The Economics of Digital Transformation: The Disruption of Markets, Production, Consumption, and Work 2021006891, 2021006892, 9781003144359

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The Economics of Digital Transformation

The unprecedented Covid-​19 crisis revealed the scale and scope of a new type of economy taking shape in front of our very eyes: the digital economy. This book presents a concise theoretical and conceptual framework for a more nuanced analysis of the economic and sociological impacts of the technological disruption that is taking place in the markets of goods and services, labour markets, and the global economy more generally. This interdisciplinary work is a must for researchers and students from economics, business, and other social science majors who seek an overview of the main digital economy concepts and research. Its down-​to-​earth approach and communicative style will also speak to businesses practitioners who want to understand the ongoing digital disruption of the market rules and emergence of the new digital business models.The book refers to academic insights from economics and sociology while giving numerous empirical examples drawn from basic and applied research and business. It addresses several burning issues: how are digital processes transforming traditional business models? Does intelligent automation threaten our jobs? Are we reaching the end of globalisation as we know it? How can we best prepare ourselves and our children for the digitally transformed world? The book will help the reader gain a better understanding of the mechanisms behind the digital transformation, something that is essential in order to not only reap the plentiful opportunities being created by the digital economy but also to avoid its many pitfalls. Katarzyna Śledziewska majored in Economics and MBA. She is Professor at the Faculty of Economic Sciences, University of Warsaw. She is Director of the Digital Economy Lab, where she coordinates numerous scientific and applied projects for business and public institutions. Her research focuses on digital transformation, international trade, regionalism, and globalisation. Renata Włoch majored in Sociology and International Relations. She is Professor at the Faculty of Sociology, University of Warsaw. She coordinates the Digital Sociology Program at the Digital Economy Lab, where she engages in numerous scientific and applied projects for business and public institutions. Her research focuses on digital transformation and globalisation.

Routledge Studies in the Economics of Innovation

The Routledge Studies in the Economics of Innovation series is our home for comprehensive yet accessible texts on the current thinking in the field. These cutting-​edge, upper-​level scholarly studies and edited collections bring together robust theories from a wide range of individual disciplines and provide in-​depth studies of existing and emerging approaches to innovation, and the implications of such for the global economy. Artificial Intelligence, Automation and the Future of Competence at Work Jon-​Arild Johannessen Capitalism, Power and Innovation Intellectual Monopoly Capitalism Uncovered Cecilia Rikap Robot Ethics and the Innovation Economy Jon-​Arild Johannessen The Co-​creative University Evaluation, Expectations and Economic Policy Implications Łukasz Mamica The Economics of Digital Transformation The Disruption of Markets, Production, Consumption, and Work Katarzyna Śledziewska and Renata Włoch The Political Economy of Digital Ecosystems Scenario Planning for Alternative Futures Meelis Kitsing For more information about this series, please visit: www.routledge.com/​ Routledge-​ S tudies- ​ i n- ​ t he- ​ E conomics- ​ o f-​ I nnovation/​ b ook-​ s er ies/​ ECONINN

The Economics of Digital Transformation The Disruption of Markets, Production, Consumption, and Work Katarzyna Śledziewska and Renata Włoch

First published 2021 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2021 Katarzyna Śledziewska and Renata Włoch The right of Katarzyna Śledziewska and Renata Włoch to be identified as authors of this work has been asserted by them 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 utilised 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. The authors wish to thank Alasdair Cullen for his work as translator/​proofreader. 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 Names: Śledziewska, Katarzyna, author. | Włoch, Renata, author. Title: The economics of digital transformation : the disruption of markets, production, consumption, and work / Katarzyna Śledziewska and Renata Włoch. Description: Abingdon, Oxon ; New York, NY : Routledge, 2021. | Series: Routledge studies in the economics of innovation | Includes bibliographical references and index. Identifiers: LCCN 2021006891 (print) | LCCN 2021006892 (ebook) Subjects: LCSH: Information technology–Economic aspects. | Information technology–Social aspects. | Information technology–Management. | Information society. Classification: LCC HC79.I55 S5958 2022 (print) | LCC HC79.I55 (ebook) | DDC 303.48/33–dc23 LC record available at https://lccn.loc.gov/2021006891 LC ebook record available at https://lccn.loc.gov/2021006892 ISBN: 978-​0-​367-​70042-​3 (hbk) ISBN: 978-​0-​367-​70044-​7 (pbk) ISBN: 978-​1-​003-​14435-​9 (ebk) Typeset in Bembo by Newgen Publishing UK

Contents

List of figures  List of tables  Preface  Acknowledgements  1 The foundations of the digital economy  What is the digital economy?  1 The foundations: the computer and the internet  5 The inflexion point: smartphones and sensors  12 The breakthrough: data and algorithms  16 The properties of the digital economy  22 Digital transformations  27 Key takeaways  29

vii xi xiii xix 1

2 How is market changing? 

45

3 How is production changing? 

77

The phenomenal career of the platform  45 Economic mechanisms of platforms  53 What makes digital platforms a challenge for traditional business?  63 Mechanisms of platformisation  66 Key takeaways  68 Industry 4.0  77 New technologies in manufacturing  80 Datafication of production  86 Intelligent product  91 Platformisation of production  92 Datafied distribution  95 The digital company  99 Key takeaways  102

vi Contents

4 How is work changing? 

118

5 How is consumption changing? 

151

6 How is globalisation changing? 

190

7 The digital economy in times of Covid-​19 

225

Automation of work  118 Platformisation of work  123 Datafication of work  129 New risks in the labour market  132 Skills for the future  134 Key takeaways  138 The new objects of digital consumption  151 From online shopping to the phygital experience  159 Platformisation of consumption  163 Collaborative consumption  167 The price of personalisation  169 Key takeaways  174 Digital flows  190 Digital trade in goods  193 Digital trade in services  198 The state in the digital global economy  201 Digital global order in the making  208 Key takeaways  210 The what-​if  225 Globalisation  227 Consumption  232 Work  237 Production  242 Market  245 The prospects for the digital economy  250 Key takeaways  257

Index 

277

Figures

1 .1 1.2

How is the economy changing? (scheme)  Moore’s Law –​number of transistors per microprocessor (in thousands, logarithmic scale, 1971–​2017)  1.3 PC/​Notebook and smartphones shipments (in billion units, worldwide, 2006/​2009–​2019)  1.4 Global ICT development indices (number per 100 people, 2000–​2019)  1.5 Smartphone penetration rate: (a) by country (2020); (b) worldwide (2016–​2020)  1.6 (a) Number of interconnected IoT devices (in billion units, worldwide, 2015 and 2022*); (b) global smart sensors market size (in billion USD, 2015 and 2022*)  1.7 Volume of data as an effect of digitalisation (in zettabytes, 2010–​2024)  1.8 Cloud infrastructure services vendor market share (in %, worldwide, 2017–​2020)  1.9 Global AI market size (in billion USD, 2015–​2023)  1.10 The mechanisms of datafication  2 .1 How is market changing? (scheme)  2.2 Big Tech market capitalisation (above 20 billion USD, 12.2020)  2.3 Blackberry’s global smartphone OS market share (in %, 2007–​2018, by quarter)  2.4 Global market share of Android and iOS (in %, 2012–​2019)  2.5 Comparison of GAFAM and BAT revenues compared to countries’ GDP (in billion USD, 2019)  2.6 Comparison of the number of platforms’ users with countries’ and regions’ population (in million individuals, 2019)  3.1 How is production changing? (scheme)  3.2 Percentage of companies investing in certain technologies (worldwide, 2020)  3.3 Projected global additive manufacturing market size (in billion USD, 2020–​2028) 

2 6 7 9 13 15 16 18 21 24 46 54 59 60 62 66 78 81 83

viii  List of figures 3.4

(a) Operational stock of industrial robots (in million units, worldwide, 2009–​2022); (b) share of traditional and collaborative robot unit sales (in %, worldwide, 2017–​2021)  85 3.5 Robot density in manufacturing sector (in units per 10,000 employees, selected countries, 2019)  86 3.6 Cloud computing services used over the internet (% of enterprises): (a) by country (2018); (b) EU28 (2014–​2018); (c) EU8 (by type, 2018)  87 3.7 Industrial Internet of Things (IIoT) market size (in billion USD, worldwide, 2017–​2025)  88 3.8 Enterprises with e-​commerce sales (% of EU28 enterprises, 2010–​2019)  96 3.9 E-​commerce platforms’ gross merchandise volume (GMV) (in billion USD, fiscal year 2019/​2020)  97 3.10 Size of the enterprise datasphere (in exabytes, worldwide, 2018) 100 3 .11 What is a digital company?  101 4.1 How is work changing? (scheme)  119 4.2 Estimated share of jobs at potential high risk of automation until 2030 (in %, European countries)  122 4.3 Share of workers using gig economy platforms (in %, worldwide, 2018, by source of income)  124 4.4 (a) Top 15 countries by searching ‘Amazon Mechanical Turk’ in Google (2004–​2021*); (b) searches of ‘Amazon Mechanical Turk’ in Google (scaled from 0 to 100, 2004–​2021*)  126 4.5 Individuals who have above basic digital skills* (in %, 2019)  136 4.6 Individuals who have written code in a programming language (in % of individuals with higher education, 2019)  136 5.1 How is consumption changing? (scheme)  152 5.2 Internet activities (% of EU28 individuals who used internet in the last 3 months, 2018 or 2019)  153 5.3 The US consumers spending on digital entertainment (in billion USD, USA, 1999–​2019)  154 5.4 Wearables unit shipments worldwide (in million units, 2014–​2019)  157 5.5 Percentage of individuals who purchased online within last 12 months (in %, 2019)  160 5.6 Percentage of EU28 individuals who purchased online certain goods (2019)  161 5.7 Barriers to buying online (% of individuals who ordered over the internet more than a year ago or who never did, EU28, 2009, 2019)  162 5.8 Number of stores which offer autonomous checkouts (in thousands, worldwide, 2018–​2024)  163 5.9 Most popular payment methods of online shoppers in selected regions (in %, 2019)  166

List of figures  ix 5.10 Percentage of (a) individuals who used any website or app to arrange an accommodation service from another individual (2019); (b) individuals who used any website or app to arrange a transport service from another individual (2019); (c) EU28 individuals who used any website or app to arrange a service from another individual (2017, 2019)  170 5.11 Time spent per day with digital versus traditional media (in minutes, USA, 2011–​2020)  171 6.1 How is globalisation changing? (scheme)  191 6.2 Cross-​border e-​commerce: percentage of individuals who purchased online from sellers abroad (a) by country in 2019; (b) in EU28, 2011 and 2019  195 6.3 Cross-​border e-​commerce: percentage of EU28 enterprises (all enterprises, without financial sector) with e-​commerce sales to (a) other EU countries; (b) to the rest of the world, 2011 and 2019  195 6.4 Global export of services (in trillion USD, 2005–​2019)  198 6.5 Number of Bitcoin transactions (30-​day average, in millions, 02.2009–​01.2021)  201 6.6 Top 10 tech companies by market capitalisation in 2020 compared with countries’ GDP in 2019 (in trillion USD)  203 6.7 The Digital Trade Restrictiveness Index  205 6.8 Structure of global export of high-​skill and technology-​ intensive goods by group of countries (in %, 1995, 2007, 2019) 207 7.1 How is Covid-​19 changing the digital economy?  226 7.2 Forecast of the tourism sector’s GDP share (in %, worldwide, 2019–​2025)  227 7.3 Global merchandise export’s growth rate (in %, year-​on-​year, 2006–​2020, by quarters)  228 7.4 Adoption of government endorsed Covid-​19 contact tracing apps in selected countries (% of individuals, 07.2020)  232 7.5 In-​home media consumption growth due to the Covid-​19 outbreak (in %, internet users, worldwide, 03.2020)  233 7.6 (a) Digital media revenue (in billion USD, worldwide, 2019, 2020); (b) digital media users (in billions, worldwide, 2019, 2020)  234 7.7 Number and growth rate of Netflix and Spotify paid subscribers (in million users and in %, worldwide, Q4 2019 –​Q3 2020)  235 7.8 Change in retail sale via mail or via internet (index of turnover, change in %, year-​on-​year change for each month, EU27, 02.2020–​10.2020)  236 7.9 Percentage of employed individuals working remotely before (2019) and during (2020) Covid-​19 pandemics (in %, EU countries with available data, 2019 and 2020)  239

x  List of figures 7.10 Number of searches of selected terms in Google (scaled from 0 to 100, 100 for the highest score, worldwide, 01.2020–​ 07.2020)  7.11 Development of industrial production (volume index 2015=100, EU27, 01.2020–​07.2020, monthly, Main Industrial Groupings, MIGs)  7.12 Year-​over year growth of GAFAM and BAT market capitalisation (in %, 2019–​2020)  7.13 Year-​over-​year growth of selected companies’ revenue (in %, 09.2019–​09.2020)  7.14 Regional ICT-​related data (2019/​2020) 

240 243 246 248 252

Tables

2 .1 2.2 2.3 2.4 2.5

Selected categories of platforms  The ecosystems of services provided by platforms  Examples of price differentiation used by platforms  Direct and indirect network effects found in platforms  Mechanisms for institutionalising trust, according to platform 

48 50 56 58 61

Preface

A time of digital revolution In March 2020 our team in the Digital Economy Lab at the University of Warsaw had its last weekly seminar in person. From then on our meetings transferred to Google Meets. We started to collaborate on our numerous scientific and commercial projects online, hopping from Slack to Microsoft Teams, from Skype to Zoom, juggling several dozen Google Docs at a time. Admittedly, we had it easier than many other organisations: working in a research institution focused on digital transformation, we knew how to transfer our work life online by extending the routines we already practised. Being academics, we were used to the tiring confluence of work and family life caused by remote and virtually never-​ending virtual work. Still, there were plenty of things we had to learn, and learn quickly.We needed to learn how to effectively teach our students scattered all across the country (and others abroad), fighting to conquer their screen fatigue. Sometimes we had to shout our lectures over the noisy lessons our children were having in an adjacent room. Our students would giggle as we had to open the door for grocery deliveries in the middle of analyses of e-​commerce patterns. With trepidation we noticed yet another symptom of growing screen addiction in our housebound kids, while we ourselves were glued to our screens while working and relaxing. Covid-​19 ruined many routines we used to take for granted: going out to work or to a restaurant, taking our kids to school, attending conferences in person, meeting our friends for a coffee or a drink, and travelling abroad. We were palpably experiencing how indispensable digital infrastructures, products, and services are, as we relied on them to work, learn, shop, seek medical advice, relax, and socialise. And in the midst of the pandemonium the pandemic wrought we persevered with finishing our book about the digital economy. For over two decades the latter has been shaped by the increasing use of digital technologies (such as artificial intelligence, the cloud, and the Internet of Things) by enterprises, public institutions, and non-​governmental organisations, employees, consumers, and citizens. But during the Covid-​19 pandemic the digital revolution actually

xiv Preface gained momentum. With amazement we observed how the mechanisms we had already described in our completed chapters were brought into the fore. The unprecedented crisis revealed the scale and scope of a new type of economy taking shape in front of our very eyes. This book sets out to identify its mechanisms and manifestations. Its most visible feature is the exponential growth of data produced by the ubiquitous connected digital devices and flowing through online networks. Application of ever more efficient tools for collection, procession, and analysis, particularly the algorithms of artificial intelligence, allows for deriving economic, social, and political value out of these abundant data (later in the book, we will use the notion of datafication to relate to this process of drawing value from abundant data by intelligent algorithms). New digital business models, such as platforms, reorganise the market, entering into new sectors of the economy. The nature of work and employment relations is being altered, along with the modes of production and consumption. The essential functions of the state are changing, along with the rules governing the global economic order. Society, the economy, and politics are all undergoing multiple digital transformations. The digital transformation is closely associated with the opportunity to develop economically, improve people’s quality of life, and realise various democratic and emancipatory ideals. At the same time, it is creating multiple and unprecedented threats. Israeli historian Yuval Noah Harari has emerged as a spokesman for those worried by the consequences of digitalisation. In his book 21 Lessons for the 21st Century (2018)1 he convincingly writes about the growing ‘tyranny of technology’. In his opinion, artificial intelligence and other advanced technologies may sound the death knell for liberal democracy and serve to introduce a system of totalitarian control, a form of digital dictatorship. Creeping automation also brings the risk of the emergence of a mass of unneeded workers. ‘The same technologies that might make billions of people in the world economically irrelevant might also make them easier to monitor and control’, writes Harari.Yet another concern is the growing use of algorithms in decisions made in the spheres of politics and the economy: according to Harari, in the future, machines may wield power over us. A host of researchers shares this technological pessimism. Shoshana Zuboff, in her book The Age of Surveillance Capitalism (2019),2 argues that large technology-​driven corporations are trying to introduce continuous surveillance to track their users’ behaviour, threatening freedom and democracy. Andrew McAfee and Eric Brynjolfsson in Platform, Machine, Crowd (2017)3 emphasise how platforms have introduced new rules to the market, which are difficult to regulate properly. This is associated with the fragmentation of the marketplace and the accelerated elimination from it of those companies and those employees who are unable to find a foothold in the new reality.This too goes for countries.Those that do not make the digital transformation a political priority may fall into the digital underdevelopment trap. One thing is certain: there is no going back. These new technologies have infiltrated our everyday lives and will continue to change them. We ourselves

Preface  xv will also change: specifically, our minds, our attitudes, and the way we live our lives and go about our work. We are experiencing disruptions to the rules that have hitherto ruled our private, professional, and public lives. The way in which enterprises and public institutions function is changing, and so too are conditions of economic and social development. A better understanding may help us reap the plentiful opportunities created by the digital economy and to mitigate its risks. We are being inundated from all sides with sensational data and information about these radical changes. We all need conceptual frameworks to help us navigate this flood and construct empirically anchored interpretations, analyses, and operational knowledge. This is also how we see the purpose of this book: in it, we track the myriad changes that have occurred due to the influence of digital technologies. Above all, we try to identify the key mechanisms of these changes: datafication and platformisation. We refer to academic insights from economics and s­ ociology while giving numerous empirical examples drawn from basic and applied research that we have carried out at the DELab UW. We are uniquely placed to narrate these changes as we are riding the wave of digital transformation ourselves. DELab UW –​the University of Warsaw’s Digital Economy Lab –​was established in 2013 thanks to a grant from Google. From the very beginning, the grant agreement guaranteed that we would retain scientific autonomy, especially with regards to selecting research subjects and the opinions we express. The grant was used to build up our institution’s potential, bring together a competent team, and join international collaboration networks. Equally importantly, we have learned to partner with companies, public institutions, and NGOs outside the university, something which has required us to acquire new competencies, ones not completely obvious to researchers: the ability to find one’s feet in business networks, to establish partnerships with institutions outside academia, and to communicate research results in an accessible way. Currently, DELab operates as an autonomous inter-​faculty project set up at the University of Warsaw, where we study the digital economy, society, and politics from an interdisciplinary perspective. We are academics –​economists, sociologists, lawyers, computer scientists, data science specialists –​but we are happy to work with business and public institutions. Over the course of several years, we have completed dozens of scientific and applied projects (e.g., NGI Forward for the European Commission, https://​fwd.delabapps.eu/​), and we shall refer to some of these later in the book. Thanks to having observed the changes taking place in specific cases and in relation to specific sectors, we have acquired considerable knowledge regarding the mechanisms involved in digital transformation. Therefore, this book is not just a review of the subject literature –​it also reports our multifaceted and varied empirical experiences. While writing this book, we have tried to organise knowledge gleaned from various sources which we have collected during our work. However, we have endeavoured to weave these diverse strands into a coherent story. We have constructed our argument with the support of a host of scientific publications on

xvi Preface new technologies and phenomena relating to the digital economy and society. Usually, these are articles on very narrow topics that contribute to the general knowledge in a field. We also reference books written for a mass audience by leading experts on the issues of digital technologies and digital transformation. Frequently, we cite data from non-​academic sources. In addition, we freely make use of, among other sources, reports published by consulting companies such as McKinsey and Deloitte.We are, of course acutely aware of their limited neutrality, their tendency to exaggerate phenomena that concern the digital economy and to overemphasise their consequences. Still, the researchers employed in these companies do have access to the types of informants, experts, respondents (in the form of, for example, global companies), and not to mention budgets, that researchers can only dream of. As a result, these reports have given us access to comparative data on a truly global scale. When assessing the consequences of digitalisation, we have tried not to adopt the over-​excited tone that permeates these publications, but doubtlessly we have not managed to avoid it everywhere. The work on creating this monograph has been truly interdisciplinary. Each of us has brought to it our own specifically disciplinary view of reality, anchored on the one hand in economics, and on the other in sociology, but filtered through many years of creative conceptual, methodological, and theoretical discussions and joint research on the digital economy and society. We are linked by the conviction that the complexity of phenomena does not have to translate into a complex message. This book is, therefore, a reflection of our institutional anchoring: it is scientifically grounded, but open to drawing from businesses and organisations that the university has contact with, and it is focused on conveying a simple yet not simplistic message.

The structure of the book Chapter 1 aims to concisely delineate the technological context of the changes that are taking place. We describe key technologies that have enabled digitalisation (computers, the internet, and smartphones) and technologies that determine the nature of the digital transformation (the cloud, the Internet of Things, artificial intelligence, robots, and blockchain). We also describe the specific features of the current technological revolution based on the multifaceted character of innovation, and resulting in invention of digital devices. Growing numbers of hyperconnected digital devices started to generate oodles of data, which coincided with the breakthrough in the research on artificial intelligence. A combination of abundant data and intelligent algorithms paved ground for datafication, and better connectivity boosted internet networks enabling platformisation. Bit by bit, the entire economy, starting from the IT sectors and engulfing the traditional sectors, is undergoing digital transformation, and becoming the digital economy. Chapter 2 describes the emergence of digital platforms, a new, network-​ based business model deftly harnessing the power of abundant data and intelligent algorithms. Platforms comprehensively adopt ‘data-​first, AI-​first’ approach.

Preface  xvii Platforms offer scalable intermediation between market sides by applying advanced matching and recommendation algorithms.They quickly expand into traditional sectors of the economy, initially affecting those companies in which data is the most critical commodity. Moreover, the platform business model was used by the Big Tech companies, such as Google, Amazon, Facebook, Apple, and Microsoft, to expand and consolidate their market position. Digital transformation embraces not only services but also the traditional ‘physical’ sectors of the economy. Rolling out of new technologies in manufacturing contributes to integrating IT with operational systems, vertical and horizontal processes, and to comprehensive datafication of the company operations. Both the company’s internal structure and relationships with customers, suppliers, and subcontractors become networked and open to platformisation. This revolution earned the name of Industry 4.0. New business models, based on the ‘data-​first, AI-​first’ approach, are adopted in manufacturing and other sectors of the traditional economy. We will write more about it in Chapter 3. The most significant organisational change, stemming from technologically-​ enabled changes in operational and business models, is happening in the area of work. Some types of human labour, both physical and mental, are increasingly automated. Intelligent machines and systems are overtaking some tasks and positions and more often working alongside people. The labour relations are increasingly intermediated by digital platforms operating on a local and global level.And the work-​life itself is being datafied and open to be monitored by intelligent systems. These are the themes of Chapter 4. Digital transformation is both propelled by –​and contributes to –​changes in consumer attitudes and expectations. All kinds of consumption are increasingly channelled through digital devices. Chapter 5 describes new objects of digital consumption: digital information goods (such as video streaming or online games) and intelligent products, complemented by a range of services provided via inbuilt software. We will show how companies use abundant data produced by digital consumers to personalise their offerings. We will also address the new processes of digital consumption enabled by digital platforms, such as online shopping and collaborative consumption. The rise in digital consumption adds to the exponential growth of cross-​ border data flows. Chapter 6 shows that the digital economy is inherently globalised. Global trade in goods is being digitalised, while global trade in services increasingly involves digital information goods (digital content), intelligent products, and services (digital and localised) provided via global digital platforms. Simultaneously, the growing preponderance of Big Techs and other digital platforms undermines the nation-​states’ traditionally understood sovereignty. In response, states engage in digital protectionisms and introduce measures to shield their data sovereignty. In conclusion, we show how the digital global economy is tinted with the growing rivalry of two dominant technology ecosystems: the American and the Chinese, overlaid with considerable geopolitical dissent between China and the USA.

xviii Preface Finally, in Chapter 7, we trace the impact of the Covid-​19 pandemic on all the areas of the emerging digital economy that we described in previous chapters. The massive turn to remote work, education, and entertainment increased households and companies’ dependence on digital infrastructures, products, and services provided by a handful of powerful Big Techs. We conclude by indicating the conditions necessary for making the most of the opportunities created by the ongoing digital revolution.

Notes 1 Harari,Y.N. 2018. 21 Lessons for the 21st Century. Random House. 2 Zuboff, S. 2019. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs. 3 McAfee, A. and E. Brynjolffson. 2017. Platform, Machine, Crowd. Brilliance Audio.

Bibliography Harari,Y.N. 21 Lessons for the 21st Century. Random House. 2018. McAfee, A. and Brynjolffson, E. Platform, Machine, Crowd. Brilliance Audio. 2017. Zuboff, S. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs. 2019.

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Acknowledgements

We would like to thank all of our DELab team for the inspiration they give us every day, for their intellectual courage and the patience they exhibit with our constant influx of research ideas. This book would not have been possible without the unwavering support and invaluable advice of the great Frances Cairncross, author of The Death of Distance (1997). Not only did she read the text critically and offer constructive suggestions on how to improve it, but she also helped us to power through the gloominess of the lockdowns. The opportunity to work with her was truly salutary. Katarzyna offers thanks to her family for their understanding, support, and help relieving her of domestic burden. And of course she must also thank her friends, with whom she could relax during their long leisurely walks. Renata would like to thank her family for their patience and support, particularly her son for not saying more than five times a day ‘When will you finish this stupid book and play with me?’

1  The foundations of the digital economy

Abstract How did the digital economy come into being? This introductory chapter takes you on a quick ride through the history of the technological ­revolution that laid the foundations for the digital economy by creating lots of mobile, hyperconnected, and mightily functional digital devices, such as smartphones. Ever more user-friendly devices paved the ground for ­ digitisation, i.e., encoding data in a machine-readable format. Next, we show how the exponential growth in the amount of digitised data, coupled with the advanced analytical tools of artificial intelligence (which we refer to as ‘datafication’) is contributing to the acceleration and intensification of the innovation processes, changing the way societies and economies work. We conclude by describing the digital economy as it has emerged so far through multiple digital transformations, and by emphasising the role of networks that ­process the growing flood of data.

What is the digital economy? You could tell that change was taking place when in 2019 UNCTAD, the UN Conference on Trade and Development, altered the traditional title of its yearly report from ‘The Information Economy’ to ‘The Digital Economy’, justifying it by the need to focus on the ‘far-​reaching and highly significant impacts expected from digitalization’.1 The concept of the information economy took off at the end of the 1970s, having grown out of the idea of the knowledge economy, a concept that had been in use for almost two decades by then.2 Both concepts emphasised the growing role of information and knowledge in economic processes, as part of the growing role of services, rather than industry, in first-​world economies. Starting in the 1990s, the idea of the internet economy or the dotcom economy gathered favour.3 In the 2010s another international organisation, the Organization for Economic Cooperation and Development (OECD), started to use the notion of the digital economy alongside the internet economy, and in 2015 it published the Digital Economy Outlook report which ‘replaced and built upon the OECD Communication Outlook and Internet Economy Outlook’ in order to ‘provide a

2  The foundations of the digital economy

Figure 1.1 How is the economy changing? (scheme). Source: Own elaboration.

more holistic overview of converging trends, policy developments and data in the digital economy on both the supply and demand sides’.4 These are no mere linguistic modifications –​they reflect the growing consensus among the economists close to the decision-​makers that we may observe the emergence of a new set of rules for economy. Much less consensual is the specification of these rules leading to the definition of the digital economy. The phrase, the digital economy, first appeared in the mid-​1990s (albeit without a precise definition) in the title of Don Tapscott’s book, The Digital Economy: Rethinking Promise and Peril in the Age of Networked Intelligence. Tapscott described an era in which intelligent machines and people were starting to connect through technology.5 Equally elusive was the definition proposed in

The foundations of the digital economy  3 2000 by Eric Brynjolfsson and Brian Kahin in their book Understanding the Digital Economy: Data, Tools, and Research. They used the term to describe ‘the recent and still largely unrealised transformation of all sectors of the economy by the computer-​enabled digitization of information’.6 The first definitions proposed by the OECD (2012) and experts at the European Commission (2013) tended to conflate the digital economy with the internet economy. The OECD acknowledged that the digital economy ‘enables and executes the trade of goods and services through electronic commerce on the Internet’,7 while the European Commission declared that it was ‘an economy based on digital technologies (sometimes called the internet economy)’.8 A team appointed by the British Economic and Social Research Council9 to study the impact of the digital economy on socio-​economic development found, in 2017, that the literature on the digital economy generally identified it simply as an economy which ‘functions primarily by means of digital technology, especially electronic transactions made using the Internet’,10 and is ‘an amalgamation of technology and people’s activities’.11 A technical note prepared in 2017 for UNCTAD emphasised that a new digital economy is developing thanks to the implementation of advanced cyber-​physical systems (connecting machines, IT systems and employees). It includes technologies and processes based ‘in one way or another’ on advanced information and communication solutions, such as the robotisation and automation of production, new data sources arising from mobile –​and ubiquitous –​internet connectivity, cloud computing, big data analytics, and artificial intelligence. These technologies ‘seem poised to dramatically reduce demand for routine tasks and transform the location, organization, and content of knowledge work’.12 A more specific description of the digital economy was one advanced by the OECD in 2015: the digital economy is characterised by an unparalleled reliance on intangibles, the massive use of data (notably personal data), the popularity of platforms as a business model, and the difficulty of determining the jurisdiction in which value creation occurs.13 In February 2018 the International Monetary Fund (IMF) emphasised that the ‘digitalization of the economic activity can be broadly defined as the incorporation of data and the internet into production processes and products, new forms of household and government consumption, fixed capital formation, cross-​border flows, and finance’.14 In 2020 OECD, having scrutinised a range of definitions, came up with a general, bind-​them-​all definition of the digital economy: it ‘incorporates all economic activity reliant on, or significantly enhanced by the use of digital inputs, including digital technologies, digital infrastructure, digital services and data. It refers to all producers and consumers, including government, that are utilising these digital inputs in their economic activities.’15 Our approach draws from the conceptual effort that OECD and International Monetary Fund experts have made, but it sets to emphasise the trends that are changing the economy. The digital economy emerges through countless, diverse, dispersed, and uneven processes of digital transformation, which consist in

4  The foundations of the digital economy changing how the consumers, employees, markets, enterprises, and other organisations function. They are made possible by the development and rolling out of breakthrough technologies for producing, collecting, processing, analysing and using data, such as connected mobile digital devices, the Internet of Things, and the cloud, and, above all, algorithms of artificial intelligence. The information gained from abundant data analysed more cheaply, quickly, and efficiently by intelligent algorithms builds a new economic layer of the digital economy through the introduction of new and ever more personalised digital products (goods and services) and development of new business models based on ever-​growing networks of connected people, organisations, and machines (such as platforms) and management prioritising on the rule of ‘data-​first, AI-​first’. This is, admittedly, a working definition that needs empirical grounding. In the next few chapters of the book we will flesh it out with more facts: we will show how rolling out of new digital technologies contributes to the digital transformation in the areas of production, consumption, work, and globalisation. But first, we want to shortly describe how we got here. Digital transformations are contingent on the bewildering pace of digital innovation, which makes use of increasing amounts of data and intelligent algorithms. The general-​purpose information and communication technologies, such as the computer and the internet, have formed the basis of a hectic ecosystem in which subsequent –​ever more efficient and user-​friendly –​inventions and innovations are rapidly accumulating. These innovations are evolving faster than ever, developing in parallel in different areas, and combining and supporting each other.16 This pattern also characterised previous technological revolutions (the first one, epitomised by the steam engine, and the second one, which brought about electrification), but it occurred at a much slower pace, partly because knowledge circulated more slowly in the pre-​digital world.17 Through the developments and innovations of the years since then, we have seen the astonishing emergence of a new world in which international trade, corporate structure, politics, health, and education –​ indeed, almost every aspect of life –​all are being transformed. This chapter is about the intricate chain of technological innovations within the third technological revolution, which led up to that historic moment –​and beyond, to what has become known as the ‘fourth technological revolution’. We aim to map out, in language that we hope the non-​technical readers can comprehend, both the mechanisms behind this revolution, and some of the ways our economic life and indeed our societies are being altered beyond past imagination. We will structure this concise description of the technological revolution around the development of the four basic components of every digital product: device (hardware), communication (network), service (software), and content (data and information).18 You may read it as a kind of an explanation of how the smartphone, the crowning result of the combinatorial innovation of the third technological revolution, and the epitome digital device, came into being. First, we show how the computing machines got smaller and

The foundations of the digital economy  5 mobile; then, we present how they began to communicate with each other; thirdly, we show how software gave them their enhanced functionality which in turn made them wildly popular among administration, business, and consumers; and lastly, we explain how, by providing access to digitised content, they started to produce huge quantities of data and opened vast new possibilities for human endeavour.

The foundations: the computer and the internet Device Today, all computers work on a principle similar to that of the steam-​driven analytical engine designed (but not built) by the British mathematician Charles Babbage in 1834. It was to be built with a store (memory for storing data, with a capacity of 675 bits) and a mill (for performing calculations). It would be programmed, he envisaged, using punch cards similar to those used in Jacquard looms; the first programmes for the Babbage machine were written by another mathematician, Ada Lovelace.19 The analytical machine would do the arduous and time-​consuming work of manual calculations.20 However, Babbage never managed to build his machine: it was just too complicated, too large, and too expensive. Babbage’s invention would have been a steam-​powered machine as big as a small locomotive. It required several other key innovations to get from there to the minicomputer, or smartphone, that you hold in your hand. Electrification was one: that allowed the basic design to become smaller and simpler. British programmable electronic machines, built in 1943–​1945, were used to decipher German military communications, and were rightly called Colossuses. The first computer designed for commercial purposes –​Britain’s Ferranti Mark 1 from 1951–​weighed half a tonne and required advanced skills to operate. The invention of the transistor in 1948 further shrank the size of the computer and replaced the inefficient vacuum tubes that were then used for calculation. A decade later, the integrated circuit appeared, bringing together all a computer’s electrical components (transistors, conductors, resistors, diodes) on one silicon chip. However, each computer function was still carried out by a separate chip. The real breakthrough came with the invention of the microprocessor. Intel’s first microprocessor in 1971 was roughly the size of a postage stamp, consisted of 2,300 transistors, and carried out 60,000 operations per second. A microprocessor produced just a year later had 3,500 transistors and could do 300,000 operations per second. This roughly confirmed the thesis proposed in 1965 by one of Intel’s founders, George Moore. He originally assumed that the number of transistors on a microprocessor would double every one and a half years. A decade later, Moore tweaked his claim: the number of microprocessors would now double every two years. Although Moore’s ‘law’ was more a norm based on observations, it has remained amazingly accurate and still seems to hold in 2020.21

6  The foundations of the digital economy

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Figure 1.2 Moore’s Law –​number of transistors per microprocessor (in thousands, logarithmic scale, 1971–​2017). Source: Own work based on Our World in Data. Moore’s Law: Transistors per microprocessor. https://​ourworldindata.org/​grapher/​transistors-​per-​microprocessor?time=1971. latest (accessed 23 January 2021).

Miniaturisation has made computers smaller and cheaper. The appearance of the Intel 8008 processor in 1972 contributed to the creation of the first microcomputers (such as France’s Micral N, launched in 1973), which in the 1970s would result in the advent of personal computers (PCs, desktop computers). In 1977, Apple Computers, founded by Steve Jobs and Steve Wozniak, began to sell the Apple II, which quickly found its way not only into offices but also into the homes of ordinary Americans. It displayed the talent for consumer-​friendly innovation that became the hallmark of Steve Jobs’s work. Unlike previous commercially available home computers, it had a colour display, a keyboard, and 48 KB of RAM (memory) –​an impressive feature at the time. Then in 1981, the Osborne company launched the first portable computer. It had no battery, but as it only weighed about 10 kg, it could be moved relatively easily from one place to another.The first true laptops appeared in the late 1980s. One, for example, was the Compaq LTE, which led The New York Times to write that ‘computing on the road becomes an almost effortless extension of computing in an office’.22 (Interestingly, this laptop, once the lightest in the world, is still used to service the car that was once the fastest in the world, the McLaren F1.)23 Another seminal moment in the development of personal computers was the first PowerBook, launched by Apple in 1991.This model set a new standard for the design of laptops. The computers were not the only devices that became rapidly smaller. Miniaturisation also affected the design of phones.The first mobile phone to go on general sale was the Motorola DynaTAC 8000x, on the market in 1984. It

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Figure 1.3 PC/​ Notebook and smartphones shipments (in billion units, worldwide, 2006/​2009–​2019). Source: Own work based on IDC. 2020. Global smartphone shipments from 2009 to 2019 (in million units). Chart. In Statista. www.statista.com/​statistics/​271491/​worldwide-​ shipments-​of-​smartphones-​since-​2009/​ (accessed 14 December 2020); Gartner. 2020. Total unit shipments of personal computers (PCs) worldwide from 2006 to 2019 (in million units). Chart. In Statista. www.statista.com/​statistics/​273495/​global-​shipments-​of-​ personal-​computers-​since-​2006/​ (accessed 14 December 2020).

weighed almost a kilo (even its designers called it ‘The Brick’), was very expensive (selling for $3,995), and the battery lasted a mere half an hour. And yet it was an instant hit, blowing away competing ‘mobile phones’, i.e., car phones, which ran off a car’s battery. Soon mobile phones became smaller, cheaper, and truly mobile thanks to a smaller and more efficient battery, became a necessity not only for business but also for the ordinary people. Connection In parallel, another revolutionary innovation was under way. Computers were increasingly powerful, but they were also huge and unmovable. The people who used them needed a way to exchange data.24 The answer arrived in the form of the network, invented in the early 1960s by a visionary psychologist and computer scientist from MIT, Joseph C.R. Licklider. In an article entitled On-​line Man-​Computer Communication, written in 1962, ‘Lick’, as his admirers called him, described how an extensive network of computers exchanging data and programs might function, and might enable long-​distance communication and indeed a global reach.25 He was the right man in the right place. He was already working at the Pentagon’s Advanced Research Projects Agency (ARPA) and his ideas

8  The foundations of the digital economy promised to solve a problem that had baffled the military. Defence systems were built radially, around one central, main computer. If that computer were to be hit by –​say –​a pre-​emptive nuclear strike, the entire system would be destroyed. Lick’s solution was to create a network of devices connected in parallel, communicating via packet switching, i.e., dividing the data stream into smaller parts, and then sending those packets via telecommunications links between network nodes. In 1969, researchers at the University of California in Los Angeles (UCLA) attempted to log on to a computer at Stanford University, 600 km away, and send data in the form of one word: ‘login’. The enthusiastic scientists delivered a running commentary over the phone as the letters gradually appeared on the target screen. After the ‘G’ appeared, the system froze. Despite this, the event marked the beginning of the internet revolution. Soon, the University of California at Santa Barbara and the University of Utah had also connected to ARPANET (the network built by ARPA). Simultaneously, other institutions were working on their own networks and technological solutions: Britain’s National Physics Laboratory (the NPL network), the University of Hawaii (ALOHAnet), Michigan Educational Research Information Triad (the Merit Network), France’s CYCLADES, Tymnet and Telenet, and others. Each of the networks worked using different network protocols. However, Getting computers to talk to one another –​networking –​had been hard enough. But getting networks to talk to one another –​internetworking –​ posed a whole new set of difficulties, because the networks spoke alien and incompatible dialects. Trying to move data from one to another was like writing a letter in Mandarin to someone who only knows Hungarian and hoping to be understood.26 Further expansion of the network therefore required the creation of a standardised data transmission system.The solution was a protocol model called TCP/​ IP (Transmission Control Protocol/​ Internet Protocol), developed in 1973 by Robert Kahn of ARPA and Vinton Cerf from Stanford University. It provided safer, more attack-​resistant transmission, and the ability to add new networks without interrupting the operations of those that already existed. Over the next decade, it replaced all previous protocols in ARPANET. In 1981, the US National Foundation for Science supported the development of a network of regional university campuses, connected to ARPANET, which eventually evolved into NSFNET (the National Science Foundation Network). NSFNET served as a skeleton for US networks until the emergence of private internet service providers. It was then that the term ‘internet’ came into general use as an abbreviation of the term ‘internetworking’, used to describe how networks used the TCP/​IP protocol to work together. Meanwhile, the commercialisation of another technology incubated by the military contributed to the growing popularity of mobile phones. The analogue telecommunication standard used by Motorola’s ‘Brick’ was not very stable or secure. In 1991 the 2G (i.e., 2nd Generation) standard was introduced.

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It offered a completely digital, encrypted signal, enabling the sending of short text messages (SMS, for ‘Short Message Service’). The digital signal improved connections, provided better coverage over a larger area and reduced battery use.27 This accelerated both demand for mobile phones and also their technological development. The 3G network, rolled out in 1998, enabled data transmission and access to the internet. Since 2009, 4G networks have enabled data transfer speeds that are ten times faster than the 3G standard, and often faster than traditional landline internet connections. In 2020 the implementation of a new mobile connection standard –​5G –​entered a decisive phase. It provides data transmission speeds of up to ten times faster than before, which minimises latency (time of response) and battery consumption (by as much as 90%). Thanks to 5G, individual users can download games faster and watch films in better quality. Above all, it will make it possible to connect a much larger number of devices, paving the way for the Internet of Things (to which we will come later in this book). Service The function of the first computers was simply to compute, or to perform quickly and efficiently the tedious calculations previously executed by humans (mainly by women).28 This explains why governments, and particularly the military, found computer technology so valuable. Not only were the Colossuses

10  The foundations of the digital economy of the 1940s used for decoding German messages and winning the strategic struggle at sea. The need to calculate the data for building a thermonuclear bomb led to the next breakthrough invention, when John von Neumann, a mathematician and early computer scientist, developed a new rule for computer architecture. Previously, computers were programmed externally with hundreds of thousands of punch cards.29 The new ones were equipped with previously inscribed programs and were thus much more user-​friendly.30 In 1955, there were only 250 computers in the world.31 But a decade later there were 20,000 and they were being used by armies, universities, public institutions, and some big corporations to support routine administrative processes. As Martin Campbell-​Kelly, an expert on the history of computing, points out, these tasks might include: payroll, billing, and report generation –​all of which tasks had already been at least partially mechanized through the use of typewriters, tabulating machines, and mechanical calculators. In many large corporations this work had already been delegated to specialist data-​processing departments. Many of the computers IBM introduced in the late 1950s were designed specifically to appeal to such departments and were in fact marketed as tools for ‘electronic data processing’, or EDP. Over the course of the 1960s, EDP would drive the majority of computer use in the corporation, despite the fact that many computer experts saw it as the least interesting application of computer technology.32 Still, the size and the cost of the computers placed them beyond the reach of small and medium-​sized businesses. A decade later, personal computers had become smaller and more affordable but were still cumbersome to use.A popular build-​it-​yourself computer called the Altair did not have a keyboard or a screen. It was operated by switches, and the results of its calculations appeared in the form of light-​emitting diodes (LEDs) that lit up. The Altair became easier to use when the company that produced it hired two Harvard students –​Paul Allen and Bill Gates –​to adapt a programming language to its requirements. Gates and Allen used the money they thus earned to establish their own company, which they called Micro-​Soft. In 1981, their company introduced DOS (i.e., Disk Operating System), which enjoyed instant popularity and would later become the basis for Windows. In parallel, Steve Jobs and Steve Wozniak were encouraging programmers to create applications for Apple. One of the most useful turned out to be the VisiCalc spreadsheet, developed in 1978: it freed the accountants from tedious and time-​consuming work on paper ledgers.33 It became one of the first ‘killer apps’, which convinced millions of companies to invest in computers. But why would anyone want a computer in their home, even if equipped with electronic spreadsheets?34 The first computers for personal use were bought mainly by enthusiastic hobbyists, often to play games on. The real explosion in computer popularity came only with the development of the internet and the

The foundations of the digital economy  11 World Wide Web. Not at once, though. The first British internet service company, Demon Internet, had nearly 3,000 customers in 1993 and according to its founder Cliff Stanford: The question we always got was: ‘OK, I’m connected –​what do I do now?’ It was one of the most common questions on our support line. We would answer with ‘Well, what do you want to do? Do you want to send an email?’ ‘Well, I don’t know anyone with an email address.’ People got connected, but they didn’t know what was meant to happen next.35 Those already connected mainly used the oldest internet application, i.e., e-​ mail, which had existed since the early 1970s. A fundamental problem with the early internet was how to search for information online. At the end of the 1980s, archiving programs began to appear: one of the first was Archie, created by Alan Emtage and Peter Deutsch, two students at McGill University in Montreal. From time to time, Archie would search all the available sites, create a list of files posted on them, and then build an index. Using it, however, was quite complicated. In 1989 Tim Berners-​Lee, a British computer scientist, and other employees at the European Laboratory for Particle Physics at CERN in Switzerland invented a protocol that made publishing, searching for, and using information online much easier. It became the basis for the World Wide Web, ‘a wide-​area hypermedia information retrieval initiative aiming to give universal access to a large universe of documents’, as Berners-​Lee once described it.36 In 1993, Berners-​Lee put the World Wide Web in the public domain, thus making it available to everyone. The secret to the World Wide Web’s success was a graphic browser, a piece of software that could retrieve on command a web page from a particular site –​and display both text and images on the same page, which greatly simplified surfing (i.e., navigating from one online page to another). Now everyone could search for digital content quickly and easily. However, some sort of system was still needed for creating a hierarchy of the content that might interest a particular user. One, based on a ranking system, was proposed by two students at Stanford University, Larry Page and Sergey Brin. Thus was the Google search engine born.37 Content Digitisation means that analogue data is encoded in a digital format, which makes it machine-​readable. To quote online Britannica, nota bene the digitised version of the voluminous paper encyclopaedia, ‘The versatility of modern information systems stems from their ability to represent information electronically as digital signals and to manipulate it automatically at exceedingly high speeds’38. The first instance of binary –​i.e., encoded in zero, one, two symbols system –​digitisation were punch cards invented by Ada Lovelace for the Babbage machine, which were to tell the machine what operations

12  The foundations of the digital economy should be executed and in what order. Text was first digitised in the 1960s to speed up the time and reduce the cost of publication of two professional abstracting journals.39 The first digitised photograph was made in 1957 with the help of a computer whose main function was to carry thermonuclear weapons calculations –​uncannily enough, it was a picture of a baby boy.40 Digitisation gained momentum in the 1980s when private companies and public institutions began linking up their desktop computers via local area networks (LANs). These Ethernet networks enabled data to be exchanged solely in digital form, which produced a host of benefits, the most obvious of which were speed and savings –​though, interestingly, it was only in 1996 that it became less expensive to archive material digitally than on paper.41 Organisations as a whole gained access to new data and information that they could use to improve efficiency. Once the process of sharing information was digitised, there was a radical increase in the volume of data generated, stored, sent, and consumed.42 Soon companies and public institutions started to use the internet to contact their partners and customers, thus beginning a transformation of these relationships. Meanwhile, the content of the internet grew rapidly. In 1993 there were no more than 200 websites, but by 1998 there were already around 2.4 million.43 But the internet soon became more than an index of static websites. As of autumn 2020, there are perhaps 5.47 billion web sites44 –​but there is also a vast array of applications, which enable people to chat, participate in forums, and buy online. One result has been the emergence of a vast online marketplace, discussed later in this book and dominated by Amazon, founded with extraordinary prescience in 1995. By the middle of the first decade of the 21st century, the internet had evolved into a space full of dynamic content, created by its users, such as amateur movies published on YouTube (2005). This process has intensified following the emergence of social media, such as Facebook (2004) and Twitter (2006). Personal computers, tablets, and then smartphones became the tools for enjoying digital goods –​digitised books, music, and movies. And early on, consumers of digital content and services started to produce a highly valuable resource: data.

The inflexion point: smartphones and sensors There were smartphones before iPhone: one of the first devices of this type, the IBM Simon, had been launched in 1994,45 but the actual term was first used to sell the lightweight and multi-​functional Ericsson R380, operating on the Symbian OS. However, it took the arrival of the iPhone to reveal the truly subversive nature of the technology. The iPhone was an example of ingenious miniaturisation that combined functions which, up until that point, were usually offered on separate devices.46 When, in January 2007, the late Steve Jobs, then the boss of Apple, unveiled the first iPhone, he announced: ‘Every once in a while, a revolutionary product comes along that changes everything.’ The iPhone, he pointed out, offered three gadgets in one: a ‘widescreen iPod with

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Figure 1.5 Smartphone penetration rate: (a) by country (2020); (b) worldwide (2016–​2020). Source: Own work based on Statista. 2020. Ranking of the smartphone penetration by country 2020. Chart. In Statista. www.statista.com/​forecasts/​1143893/​smartphone-​ penetration-​by-​country (accessed 14 December 2020); Statista. 2019. Global smartphone penetration rate as share of population from 2016 to 2020. Chart. In Statista. www. statista.com/​statistics/​203734/​global-​smartphone-​penetration-​per-​capita-​since-​2005/​ (accessed 14 December 2020).

touch controls’, a ‘revolutionary mobile phone’, and a ‘breakthrough Internet communicator’.47 The iPhone contained preinstalled games, a still camera, and a video camera, but an ordinary Nokia phone could boast these features too. Its competitive edge lay in its touchscreen and touch keyboard, integrated web browser and durable battery (the rival IBM Simon was pulled from the market after a few months because its battery only lasted an hour). Soon similar solutions were introduced by the rival technological companies, Google and Microsoft. In 2020 3.5 billion people –​45% of the world population –​owned a smartphone.48

14  The foundations of the digital economy The smartphone crowned the cumulative processes of innovation which had been building up in Information and Communication Technologies (ICT), and became the digital product marking the birth of the fourth technological revolution. The average smartphone combines the function of a mobile telephone with a portable computer that can be constantly connected to the internet. But this was not the crux of the disruption it brought about. Each smartphone can deploy a vast range of life-​simplifying applications (or apps), which allow users to do everything from tracking their bank balance to measuring their heart rate. As of 2020, there were more than 2.87 million apps in the Google Play store and 1.96 million in AppStore.49 Google’s Android –​ the system underpinning the operation of 74% of all the smartphones in the world –​allows innovators freely to gain access to the operating system (OS) and to the data that individual users constantly generate.50 The application developers and individual users are locked in a symbiotic cycle: the developers feed off the data produced by the users, providing them in exchange with applications that increase the functionality of the main device. The operating system, as an intermediary –​or platform –​between individual users and application developers, must strive to attract as many members of both these groups as possible.The OS provider also benefits from the rising inflow of data, which it uses for optimising the system and selling to advertisers. Smartphones routinely use computational resources in the cloud, which are supported by artificial intelligence. Another key device which evolved during the third technological revolution is misleadingly modest in appearance. An intelligent sensor is a combination of a sensor and a microprocessor. It not only gathers information on specific parameters of the physical environment but primarily it uses its own computational resources to analyse the information and transmit data when it detects a specific change in the environment.51 So far most intelligent sensors have been used in industry (to measure pressure, temperature, or proximity, for example). They monitor the work of machines in real-​time, which allows failures to be prevented early. They are a key factor in the automation of transport and deliveries, the optimisation of equipment and vehicle movements in factories, and they are vital in warehouse management. They also help to regulate energy consumption by matching consumption with needs. The increasing use of sensors revolutionised another innovation of the third technological revolution: the robot, i.e., a programmable machine capable of carrying out autonomous tasks and manipulating objects. In 1962, the first ‘robotic arm’ was installed at a General Motors factory; it could perform one type of repetitive operation (in this case diecasting).52 In the late 1960s, scientists at Stanford University built an arm that could move in six axes; by the 1980s, however, robots were still far from being mobile devices and were unable to sense their surroundings.This changed when smaller and cheaper sensors were coupled with computing power supported by artificial intelligence, and with advanced actuators (components that carry out movements).53 In 2019, 2.7 million industrial robots were working worldwide, 1.1 million more than in 2015.54 Most

The foundations of the digital economy  15 of them worked in the automotive, electrical/​electronic, metal, and machinery sectors.55 The development of multifunctional collaborative robots (or ‘cobots’) that will support workers in industrial and food production, health care, and packing products has allowed dramatic increases in productivity.56 Efforts are also being made to create robots that cooperate in the cloud (cloud robotics), i.e., ones able to share computing power and perform coordinated actions.57 In 2006, there were 2 billion intelligent sensors in the world; in 2020, there were probably as many as 200 billion.58 Their use accelerated considerably as their decreasing size and price (from $1.3 in 2004 to below 60 cents in 2014)59 were coupled with the growing possibility of connecting them with other devices. This depended heavily on the power of the network: with the 4G standard, 110,000 devices could be connected per square kilometre, but the 5G standard allows over a million.60 This huge rise in network capacity has helped to foster the development of the Internet of Things, a network of connections between physical objects equipped with sensors, which allow data to flow between them. Objects belonging to the network can digitally identify and communicate with other devices. It allows for the development of track-​ and-​trace systems in logistics; in manufacturing, it gives rise to smart factories. Intelligent sensors are widely used in things people wear, both in the form of devices built into clothes and of a variety of accessories such as watches, b)

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16  The foundations of the digital economy bracelets, and rings. They are used primarily to monitor health and physical activity, mainly by those trying to lead a healthy lifestyle, but they are increasingly used in healthcare. Sensors embedded in special bracelets can measure basic vital signs and alert a healthcare specialist in the event of irregularities. When I am writing these words on my PC, my smartband is sending data about my pulse to my smartphone, which at the same time streams some lulling Mozart into my wireless headphones. I experience the functioning of a ‘second economy’ in which objects are ‘talking to each other’ unbeknownst to humans.61 According to one prediction, in 2021, people will be wearing more than 900 million devices equipped with sensors.62 The development of the IoT is also a key factor in the development of smart cities, with intelligent buildings, intelligent apartments, and intelligent transport (we will write more about those in Chapter 5). Saturating the environment with devices that record all manner of activities, however, raises a whole host of concerns about data security and the protection of users’ privacy. Another challenge for building an intelligent ecosystem is also to ensure a high level of interoperability, i.e., the ability of devices to work effectively with one another.

The breakthrough: data and algorithms The Big Bang of data In just one global minute while this book was being prepared, 188 million e-​mails were sent (not only by people but also spambots), 350,000 tweets were tweeted, Google’s search engine was queried 3.8 million times, and Skype was used 180,000

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Figure 1.7 Volume of data as an effect of digitalisation (in zettabytes, 2010–​2024). Source: Own work based on IDC, Statista. 2020. Volume of data/​information created, captured, copied, and consumed worldwide from 2010 to 2024 (in zettabytes). Chart. In Statista. www. statista.com/​statistics/​871513/​worldwide-​data-​created/​ (accessed 14 December 2020).

The foundations of the digital economy  17 times. (As you will see if you go to the linked source, these data change every second.)63 Data is pouring from IT systems: it is being generated by the individual business, and institutional users of the internet and mobile applications, it is being reclaimed from the archives of public institutions and companies, and it is being gathered by an increasing number of sensors located not just in devices, but also in personal accessories and in private and public spaces. In 2015, IBM claimed that in just the previous two years (2013–​2015) 90% of all data ever generated had been produced.64 Data volumes are increasing exponentially, doubling every three years. This should come as no surprise, of course: if you go to the Internet Live Stats site listed below, you will see the reasons. The increasing application of devices equipped with sensors to track changes in the surrounding environment has accelerated the data flow. For example, an average car can be equipped with up to 200 sensors that generate 1 terabyte of data per day.65 Cautious estimates suggest that 26 billion different devices (less cautious ones go up to 50 billion) already make up the Internet of Things.66 The world’s 6.1 billion smartphones also contain sensors, mainly to detect movement. As a result, by 2020 the volume of all the data generated approached the unimaginable number of 59 zettabytes (59 times 1021 bytes).67 A large amount of the data currently being produced has specific properties: it is highly diverse, complex, and usually poorly structured. Analogue data is different, in an important way. Before being etched onto a clay tablet or noted down in an accounting book, numbers or letters were ordered in a certain way so that another user would know how to read them. Usually, digital data collected by public institutions, corporations, and NGOs is initially similarly ordered. In contrast, data generated by social media sites, server logins, online shopping, geolocation systems, and sensor readings are poorly structured. In 1997, two NASA researchers, Michael Cox and David Ellsworth, proposed calling this type of data big data. Two years later, Doug Laney, an analyst at Gartner, a consulting company, was observing the problems his clients had with data from various sources, its structure and different formats, and declared that big data was characterised by high volume, the velocity at which it was produced, and its variety.68 Over the next two decades, this list grew to 10 Vs: in ­addition to those already mentioned, one can focus on its multifacetedness and the inconsistencies within big data (its variability), its relatively low veracity, as well as its accuracy (validity), its vulnerability to cyber attacks,69 the short-​term nature of its usefulness as regards the profitability of archiving such large data sets (volatility), challenges when it comes to visualisation and its business value.70 ‘Contamination’ in large data sets (due to their diverse nature and lack of structure), and the need to resort to innovative methods to analyse the sets, has created a need for a new type of skill in data science, which is more than just data analysis.71 It is somewhat reminiscent of the process of refining so data becomes information that is useful for business (and increasingly the public sector as well). It is cleaning and organising the data that takes up the most time, on average 60%, while data mining for patterns and improving algorithms accounts for only 13%.72 Big data definitions frequently draw attention to the fact that non-​standard methods must be used to collect, process, analyse,

18  The foundations of the digital economy and visualise it, and some definitions conceive of big data more as technologies and technological structures.73 To quote OECD’s concise definition: ‘Big Data is commonly understood as the use of large scale computing power and technologically advanced software in order to collect, process and analyse data characterised by a large volume, velocity, variety and value.’74 In this book we will refrain from using the widely-​used ambiguous concept of big data, which conflates the notion of data as a raw analytical substrate with methods used to analyse it. Instead we will focus on just the former –​the notion of data as a product of digitalisation and a substrate of datafication (we will explain shortly) –​and emphasise volume as the most important trait of data nowadays by using the notion of abundant data. The sheer volume of online data has led to the development of ways that allow companies to use software that is not installed on their servers. The result has been the evolution of various computational resources –​servers, databases, software, archiving –​which are not located on a local computer, but are stored in huge data centres.75 These cloud solutions first appeared in the late 1990s and allowed companies to use software that was not installed on their servers, thus lowering the cost of infrastructural and software investments necessary for embarking on digital transformation. Cloud services may give access to infrastructure (disk space and computing power), platform or software, communication solutions or platforms (infrastructure which integrates programs and applications that operate in various operational environments). Cloud services are not just for companies: they also widely available for individuals. In 100 90 80

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The foundations of the digital economy  19 2018, six years after its launch, Google Drive had a billion users76. However, the largest share of the overall cloud services market in 2020 is claimed by Amazon (Amazon Web Services).Its one-​third share is greater than that of its three biggest competitors combined: Microsoft, IBM, and Google.77 Cloud services are also being developed by Chinese technology companies such as Alibaba Cloud, and Tencent.78 As of now, they trail behind the US-​based cloud providers, but still they control 70% of the Chinese market and plan to invest heavily to gain ground in other Asian countries.79 Intelligent algorithms The sheer volume of data is not enough to make it useful. To squeeze value out of it you need powerful analytics. Traditional data analysis based on statistical tools and simple algorithms that allow for automation is enough to spot patterns in data and to formulate predictions. But real analytical efficiency and insight require a more sophisticated technology: artificial intelligence.80 Back in the 1950s, when the research on ‘thinking machines’ was initiated, there were two approaches to the construction of ‘artificial intelligence’ –​symbolic and statistical.81 The first approach held that artificial intelligence could be created by constructing a strict set of rules that it would follow when solving problems. The ‘symbolists’ managed to build ‘Logik’, a program that used the principles of formal logic to automatically prove mathematical theorems. It is no wonder then that the 1960s were dominated by great optimism regarding the possibility of creating a machine equally –​or even more –​intelligent than a human.Yet, machines had failed to learn to recognise speech, classify images, or translate from one language into another.82 The followers of the second approach posited that a computer ‘fed’ with large amounts of data would, on its own, learn to spot trends via constant repetition, experimentation, and feedback. But they lacked properly large and digitised datasets and the computational resources of the machines they worked with were too weak. The hopes revived once computers started to offer greater calculating power and internet users generated large data sets. The statisticians finally got the opportunity to show off. They started to create algorithms that could analyse data, learn from it thanks to advanced statistical techniques, and make decisions based on the results.83 Thus machine learning was born. Meanwhile, the researchers returned to the idea of using a series of algorithms somewhat reminiscent of the structure of a human brain –​a so-​called artificial neural networks. The idea was first articulated back in the 1950s by Frank Rosenblatt.84 His Perceptron was hailed as the first ‘learning machine’, but it failed to deal with basic classifications because it operated on only one layer of neural networks. The growth of computational power allowed for building multi-​layer artificial neural networks that can recognise relationships between vast amounts of data. Each layer allows for deepening the insight as the information travels through the layers, and that is why this subset of machine learning is called deep learning.

20  The foundations of the digital economy Machine and deep learning can be supervised, unsupervised, and reinforced. In the first, the program is given data (that has already been labelled by humans or other machines), which establishes the subject to be learnt; in the second case, there are no labels, and the program just finds patterns in data according to rules. In the third version, artificial intelligence independently tests various solutions and selects the best to achieve a set goal. The potential of reinforced learning was shown in 2016, when AlphaGo, a program developed by Google’s DeepMind team, defeated a South Korean Go champion (Go is an ancient Chinese game that is much more complex than chess). The program, fed with data on games previously played by humans, learned to play at a master’s level in just three days, playing a million rounds with itself. But the real breakthrough was heralded by AlphaGo Zero.85 The self-​learning neural network was given no previous data –​it independently tested various solutions and selected the best to achieve a set goal. Just like Alpha Go in three days achieved the master level of a human, AlphaGo Zero in 40 days learned how to beat all its predecessors. Such impressive reinforced learning involves huge amounts of computational power, and the cost of training deep neural networks are exorbitant.86 Widespread rolling out of this technology requires further advances in computing and the design of the algorithms themselves. It is worth noting that today’s artificial intelligence in no way resembles the type which science-​fiction films would have us imagine. Successes in the field of building strong (deep) AI, i.e., a machine whose intellectual abilities are indistinguishable from human intellectual abilities, are so modest that some experts doubt whether it is possible at all.87 No matter: the economic, social, and political implications of rolling out of the applied or narrow AI, which relies on advanced information processing, are revolutionary enough. Kai-​Fu Lee, author of the book AI Superpowers: China, Silicon Valley, and the New World Order (2018),88 and one of the foremost experts in artificial intelligence, who was also the creator of one of the first speech recognition programs, claims that the development of artificial intelligence will proceed in four waves: •

•

Internet AI is already widely used today. It consists of user-​profiling recommendation algorithms that learn from the masses of data about what a particular person does on the web. This type of AI is responsible for correctly tailoring ads, recommending products (Amazon, Alibaba), proposing new content (YouTube), optimising user involvement through natural language processing and computer image processing, and labelling users. Business AI is increasingly being used. Algorithms can bring together threads in historical data that a human could never have associated with each other, and discover hidden correlations between data and events, something which is used in the banking and insurance sectors, and which is beginning to be used in the health service and the judicial system. This allows organisations to optimise expenses, minimise losses, and better tailor loans and insurance policies.

The foundations of the digital economy  21 •

•

Perceptive AI is on the way, thanks to which the virtual world will merge with the real world. Ubiquitous sensors of the Internet of Things will allow artificial intelligence to gain senses, accelerating AI’s evolution. This kind of artificial intelligence ‘will bring the convenience and abundance of the online world to offline reality’ and will pave the way for smart factories, homes, and shops, as well as intelligent consumption. Autonomous AI will be able to feel and respond to the real and virtual worlds surrounding it, move and act productively, and optimise its own actions. An example of this will be, for instance, drones, which thanks to computer image processing will be able to recognise and destroy weeds growing amongst crops. Alternatively, heat-​resistant drones will extinguish fires on their own, or –​most incredibly of all-​humanoid robots will be used in everyday life and the army.

In simple terms, artificial intelligence is tantamount to intelligent algorithms, most often based on supervised learning, that allow for faster and cheaper searching, analysing, matching, recommending, and predicting. Ajaj Agrawal, Joshua Gans, and Avi Goldfarb in aptly argue that the intelligence they offer is rather of ‘Central Intelligence Agency’ kind, not the ‘human intelligence’ kind.89 But it is more than enough to revolutionise the operational and business models of the companies (and the operations of other kinds of institutions).The IT companies from nine countries surveyed by Deloitte in 2020 claimed that the AI technologies allow for ‘making processes more efficient’ and enhance

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22  The foundations of the digital economy existing products and services. And most importantly, rolling out AI to automate, optimise and enhance the tasks of human workers no longer requires building an expensive inhouse software infrastructure –​it can be bought in the cloud. Only one in five surveyed companies invested more in building than buying the AI potential.90 Further democratisation of AI is provided by automated machine learning (AutoML): a company may buy a ready model of machine learning, which was already trained (it usually takes weeks and requires in-​house data science expertise) and its outcomes were analysed.91 ‘AI for hire’ is becoming the flagship product of the biggest technological companies.There is an unprecedented symbiosis going on here: it was only the emergence of huge data sets that enabled the application of artificial intelligence. No wonder that the companies to pioneer intensive investment in this area have been corporations such as Amazon, Google, and Facebook, which have access to vast amounts of client-​ generated data.92 Amazon emphasises that without machine learning it ‘couldn’t grow its business, improve its customer experience and selection, and optimise its logistic speed and quality’.93 In 2020 the 57% of its operating income was generated by Amazon Web Services, which offer, among others, the cloud-​ based AI services such as Amazon Lex (which enables building automated conversational interfaces into applications) or Amazon Rekognition (that allows for image analysis).94 Google declares that machine and deep learning are a priority for the company because they allow it to apply ‘AI to products and to new domains, and developing tools to ensure that everyone can access AI’.95 On its own website AI Google sports stories on how AI may help to advance social good. Facebook AI Research, meanwhile, is headed by Yann LeCunn, a French computer scientist who is one of the fathers of deep learning. His team say they are committed ‘to advancing the field of machine intelligence and are creating new technologies to give people better ways to communicate’.96 One of the instances of their work is GrokNet, a system that develops image recognition for commercial purposes.97 Access to operational AI becomes easier and cheaper even for small and medium companies, forming the necessary conditions for digital transformation.

The properties of the digital economy Now it is the time to get back to the properties of the digital economy and brave another approximation at its definition. Digital economy builds on the basis of the internet economy –​it takes computerisation, automation, and internet connectedness to the next level of ubiquitous computing via digital devices, intelligent automation everywhere, and platformisation. It is also characterised by the extraordinary pace of innovation. As we have shown earlier in this chapter, digital devices such as smartphones consist of four layers: device (hardware), connection (network), service (software), and content (data). Innovations may appear on each layer independently, and they frequently enhance each other, producing yet another innovation.The truly transformative

The foundations of the digital economy  23 innovations now are less often in the device or network, but in the way software and data are used. As noted by Hal Varian, Google’s chief economist, Now what we see is a period where you have Internet components, where you have software, protocols, languages, and capabilities to combine these component parts in ways that create totally new innovations. The great thing about the current period is that component parts are all bits. That means you never run out of them. You can reproduce them, you can duplicate them, you can spread them around the world, and you can have thousands and tens of thousands of innovators combining or recombining the same component parts to create new innovation. So there’s no shortage. There are no inventory delays. It’s a situation where the components are available for everyone, and so we get this tremendous burst of innovation that we’re seeing.98 The innovations in software and content contribute to the development of new organisational and business models. As we will show in the next chapter, the innovative business model of platform revolves around the use of an ingenious algorithm and abundant data. More and more companies deftly use the potential of data and networks to optimise their functioning by adopting the ‘data first, AI-​first’ rule. Digital models and solutions now permeate almost every sector of the economy in most countries, from service industries to manufacturing, and agriculture. As a result, we are seeing a change in the functioning of the market for production factors, the market for goods and services, the financial system, enterprises, governments, and households.99 Consumption, production, and work are all being revolutionised by multiple digital transformations propelled by datafication and datafied networks. Datafication Datafication is a growing tendency to create digital representations of ever more areas of the real world in order to derive value from information obtained.100 It involves extracting useful insights from data about a phenomenon or a process with the support of analytical tools. The word refers to the practical results of the virtuous circle between the growing amount of data and the growing application of intelligent algorithms. For example, I have recently datafied my sleep by wearing a smartband at night that measures my sleep efficiency. In the morning, a smartphone application tells me how well I slept. Now I know that I sleep better than 60% of users but wake too many times during the night. Perhaps I will put this information to use and quit drinking coffee after 8 pm. Individuals have access to more and more data, which they can use to make life-​related, professional, and consumer decisions. But the real beneficiaries of datafication are elsewhere. Companies –​from corporations to small and medium-​ sized enterprises –​have never before faced such a spate of data, data which can be used to increase productivity,

24  The foundations of the digital economy

Figure 1.10 The mechanisms of datafication. Source: Own elaboration.

optimise business processes, improve management, make more accurate real-​ time decisions, personalise products, adjust offerings, and expand into new markets.101 This data can be bought, but it is also generated by those using a company’s products and services and churned out during production in industrial facilities kitted out with the Internet of Things. As Erik Brynjolfsson, director of the MIT Initiative on the Digital Economy, notes: ‘More and more important assets in the economy are composed of bits instead of atoms’, and therefore data should be treated as a completely new type of capital: Computing hardware used to be a capital asset, while data wasn’t thought of as an asset in the same way. Now, hardware is becoming a service people buy in real time, and the lasting asset is the data.102 It had been said, with only slight exaggeration, that data have become for the modern economy what coal and steel were initially for the industrialised economy, followed by oil in the 20th century. Data not only affect the efficiency

The foundations of the digital economy  25 of doing business; they also determine the development of new business models, solutions and economic relations.103 Treated as capital, data have a host of interesting properties: • •

•

They are non-​fungible –​a single data set cannot be replaced by another, because it contains completely different information. Products such as barrels of oil are completely replaceable. They have a non-​r ivalrous nature –​a single data set can be used simultaneously by many algorithms or applications and analysed without losing its basic value. Meanwhile, money or a piece of equipment/​infrastructure can be used by only one actor at a time. The value of a data set is equal to the information it contains, and so this value can be assessed only after obtaining the information. However, the information acquired can be easily replicated. By contrast, the value of a durable good can be attained only by taking possession of it; merely having information about it is useless.104

This huge resource is not always properly appreciated, priced, or even noticed. Tom Godwin’s witticism has gone down in legend; in 2015 he stated that:‘Uber, the world’s largest taxi company, owns no vehicles. Facebook, the world’s most popular media owner, creates no content. Alibaba, the most valuable retailer, has no inventory. And Airbnb, the world’s largest accommodation provider, owns no real estate. Something interesting is happening.’105 He was clearly right. Companies like Uber, Alibaba, and Airbnb do not have tangible resources, but they have gigantic resources of data and the technology to derive economic value from it. According to researchers from MIT, many companies ‘are light on physical assets but heavy on data assets’.106 Specifically, standard economic indicators find it hard to capture the specificity of the new business models being developed by tech firms and platforms. A financial audit carried out at Facebook for 2011 showed the company had $6.3 billion of resources: computer hardware, office equipment, and other items. The value of the data in its possession was deemed by the auditors to be worth precisely zilch.107 This failure of standard economic indicators to deal with the new reality shows how new technologies and the deluge of data are driving a radical change in economies. The ability to derive value from data is increasingly determining firms’ competitive position in the market through the development of intelligent services and products (personalisation), automation of business processes, new ways of building networked relationships, and data-​driven management (new business models). At the most basic level, more efficient and faster analysis of large data sets allows organisations to optimise decision-​making processes. Intelligent automation makes faster, more accurate, and cheaper analysis available to an increasing number of companies, including those that cannot afford to employ a team of researchers. Better still, the analysis is as easy as using a spreadsheet. A company may, for example, go to the Data Robot platform, which cleans up and

26  The foundations of the digital economy reformats inputted data, and then runs it through dozens of algorithms. It can find a more accurate solution than those built on standard statistical models, with no prior preparation. It works ‘Out of the box, with the push of one button; that’s pretty impressive’, as one user puts it.108 In commerce, where data can be obtained not only from the marketing, sales and customer service departments but also from pricing reports and social media, the ability to process it allows for a more complete view of buyer behaviour and of the competition. Personal data, obtained by purveyors of online services, is used to create more effective marketing campaigns that reach the right target groups. Financial institutions have gained the ability quickly to detect and respond to fraud attempts. The public sector has also reaped the benefits of data analysis –​ it has, for example, made it possible to optimise public transport, thanks to the information gleaned from ticket readers, or to improve health care thanks to readings taken by various sensors worn by patients.109 Datafication lays the groundwork for new business models developed by big technology companies and platforms (for more on this, see Chapter 2). However, at the same time, datafication comes with significant social and economic consequences as it creeps into many aspects of human life, such as social relations, consumer behaviour, production processes, and political engagement. For example, childhood is being subjected to datafication, something the Children’s Commissioner for England criticised in a report entitled Who knows what about me? (2018). Children’s data is not only posted by the kids themselves or by their parents on social media; it is also collected by intelligent toys, virtual assistants (such as Siri or Alexa) and other devices connected to the internet, and it is gathered via wearable devices worn by youngsters. Data, including biometric information, is also collected by public institutions, from schools to public transport and healthcare services.110 As we will show in a detailed way later, datafication is the necessary condition for personalisation of products and services. At the same time, datafication often makes privacy a delusion. The greatest challenge for the digital economy is how to strike the balance between the companies’ –​and governments’ –​hunger for data and the rights of the consumers. Networks The digital economy takes networks that were already typical in the earlier days of the internet economy to a more sophisticated level. The rise of the internet, and then of mobile technologies and better connectivity, paved the way for society and the economy to be ‘networked’.111 That created more ties (relationships) between a larger number of actors (nodes of the network). Socially, this has meant the emergence of new relationships resulting from the possibility of freely participating in a variety of groups and circles. For example, in 2017 40% of heterosexual couples in the USA met online; the authors of the research called this phenomenon ‘disintermediating your friends’.112 In economic terms, this expansion of connected networks has changed the relationship between

The foundations of the digital economy  27 businesses and customers. Both sides now have more knowledge at their disposal. Customers know the ranges of products better, and firms know their customers’ preferences in more detail.113 In the digital economy, networks are ‘thicker’ because people and machines are connected all the time.There is no online or offline but onlife, as suggested by Luciano Floridi, of the Oxford Internet Institute. And communication is going on not only between humans but also between humans and machines and between machines themselves (by 2023 half of all connections will be machine-​to-​machine).114 Constant digitisation (turning analogue data into digital, machine-​readable data) saturates networks with more and more data. At the same time, the recommendation engines propelled by AI allow for faster and better-​tailored searches and matches between the nodes. Thicker and datafied networks have additional effects which platforms (such as Amazon, Google, or Facebook) use via their business models (for more see Chapter 2). In the traditional economy, the cost of producing a good or service generally decreased as volume rose. In the case of platforms, economies of scale enhanced by network effects occur both on the supply side (the more things are offered, the lower the costs of distribution). Meanwhile, on the demand side, the more end-​users, the more valuable the service provided.115 Platforms are connecting various parts of the market efficiently and quickly because they make use of new possibilities for data collection, processing, and analysis. As a result, platformisation is expanding into yet more sectors of the economy, and the development of networks is accelerating datafication. This in turn enables more and more personalisation in products and services, making the network even more beneficial from the point of view of consumers. Platforms are being recognised as the key feature of a digital economy. The European Parliament goes as far as to define the digital economy as ‘a complex structure of several levels/​layers connected with each other by an almost endless and always growing number of nodes. Platforms are stacked on each other, allowing for multiple routes to reach end-​users and making it difficult to exclude certain players, i.e. competitors.’116 To sum up, digital platforms with their products, services and whole ecosystems create digital infrastructures built upon existing internet networks. Platforms easily expand into traditional sectors of the economy; also their business and operating models are also emulated by companies from these sectors, adding to expanding platformisation of the economy.

Digital transformations Digital transformation is a comprehensive change in the functioning of organisations (companies and public institutions), enabled by digital technologies, and resulting in operational and business model build upon datafication and networks.117 In a wider sense, separate multiple digital transformations add to the comprehensive digital transformation of the economy and society, understood as the paradigm shift in rules governing the economic and social activity.

28  The foundations of the digital economy This process is essentially dispersed and uneven, and its effects are obviously spread over time, and thus often barely discernible. In a research conducted in 2018 by McKinsey, only 16% of respondents (out of a sample of 1,793 representatives from companies from around the world) claimed that a digital transformation in their company had increased efficiency and that the changes would be long-​lasting.118 The perception of the ‘success rate’ and the impact of digital transformation may be akin to a productivity paradox. In the USA in the 1970s and 1980s, the ICT sector was among the most dynamic and fastest growing sectors of the economy. Yet, to the considerable surprise of economists, research failed to show that ICT had any real influence on productivity; its average yearly growth in this period was a paltry 0.7%.119 In 1987 a Nobel prize winner in economics Robert Solow quipped that ‘You can see the computer age everywhere but in the productivity statistics.’ Other researchers hastened to explain that when a company adopts new technology, that may affect the productivity of the individual firm, but not necessarily of the entire sector.120 Technology can help a company to raise its market share (through better market recognition or marketing), but it does not mean that production within the sector will change. Increasing one company’s sales may mean a loss of market share for another.121 More importantly, some researchers have suggested that there may be a gap between the swift development of new technologies and the rate at which they have been applied. Besides, the technologies deployed by companies may be ineffective or mismatched –​the sheer pace of technological change leaves little time for testing solutions. Organisations require time to comprehend the possible applications of a given technology and only after a certain amount of time has elapsed do they begin to reap the rewards. Phasing in new technologies does not necessarily mean that companies see increased efficiency in the short term, but it may allow a company to respond better, more flexibly and faster to the market situation.122 Lower costs for information processing and the introduction of advanced production management systems enable enterprises to handle more products and more variants of them. Investments in new technology often require a company to introduce organisational changes and complementary investments in business processes, organising work, communication, etc. These are costly processes and do not always translate into an increase in sales volume.123 But they do translate into more flexibility, better personalisation of the products, more transparent supply networks, and at the end of the day –​ into survival on the more and more competitive market. Changes induced by technological breakthrough are occurring in the smallest businesses. Thanks to the spread of cloud services and the development of intelligent software, digital change has become far more affordable.Twenty years ago, only large companies could employ advanced warehouse management systems or accounting programs. Nowadays, any store can track sales and inventory using intelligent cash registers, which are basically personal computers with a drawer for cash. Small business owners can handle their accounts with the aid of software or online services. Because there is no need for programming skills to set up

The foundations of the digital economy  29 an online store, local, small manufacturers can now develop their sales through e-​commerce, even selling their wares globally. Inexpensive and simple solutions allow them to communicate easily with potential customers, collect data on consumer preferences, and then analyse it using AI-​based cloud solutions.124 Thanks to global digital platforms, small and medium-​ sized enterprises are gaining opportunities for global expansion. The internet straddles national boundaries and transforms conventional concepts of location and distance. Companies gain access not only to domestic markets but also to global ones at relatively low costs (more on this in Chapter 6). At the same time, those using local markets have obtained free access to global products. This creates new opportunities, but also requires considerable investment not only in technology, but also in organisational changes, and particularly in employees’ digital skills. In the next chapters of the book, we will trace those separate digital ­transformations in various areas making up the comprehensive digital transformation of economy and society.

Key takeaways • The digital economy builds upon the internet economy due to the increasing resources of data flowing from billions of hyperconnected digital devices and the development of artificial intelligence. • The digital economy is characterised by two interrelated mechanisms of datafication and expansion of networks. • Datafication boils down to deriving value (economic, social, and political) from abundant data generated en masse via digital devices and analysed in an increasingly efficient, faster, and cheaper way by intelligent algorithms. The value may consist in the processes (e.g., planning, production, and management) being made autonomous or in products (goods and services) being personalised, i.e., tailored to the needs and expectations of the customers. • The enhanced access to the internet through the digital devices contributes to the growth and thickening of online (and in consequence also offline) networks connecting people, companies, public institutions, machines, and systems. Such networks become the source of data in their own right –​ i.e., they become datafied. The emergence of the new platform business model results in strengthening some of the existing networks as well as creating new ones through the operation of matching and recommendation algorithms. This way online networks become increasingly datafied. • The intensification and extension of datafication processes into new areas of economic, social and political life is leading to a digital transformation. This is paving the way for the emergence of a new model for the functioning of markets, enterprises, households, and the public sector. Production and consumption processes are changing, as are: the nature of work, forms of employment, companies’ business models, and the way public institutions function (and, as a result, the way the global economy does too).

30  The foundations of the digital economy

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38  The foundations of the digital economy

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42  The foundations of the digital economy MIT Technology Review. Custom in partnership with Oracle. The rise of data capital. MIT Technology Review. 2016. http://​files.technologyreview.com/​whitepapers/​ MIT_​Oracle+Report-​The_​Rise_​of_​Data_​Capital.pdf?_​ga=2.225262874.1416096 816.1535754008-​552638390.1535489863 Moran, M. Evolution of robotic arms. Journal of Robotic Surgery 1(2). 2007. www.ncbi. nlm.nih.gov/​pmc/​articles/​PMC4247431/​ Morris, R.J.T. and Truskowski, B.J. The evolution of storage systems. IBM Systems Journal 42(2). 2003. National Research Council. Organizational Linkages: Understanding the Productivity Paradox. The National Academies Press, 1994. https://​doi.org/​10.17226/​2135 NESSI. Big Data: A new world of opportunities. NESSI White Paper. 2012. Newzoo. Newzoo Global Mobile Market Report 2020. Newzoo. https://​newzoo.com/​ insights/​trend-​reports/​newzoo-​global-​mobile-​market-​report-​2020-​free-​version/​ Nilsson, N.J. The Quest for Artificial Intelligence: A History of Ideas and Achievements. Cambridge University Press. 2010. Nokia. Cloud robotics and automation. Nokia. www.nokia.com/​networks/​5g/​use-​ cases/​cloud-​robotics/​ OECD. Measuring the Information Economy 2002. OECD Publishing. 2002. https://​doi. org/​10.1787/​9789264099012-​en OECD. The Digital Economy. OECD. Paris. 2013. www.oecd.org/​daf/​competition/​ The-​Digital-​Economy-​2012.pdf OECD. OECD Digital Economy Outlook 2015. OECD Publishing. Paris. 2015. http://​ dx.doi.org/​10.1787/​9789264232440-​en OECD. OECD Digital Economy Outlook 2017. OECD Publishing. Paris. 2017. https://​ doi.org/​10.1787/​9789264276284-​en OECD. A Roadmap toward a Common Framework for Measuring the Digital Economy. Report for the G20 Digital Economy Task Force. Saudi Arabia. 2020. www.oecd. org/​digital/​ieconomy/​roadmap-​toward-​a-​common-​framework-​for-​measuring-​ the-​digital-​economy.pdf Otar, C. Four ways artificial intelligence can help your small business. Forbes. 2019. www.forbes.com/​sites/​forbesfinancecouncil/​2019/​04/​09/​four-​ways-​artificial-​ intelligence-​can-​help-​your-​small-​business/​#566e8e264a1a Oxford Dictionary. Digital Economy. Oxford Dictionary. Oxford University Press. 2017. https://​en.oxforddictionaries.com/​definition/​digital_​economy Perez, C. Technological Revolution and Financial Capital. Edward Elgar Publishing. 2002. Plastics Today. Record 2.7 million robots work in factories globally. Plastics Today. www.plasticstoday.com/​automation/​record-​27-​million-​robots-​work-​f actories​globally Porat, M.U. The Information Economy: Definition and Measurement. Special Publication no. 77 –​12(1). U.S. Department of Commerce Office of Telecommunications. Washington. 1977. Press, G. Cleaning big data: Most time-​consuming, least enjoyable data science task, survey says. Forbes. 2016. www.forbes.com/​sites/​g ilpress/​2016/​03/​23/​data-​ preparation-​most-​time-​consuming-​least-​enjoyable-​data-​science-​task-​survey-​says/​ #1c7e22de6f63 Reiff, N. How Amazon makes money. Product sales, advertising, subscription services, and cloud services. Investopedia. 2020. www.investopedia.com/​how-​amazon-​makes​money-​4587523

The foundations of the digital economy  43 Rosenfeld, M., Reuben, T., and Hausen, S. Disintermediating your friends: How online dating in the United States displaces other ways of meeting. PNAS. 2019. www.pnas. org/​content/​116/​36/​17753.short?rss=1 Rouse, M. Smart sensor. IoT Agenda. 2015. https://​internetofthingsagenda.techtarget. com/​definition/​smart-​sensor Schofield, J. Ken Olsen obituary. The Guardian. 2011. www.theguardian.com/​technology/​2011/​feb/​09/​ken-​olsen-​obituary Silver, D. and Hassabis, D. AlphaGo Zero: Starting from scratch. DeepMind. 2017. https://​ deepmind.com/​blog/​article/​alphago-​zero-​starting-​scratch Simon, M. The WIRED guide to robots. Wired. 2018. www.wired.com/​story/​ wired-​guide-​to-​robots/​ Singh, S. Eight reasons why 5G is better than 4G. Altran. 2018. https://​connect.altran. com/​2018/​03/​eight-​reasons-​why-​5g-​is-​better-​than-​4g/​ Slamecka, V. Acquisition and recording of information in digital form. Britannica. www.britannica.com/ ​ t echnology/ ​ i nformation- ​ p rocessing/ ​ A cquisition- ​ a nd​recording-​of-​information-​in-​digital-​form Slamecka, V. Inventory of recorded information. Britannica. www.britannica.com/​technology/​information-​processing/​Inventory-​of-​recorded-​information Smith,R.IBM created the world’s first smartphone 25 years ago.World Economic Forum.2018. www.weforum.org/​agenda/​2018/​03/​remembering-​first-​smartphone-​simon-​ibm/​ StatCounter. Mobile operating system market share worldwide. StatCounter. GlobalStats. 2020. https://​gs.statcounter.com/​os-​market-​share/​mobile/​worldwide/​ #monthly-​201901-​202010 Synergy Research Group. Quarterly cloud spending blows past $30B; incremental growth continues to rise. Synergy Research Group. 2020. www.srgresearch.com/​articles/​quarterly-​cloud-​spending-​blows-​past-​30b-​incremental-​g rowth-​continues-​r ise Tapscott, D. The Digital Economy: Rethinking Promise and Peril in the Age of Networked Intelligence. McGraw-​Hill. 2014. Tarnoff, B. How the internet was invented. The Guardian. 2016. www.theguardian.com/​ technology/​2016/​jul/​15/​how-​the-​internet-​was-​invented-​1976-​arpa-​kahn-​cerf Tercek, R. The booming second economy. Vaporized: Solid strategies for success in a dematerialized world. 2015. https://​vaporizedbook.com/​the-​booming-​second​economy-​860089709811 Thompson, N., Greenewald, K., Lee, K., and Manso, G.F. The Computational Limits of Deep Learning. Cornell University. 2020. https://​arxiv.org/​abs/​2007.05558 Trueman, C.N. 2015. The personal computer. History Learning Site. www. historylearningsite.co.uk/​inventions-​and-​discoveries-​of-​the-​twentieth-​century/​ the-​personal-​computer/​ UNCTAD. The ‘new’ digital economy and development. UNCTAD Technical Notes on ICT for Development. no. 8. 2017. http://​unctad.org/​en/​PublicationsLibrary/​tn_​ unctad_​ict4d08_​en.pdf UNCTAD. Digital Economy Report. Value creation and capture: Implications for developing countries. UNCTAD. 2019. https://​unctad.org/​system/​files/​official-​ document/​der2019_​en.pdf von Urs, B. The Triumph of Ethernet: Technological Communities and the Battle for the LAN Standard. Stanford University Press. 2002. Warner, E. What’s the big deal with miniaturization? –​Part 1. bb7. 2016. www.bb7. com/​2016/​07/​14/​whats-​the-​big-​deal-​with-​miniaturization-​part-​1/​

44  The foundations of the digital economy Warren,T. McLaren needs a 20-​year-​old Compaq laptop to maintain its F1 supercar. 2016. www.theverge.com/​2016/​5/​3/​11576032/​mclaren-​f1-​compaq-​laptop-​maintenance World Wide Web Size. The size of the World Wide Web (The Internet). World Wide Web Size. 2016. www.worldwidewebsize.com/​ World Wide Web Size. The size of the World Wide Web (The Internet). World Wide Web Size. 2021. www.worldwidewebsize.com/​ Yoo, Y., Henfridsson, O., and Lyytinen, K. Research Commentary –​The New Organizing Logic of Digital Innovation: An Agenda for Information Systems Research. Information Systems Research. 2010. https://​pubsonline.informs.org/​doi/​abs/​10.1287/​ isre.1100.0322

2  How is market changing?

Abstract This chapter discusses how the expansion of digital platforms has disrupted traditional market models by enabling ever more efficient and tailored matching between sides of the market Abstaining from conceptual debates, we focus on those definitions and typologies that allow us to present platforms’ multiple functions in the digital economy and discuss two essential economic mechanisms underpinning their market advantages: datafication and network effects. The former means drawing value from data through efficient analytics, mainly via the use of artificial intelligence algorithms.The latter includes direct network effects (when the growth of the number of actors on the same side of the market increases the value or utility of the service) and indirect network effects (when the growth of the number of entities on the other side of the market increases the value or utility of the service). The ability to reinforce direct network effects with indirect network effects due to the efficient use of data differentiates platform from similar business models called hubs. By indicating the strengths of platforms’ business and operating models, we show two modes of platformisation: by way of innovative disruption brought to traditional sectors by platforms themselves and when traditional firms emulate the platform example and engage in digital transformation.

The phenomenal career of the platform The digital economy has already gained its own mythology, stories that have kindled the imagination of thousands of young entrepreneurs waiting for their aha moment. This moment could take the form of joint making fun of photos of uni friends, of trying to make a few dollars by renting out a mattress in their living room, or even of failing to find a taxi on a rainy day, and then become the spark that kickstarts a world-​beating business. Facebook (2004), for instance, was born of a juvenile idea of Mark Zuckerberg to compare the attractiveness of his fellow students at Harvard (or at least, that is what the official FB legend claims; a less official one, though widely known thanks to the Hollywood movie The Social Network, suggests that Zuckerberg slyly stole the idea from other students who had turned to him for help in building a website).1Airbnb (2007) would never

46  How is market changing?

Figure 2.1 How is market changing? (scheme). Source: Own elaboration.

have been created if two young designers, Brian Chesky and Joe Gebbia, had not had trouble paying the rent. Looking to earn some extra cash, they came up with the idea of inflating a couple of mattresses and renting them to conference attendees who could not find a place to stay in a hotel.2 Meanwhile, the thought of creating Uber (2009) popped into the minds of two Americans,Travis Kalanick and Garrett Camp, when they had trouble catching a taxi in Paris.3

How is market changing?  47 The story of the largest retail platform in the world, the Chinese Alibaba, began when Jack Ma, a go-​getting tour guide and former English teacher, headed to Seattle where he first encountered the internet. He typed the word ‘beer’ into Yahoo and was met with an abundance of hits. Ma decided to repeat the search but this time for ‘China beer’. Nothing came up. Despite his scant knowledge of computers, Ma borrowed $2,000 and founded the ‘China Pages’ website.4 For his next idea –​the founding of an e-​commerce portal –​Ma not only convinced a group of about a dozen friends to support it, but also the Chinese government itself. Ma called this new company Alibaba, a word which was, in his opinion, easy to spell and associated with a fairy-​tale cave full of treasure.5 All those stories have something in common: they describe the birth of a new, mighty business model based on datafication and networks –​the digital platforms.6 Each of these companies’ success is predicated upon an ingenious piece of software –​an algorithm allowing for precise connection and matching of users from multiple sides of the market. Such algorithms gain precision by feeding off the rich sources of the connected user’s data; this process is perfected and accelerated with the growing application of machine learning and deep learning. Platforms gain the upper hand wherever there is potentially useful data or lack of information between the supply and demand sides of the market. They provide the infrastructure for exchanges, ensuring that the users can interact and strike optimal deals. To quote Paul Langley and Andrew Leyshon, ‘Platforms actively induce, produce and programme circulations.’ In the past this role was played by trade fairs, matchmakers, stock exchanges, exhibitions, auctions, tenders, and even humble cork boards. Still, it was always limited by the geographical and communication boundaries of social networks.7 Digital platforms leverage the potential of the vast online networks –​they create, stabilise, and strengthen new functional connections between existing nodes, i.e., people and organisations.8 Platforms are by no means homogeneous. This business model is common to a very diverse set of companies. It can be found in the media (Facebook, YouTube), retail (Alibaba, Amazon), transport (Uber, FreeNow, iTaxi), telecommunications (WhatsApp, Messenger, Zoom, Telegram, Skype, Viber), payment facilitation (PayPal, Dotpay), music (SoundCloud, Spotify, Shazam), tourism (Airbnb, Booking.com), operating systems (iOS, Windows) and in a panoply of other sectors and industries. Some companies are born as platforms; others turn into one or adopt this business model in one area of their business activity. Platformisation was an easy and obvious choice for most big technology companies, such as Google, that transformed from a search engine into a vast platform ecosystem of Alphabet or Apple who leveraged iPhone’s potential by developing a platform. Digital platforms attract, match and connect individuals, companies, public institutions or non-​governmental organisations, acting as producers and consumers; buyers, sellers, and advertisers; employers and employees; service providers and service beneficiaries. They enable buying and selling, lending or exchanging of products, services, digital content, and

48  How is market changing? resources (such as work, housing, cars, and capital). They can be open or closed to third parties.They can expand the system of direct distribution to consumers and producers, or they can merely mediate transactions. They can offer a wide range of products or else focus on a narrow sector with one type of good or service. These differences impose different types of management, and business architecture, on a platform’s general business model. This burgeoning business reality is reflected in the plethora of platform typologies elaborated by the academic community –​an extract is presented in Table 2.1. Table 2.1 Selected categories of platforms Platforms Definition and examples differentiated by Function

Exchange platforms/​markets provide value by optimising exchanges carried out directly between the consumer and the producer. Examples: Airbnb (market for services); Alibaba (market for goods); Apple Pay (payment platform); Kickstarter (investment platform); Tinder (social platform); WhatsApp (communication platform) Maker platforms –​such as YouTube (content platform); iOS (operating systems for digital devices)-​enable producers to create products and then release them to the broader world.58

Source of Matchmaking platforms function as intermediaries between added value groups searching for each other for commercial, personal or other (OECD reasons, and provide them with easy ways to recruit employees approach) (Freelancer), get around (Lyft, Uber), book accommodation (Airbnb), meet up (Tinder, Match.com), purchase things (eBay, Amazon Marketplace, Allegro) or make payments (Dotpay, PayPal) Advertising platforms offer digital content and services and at the same time mediate the delivery of personalised advertisements from companies that form the ‘other’ side of the market. As a result, in exchange for free access to email (Gmail), a search engine (Google), videos (YouTube), music (Spotify) or user-​ generated content (Facebook), platforms acquire data about users, and can offer solutions for advertisers that allow them to send personalised ads to users of these platforms.59 Type of service provided

Innovation platforms provide a technological ecosystem which allows the (mainly) external companies to create new complementary products and services (such as smartphone applications or digital content). If the platform is free to use, the platform owner earns money by selling advertising or other complementary services. Examples of such platforms are operating systems and cloud computing services (not just Microsoft Windows, Google Android, Apple iOS and Amazon Web Services, but also Nintendo or Sony). Transactional platforms are largely intermediaries or online platforms that enable people and organisations to share information or buy, sell or access digital content, goods and services. Examples of such platforms include credit card systems

How is market changing? 49 Table 2.1 Cont. Platforms Definition and examples differentiated by

The scope of the platform

(such as Mastercard,Visa and American Express), catalogues (such as Yellow Pages), and now PayPal, Groupon and eBay. Trading/​ e-​commerce platforms create and deliver value, making it easier to buy and sell goods and services, or facilitate other interactions, enabling users to create and share content. Platform owners draw profit from transaction and/​or advertising fees. Hybrid platforms combine the features of innovation and transactional platforms. They include Alibaba, Amazon, Apple, Facebook, Google, LinkedIn, Microsoft, Tencent and Twitter.60 Infrastructural platforms offer software services and infrastructures such as search engines, social networking, apps stores that underpin the functioning of many other platforms and companies. ‘Virtually all platforms outside the Big Five constellation are dependent on the ecosystem’s infrastructural information services.’61 The most important infrastructural platforms are the Big Five: Alphabet (Google), Facebook, Amazon, Apple, and Microsoft. Sectoral platforms emerge in particular sectors or niches.62

The way users Superplatforms, such as Tencent, create one gateway for users to enter the enter many other platforms. platform Constellations of platforms, such as Google or Facebook, are interoperable and share data, but they may be accessed separately. Stand-​alone platforms operate in a given sector, such as Booking. com or Airbnb. Sources of profit

Market makers combine buyers and sellers, charging a small commission on each transaction –​eBay, Uber, Airbnb Audience builders enable users to share and consume content, which attracts advertisers who need an audience for their ad campaigns Demand coordinators or operating systems coordinate demand in a given ecosystem of end-​users.The greater number of apps available for a given operating system, the greater its usability or value, and thus the number of users rises too. Application developers can invest in app devel opment as long as the operating system has enough users –​ which allows them to make a profit –​Windows, iOS, Android

Source: own elaboration.

The most powerful platforms –​those occupying the first places on the list of the companies with the highest market valuation in the world –​are hybrids combining many different functions, drawing from many sources of profit, and handling many types of transactions. They usually develop vast ecosystems, dynamic and connected networks of functionalities and services (see Table 2.2). Even stand-​alone, sectoral platforms try to extend their services, aiming to become an ecosystem –​take Uber who created UberEats. One example of a superplatform that combines multiple functions through specific platforms is WeChat –​a Chinese messenger service connected to an

Functions

Alibaba

Amazon

Apple

Facebook

Google

Microsoft

Search

own product search Alibaba Mail

own product search Amazon SES, Amazon WorkMail Amazon SNS, Amazon Connect

–​

–​

Google

Microsoft Bing

iCloud mail

–​

Gmail

Outlook

iMessage, FaceTime

Skype

-​ Twitch, Goodreads, IMDb Amazon WorkSpaces Amazon WorkDocs, Amazon Chime AWS, Amazon Drive Zoox

–​ -​

Messenger, Google Hangouts, WhatsApp, Google Chat, Instagram Direct Google Messages, Messages Google Meet, Google Duo Facebook -​ Instagram, Facebook YouTube, Waze Gaming Workplace Google Workspace, Google Classroom

iCloud

–​

Azure, OneDrive

Project Titan

–​

AR-​enabled devices

Spark AR Studio, Facebook 360, Oculus, Quill, React 360

Electronic mail Messaging and video calling

AliWangWang

Social networking -​ Specialised social -​ networks Work and DingTalk educational spaces Aliyun Cloud services Autonomous vehicles AR /​ VR

Aliyun (Alibaba Cloud) Alibaba –​a strategic partner for Autox Buy + VR Store Amazon AR View

-​

Drive, Google Cloud Waymo

LinkedIn -​ Yammer, Microsoft Teams

Software investments

Google Lens, Live HoloLens, View, Daydream, Windows Earth VR, Mixed Reality Expeditions, Tilt Brush, VR180 Cameras, Cardboard

50  How is market changing?

Table 2.2 The ecosystems of services provided by platforms

newgenrtpdf

Voice assistant

Tmall Genie

Echo, Alexa

Advertising

Alimama

Amazon Advertising

Sales

Alibaba.com, Amazon, Amazon Taobao, Tmall, Prime Freshippo, Aliexpress, 1688. com, Alipay Amazon Pay

Payment

Operating Systems

AliOS

Fire OS

Siri, HomePod

Cortana Bing Ads, Microsoft Advertising

Microsoft Store

Microsoft Pay

Windows

Sources: Alibaba: Alibaba Group. www.alibabagroup.com/​en/​about/​businesses; Alibaba launches full VR shopping experience with Buy+, INQUIRER.net, 2016, https://​technology.inquirer.net/​56131/​alibaba-​launches-​full-​vr-​shopping-​experience-​buy; Alibaba Cloud, https://​eu.alibabacloud.com/​; Amazon: Amazon, www.amazon.com/​; Mahesh Mohan, Over 61 Facebook Products & Services You Probably Don’t Know, Minterest, 2021, www.matrics360.com/​amazon-​products-​ and-​services/​; Amazon Web Services, https://​aws.amazon.com/​?nc2=h_​lg; Zoox, https://​zoox.com/​; Amazon AR View, Amazon, www.amazon.com/​adlp/​ arview; Apple: Apple, www.apple.com/​; Support –​System Status, Apple, www.apple.com/​pl/​support/​systemstatus/​; Apple Search Ads, https://​searchads.apple. com/​, Augmented Reality, Apple, www.apple.com/​augmented-​reality/​; Reuters, Apple plans self-​driving car ‘in 2024 with next-​level battery technology’, The Guardian, 2020, www.theguardian.com/​technology/​2020/​dec/​22/​apple-​plans-​self-​driving-​car-​in-​2024-​with-​next-​level-​battery-​technology; Facebook: Products, Facebook for Developers, https://​developers.facebook.com/​products/​; Mahesh Mohan, Over 61 Facebook Products & Services You Probably Don’t Know, Minterest, 2021, www.matrics360.com/​facebook-​products-​and-​services/​; Workplace, www.workplace.com/​; Google: Produkty i usługi, Google, https://​about.google/​intl/​ALL_​ pl/​products/​; Waymo, https://​waymo.com/​, Google AR&VR, https://​arvr.google.com/​; Fuchsia, https://​fuchsia.dev/​; Microsoft: Microsoft, www.microsoft. com/​en-​us; Vijitha Chekuri, Accelerating autonomous vehicle development, Microsoft, 2019, https://​cloudblogs.microsoft.com/​industry-​blog/​automotive/​2019/​07/​ 02/​accelerating-​autonomous-​vehicle-​development/​;VR & Mixed Reality, Microsoft, www.microsoft.com/​en-​us/​store/​b/​virtualreality?icid=CNavVirtualReality.

How is market changing?  51

Google Home, Google Assistant Apple Search in News Feed, AdWords, AdSense, Ads Audience Google Network, Marketing Facebook Platform, Ads, Instagram WazeAds, Business AdMob Apple Store, App Facebook Google Play, Store, iTunes Marketplace, Chrome web Store Facebook Shops, store, Google Instagram Shopping, Shopping Google Store Apple Pay Facebook Diem, Google Pay Facebook Pay, WhatsApp Payments macOS, iPadOS, -​ Chrome OS, iOS, watchOS, Android, Wear tvOS, OS, Google watchOS Fuchsia

52  How is market changing? app store. It simultaneously enables mobile payments (by touching the phone to a payment terminal). WeChat also offers a multitude of other sales and booking services, e.g., plane tickets, places in restaurant booking queues, sports activities, Didi rides (a Chinese Uber). In 2020, WeChat already boasted over 1.2 billion users, 90% of whom were in the Chinese market.9 WeChat’s payment service –​WeChat Pay –​has approximately 800 million users (as of 2019).10 WeChat Pay’s popularity soared thanks to a promotional campaign based on the Chinese tradition of sending red envelopes containing money to family and friends to celebrate each new year. A digital version of those red envelopes and an ad campaign on Chinese television caused an increase in the number of WeChat Pay users by 70 million in the first month of 2014. In 2017 the number of red envelopes sent through WeChat in the six days around the New Lunar Year exceeded 47 billion.11 Increasing its usability by building an ecosystem of applications or services is a primary platform strategy for network tending. Platforms make use of datafication to capture better the value generated by direct and indirect network effects. They take advantage of direct network effects, which means that the value of the service increases as the number of users rises. Still, the critical factor for their development is indirect network effects, which occur when multisided markets see their sides interact with one another (for example, a higher number of network participants in one market increases usability for users in a second market). They can also deploy specific pricing based on the data insight in order to strengthen one or the other side of the market. Platforms are only one example of the business application of new technologies that change the operation of markets. For instance, Netflix changed the way movies and TV shows are made but is not a platform like YouTube. YouTube connects different sides of the market: consumers watching videos, their producers, advertisers, and companies that want to buy data that has been scattered around more or less knowingly by consumers. On the other hand, Netflix is simply an e-​commerce service that streams movies and series via the internet, illustrating the transition from a physical linear to a digital linear business model. Yet it differs from other e-​commerce services because it radically facilitates selection of the content by feeding the users data to the intelligent recommendation algorithms. The intelligent systems churn out the information about what kind of content should be produced or licensed by the company –​and how it should be promoted. The algorithms decide which products will be presented on the welcoming screen, and which trailers will attract the viewer.The more personalised the recommendations, the more users are satisfied. Thus the number of subscriptions grows, which in turn makes Netflix able to capture the value generated by direct network effects. Such technologically-​enabled business models that facilitate matching between users and providers and capture the value generated by direct network effects may be called hubs. It is worth noting that both hubs and platforms are an example of digital companies that benefit from the power of data, algorithms, and

How is market changing?  53 networks, but only the latter use their full scope and strengthen the direct network effects with indirect ones. Finally, it is, of course, the case that the world’s biggest technology companies, sometimes nicknamed Big Tech, are either platforms or increasingly turn into one by adopting elements of the platform business model. They cluster in two rival ecosystems: one located almost entirely on the west coast of the United States (and commonly equated with the Silicon Valley), the other in China, located largely in Beijing and Shenzen. The giants of the US are sometimes known by the acronym GAFAM, or as the Big Five: Google (owned by Alphabet), Amazon, Facebook, Apple and Microsoft. China’s technological giants are dubbed BAT, and consist of Baidu, Alibaba and Tencent, sometimes with the addition of Xiaomi, the largest Chinese producer of smartphones and other digital devices. Europe has some isolated cases of successful platforms, such as Spotify (based in Sweden), Zalando (a German e-​commerce firm) or Supercell (a Finnish mobile gaming company), and a few large tech companies notably SAP (an enterprise software provider, and the largest non-​American software company by revenue). But Europe has so far failed to create a technology giant, let alone an ecosystem to match the US and China. Indeed, no region in the world has matched these two clusters of technology companies. Every other country in the world is thus dependent the digital infrastructures provided by one or other of these two clusters of technology giants.

Economic mechanisms of platforms Datafication effects The success of a platform’s business model stems from efficient generation and skilful use of their users’ data. In fact, platforms may be defined as ‘(re-​)programmable digital infrastructures that facilitate and shape personalised interactions among end-​users and complementors [entities producing complementary products and services –​auth. note], organised through the systematic collection, algorithmic processing, monetisation, and circulation of data’.12 They have immediate access to abundant data left by their users and have honed the tools of squeezing value out of it. Platforms feed off the virtuous circle between data and algorithms in several ways. On the most basic level, as they are born digital, they have fully adopted the rule ‘data-​first, AI-​first’ in their organisational outlook. In fact, they are digital companies epitomised who know how to datafy the management of their operations Furthermore, platforms draw from data to develop and personalise their offer, which consolidates their market advantage and facilitates exploring new sectors of the economy. They excel in using data-​based tools to extract information about consumer preferences. In turn, tailored matching of products and services to preferences increases customer satisfaction. Until now, markets –​the basic building blocks of an economy –​operated on the basis of information

newgenrtpdf

68 MercadoLibre

Booking

Uber

46

Carvana

42 38

Match

37 Twitter

74 85

37

Amazon

90

1,196

Ebay

35

Delivery Hero Roku

35

Splunk

32 31

Facebook

Microsoft

1,617

Tencent

SAP

Alphabet

1,598

785

Apple

145 23

Yandex

23

51

57

Trip.com Spotify

YonYou

Kakao

30 Teladoc 23

Naver

Square

96

239

759

42

46

167 65

139

Pinduoduo

JD.com

251

92

Meituan

Alibaba

30

Netease

Paypal Intuit

368

21

Baidu

Slack

Samsung

20

Bilibili 22

Peloton

28 Zillow

1,973

730

Adyen

Salesforce Netflix

214

225

Prosus

182 96

Naspers

Figure 2.2 Big Tech market capitalisation (above 20 billion USD, 12.2020). Source: Fernandez R., Adriaans I., Klinge T., and Hendrikse R. 2020. Engineering digital monopolies. The financialization of Big Tech. SOMO. p. 20.

54  How is market changing?

Pinterest

Twilio Snap

How is market changing?  55 transmitted via price. However, price compressed important information too much: it informed which products are in demand, and which are scarce or costly in production, but offered no insight about precise needs and expectations of the consumers. Today’s markets are overflowing with data provided by internet users concerning the entire spectrum of their preferences, behaviours, decisions, and choices, which the companies may use for profiling their customers. The introduction of machine learning algorithms allowed for ever more efficient matching and recommendation services, creating an adaptive system that enables optimal transactions to be concluded.13 Recommendation engines offer a seamless and fitting experience for each side of the market. As platforms know how to generate useful insight from the participants’ rich data, they can design their pricing strategies accordingly. Their first ­priority is to build the network and incentivise interaction on the platform, so they may choose to attract participants on one side of the market by offering them attractively priced (or even free) products or services.The success of this strategy depends on appropriately assessing the relationship between market participants. Simultaneously, the costs (or lower remuneration) will be borne out by the participants on the other side of the market (this is called cross-​ subsidisation). For example, the credit card providers can transfer the costs of the cards to the merchants, not to the buyers.14 Spotify offers attractively priced subscriptions to individual clients but is being accused of paying low royalties to artists.15 In order to increase the number of users on one side of their business (preferably via direct and/​or indirect network effects), many platforms choose to subsidise them through investors’ cash or loans. Uber is, for example, subsidising passengers via investor money. Google subsidises the users of their free applications and services through advertisers’ fees. Such mechanisms are not possible in traditional markets, where below-​cost prices for services cannot also be market-​clearing prices, and indeed –​if they are intended to force competitors to withdraw from the market –​they constitute illegal dumping. In many cases, subsidised users do not pay a penny for using the platform (i.e., the services are free). However, by accessing these free services, users implicitly agree to provide something valuable of their own: personal and behavioural data, left to the platforms in the form of users’ digital footprints. Thanks to evolving data analytics and profiling methods, this data is becoming a precious resource that powers advertising, creating the other side of the market for the platforms. This is a strategy used by internet browsers, social media platforms, and instant messengers. Revenue from personalised ads allows them to offer free services to their users.When you use free Gmail, Google maps or any other ‘free’ Google service, you are, in fact supplying data that Google can monetise by offering the advertisers tailored access to the users.16 The platforms may also introduce price differentiation on the same side of the market based on how ‘willing to pay’ different customers are (or how flexible they are pricewise). The ability to set different prices for other customer groups allows firms to increase revenues. This type of approach depends on the

56  How is market changing? market environment, regulatory restrictions, and customer behaviour (e.g., how often people shop around for the best prices). It is not unique to platforms since traditional businesses also employ it (i.e., by airlines that diversify the tickets’ cost according to the demand using additional information about the customers’ preferences and willingness to pay).17 They can analyse data more efficiently and faster and thus introduce dynamic pricing (auctions), which is much harder for most traditional companies. This strategy can be implemented for single customers (e.g., price per order for a given contract), at the segment level –​by setting prices for selected customer groups (e.g., discounts for students), or even at the product level, where slightly different versions of a product are priced differently and marketed to various customer groups. Network effects Let’s start by unpacking the phenomenon of network effects. They come down to one simple mechanism: the greater the number of participants, the Table 2.3 Examples of price differentiation used by platforms Pricing policy Airbnb

Amazon Marketplace Facebook

Uber

YouTube

Source: own work.

The cost of booking on Airbnb depends on several elements determined by the host (daily rate, cleaning fee, additional guest fee), Airbnb (the service fee), or other factors (currency conversion fee, local taxes,VAT).63 Amazon uses dynamic pricing based on advanced data analytics (consumer patterns, competitors’ prices, margins, inventory and other factors).64 The cost of advertising on Facebook is determined via an advertising auction, and pricing is determined in two ways: via the total amount spent and the cost of each result obtained. Facebook also allows clients to control their expenses by setting ad campaign limits and account spending caps.65 Prices for the user are set depending on the class of vehicle (UberX, Select, Black,Van, and Black/​SUV) and how one travels (regular ride, UberPool). When calculating the fare, the following are taken into account: the order fee, cost per kilometre, cost per minute of waiting, a minimum fee. Differences in cost per kilometre and minute are due to supply and demand in a given place, i.e., ‘peak-​load pricing’.66 The price of services depends on a user’s chosen plan: free Youtube,YouTube Premium ($11.99 per month), or YouTube Music Premium ($9.99 per month).67 The price of YouTube Advertising ads depends on which personalised advertising plan one chooses.68

How is market changing?  57 more useful the participation in a network (understood as a system of connected market participants built around using a good or a service) becomes. At some point, the number of participants (on each side of the market) reaches critical mass, and network growth becomes self-​sustaining.18 Network effects can be divided into direct and indirect ones. Direct network effects consist of shifting some of the benefits (in the case of positive effects) or costs (in the case of negative effects) resulting from the activity of one member of a network onto other members on the same side of the network, regardless of whether they want this or not. When more participants join the network, everyone else benefits because the reach and number of interactions increases, and so too does the overall value and utility of service or good. Network effects can lead to rapid and unprecedented growth in the number of users because they create a virtuous circle –​the more users there are on one side of the market, the more valuable the service or good becomes, which attracts even more users on that side of the market. The more people used emails, the more beneficial the service was for everybody, so the number of users grew. Such effects are to be seen in social media and instant messengers. Both services are practically useless to the consumer if they are the only person using them, but their value increases as the number of other users goes up. A Messenger app is much more useful to us if used by many of our relatives and friends, not just by a handful of people. Similar effects occur in games with a multi-​player option –​they become more attractive when ‘everyone’ is playing them. A reverse and avalanche-​like process kicks in when more and more users start to dump a network –​this is termed a negative direct network effect. Platforms provide something more: positive indirect network effects. These occur when a group of users (e.g., external sellers –​the first side) benefits more because the number of users on the other side of the market increases (buyers who use the same platform –​the second side). They work in both directions in a two-​sided market –​as more users join on the one side, the platform attracts users on the other side. When new users on the other side start joining the platform, its attractiveness to the first side increases. In this process, the platforms themselves provide a valuable service by solving the problem of coordinating two or more sides.19 Examples of the two-​sided markets in the networked digital economy abound. For example, the Just Eat Takeaway platform acts as an intermediary between restaurants and customers who want to order food and have it delivered. It also serves couriers who deliver the orders. Better tailoring of their offerings to consumers translates into greater profits for the platform. The more users involved in posting ratings for books on Amazon, or apartments on Airbnb, the more complete is the information available to other users.The more applications available on the iOS platform via the App Store, the more attractive the iOS platform is for iPhone users (a direct network effect). Simultaneously, the growing number of iPhone users spurs developers to create applications for iOS (an indirect network effect).The same applies to the Android platform ecosystem. Both of these processes drive one

58  How is market changing? another. Because platforms create their business models based on data, indirect network effects can be more subtle. For example, a search engine’s algorithm becomes more accurate at predicting what users are looking for as the number of searches increases. Network effects also occur in the ‘traditional’ economy. Telephone networks linking geographically dispersed users provided direct network effects. Credit card platforms provided indirect network effects. Digital platforms feed off both direct and indirect network effects through the dexterous use of data. A crucial part of fostering network effects is the implementation of mechanisms that lower user churn rate. If users drop off a platform rapidly, the platform may turn out to be short-​lived, even if the initial interest in it was spectacular and large-​scale. Such was the fate of the Friendster, which failed to build additional features to keep its network participants active and interested. An obvious counter-​example is Facebook, which has not allowed such a thing to happen.The development of the world’s most popular social network began with direct network effects: Harvard students could communicate with each other more easily when they joined the new platform. These effects were later combined with Table 2.4 Direct and indirect network effects found in platforms Platform

Direct network effects

Airbnb

The more guests served, the The more hosts there are the more comments and ratings greater the platform’s usability they leave, and therefore the for guests, and vice versa. more information there is for other users. The more consumers there are, The more sellers there are, the the more product ratings greater the platform’s value to and recommendations they consumers, and vice versa. leave, and therefore the information becomes more usable for consumers. The more users there are, the An increase in the number of greater the possibilities for users makes the network more two-​way communication worthwhile for advertisers amongst them. and content providers (e.g., games): the more content, the more useful the platform to the users. No direct network effects on The more drivers there are, the either side of the market. more useful the service is for passengers, and vice versa. The more YouTubers, the more The more YouTubers, the more attractive the platform is value there is for subscribers for them to publish their and viewers, and vice versa. content.

Amazon Marketplace

Facebook

Uber YouTube

Source: own work.

Indirect network effects

How is market changing?  59 indirect network effects thanks to programmers who offered games and apps on the platform (e.g., FarmVille or horoscopes). Having found that it could scale up its free services, Facebook began to earn money by attracting advertisers to the platform. The latter’s benefits also resulted from the fact that Facebook could match advertising to its recipients with unusual precision because it had access to the data generated by its users. In other words, using datafication, Facebook was able to strengthen direct network effects with indirect network effects. Direct network effects are not enough to maintain market advantage. In 2009, every fifth smartphone sold worldwide was a BlackBerry, manufactured by Canada’s Research in Motion. Its defining feature –​and the reason for its popularity among businesspeople –​was its integrated micro-​keyboard, which facilitated sending emails, amongst other things. Management at RiM was so sure of its competitive advantage that they barely reacted to the iPhone’s growing popularity. Its board members were –​entirely unreasonably, as it turned out –​ convinced that customers would shy away from using a touch screen keyboard. Their second mistake was to focus too much on businesspeople and stockbrokers as their main users, and disregard the mass market.Their third basic mistake was to ignore the power of network effects. BlackBerry also benefited from them –​ its users particularly valued the free BlackBerry Messenger app, through which it was possible to communicate solely with other BlackBerry users. Apple, and 20.1

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Figure 2.3 Blackberry’s global smartphone OS market share (in %, 2007–​ 2018, by quarter). Source: Own work based on ibtimes.co.in. 2016. Global smartphone OS market share held by RIM (BlackBerry) from 2007 to 2016, by quarter. Chart. In Statista. www.statista. com/​statistics/​263439/​global-​market-​share-​held-​by-​r im-​smartphones/​ (accessed 15 December 2020) from 2007 to 2011; StatCounter. 2020. Mobile operating systems’ market share worldwide from January 2012 to October 2020. Chart. In Statista. www.statista.com/​ statistics/​272698/​global-​market-​share-​held-​by-​mobile-​operating-​systems-​since-​2009/​ (accessed 14 January 2021) from 2012 to 2018; Labels showing values for the first quarter of each year.

60  How is market changing? 100 90

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Figure 2.4 Global market share of Android and iOS (in %, 2012–​2019). Source: Own work based on StatCounter. 2020. Mobile operating systems’ market share worldwide from January 2012 to October 2020. Chart. In Statista. www.statista.com/​ statistics/​272698/​global-​market-​share-​held-​by-​mobile-​operating-​systems-​since-​2009/​ (accessed 15 December 2020).

a couple of months later Google, focused on expanding their ecosystems for app developers. Apple’s ecosystem was initially strictly controlled and hermetic. Still, in the wake of Android’s spectacular success, Steve Jobs agreed to open it up (to some degree –​the entry to the iOS platform is curated). Smartphones which expanded to include these open operating systems became multifunctional platforms. In April 2010, there were 38,000 Android applications. By the beginning of 2017, however, there were 2.8 million Android apps, 2.2 million in the Apple App Store, 669,000 in the Windows Store, and 600,000 in the Amazon Appstore. In contrast, at BlackBerry World, there were just 234,500 apps, which perfectly illustrated RiM’s failure to appeal to companies using the platform model. In 2018, Android had a 75% market share, iOS had 22%, and the share of BlackBerry OS had shrunk to 0.1%.20 Since platforms’ development hinges on increasing indirect network effects, companies are looking for solutions that allow these effects to multiply. For the same reason, gaming platforms provide development kits and a number of incentives for programmers; simultaneously, they try to lock-​in the gamers on the platform by developing or subsidising the games dedicated to a specific console. This, of course, serves to maximise indirect network effects: more games, and better games, are bound to attract more consumers interested in the games console itself, and generally, this will strengthen the competitive edge of the platform.21 A system of recommendations issued to one another by the parties involved in the transaction serves to build and maintain trust between them. Sometimes the platform provides oversight in a more direct way.

How is market changing?  61 Table 2.5 Mechanisms for institutionalising trust, according to platform Platform

Institutionalising trust

Airbnb

A system for mutual reviews of hosts and guests, and a secure communication system via the platform.69 User ratings, reviews, and comments. Blocking accounts that violate community policies (e.g., routine blocking of user accounts that FB considers to be fake, including when the user attempts to use an alias instead of their real name).70 Tools to prevent unauthorised use of personal data (sending notifications about unrecognised logins, two-​step authentication, and a privacy control function). Passenger and driver safety: call for help button, 24-​hour accident support, providing journey details to trusted contacts, security centre, mutual reviews, GPS tracking, phone number anonymisation.71 Content blocked if deemed harmful, dangerous, hateful, graphic or violent. Content identification for copyright holders. Users can report videos and users who violate policies and people’s privacy. The platform sends a warning and/​or closes accounts that violate the rules.72

Amazon Marketplace Facebook

Uber

YouTube

Source: own work.

Platforms tend to their networks by trying to prevent the users from multihoming, i.e., using similar services by other providers, be they platforms or hubs. Binge watchers may simultaneously subscribe to Netflix, HBO, and Amazon Prime Video. In many European cities, drivers and passengers are multihoming by using both FreeNow and Uber. Easy multihoming lowers user loyalty, which in turn makes the network more volatile. Multihoming is particularly dangerous to the platforms dependent on the local availability of service providers.There are only so many drivers willing to offer rides in Warsaw: if too many of them will carry the service for Uber at any given moment, Free Now may lose dissatisfied customers who will have to wait in vain for the ride. The critical mass of the network and market advantage may disappear overnight. Multihoming may be prevented by elevating the costs of switching: the user may have to learn how to use new software (procedural costs), she may lose the loyalty discounts (financial costs) or the access to contacts with other members of the network (relational costs).22 Once a platform achieves a significant position in the market, it is often not easy for users to give up its services in favour of an alternative platform, even if prices rise, quality decreases or usage conditions change. In general, platforms encourage users to invest their effort and time in building their profile or library of content. For social media, these may include setting up and personalising one’s profile, sending content, including photos, videos, posts, or

62  How is market changing? product information, as well as establishing a community of friends, followers, or customers. These investments may include becoming familiar with the appearance and way the platform operates and trusting its functioning. This is because the switching costs may increase even more rapidly when user data is associated not only with a specific platform, but with the entire ecosystem (e.g., when using various Google services). The combination of positive network effects and switching costs means that platforms tend to gain significant, often monopolistic market shares.23 This process can be lightning-​fast: Facebook reached 100 million users just four and a half years after it launched.24 It can also reach a massive scale: the number of users of some platforms well exceeds the populations of the largest nation-​states.

Chile Amazon Vietnam Apple Hungary Google (Alphabet) Kuwait Microso Myanmar Alibaba Luxembourg Facebook Cote d'Ivoire Tencent Burkina Faso Baidu

16.0 15.4

76.1 72.0 71.1 70.7 58.5 58.5

163.5 161.9 134.6 125.8

282.3 280.5 261.9 260.2

Country Company

Figure 2.5 Comparison of GAFAM and BAT revenues compared to countries’ GDP (in billion USD, 2019). Source: Own work based on Alphabet Inc. 2020. Form 10-​K. For the Fiscal Year Ended December 31, 2019. https://​abc.xyz/​investor/​static/​pdf/​20200204_​alphabet_​10K. pdf?cache=cdd6dbf (accessed 28 January 2021); Amazon. 2020. 2019 Annual Report. https://​ s 2.q4cdn.com/ ​ 2 99287126/ ​ f iles/ ​ d oc_ ​ f inancials/ ​ 2 020/ ​ a r/ ​ 2 019- ​ A nnual-​ Report.pdf (accessed 28 January 2021); Facebook. 2020. Facebook’s annual revenue from 2009 to 2019 (in million U.S. dollars). Chart. In Statista. www.statista.com/​statistics/​ 268604/​annual-​revenue-​of-​facebook/​ (accessed 28 January 2021);Apple Inc. 2020. Form 10-​K. For the Fiscal Year Ended September 28, 2019. https://​s2.q4cdn.com/​470004039/​ files/​doc_​financials/​2019/​ar/​_​10-​K-​2019-​(As-​Filed).pdf (accessed 28 January 2021); Microsoft. www.microsoft.com/​investor/​reports/​ar20/​index.html (accessed 28 January 2021); Baidu, Inc. 2020. Form 20-​F. Annual Report for the Fiscal Year Ended December 31, 2019. https://​ir.baidu.com/​static-​files/​ee02be35-​ab39-​496b-​9119-​3f30a5e99e6f (accessed 28 January 2021); Alibaba Group. 2020. Fiscal Year 2020 Annual Report. https://​ doc.irasia.com/​listco/​hk/​alibabagroup/​annual/​2020/​ar2020.pdf (accessed 28 January 2021); Tencent. 2020. 2019 Annual Report. https://​static.www.tencent.com/​uploads/​ 2020/​04/​02/​ed18b0a8465d8bb733e338a1abe76b73.pdf (accessed 28 January 2021); WorldBank Data.

How is market changing?  63 However, these procedural costs are falling because the business models and technological solutions employed by platforms are surprisingly easy to copy, as proved by the case of the Chinese copycatting described by Kai-​Fu Lee.25 This may explain the aggressive buying policy performed by tech companies to retain the competitive edge. In 2010–​2019 Apple bought 20 startups working on artificial intelligence, Alphabet –​14, Microsoft –​10, and Amazon –​7.26

What makes digital platforms a challenge for traditional business? As the OECD’s experts have succinctly put it, although not every platform brings disruptive innovation, it can indeed be said of all successful platforms.27 Platforms have many advantages over the way traditional companies work. Indeed, they apply a radically different model. Here are some of the critical differences. Platforms are immensely useful. Essentially, the platform-​based economic model brings many benefits to all parties in the market. As platforms increasingly connect recipients, manufacturers, distributors, and owners, they change how the traditional market functions towards greater access.28 For example, consumers have better access to information about products and services, they can search for those that they need more effectively, and it is easier to compare quality and price. Platforms facilitate personalisation –​a fit between consumers’ expectations and needs, and market supply. The range of available products and services is expanding, also because platforms attract other companies offering supplementary products and services. Suppliers of products and services benefit from lower operating costs, find customers faster and more easily, and have greater opportunities to trade across borders. In fact, platforms provide the digital infrastructure of everyday life and operation for a growing number of people and organisations in an ever-​increasing number of sectors. Platforms are born digital. Their operational core consists of the intelligent matching, predicting, and recommending algorithms, whose efficiency was boosted by applying machine learning. They are able to feed those algorithms with unparalleled volumes of user’s data, and this way they benefit from a virtuous circle of datafication: the more the data, the better the algorithms; the better the algorithms, the better the matches and recommendations, the more satisfied users; the more users, the more data. Datafication spurs personalisation. Because data is their business, platforms excel in datafying their operations, introducing intelligent automation and optimising internal processes whenever possible. They comprehensively espouse the ‘data-​first, AI-​first’ approach in their internal operations, optimising every aspect of their management. Platforms excel at strengthening the datafication effects with network effects. Their core advantage is digital innovation enabling effective datafication, i.e., making the most of the users’ data to optimise their operation or personalise the offer. Predominantly, the innovation takes the shape of a

64  How is market changing? self-​perfecting recommendation algorithm feeding off the users data that come from the growing network.This creates the incredibly virtuous cycle: the larger the network, the more users, the more data, the better the service, the larger the network of satisfied users. Platforms have low operating costs. They have some fixed costs (office rent, equipment, cloud services, system development, etc.). But their main assets consist of the matching and recommending algorithms that feed off the vast pools of users’ data. They usually have little physical resources to manage. Tom Goodwin’s witticism has gone down in legend; in 2015 he stated that: ‘Uber, the world’s largest taxi company, owns no vehicles. Facebook, the world’s most popular media owner, creates no content. Alibaba, the most valuable retailer, has no inventory. And Airbnb, the world’s largest accommodation provider, owns no real estate. Something interesting is happening.’29 As a result, they are capable of significant, swift and inexpensive growth (especially when compared to expanding a company’s activities in a physical goods market), because the unit costs associated with processing, storing, duplicating and transmitting data are so low.30 The ‘scale without mass’ enables platforms to develop –​even to a level where they serve hundreds of millions, perhaps even billions of people –​ without increasing investment in tangible assets or hiring new employees.31 Platforms innovate faster and cheaper. Most of them develop internal experimentation platforms to check how their users react and evaluate different versions of services and products. Having access to millions of users and billions of data, they can run thousands of experiments a year and introduce changes based on their results, even if most of them test a failed idea. Most of them are simple A/​B or split tests: they compare the two different versions of a product (e.g., an application or webpage). In 2016 commentators were awed by the fact that Google conducted as many as 7,000 such experiments within a year. Still, in 2019 the company carried the mindboggling number of 464,065 experiments, which resulted in 3,620 modifications of the Google search engine.32 ‘We test and measure almost everything we do so that we have a continuous data stream to inform our decisions.’33 Uber has over 1,000 running on their platform at any given time, enabling the company to ‘launch, debug, measure, and monitor the effects of new ideas, product features, marketing campaigns, promotions, and even machine learning models’.34 More useful and personalised products help to maintain the competitive advantage of the platforms. Platforms tend to build monopolies. This is a direct result of network effects: the greater the number of users, the better the platform is at connecting market sides. As a result, platforms ‘aspire to monopolise, often without remorse, and contribute to the rehabilitation of this concept. The argument is convincing and self-​explanatory: Facebook is more useful if everyone is on Facebook, and therefore everyone should be on Facebook.’35 Large platforms are easily able to favour their services, something that was unthinkable in the traditional economy.36 A perfect example was Google Shopping’s price comparison service and the way specific offers were displayed in search results. In 2017, the European Commission ruled that Google favoured its own services as part of a

How is market changing?  65 ‘platform powered ecosystem’, and was thus abusing its dominant market position. A penalty of €2.42 billion prompted the company to overhaul its search algorithm and allow competing price comparison websites.37 Even large tech companies may have difficulties with entering new markets. Microsoft came up against a significant barrier in the streaming games sector. Twitch is currently the dominant platform in this sector. Microsoft founded its own, called Mixer, but was unable to compete with the Twitch network’s effects, which was used by most streamers and viewers. Microsoft decided to invest in getting the most popular streamer, ‘Ninja’, to switch from Twitch to Mixer (the cost may have been as much as 50 million dollars). Only then the popularity of the Mixer app soared.38 But it was not enough to sustain its existence: in June 2020 Microsoft announced that it is shutting down the streaming platform and the users will be migrated on to the rival Facebook Gaming.39 Platforms work around regulations. Legal frameworks that have built up around the traditional models of companies fail to consider the quite different ways platforms work; this is the perfect illustration of ‘cultural lag’. The fact that institutions and their norms do not keep pace with the consequences of technological progress. Platforms usually play up their role as an intermediary and assert that they are not responsible for the actions of market sides.40 By bandying about a socially-​and ecologically-​positive sharing economy, some platforms strengthen their position in the market while bypassing the regulations in force in a given sector.41 Numerous studies have proven that the expansion of Airbnb means that it increasingly competes directly with hotels, which must respond by offering new services and lower prices.42 Besides, this expansion of the short-​term rental market has had a negative impact on the availability and cost of long-​term rental apartments.43 Similar criticism has been aimed at Uber. In 2018, taxi drivers surveyed by DELab UW underscored how Uber drivers have lower operating costs because they do not pay for licenses to drive taxis and do not buy the more expensive compulsory insurance required by taxi drivers. Their activities, therefore, bear all the hallmarks of unfair competition and should be more stringently regulated by the government.44 This problem particularly relates to how platforms comply with labour law regulations while being part of the so-​called ‘on-​demand economy’, whereby services and products are quickly delivered to the customer. Platforms such as Uber have consistently refused to consider those working through their mediation to be their employees.45 However, this argument is sometimes dented, as seen in a British court judgment concerning Aslam and Farrar versus Uber, a case in October 2016.46 The court found that, contrary to the claims of the platform, drivers are indeed de facto employees, a state of affairs which was determined based on, among other things, the following conditions: the driver does not know the exact location of the passenger until they actually get into the car, or the passenger’s name. Additionally, the route and fare for the journey are determined by Uber, which also charges fees for services rendered, settling accounts with drivers at weekly intervals.To cap it all, drivers may not exchange

66  How is market changing? Facebook East Asia and Pacific YouTube Amazon China India WeChat European Union United States Airbnb Indonesia Ethiopia Uber Philippines

2498 2068 1986

Company Country/ Region

1510 1398 1366 1165 448 328 301 271 112 111 108

Figure 2.6 Comparison of the number of platforms’ users with countries’ and regions’ population (in million individuals, 2019). Source: Own work based on Airbnb, Inc. 2020. Form S-​1. Registration Statement under the Securities Act of 1933. United States Securities and Exchange Commission. Washington. www.sec.gov/​Archives/​edgar/​data/​1559720/​000119312520294801/​d81668ds1.htm (accessed 24 January 2021); Amazon. 2019. Annual Report 2019. https://​s2.q4cdn. com/​299287126/​files/​doc_​financials/​2020/​ar/​2019-​Annual-​Report.pdf (accessed 24 January 2021); Facebook. 2020. Number of monthly active Facebook users worldwide as of 3rd quarter 2020 (in millions). Chart. In Statista. www.statista.com/​statistics/​264810/​ number-​of-​monthly-​active-​facebook-​users-​worldwide/​ (accessed 15 January 2021); Uber Investor. 2020. Uber Announces Results for Fourth Quarter and FullYear 2019. https://​ investor.uber.com/​news-​events/​news/​press-​release-​details/​2020/​Uber-​Announces-​ Results-​for-​Fourth-​Quarter-​and-​Full-​Year-​2019/​ (accessed 24 January 2021); Statista. 2020. Forecast of the number ofYouTube users worldwide from 2017 to 2026 (in millions). Chart. In Statista. www.statista.com/​forecasts/​1144088/​youtube-​users-​worldwideP(YouTube) (accessed 15 December 2020); Tencent. 2020. Number of monthly active WeChat users from 2nd quarter 2011 to 3rd quarter 2020 (in millions). Chart. In Statista. www.statista.com/​ statistics/​255778/​number-​of-​active-​wechat-​messenger-​accounts/​ (accessed 15 January 2021); WorldBank Data.

contact details with passengers, and Uber also has the right to temporarily log out any driver that refuses to make three trips in a row.47 We will return to the issue of controversial platform practices and their clashes with the regulators in the next chapters.

Mechanisms of platformisation The growing platformisation of society and economy, which Thomas Poell, David Nieborg and José van Dijck define as ‘penetration of the infrastructures, economic processes, and governmental frameworks of platforms in different sectors and spheres of life’, may take up several forms.48

How is market changing?  67 First, digital platforms are expanding into traditional sectors of the economy, bringing disruptive innovation into them; yet again, Uber and Airbnb are convenient examples of such disruption on the tourism-​and-​accommodation and taxi-​r ides market. The HighEd sector increasingly finds itself in the spotlight of the biggest tech companies, such as Google, which recently announced that it would accept its in-​house Grow with Google Career Certificates in the recruitment process, instead of the college degrees.49 The company also develops its IT Certificate Employer Consortium, a platform matching employers such as Walmart or Randstad, and Google-​certified IT specialists.50 Since 2017 you can get your master degree via Coursera, a vast e-​learning platform matching the offer of more than 200 business and university partners from 55 countries.51 Secondly, traditional firms, with a comparative advantage in a given sector, are engaging in the digital transformation. In the process, they set up hubs, build platforms, or collaborate with the existing platforms. Based on the virtuous circle between datafication and network effects, the platform’s business model has successfully penetrated those sectors of the economy that already sourced their value from data and information: IT, media and journalism, entertainment. For some time now, it has been disrupting even those sectors which were deemed essential to the operation of knowledge-​based economies and guarded by heavy regulations and elitist traditions, i.e., financial sector and higher education. Trailblazing China, the everyday financial operations are increasingly carried through apps, which are embedded in vast platform ecosystems of tech companies such as Tencent, Google, or Apple.52 Banking institutions are being reduced to providers of regulatory safety and reliable infrastructure or engage in digital transformation themselves, evolving into completely online neobanks or platform organisation collaborating with fintechs (financial technology firms) and techfins (technology firms that extend their activities into finance). Platformisation will be accelerated by the ever more effective datafication enabled by intelligent algorithms and smoother and faster flow between nodes of the growing network enabled by better connection. The transition from Internet AI to Business AI (see Chapter 1) opens new vistas for much greater integration of data from the material objects networked into the IoT.53 Platformisation will swallow the more traditional sectors because they also will become rich in data and connected. A good example is the emerging platformisation of the most traditional of all sectors –​agriculture. Farms, fields, and farming machinery are being equipped with sensors capturing the data on resources, environment, machine usage, production etc. In result, ‘the average farm went from generating 190,000 data points per day in 2014 to a projected 4.1 million data points in 2020’.54 This, in turn, motivated John Deere, a company producing agricultural equipment to set up a platform matching farmers with app providers.55 Quickly enough, the growing potential of the AgTech (Agricultural Tech) attracted the attention of the big tech companies. Recently Microsoft started to provide a cloud computing application FarmBeats,56 and IBM Watson started to support crop planning activities.57

68  How is market changing? Internet of Things makes almost every sector of the traditional economy rich in data and connected, hence opening it up for platformisation. Platforms start to mushroom in asset-​intensive industries, such as mining, construction and manufacturing, changing the production processes and organisation into more networked, distributed and horizontal models. And this takes us to the next chapter.

Key takeaways • Digital platforms typify business and operating models emerging in the digital economy, based on datafication and network effects. • Using abundant data on their users and intelligent matching, prediction and recommendation algorithms, platforms create multisided markets which attract consumers and producers; workers and employers; buyers, sellers, and advertisers; service providers and service users.Those datafication effects are self-​sustaining: the more data, the better the algorithms. And then, in turn, the better the algorithms, the more appropriate and personalised matches, predictions, and recommendations for users, so their number grows. • Digital platforms, sourcing of the internet networks, deftly use direct and indirect network effects to build and sustain their market position. Direct effects appear when the growing number of users on one side of the market translates into a more valuable service itself. Indirect effects occur when the increasing number of users on the other side of the market translates into a more valuable service. • Based on datafication and network effects, digital platforms can use pricing strategies such as cross-​subsiding and price differentiation, which both bolster their competitive edge. • Due to network effects, platforms scale easily and tend to build monopolies. The efficiency of their business and operating model facilitate their expansion into traditional sectors of the economy. Simultaneously, their successful business and operating models are emulated by traditional firms embarking on digital transformation. • Digital platforms are useful –​they offer convenient, tailored, and speedy access to many kinds of goods and services. However, some of their practices are controversial: using data to personalise their services they sometimes violate individual rights or privacy and bringing disruptive innovation into traditional markets, they make use of the fact that the new phenomena are underregulated by law.

Notes 1 Carlson, N. 2010. At last –​the full story of how Facebook was founded. Business Insider.  www.businessinsider.com/​how-​f acebook-​was-​founded-​2010-​3?IR=T (accessed 28 December 2020). 2 Carr, A. 2012. 19_​Airbnb. For turning spare rooms into the world’s hottest hotel chain. Fast Company. www.fastcompany.com/​3017358/​19airbnb (accessed 28 December 2020).

How is market changing?  69 3 The history of Uber. Uber Newsroom. www.uber.com/​newsroom/​history/​ (accessed 18 January 2021). 4 Clark, D. 2016. Alibaba:The House That Jack Ma Built. Collins Publishers. 5 Say-​Ling Lai, L. 2010. Chinese entrepreneurship in the internet age: Lessons from Alibaba.com. World Academy of Science, Engineering and Technology, International Journal of Economics and Management Engineering 4(12). https://​waset.org/​publications/​ 15135/​chinese-​entrepreneurship-​in-​the-​internet-​age-​lessons-​from-​alibaba.com (accessed 28 December 2020). 6 Evans, D.S. and Schmalensee, R. 2016. Matchmakers:The New Economics of Multisided Platforms. Harvard Business Review Press. 7 Van Dijck, J. 2013. The Culture of Connectivity: A Critical History of Social Media. Oxford University Press. 8 Abdelkafi, N., Raasch, C., Roth, A., et al. 2019. Multi-​sided platforms. Electron Markets 29: 553–​ 559. Following: L.C. Reillier and B. Reillier. 2017. Platform Strategy: How to Unlock the Power of Communities and Networks to Grow Your Business. Routledge, p. 25. https://​link.springer.com/​article/​10.1007/​s12525-​019-​00385-​4 (accessed 18 January 2021). 9 Tencent Holdings Limited. 2020. Interim Report. https://​cdc-​tencent-​com-​ 1258344706.image.myqcloud.com/​uploads/​2020/​08/​26/​c798476aba9e18d44d91 79e103a2e07f.pdf (accessed 25 January 2021). 10 Tencent Holdings Limited. 2019. Annual Report. https://​cdc-​tencent-​com-​ 1258344706.image.myqcloud.com/​uploads/​2020/​04/​02/​ed18b0a8465d8bb733e3 38a1abe76b73.pdf (accessed 25 January 2021). 11 Wade, M.R. 2018. The red envelope war in 2018. IMD. www.imd.org/​research-​ knowledge/​articles/​the-​red-​envelope-​war-​in-​2018/​ (accessed 25 January 2021). 12 Poell, T., Nieborg, D., and J. van Dijck. 2019. Platformisation. Internet Policy Review 8(4). https://​policyreview.info/​concepts/​platformisation (accessed 28 December 2020). 13 Mayer-​Schonberger, V. and T. Ramge. 2018. Reinventing Capitalism in the Age of Big Data, John Murray; Parker, G.G., van Alstyne, M.W., and S.P. Choudary. 2016. Platform Revolution: How Networked Markets Are Transforming the Economy and How to Make Them Work for You. W.W. Norton & Company, p. 3. 14 Rochet, J. and J.Tirole. 2002. Cooperation among competitors: Some economics of payment card associations. The RAND Journal of Economics 33(4): 549–​570. www. jstor.org/​stable/​3087474 (accessed 28 December 2020). 15 Ingham, T. 2018. Should Spotify change the way it pays artists? Rolling Stone. www. rollingstone.com/​music/​music-​features/​should-​spotify-​change-​the-​way-​it-​pays-​ artists-​763986/​ (accessed 28 December 2020). 16 Parker, G. and M. Van Alstyne. 2000. Internetwork externalities and free information goods. Proceedings of the 2nd ACM Conference on Electronic Commerce. New York, pp. 107–​116. 17 Elliott, C. 2020. Could dynamic pricing be influencing how much you pay for your plane ticket? Washington Post. www.washingtonpost.com/​lifestyle/​travel/​could-​ dynamic-​pricing-​be-​influencing-​how-​much-​you-​pay-​for-​your-​plane-​ticket/​ 2020/​08/​19/​7e77e182-​e17d-​11ea-​8181-​606e603bb1c4_​story.html (accessed 18 January 2021). 18 L.C. Reillier and B. Reillier. 2017. Platform Strategy: How to Unlock the Power of Communities and Networks to Grow Your Business. Routledge. 19 An Introduction to Online Platforms and Their Role in the Digital Transformation. OECD Publishing, Paris, https://​doi.org/​10.1787/​53e5f593-​en.

70  How is market changing? 20 2020. Number of available applications in the Google Play Store from December 2009 to September 2020. Chart. Statista. www.statista.com/​statistics/​266210/​ number-​of-​available-​applications-​in-​the-​google-​play-​store/​ (accessed 18 January 2021); Loesche, D. 2018. The biggest app stores. Digital image. Statista. 2 www. statista.com/​ c hart/​ 1 2455/​ number-​ o f-​ a pps- ​ available- ​ i n- ​ l eading- ​ a pp- ​ s tores/​ (accessed 18 January 2021); StatCounter. 2020. Mobile operating systems’ market share worldwide from January 2012 to October 2020. Chart. Statista. www.statista. com/​statistics/​272698/​global-​market-​share-​held-​by-​mobile-​operating-​systems-​ since-​2009/​ (accessed 18 January 2021). 21 Platform wars: Simulating the battle for video game supremacy. MIT Management Sloan School. https://​mitsloan.mit.edu/​LearningEdge/​simulations/​platform-​wars/​ Pages/​default.aspx (accessed 28 December 2020). 22 L.C. Reillier and B. Reillier. 2017. Platform Strategy: How to Unlock the Power of Communities and Networks to Grow Your Business. Routledge. 23 Iansiti, M. and K. Lakhani. 2017.The truth about blockchain. Harvard Business Review 95(1): 118–​127. https://​hbr.org/​2017/​01/​the-​truth-​about-​blockchain (accessed 28 December 2020). 24 Gebel, M. 2019. In 15 years Facebook has amassed 2.3 billion users –​more than followers of Christianity. Business Insider. www.businessinsider.com/​facebook-​has-​ 2-​billion-​plus-​users-​after-​15-​years-​2019-​2?IR=T accessed 28 December 2020). 25 Lee, K-​ F. 2018. AI Superpowers: China, Silicon Valley, and the New World Order. Houghton Mifflin Harcourt Company. 26 Cohen, J. 2020. Apple acquires more AI startups than any other tech company. Enterpreneur Europe. www.entrepreneur.com/​article/​353773 (accessed 28 December 2020). 27 OECD (2019), An Introduction to Online Platforms and Their Role in the Digital Transformation, OECD Publishing, Paris, https://​doi.org/​10.1787/​53e5f593-​ en, p. 24. 28 Evans, D. and R. Schmalensee. 2016. Matchmakers: The New Economics of Multisided Platforms. Harvard Business Review Press. 29 Goodwin, T. 2015. The battle is for the customer interface. Techcrunch. https://​ techcrunch.com/​2015/​03/​03/​in-​the-​age-​of-​disintermediation-​the-​battle-​is-​all-​ for-​the-​customer-​interface/​?guccounter=1 (accessed 28 December 2020). 30 OECD (2019), An Introduction to Online Platforms and Their Role in the Digital Transformation, OECD Publishing, Paris, https://​doi.org/​10.1787/​53e5f593-​en, p. 23. 31 OECD (2019), An Introduction to Online Platforms and Their Role in the Digital Transformation, OECD Publishing, Paris, https://​doi.org/​10.1787/​53e5f593-​en, p. 23. 32 How Google search works.Google Search.www.google.com/​search/​howsearchworks/ ​mission/​users/​ (accessed 28 December 2020). 33 Creating a culture of innovation. Eight ideas that work at Google. Google Workspace. https://​workspace.google.co.in/​intl/​en_​in/​learn-​more/​creating_​a_​culture_​of_​ innovation.html (accessed 28 December 2020). 34 Deb, A., Bhattacharya, S., and Gu, J. 2018. Under the hood of Uber’s experimentation platform. Uber Engineering. https://​eng.uber.com/​xp/​ (accessed 28 December 2020). 35 Herrman, J. 2017. Platform companies are becoming more powerful –​but what exactly do they want? The New York Times Magazine. www.nytimes.com/​2017/​ 03/​21/​magazine/​platform-​companies-​are-​becoming-​more-​powerful-​but-​what-​ exactly-​do-​they-​want.html (accessed 18 January 2021).

How is market changing?  71 36 Innovative online platform scaling-​up in the EU. European Commission. https://​ ec.europa.eu/​info/​strategy/​priorities-​2019-​2024/​europe-​fit-​digital-​age/​digital-​ services-​act-​ensuring-​safe-​and-​accountable-​online-​ e nvironment/​ e urope-​ f it-​ digital-​age-​new-​online-​rules-​businesses_​en (accessed 28 December 2020). 37 European Commission. 2017. Press release –​Antitrust: Commission fines Google €2.42 billion for abusing dominance as search engine by giving illegal advantage to own comparison shopping service. European Commission. Brussels. http://​europa.eu/​ rapid/​press-​release_​IP-​17-​1784_​en.htm (accessed 28 December 2020); Chee, F.Y. 2019. EU sees no compliance issues in Google shopping, rivals disagree. Reuters. www. reuters.com/​article/​us-​eu-​google-​antitrust/​eu-​sees-​no-​compliance-​issues-​in-​ google-​shopping-​r ivals-​disagree-​idUSKCN1SS127 (accessed 28 December 2020). 38 Owen, M. 2019. Mixer vaults to top of App Store because of ‘Ninja’ switch from Twitch. Appleinsider. https://​appleinsider.com/​articles/​19/​08/​03/​mixer-​hits-​top-​ of-​the-​app-​store-​on-​ninja-​switch-​from-​twitch (accessed 28 December 2020). 39 Warren, T. 2020. Microsoft is shutting down Mixer and partnering with Facebook Gaming. The Verge. www.theverge.com/​2020/​6/​22/​21299032/​microsoft-​mixer-​ closing-​facebook-​gaming-​partnership-​xcloud-​features (accessed 28 December 2020). 40 Helberger, N., Pierson, J., and T. Poell. Governing online platforms: From contested to cooperative responsibility. The Information Society 34(1): pp. 1–​14. www.tandfonline. com/​doi/​pdf/​10.1080/​01972243.2017.1391913 (accessed 29 December 2020). 41 Codagnone, C., Karatzogianni, A., and J. Matthews. 2019. Platform Economics: Rhetoric and Reality in the ‘Sharing Economy’. Emerald Publishing. Bingley, pp. 4–​5. 42 Gyódi, K. 2019. Airbnb in European cities: Business as usual or true sharing economy? Journal of Cleaner Production, 536–​551. 43 Nieuwland, S. and R. van Melik. 2018. Regulating Airbnb: how cities deal with perceived negative externalities of short-​terms rentals. Current Issues in Tourism. www.tandfonline.com/​doi/​full/​10.1080/​13683500.2018.1504899 (accessed 28 December 2020). 44 Mazur, J.,Włoch, R., and K, Śledziewska. 2018.Taksówkarz –​cyfrowy przedsiębiorca. [Taxi drivers as digital entrepreneurs] DELab UW. www.delab.uw.edu.pl/​wp-​ content/​ u ploads/ ​ 2 018/ ​ 1 0/ ​ R aport_ ​ C yfrowi_​ t aksowkarze_​ D ELabUW.pdf. (accessed 29 December 2020). 45 Parker, G.G., van Alstyne, M.W., and S.P. Choudary. 2016. Platform Revolution: How Networked Markets Are Transforming the Economy and How to Make Them Work for You. W.W. Norton & Company, pp. 229–​260. 46 Judgment handed down by the British Court of Appeal on 19 December 2018 regarding Uber B.V. and others versus Aslam and others, no. A2.2017/​3467, www. judiciary.uk/​wp-​content/​uploads/​2018/​12/​uber-​bv-​ors-​v-​aslam-​ors-​judgment-​ 19.12.18.pdf. 47 Evans, D.S. 2012. Governing bad behavior by users of multi-​sided platforms. Berkeley Technology Law Journal 27(2): 1247. www.researchgate.net/​publication/​228231521_​ Governing_​Bad_​Behavior_​by_​Users_​of_​Multi-​Sided_​Platforms (accessed 29 December 2020). 48 Poell, T., Nieborg, D., and J. van Dijck. 2019. Platformisation. Internet Policy Review. https://​policyreview.info/​concepts/​platformisation. 49 Dishman, L. 2020.‘No college degree required’: Google expands certificate program for in-​ demand job skills. Fast Company. www.fastcompany.com/​90527332/​no-​ college-​degree-​required-​google-​expands-​certificate-​program-​for-​in-​demand-​job-​ skills (accessed 29 December 2020).

72  How is market changing? 50 Dishman, L. 2020.‘No college degree required’: Google expands certificate program for in-​ demand job skills. Fast Company. www.fastcompany.com/​90527332/​no-​ college-​degree-​required-​google-​expands-​certificate-​program-​for-​in-​demand-​job-​ skills (accessed 29 December 2020). 51 Meet our partners. Coursera. www.coursera.org/​about/​partners (accessed 18 January 2021). 52 Westermeier, C. 2020. Money is data: The platformization of financial transactions. Taylor & Francis Online. www.tandfonline.com/​doi/​full/​10.1080/​ 1369118X.2020.1770833 (accessed 29 December 2020). 53 Lee, K-​ F. 2018. AI Superpowers: China, Silicon Valley, and the New World Order. Houghton Mifflin Harcourt Company, p. 110. 54 2018. How to turn ‘data exhaust’ into a competitive edge. Knowledge@Wharton. https:// ​ k nowledge.wharton.upenn.edu/​ a rticle/ ​ t urn- ​ i ot- ​ d ata- ​ e xhaust- ​ n ext-​ competitive-​advantage/​ (accessed 29 December 2020). 55 Kenney, M., Serhan, H., and G.Trystram. 2020. Digitalization and platforms in agriculture. Organizations, power asymmetry, and collective action solutions. ETLA. www.etla.fi/​wp-​content/​uploads/​ETLA-​Working-​Papers-​78.pdf (accessed 29 December 2020). 56 2015. FarmBeats: AI, Edge & IoT for agriculture. Microsoft. www.microsoft.com/​ en-​us/​research/​project/​farmbeats-​iot-​agriculture/​ (accessed 29 December 2020) 57 Perlman, C. 2017. From product to platform: John Deere revolutionizes farming. Digital Innovation and Transformation. MBA Student Perspectives. https://​digital. hbs.edu/ ​ p latform-​ d igit/​ s ubmission/​ f rom-​ p roduct- ​ t o- ​ p latform- ​ j ohn- ​ d eere-​ revolutionizes-​farming/​# (accessed 29 December 2020). 58 Moazed, A. and N.L. Johnson. 2016. Modern Monopolies:What It Takes to Dominate the 21st Century. Economy. St. Martin’s Press; L.C. Reillier and B. Reillier. 2017. Platform Strategy: How to Unlock the Power of Communities and Networks to Grow Your Business. Routledge. 59 OECD. 2019. An Introduction to Online Platforms and Their Role in the Digital Transformation. OECD Publishing. Paris. https://​doi.org/​10.1787/​53e5f593-​en (accessed 28 December 2020). 60 Cusumano, M., Gawer, A., and D.B. Yoffie. 2019. The Business of Platforms. Harper Business. 61 van Dijck, J., Poell, T., and M. de Waal. 2018. The Platform Society. Public Values in a Connective World. Oxford University Press, p. 15. 62 van Dijck, J., Poell, T., and M. de Waal. 2018. The Platform Society. Public Values in a Connective World. Oxford University Press, p. 15. 63 How is the price determined for my reservation? Airbnb. www.airbnb.com/​ help/​article/​125/​how-​is-​the-​price-​determined-​for-​my-​reservation (accessed 18 January 2021). 64 Mehta, N., Detroja, P., and A. Agashe. 2018. Amazon changes prices on its products about every 10 minutes –​here’s how and why they do it. Business Insider. www.businessinsider.com/​amazon-​price-​changes-​2018-​8?IR=T. (accessed 28 December 2020). 65 Facebook business. Kupowanie reklam. Kupuj reklamy na Facebooku bez względu na budżet. www.facebook.com/​business/​ads/​pricing (accessed 28 December 2020); How much it costs to advertise on Facebook. Facebook for Business. https://​en-​ gb.facebook.com/​business/​help/​201828586525529?id=629338044106215&ref= fbb_​budgeting (accessed 18 January 2021).

How is market changing?  73 66 How much does a ride with the Uber app cost? Uber. www.uber.com/​pl/​en/​price-​ estimate/​(accessed 18 January 2021). 67 Introducing YouTube Premium. 2018. YouTube Official Blog. https://​youtube. googleblog.com/​2018/​05/​introducing-​youtube-​premium.html (accessed 28 December 2020). 68 Set a budget that works for your business. YouTube Ads. www.youtube.com/​intl/​ en-​GB/​ads/​pricing/​ (accessed 28 December 2020). 69 How does Airbnb help build trust between hosts and guests? Airbnb. www.airbnb. pl/​help/​article/​4/​how-​does-​airbnb-​help-​build-​trust-​between-​hosts-​and-​guests (accessed 18 January 2021);Your safety is our priority. Airbnb. www.airbnb.pl/​trust (accessed 18 January 2021). 70 Community standards. Facebook. www.facebook.com/​communitystandards/​ (accessed 18 January 2021). 71 Driving safety forwards. Uber. www.uber.com/​pl/​en/​r ide/​safety/​ (accessed 18 January 2021). 72 Policies and safety. YouTube. www.youtube.com/​intl/​pl/​yt/​about/​policies/​ #community-​guidelines (accessed 18 January 2021).

Bibliography Abdelkafi, N., Raasch, C., Roth, A., and Srinivasan, R. Multi-​sided platforms. Electron Markets 29. 2019. Airbnb. How does Airbnb help build trust between hosts and guests? Airbnb. www.airbnb. pl/​help/​article/​4/​how-​does-​airbnb-​help-​build-​trust-​between-​hosts-​and-​guests Airbnb. How is the price determined for my reservation? Airbnb. www.airbnb.com/​ help/​article/​125/​how-​is-​the-​price-​determined-​for-​my-​reservation Airbnb.Your safety is our priority. Airbnb. www.airbnb.pl/​trust Carlson, N. At last –​the full story of how Facebook was founded. Business Insider. 2010. www.businessinsider.com/​how-​facebook-​was-​founded-​2010-​3?IR=T Carr, A. 19_​Airbnb. For turning spare rooms into the world’s hottest hotel chain. Fast Company. 2012. www.fastcompany.com/​3017358/​19airbnb Chee, F.Y. EU sees no compliance issues in Google shopping, rivals disagree. Reuters. 2019www.reuters.com/​article/​us-​eu-​google-​antitrust/​eu-​sees-​no-​compliance-​ issues-​in-​google-​shopping-​r ivals-​disagree-​idUSKCN1SS127 Codagnone, C., Karatziogianni, A., and Matthews, J. Platform Economics: Rhetoric and Reality in the ‘Sharing Economy’. Emerald Publishing. 2019. Cohen, J. Apple acquires more AI startups than any other tech company. Enterpreuner Europe. 2020. www.entrepreneur.com/​article/​353773 Coursera. Meet our partners. Coursera. www.coursera.org/​about/​partners Cusumano, M., Gawer, A., and Yoffie, D. The Business of Platforms. Strategy in the Age of Digital Competition, Innovation, and Power. Harper Business. 2019. Deb, A., Bhattacharya, S., Gu, J., Zhou, T., Feng, E., and Liu, M. Under the hood of Uber’s experimentation platform. Uber Engineering. 2018. https://​eng.uber.com/​xp/​ Dishman, L. ‘No college degree required’: Google expands certificate program for in-​demand job skills. Fast Company. 2020. www.fastcompany.com/​90527332/​no-​ college-​degree-​required-​google-​expands-​certificate-​program-​for-​in-​demand-​job-​ skills Duncan, C. Alibaba:The House That Jack Ma Built. Collins Publishers. 2016.

74  How is market changing? Elliott, C. Could dynamic pricing be influencing how much you pay for your plane ticket?. Washington Post. 2020. www.washingtonpost.com/​lifestyle/​travel/​could-​ dynamic-​pricing-​be-​influencing-​how-​much-​you-​pay-​for-​your-​plane-​ticket/​2020/​ 08/​19/​7e77e182-​e17d-​11ea-​8181-​606e603bb1c4_​story.html Entrepreneur Europe. Apple acquires more AI startups than any other tech company. Entrepreneur Europe. 2020. www.entrepreneur.com/​article/​353773 European Commission. 2017. Press release –​Antitrust: Commission fines Google €2.42 billion for abusing dominance as search engine by giving illegal advantage to own comparison shopping service. European Commission. Brussels. http://​europa.eu/​ rapid/​press-​release_​IP-​17-​1784_​en.htm. European Commission. Innovative online platform scaling-​up in the EU. European Commission.  https://​ec.europa.eu/​info/​strategy/​priorities-​2019-​2024/​europe-​ fit-​ d igital-​ a ge/ ​ d igital- ​ s ervices- ​ a ct- ​ e nsuring-​ s afe-​ a nd-​ a ccountable-​ o nline-​ environment/​europe-​fit-​digital-​age-​new-​online-​rules-​businesses_​en Evans, D. Governing bad behavior by users of multisided platforms. Berkeley Technology Law Journal 27(2). 2012. www.researchgate.net/​publication/​228231521_​Governing_​ Bad_​Behavior_​by_​Users_​of_​Multi-​Sided_​Platforms Evans, D. and Schmalensee, R. Matchmakers: The New Economics of Multisided Platforms. Harvard Business Review Press. 2016. Evans, D., Schmalensee, R., Noel, M., Chang, D., Howard, H., and Garcia-​Swartz, D.D. Platform Economics: Essays on Multi-​Sided Businesses. Competition Policy International. 2011. Facebook. Community standards. Facebook. www.facebook.com/​communitystandards/​ Facebook. How much it costs to advertise on Facebook. Facebook for Business. https://​ en-​gb.facebook.com/​business/​help/​201828586525529?id=629338044106215&ref =fbb_​budgeting Gebel, M. In 15 years Facebook has amassed 2.3 billion users –​more than followers of Christianity. Business Insider. 2019. www.businessinsider.com/​ facebook-​has-​2-​billion-​plus-​users-​after-​15-​years-​2019-​2?IR=T Goodwin, T. The battle is for the customer interface. Techcrunch. 2015. https://​ techcrunch.com/​2015/​03/​03/​in-​the-​age-​of-​disintermediation-​the-​battle-​is-​all-​ for-​the-​customer-​interface/​?guccounter=1 Google Search. How Google Search works. Google Search. www.google.com/​search/​ howsearchworks/​mission/​users/​ Google Workspace. Creating a culture of innovation. Eight ideas that work at Google. Google Workspace. https://​workspace.google.co.in/​intl/​en_​in/​learn-​more/​creating_​ a_​culture_​of_​innovation.html Gyódi, K. Airbnb in European cities: Business as usual or true sharing economy? Journal of Cleaner Production. 2019. Helberger, N., Pierson, J., and Poell, T. Governing online platforms: From contested to cooperative responsibility. The Information Society 34(1). www.tandfonline.com/​doi/​ pdf/​10.1080/​01972243.2017.1391913 Herrman, J. Platform companies are becoming more powerful –​but what exactly do they want?. The New York Times Magazine. 2017. www.nytimes.com/​2017/​03/​21/​ magazine/​platform-​companies-​are-​becoming-​more-​powerful-​but-​what-​exactly-​ do-​they-​want.html Iansiti, M. and Lakhani, K.R.The truth about blockchain. Harvard Business Review 95(1). 2017. https://​hbr.org/​2017/​01/​the-​truth-​about-​blockchain

How is market changing?  75 Ingham,T.Should Spotify change the way it pays artists? Rolling Stone.2018.www.rollingstone. com/​music/​music-​features/​should-​spotify-​change-​the-​way-​it-​pays-​artists-​763986/​ judiciary.uk. Judgment handed down by the British Court of Appeal on 19 December 2018 regarding Uber B.V. and others versus Aslam and others, no. A2.2017/​3467. www. judiciary.uk/​wp-​content/​uploads/​2018/​12/​uber-​bv-​ors-​v-​aslam-​ors-​judgment-​ 19.12.18.pdf Justpark. www.justpark.com/​creative/​sharing-​economy-​index/​ Kenney, M., Serhey, H., and Trystram, G. Digitalisation and platforms in agriculture. Organisations, power asymmetry, and collective action solutions. ETLA. 2020. www. etla.fi/​wp-​content/​uploads/​ETLA-​Working-​Papers-​78.pdf Knowledge@Wharton. How to turn ‘data exhaust’ into a competitive edge. Knowledge@Wharton. 2018. https:// ​ k nowledge.wharton.upenn.edu/ ​ a rticle/​ turn-​iot-​data-​exhaust-​next-​competitive-​advantage/​ Langley, P. and Leyshon, A. Platform capitalism:The intermediation and capitalisation of digital economic circulation. Finance and Society 3(1). 2017. Lee, K-​F. AI Superpowers: China, Silicon Valley, and the New World Order. Houghton Mifflin Harcourt Company. 2018. Loesche, D. The biggest app stores. Digital image. Statista. 2018. www.statista.com/​ chart/​12455/​number-​of-​apps-​available-​in-​leading-​app-​stores/​ Mayer-​Schönberger,V. and Ramge, T. Reinventing Capitalism in the Age of Big Data. John Murray. 2018. Mazur, J., Włoch, R., and Śledziewska, K. Taksówkarz –​cyfrowy przedsiębiorca. DELab UW. 2018. www.delab.uw.edu.pl/​wp-​content/​uploads/​2018/​10/​Raport_​Cyfrowi_​ taksowkarze_​DELabUW.pdf Mehta, N., Detroja, P., and Agashe, A. Amazon changes prices on its products about every 10 minutes –​here’s how and why they do it. Business Insider. 2018. www. businessinsider.com/​amazon-​price-​changes-​2018-​8?IR=T. Microsoft. FarmBeats: AI, Edge & IoT for agriculture. Microsoft. 2015. www.microsoft. com/​en-​us/​research/​project/​farmbeats-​iot-​agriculture/​ Moazed, A. and Johnson, N.L. Modern Monopolies: What It Takes to Dominate the 21st Century. Economy. St. Martin’s Press. 2016. Nieuwland, S. and van Melik, R. Regulating Airbnb: how cities deal with perceived negative externalities of short-​terms rentals. Current Issues in Tourism. 2018. www. tandfonline.com/​doi/​full/​10.1080/​13683500.2018.1504899 OECD. An Introduction to Online Platforms and Their Role in the Digital Transformation. OECD Publishing. Paris. 2019. https://​doi.org/​10.1787/​53e5f593-​en Owen, M. Mixer vaults to top of App Store because of ‘Ninja’ switch from Twitch. Appleinsider. 2019. https://​appleinsider.com/​articles/​19/​08/​03/​mixer-​ hits-​top-​of-​the-​app-​store-​on-​ninja-​switch-​from-​twitch Parker, G. and van Alstyne, M. Internet work externalities and free information goods. Proceedings of the 2nd ACM Conference on Electronic Commerce. New York. 2000. Parker, G., van Alstyne, M., and Choudary, S.P. Chapter 11: Policy: How platforms should (and should not) be regulated. In: Platform Revolution. How Networked Markets Are Transforming the Economy and How to Make Them Work for You. W.W. Norton & Company. 2016. Perlman, C. From product to platform: John Deere revolutionises farming. Digital Information and Transformation. MBA Student Perspectives. 2017. https://​ medium.com/​ h arvard- ​ b usiness- ​ s chool- ​ d igital-​ i nitiative/​ f rom-​ p roduct-​ t o​platform-​john-​deere-​revolutionizes-​farming-​14dc8b4e791f

76  How is market changing? Poell, T., Nieborg, D., and Van Dijck, J. Platformisation. Internet Policy Review 8(4). DOI: 10.14763/​2019.4.1425. https://​policyreview.info/​concepts/​platformisation Reiller, L.C. and Reiller, B. Platform Strategy: How to Unlock the Power of Communities and Networks to Grow Your Business. Taylor & Francis. Kindle Edition. 2017. Rochet, J-​ C. and Tirole, J. Cooperation among competitors: Some economics of payment card associations. The RAND Journal of Economics 33(4): 549–​570. 2002. www.jstor.org/​stable/​3087474 Sau-​Ling Lai, L. Chinese entrepreneurship in the internet age: Lessons from Alibaba. com. World Academy of Science, Engineering and Technology, International Journal of Economics and Management Engineering 4(12). 2010. https://​waset.org/​publications/​ 15135/​chinese-​entrepreneurship-​in-​the-​internet-​age-​lessons-​from-​alibaba.com StatCounter. Mobile operating systems’ market share worldwide from January 2012 to October 2020. Chart. Statista. 2020. www.statista.com/​statistics/​272698/​ global-​market-​share-​held-​by-​mobile-​operating-​systems-​since-​2009/​ Statista. Number of available applications in the Google Play Store from December 2009 to September 2020. Chart. Statista. 2020. www.statista.com/​statistics/​266210/​ number-​of-​available-​applications-​in-​the-​google-​play-​store/​ Sterman,J.Platform wars:Simulating the battle for video game supremacy.MIT Management Sloan School.  https://​ m itsloan.mit.edu/​ L earningEdge/ ​ s imulations/ ​ p latform-​ wars/​Pages/​default.aspx Tencent Holdings Limited. 2019. Annual Report. https://​cdc-​tencent-​com-​ 1258344706.image.myqcloud.com/​uploads/​2020/​04/​02/​ed18b0a8465d8bb733e3 38a1abe76b73.pdf Tencent Holdings Limited. 2020. Interim Report. https://​cdc-​tencent-​com-​ 1258344706.image.myqcloud.com/​uploads/​2020/​08/​26/​c798476aba9e18d44d917 9e103a2e07f.pdf Uber. Driving safety forwards. Uber. www.uber.com/​pl/​en/​r ide/​safety/​ Uber. How much does a ride with the Uber app cost?. Uber. www.uber.com/​pl/​en/​ price-​estimate/​ Uber. The history of Uber. Uber Newsroom. www.uber.com/​newsroom/​history/​ Van Dijck, J. The Culture of Connectivity: A Critical History of Social Media. Oxford University Press. 2013. Van Dijck, J., Poell, T., and de Waal, M. The Platform Society. Public Values in a Connected World. Oxford University Press. 2018. Wade, M.R. The red envelope war in 2018. IMD. 2018. www.imd.org/​research-​ knowledge/​articles/​the-​red-​envelope-​war-​in-​2018/​ Warren, T. Microsoft is shutting down Mixer and partnering with Facebook Gaming. The Verge. 2020. www.theverge.com/​2020/​6/​22/​21299032/​microsoft​mixer-​closing-​facebook-​gaming-​partnership-​xcloud-​features Westermeier, C. Money is data –​the platformization of financial transactions. Taylor & Francis Online. 2020. www.tandfonline.com/​doi/​full/​10.1080/​ 1369118X.2020.1770833 YouTube. Introducing YouTube Premium. YouTube Official Blog. 2018. https://​youtube. googleblog.com/​2018/​05/​introducing-​youtube-​premium.html YouTube. Policies and safety. www.youtube.com/​intl/​pl/​yt/​about/​policies/​ #community-​guidelines YouTube. Set a budget that works for your business. YouTube Ads. www.youtube.com/​ intl/​en-​GB/​ads/​pricing/​

3  How is production changing?

Abstract In this chapter we trace the progress of digital transformation in the production of goods, describing the emergence of Industry 4.0, aka smart or intelligent manufacturing. We start with a concise description of the key digital technologies that are revolutionising production: the Industrial Internet of Things (a network of connected data-generating devices), a new generation of mobile, flexible and AI-operated robots, and advanced tools for integrating physical and virtual reality such as digital twins. Asserting that Industry 4.0 is based on the efficient use of data and intelligent algorithms throughout the production process, we show how datafication translates into how companies organise and operate themselves, adding to the vertical and horizontal integration of processes. Datafication of the product life-cycle contributes to the emergence of new business models based on personalisation and servitisation. Turning then to platformisation, we address the budding role of industrial platforms in coordinating relations within supply chains/networks and the rapidly growing imminence of e-commerce platforms in distribution. We conclude by indicating the fundamental similarities between the processes and outcomes of digital transformation in manufacturing and in service sectors, using the example of the digital transformation of banking.

Industry 4.0 The Volkswagen factory in Poznan, Poland, is a smart factory in the making. At the assembly line for vans, digitally skilled humans work alongside cobots (collaborative robots) equipped with screwdrivers. The cobots are able to sense people around them, so there is no need to keep them in safety cages, as is the case with large industrial robots of yore. The production line, on the other hand, is fully automated. The functioning of 30 robots is monitored by one human worker –​the information about sudden breakdown is sent to his or her smartwatch. The factory removed large screens presenting the data, because human workers preferred mobile devices.The parts for the vans are transported by automated mobile trolleys, which will stop when a human gets in their way. The production uses 3D printing (additive printing) for building prototypes

78  How is production changing?

Figure 3.1 How is production changing? (scheme). Source: Own elaboration.

or production of personalised parts for the vans. While designing new parts, the engineers can use HoloLens –​a version of Mixed Reality smartglasses developed by Microsoft –​to check their functionality and prepare visualisation for the production team. But even more sublime changes are on the way. The machine maintenance is still monitored by humans case by case, but the managers are planning to introduce predictive maintenance based on data already collected from all 1200 robots working in the factory. ‘We are looking for algorithms that will inform us about the precise wear of the parts of machinery, so we are able to change them just before breakdown, but not too early’, says one of the factory managers responsible for introduction of technological innovations.The ultimate goal is to integrate and analyse data from all the sources: from more than 400 IT systems in all the departments, from production to logistics; from robots and 650 systems controlling the groups of the machines and production line; from the remaining

How is production changing?  79 700 devices equipped with sensors, such as screwdrivers, and, finally, numerous separate sensors installed throughout the factory. Integration of data within the company (so called vertical integration) will allow for automated monitoring of processes and their optimisation, making full use of intelligent algorithms.1 The decisive stage will include creating a cloud-​based, datafied network connecting the factory with suppliers and consumers (based on the horizontal integration of data). This will allow personalisation of production. ‘This is especially important today, when people expect that a vehicle in a given configuration ordered today will be ready tomorrow.’ In Europe this novel approach to building digital technologies into manufacturing was first came to the attention of industry in Europe in 2011 during the Hanover Messe international trade fair, one of the largest of the world, when members of the business, science, and political worlds presented the concept of Industry 4.0. The idea caught on in Germany, and a vision of German economic policy based on the use of new technologies seduced the federal government as well, leading it to include the concept in an initiative called ‘High-​Tech Strategy 2020 for Germany’. In 2013 a special working group developed a list of assumptions for Industry 4.0 in order to spur German economic development, developing a bold vision of enterprises operating in connected networks encompassing entire factories, machines, storage systems, and production equipment. The concept rapidly caught on elsewhere in Europe, most speedily in the Nordic countries.2 Meanwhile, in the United States, the equivalent concept is ‘Smart Manufacturing’, and in Asia ‘Smart Factories’. Everywhere, however, it is the same phenomenon: a shift from automated manufacturing toward intelligent manufacturing.3 Automated manufacturing emerged in the late 1970s thanks to the move from analogue electronics to microelectronics. Smaller and cheaper computers entered the factories, equipped with a revolutionary software for data acquisition and analysis (such as SCADA) and connected by internal, physically isolated networks (i.e., Ethernet). Communication between information technology systems (IT) and operational technology systems (OT) laid the ground for the automation of most production processes.4 Intelligent manufacturing, also known as hyper-​automation, is contingent on the growing datafication of production: the change in the way data is acquired, processed, and used in order to optimise production, logistics, and sales. This process would have been impossible without an array of innovative technologies, but much of the credit goes to a dramatic fall in the price of sensors (from $22 in 1992 to $1.4 in 2014, and $0.38 in 2020).5 Their computing power increased radically, partly because of their integration with the cloud.6 They also became smaller and more energy-​efficient, which made it possible to integrate them into existing machinery. Increasingly, multiple sensors, connected through the network of the Internet of Things, started to produce abundant data, which in turn can be quickly and efficiently processed by intelligent algorithms. Many students of digital transformation are familiar with Marc Andreessen’s witticism that ‘software is eating the world’.7 And many of them are convinced

80  How is production changing? that this relates more to the intrinsically digital industries whose main product is data or information, rather than to the physical industries, manufacturing and handling material goods.Take Michael Mandel, an economist at the Progressive Policy Institute, writing in 2018: Software has devoured any industry where the final output can be easily reduced to bits.These are the digital industries –​including communications, entertainment, finance, and even professional services. The full content of a daily newspaper can be put into a small digital file. But so far software has not been able to eat the physical world. Data is important for physical industries like manufacturing, construction, agriculture, and healthcare, but it is not the main story.The construction of a building requires huge cranes, not just a digital twin of a crane.8 Admittedly, digitalisation in physical industries such as manufacturing is much more complicated as it relies upon multiple feedback loops between constant datafication of physical processes, and the translation of data-​based decisions into those physical processes. In the case of manufacturing and other ‘physical industries’, digital transformation results not only from the fact that ‘software is eating the world’, but more specifically from the fact that ‘artificial intelligence is eating software’.9 Industry 4.0 would have been impossible without the introduction of intelligent algorithms grinding the data with unprecedented speed, often in the cloud. The adoption of digital technologies is the necessary, yet insufficient, condition for digital transformation. It needs to be supported by wide-​ranging changes in the organisation of a company (and particularly in the organisation of work). The comprehensive integration of data from sensors, connected devices, and information and operating systems, underpins the transformation of the linear value and supply chains into networks. Every stage of a product’s life cycle, from design to maintenance, can be turned into a critical node of a network, which will connect suppliers, contractors, the factory machinery and workers, as well as customers. Manufactured goods are increasingly datafied and complemented by digitally provided services, which enhance their primary functionality. This, in turn, contributes to the growing personalisation of goods and services en masse in response to the individual needs of customers. As a result, manufacturing companies inevitably adopt ‘data-​first, AI-​first’ business model.

New technologies in manufacturing As we emphasised in Chapter 1, intelligent algorithms need to be fed with abundant data. This is why the development of Industry 4.0 is predicated on the Industrial Internet of Things (IIoT).10 That concept can be defined as a dynamic network of connected physical objects equipped with detectors, autonomous sensors, a platform, and applications capable of collecting data and

How is production changing?  81 Cloud compu ng Warehouse automa on Predic ve analy cs AI Internet of Things Blockchain Machine learning Fulfillment robots Autonomous vehicles Drones Augmented reality VR and digital twins Delivery robots 3D prin ng

46% 45% 43% 40% 36% 21% 17% 13% 12% 6% 5% 3% 3% 3%

Figure 3.2 Percentage of companies investing in certain technologies (worldwide, 2020). Source: Own work based on Statista. 2020. What technologies are you currently investing in?. Chart. In Statista. www.statista.com/​statistics/​780763/​inventory-​management-​ investments-​retailers-​manufacturers/​ (accessed 21 December 2020); n = 601 (industry professionals).

sharing it amongst themselves and with their surroundings.11 In other words, machines and devices become part of autonomous networks that communicate and interact with each other in a variety of ways. The linear, point-​based process of obtaining and processing information, and then applying it to decisions on physical processes (which was characteristic of Industry 3.0) is now being replaced by an uninterrupted, cyclical, and networked process of collecting, analysing, and acting on data, which takes place almost in real-​time.12 Data points are gathered throughout and beyond the production process, including storage systems and supplier networks, and the use of intelligent algorithms allows the data to be ordered, integrated, analysed, and used efficiently.13 The sensors that collect the data may be integrated into: • The factory, encompassing the factory floor, storage spaces, buildings, vehicles, and the surrounding grounds), gathering all kinds of data about the movements of physical objects and environmental conditions such as temperature and humidity. • Wearable devices and equipment used by workers. A worker may wear a hard hat equipped with multiple sensors and camera; an intelligent vest collecting data on her movements and vital parameters; or at least a smartband which not only collects data but also vibrates if the deviation from the procedure of movement is detected.14 • Manufactured goods, which allows for monitoring their use throughout their whole life-​cycle. In result, ‘Manufacturing goes beyond production

82  How is production changing? of the physical object, because operating a smart, connected product requires a supporting cloud-​based system’, said Michael Porter and John Heppleman in an article discussing the role of smart connected products in the functioning of a company.15 • Autonomous vehicles or drones, which are able to work in a collision-​ free environment thanks to advanced sensors, their ability to communicate with other devices, fast data processing (e.g., thanks to nebular processing) and intelligent algorithms.16 In the United States, one in ten of large and middle-​sized manufacturing companies has already adopted autonomous or semi-​autonomous mobile devices, citing cost advantage as their primary motive.17 Trendsetting, in 2012 Amazon decided to buy a company which developed mobile robots (called Kiva Systems). As of 2020 more than 200,000 mobile robots carried products inside its warehouses.18 • Autonomous mobile devices are one type of a wide range of programmable machines capable of autonomous tasks and manipulation of objects that increasingly mushroom in the factories all over the world –​robots. The spread of robots and the automation of production were typical of the third industrial revolution. In 1962, the first ‘robotic arm’ was installed at a General Motors factory; it could perform one type of repetitive operation (in this case diecasting).19 In the late 1960s, scientists at Stanford University built an arm that could move in six axes; by the 1980s, however, they were still far from being mobile devices and were unable to sense their surroundings. Currently, the smaller, more efficient and cheaper sensors collect all kinds of data on their surroundings; the data is quickly processed and analysed in the cloud with the help of intelligent algorithms, increasingly with machine and deep learning; and then the decision is put into action by the advanced actuators (components that carry out movements, such as motors, hydraulic systems, signal amplifiers, and hydraulic/​pneumatic cylinders).20 As a result, robots have become more and more autonomous, able to perceive their environment better, to manipulate objects with greater dexterity and flexibility, and to interact and cooperate ever better with people.21 A rising number of collaborative robots (cobots) support workers in industrial production, as well as food production and health care.22 The advances in computer vision and 3-​D depth sensors allow for safer cooperation between human workers and large-​scale robots, which up to now worked in safety cages and performed limited movements. In 2020 there were 250 different kinds of cobots available, most of them working in the life sciences and pharmaceutical industries. Nearly half of them were used in packaging or picking and placing.23 Cobots can be operated by an employee with little experience in programming, easily reconfigured in half a day or redeployed from one department to another.24 They utilise machine learning algorithms (e.g., image recognition, remembering routes or room layouts), and so teaching them can be extremely quick and simple. For example, Lynx, manufactured by Omron Adept, a robotics

How is production changing?  83 company based in California, can memorise the layout of rooms and work out the shortest routes after a single human-​guided tour around a building. As a self-​navigating transport robot, it has proved its mettle in warehouses, but it is also employed by hospitals, as it can carry loads up to 60 kg. And then there is Panda Powertool, developed by the German company Frank Emik. It is a robotic arm with exceptional precision and flexibility and is able to perform relatively complex manual work. This cobot’s unique selling point is its small size (it fits on a tabletop) and its low price, which makes it affordable for small and medium-​sized enterprises.25 The deployment of cobots is an example of a Reconfigurable Manufacturing System, which allows the functionality and efficiency of the production infrastructure to be optimised. These systems consist of modules that, thanks to operational and IT integration, can be easily combined, separated or added to, while an integrated measuring system assesses the condition of the entire system. Mobile and flexible robots, operating on intelligent algorithms, make it easier to reconfigure production lines quickly and cheaply in order to produce small batches and respond to the changing preferences of recipients.26 This way, the technological processes that are shaping Industry 4.0 will enable advanced personalisation of the final product, resembling of crafts manufacturing, but employing mass production.27 The developments in robotics are supported by the deployment of other innovative solutions, e.g., additive (incremental) production using fast design (based, for example, on data obtained from sensors and processed by AI) together

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84  How is production changing? with 3D printing (see Figure 3.2).28 The basic raw material for 3D printing is plastic, but other innovative applications include metal object printing using a bidirectional printing technology which involves spreading metal powder and binding it during each machine pass. This results in the creation of durable metal elements at a rate that is as much as 100 times faster than in traditional production.29 3D printing is also finding more and more innovative applications in healthcare. By 2026 the market for medical, surgical and pharmaceutical applications will have increased in value from $973 million in 2018 to $3.7 billion.30 In addition to creating surgical tools, the technology is also useful for building models of organs due to undergo surgery, allowing doctors to prepare better for an intervention in the patient’s body. A separate medical application is bioprinting, i.e., applying layer upon layer of a bioink composed of living cells to create an organ. 3D printing also allows personalised implants and prostheses to be constructed.31 This will allow for true personalisation of healthcare. The number of robots in production facilities is growing steadily, rising from 1.8 million in 2016 to reach over 2.7 million in 2019.32 In 2019 70% of them were used in the automotive, electrical/​electronic, metal, or machine sectors, although more and more applications in other industries are being found, including in smaller enterprises. Robots are doing handling, welding, assembling, cleaning, dispensing, and processing. Three out of four new industrial robots are being installed in just five countries: the largest share is in China (36% of new installations), followed by Japan, the United States, South Korea and Germany.33 The design and production of robots is becoming easier and cheaper, too. Smart facilities, be they factories or offices, are equipped with ubiquitous hyperconnected devices, which makes them a perfect aim for cyberattacks. The number of malicious attacks ramped up in recent years: in 2016 each IoT device was attacked 6,000 times a year. In 2017 this number grew to 50,000.34 Moreover, 40% of security breaches are indirect and come from supply chains or business ecosystems of a company.35 In response digital companies increasingly invest in cybersecurity solutions based on intelligent algorithms, such as SOARs (Security Orchestration, Automation, and Response) or SIEM (Security Information and Event Management). They collect and analyse data on security threats, automatically respond to low-​level security breaches and allow for optimisation of security measure. Another technology which offers high level of data security is blockchain.36 Blockchain technology is an innovative combination of a number of well-​ known technologies: cryptographic tools, providing data integrity, and access control; decentralised computing; and software, which acts as a ledger for the blockchain.37 Each node in the network keeps complete copy of the database, identical to all the other copies thanks to blockchain consensus algorithm. New records are being incrementally added in blocks of data, each such addition invoking a network-​wide security-​and integrity-​assuring procedure.This guarantees that the alteration of historical records is virtually impossible. In a nutshell, it is a constantly updating distributed database. To put it even more

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Figure 3.4 (a) Operational stock of industrial robots (in million units, worldwide, 2009–​ 2022); (b) share of traditional and collaborative robot unit sales (in %, worldwide, 2017–​2021). Source: Own work based on IFR. 2019. Worldwide operational stock of industrial robots from 2009 to 2022 (in 1,000 units). Chart. In Statista. www.statista.com/​statistics/​947017/​ industrial-​robots-​global-​operational-​stock/​ (accessed 21 December 2020); IFR. 2020. Operational stock of multipurpose industrial robots worldwide from 2015 to 2019 (in 1,000 units). Chart. In Statista. www.statista.com/​statistics/​281380/​estimated-​operational-​ stock-​of-​industrial-​robots-​worldwide/​ (accessed 21 December 2020); Statista. 2019. Share of traditional and collaborative robot unit sales worldwide from 2017 to 2021. Chart. In Statista. www.statista.com/​statistics/​1018935/​traditional-​and-​collaborative-​robotics-​ share-​worldwide/​ (accessed 21 December 2020).

simply, imagine blockchain as a record of transactions kept in a Google spreadsheet shared by many users. Each user can see entries in the registry and can add information to it. However, they cannot change the entries on their own. Initially, blockchains were mainly used to create cryptocurrencies. However, it quickly became clear that the new technology –​characterised by its safety, speedy transactions, and the ability to eliminate intermediaries –​offered much greater opportunities. Blockchains can either be public and accessible to any

86  How is production changing? Singapore

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Figure 3.5 Robot density in manufacturing sector (in units per 10,000 employees, selected countries, 2019). Source: Own work based on: IFR. 2020. Manufacturing industry-​related robot density in selected countries worldwide in 2019 (in units per 10,000 employees). Chart. In Statista. www.statista.com/​statistics/​911938/​industrial-​robot-​density-​by-​country/​ (accessed 21 December 2020).

user (such as Bitcoin, for example) or private and closed, only accessible to a specific group working, e.g., in a specific industry or supply chain. Blockchain solutions ensure high security of data within the organisation. However, their introduction and maintenance is expensive and in the nearest future will be limited to large companies.

Datafication of production The changing functions of robots illustrate the growing convergence between information technology systems (IT) and operational technology systems (OT), enabling intelligent automation of all production processes.38 In the past, IT and OT functioned separately: IT was used in management, OT was used to control and monitor machinery and resources. The Industrial Internet of Things (IIoT) used in factories enables the continuous monitoring of production processes and the adjustment of the maintenance and service plan, thus preventing failures from causing downtime. Integrated with Enterprise Resource Planning (ERP) systems, it manages energy resources and power consumption, as well as optimising production processes more generally.39 In turn, the integration of the IIoT with Customer Relationship Management (CRM) systems allows companies to tailor automated customer service in real-​time, to the profile of a specific customer.40 Better integration of data enables efficient and seamless integration of the systems, and this, in turn, is reflected in comprehensive organisational and processual transformation.

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Figure 3.6 Cloud computing services used over the internet (% of enterprises): (a) by country (2018); (b) EU28 (2014–​2018); (c) EU8 (by type, 2018). Source: Own work based on Eurostat data [isoc_​cicce_​use].

How is production changing?  87

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This convergence of IT and OT is manifested in the development of cyber-​ physical systems (CPS), such as digital twins, i.e., digital replicas of physical objects and processes based data that is continuously supplied from a multitude of sensors and processed in the cloud in real-​time, using intelligent algorithms.41 Every physical product or production process can be given its own digital ‘model’ that allows it, among other things, to be experimented with safely in a virtual world. You can make a digital twin of a machine to test and adjust its design on the basis of the real-​world data (this is what Airbus does with its engines);42 you can build a digital twin of a whole production process to tweak its parameters if need arises (a pharmaceutical company Takeda created a replica of its manufacturing process to experiment and model new chemical and biological reactions);43 or even of a whole city (as is the case with Singapore, where digital twin allows for flexible response in case of disasters).44 Digital twin is more useful the greater its precision, which in turn depends on the quality of all the data on the product (or production line) parameters and the speed at which it is transmitted. Unlike Computer Aided Design (CAD) systems, which enable simulations to be carried out in the design phase, ‘digital twins’ cover the entire product life cycle.45 They are also much more interactive and even immersive, especially when used with virtual or augmented reality, e.g., experienced via special goggles, helmets, interactive display walls (powerwalls) or Cave Automatic Virtual Environments (immersive virtual reality environment),

How is production changing?  89 enabling a whole team of designers and engineers to work simultaneously on a joint project.46 Digital twin technology gives an insight into the specifics of how complex machine components operate and allows their functioning to be tested in a variety of conditions. It also makes it possible to optimise the repair and maintenance schedule based on real-​time diagnosis of wear and tear affecting machine parts. Simulated factory layouts and systems allow production to be organised better, and then for physical changes to be carried out with the aid of modules and actuators. Digital twins are especially useful for large and complex machines, such as jet engines, whose rotors are subjected to extreme temperatures that reach as high as 1600°C (higher than most metals’ melting points). They require constant maintenance, but the schedule is different for each unit, depending on various factors that cause degradation, such as conditions at airports, the number of people on board, the pilot’s flying style.47 That is why aircraft engines constructed by General Electric are equipped with over 100 sensors that continuously collect operational data.48 In addition, Boeing has found that the use of digital twins has led to a 40% improvement in the quality of aircraft parts and systems. The company’s CEO declared in 2018 that CPS technology would be the company’s biggest driver of development over the coming decade.49 Digital twins are also employed by Germany’s ThyssenKrupp, which fits out its elevators with intelligent sensors connected to the cloud. Algorithms process the collected data in real-​time, flagging potential problems in the functioning of devices and highlighting the need for maintenance. The service is supported by HoloLens –​wireless mixed-​reality glasses produced by Microsoft, thanks to which specialists can keep track of repair work performed by technical staff.50 The French rail equipment company Alstom has built a digital twin of a whole rail route between London, Glasgow, and Edinburgh, including every train in the fleet, operating timetables and maintenance regimes.51 Several companies, with Phillips and Siemens among them, are currently working on the digital twin of a heart, up to millimetres mirroring the heart of an individual patient based on 3D scanning and using real-​time data from wearables to prevent health problems.52 Now, this is a kind of predictive maintenance which may revolutionise healthcare. Integrating sensors into every part of factory equipment allows for the application of the digital twin model across whole enterprises. The workings of an ‘intelligent factory’, along with its supply chain/​ network, can be recreated virtually based on abundant data points, and management decisions can be made in a highly automated manner, based on data that is continuously connected and processed in the cloud by artificial intelligence.53 One example of the transformation of traditional production methods into a smart factory is the Hugo Boss factory in Izmir, Turkey. Employing 4,000 workers, the enterprise –​in addition to robotisation and automation –​is introducing AI systems that analyze data collected from 1,600 tablets located around the factory in order to improve machine-​and resource-​management processes in real-​time. Customers can make changes to the collections they have ordered by

90  How is production changing? using digital twins. Speedy and precise communication and collaboration with customers, taking into account their preferences, has allowed product turnaround times to plummet from six months to six weeks.54 Another example is a Siemens factory in Amberg, Germany, where industrial computer control systems are manufactured. For there is another factory, a virtual version of the physical facility that resides within a computer system. This digital twin is identical in every respect and is used to design the control units, test them, simulate how to make them and program production machines. Once everything is humming along nicely, the digital twin hands over to the physical factory to begin making things for real.55 The convergence of IT and OT systems via the IIoT, the automation of processes, together with the growing use of intelligent algorithms, have all led to upheavals in companies. Intelligent factories are seeing unprecedented vertical integration of processes, i.e., the combination of technologically separate phases of production, sales and distribution.56 Hitherto separate levels –​of devices and sensors, control equipment, the processing line or the actual production process, and planning and management –​are being brought together by an uninterrupted flow of data. Linked-​up systems and machines can autonomously respond to changes in production needs and communicate with each other in order to detect defective parts. This ensures greater flexibility and operational efficiency, especially if a company has implemented a modern ‘Manufacturing Execution System’ (or MES) to manage production.57 At the same time, the digitalisation of systems and processes from end to end, throughout all the activities aimed at delivering the product to the end-​user, is allowing the industry to reach a new level of horizontal integration. As a result, a manufacturer’s internal processes (demand planning, public procurement, logistics, and after-​sales services) are becoming linked to processes that take place where suppliers, business partners, and even consumers themselves are located. The result is a transparent network in which all partners coordinate and optimise not only their processes but also tasks and decisions throughout the entire value chain.58 Efficient integration and analysis of data –​from both vertical and horizontal operations –​is increasingly carried through a cloud-​based working environment which reduces the need to develop complex and expensive IT systems on site. These so-​called Platform-​as-​a-​Service or Infrastructure-​as-​a-​Service solutions connect devices, systems, applications, and business services, enabling data analysis and access to information in real-​time, with minimal involvement of human workers. In simpler terms, they provide for the smooth functioning of the Industrial Internet of Things. A good example of PaaS is MindSphere, developed by Siemens, and available as part of Amazon Web Services, along with an open API. It collects data from sensors, machines, and systems, allows for monitoring of the processes, application of digital twin solution, and

How is production changing?  91 advanced analytics with the use of intelligent algorithms. Company resources are connected to the cloud, which allows for monitoring their performance, performing analyses and predictive maintenance, as well as building personalised applications for internal use.59 Predix, developed by General Electric, performs similar functions by connecting industrial devices to the cloud, and enabling data analysis and access to information in real-​time.60

Intelligent product Not only are many aspects of design and production becoming digitalised: so is the lifecycle of products.The spread of ‘digital twins’ is revolutionising product life cycle management –​from conceptualisation, to ordering, development, production, distribution, use, service, and even to withdrawal from the market and perhaps recycling.61 Digital twins shorten the design cycle and allow to respond more quickly to customer needs. In 2015 the engineers at Maserati used them to shorten the time to design a new Ghibli model from 30 to 16 months.62 With the digital copy, the company was able to generate a virtual copy in parallel to the physical development of the car –​100 percent true to the original, down to the last screw. In the development process, the Maserati developers used data from the real and the virtual models simultaneously, utilized that information in parallel for continuous optimization, and were able to reduce both the costs and the time required for development by an astonishing 30 percent.63 All this contributes to the creation of a personalised product and facilitates the construction of prototypes, reducing their cost through virtual, fast, and scaled tests. As a consequence, it also optimises decision-​making processes, not only in production but also in logistics, sales and related services.64 In Airbus digital twin is used to coordinate 12,000 suppliers that provide 3 million parts for one of the engines.65 An important factor in the creation of new business models has been the increase in the number of intelligent products equipped with sensors to collect data on how they are used throughout their life cycle.66 Thanks to these, companies can improve their products and services and create a more attractive offering, thereby building a competitive advantage in the market. Technology commentators may have mocked the idea of a smart toothbrush with integrated sensors, which gather data on how scrupulously the user cleans each area in her mouth, but it does give the consumer useful information on mouth hygiene.67 Acquiring and processing data from each stage of the customer’s use of the product, in real-​time, opens up –​for instance –​the possibility of creating a digital representation of the product, one which the client can reconfigure using intuitive design tools (such as Configure One software),68 or even by using a digital twin.

92  How is production changing? Intelligent products also allow companies to create a range of complementary products and services related to a product’s use, thus expanding opportunities for servitisation (we write more about this in Chapter 5).69 A precursor to servitisation was Rolls Royce, which in 1962 began to offer customers a ‘power-​ per-​hour’ package: the purchase of an aircraft engine could be supplemented by paying a fixed price to have the engine serviced and parts replaced. In 2002, the company’s ‘CorporateCare’ package even included hardware monitoring, made possible by built-​in sensors and faster servicing in authorised centres scattered around the world. As part of the company’s current ‘TotalCare’ service package, it now rents engines and collects data from them on an ongoing basis, allowing the company to plan maintenance. Elsewhere, Caterpillar, a manufacturer of construction machinery, offers a remote tracking and monitoring service in order to provide updates and ‘preventive maintenance’.70 Another example of successful servitisation is changes introduced by IBM: in the 1990s the company began moving away from the production of computers in favour of providing consulting services for enterprises, and then to focus on creating specialised and advanced software.71 Servitisation adds to business models that involve subscribing to, or renting, a product without transferring ownership to the user.72 Ultimately, it boils down to ‘building revenue streams for manufacturers from services’.73 Rather than simply selling a piece of industrial hardware, manufacturers can sell customers a contract to provide highly streamlined, AI-​powered maintenance and repair services for that specific product. The upside for customers? Less downtime due to machine failure and fewer burdensome repair costs. The upside for manufacturers? They’re now able to leverage the data generated by IoT sensors placed within their devices into revenue that’s generated over the lifecycle of their product.74 To balance this enthusiastic approach, it is worth noting that servitisation in facts entails growing and constant dependence of the customers to the provider of the services built around the product. All physical products can be turned into a kind of connected hardware, useless without the software provided by the producer (we return to this thread in Chapter 5).

Platformisation of production The digital transformation of a production company not only changes its internal structure but may also result in a radically new business model.75 The changes here come down to the use of data’s potential to break down established value chains and at the same time open up new sources of income. Traditional companies were based on linear value chains, which often transcended national borders. The dominant model was called a pipeline as it offered a straightforward way of value creation and delivery from the supplier of raw materials

How is production changing?  93 through the producer to the customer. Traditionally, a company designs a product, a good or service; then it is manufactured or produced, and, finally, it is offered for sale to individual and business customers. The ideal process of production was lean –​a concept based on the principles and tools of the Toyota Production System (TPS). The TPS streamlined the use of the resources and the time devoted to developing new products by developing timely delivery systems, standardisation, and improvements in how staff worked.76 Currently, the growing abundance of data on each stage of value creation allows for building new connections between suppliers, producers, and customers. A simple pipeline transforms into a complex network of dynamic relations between all the participants in the production process. It is supplemented by a transition from centralised to decentralised production. The former entails carrying out complete production tasks within a single plant or in a multi-​facility organisation with a central plant and a network of organisationally related entities. Decentralisation, on the other hand, is the creation of networks of autonomous, intelligent units that exchange information and configure themselves in order to optimise the production process and achieve an efficient result. Lean manufacturing is being replaced by agile manufacturing, based on a flexible, data-​driven organisational approach and reconfigurable manufacturing systems. Focusing on smaller batch sizes or even single products, reducing time to market, and maintaining direct contact with the consumer allows companies to respond speedily to changes. It is then possible to meet individual customer needs while controlling costs and quality, and while keeping prices down. This is the idea behind production platforms. Platforms can be built around one or several of the nodes in the value chain; platforms may grow out of a product via servitisation. The integration of processes and data in the not too distant future will allow entities to operate in a distributed system, i.e., in a network.This will affect all actors in the production process, starting with those managing and controlling the production process, to those creating systems and managing suppliers and subcontractors with the aid of those systems, to those supplying materials and semi-​finished products, to engaging subcontractors and employees, to customer outreach and maintenance/​servicing. In this system, production platforms will end up as a kind of intermediary, an integrator of all the above-​mentioned actors. As Michael Mandel writes: industrial companies now have the capability to create manufacturing platforms, both open and proprietary. These platforms would be analogous to today’s multi-​sided internet platforms, like app stores, social media, or advertising networks. Platforms are built upon a ceaseless flow of small packets of data that are rapidly routed to the desired destination. By contrast, these new manufacturing platforms would be mixed cyber-​physical systems consisting of functions such as design, production, and distribution running as separate services on top of an advanced distribution network of

94  How is production changing? goods. By analogy with the digital world, it is useful to think of this new physical network of goods as being ‘packet-​switched’, indicating greater flexibility and lower costs than the previous generation of distribution.77 While production platformisation is taking place, the organisation of the value-​ added chain is also changing. The development of technology in the 20th century allowed it to fragment and paved the way for linear collaboration. On the other hand, digital technologies make it possible to rip the chain apart and distribute work across many levels. The very production process itself may soon be treated as a special service that will be available to companies and even retail customers (Manufacturing-​ as-​ a-​ service). For example, Dassault Systemes’ 3DExperience Marketplace connects potential customers digitally with producers. Customers send their projects to producers who quickly deliver a precise quotation, reducing bureaucracy and costs. The platform then passes the order to an available manufacturer that best meets the technical and location requirements.78 Another platform called Xometry, operating since 2014, provides access to 3D printing technology and metalworking. Xometry supplies clients with a geometric analyser (called 3D Hubs) that allows the order’s parameters to be tweaked, and it also comes up with almost instant order quotes based on AI analysis.79 Platforms such as Xometry give companies a fast and effective way to use another firm’s production capacity to obtain the parts or devices they need. As a result, they can reduce inventory, but above all they gain access to a wide number of potential suppliers. A report released in 2017 on Digitizing European Industry, prepared for the European Commission by a working group that deals with industrial platforms, emphasised that the creation of platforms is crucial to the way that smart factories operate. The report argued that the platforms would acquire data from machines –​not only to allow them to monitor and control applications but also to provide it to external entities that could use it to create new applications. The next step would be the creation of an ecosystem connecting multi-​sided markets, enabling the production of new and innovative products and services. New global standards will also emerge as a consequence. The report’s authors argue that platforms present a solution to many challenges facing the manufacturing industry: they enable agile and more flexible approaches to production, based, among other things, on the use of automation and robots, mass personalisation, and servitisation of the product (building the product up with additional services), and finally, they increase energy and resource efficiency.80 Industrial platforms open up completely new opportunities for companies (including small and medium-​sized ones) to reach global markets. By participating in network-​related value creation and by leveraging the results of this network, they can strengthen their competencies and compete more effectively with larger players. Their role is no longer restricted to that defined in the linear model of the value creation chain; instead, it may change depending on the project and the partnership that has been created within the network.The development of Industry 4.0 –​i.e., the combination of

How is production changing?  95 human contextual decision-​making skills and the precision and regular input of automated cyber-​physical systems driven by intelligent algorithms –​may give rise to rapid growth in productivity and speed up economic development. However, this will only happen if enterprises manage to change tack and follow a path of intensive digital transformation.

Datafied distribution One of the key manifestations of the internet revolution has been the emergence of a brand new sales channel: e-​commerce. Initially, most distance-​ based purchasing of goods and services took place over the telephone, via fax and even via television, but the increasing availability of computers for individual users, a decrease in hardware prices, the popularity of the internet, and user-​friendly graphic browsers, all created a new paradigm. The internet became fashionable, and the number of users grew rapidly. From the mid-​ 1990s to 2001, everything to do with the net seemed to have a golden future: a huge variety of online stores, auction platforms, and various forms of e-​enterprises, often devoid of realistic plans, sprang up like mushrooms after a downpour. The bursting of the dot-​com bubble in 2001, however, swept away a large number of new companies. Those that survived –​especially Amazon and eBay –​have achieved impressive financial results as the growth of the e-​commerce market has resumed.81 For businesses wading through the digital transformation, this sales channel has created unprecedented opportunities. By analysing increasingly large data sets from various online sources, the seller or advertiser can learn more and more about the consumer. Website visits, social media activity, individual clicks, comments, likes: all of these allow companies to create profiles of customers and contact them with personalised offers. The data are useful for dividing up the market and creating a variety of pricing policies, as well as for tailoring personalised, interactive and content-​ rich advertising copy and content.82 The data also allow for multi-​pronged analysis of the competition.83 The further growth of e-​commerce will depend on developments in logistics –​fast shipping goods from the seller to the customer. Companies that can quickly provide customers with tailor-​made products will gain a competitive edge. This is what Jeff Bezos understood better than anybody: ‘They want fast delivery; they want vast selection.’84 The key challenge will be the ‘last mile’ problem, i.e., the final, most unpredictable stage of delivering goods to the consumer. Smaller companies make use of external delivery companies, which increasingly develop and adjust the existing infrastructure. For example, in Poland the growing e-​commerce sector uses not only face-​to-​face delivery by the couriers and thickening network of parcel lockers, but also includes the local shops and groceries as last-​mile delivery points. Reflecting the trend characteristic to other areas of the digital economy, distribution will be increasingly dominated by online platforms. E-​commerce giants develop their own delivery platforms based on large logistics centres with myriad

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Figure 3.8 Enterprises with e-​commerce sales (% of EU28 enterprises, 2010–​2019). Source: Own work based on Eurostat data [isoc_​ec_​eseln2].

delivery points closer to the consumer: this is the model adopted by Amazon. In 2019 the company operated 500 logistics facilities in the United States and 1100 around the world.85 In 2012 it paid $775 million for Kiva Systems, a producer of logistics robots, thus kick starting a new branch of development –​Amazon Robotics. The corporation then introduced robots to warehouses and service centers around the world, cutting the time needed to prepare an online order for shipment (click-​to-​ship) to a mere quarter of an hour. It took, on average, four to five times longer for a person to do the same task. Currently, Amazon has an army of over 100,000 robots and plans to add many more.86 Some of Amazon’s warehouses also use an internal automatic transport system, made up of roller conveyors and forklifts equipped with sensors which let them manoeuvre in warehouses with narrow aisles, and which also display information about the load status, the tilting angle of the drive wheel, hours worked and lifting height.The changes that have been introduced have tripled the number of orders handled annually –​to over one and a half million currently. Additionally, since 2014 the company has invested $39 billion to build an extensive delivery network. In 2019 Amazon delivered nearly half of its 2.5 billion international packages using its network instead of external delivery companies, and according to the Bank of America Global Research it is ‘approaching a truly vertically integrated logistics network on par with the largest delivery companies in the world’.87 Amazon aims at widening the base of customers in its Prime model, introduced in 2015, promising them the next day delivery.88 The Chinese Alibaba also boasts that it can deliver anywhere in China in 24 hours, although it does not define itself as a logistics company. ‘We partner

How is production changing?  97

Alibaba Group

Taobao

524 496

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90 61

Shopify Vipshop

23

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Figure 3.9 E-​commerce platforms’ gross merchandise volume (GMV) (in billion USD, fiscal year 2019/​2020). Source: Own work based on Alibaba Group. 2020. Fiscal Year 2020 Annual Report. https://​doc.irasia.com/​listco/​hk/​alibabagroup/​annual/​2020/​ar2020.pdf (accessed 28 January 2021); eBay Inc. 2020. Form 10-​K. Annual Report for the fiscal year ended 31 December 2019. http://​d18rn0p25nwr6d.cloudfront.net/​CIK-​0001065088/​d33d35e7-​ 32e8-​4a9c-​ad67-​12baec291433.pdf (accessed 29 January 2021); Fareeha Ali. 2020. What are the top online marketplaces?. Digital Commerce 360. www.digitalcommerce360.com/​ article/​infographic-​top-​online-​marketplaces/​ (accessed 29 January 2021); Etsy. 2020. 2019 Integrated Annual Report. https://​s22.q4cdn.com/​941741262/​files/​doc_​financials/​ annual/​2019/​Etsy-​Annual-​Report.pdf (accessed 29 January 2021); Vipshop Holdings Limited. 2020. Form 20-​F. Annual report for the fiscal year ended 31 December 2019. https://​ ir.vip.com/​static-​files/​1765e7ba-​b345-​471b-​b20e-​435957118261 (accessed 29 January 2021); JD.com, Inc. 2020. Form 20-​F. Annual report for the fiscal year ended 31 December 2019.  https:// ​ i r.jd.com/ ​ s tatic- ​ f iles/ ​ f c93d5dd- ​ 9 437- ​ 4 141- ​ 9 191- ​ f 960ba46874b (accessed 29 January 2021); Shopify Inc. 2020. Form 40-​F. Annual report for the fiscal year ended 31 December 2019. https://​s23.q4cdn.com/​550512644/​files/​doc_​financials/​ 2019/​ar/​0efb0f8e-​be6a-​47d9-​b0d6-​11a92482dbd3.pdf (accessed 29 January 2021).

with others for this’, emphasised Jack Ma in 2017.89 The logistics arm of Alibaba, called Cainiao Network Technology is an open platform streamlining collaboration between merchants and 3,000 logistics partners and 3 million couriers from 15 top delivery Chinese companies and 100 international ones.90 In May 2020 Cainiao introduced a three-​year initiative to deliver packages within 24 hours in China (for 3 cents) and 72 hours globally (for $5).91 Time correspondent Charlie Cambell, writing in November 2020 from Hangzhou, observed that the company endeavours to create a single ecosystem for all logistics firms across the world to plug into, allowing for the seamless transfer of goods between companies and jurisdictions. Just as myriad smartphone makers all operate on Google’s

98  How is production changing? Android, Cainiao envisages thousands of independent logistics firms can operate within its system, sharing everything from labelling standards to customs information.92 Cainiao has already put to use a small automated vehicle called Xiao G to distribute packages nearby its depot in Hangzhou. Both the American and the Chinese e-​commerce giant are toying with the idea of drone delivery but it is still in its infancy. In 2017, Flytrex, an Israeli startup, experimented with drones to deliver goods in the suburbs of Reykjavik. To begin with, drones carried goods dispatched by a local e-​commerce store across a bay and left them at a designated place where a courier picked them up. In the summer of 2018, Flytrex drones moved on to attacking the ‘last mile’ and began delivering to suburban customers’ doors (naturally, those living in places that were relatively easy to navigate).93 In 2020 the drones were tested by Walmart to deliver groceries in Fayetteville, North Carolina.94 Most importantly, both e-​commerce behemoths know how to crunch data efficiently to ensure data-​ driven predictions of customer demand. Sangeet Choudhary emphasises that ‘data is the reason Amazon gets this right’.95 For example, data insights collected from the deliverers allow for matching the quickest routes of delivery; comprehensive datafication of warehouses allows for predictive ordering of the lacking products. Platforms are also widely used in long-​distance logistics. In 2019 nearly half of the shippers surveyed by Transport Intelligence, a British consultancy, used an online forwarding platform.96 Digital platforms connect and match shippers (manufacturing and retail companies) and service providers (logistics services, freight forwarders). One of such platforms, Flexport, connects more than 10,000 clients and suppliers around the world, offering them logistics services (ocean, air, truck and rail freight, transport of containers, and warehousing), trade services such as customs brokerage, as well as financing and insurance.97 An intuitive dashboard allows for introducing data analytics and making adjustments along the value chain. Another such platform, TradeLens, developed by a logistics company called Maersk in cooperation with IBM, uses blockchain to record the stages of the shipping process. Documentation and procedures are completed automatically and without delays.98 Admittedly, the use of information and communication technologies in logistics is nothing new: satellites began tracking sea and rail cargo several decades ago, and truck drivers have been using electronic logs for over two decades. Logistics 4.0, however, is characterised by ever more datafication: the growing volume of data obtained from an increasing number of connected sensors or devices is being more efficiently processed in the cloud by intelligent algorithms. The result is growing automation and a streamlining of the delivery process: goods can be prepared for shipment with the aid of robots,99 and thanks to the integration of processes, their shipping becomes faster and more flexible. New technologies of track-​ and-​ trace also allow for better quality control in the supply chain: Hyperledger Sawtooth monitors sensors used to tag each fish caught, and catch data is then entered into a blockchain,

How is production changing?  99 allowing consumers to find out a detailed history of a dish when they order it in a restaurant.100 Sensors and blockchains are used similarly by de Boer, one of the largest diamond producers in the world.101 Firms can manage their relationships with suppliers more efficiently –​data analysis improves auditing, affects timeliness, and allows companies quickly to spot problems with the creditworthiness of a business partner. Datafication of the supply chain/​network means better resource planning (human, material, and equipment), and this, in turn, improves process optimisation and enables faster reactions to changing market conditions.

The digital company In this chapter, we focused on the dimensions of digital transformation in manufacturing. But the production is changing in each and every sector of the digitalising economy, be it production of material goods or services. As Jack Ma of Alibaba puts it. ‘In the next ten years all industries will change due to AI, big data and cloud. Industries will be turned on their head.’102 Everywhere adoption of digital technologies results in more efficient use of ever more abundant data, achieved with the help of ever more intelligent algorithms. Companies can optimise their operations, manage their supply chains/​networks, and satisfy their customers’ needs, producing personalised goods complemented with an array of digital services. Traditional market advantage, built within a given sector, can vanish in the face of digital disruption brought about by the datafied companies, producing material goods. Here, a virtuous circle emerges: the more datafied companies can make ever more efficient use of data and network effects. Linear value and supply chains can be easily transformed into networks overcoming the sectoral divisions –​until each stage of production, each part of the company, starts to resemble a Lego block which can be easily joined with other, external blocks to build new and unexpected synergies. The internal structure of the ‘data-​first, AI-​first’ companies becomes more flat, slim, and agile.103 The developments described above have been happening already in every sector that deals with services. Digitalisation allows for scaling those services that can be delivered to large groups of people without losing their quality and specificity because they are inherently built on data. Take financial services, which are a vanguard of imminent transformation. Financial institutions have always had great access to abundant data on their customers.Yet these data were used inefficiently, because of slow and selective absorption of technologies of datafication and attachment to traditional forms of providing services. Banks focused on the development of digital banking and did not appreciate the fact that the widespread adoption of connected mobile devices opened the way for innovation in the area of contactless payments. People wanted to bank everywhere and at all times, not just in the evenings at their PCs.104 Soon banks were faced with the growing competition from fintechs –​financial startups that knew how to crunch data with ever faster and more efficient analytics based on intelligent algorithms, and how to leverage their impact through platforms accessed via mobile applications.105

100  How is production changing? Manufacturing Retail/WH Financial Services Infrastructure* Media and Entertainment Healthcare Transporta on Resource**

280 2%

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Figure 3.10 Size of the enterprise datasphere (in exabytes, worldwide, 2018). Note: Sums up to 75%, the remaining 25% covers other industries; * –​includes utilities and telecommunication, ** –​includes oil and gas (mining), transportation of oil and gas through pipelines or shipping, resource industries, petroleum and coal. Source: Own work based on Seagate. 2018. Size of the enterprise datasphere worldwide in 2018, by industry (in exabytes). Chart. In Statista. www.statista.com/​statistics/​948851/​ worldwide-​enterprise-​datasphere-​total-​size-​by-​industry/​ (accessed 19 January 2021).

Digital disruption in the financial sector was somewhat slowed down by the weight of legal regulations guarding many of the traditional functions.Traditional financial institutions gained time to learn their lesson and to seriously engage in digital transformation. But Brett King, the author of Bank 4.0, believes that the disruption will continue until banking becomes a ubiquitous experience delivered seamlessly in real-​time: ‘the bank account of tomorrow is primarily an activated, cloud-​based value store that reacts through technology where you are using your money. It’s not an app, a website or a branch.’106 Traditional banks, built around departments providing different types of products, usually offered through physical branches, will not survive, because they will be not able to offer personalisation in the shape of frictionless payments, value storage, and access to credit, backed up by intelligent recommendations. Accordingly, the banks will have to change their internal organisation, transforming into platforms built around a ‘data-​first, AI-​first’ rule. ‘AI will likely eliminate whole swathes of the org chart as it stands today, but AI and data mining and modelling will power elements of almost every interaction’, says Brett King. Such platformised banks, with a digitally standardised structure, will be able to negotiate flexible partnerships with fintechs, technological companies offering a range of complementing services, and, more importantly, with techfins, large technological companies supplying a digital layer to every kind of human experience. One such area of collaboration is online and mobile payments, with Chinese companies such as Alibaba and Tencent showing the way. Alipay developed by Alibaba can boast of 1 billion users (as of 2020), and advertises

How is production changing?  101 to ‘remove barriers between different aspects of life’ so their customers ‘can enjoy a streamlined way of living, empowered by technology’.107 The Alipay app enables frictionless online as well as in-​store payment (through QR codes) as well as management of bank account and credit card bills. Digital technologies have also changed the mode of delivery for non-​ scalable services, which may be offered to a limited number of people at a given time. Many of such services are based on personal, physical, and geographically localised contact between the provider and the receiver, for example, a hairdresser and the client, or a taxi driver and a passenger. They are intrinsically not amenable to digitalisation, but some stages of their provision can be datafied. This goldmine was first discovered by platforms such as Uber and Airbnb, which offered a simple solution to the problem of matching supply and demand for some kinds of services, and provided it via applications embedded in a mobile device. Now platformisation is beginning to expand into more traditional service sectors, such as education. Particularly at the university, datafication will devour all the passive modes of knowledge dissemination, such as lectures, which will be easily scalable through digital channels. The teaching of practical skills and competencies will still, predominantly, require personal interaction, but the process of searching for competent and efficient teachers will be increasingly mediated via platforms such as Coursera or Udemy and their recommendation algorithms. To sum up, the production of material goods and services will be increasingly datafied, and distributed via digital or digitally enhanced channels of

Figure 3.11 What is a digital company? Source: Own elaboration.

102  How is production changing? distribution. If the service is scalable, companies will aim at creating their own digital platforms. If it is not, then companies will increasingly use external B2B or B2C platforms to reach potential customers.Value chains will become more fragmented, as small technological companies take over some of their segments. In other words, as Pascal Bornet, the author of Intelligent Automation (2020) puts it, ‘All businesses are going digital: the winners will be those who do so the quickest and to the greatest extent.’108 In the next chapter, we will take a look at the prerequisite of the successful digital transformation –​digitally skilled workers. The key factor for enterprises is finding or training up appropriately qualified employees who can work alongside intelligent robots and systems that incorporate AI.

Key takeaways • •

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The rules of digital transformation apply to all sectors of the economy, from the manufacturing of goods to the production of services. In manufacturing digital transformation boils down to efficient collection, analysis and use of abundant data to optimise design, production, sales, and distribution. Data is flowing from all entities engaged in design, production, sales and distribution: i.e., digital devices and machines, vast array of robots and cobots equipped with sensors, suppliers, and contractors along the supply chain/​network, and intelligent products. This is the value provided by the key technologies that feature in Industry 4.0 (such as intelligent algorithms, the Industrial Internet of Things, a new generation of robots, and digital twins). The push to datafy all phases of production and distribution is resulting in organisational changes: the incessant flow of data and its analysis by intelligent algorithms supports vertical (within the company) and horizontal integration (within the product life-​cycle, i.e., supply chain/​network). All companies aiming to achieve competitive advantage will have to adopt a business model based on the rule of ‘data-​first, AI-​first’. Digital transformation is propelled by the drive to personalise offerings in response to the growing expectations of customers, who want tailored and yet readily available goods and services. Personalisation will require the flexible reconfiguration of manufacturing systems, based on advanced simulation of a product via digital twins, fed with specific data on customer’s needs and expectations. Personalisation is also increasingly provided through servitisation, where a physical good is complemented by a range of services that boost its basic usefulness. Datafication in all sectors of the economy, including manufacturing and services, supports platformisation, particularly in sales, distribution and logistics. Platforms use abundant data and intelligent algorithms to efficiently match producers with suppliers, contractors, deliverers, and customers. Large companies will tend to build their own platform ecosystems, while smaller firms will use the infrastructure provided by tech companies.

How is production changing?  103

Notes 1 Tadej, C., and Ł. Józefowski. 2020. Smartwatche, AR i big data w polskich zakładach Volkswagena. Platforma Przemysłu Przyszłości. https://​przemyslprzyszlosci.gov. pl/​smartwatche-​ar-​i-​big-​data-​w-​polskich-​zakladach-​volkswagena/​ (accessed 11 January 2021). 2 Kagermann, H., Wolf-​Dieter, L., and W. Wahlster. 2011. Industrie 4.0: Mit dem Internet der Dinge auf dem Weg zur 4. industriellen Revolution. VDI Nachrichten. www.dfki.de/​fileadmin/​user_​upload/​DFKI/​Medien/​News_​Media/​Presse/​Presse-​ Highlights/​vdinach2011a13-​ind4.0-​Internet-​Dinge.pdf (accessed 27 January 2021). 3 Thoben, K., Wiesner, S., and T. Wuest. 2017. Industrie 4.0 and smart manufacturing –​a review of research issues and application examples. International Journal of Automation Technology 11(1): 4–​19. www.researchgate.net/​publication/​312069858_​ Industrie_​40_​and_​Smart_​Manufacturing_​-​_​A_​Review_​of_​Research_​Issues_​and_​ Application_​Examples (accessed 30 December 2020). 4 2000. Automated manufacturing system. In: Swamidass, P.M. ed., Encyclopedia of Production and Manufacturing Management. Springer. https://​doi.org/​10.1007/​1-​ 4020-​0612-​8_​60 (accessed 30 December 2020). 5 Bank of America and Merrill Lynch. 2016. Average costs of industrial Internet of Things (IoT) sensors from 2004 to 2020 (in U.S. dollars). Chart. Statista. www. statista.com/​statistics/​682846/​vr-​tethered-​hmd-​average-​selling-​price (accessed 27 January 2021). 6 Holdowsky, J., Mahto, M., Reynor, M.E., and M. Cotteleer. Inside the Internet of Things (IoT). A Primer on the Technologies Building the IoT. Deloitte University Press.  www2.deloitte.com/​content/​dam/​insights/​us/​articles/​iot-​primer-​iot-​ technologies-​applications/​DUP_​1102_​InsideTheInternetOfThings.pdf (accessed 30 December 2020). 7 Andreessen, M. 2011.Why software is eating the world. The Wall Street Journal. www. wsj.com/​articles/​SB10001424053111903480904576512250915629460 (accessed 30 December 2020). 8 Mandel, M. 2018. The rise of the Internet of Goods: A new perspective on the digital future for manufacturers. MAPI Foundation. www.progressivepolicy.org/​ wp-​content/​uploads/​2018/​08/​Internetofgoods-​reportPPI-​2018.pdf (accessed 30 December 2020). 9 Singh, T. 2019. Software ate the world, now AI is eating software. Forbes. www. forbes.com/​sites/​cognitiveworld/​2019/​08/​29/​software-​ate-​the-​world-​now-​ai-​is-​ eating-​software/​?sh=2aaa27055810 (accessed 30 December 2020). 10 Kagermann, H. 2015. Change through digitisation –​value creation in the age of Industry 4.0. In: Management of Permanent Change. ed. Albach, H., Meffert, H., Pinkwart, A., et al. Springer, pp. 23–​45. 11 Cooper, J. and A. James. 2009. Challenges for database management in the Internet of Things. IETE Technical Review 26(5): 320–​329. www.researchgate.net/​publication/​26845114_​Challenges_​for_​Database_​Management_​in_​the_​Internet_​of_​ Things (accessed 30 December 2020). 12 Schmidt, R., Möhring, M., Härting, R-​ C., and C. Reichstein. 2015. Industry 4.0 –​potentials for creating smart products: Empirical research results. In: Business Information Systems. BIS. Lecture Notes in Business Information Processing. ed.Abramowicz, W. no. 208. Springer. pp. 16–​27; Roblek, V., Meško, M., Krapež, A. 2016. A complexity view of Industry 4.0. SAGE Open 6(2). www.researchgate.net/​publication/​ 301860128_​A_​complexity_​view_​of_​Industry_​40 (accessed 30 December 2020).

104  How is production changing? 13 Bornet, P., Barkin, I., and J. Wirtz. 2020, Intelligent Automation: Learn How to Harness Artificial Intelligence to Boost Business & Make Our World More Human. Kindle Edition. 14 DAQRI: Transforming enterprises with augmented reality. CIO Review. https://​ ibm.cioreview.com/​vendor/​2018/​daqri (accessed 30 December 2020). https://​ newsroom.ibm.com/​2019-​02-​13-​IBM-​Helps-​Organizations-​Monitor-​Their-​ Workers-​Safety-​with-​Watson-​IoT 15 Porter, M.E. and J.E. Heppelmann. 2015. How smart, connected products are transforming companies. Harvard Business Review, p. 8. https://​hbr.org/​2015/​ 10/​how-​smart-​connected-​products-​are-​transforming-​companies (accessed 30 December 2020). 16 The autonomous way to Industry 4.0 –​mobile robots: The backbone of the factory of the future. Mobile Industrial Robots. www.mobile-​industrial-​robots.com/​en/​ resources/​whitepapers/​the-​autonomous-​way-​to-​industry-​40-​mobile-​robots-​the-​ backbone-​of-​the-​factory-​of-​the-​future/​ (accessed 30 December 2020). 17 2018. Industrial mobility: How autonomous vehicles can change manufacturing. PWC.  www.pwc.com/ ​ u s/ ​ e n/ ​ i ndustrial- ​ p roducts/ ​ p ublications/ ​ a ssets/ ​ p wc-​ industrial-​mobility-​and-​manufacturing.pdf (accessed 30 December 2020). 18 Del Rey, J. 2019. How robots are transforming Amazon warehouse jobs –​for better and worse. Recode. www.vox.com/​recode/​2019/​12/​11/​20982652/​robots-​amazon-​ warehouse-​jobs-​automation (accessed 11 January 2021). 19 Moran, M.E. 2007. Evolution of robotic arms. Journal of Robotic Surgery 1(2): 103–​111. www.ncbi.nlm.nih.gov/​pmc/​articles/​PMC4247431/​ (accessed 30 December 2020). 20 Simon, M. 2020. The Wired guide to robots. Wired. www.wired.com/​story/​wired-​ guide-​to-​robots/​ (accessed 30 December 2020). 21 Cobots.ie https://​cobots.ie/​news/​palletising-​cobot-​with-​vision-​system-​works-​in-​ tight-​quarters-​in-​the-​food-​industry-​article-​by-​universal-​robots-​asia-​pacific-​food-​ industry-​mag (accessed 4 January 2021); Marr, B. 2018.The future of work: Are you ready for smart cobots? Forbes. www.forbes.com/​sites/​bernardmarr/​2018/​08/​29/​ the-​future-​of-​work-​are-​you-​ready-​for-​smart-​cobots/​#276938f8522b (accessed 4 January 2021). 22 Collaborative Robotics: State of the Market /​State of the Art. ABI Research. www. abiresearch.com/​market-​research/​product/​1022012-​collaborative-​robotics-​state-​ of-​the-​market/​ (accessed 30 December 2020). 23 Roots Analysis. 2020. The global collaborative robots (cobots) market is projected to be worth USD 18 billion by 2030, growing at a CAGR of 34.4%, claims Roots Analysis. Cision PR Newswire. www.prnewswire.com/​news-​releases/​the-​global-​ collaborative-​robots-​cobots-​market-​is-​projected-​to-​be-​worth-​usd-​18-​billion-​by-​ 2030-​-​growing-​at-​a-​cagr-​of-​34-​4-​claims-​roots-​analysis-​301133387.html (accessed 30 December 2020). 24 E-​series.. Universal Robots. www.universal-​robots.com/​pl/​e-​series/​ (accessed 11 January 2021). 25 Beaupre,. M. Collaborative Robot Technology and Applications. Robotic Industries Association –​RIA. www.robotics.org/​userAssets/​r iaUploads/​file/​4-​KUKA_​ Beaupre.pdf (accessed 30 December 2020); Pittman, K. 2016. Infographic: A brief history of collaborative robots. Engineering.com. www.engineering.com/​ AdvancedManufacturing/​ArticleID/​12169 (accessed 30 December 2020); A detailed guide to collaborative robots market. Cobot Intelligence. https://​cobotintel. com/​guide-​to-​collaborative-​robots-​market (accessed 30 December 2020).

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How is production changing?  113 Giza, A. The Sorcerer’s Apprentice, or the Social History of Marketing. Wydawnictwa Uniwersytetu Warszawskiego. 2017. Goldenberg, B. The Definitive Guide to Social CRM: Maximizing Customer Relationships with Social Media to Gain Market Insights, Customers, and Profits. Pearson Education. 2015. Google Cloud Robotics. Cloud Robotics Core: Kubernetes, Federation, App Management. Google Cloud Robotics. Core. https://​googlecloudrobotics.github.io/​ core/​ Gurley, B. Uber’s new BHAG: UberPool. Above the Crowd. 2015. https://​abovethecrowd. com/​2015/​01/​30/​ubers-​new-​bhag-​uberpool/​ Harvey, C. Symantec 2018 internet security threat report –​review. Data#3. 2018. www.data3.com/​knowledge-​centre/​blog/​symantec-​2018-​internet-​security-​threat-​ report-​review/​# Holdowsky, J., Mahto, M., Raynor, M.E., and Cotteleer, M. Inside the Internet of Things (IoT). A Primer on the Technologies Building the IoT. Deloitte University Press.  www2.deloitte.com/ ​ c ontent/ ​ d am/ ​ i nsights/​ u s/​ a rticles/​ i ot-​ p rimer-​ i ot-​ technologies-​applications/​DUP_​1102_​InsideTheInternetOfThings.pdf Hu, M. 2020. Alibaba’s logistics arm Cainiao to speed up delivery times to meet boom in online shopping. South China Morning Post. www.scmp.com/​tech/​enterprises/​ article/​3090216/​alibabas-​logistics-​arm-​cainiao-​speed-​delivery-​times-​meet-​boom Hugo Boss. Smart factory. Industry 4.0 in practice. https://​g roup.hugoboss.com/​en/​ company/​stories/​smart-​factory-​in-​izmir/​ Iansiti, M. and K.R., Lakhani. Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Kindle Edition. Harvard Business Review Press. 2020. IBM. What is blockchain security? www.ibm.com/​topics/​blockchain-​security IFR. Executive summary world robotics 2019 industrial robots. IFR. 2019. https://​ ifr.org/ ​ d ownloads/​ p ress2018/​ E xecutive%20Summary%20WR%202019%20 Industrial%20Robots.pdf IFR. IFR Press Conference. Frankfurt. 2020. https://​ifr.org/​downloads/​press2018/​ Presentation_​WR_​2020.pdf Industry 4.0: the fourth industrial revolution –​guide to Industrie 4.0. i-​SCOOP. www.i-​scoop.eu/​industry-​4-​0/​#Integrations_​in_​Industry_​40_​vertical_​and_​horizontal_​integration_​as_​all_​systems_​change Insights Team. Logistics 4.0: How IoT is transforming the supply chain. Forbes. 2018. www.forbes.com/​sites/​insights-​inteliot/​2018/​06/​14/​logistics-​4-​0-​how-​iot-​is-​ transforming-​the-​supply-​chain/​#b36af21880fc Insights Team. The IoT-​powered rethink for manufacturing: From sales to servitization. Forbes. 2018. www.forbes.com/​sites/​insights-​intelai/​2018/​09/​21/​the-​iot-​powered-​ rethink-​for-​manufacturing-​from-​sales-​to-​servitization/​#2548163a128b i-​Scoop. IT and OT convergence –​two worlds converging in Industrial IoT. i-​Scoop. www.i-​scoop.eu/​internet-​of-​things-​guide/​industrial-​internet-​things-​it-​ot/​ IYNO Advisor & Critical Manufacturing. The new MES: Backbone of Industry 4.0. IYNO Advisor & Critical Manufacturing. 2017. www.criticalmanufacturing.com/​ uploads/​resources/​The%20New%20MES.%20Backbone%20of%20Industry%20 4.0_​20170904162808.pdf?v67 Jurczak, M. Industry 4.0 in practice at the Volkswagen Distribution Center in Poland. Trans.info. 2018. https://​trans.info/​en/​industry-​4-​0-​in-​practice-​at-​the-​ volkswagen-​distribution-​center-​in-​poland-​85943

114  How is production changing? Kagermann, H. Change through digitisation –​value creation in the age of Industry 4.0. In: Management of Permanent Change. ed. Albach, H., Meffert, H., Pinkwart, A. et al., Springer. 2015. Kagermann, H., Wolf-​Dieter, L., and Wahlster, W. Industrie 4.0: Mit dem Internet der Dinge auf dem Weg zur 4. industriellen Revolution. VDI Nachrichten. 2011. www.dfki.de/​fileadmin/​user_​upload/​DFKI/​Medien/​News_​Media/​Presse/​Presse-​ Highlights/​vdinach2011a13-​ind4.0-​Internet-​Dinge.pdf Kalva, R.S. 3D printing –​the future of manufacturing (the next industrial revolution). International Journal of Innovations in Engineering and Technology 5(1). 2015. http://​ijiet. com/​wp-​content/​uploads/​2015/​02/​26.pdf King, B. Bank 4.0: Banking Everywhere, Never at a Bank. 1st Edition. Wiley. 2018. Laudon, K.C. and Guercio Traver, C. E-​Commerce 2016: Business, Technology, Society 12. Pearson. 2016. Lehmacher, W. Digital logistics platforms: The future of the industry. A fresh look at recent developments in logistics. Quicargo. https://​quicargo.com/​digital​networks-​the-​future-​of-​logistics/​ Liceras, P. 2019. Singapore experiments with its digital twin to improve city life. Tommorow. www.smartcitylab.com/ ​ blog/ ​ d igital- ​ t ransformation/ ​ s ingapore- ​ e xperiments​with-​its-​digital-​twin-​to-​improve-​city-​life/​ Ma, J. 2017. As quoted in King, B. 2018. Bank 4.0: Banking Everywhere, Never at a Bank. 1st Edition. Wiley. Mandel, M. The rise of the internet of goods: A new perspective on the digital future for manufacturers. MAPI Foundation. 2018. www.progressivepolicy.org/​wp-​content/​ uploads/​2018/​08/​Internetofgoods-​reportPPI-​2018.pdf Mandel, M. Why 2019 will be the year of the manufacturing platform. Forbes. 2019. www.forbes.com/​ s ites/​ m ichaelmandel1/​ 2 019/ ​ 0 1/ ​ 0 2/ ​ 2 019- ​ t he- ​ year- ​ o f- ​ t he-​ manufacturing-​platform/​?sh=1469329c3688 Marr, B. The future of work: Are you ready for smart cobots? Forbes. 2018. www.forbes. com/​sites/​bernardmarr/​2018/​08/​29/​the-​future-​ o f-​ work-​ a re-​ you-​ ready-​ f or-​ smart-​cobots/​#276938f8522b McWater, J. Beyond fintech: A pragmatic assessment of disruptive potential in financial services. World Economic Forum. 2017. www3.weforum.org/​docs/​Beyond_​Fintech_​-​ _​A_​Pragmatic_​Assessment_​of_​Disruptive_​Potential_​in_​Financial_​Services.pdf Mehrabi, M., Ulsoy, G., Galip, A., and Koren, Y. Reconfigurable manufacturing systems: Key to future manufacturing. Journal of Intelligent Manufacturing 11. 2000. https://​deepblue.lib.umich.edu/​bitstream/​handle/​2027.42/​46513/​10845_​2004_​ Article_​268791.pdf?sequence=1&isAllowed=y Mehta, Y. Redefining businesses through digital twin technology. DZone –​IoT Zone. 2018. https://​laptrinhx.com/​redefining-​businesses-​through-​digital-​twin-​ technology-​3421663010/​ Mejssner, B. MES –​krótki przewodnik po systemach realizacji produkcji. Computer World. 2019.  www.computerworld.pl/​news/​MES-​krotki-​przewodnik-​po-​systemach-​ realizacji-​produkcji,412205.html Microsoft. HoloLens 2. A new reality for computing. Microsoft. www.microsoft.com/​ en-​us/​hololens Mobile Industrial Robots. The autonomous way to Industry 4.0 –​Mobile robots: The backbone of the factory of the future. www.mobile-​industrial-​robots.com/​en/​ resources/​whitepapers/​the-​autonomous-​way-​to-​industry-​40-​mobile-​robots-​the-​ backbone-​of-​the-​factory-​of-​the-​future/​

How is production changing?  115 Moran, M.E. Evolution of robotic arms. Journal of Robotic Surgery 1(2): 103–​111. 2007. www.ncbi.nlm.nih.gov/​pmc/​articles/​PMC4247431/​ Mussomeli, A., Umbenhauer, B., Parrott, A., and Warshaw, L. Digital twins. Bridging the physical and digital. Deloitte Insights. 2020. www2.deloitte.com/​us/​en/​insights/​ focus/​tech-​trends/​2020/​digital-​twin-​applications-​bridging-​the-​physical-​and-​ digital.html/​#endnote-​sup-​5 Nawrat, A. 3D printing in the medical field: four major applications revolutionising the industry. Verdict Medical Devices. 2018. www.medicaldevice-​network.com/​features/​ 3d-​printing-​in-​the-​medical-​field-​applications/​ Parrott, A. and Warshaw, L. Industry 4.0 and the Digital Twin: Manufacturing Meets Its Match. Deloitte University Press. 2017. www2.deloitte.com/​content/​dam/​Deloitte/​ cn/​Documents/​cip/​deloitte-​cn-​cip-​industry-​4-​0-​digital-​twin-​technology-​en-​ 171215.pdf Perona, M., Saccani, N. and Bacchetti, A. Research vs. practice on manufacturing firms’ servitization strategies: A gap analysis and research agenda. Systems 5(19). 2017.  www. researchgate.net/​publication/​313988539_​Research_​vs_​Practice_​on_​Manufacturing_​ Firms’_​Servitization_​Strategies_​A_​Gap_​Analysis_​and_​Research_​Agenda Pickup, O. What is a digital twin and how does it keep Rolls-​ Royce machines safe? The Telegraph. 2018. www.telegraph.co.uk/​education/​stem-​awards/​digital/​ digital-​twins-​computer-​modeling/​ Pittman, K. Infographic: A brief history of collaborative robots. Engineering.com. 2016. www.engineering.com/​AdvancedManufacturing/​ArticleID/​12169 Platform Thinking Labs. Amazon is a logistic beast a detailed teardown. Platform Thinking Labs.  https://​platformthinkinglabs.com/​amazon-​is-​a-​logistics-​beast-​a​detailed-​teardown/​ Porter, M.E. and Heppelmann, J.E. How smart, connected products are transforming companies. Harvard Business Review. 2015. https://​hbr.org/​2015/​10/​how-​smart-​ connected-​products-​are-​transforming-​companies PR Newswire. The global 3D printing healthcare market was valued at $973 million in 2018, and is projected to reach $3,692 million by 2026, growing at a CAGR of 18.2% from 2019 to 2026. PR Newswire. 2019. www.prnewswire.com/​news-​releases/​ the-​global-​3d-​printing-​healthcare-​market-​was-​valued-​at-​973-​million-​in-​2018-​-​ and-​is-​projected-​to-​reach-​3-​692-​million-​by-​2026-​-​g rowing-​at-​a-​cagr-​of-​18-​2-​ from-​2019-​to-​2026-​-​300925951.html Przegalińska, A. Wearable Technologies in Organizations: Privacy, Efficiency and Autonomy in Work. Palgrave Pivot. 2019. PWC. Industrial mobility: How autonomous vehicles can change manufacturing. PWC. 2018. www.pwc.com/​us/​en/​industrial-​products/​publications/​assets/​pwc-​ industrial-​mobility-​and-​manufacturing.pdf Roblek, V., Meško, M., Bach, M.P., and Bertoncelj, A. The impact of social media to value added in knowledge-​based industries. Kybernetes 43(4). 2013. Roblek,V., Meško, M., and Krapež, A. A complexity view of Industry 4.0. SAGE Open 6(2). 2016. www.researchgate.net/​publication/​301860128_​A_​complexity_​view_​ of_​Industry_​40 Roots Analysis.The global collaborative robots (cobots) market is projected to be worth USD 18 billion by 2030, growing at a CAGR of 34.4%, claims Roots Analysis. Cision PR Newswire. 2020. www.prnewswire.com/​news-​releases/​the-​global-​ collaborative-​robots-​cobots-​market-​is-​projected-​to-​be-​worth-​usd-​18-​billion-​by-​ 2030-​-​growing-​at-​a-​cagr-​of-​34-​4-​claims-​roots-​analysis-​301133387.html

116  How is production changing? Sawtooth. A modern approach to seafood traceability. https://​sawtooth.hyperledger. org/​examples/​seafood.html Schmidt, R., Möhring, M., Härting, R-​C., and Reichstein, C. Industry 4.0 –​potentials for creating smart products: Empirical research results. In: Business Information Systems. BIS. Lecture Notes in Business Information Processing. ed. Abramowicz, W. no. 208. Springer. 2015. Schuldenfrei, M. Horizontal and vertical integration in Industry 4.0. Manufacturing. net. 2019. www.manufacturing.net/​article/​2019/​04/​horizontal-​and-​vertical​integration-​industry-​40 Schüller, N. Oral-​B iO: The latest Oral-​B toothbrush is coming out this summer. Dental Tribune. 2020. www.dental-​tribune.com/​news/​oral-​b-​io-​the-​latest-​oral-​b​toothbrush-​is-​coming-​out-​this-​summer/​ Shabalala, Z. De Beers tracks diamonds through supply chain using blockchain. Reuters. 2018. www.reuters.com/​article/​us-​anglo-​debeers-​blockchain/​de-​beers-​tracks-​ diamonds-​through-​supply-​chain-​using-​blockchain-​idUSKBN1IB1CY Siemens. Getting to market quickly. https://​new.siemens.com/​global/​en/​company/​ stories/​industry/​getting-​to-​market-​quickly.html Siemens. MindSphere. Connecting the things that run the world. https://​siemens. mindsphere.io/​en Simon, M. The Wired guide to robots. Wired. 2020. www.wired.com/​story/​ wired-​guide-​to-​robots/​ Singh, T. Software ate the world, now AI is eating software. Forbes. 2019. www.forbes. com/​sites/​cognitiveworld/​2019/​08/​29/​software-​ate-​the-​world-​now-​ai-​is-​eating-​ software/​?sh=2aaa27055810 Sukhodolov, Y.A. The notion, essence, and peculiarities of Industry 4.0 as a sphere of industry. In: Industry 4.0: Industrial Revolution of the 21st Century. ed. Popkova, E.G., Ragulina, J.V., and Bogoviz, A.V. Springer. 2019. Swecos Virtual Reality Environment Cave. SWECOGROUP. 2016. www.youtube. com/​watch?v=wyqHwz1ZIM0 T-​systems. Mirror image with potential. Digital twins promise much opportunity yet are barely being utilized. www.t-​systems.com/​de/​en/​newsroom/​best-​practice/​03-​ 2018-​digital-​twin/​digital-​twin-​use-​cases-​for-​industrial-​production T-​systems. Product lifecycle management with future. https://​plm.t-​systems-​service. com/​blob/​911514/​f0988cfc33da4fc0a7df08a34ebfd9eb/​DL_​Product_​Lifecycle_​ Management_​with_​Future.pdf Tadej, C. and Józefowski, L. Smartwatche, AR i big data w polskich zakładach Volkswagena. Platforma Przemysłu Przyszłości. 2020. Thoben, K-​ D., Wiesner, S.A. and Wuest, T. Industrie 4.0 and smart manufacturing –​a review of research issues and application examples. International Journal of Automation Technology 11(1). 2017. www.researchgate.net/​publication/​312069858_​ Industrie_​40_​and_​Smart_​Manufacturing_​-​_​A_​Review_​of_​Research_​Issues_​and_​ Application_​Examples Torn, I.A.R and Vaneker, T.H.J., Mass personalization with Industry 4.0 by SMEs: A concept for collaborative networks. 2019. https://​doi.org/​10.1016/​ j.promfg.2018.12.022 UPS. ORION: The algorithm proving that left isn’t right. 2016. www.ups.com/​us/​en/​ services/​knowledge-​center/​article.page?kid=aa3710c2 Van Houten, H. How a virtual heart could save your real one. Philips. 2018. www. philips.com/​a-​w/​about/​news/​archive/​blogs/​innovation-​matters/​20181112-​how-​ a-​virtual-​heart-​could-​save-​your-​real-​one.html

How is production changing?  117 Vandermerwe, S. and Rada, J. Servitization of business: Adding value by adding services. European Management Journal 6(4). 1988. www.sciencedirect.com/​science/​article/​ pii/​0263237388900333 Vigna, P. and Casey, M.J. The Truth Machine: The Blockchain and the Future of Everything. St. Martin’s Press. 2018. Vyse, G. Colgate targets millennials with launch of DTC smart toothbrush. Marketing Dive. 2020.    www.marketingdive.com/​news/​colgate-​targets-​millennials-​with-​launch​of-​dtc-​smart-​toothbrush/​583861/​ Ward, T. 2020. Walmart now piloting on-​ demand drone delivery with Flytrex. Walmart. https://​corporate.walmart.com/​newsroom/​2020/​09/​09/​walmart-​now​piloting-​on-​demand-​drone-​delivery-​with-​flytrex Weinberger, M. If you’re too young to remember the insanity of the dot-​com bubble, check out these pictures. Business Insider. 2016. www.businessinsider.com.au/​history-​ of- ​ t he- ​ d ot- ​ c om- ​ bubble- ​ i n- ​ p hotos- ​ 2 016- ​ 2 #The%20dot- ​ c om%20boom%20 kicked%20off Wingfield, N. As Amazon pushes forward with robots, workers find new role. The New York Times. 2017. www.nytimes.com/​2017/​09/​10/​technology/​amazon-​robots-​ workers.html Womack, J.P., Jones, D.T., and Roos, D. The Machine that Changed the World. Free Press. 1990. Zimmermann, P. Virtual reality aided design. A survey of the use of VR in automotive industry. In: Product Engineering: Tools and Methods Based on Virtual Reality. ed. Talabă, D. and Amditis, A. Springer Science + Business Media B.V. 2008. www.researchgate. net/​publication/​251102885_​Virtual_​Reality_​Aided_​Design_​A_​survey_​of_​the_​ use_​of_​VR_​in_​automotive_​industry

4  How is work changing?

Abstract This chapter deals with the bothering question of how the digital transformation will change the labour market and the nature of work and employment.We concisely recount the ongoing academic debate concerning the impact of automation on jobs, emphasising that intelligent automation will in first place affect both cognitive and physical routine tasks; that the pace of automation will strongly depend on the sector and the structure of the given economy; and that the gloomy scenarios of massive technological unemployment will not come true as new kind of tasks and new jobs appear as we write. However, the changes will affect those workers who lack adequate skills to collaborate with digital technologies. Next, we describe the surging importance of digital platforms in creating new forms of employment, often defying the traditional labour market regulations.We show that the gig economy is also skill-biased, with low-skilled online workers engaging in simple tasks known as crowd work, and low-skilled physical workers looking for gigs through platforms often deprived of social security nets. This results in the emergence of a global labour market, where employers will seek out high-skilled and well-paid professionals, and the low-skilled workers will vie for abundant, but low-paid, commissions.We conclude by presenting the growing datafication of work, which may result in the ever-increasing surveillance of workers.The leitmotif of the chapter is the everlasting importance of the skills for the future and the need for reforming the system of education.

Automation of work ‘Will a robot take your job?’The BBC website baited potential readers with this eye-​catching headline in September 2015.1 To catch their attention, it offered a search engine which allowed you to enter your profession and discover its risk of being automated within the next two decades. For instance, the work of a bank or post office clerk was 97% likely to be automated, and that of a cook had a 73% probability. Academic lecturers could, however, sleep soundly –​in their case the risk of automation turned out to be minimal (3%).The least likely jobs to disappear, the model suggested, were those of therapists, members of the clergy, and hotel owners or managers (0.4%).

How is work changing?  119

Figure 4.1 How is work changing? (scheme). Source: Own elaboration.

The BBC’s crystal ball originated in research conducted in 2013 by two researchers from Oxford University, Michael Osborne, and Carl Frey.2 The authors assumed that the automation potential for a given profession depended on the extent to which the activities it involved were routine. Automation most threatens those professions in which employees manipulate small objects precisely by performing repetitive tasks. It least affects those jobs that demand creativity, negotiating skills, and the ability to deal with people. Based on data from US employment records, Osborne and Frey put forward the thesis that nearly half of all professions (47%) may become automated in the coming years. This apocalyptic vision was quickly pounced on by the media.The scientific community approached it more circumspectly, pointing out that examining

120  How is work changing? the effects of automation in relation to individual professions makes little sense and should be replaced by an analysis of the automation potential of specific tasks performed in those professions. This approach was adopted by OECD experts who analysed data from 21 member states in 2016. According to their estimates, the percentage of professions that are highly susceptible to automation is much lower, standing at just 9%.3 Experts at McKinsey obtained similar results. After analysing 750 professions and taking into account the current state of technological development, they argued that only 5% of them might disappear completely.4 However, they reckoned that six out of ten professions were highly susceptible to automation, which could cover a third of the activities they currently involve.5 Experts from the World Economic Forum came to similar conclusions in 2018, when they examined the share of work performed by humans, in the 12 most important sectors of the economy, and predicted that it would fall from a current share of 71% of working hours to 58% in 2022. The involvement of machines and algorithms is creeping up, especially in tasks related to searching for and processing information within organisations (from 46% to 62% in 2022) as well as in activities related to decision making, administration and monitoring.6 The greatest potential for automation –​regardless of the sector of the economy –​is to be found in tasks that involve predictable, routine, and repetitive activities, both physical and cognitive. Routine physical tasks can be performed not only by automated assembly lines or large immovable cage robots, but also by increasingly flexible, new-​generation robots using machine learning and better adapted to working with humans. The first step towards automating human cognitive activities is Robotic Process Automation. RPA is a kind of a software bot using algorithms to mimic human tasks such as logging into applications, moving files, extracting or copying data, making calculations.7 Increasingly those bots will be using machine learning enabling for adapting to new tasks or improving their performance, even if working with unstructured data and processes. One intelligent RPA developed by SAP is used to process invoices. It starts by automatically opening the accountant’s inbox and identifying emails containing invoices.Then it extracts the attached documents, reads it and sends it to a machine learning application which further extracts invoice details and gives it back to the RPA bot. This ML application is being trained on large datasets including all types of invoices from any company in the world. The process is lightning fast and free of human errors.8 Automation easily penetrates those tasks that were already standardised, such as working on an assembly line, processing a loan or sorting documents, as well as data processing, analysis of textual and visual data, and some areas of customer service.9 Occupations in which contact with other people is usually valued will be less vulnerable to automation. They include education, as well as activities related to serving people and caring for them. However, services and care are usually jobs that do not require unique qualifications. Therefore they are not well paid and are unattractive for workers who carry out uncomplicated

How is work changing?  121 intellectual jobs, including those in public administration, production, transport, and logistics, whose jobs will disappear due to automation.10 How fast will these disruptions of the job market occur? Erik Brynjolfsson and Andrew McAfee, authors of the widely-​ read books The Race Against Machines (2011) and The Second Machine Age (2014)11 argue that we are on the cusp of a ‘second machine age’, in which change is occurring more widely and faster than most people realise. In their opinion, the automation of work merely seems to be occurring slowly; in actual fact, changes are accumulating and will soon pick up speed. Evidence of this can be seen, for example, in the growing involvement of machinery in the total pool of work done.12 The more balanced answer is that automation’s pace will vary from one activity, one sector, and one economy to another. Within individual occupational tasks, there are quite a few niches that are not easily automated,13 so estimating the scale and pace of automation is difficult. A good illustration of this can be found in the methodological dilemmas faced by the authors of the McKinsey report described in the previous section. Their calculations show that a slow automation rate may mean job losses for 10 million people worldwide, while a fast pace would doom 800 million by 2030. As a result, the number of people who might have to change profession or give up certain tasks could affect fewer than 10 million people –​or perhaps 375 million. The discrepancies between these extreme scenarios are pretty substantial, to put it mildly. For that reason, the report suggests adopting a middle scenario in which 400 million people may lose their jobs, and a further 75 million will be compelled to retrain.14 Drawing on data from 29 OECD countries, the authors of a report titled Will robots really steal your job? An international analysis of the potential long term impact of automation (2018), published by PwC, proposes the unsurprising thesis that the pace of automation will depend on the structure of individual countries’ economies.15 The quickest rates will be seen in countries with industrial economies, where the labour market is relatively rigid. Thus Slovakia economy may eventually see up to 44% of current jobs being automated. Service economies, such as the United States and the United Kingdom, which have numerous relatively unqualified employees, may experience a medium level of automation. In the Nordic countries –​with high employment levels in professions that are less susceptible to automation and with highly trained human capital –​automation will occur slower. In contrast, East Asian countries –​where rapid technological progress is taking place –​will experience a quicker pace and higher rate of automation in a shorter period. However, they will be a little less affected by automation in the long run due to the relatively high level of employee competencies.Taking into account the task specificity and composition in the given sector, ­automation will take place in three successive phases, contingent on the ­development of artificial intelligence.The current algorithmic phase includes the automation of simple computational and analytical tasks in sectors where there are large pools of structured data, i.e., in finance and insurance. It is reflected in massive

122  How is work changing? Slovakia Slovenia Lithuania Czech Republic Italy Germany France Spain Austria Turkey Poland Netherlands Ireland UK Denmark Cyprus Belgium Sweden Norway Russia Greece Finland

44

22

Average = 32.5

Figure 4.2 Estimated share of jobs at potential high risk of automation until 2030 (in %, European countries). Source: Own work based on PwC. 2018. Estimated share of jobs at potential high risk of automation in European countries until 2030. Chart. In Statista. www.statista.com/​statistics/​ 819133/​automation-​share-​of-​jobs-​at-​r isk-​europe/​ (accessed 4 January 2021).

uptake of Robotic Process Automation (RPA). Deloitte reports that in 2015 only 13% of the surveyed companies were planning to introduce RPA, while in 2020 78% had already done it. On the whole, business leaders predominantly expect adopting robotics solutions in two to three years’ time.16 The augmentation phase, which will reach maturity in the 2020s, will include the automation of repetitive tasks such as filling out forms, simple communication and exchanges of information, as well as statistical analysis of unstructured data obtained in a partially controlled environment (e.g., from sensors and machines connected within the Internet of Things in factories). Automation will be increasingly based on intelligent algorithms and will evolve towards cognitive automation. The economy will enter the autonomous phase in the 2030s. This will see the intelligent automation of physical work, especially that which requires manual skills, and the automation of real-​time problem solving in the ever-​changing environments found in factories and warehouses.17 The emergence of fully autonomous vehicles and robots will revolutionise sectors such as construction, transport, logistics, water resource management, and municipal services. The pace of automation will also be determined by legal regulations, the institutional environment, the profitability of rolling out technologies, and the skills available in local and global labour markets.18 For three-​quarters of

How is work changing?  123 companies planning to implement digital technologies (according to the World Economic Forum in 2018), access to qualified employees who will be able to switch to working with automated machines and systems was more important than factors such as labour costs, the flexibility of local labour laws, the availability of raw materials, or proximity to urban agglomerations.19 These factors may add to the trend of reindustrialisation, i.e., relocation of smart manufacturing to the high-​income countries, reducing the comparative advantage of poorer countries which have built industrial sectors on cheap labour.20 Most importantly, automation needs to be anchored in the wider and longitudinal digital strategy transformation of the company. According to a global survey carried out by Bain & Company in 2019, nearly half (44%) of the 796 executives questioned admitted that the adoption of automation technologies, such as RPA and artificial intelligence, as yet had not delivered the expected savings, although at the same time 45% claimed that these technologies ‘freed up staff to do higher value work’.21 There seem to be few quick and direct returns from automation, which explains why most companies choose an incremental approach and focus on automation of low-​risk areas, such as tracking customers’ preferences, instead of engaging in a comprehensive digital transformation. Yet it becomes ever more clear that both global and local markets will be dominated by those companies that adopt new business models based on ‘data-​first, AI-​first’ rule, which regards work as one of the flows that can, and indeed must, be automated. Successful automation streamlines processes and allows for unprecedented flexibility and speed of response in an increasingly turbulent world.22

Platformisation of work The automation of work is not the only manifestation of changes taking place in the labour market due to the influence of new technologies: platformisation is also taking place simultaneously. Online platforms match the supply of labour and the demand for it both on global and local markets, in all kinds of short-​ term contract work: cognitive or physical, creative or routine, low-​skilled or high-​skilled.The work is split into separate tasks –​or gigs –​and performed on demand outside the workplace, on the equipment provided by the worker.23 Work mediated by a platform can occur via direct contact between the ordering party and the contractor if both parties operate within the local market (services on demand, e.g., transport services, household maintenance or care services24), or can be provided exclusively online. In the latter case it is increasingly becoming purely virtual work; there is no contact between the client and the contractor, it is supervised and checked by intelligent algorithms embedded in the platform.25 Digital platforms, which act as online labour marketplaces, use the same principles as other types of platforms referred to in Chapter 2: they impose rules governing relations between parties; apply a system of recommendations or assessments, aimed at building and maintaining trust between them;

124  How is work changing? China

33%

India

8%

31%

Indonesia

23%

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enable and verify transactions, i.e., the exchange of work for payment via online payment environments.26 For example, the Israeli-​ based platform Fiverr withholds the money paid for a delivered work for 14 days in case of customer’s complaint. Digital platforms are conducive to the globalisation of work, as they provide a convenient form of outsourcing of specialised tasks. On platforms such as Upwork, Amazon Mechanical Turk, or OnlineJobs, companies can scour the globe to find freelancers who offer accounting, consulting, data analysis, translation, website creation, and graphic design services. Virtual assistants are also easy to locate (by using services such as Time Etc or AVirtual). Geographical distances are losing their importance –​international projects can be carried out by competent employees from any corner of the world. Cross-​border online platform work is perfectly mobile: it is rendered without delay, cheap and effective. In his book The Globotics Upheaval: Globalisation, Robotics, and the Future of Work (2019), Richard Baldwin argues that we are dealing with a situation in which talented foreigners telecommute into workplaces in high income countries, and thus compete directly with local workers. Baldwin avers that the development of artificial intelligence will reduce language barriers, so that the ranks of telemigrants will be bolstered by competent employees from all around the world.27 As a result, employee wages in developed countries may draw level out those in the developing world, which in turn may undermine welfare state models in Western countries. Companies from highly developed

How is work changing?  125 countries can thus quickly and cheaply avail themselves of the labour resources of less developed countries, without having to move production or establish branches there. The rollout of digital technologies –​including ERP and CRM systems and cloud solutions –​is increasing the demand for employees who can perform specialist tasks more flexibly while working remotely, outside an office. Decreased outsourcing costs have driven further network effects among companies, because smaller companies can also use the services of platforms. This may also reinforce the division into primary and secondary labour markets around the globe: the primary market will prevail in highly developed economies, while the latter will predominate in less developed economies. As was the case with automation processes, platform work is also ‘skill-​ biased’: the opportunities it offers are more successfully exploited by those with unique and highly-​valued competencies. Freelancers with expert knowledge or specific skills (e.g., language skills) have greater autonomy in choosing which kind of job offers to accept. For example, an English teacher or a designer working via Fiverr28 or Freelancer can decide whether to take up the gig from an interested customer.29 A large chunk of online platform-​based work is performed by many potential contractors who do not have special skills –​ this is known as crowd work or crowd employment. Platforms such as Clickworker (providing access to 2,2 million gig workers in 136 states around the world)30 or Crowd Guru31 match with people ready to perform tasks that intelligent algorithms cannot yet cope with, such as transcribing audio material, writing consumer reviews or answering customer questions (i.e., low-​skilled, but difficult to automate). Often these tasks are to help intelligent algorithms learn: the human crowd is laboriously tagging pictures or cleaning up data sets, or performs what the head of Amazon, Jeff Bezos, likes to call ‘artificial artificial intelligence’ tasks.32 The different effects of platform work were convincingly described by Wired journalist Sarah Kessler in her book Gigged: The Gig Economy, the End of the Job and the Future of Work (2018). Kessler spent some time following the fates of several platform-​based employees: an Uber driver, a ‘crowd worker’ doing clickwork to enhance ‘artificial artificial intelligence’, some cleaners, and a web designer. As it happened, in most of the cases, despite the perseverance and hard work shown by her subjects, they were not able to make ends meet in the long run. The only exception was the designer, for whom platform work turned out to be as financially rewarding as a regular full-​time job.33 The authors of The Social Protection of Workers in the Platform Economy (2017) report prepared for the European Parliament, meanwhile, came to similar conclusions. In their opinion, platform work often falls somewhere between employment and self-​ employment, but those who engage in it do not necessarily benefit from this state of affairs. Instead, they have to deal with all the normal problems associated with a lack of stable income.34 The growing popularity of platforms is one of the factors contributing to the spread of new forms of employment. In place of the classic form of full-​time employment or the various types of specific work contracts, platforms create

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How is work changing?  127 the possibility of sharing one job among several workers or of several employers sharing a single worker. Above all, however, there is a notable increase in self-​ employment and in the possibility of doing work simultaneously for many clients.35 Employment via platforms can be a reasonable solution for those who find it difficult to work in a standard job, such as students, parents of young children, or the underemployed, i.e., anyone working unwillingly part-​time or beneath their qualifications. From the point of view of self-​employed people, platforms reduce the cost of reaching customers.36 Research conducted in 2016 among Europeans workers who provide services for six large online platforms found that two-​thirds of them –​especially young and better educated people –​ identified themselves more as micro-​entrepreneurs than as platform employees and preferred a looser working relationship that offers them more freedom than traditional ‘9 to 5’ models.37 At the same time, platforms are accused of lowering labour standards by promoting a style of employment that is not secured by social guarantees. One of the impacts of platforms on work has been to make it more precarious and harder to regulate.38 Recruiting online meets the needs of employers for short-​ term workers who will only perform a certain proportion of the activities required to prepare the product or provide the service, thereby reducing the need for full-​time employees. Platforms do not consider themselves employers, but see themselves as intermediaries between the two sides of the market. As a result, people who carry out platform-​facilitated work are not connected by the traditional employer–​employee relationship with either the owners of the platform or the ordering party.39 This hamstrings institutions that protect employees’ rights, such as trade unions.40 In an analysis prepared for the International Labour Organization in 2018, Sanjeet Paul Choudhary, co-​author of Platform Strategy (2016) on the platform business model, argues that the very structure of platform makes them into exploitation mechanisms. They possess far more information about the job market than workers do, and this asymmetry creates an uneven distribution of power. This is accentuated by the way algorithms work, something over which the platforms have full control. As he points out: Workers who are managed by these algorithms, however, often have a limited understanding of how they function. This information asymmetry further empowers the platform and disempowers workers. While the platform company can alter its algorithms in response to worker behaviour, workers find it much more difficult to appropriately adjust their behaviour when the algorithm changes. Even if workers are able to change their behaviour strategically, algorithms can swiftly track the relevant changes in behaviour patterns, identify such workarounds and render them ineffective. 41 Platforms do not provide workers with the information they need to make optimal decisions. A good example is the way Uber’s app works: it reveals a

128  How is work changing? client’s details only when the driver accepts a fare, but imposes a penalty if a driver then rejects it, because it prioritises a high availability of services for clients. Additionally, any conflicts that arise between passengers and drivers are usually resolved by the platform in favour of the customer, because any outflow of customers would be more damaging than a loss of drivers. As a result, drivers must work hard to satisfy the customer, since negative feedback may reduce the number of fares they are offered.42 Furthermore, the platform allocates risk in a way that is beneficial solely to the platform (e.g., Uber drivers pay all costs associated with non-​compliance with transportation law). Uber presents a case of localisation-​dependent gig work, but the same applies to platforms providing online crowdwork. When it comes to the platform’s interests, it is best if the pool of potential workers is large and their tasks do not require specialist skills, because then they are easily replaceable. When that is the case, platforms can develop even if the workers they use frequently resign. As Choudhary points out, ‘When the cost of nurturing the worker is higher than the cost of finding a replacement worker, the platform is likely to focus its efforts on network growth rather than on network management to retain workers’.43 This dramatically hinders the fight for workers’ rights. The growing popularity of platforms among employers reflects the general trend of work becoming more flexible, datafied, and networked, because business models will be ever more flexible, datafied, and networked. Developments in technology mean enterprises need constantly to adapt to changing market conditions, and especially to the needs of consumers.The range of skills required of employees will also change. Some of a company’s employees will be trained as and when required, but more and more staff will be employed for a limited period to perform specific tasks in accordance with their skill profile. Victor Mayer-​Schönberger and Thomas Ramge, authors of Reinventing Capitalism in the Age of Big Data (2018) reach different conclusions. In their view, the creation of a work-​on-​demand system is a prerequisite for economic and social development. Key to the future of human work is unbundling ‘employment’, much as we have unbundled the CD (and the LP before it) into individual songs and let listeners create their own evolving musical mixes. We need to define the elements of work and make them flexible enough to be recombined. Enabling organisations to lend such flexibility to scale will be no small feat, nor will it be easy to bring discoverability to the various work elements so that individuals really will be able to pick and choose.44 As a result, the future job market might cater for micro-​entrepreneurs or self-​ employed people. The digital economy will make away with a concept of a profession that is learned over an extended time at an educational establishment and practised over a whole lifetime. It will also become increasingly difficult to plan a linear and predictable career path. Ursula Huws, author of Global Digital Labour (2015), claims, referring to the results of a study on the nature of

How is work changing?  129 work in a task-​based economy, that the European labour market was already moving from employment based mainly on full-​time jobs to seeing progressive platforming. Moreover, she noted, work for online platforms is only one element of a wide range of on-​demand work that is increasingly common in various sectors and professions.45 For now, it is worth emphasising that platform work is only a marginal phenomenon, although it has garnered much media interest. Research commissioned by the European Commission in 2018 showed that while one in ten workers used a platform to find orders, work found in this way was the main source of earnings for no more than 2% of the labour force in the 14 European countries examined.46 Estimates for the US economy are similar (between 1 and 2%, depending on the methodology used).47 Internationally, the largest proportion of platform employees is to be found in developing countries –​mainly in India and Bangladesh, and the largest number of jobs –​in the United States.48 On the other hand, demand for online platform work is growing at a rate of 20% per year49 –​there is evidence that more and more people employed in the gig economy are looking for work through platforms. Social security institutions, which are after all a product of the political and economic conditions that dominate in economies of the second industrial revolution, will especially need to adapt to the changes in the essence of work and functioning of the labour market.50 As Ursula Huws argues, the labour market is currently a hotchpotch of old and new solutions, such as full-​time and platform work. In her opinion, there is little point in creating special regulations to protect the rights of platform employees. Instead a new social contract should be developed that will specify the rights and obligations of all employees and all employers.This new model for employment relations will have to solve the issue of social security, including the pension system. At this point, it is also worth mentioning more revolutionary ideas, such as the concept of a universal basic income. This would be provided to every citizen by the state, not tied to regular paid work, and it would be financed, for example, by taxing technology companies or the work of robots. All in all, the states will have to make up the regulatory lag resulting from the extraordinary pace of technological progress.51

Datafication of work The efficiency of the platforms in matching workers with tasks and/​or employers stems from growing datafication and resulting algorithmic governance of every stage and every aspect of worklife, from recruitment to layoff or retirement. But not only platforms datafy the work experience. Inevitably all kinds of work will be performed in a work environment saturated with technologies. Every worker –​working along a cobot in a smart factory, complementing the work done by Robotic Process Automation in a financial institution, or taking care of patients in a hospital –​will produce ever larger pools of data that will be used for monitoring, evaluating and optimising of his or her productivity and efficiency.

130  How is work changing? Datafication starts at recruitment. Cloud software tools allow the automation of much of the recruitment process, using data provided by the candidates themselves but also data collected from other networking platforms, social media and professional sites such as LinkedIn. One such tool, AmazingHiring, integrates data for millions of profiles from 50 online sources. It also offers the tools for verifying the candidates’ skills through the use of machine learning-​ based tests. These data allow for prefiltering candidates and then matching them to the appropriate tasks or employment.52 Another tool, AllyO offers machine learning-​trained chatbots to automate repetitive recruitment tasks, such as screening the candidates.53 These tools can also be applied to layoffs, which can be streamlined through intelligent automation making use of detailed data on the employee’s performance, productivity, cooperativeness and even the social standing among the rest of the staff. Your reputation in social media, all data points you generate and digital traces you leave behind on the internet may determine your professional chances as the whole professional trajectory becomes datafied and searchable through recommendation algorithms. All this raises the risk of structural discrimination in the recruitment process, particularly when the differences between eligible candidates are marginal. Moreover –​and this applies to all kinds of algorithmic governance –​usually both the candidate and indeed the employer are unable to explain the results of the ranking/​matching provided by the black-​box of machine-​learning algorithms. The recruitment process is only a prelude to comprehensive datafication and surveillance of the workers’ actions.54 This is particularly easy when the worker performs repetitive tasks through connected software, be it in the office or via online platform (or both). Increasingly popular cloud-​based digital human-​ management tools convert work experience into data, ‘from prehire to retire’. For example, if a company implements the Kronos platform, it will integrate all internal data on an individual worker’s tasks performance, time management, benefits and time off.55 Digital technologies provide employers with tools to survey, monitor and measure the productivity of individual workers and individual teams: data can be collected from internal and external communication platforms, software for human capital management, and from sensors and cameras integrated into smart offices and factories. Software monitoring may include automatic taking regular print screens, tracing changes in documents or at disks, tracking clicks or mouse movements, or even audio/​video recordings. The employers may use automated solutions to scan emails or probe internal communication platforms such as Slack to analyse collaboration patterns, identify productivity barriers, survey opinions or carry a sentiment analysis concerning, for example, the quality of management. The datafied behaviour of workers can be skewed by gamification techniques.56 As a professional website advises: ‘You can plan of designing a program where the one who completes the work in the minimal amount of time gets appreciated. And every time he or she gets the work done in the minimum time, they earn points.’57 For example, the German-​based SAP uses

How is work changing?  131 a game called Roadwarrior to teach their sales representatives how to interact with customers. An employee is given a set of information on the goals and procedures of the corporate customer and then answers a number of questions from virtual customers to earn points and unlock further levels. The employee also gets instant feedback on the quality of the conversation.58 Gamification is one example of using psychological knowledge combined with digital technology to influence the attitude of the worker. It aims at ‘breathing new engagement into employees around the globe –​speaking in quick, instantly gratifying terms that we’ve grown accustomed to in the age of digital transformation’ and at raising the productivity by putting a gamified ‘carrot on the stick that keeps the rabbit keep chasing’.59 Some employers go further, and monitor, measure and steer employee behaviour directly through wearables: the wristband patented by Amazon tracks the workers’ movements at the company’s warehouse and vibrates when they wander off their route or perform wrong movements. Amazon praises the productivity gains achieved by optimising workers’ movements; critics fret about the loss of an employee’s privacy and dignity.60 A madcap scheme and dire privacy infringement for some, but a thing of convenience for others, some companies go even further by proposing microchipping employees, who will be able to rely on a single login to all company systems (and even buy snacks from the office vending machines).61 The insertables cannot be lost by the workers or stolen, so from the employers’ point of view, they offer a higher level of security for company offices, IT systems, and data.62 The academics working on insertables admit that social acceptance of such solutions is still the thing of the future, proposing intelligent tattoos instead,63 and some state regulators directly forbid them. Still, it is a tempting solution for more efficient worker surveillance.64 So far we have written about the datafication of work in quite gloomy terms of growing surveillance, which may turn into algorithmic exploitation. But digital technologies also offer a range of opportunities to personalise the work experience. Data-​based insight allows for better matching between tasks, competencies, and skills, and even the employee’s personality profile, developmental needs, and career path. A KPMG report on the personalisation of work experience (2019) states that ‘Employees seek a digital experience that is seamless and intuitive so that they can spend more time focusing on the task at hand.’65 Increasingly they want to be treated as ‘internal customers’, whose needs are satisfied thanks to data integration from all available sources and predictive analytics using artificial intelligence. The benefit packages may also be data-​driven, based on non-​standard predilections.66 Datafication of the work trajectory and the expansion of online platforms makes it easier to manage the individual career. Internet of Things solutions are being introduced into factories and offices to create a better workplace atmosphere that will improve not only productivity, but also security and the personal wellbeing of the worker.67 Due to predictive maintenance in smart offices ‘coffee machines will never go dry’ (a curiously oft-​repeated argument in articles focusing on this issue),68 and

132  How is work changing? your conference space can be booked automatically if you talk to Alexa for Business.69 Finally, the increased productivity and work efficiency gained through enhanced monitoring and gamification may translate into shorter work hours and a healthier balance between work and private life. If the data plainly shows that we are able to work efficiently for no more than five hours a day, why stay at work longer?

New risks in the labour market If automation and platformisation of work are inevitable, even if slow and uneven, does it mean that the digital economy will bring technological unemployment? The fear of technological unemployment has accompanied every industrial revolution that has seen tasks traditionally performed by people taken over by machines. At the beginning of the 19th century the Luddites destroyed weaving machines, and in 1930, which saw the widespread adoption of electrification and progressive automation, the British economist John Maynard Keynes wrote: We are being afflicted with a new disease of which some readers may not yet have heard the name, but of which they will hear a great deal in the years to come –​namely, technological unemployment. This means unemployment due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour.70 In fact Keynes was convinced that technological unemployment is ‘only a temporary phase of maladjustment’, characteristic of transition to another organisation of the economy. In longer perspective the composition of workers skills will be adjusted to the demand of companies. Possibly also the organisation of employment will change towards shorter hours and longer leisure. Yet the spectre of technological unemployment was again conjured when the outlines of fundamental changes in the economy became visible in the middle of 1990s. In the eye-​catchingly titled The End of Work:The Decline of the Global Labor Force and the Dawn of the Post-​Market Era, the American economist Jeremy Rifkin gloomily argued that the spread of ICT and the progress of automation would boost overall productivity and profits in global corporations, while reducing employment.71 Millions of jobs would be destroyed, especially working-​classes ones and, to a lesser extent, middle-​class ones. This would translate into a decline in consumer purchasing opportunities and, potentially, a global economic crisis. Unemployment would lead to increased crime and general societal decay. The progress of automation is grist to the mill of technological pessimism. In 2016, the American Pew Institute collected in-​depth opinions from 1,896 experts on the labour market, digital economy, ICT sector, and social policy issues. Almost half of them (48%) said that in the future robots and ‘digital

How is work changing?  133 agents’ would deprive a significant number of manual workers of their jobs, especially those in industry, thereby increasing income inequality and leading to unemployment and a breakdown in the social order. Pessimists emphasised that while the impact of automation had so far mainly threatened blue collar workers, the coming wave of innovation threatened white collar workers too. Some highly skilled workers will succeed in the new environment, but significantly more will lose their jobs permanently or will have to agree to low-​ paid jobs in service industries.72 As Daren Acemoğlu noted in 1998, the use of new technologies requires new skills, and employees who acquire them can expect a higher salary. The increasing number of people familiar with new technologies also spurs the latter’s development and increasing complexity, and servicing them requires increasingly more specialised qualifications that are better remunerated.73 For example, digital skills that allow for collaboration with cobots and systems based on intelligent algorithms will be in ever higher demand as companies step up digital transformation. Work revolves more often around projects and less around physical handling of objects and material production. To use academic lingo, it becomes dematerialised. This applies not only to intellectual work, but also to work performed in manufacturing and in some service enterprises, which has always been of a physical character, and now increasingly involves less physical work and more work controlling machines or robots, and perhaps using artificial intelligence and IT systems. As a result, the pay gap between qualified and unskilled workers is growing.74 Writing quarter of a century ago, Manuel Castells predicted that the information-​based economy would be dominated by an antagonistic division between information managers and a ‘disposable workforce’, which ‘could be subject to automation and/​or being leased, fired, or relocated abroad, depending on demand and labour costs’. His line of argumentation chimes with the notion of the dual labour market, introduced in the late 1960s by American economists, in which the labour market is divided into two basic segments. The primary labour market is made up of attractive jobs that are usually in large enterprises, well-​paid, require qualifications confirmed by a formal education, and are protected by the legal system and trade unions. The secondary labour market consists of jobs outside the main core of the economy: they are less attractive, less well-​paid, and require fewer qualifications. Flexible forms of employment, promoted the digital platforms intermediating the gig economy, dominate this segment of the market, and there is little legal and institutional protection of employee interests. This mechanism is intensifying in lockstep with the quickening pace of technological development. Automation will lead to the loss of jobs that involve simple, routine activities, both cognitive and physical, that are easily turned into algorithms, especially against a backdrop of decreasing costs when it comes to implementing and operating robots and automated systems, combined with rising labour costs.75 Ed Rensi, former Managing Director of McDonald’s, commented on this bluntly in 2015: ‘It’s cheaper to buy a robotic arm for $35,000 than to hire an employee who will inefficiently sell fries for $15 an

134  How is work changing? hour.’76 A qualified welder earns $25 per hour in the United States, while the cost of a welding robot is only $8 (taking into account a five-​year depreciation period), and in 15 years’ time, the cost is expected to fall to just $2.77 As E. Brynjolfsson and A. McAffee note: Technological progress is going to leave behind some people, perhaps even a lot of people, as it races ahead. As we’ll demonstrate, there’s never been a better time to be a worker with special skills or the right education, because these people can use technology to create and capture value. However, there’s never been a worse time to be a worker with only ‘ordinary’ skills and abilities to offer, because computers, robots, and other digital technologies are acquiring these skills and abilities at an extraordinary rate.78 There are still professions and tasks hard to automate, particularly those focused on providing care, empathy, and personal attention in aging societies. As a rule, the so-​called pink collar jobs will not require hard-​to-​acquire skills, so, although essentially important, they will remain low-​paid. Possibly they will be increasingly mediated through platforms and turned into gigs/​services on demand, without conferring stable employment contract. Progressing automation at work may also aggravate inequalities within the global labour market, affecting economies that are developing thanks to outsourcing attracted by low labour costs. Opportunities offered by the development of Industry 4.0. are facilitating the relocation of production plants back to highly developed countries, where better qualified employees are available. Increasing productivity and reduced costs for transporting products to the end consumer are not the only motives guiding global corporations –​reindustrialisation is also sometimes a perverse reaction to criticism of their violations of labour law in factories in developing countries.79 A similar mechanism applies to certain services: the development of voice assistants and AI bots is reducing, for example, the need to have helpdesks in India.80

Skills for the future To break the somewhat gloomy spell of argumentation presented so far, it is worth noticing that a small majority (52%) of the experts surveyed by the Pew Institute were more optimistic and rejected radical technological determinism. Increased productivity can help reduce time spent at work and bring about the ideal of a ‘leisure time society’ in which people have time to pursue hobbies or work in their community. The development of technology will contribute to the disappearance of some types of work, but will ultimately create more jobs. Robots or digital systems will soon take over many current professions, but thanks to human creativity, new professions, new sectors of the economy and new ways of making money will rise from the ashes. This view also pervades the reports of the World Economic Forum. As a result of how labour is divided between people and machines, 75 million jobs may disappear around the globe,

How is work changing?  135 but they will be made up for by 133 million new ones that are better suited to the needs of the digitising economy.81 The numbers may differ depending on the adopted methodology, but the trend is clear. Most importantly, technology will free us from the daily grind and allow us to define our attitude to ‘work’ in a more positive and socially useful way. Human and machine skills and competencies will complement each other, allowing people to focus on non-​ routine activities that utilise the potential of human creativity. Hard and often dangerous physical work will be replaced by intellectual work that consists in managing robots and intelligent systems equipped with user-​friendly, low-​code interfaces.82 The group of technological optimists includes H. James Wilson and Paul Daugherty, authors of Human + Machine: Reimagining Work in the Age of AI (2018). They are convinced that machine work will probably complement and support people’s work, rather than replace it, and they suggest that the integration of human and machine work be viewed through the prism of three groups of tasks. Some tasks, such as leading, empathising, creating, and judging, will still require purely human skills. Some tasks will be increasingly monopolised by the machines; this will include transacting, iterating, predicting, and adapting. But more and more often the tasks will combine human and machine skills. In some cases people will enhance the skills of the machines by training them, explaining their behaviour and sustaining their functions. In other cases machines will support and increase the physical and intellectual potential of the human workers.83 The human work will be augmented by the machines, be it robots or AI systems. Take lawyers: intelligent automation allows for faster processing and analysis of the contracts, the time-​consuming, but not particularly complex or advanced tasks. As a result, lawyers can focus on interpretation and searching for solutions to non-​obvious cases.84 In smart factories cobots piloted and easily reprogrammed by the workers perform repetitive tasks; as a result, the worker is less burdened with the actual physical work.85 Both technological pessimists and optimists agree on one thing: progressive automation processes are ‘skill biased’, i.e., they will require technical and digital skills, which allow for conscious and efficient interaction with technology.86 This group includes STEM (Science, Technology, Engineering, and Mathematics) skills as well as advanced digital skills in programming and operating advanced IT systems. However, less advanced skills are also of key importance, specifically those that consist in understanding how machines and systems operate as well as controlling them through intuitive interfaces. The ability to do simple programming is slowly becoming as basic requirement as the ability to use office software. Key competencies, however, are those that in the near future will not be achievable by algorithms and robots, but will be necessary to perform tasks that complement the work of machines and automated systems. It is difficult to automate tasks that require the ability to perceive accurately and flexibly surrounding objects, or that need creativity or social and emotional intelligence.87 Experts generally agree that it will be some time before artificial intelligence will be able to emulate the human

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How is work changing?  137 cognitive skills, such as critical thinking, solving complex problems and creativity, which allow for coping with complex and unpredictable tasks. Emotional intelligence combined with entrepreneurship and critical thinking will also be needed to deal with the challenges of a highly flexible labour market and employment instability.Working in project teams, often geographically dispersed and mediated through digital platforms, involving ‘non-​human’ employees, will require skill to ensure efficient management, coordination, and good decision making. This set of competencies is often referred to as metacompetencies or transferable skills, and they are invariably important from an employer’s perspective, regardless of the type of work actually being performed. They form a stable basis for periodic changes in the qualifications that employees in the digital economy will require.88 WEF experts have bestowed on these competencies the more catchy name of ‘skills for the future’.89 The changing demands for different skills will require some substantial changes in education. It is difficult to acquire such a range of skills in a hierarchical education system rooted in discipline and student conformism, focused on instilling knowledge gleaned from textbooks. As noted by Jack Ma (creator of the Alibaba platform and a former teacher) when he was speaking at the World Economic Forum in Davos in 2018, ‘the way we teach, the things we teach our kids, are the things from the past 200 years’.90 Additionally, the group of pessimistic experts we met in the Pew study noted that education systems are not well-​suited to preparing employees for the realities of the digital economy’s labour market. WEF indicates that more than half of all employees will need to significantly improve their qualifications. One in ten will require radical retraining that lasts more than a year. People with basic secondary education and lower cognitive skills, who perform work activities susceptible to being automated, may have greater problems with retraining in order to work supporting machines or be supported by them.91 In the broader context, the availability of employees prepared to perform hybrid tasks may determine the opportunities of a given national economy amid ever-​increasing and ever-​ intelligent automation.92 So, what changes in education will be necessary to sustain the development of the digital economy? Teaching skills for the future in practice becomes a basic requirement at every stage of education. Equally important will be gradual unbundling of siloed education curricula into shorter and more skills-​oriented certified courses, giving students palpable returns on their investments of time, effort and money.93 Many of them will be provided online or in a hybrid way (linking online instruction and monitoring with training in person when need be).94 Traditional higher education institutions will face growing competition from EdTech or Big Tech companies. Take Udemy, a large online courses platform, which has partnered with Google, Facebook, AT&T, Salesforce, and GitHub, among many others, to provide a wide choice of nanodegrees, i.e., beginner-​to-​career-​track programs in tech skills, from programming to digital marketing.95 Or there is Google itself, claiming that it will recognise Career Certificates (which can be completed remotely in six months on Coursera,

138  How is work changing? another online education platform) in its internal recruitment process as an equivalent of a bachelor degree.96 Faced with increasing platformisation of education, the universities will have to overhaul their mission towards more routine collaboration with business partners while seeking to provide their students with transferable and marketable skills. Finally, one of the essential tasks of the education systems will be teaching their students how to use technologies for their own good. Critical thinking and awareness of the risks inherent in digital infrastructures that underpin every kind of our activities, be they private, professional, or public, will be of key importance for every digital consumer. And this takes us to the next chapter.

Key takeaways • Doom-​ laden predictions of mass technological unemployment are exaggerated. Automation will not indiscriminately wipe out half of all jobs on the labour market in the blink of an eye. Still, it will change the composition of tasks carried out within the given position. In addition, its pace will be uneven, dependent on several factors, such as the structure of the economy and sector specificity. The labour market and the nature of work and employment will undoubtedly change due to the combined impact of automation, datafication, and platformisation. • In the digital economy, most people will work in datafied environments awash with digital technologies that will complement their competencies and reinforce their physical and cognitive capabilities. People will perform less ‘dull, dirty and dangerous’ work and will be able to focus on the more creative aspects of their jobs. Datafication will introduce greater surveillance and control over workers’ performance but may also lead to personalisation of the work experience. • Work in the digital economy is strongly biased towards digital and transferable skills, enhancing economic and social inequalities. The negative consequences of the labour market transformation will affect workers who perform simple and routine cognitive and physical tasks, lacking the skills to work with and alongside digital machines and systems. The labour market will be segmented between well-​paid workers equipped with technical, cognitive, and social skills and low-​paid and low-​skilled ones. • Exploitation mechanisms inherent in the operation of digital platforms can leave those who depend on income obtained through platforms in a precarious position.The platformisation of work caters to a growing flexibility of forms of employment and is loosening or eliminating the employer–​ employee relationship and the obligations that tied them together such as permanent employment, especially within the secondary labour market. • The labour market will no longer be local or national: digital platforms will globalise work, as they greatly facilitate cross-​ border sourcing of workers and enable remote and geographically dispersed collaboration. Still, these changes in the labour market necessitate action on the side of

How is work changing?  139 nation-​states and their groupings. They will need to develop new regulatory solutions concerning employment and social security, and focus on strengthening skills for the future via formal and informal long-​ life education.

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148  How is work changing? Kessler, S. Gigged: The Gig Economy, the End of the Job and Future of Work. St. Martin’s Press. 2018. Keynes, J.M. Economic possibilities for our grandchildren. In: Essays in Persuasion. Idem. W.W Norton. 1963. KPMG. Personal and customized. The future of employee experience. https://​advisory. kpmg.us/​content/​dam/​advisory/​en/​pdfs/​2019/​employee-​experience-​personal-​ customized.pdf Kronos. Human capital management: A complete HCM software solution built for the modern workforce. www.kronos.com/​products/​human-​capital-​management Lahey, S. The future employee experience is personalized. Zendesk. 2019. www.zendesk. com/​blog/​future-​employee-​experience-​personalized/​ Lee Min, K., Kusbit, D., Metsky, E., and Dabbish, L.A. Working with machines: The impact of algorithmic and data-​driven management on human workers. Proceedings of the Association for Computing Machinery (ACM) Conference on Human Factors in Computing Systems (CHI). Seoul. 2015. As cited in: Rani, U., Berg, J.M., and Furrer, M. Digital Labour Platforms and the Future of Work: Towards Decent Work in the Online World, International Labour Organization. Geneva. 2018. Lewis, N. Be careful: Gamification at work can go very wrong. SHRM. 2019. www. shrm.org/​resourcesandtools/​hr-​topics/​technology/​pages/​gamification-​at-​work-​ can-​go-​very-​wrong.aspx Liberty J. Smart office uses biosensors and machine learning to optimize individual work environments. MIT Media Lab. 2018. www.media.mit.edu/​posts/​ mediated-​atmosphere/​ Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ko, R. et al. Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages. McKinsey Global Institute. 2017. www.mckinsey.com/​featured-​insights/​future-​of-​ work/​jobs-​lost-​jobs-​gained-​what-​the-​future-​of-​work-​will-​mean-​for-​jobs-​skills-​ and-​wages Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ryan, K. et al. Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute. 2017.  www.mckinsey.com/​~/​media/​McKinsey/​Industries/​Public%20and%20 Social%20Sector/​Our%20Insights/​What%20the%20future%20of%20work%20 will%20mean%20for%20jobs%20skills%20and%20wages/​MGI-​Jobs-​Lost-​Jobs-​ Gained-​Executive-​summary-​December-​6-​2017.pdf Marshall, C. Google introduces 6-​ month career certificates, threatening to disrupt higher education with ‘the equivalent of a four-​year degree’. Open Culture. 2020. www.openculture.com/​2020/​09/​google-​introduces-​6-​month-​career-​certificates-​ threatens-​to-​disrupt-​higher-​education.html Mayer-​Schonberger,V. and Range, T. Reinventing Capitalism in the Age of Big Data. Basic Books. 2018. McCowan, T. Higher education, unbundling, and the end of the university as we know it. Oxford Review of Education 43(1): 1–​16. 2017. https://​doi.org/​10.1080/​ 03054985.2017.1343712 McKinsey Global Institute. The digital future of work: What skills will be needed? McKinsey & Company. 2017. www.mckinsey.com/​featured-​insights/​future-​of-​work/​ the-​digital-​future-​of-​work-​what-​skills-​will-​be-​needed Newman, D. How to drive employee engagement with workplace gamification. Forbes. 2017. www.forbes.com/​sites/​danielnewman/​2017/​11/​28/​how-​to-​drive-​ employee-​engagement-​with-​workplace-​gamification/​?sh=2e500fd53cf0

How is work changing?  149 Pesole, A., Urzí, B., Maria, C., Fernández-​Macías, E., Biagi, F., Ignacio, G.V. Platform workers in Europe: Evidence from the COLLEEM Survey. 2018. http://​publications. jrc.ec.europa.eu/​repository/​bitstream/​JRC112157/​jrc112157_​pubsy_​platform_​ workers_​in_​europe_​science_​for_​policy.pdf PwC. Will robots really steal our jobs? An international analysis of the potential long term impact of automation. 2018. www.pwc.com/​hu/​hu/​kiadvanyok/​assets/​pdf/​ impact_​of_​automation_​on_​jobs.pdf Rainie, L. and Anderson, J. The future of jobs and jobs training. Pew Research Institute. 2017. www.pewinternet.org/​2017/​05/​03/​the-​future-​of-​jobs-​and-​jobs-​training/​ Ranger, S. IoT in the office: Everything you need to know about the Internet of Things in the workplace. ZD Net. 2018. www.zdnet.com/​article/​iot-​in-​the-​office-​ everything-​you-​need-​to-​know-​about-​the-​internet-​of-​things-​in-​the-​office/​ Raval, N. and Dourish, P. Standing out from the crowd: Emotional labor, body labor, and temporal labor in ridesharing. Paper presented at the Conference on Computer Supported Cooperative Work. San Francisco CA. 2016. http://​wtf.tw/​ref/​raval.pdf (accessed 15 January 2021) as cited in Choudary, S.P. The architecture of digital labour platforms: Policy recommendations on platform design for worker well-​being. ILO Future of work. Research Paper Series. 2018. http://​socialprotection-​humanrights. org/​wp-​content/​uploads/​2018/​07/​wcms_​630603.pdf Rifkin, J. The End of Work: The Decline of the Global Labor Force and the Dawn of the Post-​ Market Era. Tarcher. 1996. Rodionova, Z. McDonald’s ex-​CEO says it’s cheaper to hire robots than people on minimum wage. Independent. 2016. www.independent.co.uk/​news/​business/​news/​ mcdonald-​s-​ex-​ceo-​says-​its-​cheaper-​to-​hire-​robots-​than-​people-​on-​minimum-​ wage-​a7048261.html Sánchez-​Monedero, J. and Dencik, L. 2019. The datafication of the workplace. Cardiff University https://​datajusticeproject.net/​wp-​content/​uploads/​sites/​30/​2019/​05/​ Report-​The-​datafication-​of-​the-​workplace.pdf Savignac, E. The Gamification of Work:The Use of Games in the Workplace. Wiley. 2017. Schwartz, O. Untold history of AI: How Amazon’s mechanical Turkers got squeezed inside the machine. IEEE Spectrum. 2019. https://​spectrum.ieee.org/​tech-​talk/​tech-​ history/​dawn-​of-​electronics/​untold-​history-​of-​ai-​mechanical-​turk-​revisited-​tktkt Selwyn, N. Should Robots Replace Teachers?: AI and the Future of Education (Digital Futures). Polity. 2019. Standing, G. The Precariat:The New Dangerous Class (Bloomsbury Revelations). Bloomsbury Academic. 2011. Su, Z., Togay, G. and Côté, A-​M. Artificial intelligence: a destructive and yet creative force in the skilled labour market. Taylor & Francis Online. 2020. www.tandfonline. com/​doi/​abs/​10.1080/​13678868.2020.1818513 (accessed 15 January 2021). TWB.Escaping the automation led redundancy shaping the Indian IT industry.2017.www. twb.in/​escaping-​the-​automation-​led-​redundancy-​shaping-​the-​indian-​it-​industry/​ US Bureau of Labor Statistics. Contingent and alternative employment arrangements –​ May 2017. Washington DC: US Department of Labor 2018. www.bls.gov/​news. release/​pdf/​conemp.pdf. As cited in: Gig Economy Data Hub. How many gig workers are there? www.gigeconomydata.org/​basics/​how-​many-​g ig-​workers-​are-​ there#footnote1_​u59sqi3 Valenduc, G. and Vendramin, P. Work in the digital economy: Sorting the old from the new. ETUI. 2016. www.etui.org/​Publications2/​Working-​Papers/​Work-​in-​the-​ digital-​economy-​sorting-​the-​old-​from-​the-​new

150  How is work changing? Vantage Circle. Gamification in the workplace and its importance. 2020. https://​blog. vantagecircle.com/​gamification-​in-​the-​workplace/​ Walker, T. Announcing Alexa for business: Using Amazon Alexa’s voice enabled devices for workplaces. Amazon Web Services. 2017. https://​aws.amazon.com/​blogs/​aws/​ launch-​ a nnouncing- ​ a lexa- ​ f or- ​ business- ​ u sing- ​ a mazon- ​ a lexas- ​ voice- ​ e nabled-​ devices-​for-​workplaces/​ Watkins, C. Nanodegree 101: What is a Nanodegree program? Udacity. 2016. https://​ blog.udacity.com/​2016/​07/​nanodegree-​101.html World Economic Forum. The Future of Jobs Report 2018. Insight report. Geneva. 2018. www3.weforum.org/​docs/​WEF_​Future_​of_​Jobs_​2018.pdf World Economic Forum. The Future of Jobs Report 2020. 2020. www3.weforum.org/​ docs/​WEF_​Future_​of_​Jobs_​2020.pdf Wyatt,S.,Henwood,F.,Miller,N.,and Senker,P.(eds),Technology and In/​equality:Questioning the Information Society. Routledge. 2000. Yate, M. The 7 transferable skills to help you change careers. Forbes. 2018. www.forbes. com/​sites/​nextavenue/​2018/​02/​09/​the-​7-​transferable-​skills-​to-​help-​you-​change-​ careers/​#e628f5a4c04c Yeginsu, C. If workers slack off, the wristband will know. (And Amazon has a patent for it.). The New York Times. 2018. www.nytimes.com/​2018/​02/​01/​technology/​ amazon-​wristband-​tracking-​privacy.html Zinser, M., Rose, J., and Sirkin, H. How robots will redefine competitiveness. BCG. 2015. http://​on.bcg.com/​1QGDK6I Zinser, M., Rose, J., and Sirkin, H.The robotics revolution:The next great leap in manufacturing. BCG. 2015. http://​on.bcg.com/​1jeuKeG Zysman, J. and Kenney, M. The next phase in the digital revolution: Intelligent tools, platforms, growth, employment. Communications of the ACM 61(2). 2018. https://​ cacm.acm.org/ ​ m agazines/ ​ 2 018/ ​ 2 / ​ 2 24635-​ t he-​ n ext-​ p hase-​ i n-​ t he-​ d igital-​ revolution/​fulltext

5  How is consumption changing?

Abstract In the 1990s, the internet became a new site for – and of – consumption. Digital devices – PCs, laptops, tablets, smartphones, and smart speakers – have since become networked shopping channels, media sources, cultural and entertainment venues, and tools for governing our everyday lives. This does not mean that all consumption practices have moved online, but it does mean that more and more areas of consumption have become digitalised and then datafied. In this chapter, we set out to describe how widespread adoption of connected digital devices is changing the way people engage in consumption. We discuss the characteristics of the new objects of consumption – digital information goods and intelligent products. Our leitmotif is the growing role of platforms in mediating and shaping the practices of digital consumption. Through matching and recommendation algorithms, they facilitate the choosing of digital and material goods and services and support the development of online shopping. Skilfully using data produced by connected consumers and their devices, they aim to personalise their offerings.

The new objects of digital consumption You would not exactly call Polish society a vanguard of the digital transformation. According to the Digital Economy and Society Index published by the European Commission Poland lags behind other European countries in terms of digitalisation (23rd out of 27 countries in 2020). Poles are well below the EU average for digital skills –​only 44% of people aged 16–​74 can boast basic digital skills. But still, the percentage of people using the internet is steadily growing, from 73% in 2018 to 78% in 2020.Three in four internet users read news online, watch movies, listen to music or play games, six in ten use video calls, shop online and use online banking.1 93% of Poles aged 18–​34 own a smartphone, with the average for the whole population hovering around 63%.2 Only 2.5% read e-​ books (but then only 39% read even a single book a year),3 and nearly 6.3% had a wearable device.4 Even in such relatively slow-​moving societies, digitalisation steadily, though unevenly, penetrates every sphere of consumption. As most of our daily activities are mediated, channelled and conditioned by technologies, the division between online and offline, the digital and the material,

152  How is consumption changing?

Figure 5.1 How is consumption changing? (scheme). Source: Own elaboration.

between goods and services, is becoming more and more blurred.5 The virtual internet reality is being supplemented by the physical network of connected devices known as the Internet of Things. In the digital economy, the range of the objects of consumption is being broadened by dematerialised digital information goods and digitalised material goods, i.e., intelligent products. Digital information goods Digital information goods can be defined as goods containing any kind of information, decoupled from their physical carriers such as paper, vinyl records or Blu-​ray (i.e., dematerialised). A paper edition of Pride and Prejudice is an information good; the same book in digital format on my iPad is a digital information good. Digital format offers a whole new way of consumption.Take

How is consumption changing?  153 music: you no longer need to buy a physical CD or remember to take it to play in your car –​you can stream the music any time you wish. Moreover, you are not constricted by the structure of a long-​player, which bundles together songs you really like alongside filler which you do not care for. You have a much greater choice of music genres and artists because new digital technologies and formats have decreased the costs of creative production. Coupled with the distributive, networked power of the internet, digitisation of physical content has increased access to ever cheaper and more abundant information goods, opening new vistas for the consumption of culture and entertainment.6 In 2018, 81% of internet users in the EU consumed digital information goods: they watched movies, listened to music, and played online games.7 The emergence of digital information goods has had a knock-​on effect on the business models of the creative sectors.8 These goods differ from analogue information goods in several important respects. Saved in a digital format, a prototype can be copied any number of times without a reduction in quality: the user experience remains the same for everyone. This is a highly important change: some of us still remember how every subsequent copy of a piece of music recorded on a cassette picked up more and more hiss, or how the print in each successive xerocopy of a book got even more blurry. From the consumer’s point of view, digital information goods are non-​r ival and non-​exclusive –​i.e., at the same time, with the same excellent quality, they can be enjoyed by a large group of recipients. Besides, digital information goods can readily be shared: for example by distributing a file via email or a torrent platform, or by ‘lending’ a friend your password to your favourite press website.

Watching internet streamed TV or videos

72%

Reading online news sites/newspapers/news magazines

72%

Listening to music (e.g. web radio, music streaming)

62% 33%

Playing or downloading games Watching video on demand from commercial services Online learning material

31% 20%

Figure 5.2 Internet activities (% of EU28 individuals who used internet in the last 3 months, 2018 or 2019). Source: Own work based on Eurostat data [isoc_​ci_​ac_​i].

154  How is consumption changing? 22

20.4

20 18

(in bln USD)

16 14 12 10 8 6 4 2

0.6 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2012 2013 2014 2015 2016 2017 2018 2019

Figure 5.3 The US consumers spending on digital entertainment (in billion USD, USA, 1999–​2019). Source: Own work based on Digital Entertainment Group. 2020. Consumer spending on digital home entertainment in the United States from 1999 to 2019 (in billion U.S. dollars). Chart. In Statista. www.statista.com/​statistics/​188941/​us-​consumer-​spendings-​on-​ digital-​distribution-​since-​1999/​ (accessed 20 January 2021).

However, what is useful from a consumer perspective can be a real headache for manufacturers. In the case of information goods, the first copy is the most expensive. The cost of producing a movie, hit song, or computer game is often exorbitant. The Witcher, an open-​world game produced by the Polish company CD Project, cost $81 million to create.9 Grand Theft Auto, meanwhile, hailed as ‘Scotland’s largest cultural export ever’, took five years to make and cost $265 million.10 The product can easily be replicated any number of times without sacrificing quality.11 The incremental cost of delivery to additional consumers is also negligible. Piracy has therefore become a key challenge for producers, be it illegal downloading, or copying and sharing information goods.12 This has called into question the very profitability of investing in the production of this type of good. Initially, producers and the organisations which represented them tried to deter potential pirates with the threat of high penalties for illegally downloading and sharing digital information goods. The Recording Industry Association of America sued over 18,000 people in the first decade of the 21st century.13 In 2012, one American woman was forced to pay out $220,000 for downloading and sharing just 24 works via an illegal service.14 Ever better connectivity and the development of cloud technologies have made it possible to find a better solution than this, one based on even more dematerialisation. Consumers no longer need to download files to their own hard disks; instead, they gain access to the works via the cloud. Typically the digital information goods are secured

How is consumption changing?  155 by a tool (DRM, or a ‘digital rights management’) that prohibits the unauthorised use of the content, e.g., copying or downloading. As a result, the consumer does not acquire ownership of any music or book files, just a licence which provides temporary access. The conditions of the licence are determined by the provider and often stipulate that access may be quite freely denied or withdrawn. It has been proved that users rarely read EULAs –​End User Licence Agreements, i.e.,Terms of Service –​mostly because these are written in legalese and are painfully long.15 In 2014 a cybersecurity expert set out to demonstrate that people tend merely to scan the EULA in order to click ‘I agree’ as quickly as possible. Indeed, six people were in such a hurry they ‘agreed’ to assign their firstborn child for all eternity, to a provider of free Wi-​Fi at a hotspot in the centre of London.16 Even those consumers who do take the time to read these multi-​page documents have little negotiating power –​if they do not accept the terms of the service or good, as imposed by the producer, they are not able to access them at all. A problem for the consumer arises when the licence expires. According to the Microsoft Online Service Terms, once you terminate your subscription to a service, after a specified time you lose access to all your data. As the conditions for the US version of Microsoft 365 put it: ‘All text, sound, or image files that are provided to Microsoft by, or on behalf of, the customer through the customer’s use of Microsoft 365 services.’17 The platform itself may also cease to exist. In July 2019, consumers who had bought e-​books via Microsoft’s online store lost all access to their libraries. Admittedly, they were offered a refund or credit, but this did not change the fact that they could no longer use the ‘thing’ they had paid for.18 They lost access not only to the original books but also to anything they had created, e.g., notes or highlighting. This redefines the traditional notion of ownership –​I own the thing I paid for –​towards temporary access.19 A particularly interesting illustration of one such new configuration of access and ownership is that of virtual goods, i.e., those that are used in virtual worlds, such as online games. Assigned to a specific user’s profile, they can be bought and sold in accordance with the rules of a given virtual reality.20 In the daily experiences of many players, material, and virtual consumption are mixed and combined: purchasing virtual goods can be just as satisfying as buying real goods and services since it satiates the need both for possession and for increased social status.21 Virtual goods, however, can be sold for quite real money via an intermediary, such as SkinWallet, a Polish company that trades in virtual goods used in video games such as Counter Strike and Team Fortress. Their most popular line is skins, i.e., different versions of equipment or different appearances for characters. These do not change the actual properties of items but build up a virtual image for the player in terms of aesthetics and prestige.22 A special algorithm automatically evaluates the seller’s goods, taking into account their rarity and how much in demand they are in the gaming environment. The company buys them for half the valuation and then resells them via external platforms, mainly through the highly popular Steam. Sellers may

156  How is consumption changing? decide to pay with ‘real money’, directly into a bank account, or they can send the payment to an e-​wallet.23 The discussion on changing configuration on access and ownership hinges on more basic question: are digital information goods still goods, if they have been stripped off their materiality, or are they perhaps really services? The issue of the division between goods and services is of great significance for the regulatory framework governing freedom of trade on the internal market in the EU. As there are differences in the way which goods and in which services are regulated, there is a need to state what is the character of digital information goods analysed from the legal perspective. In order to address this issue, the EU regulators resorted to some reasoning worthy of Solomon and introduced the idea of ‘digital content’, which means ‘data which are produced and supplied in digital form, such as computer programs, applications, games, music, videos or texts, irrespective of whether they are accessed through downloading or streaming, from a tangible medium or through any other means’. If, however, this digital content ‘is supplied on a tangible medium, such as a CD or a DVD, it should be considered as goods’.24 The materiality was established as the main criterion which draws the line between what should be perceived as goods and what should be categorised as digital content. Such a strategy corresponds with the traditional criterion of division between goods and services. Introducing the term digital content, however, does not provide sufficiently precise answers to the challenges which appear due to the digitisation of consumption. It does not diminish the role of the traditional division between goods and services, which might be perceived as outdated in the contemporary economy. Moreover, the definition refers to the context of consumer rights, thus digital content is the category describing data in only this limited scope. Provisions regarding contracts and consumer rights do not address other important questions which arise due to the growing popularity of digital services: is the metadata related to our digital footprints a digital content? What about digital goods within digital content, i.e., your own marginalia in an expired digital library? This is yet another example of the incompatibility of certain concepts and measures when faced with qualitatively new phenomena in the field that is the digital economy. Intelligent products In the digital economy, the difference between goods and services is again blurred when it comes to tangible, physical products which come supplied with digital applications that offer basic or additional functionalities for the consumer. This, in a nutshell, is the Internet of Things, or IoT. The most popular consumer example of the IoT is wearables –​smartwatches, smart clothing, smart wristbands, and smart jewellery. Household names and obscure startups are also scrambling to produce smart footwear. A French startup called FeetMe has designed insoles covered with 25 pressure sensors that not only monitor your daily fitness but also diagnose your health based on

How is consumption changing?  157 your gait and how you move.25 A South Korean startup that goes by the name of FootLogger uses only eight sensors, but it can record 50,000 footsteps and claims to be able to spot early signs of dementia.26 Xiaomi, a company based in Beijing, China, has launched trainers with an option to insert an intelligent, battery-​powered –​and waterproof –​module in either shoe to gather data when you run, walk or climb. Other examples are MiFit, which can monitor the calories you burn, or Google Fit,27 which can synchronise your activity data. Wearable digital devices are gaining in popularity not only as ways to improve fitness and lifestyle, but also for healthcare, security, and even measuring worker productivity. In 2018 Amazon patented a smart wristband to track its workers’ movements in its fulfilment centres.28 Intelligent products or smart objects –​from smart fridges to smart speakers –​surround us both at home and in the workplace. Even your furniture is getting smart and will be able to predict if you will fall and call for help if you do.29 An intelligent product blurs the boundaries between matter and technology, which determine how it functions.This raises dilemmas about ownership and access which echo those in the case of digital information goods. The producers of such digitalised physical goods are wont to supply the software necessary to use them bundled with digital rights management tools. This has a substantial drawback for users: it stops them from carrying out their own repairs or modifications. A case in point is John Deere, a giant American manufacturer of agricultural machinery. Its tractors operate using licensed software secured with digital rights management, which means that farmers cannot make repairs by themselves, on the spot, because they are not able to

350

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(in mln)

250 200

178

150

135

100

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102

50 29 0 2014

2015

2016

2017

2018

2019

Figure 5.4 Wearables unit shipments worldwide (in million units, 2014–​2019). Source: Own work based on IDC. 2020. Wearables unit shipments worldwide by vendor from 2014 to 2019 (in millions). Chart. In Statista. www.statista.com/​statistics/​515634/​ wearables-​shipments-​worldwide-​by-​vendor/​ (accessed 7 January 2021).

158  How is consumption changing? circumvent the software.To quote one farmer: ‘You’re paying for the metal but the electronic parts technically you don’t own it. They do.’30 The farmers’ only choice is to wait for a certified and expensive servicer, often during harvest time, or else to obtain illegal cracked software from Eastern European hackers. The inability to update and modify software may even render a machine obsolete before its time.31 The increasing digitalisation of physical products in daily use has allowed producers to collect a growing volume of data which opens up vast opportunities for personalisation. In theory, the more data a manufacturer has about the way a product is used, the greater the potential to adjust it to consumer needs, to make improvements, and to plan servicing and maintenance. The technological changes taking place in factories allow the production of limited batches of personalised goods, perfectly tailored to the needs of the consumer, and topped off with a whole host of services. This digitalisation of material goods is just the latest guise of servitisation (which we have already addressed in Chapter 3).32 The word describes the way that companies’ business models are shifting from offering goods or services, to offering goods and services, and then to providing a complete package of goods, services, and customer support throughout the product’s life cycle.33 However, only with the development of the Internet of Things, and algorithmic cloud computing, has it truly become possible to realise the full potential of servitisation to create new products which offer individualised characteristics to individual customers. A great example of servitisation is Peloton, a brand of at-​home bikes and treadmills equipped with a touchscreen and paired with a special application.34 The Peloton business model neatly illustrates the concept of digital disruption: it was brought into the traditional sector of indoor gyms by a startup that understood the combined power of datafication, platforms, and personalisation. Users can exercise in the comfort of their own homes, without the hustle and bustle of a gym and the necessity of commuting there. Nevertheless, they can still feel that they belong to a networked community of fitness fans, brought together by dedicated groups that have exploded on social media.35 This naturally doesn’t come cheap: customers pay about $2,000 for the bike –​and in addition, subscribe to an application $12.99a month). The app enables the user to access personalised courses, streamed live or on-​demand. Users can focus on developing particular skills, select their favourite music, or listen to an instructor who shouts at you just the way you like at the gym. The application adjusts the resistance, output, and cadence (speed) of the exercise to the individual abilities and needs of the user, and allows them to compare their results with others, sharing them easily on social media.36 As one user puts it: ‘Those metrics are a part of the appeal […] Instead of pedalling to the beat, instructors give you a specific target range of numbers for your cadence and resistance to fall into. Then, at the end of class, you’re given a final total for your output, a quantified number that represents all the effort you expended into that workout.The app neatly tracks and sorts all of your metrics,

How is consumption changing?  159 showing your improvement with each workout.’37 The experience of exercise is datafied and gamified –​you chase your ideal yourself (‘This is between me and me’, to quote Monica Geller from Friends) and you are indelicately nudged to try to measure up to those 5608 others in your online class who got better results than you. This ‘connected fitness’ is intentionally addictive. The charismatic instructors, many of whom have become social media celebrities with thousands of followers, help to transform monotonous and inherently boring pedalling into a varied experience.38 To sum it up, from the perspective of the user, the real value is provided by the app, not by the bike, similar to many other bikes on the market. The Internet of Things will also change the way in which collective goods are consumed, particularly in cities. By 2050, two out of three people in the world will live in cities. Advanced analysis of abundant data from various sources, including wearables and sensors scattered around the city –​is enabling better management of public infrastructure and more efficient use of scarce resources.39 The collective consumption will be increasingly facilitated and orchestrated through public and private online platforms, which will enable efficient sharing of vehicles and other mobility devices, such as scooters and bikes. One example of these efficiencies are intelligent traffic systems which harness data that flows in constantly from sensors located in public spaces or smart waste management based on sensors inbuilt in garbage cans. Some cities ambitiously aim at building digital twins (more about digital twins in Chapter 3) of their infrastructure. Virtual Singapore, a project supported by Dassault Systemes, uses a 3D experience platform of the city, which may be used for virtual experimentation or digital modelling of real-​life processes on the faithful replica of the city. The city architects can evaluate the planned construction in its surroundings beforehand, and the building owners may decide where to install solar panels on the basis of the data on sun exposition of the individual building.40 An Indian city Amaravati, a new capital of the Indian province Andhra Pradesh was built together with its digital twin, which enables the city authorities to adjust to changing patterns of traffic, as well as respond to natural disasters with more precision and speed.41

From online shopping to the phygital experience The potential of the internet as a convenient marketplace for goods and services was recognised from the start. Supposedly, the first tangible thing obtained via the internet was marijuana.Two resourceful students from Stanford and MIT, working on the ARPANET project, used the budding network some time between 1971 and 1972 to arrange the terms of the deal.42 Some commentators quibble that, technically, it was not a purchase, as it is not clear whether and how payment took place. The market, however, swiftly noted the potential inherent in this communication platform to link the producers of goods and services with customers willing to pay for them. The development of the internet as a marketplace has required secure payments and a safe way to transmit transaction data. In 1994, one

160  How is consumption changing? Phil Brandenberger purchased a Sting CD and paid for it by credit card via a data encryption program created by a small company called Net Market. ‘Attention Shoppers: Internet Is Open’, proclaimed The New York Times.43 The idea of distance shopping is certainly not new; as early as the mid-​19th century it was possible to peruse the offerings of various mail-​order stores by selecting individual goods from catalogues delivered by post. However, the internet has thrown open access to myriad non-​local goods, previously beyond the reach of consumers. Search engines have made it possible to compare individual versions of various products and to optimise their delivery.44 The next step in this revolution was then the creation and expansion of e-​commerce platforms, which greatly facilitated the searches through intelligent matching and recommendation algorithms. They also provided the mechanisms of curation between sellers and buyers, as well as online payment solutions. In 2017 one quarter of the global population –​circa 1.3 billion people –​regularly shopped online.45 Six in ten of the EU citizens shopped online, and among young internet users (16–​24 years), the proportion was considerably higher, standing at 78%.46 For the US citizen the proportion stood at 69%.47 The number of online shoppers in China has been increasing rapidly from below 34 million in 2006 to over 638 million users in 2019.48

United Kingdom Denmark Sweden Netherlands Germany Finland Luxembourg France Estonia Ireland Belgium Czechia EU28 Austria Slovakia Spain Malta Slovenia Poland Hungary Lithuania Latvia Croatia Portugal Greece Cyprus Italy Romania Bulgaria

87

63

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Figure 5.5 Percentage of individuals who purchased online within last 12 months (in %, 2019). Source: Own work based on Eurostat data [isoc_​ec_​ibuy].

How is consumption changing?  161 Overall, in 2019 the value of global business-​to-​consumer e-​commerce has hit a whopping $3.46 trillion.49 And it is no wonder: online shopping offers unprecedented access to, and diversity of, goods, combined with convenience and time savings. The possibilities of buying online have particularly enticed people who previously treated shopping purely as an instrument, without enjoying it, but who at the same time appreciated a sense of freedom and control.50 In 2017, the Global Online Consumer Report prepared by KPMG International found that consumers’ motives for online shopping included the ability to shop around the clock (mentioned by 58% of respondents), to compare prices (54%), to find lower prices (46%), save time (40%), avoid going to a store (39%), and enjoy more choice (29%).51 Improvements in logistics seem to be keeping up with customer expectations when it comes to the timely and convenient distribution of ordered goods: Eurostat research in 2019 showed that only 7% of consumers shy away from buying online because of delivery issues.52 Online shopping has been one of the most important and visible aspects of the internet revolution. Bricks-​ and-​ mortar shopping still has something important to offer, though. A visit to a high-​street store can be an intense sensory experience: stores that sell luxury goods are especially aware of this, offering their customers designer décor, complemented by a specially designed bouquet of air-​wafted aromas. Indeed, ordinary supermarkets are also wont to spray the scent of gingerbread in the run-​up to Christmas. Tellingly, those who prefer to shop in person do so because they want to see and touch things, and try them on, or else because they are loyal to their local emporia.53 For most people, shopping is inherently social –​to go shopping

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Figure 5.6 Percentage of EU28 individuals who purchased online certain goods (2019). Source: Own work based on Eurostat data [isoc_​ec_​ibuy].

162  How is consumption changing?

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Figure 5.7 Barriers to buying online (% of individuals who ordered over the internet more than a year ago or who never did, EU28, 2009, 2019). Source: Own work based on Eurostat data [isoc_​ec_​inb].

is to engage with other people. For this reason, online retailers have tried to re-​create some of the social flavour of shopping, by encouraging their more garrulous consumers to engage in ‘collaborative online shopping’. Despite being physically apart, their customers can look at the same webpages and exchange opinions.54 The new technologies of datafication has changed retailing in yet another way: by allowing the emergence of what some call ‘Bricks-​and-​Clicks’, but what in the world of marketing is better known as omnichannel shopping. This melds offline and online aspects of retailing. Customers can try out products in a shop but buy them online; they can search for information online, but buy an item in a nearby shop. The development of virtual reality technology has brought with it the promise of an ever-​improving visual experience, and perhaps –​in the not-​too-​distant future –​odours and tastes. Buying via virtual reality is already being offered.To tempt customers into trying this new manner of shopping, the China’s Alibaba made available 150,000 VR glasses, priced at just 15 cents, together with an app that lets consumers buy with just a gesture through its Buy+ platform.55 The next step in merging online and offline worlds into a physical-​digital reality –​ ‘phygital’, as the marketing world calls it –​can be glimpsed in seamless biometric payments, self check-​out stores, and more impressively –​in fully automatic physical stores such as Amazon Go.56 Sensors located in the store, built into products and shopping baskets, covertly collect data on customer behaviour and integrate it with other behavioural data gleaned from digital traces left by customers on the web.

How is consumption changing?  163 In reality, there is nothing technological about the customer experience at Amazon’s checkout-​free supermarket.You go in, take what you need from the shelves, fill your basket and leave. All the shop’s electronic equipment –​ sensors, cameras, and of course computers –​is hidden behind the scenes, out of customers’ view. From their perspective the shopping experience is no more ‘digital’ than buying a lemon on a Friday evening from your local grocer’s.57 Digital technologies have brought about a sea change in the way we purchase material goods. Even more significantly, the emergence of networked distribution channels has revolutionised the consumption of information goods, allowing for the virtual consumption of dematerialised objects in virtual worlds. Indeed, two-​thirds of international e-​commerce is now made up of services and non-​physical goods.

Platformisation of consumption For better or worse, consumption in the digital economy is increasingly mediated by platforms. 72% of EU citizens have bought something online at least once, and 76% watch videos, live-​stream or listen to music.58 The emergence of platforms using data and network effects to better organise the multisided markets has revolutionised online shopping. It has made it easier for sellers of goods and

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Figure 5.8 Number of stores which offer autonomous checkouts (in thousands, worldwide, 2018–​2024). Source: Own work based on Business Insider. 2019. Number of stores which offer autonomous checkouts worldwide from 2018 to 2024*. Chart. In Statista. www.statista.com/​ statistics/ ​ 1 033836/ ​ number- ​ o f- ​ s tores- ​ w ith- ​ a utonomous- ​ c heckouts- ​ worldwide/​ (accessed 20 January 2021).

164  How is consumption changing? services, buyers, and advertisers, to find each other. It has also increased the array of available products to an unbelievable degree. Amazon sells 12 million products, not including books, media, wine, and services; if you add the 185,000 Amazon Marketplace sellers, there are more than 353 million items to choose from.59 In the EU, one million businesses sell their products through online platforms. Even local online retail platforms offer incredible choice: 100,000 sellers piggyback on the Polish platform Allegro, trading 30 million items per month.60 Sure, you cannot download a hamburger, as the eminent sociologist of consumption George Ritzer once argued,61 but you can use a platform-​based app such as Glovo and have it delivered to you with a flick of a finger. The role of platforms is even more important in the case of digital information goods. Platforms provide access for consumers looking for content, creating near-​Borgesian libraries of books, movies and music. There are over 6 million e-​books available on Amazon’s Kindle.62 Spotify has more than 50 million songs and 700,000 podcasts.63 Quite a large bit of this cultural production exists because platforms have empowered amateur or low-​budget creators (of variable talent) to reach an audience without being hampered by gatekeepers in the form of publishing houses or music producers. This bewildering cornucopia of cultural production can make it hard for people to find online content that they know they will enjoy. There is less ‘adult curation’ –​a grand phrase for the content sifting and quality control traditionally performed by publishers. But platforms are good at solving also this conundrum.Thanks to advanced abilities in mining data left by users and tapping into the potential of artificial intelligence, platforms are able to facilitate and personalise the process of reaching content.64 And due to exponential growth in the number of data points relating to each and every consumer’s preferences, which bolster the predictive power of intelligent algorithms, they are getting better and better at this. In 2019, 167 million Netflix subscribers watched its library of 13,900 titles (with an average of 5,000 titles per country) for an average of 3.2 hours a day. The platform collected a profusion of data on how viewers interacted with content: not only on how they rated a programme, but also on binge watching patterns, and on whether they gave up on a show, or watched it more than once. As a result, approximately 80% of subscribers followed the algorithm’s recommendations as to what to watch next.65 Platforms are now performing curation through personalisation. Personalisation is particularly effective when a platform is able to integrate data points from many sources, gaining insights into many areas of consumer practices and behaviour, and all the while feeding the AI algorithms. In this respect, China’s platform ecosystem is second to none, as pointed out by Kai Fu Lee in AI Superpowers (2019). WeChat users began sending text and voice messages to friends, paying for groceries, booking doctors’ appointments, filing taxes, unlocking shared bikes, and buying plane tickets, all without ever leaving the app. WeChat became the universal social app, one in which different types of group

How is consumption changing?  165 chats –​formed with coworkers and friends or around interests –​were used to negotiate business deals, organise birthday parties, or discuss modern art. It brought together a grab-​bag of essential functions that are scattered across a dozen apps in the United States and elsewhere. China’s alternate digital universe now creates and captures oceans of new data about the real world. That wealth of information on users –​their location every second of the day, how they commute, what foods they like, when and where they buy groceries and beer –​will prove invaluable in the era of AI implementation. It gives these companies a detailed treasure trove of these users’ daily habits, one that can be combined with deep learning algorithms to offer tailor-​made services ranging from financial auditing to city planning. It also vastly outstrips what Silicon Valley’s leading companies can decipher from your searches, ‘likes’, or occasional online purchases.66 Obviously, it breeds a whole range of risks concerning the privacy of users. In 2020 Tencent, the WeChat owner, has introduced a credit scoring system, which takes into account the record of purchases performed through the app, credit records and verified personal information.67 It also draws information about the social connections of the user, which means that his or her score may be affected by the scores of friends, and vice versa. Five years earlier, a similar credit scoring system and loyalty program was developed by Ant Financial, an affiliate company of Alibaba. Zhima Credit, better known as Sesame Credit is based on payment records, tax payment history, and social media interactions. Users with higher scores are deemed trustworthy and can use many perks, such as ‘use now, pay later services’ or rent a car without advanced payment. Both companies closely cooperate with Chinese authorities who aim at building a nationwide system of Social Credit in order to assess the trustworthiness of individuals, companies and government officials.68 This may sound ominous, but the digital profiling and the curation and recommendation mechanisms used by the Western platforms can also impact consumption capabilities of individual consumers. Algorithms may, for example, discriminate against users on the basis of their age, gender, and ethnicity. One of the first studies into racial discrimination on platforms found that African Americans who used the Prosper lending marketplace were more likely than other Americans to be denied a loan or asked to pay more in interest.69 Many platforms –​take Airbnb or Uber –​operate with a mechanism based on a mutual recommendation system from both the buyer and the seller. Over time users develop a kind of personal brand to mark them as people who are deemed to be trustworthy. The building blocks of these brands are profiles on social media and comments, i.e., feedback from a multitude of one-​time interactions. Such an aggregated and visible transactional history becomes an element of one’s personal ‘reputation capital’, which can affect real-​life interactions –​for example, the lack of LinkedIn account may lose you a job. The WeChat example illustrates yet another important aspect of platformisation of consumption: the digital payment revolution.

166  How is consumption changing? Americas India Credit / debit card Asia Pacific Europe Americas PayPal, Alipay, WeChat Pay, India Asia Pacific Union Pay, etc Europe Americas Visa Checkout, Masterpass India Asia Pacific Europe Americas India Gi card, pre-paid card Asia Pacific Europe Americas Apple Wallet, Google Wallet, India Asia Pacific Baidu Wallet, etc Europe

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Figure 5.9 Most popular payment methods of online shoppers in selected regions (in %, 2019). Source: Own work based on UPS. 2019. Most popular payment methods of online shoppers in selected regions as January 2019. Chart. In Statista. www.statista.com/​statistics/​676385/​ preferred-​payment-​methods-​of-​online-​shoppers-​worldwide-​by-​region/​ (accessed 21 January 2021).

Development of mechanisms of secure online payments has been essential in the growth of e-​commerce. A safe escrow service helped jump-​start Alibaba, the largest retailer in the world. But the real breakthrough came with the widespread adoption of mobile devices, particularly smartphones. Applications known as digital wallets enabled sourcing payment credentials from the bank account, a credit card, or another digital wallet in order to pay for the purchases either online or in the physical shop. In less than a decade, there emerged an alternative payment ecosystem that disintermediates bank from payment transactions: in 2019 90% of mobile transactions in China were carried out via WeChat Pay and Alipay (connected with Ant Financial of Alibaba Group).70 Those solutions provided by platforms allowed for overcoming infrastructural barriers and leapfrogging through the credit or debit card stage: the seller does not need the chip and pin device or internet connection. The consumer scans the two-​dimensional bar code (a QR code), often printed on a sheet of paper, and carries online payment through the app. Importantly, the applications allow for transferring funds between users –​a function made popular by the Tencent brilliant idea of sending traditional red envelopes via application in 2014 –​even on a global level.71 The digital payments are now embedded in many platforms, enabling both online and physical payment. The convenience of embedded online payments facilitates rolling out of new business models such as ‘pay-​per-​use’ (also called metered services) or subscription (flat rate of payment for unlimited access to content for a specified

How is consumption changing?  167 time). To illustrate the first model, Apple’s iTunes allows users to sample files but makes them pay for downloading a file. Many streaming platforms employ a freemium model: users may use the free content bundled with advertising or else pay for ad-​free premium content.72 Take YouTube: initially, it was an open platform where you could watch uploaded videos in exchange for ogling some ads. For some time now, YouTube has allowed viewers to watch for free, but shown them tiresome ads and exhortations to fork out for a Premium subscription, which is not only ad-​free but also enables offline watching and music to play in the background, while browsing other sites or reading. Platforms employ data on how goods and services are used to differentiate the way in which they charge for their offering, steering the consumers towards more flexible model of consumption, based on access instead of ownership.73 As of 2019, 70% of American households, and 40% of British ones, had at least one video streaming subscription.74 The conceptual shift from ownership to access underpins subscription-​based business models that monetise the use of digital information goods.

Collaborative consumption The platforms’ ability to quickly and efficiently match people played a key role in the development of collaborative consumption, which consists of the simultaneous or sequential use of a given resource or good by many people. As with other concepts concerning new phenomena related to the digital economy, the definition of collaborative consumption is somewhat imprecise. In the literature on the subject (and even more often in journalism), collaborative consumption is often conflated with the sharing economy.75 The rules governing the sharing of resources have long intrigued sociologists and economists. In 1968, the ecologist Garrett Hardin used the metaphor of the tragedy of the commons to show that uncontrolled, selfish consumption of a common resource leads to its destruction and backfires on individuals.76 To prevent this from happening, people build up shared resources via a variety of institutions, the most important of which is trust, i.e., the belief that a co-​user will not cheat us, and will not abuse the resource and exclude us from using it. The problem is that trust is relatively easy to maintain in small groups, where an egoistic behaviour can be easily identified and ostracised, but in larger groups, where individuals do not know each other and cannot effectively keep an eye on each other, there is a risk of freeloading. The development of capitalism was initially associated with the emergence of institutions that allowed sellers and buyers to hedge against the transactional risk associated with interacting with strangers with unproven reputations.77 Still, social or economic sharing and exchange were limited to relatively close societal circles. Platforms offer an infrastructure for engaging in collaborative consumption with total strangers outside those close circles. They not only enable quick and easy contact between the parties to a transaction (a person or company that has a given resource and a consumer who wants to use it); they also

168  How is consumption changing? significantly lower the risk of faulty transactions through the system of mutual recommendations and verified profiles of the users. If need arises, they curate the relations between the parties and provide the means of convenient and secure payments. This is what Blabla.car does for people having ‘idle resource’ of unused space in their car, and people looking for a cheaper and more convenient alternative to a bus or train. The ideal objects for collaborative consumption are digital information goods such as e-​books that can be easily replicated and used by many people without losing its quality. But this calls into question the profitability of the publishing houses. The monetisation strategies described earlier in the chapter have effectively put an end to many cases of such collaboration, which were condemned as infringing on intellectual property rights. For instance, at the close of 2020 several scientific publishers waged a legal war in India against SciHub and LibGen, platforms that provide access to scientific articles and books respectively.78 But the revolutionary impact of the platforms consisted of the possibility to share material goods with people outside one’s close circle of family and friends. Not all material goods can be shared equally easily, and not all people share equally willingly. Certain categories of goods are deemed too ‘private’, especially items related to personal hygiene (a toothbrush) or too closely related to one’s social or economic status (mobile phone or luxury watch). At the turn of the first decade of the XXI century, the hopes were high for this idealistic version of the platform-​enabled collaborative consumption. The adherents of collaborative consumption were convinced that, if it were ever to have widespread uptake, that might change attitudes towards personal property and, consequently, the role of possessions in shaping individuals’ identities. We may be entering an era of identity being based on the propensity to share, moving from thinking that ‘what we have defines us’ to realising that ‘what we share defines us’.79 The desire to possess is being replaced by the desire to experience; consumers are increasingly guided by the principle of ‘it’s less treasure and more pleasure’.80 Books optimistically titled What’s Mine Is Yours: The Rise of Collaborative Consumption (2010),81 Sharing Is Good (2013),82 Peers Inc: How People and Platforms Are Inventing the Collaborative Economy and Reinventing Capitalism (2015), painted a vision of tamed consumerism and new social and economic relations based on digitally-​verified trust. Rachel Botsman and Roo Rogers argue that the main motivation for indulging in collaborative consumption is a desire to keep in touch with other people and to protect the environment. Beth Buczyński, in turn, focuses on ecological justifications for collaborative consumption: wild consumerism has led to the destruction of natural resources, which may bring about extremely destructive consequences. Robin Chase, founder of Zipcar, a ridesharing platform, and author of Peers Inc. argues that sharing physical resources will satisfy the needs of many people without the ecological burden, ensuring ‘abundance in a world of scarcity’.83 This over-​optimistic narrative was soon counterbalanced by more empirically-​ grounded analysis showing that people involve in collaborative consumption for

How is consumption changing?  169 several reasons, aside from the normative ones focused on the environment.84 Instrumental motives come from calculating one’s own interests and from a desire to attain economic benefits. In simpler words, people participate in collaborative consumption when they feel that it pays off for them.85 The goods that are most often shared are those that are not used very often (e.g., a drill), only during a certain period of life (e.g., a cradle), for special occasions (e.g., a tuxedo), and at the same time are expensive enough for them to be worth renting or sharing.86 Many researchers and journalists also agree with the critical argument put forward in Share or Die: Voices of the Get Lost Generation in the Age of Crisis (2012), where the authors aver that sharing and exchanging resources is simply a matter of necessity stemming from the economic crisis.87 Perhaps Millennials currently engaged in collaborative consumption will simply grow out of it and return to the earlier model of ownership-​based consumption as soon as they can afford it: apparently, there comes a moment in your life when you want to buy a car instead of using Uber, and you ditch Spotify to buy pieces of vinyl.88 More importantly, the allure of the positive notion of socially and ecologically responsible collaborative consumption/​sharing economy was cunningly exploited by platforms in their own marketing purposes while their basic motivation was no different from that of traditional companies: the pursuit of profit.89 The notion of sharing conveniently dismissed their responsibility towards the parties of transaction: Uber was not responsible for the drivers’ wellbeing, it was only intermediating between them and the passengers.The academic community protested that Uber, Lyft, and suchlike companies are only providing on-​demand services, without any real sharing. ‘Stop saying Uber is part of the sharing economy’, pleaded two researchers from Utrecht University, because ‘what is being shared besides your money?’.90 But despite these protests, the conceptual hijack performed by platforms proved effective –​the notion of sharing economy came to represent platforms that in fact offer on-​demand services or intermediate in professional renting.91

The price of personalisation Predictably, the digital consumption has changed the consumers, their behaviour and the way they employ the abilities of their minds.‘Over the last few years I’ve had an uncomfortable sense that someone, or something, has been tinkering with my brain, remapping the neural circuitry, reprogramming the memory’, claimed writer Nicolas Carr. He had identified the source of the problem pretty easily –​it was the internet, which he was using more and more intensively at work. In The Shallows:What the Internet Is Doing to Our Brains (2011), Carr notes that the internet gives seemingly unlimited and almost instant access to the information we need, indisputably an amazing achievement of our civilisation and one which has considerably accelerated the process of acquiring, collecting, and verifying knowledge. However, this has come at a price: frequent use of the internet has accustomed people to searching quickly for information, but at the same time it has changed the intellectual habits that mankind learned with

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Figure 5.10 Percentage of (a) individuals who used any website or app to arrange an accommodation service from another individual (2019); (b) individuals who used any website or app to arrange a transport service from another individual (2019); (c) EU28 individuals who used any website or app to arrange a service from another individual (2017, 2019). Source: Own work based on Eurostat data [isoc_​ci_​ce_​i].

How is consumption changing?  171 the advent of writing and developed thanks to the availability of the printed word. Scrolling through text on a computer screen (and now on a smartphone), constantly jumping from one link to another, has destroyed our ability to focus on longer text, resulting in chronic distraction.This even applies to bookworms like Carr:‘Now my concentration starts to drift after a page or two. I get fidgety, lose the thread, begin looking for something else to do. I feel like I’m always dragging my wayward brain back to the text. The deep reading that used to come naturally has become a struggle.’92 It is worth remembering that, when Carr wrote this, the number of smartphone users was only just nearing a billion. Now it is almost 4 billion, and in developed countries penetration rates have reached 95% in South Korea, 75% in France, and 63% in Poland.93 Worst of all, the way smartphone applications provide information and entertainment is –​according to commentators such as Adam Alter in Irresistible: The Rise of Addictive Technology and the Business of Keeping Us Hooked (2017), and Jenny Odell in How to Do Nothing: Resisting the Attention Economy (2019) –​geared towards nurturing behavioural addictions. Our brains are eternally looking for novelties. An average iPhone user checks their phone 80 times a day.94 Android users are even more dependent on their devices than iOS users picking up or tapping them 110 times a day. For many people, their smartphone is the first thing they touch when they wake up and the last thing they caress before going to sleep.95 A growing number of people experience mild nomophobia (‘no mobile phone phobia’), a feeling of an anxiety if they do not have their phone

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Figure 5.11 Time spent per day with digital versus traditional media (in minutes, USA, 2011–​2020). Source: Own work based on eMarketer. 2020. Time spent per day with digital versus traditional media in the United States from 2011 to 2020 (in minutes). Chart. In Statista. www. statista.com/​statistics/​565628/​time-​spent-​digital-​traditional-​media-​usa/​ (accessed 20 January 2021).

172  How is consumption changing? with them at all times.96 We’re always on, immersed in what has been dubbed the ‘onlife’ by Luciano Floridi from the Oxford Internet Institute.97 It is small wonder then that marketing departments claim that digital consumers are easily distracted, and the academic world talks about attention being a scarce resource in a world of digital entertainment.98 They are also used to variety and an abundance of choice, taking for granted instant access, convenience and seamless service.99 But above all, they expect personalisation: goods and services, and even ads, tailored to their precise needs. Undoubtedly personalisation creates great value for consumers. For example, it makes it possible to adapt products and services to the needs of those social groups that have hitherto been ‘invisible’ because of a lack of data for designers and suppliers to mine.100 Data collected from smart fitness bands, for instance, can not only help detect signs of an impending heart attack, but also record different patterns of heart attack in women and men. Unfortunately, personalisation comes at a price. And at the most basic level, that price is data.While surfing or using intelligent devices, be it in private or public, consumers constantly generate this data.101 In fact, consumers perform a kind of invisible, yet essential job: more or less willingly and knowingly, they become producers, in the sense that they produce data. Hence the notion of the prosumer. Producers crave consumers’ behavioural data so that they can profile and predict their needs and expectations. Predictive profiling allows the consumer to be presented with a personalised offering. This possibility takes on particular importance because the digital consumer, surrounded by a cacophony of communication noise, is becoming more and more resistant to traditional communications and marketing channels.102 There is a heated discussion raging as to who owns this data and who should control its use: the individual or the company that collects it? Jathan Sadowski, author of Too Smart: How Digital Capitalism is Extracting Data, Controlling Our Lives, and Taking Over the World (2020), takes a radical position, arguing that ‘common practices of data collection should be seen as theft and/​or exploitation’.103 And indeed, tech firms and platforms have at their disposal ever larger hoards of data on individual consumers (and whole groups of consumers), and these consumers have little control over who uses their data or which third parties have access to it. Even the most determined of tech users is unable to follow all the twists and turns the data take as they use their devices and applications. But the price of personalisation is in fact higher than the ownership of data per se. Personalisation is contingent on predictive profiling, which may seriously harm privacy. In 2012, a US retailer sent a catalogue of maternity products to a Minnesota teenager because algorithms that had analysed her search and purchase history concluded she was pregnant. Unfortunately, her father consequently found out everything, and the whole story went on to spark a discussion about the scale of online consumer profiling and how it violates the right to privacy. In 2014, meanwhile, Janet Vertesi, a Princeton sociologist, tried to hide her pregnancy from profiling algorithms. She made some of her purchases via the TOR browser, which uses advanced cryptography to stop network traffic from being analysed. She quickly realised, however, that by doing so she could

How is consumption changing?  173 find herself under the scrutiny of the intelligence services as a potential criminal.104 Even more sobering effects of profiling were experienced by an editor at the Washington Post, who was bombarded with advertisements for prams and diapers after giving birth to a stillborn baby.105 According to Shoshana Zuboff, the author of The Age of Surveillance Capitalism, we are currently dealing with the emergence of ‘surveillance capitalism’, a trend rooted in the use of vast behavioural data sets to extract value and covertly influence consumer choices by companies.106 She dissects the benign vision of tech companies gathering data for the good of the consumer thus: ‘They want to know how we will behave in order to know how to best intervene in our behaviour.’107 The Cambridge Analytics scandal has shed light on another danger of personalisation. To paraphrase a well-​known saying: He who owns the data, calls the tune. Platforms, particularly those that trade in digital content, exercise considerable power over the selection of which content is shown or suggested to the consumer. On Netflix one trailer for a show will be shown to a white Canadian woman who liked ‘The 100’, while another version will be shown to a German teenager who previously watched Zac Efron travelling the world. Most people might find this acceptable. But you will also get profiled news feed on Facebook, locking you into an echo chamber hewn by a profiling algorithm. In an interview for the World Intellectual Property Organization Magazine, Sangeet Choudary noted that: Platforms (…) began curating content and helping consumers find the books, films and music they wanted and to decide what was worth consuming through their recommendation systems. Because there are so many connected consumers and so many suppliers of creative content, the companies that create a platform to organise the content market occupy the most powerful position in the content market today. In effect, they determine what content is shown and to whom.108 This personalisation mechanism is being honed to perfection by TikTok’s algorithm. The For You feed of viral videos is unique for each of its more than 2 billion users. The recommendation system is based on a combination of differently weighted factors such as user interaction (shared videos, followed accounts, created content and comments); video information (captions, sounds, and hashtags); and device and account settings (language, country, and device type). At the same time, the algorithm keeps diversifying recommendations and from time to time shows a video that ‘doesn’t appear to be relevant to your expressed interests’, but which opens up vistas to other kinds of popular content, creators and experiences.109 By expanding personalisation mechanisms, platforms try to lock consumers into their choices: the content accessed, paid for, and conveniently personalised via one platform is not easily accessible from another platform. If you want to transfer your playlist from Spotify to Tidal you must use –​and pay for –​yet another app. Admittedly, personalised recommendation is blooming when it comes to digital content. Even in this case, it is quite often still quite unsophisticated: many

174  How is consumption changing? of us get frustrated when after buying, say, trekking shoes via Allegro (the largest e-​commerce platform in Poland, which will be probably swept away when Amazon finally enters our market) for the next few days you are shown never-​ ending procession of trekking boots while scrolling the net. But personalisation is still much less common in the case of material goods. The digital transformation in manufacturing –​the Reconfigurable Manufacturing Systems, digital twins operating on the constant flow of data from intelligent products (see Chapter 3) –​will soon bring yet another revolution in consumption. Digital consumption is basically based on the cross-​border flow of data, and online platforms facilitating, orchestrating and shaping its practices are usually global in their outlook and operation. In the next chapter, we will look closer at this intrinsically global character of the digital economy.

Key takeaways • In the digital economy, consumption is increasingly channelled through connected digital devices. • Digital consumption includes two new types of objects: digital information goods, such as e-​ books or streamed videos, and intelligent products, i.e., connected goods such as wearables, with software providing additional functionalities. • Digital platforms play an increasingly important role in facilitating consumption: they act as gateways for digital information goods, they enable online shopping and collaborative consumption. • Through novel tools for protecting their intellectual property rights such as streaming and DMR, as well as the growing servitisation of material goods, companies are steadily pushing to replace of the traditional ownership of things in favour of temporary access to their products, guarded by licenses and subscriptions. • Digital consumption is shaped by the imperative of personalisation. Digital consumers expect products and services matched to their needs and expectations, delivered on the spot. Personalisation relies on datafication –​the more data that is available on a consumer’s practices, the more tailored the service. This, however, opens up questions concerning privacy and the ownership of the data.

Notes 1 European Commission. Digital Economy and Society Index (DESI) 2020. Poland. https://​ec.europa.eu/​digital-​single-​market/​en/​scoreboard/​poland (accessed 25 January 2021). 2 Silver, L. 2019. Smartphone ownership is growing rapidly around the world, but not always equally. Pew Research Center. www.pewresearch.org/​global/​2019/​02/​ 05/​smartphone-​ownership-​is-​g rowing-​rapidly-​around-​the-​world-​but-​not-​always-​ equally/​(accessed 25 January 2021).

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184  How is consumption changing? Deloitte. XaaS Models: Our offerings. Making the shift to everything-​as-​a-​service. www2.deloitte.com/​us/​en/​pages/​technology-​media-​and-​telecommunications/​articles/​flashpoint-​flexible-​consumption-​technology-​platforms-​cloud.html Edelman, B., Luca, M., and Svirsky, D. Racial discrimination in the sharing economy: Evidence from a field experiment. Harvard Business School. Working Paper 16-​069. www.hbs.edu/​faculty/​Publication%20Files/​16-​069_​5c3b2b36-​d9f8-​4b38-​ 9639-​2175aaf9ebc9.pdf Euromonitor. The new consumerism: The reach of the sharing economy. 2016. http://​ blog.euromonitor.com/​2016/​04/​the-​new-​consumerism-​the-​reach-​of-​the-​sharing-​ economy.html European Commission. Digital Economy and Society Index (DESI) 2020. Poland. https://​ec.europa.eu/​digital-​single-​market/​en/​scoreboard/​poland European Union. Directive 2011/​83/​Eu Of The European Parliament And Of The Council On Consumer Rights, Amending Council Directive 93/​ 13/​ EEC And Directive 1999/​44/​EC Of The European Parliament And Of The Council And Repealing Council Directive 85/​ 577/​ EEC And Directive 97/​ 7/​ EC Of The European Parliament And Of The Council. Official Journal of the European Union. 2011. https://​eur-​lex.europa.eu/​legal-​content/​EN/​TXT/​?uri=celex%3A32011L0083 Eurostat. Individuals –​internet activities. https://​appsso.eurostat.ec.europa.eu/​nui/​ show.do?dataset=isoc_​ci_​ac_​i&lang=en Eurostat. The truth about online consumers. 2017 Global Online Consumer Report. KPMG. 2017. https://​ec.europa.eu/​eurostat/​statistics-​explained/​index.php/​E-​ commerce_​statistics_​for_​individuals#Main_​reason_​for_​not_​buying_​online FACT. Cracking down on digital piracy. Report. 2017. www.fact-​uk.org.uk/​files/​ 2017/​09/​Cracking-​Down-​on-​Digital-​Piracy-​Report-​Sept-​2017.pdf First Data /​Forrester. From rideshare, music streaming, and food delivery: The global rise of digital goods and services. Regional and demographic trends driving global cross-​border. Commerce. Forrester. 2019. www.firstdata.com/​downloads/​pdf/​Cross-​ Border_​Digital_​Goods_​Whitepaper.pdf Fitzgerald, T. How many streaming video services does the average person subscribe to? Forbes. 2019. www.forbes.com/​sites/​tonifitzgerald/​2019/​03/​29/​ how-​ m any-​ s treaming-​ v ideo-​ s ervices-​ d oes-​ t he- ​ average- ​ p erson- ​ s ubscribe- ​ t o/​ ?sh=58a3c0746301 Floridi, L. The fourth revolution: How the infosphere is reshaping human reality. Oxford University Press. 2014. Cited in: The Onlife Manifesto: Being Human in a Hyperconnected Era. ed. Luciano, Floridi. Springer. 2015. Franck, G.The economy of attention. Journal of Sociology. 2018. https://​journals.sagepub. com/​doi/​full/​10.1177/​1440783318811778 Frenken, K. and Schor, J. Putting the sharing economy into perspective. Environmental Innovation and Societal Transitions 23. 2017. https://​doi.org/​10.1016/​ j.eist.2017.01.003 Fukuyama, F. Trust. The Social Virtues and the Creation of Prosperity. Free Press. 1996. Gądek, J. Dobra cyfrowe w grach i ich wartość: skiny. Tar Heel Capital. 2020. http://​ tarheelcap.com/​dobra-​cyfrowe-​w-​grach-​i-​ich-​wartosc-​skiny/​ Gansky, L. The Mesh:Why the Future of Business is Sharing. Portfolio/​Penguin. 2010. Gerlock, G. Farmers look for ways to circumvent tractor software locks. NPR.    2017.  www.npr.org/ ​ s ections/ ​ a lltechconsidered/ ​ 2 017/ ​ 0 4/ ​ 0 9/​ 523024776/​f armers-​l ook-​f or-​ways-​t o-​c ircumvent-​t ractor-​s oftware-​l ocks?t= 1610439650297

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186  How is consumption changing? King University Online. Cell phone addiction: The statistics of gadget dependency. 2017. https://​online.king.edu/​news/​cell-​phone-​addiction/​. Klein, A. China’s digital payments revolution. Brookings. 2020. www.brookings.edu/​ research/​chinas-​digital-​payments-​revolution/​ Kowalkowski, C., Gebauer, H., Kamp, B., and Parry, G. Servitization and deservitization: Overview, concepts, and definitions. Industrial Marketing Management 60. 2017. www.sciencedirect.com/​science/​article/​pii/​S0019850116303571 KPMG International Cooperative. 2017. The truth about online consumers. https://​ assets.kpmg/​ c ontent/ ​ d am/ ​ k pmg/ ​ x x/ ​ p df/ ​ 2 017/ ​ 0 1/ ​ t he- ​ t ruth- ​ a bout- ​ o nline-​ consumers.pdf Kravets, D. 2010. Copyright lawsuits plummet in aftermath of RIAA campaign. Wired. www.wired.com/​2010/​05/​r iaa-​bump/​ Krejcar, O., Maresova, P., Selamat, A., Melero, J.F., Barakovic, S., Barakovic, H.J., Herrera-​ Viedma, E. et al. Smart furniture as a component of a smart city –​definition based on key technologies specification. IEEE Access. 2017. https://​arl.human.cornell.edu/​ linked%20docs/​Smart_​Furniture_​as_​a_​Component_​of_​a_​Smart_​City.pdf Kurutz, S. Peloton instructors ride for fitness and fame. New York Times. 2017. www. nytimes.com/​2017/​02/​01/​style/​peloton-​fitness-​cycling-​celebrity-​instructors. html Lawson, S. Transumers: Motivations of non-​ownership consumption. In: NA –​ Advances in Consumer Research no. 37. ed. Campbell, M.C., Inman, J., and Pieters, R. Association for Consumer Research. 2010. www.acrwebsite.org/​volumes/​15449/​volumes/​v37/​ NA-​37 Lee, K-​F. AI Superpowers: China, Silicon Valley, and the New World Order. Houghton Mifflin Harcourt. 2018. Lehdonvirta,V. Online spaces have material culture: Goodbye to digital post-​materialism and hello to virtual consumption. Media, Culture & Society 32(5). 2010. http://​ journals.sagepub.com/​doi/​abs/​10.1177/​0163443710378559 Lehdonvirta, V. A history of the digitalisation of consumer culture: From Amazon through Pirate Bay to FarmVille. In: Digital Virtual Consumption. ed. Denegri-​Knott, J. and Molesworth, M. Routledge. 2012. https://​papers.ssrn.com/​sol3/​papers. cfm?abstract_​id=2501350 Leswing, K. The average iPhone is unlocked 80 times per day. Business Insider. 2016. www.businessinsider.com/​the-​average-​iphone-​is-​unlocked-​80-​times-​per-​day-​ 2016-​4?IR=T Lewis, P.H. Attention shoppers: Internet is open. New York Times. 1994. www.nytimes. com/​1994/​08/​12/​business/​attention-​shoppers-​internet-​is-​open.html Markoff, J. What the Dormouse Said: How the Sixties Counterculture Shaped the Personal Computer Industry. Penguin Books. 2006. Matsakis, L. How the West got China’s social credit system wrong. Wired. 2019. www. wired.com/​story/​china-​social-​credit-​score-​system/​ Mclaughlin, M. New GTA V release tipped to rake in £1bn in sales. The Scotsman. 2013. www.scotsman.com/​whats-​on/​arts-​and-​entertainment/​new-​gta-​v-​release​tipped-​rake-​ps1bn-​sales-​2463312 Meelen, T. and Frenken, K. Stop saying Uber is part of the sharing economy. Fast Company. 2015. www.fastcompany.com/​3040863/​stop-​saying-​uber-​is-​part-​of-​the​sharing-​economy Microsoft.Books in Microsoft Store:FAQ.https://s​ upport.microsoft.com/e​ n-u ​ s/a​ ccount-​ billing/​books-​in-​microsoft-​store-​faq-​ff0b7b84-​7052-​4088-​9262-​d7e4ee22419c

How is consumption changing?  187 Microsoft.What happens to my data and access when my Microsoft 365 for business subscription ends? 2020. https://​docs.microsoft.com/​en-​us/​microsoft-​365/​commerce/​ subscriptions/​what-​if-​my-​subscription-​expires?view=o365-​worldwide Molesworth, M. and Denegri-​Knott, J. Digital virtual consumption as transformative space. In: The Routledge Companion to Digital Consumption Routledge. ed. Belk, R.W. and Llamas, R. Routledge Handbook Online. 2012. www.routledgehandbooks. com/​doi/​10.4324/​9780203105306.ch20 Murray, M.R. Peloton: Exercising strategic thinking for a new business model. UVA Darden Ideas to Action. 2020. https://​ideas.darden.virginia.edu/​peloton-​exercising​strategic-​thinking Nanalyze. 11 smart shoes that are digitally connected. 2019. www.nanalyze.com/​2019/​ 02/​smart-​shoes-​digitally-​connected/​ National Research Foundation. Virtual Singapore. Prime Minister’s Office. Singapore. www.nrf.gov.sg/​programmes/​virtual-​singapore Peloton. www.onepeloton.com/​ Peloton. Indoor exercising bike with online streaming classes. www.onepeloton.com/​ bike Peloton. Introducing Instagram story sharing. Share. Connect. Explore. The Output. https://​blog.onepeloton.com/​peloton-​instagram-​story-​sharing/​ Perez, C.C. Invisible Women. Data Bias in a World Designed for Men. Abrams Press. 2019. Ponelis, S. Information as economic good: Its origins, characteristics, pricing, and associated legal and ethical issues. In: Approaches and Processes for Managing the Economics of Information Systems. ed.Tsiakis,T., Kargidis,T. and Katsaros, P. IGI Global. 2014. www.igi-​global.com/​gateway/​chapter/​94275#pnlRecommendationForm Pope, D.G. and Sydnor, J.R. What’s in a picture? Evidence of discrimination from Prosper.com. Journal of Human Resources 46(1). https://​faculty.chicagobooth.edu/​ devin.pope/​assets/​files/​Website_​Prosper.pdf Prasad, M., Patthi, B., Singla, A., Gupta, R., Saha, S., Kumar, J., Krishna, M.R., et al. Nomophobia: A cross-​sectional study to assess mobile phone usage among dental students. Journal of Clinical & Diagnostic Research. www.ncbi.nlm.nih.gov/​pmc/​articles/​PMC5376814/​ Retail Touchpoints. How many products does Amazon carry? https://​retailtouchpoints. com/​resources/​how-​many-​products-​does-​amazon-​carry Ritzer, G. The McDonaldization of Society. Into the Digital Age. Ninth Edition. International Student Edition. University of Maryland. 2018. Sadowski, J. Too Smart: How Digital Capitalism is Extracting Data, Controlling Our Lives, and Taking Over the World. The MIT Press. 2020. Scally, D. Child smartphone addiction growing, says German drug agency. The Irish Times. 2017.  www.irishtimes.com/​news/​world/​europe/​child-​smartphone-​addiction​growing-​says-​german-​drug-​agency-​1.3101529 Scaria, A.G. Sci-​Hub case: The court should protect science from greedy academic publishers. The Wire. 2020. https://​thewire.in/​law/​sci-​hub-​elsevier-​delhi-​high-​ court-​access-​medical-​literature-​scientific-​publishing-​access-​inequity Siegel, E. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Wiley. 2016. Silver, L. 2019. Smartphone ownership is growing rapidly around the world, but not always equally. Pew Research Center. www.pewresearch.org/​global/​2019/​02/​05/​ smartphone-​ownership-​is-​g rowing-​rapidly-​around-​the-​world-​but-​not-​always-​ equally/​

188  How is consumption changing? Skinwallet. www.skinwallet.com/​pl/​ Sorensen, K. Phygital –​the new marketing frontier. Konstrukt Digital. 2020. https://​ serviceplan.blog/​en/​2019/​02/​as-​technology-​vanishes-​black-​magic-​appears/​ Statista. Wearables. www.statista.com/​outlook/​319/​146/​wearables/​poland#market​revenue Stoll, J.D. Is Peloton a fitness fad or a tech company? Everything’s riding on the answer. The Wall Street Journal. 2020. www.wsj.com/​articles/​is-​peloton-​a-​fitness-​fad-​or-​a-​ tech-​company-​everythings-​r iding-​on-​the-​answer-​11579273632 Stus, M. Kilka tysięcy dolarów za ‘topowe kosy’ w grze. ‘Dajemy możliwość wyrażenia siebie’. Esportmania. https://​esportmania.pl/​big-​stories/​handel-​skorkami-​w-​cs-​go-​ na-​czym-​polega-​skinwallet/​jxcd3bg Temperton, J. I tried to keep my unborn child secret from Facebook and Google. Wired. 2019. www.wied.co.uk/​article/​the-​internet-​hates-​secretsTikTok. How TikTok recommends videos #ForYou. https://​newsroom.tiktok.com/​en-​us/​ how-​tiktok-​recommends-​videos-​for-​you Trindade, E.P., Hinnig, M.P.F., Moreira da Costa, E., Sabatini Marques, J., Cid Bastos, R., and Yigitcanlar,T. Sustainable development of smart cities: A systematic review of the literature. Journal of Open Innovation: Technology, Market, and Complexity 3(11). 2017. https://​jopeninnovation.springeropen.com/​articles/​10.1186/​s40852-​017-​0063-​2 UNCTAD. Global e-​Commerce sales surged to $29 trillion. Geneva. 2019. https://​ unctad.org/​en/​pages/​PressRelease.aspx?OriginalVersionID=505 Vandemerwe, S. and Rada, J. Servitisation of business: Adding value by adding services. European Management Journal 6(4). 1988. www.sciencedirect.com/​science/​article/​ pii/​0263237388900333. Vertesi, J. My experiment opting out of big data made me look like a criminal. Time. 2014. http://​time.com/​83200/​privacy-​internet-​big-​data-​opt-​out/​ Waldfogel, J. Digital Renaissance: What Data and Economics Tell Us about the Future of Popular Culture. Princeton University Press. 2018. Wang, H.H. From virtual reality to personalised experiences: Alibaba is bringing us the future of retail this singles day. Forbes. 2016. www.forbes.com/​sites/​helenwang/​2016/​ 11/​06/​how-​alibaba-​will-​use-​the-​worlds-​biggest-​shopping-​day-​to-​transform-​ retail/​#5bd808e06d4e Watkins, R. and Denegri-​Knott, J. Do we own our digital possessions? Journal of Marketing Management. 2016. www.jmmnews.com/​do-​we-​own-​our-​digital-​possessions/​ Watkins, R., Denegri-​Knott, J., and Molesworth, M. The relationship between ownership and possession: Observations from the context of digital virtual goods. Journal of Marketing Management 32(1–​ 2). 2016. http://​dx.doi.org/​10.1080/​ 0267257X.2015.1089308 Winnick, M. Putting a finger on our phone obsession. Mobile touches: a study on how humans use technology. Blog Dscout. 2016. https://​blog.dscout.com/​mobile-​touches Winograd, M. and Hais, M.D. Millennial Momentum: How a New Generation Is Remaking America. Rutgers University Press. 2011. Wolfinbarger, M. and Gilly, M.C. Shopping online for freedom, control, and fun. California Management Review 43(2). 2001. https://​journals.sagepub.com/​doi/​ 10.2307/​41166074 Ye, T., Alahmad, R., Pierce, C., and Robert, L.P. Race and rating on sharing economy platforms: The effect of race similarity and reputation on trust and booking intention in Airbnb. Conference: Proceedings of the 38th International Conference on Information

How is consumption changing?  189 Systems (ICIS 2017). Korea. Seoul. 2017. www.researchgate.net/​publication/​ 319957147_​Race_​and_​Rating_​on_​Sharing_​Economy_​Platforms_​The_​Effect_​of_​ Race_​Similarity_​and_​Reputation_​on_​Trust_​and_​Booking_​Intention_​in_​Airbnb Young, J. Global ecommerce sales to reach nearly $3.46 trillion in 2019. Digital Commerce. 2019. www.digitalcommerce360.com/​article/​global-​ecommerce-​sales/​ Yue, H. and Yingzhe, G. 2020. Tencent launches credit scoring system based on WeChat purchases. CX Tech. www.caixinglobal.com/​2020-​06-​08/​tencent-​launches-​credit-​ scoring-​system-​based-​on-​wechat-​purchases-​101564336.html Zuboff, S. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. Profile Books. 2019.

6  How is globalisation changing?

Abstract In this chapter, we shall examine how changes in modes of production and consumption are ushering in a new phase of globalisation – digital globalisation. We briefly characterise the flows of data as a new kind of cross-border flows, while emphasising the importance of physical infrastructure (such as the internet cables and data centres), which is largely controlled by private companies. Next, we describe the changes brought into international trade by processes of digitalisation. Mounting adoption of digital technologies such as the Internet of Things, blockchain, and intelligent automation will accelerate the trade in goods, for they enable the monitoring of supply chain and facilitate customs procedures. However, the development of Industry 4.0 may decrease the volume of trade in goods, as it may support the relocalisation of production. On the other hand, trade in services will unwaveringly grow, mainly because of the increasing datafication of goods and services and the efficient market intermediation by digital platforms on a global scale. In the last part of the chapter, we address the rise of digital protectionism and digital sovereignty as new developments underpinning the transformation of global order.

Digital flows Globalisation as we currently know it started right after WWII, when war-​weary states agreed to liberalise international trade. A series of negotiating rounds took place as part of the General Agreement on Tariffs and Trade. Gradually, trade barriers were lowered, clearing a path for large corporations from developed countries to enter global markets. They started to spread their linear value chains (i.e., activities resulting in bringing a product to market, from design through production to distribution) across borders, eager to find new markets, new sources of cheaper production (particularly labour) and new resources. Cross-​border flows consisted predominantly of goods, which necessitated the development of transport infrastructure, epitomised by the introduction of the container, readily loaded on to leviathan-​like container ships. Trade in services expanded much more slowly: international communications were expensive and technologically limited. It was only the arrival of undersea glass-​fibre

How is globalisation changing?  191

Figure 6.1 How is globalisation changing? (scheme). Source: Own elaboration.

cables and the spread of the internet in the 1990s that dramatically lowered the costs of communicating, while at the same time increasing the capacity and the quality. International corporations became bolder in fragmenting and distributing production processes across multiple countries, as it became easier to communicate between foreign divisions.1 Software solutions, such as Enterprise Resource Planning (ERP), enabled the complex coordination of physical flows within supply chains.2 The internet also facilitated the provision of services,

192  How is globalisation changing? particularly software and client services (such as human resources management software). But this was just the beginning. Around 2010, the massive adoption of connected mobile devices, the growing digitisation of content, and the platform-​enabled thrust of commercial and non-​commercial networks across borders, all kicked off a new, digital phase of globalisation. The digital economy is now globalised to its very core. Two-​thirds of data flowing through the web –​more aptly called the World Wide Web –​crosses state borders.3 Before your email reaches you, it may have visited several other countries via several dozen computers. If you are browsing the web in Poland, your computer probably requests data from a US data centre.4 Between 2005 and 2014, the global flow of data rose 45 times.5 The value of this global data flow is also rising: in 2014 it stood at $2.3trillion,6 while in 2025 it is predicted to hit $11trillion.7 However, most of these flows are data and services delivered to and from end-​users for free (free emails, search engine results), often taking the form of digital information which, (as mentioned in Chapter 5), is instantly accessible from almost anywhere in the world.8 Moreover, the growth of video content means that, by 2021, it will account for almost 80% of global data flow.9 However, data is also a component of a growing number of internationally traded material goods and underpins the international trade in services. Material goods are increasingly being datafied and turn into intelligent products: i.e., they are being equipped with intelligent sensors and often connected to the internet, which allows them to be complemented with additional services. An example of such goods might be a smart fridge that stores food and can also check expiry dates and order new items based on an intelligent prediction of the user’s needs.10 Meanwhile, goods that once took material form –​such as written texts, music or videos –​are now often provided in electronic form as digital information goods, and therefore defined as a kind of service and incur a subscription.The traditional division between goods and services is thus increasingly blurred. Is a Kindle e-​book bought through Amazon a good or a service? Should we classify such a purchase –​if, for example, you made it sitting in your living room in Nairobi –​as an item of international trade? Processes of international trade are increasingly being datafied and are more and more often carried via platforms. All in all, data ‘is not only a means of production, it is also an asset that can itself be traded, and a means through which GVCs [global value chains] are organised and services delivered’.11 This dematerialised and globalised digital economy is utterly dependent on a very physical fabric: the vast, material infrastructure that enables data flow and storage. This consists of more than 1.2 million kilometres of underwater fibre-​ optic cables; in fact, 95% of global data flows through just 200 such underwater cables.12 Moreover, it is content providers that own or lease more than half of all submarine cables’ capacity. Google, for example, has bankrolled the construction of at least 14 cables around the globe.The big tech companies are intensely developing a network of data centres, essential in provision of the cloud services. The first Google data centre was built in 2006, around the time when

How is globalisation changing?  193 the need to process and store data began to rise exponentially. As of 2020, the company had 13 data centres scattered around the world, with another eight under construction –​all to support billions of searches, emails, and cloud computing services. Amazon, Facebook, and Microsoft have used their own cables to connect data centres on all the continents.13 Demand for infrastructure will continue to grow –​more than half of the world’s population now uses the internet, and fast data transmission is also required by the nascent Internet of Things, while companies now routinely use cloud computing.14 Hence, control over this strategic infrastructure, which is almost completely in the hands of private companies, has become critical. Some states try to assert greater control: for example, in 2017, Australia blocked the plans of Chinese tech giant Huawei to lay a cable connecting it to the Solomon Islands, fearing that the Chinese government would gain access to Australian data; in 2019 the country declared Huawei a high-​r isk vendor and banned it from providing 5G infrastructure on its territory.15 This is but one example of the changing hierarchies of power in the age of digital globalisation, including the redefinition of the traditional state sovereignty challenged by the growing influence of the big tech companies. Before we address these issues, we will first analyse the impact of digital technologies on digital trade in goods and services.

Digital trade in goods The growing adoption of digital solutions is facilitating and accelerating the flow of material goods across borders (i.e., adding to the trend of datafied distribution described in Chapter 3).16 Some of the solutions employed are simple, but nonetheless revolutionary. For instance, cheap radio-​frequency identification tags on goods enable a delivery route to be tracked through extensive and labyrinthine supply chains.17 Intelligent sensors built into goods, vehicles collect data that may be used to optimise transport and logistics.18 The application of machine learning in translation and better text recognition algorithms can reduce language barriers. When eBay introduced machine translation, which was trained on eBay data and data scraped from the web, US exports via this platform to Spanish-​speaking Latin American countries grew by 17.5%.19 The data on resources and production processes may be safeguarded by blockchain, a technology that can safely encrypt data.20 Such technology-​enabled certification and verification are vital for vulnerable goods, such as pharmaceuticals, but they are also proving useful in the case of luxury goods such as Breitling watches (blockchain warrant instead of paper certificates of authenticity).21 Consumers worldwide increasingly expect the whole supply chain to be transparent and easily verifiable by just scanning a product label.22 For example, the Swiss company Nestlé uses IBM Food Trust blockchain technology to prove the ethical and ecological origin of its Zoégas coffee brand to customers who need only to scan the QR code on the packaging to trace the origins of the beans.23 Further transformations in international exchange are being driven by the proliferation of the Internet of Things and intelligent algorithms. These

194  How is globalisation changing? are spurring the transformation of linear supply chains into cross-​ border networked supply ecosystems coordinated by central platforms. Continuously collected and processed data (on workers, contractors, customers, objects, and processes) optimise the functioning within the supply network. For example, ever more datafied and automated supply networks can then react more flexibly to disruptions; and supplies of both semi-​products and final products are sped up, which in turn leads to a smaller environmental footprint. The application of digital technologies lowers costs associated with clearing customs, and it can actually streamline customs procedures themselves. Algorithms monitor any changes to customs regulations and aid coordination between different countries’ customs services. They make it easier for companies to wade through the mire created by preferential trade agreements, which define the different levels of certain duties and the precise criteria of preferential treatment. Introducing blockchain solutions boosts value chains’ transparency and enables coordination between national customs services (mutual recognition enables paperless global cross-​border trade). For example, TradeLens (a digital shipping platform based on blockchain) provides a ‘shared, immutable ledger that records transactions and tracks tangible and intangible assets’.24 As a result, many steps involved in clearing customs, e.g., tariffs payments, can be automated, shortening the long queues of lorries at the borders. The increasing automation of the loading and unloading process is further accelerating trade. For instance, machine learning helps to optimise the way robotic cranes stack freight containers (a solution used in Rotterdam port).The next stage in the transformation will be the full automation of (re)loading ports and the entire logistics system, although this technologically complex transformation is for now in its infancy.25 As of 2019, only 1% of terminals were fully automated, and just 2% were semi-​automated.26 Perhaps the most important of all the roles digitalisation is playing in revolutionising international trade in goods belongs to platforms. In his blog on platform strategies, Sangeet Paul Choudary claims that ‘global trade is moving form pipelines to platforms’.27 Software platforms have lowered entry barriers to global markets by providing cheap and scalable tools for management and sales applications based on AI-​powered computing. Social media platforms offer relatively inexpensive and scalable marketing tools built upon social networks: more than three million businesses advertise through Facebook, aiming to attain worldwide visibility. Matchmaking platforms, such as Alibaba, Amazon, eBay, Flipkart, and Rakuten, streamline cross-​border e-​commerce. They have driven down the costs of finding buyers and suppliers, as they provide the infrastructure to build trust among (and provide support for) foreign producers, contractors, and customers. Even small companies, or fledgling startups, can use them to seek out markets and individual clients abroad, or to hire the employees they need in international markets and find financial backing outside the country in which they physically operate.28 Individual artists and their customers can carry out transactions on Etsy, an arts, crafts, and vintage goods market: almost 30% of its gross sales are international.29 Over

How is globalisation changing?  195 a)

b)

29%

30 25

(%)

20 15

12%

10 5 0 37

2011

2019

Figure 6.2 Cross-​border e-​commerce: percentage of individuals who purchased online from sellers abroad (a) by country in 2019; (b) in EU28, 2011 and 2019. Source: Own work based on Eurostat data [isoc_​ec_​ibuy].

a)

b)

10

10

9%

9

9

8 6

8 7

6% (%)

(%)

7 5

6 5

4

4

3

3

2

2

1

1 2011

2019

5% 4%

2011

2019

Figure 6.3 Cross-​border e-​commerce: percentage of EU28 enterprises (all enterprises, without financial sector) with e-​commerce sales to (a) other EU countries; (b) to the rest of the world, 2011 and 2019. Source: Own work based on Eurostat data [isoc_​ec_​eseln2].

20,000 independent designers and artists showcase their work on Pinkoi, a Taiwanese online marketplace. The company has reached out to customers in more than 93 countries via Facebook to expand its range across the Asia-​Pacific region.30 As of 2020 Amazon, worked with 2.4 million third-​party sellers.31

196  How is globalisation changing? Alibaba has attracted around 10 million small businesses from 190 countries and regions to its ecosystem; one-​third of the order volume comes from the firms based in the USA.32 In 2017, the CEO of Alibaba revealed that his company was working on a new global trading platform –​eWTP –​to iron out logistics, customs issues and guarantee favourable tariffs.33 Alibaba revenue from international retail sales had risen from $58.4 million in 2013 to $2119 million in 2018.34 Nowadays, the process of building a new company no longer has to include an incubation stage in the local or national market; some firms, especially technology startups, become established immediately as global companies (in effect, they are ‘born global’).This is particularly important for startups from emerging markets, desperate to overcome barriers to their development in domestic markets.35 For example, 48% of Polish startups export their products and services, in comparison to a paltry 4.4% of more established Polish small and medium-​sized enterprises.36 In 2019, UNCTAD estimated the value of global e-​commerce at $25.6 trillion, with more than 300 million people who made a cross-​border online purchase.37 By 2021, China will be the largest participant in cross-​border online trade: its share will amount to 41%. Curiously, in the EU, internal cross border e-​commerce is still relatively low, despite the existence of the internal market, designed to boost trade among EU countries. The share of consumers buying from abroad is in average 29% (Figure 6.1) while the share of EU companies selling to the internal market via e-​commerce is 9%; and it is even smaller (5%) in trade with non-​EU countries (Figure 6.2).38 These figures may be an under-​ estimate: official trade statistics do not usually take into account the value of the large proportion of all international trade accounted for by transactions that take place via online platforms such as Alibaba, Amazon, or eBay. Often these transactions involve small parcels, the shipment of which is not recorded by tax offices and is therefore not included in countries’ statistics. An important part of the growth of international e-​commerce has been the development and maintenance by platforms of reliable, cheap (or even free), and fast cross-​border payment systems, with the Chinese platforms taking the lead. Alibaba’s own mobile payment system, Alipay, was developed by its subsidiary Taobao –​an online platform for small businesses. As Chinese customers were reluctant to pay for goods before receiving them, a system needed to be set up in which payment, although recorded, is not transferred to the seller’s account until the customer has completely accepted the goods. In 2008, the system was transformed into mobile ‘wallets’, which took the form of a smartphone application linked to a bank account. Payments are made using Quick Response (QR) codes, square black and white dot matrixes that have become ubiquitous in China. Ant Group (formerly Ant Financial and Alipay) has been investing in local mobile payment services in India, Indonesia, Malaysia, the Philippines, Singapore, South Korea, and, more recently, Pakistan.39 In 2019, Alibaba’s arch rival Tencent gained a licence for electronic payments in Malaysia via the WeChatPay application, which customers can link to their credit card. International transactions typically incur transaction fees or exchange rate costs.

How is globalisation changing?  197 To reduce these, more and more companies, both in China and elsewhere are using blockchain technologies. Blockchain has other uses in countries where the banking system is not sufficiently developed to support international trade: it can help companies by securing the flow of financial documents necessary to obtain a loan or financial guarantee. So far we have argued that the digital technologies greatly facilitate and accelerate the processes of international trade. However, the ongoing digital transformation can also bring some adverse effects as to the volume of the global flow of goods. Development of Industry 4.0 is the main suspect in the curious phenomenon of ‘deglobalisation’.40 New manufacturing technologies, including 3D printing, have made it profitable for companies to move production closer to consumer markets and innovation centres, and to invest in smart factories (see Chapter 3). Access to cheap labour is no longer such an asset. Much more important is access to the digitally skilled labour, to the robust connectivity allowing for application of the Industrial Internet of Things, and to local markets, allowing for a radical shortening of delivery times. McKinsey believes that the value of global trade in goods may decrease by as much as 10% by 2030.41 Relocating production closer to consumer markets and innovation centres will change the whole structure of international trade, particularly in relation to knowledge-​intensive products (i.e., those goods and services whose production requires highly competent human capital).42 So far those products were designed in the highly developed countries and cheaply manufactured in the countries patronisingly dubbed ‘developing’. The fast evolution of the Chinese technological ecosystem is inevitably overturning this long-​ term pattern. For example, for some time now China has been doubling its efforts to break free from dependence on the foreign suppliers of semiconductors or chips (used in production of all kinds of electronic and digital devices; the most advanced one are instrumental in processing abundant data). In 2019 China imported more than $300 billion of semiconductors, mainly from the US companies, which made the Chinese producers of electronic and digital devices vulnerable to the American trade blacklisting.43 As of 2020 China did not have any cutting-​edge semiconductor manufacturing facility, but the scale of the planned investments will certainly change this situation. As emphasised by one Bloomberg columnist: Foreign companies –​notably in semiconductors, software or materials –​ that still believe China is a viable long-​term business are kidding themselves. Only those supplying crucial products and services not available locally will have any shot at sustained market access, and even then only until a domestic alternative comes along.44 In October 2020 the Central Committee of the Chinese Communist Party announced that ‘technological self-​sufficiency’ would become ‘a strategic pillar of national development’. Earlier that year, China declared plans to invest $1.4 trillion by 2025 in spreading the 5G networks all over the country, installing

198  How is globalisation changing? cameras and sensors in public spaces (this is one of the reasons China craves large number of affordable semiconductors), and in developing technologies based on artificial intelligence.45 In result, the supply chains feeding the Chinese technological development will be increasingly indigenous, which will limit the trade with the Western countries.

Digital trade in services

(in tln USD)

The digitalisation-​driven changes in international trade in goods have been truly astonishing. Yet, they pale in comparison with the revolutionary changes taking place in the international trade in services, which doubled in volume between 2005 and 2017 (Figure 6.6).46 Datafication and platforms are creating new ways of buying and selling services internationally.47 This is down to three major factors. Firstly, up until very recently, many services were considered ‘non-​tradeable’, because they could be delivered only if the service provider was physically present in the vicinity of the service receiver, or at least if both had access to a reasonably-​priced, good-​quality virtual communication channel (i.e., affordable phone connection). In recent years, however, the price of internet connections has plummeted, the introduction of 5G wireless networks will enable the immediate transmission of information in the form of excellent quality audio and video, and the use of virtual and augmented reality solutions is making possible such activities as remote device-​servicing in factories or even remote plastic surgery.48 In fact, previously non-​tradable services have now become hyper-​tradable.49

6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.7 2.5 2.0 1.5 1.0 0.5

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4.1

4.5 4.6 3.6

4.9

5.2

5.0

5.1

6.1

5.5

4.0

3.0

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Figure 6.4 Global export of services (in trillion USD, 2005–​2019). Source: Own work based on UNCTADstat data.

How is globalisation changing?  199 Secondly, digital platforms and their constellations enable the international scaling of services by efficiently connecting actors throughout the global market, mediating contacts between suppliers and clients, and providing the infrastructure to make deals and execute payments. More and more on demand services provided in local markets are cross-​border in nature, as they are intermediated by global companies (e.g., Free Now taxis,Takeaway.com deliveries, and renting an apartment on Airbnb).50 In this way, gig-​economy platforms offer a new channel to draw on the international availability of labour.51 Thirdly, and most important, as has already been argued in Chapter 5, the definition of a service has been growing wider, to the point where it now includes virtually all data flows, and particularly data in the form of digital information goods. If you listen to music on Spotify, you are probably taking part in cross-​border trade. A document edited on Google drive, a post published on Facebook, or a short ride on an electric scooter –​these are all in fact services based on cross-​border data exchange. Additionally, the international exchange of services is also driven by intelligent products, equipped with sensors collecting data on the products’ life cycles, and swarming with additional services.This applies, in fact, to every third item sold to a client abroad.52 And if these digitalised devices are operated by companies based in another country, then again it all contributes to international trade. Significantly, much of the cross-​border flow of digital information goods –​e-​ books, audiobooks, applications, online games, mp3 music files, software, cloud computing services, and streaming services sent instantly to consumers worldwide –​is not picked up by statisticians. Digital information goods are made available for free or are increasingly sold using a subscription model. This is of course hardly a novelty –​for example, it has been used by companies providing antivirus software since the late 1990s. On average, a digital consumer subscribes to two services that give access to digital media and information goods. Globally, every third consumer subscribes to a music streaming service. Naturally, the popularity of such services depends on how wealthy a given society is, on access to internet infrastructure, and on local intellectual property regulations (licensing options and the severity of penalties for content piracy). Streaming subscriptions are common in Scandinavian countries, but they are less popular in Poland, where consumers prefer to download music files.53 One of the challenges for Baidu’s iQiyi, a Chinese service which has 100m paying customers, and is thus the world’s second most popular streaming medium after Netflix, is how to convince consumers from South-​East Asian countries to pay for access. The subscription model is gaining in popularity when it comes to providing software services, particularly in business-​ to-​ business relations (B2B). The seller increases the predictability and long-​term profitability of the product, while the subscribing company gets temporary access to advanced technological applications. For example, Salesforce Einstein offers a software platform (with ‘minimal programming’) to build AI-​powered apps for employees

200  How is globalisation changing? and customers easily. Its Customer Relationship Management system offers predictive analysis services based on machine learning.54 Oracle, meanwhile, offers automatic processing of transactions and autonomic databases. As a result, companies worldwide are finding it easier to undertake digital transformation, starting with the automation of various business processes (ranging from recruitment to marketing). This is a good example of cross-​border disruption, as the digitally-​transformed companies easily gain the upper hand over their local counterparts. At the same time, as with the global trade in goods, the digital transformation may cause a decline in the global market for certain types of services. Thus, some companies will automate those services that have so far been outsourced or transferred overseas, e.g., customer service. Many aspects of customer service are already ‘staffed’ by virtual agents (e.g., chatbots), which, thanks to intelligent algorithms, boast natural language processing capabilities and are beginning to handle an ever wider range of tasks.55 With time, as their machine learning algorithms get better, the customers will find them less maddening. Innovative (and controversial) forms of international capital flows, such as cryptocurrencies, may also be considered a kind of cross-​border service. A cryptocurrency is a digital asset that is used as a medium of exchange in peer-​to-​peer online transactions. Cryptocurrencies are based on decentralised community control, unlike currencies issued through traditional central banking systems. Decentralisation in each cryptocurrency works via distributed ledger technology, usually a blockchain, which serves as a public database of all financial transactions. While with conventional currencies the role of a central authority, responsible for issuing a given currency and controlling monetary policy, is necessary, in the case of cryptocurrencies –​including Bitcoin –​there is no such body.56 The mechanisms that exist to protect Bitcoin from a crash stem from the very architecture of the cryptocurrency system. There is currently an animated discussion going on among economists as to whether, from the perspective of economic theory, a cryptocurrency is a form of digital asset or if it is also a digital currency that can be used as an ordinary currency. In its most basic sense, a cryptocurrency is merely a digital record of transactions (a code). Some experts aver that a handful of cryptocurrencies may in the future play the role of a global stablecoin, facilitating and accelerating international payments. Of course, that will happen only if there are solutions to the many problems related to their safety and security, especially regarding personal data protection, can be solved, and if their compatibility with national financial systems is ensured.57 Summing up, the digital economy changes the structure of international trade. Goods will flow over border faster, but the development of the Industry 4.0 will enhance relocalisation of production closer to market, thus shortening the supply chains and downsizing the volumes of international exchange. On the other hand, the growing number of services will be provided through global digital platforms, adding to the cross-​border flow of data.

How is globalisation changing?  201

350

326.1

300

(in mln)

250 200 150 100 50 0

0.1 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Figure 6.5  Number of Bitcoin transactions (30-​day average, in millions, 02.2009–​ 01.2021). Source: Own work based on Blockchain.com. www.blockchain.com/​charts/​n-​ transactions (accessed 29 January 2021).

The state in the digital global economy The global digital economy brings a host of new challenges for nation-​states, the fundamental institutions framing social, economic, and political life, and the traditional actors of international politics. They are faced with new responsibilities as their economic situation is increasingly determined by the quality of the digital infrastructure, the pace of the digital transformation of local companies and the ability of the educational system to provide digital skills to the workforce. Nudged by their own citizens, who increasingly expect the same standard of service and personalisation as that offered by platforms, they themselves engage in digital transformation. For example, state authorities are setting up online contact channels and digitalising administrative processes to provide public services faster and more smoothly.58 The most comprehensive approach was taken by Estonia, which in 2001 launched a service called x-​road, enabling interoperability of databases, information exchange for public institutions, citizens and firms. Nowadays almost all public services in Estonia are online: an Estonian can use the e-​identity card, sign her documents with an e-​signature, pay taxes electronically, and vote online.59 In fact, in the developed countries, public administration is adopting cloud services faster than private companies, often using technological solutions provided by behemoths such as Amazon or Microsoft. In the USA, 6500 government agencies use Amazon Web Services.60 Since 2013 public institutions in the UK are obliged to use the cloud when developing public services.61 The states are learning to make use of the abundant data they have in their

202  How is globalisation changing? disposal (although all too often collected in mutually incompatible formats and locked in institutional silos). They also explore the application of algorithms in public policy provision and governance, although with mixed effects, as there are plenty examples of a discriminatory results brought by skewed algorithms.62 The oft-​cited example of a state building a comprehensive system of datafication of its citizens activities is China with its attempt to build Social Credit System, but in fact all states, be they democratic or authoritarian, are increasingly collecting all kinds of data about their citizens and applying digital technologies to analyse them. Remarkably, relations between nation states and digital companies are further complicated by the fact that the technological solutions provided by the latter underpin the functioning of state bureaucracies all around the world. The growing impact of the globalised digital economy is undermining states’ traditional sources of economic power and altering the essence of conventional state sovereignty. It is already much more difficult for nation states to control tangible and intangible flows of goods, services, and finance crisscrossing borders, to monitor and control the activities of political, economic, and social actors in their territory, and to tax economic activity efficiently. The process of digital globalisation described in this chapter rests heavily on a small group of large tech companies, mostly located on the west coast of the United States (GAFAM) or in China (BAT). This is of course just a rough measure of how traditional global hierarchies of power have been reshuffled, but in 2020 the market capitalisation of many of the largest tech companies exceeded the gross national products of many nations. For many countries, the emergence of the digital economy has been associated with the emergence of many phenomena and processes that fail to fit into the legal and institutional order. Platforms, especially, disrupt the operations of many traditional sectors of the economy, promoting new products and services and new ways of operating in many areas of a nation’s economic and social life. For instance, they are chipping away at the long-​held distinctions between employee and employer, or introducing mobile financial services that bypass the traditional banking system. Regulation is rarely able to keep up with technological advances, which is why platforms such as Uber and Airbnb have been able to use regulatory loopholes to their advantage. However, cases involving these two companies have shown that local and national authorities can challenge these giants, as long as they show a determination to regulate how new business models function. In 2020 Barcelona decided to delegalise short-​term apartment renting on grounds that operations of Airbnb and similar platforms harm local hotels and raise prices for the local inhabitants. Big Tech has shown a great knack for gaining monopolistic positions in markets, which hamper the development of local companies (see Chapter 2). These giants use digital technologies and intangible resources such as intellectual property in the form of software and algorithms to expand their activities across national borders. The states find it difficult to control the digital infrastructures and services, which are not physically present within their territories.63 In other

How is globalisation changing?  203 2.6 2.4 2.2

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Apple

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Figure 6.6 Top 10 tech companies by market capitalisation in 2020 compared with countries’ GDP in 2019 (in trillion USD). Source: Own work based on Companiesmarketcap.com, https://​companiesmarketcap. com/​tech/​largest-​tech-​companies-​by-​market-​cap/​; WorldBank data (GDP).

words, the digital companies, and particularly platforms, are blessed with ‘cross-​ jurisdictional scale without mass’.64 Digital platforms obviously challenge the law, and this is a key feature and consequence of their operations. They like to show how the law is out-​of-​date with the new economy, and they even appear alien to the law. Indeed, they tend to negate the territorial aspect of the (State) law. To be constrained by rules applicable on a national territory appears an anachronism for platforms which have a global perspective and outreach. In addition, those platforms, typically U.S.-​based companies, are sophisticated operators which regularly use legal engineering, for instance to minimize their tax burden –​the disruption of tax perception, one of the traditional functions of national States, appears also programmed in many platforms’ genes.65 They participate in the economic life of a multitude of countries, impacting their citizens, but they do not feel bound by local laws or obligations (including paying taxes). For example, in 2019 Facebook paid just $1.1 million in taxes in Poland, a country with 16 million FB users.66 Several countries have recently taxed, or are preparing to tax, digital services: for example, France introduced a 3% tax on the total annual revenues of the largest tech companies that provide services to French consumers, dubbed the GAFA tax (Google, Amazon, Facebook, and Apple).67 Also, the Organisation for Economic Co-​operation and

204  How is globalisation changing? Development (OECD), a grouping of 37 high-​income countries, has recently focused on how to tax multinational corporations, what principle to employ effectively (i.e., how much of their profits should be taxable in those countries where the customers, or users, of the goods or services, are located), and how to create an effective system to enforce the minimum level of taxation.68 Increasingly, states attempt to regain control over data flows crossing their borders by asserting ‘data sovereignty’: all data collected on their territory must be stored and processed at domestic data centres.69 As Susan Aaronson, a professor at the Elliott School of International Affairs in the United States, posits that the approaches countries adopt in relation to data governance will contribute to the emergence of three distinct regimes.The United States is committed to the notion that data is primarily owned by companies, and that cross-​border data flows should be free. On the other hand, the European Union is trying to regulate the cross-​border flow of data, is increasingly protective of privacy issues, and considers both consumers and enterprises to own their data. Meanwhile, China restricts the flow of data, does not care about protecting privacy, and considers all data the property of the state.70 For example, the Swedish logistics company Scania needs to operate local data centres in China to process data from sensors built into its fleet of trucks.71 Additionally, China has instituted the most radical legal and technological barriers to foreign companies. Since about 2010, the state has successfully blocked access to many foreign websites with the Great Firewall of China. Shunning or forbidding operations of the American-​based Big Tech and other digital companies (such as Netflix) was critical in making space for the development of the indigenous technological ecosystem, with Baidu, Alibaba, and Tencent in the lead. In January 2018, China introduced a law setting local standards for the way enterprises should operate, which indirectly favours Chinese companies.72 China is way out ahead in The Digital Trade Restrictiveness Index (prepared by the European Center for International Political Economy, ECIPE) followed by Russia, India, Indonesia, and Vietnam. The index takes into account four clusters of digital trade policy: fiscal restrictions and market access; establishment restrictions; restrictions on data; and trading restrictions. The most open country is New Zealand, followed closely by Iceland, Norway, Ireland, and Hong Kong. Critically, all of these are small service economies whose economic growth depends on the extent to which their market is open. More and more often, the adoption of digital protectionist measures results in fraught international tensions.73 The most intense are taking place between China or the United States. In 2019, the US government accused China of using some of its companies as vehicles for espionage. As a consequence, several American tech companies, including Google, Intel and Qualcomm, stopped selling software, hardware and licences to Huawei, the Chinese phone and ICT manufacturer. Google even cut off Huawei’s access to various functionalities in the Android mobile operating system (e.g., the ability to update or access Google’s flagship products such as Gmail). The American government used similar grounds to try to ban TikTok, a social media platform based in China,

How is globalisation changing?  205 0.8 0.7

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0.1

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Figure 6.7 The Digital Trade Restrictiveness Index. Note: The Digital Trade Restrictiveness Index includes over 100 policy categories covering 65 countries around the world. Source: Own work based on M.F. Ferracane, H. Lee-​Makiyama and E. van der Marel. 2018. Digital Trade Restrictiveness Index. European Centre for International Political Economy (ECIPE). https://​ecipe.org/​dte/​dte-​report/​ (accessed 27 January 2021).

arguing that it posed ‘an immediate danger’ to national security (as we write these words, a federal judge has granted the company’s request for a temporary injunction).74 In the coming years, the geopolitical landscape will be coloured by the increasingly divergent economic interests of two technological ecosystems supported by the American and Chinese nation-​state apparatus. Tensions will be aggravated by the expansion of GAFAM and BAT into new markets. At the beginning of 2020 Amazon announced $1 billion worth of investments to strengthen its foothold in India, adding to more than $5 billion invested since 2015. (Admittedly, in more than 300 Indian cities the pledge was met with protests of small traders who believe that the e-​commerce giant harms local retail market by price dumping.)75 In July 2020 it was Google’s turn to proclaim $10 billion in investments concentrated on enabling affordable access to the internet and developing new products and services.76 At the same time the Chinese Big Tech is also stepping up investment, but taking up another approach. According to the Economist, the Chinese giants are taking a different tack, buying stakes in local firms and weaving them together into complex tapestries of services. The ecosystem of Tencent and Alibaba, with over 1,000 stakes in foreign firms, includes dozens in emerging markets. Along with Ant, they have backed

206  How is globalisation changing? 43% of all Asian unicorns, startups worth more than $1bn. Chinese tech firms pumped $5bn into Indian startups in 2017, a fivefold increase on the year before. America’s tech giants are wearing uniform abroad; China’s melt into the background.77 Other states and their grouping will have to assert their position in relation to the relentless growth of those two dominant technology ecosystems. In this respect, the European Union is leading the way with a bill presented by the Commission in 2015 to create a digital single market. Since then, several legal acts have been passed to implement the objectives set out in the strategy –​ which is, above all, to foster the development of the digital economy in Europe. The three main areas for action are (a) to provide consumers and businesses with easier access to goods and services across Europe, (b) to create conditions conducive to the development of digital networks and services, and (c) to maximise the growth potential of the digital economy. More precisely, these activities will deal with consumer rights on the internet, unjustified geo-​blocking (restricting access to online content based on the location of the user), copyright issues, audiovisual services, and the regulation of telecom companies. Many of the changes introduced are of a more technical nature or constitute an update and unification of the existing legal framework. Only a few of the adopted legal acts have triggered any kind of media criticism or aroused controversy amongst the wider public –​notably a directive on copyright in the Digital Single Market.78 At the root of the EU’s efforts is a normative doctrine that aims to give individuals control over their own data. The EU scrupulously protects the rights of citizens in issues regarding the flow of personal data. Citizens have the right to access, correct, and determine who can use their data and how. This is the very essence of the General Data Protection Regulation (GDPR), the rules of which have already been copied by many countries around the world. The next step is to enable interoperability between services so that users can easily switch between service providers, moving to companies that offer better financial terms or to those which treat their customers more ethically.The European Union is also introducing robust monitoring of how far tech companies comply with competition rules. Alphabet, the parent company of Google, has come in for particular scrutiny from the Commissioner for Competition. In June 2017, the EU fined it €2.4 billion for the way its browser used Google Shopping to distort price comparability. In June 2018, the Commission imposed a fine of €4.3 billion for refusing a demand that Google browsers be installed on Android devices, and then in March 2019, Alphabet was fined a further €1.49 billion for restricting choice in their AdSense application.79 In February 2020, the European Commission announced new requirements for systems that use AI to underpin the provision of the increasing number of cross-​border digital services. The requirements include not only the mandatory sorting of data used to train algorithms in order to counteract discrimination, but also the need to store data and analyse results. Regulators are to be provided with access to documentation on developed systems (including

How is globalisation changing?  207 the methodology).80 In August 2020, the USA warned that the EU bid to tax big American tech companies and attempts to monitor and control the use of the AI algorithms might result in a trade war. In December 2020 the EU introduced the Digital Services Act which compels platforms to behave fairly so that they can be challenged by new entrants and existing competitors, ensuring that consumers have a wide choice and the Single Market remains competitive and open to innovation.81 Time will tell how these ambitious and normative regulations will impact the operations of Big Tech, particularly while the EU technology ecosystem is in its infancy. In 2020 the value of European tech companies was four times more than in 2015, amounting to €618 billion, but still a long way off the staggering $5.2 trillion market value of GAFAM alone.82 Finally, it is worth noting that the progress of the digital economy is not limited to the several most digitally matured states that dominated our narrative. The mixture of opportunities and risks characteristic of the digital economy is particularly noticeable in the case of poorer countries. Some of these countries are using digital technologies to leapfrog developmental barriers (which allow them to compete in production of high-​skill and technology-​intensive goods, see Figure 6.7). Rwanda, a country which suffered horrible genocidal conflict in 1990s, engaged in digital transformation, providing its citizens with comprehensive online services and supporting the digitalisation of its companies.83 In Kenya introduction of mPesa mechanisms for mobile payments supplemented its inadequate banking infrastructure.84 Digital technologies may help a number of companies from emerging markets to achieve global or regional success. A case in point is an Argentine e-​ commerce platform MercadoLibre or Interswitch, a Nigerian company specialising in processing payments. A World Bank estimate shows that, in the case of developing countries, a 10% increase in access to broadband translates into a 1.38% increase in GDP.85

Developed economies

Developing economies

73%

1995

2007

2019

Transi on economies

60%

50%

26%

39%

49%

Figure 6.8 Structure of global export of high-​skill and technology-​intensive goods by group of countries (in %, 1995, 2007, 2019). Source: Own work based on UNCTADstat data.

208  How is globalisation changing? On the other hand, the development of Industry 4.0, transformation of industries towards knowledge-​ intensive production, and the related relocalisation closer to markets as well as a digitally skilled workforce, may destroy jobs in countries that have hitherto found their competitive advantage in cheap labour. As emphasised by UNCTAD Secretary-​General Mukhisa Kituyi ‘If left unaddressed, the yawning gap between under-​connected and hyper-​ digitalised countries will widen, exacerbating current inequalities.’86 The result may resemble visions out of sci-​fi movies: a clash between the digital haves and analogue have-​nots will be detrimental to all.

Digital global order in the making The international regime constructed after WWII was one of trade liberalisation, and for several decades it facilitated flows of goods, services, and factors of production. Additionally, some states (or groupings of states) intensified their cooperation and liberalised trade on preferential terms, resulting in hundreds of bilateral and multilateral international agreements (e.g., the European Economic Community in 1957 or North American Free Trade Organization, NAFTA, in 1992. The end of the Cold War expedited the institutional consolidation of the GATT negotiations, creating the World Trade Organization in 1995. The agreement was supplemented by the General Agreement on Trade in Services, which indirectly regulated some aspects of the internet economy. In reaction to growing cross-​border online trade, the WTO initiated the Working Programme on E-​Commerce. At the same time, the organisation introduced a temporary moratorium on tariffs on electronic transmissions.87 The moratorium has been regularly extended by consensus –​most recently in 2017 –​but it is crumbling because the interests of net exporters and net importers of digital goods and services are diverging.88 As the OECD remarked in a report on the impact of digitalisation on trade, ‘In the world of digitalisation, old trade issues may have new consequences.’89 In other words, under pressure from the digital transformation, a regime of trade liberalisation needs to change by absorbing new rules for cross-​border data flows and digital trade in goods and services. The global digital economy will need a new institutional architecture. Yet, the differences between states regarding issues such as who owns data, how data flows should be regulated and taxed, and how to regulate e-​commerce, are all hampering current WTO negotiations. The WTO’s Working Programme on E-​Commerce, established in 1998, has not produced a single agreement. Issues relating to e-​commerce and digital trade are among the many bones of contention in the protracted negotiations regarding the Trade in Services Agreement (TiSA), which are being conducted by a group of 23 WTO members, including the EU and the USA. The negotiations aim to liberalise international trade in services, from banking to healthcare. The draft chapter on digital trade, published by WikiLeaks, addresses issues connected with e-​commerce (e.g., tariffs on digital goods), territorial restrictions on data flows, and solutions related to e-​identification. Negotiations

How is globalisation changing?  209 have virtually halted because of fundamental differences in approach to regulating personal data in the USA and the EU.The European Commission believes that provisions on data flows and protecting privacy should be included in the TiSA’s e-​commerce annexe.90 In 2017, a wider group of 71 of 164 WTO members commenced negotiations to create a multilateral framework for e-​commerce and digital trade, which has resulted in a Joint Statement on Electronic Commerce signed in January 2019 by 76 states.91 The negotiations are focused on e-​contracts and e-​signatures, data localisation requirements (i.e., data sovereignty), disclosure of source code, and customs duties on electronic transmissions (the concise presentation of negotiation stances can be found in a note prepared by CUTS International).92 The issues of data flows and privacy remain contentious among the initiators (with the EU and the USA among them, and with China joining at the last minute),93 and it is still unclear what will be the legal form of the deal. Nevertheless, ‘participants wish to modernise trade rules to fit the digital age and show that the WTO’s negotiating function can deliver’.94 In 2020 the number of participants grew to 86, accounting for 90% of the global trade. The gaps in the global multilateral regime are, to some extent filled by bilateral and multilateral agreements (known as Regional Trade Agreements, or RTAs).95 For example, a deal signed in 2020 between Singapore and Australia allows for the free flow of data for business purposes and removes localisation requirements for data storage.The agreement provides legal protection in intellectual property law, and eliminates the requirement to disclose the source codes when selling software. Both countries also gained access to each other’s public data for economic, social and scientific research.96 An analysis commissioned by the WTO in 2017 found that out of 275 regional trade agreements examined, 75 included e-​commerce clauses.97 These regulations most often concern: the abolition of customs duties for cross-​border digital trade, the elimination of any obligation to create paper documentation or an announcement that such an obligation will be done away with, the issue of electronic signatures and their mutual recognition, the issue of transparency (e.g., in order to monitor if the states that are party to the agreement are complying with it), and above all, the prohibition of discrimination, which means that digital products from the signatory states must receive national treatment. The problem is, however, that when it comes to this group of products, determining the ‘country of origin’ is extremely difficult, and requires agreement on detailed criteria (e.g., regarding the creation, production, publication, storage, contracting, ordering, or how it is made available to the public for the first time for commercial purposes in the territory of a signatory country). The efforts to build a new international trade regime are fragmented, inconclusive, and tinged with political frictions. However, as emphasised by the OECD: Given the strong cross border effects of the digital economy, solutions limited to the domestic domain will no longer suffice. International

210  How is globalisation changing? regulatory cooperation is needed to avoid arbitrage; protect consumer rights effectively; and promote interoperability across regulatory frameworks and enforcement, whilst creating a favourable environment for the digital economy to thrive.98 Additionally, states working together towards shared understanding of the basic definitions and rules may prove to be the only way to mitigate the risk of turning digital trade wars into major political conflict. In this respect the emerging foundations of the new regime may play a role analogous to that of the Bretton Woods regime after WWII. And, even more importantly, they are leveraging the digital technologies in order to cope with the consequences of the unprecedented global crisis that is the coronavirus pandemic. This takes us to the last chapter, in which we will trace the impact of Covid-​19 in the various areas of the digital economy.

Key takeaways •

Data do not respect state borders –​they flow over them incessantly in the shape of digital information goods, digital services and digital components of material goods.This is why the digital economy is inherently global. • The application of digital technologies in trade in goods increases the velocity of the flow in goods. On the other hand, the way Industry 4.0 takes advantage of digital technologies may decrease the volume of flows in goods, as it may shorten geographically dispersed supply chains and relocate production closer to end-​consumers and to digitally skilled workforces. • Digital trade in services will grow, partly because digitalisation widens the definition of services, and partly because digital platforms help to convert non-​tradable and highly localised services into highly tradable and globalised services. This process is particularly important for its impact on local labour markets. • In the digital economy, the traditional notion of state sovereignty is slowly dissolved by the cross-​border flows of data and the growing preponderance of big technology companies, which already control the physical infrastructure that enables cross-​border flows of data. • Relations between nation states and digital companies are further complicated by the fact that the technological solutions provided by the latter underpin the functioning of state bureaucracies all around the world. In addition, and for now, the shape of the global digital economy is being defined by the rivalry between two technological ecosystems consisting of digital companies based in China and United States, with the European Union trying to set the normative tone to the discussion, but without a digital ecosystem to match those of the two giants. • International regimes, designed decades ago, are not adjusted to cope with the specific challenges posed by the digital economy. The new international regimes are slowly taking shape in the process of negotiating definitions and interests between the several dozens of engaged states.

How is globalisation changing?  211

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220  How is globalisation changing? The Economist. How mobile money is spreading. 2018. www.economist.com/​special-​ report/​2018/​05/​03/​how-​mobile-​money-​is-​spreading The Economist. Big tech faces competition and privacy concerns in Brussels. 2019. www.economist.com/​briefing/​2019/​03/​23/​big-​tech-​f aces-​competition-​and​privacy-​concerns-​in-​brussels Etsy. Seller policy. www.etsy.com/​legal/​sellers/​ Eubanks,V. Automating Inequality: How High-​Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press. 2018. European Central Bank. Digital challenges to the international monetary and financial system. 2019. www.ecb.europa.eu/​press/​key/​date/​2019/​html/​ecb.sp190917~ 9b63e0ea23.en.html European Commission. The Digital Services Act package. https://​ec.europa.eu/​digital-​ single-​market/​en/​digital-​services-​act-​package European Commission. Trade in Services Agreement (TiSA): Factsheet. 2016. https://​ trade.ec.europa.eu/​doclib/​docs/​2016/​september/​tradoc_​154971.doc.pdf European Commission. Communication from the Commission to the European Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions on the Mid-​Term Review on the Implementation of the Digital Single Market Strategy: A Connected Digital Single Market for All. COM(2017)228 final. Brussels. 2017. https://​eur-​lex.europa.eu/​legal-​content/​PL/​TXT/​? European Commission. Communication from the Commission to the European Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions on the Mid-​Term Review on the Implementation of the Digital Single Market Strategy a Connected Digital Single Market for All. COM/​ 2017/​0228 final. Brussels. 2017. uri=COM:2017:228:FIN European Commission. Antitrust: Commission fines Google €4.34 billion for illegal practices regarding Android mobile devices to strengthen dominance of Google’s search engine. Brussels. 2018. https://​europa.eu/​rapid/​press-​release_​IP-​18-​4581_​en.htm European Commission. Antitrust: Commission fines Google €1.49 billion for abusive practices in online advertising. Brussels. 2019. https://​ec.europa.eu/​commission/​ presscorner/​detail/​en/​IP_​19_​1770 European Commission. White paper on artificial intelligence –​a European approach to excellence and trust. COM(2020) 65 final. Brussels. 2020. https://​ec.europa. eu/​info/​sites/​info/​files/​commission-​white-​paper-​artificial-​intelligence-​feb2020_​ en.pdfeWTP. Electronic World Trade Platform. www.ewtp.org/​ Ferracane, M.F., Kren, J., and van der Marel, E. Do data policy restrictions impact the productivity performance of firms and industries? European Centre for International Political Economy. DTE Working Paper 01. https://​ecipe.org/​wp-​content/​uploads/​ 2018/​10/​Do-​Data-​Policy-​Restrictions-​Impact-​the-​Productivity-​Performance-​of-​ Firms-​and-​Industries-​final.pdf First Data /​Forrester. From Rideshare, music streaming, and food delivery: The global rise of digital goods and services regional and demographic trends driving global cross-​border commerce. A Forrester Consulting White paper Commissioned by First Data, now Fiserv. 2019. www.firstdata.com/​downloads/​pdf/​Cross-​Border_​Digital_​ Goods_​Whitepaper.pdf Fratocchi, L. 2017. Is 3D printing an enabling technology for manufacturing reshoring? In: Reshoring of Manufacturing: Drivers, Opportunities, and Challenges (Measuring Operations Performance). ed.Vecchi, A. Springer International Publishing.

How is globalisation changing?  221 Fung, B. Judge rules TikTok can avoid a ban in the US, for now. CNN Business. 2020. https://​edition.cnn.com/​2020/​09/​27/​tech/​tiktok-​ban-​judge/​index.html Ganne, E. Can blockchain revolutionise international trade? World Trade Organization. Geneva. 2018. www.wto.org/​english/​res_​e/​publications_​e/​blockchainrev18_​ e.htm Gao, H.S. Across the Great Wall: E-​commerce Joint Statement Initiative Negotiation and China. 2020. Available at SSRN: https://​ssrn.com/​abstract=3695382 or http://​ dx.doi.org/​10.2139/​ssrn.3695382 Garcia-​Israel, K. and Grollier, J. Electronic Commerce Joint Statement: Issues in the negotiations phase. CUTS International. Geneva. 2019. www.cuts-​geneva.org/​pdf/​ 1906-​Note-​RRN-​E-​Commerce%20Joint%20Statement2.pdf Government Digital Service. Government cloud first policy. www.gov.uk/​guidance/​ government-​cloud-​first-​policy Heeks, R. and Bailur, S. Analysing e-​government research: perspectives, philosophies, theories, methods, and practice. Government Information Quarterly 24(2). 2007. www. sciencedirect.com/​science/​article/​pii/​S0740624X06000943 IBM. IBM Food Trust.A new era for the world’s food supply. www.ibm.com/​blockchain/​ solutions/​ f ood-​ t rust?utm_​ m edium=OSocial&utm_ ​ s ource=Blog&utm_​ content=000026VK&utm_​term=10008219&utm_​id=From-​lettuce-​to-​luxury-​ goods-​ b lockchain-​ h elps-​ i ndustr ies-​ t hr ive-​ I n-​ Text&cm_​ m mc=OSocial_​ Blog-​ _ ​ - ​ B lockchain+and+Strategic+Alliances_​ B lockchain-​ _ ​- ​ W W_​ W W-​ _ ​ -​ From-​lettuce-​to-​luxury-​goods-​blockchain-​helps-​industries-​thrive-​In-​Text&cm_​ mmca1=000026VK&cm_​mmca2=10008219 Ismail, N.Value of European tech companies soars to €618 billion. Information Age. 2020. www.information-​age.com/​value-​of-​european-​tech-​companies-​soars-​to-​e618-​ billion-​123492448/​ Jones, R.W. and Kierzkowski, H. Horizontal aspects of vertical fragmentation. In: Global Production and Trade in East Asia. ed. Cheng, L.K-​H. and Kierzkowski, H. Springer. 2001. Kanellos, M. Hold the laughter:Why the smart fridge is a great idea. Forbes. 2016. www. forbes.com/​sites/​michaelkanellos/​2016/​01/​13/​hold-​the-​laughter-​why-​the-​smart-​ fridge-​is-​a-​great-​idea/​?sh=531f62c57d40 Kattel, R. and Mergel, I. Estonia’s digital transformation: Mission mystique and the hiding hand. UCL Institute for Innovation and Public Purpose Working Paper Series. no. 9. 2018. www.ucl.ac.uk/​bartlett/​public-​purpose/​publications/​2018/​sep/​ estonias-​digital-​transformation-​mission-​mystique-​and-​hiding-​hand Khan, T., Srinivasan, K., Godbole, S. and Namboodiri, L.K. Research paper –​initial findings on how emerging technologies support mutual recognition of cross border electronic documents. www.unescap.org/​sites/​default/​files/​Agenda%20Item%20 2%28e%29_​Emerging%20technologies.pdf Larsson, A. and Teigland, R. 2020. The Digital Transformation of Labor: Automation, the Gig Economy and Welfare. Routledge. Levy, A. Big Tech is worth over $5 trillion now that Alphabet has joined the four comma club. CNBC. 2020. www.cnbc.com/​2020/​01/​16/​big-​tech-​worth-​over-​5-​trillion-​ with-​alphabet-​joining-​four-​comma-​club.html Liu, R. China’s one trillion-​ dollar tech campaign to beat the US. Medium. 2020. https://​ m edium.com/​ c ornertechandmarketing/​ c hinas-​ t rillion-​ d ollar-​ t ech​campaign-​to-​beat-​the-​us-​34fbb5bca8d8

222  How is globalisation changing? Lopez-​Gonzalez, J. and Jouanjean, M-​A. Digital trade: Developing a framework for analysis. 2017. www.researchgate.net/​publication/​319667734_​Digital_​Trade_​ Developing_​a_​Framework_​for_​Analysis Lund, S. and Bughin, J. Next-​generation technologies and the future of trade. VOX CEPR Policy Portal. 2019. https://​voxeu.org/​article/​next-​generation-​technologies​and-​future-​trade Manyika, J., Lund, S., Bughin, J., Woetzel, J., Stamenov, K., and Dhingra, D. Digital globalisation: the New Era of Global Flows. McKinsey Global Institute. 2016. www. mckinsey.com/​~/​media/​McKinsey/​Business%20Functions/​McKinsey%20Digital/​ Our%20Insights/ ​ D igital%20globalisation%20The%20new%20era%20of%20 global%20flows/​MGI-​Digital-​globalisation-​Full-​report.ashx Marketplace Pulse. Number of Sellers on Amazon Marketplace. www.marketplacepulse. com/​amazon/​number-​of-​sellers Mbiti, I. and Weil, D.N. Mobile banking:The impact of MPesa in Kenya,Working Paper. no. 2011–​13. Brown University. Department of Economics. 2011. www.econstor.eu/​bitstream/​10419/​62662/​1/​668481188.pdf McKinsey Global Institute. 2016. Digital globalization: The new era of global flows. McKinsey&Company.  www.mckinsey.com/ ​ ~ / ​ m edia/ ​ M cKinsey/ ​ B usiness%20 Functions/​McKinsey%20Digital/​Our%20Insights/​Digital%20globalization%20 The%20new%20era%20of%20global%20flows/​MGI-​Digital-​globalization-​Full-​ report.pdf Meltzer, J.P. and Lovelock, P. Regulating for a digital economy. Understanding the importance of cross-​border data flows In Asia. Global Economy and Development at Brookings. 2018. www.brookings.edu/​wp-​content/​uploads/​2018/​03/​digital-​ economy_​meltzer_​lovelock_​web.pdf Milnes, H. Breaking down Alibaba’s global ambitions. Digiday UK. 2019. https://​digiday. com/​retail/​breaking-​alibabas-​global-​ambitions/​ Monteiro, J-​A. and Teh, R. The provisions on electronic commerce in regional trade agreements. WTO Working Paper. ERSD-​2017-​11. 2017. www.wto.org/​english/​res_​ e/​reser_​e/​ersd201711_​e.pdf Mueller, M. and Grindal, K. Is it ‘trade?’ Data flows and the digital economy. TPRC 46: The 46th Research Conference on Communication. Information and Internet Policy. 2018. https://​ssrn.com/​abstract=3137819 Mukul, P. Explained: What the $10 billion investment means for Google, and India. The Indian Express. 2020. https://​indianexpress.com/​article/​explained/​ google-​10-​billion-​usd-​investment-​in-​india-​explained-​6505026/​ National Board of Trade. No transfer, no trade –​the importance of cross-​border data transfers for companies based in Sweden. Kommerskollegium. no.1. 2014. https://​ unctad.org/​meetings/​en/​Contribution/​dtl_​ict4d2016c01_​Kommerskollegium_​ en.pdf Nugraha,Y.K. and Sastrosubroto, A.S. Towards data sovereignty in cyberspace, 2015 3rd International Conference on Information and Communication Technology (ICoICT). Nusa Dua. 2015. www.cs.ox.ac.uk/​files/​7463/​Towards%20Data%20Sovereignity%20 in%20Cyberspace_​Nugraha.pdf OECD. Regulatory effectiveness in the era of digitalization. 2019. www.oecd.org/​gov/​ regulatory-​policy/​Regulatory-​effectiveness-​in-​the-​era-​of-​digitalisation.pdf OECD. Tax and digitalisation. OECD Going Digital Policy Note. Paris. 2019. www.oecd. org/​going-​digital/​tax-​and-​digitalisation.pdf Pinkoi. About Pinkoi. https://​en.pinkoi.com/​about

How is globalisation changing?  223 Pipelines to Platforms. How the platform economy is reshaping global trade. https://​ platformthinkinglabs.com/​how-​the-​platform-​economy-​is-​reshaping-​global-​trade/​ Qiang, W. Rossotto, C.M. and Kimura, K. Economic impacts of broadband. In: World Bank. 2009. 2009 Information and Communications for Development: Extending Reach and Increasing Impact. https://​openknowledge.worldbank.org/​handle/​10986/​2636 Rolland, S.E. Consumer protection issues in cross-​border ecommerce. In: Research Handbook on Electronic Commerce Law. ed. Rothchild, J.A. 2016. Edward Elgar Publishing. Rudnicka-​Reichel, A., Jonak, Ł., and Włoch, R. Digitalization of supply chain transparency: The case of ChainReact. In: ed. Grzybowska, K. Awasthi, A., and Sawhney, R. Sustainable Logistics and Production in Industry 4.0 (978-​3-​0303-​3368-​3). Springer. 2020. [DOI] Salesforce. Quickly build AI-​powered apps for employees and customers on a complete artificial intelligence platform. www.salesforce.com/​products/​einstein/​features/​ Satariano, A. How the internet travels across oceans. The New York Times. Technology. 2019.  www.nytimes.com/​interactive/​2019/​03/​10/​technology/​internet-​cables-​ oceans.html Sharlach, M. New tool helps users control which countries their internet traffic goes through. Princeton University. 2018. www.princeton.edu/​news/​2018/​08/​02/​ new-​tool-​helps-​users-​control-​which-​countries-​their-​internet-​traffic-​goes-​through Skala, A. Digital Startups in Transition Economies. Challenges for Management, Entrepreneurship and Education, Palgrave Pivot. 2019. DOI: https://​doi.org/​10.1007/​ 978-​3-​030-​01500-​8 Słok-​Wódkowska, M. and Śledziewska, K. Anatomy of the EU regional trade agreements –​what really influences economic integration?. Working paper. DELab. Warsaw. 2015. Smyth, J. Australia banned Huawei over risks to key infrastructure. Financial Times. 2019. www.ft.com/​content/​543621ce-​504f-​11e9-​b401-​8d9ef1626294 Strowel, A. and Wouter, V. Digital platforms: To regulate or not to regulate? 2016. https://​ec.europa.eu/​information_​society/​newsroom/​image/​document/​2016-​7/​ uclouvain_​et_​universit_​saint_​louis_​14044.pdf Submarine cable map. TeleGeography. www.submarinecablemap.com/​#/​ Sunak, Rishi. Undersea cables. Indispensable, insecure. Policy Exchange. 2017. https://​ policyexchange.org.uk/​wp-​content/​uploads/​2017/​11/​Undersea-​Cables.pdf Thomas, C.A. Lagging but motivated: The state of China’s semiconductor industry. Tech Stream. 2021. www.brookings.edu/​techstream/​lagging-​but-​motivated-​ the-​state-​of-​chinas-​semiconductor-​industry/​ Titevskaia, J. WTO e-​commerce negotiations. European Parliament Think Tank. 2020. www.europarl.europa.eu/​ t hinktank/​ e n/​ d ocument.html?reference=EPRS_​ ATA(2020)659263 Tradelens. Trade Made Easy. www.tradelens.com/​ UNCTAD. Key statistics and trends in international trade 2017. New York and Geneva. 2018. https://​unctad.org/​system/​files/​official-​document/​ditctab2017d6_​en.pdf UNCTAD. Global e-​commerce hits $25.6 trillion –​latest UNCTAD estimates. 2020. https://​unctad.org/​press-​material/​global-​e-​commerce-​hits-​256-​trillion-​latest-​ unctad-​estimates Valladão, A. de Gama e Aubreu. Masters of the Algorithms, The Geopolitics of the New Digital Economy from Ford to Google. The German Marshall Fund of the United States. 2014.

224  How is globalisation changing? Woods, A.K. Litigating data sovereignty. The Yale Law Journal 128. 2018. www. yalelawjournal.org/​pdf/​Woods_​i233nhrp.pdf World Bank Group. Rwanda economic update. Accelerating digital transformation in Rwanda. 2020. http://​documents1.worldbank.org/​curated/​en/​ 912581580156139783/​pdf/​Rwanda-​Economic-​Update-​Accelerating-​Digital-​ Transformation-​in-​Rwanda.pdf World Trade Organization. Electronic commerce. www.wto.org/​english/​tratop_​e/​ ecom_​e/​ecom_​e.htm World Trade Organization. World Trade Report 2018. The Future of World Trade: How Digital Technologies Are Transforming Global Commerce. Geneva. www.wto.org/​ english/​res_​e/​publications_​e/​world_​trade_​report18_​e_​under_​embargo.pdf World Trade Organization. Joint statement on electronic commerce. 2019. https://​ trade.ec.europa.eu/​doclib/​docs/​2019/​january/​tradoc_​157643.pdf World Trade Organization. World Trade Report 2019. The Future of Services Trade. World Trade Organization. www.wto.org/​english/​res_​e/​booksp_​e/​00_​wtr19_​e.pdf World Trade Organization. WTO members examine e-​commerce moratorium. World Trade Organization. 2019. www.wto.org/​english/​news_​e/​news19_​e/​ecom_​29apr19_​ e.htm Xin, T. Fragmentation of production. Wiley Online Library. 2017. https://​onlinelibrary. wiley.com/​doi/​abs/​10.1002/​9781118786352.wbieg0715 Zięba, D., Kokoszczyński, R. and Śledziewska, K. 2019. Shock transmission in the cryptocurrency market. Is Bitcoin the most influential? International Review of Financial Analysis. Elsevier. v.64(C). 2019. DOI: 10.1016/​j.irfa.2019.04.009 Zielińska, U. Podatki. Facebook i Google nadal płacą niewiele. Rzeczpospolita. 2020. www.rp.pl/​Budzet-​i-​Podatki/​309069935-​Podatki-​Facebook-​i-​Google-​nadal-​ placa-​niewiele.html Zimmer,J.Google owns 63,605 miles and 8.5% of submarine cables worldwide.Broadbandnow. 2018. https://​broadbandnow.com/​report/​google-​content-​providers-​submarine​cable-​ownership/​

7  The digital economy in times of Covid-​19

Abstract In this chapter we trace the impact of the unprecedented Covid-19 crisis on all the aspects of digital transformation discussed in the book.We start by showing how a crisis born out of globalisation has pushed the world towards digital globalisation. Cross-border flows of data surged with the sharp rise in the consumption of digital goods and services, online shopping, remote work, and education. We discuss the long-term consequences of the massive shift towards remote work, focusing on the changing work culture, progressing datafication of work and growing rift in the labour market between the position of highly-skilled and lowskilled workers. Next, we assert that the fraying of value and supply chains may advance the budding trend to relocalize production.We emphasise that the crisis proved the virtues of digital transformation, as it was the more digitally mature companies that survived best.Turning to markets, we take note of the mounting dominance of the BigTech companies, stemming from the functional importance of digital infrastructures, products, and services during the Covid-19 crisis. Their dominance, in turn, contributes to growing anxiety on the part of the traditional sources of power: the nation-states. In our conclusion, we discuss the prospects of the digital economy by returning to its two defining features: datafication and networks.We argue that only robust networks, providing an equal access to every user, and datafication that benefits all, will ensure that the humanity makes the most of the opportunities that the digital technological revolution is creating.

The what-​if 2020 was the year that a raft of virtually unimaginable ‘what-​ifs’ came true. What if we stopped globalisation for a while –​stopping people and goods from moving across state borders? What if we learned, worked, relaxed, had fun, exercised, and contacted our nearest and dearest entirely through the screens of our digital devices? What if we shouted at our kids to pore over screens instead of going out, and not the other way around? What if we routinely consulted our doctors via Skype? What if we shopped for our groceries online, and our purchases were –​for reasons of hygiene –​packed by a small but versatile cobot? What if our already vital digital networks became life-​support systems for our

226  The digital economy in times of Covid-19

Figure 7.1 How is Covid-​19 changing the digital economy? Source: Own elaboration.

The digital economy in times of Covid-19  227 personal lives and business careers? What if the functional importance of the big tech companies, which spin and sustain those networks, became even greater, underpinning nigh on each and every activity that shapes our existence? What if a sudden crisis threw into stark relief what technology can do for us ... and what it cannot? It was as if the social, economic and political consequences of the pandemic were iron filings sprinkled on the economy, revealing the magnetic undercurrents of all that is digital. The pandemic delineated the boundaries of the digital economy and proved that the digital is ubiquitous, and that it underpins our everyday reality. So in this last chapter we trace those undercurrents, seeking to understand how the coronavirus pandemic accelerated and intensified the process of digital transformation.We propose to track the process through the areas dealt with in each chapter, but backwards –​travelling from globalisation to consumption, work, production, the market, and back to the digital economy itself.

Globalisation A spur towards the digital globalisation and reorganisation of global value chains The coronavirus pandemic –​as with every other plague in history –​resulted from connections between people. In our ever more linked-​up world, with 1.5 billion people travelling abroad on holiday (as of 2019),1 the virus spread quickly and ruthlessly, striking via those unprecedentedly dense ties. All around

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228  The digital economy in times of Covid-19 the world people went into lockdown, confined to their homes, while airplanes were grounded, and train and bus travel heavily restricted. The cross-​border flow of goods –​another hallmark of globalisation –​was severely disrupted when one country after another closed its borders. In the second quarter of 2020, the volume of global trade plummeted by 19% (as compared to 2019, and 9.2% in 2020).2 At the same time, the flow of data skyrocketed as digital infrastructures stretched to accommodate the millions of people who took to remote education, work, and socialising, further boosting digital globalisation. The pandemic will not stop globalisation as we know it, but it will change it. Some of these crisis-​induced changes are here to stay, and they will add to current trends of digital transformation.3 For example, international tourism will rebound as soon as the virus abates or vaccines prove their efficacy. But business travel –​particularly short trips for one-​day meetings or conferences –​ will to some extent be replaced by much cheaper online meetings. Spending the year cooped up at home provided people with a unique training experience in how to learn, work, and interact more effectively online. Both global brands and local companies will find it easier to source talent via global platforms and then manage workflows in geographically dispersed and culturally diverse teams, as workers have already learned how to collaborate remotely with the use of digital tools. More digitally savvy consumers and companies will find it easier to use online services provided by foreign professionals, which will provide a fillip to the cross-​border trade in services. To some extent, and particularly in the area of intellectual work, physical flows of workers will be supplanted by digital flows of work-​related data.

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Figure 7.3 Global merchandise export’s growth rate (in %, year-​on-​year, 2006–​2020, by quarters). Source: Own work based on UNCTAD stat data.

The digital economy in times of Covid-19  229 The experience of closed borders and disrupted supply chains –​particularly shocking to those Europeans who relatively recently managed to forget the Iron Curtain –​will add to the growing trend for relocating production. The coronavirus crisis laid bare the inherent risks of the ‘lean inventory model’ and ‘just-​in-​time’ deliveries from global supply chains straddling state borders and spanning continents in search of the highest quality at the lowest price. Companies were able to save on storage and warehouse space, but were hit by an almost immediate halt in production when the supply chain snapped.4 This is exactly what happened when factories in China, for more than two decades the cornerstones of the international trade in intermediate goods, shut down at the beginning of 2020. The aftershocks affected production in numerous industries around the world, particularly in the automotive and electronics sectors. Take Apple: three-​quarters of its 200 top suppliers had at least one production site located in China. As a consequence, in March 2020 Apple was forced to limit the online sale of its iPhones to two units per customer.5 In July 2020, the Bank of America reported that US companies in over 80% of globally-​operating sectors had had their supply chains disrupted.6 The organising logic of extended, and complex, global value chains will not change overnight, especially since –​in the face of an unprecedented crisis –​the most essential chains ultimately proved resilient.7 Despite panic stockpiling of cans of food and pasta all around the world, and long queues of freight-​laden lorries at borders, food supply chains generally held up. Incidentally, in many countries people came to realise that the bulk of their daily diet consisted of imported food (worldwide, four in every five people eat some imported food every day).8 In March 2020, the Polish authorities tried to calm consumers by reminding them that Poland was one of the largest food exporters in Europe and that, even with borders closed tight, Poles would not lack for dairy products or other foodstuffs. In the event, throughout 2020, Poles were able not only to continue drinking Polish milk and eating domestic poultry, but they also could still enjoy Israeli avocados and Spanish cheese. Nevertheless, in strategically important sectors, depending on faraway subcontractors and long delivery routes proved to be too risky.9 For example, pharmaceutical production is based on extreme international specialisation within fragmented global value chains.10 In Europe, 90% of active pharmaceutical ingredients (APIs, i.e., basic substances for drug production) come from China and India. The USA, meanwhile, is dependent on Europe for 31% of its APIs.11 For security reasons, governments will now seek to relocate the production of strategic drugs and medical supplies, prioritising secure delivery over price. According to a 2020 UNCTAD report on international production after the pandemic, this sector will see value chains shorten and production move to distributed manufacturing spread across a network of factories equipped with high quality digital infrastructure. Digital-​twin technologies and 3-​D printing will allow for flexible and customised responses to rapidly growing demand in the event of another crisis.12

230  The digital economy in times of Covid-19 Technologically advanced sectors that are already heading towards Industry 4.0 will increasingly reshore their production, moving it closer to highly-​ skilled workers who will be able to work alongside cobots and intelligent systems, and also nearer to customers, who expect speedy deliveries and personalised offerings.Value chains will be much shorter and less fragmented.13 In some industries –​for example in production, food processing, and the chemical industry –​value chains will remain fragmented, but they will still be concentrated within regional industrial clusters. Production will take place in datafied factories, connected and coordinated through regional production platforms. As a result, investments will be less focused on achieving global efficiency, and more on regional markets. Relatively long and fragmented value chains will still prevail in services and industries that exhibit medium to low technological intensity (e.g., clothing and textiles). But even in those cases, the digital transformation will also bring about a sea change. Supply chains will be increasingly transparent and manageable, thanks to the growing adoption of various Internet-​of-​Things and blockchain-​based technologies.14 Data gathered throughout a supply chain/​ network and processed by intelligent algorithms will allow inventories to be optimised and crises to be rapidly dealt with. Critical nodes in supply chains/​ networks, at each stage of a product’s life cycle, will be increasingly overseen by platforms, which will match producers, suppliers, and contractors.15 To enter these increasingly datafied supply chains and make full use of the data, companies will need to digitally transform their internal processes and even their organisational structure.16 In fact, the pandemic was a dramatic incentive to carry out digital transformation in almost every kind of organisation. Data insights into the development of the Covid outbreak proved crucial in designing tailored public policy responses to the imminent danger of a complete breakdown of the healthcare system. Those countries with more advanced e-​government solutions coped better and faster when it came to ensuring the continuity of public services and setting up remote bureaucracy. For example, in the UK the government dropped lengthy processes for rolling and authorised a rapid adoption route for rolling out new services (such as the Coronavirus Job Retention Scheme, launched in less than five weeks in order to provide economic support to furloughed workers). As a result, by the end of May 2020 various government departments had delivered 69 new digital services and had another 46 in the works.17 The state had been given, as renowned scholar of globalisation Ian Goldin, professor of globalisation at Oxford University, noted, ‘a fresh lease on life, after many decades of being seen as a junior partner to markets, corporations, multilateral agencies, and media organizations’.18 It was the state, wielding its authority over its citizens and its territory, that closed borders and imposed lockdowns. The state could lock people in their homes and close down numerous sectors of the economy. Still, the transition to remote services exposed the difficulties related to siloed data domains within each government’s bureaucracy, data locked in

The digital economy in times of Covid-19  231 by specific software choices, thereby hampering efficient interdepartmental collaboration. The year of 2020 was not just a high point of state sovereignty: it also delineated the state’s limitations by revealing the tense dynamics between the state and tech companies. On the one hand, the tech companies provide the digital infrastructures, platforms, and tools for channelling essential public services, from healthcare to education. As a result, they have entrenched their importance in a world increasingly dependent on the digital infrastructures they provide, and they have begun to manifest their power by refusing to abide by government demands, if they do not agree with them. For example, Apple and Google –​whose operating systems power 99% of smartphones around the world –​denied governments access to user data gathered via Bluetooth that could have been used to track contacts between potential spreaders of Covid.19 Some states protested volubly: the French minister of digital affairs, Cedric O, talked about constraints on French sovereignty. In October 2020, together with his Dutch counterpart, he called on the EU Commission to prevent large tech companies from blocking access to their services unless they had ‘an objective justification’.20 The French government’s app, with data stored on a central server, failed spectacularly, as it managed to catch only 14 cases of Covid transmission.21 In the meantime Google and Apple launched a new software framework aimed at helping states to develop their own contact tracing apps –​but limited to just one app per country.22 Several European countries took a decentralised approach and shared data (Germany, Denmark, Italy, Ireland, the Czech Republic, and Latvia).23 Interestingly, around the world, governments achieved very limited success in convincing their citizens to download contact tracing applications. In Poland, only 1 million people installed the Stop Covid application.24 The reason may have been people’s growing awareness of how corporations and the state might be using data for what Raluca Csernatoni called ‘state-​corporate techno-​surveillance’.25 Finally, the Covid outbreak threw into sharp relief the changing global hierarchies of power. As we argued in Chapter 6, the digital economy, propelled by cross-​border data flows, is intrinsically global. However, it is upheld by two main digital ecosystems, the American and the Chinese ones, both dwarfing all others. The American ecosystem consists of GAFAM, while the one in China is made up of Baidu, Alibaba, Tencent, Huawei, and Byte-​Dance (owners of Tik-​Tok). The two ecosystems block each other mutually. This growing conflict between China and USA will define the rules for the digital economy, compromising the choices of other states and regions. Some commentators indicate that the strict regulatory approaches, such as the one taken by the EU, may tip the technology and geopolitical balance against their fundamental political interest. ‘If the U.S. technology giants are broken up, the result would be a vastly uneven global playing field, pitting fragmented U.S. companies against consolidated state-​protected Chinese firms’,26 says Bhaskar Chakravorti, Dean of Global Business at Tufts University.

232  The digital economy in times of Covid-19 21.6

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Figure 7.4 Adoption of government endorsed Covid-​19 contact tracing apps in selected countries (% of individuals, 07.2020). Note: 14 years and older; among countries with a minimum population of 20 million; excluding residents who do not have access to a smartphone. Source: Own work based on Sensor Tower. 2020. Adoption of government endorsed Covid-​ 19 contact tracing apps in selected countries as of July 2020. Chart. In Statista. www.statista. com/ ​ s tatistics/ ​ 1 134669/ ​ s hare- ​ p opulations- ​ a dopted-​ c ovid-​ c ontact-​ t racing-​ a pps-​ countries/​(accessed 29 January 2021).

Consumption Accelerating adoption of online shopping and digital consumption In November 2020, a German government advertisement went viral. It showed an elderly gentlemen tenderly recalling the winter of 2020. ‘An invisible danger threatened everything we believed in. Suddenly the fate of our country was in our hands. So, we mustered all our courage and did what was expected. The only right thing. We did –​nothing. Absolutely nothing. Being as lazy as racoons. Day and night we kept our arses at home and fought the spread of the coronavirus. Our sofa was our front and our patience our weapon.’27 With a strict lockdown introduced by Angela Merkel, German Chancellor, in the middle of December 2020, this was quite a good reflection of what was going on in many German households –​and in many households across Europe and indeed the world. Electronic screens became the main portals to enjoy culture and entertainment.With cinemas and theatres shuttered, housebound consumers turned to comfort-​watching, infinite scrolling through their newsfeed, and playing games. Consumption of such digital information goods rose to such an extent that on 18 March 2020 Thierry Breton (commissioner for the EU’s internal market)

The digital economy in times of Covid-19  233 67%

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Figure 7.5 In-​home media consumption growth due to the Covid-​19 outbreak (in %, internet users, worldwide, 03.2020). Source: Own work based on GlobalWebIndex. 2020. In-​home media consumption due to the coronavirus outbreak among internet users worldwide as of March 2020, by country. Chart. In Statista. www.statista.com/​statistics/​1106498/​home-​media-​consumption-​coronavirus-​ worldwide-​by-​country/​ (accessed 22 December 2020).

asked Reed Hastings (CEO of Netflix) to switch to standard quality of its streaming to ensure the smooth working of telecommunication infrastructure.28 In fact, Netflix gained 26 million subscribers around the world in the first six months of 2020.29 As a result, its market capitalisation soared in 2020 by $100 billion, and the value of its shares –​by 75%.30 In April 2020, the company, recognising how important it had become for customers’ wellbeing, felt compelled to reassure viewers that, despite downtimes in production, it would not run out of content in the foreseeable future. These changes to our daily routines marked out new patterns of digital consumption. Housebound consumers turned to audiobooks and podcasts, both versatile forms of entertainment and learning, as one can listen while taking care of the children or doing household chores. Overall podcast consumption via Spotify –​a relatively new entrant to the podcast market, but one already boasting a library of 700,000 odd podcast titles –​doubled in 2020.31 During lockdown many people even took to watching theatre shows online. The British National Theatre noted a rise in subscriptions to its streaming services for current and historic performances, equivalent to the size of the audiences who would fill all three of its spaces for 11 years.32 On the other hand, some types of cultural activities proved sadly immune to digitalisation. Virtual museum tours registered only a short peak in popularity –​apparently,

234  The digital economy in times of Covid-19 a)

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Figure 7.6 (a) Digital media revenue (in billion USD, worldwide, 2019, 2020); (b) digital media users (in billions, worldwide, 2019, 2020). Source: Own work based on Statista. 2020. Digital Market Outlook. Digital Media –​ worldwide. www.statista.com/​outlook/​200/​100/​digital-​media/​worldwide (accessed 22 December 2020).

people regard looking at paintings as authentic and fulfilling only when experienced directly, in person.33 Physical activity was also digitally distributed as many people were unable to visit their gym.The fitness startup Peloton (which we mentioned in Chapter 5), increased its revenue by two-​thirds in the third quarter of 2020 alone, while the number of its subscribers grew in that period by a whopping 94%.34 In April 2020, one of its remote exercise sessions was ‘attended’ digitally by a staggering 23,000 eager cyclists.35 Unfortunately, many people complemented their binge watching with binge eating, or tried to drown their stress and anxiety by upping their consumption of alcohol and other drugs.36 The World Health Organisation warned that the long-​term consequences of strict lockdowns, physically and mentally, might range from obesity to increased screen addiction, particularly amongst the young.37 As work, education, and entertainment all arrived on digital devices, so did screen fatigue and mental health issues.

The digital economy in times of Covid-19  235 Netflix

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Figure 7.7 Number and growth rate of Netflix and Spotify paid subscribers (in million users and in %, worldwide, Q4 2019 –​Q3 2020). Source: Own work based on Netflix. 2020. Number of Netflix paid subscribers worldwide from 3rd quarter 2011 to 3rd quarter 2020 (in millions). Chart. In Statista. www. statista.com/​statistics/​250934/​quarterly-​number-​of-​netflix-​streaming-​subscribers-​ worldwide/​ (accessed 22 January 2021); Spotify. 2020. Number of Spotify premium subscribers worldwide from 1st quarter 2015 to 3rd quarter 2020 (in millions). Chart. In Statista. www.statista.com/​statistics/​244995/​number-​of-​paying-​spotify-​subscribers/​ (accessed 22 January 2021).

Not every business built on digital distribution did as well as Netflix and Spotify. The fear of infection struck the sharing economy hard. Both Airbnb and Uber recorded grim losses when people abandoned travelling. In April 2020, Airbnb applied to the banks for loans amounting to $2 billion, and announced a 25% cut in its workforce, justified by a 90% drop in bookings compared with 2019.38 Some commentators suggested that the sharing economy was being replaced by an ‘isolation economy’, with minimal contact between people at work or leisure, and filtered through digital devices. For example, Kumar Mehta, the author of a book Innovation Biome (2017), commented that: This transformation is already upon us. Going to the office is being replaced by working from home. Driving to Safeway is replaced by home delivery. Going to the gym is replaced by streaming fitness as Peloton sales surge and innovations like Mirror39 come into vogue. Going to the movies or visiting the mall is increasingly a thing of the past. Schools and universities will encourage more online learning, just as doctor visits will move towards telemedicine. The early winners of the Isolation Economy are clear. While Uber, Airbnb and WeWork were the poster children for the sharing economy, companies like Zoom, Peloton and Netflix epitomize the Isolation Economy.40

236  The digital economy in times of Covid-19 Lockdowns administered by governments all around the world literally drove consumers online, enhancing the scale and scope of e-​commerce. When a large share of bricks-​and-​mortar retail stores downsized their operations or closed completely, online shopping became a necessity, not an option. Digital channels became the main, and sometimes only, medium for customer engagement. The number of people using e-​commerce platforms surged, and included some consumers who had traditionally shied away from online shopping, such as older people.41 Millions of consumers had to change their usual shopping habits and learn new digital skills to buy everyday groceries, clothing, and other necessities online. This formidable challenge was matched by those businesses that already used digital, user-​friendly solutions, or else were able to move quickly to e-​commerce. In Poland, the Chamber of E-​commerce noted that local food producers increasingly set up their own online platforms, or even simple e-​ commerce shops.42 This change is here to stay: when UNCTAD (the United Nations Conference on Trade and Development) surveyed consumers in nine countries (Brazil, China, Germany, Italy, South Korea, Russia, South Africa, Switzerland, and Turkey) in June 2020 more than half of consumers surveyed declared that they would shop more often online after the pandemic, citing time savings and convenience. The largest increase in online shopping was observed in countries that previously had the lowest levels of e-​commerce.43 Still, people without internet access or digital skills will be excluded from online shopping. Interestingly, it was not the first time that a coronavirus epidemic had kick-​started e-​commerce. In 2004, during the first outbreak of SARS in China, many house-​ bound Chinese decided to try shopping online via one of the new e-​commerce platforms. Alibaba was one of the first companies hit by SARS: its team of 500

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Figure 7.8 Change in retail sale via mail or via internet (index of turnover, change in %, year-​on-​year change for each month, EU27, 02.2020–​10.2020). Source: Own work based on Eurostat data [sts_​trtu_​m].

The digital economy in times of Covid-19  237 had to quarantine at home after one employee got infected at a trade fair, so they took their PCs home and powered through the crisis, launching Taobao, a C2C e-​ commerce platform. Alibaba ended that year with 1.4 million suppliers connected to its e-​commerce platforms,44 and since 2005 the company has annually celebrated its house-​bound employees’ feat of keeping the platform active.45 The SARS outbreak had another interesting effect: fear of contagion induced many Chinese to try another novelty –​contactless payments via their mobile phones.46 A similar accelerated uptake of digital payments has taken place 15 years later. During lockdowns panic buying gave way to routine shopping highlighting the importance of logistics networks, particularly outside urban areas. Arguably, customers accustomed to buying online will also be more open to phygital (physical-​digital) buying experiences in new areas of commerce, for example when buying more expensive goods such as cars.47 In that case, physical shops will be increasingly become showrooms, with the boring act of buying shifting online. Already, the number of physical shops is falling as e-​commerce grows. For example, one of Britain’s biggest department store chains, Debenhams, collapsed in 2020. An online-​only clothing retailer bought its brand name –​ but nothing else.48 The development of e-​commerce in China provides a convenient glimpse into the future. China has far fewer shopping malls than the USA (which may have 30 times as many). Middle-​class Chinese are not satisfied with their quality and range, and prefer to shop conveniently and safely online.49 This specific mingle of digital and physical shopping was described thus in the January 2021 edition of The Economist: A high population density makes delivery cheaper for consumers. The result is a mix of shops, entertainment venues, food courts, games arcades and gathering places that replicates the 20th-​century American mall in digital form, and hybrid links of the virtual with the physical.Videos show something being crafted by hand. Influencers draw attention to how the item is used. Friends recommend it (or not) on social media. Shoppers band together with other netizens to buy it in bulk at a discount. Live broadcasts turn the whole process into entertainment. And a network of real-​world businesses delivers the purchases.50 Consumers all around the world, shaken by the endless months of the pandemic, will doubtless follow Chinese consumers’ lead, expecting seamless and satisfying shopping experiences through e-​commerce platforms with embedded payment services.

Work Datafied work in distributed workplaces The mandated lockdowns mapped the potential and limits of remote work (or telework) enabled by technology. During the pandemic, nearly 45% of

238  The digital economy in times of Covid-19 employees in the EU worked from home.51 This number largely matches estimates made by the Joint Research Centre of the European Commission, which found that some 37% of dependent work within the EU could be performed remotely. However, only 13% can actually be carried out from afar without suffering a major loss in quality or efficiency, being less dependent on social interactions and regular face-​to-​face contact with other co-​workers, managers, and clients.52 Still, in 2019 only 5.4% of employees in the EU teleworked usually, and a further 9% had done so at some point. The incidence of telework was tied to several factors, beginning with basic technical requirements, such as access to hardware and a fast internet connection. In recent years, smoother virtual collaboration in dispersed teams has been made possible by the adoption of cloud solutions, providing access to data and joint projects. People living in urban areas, with fast internet connections, naturally telework more often. White-​collar occupations are, for obvious reasons, much more teleworkable than blue-​collar ones, which involve physical tasks, manipulating objects and/​ or interacting with people face-​to-​face. Sectoral differences also determine the frequency of telework: in IT and knowledge-​intensive fields (connected with producing and distributing information and information-​based products and services), 40% of employees work remotely at least occasionally. The proportion was lower amongst those working in education and publishing (30%), and in telecommunications, finance, and insurance, where it was 20%.53 Overall, countries with larger knowledge-​intensive sectors had higher incidences of telework, with Netherlands much more likely to engage in it than Romania, for example. Ultimately, the EU study found that the key barriers to telework were cultural and legal. This explains why, in the Netherlands, six out of ten IT professionals worked remotely, compared with only one in ten in Italy.54 The culture of work and organisational hierarchies meant that people whose work was deemed more autonomous and self-​directed, such as managers, worked remotely more often. Working from home also correlated with higher education and higher income. Workers lower down the organisational hierarchy –​junior administrative personnel, technicians and support workers –​ rarely teleworked, presumably because their employer assumed that their work needed to be managed and closely monitored on site, even if the nature of the task suggested otherwise.55 It was clear by the end of 2020 that the experience of months of remote working was instrumental in forming a new work culture. Both employees and employers had ample time to rethink the case for a nine-​to-​five, five-​ day-​a week model performed at premises provided by the employer. More than half of pandemic teleworkers wanted to continue working remotely for at least a few days a week.56 Two in three American workers who worked remotely wished to carry on doing so.57 According to forecasts by the World Economic Forum, the number of people working remotely is set to double in 2021.58

The digital economy in times of Covid-19  239 Belgium Denmark Ireland Italy Spain Portugal Finland Austria Lithuania France Czechia EU27 Greece Netherlands Germany Estonia Slovenia Sweden Hungary Croa­a Slovakia Romania 1 Bulgaria 1

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Figure 7.9 Percentage of employed individuals working remotely before (2019) and during (2020) Covid-​19 pandemics (in %, EU countries with available data, 2019 and 2020). Source: Own work based on Eurofound data (2020) [% of ‘Yes’ answers for: During the Covid-​19 pandemic, where did you work? –​At home] and Eurostat data (2019) [Employed persons working from home –​Usually and Sometimes, [lfsa_​ehomp]].

The fact that so many types of white-​collar tasks can be carried out remotely, at least to a certain extent, will change the organisation of work. Lockdowns led to rethinking which tasks must be performed on site, but not necessarily at the main office, and which could be performed remotely without suffering much in terms of quality and efficiency. It now seems likely that some work will be performed at home, some in co-​working spaces near home, and only a few activities –​conferencing, brainstorming, idea-​building –​in the office. If so, the change will have both positive and negative impacts. Workers will spend less time commuting.The demand for office space in the centres of cities will decrease, which may free up resources for living spaces and bring about a revitalisation of many urban areas. At the same time, thousands of people who run commuting services working in the support sectors that have emerged around the commuting process may well lose their jobs. There is also a risk that employers will increasingly palm off on their employees the responsibility for finding a place to work, which will be hard for anyone who lives in

240  The digital economy in times of Covid-19 100

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cramped accommodation. Additionally, a distributed workplace will exacerbate the instability of employment, as employers will find it easier to reorganise workflows into geographically dispersed projects carried out by temporary workers. The pandemic brought to the fore widespread difficulties connected with working from home. Many people found themselves in a small home, often with a partner engaged in lively online meetings while young children were being remotely schooled only a few steps away. The boundaries between work and private life, already porous, were further eroded. Some academics ventured that it was as if we were back in the Middle Ages, with the whole family living and working in the same space, cheek by jowl, all day long. For many workers this upending of their work–​life balance, often topped off with the additional workload of childcare and caring for other dependent family members, worsened mental wellbeing and often led to burnout. This was particularly true for women, who had to juggle their home and work duties. At the same time, the experience of remote work revealed the intrinsic value of work as an important dimension of sociability and a defining element of social identity. This perception should inform future public policy whenever a sector is hit by technological unemployment. Remote work clearly requires new kinds of professional skills in such areas as sustaining focus, avoiding burnout and screen fatigue, and reconciling efficiency with long-​term mental and physical wellbeing. Interestingly, the pandemic became a large-​scale experiment in skill upgrading, as digital skills became the basic requirement for most kinds of mental work. Workers had to

The digital economy in times of Covid-19  241 learn, quickly and hands-​on, how to use digital tools to do their own work and to contact their colleagues and managers. In most cases these tasks proved to be less difficult than might have been expected, partly because, digital software provides ever more user-​friendly interfaces. It was a real-​life case of learning through doing, which accelerated a fundamental shift in the attitudes of both employers and employees. Further changes in how work is organised will require new models for managing teams that are dispersed geographically and/​or functionally. Cloud-​ based software offers ways to monitor the efficiency of remote workers.59 Calibrated management tools will enable teleworking for more junior staff, who up to now have been more likely to be managed in the workplace rather than in their home.60 Changes in management techniques will go hand in hand with changes in the organisation and processes employed by companies that are undergoing digital transformation. Middle management, whose function has always been to oversee the work of underlings, will be increasingly expendable. Supervision will be increasingly performed by advanced management systems that use intelligent algorithms, gorging themselves on data produced by remote workers using digital devices. The growing incidence of remote work will normalise telecommuting by skilled workers from distant regions and far-​flung countries. Companies will be able to source talent outside the local labour market. On the one hand this may boost the opportunities of local companies looking for skilled and inexpensive staff, but on the other it will undermine the position of white-​collar workers in high-​income countries, who will increasingly compete for jobs with their foreign counterparts, who will ask for less pay.This trend will intensify with the growing role of platforms, which are ever more efficiently able to match the demand and supply of talent from all over the world. Work will become more and more fragmented: people will perform tasks for multiple projects from several employers. The pandemic also brought into sharp focus the growing bifurcation of the labour market. The prevalence of remote work was highest amongst workers with a higher education, engaged in some kind of intellectual, white collar work.61 But many occupations, particularly in healthcare and services, are inherently not teleworkable. They are praised as being essential, but are low paid.Three in four workers from the highest salary quintile are able to telework, but only 3% of those in the lowest paid quintile can do so.62 The pandemic also exposed the essential vulnerability of people working via physical service platforms (such as Uber), who suffered most from lockdowns but did not have the social safety net enjoyed by full-​time employees or access to the sort of financial support that many governments provided to the unemployed.63 Their plight will probably weigh strongly in the growing clamour for legal regulation of the platform economy. Prolonged lockdowns also underscored the potential, and indeed the necessity, for automation, in both highly-​skilled and low-​skilled professions, in accordance with the conclusions we presented in Chapter 4. The simpler, more

242  The digital economy in times of Covid-19 routine and repetitive tasks found in highly-​skilled jobs will be automated or performed with the support of intelligent systems, because this will free up time for more complex duties. In healthcare, routine check-​ups and prescriptions can be easily automated. The potential for telemedicine is also huge –​some medical advice can be given remotely, particularly with the aid of smart devices to provide data on the wellbeing of the patient. Automation in low-​skilled jobs will accelerate because the pandemic laid bare the risks to job performance continuity posed by frail human workers.

Production Digital maturity to weather the crisis One day in March 2020, groceries from Frisco (a Polish online supermarket, bought in 2020 by the Eurocash group) arrived at home of one of the authors neatly packed in plastic bags and with a leaflet explaining that the packing had been done by a Kuka cobot in an automated logistics centre to ensure minimal contact with human workers who otherwise might inadvertently have breathed the coronavirus onto my vegetables. This is a good example of how the pandemic may have enhanced the trend towards automation in production wherever human employees may constitute a risk factor.There is no doubt that the Covid outbreak will accelerate automation in those areas of social and economic life where human work is not just routine and predictable (i.e., dull for most people), but also dirty and dangerous. This will not be confined just to hospitals, but it will also affect factories, offices, shops, and anywhere the health and safety of workers –​and customers –​is at stake. Automation will receive a particular boost in healthcare thanks to the great discrepancy between ‘future talk’ (about the possibilities of automation) and the sobering reality of how limited the use of robotics in medicine is, something that was again revealed by the Covid-​19 outbreak. ‘As epidemics escalate, the potential roles of robotics are becoming increasingly clear’, an international group of researchers wrote in the journal Science Robotics in March 2020.64 But they went on to argue that this potential was not being fulfilled because funding for research on medical robotics remained ‘expensive, rare and directed to other applications’.65 Heartening stories about hospital robots such66 as Tug67 or Tommy carrying supplies or distributing drugs (as well as other robots measuring vital signs and keeping infected patients company), or less sophisticated but immensely useful robots equipped with UV lamps for disinfecting surfaces, did not reflect the reality of care in the majority of healthcare units around the world.68 This will surely change now, as the world has seen the benefits of easily disinfected, mobile cobots that can support human workers. In other areas of corporate life prospects for automation are more constrained, because they greatly depend on the digital maturity of a given company. Digitally mature companies –​ones that truly adhere to the rules of data-​first and AI-​first –​were better equipped to cope with the crisis, as they were able

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04.20

05.20

06.20

07.20

Figure 7.11 Development of industrial production (volume index 2015=100, EU27, 01.2020–​07.2020, monthly, Main Industrial Groupings, MIGs). Note: Seasonally and calendar adjusted data. Source: Own work based on Eurostat data [sts_​inpr_​m].

to respond to its challenges more flexibly. They had already established digital channels of communication with their customers and generally knew how to leverage the channels efficiently to power through the crisis.The banking sector is a good example here –​employees switched to working remotely and any remaining non-​digital procedures were hastily digitalised. The pandemic was thus a strong push in a direction they were already taking, and encouraged even more closures of branches, something which had already been under way for some time. Several research reports prepared in 2020 by large consultancies back the view that the pandemic intensified the digitalisation of customer relations, of supply chains, and of companies’ internal operations. For example, the majority of top executives and senior managers surveyed by McKinsey in July 2020 were convinced that digital transformation had been accelerated by three to four years.69 It had become the top business priority because of the changing needs of customers and the need to reorganise work. A survey by Deloitte found that two-​thirds of a sample of 441 managers from 29 countries claimed to have used some kind of automated solution to cope with the challenges posed by the pandemic, and one third reported that their company had accelerated cloud-​based automation.70 The World Economic Forum’s report in 2020 on the Future of Jobs suggests that 94% of UK-​based companies accelerated the processes of digitalisation.71 Undoubtedly, automation is on the rise. Deloitte reports that in 2015 only 13% of surveyed companies were planning to introduce Robotic Process Automation (software to automate rule-​based processes), while by 2020 78% had already done so.72 Indeed, business leaders surveyed in

244  The digital economy in times of Covid-19 2020 overwhelmingly said that they expected robotics solutions to have been adopted in two to three years’ time. On the other hand, only one-​third of the same group of managers claimed that their companies were rolling out some kind of intelligent automation.73 In manufacturing the pandemic might accelerate the digital transformation both of companies that already boast adequate data infrastructure, allowing IT and OT (operational technology) to be integrated, and (unsurprisingly) of companies that have the financial resources to invest in new technology and organisational makeovers. For the latter reason, this acceleration may largely bypass small and medium-​sized enterprises. Investing in expensive technologies to carry out intelligent automation may be delayed by the looming economic crisis, swelling the pool of cheap human labour that will result from redundancies.74 Only digitally mature companies will be able to embark on end-​to-​end intelligent automation of production and supply chains. Drawing on their internal data pools and increasingly datafied processes, they will gain an unbeatable advantage over their competition by having more efficient and flexible operations.75 When it comes to the production of services, the coronavirus crisis resulted in a push for datafication, particularly in those areas that proved to be easily scalable. Sadly, the pandemic turned into an experiment in the possibilities of datafying health and telemedicine. At the end of November 2020, the staggering number of infected people caused the Polish healthcare system to collapse. Unable to provide hospital care to all those infected, the government instructed people aged over 55 and diagnosed with Covid-​19 to stay at home, and equipped them with oximeters. Patients had to monitor their blood oxygen level and enter the data manually into an application. If their oxygen saturation dropped to a dangerous level, the patient was transported to hospital.76 Of course, an oximeter is not exactly wearable and those provided by the government were not smart –​i.e., not connected to a central system and capable of automatically collecting and analysing data. But in the near future, smart oximeters and other healthcare wearables connected to a monitoring system will aid diagnostics and prevention. Scaling up via the use of digital channels and automating certain tasks could also be observed in education, particularly in traditionally passive forms of instruction such as lectures, but also in testing and examinations. Both teachers and students upgraded their digital skills and learned how to use educational platforms for their own benefit. Now, educational institutions have scented an opportunity to use platforms to sell their products to a wider audience. This opportunity is particularly attractive to companies that specialise in educational technology and has also caught the eye of some Big Tech companies. Google developed Career Certificates, which can be completed remotely in six months on Coursera (an online education platform) at a reasonable price, and which, whenever Google is hiring, will be treated in the recruitment process as the equivalent of a bachelor’s degree.77

The digital economy in times of Covid-19  245 Incidentally, the pandemic exposed a few critical risks resulting from the short-​sighted adoption of intelligent algorithms. Faced with the dreaded prospect of cancelling exams, Britain’s educational authorities decided to estimate A-​level grades on the basis of an algorithm that took into account the historical grade distribution of schools, teachers’ estimated grades for a student, and the previous exam results of a given student in each subject. That first factor in particular introduced a high level of bias, discriminating against students from underprivileged areas. Nearly 40% of students around the UK received lower grades than those recommended by their subject teachers, causing an understandable outcry. In August, the government announced a humiliating u-​turn, scrapping algorithmically-​assessed grades in favour of teachers’ assessments.78

Market With the growing domination of tech companies –​is a backlash possible? On 14 December 2020, in the middle of the working day, Google underwent a minor collapse with major consequences. The authors of this book lost access to their all-​important calendars and emails. One of us was in the middle of an online MA seminar. We also had a meeting on Google Meet planned in order to hire a new scientific manager for our project. Our university no longer could provide its internal emailing system –​everything was routed through Google services on the basis of a contract signed in 2016. The world stopped for some 40 minutes and the experience was both bewildering and utterly sobering. For some time prior to this, we had known that digital infrastructures were essential for the smooth functioning of today’s economy and society. The coronavirus outbreak showed just how important they were; they had become invisible yet indispensable utilities. They enabled large swathes of the adult population to telework, which kept the economy ticking over despite the crisis. Even public services –​from healthcare to education –​were channelled digitally, more often than not through one of the dominant platforms.79 In many countries children used digital tools provided by Google or Microsoft. People could avoid cinemas and theatres by browsing through streaming platforms, and shun shops thanks to e-​commerce platforms. All around the world large and small companies turned to platforms to sell their products and services, and leaned heavily on cloud-​based software and computing power to maintain their operations while their staff worked remotely. When their supply chains got disrupted or broken, they used platforms to search for new contractors and suppliers. If the pandemic had struck, say, 20 years ago, none of this would have been possible. Now though, institutions and companies could not only use the communication power offered by the internet itself, but they could also reach for off-​the-​shelf, cloud-​based solutions touted by tech companies without the need to develop their own internal IT infrastructure. To quote Satya Nadella,

246  The digital economy in times of Covid-19 chief executive of Microsoft, ‘We’ve seen two years’ worth of digital transformation in two months. From remote teamwork and learning, to sales and customer service, to critical cloud infrastructure and security –​we are working alongside customers every day to help them adapt and stay open for business in a world of remote everything.’80 Together with their functional importance, the market value of the big technological companies soared off charts.81 Take Amazon: in January 2021 its market capitalisation hit $1.57 trillion.82 Jeff Bezos’s already mind-​boggling assets ballooned by an extra $67.5 billion to $182.2 billion.83 In just the third quarter of 2020, the net profit of Amazon, Apple, Alphabet, and Facebook combined was $38 billion.84 From the start, platforms and other tech companies were much better placed to cope with the situation than other businesses. By definition they already had an extensive digital infrastructure and access to cloud resources. Additionally, they were lean when it came to staff, and their employees were used to remote work. Moreover, they could easily scale up their sought-​after products, vastly expanding their customer base. The capacity of internet connections was their only limit. By April 2020, Google was gaining two million new users of Meet every day.85 Both Google and Microsoft extended and integrated their product ecosystems: Microsoft integrated Teams into Office, while Google developed new features for its Hangouts/​Meets, integrating them with their emailing and calendar service. Both companies were scrambling to emulate the best features of their much smaller and nimbler rivals, such as Slack and Zoom.86 They also unabashedly used the opportunity to hasten the buying up of promising digital

Amazon

+ 74%

Baidu

+ 69%

Apple

+ 62%

Tencent

+ 52%

Facebook

+ 37%

Microsoft

+ 35%

Google (Alphabet)

+ 34%

Alibaba

+ 14%

Figure 7.12 Year-​over year growth of GAFAM and BAT market capitalisation (in %, 2019–​2020). Source: Companiesmarketcap.com, https://​companiesmarketcap.com/​ (accessed 31 January 2021).

The digital economy in times of Covid-19  247 startups, increasing their market share in the digital economy.The same strategy was taken by the Chinese BAT.87 The coronavirus crisis will consolidate the market dominance of the Big Tech not only because of their aggressive acquisition strategies or growing customer base, but also because it created unprecedented opportunities for collecting mind-​bogglingly vast amounts of data on consumer behaviour. Think about the richness of the data Microsoft will have gathered –​not only from all of the schools in Warsaw that used Teams (speedily purchased and rolled out by the city’s authorities), but also from 183,000 other educational institutions scattered around the world.88 Such pools of data will feed into intelligent algorithms and spur the development of even more convenient and personalised tools. Even more importantly, the pandemic familiarised billions of people with digital tools and taught them basic yet highly practical digital skills. This partly explains why Big Tech so eagerly helped the public sector, keen to worm its way even deeper into our private and professional lives. Hospitals were offered free subscriptions for AI-​enhanced software to help with diagnosing and registering patients. Google and Microsoft offered extended free trials for educational institutions. As Franklin Foer, a journalist at The Atlantic noted, ‘The government has failed in its response to the pandemic, and Big Tech has presented itself as a beneficent friend, willing to lend a competent hand.’89 The first months of coronavirus laid bare the hazards inherent in platforms’ advertising revenue. Google may have lost as much as $10 billion during the first half of 2020.90 But this part of their business had already been coming in for heavy criticism from civil society advocates and state regulators. Google’s micro-​targeted advertising was accused of violating privacy and distorting the flow of information.91 Maëlle Gavet, author of Trampled by Unicorns: Big Tech’s Empathy Problem, and How to Fix It (2020), writing in the Harvard Business Review, suggests that ‘Web-​based advertising platforms will likely limit micro-​ targeting to a very narrow subset of categories and advertisers, while moving towards some kind of “freemium” model, more acceptable to regulators and users.’92 This is why many of the big tech companies, from Google to Amazon, are likely to develop a more promising source of income, cloud services, and particularly those based on artificial intelligence. As we wrote earlier, the pace of digital transformation will be uneven, with the leaders speeding up and leaving their competition trailing in their wake. Nonetheless, the rule ‘transform or die’ will only gain further traction. Massive digitalisation of production, consumption, and work will eventually spur digital transformation in companies (as well as public organisations), both big and small, and the mantra ‘data first, AI first’ will prevail. The demand for external cloud infrastructure will surge, as will the need for the matching and recommendation services of platforms, which will enable digital companies to enter into symbiotic collaboration with other companies. It may well be that the excessive market dominance of Big Tech and growing dependence of public and private institutions’ (as well as customers’) on digital

248  The digital economy in times of Covid-19 35%

Amazon 25%

Netflix

24%

Youtube 17%

Facebook

13%

Microsoft 9%

Google 4%

Apple -12%

Uber Airbnb

-32%

Figure 7.13 Year-​ over-​ year growth of selected companies’ revenue (in %, 09.2019–​09.2020). Source: Own work based on: Alphabet Inc. 2020. Form 10-​Q. Quarterly report for the quarterly period ended September 30, 2020. https://​abc.xyz/​investor/​static/​pdf/​20201030_​ alphabet_​10Q.pdf?cache=4d557b4 (accessed 31 January 2021); Facebook Inc. 2020. Facebook Reports Third Quarter 2020 Results. https://​investor.fb.com/​investor-​news/​ press-​release-​details/​2020/​Facebook-​Reports-​Third-​Quarter-​2020-​Results/​default. aspx (accessed 31 January 2021); Amazon.com Inc. 2020. Amazon.com Third Quarter Results. https://​s2.q4cdn.com/​299287126/​files/​doc_​financials/​2020/​q3/​AMZN-​ Q3-​2020-​Earnings-​Release.pdf (accessed 31 January 2021); Airbnb, Inc. 2020. Form S-​1. Registration Statement under the Securities Act of 1933. United States Securities and Exchange Commission. Washington. www.sec.gov/​Archives/​edgar/​data/​1559720/​ 000119312520294801/​d81668ds1.htm (accessed 24 January 2021); Uber Investor. 2020. Uber Announces Results for Third Quarter 2020. https://​investor.uber.com/​news-​events/​ news/​press-​release-​details/​2020/​Uber-​Announces-​Results-​for-​Third-​Quarter-​2020/​ (accessed 31 January 2021); Netflix. 2020. 2020 Third Quarter Earnings. https://​ir.netflix. net/​financials/​quarterly-​earnings/​default.aspx (accessed 31 January 2021); Microsoft. 2020. Earnings Release FY20 Q3.www.microsoft.com/​en-​us/​Investor/​earnings/​FY-​ 2020-​Q3/​press-​release-​webcast (accessed 31 January 2021); Microsoft. 2020. Earnings Release FY20 Q4. www.microsoft.com/​en-​us/​Investor/​earnings/​FY-​2020-​Q4/​ press-​release-​webcast (accessed 31 January 2021); Microsoft. 2020. Earnings Release FY21 Q1. www.microsoft.com/​en-​us/​Investor/​earnings/​FY-​2021-​Q1/​press-​release-​ webcast (accessed 31 January 2021); Apple Inc. 2020. Condensed consolidated statements of operations (Unaudited) FY20 Q4.https://​s2.q4cdn.com/​470004039/​files/​doc_​ financials/​2020/​q4/​FY20_​Q4_​Consolidated_​Financial_​Statements.pdf (accessed 31 January 2021); Apple Inc. 2020. Condensed consolidated statements of operations (Unaudited) FY20 Q1.https://​s2.q4cdn.com/​470004039/​files/​doc_​financials/​2020/​q1/​Q1-​FY20-​ Consolidated-​Financial-​Statements.pdf (accessed 31 January 2021).

The digital economy in times of Covid-19  249 infrastructures they provide, thrown into harsh relief by the 2020 crisis, added to states’ (and groups of states’) desire to reassert their digital sovereignty. In February 2020, the European Commission issued a White Paper called Artificial intelligence: A European approach to excellence and trust, which proposed drawing up a list of sectors in which certain ways of using AI might be considered high-​r isk. These sectors would therefore be subject to a number of requirements regarding, e.g., standards relating to the data used in such systems, or safety requirements regarding data storage. In December 2020, the Commission presented a proposal for a Digital Services Act and a Digital Markets Act. The latter included provisions setting out, for instance, an obligation for online platforms to take responsibility for illegal content, a requirement for increased transparency, and a further requirement to researchers with access to data.93 Digital services were defined as ‘a large category of online services, from simple websites to internet infrastructure services and online platforms’.94 In short, the two new Acts tried to redefine the rules of the game in accordance with the European approach to privacy, innovation, and data ownership.The European Commission justified the planned regulation thus: The accelerating digitalisation of society and the economy has created a situation where a few large platforms control important ecosystems in the digital economy.They have emerged as gatekeepers in digital markets, with the power to act as private rule-​makers. These rules, however, sometimes result in unfair conditions for businesses using these platforms and less choice for consumers.95 Moreover, since 2019, a group of 22 French and German companies (including Deutsche Telekom, Orange, Bosch and Siemens, BMW, Atos, Dassault Systemes, OVHcloud, and SAP), under the aegis of French-​ German governmental cooperation, has been building a European-​based cloud data infrastructure called Gaia-​X, which is expected to develop into a European data ecosystem. It aims to decrease the dependence of European companies and public institutions on American-​based cloud providers, mainly Amazon, Google, and Microsoft. The project is also open to other European governments. In the first round it will set out rules for data privacy and governance. In June 2020, while describing Gaia-​X’s data infrastructure at an online conference, the French Minister for Economy and Finance, Bruno Le Maire, emphasised that ‘We are not China. We are not the United States. We are European countries with our own values and with our own economic interest that we want to defend.96,97 In response, his German counterpart underlined the need to bolster digital sovereignty. The main principle of the European cloud will be ‘reversibility’, which will allow users to switch providers without any difficulty. Meanwhile, in December 2020 antitrust resentment of Big Tech led a group of US states to complain that Google had been colluding with Facebook in order to monopolise the online ad market.98 Earlier that year, Google had been accused of striking a deal with Apple to block the distribution of search engines

250  The digital economy in times of Covid-19 made by other companies. Even more surprisingly, in the last week of 2020, China’s top market regulator decided to discipline Alibaba and initiated an investigation into the company’s practice of forcing merchants to sign exclusivity contracts (preventing them from selling on rival platforms).99 Even though Chinese big tech firms obediently cooperated with the authorities in building systems for social surveillance, their size, influence, and aggressive acquisition policies, which had constrained innovation in the Chinese market, had grown just too large for the authoritarian state, jealous of their power and independence, to tolerate.100 There is no doubt that the shape of the digital economy will be determined by the changing dynamics of relations between the two fundamental institutions of modern life: on the one hand, the nation state: and on the other, the Big Tech companies, both America’s GAFAM (Google, Amazon, Facebook,Apple, and Microsoft) and China’s BAT (Baidu,Alibaba, and Tencent).

The prospects for the digital economy What technology can do –​and what it cannot do It is a sobering thought –​that all the technological tools that humanity could muster in the face of a severe crisis did not prevent the global spread of an infectious disease. Despite the optimistic reports prepared by global consultancies, hailing the dawn of a new world of technological bounty that would solve all the humanity’s problems, at first people had to resort to the simplest measures known since the antiquity: distancing and isolation. No wise person would claim that even the most advanced technology offers a silver bullet for the multiple problems of modernity. Still, let us imagine, just for a moment, what would happen to our societies and economies if this stupendous crisis struck three decades ago, when such a massive and rapid shift to remote work, education and socialising would have been technically impossible. With its technologies, solutions, products, and services, the digital economy had shown its strength and quite unexpected capabilities for allowing economic and social activities to continue. However, this crisis will not be the last –​and quite possibly not the gravest one –​that we will have to face in the foreseeable future. The ever more connected world will produce them with great regularity; the higher complexity of any given system, the higher the risk of both expected and unexpected crises. The coronavirus pandemic offers, in a sense, a global test for the swelling environmental crisis, compounded by the still unresolved rift of economic, social, and political inequalities, both within the individual societies and globally. Digital products and services have great potential for elevating the quality of life and levelling the playing field for people worldwide. Cumulative innovation, stemming from abundant data churned by ever more efficient intelligent algorithms, may prompt solutions to the environmental crisis. It is tough to make predictions, especially about the future, as Yogi Berra once quipped, and so we will not attempt to delineate scenarios for the post-​Covid digital

The digital economy in times of Covid-19  251 economy. However, the pandemic taught us that two conditions are essential for developing a model of the digital economy that would help to provide a rapid and adequate response to any future crises. Those two conditions, arising from the definition of the digital economy set out in Chapter 1, are robust networks and beneficial datafication. Robust networks Robust networks provide stable, safe, and equal access to the digital products and services offered by the digital companies and digital governments, and hence determine the economic fate of people, companies and whole national economies. The number of people, devices, machines, and systems connected to the internet will continue to grow. In 2023 nearly two-​thirds of the world population –​5.3 billion people –​will have access to the internet. There will be 29.3 billion connected devices such as smartphones and personal computers (up from 18.4 billion in 2018), and the connections between them will make up half of all the connections.101 Still, one-​third of the world population will not have basic access to the products, services, and economic opportunities offered by the digital economy or to the public services that will be increasingly supplied through online channels. Take a simple registration procedure for a Covid vaccine shot: in Poland only those with internet access and basic digital skills could use the government app that generated an individual electronic referral. The alternatives were to spend several hours on the phone or in the queue outside the local medical centre in the freezing cold –​but this was what many octogenarians did. The lack of access to the internet will steadily worsen social, economic, and political exclusion. As of 2019, 62% of the Sub-​Saharan population did not have access to the internet.102 As Mahtar Diop, the World Bank’s Vice President for Infrastructure, pointed out in 2020: we must power digital transformation in some of the world’s poorest countries by massively scaling up resources dedicated to building the foundations of a thriving digital economy. This crisis painfully shows that the benefits and opportunities of technology are not equally distributed. In the informal economy, there is no such thing as telecommuting. In poor countries, even established businesses, more often than not, do not have the capability to move to online operations. Teachers, students, and government officials need connectivity, but also digital skills to use these tools effectively. Economies are increasingly relying on fintech to stay afloat, and demand for services such as mobile payments, food delivery, and e-​ commerce will grow exponentially.103 Broadening access to the internet necessitates massive investments in hard infrastructure, particularly in fibre optic cables, 5G stations and data centres. Connecting over 1 billion new users in Africa by 2030 will require building

252  The digital economy in times of Covid-19 Individuals using the Internet (per 100 peope) Europe The Americas Asia and Pacific Arab States Africa

International bandwidth per Internet user (kbit/s)

83

211

77

130

45

111

55

101

29

Active Population covered mobile-broadband by at least an subscriptions (per LTE/WiMAX mobile 100 people) network (%)

31

100

97

99

89

77 60 33

94 62 44

Figure 7.14 Regional ICT-​related data (2019/​2020). Source: Own work based on ITU Global and Regional ICT data.

nearly 250,000 new base stations for networks with at least 4G quality and laying at least 250,000 kilometres of fibre.104 This is a tremendous undertaking that will cost $100 billion, and most African states will not be able to bear the expense on their own. The cost of building a broadband infrastructure is stupendous even for the developed countries. In 2018 the British government announced that the cost of introducing 5G and broadband across the country by 2027 would reach £6.8 billion, necessitating cooperation with the private sector to carry part of the cost.105 In most cases, countries reach out to telecom companies who traditionally financed and provided the internet’s backbone infrastructure. This began to change in 2016 when the big tech companies, which are also content providers, started to invest in undersea cables and build impressive control over the digital infrastructure.106 However, investments in building networks is essential for any country that wants to participate in the global digital economy.107 No country understands it better than China. In 2005 only 8.5% of the Chinese population had access to the internet; at the same time, it was true for 68% of US citizens.108 In 2019 more than 60% of Chinese people were internet users (in USA it stood at 90%). Admittedly, that is still a long way from universal access, particularly for people living in rural areas. However, in absolute numbers, the number of internet users in China leapt from 111 million in 2005 to 904 million in March 2020.109 Almost all of these connections are made through mobile phones. This is one reason why by the end of 2020 China had built 690,000 base signal stations for 5G, compared to 50,000 in the USA110. By 2025 5G should enable half of all connections initiated in China, Japan, and South Korea. Europe seems likely to lag, as only one-​third of connections will use 5G by then.111 This is particularly important as the 5G standard allows the development of the Internet of Things, which, as we showed in previous chapters, underpins many new production

The digital economy in times of Covid-19  253 and consumption methods. The Covid-​19 crisis proved that it pays to invest in infrastructure preemptively so that it can bear the pressure of unexpected occurrences. The Covid-​19 crisis also proved the importance of the cloud, which enabled the transition to the remote mode. As we repeatedly indicated throughout the book, cloud computing allows companies and other organisations to engage in digital transformation without the need to invest in their internal IT systems. This component of digital infrastructure is under the exclusive control of the big technological companies which are accelerating their investments in infrastructure that will enable data storage and processing. In May 2020 Microsoft announced plans to build a large data centre for the whole Eastern Europe near Warsaw. In June, Google Cloud made a similar announcement. The $2 billion investment will create the company’s sixth data centre in Europe. Both technological giants chose Poland partly to streamline the flow of data in the region, but also because of the availability of very affordable energy and access to a skilled, but reasonably priced, workforce. Poland can boast of competent IT specialists in the world who will still work for less than Western Europe professionals. The indispensability of digital infrastructures makes them a perfect target for destabilising attacks. Cyber attacks are a common occurrence in the digital economy, and sometimes they bring dire consequences. In June 2017 a computer ransomware dubbed NotPetya paralysed several global companies (e.g., a pharmaceutical company Merck and a logistic giant Maersk) and brought down part of the Ukrainian energy and banking system, causing losses of $10 billion.112 During the lockdowns, when the digital infrastructure was more important and more fragile than ever, whole companies and departments worked with home Wi-​Fi. Not surprisingly, cyber attacks grew in number. In the UK, the National Security Centre noted a 20% surge in attacks compared with the annual average since 2016.113 More worryingly, some attackers zeroed in on the organisations and companies working on the coronavirus vaccine. In November 2020 a malware attack caused a system outage in Miltenyi, a biotech company located in Germany which supplies SARS-​CoV-​2 antigens for research firms working on Covid-​19 treatment.114 Microsoft confirmed that earlier that year it had detected cyber attacks, launched by Russian and North Korean hackers, on ‘seven prominent companies directly involved in researching vaccines and treatments for Covid-​19’ from Canada, France, India, South Korea, and the United States.115 Another series of phishing attacks targeted companies forming the ‘cold chain’ of distribution of the BioNTech-​Pfizer vaccine (which must be stored and transported at -​70 Celsius degrees).116 In December 2020 a massive cyberattack on the European Medicine Agency (an EU agency responsible for evaluating, monitoring, and supervising new medicines) leaked documents on the regulatory submission by Pfizer and BioNTech for their Covid-​19 vaccine, raising concerns that the documents might be used to produce false information about negative side-​effects of the vaccine.117 The future development of the digital economy will hinge on technological solutions (such as blockchain)

254  The digital economy in times of Covid-19 and institutional architectures that will guarantee the safety and security of the web. Finally, robust online networks need to allow ready access to citizens and consumers. In the digital economy, access to the internet is tantamount to access to the basic amenities of everyday life: products and services offered by private companies, and public services and information. Indeed, in 2020, many people worldwide realised that being connected to the internet was a human right, a stance that the United Nations had taken since 2016.118 That concept has already been frequently disregarded: the Great Chinese Firewall blocks access to foreign websites and censors online content, the Russian government emulates the Chinese approach, the Trump administration in the US tried to block WeChat and Tik Tok: all are undermining the idealistic notion of the internet freedom and universality.119 Still, even a splintered internet, divided into several autonomous domains and resembling intranet guarded by the rule of digital sovereignty, as exists in China, at least offers connectivity to people and companies.120 More harmful are the internet shutdowns, imposed by countries such as Belarus,Yemen, Myanmar, and Azerbaijan. In 2020 the cumulative length of such government shutdowns was 50% longer than the year before. The inglorious first place for inflicting closures belonged to the Indian government. It ordered 59 shutdowns, most of them in Jammu and Kashmir, which limited the dissemination of information about the dangers of coronavirus and may have contributed to its spread.121 The repeated closures also hindered access to remote education for thousands of children, the operation of local companies, and, most crucially, access to telemedicine.122 It is not just states that wield gatekeeper power. So do digital platforms, particularly those that create, control, and sustain specialised networks (e.g., act as content providers). Twitter or Facebook can exclude unwanted content or profiles that transgress their internal censorship rules. For example, on 8 January 2021,Twitter decided that one of the world’s most powerful politicians broke the rules on glorification of violence and suspended the @realDonaldTrump account, with nearly 88 million followers.123 Beneficial datafication The Covid-​19 crisis proved the importance of datafication, showing that the lack of accurate data hinders rapid and tailored response to imminent threats. At the beginning of the pandemic, epidemiologists lacked all kinds of data on the new coronavirus disease: how it spread, how infectious it was, who got ill and who was most likely to die. Data on Covid-​19 infections were gathered in different formats, making it difficult to predict the infection’s pace and spread. In Poland, these data were so blatantly incomplete that one 19-​year old ‘data hobbyist’ felt compelled to start collecting them in one file, presented through his Twitter account. He consistently pointed out huge discrepancies between the central authorities’ figures and those reported by

The digital economy in times of Covid-19  255 the local sanitary and health authorities. In the end, the scientists from the University of Warsaw used his database to build epidemiological models, which in turn determined the public policy measures that the government introduced to try to containing the spread of the disease.124 The lack of data hindered the efficient use of artificial intelligence. As Jeremy Kahn, a Fortune journalist, aptly notices: While some AI software helped sound early warnings that a worrisome new respiratory virus seemed to be circulating in Wuhan, China, the technology certainly did not help prevent the pandemic. And its impact on epidemiological modelling and policymaking has been minimal. It’s had limited impact in the quest to find COVID-​19 treatments and develop vaccines. Some have quipped that AI would be ready to combat the next pandemic, but not this one.125 As the virus spread, applications of AI started to follow its course. Flinders University in Australia, working with a biotech company called Vaxine, used machine learning tools to analyse the spike proteins of the coronavirus (the parts used by the virus to attach to the attacked molecules) and the results will be used to produce medicines based on antibodies.126 Elsewhere around the world, several academic and non-​profit organisations built machine-​readable and AI-​searchable sites with links to thousands of scientific articles on coronavirus. The international exchange and sharing of knowledge certainly accelerated the process of vaccine creation.127 With the help of AI tools, the scientists from MIT proved that some vaccines could be less effective for people of African or Asian origin.128 The UK government offered a software company, Genpact UK, £1.5m to build an AI tool to process the data on adverse drug reaction to Covid-​19 vaccine.129 There is no doubt that the technological revolution will continue to deliver significant innovations. Indeed, in December 2020, the University of Science and Technology in Hefei, China, announced that its staff had carried out a photonic quantum computation that would take a typical computer 2.5 billion years to perform. The computer was not programmable, and it could perform only a single specific type of calculation (boson sampling),130 but this was still an astonishing achievement. Globally, several teams in academia and industry are working on different forms of quantum computers: in 2019, Google announced it had created a quantum computer that worked by using so-​called qubits based on superconducting circuits.131 Nevertheless, for several years ahead, maybe even for several decades, the progress of the digital economy will be built around the massive application, calibration, and adoption of technologies that already exist, and particularly around intelligent algorithms, the cloud, the Internet of Things. All of these are technologies that enable efficient datafication by the companies large and small, by governments and by other kinds of organisations. The year 2020 made it clear that datafication is, in fact, inevitable: you cannot live

256  The digital economy in times of Covid-19 in a modern society without being monitored by sensors scattered in the public space and without leaving a rich trail of data whenever you use your digital devices. Datafication –​the extraction of value from abundant data through the use of intelligent algorithms –​has turned out to be the most critical normative and regulatory issue at the crossroads of interests of various political, economic, and social actors. The issue is not whether all our activities should be measured in data, but rather who will benefit from datafication and how. During lockdowns, people all around the world produced enormous quantities of data. Companies will use these data to develop more digital products and services and to personalise their offers. But will they be used to benefit humanity –​and particularly to benefit those who are most in need? Ultimately, in the digital economy, the risk to humanity will lie in inadequate, insufficient, or misused datafication: that is the situation when your data is incomplete or processed and analysed incorrectly or used against your vital interests and rights. Even more dangerous will be the lack of datafication –​those people whose activities will be not datafied will become invisible to systems based on datafication.This is why it is so important to build robust networks, which will enable universal access to the opportunities that the digital economy creates. Hence we return to the issues, which surfaced several times in this book, of the ownership of data and the control of algorithms.With the growing adoption of machine learning and of deep learning algorithms will come worries about their accuracy and reliability, generated by the so-​called black-​box problem, or the inability to explain the machine’s decision.132 However, even simple, rule-​based algorithms can create discrimination in access to essential resources. A case in point is an algorithm used in a research project by scientists at Stanford University in the US. The project, to work out priorities for the vaccination of medical staff, took account of several factors such as age, employment (remote or in contact with people), and public health guidance. However, it omitted to take account of the actual exposure of individuals to patients infected with the coronavirus. As a result, the vaccine was offered to the administrators and other employees who worked from home, but only to seven of 1,300 medical residents. The hospital authorities tried to blame ‘a very complex algorithm’, but in fact, the fault lay with the individuals who set up the project and took the wrong decisions in designing the rules.133 This is why we need to approach the enthusiasm revealed by some scientists that AI can solve the dilemma of equal access to the limited resources such as vaccine with justified reservations (‘By using AI to determine priority, each country and organisation can be certain that the order in which they roll out the vaccine is reflective of the actual pro bability of their people dying and/​or being infected.’).134 It will not: all kinds of political, economic, and social factors will come into play. This is why, for example, the supplies of the vaccines were bought out by the wealthiest coun tries, leaving the poorer countries to cope for themselves, despite the appeals of the World Health Organization. For example, 82% of the promised doses of

The digital economy in times of Covid-19  257 the BioTechN/​Pfizer vaccine were preordered by several developed countries accounting for only 14% of the world population.135 To survive and thrive in the digital economy and make most of its opportunities, we need to develop and nurture an attitude of healthy distrust towards the masters of datafication, such as digital companies and states. It will require considerable effort, as the digital infrastructures blend into our everyday lives, and as ever more personalised products and services provide convenience and comfort. It will also necessitate critical thinking and creativity, which should be essential skills, taught throughout the educational systems. Ultimately, what we need to keep in mind as citizens, consumers, and humans, is that all kinds of technologies are what we make out of them within the world’s diverse social, cultural, economic, and political frameworks.

The key takeaways •

The Covid-​19 crisis brought to the fore the scale and scope of the digital economy; the mechanisms described in previous chapters become stronger and more intense. Digital infrastructures, technologies, products, and services proved instrumental in sustaining the functioning of societies and economies worldwide, and they will determine the shape of the post-​Covid world. • Closed borders and diminished flows of people and goods accentuated the global dimension of the digital economy manifested in the further growth of cross border data flows (including digital information goods and digital services). • The pandemic accelerated the digital transformation, not only through increased uptake of digital technologies by organisations but mainly through changed attitudes and upgraded digital skills amongst people who en masse took to remote education, work, entertainment, and socialising. • In the digital economy, success will be enjoyed by those companies and organisations (including states) that leverage the power of abundant data, intelligent algorithms, and networks. Half-​hearted and ad-​hoc application of technology will not do: digital transformation requires organisational and operational overhaul, aiming to achieve digital maturity and providing personalised products and services. • The digital economy might be instrumental in building prosperous future for all humanity only if it is based on robust networks (i.e., inclusive and safe access to the internet and digital infrastructures) and beneficial datafication (focused on benefits to the user, citizen and consumer). • The digital economy’s progress will be fraught with tensions between nation-​states, which during the pandemic wielded their traditional prerogatives of sovereignty but were still dependent on digital infrastructures, and Big Tech and other digital platforms, whose market

258  The digital economy in times of Covid-19

•

dominance has grown unchecked.The latter’s position will be consolidated thanks to the enormous amount of data inflowing from housebound consumers and workers, companies and state agencies, and constant investments in new technologies based on artificial intelligence. The two silver bullets for the digital economy available to all are r­ egulation and education. Regulation of globe-​straddling digital companies’ ­activities will require an orchestrated effort from various states towards building a new international regime. Focusing the educational ecosystem on skills for the future will pave the way for the ongoing digital transformation of ­companies and other organisation, thus determining the competitive advantage of national economies. Education should focus on developing not only technical and digital skills but also such skills as ­critical thinking, creativity, and cooperation, instrumental in keeping at bay the negative social, economic, and political consequences of technological development.

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The digital economy in times of Covid-19  271 Freeguard, G., Shepheard, M., and Davies, O. 2020. Digital government during the coronavirus crisis. Institute for Government. 2020. www.instituteforgovernment.org.uk/​ sites/​default/​files/​publications/​digital-​government-​coronavirus.pdf Gavet, M. Trampled by Unicorns: Big Tech’s Empathy Problem, and How to Fix It. Wiley, 2020. Gavet, M.What’s next for Silicon Valley? Harvard Business Review. 2020. https://​hbr.org/​ 2020/​09/​whats-​next-​for-​silicon-​valley The Global Economy. Internet users –​country rankings. www.theglobaleconomy.com/​ rankings/​Internet_​users/​ Global Food Security. Your food is global. www.foodsecurity.ac.uk/​challenge/​ your-​food-​is-​global/​ Global Justice Now. Most of Pfizer’s vaccine already promised to richest, campaigners warn. 2020. www.globaljustice.org.uk/​news/​2020/​nov/​11/​most-​pfizers-​vaccine​already-​promised-​r ichest-​campaigners-​warn Goldin, I. Covid-​19 proves globalisation is not dead. Financial Times. 2020. www.ft.com/​ content/​d99fa0e9-​2046-​4587-​b886-​7d42252b6fc9 Graze, M. Museums are going digital –​and borrowing from Data Viz in the process. Medium. 2020. https://​medium.com/​nightingale/​museums-​are-​going-​digital-​and-​ borrowing-​from-​data-​viz-​in-​the-​process-​b5e3828b4000 Greenberg, A. The untold story of NotPetya, the most devastating cyberattack in history. Wired. 2010. www.wired.com/​story/​notpetya-​cyberattack-​ukraine-​ russia-​code-​crashed-​the-​world/​ Griffin, R. Is the Covid-​19 pandemic a victory for Big Tech? SciencePo. www.sciencespo.fr/​ public/​chaire-​numerique/​en/​2020/​08/​13/​is-​the-​covid-​19-​pandemic-​a-​victory-​ for-​big-​tech/​ Gross, A. UK plans to use AI to process adverse reactions to Covid vaccines. Financial Times. 2020. www.ft.com/​content/​17a306cd-​be75-​48b4-​996e-​0c2916b34797 Holmes, A. Tech leaders have long predicted a ‘splinternet’ future where the web is divided between the US and China. Trump might make it a reality. Business Insider. 2020. www.businessinsider.com/​splinternet-​us-​china-​internet​trump-​pompeo-​firewall-​2020-​8?IR=T Huixi, D., Yang, F., Lu, X., and Hao, W. Internet addiction and related psychological factors among children and adolescents in China during the coronavirus disease 2019 (Covid-​19) epidemic. Frontiers in Psychiatry. 2020. www.frontiersin.org/​articles/​10.3389/​fpsyt.2020.00751/​full Hurst, A. Two-​ thirds of business leaders used automation for Covid-​ 19 response –​Deloitte. Information Age. 2020. www.information-​age.com/​two-​thirds-​ business-​leaders-​used-​automation-​for-​covid-​19-​response-​123492795/​ Infosys. Second shift: Manufacturing after Covid-​ 19. www.infosys.com/​industries/​ industrial-​manufacturing/​insights/​being-​resilient-​new-​normal-​covid19.html ITU. Economic impact of Covid-​19 on digital infrastructure. GSR-​20 Discussion Paper. 2020. www.itu.int/​en/​ITU-​D/​Conferences/​GSR/​2020/​Documents/​GSR-​20_​ Impact-​COVID-​19-​on-​digital-​economy_​DiscussionPaper.pdf Jacob, S. and Lawarée, J. The adoption of contact tracing applications of Covid-​19 by European governments. Policy Design and Practice. www.tandfonline.com/​doi/​full/​ 10.1080/​25741292.2020.1850404 Kahn, J. Vaccinating the world against Covid is off to a slow start. These firms think A.I. and blockchain could help. Fortune. 2021. https://​fortune.com/​2021/​01/​06/​ covid-​vaccine-​rollout-​distribution-​vaccination-​coronavirus-​ai-​blockchain/​

272  The digital economy in times of Covid-19 Kolibabski, K. Aplikacja ProteGo Safe okazała się porażką. Powiadomienia włączyło ... 420 osób. Gazeta.pl Next. 2020. https://​next.gazeta.pl/​next/​ 7,173953,26452567,aplikacja-​protego-​safe-​okazala-​sie-​porazka-​powiadomienia-​ wlaczyly.html Krasoń-​Wałęsiak, M. E-​commerce w czasach koronawirusa. ICAN Institute. 2020. www. ican.pl/​a/​e-​commerce-​w-​czasach-​koronawirusa/​DjzGEH6BB Kynge, J. and Ruehl, M. Apple suffers further supply chain setbacks in China. Financial Times. 2020. www.ft.com/​content/​973e5260-​52ed-​11ea-​8841-​482eed0038b1 Lee, W. How the coronavirus crisis has helped Spotify’s podcast business. Los Angeles Times. 2020. www.latimes.com/​entertainment-​arts/​business/​story/​2020-​04-​27/​ coronavirus-​podcasts-​spotify-​finneas Leswing, K. States are finally starting to use the Covid-​tracking tech Apple and Google built –​here’s why. CNBC. 2020. www.cnbc.com/​2020/​10/​03/​covid-​app-​exposure-​ notification-​apple-​google.html Lin, W. China’s internet users reach 900 million, live-​streaming ecommerce boosting consumption: report. Global Times. 2020. www.globaltimes.cn/​content/​1187036. shtml Ling, C. and McElveen, R. Will China’s e-​commerce reshape a reopening world?. Brookings. 2020. www.brookings.edu/​articles/​will-​chinas-​e-​commerce-​reshape-​a​reopening-​world/​ Liu, G., Carter, B., and Gifford, D.K. Predicted cellular immunity population coverage gaps for SARS-​CoV-​2 subunit vaccines and their augmentation by compact peptide sets. bioRxiv. 2020. www.biorxiv.org/​content/​10.1101/​2020.08.04.200691v2. full.pdf Lomas, N. Netflix and other streaming platforms urged to switch to SD during Covid-​19 crisis. TechCrunch. 2020. https://​techcrunch.com/​2020/​03/​19/​keep-​ calm-​and-​switch-​to-​sd/​ Loosemore, T. Google and Apple’s diktat to governments on coronavirus contact-​ tracing apps is a troubling display of unaccountable power. Business Insider. 2020. www.businessinsider.com/​opinion-​google-​apple-​contact-​tracing-​app-​troubling-​ governments-​2020-​6?r=US&IR=T Marr, B. Tech Trends in Practice: The 25 Technologies that are Driving the 4th Industrial Revolution. Wiley. 2020. Marshall, C. Google introduces 6-​ month career certificates, threatening to disrupt higher education with ‘the equivalent of a four-​year degree’. Open Culture. 2020. www.openculture.com/​2020/​09/​google-​introduces-​6-​month-​career-​certificates-​ threatens-​to-​disrupt-​higher-​education.html Masso, G.Tens of millions watching streamed theatre shows worldwide. The Stage. 2020. www.thestage.co.uk/​news/​tens-​of-​millions-​watching-​streamed-​theatre-​shows-​ worldwide McKausland, T. 2020. Covid-​19’s Impact on Globalization and Innovation. Routledge. 2020. www.tandfonline.com/​doi/​full/​10.1080/​08956308.2020.1813506 McKinsey & Company. How Covid-​19 has pushed companies over the technology tipping point –​and transformed business forever. 2020. www.mckinsey.com/​business-​ functions/​strategy-​and-​corporate-​finance/​our-​insights/​how-​covid-​19-​has-​pushed-​ companies-​over-​the-​technology-​tipping-​point-​and-​transformed-​business-​forever Mehta, K.Welcome to the isolation economy (goodbye sharing economy). Forbes. 2020. www.forbes.com/​sites/​kmehta/​2020/​03/​23/​welcome-​to-​the-​isolation-​economy-​ goodbye-​sharing-​economy/​?sh=4435265a8d88

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Index

Note: Page numbers in italics indicate figures and in bold indicate tables on the corresponding pages. abundant data xiv, xvi, 4, 18, 23, 29, 68, 257; Covid-​19 and 250; datafication and 53, 102, 256; digital company and 99; globalisation and 197, 201–​202; Internet of Things and 79, 159; manufacturing technologies and 80, 89; 102 Advanced Research Projects Agency (ARPA) 7–​8 advertising platforms 48 Airbnb 25, 45–​47, 48–​49, 101, 165; direct and indirect network effects 58; as disruptive innovation 67; institutionalising trust 61; low operating costs of 64; number of users 66; pricing policy 56; working around regulations 202 algorithmic phase 121 algorithms, intelligent see intelligent algorithms Alibaba 25, 47, 48–​49, 53, 96–​100, 137, 194; Cloud 19; globalisation of 196; low operating costs of 64; revenues compared to countries’ GDP 62 Alipay 100–​101 Allegro 48, 174 Allen, Paul 10 AllyO 130 Alphabet 49, 53; revenues compared to countries’ GDP 62 AlphaGo 20 Amazon 47, 49, 53, 95, 125, 194; Covid-​19 and 246, 246–​247, 246–​250; data centres 193; direct and indirect network effects 58; globalisation of

195, 196; Go 162–​163; institutionalising trust 61; Kindle 164, 192; logistics facilities 96, 98; Marketplace 48, 56, 58, 61, 164; Mechanical Turk 124, 126; number of users 66; pricing policy 56; Prime Video 61; revenues compared to countries’ GDP 62; wearable devices work by workers at 131; Web Services 19, 22, 48, 90, 201–​202 American Express 49 Adnreessen, Marc 79–​80 Ant Financial 165 Ant Group 196 Apple 6, 10, 47, 53; Covid-​19 and 246–​247, 246–​250; Covid-​19 contact tracing app developed by 231; Covid-​19 pandemic and 229; as disruptive innovation 67; iOS 47, 48, 49; iPhone 12–​13, 59–​60, 60, 171; iTunes 167; Pay 48; revenues compared to countries’ GDP 62 Archie 11 ARPANET 8, 159 artificial artificial intelligence 125 artificial intelligence (AI) xiv, 1, 19–​22, 21; applied 20; autonomous 21; business 20; Covid-​19 and 255; Internet 20; narrow 20 AT&T 137 automated machine learning 22 automated manufacturing 79 automation: Covid-​19 and 244; future work skills and 134–​138, 136; intelligent 22, 102; legal regulation of 122–​123; technological unemployment

278 Index due to 132–​134; of work 118–​123, 122, 241–​242 autonomous checkouts 162–​163, 163 autonomous phase 122 autonomous vehicles 82 Babbage, Charles 5 Baidu 53, 199; revenues compared to countries’ GDP 62 BAT companies 202, 205, 231; Covid-​19 and 246–​247, 246–​250 Berners-​Lee, Tim 11 Bezos, Jeff 95, 125, 246 bifurcation of the labour market 241 big data 3, 17–​18, 99, 128 Big Five see GAFAM Big Tech 53, 54; Chinese 205–​206, 231; Covid-​19 and 246–​247, 246–​250; education changes and 137–​138 Bitcoin 86, 201 Blabla.car 168 BlackBerry 59, 59, 59–​60 blockchain 84–​86, 99, 193, 197 Booking.com 47, 49 Brin, Sergey 11 browsers, Internet 11, 172 business-​to-​business relations 199–​200 business model xiv, xvi–​xvii, 3–​4, 25, 29, 45; creative sector 153; datafication and 26, 47, 128; data-​first, AI-​rule 123; digital technologies and 27, 80; globalisation and 202; intelligent algorithms and 21, 23; pay-​per-​use 166–​167; personalisation and servitisation and 77, 92, 158; platforms and 47–​48, 52–​53, 58, 63, 67, 92, 127; sensors and 91 Cainiao Network Technology 97–​98 Cambridge Analytica scandal 173 children’s data 26 Clickworker 125 cloud computing 18, 18–​19, 87, 130, 193 cobots 82–​83 collaborative consumption xvii, 162, 167–​169, 174 collaborative robots 82–​83 Computer Aided Design (CAD) systems 88 computers: data digitisation and 11–​12; development history of 5–​7, 6–​7; networking of 7–​9, 9; services provided by 9–​11

connection technologies 7–​9, 9 constellation platforms 49 consumption: collaborative 167–​169; Covid-​19 and 232–​237, 233–​236; digital information goods 152–​156, 153–​154, 232–​237, 233–​236; flexible model of 167; intelligent products 156–​159, 157; new objects of digital 151–​159, 152; platformisation of 163–​167, 166; price of personalisation in 169–​174, 170–​171 Coursera 67, 101, 137–​138 Covid-​19 pandemic xiii–​xiv; beneficial datafication and 254–​257; boundaries between work and private life with 240; changes in the digital economy with 225–​227, 226; consumption and 232–​237, 233–​236; digital transformation and 230–​231; globalisation and 227–​228, 227–​231, 232; isolation economy and 235–​236; market changes and 245–​250, 246, 248; new work culture and 238; production and 242–​245, 243; prospects for the digital economy with 250–​257, 252; robust networks and 251–​254, 252; tourism and 227, 227–​231; tracing apps for 231, 232; work and 237–​242, 239–​240 Crowd Guru 125 crowd work/​employment 125 cryptocurrencies 85–​86, 200, 201 Customer Relationship Management systems 200 cyberphysical systems (CPS) 88 data xiv, 1, 77; abundant 18; big bang of 16, 16–​19, 18; blockchain security 85–​86; children’s 26; cloud solutions for 18, 18–​19; flows xvii, 17, 29, 192, 199, 204, 208–​209, 231, 257 datafication xv, 1, 23–​26, 24, 45; Covid-​19 and beneficial 254–​257; of distribution 95–​99, 96–​97; platforms and 53–​56; of production 86–​91, 87–​88; of work 129–​132; of work during the Covid-​19 pandemic 237–​242, 239 data-​first, AI-​first approach 63, 99, 100, 123 Data Robot platform 25–​26 data science 17–​18 decentralisation 93

Index  279 deep learning 19–​20, 22, 47, 82, 165, 256 deglobalisation 197 demand coordinators 49 dematerialisation 154 dematerialised work 133 digital companies 52–​53, 99–​102, 100–​101 digital content 156; personalisation of 173–​174 digital devices xiv, xvi–​xvii, 1, 4, 29, 48, 53, 102, 151, 225; isolation economy and 235; trail of data left by 256; ubiquitous computing via 22; wearable 81, 131, 157; workers using 241 digital economy: Covid-​19 and 225–​227, 226, 250–​257, 252; defining 1–​5, 2; first appearance of phrase 2–​3; globalised to its very core 192; in poorer countries 207–​208; properties of 22–​27, 24; the state in global 201–​208, 203, 205, 207 digital global order 208–​210 digital information goods 151, 152–​156, 153–​154; Covid-​19 and 232–​237, 233–​236 digital infrastructures 27, 245 digital innovation 1, 4 digitalisation: market changes and 45, 46; of production 77, 78 digital maturity 242–​243 digital payment revolution 165–​166 digital rights management (DRM) 155 Digital Services Act 207 digital services 3, 99, 156, 203, 206, 207, 210, 230, 249, 257 digital single market 206 digital trade: in goods 193–​198, 195; in services 198, 198–​200, 201 digital transformation xiii–​xv, 1, 3–​4, 27–​29, 45, 77, 99–​102; AI and 22; costs of 18; Covid-​19 pandemic and 230–​231; datafication and 23; datafied distribution and 95–​99; digital consumption and 151–​159; digital technologies for 79–​80; globalisation and 197, 200–​207, 207; platformisation and 92–​95; by traditional firms 67; of work 118, 119, 123, 131, 133 digital twins 77, 88–​89; Covid-​19 pandemic and 229 digitisation 11–​12, 24; volume of data 16, 16–​17

discrimination 130, 165, 206–​207, 209, 256 disintermediating your friends 26 distribution, datafied 48, 77, 90, 91, 95–​99, 96–​97, 102, 193 disruption xv, 14, 45, 67, 99–​100, 121, 158, 194, 200, 203 Dotpay 48 drones 82, 98 dual labour market 133 eBay 48, 49, 95, 193, 194; globalisation of 196 e-​commerce 95–​99, 96–​97, 159–​163, 160–​161; global digital trade in goods 193–​198, 195; online payments 165–​166, 166 education, changes in 4, 67, 101, 118, 120, 128, 133–​138, 136, Covid–​19 and 225, 228, 231, 234, 238, 241, 244–​245 Enterprise Resource Planning (ERP) systems 86, 191 Ericsson 12 European Commission 3 e-​wallets 156 Facebook 12, 22, 25, 45, 47, 48–​49, 53, 62; advertising on 59; data centres 193; direct and indirect network effects 58; education changes and 137; globalisation of 195; institutionalising trust 61; international taxes paid by 203; low operating costs of 64; as monopoly 64; number of users 66; pricing policy 56; revenues compared to countries’ GDP 62 factories, intelligent 81, 89–​90 financial sector, digital disruption in 99–​101 5G network 9, 15, 197–​198; Covid-​19 and 251–​252 Fiverr 125 flexible model of consumption 167 Flipkart 194 4G network 9, 15 Freelancer 48 freemium model 167, 247 FreeNow 47, 61 Friendster 58 GAFAM companies 49, 53, 54, 62, 202, 205, 231; Covid-​19 and 246–​247, 246–​250

280 Index gamification 130–​131 gatekeeper power 254 Gates, Bill 10 General Data Protection Regulation (GDPR) 206 General Electric 91 gig economy 118, 123–​124, 124, 129 GitHub 137 global hierarchies of power 193, 231 globalisation: changes in 190, 191; changing hierarchies of power in 193; Covid-​19 pandemic and 227–​228, 227–​231, 232; cryptocurrencies and 200, 201; digital flows in 190–​193; digital global order in 208–​210; digital trade in goods and 193–​198, 195; digital trade in services and 198, 198–​200, 201; the state in the digital global economy and 201–​208, 203, 205, 207; of work 124–​125 global labour market 118; inequalities within 134 Gmail 48, 55 Google 11, 16, 19, 22, 47, 48, 49, 53; Android 48, 49, 59–​60, 60; Covid-​19 and 246–​247; Covid-​19 contact tracing app developed by 231; DeepMind 20; digital infrastructure of 245; as disruptive innovation 67; Drive 19, 199; education changes and 137–​138; faster and cheaper innovation by 64; Fit 157; globalisation of 192–​193, 204; maps 55; as monopoly 64–​65; revenues compared to countries’ GDP 62; smartphones 13, 14 GrokNet 22 Groupon 49 HBO 61 horizontal integration 90 Huawei 193, 204 hubs 45, 52 hyper-​automation 79 IBM 19, 67; Simon smartphone 12, 13 Industrial Internet of Things (IIoT) 77, 80–​81, 86, 88, 197; convergence of IT and OT in 89–​90 Industry 4.0 77–​80, 94–​95; Covid-​19 pandemic and 230; deglobalisation and 197 inequalities within global labour market 134

Information and Communication Technologies (ICT) 14, 28 infrastructural platforms 49 Infrastructure-​as-​a-​Service 90 innovation platforms 48, 64, 67 insertables 131 institutionalising trust 61 integration, vertical and horizontal 90 Intel 5 intelligent algorithms xiv, xvi, 1, 4, 19–​22, 21, 29, 77; in automation 98–​99, 122; consumption and 164, 200; Covid-​19 and 230, 241, 245; globalisation and 193–​194, 200; manufacturing and 79–​84, 88, 90–​91, 95, 102; platformisation and 67, 123, 125; in work 133 intelligent automation 22, 25, 63, 102; Covid-​19 and 244; datafication of production and 86; globalisation and 190; work and 118, 122, 130, 135, 137 intelligent factories 81, 89–​90 intelligent manufacturing 79 intelligent products xvii, 91–​92, 102, 151–​152, 156–​159, 157, 174, 192, 199 intelligent sensors 14–​16, 99; collecting data in manufacturing 81–​82 International Labour Organization 127 International Monetary Fund (IMF) 3 Internet, development of the 7–​9, 9 internet economy 1 Internet of Things (IoT) 9, 15, 15–​17, 21, 68, 152, 156–​159, 157; Covid-​19 and 252–​253 Interswitch 207 iPhone 12–​13, 59–​60, 60, 171 iQiyi 199 isolation economy 235–​236 iTaxi 47 Just Eat Takeaway 57 Kickstarter 48 Kiva Systems 96 knowledge economy 1 Kronos platform 130 ‘last mile’ problem 95 lean manufacturing 93 LibGen 168 LinkedIn 49 Logistics 4.0 98–​99

Index  281 Lovelace, Ada 5, 11 Lyft 48, 169

Nintendo 48 Nokia 13

machine learning 19–​20, 22, 47, 55, 63–​64, 81–​82, 120, 130, 256; globalisation and 193–​194, 200 maker platforms 48 manufactured goods: e-​commerce and 95–​99, 96–​97; sensors and 81–​82 manufacturing: Covid-​19 and 242–​245, 243; global market size 83; intelligent 79; investment in new technologies for 81; lean 93; new technologies in 80–​86, 81, 83, 85–​86; 3D printing 84; see also production market changes 45, 46; Covid-​19 and 245–​250, 246, 248 Mastercard 49 Match.com 48 MercadoLibre 207 Messenger 47 metacompetencies 137 microcomputers 6 micro-​entrepreneurs 128–​129 microprocessors 5 Microsoft 10, 13, 19, 53, 201; Covid-​19 and 246–​247, 246–​250; data centres 193; digital infrastructure of 245–​246; as disruptive innovation 67; HoloLens 78; as monopoly 65; Online Service Terms 155; revenues compared to countries’ GDP 62; Windows 47, 48, 49 MiFit 157 MindSphere 90 miniaturisation 6–​7 MIT Initiative on the Digital Economy 24 Mixer 65 mobile phones 6–​7; smartphones 12–​14, 13 monopolies, platforms building 64–​65 Motorola 6–​9, 9 multihoming 61

omnichannel shopping 162 on-​demand economy 65, 123 onlife 27, 127 OnlineJobs 124 online shopping 151; Covid-​19 and 232–​237, 233–​236; phygital experience and 159–​163, 160–​162 operating systems 47, 49 Organization for Economic Cooperation and Development (OECD) 1, 3, 121, 203–​204, 208, 209–​210 outsourcing 124–​125, 134 Oxford Internet Institute 172

Nestlé 193 Netflix 52, 164, 173; Covid-​19 and 233, 235; multihoming and 61 network effects 56–​63; direct 45, 57, 58; indirect 52, 57–​58, 58 networks xiv, 1, 4, 26–​27; development of computer 7–​9, 9; hubs 45, 52; robust 251–​254, 252

Page, Larry 11 PayPal 48, 49 pay-​per-​use services 166–​167 Peloton 158 perceptive AI 21 personalisation 25–​28, 77, 201; of consumption 158, 164, 169; curation through 164; platform 63, 164–​165; price of 169–​174, 170–​171; of production 79–​80, 83–​84, 94, 100, 102; work and 131, 138 phygital experience 159–​163, 160–​162 Pinkoi 195 Platform-​as-​a-​Service 90 platformisation xv, 22, 27, 45; of consumption 163–​167, 166; mechanisms of 66–​68; of production 92–​95; of work 123–​129 platforms: as born digital 63; building monopolies 64–​65; categories of 48–​49; as challenge for traditional business 63–​66, 66; credit card 58; datafication effects 53–​56; economic mechanisms of 53–​63; ecosystem of services provided by 50–​51; exchange 48; excelling as strengthening datafication effects with network effects 63–​64; heterogeneity of 47–​48; hybrid 49; as immensely useful 63; innovating faster and cheaper 64; low operating costs of 64; matchmaking 48, 194; mechanisms for institutionalising trust 61; multihoming of 61; multiple functions in the digital economy 45; network effects and 27, 56–​63; phenomenal career of 45–​53, 46,

282 Index 48–​51; price differentiation by 55–​56, 56; sectoral 49; social media 194; software 194; switching costs for users of 61–​62; for telecommuting 241; transactional 48–​49; user populations compared between countries/​ regions and 66; working around regulations 65–​66 Predix 91 price differentiation by platforms 55–​56, 56 privacy 16, 26, 61, 68; in consumption 165, 172, 174; Covid-​19 and 247, 249; globalisation and 204, 209; at work 131 production 78; Covid-​19 and 242–​245, 243; data collection in 81–​82; datafication of 86–​91, 87–​88; deglobalisation of 197; digitalisation of 77, 78; digital maturity and 242–​243; Industry 4.0 77–​80; new technologies in manufacturing 80–​86, 81, 83, 85–​86; platformisation of 92–​95; reshoring of 230; sensors in 81–​82; see also manufacturing production platforms 93–​94 productivity paradox 28 product life cycle management 91–​92 Rakuten 194 Reconfigurable Manufacturing Systems 83, 174 recruitment, employee 130 regional industrial clusters 230 reinforced learning 20 Reinveting Capitalism in the Age of Big Data 128 remote work 237–​242, 239–​240 reshoring 230 Robotic Process Automation (RPA) 120–​121, 129 robots 14–​15, 77; Amazon 96; collaborative 82–​83; Covid-​19 and 243, 243–​244; growth in use of 84, 85–​86; see also automation robust networks 251–​254, 252 Salesforce 137, 199–​200 SciHub 168 Second Machine Age, The 121 self-​employed people 128–​129 sensors, intelligent see intelligent sensors services, hyper-​tradable 198 servitisation 77, 92, 158 Sesame Credit 165

sharing economy 65, 167, 169, 235 Siemens 90 skills, future work 134–​138, 136 SkinWallet 155 Skype 16, 47 Slack 130 smart manufacturing 79, 84 smart objects 157 smartphone 12–​14, 13; addiction to 171, 171–​172; direct network effects and 59, 59–​60 Social Credit System, China 202 social media platforms 194; see also Facebook social security institutions 129 software eating the world 79–​80 software platforms 194 Sony 48 splintered internet 254 Spotify 48, 53, 169, 173, 199; Covid-​19 and 233, 235 stand-​alone platforms 49 STEM skills 135 strong (deep) AI 20 subscription: model of 199–​200; services in 166–​167 supercell 53 superplatforms 49 supply chains 80, 98–​99; disrupted by Covid-​19 pandemic 229 taxation, international 203–​204 tech companies xvii, 53, 63, 102; consumption and 173; Covid-​19 and 225, 227, 231, 244–​247, 250, 252; education sector 67, 137; entering new markets 65; globalisation and 192–​193, 202–​204, 206–​207 technological revolution 1, 27–​29 technological unemployment 132, 138, 240 telecommuting 241 Telegram 47 telephone networks 58 Tencent 19, 49, 53, 100, 165; as disruptive innovation 67; revenues compared to countries’ GDP 62 3D printing 84 Tidal 173 TikTok 173, 204–​205 Tinder 48 TOR browser 172 Toyota Production System (TPS) 93 transferable skills 137

Index  283 transistors 5, 6 Transport Intelligence 98 Twitch 65 Twitter 12, 49 Uber 25, 46–​47, 48–​49, 49, 101, 127–​128, 165, 169; direct and indirect network effects 58; as disruptive innovation 67; institutionalising trust 61; multihoming and 61; number of users 66; pricing policy 56; working around regulations 65–​66, 202 UberEats 49 ubiquitous computing 22 Udemy 101, 137 UN Conference on Trade and Development (UNCTAD) 1, 3, 196; Covid-​19 pandemic and 229 Understanding the Digital Economy: Data, Tools, and Research 3 Upwork 124 vertical integration 90 Viber 47 virtual assistants 124 virtual goods 155–​156 Virtual Singapore 159 Visa 49 Volkswagen 77–​78 wearable devices 81, 131, 157 WeChat 49, 52, 165–​166, 196; number of users 66

WhatsApp 47, 48 WikiLeaks 208 Windows OS 47, 48, 49 work: automation of 118–​123, 122; Covid-​19 and 237–​242, 239–​240; datafication of 129–​132, 237–​242, 239; dematerialised 133; gamification of 130–​131; globalisation of 124–​125; intrinsic value of 240; new risks in the labour market and 132–​134; platformisation of 123–​129; skill-​biased platform 125; skills for future 134–​138, 136 World Economic Forum 137 World Health Organisation 234 World Trade Organization 208–​209 World Wide Web 11 Wozniak, Steve 6, 10 Xiaomi 157 Xometry 94 Yellow Pages 49 YouTube 12, 47, 48, 52; direct and indirect network effects 58; free services 167; institutionalising trust 61; number of users 66; pricing policy 56 Zalando 53 Zoom 47 Zuckerberg, Mark 45