. In contrast, adoption of ICTs in highly developed countries and regions occurs linearly and equally. According to Lopez Peláez (2014), the technology, digital or robotics divide, a gap between the ‘haves’ and ‘have nots’, can be extended to possession of robots. Owning robots can become a visible sign of power and wealth at three different levels: states, companies and individuals. For individuals, Lopez Peláez (ibidem) suggests that having limited access to robots can lead to social exclusion both physically, by defining the burden (e.g. cleaning or mobility) of people’s everyday lives, and mentally, inter alia by defining one’s access to educational resources. For companies, he links having robots with higher automation, better productivity and accessibility to new business niches. For states, better accessibility to advanced robotics would play a role in increased military power and higher levels of technological innovation. Finally, new ICTs are creating new opportunities for people to use their innate human skills, ones in which machines can be outperformed –​complex problem-​solving, creativity, coordination between multiple agents, natural language understanding, emotional intelligence and mobility in diverse environments. Computers on the other hand are better especially at resolving routine-​based (predictable) and rote tasks, which has resulted in increased automation of various business and production processes.The authors of a McKinsey report maintain that automation, by decreasing the share of rote tasks conducted by people, ‘could make us all more human’ (Manyika et al., 2017, p.18).

Acknowledgement This research has been supported by Project no.  2015/​ 19/​ B/​ HS4/​ 03220 financed by the National Science Centre, Poland.

References Abramovitz, M. (1986). Catching up, forging ahead, and falling behind. Journal of Economic History, 46(2), 385–​406. Ames, E., and Rosenberg, N. (1963). Changing technological leadership and industrial growth. Economic Journal, 73(289), 13–​31. Arner, D., Barberis, J., and Buckey, R. P. (2016). FinTech, RegTech, and the reconceptualization of financial regulation. Northwestern Journal of International Law and Business, 37, 371–​414. Avineri, S. (1971). Labor, alienation, and social classes in Hegel’s Realphilosophie. Philosophy & Public Affairs, 1(1) (Autumn), 96–​119.

10  Ewa Lechman et al. Baumol, W. (1986). Productivity growth, convergence, and welfare: what the long-​run data show. American Economic Review, 76, 1072–​1084. Baxter, G. and Sommerville, I. (2011). Socio-​technical systems: from design methods to systems engineering. Interacting with Computers, 23(1), 4–​17. Behar, A. (2016). The endogenous skill bias of technical change and wage inequality in developing countries. Journal of International Trade & Economic Development, 25(8), 1101–​1121. Bijker,W. (1995). Sociohistorical technology studies. In S. Jasanoff, G. Markle, J. Peterson and T. Pinch (eds.), Handbook of Science and Technology Studies, ed. Thousand Oaks, CA: Sage. Bijker, W. E., Hughes, T. P., Pinch, T., and Douglas, D. G. (2012). The social construction of technological systems: new directions in the sociology and history of technology. Anniversary edn. Cambridge, MA: MIT Press. Bijker, W. E., and Law, J. (1992). Shaping technology/​building society: studies in sociotechnical change. Cambridge, MA: MIT Press. Bresnahan, T. F., and Trajtenberg, M. (1995). General purpose technologies: ‘Engines of growth’? Journal of Econometrics, 65(1), 83–​108. Cairncross, F. (2001). The death of distance: how the communications revolution is changing our lives. Boston, MA: Harvard Business School Press. Castells, M., and Cardoso, G. (eds.) (2006). The network society: from knowledge to policy. Washington, DC: Johns Hopkins Center for Transatlantic Relations. Cimoli, M., and Dosi, G. (1995).Technological paradigms, patterns of learning and development: an introductory roadmap. Journal of Evolutionary Economics, 5(3), 243–​268. Cofta, P. (2007). Trust, complexity and control: confidence in a convergent world. Chichester: John Wiley. Dicken, P. (2007). Global shift: mapping the changing contours of the world economy. London: Sage. Deng,Y. (2019). Construction of ideal model of social development under the political background of mind philosophy. Cognitive Systems Research, 57, 1–​10. Dupré, L. (1972) Hegel’s concept of alienation and Marx’s reinterpretation of it. Hegel-​ Studien, 7, 217–​236. Fagerberg, J., Feldman, M. P., and Srholec, M. (2013).Technological dynamics and social capability:  US states and European nations. Journal of Economic Geography, 14(2), 313–​337. Fagerberg, J., and Srholec, M. (2008). National innovation systems, capabilities and economic development. Research Policy, 37(9), 1417–​1435. Galor, O., and Tsiddon, D. (1997). The distribution of human capital and economic growth. Journal of Economic Growth, 2(1), 93–​124. Giddens, A., and Pierson, C. (1998). Conversations with Anthony Giddens: making sense of modernity. Stanford University Press. Giddens, A. (1984). The constitution of society: outline of the theory of structuration. Berkeley: University of California Press. Giddens, A. (2013). Modernity and self-​identity:  self and society in the late modern age. Hoboken, NJ: Wiley. Hakansson, H. (2015). Industrial technological development: a network approach. Abingdon: Routledge. Hanna, N. K. (2010). Transforming government and building the information society: challenges and opportunities for the developing world. New York: Springer.



ICT and social development 11 Hegel, G. W.  F. (1977) [1807]. Phenomenology of spirit, trans. A.V. Miller. Oxford: Clarendon Press. Heidegger, M. (1962). Being and time, trans. John Macquarrie and Edward Robinson. London: SCM Press. Heidegger, M. (1977). The question concerning technology and other essays. New York and London: Garland. Inglehart, R., and Welzel, C. (2005). Modernization, cultural change, and democracy:  the human development sequence. Cambridge University Press. Jovanovic, B., and Rousseau, P. L. (2005). General purpose technologies. Handbook of economic growth, 1, 1181–​1224. Katz, M. L., and Shapiro, C. (1985). Network externalities, competition and compatibility. American Economic Review, 75(3), 424–​440. Kahn, R., and Kellner, D. (2004). New media and internet activism: from the ‘Battle of Seattle’to blogging. New Media & Society, 6(1), 87–​95. Lechman, E. (2015). ICT diffusion in developing countries: towards a new concept of technological takeoff. Cham: Springer. Lechman, E. (2017). The diffusion of information and communication technologies. New York: Taylor & Francis. López Peláez, A. (2014). From the digital divide to the robotics divide? Reflections on technology, power, and social change. In A. López Peláez (ed.), The robotics divide. Heidelberg: Springer. Mackay, H., and Gillespie, G. (1992). Extending the social shaping of technology approach: ideology and appropriation. Social Studies of Science, 22(4), 685–​716. MacKenzie, D., and Wajcman, J. (eds.) (1985). The social shaping of technology:  how the refrigerator got its hum. Milton Keynes: Open University Press. MacKenzie, D., and Wajcman, J. (eds.) (1999). The social shaping of technology:  how the refrigerator got its hum. 2nd edn. Milton Keynes: Open University Press. Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K.,Willmott, P., and Dewhurst, M. (2017). A future that works: automation, employment, and productivity. San Francisco, CA: McKinsey Global Institute. Marszk, A., Lechman, E., and Kato, Y. (2019). The Emergence of ETFs in Asia-​Pacific. Cham: Springer. Marx, K. (1981). The economic and philosophic manuscripts of 1844. London: Lawrence and Wishart. Marx, K. (2015) [1867]. Capital: a critique of political economy, volume I. Moscow: Progress Publishers. Mattsson, H. (2007). Locating biotech innovation: places, flows and unruly processes. PhD thesis. Uppsala University. May, J., and Diga, K. (2015). Progress towards resolving the measurement link between ICT and poverty reduction. In A. Chib, J. May and R. Barrantes (eds.), Impact of information society research in the Global South. Singapore: Springer. Mészáros, I. (1970). Marx’s theory of alienation. London: Merlin Press. Miettinen, R. (2016). Sivistys kilpailuyhteiskunnassa:  Mitä annettavaa Hegelillä on tänään? Kasvatus & Aika, 10(3), 57–​75. Molina, A. H. (1989). The transputer constituency: building up UK/​European capatilities in information technologies. Research Centre for Social Sciences, University of Edinburgh. Mowery, D. C., and Rosenberg, N. (1991). Technology and the pursuit of economic growth. Cambridge University Press.

12  Ewa Lechman et al. Nelson, R. R., and Phelps, E. S. (1966). Investment in humans, technological diffusion, and economic growth. American Economic Review, 56(1/​2),  69–​75. Perez, C., and Soete, L. (1988). Catching-​ up in technology:  entry barriers and windows of opportunity. In G. Dosi, et al. (eds.): Technical change and economic theory. London: Pinter. Reijers,W., O’Brolchain, F., and Haynes, P. (2016). Governance in blockchain technologies & social contract theories. Ledger, 1, 134–​151. Reeves, B., and Nass, C. I. (1996). The media equation: how people treat computers, television, and new media like real people and places. Cambridge University Press. Rosenberg, N. (1982). Inside the black box: technology and economics. Cambridge University Press. Saviotti, P., and Metcalfe, S. (eds.) (2018). Evolutionary theories of economic and technological change: present status and future prospects. London: Routledge. Siltala, J. (2007). Työelämän huonontumisen lyhyt historia:  muutokset hyvinvointivaltioiden ajasta globaaliin hyperkilpailuun. Helsinki: Otava. Soete, L., and Verspagen, B. (1993). Technology and growth: the complex dynamics of catching-​ up, falling behind and taking over. In A. Szirmai (ed.), Explaining economic growth. Amsterdam: North-​Holland. Stergiopoulos, S. (ed.) (2009). Advanced signal processing: theory and implementation for sonar, radar, and non-​invasive medical diagnostic systems. 2nd edn. Hoboken, NJ: CRC Press. Williams, R., and Edge, D. (1996). The social shaping of technology. Research Policy, 25(6), 865–​899. Verspagen, B. (1991). A new empirical approach to catching up or falling behind. Structural Change and Economic Dynamics, 2(2), 359–​380. Violante, G. L. (2008). Skill-​biased technical change. In Steven Durlauf and Lawrence Blume (eds.), The New Palgrave Dictionary of Economics, 2nd edn. Basingstoke: Palgrave Macmillan.



2  I, Robot Between angel and evil Magdalena Popowska

2.1  Introduction Artificial intelligence (AI) is nowadays the fastest-​developing, in terms of theoretical research and of real applications, in the growing number of ICT disciplines. According to International Data Corporation,1 by 2021, AI and cognitive spending will hit $52.2 billion, and by PricewaterhouseCoopers2 estimations AI could add $15.7 trillion to the global economy by 2030. AI solutions, shaped by humans, enable most activities that were previously reserved for humans to go beyond human capability (Ashrafian, 2014; Ashrafian, 2015). Ongoing developments and innovations in AI and robotics may engender, in the near future, the capacity for computer consciousness, sentience, rationality and autonomy. While the first elements bring a lot of hope, the last provokes fear.The anxiety does not apply only to average people. Experts as well, including scientists and innovators, warn against the irresponsible application of AI, especially with regard to the possible introduction of artificial general intelligence (AGI), performing at or above a human level (Kurzweil, 2005; Sandberg and Bostrom, 2011; Muehlhauser and Salamon, 2012; Sotala and Yampolskiy, 2013). In 2014, Stephen Hawking warned that ‘the development of full AI could spell the end of the human race’,3 and Elon Musk alerted us that ‘We need to be super careful with AI. Potentially more dangerous than nukes’.4 The race to build a machine with human-​level intelligence within AGI has been speeded recently by Microsoft, which offered a San Francisco-​based AI research group5 $1 billion to pursue this project over five years (Waters, 2019). The biggest apprehension we have is that AI will become so sophisticated that it will surpass human brain capabilities and eventually will take deliberate control over our lives as we will have to rely more and more on its ability to make choices anddecisions. From this arises the discussion on the need to create AI systems designed to make appropriate moral choices, a discussion deepened by considerations on ‘machine ethics’ (Wallach, Allen and Smit, 2008; Anderson and Anderson, 2011; Sotala and Yampolskiy, 2013; Brundage, 2014).The challenge of ensuring safe and responsible deployment of AI has been gradually placed at the heart of the debate about these technologies. Undoubtedly, the ability to create responsible AI systems, and to design business models making it available to a large public, may be a source of amazing

14  Magdalena Popowska benefits for the whole human race. Is there any space for ethics and responsibility of the devices based on AI while their creators are not always responsible themselves? How to ensure a minimum level of accountability of more and more independent intelligent devices? Are there already some lessons to learn and consider when designing future progress? These are the main questions raised by researchers nowadays. At the same time, there are voices arguing that now, rather than continue to enumerate potential issues connected with AI, we should frame responses around already identified problems (Liu, 2018). Taking into account the growing deployment scope of AI solutions, machine ethics research should be analysed in the broader complex social context in which humans and computational agents will find themselves very soon (Brundage, 2014). First, it is necessary to understand the progress –​to consider the past, present and future learning capacity of AI systems, to see what are their current and future applications and, in connection with this, to discuss the moral challenges we will face in the near future. Consequently, this study aims at uncovering the main challenges raised by researchers and experts in connection with AI ethical concerns, taking into account the field of application, and the AI solutions and recommendations provided. The topic analysis was based on a literature review and the research sample was based on two main databases: EBSCO and Taylor & Francis, as the most representative for the management field. The selected timespan for the research was 2008 to 2019. This choice was dictated by preliminary review of works on the selected topic, which revealed that the most important literature, forming a kind of key point for further considerations, was published in the last ten years. This may be explained by growing interest in the recent emergence of new AI applications. However, since the concept of AI has been discussed for over two decades, some important sources preceding the initial time frame had to be analysed. Several search paths with the following key expressions were used for the selection: artificial intelligence and ethics and responsible artificial intelligence. Initially, 46 papers were selected based on abstracts, and in the second phase, after a deeper review of the abstracts, only 35 papers were chosen for further analysis. Next, since several papers contained other possibly related references, 12 additional papers were added as relevant for further review. During a snowballing review, only nine sources were picked as pertinent for additional analysis. At this stage, a sample of 44 papers was established for the main study. Summarizing, this chapter focuses on a review of the literature discussing the opportunities connected to deployment of AI technologies and the ethical challenges related to the general use of these solutions. It is organized in six sections. Section 2.2 briefly introduces the evolution of AI systems. Section 2.3 presents the possible impact of recent AI-​based innovations on humans’ lives. Section 2.4 discusses the main ethical challenges in order to provide some institutional and individual answers in Section 2.5. Section 2.6 outlines some conclusions and final recommendations.



I, Robot 15

2.2 Artificial intelligence’s path to independent decision-​making The term artificial intelligence appeared for the first time in a research grant proposal prepared by John McCarthy (1955) and his team of computer scientists. At that time, AI was considered to be the science and engineering of making intelligent machines, especially intelligent computer programs. That was not a long-​run perspective, but brought a substantial basis, not only linguistic, to what AI means today. From the 1950s to the 1990s, most of AI was about programming computers and using statistical approaches encoded in the algorithms. Nowadays, machines are able to learn from scientists –​they are acquiring and gathering more and more human knowledge in numerous disciplines. Unsurprisingly, the definition of AI has changed over the years, reflecting the evolution of goals that were achieved with technological progress. Today, modern dictionary definitions focus on AI as a sub-​field of computer science, in which the machines imitate human intelligence. The Oxford English Dictionary, for example talks about the theory and development of computer systems able to perform tasks normally requiring human intelligence. Scholars, defining AI, underline the power of intelligence acquired by machines, enabling them to function appropriately and with foresight in their environment (Nilsson, 2010). Heretofore, all definitions have described AI as being human-​ like rather than becoming human. It is an intelligence demonstrated outside the human mind, essentially by machines, or the activity of teaching a machine how to do a task that was thought to be human. The term covers four basic areas: (1) learning, (2) speech, (3) vision and (4) language (Arruda, 2017; Hamet and Tremblay,  2017). Nowadays, we are in the age of the machine learning, where computers are learning from data, and no longer from humans (Katare, Padihar and Quereshi, 2018). There are three types of machine-​learning algorithms: (1) unsupervised (ability to find patterns), (2) supervised (classification and prediction algorithms based on previous examples), (3) reinforcement learning (use of sequences of rewards and punishments to form a strategy for operation in a specific problem space), and (4) deep learning, which uses artificial neural networks, modelled on human brain functioning. Supervised learning is a training process using a labelled set of example training data, while unsupervised learning refers to the use of unlabelled input data. Machine-​learning systems are only ever as good as their training data (Scharre, 2019). For now, the algorithms that underpin the technology need to be fed millions of labelled examples to teach them to see. A McKinsey report (Manyika and Bughin, 2018) listed data labelling as the biggest obstacle to AI adoption in industry. Both deep learning and reinforcement learning are machine-​learning functions, which enable a computer to develop rules on its own to solve problems. Put differently, deep learning involves training computers to learn patterns without being explicitly programmed for those patterns. A machine learns to represent complex and abstract concepts in

16  Magdalena Popowska terms of multiple simpler concepts by passing inputs through a large number of layers of interconnected non-​linear processing units (Scharre, 2019). There are four main applications of AI by companies:  automated intelligence, assisted intelligence, augmented intelligence and the most advanced form –​autonomous intelligence (PricewaterhouseCoopers, 2017; Garbuio and Lin, 2019). Automated intelligence provides a possibility to automate manual and cognitive routine and non-​routine tasks. Assisted intelligence helps improve the current operation of a company by amplifying the value of these activities by data verification and simulation to test business decisions before incurring risk. Augmented intelligence provides organizations with new capabilities and differs from assisted intelligence because it alters the nature of an activity, which as a consequence requires changes in the business model. Autonomous intelligence, which is currently being developed, acts on its own and chooses its action in relation to business goals –​it allows an automation of decision-​making. So far, human-​independent decision-​making capabilities are not in widespread use beyond automated stock trading and facial recognition applications, but this form is the most promising and at the same time the most frightening for the future. Shortly, intelligent machines will be able to design other, increasingly faster and more intelligent machines –​this speed and intelligence explosion is a part of the self-​improving system (Chalmers, 2010; Hutter, 2012; Sotala and Yampolskiy, 2013). Undoubtedly, AI’s potential to help or harm us depends on its application and control. The decision-​making process is extremely important in all human activities. A decision is a choice made by an individual consciousness, and each decision is world-​changing and world-​creating (Harris, 2018). In the near future, AI might create overwhelming military, economic, or political power for the groups that control it (Bostrom, 2002; Sotala and Yampolskiy, 2013).

2.3  Recent applications of AI and their possible impact on our lives The application scope of AI has been growing exponentially in the last few years. There are already some leading fields, in particular health care, law and marketing. Regarding medicine, according to Hamet and Tremblay (2017), we can distinguish two branches for AI application: a virtual branch with softbots and a physical branch with carebots. In the first case, the a combination of novel, evolutionary algorithms permitted prediction of over 5,000 protein complexes and the identification of the DNA variants as predictors of diseases or traits. One system fed with thousands of brain tumour images allows a rural doctor without access to a large hospital to be able to analyse his scan and suggest, with a percentage of certainty, what sort of tumour he is looking at. Software is not formally diagnosing the tumors, but it provides an amazing tool to help doctors in making diagnoses (Arruda, 2017). The second form, carebots, includes very sophisticated robots, specialized in the delivery of care to elderly or disabled people, in communicating with and educating autistic children, or in assisting



I, Robot 17 surgeons during very complex operations (Hamet and Tremblay, 2017). In parallel, in the health care market, it is now possible to observe an increased appearance of start-​ ups based on solutions using AI. Current research has classified health care AI start-​ups, considering the type of problem addressed, for example whether they provide telemedicine services, virtual assistance or image recognition (Garbuio and Lin, 2019). As far as law is concerned, AI offers an unprecedented possibility to create a collective knowledge database of millions of cases and previous examples and it allows a simultaneous, ready-​to-​use transfer of information and knowledge. According to Arruda (2017), lawyers will soon have an ethical obligation to use AI-​based solutions, in order to provide their client with maximum service quality. Other deployment fields include finance, marketing, transportation, military solutions, social networks, etc. The self-​driving cars industry is progressing very fast. In finance, experts utilize a tool called high-​frequency trading, where millions of trades are registered and explored to predict trends and make trades on their own (Arruda, 2017). Facebook developed a suicide prevention tool, which becomes more and more efficient (Gomes de Andrade et al., 2018). In 2015, Google released its free and open-​source software library TensorFlow, also used for machine-​learning applications and offering solutions to difficult practical problems that previously have been beyond the capabilities of AI systems (Schuurman, 2019). Certainly, among the widest business applications, as involving the whole community of Internet users, is the use of AI algorithms in personalized advertising, and the proliferation of chatbots, new tools designed to simplify the interaction between humans and computers, particularly useful in banking. Although, according to many researchers, AI will likely continue improving our lives gradually, under human control, providing important economic and societal benefits in the process (Atkinson, 2018), some important challenges exist. The applications of AI mentioned above, and others as yet unknown, may also have heavy, and not always positive, impact on our future lives. According to several researchers, AI will change dramatically the structure of the market. Zovko (2018) forecasts the rise of prosumers  –​corporations will move from their role of wealth creators toward the role of public services. AI may boost the economy (Scharre, 2019) but also deeply change labour market balances. Right now, the largest IT companies, as mentioned before, do not want to be in the business of data-​training –​they want to own customer relationships, so they hire subcontractors from cheap labour markets (Murgia, 2019). There is also no doubt that AI as disruptive technology is enhancing productivity through cost savings and revenue generation as demonstrated in a variety of industries from automotive to customer services (Subramanyam and Patagundi, 2018). However, there is a risk its influence on labour structures may be much more profound (Liu, 2018). AI will encourage a gradual evolution in the job market through greater combination of human and machine, which will become the permanent characteristic of the workforce of the future (Atkinson, 2018).While

18  Magdalena Popowska Autor, Levy and Murnane (2003) warn that technology will eventually replace human labour in routine tasks, many others claim that very shortly, the AGI systems wil apply also to more sophisticated and creative jobs (Korinek, 2019). Certainly, in a relatively short-​term perspective, the labour market may even encounter a shortage of advanced skills related to this technology, as the technology creates new professions (Katare, Padihar and Quereshi, 2018). Countries are moving full ahead to automation and artificial intelligence. A joint report by the China Development Research Foundation and Sequoia China6 stated that in the future around 70 per cent of occupation in China will likely be replaced by artificial intelligence (AI).7 Therefore, one of the prime challenges for humanity in the age of AI will be to ensure that humans continue to prosper and obtain a fair share of the resources produced by the shared human–​AI economy. It is also supposed that political systems will be transformed by the interference of AI. Some signs of the occurring shifts are visible today –​it is enough to mention bot manipulations on the eve of political elections in several countries. The next step could be centralization of real power and authority toward a small group of ‘technowizards’, which, according to Bartlett (2018), could eventually kill democracy and introduce a ‘machinocracy’. ‘ “It was the algorithm” will become our politicians’ favorite non-​apology when something goes wrong’ says the author and gives a couple of examples from recent years, including Putin’s words proclaimed in 2017 about the nation that leads the development of AI as a potential ruler of the world (Bartlett, 2018; Scharre, 2019). Most of these transformations and challenges will come gradually, and we should be able to reshape our behaviours, business practices and legal environment in order to prevent the detrimental consequences of AI’s disruptive power or to ease in its prosperity-​bringing capabilities.

2.4  Responsible and ethical artificial intelligence Discussions on AI morality and ethics started as soon as the human imagination was able to predict the future independence of artificial intelligence and its consequences. Yudkowsky (2011) argues that a failure to develop a sufficiently well-​formed computational morality will result in the disastrous effects of developing super-​intelligent machines, because at some point the goals of self-​perfecting systems seeking to maximize some arbitrary utility function will come into conflict with human interests (Brundage, 2014). According to Subramanyam and Patagundi (2018), creating a balance between technological advancement and the human labor value added becomes an urgent imperative. Ethics and responsibility of artificial intelligence may be appraised from different perspectives. Ashrafian (2014, 2015), in his philosophical considerations ranging from determinism to libertarianism, claims for humanity an obligation to control and institutionalize AI progress, in order to advance both human and artificial intelligence societies. In machine ethics, researchers often encounter issues that are both philosophical and technical (Powers, 2011).There



I, Robot 19 are at least four main fields of ethical conflict in the context of AI (Torrance, 2008; Wallach and Allen, 2008; Anderson and Anderson, 2011; Lin, Abney and Bekey, 2011; Köse, 2018), including machines’ rights, duties, human welfare and justice (Wallach, 2010; Hibbard, 2014; Schneider, 2016), which, in relation to machines’ access to information, reasoning capacity and control issues, are very important topics for further investigation. The intrinsic imperfectability of machine ethics has already been discussed by several scholars. According to some of them, machines, like humans, will inevitably make some mistakes, challenging the possibility of obtaining positive outcomes (Sotala and Yampolskiy, 2013). Humans themselves, differentiated in their values, imperfect in their decision-​ making and exposed to moral dilemmas in their private and professional lives, may not be able to create something superior, ready to deal with moral hazards and dilemmas. Globalization amplifies the complexity of moral dilemmas due to the diversity of cultures and ethical standards. Moral rules are often ambiguous and broken, and full of exceptions, and there is an ongoing discussion about the conditions in which such exceptions should be made (Brundage, 2014).Therefore, there is a need to balance and ponder values, issues and interests in accordance with a given application and institutional practices (Gomes de Andrade et al., 2018). According to Wallach and Allen (2008), software agents and robots have to be imbued with explicit ethical principles to govern their behavior. AI application designers have to understand their context through the lens of ethical considerations and have to regulate the behaviour of robots with a computational logic, so that all actions they perform are provably ethically permissible (Bringsjord et al., 2011). Ethical decision-​making requires an agent to estimate the wider consequences of its actions. Software engineers should behave as moral agents while designing, because humans need to be ultimately responsible for the actions of today’s AI systems. At the same time, as the application context may vary considerably for the same AI tool, multiple actors can be responsible for different aspects of an application context. This moral responsibility, in order to be effective, needs to be transformed into legal responsibility submitted to appropriate legal authorities (Garbuio and Lin, 2019). Cath et al. (2018), pointing out the lack of legal framework, advocate the foundation of a council on AI and data ethics. Zeitoun (2019) considers that, since AI is and will be deployed in a growing number of disciplines, there is a need to create separate ethics committees depending on the field of action. On the other hand, Zhao (2018), for ensuring safe and responsible application of AI tools, proposes to use the ISO 26000 core subjects: human rights, labour practices, the environment, fair operating practices, consumer issues and community involvement and development. Since the challenges connected with AI relate to trust, preservation of human dignity, identity and safety, innovation policies should require adherence to ethical standards as a prerequisite (Keskinbora, 2019). Brundage (2014) identifies several situations of possible machine-​ ethics failure:  (1) insufficient knowledge and/​ or computational resources for the situation at hand (making an inappropriate exception or not making it when

20  Magdalena Popowska morally relevant factors exist); (2)  moral dilemmas of the agent; (3)  wrong morals modelled by the system due to shortages of training data or ‘defective’ or extrapolated embedded human morality; (4) loss of understanding or control of ethical AGI systems due to their complexity or extrapolation of human values. On the other hand, human agents may try to blame AI systems for their own mistakes.The evidence from the world of events shows that in the case of ethical problems humans tend to blame others –​this now involves, more and more frequently, an applied AI algorithm (Shank and DeSanti, 2018).Therefore, there is a necessity to uncover and confirm the processes and mechanisms that distinguish moral violations by AIs from those by humans. Scharre (2019), warning against application of AI in manipulation by social media (bots) as a main tool of political propaganda, urges increased protection of AI systems and their supervised learning. To avoid abusive and uncontrolled behaviours, AI operators should be licensed, just as many other professions are, e.g. pharmacists and civil engineers (Balan, 2019; Dennett, 2019). Autonomous systems should be monitored while in operation, and updated or corrected as needed. Furthermore, AI systems must be data-​responsible. They should use only what they need and delete it when it is no longer needed (‘data minimization’).Today, binding laws have not everywhere been adopted for privacy or data protection, to slow down the use and processing of data, such as the new General Data Protection Regulation (GDPR) rules in Europe. It seems irresponsible to leave this issue to the choice of a company: today, for example Chinese tech conglomerate Alibaba has no internal ethics division. Therefore, the AI systems should encrypt data in transit and at rest, and restrict access to authorized persons (‘access control’). AI systems should only collect, use, share and store data in accordance with privacy and personal data laws and best practices (Keskinbora, 2019). To all these ethical challenges may be added another one, referring to ethical AI supply chains. The subcontractors of IT multinationals, providing the data-​labelling services, are located in low-​wage economies such as India, Kenya and the Philippines (Murgia, 2019). Therefore, the challenge is to ensure an ethical supply chain as the growing sector employs large numbers of underpaid workers.

2.5  Some existing answers to the challenge of ethical/​ responsible AI In response to those ethical issues that arise in AI, several solutions have already been provided. First of all, several organizations have been established, mostly within leading world-​class universities. At the University of Oxford there is the Future of Humanity Institute, while New York University has established the AI Now Institute –​an interdisciplinary research center dedicated to understanding the social implications of artificial intelligence.8 MIT Media Lab and the Berkman Klein Center for Internet and Society at Harvard University are participating in a global initiative to fund and advance AI research for the public good (Schuurman, 2019). In early 2015, the United Nations Interregional



I, Robot 21 Crime and Justice Research Institute launched its programme on artificial intelligence and robotics.9 In 2017, in The Hague, the Centre for Artificial Intelligence and Robotics was founded. It is dedicated to understanding and addressing the risks and benefits of AI and robotics from the perspective of crime and security through awareness-​raising, education, exchange of information and harmonization of stakeholders. Responsible AI also becomes a concern of non-​governmental organizations. One of them, the Foundation for Responsible Robotics, created in 2015 by computer scientist Noel Sharkey and robot ethicist Aimee Van Wynsberghe, has an ambition to spread knowledge of robotics and AI beyond the academic world. The organization focuses on the ethics of robotics and makes an attempt to bring the academic knowledge in this field to businesses and governments. Since 2017, many legislative national initiatives aiming at examining and addressing the impact of AI on society have emerged.The examples range from the UK government’s Industrial Strategy (November 2017), whose section on Grand Challenges features AI,10 to an Estonian government report (May 2019)  about accelerating AI in private and public sectors throughout the country.11 In 2019, the US President Donald Trump issued an executive order launching the American AI Initiative. At the same time, several significant acts marked a legislative AI framework: in October 2017, Saudi Arabia became the first country to give citizenship to a humanoid robot, Sophia;12 in June 2017, a chatbot programmed to be a seven-​year-​old boy has become the first AI bot to get official residence in Tokyo, Japan. On 31 May, 2016, the European Parliament’s Committee on Legal Affairs (JURI) published the draft report on Civil Law Rules on Robotics with recommendations to the European Commission.13 The report calls for the creation of a ‘European Agency for Robotics and AI’ consisting of regulators and external technical and ethical experts, who can monitor AI-​and robotics-​based trends, identify standards for best practice, recommend regulatory measures, define new principles and address potential consumer protection issues (Cath et  al., 2018). Regarding ethical principles, this document considers that the existing EU legal framework should be updated and complemented, where appropriate, by guiding ethical principles in line with the complexity of robotics and its many social, medical and bioethical implications. This framework should be based on the principles of beneficence, autonomy, justice and other values from Article 2 of the Treaty on European Union and from the Charter of Fundamental Rights. In the annex of this resolution, one can find also a framework in the form of a charter consisting of a code of conduct for robotics engineers, a code for research ethics committees when reviewing robotics protocols, and model licences for designers and users. The resolution highlights the principle of transparency and proposes to equip advanced robots with a sort of black box that will record data on every operation, including information on the logic that contributed to its decisions.This idea may find its extension in the concept of creation of codes of ethics for researchers and other agents connected with AI development (Boddington, 2017).

22  Magdalena Popowska The Next Generation Internet (NGI) initiative, launched in 2016 by the European Commission, aims to shape the future Internet with European values: openness, inclusivity, transparency, privacy, cooperation and protection of data.14 In 2018, NGI conducted a consultation around the subject of responsible AI, engaging multidisciplinary experts in this field. The outcome report of the HUB4NGI15 covers several key areas and related issues, including ethics and responsibility. The recommendations underline the importance of the design moment and the designer agent for the assessment of the ethical impacts of machines; while in use, the responsible moral agent may be the user, and the impacts may depend on the application context. The institutional solutions anticipate the recommendations proposed by researchers and experts in the field of AI, and thus meet the threats and challenges discussed in this chapter. The general outline of the possible recommendations is presented in Table 2.1.

Table 2.1 Main recommendations in the context of ethical challenges connected with AI systems Author

Discussed application

Recommendations

Wallach and Allen (2008)

Any kind

Bringsjord et al. (2011)

Any kind (battlefield, hospitals, law enforcement) Military/​political

Software agents and robots be imbued with explicit ethical principles to govern their behaviour. Regulate the behaviour of robots with a computational logic, so that all actions they perform are provably ethically permissible. Government agencies will have to reconsider their traditional approaches to testing new systems, going far beyond the simple checking of their compliance with design specifications. Protection of AI systems as huge data repositories. License AI systems. Create a council on AI and data ethics. Need for legal authority specific to AI.

Scharre (2019)

Balan (2019) Cath et al. (2018) Garbuio and Lin (2019) Hamet and Tremblay (2017)

Any kind Any kind Health start-​ups Medicine

Kose (2018)

Any kind

Subramanyam and Patagundi (2018)

Any kind

Create ethical standards, develop measures of success and effectiveness, available to the mainstream. Design and develop some algorithmic solutions for ensuring safe intelligent systems. Creating a balance between technological advancement and the value addition of human beings. (continued)



I, Robot 23 Table 2.1 Cont. Author

Discussed application

Recommendations

Gomes de Antrade et al. (2018)

Social media (Facebook)

Zhao (2018) Zeitoun (2019)

Any kind Any kind

Brundage (2014)

Any kind

Boddington (2017) Keskinbora (2019)

Any kind

Shank and DeSanti (2018)

Events

Atkinson (2018)

Any kind

Liu (2018)

Any kind (power balances)

Murgia (2019)

Supervised learning

Need to balance and ponder values, issues and interests in accordance with our mission and institutional practices. Apply ISO 26000. Ethics committees scrutinize an application’s field of action. Machine ethics research may have some social value, but it should be analysed through a broader lens of the inherent difficulty of intelligent action in general and the complex social context in which humans and computational agents will find themselves in the future. Develop codes of ethics for AI researchers and operational agents. Innovation policies should require adherence to ethical standards as a pre-​ requisite. Ensure interpretability and safety of AI systems. Reveal and confirm the processes and mechanisms that distinguish moral violations by AIs from those by humans. Proceeding on the assumption that AI will be fundamentally good, and while it will present some risks, as does every technology, address them. Joint work of ethicists, scientists and lawyers to prevent myriad risks and challenges. Empowering unskilled workers in low-​ income economies.

Medicine, neuroethics

Source: author’s elaboration.

The range of actions proposed to ensure that technologies based on artificial intelligence are safe for people, i.e. focus on improving our lives, extends from technical solutions to legal regulations.The great majority of scholars urge the creation of special authorities, councils/​committees or other legal bodies, which will directly address all the ethical issues and permanently improve the legal framework of the emergent technology. Several researchers look rather for technology-​based solutions, algorithms and computational logic, embodied in AI software codes. Doubtless, bringing an exact and complex answer to this increasing challenge would require a concerted contribution of scholars and experts from a great number of scientific disciplines.

24  Magdalena Popowska

2.6  Concluding remarks Artificial intelligence has become one of the hot topics among ethicists, philosophers, engineers and other scholars in the last five to ten years. Certainly, this interest has been stimulated by the increased development of technologies based on artificial intelligence. Since AI solutions, through their omnipresence, affect an average human everyday life, they attract the attention of a large public, including various social groups and the corporate world. This chapter aimed at gathering scientific evidence on available approaches to address the growing ethical challenge of fast-​developing AI systems. The outcome of this literature review highlights several key solutions that should be designed and implemented as soon as possible, since in some respects we are behind schedule. However, the most important conclusion, even if a little naïve, is that, despite sometimes contradictory national interests, there is a need to establish a supranational multidisciplinary organization, a kind of Ethical World Council for AI, able to coordinate research in this field and accompany technological progress with a legislative framework. Nowadays, there are myriad organizations trying to ‘individually’ tackle ethical issues, starting with IT corporations, private foundations and research institutions. The legal framework emerges from national governments and a few international authorities such as the European Commission. In our opinion, as with peace, the environment and nuclear technology, it is one of those questions which, due to the complexity of their management, require an effort of global governance. Therefore, it would be beneficial to merge all these scattered efforts in one organization, acting as a global guard for human safety, as is the case, relatively speaking, for the nuclear industry, with the International Atomic Energy Agency based in Vienna. Future regulatory initiatives should urgently address the issue of data protection –​the EU General Data Protection Regulation (GDPR) constitutes only the first step, and should soon become a generalized policy. Regarding innovation policies, they should require adherence to set ethical standards as a prerequisite, without any space for exceptions, and violations should be registered and stigmatized. As a final optimistic remark, we would like to stress that heretofore ethics and innovation have been progressing together –​ethical questions about the safety of technology eventually led to further innovations addressing the safety issue. We believe that this rule is valid also in the case of AI, and future development in robotics will allow the use of AI ethics to support human rights and well-​being, rather than to harm them.

Notes 1 www.idc.com/​getdoc.jsp?containerId=IDC_​P33198 (accessed 5 June 2019). 2 www.pwc.com/​gx/​en/​issues/​data-​and-​analytics/​publications/​artificial-​intelligencestudy.html (accessed 5 June 2019). 3 www.bbc.com/​news/​technology-​30290540 (accessed 7 July 2019).



I, Robot 25 4 www.kurzweilai.net/​ai-​is-​potentially-​more-​dangerous-​than-​nukes-​says-​musk (accessed 7 July 2019). 5 The group includes Elon Musk, Peter Thiel and Reid Hoffman, the greatest authorities in the tech industry. 6 www.prnewswire.com/​news-​releases/​cdrf-​and-​sequoia-​china-​publish-​report-​on-​ human-​capital-​investment-​in-​the-​ai-​era-​300700810.html (accessed 25 May 2019). 7 https://​talentmind.ai/​blog/​insight/​1450 (accessed 23 May 2019). 8 https://​ainowinstitute.org (accessed 5 June 2019). 9 www.unicri.it/​topics/​ai_​robotics/​centre (accessed 5 June 2019). 10 https://​futureoflife.org/​ai-​policy-​united-​kingdom (accessed 25 June 2019). 11 https://​futureoflife.org/​ai-​policy-​estonia (accessed 10 July 2019). 12 https://​en.wikipedia.org/​wiki/​Sophia_​(robot) (accessed 26 June 2019). 13 This report was adopted in a modified form by the European Parliament on 16 February 2017 and took the form of a resolution: https://​eur-​lex.europa.eu/​legal-​content/​EN/​ TXT/​HTML/​?uri=CELEX:52017IP0051&from=EN      (accessed 5 June 2019). 14 www.ngi.eu/​about (accessed 24 June 2019). 15 Responsible AI  –​Key Themes, Concerns & Recommendations for European Research and Innovation (Summary of Consultations with Multidisciplinary Experts (June 2018).

References Anderson, M., and Anderson, S. L. (2011). Machine ethics. New  York:  Cambridge University Press. Arruda, A. (2017). An ethical obligation to use artificial intelligence? An examination of the use of artificial intelligence in law and the model rules of professional responsibility. American Journal of Trial Advocacy, 40, 443–​458. Ashrafian, H. (2014). AIonAI: a humanitarian law of artificial intelligence and robotics. Science and Engineering Ethics, 21(1), 29–​40. Ashrafian, H. (2015). Artificial intelligence and robot responsibilities: innovating beyond rights, Science and Engineering Ethics, 21(2), 317–​326. Atkinson, R. D. (2018). ‘It is going to kill us!’ and other myths about the future of artificial intelligence. IUP Journal of Computer Sciences, 12(4), 7–​56. Autor, D. H., Levy, F., and Murnane, R. J. (2003).The skill content of recent technological change: an empirical exploration. Quarterly Journal of Economics, 118(4), 1279–​1333. Balan, S. M. (2019). Artificial intelligence and law: a review of the role of correctness in the general data protection regulation framework. Curentul Juridic, 76, 45–​53. Bartlett, J. (2018). How AI could kill democracy: are we willing to give up our freedom in exchange for efficient, data-​driven public services owned and run by tech giants? New Statesman, 15 August 2018, 29–​32. Boddington, P. (2017). Towards a code of ethics for artificial intelligence. Artificial intelligence: foundations, theory, and algorithms. Cham: Springer. Bostrom, N. (2002). Existential risks: analyzing human extinction scenarios and related hazards. Journal of Evolution and Technology, 9, www.jetpress.org/​volume9/​r isks.html. Bringsjord, S., Taylor, J., van Heuveln, B., Arkoudas, K., Clark, M., Wojtowicz, R. L. (2011). Piagetian roboethics via category theory:  moving beyond mere formal operations to engineer robots whose decisions are guaranteed to be ethically correct. In M. Anderson and S. L. Anderson (eds.), Machine ethics, pp. 361–​374. New York: Cambridge University Press.

26  Magdalena Popowska Brundage, M. (2014). Limitations and risks of machine ethics. Journal of Experimental and Theoretical Artificial Intelligence, 26(3), 355–​372. Cath, C.,Wachter, S., Mittelstadt, B.,Taddeo, M., Floridi, L. (2018). Artificial intelligence and the ‘good society’: the US, EU, and UK approach. Science and Engineering Ethics, 24, 505–​528. Chalmers, D. J. (2010). The singularity: a philosophical analysis. Journal of Consciousness Studies, 17(9–​10),  7–​65. Dennett, D. C. (2019). Possible minds: twenty-​five ways of looking at AI, ed. John Brockman. New York: Penguin. Frank, A. (2018). How artificial intelligence will impact the future of work. CMS WiRE, 16 May 2018, www.cmswire.com/​digital-​workplace/​how-​artificial-​intelligence-​ will-​impact-​the-​future-​of-​work (accessed 24 June 2019). Garbuio, M., and Lin, N. (2019). Artificial intelligence as a growth engine for health care startups:  emerging business models, California Management Review, 61(2),  59–​83. Gomes de Andrade, N. N., Pawson, D., Muriello, D., Donahue, L., Guadagno, J. (2018). Ethics and artificial intelligence:  suicide prevention on Facebook. Philosophy & Technology, 31, 669–​684. Hamet, P., and Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism: Clinical and Experimental, 69, 36–​40. Harris, J. (2018). Who owns my autonomous vehicle? Ethics and responsibility in artificial and human intelligence. Cambridge Quarterly of Healthcare Ethics, 27(4), 599–​609. Hibbard, B. (2014). Ethical artificial intelligence. arXiv preprint arXiv:1411.1373. Hutter, M. (2012). Can intelligence explode? Journal of Consciousness Studies, 19(1–​2), 143–​166. Katare, G., Padihar, G., and Quereshi, Z. (2018). Challenges in the integration of artificial intelligence and Internet of things. International Journal of System and Software Engineering, 6(2), 10–​15. Keskinbora, K. H. (2019). Medical ethics considerations on artificial intelligence. Journal of Clinical Neuroscience, 64, 277–​282. Korinek, A. (2019). Labor in the age of automation and artificial intelligence. Economists for Inclusive Prosperity research brief. https://​econfip.org/​wp-​content/​uploads/​2019/​ 02/​6.Labor-​in-​the-​Age-​of-​Automation-​and-​Artificial-​Intelligence.pdf (accessed 15 June 2019). Köse, U. (2018). Are we safe enough in the future of artificial intelligence? A discussion on machine ethics and artificial intelligence safety, BRAIN –​Broad Research in Artificial Intelligence and Neuroscience, 9(2), 184–​197. Kurzweil, R. (2005). The singularity is near:  when humans transcend biology. New  York: Viking. Lin, P., Abney, K., and Bekey, G. A. (2011). Robot ethics: the ethical and social implications of robotics. Cambridge, MA: MIT Press. Liu, H-​Y. (2018). The power structure of artificial intelligence. Law, Innovation and Technology, 10(2), 197–​229. Manyika, J., and Bughin, J. (2018). The promise and challenge of the age of artificial intelligence. McKinsey Global Institute. www.mckinsey.com/​featured-​insights/​artificial-​ intelligence/​ t he-​ p romise-​ a nd-​ c hallenge-​ o f-​ t he- ​ a ge- ​ o f- ​ a rtificial- ​ i ntelligence (accessed 19 May 2019).



I, Robot 27 McCarthy, J., et al. (1955). A proposal for the Dartmouth summer research project on Artificial Intelligence. http://​jmc.stanford.edu/​articles/​dartmouth/​dartmouth.pdf (accessed 20 June 2019). Muehlhauser, L., and Salamon, A. (2012). Intelligence explosion: evidence and import. In A. H. Eden et al. (eds.), Singularity hypotheses: a scientific and philosophical assessment. Berlin: Springer. Murgia, M. (2019). AI’s rise means work for army of data labellers. Financial Times, 25 July 2019, 13. Nilsson, N. J. (2010). The quest for artificial intelligence: a history of ideas and achievements. Cambridge University Press. Powers,T. M. (2011). Incremental machine ethics. IEEE Robotics & Automation Magazine, 18(1),  51–​58. PricewaterhouseCoopers (2017). Sizing the prize:  what’s the real value of AI for your business and how can you capitalize? www.pwc.com/​gx/​en/​issues/​data-​and-​analytics/​ publications/​artificial-​intelligence-​study.html. Sandberg, A., and Bostrom, N. (2011). Machine intelligence survey. Technical report 2011-​ 1. Future of Humanity Institute, University of Oxford. www.fhi.ox.ac.uk/​reports/​ 2011-​1.pdf. Scharre, P. (2019). Killer apps: the real dangers of an AI arms race. Foreign Affairs, May/​ June 2019. Schneider, S. (ed.) (2016). Science fiction and philosophy: from time travel to superintelligence. Chichester: Wiley-​Blackwell. Schuurman, D. C. (2019). Artificial intelligence:  discerning a Christian response. Perspectives on Science and Christian Faith, 71(2), 75–​82. Shank, D. B., and DeSanti, A. (2018). Attributions of morality and mind to artificial intelligence after real-​world moral violations. Computers in Human Behavior, 86, 401–​411. Sotala, K., and Yampolskiy, R. V. (2013). Responses to catastrophic AGI risk:  a 2. Berkeley, CA:  Machine Intelligence Research survey. Technical report 2013-​ Institute. Subramanyam, N., and Patagundi, B. (2018). Automation and artificial intelligence  –​ boon or bane:  a humanistic perspective. Organization and Management Scientific Quarterly, 4(44), 151–​158. Torrance, S. (2008). Ethics and consciousness in artificial agents. AI & Society, 22(4), 495–​521. Wallach, W. (2010). Robot minds and human ethics:  the need for a comprehensive model of moral decision making. Ethics and Information Technology, 12(3), 243–​250. Wallach, W., and Allen, C. (2008). Moral machines:  teaching robots right from wrong. New York: Oxford University Press. Wallach, W., Allen, C., and Smit, I. (2008). Machine morality: bottom-​up and top-​down approaches for modelling human moral faculties. AI & Society (special issue: Ethics and Artificial Agents), 22(4): 565–​582. Waters, R. (2019). The billion-​dollar bet to reach human-​level AI. Financial Times, 5 August 2019, p.7. Yudkowsky, E. (2011). Complex value systems are required to realize valuable futures. In J. Schmidhuber, K. R. Thórisson, and M. Looks (eds.), Proceedings of AGI 2011, pp.388–​393. New York: Springer.

28  Magdalena Popowska Zeitoun, H. (2019). L’intelligence artificielle… pour plus d’intelligences. Stratégie, 27 March 2019, 30–​32. Zhao, W.-​W. (2018). Improving social responsibility of artificial intelligence by using ISO 26000. IOP Conference Series: Materials Science and Engineering, 428, conference 1. Zovko, V. (2018). Management in the year 2050. Interdisciplinary Description of Complex Systems, 16(3-​B), 417–​426.



3  Next-​generation networks as general-​purpose technologies The impact on economic development Angelo Castaldo, Alessandro Fiorini and Filippo Reganati 3.1  Introduction: background and purpose The evolution path of societies has always been strongly interdependent with technological shifts (von Hayek, 1945). In this vein, starting with Internet connections and their relevant role in reshaping economic and social interactions, governments have always been active in promoting the deployment of broadband (BB) and ultra-​wideband communication networks (i.e. next-​generation networks, or NGNs). However, it is worth noting that if on the one hand, representing a general-​purpose technology (GPT), electronic communication networks may be identified as crucial drivers for economic and social development, on the other they have raised important issues in terms of both potential opportunities and challenges (Majumdar, Carare and Chang, 2009). As infrastructure platforms, NGNs are able to generate relevant positive spillover effects on the production and diffusion of knowledge and, as a result, they promote structural changes by enhancing factor productivity across all private and public sectors (Bloom and Van Reenen, 2007; Qiang and Rossotto, 2009; Greenstein and McDevitt, 2011; Kolko, 2012; Kretschmer, 2012; Cardona, Kretschmer and Strobel, 2013). At the same time, however, given the significant spillovers originated by broadband infrastructures and services, the presence of an ‘invisible hand’ private infrastructure market might hamper the attainment of the optimal level of investment. For this reason, the interplay between public and private agents is a key determinant. However, the stimulus arising from the European Digital Agenda (DAE), with its policy target of 50 per cent of households having a subscription to an Internet connection above 100 Mbps by 2020, appears to be a mirage compared to the last collected data by Eurostat that reveals coverage of just 26 per cent in June 2018. There is a broad empirical literature (Röller and Waverman, 2001; Datta and Argawal, 2004; Crandall, Lehr and Litan, 2007; Koutroumpis, 2009; Czernich et  al., 2011; Gruber and Koutroumpis, 2011; Ng, Lye and Lim, 2013; Arvin and Pradhan, 2014; Gruber, Hätönen and Koutroumpis, 2014; Ghosh, 2016; Castaldo, Fiorini and Maggi, 2018; Mayer, Madden and Wu, 2019) that has tried to evaluate the impact of NGNs on economic growth in order to help

30  Angelo Castaldo et al. policymakers in weighing gains and missed opportunities related to specific GPT networks. Using a panel referring to the OECD countries over the period 1996–​2017, the main purpose of this chapter is to measure and evaluate the impact of BB penetration rate (fixed and mobile BB connections) on economic growth. More specifically, the research questions we want to address are the following: to what extent can NGNs contribute to economic growth? And, if so, to what extent is such an impact different among countries? Exploiting the time horizon of our dataset, does the impact on economic growth change according to the double transition from traditional copper to partially fibre networks, and from partially to totally fibre networks? Is there an ongoing convergence or a substitution process among fixed and mobile broadband connections? Finally, did the impact of BB hold during the 2007/​2008 economic turmoil or did it experience a structural break? The remainder of the chapter is organized as follows. In Section 3.2, we discuss the characteristics of our dataset and give some descriptive statistics. Section 3.3 outlines our dynamic panel econometric strategy. Section 3.4 provides the main empirical results and, finally, Section 3.5 ends with some concluding remarks and policy implications.

3.2  Data and descriptive statistics Our empirical analysis is based on a panel dataset covering the OECD countries over the period 1996–​2017. All the data used were collected from three different sources:  the OECD, the International Telecommunication Union (ITU) and the World Bank. For each country, we have retrieved data on real gross domestic product (GDP), gross fixed capital formation (GFCF), fixed broadband subscriptions (FBB), mobile broadband subscriptions (MBB), labour force (LBF), tertiary educational attainment (EDU), yearly average population (POP), working-​age population (WPOP), and years since first appearance of broadband connections (Tb). The detailed description of all variables and the summary statistics are reported, respectively, in Appendix Tables 3.A1 and 3.A2. Before going into a formal regression analysis, we present some summary data for our sample of countries. Figure 3.1 reports the distribution of OECD countries by the average GDP per working-​age population and the year of first appearance of BB penetration rate. The existence of a high degree of heterogeneity among countries is clear, compared to the median of the all-​sample distribution that sets the first appearance of BB in 2001. Countries such as Canada, Japan and the United States are first movers, exhibiting penetration rates since 1998. With reference to European countries, the leading economies are Belgium, France and the Netherlands, where the first broadband Internet services were realized in 1999, while Greece is the latest adopter. The average BB subscription rate confirms countries’ heterogeneity (see Appendix Table 3.A2). Northern European countries lead the ranking: Sweden (82.4 per cent), Denmark (78.7 per cent) and Norway (75.8 subscribers over



240

2003

200

2002

160

2001

120

2000

80

1999

40

1998

0

USD thousand

2004

Canada Japan United States Belgium France Netherlands Austria Chile celand Mexico New Zealand Australia Denmark Germany Hungary taly Latvia Norway Portugal Spain Sweden Switzerland Turkey United Kingdom Estonia srael Lithuania Luxembourg Poland Slovenia reland Greece

Year

Next-generation networks 31

GDP/Working age populaon

Tb

Figure 3.1 Comparison between average GDP per working-​age population and year of the first appearance of BB penetration rate.

100 inhabitants). It is worth noting that Sweden and Denmark are, respectively, countries with higher levels of BB penetration rate at the first appearance: 2.8 per cent and 1.25 per cent. This can be a consequence of a comparatively advanced readiness of the population to adopt innovations in ICT. By reporting some graphs that depict the evolution of the penetration rate of both fixed and mobile broadband over time (see Appendix Table 3.A3), we see that beyond the regularity of an S-​shaped curve, countries substantially differ in their evolution path both for magnitude and for speed. Although for fixed broadband connections all OECD countries have almost completed their growth path, the attainment of a saturation level for mobile connections is still an open question. This seems to be the case for some developed or leading countries such as Australia, Luxembourg, Norway, Sweden and the United Kingdom. On the other hand, countries such as Belgium, Germany and Japan, even if they show a decreasing pace of growth for mobile connections penetration, have experienced a relevant jump around 2014 due to the deployment of next-​ generation mobile networks (4G). Finally, in Greece, Poland and Portugal the broadband penetration rate is still at an early adoption stage. From these figures, it seems evident that rather than a convergence process among platforms, a substitution process of mobile with respect to fixed broadband connections has entered into force. By plotting the average GDP per working-​age population and the BB penetration rates (Figure 3.2), we observe a tight relationship between the wealth of a country and the deployment of electronic communication networks. Indeed, several countries (Norway, the United States, Switzerland, Ireland, Italy, Spain, Germany, the Netherlands, France, New Zealand, Israel, Lithuania and Latvia) are compelling cases for this close relationship. By contrast, other countries such as Luxembourg and Sweden appear to be less representative.

BB penetraon rate (per 100 nhab tants)

32  Angelo Castaldo et al. 90

SE

80

DK

JP

AU

70 EE

60 50

NZ

PL LV

40 30 20 20

IL

LT SI HU

CL

IS

UK FR IT

EL ES CA PT

DE

NO US CH

NL

LU

IE

AT BE

TR MX 40

60

80

100

120

GDP/Working age populaon (USD thousand)

Figure 3.2 Comparison between GDP per working-​age population and BB penetration rate (average values: 1996–​2017).

3.3  Econometric methodology and identification strategy Following the literature on endogenous growth with externalities (Lucas, 1988; Romer, 1990; Barro, 1991; Aghion and Howitt, 1992), our empirical strategy is based on an adaptation of the Mankiw, Romer and Weil (1992) model where the broadband penetration rate (BB_​rate) is plugged into the technological component. To build our regression model, we start from the following steady-​ state output relationship: gdpw itss = techt + β1lbf it + β2 gfcf it + β3eduit + γ X it

with: i = 1,…, n, t = 1,…, m

(1) where i and t denote respectively the cross section and time index, gdpw itss represents the real output per worker, lbfit the rate of change of the workforce, gfcfit the share of gross fixed capital formation in real GDP, eduit the share of working-​age population with a third-​level degree of education, and Xit is a matrix of control variables. All variables of equation (1) are expressed as natural logarithms. The technological component (tech) is specified as: techt = β0 + f (t ) With :

f (t ) = βBB _ rateit (2)

where BB_​rateit is the sum of both fixed (FBB) and mobile broadband (MBB) subscriptions per 100 inhabitants.



Next-generation networks 33 In line with other empirical studies on the determinants of economic growth (Roller and Waverman, 2001; Datta and Argawal, 2004; Koutroumpis, 2009;Vu, 2011; Ng, Lye and Lim, 2013; Castaldo, Fiorini and Maggi, 2018), we set up a dynamic specification of our regression model that allows us to examine the transitional dynamics of the GDP growth –​that is, how an economy’s per capita income converges towards its own steady-​state value. In addition, such a specification is likely to produce more robust results correcting the estimates from possible bias due to reverse causality and endogeneity (Holt and Jamison, 2009). Taking all these issues into account, the empirical adjustment of actual output to its steady-​state equilibrium is estimated by the following lagged equation: gdpw it = (1 − δ ) gdpwt −1 + δ[β0 + β1lbf it + β2 gfcf it + β3eduit + β4BBrate it + β5Tbit + β6 crisisit + β7tech _ changeit ] + εit

(3)

where δ represents the speed of adjustment at which out-​of-​the steady-​state countries move towards their long-​run equilibria, and gdpw-​1 is the lagged real GDP per worker to capture the persistence of growth pattern. In equation (3) we have also inserted a trend component (Tb) which indicates the number of years since broadband’s first appearance (Gruber and Verboven, 2001), a dummy variable crisis that assumes the value of 1 in the years 2008–​2010 to control for the existence of possible structural breaks that could have affected the impact of broadband connections on economic growth; and, finally, the variable tech_​change  –​measured by the ratio of fixed over mobile broadband subscriptions –​to test if the fixed to mobile substitution process into force alters the impact of fixed-​wired networks on economic growth. Our econometric strategy presents two main advantages:  first, the adjustment process allows capturing of the non-​linear growth path of GDP; second, it allows internalizing of the time span required by the economic system for absorbing the network externalities arising from NGN. In this vein, following Islam (1995), studying the short-​run autoregressive behaviour of the GDP, we estimate the speed of adjustment ( δ ) and the mean time lag ( τ = 1/ δ ).

3.4  Estimation results The empirical analysis is divided into three parts. We first identify the relationship between the broadband penetration rate and the economic growth by simply replicating the Mankiw, Romer and Weil (1992) specification (Model 1).Then, we add two more control variables (i.e. edu and Tb) to test the stability of the effects exerted by the technological component. In particular, the first regressor (EDU) is aimed at verifying whether the share of high-​skilled labour generates an additive impact on economic growth compared to that of the overall labour force. The inclusion of the time trend component (Tb) is useful for considering the time path of the positive spillovers generated by the BB

34  Angelo Castaldo et al. Table 3.1 Dynamic panel GMM (Arellano-​ Bond) estimation output for OECD countries Dep.Variable: gdpwt

Model 1

Model 2

Model 3

gdpwt-​1

0.7877*** (0.0173) 0.1372*** (0.0295) 0.4388*** (0.0966) 0.0942*** (0.0112)

0.7833*** (0.0214) 0.3598*** (0.0923) 0.4598*** (0.106) 0.1128*** (0.0127) 0.0867*** (0.0278) –​0.0050*** (0.00148)

2.0265*** (0.174) 7312.55*** 32

1.7581*** (0.219) 4199.131*** 31

0.7041*** (0.0369) 0.2854* (0.168) 0.2421* (0.141) 0.1404*** (0.0200) 0.0867* (0.0464) -​0.0013 (0.00233) -​0.0093** (0.00463) 1.329e-​05 (0.000133) 2.4953*** (0.365) 1633.09*** 31

BB_​rate lbf gfcf edu Tb crisis tech_​change Cons. Wald chi-​squared N. Countries

Standard errors in parentheses: * p