Handbook on Digital Platforms and Business Ecosystems in Manufacturing 1035300990, 9781035300990

Citation preview

HANDBOOK ON DIGITAL PLATFORMS AND BUSINESS ECOSYSTEMS IN MANUFACTURING

Handbook on Digital Platforms and Business Ecosystems in Manufacturing Edited by

Sabine Baumann Professor of Digital Business, Berlin School of Economics and Law, and Scientific Director, OFFIS Institute for Information Technology, Germany

Cheltenham, UK • Northampton, MA, USA

© The Editor and Contributors Severally 2024

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. Published by Edward Elgar Publishing Limited The Lypiatts 15 Lansdown Road Cheltenham Glos GL50 2JA UK Edward Elgar Publishing, Inc. William Pratt House 9 Dewey Court Northampton Massachusetts 01060 USA A catalogue record for this book is available from the British Library Library of Congress Control Number: 2023952158 This book is available electronically in the Business subject collection http://dx.doi.org/10.4337/9781035301003

ISBN 978 1 0353 0099 0 (cased) ISBN 978 1 0353 0100 3 (eBook)

EEP BoX

Contents

List of contributorsviii 1

Introduction to the Handbook on Digital Platforms and Business Ecosystems in Manufacturing1 Sabine Baumann

PART I

BUSINESS MODEL TRANSFORMATIONS

2

Revolutionizing manufacturing: how digital technologies and digital industrial platforms drive business model transformation Claudia Franzè and Danilo Pesce

3

Converging challenges: industrial data ecosystems and a vital business model Marc Brechtel and Dimitri Petrik

4

Ecosystem emergence in the manufacturing sector: exploring transformation processes of product-focused firms Joachim Stonig, Torsten Schmid and Günter Müller-Stewens

42

5

Social exchange- versus economic exchange-driven processes: the emergence of peer-to-peer start-up business models in Denmark Susanne Gretzinger, Birgit Leick and Anna Marie Dyhr Ulrich

55

6

It’s all connected: IoT-affordances and connectivity-based business model innovation Luke Treves, Mika Ruokonen and Paavo Ritala

71

PART II

10 26

ECOSYSTEM DESIGN AND GOVERNANCE

7

Fishing for complements: partnership scouting routines of non-focal B2B firms in emerging-technology digital business ecosystems Christian Zabel, Jonathan Natzel and Daniel O’Brien

88

8

Designing innovation ecosystems with functional roles: the case of industrial, intelligent manufacturing Julius Kirschbaum, Tim Posselt and Kathrin M. Möslein

106

9

Changing the role of a focal firm: the transition of a B2B SME to ecosystem leadership in manufacturing industry Lukas Budde, Leonardo Laglia, Thomas Friedli and Roman Hänggi

126

v

vi  Handbook on digital platforms and business ecosystems in manufacturing 10 Establishing platform-based ecosystems: how automotive manufacturers adapt their value creation to a digital end147 Nicolas Böhm, Laura Marie-Luise Watkowski and Christoph Buck 11

Connecting the ecosystem: enabling interaction between manufacturing companies164 Jonas Kallisch

12

Relevance of technology and focal product for collaboration: exploring additive manufacturing ecosystems Dominik Morar, Simon Hiller and Dimitri Petrik

13

Enabling digital business ecosystems: an empirical analysis of the impact of smart manufacturing technologies on firms’ financial performance Francesco Arcidiacono, Alessandro Ancarani, Carmela Di Mauro and Florian Schupp

178

194

PART III SUSTAINABILITY AND CIRCULAR ECONOMY 14

Profitable sustainability: the tribrid business model for environmental, social and economic value creation in digital platforms Susanne Royer, Sabine Baumann and Paweł Głodek

206

15

Design thinking in the circular economy: augmenting digital ecosystems and platforms with local entrepreneurial ecosystems Philip T. Roundy

221

16

Evaluation of sustainability of smart-circular product-service ecosystems using the example of 3D printing Verena Luisa Aufderheide

239

17

The role of digital platforms as circularity broker: an updated SCOR perspective on circular supply chain performance Tom Pettau and Laura Montag

257

18

New work in manufacturing: current and future implications to the paradigm shift in global manufacturing work Seth Powless and Ashley Church

278

PART IV INDUSTRY APPLICATIONS AND CASE STUDIES 19

Digital business ecosystems for digital spare parts Sabine Baumann and Marcel Leerhoff

295

20

Space digital platforms: empirical evidence from a case study Daniele Binci, Andrea Appolloni and Wenjuan Cheng

310

21

Maintenance 4.0: applying IoT technologies to increase uptime and efficiency of critical infrastructures Alexander Herzfeldt, Christoph Ertl and Sebastian Floerecke

327

Contents  vii 22 A digital platform for heterogeneous fleet management in manufacturing intralogistics344 Nitish Singh, Alp Akçay, Quang-Vinh Dang, Ivo Adan and E. A. Thijssen 23

Data-driven traffic management on the last mile: understanding manufacturing industry in smart city ecosystems Alisa Lorenz and Nils Madeja

358

24

Digital transformation and the role of platforms in fostering co-creation: the case of the Open Italy program by ELIS consortium Nicola Del Sarto, Alberto Di Minin, Giulio Ferrigno and Asia Mariuzzo

377

25

Digital platforms in the Norwegian food industry: an ecosystem perspective on the nation’s dairy and beef production Victoria Slettli

391

Index407

Contributors

Ivo Adan is a Full Professor in the Department of Industrial Engineering at Eindhoven University of Technology. His current research interests include modelling, analysis, design and control of manufacturing and warehousing systems, and more specifically, mathematical analysis of multidimensional Markov processes and queueing models. Alp Akçay is an Associate Professor in the Department of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology. He received his PhD in Operations Management and Manufacturing from Carnegie Mellon University. His research interest is data-driven decision-making with applications in smart industry, focusing on planning and control of manufacturing operations and after-sales services of capital goods. Alessandro Ancarani, PhD, is Full Professor in Management Engineering at the University of Catania, Italy. His research focuses on Industry 4.0 and smart manufacturing, analysis of intangibles in public service organizations, public procurement, and behavioral operations management. He has published in leading journals including the British Journal of Management, International Journal of Production Economics and the International Journal of Production & Operations Management. Andrea Appolloni is an Associate Professor at the University of Rome Tor Vergata in Italy. He is also a permanent visiting fellow at Cranfield University in the UK. His research areas and teaching activities are concentrated on operations, supply chain, and procurement management with a focus on innovation and sustainability. He is responsible for several national and international research projects. At the University of Tor Vergata, he is the lead of the specialization on Supply Chain Management at the Master of Science in Business Administration. He is also the coordinator of the ESA​_LAB@​UNITOV, a research space center supported by the European Space Agency. Francesco Arcidiacono, PhD, works as Industry 4.0 Specialist in the Purchasing & Supplier Management department of Schaeffler Automotive Technologies, Germany. His research focuses on Industry 4.0 and smart manufacturing and on human resource management. His research has been published in international refereed journals including the International Journal of Production & Operations Management, Public Management Review and IEEE Engineering Management Review. Verena Luisa Aufderheide is a Research Assistant at the Center for Environmental Management, Resources and Energy at Ruhr University Bochum. She completed her bachelor’s degree in Management and Mathematics and her master’s degree in Management at Ruhr University Bochum. Since 2019, she has been conducting research in various areas of sustainability and digitalization. She completed her PhD in the topic area of Smart-Circular Product-Service-Systems. Sabine Baumann is Professor for Digital Business at Berlin School of Economics and Law and Scientific Director at OFFIS Institute for Information Technology. Before rejoining academia she worked in senior management positions for Bertelsmann, Germany’s largest media viii

Contributors  ix conglomerate. Dr. Baumann has edited several books, including the Handbook on Digital Business Ecosystems, and a special issue on Strategic Media Management for the Journal of Media Business Studies. Her research areas are all interdisciplinary, involving either changes in business models due to new technologies or the strategic exploitation of network structures. Her work has been published in influential journals and handbooks. Daniele Binci is Assistant Professor in Management at Tor Vergata University, Department of Management and Law, Rome, Italy. He received his PhD in Management from the same university. His main research interests include innovation management, digital transformation and sustainability. His teaching areas are focused on management and innovation management at undergraduate, graduate and executive level. He has published articles for several international journals and presented papers at international conferences. He is a Member of the Board of the Master Program on ‘Administrative science and innovation on public administration’ at the University of Macerata and University of Urbino, Italy. Nicolas Böhm is a Research Assistant at the Fraunhofer Institute for Applied Information Technology FIT Branch Business & Information Systems Engineering (Germany), while currently pursuing the Mannheim Master in Management at the University of Mannheim (Germany). He completed his Bachelor in Business Administration at the University of Bayreuth (Germany). In his studies, he specializes in information systems and management of innovation. His research so far focuses on digital innovation in business, including platform ecosystems. Additionally, he has gained experience in project management and strategy consulting. Marc Brechtel is an External Doctoral Student at the Faculty of Economics and Social Sciences at the University of Potsdam. He holds a master’s degree (M.Sc.) in Industrial Engineering from the University of Applied Sciences in Mannheim. His research interests lie with digital platforms and industrial data ecosystems. Marc has ten years of professional experience in various positions at Freudenberg. There, he gained experience in technical sales, application engineering and digital business development. Currently, he is employed as a project manager in the field of advanced product development focusing on digitalization and the internet-of-things at Freudenberg FT GmbH. Christoph Buck is Professor for Entrepreneurship and IT Innovation Management at the Augsburg University of Applied Sciences, an adjunct Associate Professor at the Queensland University of Technology (Australia) as well as Deputy Academic Director of the FIM Research Center for Information Management. He also works in a leading position in the Branch Business & Information Systems Engineering of Fraunhofer FIT, where he is co-heading a research group and managing the Digital Innovation Lab as one of the cofounders. His research and teaching interests include digital transformation and innovation, digital ecosystems, digital sports and health and entrepreneurship education. Lukas Budde is Post-Doc and Division Deputy in the field of Production Management at the Institute for Technology Management at the University of St. Gallen. His research focus lies on patterns for smart manufacturing and digital platform ecosystems in manufacturing companies. Lukas has supported companies in many digital transformation projects. He is a lecturer for operations management courses and for custom programs of the Executive School of the University of St. Gallen. Since 2018 he has also been the managing director of INOS, the largest regional innovation network for SMEs in Switzerland.

x  Handbook on digital platforms and business ecosystems in manufacturing Wenjuan Cheng, PhD, is a Postdoctoral Research Fellow specializing in sustainable procurement management. She is affiliated with the Department of Management and Law at the University of Rome Tor Vergata in Italy, where she holds a PhD in Environmental Economics and Law. As a Postdoctoral Research Fellow, her research interests encompass green and sustainable procurement, sustainable supply chain management, environmental policy, innovation, and sustainable management in public administration, food, space economy, industrial and manufacturing sectors. She actively contributes to various research projects exploring innovation and sustainable management. Ashley Church is a Sales and Research Associate at Dewey Community, an EdTech start-up, and recently graduated from Earlham College with a bachelor’s degree in Global Management with a focus on marketing. As a student at Earlham College, she participated in multiple research projects focused on supply chain, marketing, and new work practices within the United States. Currently, she researches and markets the benefits of parental engagement in all areas of education within schools and districts across the United States. Quang-Vinh Dang is an Assistant Professor in the Department of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology. He received his Ph.D. degree in Operations Research from Aalborg University in Denmark. His research interest is in developing methodologies for the planning and scheduling of automated manufacturing/ services systems, including the interconnection between manufacturing processes and material handling operations and the collaboration between autonomous mobile robots and human operators. Nicola Del Sarto is an Assistant Professor at University of Florence. He received a PhD in Management of Innovation, Sustainability and Healthcare from Scuola Superiore Sant’Anna in 2019. Nicola’s research interests focus on small businesses and start-ups and support mechanisms such as incubators, accelerators and corporate accelerator programs. Moreover, he is investigating the processes of business creation under the open innovation paradigm. Nicola holds a master’s degree in Economics from University of Pisa and a postgraduate master’s in Management, Innovation and Engineering of Services from Scuola Superiore Sant’Anna. Nicola’s work has been published in high-quality peer-reviewed journals. Carmela Di Mauro, DPhil, is Associate Professor in Management Engineering, University of Catania, Italy. Her current research focuses on Industry 4.0 and smart manufacturing, health care management, organization, behavioral operations management and public procurement. Her most recent research has been published in international refereed journals, including the Journal of World Business, International Journal of Production Economics and the British Journal of Management. Alberto Di Minin is Full Professor of Management at Sant’Anna School of Advanced Studies and Research Fellow with the Berkeley Roundtable on the International Economy (BRIE), University of California, Berkeley. Alberto teaches innovation management and innovation policy, and he is Co-Director of the Confucius Institute of Pisa, and Director of the Galileo Galilei Italian Institute in Chongqing University. Alberto is Co-Editor in Chief with R&D Management Journal, and his research deals with open innovation, appropriation of innovation and science and technology policy.

Contributors  xi Anna Marie Dyhr Ulrich is PhD and Associate Professor of B2B Marketing at the Department of Business and Sustainability at the University of Southern Denmark. Her research interests are within B2B marketing, international marketing, IoT and relationship marketing. Anna Marie has published articles within these topics in well-recognized international journals and books. She has extensive national and international teaching and research experience. Furthermore, she has practical experience from jobs as project manager, owner of her own consultancy business and as Senior Consultant in the international department of the Confederation of the Danish Industry, Copenhagen. Christoph Ertl (Prof. Dr) is a Professor for Business Information Systems at Munich University of Applied Sciences. Concurrent with his PhD completion at the Technical University of Munich in 2019, he directed the Controlling Department of Munich Airport’s Engineering and Facilities unit from 2015 to 2020. His research explores the impact of Digital Transformation on Business Processes, synthesizing theoretical insights with practical knowledge gained from implementing innovative technological solutions in critical infrastructures. Giulio Ferrigno is a Senior Assistant Professor at Sant’Anna School of Advanced Studies of Pisa. He has held visiting positions at the University of Cambridge, Tilburg University and the University of Umea. His main research themes include strategic alliances, big data, and Industry 4.0. His works have been published in Small Business Economics, Technological Forecasting and Social Change, International Journal of Management Reviews, R&D Management, Technology Analysis & Strategic Management, Review of Managerial Science, European Journal of Innovation Management and International Journal of Entrepreneurial Behavior & Research. He is an Associate Editor of Technology Analysis & Strategic Management. Sebastian Floerecke (Dr) is currently employed as an Enterprise Architect at Munich Airport. He is also a Research Affiliate at the Chair of Information Systems (Information and IT Service Management) at the University of Passau, where he previously worked as a Research Assistant. His dissertation project focused on investigating cloud computing business models and ecosystems. Claudia Franzè is a PhD Candidate in Management Engineering at Politecnico di Torino and Industry 4.0 Consultant at the National Competence Center CIM4.0 in Turin, Italy. She was a visiting PhD candidate at OFFIS – Institut für Informatik in Oldenburg, Germany. She holds a master’s degree in Business Management and a bachelor’s degree in Business Administration from Turin’s School of Management and Economics. She also has a master’s in Marketing and Digital Communication from IlSole24ore Business School of Milan. Her research focuses on digital transformation, business model innovation, sustainability and circular economy. Thomas Friedli is a professor of production management at the University of St. Gallen (Switzerland). His main research interests are in the field of managing operational excellence, global production management and management of industrial services. He is a lecturer in the (E)MBA programs in St. Gallen, Fribourg and Salzburg, and spent several weeks as Adjunct Associate Professor at Purdue University in West Lafayette, USA. Prof. Friedli leads a team of 15 researchers who develop new management solutions for manufacturing companies in today’s business landscape. He is also the editor, author, or co-author of 14 books and various articles.

xii  Handbook on digital platforms and business ecosystems in manufacturing Paweł Głodek is Associate Professor at the Faculty of Management at the University of Lodz, Poland. His research concerns innovation, entrepreneurship, business advice and small business economics. He has published papers in international journals such as Entrepreneurship Theory and Practice and presented his work at leading international conferences such as RENT and the International Council for Small Business (ICSB) World Conference. Author and coauthor of expert reports prepared for the European Commission, Polish Ministry of Science, and several regional authorities in Poland (including Regional Innovation Strategies). Paweł is an experienced project manager and researcher in several national and EU-funded projects (EIT, Erasmus, INTERREG and others). Susanne Gretzinger is Associate Professor (PhD) of B2B Marketing and Regional Development at the Department of Business and Sustainability at the University of Southern Denmark. Her current research interests are within B2B marketing, regional development and rural enterprise economics. Her current interests include the sharing economy, the start-up process, IoT-driven business models, participation in a rural regional context and emerging social capital. Roman Hänggi is Professor for Production Management at the University of Applied Science, OST, in Switzerland. Before joining academia, he held various senior management positions at Leica, Bosch, Arbonia and Hilti for 25 years where his focus was on changing industrial companies globally through lean and digitalization initiatives. His research areas are the development and implementation of the smart factory and the implementation of lean management in small and medium-sized companies. He publishes on these topics in scientific journals and books. Alexander Herzfeldt (Dr) is a Management Consultant/Advisor and works at the interface of management consulting and custom software development. Simon Hiller is a Research Assistant at the Ferdinand Steinbeis Institute in Heilbronn and Stuttgart, Germany. He conducts research in the field of Industry 4.0 and Industrial Internet of Things, focusing on value creation through cyber-physical systems, especially with regard to additive manufacturing. He is also project manager of several cross-domain Industrial Internet of Things projects at the Ferdinand Steinbeis Institute, so-called Micro Testbeds. Jonas Kallisch is a Research Assistant at Emden/Leer University of Applied Sciences (Germany). He is researching the networking of manufacturing companies and their manufacturing infrastructures as part of the Future Lab Production project. He holds a master’s degree in Industrial Engineering. His research interests are data analytics, digital business ecosystems and federated data architectures. Before starting his dissertation, he gained experience in ERP consulting with manufacturing companies and as a software engineer in the defense sector. Julius Kirschbaum is currently a Research Associate and Doctoral Candidate at the School of Business, Economics and Society of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). His research is focused on industrial innovation ecosystems and platforms, as well as artificial intelligence, especially in the field of natural language processing. He has a Master’s of Science in Industrial Engineering – Material Science, which he studied at RWTH University until 2018. His studies were focused on innovation management and metallurgy. Leonardo Laglia heads the Marketing & Sales department of Swiss recycling firm soRec. Before his current position, he was a Research Assistant at the Institute of Technology

Contributors  xiii Management of the University of St. Gallen. His research focuses on the design, development and growth of digital platform ecosystems as well as their implications for sustainable business models. Marcel Leerhoff is working at thyssenKrupp Electrical Steel GmbH Gelsenkirchen, Germany. He is responsible for the automation and digitization of the business processes with process mining. Before that, he worked at a computer science research facility and supported SMEs with digitization projects. He also conducted research with a strong focus on digital business ecosystems and additive manufacturing in combination with digital spare parts. In 2021 he completed his master’s degree in Industrial Engineering. Birgit Leick is Professor of Innovation and Entrepreneurship in the School of Business (Department of Business and IT) of University of South-Eastern Norway. Her current research interests are regional entrepreneurship in the Nordic sharing economy, creative entrepreneurship in rural-peripheral locations and leadership in relation to entrepreneurship. Alisa Lorenz is a Research Associate and Lecturer for Business Intelligence at the THM Business School in Germany while pursuing her doctoral studies in Information Systems at the University of Cologne. She is a data and analytics expert with several years of industry experience in the domains of consulting, finance, telecommunication and construction. She holds a master’s degree in Financial Controlling and a bachelor’s degree in Management with focus on medium-sized businesses. Her current research focuses on smart mobility, where she explores the influences of citizen-centric initiatives in development towards a smart city and the influences on technology acceptance. Nils Madeja has been serving as Professor of Business Administration with a special emphasis on digital business at the Technische Hochschule Mittelhessen (THM) University of Applied Sciences in Gießen since 2018. His research and teaching activities focus on digital business models, digital ecosystems and the digital transformation – drawing on his practical experience as a former venture capitalist. At THM Business School, Nils heads the Master of Science program in Digital Business. He holds a graduate degree in Electrical Engineering from the University of Kiel and a doctorate degree in Business Administration from the WHU – Otto Beisheim School of Management. Asia Mariuzzo is a Consultant at SDG Group (Verona) in the field of Corporate Performance Management. After her bachelor’s degree in Philosophy, International Studies and Economics at Ca’ Foscari University of Venice, she completed a master’s degree in Innovation Management at Sant’Anna School of Advanced Studies (Pisa), where she carried out a dissertation on digital transformation and partnerships between corporates and start-ups. Today, her focus is on business analytics and the development of data-driven solutions to provide insights on corporate performances and support companies in making informed decisions. Laura Montag (Dr) is a Head of Business Administration and Sustainability at the German National Association for Student Affairs in Berlin. She obtained her Doctorate Degree at the Ruhr University in Bochum and worked as a Research Assistant at the Chair of Production Management. Her research focuses on the interrelationship between circular economy, sustainability and supply chain management as well as the interlinkages between digitization and circular economy.

xiv  Handbook on digital platforms and business ecosystems in manufacturing Dominik Morar is Postdoctoral Researcher at the Ferdinand Steinbeis Institute in Heilbronn and Stuttgart, Germany. His research focus is on the digital transformation of business processes in manufacturing. He examines additive manufacturing (3D printing) from the perspective of information systems, e.g. information sharing and analysis. In addition, he oversees research and transfer projects on business ecosystems for product development and manufacturing applications. Kathrin M. Möslein is Vice President and professor at the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). She holds the Chair for Information Systems I – Innovation & Value Creation at FAU’s School of Business, Economics and Society and is a Research Professor and Academic Director of the Center for Leading Innovation and Cooperation (CLIC) at the HHL Leipzig Graduate School of Management (HHL). She is a Founding Member, Fellow and former President of the European Academy of Management (EURAM) and former Vice President of the European Institute for Advanced Studies in Management (EIASM). Her research focuses on strategic innovation and ecosystems development. Günter Müller-Stewens (Prof. em. Dr) was Professor at the University of St. Gallen (1991–2017) and Director of the Institute of Management & Strategy, Dean of the Business Administration Department, Founder and Academic Director of the Master of Strategy and International Management and of the Master of General Management. He studied business administration at the University of Regensburg (1977, Dipl.-Kfm.), received his doctorate from the University of Munich (1981, Dr. rer. pol.) and his habilitation at the University of Stuttgart (1987, Dr. rer. pol. habil). His research and work focusses on strategic management, a field in which he has extensively published and received numerous awards. Prof. Müller-Stewens also serves as an advisory board member, trainer and consultant to international companies. Jonathan Natzel is a Strategy and Organization Consultant to startups and big corporations who combines his research interest with industry knowledge around teams in dynamic and mostly digital business environments. His industry expertise in media is based on his previous profession, within which he took the role as Founder and Managing Director of a virtual reality startup and was heading huge broadcasting technology projects in Europe. Daniel O’Brien is a Research Assistant at the Technische Hochschule Köln. He has been supporting Prof. Zabel in his research there since 2022. Previously, he was a research assistant at the University of Cologne at the Chair of Media and Technology Management where he also obtained his PhD on the digital transformation of entrepreneurial journalism in 2022. In addition, he works as a freelancer in research and teaching. Danilo Pesce received his PhD in Management from the Politecnico di Torino in 2019. He is currently an Assistant Professor of Strategy and Organization with the Politecnico di Torino, Italy. His research interests include organizational- and industry-level changes triggered by digital technologies adoption. His research on technology strategy and digital transformation has been published in leading outlets including the Journal of Product Innovation Management, Information & Management, Technological Forecasting and Social Change and IEEE Transactions on Engineering Management. Dimitri Petrik is a Postdoctoral Researcher at the Department for Information Systems II at the University of Stuttgart and a Research Group Leader at the Graduate School of Excellence Advanced Manufacturing Engineering. His research concentrates on digital platforms, ecosystems and circular economy and has been published in the Information Systems Management

Contributors  xv Journal, Schmalenbach Business Review and the International Conference on Information Systems. He serves as an Organizing Committee Member of the International Conference on Software Business and the International Workshop on Software-intensive Business and is a Management Committee Member of the Software Product Management Division of the German Informatics Society. Tom Pettau (MSc) is a Doctoral Student and Research Assistant at the Chair of Production Management at Ruhr University Bochum. After his bachelor’s degree in Management and Economics, he graduated with a master’s degree in Management at Ruhr University Bochum with a specialization in Operations Research and Production Management. His doctoral thesis focuses on the interrelationships between circular economy, holistic sustainability, resilience, digitalization and supply chain management. Tim Posselt is a Postdoctoral Researcher at the School of Business, Economics and Society of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He received his doctorate on Organizational Competence for Servitization from the Chair of Information Systems – Innovation and Value Creation at FAU in 2017. His research focuses on value co-creation in complex service systems. Specifically, he aims to understand how such systems organize for distributive and regenerative value creation. Previously, he was a Research Assistant as well as a Group Manager at the Fraunhofer Center for Applied Research on Supply Chain Services in Nuremberg from 2011–2018. Seth Powless, PhD, is an Assistant Teaching Professor of Business at Penn State University with teaching and research interests in project management, operations management, supply chain, logistics, international business and data analytics. He is also the Honors Program Coordinator for Penn State University – Beaver Campus. His research focuses on the intersection between operational constraints (Theory of Constraints) and contemporary issues in supply chain, project management, business analytics and new work. Dr Powless taught at the University of Toledo and held a tenured faculty position at Earlham College before joining Penn State University. He regularly presents at the Production Operations Management Society (POMS) and Decision Sciences Institute (DSI). Paavo Ritala is a Professor of Strategy and Innovation at the Business School at LUT University, Finland. His main research themes include networks, ecosystems and platforms, the role of data and digital technologies in organizations, business model innovation and circular and regenerative economy. His research has been published in journals such as the Journal of Management, Research Policy, Journal of Product Innovation Management, R&D Management, Technovation, Long Range Planning, Industrial and Corporate Change, California Management Review and Journal of Business Strategy. He is closely involved with business practice through research projects, executive and professional education programs and in speaker and advisory roles. Philip T. Roundy is Mary Harris Distinguished Professor of Entrepreneurship at the University of Tennessee (Chattanooga). His research focuses on understanding how entrepreneurial and business ecosystems can be tools for economic and community revitalization. He is particularly interested in the role of entrepreneurship in the renewal of struggling cities and ‘dying’ industries. He is Associate Editor at the Journal of Business Venturing Insights and serves on the editorial boards of Strategic Entrepreneurship Journal, Small Business Economics, Strategic Organization and Entrepreneurship Research Journal.

xvi  Handbook on digital platforms and business ecosystems in manufacturing Susanne Royer is a professor of strategic and international management at Europa-Universität Flensburg, Germany. Her research interests include changing forms of value creation structures, resources and capabilities and competitive advantage. She has published numerous papers, (co-)authored several books and edited the International Journal of Globalisation and Small Business till January 2024. Mika Ruokonen is an Industry Professor of Digital Business at the Business School at LUT University, Finland. His research interests include data, artificial intelligence and digital business models and ecosystems. Mika has broad experience in digital business, in managerial and consulting roles and he holds several positions on different corporate boards. Torsten Schmid is a senior researcher and lecturer at the University of St. Gallen (ES-School of Entrepreneurs) in the fields of strategic change and organizational design at large corporations. As a ‘pracademic’, he also works as a strategy consultant and executive trainer. Florian Schupp (Dr) works as Senior Vice President Purchasing & Supplier Management at Schaeffler Automotive Technologies, Germany. Since 2019 he has been Adjunct Professor for Logistics at the Jacobs University Bremen. His main research interests encompass purchasing strategy, behavioral aspects in purchasing, purchasing in nature and supplier innovation. His research has been published in international refereed journals including the International Journal of Production & Operations Management, Journal of Purchasing and Supply Management, and International Journal of Production Research. Nitish Singh is a PhD Candidate in the School of Industrial Engineering at Eindhoven University of Technology. His research interests include simulation-based optimization and data-driven operations management, focusing on applications in manufacturing systems and transportation systems. Victoria Slettli works in the position of an Associate Professor in Organization and Management at the Inland Norway School of Business and Social Sciences. In her research, Dr Slettli is especially interested in such topics as organizational and individual learning, knowledge sharing, intellectual capital, smart innovation and management perspectives on big data. Victoria has published her research in such journals as the Electronic Journal of Knowledge Management, Journal of Entrepreneurship, Management and Innovation and Scandinavian Journal of Business Research. Joachim Stonig is a Postdoctoral Researcher and Lecturer, leading the Competence Center for Ecosystems at the Institute for Management & Strategy at the University of St. Gallen. Joachim’s research focuses on transformation processes of established companies in the context of changing ecosystems. His research has been published in leading academic and practice-oriented journals, including the Strategic Management Journal. Prior to joining the University of St. Gallen, he worked as a strategy consultant in Paris for four years. E. A. Thijssen received his master’s degree in Manufacturing Systems at Eindhoven University of Technology. His current interest revolves around modular communication frameworks for Industry 4.0. He is the founder of ‘Ennio Smart Solutions’ and works on the project ‘Digital Factory’ at Brainport Industries Campus in Eindhoven and the European project ‘DIMOFAC’ (Digital and Intelligent Modular Factories).

Contributors  xvii Luke Treves, DSc, is a Postdoctoral Researcher at the Business School at LUT University, Finland. His main research interests include business model innovation and process design, particularly its intersection with Industry 4.0/5.0 and the digital transformation of the economy and society. He has worked in various advisory roles within the United Kingdom Government’s former Department for Business, Innovation and Skills. Via this work, he has been involved in numerous projects through which he has accumulated experience in business administration, trade facilitation and strategic controls. His academic works have considered artificial intelligence, the Internet of Things, cloud computing, and product-service systems. Laura Marie-Luise Watkowski is a PhD Candidate at the University of Bayreuth, the Research Center Finance and Information Management (FIM) and the Branch Business and Information Systems Engineering of the Fraunhofer Institute of Applied Information Technology (FIT). She focuses her research in the field of digital innovation, digital platform ecosystems and digital transformation of organizations. She has further gained practical experience within various digitalization projects focused on developing digital services and introducing enterprise resource planning modules. Christian Zabel is Full Professor for Innovation and Corporate Management at Technische Hochschule Köln. His research focuses on production and distribution in digital media, digital ecosystems, emergent media technologies and the digital transformation of (media) companies. Previously, he headed the product management of t-online.de, Germany’s largest online publisher and was executive assistant to Deutsche Telekom’s CEO René Obermann.

1. Introduction to the Handbook on Digital Platforms and Business Ecosystems in Manufacturing Sabine Baumann

EXPLORING DIGITAL PLATFORMS AND BUSINESS ECOSYSTEMS A digital business ecosystem (DBE) is a network of organizations such as manufacturers, suppliers, distributors, customers, competitors, government agencies, etc. that together create and deliver a specific product and/or related service in an environment that is partially or fully driven by digital technologies. The concept of a business ecosystem draws an analogy from biological ecosystems, suggesting that its actors are interdependent and the system evolves with the inclusion or departure of actors (Kapoor, 2018; Baumann, 2022). These actors collaborate for shared goals while also competing for limited resources (Kapoor and Lee, 2013; Kapoor, 2018). The core of a business ecosystem lies in the absence of a sole actor controlling all solution components. Instead, the ecosystem’s value creation surpasses the sum of individual contributions from its actors. Consequently, DBEs redefine organizational structures, shifting from vertical hierarchies and linear supply chains to collaborative and distributed models, fundamentally altering business processes (Subramaniam, 2020; Baumann, 2022). Within DBEs, value creation transcends individual company boundaries, emerging from collaborative partner efforts (value co-creation) (DeFillippi, 2014; Beirão et al., 2017; Bähr, 2022; Robra-Bissantz et al., 2022). Facilitated by digital technologies like artificial intelligence, digital twins, data analytics and cloud computing, agile networking and cross-company data exchange become achievable, which is essential for crafting innovative business models and resilient supply chains. As a result, information transforms into a valuable and essential resource (Wunck and Baumann, 2017). Notably, DBEs transcend traditional industrial frameworks, enabling firms from various sectors and locations to belong to the same DBE while offering distinctive, tailor-made solutions to individual clients, a notion now known as Industry 4.0 (Baumann, 2022; Culot, 2022). Besides, the leveraging of geographical cost variations and resource accessibility has prompted companies to decentralize operations and collaborate with external partners, adopting distributed, modular and increasingly virtual structures (Baumann, 2013). Extensive discussions have revolved around naming these business networks with propositions ranging from value creation networks or architectures (e.g. Keen and Williams, 2013) to value adding webs (e.g. Gretzinger and Royer, 2014)). Dealing with the challenge of simultaneously competing and collaborating in DBE have spurred research about tensions, strategic positioning and necessary dynamic capabilities (Maijanen, 2022; Reisinger and Lehner, 2022). A significant and intense discourse concerns the role of platforms in DBEs (e.g. Gawer, 2011; Rietveld 1

2  Handbook on digital platforms and business ecosystems in manufacturing and Schilling, 2020; Kretschmer et al., 2022; Mosch and Obermaier, 2022), spurred by the growth of digital platform giants like Facebook, Amazon, Apple and Google. However, while many DBEs are platform-oriented, a platform is not a prerequisite for DBE success (Baumann, 2022). Although digital platforms and business ecosystems are widely discussed, numerous questions have not yet been adequately resolved: How can business ecosystems be initiated and established across industries? How is the role of the focal actor changing? What shapes value co-creation and value capture? What factors influence the success of digital platforms and DBE? How can technical and organizational barriers to data exchange between actors in a DBE be removed? How do actors position themselves strategically within DBEs? How is competition between DBEs shaping up? Are industries evolving into ecosystems of ecosystems? What might a technological maturity assessment of digital platforms and ecosystems look like? How can digital platforms and DBE support circular economy concepts? Are DBEs a suitable approach with which to implement sustainable business models that follow social, environment and economic objectives? These questions highlight the intricate nature of digital business ecosystems, where interconnected organizations driven by digital technologies collaborate and compete. DBEs redefine structures, favoring collaboration, and value co-creation. Powered by AI, digital twins, analytics and cloud computing, DBEs transcend traditional industry boundaries and may offer tailored solutions for manufacturing advanced products combined with innovative services. Because the abovementioned fundamental questions persist, this handbook delves deeply into the landscape of digital platforms and business ecosystems in manufacturing.

OBJECTIVES AND APPROACH OF THE HANDBOOK DBEs are not only of interest in management and economics, but also extend to areas like information systems and engineering. While information systems and engineering focus on technical feasibility, management and economics explore strategies and business models leveraging data streams. A mapping study by (Baumann and Leerhoff, 2022) revealed isolated research strands even within disciplines, also highlighting a lack of in-depth industry applications and recommendations for DBE design and management amidst abundant theoretical papers. In addition, the authors found it all the more surprising that research on digital platforms and digital business ecosystems in industrial engineering, and particularly in manufacturing, is still underdeveloped compared to other disciplines such as computer science, management and economics. Therefore, the objective of this handbook is to fill this void and explore an integrated approach to manufacturing that brings together physical machines, data and human beings to transform the manufacturing process and its related ecosystem. The use of sensors, connectivity and advancements in analytics and machine learning have been driving manufacturing in the digital age. Consequently, embracing the business ecosystem model and harnessing digital technologies opens up new business opportunities, particularly through the use of digital platforms, while advancing product quality, productivity and operational efficiency in competitive markets. Business ecosystems and platforms are also known to be better able to address the manifold disruptions in supply chains and provide the necessary resilience for continuous and sustainable operations.

Introduction  3 This handbook provides a comprehensive and detailed exploration of the evolution and current state of digital platforms and ecosystems in manufacturing. It brings together scholars from relevant disciplines (management, engineering and computer science) and investigates different perspectives (business model transformations, platforms technologies, governance, and sustainability and circular economy) on digital platforms and ecosystems in manufacturing. This integrative approach is vital to capture the scope of economic and technological factors that interact in the emergence and evolution of digital platforms and ecosystems in manufacturing in order to understand the underlying processes. This compendium navigates the integration of digital technologies and collaborative digital platforms and business ecosystems for manufacturing. The Handbook not only provides guidance for researchers unfamiliar with the topic, but also for managers who have to develop and operate increasingly complex digital platforms and business ecosystems in manufacturing for their companies to remain competitive.

ORGANIZATION OF THE HANDBOOK The objective for the Handbook is to bring together perspectives and approaches that represent the diversity of disciplines and the many facets of digital platforms and DBE development in manufacturing practice. The Handbook includes 25 chapters from over 60 international scholars and practitioners who share their research and experiences on and in DBEs. The Handbook comprises four primary sections, each centered around a central theme. Given the transdisciplinary nature of digital platforms and DBEs, several chapters address multiple topic areas but are categorized under the theme that best encapsulates their overall contribution. While chapters may exhibit some thematic overlap, each chapter offers a distinct viewpoint and is designed to be independently informative. The following outline offers a comprehensive view of the Handbook’s themes and a brief summary of the included chapters. Part I (which follows this introductory chapter) presents business model transformations of DBEs in manufacturing. Contributions concentrate on how platforms and DBEs facilitate the development and implementation of innovative business models. Claudia Franzè and Danilo Pesce (Chapter 2) examine how the convergence of digital platforms and technologies reshapes business models, particularly in traditional manufacturing firms. Through a case study in the medical device sector, they showcase a multidimensional business model transformation driven by product personalization and servitization. They propose an integrative framework to systematize research on digital transformation’s impact on business models. Marc Brechtel and Dimitri Petrik (Chapter 3) interviewed industry experts to determine why industrial data remains siloed although potentials of cross-firm data sharing are commonly recognized. They identify technology, people and organizational structures, as well as a crucial absence of well-defined and attractive business models to create and distribute value equitably among participants, as major obstacles. In Chapter 4, Joachim Stonig, Torsten Schmid, and Günter Müller-Stewens provide insights into the transitional challenges faced by manufacturing firms adopting platform-based ecosystems for digitalized production. Their case study highlights a machine manufacturer’s shift from products to ecosystem-based value propositions through a learning process, showcasing stages of evolution towards Industry 4.0 ecosystems. Susanne Gretzinger, Birgit Leick and Anna Marie Dyhr Ulrich (Chapter 5) analyze how peer-to-peer platform-driven business models enable companies to expand by combining core strengths

4  Handbook on digital platforms and business ecosystems in manufacturing with sharing capabilities, opening doors for innovative startups in emerging markets through role-making and role-taking. Their chapter uncovers that social exchanges during business model formation facilitate resource access and robust role-based models. However, in mature markets with standardized products, economic exchanges are vital yet may restrict startups in accessing existing networks. Concluding Part I, in Chapter 6, Luke Treves, Mika Ruokonen and Paavo Ritala introduce a typology of business model orientations (BMO) arising from IoT connectivity advances: show and visualize, propose and compare and optimize and automate. Their typology aids scholars and practitioners in leveraging IoT for value creation, cost reduction and competitive advantage in the digital economy. Part II focuses on ecosystem design and governance. Contributions cover approaches and frameworks supporting DBE design, implementation and operation. Christian Zabel, Jonathan Natzel and Daniel O’Brien, in Chapter 7, examine the role of partnership scouting in a firm’s dynamic sensing abilities within digital business ecosystems. Based on interviews with non-focal B2B firms in the German VR market, they highlight the influence of technology trends, company size and coopetition dynamics on partnership scouting routines. Their findings underscore the importance of continuous scouting for adaptation in rapidly changing environments and contribute to understanding dynamic capabilities and non-focal actors in DBEs. Julius Kirschbaum, Tim Posselt and Kathrin M. Möslein (Chapter 8) introduce a novel logic for organizations seeking to establish mutual resource and capability allocation agreements among members of innovation ecosystems. Their chapter outlines archetypical functional roles to facilitate structured discussions and understanding within innovation ecosystems and explores case studies of industrial organizations to illustrate three platform types: industrial IoT, data marketplace and innovation lab. These serve to map and analyze existing ecosystems and guide the exploration of new designs and business models for organizations aiming to implement such ecosystems. In Chapter 9, Lukas Budde, Leonardo Laglia, Thomas Friedli and Roman Hänggi examine a B2B SME’s actions in fostering an ecosystem and the associated roles for an integrated value proposition in manufacturing. Leveraging institutionalist theory, they map role change through external and internal institutional work, illustrating how SMEs drive ecosystem development for industrial firms. Nicolas Böhm, Laura Marie-Luise Watkowski and Christoph Buck (Chapter 10) explore how automotive OEMs, facing evolving customer demands and new market entrants, are turning to platform-based ecosystems (PBE) to integrate external partners and adapt their supply chains. Through a literature review and analysis of 20 real-world cases, their study identifies distinct PBE strategies in the automotive sector: premium, cost-value, digital native and hybrid. Jonas Kallisch, in Chapter 11, examines the establishment of business ecosystems among manufacturing companies to determine both potentials and impediments of manufacturing ecosystem development in order to derive viable concepts for connecting manufacturing systems. Dominik Morar, Simon Hiller and Dimitri Petrik (Chapter 12) focus on manufacturing ecosystems, emphasizing the importance of technology and end products. They investigate collaboration specifics and non-generic value propositions, using additive manufacturing as an illustrative example. Their findings offer insights into role positioning and improved classification for manufacturing ecosystems. The second part concludes with Francesco Arcidiacono, Florian Schupp, Carmela Di Mauro and Alessandro Ancarani exploring why the uncertain impact of smart manufacturing (SM) on firms’ profitability discourages smaller businesses from digital technology investments into their supply chain. They create a model linking SM adoption to financial performance, using operational performance improvements as mediators. Results indicate a direct negative impact

Introduction  5 of SM adoption on financial performance and that operational improvements resulting from SM adoption do not significantly affect financial performance. Part III of the Handbook is oriented towards sustainability and circular economy. Contributions explore how digital platform DBEs can support more sustainable business models in manufacturing, but also capture societal challenges in the context of new work. In Chapter 14, Susanne Royer, Sabine Baumann and Pawel Głodek present the concept of tribrid business models, which aim to combine economic, environmental and social value creation within digital platforms. Using a qualitative approach, they develop a framework for conceptualizing tribrid business models, which is then illustrated with practical examples. Philip T. Roundy, in Chapter 15, demonstrates how circular economy (CE) entrepreneurs utilize entrepreneurial ecosystems – the interconnected actors and forces in local communities that support entrepreneurship – in the design thinking process. To address the resource challenges of CE entrepreneurs, the concept of ‘entrepreneurial ecosystem-enhanced design thinking’ is proposed and a conceptual framework is developed which explains how entrepreneurs augment their use of digital ecosystems and platforms by leveraging local startup communities. In Chapter 16, Verena Luisa Aufderheide explores the value creation opportunities of smart-circular product-service-systems for 3D printers from an ecosystem perspective by using digital platforms. In addition, she develops a new quantitative approach for sustainability assessment and applies it to 3D printers in order to achieve an even evaluation of the three dimensions of economy, ecology and social. Tom Pettau and Laura Montag (Chapter 17) investigate barriers to a circular economy, notably a lack of cross-sectoral relationships causing ‘circularity holes’. Digital platforms can address this by acting as circularity brokers, aided by technologies like blockchain to overcome trust and security issues. Their study introduces a holistic performance framework with four sustainability dimensions (circular, economic, environmental and social) along with a digital component for comprehensive evaluation of platform-based ecosystem sustainability and performance. Seth Powless and Ashley Church (Chapter 18) conclude this part with their investigation into the paradigm shift in manufacturing through the integration of new work concepts in DBEs. Defined as a flexible work practice, new work involves practices already relevant in nonmanufacturing industries, including distributed workforces, remote teams and flexible work schedules. Using Goldratt’s Theory of Constraints as a theoretical framework, findings include that by adopting and implementing new work principles and adopting digital technologies in manufacturing, waste can be reduced and efficiency improved to support long-term sustainability. Part IV introduces a variety of digital platforms and DBE industry applications and case studies. Contributors delve into industrial examples to illustrate organizing principles of platforms and DBEs and analyze their benefits and limitations in manufacturing practice. Sabine Baumann and Marcel Leerhoff, in Chapter 19, set the scene by examining the concept of digital spare parts (DSP) that aims to replace centralized conventional manufacturing with decentralized additive manufacturing. While existing studies on DSP have primarily focused on individual process steps or various supply chain configurations, lacking a comprehensive examination of its impact at the ecosystem level, this chapter explores how DSP can facilitate the development of a more resilient and adaptable supply chain and ecosystem. In Chapter 20, Daniele Binci, Andrea Appolloni and Wenjuan Cheng explore the specificities of the sociotechnical perspective on platforms and DBE with the aim of building a comprehensive framework for exploring the main input, processes and output of the digital platforms in the downstream space sector. Alexander Herzfeldt, Christoph Ertl and Sebastian Floerecke

6  Handbook on digital platforms and business ecosystems in manufacturing (Chapter 21) investigate applications of Maintenance 4.0 in existing infrastructures and present a related key capabilities framework. The framework can be used by infrastructure companies trying to start building up Maintenance 4.0 capabilities. Nitish Singh, Alp Akçay, Quang-Vinh Dang, Ivo Adan and E. A. Thijssen, in Chapter 22, present the Brainport Industries Campus (BIC), a unique initiative by the high-tech suppliers of the Eindhoven region to work towards collaborative consumption of shared resources, such as production machines, automated guided vehicles, clean rooms and storage spaces, in order to jointly meet their customers’ demands, reduce cost of ownership and better manage risks during uncertain demand and supply cycles. The authors propose a novel digital platform for serving multiple tenants of BIC, sharing a fleet of heterogeneous automated guided vehicles (AGVs). Alisa Lorenz and Nils Madeja (Chapter 23) identify interdependencies in cities as complex systems to determine which data-driven traffic management solutions have the highest impact and where they should be applied. They present a systematic approach to evaluate key variables of both an industry and city ecosystem by using approaches from Vester’s sensitivity model and apply it in the context of a German city ecosystem. In Chapter 24, Nicola Del Sarto, Alberto Di Minin, Giulio Ferrigno and Asia Mariuzzo showcase ELIS, an exemplary platform where Italian corporations collaborate with startups to implement digitalization-focused manufacturing projects to explore the emergence of open innovation ecosystems triggered by digital transformation. Victoria Slettli (Chapter 25) concludes Part IV and the Handbook with her study on DBEs in Norwegian dairy and beef production. The chapter provides an ecosystem perspective on the national food industry, which has traditionally been presented in terms of a linear pipeline value chain. An ecosystem perspective highlights the complex, interactive and highly interdependent nature of the relationships between beef and dairy farmers, research organizations, slaughterhouses, food manufacturers and public organizations.

CONCLUSION The field of platforms and DBEs has come of age and needs to make its way into manufacturing, a sector that is lagging behind in its adaptation. The Handbook on Digital Platforms and Business Ecosystems in Manufacturing lays essential groundwork for advancing both theoretical development and practical applications in industrial domains. It offers a comprehensive examination of the development and present status of digital platforms and DBEs in manufacturing. It convenes experts and professionals across disciplines like management, computer science and engineering, exploring diverse viewpoints such as business models, platform and ecosystem design, governance, value creation, sustainability or societal implications. The focus extends to industrial implementations of digital platforms and DBEs within manufacturing. It also opens doors to unexplored research directions, especially those that integrate diverse disciplines and viewpoints. Ultimately, I trust that the interdisciplinary collaboration within this handbook marks a pivotal stride in this progression and sparks meaningful endeavors for further research and real-world implementations in the future. This handbook could not have been completed without the support of numerous people. My sincere gratitude goes to all the contributors for their consistent dedication and timely submissions of draft chapters, meticulous reviews of each other’s chapters and their excellent final contributions. I would also like to thank the editorial staff at Edward Elgar Publishing, in particular Alan Sturmer and Katia Williford, for their guidance and support in completing

Introduction  7 this important and much needed Handbook on Digital Platforms and Business Ecosystems in Manufacturing.

REFERENCES Bähr, K. (2022) Customer roles in digital business ecosystems: different forms of customer contribution in value creation. In: Baumann, S. (Ed.) Handbook on digital business ecosystems: Strategies, platforms, technologies, governance and societal challenges. Edward Elgar Publishing Ltd: Cheltenham, pp. 194–211. Baumann, S. (2013) Adapting to the brave new world. Innovative organisational strategies for media companies. In: Storsul, T. and Krumsvik, A. H. (Eds.) Media innovations: A multidisciplinary study change. Nordicom: Göteborg, pp. 77–92. Baumann, S. (2022) Introduction to the handbook on digital business ecosystems: strategies, platforms, technologies, governance and societal challenges. In: Baumann, S. (Ed.) Handbook on digital business ecosystems: Strategies, platforms, technologies, governance and societal challenges. Edward Elgar Publishing Ltd: Cheltenham, pp. 1–9. Baumann, S. and Leerhoff, M. (2022) Networks, platforms, and digital business ecosystems: mapping the development of a field. In: Baumann, S. (Ed.) Handbook on digital business ecosystems: Strategies, platforms, technologies, governance and societal challenges. Edward Elgar Publishing Ltd: Cheltenham, pp. 11–24. Beirão, G., Patrício, L. and Fisk, R. P. (2017) Value cocreation in service ecosystems. Journal of Service Management, 28(2), 227–49. Available from: https://​doi​.org/​10​.1108/​JOSM​-11​-2015​-0357. Culot, G. (2022) Digital ecosystems in manufacturing: emerging models for Industry 4.0. In: Baumann, S. (Ed.) Handbook on digital business ecosystems: Strategies, platforms, technologies, governance and societal challenges. Edward Elgar Publishing Ltd: Cheltenham, pp. 727–42. DeFillippi, R. (2014) Co-creation in the era of social business. Strategy & Leadership, 42(4). Available from: https://​doi​.org/​10​.1108/​SL​-05​-2014​-0039. Gawer, A. (2011) Platforms, markets and innovation: an introduction. In: Gawer, A. (Ed.) Platforms, markets and innovation. Edward Elgar Publishing: Cheltenham, pp. 1–18. Gretzinger, S. and Royer, S. (2014) Relational resources in value adding webs: The case of a Southern Danish firm cluster. European Management Journal, 32(1), 117–31. Kapoor, R. (2018) Ecosystems: Broadening the locus of value creation. Journal of Organization Design (J Org Design), 7(12), 1–16. Available from: https://​doi​.org/​10​.1186/​s41469​-018​-0035​-4. Kapoor, R. and Lee, J. M. (2013) Coordinating and competing in ecosystems: How organizational forms shape new technology investments. Strategic Management Journal, 34(3), 274–96. Available from: https://​doi​.org/​10​.1002/​smj​.2010. Keen, P. and Williams, R. (2013) Value architectures for digital business: Beyond the business model. MIS Quarterly, 37 (2), 643–7. Kretschmer, T., Leiponen, A., Schilling, M. and Vasudeva, G. (2022) Platform ecosystems as meta‐ organizations: Implications for platform strategies. Strategic Management Journal, 43(3), 405–24. Available from: https://​doi​.org/​10​.1002/​smj​.3250. Maijanen, P. (2022) Digital business ecosystems and dynamic capabilities. In: Baumann, S. (Ed.) Handbook on digital business ecosystems: Strategies, platforms, technologies, governance and societal challenges. Edward Elgar Publishing Ltd: Cheltenham, pp. 50–62. Mosch, P. and Obermaier, R. (2022) Digital platforms in the industrial sphere: value creation logics and platform types. In: Baumann, S. (Ed.) Handbook on digital business ecosystems: Strategies, platforms, technologies, governance and societal challenges. Edward Elgar Publishing Ltd: Cheltenham, pp. 177–93. Reisinger, S. and Lehner, J. M. (2022) Dealing with strategic tensions in digital business ecosystems. In: Baumann, S. (Ed.) Handbook on digital business ecosystems: Strategies, platforms, technologies, governance and societal challenges. Edward Elgar Publishing Ltd: Cheltenham, pp. 63–79.

8  Handbook on digital platforms and business ecosystems in manufacturing Rietveld, J. and Schilling, M. A. (2020) Platform competition: a systematic and interdisciplinary review of the literature. Journal of Management, 47(6), 1528–63. Available from: https://​doi​.org/​10​.1177/​ 0149206320969791. Robra-Bissantz, S., Lattemann, C., Laue, R., Leonhard-Pfleger, R., Wagner, L. and Gerundt, O., et al. (2022) Methoden zum design digitaler plattformen, geschäftsmodelle und service-ökosysteme. HMD Praxis der Wirtschaftsinformatik, 59(5), 1227–57. Available from: https://​doi​.org/​10​.1365/​s40702​ -022​-00906​-4. Subramaniam, M. (2020) Digital ecosystems and their implications for competitive strategy. Journal of Organization Design, 9(1). Available from: https://​doi​.org/​10​.1186/​s41469​-020​-00073​-0. Wunck, C. and Baumann, S. (2017) Towards a process reference model for the information value chain in IoT applications. In: Weber, C., Bierwolf, R. and Holzmann, T. (Eds.) ‘Digital innovation for advanced manufacturing managing technological and entrepreneurial challenges’: Proceedings of the 2017 IEEE European Technology and Engineering Management Summit (E-TEMS): October, 2017, 2017 IEEE European Technology and Engineering Management Summit (E-TEMS), 10/17/2017–10/19/2017, Munich. IEEE: Piscataway, NJ, pp. 1–6. Available from: https://​ieeexplore​ .ieee​.org/​document/​8244228

PART I BUSINESS MODEL TRANSFORMATIONS

2. Revolutionizing manufacturing: how digital technologies and digital industrial platforms drive business model transformation Claudia Franzè and Danilo Pesce

INTRODUCTION Over the past decade, the emergence of new digital technologies has radically revolutionized the traditional ways of doing business (Coskun-Setirek and Tanrikulu, 2021). In particular, digitalization and connectivity are creating new trade-offs in consumer needs, increasingly leading to the search for personalized products and services (Lanzolla et al., 2023). Consequently, personalization becomes crucial to respond to increasingly heterogeneous customer needs that shift competition from ‘linear’ product-related supply chain logics to the development of personalized solutions that exploit the connections and platform logics enabled by digital technologies (Wang et al., 2017a; Yang et al., 2020). Nowadays, digital industrial platforms play a significant role in fostering new connections and interdependencies between different actors in the supply chain, enabling a shift from product-only to product-service bundles (de Reuver et al., 2018; Baumann and Leerhoff, 2021; Veile et al., 2022). However, the incorporation of services into product-oriented activities requires companies to rethink their business models (Shen et al., 2023). By carefully considering how they create, capture and deliver value, companies can transform their business models by offering bundles of customer-focused combinations of goods, services, support, self-service and knowledge, with services beginning to dominate (Cenamor et al., 2017; Tien, 2020). Such servitization logics can now be considered as the customer-facing component of Industry 4.0 (I4.0) and are becoming increasingly crucial for manufacturing companies, enabling them to move away from linear product-focused logics and bring organic and circular benefits to the end customer, the supplier, the environment and society as a whole (Paschou et al., 2020). Long successfully applied in the business-to-consumer (B2C) sectors, digital platforms have only recently entered the business-to-business (B2B) industrial world, and, despite the growing body of research, the related literature is still at an early stage (Gebauer et al., 2021). Therefore, the growing importance of digital industrial platforms and their disruptive potential for transforming business models towards service-centric logics require further attention and deeper reflection. While digital-native companies seem to be obvious candidates for adopting digital industrial platforms and technology-driven improvements, manufacturing companies – traditionally bound to product-centric logics and the constraints imposed by linear supply chain models – are facing new and important challenges (Simonsson et al., 2020). These include the transition to new business models and how these are reconfigured in line with the combined adoption of digital industrial platforms – identified as a critical success factor for servitization – and digital technologies – identified as a key driver for personalization (Wang et al., 2017a; Gebauer et al., 2021). To fill this gap, this book chapter aims to answer 10

Revolutionizing manufacturing: digital technologies and platforms  11 the following research question: how do traditional manufacturing companies transform their business model through the combined adoption of digital technologies and platforms that aim at personalization and servitization of their value proposition? Providing an integrative framework based on a ‘revealing’ case study (hereafter Alpha1) in which the phenomenon of interest can be ‘transparently observed’ (Yin, 1994, p. 40), this chapter explores how a traditional manufacturing company integrates digital technologies and platforms to transform its business model by moving from product-only to product-service bundles. In addition, a series of implications are proposed to delve into the dynamics of business model transformation, the elements that drive change and the challenges that manufacturing companies face in ‘leading’ digital transformation without being overwhelmed by it.

THEORETICAL BACKGROUND Digital Technologies and Personalization Digital technologies – viewed as combinations of information, computing, communication and connectivity technologies – and the subsequent digital transformation process have fundamentally revolutionized business strategies, processes, firm capabilities, products and services and key interfirm relationships in extended business networks. Over the last 15 years, this digital transformation process culminated in the paradigm shift of I4.0, which has created disruptions across the system, altered the value creation paths of the companies and sowed profound structural changes (Bharadwaj et al., 2013). I4.0, in fact, does not concern a single radical invention but encompasses various ‘“tech ingredients” that are still evolving into new enabling technologies by convergence and mutual combination’ (Culot et al., 2020, p. 5). Indeed, the acquisition and implementation of more advanced technologies (i.e. core technologies) heavily rely on integrating various traditional technologies (i.e. facilitating technologies). Core technologies are modern technological innovations, which have been under development for several decades, and have reached an adequate level of maturity to become commercially available within the past ten years (i.e. additive manufacturing, big data and analytics, blockchain, cloud computing, cyber-physical systems, cybersecurity, digital twin, Industrial Internet of Things, virtual and augmented reality). In contrast, facilitating technologies are well-established, widely adopted and mature information and operations technologies that allow the core technologies of I4.0 to carry out their intended functions, which have been commercially available and commonly utilized across various industries for several decades (i.e. communication interface, computer numerical control system, high-performance computing, industrial actuator and sensors, industrial embedded systems, intelligent enterprise resource planning, machine and process controller, smart manufacturing execution system) (Ghobakhloo et al., 2021). The confluence of core and facilitating digital technologies triggers the convergence between physical and digital spaces (Lanzolla et al., 2023), enhancing the limited connectivity inside the firm and the widespread connectivity along the supply chain (Culot et al., 2020). To realize the full benefits of I4.0 technologies, companies should be oriented toward implementing several I4.0 design principles2 (Kagermann et al., 2013; Ghobakhloo et al., 2021). Among them, personalization is the focus of this chapter, as a market-pull approach is adopted,3 hence emphasizing the orientation towards customers, i.e. placing customer-centricity as one of the

12  Handbook on digital platforms and business ecosystems in manufacturing core digital transformation objectives under I4.0 (Ghobakhloo, 2018). In this sense, digital technologies aim to strengthen the customer experience by delivering personalized products and services associated with unique, inimitable and memorable experiences (Tien, 2020). Over the four industrial revolutions, the personalization process has evolved from tailored production (Industry 1.0) across mass production (Industry 2.0) and mass customization (Industry 3.0) to mass personalization production (I4.0) (Wang et al., 2017b). In particular, mass customization and mass personalization’s main differences lie in (i) the enabling technologies, (ii) the involvement of the customer, (iii) the offer’s characteristics, and (iv) the production costs. Mass customization has been mainly enabled by the use of automation, computer numerical control machines and robots. Customers have a restricted involvement as they make choices passively from established offerings, guided by producers, at the cost near mass production. Mass personalization is associated with intelligent operations in the context of I4.0. Customers are actively and intensively involved in the product design process as each of them will have a different experience according to their needs and interests, with shorter cycle times and lower costs (Pech and Vrchota, 2022). Such novel forms of personalization enabled by I4.0 allow the conflict between demand diversification and large-scale manufacturing to be solved, providing the modern manufacturing enterprise with the advantages of cost, quality, flexibility, time and variety. In this way, personalized products can be offered with affordable fulfilment costs and bring more value to customers and manufacturers. Based on the concepts of I4.0, mass personalization production efficiently and effectively satisfies customer requirements by providing individually different products with a positive user experience (Wang et al., 2017b; Pech and Vrchota, 2022). With the emergence of the mass-personalization paradigm, the business model of traditional manufacturing companies is subjected to a transformation – total or partial – towards highly specialized and highly personalized production schemes by integrating the whole supply network for a more dynamic way of manufacturing (Tien, 2020). Drawing on a stream of such research, the first dimension of the proposed integrative framework considers digital technologies as enablers of mass personalization production driving business model transformation. Digital Industrial Platforms and Servitization From an I4.0 perspective, digital technologies and digital industrial platforms are reshaping traditional value creation by driving horizontal and vertical interconnection and fostering ecosystems (de Reuver et al., 2018; Veile et al., 2022). Pauli et al. (2021) defined4 digital industrial platforms as platforms that: (i) collect and integrate data from a heterogeneous set of industrial assets and devices, (ii) provide this data and additional technological support to an ecosystem of third-party organizations who develop and enable complementary solutions that (iii) affect the operation of industrial assets and devices, and (iv) provide a marketplace to facilitate interactions between platform owner, third-parties and business customers.

Operating in a B2B setting, such a platform differs from the online platforms that facilitate interactions and matching between users (C2C) and buyers and sellers (B2C). Indeed, online platforms might create value in two ways: by collecting and using data about consumer interactions and generating and taking advantage of network effects or by allowing firms to extend their operations beyond their home state, catering to consumers across the global economy.

Revolutionizing manufacturing: digital technologies and platforms  13 Examples of such transaction platforms include app stores, search engines, social media and platforms for the collaborative economy (Gawer, 2014). Considering such a new paradigm, traditional manufacturing firms initially felt pressure to incorporate digital industrial platforms into their existing business more than ever to survive (Van Alstyne et al., 2016; Verhoef et al., 2021). Then, the recent unlocking of the real potential of digital industrial platforms has helped companies – not only in high-technology industries but also in several other industries – to create greater value by improving their digital transformation process (Li et al., 2018; Warner and Wäger, 2019). In this sense, traditional manufacturing companies adopt digital industrial platforms to integrate digital technologies into their legacy products and processes, resulting in pivotal changes in the ways of doing business (Ondrus et al., 2015; Papadopoulos et al., 2020). As digital industrial platforms perform a connecting role between devices (upstream) and applications (downstream), they act as a bridge by collecting and integrating industrial asset data centrally and leveraging this data for the creation of smart applications and services that optimize industrial operations and provide new services (Pauli et al., 2021). Such a concept is shared among different authors, which identified platforms as an interesting approach to enable and support several services, fostering traditional manufacturing companies towards a digital servitization path (cf. Gebauer et al., 2013; Eloranta and Turunen, 2016; Cenamor et al., 2017). Digital servitization describes the convergence of servitization and digitalization. The first phenomenon – servitization – is predominantly related to the transformation from a product-only approach to include a larger share of services and has been investigated for decades. The second trend – digitalization – is broadly defined as the adoption of or increase in digital or computer technology use by an organization, industry, country, etc. (Gebauer et al., 2021). For example, Cenamor et al. (2017) investigated how the leverage of a platform approach helps the transition of manufacturing companies to a digital service offering, finding that such an approach leverages the value of information to increase operational efficiency while simultaneously allowing for personalized and flexible offerings. Furthermore, since the coordination between the back-end (e.g. R&D unit) and the front-end (e.g. market and sales unit) represents a key aspect in implementing services (Silvestro and Lustrato, 2015), more coordinated organizational structures are needed to exploit the interdependencies among the product and service divisions (Windahl and Lakemond, 2006). In this sense, companies can use digital industrial platforms to reorganize the allocation of responsibilities and change how value is created by different actors within the company through the development, configuration, and delivery of offerings (Gawer, 2014; Thomas et al., 2014). In the B2B sector, extending the service business in addition to the traditional core product business may assure long-term growth and strengthen competitive advantages (Gebauer et al., 2021). Specifically, manufacturing firms implement the servitization approach for several reasons: to strengthen the relationship with customers, which in turn can increase sales of products over a long period and enhance customer satisfaction (Reinartz and Ulaga, 2008; Parida et al., 2014b); as an opportunity to move up in the value chain, as bundles of products and services provide customers with more total value (Tongur and Engwall, 2014; Noke and Hughes, 2010); to keep their profit margins in contrast to the increased competition for products (Oliva and Kallenberg, 2003; Parida et al., 2014a; Gaiardelli et al., 2015); to overcome the service paradox5 (Pekkarinen and Ulkuniemi, 2008; Eloranta and Turunen, 2016); or to access new business opportunities (Oliva and Kallenberg, 2003), improve efficiency (Eggert

14  Handbook on digital platforms and business ecosystems in manufacturing et al., 2014) and enhance the offering differentiation (Kohtamäki et al., 2013). Thus, a digital platform approach may be useful in advanced service implementation, facilitating personalization and efficiency (Cenamor et al., 2017). Drawing on such a stream of research, the second dimension of the proposed integrative framework considers digital industrial platforms as enablers of servitization driving business model transformation. Personalization and Servitization: Toward an Integrative Framework Personalization and servitization enabled by digital technologies and digital industrial platforms push traditional manufacturing companies to adapt their current business models6 to external changes and new customer needs (Lanzolla and Markides, 2021). Since business models are subject to adjustments over time, as they are not static configurations, a change in (one or more) design elements of the current business model or a reconfiguration among existing constituent elements leads to a transformation process towards new business models (Şimşek et al., 2022). Research and practice agree that digital technologies and digital industrial platforms disrupt and transform traditional businesses, along with critical changes in industrial value creation (de Reuver et al., 2018). Consequently, to study how traditional manufacturing companies reconfigure their business model through the combined adoption of digital technologies and digital industrial platforms, it is necessary to identify the most basic building blocks affected by this transformation: the personalization and servitization of their value proposition.

Figure 2.1

Personalization and servitization: toward an integrative framework

Revolutionizing manufacturing: digital technologies and platforms  15 Personalization is represented by any shift along the horizontal axis of Figure 2.1 when adopting digital technologies enables a shift from standard offerings to more personalized ones. The shift along the vertical axis of Figure 2.1 illustrates servitization, where the adoption of digital platforms enables product-centric companies to create increasing value by integrating more and more services into their offerings to meet specific customer needs.

A CASE STUDY IN THE MEDICAL DEVICE SECTOR Company Profile Alpha is a global medical device company based in Italy, which manufactures and distributes innovative and high-quality reconstructive orthopedic solutions – including hip, knee, and extremities implants and fixation devices – as well as custom-made implants designed for patients with particular orthopedic needs, requiring high levels of surgical expertise. The company was founded in the 1940s to produce surgical instruments in response to the shortage caused by the Second World War. In the following decades, Alpha specialized in processing titanium – a core material used in most orthopedic implants today – also providing components for other sectors, such as aeronautics and automotive. In the 1970s, the company developed and adopted a strict quality system for its aerospace offering. The system was immediately used to produce orthopedic implants, resulting in worldwide recognition and trust from surgeons. Since the 2000s, Alpha has turned back to focusing exclusively on its core business related to medical devices with a pioneering approach to additive manufacturing (AM) for the development of titanium prostheses. Today, with over 1000 staff members and three production plants worldwide, Alpha has established more than 25 direct subsidiaries in Europe, the USA, Asia-Pacific and Latin America. Combined with a network of distributors, Alpha operates in nearly 50 countries, with over 200M€ total revenues in 2022 (+15 percent from 2020). Baseline: Building Competence in Titanium Processing In the first phase, from its foundation to the 2000s, the company has exploited its core competencies in titanium processing to diversify its business over three sectors (automotive, aeronautical and orthopedic). Such heritage in the automotive and aeronautical industries is evidenced in the company’s highly automated and efficient manufacturing processes. The expertise in processing titanium is the foundation of the subsequent competitive advantages deriving from its exploitation along with 3D printing technology. In the orthopedic sector, the company’s value proposition focuses on offering standard prostheses (Quadrant A in Figure 2.1) with high sales volume (over 500 000 products/year). The prosthetic products manufactured by Alpha differ in size and other anatomical and structural characteristics, which depend in part on the characteristics of the specific customers, but production mainly takes place in series. Over the years, the company’s R&D department dedicated its effort in two directions. The first relates to the key resources (technological and human) of its business model, as the company focused on improving production processes by developing AM and digital twin (DT) product technologies with the conviction that, despite technological advances, labor and human skills could not be replaced. The second relates to

16  Handbook on digital platforms and business ecosystems in manufacturing the customer relationship component of its business model, as the idea has always been to embrace platform logic to collaborate closely with surgeons. Phase 1: Personalization Enabled by Digital Technologies Phase 1 began in the 2000s when the company focused exclusively on its core medical device business. While carrying a full traditional product line of orthopedic implants, in the early 2000s, Alpha began developing a bundle of digital core technologies to both overcome the technical constraints of the traditional manufacturing processes and strive for a higher level of product personalization, resulting in a first shift of Alpha’s offer from Quadrant A (standardized products) to Quadrant D (personalized products) in Figure 2.1. First, it invested in electron beam melting (EBM) technology, a form of AM, to address the functional limitations of the coatings applied to traditional prosthetic implants. 3D Printing machines allowed Alpha’s engineers to create new shapes and materials – notably, the invention of BioMaterial, a 3D printed biomaterial that forms a unique net geometric structure imitating bone morphology. This new 3D-printed technology better supports new bone formation. In the same year, the company’s first acetabular cup featuring BioMaterial was developed and implanted for the first time in Italy. Alpha began leveraging the capabilities of 3D printing before titanium EBM printers were commercially available. AM is complemented by the application of artificial intelligence (AI) that makes it possible to test the performance of products without the need to carry out destructive tests on product samples. Second, it started leveraging such digital technologies to personalize its products fully. Indeed, the AM technology was the pillar for creating the Custom Division in 2015, a dedicated engineering design service allowing patients to custom-make orthopedics implants, where the personalized portion of the device can be combined with the company’s standard device, creating a complete implant both time- and cost-efficient. Custom Division is dedicated to designing and producing patient-specific orthopedic implants – including an extensive range of anatomical districts, large joints like hip and knee, shoulder, elbow, wrist, and ankle, and combination implants that can join two different joints into a single implant. With AM, it is possible to create an advanced porous surface to increase the implant’s primary stability and enhance biologic integration, thus making the prosthesis an integral and personalized part of the human body. In this new design perspective enabled by AM, the production of biomedical components is fully dedicated to serving the patient, as it allows not only the creation of complex objects with intricate geometries that would be difficult or impossible to achieve with traditional methods but also the development of personalized objects to meet specific customer needs. Since each prosthesis retains independent characteristics in terms of size and shape, AM enables the creation of prostheses that are optimal for proper fixation and with a high level of accuracy. Moreover, this technology can reduce production costs by eliminating the need for expensive production tools and reducing the time required to manufacture the object. Finally, 3D printing is highly efficient as it uses only the necessary amount of material to produce the object, minimizing waste. The technological processes and materials used in AM optimize support for patients and assist surgeons in working in a simple and timely manner. Even if Custom Division develops a niche product – around 1 percent of the total production – it combines expertise in titanium processing with the benefits of digital technologies, representing a highly profitable personalized offer. Such a combination changes the business model towards per-

Revolutionizing manufacturing: digital technologies and platforms  17 sonalization and decentralization of production, which is done directly in the hospital, thus creating the basis for a servitization strategy, as explained in the following section. Phase 2: Service-Centered Logics Enabled by the Digital Industrial Platform The second phase continues with the development in the Custom Division of a ‘surgeon-to-engineer interface platform’ where the engineers continuously exchange information with surgeons during all phases of personalized product development. The division is able to offer a pool of engineers and material scientists directly to surgeons that require a tailored implant for their patient. The design process of personalized Custom Division products – enhanced by the interface platform – enabled the establishment of new, and the strengthening of existing, relationships with surgeons, who appreciate such highly specialized solutions, creating a pull-through for the other standard product lines. Surgeons are among the key decision-makers in purchasing orthopedic implants, who choose the supplier who can provide the added value of the product-service package offered in terms of training, after-sales services, visibility and reputation. This results in a second shift of Alpha’s offer from Quadrant A (product) to Quadrant B (service) in Figure 2.1. Phase 3: Exploiting Connections by Providing Personalized Solutions that Fulfill Customers’ Needs The third phase begins with the acquisition of a medical device software company located in the USA to offer services in the form of digital applications that complement the hardware portfolio. They created Digital Planner: a 3D digital preoperative and intraoperative surgical guidance platform based on a proprietary machine learning (ML) algorithm built from an extensive database of morphological data points. Digital Planner supports surgeons in designing the prostheses directly from their office thanks to a combination of data extracted from the patient’s CAT scan, the digital atlas of the human body, and the 3D catalog of standard prosthetic components. Moreover, it assists the surgeon during implant positioning, offering real-time feedback, thus delivering performance and precision. The Digital Planner – a physical and digital environment – can be considered a digital industrial platform because, in the future, it will be capable of supporting multiple applications: shoulder, hip and knee. On the one hand, the platform interface connects surgeons with engineers during the development and production phase of the prosthesis. At the same time, 3D space brings together different information and supports surgeons in prostheses design. They are two types of platforms that are based on different value-creation mechanisms. The virtual modeling of the patient and the digital reproduction of the prosthesis allow the physical and digital space to be integrated during the production phase. This aspect has profound effects on the ability to manage ever higher geometric quality requirements of the product; it tends to make the relationship between production and quality managers much more collaborative rather than conflictual (as non-quality elements tend to become less frequent and more easily measurable with objective data), as well as the importance of manual dexterity being relatively reduced to manage high precision and geometric tolerances in the production phase. Leveraging the legacy and digital skills accumulated during Phase 1 and Phase 2 and the progressive shifts toward personalized products (Quadrant D in Figure 2.1) and services (Quadrant B in Figure 2.1) enabled Alpha to take another step toward transforming its business

18  Handbook on digital platforms and business ecosystems in manufacturing model. In fact, in 2020, Alpha opened the first AM facility for complex personalized implants in the medical field to offer faster and more affordable care to patients who need specific solutions for their complex orthopedic conditions by converging to Quadrant C in Figure 2.1.

DISCUSSION The case study shows that the transformation of business processes and strategies is becoming an increasingly critical aspect of survival and competitiveness in the digital age, especially for more traditional manufacturing firms (Coskun-Setirek and Tanrikulu, 2021). In particular, the combined adoption of digital technologies and industrial platforms can lead to a complete transformation of the business model of traditional manufacturing firms, resulting in a joint shift toward personalization and servitization of the value proposition (de Reuver et al., 2018). Figure 2.2 summarizes the three stages of Alpha’s ‘digital journey’ toward the transformation of its business model within the four quadrants of the integrative framework proposed in this study.

Figure 2.2

Alpha’s business model transformation

The transformation process begins with two incremental changes in the business model (Phase 1 and Phase 2). The main change occurs in Phase 1, in which Alpha innovates its business model by personalizing its standard product offerings through the use of digital technologies such as AM and DT product. In line with the expertise Alpha has developed in titanium processing, AM has revolutionized the development of prostheses for the human body, allowing complex shapes and structures to be developed more efficiently with shorter set-up times,

Revolutionizing manufacturing: digital technologies and platforms  19 lower material costs and less waste than traditional manufacturing7 (Weber, 2019). The combination of AM and DT technologies has proven effective in producing implants and prostheses that precisely fit patients and performing precise and safe surgeries. In fact, the interdependencies generated by the combination of AM and DT technologies promote the correct positioning and alignment of the prosthesis, the selection of the model based on the patient’s body and structure, and the avoidance of improper implant design that can lead to implant deterioration and failure (da Silva et al., 2021). This demonstrates how, in the context of I4.0, changes in the business model are not limited to the simple adoption of specific individual technologies but are shaped in their combination, enabling the enterprise to create value in new ways (Schneider, 2018; Gebauer et al., 2021). Over the years, Alpha has successfully navigated the significant challenges associated with the adoption of technologies, such as AM and DT, which are beyond its traditional domains of expertise. This success can be attributed to the company’s sustained and substantial investments in skills development, ongoing training of highly qualified personnel, as well as in state-of-the-art equipment, materials and operations management. These investments have been driven by a clear strategy of enterprise-wide technology adoption and related value-added applications aimed at meeting customer needs in new ways (Betti et al., 2022). Subsequently, investments in the development and implementation of industrial digital platforms kick off Phase 2, in which Alpha pursues a second incremental change in its business model by embracing service-centric logics. In addition to the mass production of standard and personalized prosthetics with AM and DT technologies, the company’s value proposition is further enriched with digital services. The servitization model implemented by Alpha is driven by long-term investments in software development activities and industrial digital platforms, which facilitate the connection of products (prosthetics), services (platform), processes (manufacturing) and (eco)systems (engineers, surgeons, patients) (cf. Hsu, 2007). This is made possible through a policy of joint investments in human and technological capital that demonstrate how the accumulation of legacy and digital knowledge and skills is critical to evolving the business model and maintaining competitiveness in the digital age (Lanzolla et al., 2021). In fact, over the years, Alpha has created a digital ecosystem in which different players collaborate by leveraging technology to facilitate communication, coordination and information exchange, enabling efficient and seamless collaboration (Veile et al., 2022). Building on the initial collaboration with surgeons, the company enhanced this collaborative relationship and eventually enabled the co-creation of value between engineers and surgeons to build a collaboration ecosystem. This ecosystem not only integrates digital technologies into the company’s traditional non-digital products and processes but also combines technical skills with digital skills, leading to fundamental changes in the way business is done (Papadopoulos et al., 2020). The shift from standardized products (Quadrant A in Figure 2.2) to personalized products (Quadrant D in Figure 2.2) and service-centric logics (Quadrant B in Figure 2.2) has enabled Alpha to take the next step in its digital journey, which takes the form of the transformation of its business model (Quadrant C in Figure 2.2). In Phase 3, Alpha, in fact, leverages the connections enabled by digital technologies (Phase 1) and digital industrial platforms (Phase 2) by providing personalized solutions (bundles of personalized products and services) that fulfill customers’ needs. The business model transformation implemented by Alpha in Phase 3 is an excellent example of the connections between digital technologies and industrial digital platforms: (i) upstream Alpha collects and integrates different types of data from various sources – the 3D catalog of standard prosthetic components from the company’s engineers,

20  Handbook on digital platforms and business ecosystems in manufacturing the digital atlas of the human body developed with the acquired company, the patient’s CT scan from the surgeon; (ii) these data are stored, analyzed and processed by the digital platform (artificial intelligence and machine learning), which also serves as the operating system for the applications; (iii) downstream, the data are leveraged not only for the production of personalized products but also for the creation of advanced services aimed at a broad ecosystem of actors. For example, the digital platform’s preoperative application virtually simulates the surgery, then its intraoperative application supports the surgery according to pre-operative planning, and finally, sensors guide surgeons in bone preparation and component placement, providing real-time feedback. In addition, the business model introduced by Alpha in Phase 3 allows for further improvement in customer relationship management and the fulfillment of customer needs by creating a collaborative ecosystem (Cenamor et al., 2017) in which (i) activities are coordinated among different actors in a way that leverages value co-creation; (ii) interactions are facilitated among different stakeholders in the supply chain; and (iii) responsibilities are distributed according to the core strengths and capabilities of each actor in the supply chain (i.e. Alpha’s technologies, practices and skills, the design and manufacturing skills of engineers and the medical skills of surgeons). In this sense, Alpha’s ability to transform its business model according to the needs of its customers fosters interdisciplinarity and cross-sectoriality at the ecosystem level. In fact, the team of developers – composed of mechanical or biomedical engineers – has DT and AM skills that, interacting with medical and biomechanical ones, allow it to create personalized products and deliver complex services that could not have been provided with traditional solutions. Finally, Alpha consolidates the transformation of its business model by merging physical reality with digital reality. By combining its legacy experience in titanium processing with the digital expertise it has gained over the years, Alpha is able to decentralize the production of prosthetics directly in the hospital, thus providing personalized products and highly complex services in a short time frame (cf. Ben-Ner and Siemsen, 2017).

CONCLUSIONS This chapter analyzes how traditional manufacturing companies transform their business model through the combined adoption of digital technologies and digital industrial platforms that aim at the personalization and servitization of their value proposition. An integrative framework based on a two-by-two matrix is proposed to provide an initial answer to this important question. The x-axis distinguishes between standard and personalized product offerings enabled by digital technologies. The y-axis, on the other hand, distinguishes between product and service offerings enabled by digital industrial platforms. The integrative framework developed at the theoretical level was tested with a case study of a manufacturing company (Alpha) operating in the medical device sector. The case study shows how the combined adoption of digital technologies and the development of digital industrial platforms enabled Alpha to transform its business model from producing standardized products to providing personalized solutions that embraced both product and service logics. The case study makes it possible to generalize some important implications about the transformation of the business model of manufacturing firms enabled by digital transformation. First, Alpha changed several dimensions of its business model simultaneously rather than changing one at a time. This indicates that business model transformation is a systemic process

Revolutionizing manufacturing: digital technologies and platforms  21 in which the whole system is more significant than its individual components. Second, these multidimensional transformations can be achieved by combining industrial digital technologies and platforms that connect all players in the business ecosystem and share data and information transparently and fluidly. This highlights the importance of an increasingly relational model based on human-machine and human-human interactions that facilitate the co-creation of value throughout the process. Third, the case study highlights that one must start with strategy rather than technology to pursue the opportunities enabled by digital transformation. Alpha can now offer a successful combination of personalized products and advanced services because it has used technology in a way that is functional to its business model and the needs of its customers. Traditionally, manufacturing firms address the challenges and opportunities of digital transformation by taking a top-down approach (see Figure 2.3a): they invest in technology in the hope that the business model with which they serve their customers in the physical world will continue to work in the digital world. The empirical evidence gathered in this book chapter shows that the top-down approach does not work. The correct approach to leading digital transformation (without being overwhelmed by it) is not to start with technology but to work back to strategy. In other words, identify changes in customer needs and define how the company intends to create and capture value (i.e. the business model) and the organizational changes needed to support the transformation. After defining these two elements of the transformation journey (or ‘digital journey’, as Alpha’s Top Management Team called it), the next step is to build the digital core. Building the digital core is now a necessary but not sufficient condition for addressing the digital transformation process. However, starting from building the digital core and using technology to serve the customer, following a top-down approach may not lead to the desired results. The case study shows how technology cannot be just dumped in the organization, but it is necessary to follow a bottom-up approach that starts from the analysis of changes in customer needs. The digital core then becomes the tool that, by permeating the entire organization, enables the transformation of the business model by providing what is no longer an innovative product but a personalized solution aimed at fulfilling customers’ deepest needs (see Figure 2.3b).

Figure 2.3

Leading digital transformation: Implications for business model transformation

22  Handbook on digital platforms and business ecosystems in manufacturing

NOTES 1. The company (Alpha), its projects (Custom Division), inventions (BioMaterial) and technologies (Digital Planner) have been anonymized for confidentiality reasons. 2. The design principles identified and studied in the literature are mainly the following: agility, decentralization, human-machine integration, interoperability, modularity, personalization, real-time data management, service orientation and virtualization (Kagermann et al., 2013; Hermann et al., 2016; Salkin et al., 2018). 3. Both the technology-push and the market-pull approaches support the I4.0 paradigm (Lasi et al., 2014). In the technology-push approach, innovation is ‘pushed to the market through technology development within the firm’s R&D department or technology transfer from research organizations to firms’ (Boyer and Kokosy, 2022, p. 4). In the market-pull approach, ‘the impulse to develop or use new technologies comes from consumers, end-users, or other individuals or groups that express their needs’ (Boyer and Kokosy, 2022, p. 5). 4. There are other definitions and classifications of platforms proposed in the literature, see, for example, Gawer, 2014; Gawer and Cusumano, 2014; Thomas et al., 2014; Veile et al., 2022. 5. The service paradox occurs when substantial investment in extending the service business leads to increased service offerings and higher costs but does not generate the expected correspondingly higher returns (Gebauer et al., 2005). 6. There are several conceptualizations of business model, it: ‘is simply a business concept that has been put into practice’ (Hamel, 2002, p. 117); ‘depicts the design of transaction content, structure, and governance so as to create value through the exploitation of business opportunities’ (Amit and Zott, 2001, pp. 494–5); or ‘describes the design or architecture of the value creation, delivery and capture mechanisms employed’ (Teece, 2010, p. 191). 7. The AM process can be described as follows: (1) acquisition of two-dimensional medical images by CT (computed axial tomography) or NMR (nuclear magnetic resonance); (2) transformation of the medical images into three-dimensional virtual models; (3) modeling of the virtual prosthesis and of 3D CAD fixation systems; (4) fabrication of the model (prosthesis and fixation systems) by AM technologies; and (5) fabrication of the prostheses with biocompatible materials (da Silva et al., 2021).

REFERENCES Amit, R. and Zott, C. (2001) Value creation in E-business. Strategic Management Journal, 22(6–7), 493–520. Available from: https://​doi​.org/​10​.1002/​smj​.187 Baumann, S. and Leerhoff, M. (2021) Networks, platforms, and digital business ecosystems: mapping the development of a field. In Handbook on Digital Business Ecosystems, Cheltenham, UK: Edward Elgar Publishing. Available from: https://​doi​.org/​10​.4337/​9781839107191​.00007 Ben-Ner, A. and Siemsen, E. (2017) Decentralization and localization of production. California Management Review, 59(2), 5–23. Available from: http://​dx​.doi​.org/​10​.1177/​0008125617695284 Betti, F., Seidel, C. and Meboldt, M. (2022) An Additive Manufacturing Breakthrough: A How-to Guide for Scaling and Overcoming Key Challenges, World Economic Forum. Bharadwaj, A., El Sawy, O., Pavlou, P. and Venkatraman, N. (2013) Digital business strategy: toward a next generation of insights. MIS Quarterly, 37(2), 471–82. Available from: https://​doi​.org/​10​.25300/​ MISQ/​2013/​37:​2​.3 Boyer, J. and Kokosy, A. (2022) Technology-push and market-pull strategies: the influence of the innovation ecosystem on companies’ involvement in the Industry 4.0 paradigm. The Journal of Risk Finance, 23(5), 461–79. Available from: https://​doi​.org/​10​.1108/​JRF​-12​-2021​-0193 Cenamor, J., Rönnberg Sjödin, D. and Parida, V. (2017) Adopting a platform approach in servitization: Leveraging the value of digitalization. International Journal of Production Economics, 192, 54–65. Available from: https://​doi​.org/​10​.1016/​j​.ijpe​.2016​.12​.033

Revolutionizing manufacturing: digital technologies and platforms  23 Coskun-Setirek, A. and Tanrikulu, Z. (2021) Digital innovations-driven business model regeneration: A process model. Technology in Society, 64, 101461. Available from: https://​doi​.org/​10​.1016/​j​ .techsoc​.2020​.101461 Culot, G., Nassimbeni, G., Orzes, G. and Sartor, M. (2020) Behind the definition of Industry 4.0: Analysis and open questions. International Journal of Production Economics, 226, 107617. Available from: https://​doi​.org/​10​.1016/​j​.ijpe​.2020​.107617 da Silva, L. R. R., Sales, W. F., Campos, F. dos A. R., de Sousa, J. A. G., Davis, R., Singh, A., Coelho, R. T. and Borgohain, B. (2021) A comprehensive review on additive manufacturing of medical devices. Progress in Additive Manufacturing, 6(3), 517–53. Available from: https://​doi​.org/​10​.1007/​s40964​ -021​-00188​-0 de Reuver, M., Sørensen, C. and Basole, R. C. (2018) The digital platform: a research agenda. Journal of Information Technology, 33(2), 124–35. Available from: https://​doi​.org/​10​.1057/​s41265​-016​-0033​-3 Eggert, A., Hogreve, J., Ulaga, W. and Muenkhoff, E. (2014) Revenue and profit implications of industrial service strategies. Journal of Service Research, 17(1), 23–39. Available from: https://​doi​.org/​10​ .1177/​1094670513485823 Eloranta, V. and Turunen, T. (2016) Platforms in service-driven manufacturing: Leveraging complexity by connecting, sharing, and integrating. Industrial Marketing Management, 55, 178–86. Available from: https://​doi​.org/​10​.1016/​j​.indmarman​.2015​.10​.003 Gaiardelli, P., Martinez, V. and Cavalieri, S. (2015) The strategic transition to services: a dominant logic perspective and its implications for operations. Production Planning & Control, 26(14–15), 1165–70. Available from: https://​doi​.org/​10​.1080/​09537287​.2015​.1033498 Gawer, A. (2014) Bridging differing perspectives on technological platforms: Toward an integrative framework. Research Policy, 43(7), 1239–49. Available from: https://​doi​.org/​10​.1016/​j​.respol​.2014​ .03​.006 Gawer, A. and Cusumano, M. A. (2014) Industry platforms and ecosystem innovation. Journal of Product Innovation Management, 31(3), 417–33. Available from: https://​doi​.org/​10​.1111/​jpim​.12105 Gebauer, H., Fleisch, E. and Friedli, T. (2005). Overcoming the service paradox in manufacturing companies. European Management Journal, 23(1), 14–26. Available from: https://​doi​.org/​10​.1016/​j​.emj​ .2004​.12​.006 Gebauer, H., Paiola, M. and Saccani, N. (2013) Characterizing service networks for moving from products to solutions. Industrial Marketing Management, 42(1), 31–46. Available from: https://​doi​.org/​10​ .1016/​j​.indmarman​.2012​.11​.002 Gebauer, H., Paiola, M., Saccani, N. and Rapaccini, M. (2021) Digital servitization: Crossing the perspectives of digitization and servitization. Industrial Marketing Management, 93, 382–8. Available from: https://​doi​.org/​10​.1016/​j​.indmarman​.2020​.05​.011 Ghobakhloo, M. (2018) The future of manufacturing industry: a strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29(6), 910–36. Available from: https://​doi​.org/​ 10​.1108/​JMTM​-02​-2018​-0057 Ghobakhloo, M., Fathi, M., Iranmanesh, M., Maroufkhani, P. and Morales, M. E. (2021) Industry 4.0 ten years on: A bibliometric and systematic review of concepts, sustainability value drivers, and success determinants. Journal of Cleaner Production, 302, 127052. Available from: https://​doi​.org/​10​.1016/​ j​.jclepro​.2021​.127052 Hamel, G. (2002) Leading the revolution: how to thrive in turbulent times by making innovation a way of life. Harvard Business School Press. Hermann, M., Pentek, T. and Otto, B. (2016) Design principles for Industry 4.0 scenarios. In: 2016 49th Hawaii International Conference on System Sciences (HICSS). January 2016 IEEE. pp. 3928–37. https://​doi​.org/​10​.1109/​HICSS​.2016​.488 Hsu, C. (2007) Scaling with digital connection: Services innovation. In: 2007 IEEE International Conference on Systems, Man and Cybernetics. October 2007 IEEE. pp. 4057–61. Available from: https://​doi​.org/​10​ .1109/​ICSMC​.2007​.4414260 Kagermann, H., Wahlster, W. and Helbig, J. (2013) Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Securing the future of German manufacturing industry. acatech Kohtamäki, M., Partanen, J., Parida, V. and Wincent, J. (2013) Non-linear relationship between industrial service offering and sales growth: The moderating role of network capabilities. Industrial Marketing Management, 42(8), 1374–85. Available from: https://​doi​.org/​10​.1016/​j​.indmarman​.2013​.07​.018

24  Handbook on digital platforms and business ecosystems in manufacturing Lanzolla, G., and Markides, C. (2021) A business model view of strategy. Journal of Management Studies, 58(2), 540–53. Available from: https://​doi​.org/​10​.1111/​joms​.12580 Lanzolla, G., Pesce, D. and Tucci, C. (2023). The digitalization of physical reality: theoretical lenses to incorporate digitalization into management research. In Cennamo, C., Dagnino, G. and Zhu, F. (Eds.), Research Handbook on Digital Strategy, Cheltenham, UK: Edward Elgar Publishing Available from: https://​doi​.org/​10​.4337/​9781800378902​.00010 Lanzolla, G., Pesce, D. and Tucci, C. (2021) The digital transformation of search and recombination in the innovation function: Tensions and an integrative framework. Journal of Product Innovation Management, 38(1), 90–113. Available from: https://​doi​.org/​10​.1111/​jpim​.12546 Lasi, H., Fettke, P., Kemper, H.-G., Feld, T. and Hoffmann, M. (2014) Industry 4.0. Business & Information Systems Engineering, 6(4), 239–42. Available from: https://​doi​.org/​10​.1007/​s12599​-014​ -0334​-4 Li, L., Su, F., Zhang, W. and Mao, J.-Y. (2018) Digital transformation by SME entrepreneurs: A capability perspective. Information Systems Journal, 28(6), 1129–57. Available from: https://​doi​.org/​10​ .1111/​isj​.12153 Noke, H. and Hughes, M. (2010) Climbing the value chain. International Journal of Operations & Production Management, 30(2), 132–54. Available from: http://​dx​.doi​.org/​10​.1108/​ 01443571011018680 Oliva, R. and Kallenberg, R. (2003) Managing the transition from products to services. International Journal of Service Industry Management, 14(2), 160–72. Available from: https://​doi​.org/​10​.1108/​ 09564230310474138 Ondrus, J., Gannamaneni, A. and Lyytinen, K. (2015) The impact of openness on the market potential of multi-sided platforms: a case study of mobile payment platforms. Journal of Information Technology, 30(3), 260–75. Available from: https://​doi​.org/​10​.1057/​jit​.2015​.7 Papadopoulos, T., Baltas, K. N. and Balta, M.E. (2020) The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice. International Journal of Information Management, 55, 102192. Available from: https://​doi​.org/​10​.1016/​j​.ijinfomgt​.2020​ .102192 Parida, V., Sjödin, D. R., Wincent, J. and Kohtamäki, M. (2014a) A survey study of the transitioning towards high-value industrial product-services. Procedia CIRP, 16, 176–80. Available from: https://​ doi​.org/​10​.1016/​j​.procir​.2014​.01​.019 Parida, V., Sjödin, D.R., Wincent, J. and Kohtamäki, M. (2014b) Mastering the transition to product-service provision: Insights into business models, learning activities, and capabilities. Research Technology Management, 57(3), 44–52. Paschou, T., Rapaccini, M., Adrodegari, F. and Saccani, N. (2020) Digital servitization in manufacturing: a systematic literature review and research agenda. Industrial Marketing Management, 89, 278–92. Available from: https://​doi​.org/​10​.1016/​j​.indmarman​.2020​.02​.012 Pauli, T., Fielt, E. and Matzner, M. (2021) Digital industrial platforms. Business & Information Systems Engineering, 63(2), 181–90. Available from: https://​doi​.org/​10​.1007/​s12599​-020​-00681​-w Pech, M. and Vrchota, J. (2022) The product customization process in relation to Industry 4.0 and digitalization. Processes, 10(3), 539. Available from: https://​doi​.org/​10​.3390/​pr10030539 Pekkarinen, S. and Ulkuniemi, P. (2008) Modularity in developing business services by platform approach. The International Journal of Logistics Management, 19(1), 84–103. Available from: https://​ doi​.org/​10​.1108/​09574090810872613 Reinartz, W. and Ulaga, W. (2008) How to sell services more profitably. The idea in brief. The idea in practice. Harvard Business Review, 86(5), 1–9. Salkin, C., Oner, M., Ustundag, A. and Cevikcan, E. (2018) A conceptual framework for Industry 4.0. In: A. Ustundag and E. Cevikcan (eds.). Industry 4.0: Managing The Digital Transformation. Springer International Publishing Switzerland. pp.  3–23. Available from: https://​doi​.org/​10​.1007/​978​-3​-319​ -57870​-5​_1 Schneider, P. (2018) Managerial challenges of Industry 4.0: an empirically backed research agenda for a nascent field. Review of Managerial Science, 12(3), 803–48. Available from: https://​doi​.org/​10​ .1007/​s11846​-018​-0283​-2

Revolutionizing manufacturing: digital technologies and platforms  25 Shen, L., Sun, W. and Parida, V. (2023). Consolidating digital servitization research: a systematic review, integrative framework, and future research directions. Technological Forecasting and Social Change, 191, 122478. Available from: https://​doi​.org/​10​.1016/​j​.techfore​.2023​.122478 Silvestro, R., and Lustrato, P. (2015) Exploring the ‘mid office’ concept as an enabler of mass customization in services. International Journal of Operations & Production Management, 35(6), 866–94. Available from: https://​doi​.org/​10​.1108/​IJOPM​-03​-2013​-0154 Simonsson, J., Magnusson, M. and Johanson, A. (2020) Organizing the development of digital product-service platforms. Technology Innovation Management Review, 10(3), 37–48. Available from: https://​doi​.org/​10​.22215/​timreview/​1335 Şimşek, T., Öner, M. A., Kunday, Ö. and Olcay, G. A. (2022) A journey towards a digital platform business model: a case study in a global tech-company. Technological Forecasting and Social Change, 175, 121372. Available from: https://​doi​.org/​10​.1016/​j​.techfore​.2021​.121372 Teece, D. J. (2010) Business models, business strategy and innovation. Long Range Planning, 43(2–3), 172–94. Available from: https://​doi​.org/​10​.1016/​j​.lrp​.2009​.07​.003 Thomas, L. D. W., Autio, E. and Gann, D. M. (2014) architectural leverage: putting platforms in context. Academy of Management Perspectives, 28(2), 198–219. Available from: http://​dx​.doi​.org/​10​.5465/​ amp​.2011​.0105 Tien, J. M. (2020) Toward the fourth industrial revolution on real-time customization. Journal of Systems Science and Systems Engineering, 29(2), 127–42. Available from: https://​doi​.org/​10​.1007/​s11518​-019​ -5433​-9 Tongur, S. and Engwall, M. (2014) The business model dilemma of technology shifts. Technovation. 34(9), 525–35. Available from: https://​doi​.org/​10​.1016/​j​.technovation​.2014​.02​.006 Van Alstyne, M. W., Parker, G. G. and Paul Choudary, S. (2016) Pipelines, platforms, and the new rules of strategy. Harvard Business Review, 94, 2–9. Veile, J. W., Schmidt, M.-C. and Voigt, K.-I. (2022) Toward a new era of cooperation: How industrial digital platforms transform business models in Industry 4.0. Journal of Business Research, 143, 387–405. Available from: https://​doi​.org/​10​.1016/​j​.jbusres​.2021​.11​.062 Verhoef, P. C., Broekhuizen, T. L., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N. E. and Haenlein, M. (2021) Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. Available from: https://​doi​.org/​10​.1016/​j​.jbusres​.2019​.09​.022 Wang, Y., Lee, J., Fang, E. and Ma, S. (2017a) Project customization and the supplier revenue–cost dilemmas: the critical roles of supplier–customer coordination. Journal of Marketing, 81(1), 136–54. Available from: https://​doi​.org/​10​.1509/​jm​.15​.0300 Wang, Y., Ma, H.-S., Yang, J.-H. and Wang, K.-S. (2017b) Industry 4.0: a way from mass customization to mass personalization production. Advances in Manufacturing, 5(4), 311–20. Available from: https://​doi​.org/​10​.1007/​s40436​-017​-0204​-7 Warner, K. S. R. and Wäger, M. (2019) Building dynamic capabilities for digital transformation: an ongoing process of strategic renewal. Long Range Planning, 52(3), 326–49. Available from: https://​ doi​.org/​10​.1016/​j​.lrp​.2018​.12​.001 Weber, A. (2019) Additive manufacturing for medical device production assembly. Assembly, 61(5), 58–63. Windahl, C. and Lakemond, N. (2006) Developing integrated solutions: the importance of relationships within the network. Industrial Marketing Management, 35(7), 806–18. Available from: http://​dx​.doi​ .org/​10​.1016/​j​.indmarman​.2006​.05​.010 Yang, D., Li, L., Jiang, X. and Zhao, J. (2020) The fit between market learning and organizational capabilities for management innovation. Industrial Marketing Management, 86, 223–32. Available from: https://​doi​.org/​10​.1016/​j​.indmarman​.2019​.12​.007 Yin, R. (1994) Case Study Research Design and Methods: Applied Social Research and Methods Series (2nd ed.). SAGE Publications, Inc.

3. Converging challenges: industrial data ecosystems and a vital business model Marc Brechtel and Dimitri Petrik

INTRODUCTION The advancing industrial adoption of the IoT paradigm is shifting industrial firms’ value creation from selling traditional products to offering connected products and digital services (Marheine et al., 2021). Connected products can generate data that can be processed to create value-added services for solving previously difficult to solve problems. In particular, data can be used to realize new business potential through digital services. In turn, these services can provide foundations for completely new business models (Yoo et al., 2010). Consequently, the accessibility of data is crucial to generate added value. If firms can expect added value, they will make their data available to third parties, and system value can be generated (Schweihoff et al., 2022). In many cases, the potential of leveraging added value from data has not been tapped (Azkan et al., 2022). Meanwhile, firms have increasingly invested in and built up digital infrastructures such as data lakes to process the various data streams firm-internally (Marheine et al., 2021). Recently, industrial firms have started considering sharing data for collaboration purposes, in so called data ecosystems (Oliveira et al., 2019). Collaboration on a data level among ecosystem actors can unlock tremendous potential (Oliveira et al., 2019). Following the ecosystem approach, industrial firms may take advantage through improved effectiveness and efficiency (Bauer et al., 2022). Further, the environmental and social goals, legally enforced by policies such as the ‘Act on Corporate Due Diligence Obligations in Supply Chains’, can only be achieved by traceability along the whole supply chain (Bauer et al., 2022). Finally, collaborative data sharing sets the stage for new business models and use cases with advantages for all actors involved. However, they have not experienced mass adoption yet (Bauer et al., 2020). Although ecosystem coordination efforts are increasing in number, data is still siloed in many industrial firms, and the potential that lies with data is not skimmed (Heimstädt et al., 2015). Consequently, ‘the full potential of data ecosystems remains untapped’ (Zhiwei et al., 2021, p. 26). Irrespective of political enthusiasm and previous research efforts on infrastructure and software to facilitate data sharing across organizations (Baumann and Leerhoff, 2022), most industrial firms continue to not push for data sharing if they consider this data (potentially) critical to their business performance relative to competitors (Gelhaar and Otto, 2020). Thus, many firms struggle with ecosystem-wide data sharing. In particular, industrial firms lack a clear understanding of participating and capturing added value from data sharing in data ecosystems (Gelhaar et al., 2021). Despite this contradiction between potential and reality, extant research offers limited understanding of how to ensure value creation, value delivery and value capture from participating in data ecosystems. Even though primary research considering business models for data ecosystems is available, ‘the connection to the structures of an ecosystem still holds a lot 26

Industrial data ecosystems and a vital business model  27 of potential’ (Schweihoff et al., 2022, p. 340). So far, previous publications predominantly focus on the technical nature of data ecosystems (Oliveira et al., 2019). At the same time, we witness that ‘data ecosystem initiatives emphasize the importance of considering all components that constitute a socio-technical system’ (Oliveira et al., 2019, p. 614). Baumann (2022, p. 2) stresses that ‘an interdisciplinary approach is vital, in order to capture the scope […] of [digital business ecosystems]’, to which we would also assign data ecosystems. Following her, ‘many important [digital business ecosystems] research strands are still unconnected and underexplored’ (p. 3). We conclude that more empirical knowledge about the sociotechnical design of digital business ecosystems, i.e. data ecosystems, is needed. This chapter explores the phenomenon of insufficient data sharing between industrial firms in data ecosystems from a sociotechnical perspective. Based on a qualitative approach, we seek to reason the absence of industrial data ecosystems by asking the following question: Why do most industrial firms hesitate to join or form data ecosystems? We answer this question and reveal reasons for the absence of scaled industrial data ecosystems by analyzing 31 interviews with industrial firms having experience of, or at least interest in, collaborative data-driven services and data ecosystems. Providing empirically backed knowledge, this study advances the understanding of how to scale data ecosystems by recognizing their challenges aggregated to four dimensions: technology, people, organization and value.

THEORETICAL BACKGROUND Originating from the field of biology, ecosystems were reinterpreted by management scholars to analyze the different interactions and multilateral relations within a network composed of entities and factors aiming for a strong value proposition (Adner, 2017). Today, ecosystems are defined as structures of initially loosely coupled actors that increasingly cooperate and compete simultaneously, resulting in the actors’ interdependency (Adner, 2017). Moore (1993) originally popularized the concept of business ecosystems as ‘a novel organizational form reshaping traditional market hierarchies and business networks’ (Otto et al., 2019, p. 15). In advancing digitalization, the organization of value creation in ecosystems becomes increasingly digital (Senyo et al., 2019). The resulting digital ecosystems are based on digital technologies mediating value by combining software and data of connected products, which may use digital platform technologies (Subramaniam, 2020). A more narrowed and application-oriented term in the emergence of the ecosystem construct is platform ecosystems. These are composed around ‘a digital platform between the platform owner and an ecosystem of autonomous complementors and consumers’ (Hein et al., 2019, p. 90). Prior researchers have already observed that digital platform owners may hold a focal position in ecosystems and thus can be seen as keystone players (Iansiti and Levien, 2004). Owners, therefore, have major leverage over platform ecosystem value as relevant data are processed centrally on their proprietary digital resources (Thomas et al., 2014). Consequently, platform ecosystems are sensitive to power asymmetries between the keystone player and other actors (Cutolo and Kenney, 2020). This increases the risk for firms to become dependent and be commoditized by the keystone player. As a consequence, scholars are discussing new pathways for ‘bringing the algorithms to the data instead of bringing the data to the algorithms’ (Van Alstyne et al., 2021, p. 35).

28  Handbook on digital platforms and business ecosystems in manufacturing Following the latest discussions of leading platform scholars, we focus on industrial data ecosystems, i.e. B2B networks with relevant upstream orientation dedicated to value creation, complementing the dominant downstream orientation of the many well-known, often end-consumer oriented and transaction focused, digital platforms. Data ecosystems borrow aspects from former ecosystem constructs (Oliveira and Lóscio, 2018). We consider them an evolutionary step towards utilizing value from data without dependency on central digital platforms. They are defined as distributed, open and adaptive networks consisting of autonomous actors owning proprietary data connected to each other. In comparison to centralized digital platforms, they retain maximum control over whether to consume, produce and contribute resources such as data and related software, services or infrastructure to create added value (Oliveira and Lóscio, 2018). In contrast to centralized digital platforms, functionalities such as the execution of applications can be implemented in decentralized connectors as part of the architecture (Otto and Jarke, 2019). Data ecosystems develop through the interaction of its actors (Heimstädt et al., 2015). They allow their actors to explore data and create value collaboratively (Oliveira et al., 2019). Data ecosystems are closely intertwined with the concept of business models since they are likely to unlock new ways of creating and capturing value (Osterwalder et al., 2005) for the organizational actors involved based on sharing data. Data ecosystem actors assume that more value can be created based on processing shared data compared to separate analysis of individual data only. Consequently, such ecosystems are seen as a strong driver for multilateral innovation (Otto et al., 2019) for which a logic on how to create and deliver value based on data to customers, i.e. business model, needs to be found (Teece, 2010). Further, data ecosystems are seen as sociotechnical systems (Oliveira et al., 2019). Sociotechnical system models combine technological as well as human and organizational dimensions (Brandt and Cernetic, 1998). Even though scholars agree that adopting a sociotechnical approach can help improve system acceptance, we hardly find such approaches in practice (Baxter and Sommerville, 2011). We propose that this is equally valid for the current development of data ecosystems in their emergence phase. In particular, we began our study on the assumption that firms carefully evaluate the opportunities and risks for future and existing business before joining ecosystems. Extant research has so far focused on selected components essential for the technology of data ecosystems. We are missing a comprehensive view of their sociotechnical character (Oliveira and Lóscio, 2018), which include a strong business perspective, and choose to pursue previous proposals in the literature on ‘figuring out how to earn revenues from providing data […] is a key element’ for data ecosystems (Oliveira et al., 2019, p. 618). We will pay particular attention to the core purpose of data ecosystems: sustainable creation of benefits for each individual actor and for the whole ecosystem (Otto et al., 2019).

METHODOLOGY The topic’s novelty and the rarity of empirical findings on data ecosystems encouraged us to apply a qualitative-empirical research design to learn more about the reasons for the absence of industrial data ecosystem implementations. To explore the reasons for missing adoption by industrial firms, we relied on a qualitative study design to reveal the challenges in the context of business decisions in a real-world setting (Myers, 2013).

Industrial data ecosystems and a vital business model  29 For our study we interviewed representatives from industrial firms with expertise in the field of data ecosystems. We identified them through their active participation or a communicated interest in national and international government-supported initiatives for data ecosystems, such as Gaia-X (2023) and Catena-X (2023). The goal of Gaia-X is to be an open-source, federated data ecosystem in Europe aiming for sovereign and secure data sharing across firms’ boundaries (Ahle and Hierro, 2022). Catena-X is one of the first lighthouse projects based on the fundamentals of Gaia-X for the automotive industry. It aims for consistent data chains leveraging additional value creation by addressing ten initial use cases (Catena-X, 2023). Both ecosystems provide overviews of firms involved and even in some cases specific representatives contributing in working groups, which we considered as experts for our study. Further, we adopted the snowballing concept by asking for additional contacts at the end of each interview. The majority were incumbent firms with a leading market position in their field of business. At the same time, successful mid-sized firms and start-ups were not excluded from the data sample but made up a much smaller part (see Table 3.1). We used semi-structured interviews to derive deep insights through a combination of pre-formulated questions as part of the interview guide and situational follow-up questions. The interviews are considered as rich opportunities to gather data on ecosystem requirements and help reconstruct the context of decision inhibitors from potential stakeholders eager to participate in a future data ecosystem. We designed the interview guide as problem-centered, which enabled us to access implicit dimensions of the experts’ knowledge beyond our staging questions (Döringer, 2021). Thereby, we succeeded in openly discussing challenges that interviewees experienced in their firms’ attempts to collaborate with peers on a data level across firms’ boundaries. The interview guide itself was structured as five sections: First, we clarified the expert’s role and responsibility before, second, we found definitional alignment on the term of data ecosystems. Third, we focused on the challenges our interviewees were and are confronted with regard to data ecosystems, before, fourth, appropriate solution pathways were the subject of investigation. Finally, we asked our interviewees to summarize the three most crucial challenges combined with the question on how they expect their firm would most likely collaborate with other businesses on data in the year 2030. In sum, we interviewed more than 25 firms, represented by the voice of 31 interviewees, between June and November 2021. Using an iterative, open coding process, we noticed that the last interviews did not contribute any new challenges. Therefore, the researcher team agreed on having reached theoretical saturation (Myers, 2013). All interviews were recorded, transcribed and anonymized. While coding data, we conceptualized the derived results, focusing on building relevant data clusters, i.e. challenges, hindering firms joining or forming industrial data ecosystems. In addition to the interview recordings, all researchers took summarizing notes during each interview. After each interview, these notes were exchanged among the researchers to achieve a consensus in interpreting the gathered data and helped in guiding the first phase of open coding. Accordingly, we adopted the data analysis techniques proposed by Corbin and Strauss (1988) to derive challenges in the emergence of industrial data ecosystems, using MaxQDA as a software tool to create a codebook (for excerpts, see Table 3.2) and organize the codes accordingly (Myers, 2013; Gioia et al., 2013). During the labeling, the authors jointly assessed whether the mentioned challenges are really specific to data ecosystems. After passing this criterion, the remaining codes were used to build a list of first-order concepts. Following Corbin and Strauss (1988) we condensed these first-order concepts to 13 second-order themes via axial coding. During the coding process we kept the elements char-

30  Handbook on digital platforms and business ecosystems in manufacturing acterizing a sociotechnical system continuously in perspective (Brandt and Cernetic, 1998). Sociotechnical systems incorporate technology, people and organization, serving as the aggregated dimensions of our analysis. Through the inductive code development and supported by Jacobides et al. (2020), stressing that ecosystems focus on how firms can come together to provide joint value, we expanded the sociotechnical thinking by value as a fourth dimension.

RESULTS This section describes the challenges (see Figure 3.1) that equate to the barriers of data ecosystem utilization in the industry and explains selected challenges. Technology-Related Challenges Technology-wise, we see three major challenges related to industrial data ecosystems. First, firms continue to fear that insufficient security fails to ensure that sensitive data does not get into the wrong hands. Interviewee [#1] representatively claimed that today’s available cyber security does not reach a sufficient level to leverage data ecosystems. Second, today’s technical complexity and poor system interoperability among and even within firms form a major technical barrier – poor compatibility. One interviewee underpinned that the technology needed in practice varies by application [#19], which in turn results in a variety of data structures and interfaces. Third, we recognized a fundamental lack of digital readiness in many industrial firms that we talked to. Many firms still face problems regarding the connectivity of their brownfield installations and thus suffer data availability in general [#19]. So before committing themselves to data ecosystems and sharing data along the supply chain, they need to be able to provide data at all – an issue that can only be overcome with huge, often manual, integration efforts due to lack of standards and harmonized interfaces [#17]. People-Related Challenges We see three types of people-related challenges hindering the successful establishment of industrial data ecosystems. First, we identified challenges related to the available knowledge on data ecosystems in the respective organizations. In our interview, we took notice of a knowledge gap between the C- and operational levels [#26]. On the C-level, commitment between different firms was found quite often (see Catena-X, 2023). But once firms go beyond a letter of intent, middle management notices that the C-level management does not consider multiple sociotechnical issues, which they have to master as a consequence. Furthermore, the interviewed firms stated how they lack knowledge carriers in the field of data economy. This is due to new ‘technologies and thought structures [that] are completely different from anything that has come before’ [#27]. Both challenges are complemented by the stated missing business experience. This results in wrong expectations as decision makers can’t even assess what ups and downs they can get confronted with [#26]. Second, we see a leadership issue coming into play regarding data ecosystems. As admitted by one interviewee [#12], a missing sense of urgency to join data ecosystems hampers their adaptation. Instead, firms still rely on successful business models established in the past, focusing on their exploitation instead of looking out for seminal collaborative digital business models. Further, our data highlights the mana-

Supply Chain Position

Raw Material Supply

Raw Material Supply

Raw Material Supply

Component Supply

Component Supply

Component Supply

Component Supply

Component Supply

Component Supply

Component Supply

Component Supply

Component Supply

System Integrator

System Integrator

System Integrator

System Integrator

System Integrator

System Integrator

System Integrator

System Integrator

System Integrator

System Integrator

System Integrator

System Integrator

Service Provider

Service Provider

Service Provider

Service Provider

Service Provider

Service Provider

Service Provider

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

Z

Y

X

W

V

U

T

S

R

Q

P

O

N

M

L

L

K

J

J

I

H

G

F

E

D

C

B

B

A

A

A

Firm

List of interviewees

#

Table 3.1

Corporate

Corporate

Corporate

Corporate

Start-up

Start-up

Start-up

Corporate

Corporate

Corporate

Corporate

Corporate

Corporate

Corporate

Corporate

Corporate

Corporate

Corporate

Corporate

Corporate

Corporate

Corporate

Corporate

SME

Corporate

Corporate

Corporate

Corporate

Corporate

Corporate

Corporate

Firm Type

Program Lead, Data Intelligence

Platform Product Owner

Business Owner Master Data

Head of Digital Businesses

Chief Executive Officer

Chief Executive Officer

Platform Consultant

Project Manager, Digitalization

Director, Digital Products & IIoT

Head of Digitalization

Head of Digital Services

Product Marketing Manager, Digital Solutions

Senior Manager, Digital Innovation

Global Business Partner, Digitization & IIoT

Product Manager, Digital Solutions

Global Product Manager

Head of Business Unit Digital Innovations

Business Development Manager, EaaS

Head of Incubation & Digital Business Models

Director, Automotive Ecosystems

Vice President, Strategic & Digital Business Dev.

Vice President, Digital Systems Product Engineering

Head of Product Management, Spares & Services

Chief Digital Officer

Business Ecosystem Manager

Director, Digital Business & Project Lead Catena-X

Project Manager, Catena-X & Gaia-X

Director, Strategy for Partnerships & Alliance Mgmt.

Digital Expert, New Business Development

Senior Manager, Digitalization

Senior Specialist, Digitalization

Job Title

16.11.21

14.09.21

23.07.21

03.08.21

27.08.21

03.08.21

21.07.21

16.09.21

22.07.21

19.07.21

21.09.21

27.08.21

13.08.21

10.08.21

30.07.21

01.07.21

30.07.21

13.08.21

12.10.21

12.08.21

07.07.21

10.07.21

07.09.21

24.06.21

07.07.21

09.07.21

29.07.21

29.07.21

27.07.21

22.07.21

28.07.21

Date

Industrial data ecosystems and a vital business model  31

32  Handbook on digital platforms and business ecosystems in manufacturing

Figure 3.1

Overview of challenges that explain the absence of industrial data ecosystems

gerial aversion to data ecosystems due to a limited venture-driven mindset. One interviewee [#7] put it like this: ‘few are pioneers and […] simply implement data ecosystems’. Another aspect we observed in conjunction with leadership issues are inappropriate incentive systems [#28]. The interviews revealed the existence of bonus programs with a one-year orientation, whereas the successful establishment of a data ecosystem takes several years and is therefore not compatible with bonus payments strictly based on annual top- or bottom-line results.

Industrial data ecosystems and a vital business model  33 Lastly, decision-making managers often receive only rough estimations regarding the return on investment [#11]. As a result, they are hesitant about the approval of budgets for joining or forming data ecosystems with peers. Third, we see missing trust among firms as another barrier. We argue that a certain level of trust among all involved actors is a prerequisite for the emergence of industrial data ecosystems. Otherwise, industrial firms will continue refraining from sharing sensitive data with external parties [#9]. Moreover, even in data ecosystems the OEMs are still perceived by Tier-X suppliers as the decisive force [#24]. This, in turn, manifests the existing power asymmetries along the supply chain – even in federated ventures like Catena-X. Organization-Related Challenges We find that cultural, legal and accountability challenges in organizations obstruct the emergence of industrial data ecosystems. First, our data shows that even though industrial firms have committed to working in environments that strive for data ecosystems, they mostly stick to their own agenda, which is not communicated with others [#5]. Revealing and bringing these hidden agendas together appears to be a major cultural challenge while forming data ecosystems. Instead, the great majority of industrial firms limit themselves to developing their own digital verticals, resulting in many redundant solutions [#19]. This silo thinking is most probably attributable to the ‘not-invented-here’ syndrome [#13]. Hence, firms tend to stick to their internal capabilities and prefer proprietary solutions. Further, our experts agreed that most industrial firms lack pragmatic approaches – especially their legal departments [#25]. One of our interviewees [#5] even claimed that the legal world has not yet arrived in the world of data ecosystems. Second, we noticed several legal-related barriers that need to be overcome to further develop the idea of data ecosystems. In regulated industries, such as the power industry, it is even forbidden to share data as they are assigned to critical infrastructure [#16]. Given such clear regulations for the critical infrastructure, poor data regulations lead to grey-zone activities in other industries [#6]. As a result, e.g. data ownership rights need to be defined case by case, which may be time-intensive for all involved actors. Furthermore, we noticed the lack of a scalable legal framework that is applicable in the formation process of data ecosystems. Today, interested firms are confronted with a rat tail of high entry barriers from that standpoint [#19]. Third, lacking clarity on intra- and interfirm data responsibility [#26], let us list another firm-internal necessary groundwork for industrial data ecosystems: accountability. Value-Related Challenges We identified five value-related challenges for emerging industrial data ecosystems. First, we notice that start-ups and SMEs confirm doubts about the added business value for them [#23]. However, their active participation in industrial data ecosystems is proving critical as they bridge the gap between bigger Tier-suppliers and OEMs. Simply put, without SMEs participation, end-to-end data consistency along the automotive supply chain cannot be ensured. In addition, most firms have difficulties determining the actual value of the data [#24]. This keeps them from sharing data with peers as competitors could potentially use the data in a more value-adding manner than originally expected. To move on, industrial firms and especially SMEs require high investments to achieve a sufficient level of digital readiness [#28]. At the

34  Handbook on digital platforms and business ecosystems in manufacturing same time, we witness incorrect return-on-investment assumptions [#17] due to inappropriate inspiration. Many firms aim for success stories as they see them in the B2C context, which simply does not work out in the data-sensitive B2B environment. Consequently, the high expectations are not met, and project activities are already discontinued before such ventures can become profitable. As a third aspect, we see that firms, and especially SMEs, can’t even sense relevant use cases and thus business value related to data ecosystems so far [#5]. As one interviewee [#26] stressed, the successful realization of use cases seems to be incredibly difficult. Often firms are stuck in the realization of bilateral use cases and do not make it towards multilateral adoption of the data ecosystem idea. Proof-of-success for such use cases is still absent. Finally, we see a deceleration for data ecosystems with the remaining competitive thinking expressed by the fear of increasing transparency over the supply chain. The reasons are the economic rents gained from intransparency [#2]. Consequently, such firms are unwilling to share data with others as they assume that data sharing will dissolve the intransparency, diminish their competitive advantage and disrupt their currently (still) profitable business model.

DISCUSSION The present study examines the reasons for firms’ hesitant behavior regarding data sharing in a B2B environment. Our findings add empirical evidence to the primarily conceptual understanding of data ecosystem design (Oliveira et al., 2019). Our study assists data ecosystem designers in addressing and overcoming them. The qualitative analysis and synthesis of the challenges enables us to confirm prior research, especially in the field of technology-, peopleand organization-related aspects. Most importantly, we present a proposition on key business requirements for successful promotion of data ecosystems. We confirm that security, compatibility, and an appropriate level of digital readiness are vital technology requirements for the development of industrial data ecosystems. First, our results confirm the omnipresent perception of potential cyber security threats in the context of digitalization in general (Jeglinsky and Winkler, 2022) and further among (potential) data ecosystem actors in specific (Zhiwei et al., 2021). We underpin the fact that a secure environment for sharing data is needed to proactively address the fear of industrial firms that shared data could be disclosed to non-authorized consumers, e.g. competitors (Gelhaar and Otto, 2020). In this regard, we also confirm that cyber security should meet the highest possible security standards (Bauer et al., 2022). Second, technical interoperability needs to be addressed to establish data ecosystems successfully. Our results support the call for more technical standards as they can be seen as one of the fundamental bases of data ecosystems (Oliveira et al., 2019; Gelhaar and Otto, 2020). Third, like the Capgemini Research Institute (Zhiwei et al., 2021), we conclude that missing data availability hampers the mass adoption of industrial data ecosystems. We go beyond their quantitative findings by stressing that many firms avoid huge data integration efforts needed due to their poor digital readiness level. Based on our results, we detect awareness for technical hurdles and notice that the working groups aiming for industrial data ecosystems have already started to tackle these issues. Our study empirically substantiates that industrial firms lack the knowledge and leadership skills needed to successfully establish data ecosystems. Further, it underlines the centrality of building trust among data ecosystem actors.

Trust

Technology

People

Leadership

Knowledge

Digital

Compatibility

Sec.

Table 3.2

#1: So far, cyber security is not built on the best foundations to leverage data ecosystems.

#26: And that’s exactly why we often knock down the doors of a board of directors and it’s totally easy for us to get a meeting. But to get the

middle manager […] to say that he is spending his budget and allocate resources […]. That’s just difficult.

Knowledge gap

between C- &

before.

#26: But from an experience perspective – they can’t even assess what ups and downs they should expect in a data ecosystem project.

carriers

Missing business

#7: Today’s managers always want to see and copy use cases. Few are pioneers and […] simply implement data ecosystems.

Poor venture-driven

management, and then there have to be people who do it.

#11: Managers always have the ROI in mind – especially in a medium-sized company. And […] this is hard to calculate – nearly impossible.

system

Fear of

#24: It is already relatively clear who is the cook and who is the waiter. The car OEMs are still the bosses, and the Tier-1 suppliers are the first

Power asymmetries

guard, the Tier-2 suppliers, and their IT service providers are allowed to join. […] they are just transferring their ecosystem into a digital one.

#9: Everyone realizes okay, we need data from others but don’t get it. My firm doesn’t want to give away any data either. It is an issue of trust.

Lack of trust

decision-making

#28: Incentivization, I would say, is also a huge topic. Who is incentivized to look for new solutions today? That should really be the

Unsuitable incentive

mindset

#12: Everyone acts like, what do you want to tell me new? I have my process under control. So, why should I share data?

No sense of urgency

experience

#27: We see there a knowledge issue. The needed technologies and thought structures are completely different from anything [that] has come

Lack of knowledge

operational levels

#17: […] the system landscapes are not sufficiently standardized. Hence, the manual effort, to bring this data together is very, very high.

about data ecosystems.

#19: Most of the manufacturing firms haven’t even managed to get their machines onto a technical footing. Hence, they don’t even need to talk

different lengths. The longer standards are in place […], the easier it is to implement data spaces.

#26: Data structure homogeneity is not particularly high across companies. This is […] because industries […] have been tackling this issue for

it’s not that much. [..] If you go to an injection molding machine […] [-] the technology you need for this is completely different.

Integration efforts

Poor data availability

Poor interoperability

Technical Complexity #19: If we look at a measuring machine and see how many elements, it contains that are relevant for condition monitoring. Then I realize that

Cyber security risks

Excerpt of interview statements on the challenges related to data ecosystems

Industrial data ecosystems and a vital business model  35

Readiness

Organization

Value

Culture

Legal

Acc.

Business

Profitability

Use Case

Competition

Suddenly, we’re supposed to collaborate and share something with peers. That’s simply contrary to the principles that have prevailed.

#25: If I want to scale […] with my data ecosystem but hide behind NDAs and first need a LEITZ folder full of documents to get started –

Lack of pragmatism

the market. […] and there are legislations that simply prohibit it.

#6: So, what is being discussed here is to regulate the topic of data use. I believe that this is necessary to create legal certainty. Firms that aim

markets

Poor data regulations

#26: The question is who is responsible for the data in the industry or in the individual firm - and the question is far from settled.

Unclear data

many firms are not willing to invest.

#17: That’s one of the major issues, that I’m still spending a bunch of money and can’t quite quantify what the new thing is going to bring.

Incorrect ROI

decentralized and why not on a central platform?

#26: I have been active in International Data Spaces Association since 2016 and since then we have not been able to go live, although the

specifications have been clear for five years.

#2: There are many areas where one lives relatively well from intransparency. You know something that someone else doesn’t know. And this

use cases

Missing

proof-of-success

Fear of transparency

can be monetized quite well. I think it is rather this issue that this step from intransparency to transparency fills many with concern.

#5: For SMEs the use cases are still not clearly defined, and the business value is not clear. And simply the argumentation, why now

Poor accessibility of

assumptions

#28: To become data ecosystem-ready you get confronted with six-figure, seven-figure sums without having earned a single euro. That is why

investment

does not know the context of the problem and thus the value.

#24: Data has no value. It only becomes valuable in the context of problems – when data becomes information. But the one who offers the data

Need of high initial

Unclear data value

Unclear business value #23: In the end, there always must be added value. Everyone must capture a fair portion of it and that just doesn’t work out somehow yet.

responsibility

How is it valued in the balance sheet? So, it’s a whole rat tail resulting in high entry barriers.

frameworks

Lack of scalable legal #19: And to set up a company jointly – that is very time-consuming [...]. Who benefits and to what extent? Who is held accountable for what?

for data sharing are in the grey zone – at least today.

#16: So, the first challenge are national restrictions, norms, standards, legislation for critical infrastructure. That’s a problem that comes with

Highly regulated

I think that’s the wrong approach.

#13: We place a lot of value on our patents and the protection of our inventions. That’s how we’ve simply worked, protecting what we know.

syndrome

optimize its portfolio by adding digital capabilities on top. This leads to […] many solutions emerging side by side.

#19: We are seeing that every machine tool manufacturer is currently developing its own verticals. Each manufacturer is trying to further

together – that’s the challenge.

#5: […] the political component should not be underestimated, because everyone has its own agenda. I think revealing them and bringing them

Not-invented-here

Silo thinking

Hidden agendas

36  Handbook on digital platforms and business ecosystems in manufacturing

Industrial data ecosystems and a vital business model  37 First, we can confirm that the lack of knowledge carriers, as Alharthi et al. (2017) and Curry et al. (2021) have shown in the context of big data research, or like Jeglinsky and Winkler (2022) did in the context of digitalization projects for digital business ecosystems, is also valid for data ecosystem establishment. Therefore, the need for experts that understand data-driven businesses and are equipped with technical expertise rises accordingly. However, we see how firms continue exploiting their traditional businesses and do not see the need to further push the topic of digitalization (Jeglinsky and Winkler, 2022) and adopt the idea of data ecosystems. That is why we call for managerial attention to entrepreneurial urgency. Second, our study results enrich extant research regarding the centrality of trust to motivate firms to participate in a data ecosystem (Gelhaar and Otto, 2020). We claim that the traditional logic of trust is not fully transferable to data-driven collaboration asking for new sociotechnical solutions (compare Gaia-X, 2022). Although previous research recognizes the existence of research studies on technical solutions that enable data sharing (Baumann and Leerhoff, 2022), our empirical findings show that it takes more than technical solutions to enable data sharing across organizations. We support and selectively enrich previous findings on the need to address specific cultural, legal and accountability issues. First, we can confirm that one of the major cultural barriers for active engagement in data-driven business is the ‘not-invented-here-syndrome’ (Marheine et al., 2021). Second, we see and confirm that legal uncertainties prevent industrial firms from sharing data with industrial peers. Capgemini Research Institute (Zhiwei et al., 2021) attributes this to the fact that we still have no legal clarity on the distribution of intellectual property regarding data sharing. With our study, we acknowledge and support Gelhaar and Otto (2020, p. 7) on legal measures to create “a trustworthy and secure environment for the sharing of data”. Lastly, and following Fadler and Legner (2021) we see that clarity on data ownership and data responsibility, is essential for industrial firms benefiting from their data. In particular, it is important to give firm-specific answers to the questions of who is responsible for which data assets and under which conditions they can be shared with whom and to what extent. Consequently, we ask industrial firms to answer the question of internal data accountability first, before entering or forming a data ecosystem with peers. We propose that missing attention to essential value-related requirements concerning business aspects, profitability, use cases and competition is keeping firms from building or joining industrial data ecosystems. Our observations point towards the need for coherent business models which explain how data ecosystems’ value cannot only be created but also how it can be captured and fairly distributed among all contributing participants. First, we claim that industrial data ecosystems can only begin developing sustainably if they provide clear answers to what value they add for all actors and how it can be fairly distributed among them, e.g. along the supply chain. Today, most data ecosystem initiatives miss such clear answers, as our empirical data reveals. Instead, for many potential data ecosystem actors, it remains unclear what added value they can expect from their participation (Gelhaar et al., 2021). Second, interested firms continue to shy away from the mostly necessary high initial investment to become compliant with the data ecosystem. Agreeing with Jürjens et al. (2022, p. 91) we see a ‘significant challenge […] to incentivize the participants to invest in setting up the infrastructure’. Further, we state that decision-makers in firms require implemented showcases. Our empirical data agrees with Curry et al. (2021) that the broad acceptance of data ecosystems requires visible and successful use cases. Finally, transparency in the context

38  Handbook on digital platforms and business ecosystems in manufacturing of data ecosystems seems to be a two-sided coin, by differentiating between data transparency and supply chain transparency. On the one hand, data transparency is and needs to be one of the core principles of federated data ecosystems like Gaia-X. On the other hand, occurring supply chain transparency in data ecosystems worries potential data ecosystem actors about too much transparency – especially regarding production knowhow and data assets of their core operational processes. Even though the claim that data ecosystems need to ‘show that all actors can benefit from […] participating in the ecosystem’ (Gelhaar and Otto, 2020 p. 8), we see no established solution pathways toward this requirement yet. Clear recommendations on how value creation, delivery and capture may work in practice are not only missing for general digitalization projects across firms’ boundaries (Jeglinsky and Winkler, 2022), but also for industrial data ecosystems. We suggest that there is a strong need for comprehensive data ecosystem business models: the ecosystem and its actors need to understand how value is created, captured and continuously distributed fairly among one another. Theoretical Contribution Our study enriches the currently limited understanding and knowledge on different abstraction levels about designing and establishing data ecosystems in an industrial context. Further, we do justice to the call of empirical research combining it with the content of sociotechnical system theory (Oliveira et al., 2019). We demonstrate the sociotechnical multidimensionality and complexity of data ecosystems by providing a long but also structured list of challenges. Our results support enhanced inductive modeling of data ecosystems in line with previous findings regarding missing theoretical constructs (Oliveira et al., 2019). We see our study as a foundation to advance the design knowledge on industrial data ecosystems. The main contribution of our study highlights the need for and importance of convincing business models for data ecosystems. These business models need to encourage industrial firms to leverage the potential of data sharing in data ecosystems. Our study specifically highlights the importance of considering not only joint value creation but also attractive value capturing and fair value delivery to all ecosystem participants. Appropriate business models that satisfy the needs of all involved actors must be developed and proven reliable. With that, we provide the empirical basis for calling for the creation of ‘new business models for new products and services grounded in data spaces and data sovereignty’ (Cirullies et al., 2021, p. 5). Practical Contribution Our findings support data ecosystem design and data sharing between industrial firms to establish manufacturing ecosystems based on shared data as a key resource. The study offers a better understanding of why many industrial firms still hesitate to embrace the collaborative opportunities of data ecosystems. We provide a list of reasons and thus an explicit explanation for the absence of industrial data ecosystems. Our findings enable firms to identify their own and potential reasons for peers for not collaborating on a proprietary data level. We witness that especially SMEs are still catching up with the requirements of digital business environments. They need support in learning how they can create value for themselves while enabling value generation for partners in a data ecosystem. Thus, we encourage ecosystem promotors to actively engage with SMEs.

Industrial data ecosystems and a vital business model  39 In particular, we have seen that sustainable access to the value captured in industrial data ecosystems remains unclear for most actors. This shows that fair value distribution in data ecosystems must be a critical determinant for industrial data ecosystem designers. More attention needs to be paid to calculating the profit formula for all participants as early as possible. It must be considered as one, if not the key, subject of negotiation in every collaborative data-driven use case in data ecosystems.

LIMITATIONS AND OUTLOOK The retrieved findings are subject to three relevant limitations. First, we refer to empirically gathered expert interview data only. We did not run a quantitative survey to triangulate our data yet. Although we considered 31 industry experts to get to our results, we cannot claim statistical generalizability. Second, we focused on the identified challenges perceived by (potential) data ecosystem actors. We intentionally did not include measures to overcome the derived challenges to give this chapter a clear scope. Finally, our analysis builds on the opinion of firm representatives either actively shaping or at least being interested in data ecosystems. We did not consider voices of data ecosystem-averse businesses or even data ecosystem opponents. These limitations open up new opportunities for future research: First, to increase the validity of our findings, we propose data triangulation and considering the perspective of industrial firms that are not interested in data ecosystems at all. Second, we see the chance that our study results form the basis for subsequently mapping suitable solutions to the revealed list of challenges. Agreeing with Schweihoff et al. (2022), we see essential insights into the business model elements of data ecosystems as being highly under-researched. Finally, a particular focus of future research should be on the development of innovative and feasible data-driven business models to finally enable broad adoption of industrial data ecosystems. Future research, we believe, will be able to highlight the specifics of fundamental and shared elements of such business models valid for the main types of data ecosystem actors.

REFERENCES Adner, R. (2017) Ecosystem as structure – An actionable construct for strategy. Journal of Management, 43(1), 39–58. Ahle and Hierro (2022) FIWARE for data spaces. In Otto, B., Ten Hompel, M. and Wrobel, S. (Eds.) Designing data spaces: The ecosystem approach to competitive advantage. Cham, Switzerland: Springer International Publishing, 91–108. Alharthi, A., Krotov, V. and Bowman, M. (2017) Addressing barriers to big data. Business Horizons, 60(3), 285–92. Azkan, C., Möller, F., Iggena, L. and Otto, B. (2022) Design principles for industrial data-driven services. To be published in IEEE Transaction on Engineering Management. [Preprint]. Available from: https://​ieeexplore​.ieee​.org/​stamp/​stamp​.jsp​?tp​=​&​arnumber​=​9789746 [Accessed 3 October 2022]. Bauer, K., Bienek, M., Haße, H., Hill, M., Hoppe, G., Hünsch, T., et al. (2020) Collaborative condition monitoring – How cross-company collaboration can generate added value. Berlin: Federal Ministry for Economic Affairs and Energy. Bauer, K., Bienel, M., Drost, M., Grüner, S., Haße, H., Hill, M., et al. (2022) Multilateral data sharing in industry – Concept using ‘collaborative condition monitoring’ as a basis for new business models. Berlin: Federal Ministry for Economic Affairs and Climate Action.

40  Handbook on digital platforms and business ecosystems in manufacturing Baumann, S. (2022) Introduction to the handbook on digital business ecosystems, strategies, platforms, technologies, governance and societal challenges. In Baumann, S. (Ed.): Handbook on Digital Business Ecosystems: Strategies, Platforms, Technologies, Governance and Societal Challenges. Cheltenham, UK: Edward Elgar Publishing, 1–9. Baumann, S. and Leerhoff, M. (2022) Networks, platforms, and digital business ecosystems: Mapping the development of a field. In Baumann, S. (Ed.): Handbook on Digital Business Ecosystems: Strategies, Platforms, Technologies, Governance and Societal Challenges. Cheltenham, UK: Edward Elgar Publishing, 11–24. Baxter, G. and Sommerville, I. (2011) Socio-technical systems: From design methods to systems engineering. Interacting with Computers, 23(1), 4–17. Brandt, D. and Cernetic, J. (1998) Human-centered approaches to control and information technology: European experiences. AI & Society, 12(1–2), 2–20. Catena-X (2023) Catena-X Automotive Network. Available at: https://​catena​-x​.net/​fileadmin/​user​ _upload/​Vereinsdokumente/​Catena​-X​_general​_presentation​.pdf [Accessed 18 May 2023]. Cirullies, J., De Luca, G., De Rodde, M., Mertens, C., Pettenpohl, H., Langdon, C., et al. (2021) New business models for data spaces grounded in data sovereignty. Available at: https://​int​ernational​ dataspaces​.org/​wp​-content/​uploads/​IDSA​-Position​-Paper​-New​-Business​-Models​-sneak​-preview​ -version​.pdf [Accessed 17 September 2022]. Corbin, J. and Strauss, A. (1988) Basics of qualitative research – Techniques and procedures for developing grounded theory. Thousand Oaks: SAGE Publications Inc. Curry, E., Metzger, A., Zillner, S., Pazzaglia, J.-C. and Robles, A. (Eds.) (2021) The elements of big data value: Foundations of the research and innovation ecosystem. Cham: Springer International Publishing. Cutolo, D. and Kenney, M. (2020) Platform-dependent entrepreneurs: Power asymmetries, risks, and strategies in the platform economy. Academy of Management Perspectives, 35(4), 584–605. Döringer, S. (2021) ‘The problem-centred expert interview’ – Combining qualitative interviewing approaches for investigating implicit expert knowledge. International Journal of Social Research Methodology, 24(3), 265–78. Fadler, M. and Legner, C., (2021) Data ownership revisited: Clarifying data accountabilities in times of big data and analytics. Journal of Business Analytics, 5(19), 1–17. Gaia-X (2022) Gaia-X Trust-framework – 22.04 Release, Available at: https://​gaia​-x​.eu/​wp​-content/​ uploads/​2022/​05/​Gaia​-X​-Trust​-Framework​-22​.04​.pdf [Accessed 25 November 2022]. Gaia-X (2023) About Gaia-X, Available at: https://​gaia​-x​.eu/​what​-is​-gaia​-x/​about​-gaia​-x/​ [Accessed 25 November 2023]. Gelhaar, J., Gürpinar, T., Henke, M. and Otto, B. (2021) Towards a taxonomy of incentive mechanisms for data sharing in data ecosystems. In: Vogel, D., Shen, K.N. and Ling, P.S. (Eds.): Proceedings of the Twenty-fifth Pacific Asia Conference on Information Systems. Dubai, UAE. Gelhaar, J. and Otto, B. (2020) Challenges in the emergence of data ecosystems. In: Vogel, D., Shen, K. N. and Ling, P. S. (Eds.): Proceedings of the Twenty-fourth Pacific Asia Conference on Information Systems. Dubai, UAE. Gioia, D. A., Corley, K. G. and Hamilton, A. L. (2013) Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods, 16(1), 15–31. Heimstädt, M., Saunderson, F., and Heath, T. (2015) Conceptualizing open data ecosystems: A timeline analysis of open data development in the UK. In: Edelmann, N. and Parycek, P. (Eds.): Proceedings of the International Conference for E-Democracy and Open Government. Krems, Austria. Hein, A., Schreieck, M., Riasanow, T., Setzke, D., Wiesche, M., Böhm, M. and Krcmar, H. (2019) Digital platform ecosystems. Electronic Markets, 30(1), 87–98. Iansiti, M. and Levien, R. (2004) Strategy as ecology. Harvard Business Review, 82(3), 68–78. Jacobides, M. G., Cennamo, C. and Gawer, A. (2020) Distinguishing platforms and ecosystems – Complementarities, value creation, and coordination mechanisms. To be published in n/a. [Preprint] Available from: https://​www​.jacobides​.com/​_files/​ugd/​0b15b1​_7e​0678d28155​41bb920c77​ec41e4d305​ .pdf. [Accessed: 2 March 2023]. Jeglinsky, V. and Winkler, H. (2022) Improved decisions for digitalization projects in digital business ecosystems. In Baumann, S. (Ed.): Handbook on Digital Business Ecosystems: Strategies, Platforms,

Industrial data ecosystems and a vital business model  41 Technologies, Governance and Societal Challenges. Cheltenham, UK: Edward Elgar Publishing, 307–26. Jürjens, J., Scheider, S., Yildirim, F., and Henke, M. (2022) Tokenomics: Decentralized incentivization in the context of data spaces. In Otto, B., Ten Hompel, M. and Wrobel, S. (Eds.) Designing data spaces: The ecosystem approach to competitive advantage. Cham, Switzerland: Springer International Publishing, 91–108. Marheine, C., Engel, C. and Back, A. (2021) How an incumbent telecoms operator became an IoT ecosystem orchestrator. MIS Quarterly Executive, 20(4), 297–314. Moore, J. F., (1993) Predators and prey – A new ecology of competition. Harvard Business Review, 71(3), 75–86. Myers, M.D. (2013) Qualitative research in business & management. 2nd edition, London: SAGE Publications Inc. Oliveira, M. I. S., Lima, G. and Lóscio, B.F. (2019) Investigations into data ecosystems – A systematic mapping study. Knowledge and Information Systems, 61(2), 589-630. Oliveira, M.I.S. and Lóscio, B. F. (2018) What is a data ecosystem. In: Janssen, M., Chun, S., Weerakkody, V., Zuiderwijk, A. and Hinnant, C. (Eds.): Proceedings of the 19th Annual International Conference on Digital Government Research. Delft, Netherlands. Osterwalder, A., Pigneur, Y. and Tucci, C.L. (2005) Clarifying Business Models: Origins, Present, and Future of the Concept. Communications of the Association for Information Systems, 16, 1–25. Otto, B., Lis, D., Jürjens, J., Cirullies, J., Opriel, S., Howar, F., Meister, S., Spiekermann, M., Pettenpohl, H. and Möller, F. (2019) Data ecosystems – Conceptual foundations, constituents and recommendations for actions. Fraunhofer Institute for Software and Systems Engineering. ISST-Report. Otto, B. and Jarke, M. (2019) Designing a multi-sided data platform – Findings from the international data spaces case, Electronic Markets, 29, 561–80. Schweihoff, J.C., Jussen, I., Stachon, M. and Möller, F. (2022) Design options for data-driven business models in data ecosystems. In: Demmler, D., Krupka, D. and Federrath, H. (Eds.), Jahrestagung der Gesellschaft für Informatik. Bonn, Germany. Senyo, P. K., Liu, K. and Effah, J. (2019) Digital business ecosystem: Literature review and a framework for future research. International Journal of Information Management, 47, 52–64. Subramaniam, M. (2020) Digital ecosystems and their implications for competitive strategy. Journal of Organization Design, 9(12), 1–10. Teece, D. J. (2010) Business models, business strategy and innovation. Long Range Planning, 43(2-3), 172–94. Thomas, L., Autio, E. and Gann, D. (2014) Architectural leverage: Putting platforms in context. The Academy of Management Perspectives, 28(2), 198–219. Van Alstyne, M. W., Petropoulos, G., Parker, G. and Martens, B. (2021) In-situ data rights. Communications of the ACM, 64(12), 34–5. Yoo, Y., Henfridsson, O. and Lyytinen, K. (2010) The new organizing logic of digital innovation: An agenda for information systems research. Information Systems Research, 21(4), 724–35. Zhiwei, J., Thieullent, A., Jones, S., Perhirin, V., Baerd, M., Shagrithaya, P., Cecconi, G., Isaac-Dognin, L., Buvat, J., Khadikar, A., Khemka, Y. and Nath, S. (2021) Data sharing masters – How smart organizations use data ecosystems to gain an unbeatable competitive edge. Capgemini Research Institute.

4. Ecosystem emergence in the manufacturing sector: exploring transformation processes of product-focused firms Joachim Stonig, Torsten Schmid and Günter Müller-Stewens

INTRODUCTION Digital transformation has been a prime concern for many academics, consultants and practitioners, but our knowledge about what this change entails for established companies remains limited (Altman et al., 2022; Furr et al., 2022). Technological changes that introduce modular interfaces in established sectors drive the emergence of ecosystems (Holgersson et al., 2022). We define ecosystems as an organizational form that connects multiple complementary actors to provide an integrated value proposition, often building on a digital platform (Adner, 2017; Kapoor, 2018; Müller-Stewens and Stonig, 2019). Platform-based ecosystems have become an important organizational form to enable digitalized production processes (Baumann, 2022). However, manufacturing firms often struggle to implement and transition to these new strategies. Empirical surveys indicate that around 75 percent of all ecosystems fail to reach a sustainable market position (Reeves et al., 2019). Transitioning from a focus on machines to ecosystem orchestration requires a challenging adaptation process (Gawer and Phillips, 2013; Khanagha et al., 2022) with uncertain outcomes (Snihur et al., 2018). Prior research has theorized the benefits that ecosystems can provide compared to other forms to organize economic value creation (Jacobides et al., 2018; Holgersson et al., 2022). Compared to market-based transactions, ecosystems can realize complementarities between products and thus achieve higher value creation (Kapoor, 2018). Compared to hierarchical system integration, ecosystems can leverage modular interfaces to reach unprecedented variety and efficiency (Baldwin and Clark, 2000). However, our understanding of how firms can successfully transition when technological changes drive their sector towards ecosystems remains limited. Hence, we ask: How do firms successfully transform from a focus on products to integrated value propositions based on digital ecosystems? This gap is especially salient in the context of manufacturing, as most studies on ecosystem emergence focus on the B2C sector (Altman et al., 2022). Hence, further research is needed on how ecosystems in these contexts emerge and evolve over time. Exploring how manufacturing firms may transition towards ecosystem-based value propositions is therefore our focus in this chapter. Building on Porter (1991), we address the research question in two ways. Porter distinguished the cross-sectional problem from the longitudinal problem in strategy. The cross-sectional problem refers to the causes of superior performance at any given point in time, whereas the longitudinal problem refers to the dynamic process by which competitive positions are attained. In line with this distinction, extant research on platform-based ecosystems

42

Ecosystem emergence in the manufacturing sector  43 remains limited in two ways, in particular in regard to explaining the longitudinal transformation of the manufacturing sector: 1. Longitudinal problem: The transformation of a sector towards platform-based ecosystems is primarily portrayed as a process of radical and disruptive change in prior research (Ansari et al., 2016; Cozzolino et al., 2018). In the manufacturing context, however, ecosystem emergence may be a more incremental process, given the capital-intensive asset base and client inertia. 2. Cross-sectional problem: Extant research explains the superior performance of platform-based ecosystems compared to other organizational forms with network effects based on the quantity and variety of products and/or users in the ecosystem (e.g. Rietveld and Eggers, 2018; Cennamo and Santaló, 2019). This research has an emphasis on B2C markets, such as mobile operating systems or gaming consoles. It remains unclear if there are other sources of ecosystem advantage in the context of B2B and more specifically, manufacturing. To address our research question, we draw on the case of CASTER, a global leader in die-casting machines. A detailed overview of the transformation of this firm is provided in Stonig et al. (2022). One fascinating aspect that we did not explore in that article was the historical evolution of the firm towards an ecosystem. CASTER did not start out with an ecosystem-based strategy, but engaged in a long-term organizational learning process, moving from product differentiation to system integration, and eventually to ecosystem orchestration. In this emergent process, the firm identified new sources for an incumbent advantage that were not based on network effects arising from the number of users/products. Through this chapter, we add two contributions to the literature on ecosystems in the context of the manufacturing industry. First, we provide an answer to the longitudinal problem regarding the process of ecosystem emergence and the associated managerial choices. Extant research contrasts platform ecosystems as potentially superior alternatives to other organizational forms, such as product differentiation and system integration. However, it remains limited in describing and explaining the process of how manufacturing firms transition towards platform ecosystems and develop necessary capabilities for ecosystem orchestration (Foss et al., 2022; Helfat and Raubitschek, 2018). We develop a process model, showing how organizational forms may represent stages in the long-term evolution of a focal manufacturing firm and its sector towards an Industry 4.0 ecosystem. In our case, the focal firm moved from product differentiation (selling superior machines) to system integrator (a superior production system hierarchically controlled by the focal firm), and eventually to ecosystem orchestration (a superior production system on the basis of partner products orchestrated by the focal firm). We show how these different organizational forms interplay. In our case, earlier stages, while not directed at ecosystem creation, afforded the firm with resources and insights that were important for both the development of the emerging ecosystem and the focal firm’s orchestration capabilities. Hence, the firm’s organizational learning may be an important mechanism for manufacturing firms to digitalize industrial production in a broader sense, not only focused on the machine. Second, we elaborate the potential origins of ecosystem advantage in a cross-sectional comparison. Extant research focuses mostly on high-tech, asset-light industries, such as software and online platforms, and thus on the number of users/products as a source of network effects. We add an additional explanation for why ecosystems may emerge, namely the quality and

44  Handbook on digital platforms and business ecosystems in manufacturing amount of data generated and processed in the ecosystem (Gregory et al., 2021). This requires, at the ecosystem level, the establishment of shared standards, supporting and optimizing data exchange, as well as ‘smart’ components with superior data generation capacity. At the level of the focal firm, it requires ownership of an integrative platform for data processing and a central component to facilitate hardware adaptation. This view of ecosystem as data processing structures nuances extant conceptualizations of network effects and may be particularly important in manufacturing, because compared to B2C sectors, the number of users/products is often more limited and the interaction between ecosystem participants is closer. In the next sections, we provide a brief overview of ecosystem research, elaborate on the process model and the case observations and discuss its implications for ecosystem emergence in the manufacturing sector.

ECOSYSTEM CREATION IN ESTABLISHED FIRMS Ecosystems are a new organizational form that has gained immense popularity during recent years. Technological innovation and new management styles shift established market interactions or integrated organizations towards ecosystems (Holgersson et al., 2022). Jacobides et al. (2018) theorize that ecosystems are one way to solve problems of interfirm coordination when multiple actors focus on providing a joint value proposition. In contrast to markets, ecosystems allow for the coordination of the interdependent actors’ activities to realize complementarities (Kapoor, 2018). In contrast to hierarchical system integration, ecosystems leverage the power of modularity (Baldwin and Clark, 2000) to reduce the costs associated with the coordination of a large number of participants. The development of ecosystems has become a priority for many established firms (Jacobides, 2022; Reiter et al., 2024). Companies are advised to transition from product-focus to ecosystem leadership to remain competitive (Van Alstyne et al., 2016; Zhu and Furr, 2016). However, transitioning from a focus on products to ecosystem orchestration requires a challenging adaptation process (Gawer and Phillips, 2013). In particular, ecosystems require the development of new managerial capabilities (Helfat and Raubitschek, 2018; Foss et al., 2022) to engage other firms in collaborative value creation (Altman et al., 2022; Thomas and Ritala, 2022). There have been numerous case studies of established firms, often focused on products, that struggle to compete when ecosystem-based competitors enter their market (Ansari et al., 2016; Snihur et al., 2018; Cozzolino and Verona, 2022). The transformation to competing in ecosystems requires several strategic choices from established firms. First, they need to determine whether to become an ecosystem orchestrator or become a participant in another ecosystem (Adner, 2022). This choice depends on the capability of the firm to mobilize complementary contributions from independent actors compared to the capability of developing these activities in-house (Fuller et al., 2019; Altman et al., 2022). Ecosystems are only successful when they realize an ecosystem premium (Stonig and Müller-Stewens, 2019), that is, the value created by using ecosystems as an organizational form exceeds the costs of ecosystem orchestration. Second, the established firm needs to determine the right governance approach for the ecosystem (Wareham et al., 2014; Reiter et al., 2024). Effective governance makes it possible to identify a value proposition that is attractive to customers (Dattée et al., 2018) and to bring partners from the established sector on board (Khanagha et al., 2022).

Ecosystem emergence in the manufacturing sector  45 The comparative perspective between the position of an established firm as a product manufacturer or ecosystem orchestrator, however, underplays the important longitudinal perspective that might enable or impede the creation of ecosystems. We require a more nuanced understanding of transformation processes that goes beyond a ‘from-to’ mindset (Furr et al., 2022). In the next section, we will therefore provide an in-depth longitudinal analysis of one transformation towards an ecosystem.

FROM PRODUCT DIFFERENTIATION TO ECOSYSTEM ORCHESTRATION: THE CASE OF CASTER Our case focuses on a leading manufacturer of machines used mainly in the automotive supply chain. This focal firm transitioned from a product differentiation strategy to an integrated value proposition using an ecosystem orchestration strategy. Based on our longitudinal research,1 this chapter outlines how the firm realized that its own capabilities and resources were not sufficient, and then, reluctantly, engaged partners to provide a new value proposition. We also show how data generation and processing was critical to establish the new value proposition with an ecosystem as the organizational form of choice. To support a theory-driven analysis, we use an activity system lens (Siggelkow, 2001; Siggelkow, 2002). We present the configuration of core activities that underlie each strategy in each stage of firm evolution and highlight the changes the firm introduced in its activity system during each stage. We discuss the implications of original and new activities for moving towards an ecosystem-based strategy. Stage 1 (Prior to 2001): Product Differentiation Strategy Product-focused activity system Before 2001, CASTER followed a product differentiation strategy. Under this strategy, the core activities involved engineering high-quality machines. These activities centered on the technological design of the machine and its software; the manufacturing of the machine hardware was partly outsourced. The key performance metric for this activity was machine performance, measured with criteria like injection quality, locking force and machine speed. Another core activity was maintaining close service relationships with clients. CASTER had to visit the client’s site frequently in order to perform necessary interventions during the installation and aftersales maintenance of the machine. Such interventions were crucial, considering that the high temperatures and pressures of the die-casting process often cause machine malfunctions. CASTER was committed to providing a high level of aftersales service to resolve these issues, and the company was perceived to intervene faster and more effectively than rivals. As a further activity, CASTER effectively executed the product differentiation strategy through an organizational design featuring a functionally differentiated organizational structure, set up along value chain steps of machine manufacturing such as research and development, production, sales and finance. Centralized decision-making by top management characterized the strategic choices in regard to the product differentiation strategy. For instance, the CEO decided to take a top-down approach for new machine types and technologies and assigned their development to the R&D department to focus on. An employee recalls that this type of decision-making led to the introduction of a new machine type: ‘The CEO returned from a trip to Japan and said: “we need such a type [of machine] as well.”’ However, low-cost

46  Handbook on digital platforms and business ecosystems in manufacturing competitors were increasingly able to match certain key aspects of CASTER’s premium machine performance at a lower price, which led to an increase in competitive pressure. Sensing a transition to integrated value propositions CASTER sensed the shift towards integrated value propositions ahead of its competitors, based on the core activities of its existing product differentiation strategy and efforts to sustain this strategy. Being at the forefront of R&D knowledge in die-casting, the firm’s engineers perceived a flattening of the incremental improvement potential of the machine at a relatively early phase. In an effort to sustain its product differentiation strategy and premium margins, CASTER’s management started expanding the services offered to existing machine clients. These services included client training, framework contracts, and consulting services. As part of these service expansion initiatives, CASTER’s management conducted regular interviews with its key clients to learn proactively about their changing needs. Close interactions between service technicians and clients to improve and customize the machines, a key aspect of the product differentiation strategy, helped CASTER to identify a large potential for efficiency improvements in the client’s production process adjacent to the existing product. CASTER learned that the clients’ main concern was to improve the efficiency of the entire die-casting process, rather than to purchase a standalone machine with better performance. This also helped CASTER’s management team to predict that the client demand would shift to integrated offerings that can help clients optimize the entire die-casting process. Thus, CASTER’s top management decided to use process efficiency as a novel performance metric for the internal innovation projects. Traditionally used by clients, this metric measured the costs per part produced. CASTER’s adoption of this new performance metric implied a broader scope of innovation activities, which reduced the inefficiencies between the machine and adjacent products in the die-casting system. Soon after starting these innovation projects, CASTER’s engineers started to fully apprehend the tremendous potential for performance improvements that could stem from optimizing the entire die-casting system. Stage 2 (2001–13): System Integrator Strategy Shift towards an integrated value proposition CASTER tried to build on its historically successful core activities to implement the new integrated value proposition. Based on the insights gained from the new initiatives to improve efficiency, CASTER’s CEO decided on a reorientation to a system integrator strategy. The firm started to focus on a new vision to provide an integrated offer to clients, driven by the objective to become a ‘solution provider’. CASTER communicated this new vision to become a provider of integrated die-casting solutions internally and externally, for example, with the slogan ‘welcome to productivity’. This new vision entailed a fundamentally different type of interaction with clients. Instead of optimizing the product for a specific function of the die-casting value chain, CASTER was advocating the integration of previously unrelated products to achieve better overall performance. Management decided to deliver the new value proposition by adapting its product-differentiation strategy to a system integrator strategy, that is, by producing the components of the new value proposition internally. Thus, it maintained the original focus on superior product innovation and hierarchical control over suppliers. CASTER internally developed an integrated die-casting system. This system aimed at creating better connections between CASTER’s

Ecosystem emergence in the manufacturing sector  47 machine and other products of the die-casting process, in order to eliminate bottlenecks and improve process efficiency. An example of such a bottleneck was the alloy transport and the heat balance between the machine and the die. Linking machine and die would enable faster production and fewer defective parts. Initially, CASTER’s engineers developed solutions to optimize the entire die-casting process conceptually, relying, for instance, on their knowledge of thermodynamic problems encountered during machine engineering. To build a client offer based on these conceptual innovations, CASTER’s R&D department engineered products adjacent to the machine designed to improve the linkages between products. For instance, a furnace with an extremely high dosage accuracy for special alloys would enable the machine to perform higher quality injections. CASTER’s machine engineering knowhow enabled them to develop these products. Subsequently, CASTER used suppliers to manufacture them according to their specifications. Although CASTER retained the intellectual property of these products on an exclusive basis, it needed to cooperate bilaterally with some suppliers to find solutions to technical issues that could not be addressed by its R&D due to their lack of competencies. CASTER’s R&D modified the machine software to control all products of its integrated die-casting system. They adapted the machine software code and its interfaces to steer all adjacent products in sync with the machine. This machine software was a proprietary technology, which could not be accessed by clients. Misfits of an extended product-focused activity system Despite a shift to integrated value propositions, CASTER only extended its activities system to provide multiple products as a system integrator. This caused external and internal misfits. Since CASTER had initiated a shift to integrate offerings as a first-mover, most of its clients were initially reluctant to purchase a die-casting system from CASTER. Small foundries, which represented one of CASTER’s core client groups, were particularly hesitant to invest because an integrated system entailed fewer opportunities to customize the die-casting process. It also had lower differentiation potential, owing to the fact that several rival foundries would use the same process configuration developed by CASTER. Therefore, CASTER was forced to customize its integrated systems to achieve initial successes with the commercialization. Clients’ customization requests included, in particular, combining elements of CASTER’s integrated system with selected products of third-party firms. Furthermore, CASTER largely maintained its existing organizational design. Two key parts of the existing product differentiation strategy were centralized decision-making, in the form of a top-down strategy process, and functional differentiation along the value chain, with limited integration across departments. The system integrator strategy largely maintained these organizational design features. However, coordinating the delivery of the integrated system through the functionally differentiated organization increased conflicts and inefficiencies. The R&D team possessed the required technical knowhow to engineer an integrated client solution. They performed system integration activities in coordination with the sales team who interacted with the client. However, the different functional departments struggled to agree on key decisions for the integrated offerings. For instance, the sales team pushed for low system prices to facilitate the sale, while the technological staff pushed to add more financial cushion to absorb potential problems and delays during the customization process. Furthermore, CASTER’s R&D struggled with the upgradation of a large number of internally developed products. After designing the core features required for the integrated system, the R&D team had limited resources and few incentives to continue making incremental

48  Handbook on digital platforms and business ecosystems in manufacturing improvements. These incremental improvements to the components, however, were important for the clients. As a result, some of CASTER’s in-house products were withdrawn from the market after their release, due to insufficient demand. Sensing the potential of an ecosystem approach Experimenting with a system integrator strategy produced new insights that eventually enabled CASTER to transition to an ecosystem strategy. In particular, CASTER shifted from addressing higher process efficiency toward using the integrated value proposition to address emerging client needs in the form of complex and thin aluminum parts. For example, when the R&D projects were launched to improve process performance, CASTER’s engineers started to apprehend the potential for other value propositions that can be achieved by better linking die-casting products. CASTER collaborated with key machine clients to test, customize and install integrated solutions. Through these collaborations, CASTER learned that the integrated offer originally developed to achieve higher process efficiency could also be used to produce new, more complex and high-margin aluminum parts. For instance, an integrated system enabled the production of large and complex motor blocks or thin-walled chassis parts. While not intended initially, this discovery implied a robust growth of the die-casting market, since these parts previously could only be produced by other, competing metal casting technologies. For CASTER’s clients, investing in the production of these parts provided an opportunity to increase their value addition and command higher margins than those for the commoditized die-casting parts. As a result, CASTER’s clients became more willing to invest in and buy CASTER’s integrated die-casting systems. Pioneering clients, mainly large foundries, successfully used the new die-casting systems to produce thin-walled parts to make cars lighter and more fuel-efficient, in the context of the new environmental regulations. Die-casting systems also proved popular with clients in the fast-growing emerging markets and with those lacking skilled personnel. The demand for customizing such solutions surged to the point that CASTER lacked personnel resources to complete contracts. However, the rigidity of the system integration activities impeded the scalability of the new value proposition beyond a limited number of pioneering clients. These misfits manifested in the form of internal conflicts when coordinating new integrated offerings across functionally differentiated departments. Furthermore, the incumbent could not focus on improving all products of the integrated system, which resulted in quality problems and a slow pace of product innovation, eventually triggering a shift towards an ecosystem strategy. Stage 3 (2014–Present): Ecosystem Strategy Integrated value proposition through ecosystem orchestration By observing the misfits emerging from the system integrator strategy, it became clear that efficiently integrating third-party products was critical to providing the integrated offers that fulfill clients’ requests. Therefore, CASTER shifted to a strategy to become the orchestrator of an ecosystem of independent partners encompassing the entire die-casting process. As an ecosystem orchestrator, CASTER refined its system-level activities to enable cooperation with partners around the integrated value proposition and, simultaneously, adapted the existing product to support this ecosystem. With this new strategy, the governance of the integrated

Ecosystem emergence in the manufacturing sector  49 value proposition shifted from hierarchical control by a system integrator toward an orchestration of partners to provide the integrated value proposition. The origins of this shift lay in an open strategy process. The CEO had started a review of CASTER’s system integration strategy, involving the extended senior management team in team-based strategic decisions. He conducted multiple meetings and off-site workshops that engaged senior managers with functional and regional responsibilities. Over several months, this strategy process encouraged an open discussion on the strategic assumptions and past performance. The discussion also focused on the system integrator strategy as well as the company’s vision as a solution provider. The CEO elaborated: ‘As part of the strategy, we discussed our identity: Are we a machine supplier? Are we a solution provider? If we are a solution provider, what does that mean?’ The management team intensively discussed the performance of the system integrator strategy, using concrete examples of in-house products to analyze the problems and potential benefits. Building on the past experiences of senior managers, the eventual strategic decision was to pursue the integrated value proposition through an ecosystem strategy but continue providing differentiated machines. CASTER’s senior managers chose ‘marrying’ the machine and the integrated offer as a metaphor for this approach. This metaphor highlighted the central role of the machine with the coordination of partners in the ecosystem. The most imminent change was abandoning most in-house products in response to past failures and halting plans for acquisitions of product manufacturers. In lieu, managers reallocated resources to ecosystem projects, such as the communication protocol and the control system, while continuing investment in machine innovations. The new ecosystem strategy implied that CASTER maintained its focus on one differentiated product but collaborated with a large ecosystem of interdependent complementors in order to provide the integrated value proposition. The process involved establishing touchpoints between the product and the ecosystem activities, such as a standardized communication protocol that can process information provided by machine sensors. Furthermore, it included coordinated innovation at the product- and the ecosystem-level. A modular machine design provided the necessary versatility to customize integrated value propositions efficiently at the ecosystem level and the use of ecosystem-level data provided information to develop new machine innovations. Ecosystem-level adaptation In order to connect its machine with adjacent third-party products and coordinate the entire manufacturing ecosystem, CASTER set out to establish an industry-wide technological standard for a communication protocol. This enabled CASTER to specify the digital interface standard that each product of the die-casting process should use to send and receive information. CASTER enrolled eight large firms as lead partners to develop the technological basis of this communication protocol. CASTER had worked with all these partners in the past, for instance, for collaborating on client-specific projects, which facilitated the formation of the multi-partner project. The project started on an exclusive basis, where only the selected partners were involved in the technology development for the communication protocol. This period of exclusivity enabled the lead partners to learn about and shape the communication protocol ahead of time, which enabled them to adapt their products and gain a competitive edge in the market. After the initial development phase and field tests, CASTER and its part-

50  Handbook on digital platforms and business ecosystems in manufacturing ners opened the specifications of the communication protocol to the entire industry, including their competitors. Since several technologically advanced firms had participated in the project, the technological cornerstones of this communication protocol were adopted by the industry’s standard-setting association, wherein several firms furthered the development of the standard. This open communication protocol efficiently integrated third-party innovation into CASTER’s solution. New product lines and partners’ updates could be connected to the solution without any intervention by CASTER’s engineers. A focus on the modular integration of partner products allowed CASTER to cater to several clients who felt that their idiosyncrasies, that is, preferences for certain suppliers or types of equipment, could not be met with a system integration strategy. CASTER’s R&D equipped its machines with sensors and new interfaces, in accordance with the specifications of the communication protocol, to provide as much data as possible to the ecosystem. It encouraged partnering firms in the ecosystem to also improve their products in terms of data connectivity. The openness of the communication protocol and the support of many leading firms in the sector facilitated the adoption of the protocol’s interfaces. Focal firm adaptation Based on this communication protocol, CASTER built a standalone control system to coordinate all products involved in the integrated value proposition. CASTER’s engineers envisioned the control system as becoming the ‘brain of the die-casting process’. This control system was designed as a standalone product with its own hardware, including a touchscreen user interface. It was linked to the machine and to all products in the cell; based on the technical and thermodynamic parameters received via the communication protocol, the control system used algorithms to decide when and how each product in the ecosystem could became an active part of the die-casting process. CASTER equipped the control system with modular interfaces connecting to clients’ corporation-wide software protocols. CASTER used its US and Asia R&D teams to ensure the compatibility of the cell control module with these regionally specific software protocols. Complementary to the control system, CASTER modularized its machine and disintegrated it from other products. CASTER reduced the functional scope of these machines to the injection process only and transferred functionalities to the newly established control system. Furthermore, CASTER’s management decided to modularize the machines, and thereby increased their versatility for various applications in different client-specific integrated offers. Value creation through data generation and processing The performance of CASTER’s client-facing solution was dependent on value creation at both the product level and the ecosystem level. The full functionality of the machine was exploited when it was integrated with CASTER’s control system and the system, in turn, relied on connected and adapted products to implement its coordination function. While CASTER’s ecosystem innovations could also function in a cell working with a competitor’s machine, the modularization of the machine as part of the ecosystem still had a considerable performance impact. The ecosystem strategy helped CASTER to develop system-wide innovations complementary to the product activities. This alignment of machine and ecosystem was only possible through extensive generation and processing of data at both levels. The first gains realized from information exchange were very basic functionalities, such as a simultaneous reset of all

Ecosystem emergence in the manufacturing sector  51 components in the die-casting process – much faster than a manual reset where a worker had to push a button at every piece of equipment. CASTER’s engineers also developed coordination algorithms that reduce bottlenecks between products, such as thermal imbalances, and thus enable a faster and more efficient production of new and complex aluminum parts. With increasing amounts of data, CASTER is now realizing more advanced applications, such as predictive maintenance algorithms. By analyzing data patterns of die-casting problems, clients are informed about risky components and can replace them before an unplanned outage occurs. Predictive quality applications, where data patterns are analyzed to identify faulty production outcomes, are applied at an early stage, before a large number of these parts are produced. These applications are only possible because all components in the cell generate large amounts of data and CASTER’s integrative platform can process this data into valuable outcomes for the clients. Because of the open standard, this data is in principle open to all actors in the die-casting sector. CASTER trusts that it will be able to learn faster and better from this data than competitors and therefore offer more value to clients on the basis of knowledge creation rather than data control. Data control remains a choice of CASTER’s clients, an important factor for their adoption of the ecosystem-based solution.

ECOSYSTEM CREATION IN THE MANUFACTURING SECTOR Based on this single case study, we discuss two important findings for the emergence of ecosystems in manufacturing. First, we propose that a transition through different stages of organizational forms might be a promising strategy in markets with high asset intensity and slow clockspeed. Second, we observe that ecosystems as an organizational form might be particularly relevant when data network effects arise from a better integration of the ecosystem and its components. Figure 4.1 illustrates the key elements of this type of ecosystem creation. First, our case shows that organizational forms, which prior research differentiates as alternative options, can actually represent stages that build on each other in the long-term evolution of a focal manufacturing firm and its sector towards ecosystem-based competition. Each stage afforded the firm with complementary assets that were important for the development of the emerging ecosystem and the organizational learning required to developing the focal firm’s orchestration capabilities. For instance, without the market position as a product differentiator, the firm would not have sensed the shift to integrated value propositions, and without the system integration, it would not have been able to develop the relationships with future ecosystem partners. We thus see two advantages to a gradual evolution towards ecosystem orchestration that are particularly salient in the context of the manufacturing sector. We propose that their dynamic progression through different organizational forms, such as product differentiation, system integration, and ecosystem (Jacobides et al., 2018), can support the evolution of interfirm relationships toward an ecosystem. Given the slow clock speed of many manufacturing industries, a radical transformation of one actor towards ecosystem orchestration might create frictions with potential partners and clients. These other actors also have to develop some level of ecosystem capability, even as a participant and not an ecosystem orchestrator. Speaking to clients of CASTER, one plant manager complained jokingly that everyone was trying to sell Industry 4.0, but his firm was not even at an Industry 1.0 stage. Dynamically linking different organizational forms might help demonstrate the viability of integrated value proposition and preparing the industry for a digital transformation.

52  Handbook on digital platforms and business ecosystems in manufacturing

Figure 4.1

A longitudinal approach to ecosystem creation in the manufacturing sector

Furthermore, creating an ecosystem in an established sector means establishing a new sociotechnical system that is unknown to the orchestrator. The literature on technological modularity shows that, in these cases, firms are ‘better off erring on the side of integration rather than greater modularity’ (Ethiraj and Levinthal, 2004: 171) to find the right technologies and organizational form. Thus, the sequencing of a system integration strategy followed by an ecosystem strategy can help an incumbent gain valuable information about the interdependencies required to provide the value proposition and develop orchestration capabilities (Helfat and Raubitschek, 2018; Foss et al., 2022). This corresponds to Dattée et al. (2018)’s observation that many of the ecosystem orchestrators they studied did not initially have an ecosystem in mind. Second, our case offers additional insights on why ecosystems may emerge as a superior form of organizing as compared to product differentiation or system integration. While value creation in B2C ecosystems often relies on network effects that increase the quantity of participants and thus the availability and utility of the value proposition (e.g. Cennamo and Santaló, 2013), the use of information may be the key mechanism for value creation in many B2B markets. Learning, for example with the use of AI technologies, is thus the key mechanism for value creation (Gregory et al., 2021). This learning does not only occur at the ecosystem level, but also at the level of components of the ecosystem. These components are improved through better integration in the ecosystem. A positive feedback loop thus emerges between an ecosystem that is able to learn from the data provided by its components, and the components in turn learn from the integration in the ecosystem. We argue that this type of network effect is particularly salient in manufacturing and other B2B sectors that rely on sophisticated products the value creation of which significantly affects the operations of client firms.

Ecosystem emergence in the manufacturing sector  53 Data network effects provide an opportunity for learning advantages for incumbent firms compared to ‘born digital’ competitors, as they often have large amounts of data already in house and access to the components of the ecosystem. This data can provide an advantage in developing integrated value propositions and can constitute a first step towards legitimizing ecosystem orchestration vis-à-vis clients and partners (Thomas and Ritala, 2022). An ecosystem creation process in a progression through different organizational forms, as described above, might be particularly suited to creating and exploiting data network effects.

CONCLUSION Integrated value propositions emerge in manufacturing and many other B2B sectors. Instead of selling standalone pieces of equipment, many firms now provide an optimization of the entire production and service process, for instance under the slogan ‘power by the hour’ (e.g. Rolls Royce) or ‘pay per part’ (e.g. Trumpf). Hence, a detailed understanding of how manufacturing firms successfully transition from a focus on machines to integrated value propositions based on digital ecosystems is vital. The contribution of this chapter to ecosystems in manufacturing is twofold. First, we show that a dynamic linking of different organizational forms on the way to ecosystem orchestration might provide important benefits to established firms, as it aligns their transformation with the changes of other actors in their sector. Second, we argue that this process is particularly suited when the use of large amounts of data creates significant customer value.

NOTE 1. While this chapter uses the same database as Stonig et al. (2022), it presents new data that we did not use in prior work and focuses on new and more detailed theoretical insights.

REFERENCES Adner, R. 2017. Ecosystem as structure: an actionable construct for strategy. Journal of Management, 43, 39–58. Adner, R. 2022. Sharing value for ecosystem success. MIT Sloan Management Review, 63, 85–90. Altman, E. J., Nagle, F. and Tushman, M. L. 2022. The translucent hand of managed ecosystems: engaging communities for value creation and capture. Academy of Management Annals, 16, 70–101. Ansari, S., Garud, R. and Kumaraswamy, A. 2016. The disruptor’s dilemma: TiVo and the U.S. television ecosystem. Strategic Management Journal, 37, 1829–53. Baldwin, C. Y. and Clark, K. B. 2000. Design Rules: The Power of Modularity, Boston, MA, MIT Press. Baumann, S. 2022. Handbook on Digital Business Ecosystems: Strategies, Platforms, Technologies, Governance and Societal Challenges, Cheltenham, Edward Elgar Publishing. Cennamo, C. and Santaló, J. 2013. Platform competition: strategic trade-offs in platform markets. Strategic Management Journal, 34, 1331–50. Cennamo, C. and Santaló, J. 2019. Generativity tension and value creation in platform ecosystems. Organization Science, 30, 617–41. Cozzolino, A. and Verona, G. 2022. Responding to complementary-asset discontinuities: a multilevel adaptation framework of resources, demand, and ecosystems. Organization Science, 33, 1990–2017.

54  Handbook on digital platforms and business ecosystems in manufacturing Cozzolino, A., Verona, G. and Rothaermel, F. T. 2018. Unpacking the disruption process: new technology, business models, and incumbent adaptation. Journal of Management Studies, 55, 1166–202. Dattée, B., Alexy, O. and Autio, E. 2018. Maneuvering in poor visibility: how firms play the ecosystem game when uncertainty is high. Academy of Management Journal, 61, 466–98. Ethiraj, S. K. and Levinthal, D. 2004. Modularity and innovation in complex systems. Management Science, 50, 159–73. Foss, N. J., Schmidt, J. and Teece, D. J. 2022. Ecosystem leadership as a dynamic capability. Long Range Planning, 102270. Fuller, J., Jacobides, M. G. and Reeves, M. 2019. The myths and realities of business ecosystems. MIT Sloan Management Review, 60, 1–9. Furr, N., Ozcan, P. and Eisenhardt, K. M. 2022. What is digital transformation? Core tensions facing established companies on the global stage. Global Strategy Journal. Gawer, A. and Phillips, N. 2013. Institutional work as logics shift: the case of Intel’s transformation to platform leader. Organization Studies, 34, 1035–71. Gregory, R. W., Henfridsson, O., Kaganer, E. and Kyriakou, H. 2021. The role of artificial intelligence and data network effects for creating user value. Academy of Management Review, 46, 534–51. Helfat, C. E. and Raubitschek, R. S. 2018. Dynamic and integrative capabilities for profiting from innovation in digital platform-based ecosystems. Research Policy, 47, 1391–9. Holgersson, M., Baldwin, C. Y., Chesbrough, H. and Bogers, M. 2022. The forces of ecosystem evolution. California Management Review, 64, 5–23. Jacobides, M. G. 2022. How to compete when industries digitize and collide: an ecosystem development framework. California Management Review, 64, 99–123. Jacobides, M. G., Cennamo, C. and Gawer, A. 2018. Towards a theory of ecosystems. Strategic Management Journal, 39, 2255–76. Kapoor, R. 2018. Ecosystems: broadening the locus of value creation. Journal of Organization Design, 7, 1–16. Khanagha, S., Ansari, S., Paroutis, S. and Oviedo, L. 2022. Mutualism and the dynamics of new platform creation: a study of Cisco and fog computing. Strategic Management Journal, 43, 476–506. Müller-Stewens, G. and Stonig, J. 2019. Business ecosystems and platforms: towards a shared understanding. Die Unternehmung, 73, 381–6. Porter, M. E. 1991. Towards a dynamic theory of strategy. Strategic Management Journal, 12, 95–117. Reeves, M., Lotan, H., Legrand, J. and Jacobides, M. G. 2019. How business ecosystems rise (and often fall). MIT Sloan Management Review, 60, 1–6. Reiter, A., Stonig, J. and Frankenberger, K. (forthcoming) 2024. Managing multi-tiered innovation ecosystems. Research Policy, 53, 104905. Rietveld, J. and Eggers, J. 2018. Demand heterogeneity in platform markets: implications for complementors. Organization Science, 29, 304–22. Siggelkow, N. 2001. Change in the presence of fit: the rise, the fall, and the renaissance of Liz Claiborne. Academy of Management Journal, 44, 838–57. Siggelkow, N. 2002. Evolution toward fit. Administrative Science Quarterly, 47, 125–59. Snihur, Y., Thomas, L. D. W. and Burgelman, R. A. 2018. An ecosystem-level process model of business model disruption: the disruptor’s gambit. Journal of Management Studies, 55, 1278–316. Stonig, J. and Müller-Stewens, G. 2019. Navigating the Challenges of Ecosystem Emergence: A Multi-Level Review of Leader and Complementor Strategies. Die Unternehmung, 73, 288–307. Stonig, J., Schmid, T. and Müller-Stewens, G. 2022. From product system to ecosystem: how firms adapt to provide an integrated value proposition. Strategic Management Journal, 43, 1927–57. Thomas, L. D. and Ritala, P. 2022. Ecosystem legitimacy emergence: a collective action view. Journal of Management, 48, 515–41. Van Alstyne, M. W., Parker, G. G. and Choudary, S. P. 2016. Pipelines, platforms, and the new rules of strategy. Harvard Business Review, 94, 54. Wareham, J., Fox, P. B. and Giner, J. L. C. 2014. Technology ecosystem governance. Organization Science, 25, 1195–215. Zhu, F. and Furr, N. 2016. Products to platforms: making the leap. Harvard Business Review, 94, 72–8.

5. Social exchange- versus economic exchange-driven processes: the emergence of peer-to-peer start-up business models in Denmark Susanne Gretzinger, Birgit Leick and Anna Marie Dyhr Ulrich

INTRODUCTION The digitalisation of manufacturing, which has currently reached the stage ‘Industry 4.0’, and the services around the various manufacturing industries have created new opportunities for both incumbent companies and start-up businesses (Frank, 2019; Vinogradov et al., 2021). According to Tian et al. (2021), the Industry 4.0 context forces manufacturers to transform their current business models from exclusively manufacturing to co-creation of customisation, which leads to a stretching of the current boundaries of exchanges and resource integration between companies. Particularly, the logic of peer-to-peer-platform1-driven business models has enabled companies to co-create an enlarged product/service portfolio through combining the focus on core competences with sharing capabilities (Frank, 2019; Jussen et al., 2021). This process is, however, in its initial phase of development, in which it remains often unclear for both incumbent companies and start-up businesses how they could leverage platform thinking for adapting their current business models. Such an adaptation of business models through the use of peer-to-peer platforms would lead to three major advantages (Jussen et al., 2021): the creation of new knowledge, the availability of data-based mass products/services and a faster rollout of product and service improvements. Even if the gains are obvious, incumbent companies (particularly SMEs) are often reluctant regarding investments into further digitalisation or do not have the necessary resources to become more digitised without co-creators (Kazantsev and Martens, 2021). To this end, start-up businesses that are specialised in peer-to-peer platforms are drivers in the process of developing new business models and may as well be vital partners to complement the more reluctant or indecisive incumbent companies. Against the backdrop of these developments, this chapter is motivated by the under-explored aspect of how start-ups would complement the process of developing peer-to-peer business models along the entire supply and value chains and how they would use various roles for improving their start-ups (Tian et al., 2021; Vinogradov et al., 2021)2. For capturing this process, the logic of overlapping exchange networks is approached (Cook et al., 1983; Cook and Hahn, 2021; Tian, 2021). The process of sharing or reshaping platform models is understood as being embedded in networks and activities of social and economic exchanges (Cook et al., 1983; Cook and Hahn, 2021). As of now, only limited insights are available regarding how start-up businesses develop these initial business relationships through various exchanges 55

56  Handbook on digital platforms and business ecosystems in manufacturing (Baraldi et al., 2019; Slávik, 2019; Baraldi et al., 2020; La Rocca and Snehota, 2021) and how the structure of exchanges, in return, influences the emergence of business models. One mechanism to orchestrate relationships is the development of particular roles (Heikkinen et al., 2007) that support reciprocity in the relationships (Cook et al., 1983; Cook and Hahn, 2021). Start-up businesses and the entrepreneurs behind start-up businesses, who use roles in a targeted way, can gain positions and mould relationships that support the creation of their initial business models (Heikkinen et al. 2007). This chapter explores the associations between the structure of exchanges (economic versus social exchange) and the roles developed and applied by start-up entrepreneurs. By illustrating how start-up entrepreneurs use role-making and role-taking to shape and/or contribute to new digitalised business models, the research question can be formulated as follows: How does role-taking and role-making interact with the structure of exchanges (i.e. a situation when social exchange is dominant versus a situation in which economic exchange is dominant) during the emergence of start-up business models in the peer-to-peer platform-based sharing economy? The remainder of the paper is organised as follows: In the next section, the theoretical framework will be presented through the model assumptions and an adapted theory of social and economic exchange that fits the context of this investigation. This will be followed by an empirical section. The subsequent discussion and conclusion section will take up the research question, provide some answers to it and present an avenue for future research.

THEORETICAL FRAMEWORK Exchange Networks and the Theory of Exchange The term ‘exchange theory‘ describes a family of theories which share a set of analytical concepts, i.e. actors, resources, structures and processes. In this chapter, the focus is set on social versus economic exchange (Molm, 2003). In both cases, resources may switch from one owner to another. What makes the difference is how these processes are mediated (Röpke, 1970; Matiaske, 2010) during the exchange. In particular, the medium of exchange and the composition of established rights which govern the exchange of the resources determine whether the transaction represents an economic or a social exchange. While a social exchange is informal, an economic exchange – even if a social resource is exchanged – is approached through a formalised and thus institutionalised medium (Emerson, 1976; Matiaske, 2010). Cook et al. (1983) relate such exchanges – both social and economic exchange – to networks by referring to ‘exchange networks’ (see also Cook and Hahn, 2021), which means that processes of exchange constitute business networks. Therefore, in this chapter, the emergence of new start-up business models is assumed to take place in business networks and represent a process of exchange among key actors (here, the start-up entrepreneur as focal actor and other actors, such as the entrepreneur’s main business partners)3. Economic and Social Exchange in Business Networks More generally, an exchange is defined as a process in which two or more actors interchange resources, for instance, physical resources (e.g. final or semi-finished products) or intangible

Social exchange- versus economic exchange-driven processes  57 services, such as financial payments or knowledge. An exchange can be characterised as an economic exchange when the exchange process is standardised, well-defined and/or institutionalised (Madanoglu, 2018). A social exchange, by contrast, is not to the same extent institutionalised and can be understood as a series of sequential and less formalised transactions between two or more actors (Mitchell et al., 2012). Economic exchange can trigger new social exchange, and vice versa, social exchange can turn into economic exchange. A relationship in a business network might be initially purely contract-based (economic), but through an increase of the exchange of information and intensified communication, a social exchange might also emerge over time because, for instance, deeper information levels in the communication might appear beyond the rather standardised information exchanges. At the same time, new institutional elements emerge or get fostered, e.g. commitment and trust (Molm, 2003; Madanoglu, 2018). In return, this can lead to higher levels of social exchange. The more reciprocal these processes are, the more likely it becomes that they turn into long-lasting, institutionalised relationships. However, both types of exchange follow the logic of demand and supply when relating to business models: one actor (or one group of actors) demands a product, service, information, etc., from another actor (or a group of actors) who can supply the product, service, information, etc. The quality of the resources exchanged according to this market logic is often influenced by the interactions themselves and the relationships of the exchanging actors (Blau, 1968; Cropanzano et al., 2017). Economic and Social Exchange During the Start-Up Process with Peer-to-Peer Platform-Based Entrepreneurs Start-up entrepreneurs with their businesses that are generally characterised as being scarce in resources (for example, investment capital, knowledge, human resources) need to shape and foster new networks around them to overcome their liability of being a small and unknown player in a competitive market (Aldrich and Auster, 1986; Snehota, 2011; La Rocca et al., 2019). In the context of peer-to-peer platform-based business models, this means that start-up entrepreneurs need to connect with various new actors to establish their initial business models, such as co-creators from various industries or supply chains, or users and customers, who are placed within different exchange networks (Kumar et al., 2018; Apte and Davis, 2019; Cook and Hahn 2021). However, the literature neither describes nor explains how the connections with different actors across the various business networks and supply chains during the entrepreneurial start-up process unfold in platform-based and technology-driven contexts (Sussan and Acs, 2017; Baraldi et al., 2020; Schiavone et al., 2020). From an exchange theoretical perspective, roles are vital in this context. Particularly due to the fact that, in the peer-to-peer platform economy, where roles are more blurred and ambiguous (Schiavone et al., 2020), social exchanges are vital as this exchange enables actors to explore the potential for role-making and role-taking. Roles are important as they can be used for orchestrating activities and resources during the development of initial business models. Undoubtedly, the search for and establishment of partnerships with actors to develop initial network capabilities within exchange networks represents a vital task for entrepreneurs in such environments if they want to be able to produce a new, innovative solution that can be translated to an effective business model (Håkansson and Waluszewski, 2007; Baraldi et al., 2020).

58  Handbook on digital platforms and business ecosystems in manufacturing Shaping and Sharing Resources and Capabilities for Start-Up Entrepreneurs Resources represent input factors to which actors can attribute a current or potential use (Håkansson and Snehota, 1995; Baraldi et al., 2012). Another element within exchange networks are capabilities, which according to Teece et al. (1997) are defined as invisible firm resources that are developed over time through complex interactions. To put it in easy words, resources are always there, but capabilities in terms of skills and action scripts that enable a translation of resources into business models are not necessarily fully available. Start-up entrepreneurs in the peer-to-peer platform economy may have their initial business ideas and concepts ready about how to make new relationships, but they might lack experience of performing these tasks. Johnson and Ford (2006) stress in this context that capability development is not only based on a well-defined set of investment alternatives (Teece, 1990). Instead, actors, resources and activities, which are sequenced within exchange networks, influence and shape resources (Bizzi and Langley, 2012). Peer-to-peer platforms can be used as common places for sharing and developing resources and capabilities (Frank et al., 2019). However, even if it is highlighted that this would still enable entrepreneurs to focus on the core-competences (Frank, 2019), due to lack of resources, start-up businesses experience the risk of losing control over their competitive edge (Cook et al., 1983). The role-taking within exchange relationships is one possible approach to attribute a current or potential use to resources and/or capabilities. Speeding up this process is vital for an entrepreneur when it comes to establishing their ‘own core-competences‘ and utilising them, which, in return, increases the power position of entrepreneurs within exchange relationships. In this context, roles are understood as behaviours that are shaped and constrained on both the individual and meso levels by the scripts that actors apply by intention when they want to conquer an advantageous position within the relevant exchange (i.e. business) networks (Biddle, 1986; Heikkinen et al. 2007). From the perspective of a focal start-up entrepreneur, the emergence and the establishment of roles (Håkansson et al., 2009; Bizzi and Langley, 2012) enable entrepreneurs not only to create but also capture value within the value creating network. This is described by McDonald and Eisenhardt (2020), who view business models during the entrepreneurial start-up stage as cognitive schemata and interconnected activities performed by a focal firm. Accordingly, start-up entrepreneurs develop their business models in the framework of ‘parallel play’, in which cognition, action and timing intersect to enable the formation of initial business models. Start-up entrepreneurs hence borrow through exchange processes and relationships built through the exchange processes from peers; they pause and reflect to develop roles and conduct activities that shape these business models (McDonald and Eisenhardt, 2020). Concludingly, this process is accompanied by both role-making and role-taking. Both types of exchanges, social exchange and economic exchange, are at play when roles are made or taken. Start-up entrepreneurs in a peer-to-peer platform context, in particular, need to take care that social exchange maybe is helpful for understanding and co-creating, while at later stages, translating social exchange into well-defined products and subscription models, namely economic exchange, is vital.

Social exchange- versus economic exchange-driven processes  59

METHODOLOGICAL CONSIDERATIONS The units of analysis in this chapter are dyadic relationships, organised around start-up entrepreneurs (6 cases: C1–C6). The start-up entrepreneur with his/her business is understood and considered as a process of exchange networks in which the entrepreneur takes part. More specifically, the process is explored in which a start-up business model (i.e. an initial business model for the entrepreneurship) has been developed by the start-up entrepreneur. Methodologically, this contribution will apply a qualitative approach to explore networks from an industry network approach (Bizzi and Langley, 2012). The chapter will present the emergence of start-up business models based on peer-to-peer platforms in Denmark as a regional case study. As Denmark hosts manifold new and emerging sharing-economy businesses, including recent start-up entrepreneurs, with different business activities, the business models that the entrepreneurs have developed and the markets in which they operate range from for-profit businesses with national and international networks to social initiatives at the local-regional and even municipal level. Given this variety, Denmark represents a relevant and appropriate country context for the present empirical study. The sampling process was organised as a combination of internet searches (e.g. through search engines and keywords) and a snowballing approach by following leads that were provided by regional business actors in Denmark. Through this sampling strategy, an initial list of more than 30 start-ups in Denmark in the peer-to-peer platform-based sharing economy was established. These start-up businesses were contacted, and a total of six businesses agreed to take part in the empirical fieldwork. Hence, a total of six interviews were held with the respective start-up entrepreneurs, who were willing to participate. The data collection was conducted between autumn 2018 and spring 2019 and based on structured interview guides. All interviews were taped and transcribed subsequently. For the analysis, a multiple-step coding concept in line with Saldaña (2016) was adopted that reflects grounded-theory thinking (Boeije, 2009). As a first-cycle coding, an open macro-level coding technique was applied that aims to deduce a code mapping and landscaping to identify the basic themes and issues that are inherent to the data and constitute the phenomena of interest in the context studied (here, roles during the various start-up phases of entrepreneurs in the peer-to-peer platform-based sharing economy). This first step was informed by the key building blocks of the industry network approach (Bizzi and Langley, 2012), with its focus on exchange interactions within exchange networks. While the first-cycle macro-level coding represents preparatory groundwork for a more detailed subsequent coding of data, the second-cycle coding (Saldaña, 2016) analyses and reanalyses data coded in the first cycle. As a result of these coding steps, the key themes were identified by means of applying the NVivo software, which supported the data analysis of the entire material in a structured and systematic way and facilitated the saving of data so that quotations could be easily allocated to emerging themes (Boeije, 2009; Saldaña, 2016). The structure can be depicted as follows: ● For each case (C1–C6) in the first coding interaction, the various exchange actors were identified. ● First-cycle codes were based on the theoretical framework, including the following concepts: social/economic exchange, resources/capabilities of the exchange, role of each particular actor (including the start-up entrepreneur or business), further characteristics of

60  Handbook on digital platforms and business ecosystems in manufacturing the relationship, role or roles created or taken up by each actor, the history of the business, its value proposition, strategy, specific activities and future plans. ● Second-cycle codes were built on the first-cycle codes, but developed the themes through an interpretation of various descriptions from the first cycle, but also interactions among the various descriptions. As an example from C1, we were able to identify for this case company a total of seven key actors in terms of co-creators and customer groups. Based on the understanding that SE denotes social exchange, which is not based on defined characteristics or rules, and EE refers to economic exchange, based on defined characteristics or rules, we evaluated each actor and relationship identified. Some relationships or activities, such as the programming of an app based on a contract, the description of a business model that was just implemented, or a low turn-over through a subscription income model, were interpreted as being mainly EE, whereas other actors or activities, e.g. presentations, visits, communication with customers without specifying the exchange, were considered as SE. In the next coding cycle, we clustered the findings from the first-cycle coding in an interpretive fashion. Thereby, further interpretations were derived, for instance, that extensive social exchange, which, at the same time, shapes a socioeconomic structure, opens opportunities for the entrepreneur to be more exclusive and have more economic exchange in the future.

THE EMERGENCE OF PEER-TO-PEER PLATFORM-BASED BUSINESS MODELS WITH DANISH START-UP ENTREPRENEURS IN THE SHARING ECONOMY Description and Discussion of Six Case Studies In the following, the six cases from Denmark will be introduced and described both individually and in a comparative manner (Tables 5.1 and 5.2) in order to investigate to what extent their business models have been influenced by social and economic exchanges and role-taking/ role-making during the exchange processes. Case 1 (C1) The business in case 1 is based on a platform (app) through which closed communities can both lend and borrow things. The intention of the app is to develop an exclusive sharing environment in which it is more convenient to rent and lend items than to own them. This supports the green transition and strengthens the social cohesion within communities, such as companies and housing associations. C1 has five employees with educational backgrounds in marketing, engineering, software development and multimedia design. C1 is currently conducting a pilot test in eight communities, which means that around 8000 people have access to the platform. To access the platform, the community pays a monthly subscription fee, and the individual user in the community furthermore pays a transaction fee. C1 is a company in its very early stage, which began as a one-man university start-up, with the cofounder being a former classmate from the university. Financially, the business start-up was based on the founder’s savings and study grants, and later the entrepreneur received an innovation grant from the Danish government. Since the company needed to develop compe-

business partners.

● Some social exchange in a very targeted way with key

Case C4

● Some social interaction with key customers.

Case C3

● Initial business model has been developed and revenue creation has started.

low

SE =

high

● Using a classical economic exchange model that functions like a stock exchange.

clear business model, they can focus on economic exchange.

● Altogether, they have established a good position on the market and, due to a strong back up and

● Well-developed business model.

EE =

● Operating under the ‘roof’ of a well-established company.

low

EE =

high

EE =

low

EE =

● Can borrow the structure from a strong business partner.

● Protection mechanisms of the business model are not clear.

● Fee-model is established for rent creation.

● Low degree of relationship-building to acquire an exclusive position on the market. ● Very vague ideas about how the business model will be formulated.

low

● Continuing with developing and establishing the infrastructure to become more resilient.

● Has managed to develop a mature infrastructure for their business.

● Has established a solid business model.

● Strong and close interaction with platform developers (but based on contracts).

● Infrastructure is emerging.

● Sociotechnical structure is used to become established.

● Financing mainly through institutionalised crowd funding.

SE =

low

sharing of resources via a platform.

SE =

● Social exchange is an intrinsic part of their activities.

high

SE =

● The marketing of the business idea happened through the

Case C2

● Extensive social exchange that has created reciprocity.

as a start-up business (also big industrial players).

● Interaction with any kind of actors to become recognised

Case C1

Economic exchange (EE): (Institutionalised) Activities

The start-up entrepreneurs and their exchanges

Social exchange (SE): (Social) Activities

Table 5.1

Social exchange- versus economic exchange-driven processes  61

● Acting as a finance business that has traditionally been institutionalised regarding exchanges.

Source:  Own illustration.

● Feedback from users was a vital development tool.

● Developing complementary capabilities in-house.

traditional car industry as an institutionalised strong relationship.

● Empowerment through establishing and utilising a strong and exclusive cooperation within the

● Being proactive and trying to achieve close interaction.

● Delivering ‘in-house capabilities’ to become empowered to collaborate externally.

labelled this incubation).

High

exchange across various in-house departments (they

EE =

● Interconnections through platforms across the internal network partners.

high

EE =

● Covering the entire sector based on established relationships.

well-defined algorithms.

● Business expansion and marketing for business expansion through the platform using

● Using precisely defined contracts both for downstream and upstream activities.

● Tracing out their own business based on a clear business model (spin-off logic).

High

SE =

low

SE =

Economic exchange (EE): (Institutionalised) Activities

● Screening and analysing the landscape through social

activities.

● The managers’ networks are used for social exchange

Cases C6&C7: C7 as a spinoff under the roof of case C6

sionalised and functionalistic) way.

● Social exchange taking place in a very targeted (profes-

Case C5

Social exchange (SE): (Social) Activities

62  Handbook on digital platforms and business ecosystems in manufacturing

Social exchange- versus economic exchange-driven processes  63 tencies within coding, data science, software development and design, the fellow students of the entrepreneur provided these competencies. The platform/app is under ongoing development, and the feedback from the communities is important for the development process. The social exchange that C1 has with the customers (communities), users, advisory board, etc., currently plays a bigger role in the development of the business model than the economic exchange. C1 still needs to develop the business model; therefore, the start-up business is taking part in an acceleration programme for start-ups with the purpose of becoming more professional and developing into a profitable company. The start-up business managed to establish a basis to increase economic exchange in the future. Case 2 (C2) C2 started in 2005 as a member-based car sharing association in Aarhus and Copenhagen. In 2007, they developed into a non-profit foundation. In 2014 and 2015, the C2 Fleet System was created, which is an online reservation system. In 2020, C2 created a for-profit company of which 87 per cent is owned by the car-sharing foundation and the last 13 per cent is owned by internal employees and external investors. Today C2 has 300 cars with reserved parking spaces in Copenhagen and another 30 cars in Aarhus, Odense and Roskilde. C2 and C2 Fleet System together contain 25 fulltime employees and have 4700 members. The business model is based on a membership fee and a transaction fee for each kilometre that the user drives. Because the users in the bigger cities in Denmark commonly need a car only occasionally and not on a daily basis, a few volunteers started the association ‘Delebilklub Århus og Copenhagen’ in 2005. In the beginning, the focus was on the social and the environmental aspect of sharing a car among more users to avoid each household having its own car. The economic focus was not on a company, but on an individual level, as it is cheaper for a household to share a car among more users instead of owning a car. As the association over the years developed into both a profit and non-profit company and started to hire employees, the economic exchanges of the company came more into the focus. C2 is still aware of maintaining the social exchange with and among its members; they also use the feedback to quality check the product and services, improve the business and develop the business further. A next step that C2 is considering is a rental platform of e-bicycles in the major cities in Denmark. C2 is considered to be a mature company with a clear vision and a well-formulated business model for the years to come. They focus on maintaining a financially stable and profitable company and the social exchange with the members is used to reach that. Case 3 (C3) C3 was founded in 2015 by two brothers who grew up on a farm in Denmark. They had noticed that farming had changed over recent years. The farms have been becoming larger and more professional in the way they are managed. To run a farm requires different, expensive machinery for the different processes on the farm, e.g. for ploughing, sowing, harvesting, etc. The various machines are typically used for shorter periods depending on the season, which also means that the same machines are standing idle in other periods. Based on this realisation, the two brothers founded the company C3. The business is based on a peer-to-peer platform on which farmers can rent machinery from each other. To access the platform, the users (farmers) pay a fee. Later, the start-up entrepreneurs expanded the platform so that it now also provides the farmers with the possibility to document their tasks, products, machinery, employees, etc.

64  Handbook on digital platforms and business ecosystems in manufacturing The founders started their business by requiring a license to a commercial platform solution from a third-party supplier. Soon they realised the lag of flexibility on such a ‘white label’ platform and therefore teamed up with a new partner and investor with whom they jointly built a customised platform for C3. Financially, C3 was supported by the father of the founders, and later they also received funding from the Danish Innovation Foundation. C3 is characterised to be still in the development stage both regarding the platform and the business model. In the start-up phase and before the launch of the platform, the founders had been in close interaction with the users/customers, and they still spend a lot of time on getting feedback from and having a close interaction with the customers to develop the platform and their business further. C3 is encountering some difficulties in developing the business into one that is sustainable and profitable. Case 4 (C4) C4 is a peer-to-peer marketplace (platform/app) for buying and selling second-hand clothing, accessories and used lifestyle goods. The platform is built up in such a way that it is the users that both drive the demand and the supply; the role of the platform C4 is to facilitate that process. C4 was established in 2021 as a further development of the platform of a parent company, and C4 still owns the intellectual property rights for that platform. Originally, the parent company was financed by a venture fund and by a private investor. The business model is based on the transaction fee method, and the users have the possibility to buy other transaction-related items, such as freight, promotion of products or shared products (lending instead of buying). C4 is using the feedback from the customers/users in different ways. At the start of the process of building the platform, the users tested features, functionalities, etc., and C4 used the feedback to design the best user flow and journey. Later, the focus was on getting feedback from different seller profiles on various changes on the platform, e.g. a new design, the flow, etc. The community (influencers and Instagram profiles) plays an essential role for the branding and image of C4, and the company views community development as one of the most important current tasks. C4 is a very young company, but since it is based on the earlier company, it is not to be considered in the start-up phase. The economic exchange is structured and a vital part of the company. There is a clear business model, including contracts with the suppliers, and the business has a vision for the development and growth of the company. The social exchange is also an important aspect for C4 as they are at the beginning of getting a community established supporting the company. Case 5 (C5) C5 operates an online crowd-lending platform on which it facilitates loans between individuals and companies. Crowd-lending is a form of crowdfunding where a company or a private person borrows from a group of people through an online platform. A loan is split into smaller shares, which makes it possible for many individuals to join to finance a loan. C5 was founded in 2014 by four cofounders with a vision to make it easier and faster to borrow and at the same time create an attractive lending option for investors. Now they are three partners, and two of them are working in the company. The founders of C5 developed the platform themselves in close interaction with the users, and they are still using the user feedback to improve the platform. C5 does not have much

Social exchange- versus economic exchange-driven processes  65 contact with the users of the platform, as it is the intention that the platform will work in the same way as the stock exchange. C5 can be characterised as a formalised company, and the business model is based on a loan fee, which is also known from the physical banks. It is financed by investors, funds and venture capitalists. Borrowing and lending money is a very trust-dependent task, and to get that trustworthiness in the market, C5 established a board of directors with experience in crowdfunding. The board of directors should, besides that, also help to develop C5 into a profitable business with a potential for scalability, which from the beginning was a clear goal for the founders. The social exchange, which is not formalised, is mainly with lawyers, accountants and advisors, and they reach out to these communities with the aim of creating awareness about the company. Case 6 (company: C6 and focal actor: C7) C7, which is a spinoff of the incubator C6, is a marketplace for buying and selling cars, and services related to the selling and buying of cars, e.g. car checks, secure payment options, contracts, etc. C6, furthermore, has partnerships with financial and insurance companies and offers financial and insurance services. These services are not only for buyers and sellers of cars, but also for owners of cars. The business consists of different platforms that are linked to each other. The target group is both professional and private buyers, sellers and car users, and the business model is based on transaction fees for the services offered. The spinoff company (C7) became an independent business unit in 2021, but the roots of the original company (C6) in which C7 is embedded go back 10–15 years. The founders financed the start-up of C7 themselves. They had the company running under the umbrella of C6 until it started to generate a profit, then it was organised as a separate and independent company with its own managers and employees. The platforms are built and developed in-house with support from the incubator C6. The feedback and the behaviour of users on the platforms represent important knowledge sources that the business utilises for its further development of the platforms and the business itself. The manager’s personal and professional networks are used to discuss and develop new business ideas (that is, services), which is taking place in an unformalised way. C7 is considered to have a strong formalised business model, and it is a clear goal to run a profitable company with a focus on the economic exchange. Comparative Discussion of the Cases Following the description of the individual six start-up entrepreneurs and their business models, Table 5.2 summarises the key findings about the configurations of exchanges identified with the entrepreneurs and their role-taking and role-making during these exchanges. Four different configurations can be identified. 1. The case of both low levels of social exchange (SE) and economic exchange (EE) (the bottom left-hand side cell in Table 5.2) can be described as the very beginning of business-model formation with entrepreneurs in the peer-to-peer platform-based sharing economy. Nothing is standardised, and it is not clear how the product or service will translate to a fully-fledged business model. Roles are blurred for start-up entrepreneurs in such contexts, so that it can be beneficial if entrepreneurs increase their social exchange activities to screen markets for new opportunities.

66  Handbook on digital platforms and business ecosystems in manufacturing Table 5.2

The social/economic exchange configurations identified with the Danish start-up entrepreneurs in the peer-to-peer platform-based sharing economy

 

Economic exchange (EE) = low level

Economic exchange (EE) = high level

Social exchange (SE) = high

● Extensive social exchange has created

● Extensive social exchange.

level

reciprocity. ● Performance on successful financing instead of strong business model.

● Constantly screening and analysing. ● Constantly developing complementary capabilities.

● Peer-to-peer business model still in the initial ● Reciprocity turns into exclusive relationships. stage, lacking sufficient clearly-defined eco-

● Exclusive cooperation.

nomic exchange elements.

● Delivering ‘in-house capabilities’.

● Infrastructure still emerging. Platform is used ● Translating social benefits into a fully-fledged to shape the infrastructure and bind the exclusive members (functionalist method). Social exchange (SE) = low level

● Too little relationship-building in terms of

business model. ● Peer-to-peer business model has broad coverage upstream and a solid back-up downstream. ● Under the ‘roof’ of a well-established company.

acquiring an exclusive position on the market. ● Can borrow the structure from a strong business ● Vagueness about the specific business model. ● Payments through fees are implemented to create rents, but not in such a way that the start-up company achieves breakeven.

partner. ● Well-developed business model. ● Peer-to-peer platform model is used in a targeted (functionalist) way.

● Protection mechanisms (mechanisms that the ● Altogether, established position on the market, entrepreneur might use to shield the business model) and value propositions (reasons why consumers should use the product / service offered) are not clear.

strong back up and clear business model focus, thereby focusing on economic exchange. ● Using a classical economic exchange model that functions like a stock exchange.

● Lack of exclusive relationships.

Source:  Own illustration.

2. Later, during the business-model formation when the peer-to-peer platform-based business model becomes clearer and is thus emerging (the configuration of social exchange (SE) = high / economic exchange (EE) = low; the upper left-hand side cell in Table 5.2), the social exchange activities of start-up entrepreneurs can be used to create reciprocity with their key partners in the networks. Increased reciprocity will increase the chance that the start-up entrepreneurs can access necessary resources, such as complementary resources, which they lack themselves. Hence, in this configuration, a lack of economic exchange might be considered as a chance to use social interaction with customers because customers will not have access to a standardised business product and service and might provide valuable input to the further business development (including the business model formulation). Such a context may thus motivate customers to participate in social interactions, gaining a better understanding of the value that the new platform-based solution provides and how they can apply it. In this context, the peer-to-peer platform is both the medium to establish the business model through describing and explaining the product or service and the medium to get as many actors as possible on board, for instance, co-creators, customers and users. 3. When the peer-to-peer platform-based business model has a configuration in which there are high levels of both social exchange (SE) and economic exchange (EE) (the upper right-hand side cell in Table 5.2), it becomes realistic that the business model becomes

Social exchange- versus economic exchange-driven processes  67 mature. Reciprocity turns into exclusive relationships at this stage and a start-up entrepreneur that builds a business model which is embedded in such a context delivers in-house capabilities and is furthermore protected through membership in exclusive cooperation. Social exchange that has led to benefits is translated to specific services and products and thereby becomes an integral part of the business model. The peer-to-peer platform-based business model then gains a broad coverage – both upstream and downstream in the supply chain – and enables market penetration. 4. Finally, the combination of low social exchange (SE) and high economic exchange (EE) (the bottom right-hand side cell of Table 5.2) indicates that the peer-to-peer platform-based business model is mature and can be applied in a mature market. Social exchange is used for marketing activities and to further explore add-ons; however, altogether, the business model is well-defined at this stage and needs to compete in the market because such a market is often functioning like a stock exchange. The comparison of these cases reveals furthermore that the roles which start-up entrepreneurs adopt in such contexts are shaped by the structure of their social and economic exchanges. In contexts in which economic exchange is still low, the social exchange bears great potential for developing and applying roles that help entrepreneurs accessing resources and developing fully-fledged, and ideally solid, business models. On mature markets in which platform-based products and services are more standardised, there is less room for start-up entrepreneurs, who are embedded in various networks, to use social exchange as a mechanism that helps in developing particular roles and applying them.

DISCUSSION AND CONCLUSION A first key finding of the case study presented is that a process of frequent exchange, particularly of social exchange, bears the potential for applying role-taking and role-making. In particular, social exchange during initial business-model formation supports entrepreneurs to access resources and develop a fully-fledged, and, ideally, solid, business model based on roles. Roles can be used for precisely identifying and adapting complementary resources and business models to new emerging opportunities. A second key finding is that, in mature markets in which platform-based products and services are more standardised, there is less room for start-up entrepreneurs to use social exchange as a mechanism that helps developing particular roles and applying them. The (necessary) focus of these start-up entrepreneurs on economic exchange then rather bears the risk of becoming locked in existing networks. Notwithstanding, in these cases, it is the economic exchanges which seem to trigger and support role-taking and role-making, but in a narrow way. However, possibilities of how to extend social exchange should be explored in such contexts. Finally, another finding is that entrepreneurs, who attempt to reach higher levels of social exchange in general, and during initial business formation in particular, have the potential for acquiring new information and thereby can trigger the innovativeness of the new emerging business models. Several scholars (McLean Parks and Smith, 1998; Thompson and Bunderson, 2003) have argued that, in addition to the social and economic exchanges of resources, ideological resources (e.g. an overarching mission or idealistic goal that drives entrepreneurs) may serve as a basis for the relationships of entrepreneurs, even though they might not have formulated

68  Handbook on digital platforms and business ecosystems in manufacturing a business model yet. Hence, social and economic exchange in comparison to purely ideological resource acquisition and resource sharing might improve the chance for entrepreneurs to learn and reflect. Finally, improving the chance to acquire essential resources will enable start-ups to overcome the limitations of such ideology-based resources during later stages of business-model formation. Beyond this, reflections of how the structure of exchange impacts the process of business model development and conclusions regarding the general industrial context can be drawn. According to Tian et al. (2021), many industrial companies, both incumbent and start-up businesses, do currently not know how to leverage platforms for stretching the boundaries of digital servitisation, while, at the same time, there is an ever-rising number of actors starting to develop and apply peer-to-peer platform-driven business models. This indicates a huge potential for achieving higher levels of co-creation and integration. Furthermore, this points to new opportunities presumably at any stage of the supply and value chains. To this end, it will not only be upstream co-creators (e.g. suppliers), but also traders, end-users and customers (important downstream actors) that will matter for such co-creation and sharing of capabilities. The examples from the automobile and farming cases provide evidence of this finding. A central conclusion for the industrial context is thus that start-up entrepreneurs are a vital resource for the established industrial firms when it comes to the peer-to-peer platform-based business models, independent of whether they are located vertically or horizontally in the supply chains. Even if peer-to-peer platform business models would be applied in trade related to users and consumers, they can lead to synergies for industrial actors (through, e.g. a faster rollout of product and service improvement; see Jussen et al., 2021). Industrial companies, SMEs as well as large companies need to focus on their core competences but also utilise digital platforms for the sake of better customisation (Tian et al., 2021). Hence, the integration of start-up entrepreneurs in this domain by incumbent companies can represent a vital process of co-shaping and sharing capabilities. For the start-up entrepreneurs, a focus on social exchange seems an appropriate strategy to start their business; however, for established industrial companies, social exchange (e.g. with start-up businesses) might also represent an important mechanism to identify new opportunities through the integration of start-up businesses into their existing supply and value chains. For start-up businesses, the process of establishing economic exchanges as early as possible is vital for getting to value capturing and thereby getting foothold on the market. For established industrial players, instead, a central key task is the early integration of start-up businesses in such a way that the incumbent actors keep or even extend their power position as well as their share in value capturing. Despite these insights, the present chapter has some limitations: Firstly, the study presented is limited due to its exploratory nature and the empirical case-study approach (including six cases from Denmark). Secondly, the chapter presents a regional case from Denmark. Hence, follow-up research should validate the conceptual framework used here by means of larger, quantitative and representative surveys among start-up businesses in this sector and utilising broader qualitative investigations that work with richer data.

NOTES 1. Peer-to-peer platform-driven business models use platforms for facilitating actors to exchange and transact with one another (Vinogradov et al., 2021).

Social exchange- versus economic exchange-driven processes  69 2.

Sharing economy start-ups are defined as actors who develop peer-to-peer platform-driven business models. The sharing economy is understood as an industry that is spread and interconnected with various other industries. 3. According to this logic, an industrial ecosystem would consist of various interconnected networks and include the industry-specific infrastructure. The supply chain in the logic of the exchange theory would be understood as a particular exchange network that fulfills a specific function, whereas the value chain would be understood as a set of resources and activities that support the manufacturing and development of products and services.

REFERENCES Aldrich, H., and Auster, E. R. (1986). Even dwarfs started small: Liabilities of age and size and their strategic implications. In: Staw, B., and Cummings, L. (Eds.), Research in organizational behaviour, 8, JAI Press, Greenwich, CT, pp. 65–198 Apte, U. M. and Davis, M. M. (2019). Sharing economy services: Business model generation. California Management Review, 61(2), 104–31. Baraldi, E., Gressetvold, E., and Harrison, D. (2012). Resource interaction in inter-organizational networks: Foundations, comparison, and a research agenda. Journal of Business Research, 65(2), 266–76. Baraldi, E., Havenvid, M. I., Linné, Å., and Öberg, C. (2019). Start-ups and networks: Interactive perspectives and a research agenda. Industrial Marketing Management, 80, 58–67. Baraldi, E., La Rocca, A., Perna, A., and Snehota, I. (2020). Connecting IMP and entrepreneurship research: Directions for future research. Industrial Marketing Management, 91, 495–509. Boeije, H. (2009). Analysis in Qualitative Research. Thousand Oaks: SAGE. Biddle, B. J. (1986). Recent development in role theory. Annual Review of Sociology, 12, 67−92. Bizzi, L. and Langley, A. (2012). Studying Processes in and around networks. Industrial Marketing Management, 41(2), 224–34. Blau, P. M. (1968). Social exchange. International Encyclopedia of the Social Sciences, 7(4), 452–7. Cook, K. S., Emerson, R. M., Gillmore, M. R., and Yamagishi, T. (1983). The distribution of power in exchange networks: Theory and experimental results. American journal of sociology, 89(2), 275–305. Cook, K. S., and Hahn, M. (2021). Social exchange theory: Current status and future directions. Theoretical Sociology, 179–205. Cropanzano, R., Anthony, E. L., Daniels, S. R., and Hall, A. V. (2017). Social exchange theory: A critical review with theoretical remedies. Academy of management annals, 11(1), 479-516. Emerson, R. M. (1976). Social exchange theory. Annual Review of Sociology, 2, 335–62. Frank, A. G., Dalenogare, L. S., and Ayala, N. F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210, 15–26. Håkansson, H. and Snehota, I. (1995). Developing Relationships in Business Networks. London: Routledge. Håkansson, H., Ford, D., Gadde, L. E., Snehota, I. and Waluszewski, A., (2009). Business in networks. John Wiley & Sons. Håkansson, H., and Waluszewski, A. (Eds.). (2007). Knowledge and innovation in business and industry: The importance of using others (Vol. 5). Routledge. Heikkinen, M. T., Mainela, T., Still, J., and Tähtinen, J. (2007). Roles for managing in mobile service development nets. Industrial Marketing Management, 36(7), 909–25. Johnson, R. E., and Ford, D. (2006). Interaction capability development of smaller suppliers in relationships with larger customers. Industrial marketing management, 35(8), 1002–15. Jussen, P., Meinel, M., and Hosenfeldt, T. (2021). Digital business models for industrial suppliers—the case of Schaeffler OPTIME. Digital Business Models in Industrial Ecosystems: Lessons Learned from Industry 4.0 Across Europe, 71–85. Kazantsev, N., and Martens, I. (2021). Industry 4.0 digital platforms: Collaborative business models for SMEs. Digital Business Models in Industrial Ecosystems: Lessons Learned from Industry 4.0 Across Europe, 125–34.

70  Handbook on digital platforms and business ecosystems in manufacturing Kumar, V., Lahiri, A., and Dogan, O. B. (2018). A strategic framework for a profitable business model in the sharing economy. Industrial Marketing Management, 69, 147–60. La Rocca, A., Perna, A., Snehota, I., and Ciabuschi, F. (2019). The role of supplier relationships in the development of new business ventures. Industrial Marketing Management, 80, 149–59. La Rocca, A., and Snehota, I. (2021). Mobilizing suppliers when starting up a new business venture. Industrial Marketing Management, 93, 401–12. McDonald, R. M., and Eisenhardt, K. M. (2020). Parallel play: Startups, nascent markets, and effective business-model design. Administrative Science Quarterly, 65(2), 483–523. McLean Parks, J., and Smith, F. L. (1998). Organizational identity: The ongoing puzzle of definition and redefinition. Washington University, John M. Olin School of Business, Working Paper No. OLIN-98-01. Matiaske, W. (2010). Exchange – A baseline model for socio-economics. Contemporary Perspectives on Justice, 6, 245. Madanoglu, M. (2018). Theories of economic and social exchange in entrepreneurial partnerships: an agenda for future research. International Entrepreneurship and Management Journal, 14(3), 649–56. Mitchell, M. S., Cropanzano, R. S., and Quisenberry, D. M. (2012). Social exchange theory, exchange resources, and interpersonal relationships: A modest resolution of theoretical difficulties. In Handbook of social resource theory (pp. 99–118). Springer, New York, NY. Molm, L. D. (2003). Theoretical comparisons of forms of exchange. Sociological theory, 21(1), 1–17. Röpke, J. (1970). Primitive economics, cultural change and the diffusion of innovations. Primitive economics, cultural change and the diffusion of innovations. Tübingen, 1970. Schiavone, F., Tutore, I., and Cucari, N. (2020). How digital user innovators become entrepreneurs: a sociomaterial analysis. Technology Analysis & Strategic Management, 32(6), 683–96. Saldaña, J. (2016). The Coding Manual for Qualitative Researchers. London: Sage. Slávik, Š. (2019). The Business model of start-up—Structure and consequences. Administrative Sciences, 9(3), 69. Snehota, I. (2011). New business formation in business networks. The IMP Journal, 5(1), 1–9. Sussan, F. and Acs, Z. J. (2017). The Digital Entrepreneurial Ecosystem. Small Business Economics, 49, 55 –73. Teece, D. J., Pisan, G., and Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic management journal, 18(7), 509–33. Teece, D. (1990). Firm capabilities, resources and the concept of strategy. Economic analysis and policy. Thompson, J. A., and Bunderson, J. S. (2003). Violations of principle: Ideological currency in the psychological contract. Academy of management review, 28(4), 571–86. Tian, J., Vanderstraeten, J., Matthyssens, P., and Shen, L. (2021). Developing and leveraging platforms in a traditional industry: An orchestration and co-creation perspective. Industrial Marketing Management, 92, 14–33. Vinogradov, E., Leick, B., and Assadi, D. (2021). Digital Entrepreneurship and The Sharing Economy. London: Routledge. Wirtz, J., So, K. K. F., Mody, M. A., Liu, S. Q., and Chun, H. H. (2019). Platforms in the peer-to-peer sharing economy. Journal of Service Management, 30(4), 452–83.

6. It’s all connected: IoT-affordances and connectivity-based business model innovation Luke Treves, Mika Ruokonen and Paavo Ritala

INTRODUCTION Connectivity supported by increasing computing power and innovative algorithms is a primary driver of digital transformation – the process by which companies embed technologies across their businesses to drive fundamental change in their organizational and operational processes and business models (Porter and Heppelmann, 2014). Connectivity between different machines, sensors and devices allows companies and their associated business ecosystems to collect and exchange vast amounts of valuable data, enabling increasing amounts of predictive analytics and automation for various business purposes, leading to the development of diverse types of ‘smart solutions‘ and ‘smart products’ (Pardo et al., 2020; Huikkola et al., 2022). IoT has become a key enabling technology of digital transformation by providing connectivity to data-driven companies. Blending physical equipment and software equipped with ubiquitous intelligence, IoT facilitates the sharing, collection and communication of data with minimal human intervention (Atzori et al., 2017; Martens et al., 2022). In these hyperconnected environments, digital platforms and systems can record, monitor, cooperate and adjust each interaction between connected ‘things’, enabling the development of new value-adding service applications and business model orientations (BMO) (Leminen et al., 2018). We refer to these developments as ‘IoT-connectivity,’ which provides value through enhancing monitoring, control, optimization and autonomy of connected devices and objects (Porter and Heppelmann, 2014), advancing the service economy wherein connectivity and smart aspects become more important than the physical part of the ‘thing’ (Leminen et al., 2018). Realizing value through IoT-connectivity is not only a technical feat, it requires the renewal or development of new radical service-oriented business models and business model innovation (Foss and Saebi, 2017) built upon value derived from data it collects. However, to date, research focusing on the business model innovation aspects remains sparse, particularly on how companies can leverage IoT-connectivity to adapt or develop new business models that identify, create and capture new sources of value in meaningful ways (Sjödin et al., 2020; Linde et al., 2021). Additionally, there is a need for an overarching understanding of IoT-connectivity business models, particularly from a multidimensional and service perspective (Leminen et al., 2018). In this chapter, we address these research gaps and develop a theory-driven conceptualization of IoT-connectivity affordances for value identification, creation and capture through innovative IoT-connectivity BMOs, which contribute to strengthening business ecosystems and digital platforms. To guide our research, we address two questions: 1. What types of BMO are emerging because of IoT-connectivity affordances? 2. What effects are these affordances and BMOs having on platforms and business ecosystems?

71

72  Handbook on digital platforms and business ecosystems in manufacturing To answer these questions we draw on two streams of academic theory: (1) (digital) technology affordance theory (Sony and Naik, 2020; Belitski et al., 2021), which helps explain what type of action can be taken based on the features of a technology; and (2) business model theory, which helps distinguish between value identification, creation and capture dimensions in technology-based business models (Atzori et al., 2017; Teece, 2018; Leminen et al., 2020; Sjödin et al., 2020; Sony and Naik, 2020). With these foundations, this chapter proposes a typology for reimagining or developing new IoT-connectivity BMOs. The proposed typology and definitions are sufficiently precise, parsimonious and logically consistent in taxonomies that are syntactically and semantically compatible with common conceptualizations, which enable academia, and practitioners to identify, create and capture new sources of value, thus enabling them to compete more effectively in the digital economy. The remainder of the chapter is structured as follows. The following section describes the key concepts used in the chapter. We then introduce a typology of IoT-connectivity business models and their affordances, and three emerging IoT-connectivity BMOs. We end with a discussion of the implications of our study to academia and business practitioners and our research delimitations and future opportunities.

BUSINESS MODELS AND BUSINESS MODEL INNOVATION The business model concept has grown in prominence in recent years, both in practice and academia, resulting in the development of several streams of focus, each of which follows its own methodologies and theories (Leiting et al., 2022). Although research on business models is diverse, most researchers agree that in essence they outline how a company does business (Amit and Zott, 2012; McDonald and Eisenhardt, 2020) and are built upon on three key elements of a business model: (1) value proposition/identification, (2) value creation, and (3) value capture (Clauss, 2017; Leiting et al., 2022). In this chapter, we focus on these three major business model components and investigate the impact the innovative technology paradigm surrounding IoT-connectivity has on business models and their innovation, referred to hereafter as ‘business model innovation’. Business model innovation refers to the deliberate modification of an existing business model or the creation of a new one to enable companies to better meet customers’ needs than existing business models and gain an advantage over their competitors. These changes can impact a company’s overall business model(s), one or a combination of its value identification, creation and capture elements, as well as their interrelationships and associated business ecosystems (Geissdoerfer et al., 2018). According to the literature, companies can implement multiple business models concurrently to diversify their revenue streams, access new markets, or serve different segments more successfully. The emphasis of business model innovation here is on how a company can create and capture profitability, competitive advantage and value creation via changes to their value proposition delivery decisions and supporting elements. This perspective is relevant when examining business ecosystems, as members frequently work in multiple ecosystems, each with their distinct business model(s). It is therefore critical for ecosystem members to determine their stakeholders and their importance (i.e. contribution to value identification, creation and capture) within each business ecosystem in which they they participate to collectively generate legitimacy and traction for a particular ecosystem (Thomas and Ritala, 2022). Consequently,

IoT-affordances and connectivity-based business model innovation  73 these choices will determine the nature of competitors or the digital technology infrastructure within which a business ecosystem develops (Demil et al., 2018). Additionally, business ecosystems enhance business model innovation through its members pooling their resources and collaborating to co-generate, co-design, co-monitor, co-evolve, co-implement and co-develop product/services, reducing financial and organizational resource burden (Tunn et al., 2020). This observation is critical in the context of this chapter, as research suggests that companies and their associated business ecosystems might need to disengage from their traditional way of thinking and initiate business model innovations that identify, create and capture value from advancements in digital technologies like IoT-connectivity in order to be competitive in the long run (Leiting et al., 2022).

THE EMERGENCE OF IOT-CONNECTIVITY BUSINESS MODELS Kevin Ashton coined the term ‘Internet of Things‘ in 1999 to describe the linking of physical items by adding radio frequency identification and other sensors for various functions like sensing, communication and data collecting (Suppatvech et al., 2019). This chapter builds upon this and subsequent definitions of IoT, to describe it as an enabling technology in which objects, spaces and humans interconnect with each other at any point and location to generate large-scale, real-time, linked data, which can improve a company’s value identification, creation and capture. Central features of IoT are connectivity and interoperability between ‘smart objects/devices’, which provide users with new IoT-connectivity-enhanced affordances – the specific capabilities or functionalities of IoT devices or systems that allow users to interact with them and accomplish tasks. Based on an integrative review of previous academic literature on IoT-connectivity (Lee and Lee, 2015; Atzori et al., 2017), we identify seven key affordances: 1. 2. 3. 4. 5. 6.

Sensors that enable connectivity, identification and recognition and communication. Data collection and processing (mining). Access and visualization of mined data in convenient and meaningful ways. Aggregation, including the management, mining and exchange of data. Computing power facilitated by the cloud. Algorithms that enhance reports and dashboards with intelligent operational oversights, recommendations and proposals, comparisons and benchmarks. 7. Feedback loops enabled by connectivity that provide advanced metrics. IoT-connectivity therefore extends (internet-based) connectivity beyond traditional devices like desktop and laptop computers to a diverse range of devices and everyday objects that use embedded technology to communicate and interact with external environments via the internet and digital platforms, significantly reshaping platforms and business ecosystems as they increasingly shift product/service offerings from purely physical products toward digital ones (Leiting et al., 2022). Additionally, IoT-connectivity affordances drive business model innovation as they offer companies the ability to generate and collect large-scale, real-time, linked data on user behavior, attitudes, consumption habits and choices (Martens et al., 2022). This data enables business ecosystems to enhance their current business model(s) or develop novel IoT-connectivity BMOs that focus on delivering integrated bundles of tangible products, intangible services

74  Handbook on digital platforms and business ecosystems in manufacturing and digital architectures that fulfil individual consumer demands digitally (Atzori et al., 2017). IoT-connectivity also enables business ecosystems to monetize data and enhance existing offerings through new service-level agreements that focus on identifying, creating and capturing value by leveraging the pervasiveness of IoT-connectivity and the rapid advancement of IoT-enabled technologies (Leiting et al., 2022; Martens et al., 2022). We refer to these types of business model innovations as ‘IoT-connectivity business models’.

THE ROLE OF DIGITAL PLATFORMS AND BUSINESS ECOSYSTEMS Platforms are two-sided markets that allow various groups to interact, and which help to host ecosystems that provide benefits and value to actors that interact with and on them. In recent years, platform innovation has become the go-to strategy for achieving sustainable revenues, particularly in information communication technology (ICT) and mobile connectivity sectors (Kim, 2016). We expand upon this view of platforms to discuss digital platforms which are interconnected systems of digital services and technologies that allow for seamless communication, collaboration and exchange of data and novel sources of value within business ecosystems via IoT-connectivity affordances (see e.g. Jovanovic et al., 2022). These developments facilitate the development of new BMOs based on connectivity and the exchange of information, products and services (Jacobides et al., 2018). Another important characteristic of digital platforms and ecosystems is the presence of ‘network effects’, where value increases together with the number of people or participants (buyers, sellers, or users) in an ecosystem who co-create, co-design, co-monitor, co-evolve, co-implement and co-develop digital platforms and products/services associated with them (Tunn et al., 2020). These interactions improve the value of a product, service, or platform depending on the numbers of those who leverage it, which can radically strengthen the advantages of the platform and associated business ecosystem (Kim, 2016). Additionally, platform owners often create governance measures to facilitate value-creating mechanisms with an ecosystem of autonomous complementors and consumers. The complementors in an ecosystem are the organizations, companies and individuals who provide complementary resources, skills, products or services that enhance the overall value proposition. Platforms provide boundary-spanning resources, such as software development kits, that enable users to create specialized products/services, which benefit consumers and the business ecosystem by providing monetary contributions and unique consumer information, which they can use to improve the digital platform and products/services and enter new markets (Karhu and Ritala, 2020).

IOT-CONNECTIVITY BUSINESS MODEL ORIENTATIONS IDENTIFICATION AND DESCRIPTIONS Based on the outcome of our theoretical development using an integrative literature review of peer-reviewed academic articles, we examined representative empirical examples of B2B companies – available in public domain and existing academic literature, case studies or empirical reports – that are known for their adoption of IoT technologies to assess their fit and effect on business model innovation in practice. This cross-pollination across the literature

IoT-affordances and connectivity-based business model innovation  75 and company examples enabled us to understand how companies embed these affordances into their current business models or influence business model innovation. From this review, we observe three archetypical IoT-connectivity BMOs: (BMO#1) show and visualize, (BMO#2) propose and compare and (BMO#3) optimize and automate. The three BMOs display differing and progressively complex IoT-connectivity affordances and contractual relationships between the provider and the customer (Table 6.1). Whilst these business model types mark a progression in complexity, a company can choose a BMO that best suits the specific maturity level of their product(s) and/or service(s), needs and/or situation. They can also employ more than one of these BMOs at the same time. Importantly, we distinguish how the IoT-connectivity affordances provide distinct value identification, creation, delivery and capture opportunities for the three BMOs, as summarized in Table 6.2. BMO#1: Show and Visualize BMO#1 relies on companies storing, categorizing, processing and analyzing data collected by IoT-connectivity devices and making it available to their customers in appropriate ways. For example, in BMO#1 visualizations may be presented in general and comparable reports or dashboards that provide for a wide range of different customers, or customized based on specific customer requirements. Additionally, in these BMOs, a company often supplements its ‘physical’ product offering(s) with a digital aspect that generates value for customers who want to better understand the functionality of its equipment or physical surroundings. Value is often captured through a license fee or subscription that grants the customer access to the data and accompanying insights. Further value-capture strategies are also feasible, and in certain situations, data visualizations may be provided to customers for free (for example, as part of the device purchase charge), with the goal of increasing customer intimacy and loyalty. Examples in practice Vodafone, the British telecommunications company, collects connectivity-enabled GPS location data about its mobile phone user customers in their cars and sells anonymized and raw location data to TomTom, the Dutch location technology company, which uses the data to create navigation services through a better understanding of how subscribers drive, including where they are and how fast they are driving, enabling TomTom to make optimized navigation suggestions (Parvinen et al., 2020). For example, by calculating the real-time speed and the location of the mobile phone, the TomTom technology can create a general picture of the region and thus it can provide users with smarter and faster ways out of traffic jams (Gatlan, 2006). Connectivity provides data that is shown and visualized to the customers on a large scale, because of a partnership of two companies. The US-based jet engine manufacturing company Boeing has opened access to its connectivity-enabled equipment data through its AnalytX platform, which is an automation tool for optimizing operations and providing insight into the product’s future. The platform allows users to devote more time to solution evaluation, planning and management. It has three categories of (i) analytics-driven, (ii) combinable products and (iii) services (Boeing, 2018). For example, airline companies can use the platform to perform self-service analytics, enabling them to discover new insights or improve their operational performance, e.g. flight planning. One of the platforms features is a fuel dashboard, which visualizes plane fuel con-

3. Optimize and automate

2. Compare and propose

1. Show and visualize

products and services they use, rather than

informed comparisons and decisions.

benchmarks so that customers receive

outcome. hazards associated with algorithmic

demonstrated.

is tied to the level of effort or performance

● Performance-based payments – payment

reliability requirement and removal of

algorithms.

decision-making.

to the achievement of a specific goal or

essary due to a high dependability/

concepts enabled by sophisticated data and

● Outcome-based payments – payment is tied

product/services.

workforce connected to simulations and/or possibilities.

simulations of anticipated outcomes of

● Sophisticated algorithms are nec-

gaining access to talent, its expertise and/or business lines and breakout growth

decisions and create new product-service

charges customers a fee per hour or day for services, plus the release of new

to develop. Companies make sophisticated

on demand. ● Data permits enhancements to existing ● Per-hour type consultancy fee – a company

itself, and the customer pays a fee for usage

and benchmarks at the needed scale.

business model renewal and expansion begins

Building upon BMO#2, a more disruptive

of the product/service lie with the company

tailored recommendations, comparisons

algorithms that help companies deliver ● Pay-per-use – ownership and responsibility

purchasing outright.

require subscribers to pay once a year for the

customers. Enabling them to make

recommendations, comparisons and

● Creation of insights commonly requires

services they are using. Annual subscriptions

product-services is available to other

actionable insights.

make monthly payments for products or

tomers interactions with a company’s

‘premium’ pricing. ● Monthly/annual subscription fee – customers

reports and dashboards with suggestions,

● Benchmark data and insights on cus-

elements, i.e. a combination of ‘free’ and

● Contracts can also include ‘freemium’

rather than purchasing it outright.

annually) for access to the product or service,

customers pay a recurring fee (monthly or

provide customers with enhanced

Building upon BMO#1, whereby companies

tomizable in the digital formats.

● Data is presented to customers as cus-

the product for a specific period. Typically,

customers.

tracts – customers pay for the right to use

IoT-connectivity devices.

new sources of service orientated value for

● Licensing and/or subscription-based con-

customer

Contract/relationship between company and

process and analyze data collected by

● Companies actively store, label,

Main characteristics

offering(s) with a digital aspect that generates

Companies supplement ‘physical’ product

Business model innovation

IoT-connectivity business model orientations (BMO)

BMO# by Level of Capabilities

Table 6.1

76  Handbook on digital platforms and business ecosystems in manufacturing

the data.

and related insights. Enhanced reports and dashboards Supplier: Monthly/

internal/external customers. Algorithms and digital Need for enhanced data

2. Compare Aggregation,

and propose computing

automate

and

feedback loop

3. Optimize Algorithms,

services to improve blasting productivity, efficiency and outcomes of its clients. KONE helps its clients to prolong elevator

performance-based

decisions.

and/or intervention and

combining products, services,

also outcome- and

resources).

John Deere helps its customers to do more business lines and breakout efficient management of farming operations

nonhuman decision making.

based on equipment data.

customers reduce downtime of their fleets

monitoring of system log data, which helps

Kalmar services enable automated 24/7

performance indicators.

in efficiency and/or output according to key

GE Power offers guaranteed improvements

and drive increase in crop yields.

maintenance. to existing offerings, new

elimination of risks in

growth scenarios.

life through predictive services and proactive Customer: New additions

enabling reliability and

eliminating waste (time and Sophisticated algorithms

payments

decision reducing the need simulations of anticipated outcomes of data and automated services.

Orica provides data-enabled consulting

needed scale. Supplier: per-hour

Comprehensive offering(s)

for human interaction

Sophisticated

recommendations at the

type consultancy fee,

Need for automated

and fuel consumption.

recommendations,

recommendations. comparisons and

compare different machine’s productivity

valuable insights, tailored

operational oversights/

and benchmarks.

on which reliable decisions with intelligent

algorithms

and actions can be based.

Ponsse allows its clients to analyze and

pay-per-use.

recommendations, comparisons

provides actionable insights reports and dashboards Customer: Continuous

performance of the equipment of its clients.

annual subscription fee,

(platforms) with suggestions,

processing presentation that solutions enhance

power,

Boeing visualizes equipment data to airlines. Glaston enables comparison of the

that visualizes navigation paths revealed by

Customer: Access to data

operational performance.

Vodafone sells location data to TomTom

is meaningful and useful to data presentation.

visualization

Creation of customer engagement Supplier: Licensing fee or

Examples

subscription.

data generic or tailored

visualized in a way(s) that

Value capture

maps, new insights, improved

Digital processing of

Need for data that is

access and

Value creation

visualize

identification

IoT affordances Value Proposition /

Overview of business model orientations afforded by IoT-connectivity

1. Show and Sensors, data,

BMO

Table 6.2

IoT-affordances and connectivity-based business model innovation  77

78  Handbook on digital platforms and business ecosystems in manufacturing sumption and helps to identify opportunities for savings and improvements, per each flight or per entire airplane fleet (Boeing, 2018). BMO#2: Propose and Compare BMO#2 represents an evolutionary development that takes companies beyond ‘just’ showing and visualizing data to providing valuable customer insights; thus, extending their value proposition to customers and the value identification, creation, and capture for themselves. In BMO#2, a company enhances its reports and dashboards with suggestions, recommendations, comparisons and benchmarks, so that customers receive actionable insights on which decisions and improvements to make. Typically, benchmark data is available from similar customers, so that it is possible for all customers to see ‘what good looks like’, to make other relevant observations, and for the supplying company to capture relevant customer insights. Creation of insights commonly requires algorithms that help companies deliver tailored recommendations, comparisons and benchmarks at the needed scale. As in BMO#1, the value capture logic can be a recurring monthly or annual licensing or subscription fee. Alternatively, customer-specific tailored recommendations could be purchased via a ‘pay per use’ logic, for instance, a fee per each recommendation is given. Examples in practice Glaston, a Finland-based glass heat treatment machines manufacturer, has implemented sensor technologies that collect data from its equipment while the clients use them. Consequently, Glaston can analyze the functionality of all their equipment outfitted with IoT-connectivity technologies as a swarm and ecosystem in decentralized, self-organized systems that can react quickly in a coordinated manner. Glaston has also implemented a GlastOnline customer portal that serves as the backbone to its digital offering. On the platform, customer-specific data and insights on equipment usage and performance is available for a recurring monthly fee. The customer can also use data and insights to compare its own equipment against its peers to understand how to improve their machine uptime and productivity (Kuusela, 2016; Glaston, 2021). Ponsse, a Finland-based manufacturer and marketer of forestry vehicles and machinery, has developed a digital operations management system for forest machine entrepreneurs named ‘Ponsse Manager.’ The system allows entrepreneurs to access a platform through which they can remotely access valuable information concerning their fleet, ensuring the correct machinery is in the correct place at the correct time. This in turn enables them to monitor the progress of timber stands (all trees occupying an area capable of producing timber) by comparing the estimated timber volume to the cut timber volume, allowing entrepreneurs to plan their move to the next stand; thus, improving real-time management of productivity and operational efficiency. The platform also allows forest entrepreneurs to analyze and compare different machinery’s productivity and monitor fuel consumption of any machine. For example, it presents clearly and precisely amounts of cut assortments and average stem sizes (Ponsse, 2022). BMO#3: Optimize and Automate Optimize and automate BMOs enable companies to make sophisticated simulations of anticipated outcomes of decisions; thus, a progressive step-up from BMO#2. Typically, companies

IoT-affordances and connectivity-based business model innovation  79 create new product and service concepts that are enabled by data and algorithms. As a result, consulting and enhancement services are frequently provided. Because of connection, a more disruptive business model renewal and expansion begins to develop BMO#3. Data not only permits fresh enhancements to existing services, but may also release entirely new business lines and breakout growth possibilities for companies. More sophistication is required from the algorithm’s perspective, as optimization and automation often necessitate high dependability and the removal of hazards associated with algorithmic decision-making. Value capture for optimization and automation services can occur through traditional per-hour consulting fees, as well as various performance- and outcome-based payments. The IoT-connectivity business model often yields a holistic offering that integrates products, services, data and automated processes. Examples in practice Orica, an Australia-based company, supplies commercial explosives and blasting equipment to the mining, quarrying, oil and gas and construction sectors. Orica collects data from its clients’ blasting activities using sensors and stores it on its BlastIQ platform, which it designed for blasting data analysis, storage and sharing. It specifically gives the user comprehensive control over the blasting operation, including quality control management of blast design execution through better visibility and control of bench operations. It also aids in systematized access and design implementation, as well as loading rules. Various consulting services, such as hole loading, blast loading and technical blast management, are also available to increase blasting productivity, efficiency and results (Orica, 2023). KONE, a Finland-based global leader in the elevator and escalator industry, provides its customers with the concept of KONE Care DX, which adds greater safety, transparency and intelligence to elevator maintenance (KONE, 2021). Using AI (artificial intelligence) and connectivity, KONE Care DX affords their customers seamless predictive maintenance, automatic updates, up-to-date status reports, remote servicing and in-person or digital support. These features enable them to extend elevator life through predictive services such as proactive maintenance planning and repairs, which reduce unexpected downtime. The solutions also enable the creation of hand-picked digital services that can be quickly altered, activated and expanded to improve the experiences of their customers. John Deere, an American agricultural machinery company, was a pioneer in incorporating GPS mobile sensor suites with computing capabilities into their field machines. For example, under the FarmSight brand, the firm released a line of machines and data services in 2011. John Deere uses this branding to place sensors on all new tractors as soon as they leave the manufacturing line, making data collecting in the field easier and faster. Machinery may send data to servers via a mechanism known as JDLink. A farm management platform is provided by Mobile Farm Manager, a mobile application, and its PC (process control) equivalent, Operation Center (Pham and Stack, 2018; John Deere, 2022). As a result, the company’s offerings encompass a full system of equipment, data, analysis and automation. John Deere can tailor their offerings to customers by providing personalized care (precise quantity of water, fertilizers and pesticides) at scale to each of the tens of thousands of plants per acre (multiplied by thousands of acres per farm) thanks to this technological stack (Connerty et al., 2016; Leano, 2021). In practice, John Deere is now an outcomes-oriented company that helps its customers manage agricultural operations more efficiently and boost crop yields, allow-

80  Handbook on digital platforms and business ecosystems in manufacturing ing them to establish an unparalleled predictive business model over every part of farming (Carbonell, 2016). General Electric (GE) Power, the power generation and water technologies division of General Electric, has implemented sensors widely in its equipment, and has used digital twins in its manufacturing processes to monitor and manage equipment operations since 2013 (Saracco, 2019). They are employing digital twins, for example, to model and test scenarios that may indicate equipment failure or possibilities to enhance and optimize performance of turbines, generators and other huge and complicated devices (Tao and Qi, 2019). GE Power has also adjusted its equipment manufacturing business to give guaranteed efficiency and/or production increases based on key performance metrics provided by its power producer and power distributor customers. There are incentives and penalties for GE Power to meet predefined objectives, resulting in a symbiotic relationship between GE Power and its customers (Gillin, 2017). Kalmar, a Finland-based provider of eco-efficient cargo handling equipment and automated terminal solutions, software and support services, has introduced new remote services that help terminal operators optimize equipment availability, minimize disruptions, enable continuous improvement and extend the equipment’s lifecycle wherever they are in the world. Terminal operators, for example, can spot problems before they arise and, if necessary, pass them on to service workers for further in-depth study and answers thanks to access to advanced analytic algorithms. Furthermore, through remote connectivity and automatic continuous monitoring, specialists may assist in identifying, anticipating and resolving difficulties with automation systems or equipment that would have previously needed proactive measures by human operators. As a result, regardless of where they are situated, downtime is minimized while availability and performance are maximized (Kalmar, 2021).

DISCUSSION AND IMPLICATIONS In this chapter, we argue that IoT connectivity creates new technological affordances that, when embedded in appropriate business model(s), can enhance company, business ecosystem and platform transformation. B2B models traditionally involve selling products, providing access to services, or a combination of both to other businesses rather than consumers. Remuneration of these transactions typically involves three payment forms: 1. Remuneration for each product upon purchase, within an agreed-upon period or on an instalment basis (PONSSE). 2. Flexible or fixed per-hour or per-month remuneration for accessing services and upgrades (KONE). 3. A combination of both product and service remuneration types. For example, various maintenance contracts include combinations of physical products and service-based elements (Vodafone). The BMOs we describe in this chapter supplement existing business models by exploiting IoT-connectivity affordances to transform and create new business opportunities through business model opportunities. Specifically, BMO#1 allows companies to innovate their business models by adding data elements. BMO# 2 is a progressive step up from BMO#1 by enabling access to more detailed data-based insights, which can help consumers/users make

IoT-affordances and connectivity-based business model innovation  81 enhanced decisions. Finally, BMO#3 marks a more radical business model innovation that can lead to more outcome-orientated and ‘as-a-service’ business models. Here, replacement of earlier business model types can occur as solutions (combinations of products and services) begin to include outcome-based elements, including bonuses or sanctions if providers fail to meet specified outcomes or targets. These progressive BMOs allow companies and broader business ecosystems to identify, create and capture sources of value through IoT-connectivity affordances. Managerial and Practical Implications: An Evolutionary Model of IoT-Connectivity Business Model Innovation Our research contributes to the nascent literature on IoT-connectivity affordances. It provides descriptions of radical service-oriented BMOs and connected value identification, creation and capture mechanisms emerging from companies utilizing IoT-connectivity. It addresses the theoretical gap regarding the overarching understanding of business models based on IoT-connectivity from a multidimensional and service perspective. We also demonstrate that IoT-connectivity affordances facilitate business model innovation and the development of novel BMOs based on real-time and cumulative data from connected devices and objects. This innovation and development can lead to improved business models for ‘smart products’, digital servitization and automation and optimization of production and service systems. We demonstrate that companies and their associated business ecosystems typically increase their connectivity, data, analytics and machine learning capability gradually over time as there typically are no quick wins in building such complex digital systems. The transition to sophisticated IoT-connected business models represents a systemic shift, with software systems running daily operations and humans developing and maintaining them. Companies’ commercial and technological maturity grows in lockstep, allowing them to introduce new products and services more quickly. As a company progresses from BMO#1 to BMO#3 (Figure 6.1), its strategy frequently shifts as connectivity-enabled data becomes a critical component of its future offerings. Thus, the transition to sophisticated IoT-connectivity business models is a systemic shift: companies are becoming automated information processing systems, with software systems running everyday operations and humans developing and maintaining them. The three connectivity-enabled BMOs discussed are not mutually exclusive, and a company can provide multiple BMOs concurrently. Boeing, the jet engine manufacturing company, is an example of a company that makes creative and comprehensive use of various IoT-connectivity BMOs in parallel. They provide dashboards that display and visualize data (BMO#1), as well as digital real-time analytics tools (BMO#2) and optimization and consultancy services (BMO#3). Individual companies and business ecosystems should analyze their customers, markets and competition parallel with their IoT-connectivity affordances and business ambitions to make well-informed decisions on which BMO option(s) to pursue. It is important to remember that a business model is only ‘good’ if it generates more revenues than costs, and a ‘new’ business model must generate new value-adding sources, mechanisms or logic by identifying new value-generating opportunities, developing new products and services or developing new methods of producing, delivering and capturing them. Additionally, whilst we identify the benefits of IoT connectivity business models in the context of digital platforms

82  Handbook on digital platforms and business ecosystems in manufacturing

Figure 6.1

The evolutionary development path of three connectivity-enabled business models

and business ecosystems. There are also negative aspects of these business models to be aware of, including: 1. Security risks grow as more devices connect to the internet. IoT devices are susceptible to hacking and breaches. 2. Privacy concerns may occur due to the sharing of data collected by IoT devices being shared with third parties. The amount and use of data collected may be of concern to consumers. 3. IoT’s dependence on connectivity means business ecosystems and digital platforms may experience problems if connection is interrupted. 4. Compatibility issues: IoT devices and platforms can have compatibility issues due to the wide variety on the market. There may be incompatibilities between devices or the need for specialized software. Finally, our BMO typology allows academics and practitioners to understand and reap the benefits of IoT-enabled connectivity when developing and implementing IoT-connectivity business models. It also identifies the potential negative aspects of IoT connectivity that academia and practitioners must consider when implementing these technologies.

IoT-affordances and connectivity-based business model innovation  83

CONCLUSION – FUTURE IOT-CONNECTIVITY BUSINESS MODELS IoT-connectivity business models and their associated affordances offer significant potential for companies who seek to identify, create and capture value based on digital platforms with connected devices, machines and sensors. In our conceptual study, we identify and describe three BMOs that employ IoT-connectivity to offer products and services in radically new ways. To contextualize these options, we provide illustrative examples for each as an initial step in understanding of emerging IoT-connectivity business model types and how they are being developed in practice. Practitioners can use our results in experimenting with and eventually implementing different BMOs. Our model describes how the options become progressively more data- and algorithmic-driven, as they move from ‘show and visualize’ to ‘compare and propose’ and finally to ‘optimize and automate’. Firms can select to implement one or several of these business model options depending on their own digital maturity, availability of IoT devices and systems, the availability of suitable ecosystem complementors, as well as the customer readiness to buy the different solutions. In addition to the three BMOs, there is potential for developing increasingly radical and disruptive BMOs focused on ‘transforming and renewing’. For example, building on BMO#3, business ecosystem members can approach their operations through the lens of opportunities afforded by data and algorithms (such as artificial intelligence) to obtain marketing results or performance data, which can be quantified and confirmed using data and analytics to produce unique insights on product-service usage and condition to determine the need for corrective measures or maintenance. These BMOs allow business ecosystems to provide services, including, e.g. insurance for their product/service offering. Hence a company’s business profile could shift from a product business to a service/insurance business and from one-time sales to insurance contracts. Tesla, for example, initiated such BMOs by first providing its auto insurance in California (Haselton and Kolodny 2019) and later expanded the business to other US states to disrupt the traditional insurance market. IoT-connectivity BMOs also offer breakout situations and potentially important opportunities for growth for the companies who implement them. Business ecosystems and their individual members can shift away from selling physical products and focus more on delivering digital, data and analytics aspects of their products/services and overarching value proposition, creation and capture mechanisms. IoT affordances also offer significant potential digital platforms and business ecosystems that seek to identify, create and capture value based on connected devices, machines and sensors in their business model innovation. Finally, while our research provides an in-depth analysis and description of IoT affordances and connectivity business models, it is limited by its conceptual nature and limited number of case examples. In the future, empirical research should provide more in-depth analyses and descriptions of current and future IoT-connectivity BMOs on a broader range of companies representing different business sectors, types, locations and sizes.

84  Handbook on digital platforms and business ecosystems in manufacturing

REFERENCES Amit, R. and Zott, C. (2012) ‘Creating value through business model innovation’, MIT Sloan Management Review, 53(3), pp. 41–9. Atzori, L., Iera, A. and Morabito, G. (2017) ‘Understanding the Internet of Things: definition, potentials, and societal role of a fast evolving paradigm’, Ad Hoc Networks, 56, pp. 122–40. doi: 10.1016/j. adhoc.2016.12.004. Belitski, M., Korosteleva, J. and Piscitello, L. (2021) ‘Digital affordances and entrepreneurial dynamics: new evidence from European regions’, Technovation, (December 2020), p. 102442. doi: 10.1016/j. technovation.2021.102442. Boeing. (2018). ‘Boeing Global Services’ Enhanced Digital Solutions Focus on Customer Speed and Operational Efficiency.’ Boeing. Available at: https://​ investors​ .boeing​ .com/​ investors/​ news/​ press​-release​-details/​2018/​Boeing​-Global​-Services​-Enhanced​-Digital​-Solutions​-Focus​-on​-Customer​ -Speed​-and​-Operational​-Efficiency/​default​.aspx. Carbonell, I. M. (2016) ‘The ethics of big data in big agriculture’, Internet Policy Review, 5(1), pp. 1–13. doi: 10.14763/2016.1.405. Clauss, T. (2017) ‘Measuring business model innovation: conceptualization, scale development, and proof of performance’, R&D Management, 47(3), pp. 385–403. doi: 10.1111/radm.12186. Connerty, M., Navales, E., Kenney, C. and Bhatia, T. (2016) ‘Manufacturing companies need to sell outcomes, not products’, Harvard Business Review. Available at https://​hbr​.org/​2016/​06/​manufacturing​ -companies​-need​-to​-sell​-outcomes​-not​-products. Demil, B., Lecocq, X. and Warnier, V. (2018) ‘“Business model thinking”, business ecosystems and platforms: the new perspective on the environment of the organization’, Management (France), 21(4), pp. 1213–28. doi: 10.3917/mana.214.1213. Foss, N. J., and Saebi, T. (2017). ‘Fifteen years of research on business model innovation: How far have we come, and where should we go?’ Journal of Management, 43(1), 200–27. https://​doi​.org/​10​.1177/​ 0149206316675927. Gatlan, S. (2006) ‘Real-time traffic data network coming from Vodafone and TomTom’, Softopedia. Available at https://​news​.softpedia​.com/​news/​Real​-time​-Traffic​-Data​-Network​-Coming​-from​-Vodafone​ -and​-TomTom​-38911​.shtml. Geissdoerfer, M., Vladimirova, D. and Evans, S. (2018) ‘Sustainable business model innovation: a review’, Journal of Cleaner Production, 198, pp. 401–16. doi: 10.1016/j.jclepro.2018.06.240. Gillin, P. (2017) ‘Selling “outcomes”: GE shows that old rules don’t apply to digital business’, SiliconANGLE Media. Available at https://​siliconangle​.com/​2017/​06/​24/​selling​-outcomes​-ge​-shows​ -old​-rules​-dont​-apply​-digital​-business. Glaston (2021) ‘Glaston online’. Available at https://​glaston​.net/​fi/​glastonline. Haselton, T. and Kolodny, L. (2019) ‘Tesla starts selling its own car insurance in California’, CNBC. Available at https://​www​.cnbc​.com/​2019/​08/​28/​tesla​-starts​-selling​-its​-own​-car​-insurance​-in​ -california​.html. Huikkola, T., Kohtamäki, M., Rabetino, R., Makkonen, H. and Holtkamp, P. (2022) ‘Overcoming the challenges of smart solution development: co-alignment of processes, routines, and practices to manage product, service, and software integration’, Technovation, 118. doi: 10.1016/j. technovation.2021.102382. Jacobides, M. G., Cennamo, C. and Gawer, A. (2018) ‘Towards a theory of ecosystems’, Strategic Management Journal, 39(8), pp. 2255–76. doi: 10.1002/smj.2904. John Deere. (2022). ‘John Deere Mobile Farm Manager.’ John Deere. Available at: https://​www​.deere​ .com/​en/​technology​-products/​precision​-ag​-technology/​data​-management/​john​-deere​-mobile​-farm​ -manager/​. Jovanovic, M., Sjödin, D. and Parida, V. (2022) ‘Co-evolution of platform architecture, platform services, and platform governance: expanding the platform value of industrial digital platforms’, Technovation, 118, p. 102218. doi: 10.1016/j.technovation.2020.102218. Kalmar (2021) ‘Remote services give terminals 24/7 access to advanced analytics and top expertise’, Cargotec Finland Oy. Available at https://​www​.kalmarglobal​.com/​news​-​-insights/​articles/​2021/​ 20211209​_remote​-services.

IoT-affordances and connectivity-based business model innovation  85 Karhu, K. and Ritala, P. (2020) ‘Slicing the cake without baking it : opportunistic platform entry strategies in digital markets’, Long Range Planning, 54(5), p. 101988. doi: 10.1016/j.lrp.2020.101988. Kim, J. (2016) ‘The platform business model and business ecosystem: quality management and revenue structures’, European Planning Studies, 24(12), pp. 2113–32. doi: 10.1080/09654313.2016.1251882. KONE (2021) ‘KONE CareTM DX’, Kone Oyj. Available at https://​www​.kone​.com/​en/​products​-and​ -services/​maintenance​-and​-modernization/​care​-dx​.aspx. Kuusela, K. (2016) ‘What can online access to my machinery data (IoT) do for me?’, Glastory. Available at https://​www​.glastory​.net/​iot​-online​-access​-to​-machinery​-data. Leano, H. (2021) ‘Down to the individual grain: how John Deere uses industrial AI to increase crop yields through precision agriculture’, Databricks. Available at https://​databricks​.com/​blog/​2021/​07/​ 09/​down​-to​-the​-individual​-grain​-how​-john​-deere​-uses​-industrial​-ai​-to​-increase​-crop​-yields​-through​ -precision​-agriculture​.html. Lee, I. and Lee, K. (2015) ‘The Internet of Things (IoT): applications, investments, and challenges for enterprises’, Business Horizons, 58(4), pp. 431–40. doi: 10.1016/j.bushor.2015.03.008. Leiting, A. K., De Cuyper, L. and Kauffmann, C. (2022) ‘The Internet of Things and the case of Bosch: changing business models while staying true to yourself’, Technovation, 118(March), p. 102497. doi: 10.1016/j.technovation.2022.102497. Leminen, S., Rajahonka, M., Westerlund, M. and Wendelin, R. (2018) ‘The future of the Internet of Things: toward heterarchical ecosystems and service business models’, Journal of Business and Industrial Marketing, 33(6), pp. 749–67. doi: 10.1108/JBIM-10-2015-0206. Leminen, S., Rajahonka, M., Wendelin, R. and Westerlund, M. (2020) ‘Industrial internet of things business models in the machine-to-machine context’, Industrial Marketing Management, 84, pp. 298–311. doi: 10.1016/j.indmarman.2019.08.008. Linde, L., Sjödin, D., Parida, V. and Wincent, J. (2021) ‘Dynamic capabilities for ecosystem orchestration: a capability-based framework for smart city innovation initiatives’, Technological Forecasting and Social Change, 166. doi: 10.1016/j.techfore.2021.120614. Martens, C. D. P., Da Silva, L. F., Silva, D. F. and Martens, M. L. (2022) ‘Challenges in the implementation of internet of things projects and actions to overcome them’, Technovation, 118, p. 102427. doi: 10.1016/j.technovation.2021.102427. McDonald, R. M. and Eisenhardt, K. M. (2020) ‘Parallel play: startups, nascent markets, and effective business-model design’, Administrative Science Quarterly, 65(2), pp. 483–523. doi: 10.1177/ 0001839219852349. Orica (2023) ‘BlastIQ’. Available at https://​www​.orica​.com/​products​-​-​-services/​blasting/​blastiq/​home​#​ .ZCqnD4RBxZc. Pardo, C., Ivens, B. S. and Pagani, M. (2020) ‘Are products striking back? The rise of smart products in business markets’, Industrial Marketing Management, 90(June), pp. 205–20. doi: 10.1016/j. indmarman.2020.06.011. Parvinen, P., Pöyry, E., Gustafsson, R., Laitila, M., and Rossi, M. (2020). ‘Advancing data monetization and the creation of data-based business models.’ Communications of the association for information systems, 47, 25–49. https://​doi​.org/​10​.17705/​1CAIS​.04702. Pham, X. and Stack, M. (2018) ‘How data analytics is transforming agriculture’, Business Horizons, 61(1), pp. 125–33. doi: 10.1016/j.bushor.2017.09.011. www​ .ponsse​ .com/​ en/​ services/​ online​ Ponsse (2022) ‘Ponsse Manager’, Ponsse. Available at https://​ -services/​ponsse​-manager​#. Porter, M. E. and Heppelmann, J. E. (2014) ‘How smart, connected products are transforming competition’, Harvard Business Review, 92, pp. 64–88. Saracco, R. (2019) ‘Digital twins: bridging physical space and cyberspace’, Computer, 52(12), pp. 58–64. doi: 10.1109/MC.2019.2942803. Sjödin, D., Parida, V., Jovanovic, M. and Visnjic, I. (2020) ‘Value creation and value capture alignment in business model innovation: a process view on outcome-based business models’, Journal of Product Innovation Management, 37(2), pp. 158–83. doi: 10.1111/jpim.12516. Sony, M. and Naik, S. (2020) ‘Critical factors for the successful implementation of Industry 4.0: a review and future research direction’, Production Planning and Control, 31(10), pp. 799–815. doi: 10.1080/09537287.2019.1691278.

86  Handbook on digital platforms and business ecosystems in manufacturing Suppatvech, C., Godsell, J. and Day, S. (2019) ‘The roles of internet of things technology in enabling servitized business models: a systematic literature review’, Industrial Marketing Management, 82, pp. 70–86. doi: 10.1016/j.indmarman.2019.02.016. Tao, F. and Qi, Q. (2019) ‘Make more digital twins’, Nature, 573(7775), pp. 490–1. doi: 10.1038/ d41586-019-02849-1. Teece, D. J. (2018) ‘Business models and dynamic capabilities’, Long Range Planning, 51(1), pp. 40–9. doi: 10.1016/j.lrp.2017.06.007. Thomas, D. W. and Ritala, P. (2022) ‘Ecosystem legitimacy emergence: a collective action view’, Journal of Management, 48(3), pp. 515–41. doi: 10.1177/0149206320986617. Tunn, V. S. C., van den Hende, E. A., Bocken, N. M. P., and Schoormans, J. P. L. (2020). ‘Digitalised product-service systems: Effects on consumers’ attitudes and experiences.’ Resources, Conservation and Recycling, 162, [105045]. https://​doi​.org/​10​.1016/​j​.resconrec​.2020​.105045.

PART II ECOSYSTEM DESIGN AND GOVERNANCE

7. Fishing for complements: partnership scouting routines of non-focal B2B firms in emerging-technology digital business ecosystems Christian Zabel, Jonathan Natzel and Daniel O’Brien

INTRODUCTION With accelerating digitalization, today’s competitive markets are increasingly organized as digital business ecosystems (DBEs). This is particularly relevant in the manufacturing industry, where platform-based value creation is leading the ongoing digitalization (Lager, 2017; Suuronen et al., 2022). Navigating these is as relevant as it is difficult. Not only do manufacturing companies need to identify opportunities to cooperate – or compete – with other actors within a DBE or even across different DBEs. They also have to adapt their activities to their dynamically changing environment (Hess et al., 2016; McLaughlin, 2017; Schoemaker et al., 2018; Vial, 2019). Digital business ecosystems represent ‘new forms of value creation in networks where the digital infrastructure enhances mechanisms of self-organization’ (Lenkenhoff et al., 2018: 167). Ecosystem actors develop and provide complementarities without being hierarchically bound (Jacobides et al., 2018: 2264). Platform operators, so-called focal firms, act in cooperation with non-focal firms. The latter operate on the platform, use the platform’s tools and services and co-create value (Cenamor, 2021; Hurni et al., 2021). They also constitute by far the largest portion of firms participating in DBEs. Since value creation depends on the interaction of ecosystem actors, identifying potential partners and forms of cooperation (as well as competition) is vital for firms participating in DBEs. This is particularly true for the manufacturing industry, where the digital transformation of firms – accelerated by emerging technologies such as artificial intelligence (AI) or big data – often involves platform solutions which require interaction with focal and non-focal actors (Suuronen et al., 2022). Since most of these companies are non-focal actors (i.e. not running their own ecosystem), the analysis of non-focal firms’ partnership scouting routines is of high practical relevance for their ability to effectively participate in DBEs. Doing so is even more challenging in highly dynamic and technologically uncertain environments (Carlo et al., 2012), such as the VR market. VR has a wide range of applications in manufacturing, including digital twins, predictive maintenance and virtual reality training (Chandra Sekaran et al., 2021; Suuronen et al., 2022), often requiring partnerships for implementation. The research question, therefore, is: What are the partnership scouting routines of non-focal B2B firms in emerging-technology digital business ecosystems? In emerging-technology DBEs, partnership scouting can be conceptualized as being part of a firm’s dynamic capabilities (DCs). According to Teece (2007), DCs represent ‘the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments’ (p. 516) and can be differentiated into sensing, seizing and 88

Partnership scouting routines of non-focal B2B firms  89 transforming DCs. In an ecosystem context, dynamic sensing capabilities have been shown to contain opportunity screening routines (i.e. scanning markets and evaluating technologies) and partnership scouting routines (i.e. identifying complementarities with ecosystem actors and exploring alternative DBEs (Linde et al., 2021; Zabel et al., 2023)). Partnership scouting as part of dynamic sensing capabilities has been addressed in the literature mostly on a conceptual level (Tan et al., 2020; Maijanen, 2022) or from a focal firms’ perspective (Linde et al., 2021). Also, taxonomic differentiation of non-focal DBE actors is not well developed (Battistella et al., 2013; Teece, 2018). Hence, in our explorative multiple case study, we aim to explore non-focal firms’ partnership routines in emerging-technology DBEs as part of the firm’s dynamic sensing capabilities. To do so, technology and commercial leaders from 12 Germany-based companies producing VR software for the B2B sector were interviewed.

LITERATURE REVIEW Dynamic Capabilities in Digital Business Ecosystems Dynamic capabilities and their application in the DBE context have been widely researched. Building on Teece’s (2007) definition of sensing, seizing and transforming, several studies have demonstrated DCs to be closely related to implementing and monetizing emerging technologies successfully (McLaughlin, 2017), to conducting radical technological innovation (Chiu et al., 2016) and to profiting from such innovation (Teece, 2018). In the context of DBEs, DC research has mostly focused on focal firms (Tan et al., 2020). Hence, there remains a ‘dearth of literature’ (Selander et al., 2013: 183) around non-focal firms that is not limited to a conceptual level. Cenamor (2021) argued that such actors form networks where they partner to tackle common challenges while sharing resources and knowledge. Closer connectedness between actors can intensify coopetition and make a network more effective. Tan et al.’s (2020) literature review of DCs for firms participating in DBEs highlighted the crucial role of partnering through network capabilities. The literature also frequently emphasizes the importance of identifying trends and changes concerning DBEs and emerging technologies (Helfat and Raubitschek, 2018; Hanelt et al., 2021; Linde et al., 2021). Therefore, dynamic sensing capabilities can be considered crucial, especially for emerging-technology DBEs. Partnership Scouting as Microfoundation of Dynamic Sensing Capabilities Partnership scouting can be conceptualized as a microfoundation of dynamic sensing capabilities, i.e. the first activity cluster in Teece’s DC framework (Linde et al., 2021). Microfoundations describe the activities and structures (e.g. skills, procedures, processes, structures, rules and behaviour) that are building blocks of the higher-order DCs (Teece, 2007). Partnership scouting describes the importance of scanning beyond existing partnerships to leverage opportunities from collaboration with other ecosystem actors. It is important to note that partnership scouting depends on the position of the firm in the DBE. Whereas focal firms mostly focus on partnerships within their respective platform, non-focal firms may be active on different platforms simultaneously. They might aim for alternative ecosystems, so-called multi-homing decisions as a form of strategic hedging (Cenamor, 2021).

90  Handbook on digital platforms and business ecosystems in manufacturing Operationalization of Partnership Scouting Partnership scouting can be differentiated into ‘searching for collaboration opportunities’ within a given DBE and ‘scanning and exploring new business opportunities’ outside an ecosystem (Maijanen, 2022: 59). The microfoundations identified by Linde et al. (2021) and Maijanen (2022) have been operationalized differently in empirical studies. Routines that help to understand focal firm’s strategy and governance are important (O’Mahony and Karp, 2022). This may include the monitoring of a focal firm’s market behaviour and practices (Hurni et al., 2021). In addition, non-focal firms need ways to engage with the focal firm in general and to generate privileged access to the platform benefits in particular (Wareham et al., 2014; Kretschmer et al., 2022). This may include generating visibility and, more importantly, designing and safeguarding the collaboration with the focal firm, e.g. by entering into partnerships (Kapoor, 2018; Suominen et al., 2019). Furthermore, non-focal firms consider alternative DBEs by evaluating commercial and technical aspects, such as the complexity of the ecosystem (Chen et al., 2022). Here, non-focal firms need processes that allow them to identify and exploit unique partnership opportunities in other DBEs (Chou et al., 2011) by sharing resources and risk, streamlining decision-making processes and coordinating their aims with partners (Shipilov and Gawer, 2020; Cenamor, 2021). Partnership scouting routines also address the collection and exchange of knowledge with complementors, e.g. suppliers and even competitors (Jacobides et al., 2018; Cenamor, 2021), as these actors can enhance a company’s understanding of the business (Mikalef and Pateli, 2016). In addition, coopetition with complementors within DBEs (i.e. co-development, commercial/technological standardization) can be routinized, allowing the standardization of products and services, the evolution of common governance, e.g. through regulations (Kapoor and Agarwal, 2017; Shipilov and Gawer, 2020) or the disclosure/licensing of IP (Miller and Toh, 2022). These routines may be shaped by the openness of the platform (Zhang et al., 2022). A different strand of partnership scouting activities specifically focuses on the role of customers, particularly in the B2B sector. Researchers emphasize the relevance of understanding customer needs through feedback and regular customer interactions (Helfat and Raubitschek, 2018; Khan et al., 2020). Routines may include customer-led development efforts and co-creation routines, i.e. in the company’s product development (Carlo et al., 2012; Jantunen et al., 2012). In the manufacturing industry, co-creation is increasingly important for product and process innovation, asking for projects involving several actors and knowledge exchange or creation with end-users (Sjödin, 2019). It has to be stated that partnership scouting routines build on and have significant overlaps with other opportunity scanning activities (Linde et al., 2021). Monitoring market trends (Plattfaut et al., 2015) or best practices (Mikalef and Pateli, 2016) may yield significant insights for partnership scouting as well. In the same vein, Khan et al. (2020) and Gelhard and von Delft (2016) demonstrated that networking in the form of conferences, seminars, trade fairs, meetings and other events helps to identify threats to the business model – but also enables partnering options between company founders and/or representatives as well as developers and specialists, who serve as a source of tacit knowledge to the firms. This is also true for technology evaluation activities: scanning the research output with exogenous science and technology organizations, such as universities (Castiaux, 2012; Teece, 2007), may open up cooperation opportunities, such as jointly developing technology (Khan et al., 2020). Finally, practical experimentation with technology, through trial-and-error and rapid-fire prototyping

Partnership scouting routines of non-focal B2B firms  91 (Zahra et al., 2006), may yield valuable insights, which can also inform partnership scouting activities. Given the state of the literature on non-focal firm DCs, it is not surprising that taxonomic differentiation between non-focal firms is just beginning to evolve. Zapadka et al. (2022) empirically examined the focus on B2B and/or B2C markets. They emphasized that firms with a mix of customers free up different knowledge resources. Another factor of taxonomic differentiation is company size. Arend (2014) argued that firm size and age influence the benefits provided by DCs. Parida et al. (2016) asserted a positive direct effect of firm size on innovation capability and network capability. Likewise, Tavalaei and Cennamo (2021) argued that firm size influences the resources that can be employed.

METHOD This research is designed as an exploratory multiple case study of 12 companies that are providing B2B products or services in the German VR market. Insights were generated based on 21 semi-structured interviews with technology and commercial executives as well as secondary data analysis. The study focuses on firms using virtual reality. VR can be considered a key base technology for potentially vast business opportunities such as the metaverse by enabling full user immersion (Lee et al., 2021; Park and Kim, 2022). It can be applied for different manufacturing use cases, ranging from digital twin or predictive maintenance to virtual reality training (Chandra Sekaran et al., 2021; Suuronen et al., 2022). Whereas the technology has received substantial academic and popular attention, the market (and with it the VR-related DBEs from software and hardware providers) is still emerging. According to estimates, the US VR industry generated a $3.3 billion turnover in 2021 (Grand View Research, 2021). In Germany, the VR sector totalled approx. €550 million in 2021, mostly in the B2B sector (Zabel et al., 2022). Though VR has not fully reached mass market adoption yet, it can be stated that the technology is rapidly evolving (Kunz et al., 2022). Therefore, VR is a suitable object of investigation for studying emerging-technology based DBEs (Zabel et al., 2023). To reach theoretical saturation and develop taxonomic differentiation, sample selection was based on areas of application (e.g. corporate training, collaboration platforms), target markets with strong B2B focus (e.g. manufacturing, medicine) and firm size (fewer than vs more than 50 employees). We interviewed two executives per company; one from the commercial/ market side (e.g. the managing director) and one with a technology focus (most often the chief technology officer (CTO), cf. Table 7.1). This allowed us to validate and elaborate on different aspects raised by the interviewees. In three very small firms, we interviewed only the managing director. All conversations were held via Zoom calls in June 2022, lasting between 30 and 75 minutes. To protect the identity of the interviewees, their responses were anonymized. The questionnaire included general company information (age, number of employees, business model and products) before questions around overall sensing activities and more specifically partnership scouting activities were raised. It followed a semi-structured interview approach in which predefined questions guided the conversation while allowing for spontaneous deviation to other topics of interest to the researchers. Questions were based on previous research, with a particular focus on sensing. Since partnership scouting represents an important – but not the only – facet of sensing activities, general sensing activities were queried in

92  Handbook on digital platforms and business ecosystems in manufacturing Table 7.1 Interviewee

Participants. In the interviewee column, C denotes an interview partner from the commercial side and T an interviewee from the technical side Company

Position

Company size

Business model

(employees)

type (B2B/B2C)

Activities

C1

1

Managing Director

15

B2B

VR training software

T1

1

VR Developer

15

B2B

VR training software

C2

2

Managing Director

25

B2B

VR authorship tool

T2

2

Director Key Account

25

B2B

VR authorship tool

Management T3

3

CTO

24

B2B

VR agency/training software

C3

3

Managing Director

24

B2B

VR agency/training software

C4

4

Managing Director

12

B2B

VR full-service agency

T4

4

Marketing Manager

12

B2B

VR full-service agency

C5

5

Vice President XR

53 000

B2B/B2C

ICT

T5

5

Senior Director XR

53 000

B2B/B2C

ICT

C6

6

Managing Director

14

B2B

VR collaboration/full service

T6

6

CTO

14

B2B

VR collaboration/full service

T7

7

CTO

17

B2B

VR meeting/full service

C7

7

Head of Development

17

B2B

VR meeting/full service

C8

8

Product Owner

550

B2B/B2C

Game engine

T8

8

Director of Game

550

B2B/B2C

Game engine

Engine C9

9

Managing Director

7

B2B

VR training software

C10

10

Managing Director

13

B2B

360° video agency

C11

11

Managing Director

2

B2B

VR coaching

C12

12

Managing Director

100

B2C/B2B

VR experiences/tours

T9

12

Head of Development

100

B2C/B2B

VR experiences/tours

the first part, helping to uncover linkages of interest. In the second part, DBE-related sensing activities were examined. For this purpose, we asked the participants to name the most important DBE for their company and then focused our questions on this specific DBE. Still, we also touched on alternative DBEs (i.e. for multi-homing purposes). Then, in the third and final section, we specifically addressed sensing activities concerning the metaverse. Here, the interviewees were provided with a definition of the term metaverse, based on Ball (2022). Overall, we included open-ended questions concerning observation, evaluation and action, for example, ‘Do you have a strategy for dealing with ecosystem companies/DBEs? If yes, what does this look like?’ and ‘How do you observe actions of the ecosystem/DBE operator and other ecosystem actors?’ The interviews were recorded and transcribed with the consent of the participants. They were then controlled for quality, analysed and coded in MaxQDA by three researchers. Following the Gioia method, we structured the data in first-order concepts and second-order themes (i.e. routines (Gioia et al., 2013)). The insights from the literature review defined the overarching coding topics linking to the microfoundations of sensing (Khan et al., 2020; Linde et al., 2021). This structure was then updated and subsequently adapted following the ongoing interviews. Thus, data collection and analysis happened synchronously and through multiple iterations. Initial codes were created by independent reading of transcribed data, notes and secondary data. Then, every transcript was read several times and passages related to the research purpose and relevant literature were marked, as were trends and topics frequently brought up

Partnership scouting routines of non-focal B2B firms  93 in the interviews. The applied codes were checked by at least two coders and were iteratively refined through the coders’ joint discussion. In total, 12 of the 44 sub-codes could then be directly linked to the subroutines of partnership scouting. After the coding phase, we scrutinized the coded segments for recurring themes, also highlighting contradictory statements. In the final phase, the researchers rescreened the entire transcripts to confirm the identified thematic patterns and to extract various exemplary quotes.

FINDINGS In our study, we noticed a common but somehow hands-on understanding of dynamic sensing activities in general: Every company in our sample was permanently engaged in sensing activities, be it market/technology-related activities (opportunity screening) or partnership scouting activities. Regarding opportunity screening, many activities involved information gathering of publicly available information (markets and technology), social sensing routines (specifically with regards to competitors and customers) or the practical evaluation of technology. Even though all interviewees reported conducting such activities, the degree of formalization, extent, focus and the people involved varied significantly between firms. In smaller companies, only a handful of respondents reported having dedicated structures, units or personnel for these tasks, whereas interviewees from larger and established firms described being able to apply a more structured approach. These opportunity screening activities also feed into partnership scouting activities. In line with Maijanen (2022), partnership scouting activities as part of dynamic sensing capabilities take place in two different settings. First, within a specific DBE, non-focal firms try to identify collaboration opportunities with other ecosystem actors, including the DBE’s focal firm, competitors, suppliers and customers. Second, firms can evaluate business and collaboration opportunities in alternative DBEs and approach DBE-agnostic actors, most importantly research institutions (Table 7.2). In the following, we first present overarching findings, before addressing the findings regarding inside- and outside-DBE partnership scouting. In general, identifying complementarities within and outside a specific ecosystem requires various qualitative information, which may be primarily gathered from direct exchanges. This underlines the importance of participating in networking and exchange initiatives such as trade fairs or industry associations. These routines can be time- and resource-consuming, hence, almost all small complementors did not scout in a structured and formalized way as they often lacked the resources to do so. Also, given the importance of partnership scouting and the confidentiality of topics (e.g. partnership agreements) in smaller firms, such activities were often carried out by the companies’ founders or managing directors, which adds to the resource constraint. The larger the company surveyed, the more these routines were delegated to the department level, depending on the expected importance of the markets and partnerships. Whereas all interviewees could recount examples of partnership scouting activities, these arose in specific contexts but were not part of daily routines. In contrast to opportunity screening of markets and technologies, the standardization of such efforts seems not to be linked to the firm size but to the business model. Whereas companies with a full-service offering scouted rather randomly, firms with defined software products screened in a more structured

‘You already try to be visible to [ecosystem] providers. How do you do that? Of course, through personal contact. But you also look at social media, follow

Customer firms

because of course there are really cool solutions that we cannot develop ourselves.’ (Head of development VR entertainment company)

suppliers)

customer.’ (Director at VR authoring tool firm)

customer a perfect puzzle. We try to look for interfaces between complementary solutions where we need to do a bit of development to increase value for our

‘At one customer there are three partners for VR, and we try to understand exactly what they do. And we try not to block each other, but we try to offer our

orders.’ (CEO of VR content and product company)

development. […] Of course we had to learn something about the subject matter […] and the knowledge came primarily from the companies that gave us the

‘We come from a service. That’s why customers came to us first and foremost. This then developed into a bit of a hybrid and now also goes into product

public.’ (CEO of VR collaboration platform)

‘Sometimes we also cooperate with our customers because they get the equipment faster. We then have access to prototypes that are not yet available to the

continuous consultation on this.’ (CEO of VR training software)

still have a relatively new technology, we are now also coordinating very closely in which constellations we still have to go to the customers. So, there is

‘So, the collaboration is really just starting. But it looks like we’ll also be collaborating [with other non-focal firm] in business development. […] Since we

the solutions are almost identical, which doesn’t happen that often, but it does happen.’ (CTO of VR full-service firm)

‘I would say, the market is still too small for too much competition. Actually, it is very cooperative. That’s also very surprising sometimes, especially when

new glasses and new technologies relatively quickly.’ (CEO VR entertainment company)

‘We also have quite close contact with suppliers, who also give us test orders at our disposal. […]. And we’re lucky enough to receive those test orders for

‘We sat down with them [another non-focal firm] and thought about how we could collaborate. So, what are the technical things that we can exchange,

Complementors (e.g. competitors,

development VR entertainment)

but also when we have solved problems. Where we then discuss with other developers (…) what are possible ways to address these problems then.’ (Head of

kinds of platforms that are used for communication, and of course we also exchange information, so that’s quite clear somehow. Because we have problems,

‘In the form of problems, (…) our developers are in some form in the appropriate forums on the road or communities or Discord Channels. There are all

regularly with the decision-makers, as far as possible, to discuss matters.’ (CEO VR authoring tool)

‘Of course, we try to obtain information. This includes strong relationships with companies that are driving technological progress forward. We meet

key employees, give them a like or something.’ (CTO of VR training software)

Partnership scouting (Quotes)

Focal firm(s)

Exemplary quotes of partnership scouting activities of B2B non-focal firms

Partnerships with:

Table 7.2

94  Handbook on digital platforms and business ecosystems in manufacturing

‘Then I’m also on the road a lot at such conferences and festivals. That’s where you get to hear an incredible amount. These conferences are often the places

Alternative DBEs

universities that we can somehow manage such a science transfer. But of course, you also have to say that start-ups also have certain limits for their own

institutions)

questioning, so that in the end we also get something out of it, can then share the knowledge or use it and take it further.’ (CTO VR full-service agency)

have customers who are very interested in working on questions within their partnership with a university or a doctorate, where we then support them in their

‘On the other hand, we always try to use small teams, students, and universities to support us by asking questions and getting answers. In some cases, we also

it’s going and you suddenly realize in the process: Oh, how exciting is that?’ (CEO VR full-service agency)

for us regarding the priorities we have in mind. But from the experience of the last 10, 15, 20 years, I know that sometimes you don’t know exactly where

‘Of course, there are also research projects where we are sometimes asked whether we want to participate. […] Perhaps it is not so fundamentally important

(CEO VR agency / training software)

R&D activities – especially in an emerging technology area like here, for example, it is very expensive to try out many things that no one has done before.’

‘We try to cooperate with universities on research projects. So, there are also a few research projects going on where we are looking together with

DBE-agnostic actors (research

software has continued to evolve as a standalone platform.’ (CEO VR collaboration platform)

many customers from our custom projects […] So we put the prototype on their headsets and asked them: “What do you think of this?” […] Since then, this

briefly work on anything together. So, we developed a prototype for ourselves relatively quickly. […] We liked this prototype so much and we already had

‘And at the beginning of Corona, we also had the problem when we were working in VR projects that there just wasn’t anything suitable where we could

platform.’ (CEO of VR platform provider)

‘We have very good contacts with them [alternative DBE] because they work differently. […] There, our contact person supported us very early on with our

sector, it may even already be known. So, it’s more in that area, I would say.’ (CEO VR full-service agency)

where you get to hear and see what others are working on. What might not really be available to consumers until a year or so from now. In the business

Partnership scouting (Quotes)

Partnerships with:

Partnership scouting routines of non-focal B2B firms  95

96  Handbook on digital platforms and business ecosystems in manufacturing way. This was even more noticeable for those companies aiming to build a platform-based business (see Table 7.2.). The largest companies in our sample (which also provided a software platform) additionally structured such activities with key account and partner management positions. These were tasked to discuss confidential and potentially wide-ranging opportunities to cooperate. One example: To track trends emerging from start-ups and engage with them individually or through specific initiatives such as start-up competitions, accelerator programs or corporate venture capital initiatives. Possible benefits include getting hold of new technologies, influencing product development at an early stage and identifying new target customers. Partnership Scouting within a Specific Ecosystem It is not surprising that all non-focal firms were trying to actively partner with the DBE provider, as such providers constitute a certain degree of power, due to their role in providing central technologies and governing collaboration and rules within the ecosystem. However, non-focal firms might not be visible to the focal firm due to their small size, divergent target market or product. Smaller non-focal firms reported having limited access to information (about products, standards, roadmaps, etc.) and other resources of the DBE provider. This not only limits their options for identifying new business opportunities within the DBE but also requires non-focal firms to actively monitor (and then quickly react) to actions undertaken by the focal firm to protect their business. Non-focal firms hence strive to strengthen relationships with focal firms by establishing personal connections. This may include reaching out to employees of the focal firm or following and liking comments on social media. Other interviewees mentioned publishing a blog to increase the visibility to the focal firm. However, interviewees (specifically from smaller companies) considered direct contact with focal firms as essential but oftentimes not satisfying. On the other side, designing these interactions with many (small) complementors represented a challenge for focal firms, too. All interviewees highlighted the role of institutionalized, large-scale exchanges like developer programs, blogs or forums. Such interactions are either facilitated by the DBE provider or self-governed by complementors, in this case drawing even less on the DBE’s resources. These exchanges may grant staggered access to a focal firm’s resources, depending on the (expected) relevance a non-focal actor has for the ecosystem operator. As a result of this tiering approach, the smallest firms (for example newly founded start-ups) may struggle to get qualified access at all. This is different for the most important non-focal firms (as measured by scale, strategic fit or growth potential): Larger and strategically aligned complementors offered a different picture of their partnership opportunities with the focal firm. Oftentimes they are granted exclusive access to people resources or technologies (e.g. beta versions) to align their products and services before such new offerings hit the market. Another way to improve DBE relations for non-focal firms is establishing direct contacts and partnerships with technology partners (e.g. suppliers, resellers), which provide access to and testing of technological opportunities, such as VR hardware. Entering these cooperations constituted a major case of partnership scouting with other complementors. Here, the most basic form of engaging with other non-focal firms consisted of participating in network organizations. The majority of non-focal companies in our sample mentioned being active in one or more associations to reach out to other complementors.

Partnership scouting routines of non-focal B2B firms  97 Few interviewees additionally reported having intensive exchanges with co-localized actors. Conversations with executives from suppliers, focal firms and competitors help to qualify and make sense of openly available information. This is especially important for manufacturing companies when navigating uncertain environments such as VR technologies and markets. Several respondents indicated that they engage in such discussion through active outreach (e.g. public speeches). This can be used to increase knowledge transfer between actors through expert presentations and sharing best practices. By producing and providing valuable information themselves, they also position their firm as a valuable partner for others in the ecosystem(s). Multiple respondents generally alluded to a collaborative environment between companies, which is associated with the maturity and complexity of the VR market. Consisting of niche markets with specific requirements and based on technological frameworks, differentiation by such niches allows for a relatively co-opetitive environment. Cooperation between non-focal firms was particularly pronounced on the technology side, being a result of the high complexity of the emerging technology landscape. But it can also touch on commercial aspects. Firms scouted for additional distribution channels, often created by other non-focal firms that target similar niches (e.g. corporate training applications for manufacturing companies). Several respondents also mentioned passing on contracts to other firms to make the best of their complementary competencies and limited resources. Cooperation was also necessary when encountered firms were addressing the same customers. Here, commercial-side managers (often at C-level) needed to reach out to other complementors to ensure compatibility of their products with common B2B customers. Still, such forms of partnership activities appear rather randomly; interviewees reported that more standardized forms of cooperation have yet to be developed. This might eventually lead to more integrated value chains. In addition to the findings presented above, client firms were also mentioned by almost all respondents as crucial targets in their partnership scouting routines. In addition to acquiring additional business from new customers (which is part of the market opportunity screening), firms may gain significant insights into market developments and specialized industry knowledge from their existing customers. Considering that VR companies oftentimes specialize in a specific industry, expanding activities to a different industry (e.g. a VR company historically working in media now approaching a manufacturing firm) requires working closely with knowledgeable actors (e.g. manufacturers) to understand the specifics of the industry. Since many of the companies that were interviewed work across industries, the market-scanning activities – particularly of the smaller firms – are otherwise thinly stretched. In addition, customer-led development was expressed not only through a focus on customer demands but also through co-creation between customers and non-focal firms, ranging from service relationships to more intensive exchanges, such as co-development with customers. Co-creation examples include projects where actors from the manufacturing industry, universities and other ecosystem actors jointly develop and assess prototypes (e.g. VR training for blue-collar workers). Such projects may even be formalized, such as in a joint venture. Partnership Scouting Outside a Specific Ecosystem Looking for business opportunities in alternative ecosystems outside a specific DBE was pervasive among the firms in our sample. All interviewees reported either pursuing a multi-homing strategy to avoid becoming dependent on a single DBE or looking regularly

98  Handbook on digital platforms and business ecosystems in manufacturing for new DBEs to understand which solution might become an alternative, given the limitations of the one currently used. Here again, exploring alternative DBEs was built on opportunity screening (market and technology) activities. The analysis of new equipment or software, customer demands or competitor movements may already uncover to some extent the attractiveness of alternative DBEs. There are, however, more specific aspects, which require a strategic assessment: Almost all interviewees mentioned that the intentional exploration of alternative DBEs pays specific attention to customer needs. Non-focal firms try to find an ecosystem that enables them to fully deliver on all aspects of the customer journey, something being described to strongly differ across industry needs (e.g. manufacturing vs media). Here, interviewees named both hardware requirements (e.g. size, weight, battery of a head-mounted display) and the ease of use or specific features of the solution as important criteria. In addition to this performance assessment, the market reach of the DBE and customer familiarity with a specific product or brand – often the result of marketing efforts of the DBE provider or prior customer experience – were considered particularly important. In effect, commercial and technical aspects were perceived as interwoven, revealing a commercial side to technical specificities and vice versa. Here, partnership-scouting activities must also assess customer-relevant aspects of platform governance. Features (e.g. privacy settings), market conduct and transparency around roadmaps or the ownership of the platform may affect the trustworthiness of alternative DBEs. These aspects were considered especially important in the B2B space or when dealing with sensitive data. Regarding the architecture of the alternative platform ecosystem, interoperability across systems (in the case of virtual reality, the usage of open technologies like OpenXR) was considered a deciding factor for smaller and larger companies alike, since interoperability facilitates multi-homing, making risk diversification for non-focal firms easier. This becomes even more relevant when looking at the series of emerging DBEs in the VR market, where the number of different technologies and platforms makes sensing and partnering highly complex. Despite open standards, multi-homing of the sample firms was significantly limited due to resource rigidity on a coding and/or technology level. Thus, a majority of companies specialize in the utilized software. Switching costs were reported to be considerably lower for hardware-based DBEs in the virtual reality market because of underlying software standards and the decreasing cost of end-user hardware, which allow operating different hardware stacks and thus mitigate risks. The challenges of designing interactions with non-focal firms and keeping them in a platform ecosystem provide an opening for new market entrants to proactively approach other non-focal firms. Such new entrants, oftentimes coming from the position of a non-focal firm themselves, allocate more resources to partnership scouting and actively approach small non-focal firms to directly understand their needs, to create transparency around product development road maps and to even provide premium support for smaller firms. Examples of non-focal firms in a specific ecosystem becoming focal firms themselves outside this ecosystem have been mentioned repeatedly in our interviews. Given the high resource demands, this development often stems from a dedicated product strategy: all the interviewees representing B2B companies in our study stated their goal to establish a scalable platform upon which other companies (often customers, rarely other complementors) could build their solutions. Here, again, customer demand and technical feasibility were considered crucial factors. Also, bigger firms based such strategies on a structured evaluation of the opportunities and risks

Partnership scouting routines of non-focal B2B firms  99 before deciding whether to allocate resources to them. Smaller complementors acted more opportunistically, reacting spontaneously to market developments, often described as a ‘pull’ effect. Even where a decision to build a platform might have been planned strategically, implementation could occur spontaneously and was often driven by customer demands. Due to the strategic and forward-looking business demands on platform operators, partnership scouting required experimentation with and development of more formalized partnering strategies, something which emerges with non-focal to focal actors over time. Finally, partnership scouting with DBE-agnostic partners – in this case, collaborations with research institutions – was widespread amongst the firms in our sample. Interviewees underscored that, although such partnerships may be helpful in technology innovation, their scope remains somewhat limited: Oftentimes they are conflicting with the rather short time horizon of players acting in rapidly changing ecosystems. In addition, basic research was also considered too specialized, which would require focusing on a specific (sub‑) technology, thus creating path dependencies, and further reducing the scope of evaluating alternative technologies. Hence, formalized scientific research cooperations with universities or scientific institutions were only mentioned by the two largest companies in our sample. Many of the interviewees representing smaller companies cited a lack of financial or human resources as a major hurdle to engaging in such long-term cooperations. However, focusing on applied research or sharing knowledge between the science and industry communities was more common. This again involves industry partners and was particularly developed among firms working in the manufacturing industry. When jointly applying for research grants, a major motivator for smaller non-focal firms was the opportunity to generate an additional source of income, whereas their industry partners rather aimed to reduce the cost and risk linked to such uncertain projects, both demonstrating a form of risk hedging in highly volatile DBEs. In addition to such cooperations, non-focal firms also engaged in teaching at universities, which pays into the exchange of knowledge and future research cooperations but was also seen as a relevant stream for training and hiring future employees for the companies.

DISCUSSION Our study conceptualizes the partnership scouting activities of non-focal firms in DBEs, differentiating such firms into smaller or bigger firms, and particularly looking at B2B companies. This deepens the understanding of how partnership scouting activities – being part of dynamic sensing capabilities – are influenced by environmental turbulence, which in our case of the VR market was particularly high. We found that non-focal firms oftentimes screen for partnerships inside or outside specific ecosystems rather intuitively. Partnership scouting routines focus on four different groups: focal firms, complementors, customers (e.g. manufacturing companies) and platform-agnostic actors (e.g. universities). Depending on whether partners are scouted within an ecosystem or outside of it, different patterns emerge, with profound managerial implications. Resource Constraints Require Productive Opportunism Resource constraints play a significant role in the dynamic sensing capabilities of firms, in this case for partnership scouting activities. Even though differences can be seen between

100  Handbook on digital platforms and business ecosystems in manufacturing smaller and bigger companies, managers still need to consider carefully where to allocate their resources. Smaller non-focal firms pursue rather opportunistic and less formalized forms of partnership scouting since the sheer complexity of the environment limits their ability to strategically plan and execute. This bricolage behaviour, where firms adopt a ‘could-do’ logic (Bicen and Johnson, 2015: 290), privileges short-term actionability in sensing over long-term strategy. It enables smaller players to quickly adapt to market changes and customer requests as seen with the pivot of companies during the Covid-19 pandemic (see Table 7.2). Manufacturing companies could profit from this agility by facilitating partnerships with non-focal actors, e.g. by reducing procurement barriers for prototyping projects, supporting external partners with internal teams or developing prototypes in iterative approaches (Zabel and Telkmann, 2021). At the same time, this may limit longer-term cooperations (for example with research institutions) if they require a very specific, ‘narrow’ focus and thus could create significant path dependencies. Strikingly, even the much larger focal firms face a similar trade-off regarding their resources. They must consider which partnership activities bring the most value to their platform and hence favour some non-focal firms over others. While this is required by the sheer endless numbers of potential partners, it may severely limit the sensing capabilities of smaller firms and thus the value that such firms create for the platform operator. For focal players there is a significant risk of misinterpretation in differentiating between partners: Such governance can inadvertently be considered as arrogance, reducing trust and complementor dedication (Hurni et al., 2021). This also applies to non-focal firms pivoting to become focal players themselves. A proactive partnering approach – which can be observed by newer DBE providers entering the market – helps to decrease the resource needs in sensing activities, as access to information is more easily approachable and partnerships are shaped towards the needs of smaller non-focal firms. Manufacturing companies might take this into account when considering establishing their own ecosystem or joining an existing DBE. Partnership Scouting Activities are Focused on Customer Companies Dynamic sensing capabilities in ecosystem contexts depend heavily on social sensing activities (Zabel et al., 2023). This is also true for partnership scouting, where the B2B companies studied were found to be strongly focused on the demands and needs of the customer, both in existing ecosystems and outside. The customer has a decisive influence on a company’s search for information and participates in the process of knowledge creation and value co-creation (Cenamor et al., 2017; Sjödin et al., 2020). Through these co-creational aspects, they have a huge impact on companies’ business model (e.g. pricing) and strategy (e.g. multi-homing decision), particularly in B2B markets (Woo et al., 2021). Also, customers might enable access to resources (e.g. early access to products), which might otherwise not be attainable. Our study shows that co-creation with customers is a relevant element of market discovery since it allows non-focal firms to tap into pockets of expertise that would otherwise be hard to reach. For partners such as manufacturing companies this can be considered a huge opportunity in knowledge sharing: Learning about new markets and technologies from non-focal actors while sharing industry-specific needs with such players might not only open up opportunities to enter new markets (e.g. the metaverse) but also grant early access to ecosystem innovation from other players (e.g. blue-collar training applications). Here, personal contacts to selected customer and provider firms play a crucial role; for some firms, spatial co-location is particularly

Partnership scouting routines of non-focal B2B firms  101 relevant (Zabel and Telkmann, 2022) At the same time, we found that the high heterogeneity of clients (concerning application areas and industries) and the growth of the overall market facilitated cooperation, making partnership scouting worthwhile. Partnering with Co-Opeting Non-Focal Actors is Relevant for Mutual Growth The B2B focus of the sample group allowed to analyse the interplay of partnership activities because market interaction is more developed. We found a high degree of collaboration between competing non-focal firms. The degree of co-opetition may depend on the structure of the platform ecosystem as well as on the fragmentation of target markets and industries (e.g. corporate training applications in manufacturing). Another major factor is the high product and technology heterogeneity, which requires customer-facing consolidation of fragmented products and solutions. In the emerging DBEs of the VR market, this might not yet be fully developed, resulting in customized and casuistic partnership scouting and cooperation approaches. These activities could evolve, when sufficient cooperation experience (e.g. with outsourcing to other non-focal actors) has been accumulated and complementor co-opetition becomes fully viable. These findings are also highly relevant for managers at platform operators or non-focal firms aiming to set up their own platform ecosystem. In line with the literature, we saw that firms – specifically in B2B markets – may strategically include selected competitors in their offerings (Broekhuizen et al., 2021; Cenamor and Frishammar, 2021), alliances that are also relevant in manufacturing. To assure sustained complementor dedication (Hurni et al., 2021), focal firms have to avoid misinterpretations resulting from a lack of information. This may require signalling strategic intentions, but also creating a high degree of transparency by granting access to the focal firm’s shared resources, core and complements. This creates the risk of unwanted exploitations through other ecosystem actors. This may pose a specific challenge for complementors becoming platform operators themselves. Such development leads to several DBEs being built on top of one another. Our study develops the understanding of DBEs that are organized in a downstream–upstream relationship and the challenges for firms navigating such an environment. These highly interconnected DBEs often share the same non-focal firms and customers. Therefore, joint projects with partners (e.g. VR provider and manufacturing firm) could not only lead to learnings around one specific ecosystem but also grant some early experiences with others. In such a setting, non-focal actors may play a hybrid role, being non-focal in one context and focal in the other. This also impacts partnerships with and between non-focal actors. Figure 7.1 summarizes the core insights of the present chapter.

LIMITATIONS AND FUTURE RESEARCH Regarding limitations, it must be said that our research is based on selected cases and the specifics of the German VR market. Therefore, it cannot be ruled out that other non-observed firm specifics may affect the outcome. While our research deliberately focused on non-focal firms, the role of focal actors is of central importance to DBEs. Hence, future research could deliberately address both sides and address other industries, segments and markets. Also, there is no external validation of the interviewees’ perceptions or of firm performance. Given the emergent nature of the VR market, these aspects cannot yet be independently

102  Handbook on digital platforms and business ecosystems in manufacturing

Figure 7.1

Partnership scouting of non-focal B2B firms

assessed. This also points to an interesting line of further research: Our qualitative insights could now be further validated in quantitative studies, yielding insights into the relative importance of partnership activities in DBEs. Another further research stream could add to the insight of DBEs as vertically integrated ecosystems that build on one another. Here, firms need to adapt their partnership activities (as well as, presumably, other elements of DCs) to the fact that they are focal and non-focal firms at the same time. Although the challenges of entering the platform game have already been pointed out in the literature (Khanagha et al., 2022), in these cases additional co-dependencies have to be considered since existing ecosystem relationships add a further layer of complexity.

REFERENCES Arend RJ (2014) Entrepreneurship and dynamic capabilities: how firm age and size affect the ‘capability enhancement–SME performance’ relationship. Small Business Economics 42(1): 33–57. DOI: 10.1007/s11187-012-9461-9. Ball, Matthew. The Metaverse: and How it Will Revolutionize Everything. Liveright Publishing, 2022. Battistella C, Colucci K, De Toni AF, et al. (2013) Methodology of business ecosystems network analysis: A case study in Telecom Italia Future Centre. Technological Forecasting and Social Change 80(6): 1194–210. DOI: 10.1016/j.techfore.2012.11.002.

Partnership scouting routines of non-focal B2B firms  103 Bicen P and Johnson W (2015) Radical innovation with limited resources in high-turbulent markets: the role of lean innovation capability. Creativity and Innovation Management 24. DOI: 10.1111/ caim.12120. Broekhuizen TLJ, Emrich O, Gijsenberg MJ, et al. (2021) Digital platform openness: Drivers, dimensions and outcomes. Journal of Business Research 122: 902–14. DOI: 10.1016/j.jbusres.2019.07.001. Carlo JL, Lyytinen K and Rose GM (2012) A knowledge-based model of radical innovation in small software firms. MIS quarterly: 865–95. Castiaux A (2012) Developing dynamic capabilities to meet sustainable development challenges. International Journal of Innovation Management 16(06). World Scientific Publishing Co.: 1240013. DOI: 10.1142/S1363919612400130. Cenamor J (2021) Complementor competitive advantage: a framework for strategic decisions. Journal of Business Research 122: 335–43. DOI: 10.1016/j.jbusres.2020.09.016. Cenamor J and Frishammar J (2021) Openness in platform ecosystems: Innovation strategies for complementary products. Research Policy 50(1): 104148. DOI: 10.1016/j.respol.2020.104148. Cenamor J, Rönnberg Sjödin D and Parida V (2017) Adopting a platform approach in servitization: Leveraging the value of digitalization. International Journal of Production Economics 192: 54–65. DOI: 10.1016/j.ijpe.2016.12.033. Chandra Sekaran S, Yap HJ, Musa SN, et al. (2021) The implementation of virtual reality in digital factory – a comprehensive review. The International Journal of Advanced Manufacturing Technology 115(5): 1349–66. DOI: 10.1007/s00170-021-07240-x. Chen L, Yi J, Li S, et al. (2022) Platform governance design in platform ecosystems: Implications for complementors’ multihoming decision. Journal of Management 48(3): 630–56. Chou, JC-L, Hung C-L, and Li WT (2011) Achieving platform leadership: Application of inverting and porting in system development. In: 2011 IEEE International Conference on Industrial Engineering and Engineering Management, 6 December 2011, pp. 1898–901. DOI: 10.1109/IEEM.2011.6118245. Chiu W-H, Chi H-R, Chang Y-C, et al. (2016) Dynamic capabilities and radical innovation performance in established firms: a structural model. Technology Analysis & Strategic Management 28(8): 965–78. Gelhard C and von Delft S (2016) The role of organizational capabilities in achieving superior sustainability performance. Journal of Business Research 69(10): 4632–42. DOI: 10.1016/j.jbusres.2016.03.053. Gioia DA, Corley KG and Hamilton AL (2013) Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods 16(1): 15–31. DOI: 10.1177/1094428112452151. Grand View Research (2021) Virtual Reality Market Size & Share Report, 2022–2030. Available at: https://​ www​.grandviewresearch​.com/​industry​-analysis/​virtual​-reality​-vr​-market (accessed 5 December 2022). Hanelt A, Bohnsack R, Marz D, et al. (2021) A systematic review of the literature on digital transformation: insights and implications for strategy and organizational change. Journal of Management Studies 58(5): 1159–97. DOI: 10.1111/joms.12639. Helfat CE and Raubitschek RS (2018) Dynamic and integrative capabilities for profiting from innovation in digital platform-based ecosystems. Research Policy 47(8): 1391–9. Hess T, Matt C, Benlian A, et al. (2016) Options for formulating a digital transformation strategy. MIS Quarterly Executive 15(2). Hurni T, Huber TL, Dibbern J, et al. (2021) Complementor dedication in platform ecosystems: rule adequacy and the moderating role of flexible and benevolent practices. European Journal of Information Systems 30(3): 237–60. DOI: 10.1080/0960085X.2020.1779621. Jacobides MG, Cennamo C and Gawer A (2018) Towards a theory of ecosystems. Strategic Management Journal 39(8): 2255–76. DOI: 10.1002/smj.2904. Jantunen A, Ellonen H-K and Johansson A (2012) Beyond appearances – Do dynamic capabilities of innovative firms actually differ? European Management Journal 30(2): 141–55. DOI: 10.1016/j. emj.2011.10.005. Kapoor R (2018) Ecosystems: broadening the locus of value creation. Journal of Organization Design 7(1): 12. DOI: 10.1186/s41469-018-0035-4. Kapoor R and Agarwal S (2017) Sustaining superior performance in business ecosystems: evidence from application software developers in the iOS and Android smartphone ecosystems. Organization Science 28(3): 531–51. DOI: 10.1287/orsc.2017.1122.

104  Handbook on digital platforms and business ecosystems in manufacturing Khan O, Daddi T and Iraldo F (2020) Microfoundations of dynamic capabilities: Insights from circular economy business cases. Business Strategy and the Environment 29(3): 1479–93. DOI: 10.1002/ bse.2447. Khanagha S, Ansari S (Shaz), Paroutis S, et al. (2022) Mutualism and the dynamics of new platform creation: A study of Cisco and fog computing. Strategic Management Journal 43(3). John Wiley & Sons, Ltd: 476–506. DOI: 10.1002/smj.3147. Kretschmer T, Leiponen A, Schilling M, et al. (2022) Platform ecosystems as meta‐organizations: Implications for platform strategies. Strategic Management Journal 43(3): 405–24. DOI: 10.1002/ smj.3250. Kunz RE, Zabel C and Telkmann V (2022) Content-, system-, and hardware-related effects on the experience of flow in VR gaming. Journal of Media Economics 34(4): 213–42. DOI: 10.1080/08997764.2022.2149159. Lager T (2017) A conceptual framework for platform-based design of non-assembled products. Technovation 68: 20–34. DOI: 10.1016/j.technovation.2017.09.002. Lee L-H, Braud T, Zhou P, et al. (2021) All one needs to know about metaverse: A complete survey on technological singularity, virtual ecosystem, and research agenda. arXiv preprint arXiv:2110.05352. Lenkenhoff K, Wilkens U, Zheng M, et al. (2018) Key challenges of digital business ecosystem development and how to cope with them. Procedia Cirp 73: 167–72. Linde L, Sjödin D, Parida V, et al. (2021) Dynamic capabilities for ecosystem orchestration A capability-based framework for smart city innovation initiatives. Technological Forecasting and Social Change 166: 120614. Maijanen P (2022) Digital business ecosystems and dynamic capabilities. In: Handbook on Digital Business Ecosystems. Edward Elgar Publishing. McLaughlin SA (2017) Dynamic capabilities: taking an emerging technology perspective. International Journal of Manufacturing Technology and Management 31(1–3): 62–81. Mikalef P and Pateli AG (2016) Developing and validating a measurement instrument of IT-enabled dynamic capabilities. In: ECIS, 2016. Miller CD and Toh PK (2022) Complementary components and returns from coordination within ecosystems via standard setting. Strategic Management Journal 43(3): 627–62. DOI: 10.1002/smj.3143. O’Mahony S and Karp R (2022) From proprietary to collective governance: How do platform participation strategies evolve? Strategic Management Journal 43(3): 530–62. DOI: 10.1002/smj.3150. Parida V, Oghazi P and Cedergren S (2016) A study of how ICT capabilities can influence dynamic capabilities. Journal of Enterprise Information Management 29(2). Emerald Group Publishing Limited: 179–201. DOI: 10.1108/JEIM-07-2012-0039. Park S-M and Kim Y-G (2022) A Metaverse: taxonomy, components, applications, and open challenges. IEEE Access. Plattfaut R, Niehaves B, Voigt M, et al. (2015) Service innovation performance and information technology: An empirical analysis from the dynamic capability perspective. International Journal of Innovation Management 19(04): 1550038. Schoemaker PJ, Heaton S and Teece D (2018) Innovation, dynamic capabilities, and leadership. California Management Review 61(1): 15–42. Selander L, Henfridsson O and Svahn F (2013) Capability Search and Redeem across Digital Ecosystems. Journal of Information Technology 28(3): 183–97. DOI: 10.1057/jit.2013.14. Shipilov A and Gawer A (2020) Integrating research on interorganizational networks and ecosystems. Academy of Management Annals 14(1). Academy of Management: 92–121. DOI: 10.5465/ annals.2018.0121. Sjödin D (2019) Knowledge processing and ecosystem co-creation for process innovation: Managing joint knowledge processing in process innovation projects. International Entrepreneurship and Management Journal 15(1): 135–62. DOI: 10.1007/s11365-018-0550-3. Sjödin D, Parida V, Kohtamäki M, et al. (2020) An agile co-creation process for digital servitization: A micro-service innovation approach. Journal of Business Research 112: 478–91. DOI: 10.1016/j. jbusres.2020.01.009. Suominen A, Seppänen M and Dedehayir O (2019) A bibliometric review on innovation systems and ecosystems: a research agenda. European Journal of Innovation Management 22(2): 335–60. DOI: 10.1108/EJIM-12-2017-0188.

Partnership scouting routines of non-focal B2B firms  105 Suuronen S, Ukko J, Eskola R, et al. (2022) A systematic literature review for digital business ecosystems in the manufacturing industry: Prerequisites, challenges, and benefits. CIRP Journal of Manufacturing Science and Technology 37: 414–26. DOI: 10.1016/j.cirpj.2022.02.016. Tan C, Dhakal S and Ghale B (2020) Conceptualising capabilities and value co-creation in a digital business ecosystem (DBE): a systematic literature review. Journal of Information Systems Engineering and Management 5. DOI: 10.29333/jisem/7826. Tavalaei MM and Cennamo C (2021) In search of complementarities within and across platform ecosystems: Complementors’ relative standing and performance in mobile apps ecosystems. Long Range Planning 54(5): 101994. DOI: 10.1016/j.lrp.2020.101994. Teece DJ (2007) Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic management journal 28(13): 1319–50. Teece DJ (2018) Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world. Research Policy 47(8): 1367–87. DOI: 10.1016/j. respol.2017.01.015. Vial G (2019) Understanding digital transformation: A review and a research agenda. The journal of strategic information systems 28(2): 118–44. Wareham J, Fox PB and Cano Giner JL (2014) Technology ecosystem governance. Organization Science 25(4): 1195–215. DOI: 10.1287/orsc.2014.0895. Woo H, Kim SJ and Wang H (2021) Understanding the role of service innovation behavior on business customer performance and loyalty. Industrial Marketing Management 93: 41–51. DOI: 10.1016/j. indmarman.2020.12.011. Zabel C and Telkmann V (2021) The adoption of emerging technology-driven media innovations. A comparative study of the introduction of virtual and augmented reality in the media and manufacturing industries. Journal of Media Business Studies 18(4): 235–66. DOI: 10.1080/16522354.2020.1839172. Zabel C and Telkmann V (2022) The role of location for non-focal firms in emerging digital business ecosystems. Relevance of agglomeration factors in the German XR industry. In: Baumann S (ed.) Handbook on Digital Business Ecosystems: Strategies, Platforms, Technologies, Governance and Societal Challenges. Research Handbooks in Business and Management. Cheltenham: EDWARD ELGAR, pp. 250–68. Zabel C, Heisenberg G and O’Brien D (2022) Extended / Cross Reality (XR) in Deutschland 2022. Metaverse, digitale Ökosysteme & Entwicklung der XR-Branche. Köln. Zabel C, O’Brien D and Natzel J (2023) Sensing the metaverse: The microfoundations of complementor firms’ dynamic sensing capabilities in emerging-technology ecosystems. Technological Forecasting and Social Change 192: 122562. DOI: 10.1016/j.techfore.2023.122562. Zahra SA, Sapienza HJ and Davidsson P (2006) Entrepreneurship and dynamic capabilities: a review, model and research agenda. Journal of Management studies 43(4): 917–55. Zapadka P, Hanelt A and Firk S (2022) Digital at the edge – antecedents and performance effects of boundary resource deployment. The Journal of Strategic Information Systems 31(1): 101708. DOI: 10.1016/j.jsis.2022.101708. Zhang Y, Li J and Tong TW (2022) Platform governance matters: How platform gatekeeping affects knowledge sharing among complementors. Strategic Management Journal 43(3): 599–626. DOI: 10.1002/smj.3191.

8. Designing innovation ecosystems with functional roles: the case of industrial, intelligent manufacturing Julius Kirschbaum, Tim Posselt and Kathrin M. Möslein

INTRODUCTION Utilizing digital and especially data-driven technologies in manufacturing often requires organizations to acquire new sets of resources and capabilities that differ from those traditionally used within manufacturing processes (e.g. De Carolis et al., 2017). On the resource side, organizations extend their physical assets like machinery and tools required for manufacturing with digital infrastructures such as IT networks, sensors, edge devices and servers (Henfridsson and Bygstad, 2013). On the capability side, traditional manufacturing capabilities have to be complemented by digital ones (Nambisan et al., 2017), for example data-management or human computer interaction (Barenfanger and Otto, 2015). In particular data-driven information system technologies, such as artificial intelligence (AI) create new requirements for organizations. They need to be able to handle large amounts of data (Mikalef et al., 2020), integrate advanced algorithms such as machine learning (Krakowski et al., 2022), have access to the required IT infrastructure (Jöhnk et al., 2021) and combine these requirements with their domain expertise (Agrawal et al., 2019). Neither resources nor capabilities have to be available in-house. A common approach of industrial organizations is the joint development of data-driven solutions, which provides organizations access to such resources and capabilities that are scarcely available within their departments (Obradović et al., 2021). In industrial manufacturing, the successful integration of other actors is a challenge because all actors contributing to a solution have to mutually agree on their allocation of resources and capabilities (Adner, 2017). This is often the case for data-driven use-cases, such as those in the context of AI (Aquilani et al., 2020), which enable intelligent manufacturing. Use-cases are understood as application scenarios of sets of technologies for a certain context that have the goal to provide value to a target group, either by solving a problem or exploiting an opportunity (Kirschbaum et al., 2022). This may be in the form of new products, services, or as internal process optimizations. Imagine an audio device producer that aims to predict the quality of its headphones. The value proposition of such a use-case is to provide the ability to decide whether an intermediate product should be further processed or scrapped. To fulfill the requirements for such a use-case, producers can partner with other organizations, or integrate the respective resources and capabilities as a service. Both options create different types of interdependencies between the organizations (Jacobides and Billinger, 2006; Mudambi and Tallman, 2010; Badr et al., 2018). As a result, they have to assess which organizations can contribute the required resources and capabilities to conduct the activities needed to develop and implement a use-case. The individual consortia of organizations that form around such use-cases are referred to as industrial innovation ecosystems (IE) (Rao and Jimenez, 2011; Ikävalko et al., 2018; Granstrand and Holgersson, 2020). This is illustrated in Figure 8.1. 106

Designing innovation ecosystems with functional roles  107

Figure 8.1

Innovation ecosystem visualization (own visualization)

IEs succeed if organizations align themselves with each other (Visscher et al., 2021) by reaching mutual agreement on the allocation of resources and capabilities (Adner, 2017), as well as on their value contribution and capture (Talmar et al., 2020). The positions and roles of each actor and the relations between them are referred to by Adner (2017) as the alignment structure of an IE. Creating this structure is one core challenge of organizations engaging in IE (Adner, 2017). While recent research has aimed to describe this challenge for industrial organizations, little research has developed approaches to support them (Akter et al., 2016; Pappas et al., 2018; Visscher et al., 2021; Piller et al., 2022). Hence, the purpose of this study is to investigate how organizations can create successful alignment structures for IE around use-cases in the context of industrial, intelligent manufacturing (IIM). To do this, three different types of platforms are investigated, which are used by industrial organizations to gain access to scarce resources and capabilities (Bogers et al., 2017; Eckhardt et al., 2018; Jingyao et al., 2021). We introduce a new logic for ecosystem design, which suggests that instead of matching individual organizations – i.e. actors – to the requirements of a use-case, organizations should first define a set of roles that is useful for a particular use-case. Organizations that possess suitable competencies can then be matched to those roles (cf. Figure 8.1). This is useful to communicate and align the purposes and tasks of each

108  Handbook on digital platforms and business ecosystems in manufacturing member of an ecosystem. We conceptualize an initial set of archetypical roles that can be used to create alignment structures, which support organizations with the allocation of resources and capabilities (Adner, 2017; Gawer, 2014). The roles are archetypical for the domain of IIM, as in any particular instance the exact characteristics may vary according to the individualities of the organizations. While individual resource and task responsibilities may be adapted, the fundamental purpose of a role stays the same. Defining such roles is useful because they enable the identification of dependencies between actors (Adner and Feiler, 2019; Sjödin, et al. 2022) and are used to resolve bottlenecks, as well as resource and capability gaps within an ecosystem (Adner and Kapoor, 2010). Additionally, the set of archetypical roles conceptualized in this study allows an exploration of new ecosystem designs by adding or removing roles or changing the organizations that fulfill them. Thereby, organizations can erase unwanted dependencies or create them deliberately, which allows for better strategic decision-making and positioning in ecosystem constellations (Adner and Kapoor, 2010). Moreover, not all IIM use-cases have the same requirements and therefore require different sets of roles (Lusch and Nambisan, 2015). Consequently, roles can be grouped and specified to create templates/blueprints for individual use-cases (e.g. Ikävalko et al. 2018). For instance, referring back to the abovementioned predictive quality use-case for headphones, the producer and its partners can use such templates to ensure that they collectively fulfill those roles defined as relevant for their use-case and agree on who fulfills which role. Thus, this study aims to conceptualize an initial set of roles that are described by archetypical characteristics that can afterward be specified for individual use-cases. Platforms are used as a research subject for this purpose because they involve diverse sets of actors in different roles (Jingyao et al., 2021) and are often part of industrial use-cases, in which different platform types fulfill different use-case requirements (Ikävalko et al. 2018; Stichweh et al. 2021). We address the research question: Through which archetypical roles can organizations align themselves in industrial IE? The following section further elaborates on the role of platforms and ecosystems in IIM contexts. Afterward, the research design is described and the results are presented and discussed. The chapter concludes with its practical implications and contributions to the academic literature, as well as a section on the limitations and ideas for future research.

LITERATURE REVIEW Conceptualizing archetypical roles on a variety of platform types is useful because organizations have different views and ideas of the roles they play depending on the platforms they operate on (Dedehayir et al., 2018). Technological platforms are understood as building blocks that form the ‘[…] foundation upon which an array of firms (sometimes called a business ecosystem) can develop complementary products, technologies or services’ (Gawer, 2009a, p. 45). Such a platform enables industrial organizations to implement use-cases of the Industrial Internet of Things (IIoT) (Gerrikagoitia et al., 2019). One benefit is that they ensure interoperability of different technological systems, such as edge devices, enterprise resource planning (ERP) systems, industrial robots or digital services (Gawer, 2009b, p. 137f). Marketplaces are a type of platform that facilitates the transaction between a supply- and demand-side (Täuscher and Laudien, 2018), for example for datasets or AI services. They enable organizations to find appropriate resources, solutions and partners, for example, based

Designing innovation ecosystems with functional roles  109 on the requirements for a use-case. Lastly, innovation labs as a particular type of innovation platform (Greve et al., 2020) can incorporate technological platforms that provide ‘[…] a base technology and distribution system to which other companies can add their own innovations, increasing the value for the system as a whole’ (Teece, 2018, p. 10). Simultaneously, they are enablers of innovation processes because platform-based innovation strategies create ‘[…] opportunities for new ventures that involve developing products and services that complement the platform’ (Bogers et al., 2017, p. 15). In industrial settings, organizations conduct experiments and proof-of-concept studies in innovation labs to benefit from diverse and specialized resources and capabilities available in these labs (Jacobides and Billinger, 2006). Platforms and Ecosystems in Industrial, Intelligent Manufacturing In this study, the focus is set on such phenomena, in which multiple organizations jointly aim to develop a use-case for a set of technologies to address a need of a target group; in other words, a group of organizational actors that combine and complement their resources and capabilities to solve a particular problem. This may result in a new product or service offering, but can also lead to process optimizations. For instance, the goal of a use-case may be to minimize machine power consumption (Kant and Sangwan, 2014), to predict product quality or to enable new maintenance services (Demlehner et al., 2021). While the term use-case is widely used in practice (e.g. Chui et al., 2018) and academia (e.g. Demlehner et al., 2021) to describe such initiatives of organizations, there exist different understandings and conceptualizations. In this research, use-cases are understood as application scenarios of sets of technologies for a given context (Kirschbaum et al., 2022). Organizations combine these technologies in such a way that they solve a problem in a better or novel way, which often involves multiple interdependent actors contributing their resources and capabilities. Use-cases have a value proposition at their center that represents the value for a customer or user (Osterwalder et al., 2014). One of the value propositions of predicting product quality, for example, is that intermediary products with defects or of insufficient quality can be discarded at an early production stage, preventing unnecessary processing. The set of organizations coming together to combine and complement their resources and capabilities in a value proposition is referred to as an IE (e.g. Adner 2017; Jacobides et al. 2018). The integration of the use-case and IE concept is illustrated in Figure 8.1. Based on Adner and Kapoor (2010), we use the IE concept to describe phenomena in which multiple organizational actors enter a state of interdependence because they aim to jointly create a solution, i.e. a use-case. Adner (2017) refers to this value proposition-centric conceptualization of IE as ‘ecosystems-as-structure’. This is in line with Jacobides et al. (2018) and others (e.g. Cobben et al., 2022), who identify a similar stream of research that conceptualizes IE around a certain innovation or value proposition. Additionally, we follow the view of Visnjic et al. (2016) and others (e.g. Rehm et al., 2017; Walrave et al., 2018) by adopting an ecosystem of ecosystems logic. That is, organizations can be part of multiple IE and may fulfill the same or different roles within them. In an IIM context this multilayered ecosystems perspective is useful because organizations operate on different types of platforms and cooperate with different actors on different use-cases, which makes them members of a variety of IE (Walrave et al., 2018). For example, IIoT platforms are a widely used approach to provide organizations access to digital services and to ensure interoperability (Gawer 2009b, p. 137f). They enable organizations to implement multiple use-cases with the same platform. While such an

110  Handbook on digital platforms and business ecosystems in manufacturing organization may use an IIoT platform to implement AI technologies, for example to optimize their energy consumption, it may use the same datasets to sell them on a data marketplace. On the marketplace platform, the organization has a different role than on the IIoT platform. This illustrates that conceptualizing archetypical roles on a variety of platform types is useful because organizations have different views and ideas of the roles they play depending on the platforms they operate on (Dedehayir et al., 2018). Our framework enables the investigation of IE in which multiple organizations face the challenge to organize themselves in a variety of different roles (Mäkinen and Dedehayir, 2012). The following section focuses on how organizations can design IE (e.g. Pidun et al., 2020; Guggenberger et al., 2021; Jacobides and Billinger, 2006), i.e. how they can combine resources and activities effectively by using archetypical functional roles to create alignment structures. Structuring and Designing Ecosystems with Functional Roles Designing an ecosystem is about modeling the structure of the ecosystem (Gawer, 2014; Adner, 2017), the alignment between actors (Adner, 2017; Visscher et al., 2021) and identifying interdependencies (Adner and Feiler, 2019; Sjödin et al., 2022). Structural models and design principles for platforms and ecosystems (e.g. for IIoT platforms (Lockl et al., 2020), for data marketplaces (Abbas, 2021) or for innovation platforms (Michalke, 2022)) are viewed as a basis for design and modeling methods (e.g. Talmar et al., 2020). The result of these methods is instantiations of an ecosystem model, which can be used to analyze and explore strategies and business models (Visscher et al., 2021) or to rearrange actors’ positions and roles (Moerchel et al., 2020). Different models, however, have different structural elements and are made for different situations/phenomena. For example, two different approaches are ecosystem network analysis, which is a quantitative approach for analyzing large networks in business ecosystems (Basole, 2009; Basole and Karla, 2011), and what we call value-driven modeling, which is a qualitative approach for analyzing and modeling the affiliations of individual organizations in ecosystem constellations (e.g. Moerchel et al., 2020; Talmar et al., 2020). Most research on platform and ecosystem modeling includes organizational entities and links/interaction mechanisms between them as structural elements (e.g. Basole, 2009; Adner and Kapoor, 2010). Additionally, some authors, like Adner (2017) or Shipilov and Gawer (2020), have suggested that the position an organization has within an ecosystem is another useful structural element. Positions specify organizations’ ‘[…] locations in the flow of activities across the system’ (Adner, 2017, p. 44). While positions and roles are considered useful elements to organize multilateral business constellations like ecosystems (Shipilov and Gawer, 2020), there exist various types and conceptualizations. In the reviewed literature, we find a bipartite notion of these roles and positions. One group considers what we call strategic roles, which are mainly discussed by business ecosystem researchers (e.g. Iansiti and Levien, 2004; Kay et al., 2018; Mei et al., 2019). Iansiti and Levien (2004), for instance, identify a dominator-, keystone- and niche-player-role that can be used to describe an organization’s position within an ecosystem structure. Teece (2014) finds similar roles, such as an ecosystem captain and an ecosystem initiator role. The common characteristic of such strategic roles is that any organizational member in an ecosystem may aim to fulfill any of these roles, as the choice is strategic. They define the strategic positioning of an organization, independent of their operational tasks and activities (Alam et al., 2022).

Designing innovation ecosystems with functional roles  111 A second group of authors considers what we call functional roles (e.g. Rong and Shi, 2015, p. 172/240; Kress et al., 2016). Functional roles are, for example, identified by Dedehayir et al. (2018), who describe a supplier-, assembler-, complementor- and a user-role. Similarly, Adner and Kapoor (2010) structure their ecosystems through a focal firm, suppliers (component providers), complementors and customers. Such roles are defined by the types of contributions organizations make. Consequently, functional roles focus less on strategic alignment and more on value alignment (Boha, 2021). However, this kind of functional role is generic and static, as they can be applied to any arbitrary IE. For example, in a car manufacturing value network, an organization has few options to change its generic function from that of a supplier to that of an original equipment manufacturer (OEM). We argue that IE and the roles useful for their design are context dependent. For instance, the functional roles in an industrial manufacturing context differ from the roles in a software development context or an agricultural one. This is because different contexts require different resources and capabilities and have different value contributions (Talmar et al., 2020), which can be used to define archetypical roles for that context. For example, a farmer role or a groceries vendor role make sense in an intelligent farming or food logistics context but are not meaningful for use-cases in IIM. It is often the case, however, that different contexts mix. For instance, in the previously mentioned intelligent farming and intelligent manufacturing cases, ecosystems in both contexts utilize technologies like AI (De Carolis et al., 2017). Consequently, roles such as software developers and data scientists are relevant for use-cases in both contexts. Some research has acknowledged these aspects and started to investigate functional roles for specific contexts. For instance, Adner and Kapoor (2010), as well as Peltola et al. (2016) identify domain-specific functional roles in the lithography and mobile industry, respectively. However, in none of the identified papers did the authors use particular characteristics to conceptualize domain-specific functional roles. Therefore, our aim is to complement existing research on ecosystem design by shifting the focus from strategic and generic functional roles to domain-specific functional roles that enable organizations to design their IE in IIM contexts. Specifically, we aim to systematically describe these roles by applying the ecosystem model by Talmar et al. (2020), who characterize actors in an IE through (A) their resource contributions, (B) their conducted activities, (C) their resulting value contributions, (D) the value capture they make, (E) the position of an actor in the value network, (F) the links between them and the (G) interdependencies among the actors. While the approach by Talmar et al. is originally designed to model actors/organizations, in this research it is used to model roles that are fulfilled by these actors (cf. Figure 8.1).

RESEARCH DESIGN For this research, a multiple case study according to Eisenhardt (1989) is conducted via an action research approach (Baskerville and Myers, 2004) combining the results of value proposition modeling tools and workshops (Thoring et al., 2020). Prior to the workshops, the three platforms’ value propositions were developed together with the participants based on ‘The digitization-enabled Value Proposition (VdieP-) developer’ canvas from Genennig et al. (2018) and Geoff Moore’s (Moore 2014, p. 186) value proposition template (cf. Table 8.1). This was done asynchronously, where each participant was providing his/her thoughts and ideas separately. Afterward, participants were reviewing each other’s inputs, which were then reworked

112  Handbook on digital platforms and business ecosystems in manufacturing Table 8.1

Value proposition template for each case (based on (Moore 2014, p. 186))

 

Case1: IIoT Platform

Case2: Data Marketplace

Case3: Industrial Innovation Lab

For:

industrial manufacturing

intelligent manufacturing

organizations from the field of

organizations

organizations

industrial manufacturing

utilize data to improve their

want to monetize their datasets or

aim to develop data-driven use-cases

industrial manufacturing processes

want to obtain external datasets

Our:

IIoT platform

data marketplace

Which is:

an open-source platform

a technological platform

an innovation platform

That:

provides the infrastructure for the

provides the technological

provides access to an experimentation

interoperability of machines and

and legal basis for concluding

and evaluation environment for

other devices (e.g. edge devices) in

contracts, in which datasets are

proof-of-concept studies with

the context of IIM

sold and bought

professional support

Who:

industrial innovation lab

by each participant. At the start of each workshop the formulations for the platforms’ value propositions were finalized synchronously based on the individual input of each participant. This initial discussion to unify the participants’ views ensured that all participants had the same understanding of the purpose and value provided by the respective platform. For each of the three cases, a four-hour workshop was held with the development team of the respective platform. The first case is an IIoT platform (Pauli et al., 2020), the second an industrial data marketplace platform (Stahl et al., 2016) and the third a manufacturing innovation lab (Wolpert and Mengual, 2020), which serves as a platform for data-driven experimentation for AI in manufacturing. The methodology is based on the ecosystem model provided by Talmar et al. (2020). The following steps were taken during the workshops: (1) finalization of the platform value proposition, (2) expectation on the workshop of each participant, (3) introduction to the methodology, (4) conceptualization of up-stream and down-stream roles/ positions, as well as demand- and supply-side roles, (5) conceptualization of additional roles, (6) identification of interdependencies between roles, and (7) collection of feedback on the workshop. Steps (4)–(6) were conducted iteratively. Each phase provided room for each expert to bring in their opinions and views, which were discussed in the complete group. A whiteboard tool was used to document the results of the virtually held workshops. A case study approach was chosen because there exist different types of platforms within the IIM context and each covers different intelligent manufacturing roles. For example, the resources and activities in an IIoT setting (e.g. Guggenberger et al., 2021) differ from those in data economy (e.g. Otto and Aier, 2013) and industrial innovation labs (van der Meer et al., 2021). Hence, we expected to obtain different archetypical roles from each of the cases that complement each other. Action research with a workshop format was chosen because applying the ecosystem modeling method by Talmar et al. (2020) to the three platform types required the participants to first understand the modeling approach and then to interactively conceptualize the functional roles. This put us in need of a moderation of the modeling process during the workshops. The approach enabled participants to actively engage with each other and to complement their individual views. Participants’ positions and backgrounds are described in Table 8.2.

Designing innovation ecosystems with functional roles  113 Table 8.2

Description of participants

 

Case1: IIoT Platform

Case2: Data Marketplace

Case3: Industrial Innovation

P1

Position: Head of Innovation

Position: Lawyer

Position: Research Associate

Field: Industry 4.0

Field: Legal Informatics

Field: Industrial Process Control

Position: Postdoctoral Research

Position: Research Associate

Position: Research Associate

Associate

Field: Data Economy

Field: Numerical Control

Lab

P2

Simulation

Field: Data Science P3 P4

Table 8.3

Position: Research Associate

Position: Software Engineer

Position: Product Owner

Field: Informatics

Field: Data Marketplaces

Field: Industrial Processes

Position: Innovation Manager

Position: Innovation Engineer Field:

 

Field: Digital Business Models

Data Economy

Archetypical, domain-specific functional roles for different platform types

 

Case1: IIoT Platform

Case2: Data Marketplace

Case3: Industrial Innovation Lab

R1

Machine Operator

Data Provider (Supply)

Innovation Lab Operator

R2

Machine Manufacturer

Data User (Demand)

Facilities and Technology Provider

R3

Machine Component Provider

Marketplace Provider

IIoT and AI Expert

R4

System Integrator

Platform Operator

Researcher

R5

Platform Operator

Platform Developer

Intermediary Organization

R6

Incremental Service Provider

Validator for Data Rights and

Hardware and Software Provider

Licenses R7

Full Application Provider

Onboarding Consultant

Machine Operator

R8

 

Escrow Agent

 

R9

 

Appraiser for Certification

 

ECOSYSTEM ROLES FOR INDUSTRIAL INTELLIGENT MANUFACTURING From each case in this study, a set of archetypical roles is conceptualized. In the following section, these roles are presented. In addition, the different identified ecosystem designs, which all follow the architectural model shown in Figure 8.1, are discussed. Due to the amount of data collected in this study, results are provided in an interactive tool that allows readers to explore the functional roles in IIM IE (Link: https://​roletool​.ecosystem​-tools​.de/​Ecosystem​ -Designer​.html). Case1: IIoT Platform From the IIoT case, seven archetypical roles were conceptualized. During the workshop, participants were using representative use-case examples to illustrate individual IE, as they would form on the platform (cf. Figure 8.1). The initially created up- and down-stream roles were a Software Solution Provider role, as well as a Machine Provider and Machine Operator role. Start-ups, service providers as well as incumbent organizations that develop IT solutions for IIM were considered typical organization in a Software Solution Provider role. Machine Providers and Machine Operators were viewed as a traditional duet of organization that are highly dependent on each other due to

114  Handbook on digital platforms and business ecosystems in manufacturing the high investment costs for machines and the after-sales services that bind seller and buyer together. In the first round of discussion, participants suggested splitting the Machine Provider’s role into a Machine Manufacturer and Machine Component Provider role. Machine Manufacturers design and develop machines, for which they use individual components of Component Providers, including sensors, edge devices and IIoT interfaces. Both roles are viewed as main resource contributors, as both aim to enable their machines/components for IIoT use-cases. To adapt their products to meet requirements for such use-cases, Machine Manufacturers, as well as the Component Providers, often work in tandem. For example, the data produced by a component during manufacturing is valuable for the Component Provider, but to gain access to that data, Component Providers have to be aligned with the Machine Manufacturer on a contractual basis (Lee, 2019). This includes, for example, standardization for interfaces but also the adaption to IT solutions offered in the IIoT sector. Machine Operators, as the most down-stream role, are the actual users of a machine and provide most of the data utilized in IIoT use-cases. They compare different machines/components and adapt their IIM infrastructure accordingly. Machine Manufacturers are chosen with great care as partners because machine investments are expensive and not easily changed afterward. The same accounts for IIoT platforms. Once committed to a platform, reverting back or changing to another is difficult because of the integration efforts (Minchala et al., 2020). Machine Operators, therefore, collaborate with Machine Manufacturers and platform developers to ensure maximum impact across their productions. Because the IIoT platform in this case was built as an open-source technology platform, it is often implemented by manufacturing organizations themselves, without the need of an external organization hosting the platform. Hence, these organizations are not only Machine Operators but also Platform Operators. This is important because other IIoT platforms are operated by the platform providers, for example in the case of Siemens’ IIoT platform MindSphere (Sauer et al., 2020). Machine Operators are often the initiator of new IE because they aim to implement IIM use-cases. There are various means by which a Machine Operator identifies new use-cases, for example because of market screening, through their network, or by their own research (Brunnbauer et al., 2021). Because there are various individual solutions used within IIoT use-cases, another distinction was made between the Incremental Service Provider and the Full Application Provider that replaced the Software Solution Provider role. Participants argued that developing a full application that solves a business problem is different than programming incremental IT services that fulfill small technical tasks. For example, a predictive maintenance solution was viewed as a full application that can be deployed on a manufacturing system or individual machines. An incremental service, on the other hand, was conceptualized as a small solution that may change data from one format to another or cleans a dataset from incomplete entries. Full applications thereby consist of multiple of these incremental services and may be offered as a stand-alone offering by a developer. Machine Operators usually assess to what degree a solution fulfills their requirements. When designing their ecosystem, organizations select Machine Manufacturers and Component Providers that are already technologically aligned with service and application providers. They often collaborate with multiple to ensure the fulfillment of the use-case requirements. This is important because plug-and-play solutions are rare in an IIM context (Banerjee et al., 2021; Görzig et al., 2019); i.e. there is always some degree of individualization required, where some incremental services are adapted or exchanged for others. Manufacturing organizations are

Designing innovation ecosystems with functional roles  115 often required to combine applications and services in a useful way and can even develop their own solutions. Those manufacturing organizations are not only in a Machine Operator role, but also in a Service/Application Provider role. The last differentiation that was discussed during the workshop was to conceptualize the System Integrator in a separate role. System Integrators mediate between Machine Operators and the Service/Application Providers. They are conceptualized as points of contact that support the deployment of software and hardware solutions into processes (Pauli et al., 2020). The motivation for this was the need for a role that combines the expertise with individual manufacturing machines and also with the applications and services that should be deployed. The separation is useful because not all manufacturers that implement IIoT use-cases have employees with this expertise. In some cases, this role is fulfilled by the same organization that is also contributing the core application. In other cases, the System Integrator role is fulfilled by the Machine Manufacturer or even a third party specialized for deploying solution on certain types of machines. Hence, it is useful for ecosystem analysis and modeling to separate this role. Several ecosystem design possibilities can be explored by modeling these possibilities based on the archetypical roles conceptualized in the IIoT case. This also enables organizations to identify new business model opportunities that are available to organizations in such ecosystems (Hodapp et al., 2019; Kohtamäki et al., 2019). For example, a Machine Manufacturer may contribute the expertise about their machines in several AI-use-cases. Over time, they can obtain the know-how about deploying AI solutions, which enables them to take a System Integrator role, as well, opening new value capture possibilities. Additionally, participants discussed possibilities for organizations in a Machine Operator role to host their own IIoT platforms (Stichweh et al., 2021) vs. acquiring an external IIoT platform (e.g. Sauer et al., 2020). Both situations can be modelled with the current set of roles because the Platform Operator role is separate and can be taken by a variety of organizations. For example, large corporate enterprises like Siemens and GE can be in any of the roles conceptualized in this case, but are rarely fulfilling them all in one particular IE. Other, smaller organizations cannot take several roles, but may offer their own platform (Platform Operator) along with native applications and services (Service/Application Provider) that can be used by any platform user. Roles that cannot be fulfilled by the organization providing the platform would then need to be fulfilled by others. Case2: Data Marketplace Nine archetypical roles were conceptualized in the data marketplace case. In the same manner in which illustrative scenarios were used in the workshop on the IIoT platform, similar exemplifications of concrete use-cases were considered by participants of the marketplace workshop. An important characteristic of the marketplace in our study is that the datasets that are bought and sold are not exchanged through the technological platform underlying the marketplace. Only the meta-data about the datasets can be viewed and data users review these meta-data to decide from which data provider they want to acquire relevant data for their use-cases. The operator of the platform (Platform Operator) ensures the functionality and that the platform is working properly and according to contracts and laws. In this scenario, the Platform Operator role must not be with the same organization as the Marketplace Provider.

116  Handbook on digital platforms and business ecosystems in manufacturing The former is only responsible for the technological aspects of hosting the platform, while the latter is the business operator of the marketplace (Abbas et al., 2021). For example, one option is that an organization is hosting the technological platform underlying a data marketplace and offers the hosting as a service to various marketplace providers. in this way, the marketplace providers do not have to ensure the technological stability and security of the platform. Instead they use the platform technology to provide a marketplace, on which a data-demand and -supply side are brought together (Parker and Van Alstyne, 2005). Both are interdependent of the Platform Developer, who ensures that the platform is up to date and new functionalities are developed. For example, developers ensure that marketplace providers can define a ruleset that enables data providers to choose which groups can view the meta-data of a dataset. This is important to prevent competitors from viewing or even buying an organization’s datasets. The role is separate because not all Platform Developers host their own instantons of the platform, for instance if they are the result of a decentralized, community-based development (Chen et al. 2021) A main difference to Case1 were the contributions by the legal expert within the group, as they added an additional perspective. As a result, some debates during the workshop were focused on the activities and responsibilities related to legal topics for organizations utilizing data marketplaces. In particular, activities that concern the adherence to data laws/acts and those related to licensing and certification were discussed. Following this discussion, the group conceptualized three roles that can be related to legal aspects of data-driven use-cases, a Validator for Data Rights and Licenses, an Escrow Agent and an Appraiser for Certification role. The main contribution from an Escrow Agent is to overlook the actual data throughout the legal transaction by the data provider and data user. It was added as a stand-alone role because it can be fulfilled by a variety of organizations, which is ultimately up to the contracting partners to decide. The roles for Data Rights and License Validation, as well as the certification of marketplace members by the Appraiser were created because participants reported various examples of possible ecosystem designs. In some examples, chanceries were taking the Validator and or Certifier role. In other examples the Platform Operator itself was offering legal services to fulfill these roles. This shows that various constellations are possible and different value capture combinations can be created, depending on the business models of the organizations within the ecosystem. The best alignment can be achieved by exploring these different options. This discussion also led to the conceptualization of an Onboarding Consultant role. Participants argued that multiple organizations can be part of the development community that continuously debugs and improves the technological platform enabling new features on the marketplace. For instance, many industrial open-source platforms are community driven and the community consists of different types of organizations that rely on these platforms to different degrees (Oyekanlu, 2017). Consequently, using such open-source technologies may require data users to also take a (Co-)Developer role (Kim et al., 2019). The community has to ensure that there are means by which new data users and providers are acquired for the platform. A key role is to introduce new platform users to the functionality and means of operation. It is possible for various organizations to take over the onboarding activities for the platform. Participants reported that in some cases service providers and IT consultancies were taking part in the platform development and were offering onboarding services in addition to their consultancy services. In this scenario, these were the main channel through which new platform users were acquired and onboarded.

Designing innovation ecosystems with functional roles  117 Case3: Industrial Innovation Lab In the industrial innovation lab case seven archetypical roles were conceptualized. The first roles conceptualized were the Lab Operator, which provides an experimentation platform that brings together Machine Operator, as well as the Machine Manufacturers and the Solution Providers that develop hard and software products. One important note is that, because the investigated lab is embodied within a higher education institution (HEI), additional roles may be relevant for innovation labs that are embodied within industrial organizations (van der Meer et al., 2021). For example, the Researcher role was conceptualized as a source of methodological expertise and access to state-of-the-art scientific knowledge. Researchers are interested in engaging in innovation labs because they can collect data and find new partners for research projects. In return, they contribute the experience gained from those projects back to the lab. To complement the Researchers’ perspective, an IIoT/AI Expert role was conceptualized, which is contributing practical knowledge on intelligent manufacturing. Such experts have expertise on the requirements of individual use-cases, but also evaluate use-cases from a neutral point of view. Hence, to retain an objective view, the IIoT/AI Expert role should be fulfilled by a separate organization that is not contributing hard or software solutions. Running an industrial experimentation lab requires a physical component in the form of facilities and machines, as well as business components that ensures that new projects are acquired. In line with Case2, participants were discussing such a differentiation between the technological and business operation of an innovation lab. This was accredited for by distinguishing the Innovation Lab Operator and the Facilities and Technology Provider roles. While the former is covering the business operations of the lab, the latter is providing the infrastructure that is needed for the lab to be operational and attractive to Machine Operators, on the one side, but also to Hardware and Software Providers on the other. Hardware and Software Providers, such as start-ups, component providers and software developers come together with Machine Operators to explore and develop new use-cases in a testing environment at the lab. Hence, Lab Operators enable Machine Operators to explore use-cases without disruption to their ongoing production and provide the IIoT infrastructure that would require high initial investment costs from a manufacturer. Lab Operators can be paid by Machine Operator or by Hardware and Software Providers, which depends on the market power and needs of both groups. Different scenarios were considered by participants, who reported that they were involved in cases in which a university was in a Lab Operator role, but the technological infrastructure was provided by external organizations from the industry. In other cases, the HEI took the Lab Operator and Technology Provider roles simultaneously. We see this as further evidence that differentiating the technological and business operator role is useful for industrial IE because it allows organizations to construct more individualized ecosystem designs that better fit the requirements for the use-case. For instance, university-industry spin-offs are a common approach to bring new solutions to a market (Walter et al., 2011). These spin-offs are often owned by multiple organizations, including the university. Hence, universities and industrial partner organizations can split or even share the two operator roles in different ways. Participants also referred to a common case, in which machines are sponsored to the lab by machine manufacturers to enable the lab to conduct experiments on their machine. Start-ups were joining the lab to prototype their solutions on those machines, which created benefits for all parties. Start-ups could evaluate their solutions, the machine manufacturers got insights into the requirements for their machines, the producers of industrial goods could test applica-

118  Handbook on digital platforms and business ecosystems in manufacturing tion scenarios for the start-ups’ solutions and the Researchers got data for their publications. In such scenarios, it is not only possible to run a Lab Operator business model, but also to have a Facilities and Technology Provider that offers the required infrastructure as a service. In such situations, the roles conceptualized in this case support organizations in achieving alignment between all these partners and also to communicate the value provided by such labs to the respective target groups. This is further discussed in the following section. Designing Ecosystems with Functional Roles We see support for our initial argument that different types of platforms should be considered for obtaining a diverse set of archetypical roles. Intelligent manufacturing use-cases require a variety of roles, not necessarily found on only one platform type. For example, an industrial goods producer may implement an IIoT platform to fulfill interoperability and asset management requirements needed for the deployment of a predictive quality solution. Data requirements could be fulfilled by acquiring datasets through a data marketplace and integrating them through the IIoT platform. A first proof-of-concept study could be conducted in an innovation lab where an instantiation of the IIoT platform is implemented. In such a scenario, all three platform types fulfill some of the requirements needed to implement a predictive quality use-case. Moreover, the conceptualized roles are not tied to one of the platform cases. Use-case templates may be created by combining roles from all three cases. Assume a Machine Manufacturer offers an AI-based energy optimization solution, i.e. the electricity usage is optimized. This after-sales service is individualized for the manufacturing systems of each customer because the requirements depend on the infrastructure of the customer. Some of them may have an IIoT platform implemented in their production system, others don’t aim to implement these use-cases without hosting their own IIoT platform or by acquiring one as a service. Consequently, Platform Operator and Platform Developer roles are sometimes required. Additionally, because machines from different Machine Manufacturers use different standards and have a variety of individualities, a System Integrator role is needed to be able to deploy IIoT applications. If available internally, the Machine Manufacturers themselves can fulfill the System Integrator role. Such a scenario may additionally require some of the legal roles that were conceptualized in Case2, especially in cases in which data is utilized across multiple organizations. Depending on the different deployed solutions, further roles can be added to design the ecosystem of this scenario. The roles presented in this study enable organizations to keep track of the various roles they fulfill within their ecosystems, in particular for more complex use-cases that involve a variety of different contributors, as illustrated in the scenario above. This is further concluded in the following.

CONCLUSION Contributions to Academic Literature This study makes several contributions to the academic literature. Our approach presented arguments for conceptualizing organizational multi-actor constellations as IE that jointly develop value propositions in the form of use-cases. In such IE, actors agree on the resource

Designing innovation ecosystems with functional roles  119 contributions and activity responsibilities of each organization. The study shows that practitioners mentally construct roles for organization in IIM contexts, but they do this unsystematically and participants showed a heterogeneous understanding of these roles. This research has demonstrated that the approach taken is capable of distilling these mentally constructed roles, but further refinement has to be made to develop a validated mythological approach. Furthermore, results indicate that the alignment structure of IE can be achieved through the archetypical roles identified in this study. These roles are specified for the domain of IIM and help organizations develop a more homogeneous understanding of their role within an IE. Future research could investigate other domains to validate the usefulness of such roles for designing IE. Moreover, our research shows that different ecosystem designs can be modeled and explored by using functional roles. The initial set of roles conceptualized in this study can be used to create use-case-specific ecosystem templates that enable various ecosystem designs. Creating these templates in future research is useful because they contain relevant best practices for ecosystem design and enable organizations to adapt their strategic positioning in various ecosystems in which they participate. Practical Implications Functional roles for intelligent manufacturing enable industrial organizations to achieve mutual agreement on and improve the allocation of resources and capabilities contributed to a use-case. The collection of organizations contributing resources and activities to a use-case is referred to as an IE in this study, which corresponds to the value proposition-centric view adopted in Figure 8.1. Collectively, organizations have to fulfill the requirements of the intended use-case. The archetypical roles conceptualized in this study enable these organizations to align themselves in such a way that they can agree on individual contributions of resources and capabilities to fulfill specific requirements. Depending on the use-case, organizations can choose roles from our initially conceptualized set of roles that are needed to cover all requirements. This supports the positioning within a market, improves the efficient allocation of resources and offers the exploration of new business models. Organizations can define their use-case templates/blueprints that can be reused in similar scenarios, which improves the early development stages, because actors can evaluate if they collectively fulfill all roles defined by a use-case template. By using functional roles to create alignment in IE, organizations can identify resource and capability gaps and systematically close them. For instance, consider a consortium or organizations aiming to implement a predictive quality use-case. Assume they struggle with the deployment of the solution to the manufacturing system. This may be due to the lack of a System Integrator that has the expertise on the machines and applications. The initial creation of a role template for such a use-case can prevent this capability cap. In this way, the archetypical roles from this study can be used to model existing IE, or to create new ones. Creating IE templates for individual use-cases could be part of future research, which would enable the matching roles to concrete requirements.

120  Handbook on digital platforms and business ecosystems in manufacturing Limitations The approach taken in this study has limitations stemming from the qualitative nature of data collection and the drawbacks of action research. First, although three different platform types were selected to conceptualize the archetypical roles, they are most likely not enough for obtaining a sufficiently complete set of roles. Other types of marketplace platforms for data and AI applications may add additional insights. Similarly, different IIoT platforms or collaboration and innovation platforms could fulfill other requirements and therefore different roles would be conceptualized. Another aspect that was left out is secondary (passive) contributions, such as those contributed by the cloud, software providers and other infrastructure providers. Depending on the relevance for an ecosystem, it might make sense to include roles for such organizations because a use-case might depend on their contributions. Research on ecosystem boundaries could benefit from these design approaches for ecosystems (e.g. Tsujimoto et al., 2015). Another key limitation is the background of the participants in each workshop. As Case2 illustrated, having participants with diverse backgrounds, such as the legal sector, is highly relevant for constructing new roles. This can be accounted for in future applications of the roles for ecosystem design. We suggest future research to conceptualize new roles while modeling new ecosystems with the initial set of roles from this study.

REFERENCES Abbas, A. E. 2021. ‘Designing Data Governance Mechanisms for Data Marketplace Metaplatforms’, in 34th Bled EConference Digital Support from Crisis to Progressive Change: Conference Proceedings, University of Maribor Press, pp.  691–703. (https://​doi​.org/​10​.18690/​978​-961​-286​-485​-9​.49). Abbas, A. E. et al. 2021. ‘Business Data Sharing through Data Marketplaces: A Systematic Literature Review’, Journal of Theoretical and Applied Electronic Commerce Research (16:7), pp. 3321–39. (https://​doi​.org/​10​.3390/​jtaer16070180). Adner, R. 2017. ‘Ecosystem as Structure: An Actionable Construct for Strategy’, Journal of Management (43:1), pp.  39–58. (https://​doi​.org/​10​.1177/​0149206316678451). Adner, R., and Feiler, D. 2019. ‘Interdependence, Perception, and Investment Choices: An Experimental Approach to Decision Making in Innovation Ecosystems’, Organization Science (30:1), pp. 109–25. (https://​doi​.org/​10​.1287/​orsc​.2018​.1242). Adner, R., and Kapoor, R. 2010. ‘Value Creation in Innovation Ecosystems: How the Structure of Technological Interdependence Affects Firm Performance in New Technology Generations’, Strategic Management Journal (31:3), pp.  306–33. (https://​doi​.org/​10​.1002/​smj​.821). Agrawal, A. et al. 2019. The Economics of Artificial Intelligence, (1st ed.), Chicago and London: University of Chicago Press. (https://​doi​.org/​10​.7208/​chicago/​9780226613475​.001​.0001). Akter, S. et al. 2016. ‘How to Improve Firm Performance Using Big Data Analytics Capability and Business Strategy Alignment?’, International Journal of Production Economics (182), Elsevier, pp.  113–31. (https://​doi​.org/​10​.1016/​j​.ijpe​.2016​.08​.018). Alam, M. A. et al. 2022. ‘From Ego‐systems to Open Innovation Ecosystems: A Process Model of Inter‐firm Openness’, Journal of Product Innovation Management (39:2), pp. 177–201. (https://​doi​ .org/​10​.1111/​jpim​.12615). Aquilani, B. et al. 2020. ‘The Role of Open Innovation and Value Co-Creation in the Challenging Transition from Industry 4.0 to Society 5.0: Toward a Theoretical Framework’, Sustainability (12:21), p.  8943. (https://​doi​.org/​10​.3390/​su12218943). Badr, N. G. et al. 2018. ‘Exploring Service Science’, Lecture Notes in Business Information Processing (Vol. 331), Lecture Notes in Business Information Processing, (G. Satzger et al., eds.), Cham: Springer International Publishing. (https://​doi​.org/​10​.1007/​978​-3​-030​-00713​-3).

Designing innovation ecosystems with functional roles  121 Banerjee, I. et al. 2021. ‘Building a Tech-Services Ecosystem to Deliver Products – Not Applications’, Banerjee, Indraneel Bhatnagar, Abhi Elsner, Thomas Fuchs, Christoph Shirdade, Vikrant (July), McKinsey & Company. Barenfanger, R., and Otto, B. 2015. ‘Proposing a Capability Perspective on Digital Business Models’, in 2015 IEEE 17th Conference on Business Informatics (Vol. 1), IEEE, July, pp. 17–25. (https://​doi​.org/​ 10​.1109/​CBI​.2015​.18). Baskerville, R., and Myers, M. D. 2004. ‘Special Issue on Action Research in Information Systems: Making IS Research Relevant to Practice: Foreword’, MIS Quarterly (28:3), pp. 329–35. (https://​doi​ .org/​10​.2307/​25148642). Basole, R. C. 2009. ‘Visualization of Interfirm Relations in a Converging Mobile Ecosystem’, Journal of Information Technology (24:2), pp.  144–59. (https://​doi​.org/​10​.1057/​jit​.2008​.34). Basole, R. C., and Karla, J. 2011. ‘On the Evolution of Mobile Platform Ecosystem Structure and Strategy’, Business & Information Systems Engineering (3:5), pp.  313–22. (https://​doi​.org/​10​.1007/​ s12599​-011​-0174​-4). Bogers, M. et al. 2017. ‘The Open Innovation Research Landscape: Established Perspectives and Emerging Themes across Different Levels of Analysis’, Industry and Innovation (24:1), Routledge, pp.  8–40. (https://​doi​.org/​10​.1080/​13662716​.2016​.1240068). Boha, J. 2021. ‘Managing Value Propositions in Service Ecosystems’, Markt- Und Unternehmensentwicklung Markets and Organisations (MAU), Wiesbaden: Springer Fachmedien Wiesbaden. (https://​ link​ .springer​.com/​10​.1007/​978​-3​-658​-31713​-3). Brunnbauer, M. et al. 2021. ‘Idea-AI: Developing a Method for the Systematic Identification of AI Use Cases’, in Americas Conference on Information Systems (AMCIS) 2021. Chen, Y. et al. 2021. ‘Decentralized Governance of Digital Platforms’, Journal of Management (47:5), pp.  1305–37. (https://​doi​.org/​10​.1177/​0149206320916755). Chui, M. et al. 2018. ‘Most of AI’s Business Uses Will Be in Two Areas’, Harvard Business Review (July). (https://​hbr​.org/​2018/​07/​most​-of​-ais​-business​-uses​-will​-be​-in​-two​-areas). Cobben, D. et al. 2022. ‘Ecosystem Types: A Systematic Review on Boundaries and Goals’, Journal of Business Research (142:April 2021), Elsevier Inc., pp. 138–64. (https://​doi​.org/​10​.1016/​j​.jbusres​ .2021​.12​.046). De Carolis, A. et al. 2017. A Maturity Model for Assessing the Digital Readiness of Manufacturing Companies, pp.  13–20. (https://​doi​.org/​10​.1007/​978​-3​-319​-66923​-6​_2). Dedehayir, O. et al. 2018. ‘Roles during Innovation Ecosystem Genesis: A Literature Review’, Technological Forecasting and Social Change (136), Elsevier Inc., pp. 18–29. (https://​doi​.org/​10​ .1016/​j​.techfore​.2016​.11​.028). Demlehner, Q. et al. 2021. ‘How Can Artificial Intelligence Enhance Car Manufacturing? A Delphi Study-Based Identification and Assessment of General Use Cases’, International Journal of Information Management (58:December 2020), Elsevier Ltd, p. 102317. (https://​doi​.org/​10​.1016/​j​ .ijinfomgt​.2021​.102317). Eckhardt, J. T. et al. 2018. ‘Open Innovation, Information, and Entrepreneurship within Platform Ecosystems’, Strategic Entrepreneurship Journal (12:3), pp.  369–91. (https://​doi​.org/​10​.1002/​sej​ .1298). Eisenhardt, K. M. 1989. ‘Building Theories from Case Study Research’, The Academy of Management Review (14:4), pp. 532–50. Gawer, A. 2009a. ‘Platform Dynamics and Strategies: From Products to Services’, in Platforms, Markets and Innovation, Edward Elgar Publishing, pp.  45–76. (https://​doi​.org/​10​.4337/​9781849803311​.00009). Gawer, A. 2009b. Platforms, Markets and Innovation, (1st ed.), (A. Gawer, ed.), London: Edward Elgar Publishing Limited. Gawer, A. 2014. ‘Bridging Differing Perspectives on Technological Platforms: Toward an Integrative Framework’, Research Policy (43:7), Elsevier B.V., pp.  1239–49. (https://​doi​.org/​10​.1016/​j​.respol​ .2014​.03​.006). Genennig, S. M. et al. 2018. ‘Value Propositions in Service Systems Enabled by Digital Technology: A Field Based Design Science Approach’, Journal of Service Management Research (2:4), pp. 6–21. (https://​doi​.org/​10​.15358/​2511​-8676​-2018​-4​-6). Gerrikagoitia, J. K. et al. 2019. ‘Digital Manufacturing Platforms in the Industry 4.0 from Private and Public Perspectives’, Applied Sciences (9:2934). (https://​doi​.org/​10​.3390/​app9142934).

122  Handbook on digital platforms and business ecosystems in manufacturing Görzig, D. et al. 2019. ‘Advances in Production Management Systems. Production Management for the Factory of the Future’, IFIP Advances in Information and Communication Technology (Vol. 566), IFIP Advances in Information and Communication Technology, (F. Ameri et al., eds.), Cham: Springer International Publishing. (https://​doi​.org/​10​.1007/​978​-3​-030​-30000​-5). Granstrand, O., and Holgersson, M. 2020. ‘Innovation Ecosystems: A Conceptual Review and a New Definition’, Technovation (90–91:June 2018), p.  102098. (https://​doi​.org/​10​.1016/​j​.technovation​ .2019​.102098). Greve, K. et al. 2020. ‘7 Unlocking Unique Value Through Co-Creation in Open Laboratories’, in Innovating in the Open Lab, pp.  81–92. (https://​doi​.org/​10​.1515/​9783110633665​-007). Guggenberger, T. M. et al. 2021. ‘How to Design IIoT-Platforms Your Partners Are Eager to Join: Learnings from an Emerging Ecosystem’, in Innovation Through Information Systems, Springer International Publishing, pp.  489–504. (https://​doi​.org/​10​.1007/​978​-3​-030​-86800​-0​_34). Henfridsson, O., and Bygstad, B. 2013. ‘The Generative Mechanisms of Digital Infrastructure Evolution’, MIS Quarterly (37:3), pp.  907–31. (https://​doi​.org/​10​.25300/​MISQ/​2013/​37​.3​.11). Hodapp, D. et al. 2019. ‘Business Models for Internet of Things Platforms: Empirical Development of a Taxonomy and Archetypes’, Wirtschaftsinformatik, pp. 1769–83. Iansiti, M., and Levien, R. 2004. The Keystone Advantage: What the New Dynamics of Business Ecosystems Mean for Strategy, Innovation, and Sustainability, (1st ed.), Harvard Business Press. (https://​ebookcentral​.proquest​.com/​lib/​erlangen/​reader​.action​?docID​=​5560206). Ikävalko, H. et al. 2018. ‘Value Creation in the Internet of Things: Mapping Business Models and Ecosystem Roles’, Technology Innovation Management Review (8:3), pp. 5–15. (https://​doi​.org/​10​ .22215/​timreview/​1142). Jacobides, M. G. et al. 2018. ‘Towards a Theory of Ecosystems’, Strategic Management Journal (39:8), pp.  2255–76. (https://​doi​.org/​10​.1002/​smj​.2904). Jacobides, M. G., and Billinger, S. 2006. ‘Designing the Boundaries of the Firm: From “Make, Buy, or Ally” to the Dynamic Benefits of Vertical Architecture’, Organization Science (17:2), pp. 249–61. (https://​doi​.org/​10​.1287/​orsc​.1050​.0167). Jingyao, M. et al. 2021. ‘Governance Mechanisms Implementation in the Evolution of Digital Platforms: A Case Study of the Internet of Things Platform’, R and D Management. (https://​doi​.org/​10​.1111/​ radm​.12494). Jöhnk, J. et al. 2021. ‘Ready or Not, AI Comes – An Interview Study of Organizational AI Readiness Factors’, Business & Information Systems Engineering (63:1), pp.  5–20. (https://​doi​.org/​10​.1007/​ s12599​-020​-00676​-7). Kant, G., and Sangwan, K. S. 2014. ‘Prediction and Optimization of Machining Parameters for Minimizing Power Consumption and Surface Roughness in Machining’, Journal of Cleaner Production (83), Elsevier Ltd, pp.  151–64. (https://​doi​.org/​10​.1016/​j​.jclepro​.2014​.07​.073). Kay, N. M. et al. 2018. ‘The Role of Emergence in Dynamic Capabilities: A Restatement of the Framework and Some Possibilities for Future Research’, Industrial and Corporate Change (27:4), pp. 623–38. (https://​doi​.org/​10​.1093/​icc/​dty015). Kim, M. et al. 2019. Open-source Based Industrial IoT Platforms for Smart Factory: Concept, Comparison and Challenges, pp.  105–20. (https://​doi​.org/​10​.1007/​978​-3​-030​-24311​-1​_8). Kirschbaum, J. et al. 2022. ‘Use-Case-Based Innovation for Artificial Intelligence – an Ontological Approach’, in ECIS 2022 Proceedings, AIS Electronic Library (AISeL). (https://​aisel​.aisnet​.org/​ ecis2022​_rip/​64). Kohtamäki, M. et al. 2019. ‘Digital Servitization Business Models in Ecosystems: A Theory of the Firm’, Journal of Business Research (104), pp.  380–92. (https://​doi​.org/​10​.1016/​j​.jbusres​.2019​.06​.027). Krakowski, S. et al. 2022. ‘Artificial Intelligence and the Changing Sources of Competitive Advantage’, Strategic Management Journal (January 2020), pp.  1–28. (https://​doi​.org/​10​.1002/​smj​.3387). Kress, P. et al. 2016. ‘Role Model of Production Value Networks’, in 14. Fachtagung EKA 2016 – Entwurf Komplexer Automatisierungssysteme, ifak - Institut für Automation und Kommunikation e. V. Lee, H. 2019. ‘Effective Dynamic Control Strategy of a Key Supplier with Multiple Downstream Manufacturers Using Industrial Internet of Things and Cloud System’, Processes (7:3), p. 172. (https://​doi​.org/​10​.3390/​pr7030172).

Designing innovation ecosystems with functional roles  123 Lockl, J. et al. 2020. ‘Toward Trust in Internet of Things Ecosystems: Design Principles for Blockchain-Based IoT Applications’, IEEE Transactions on Engineering Management (67:4), pp.  1256–70. (https://​doi​.org/​10​.1109/​TEM​.2020​.2978014. Lusch, R. F., and Nambisan, S. 2015. ‘Service Innovation: A Service-Dominant Logic Perspective’, MIS Quarterly: Management Information Systems (39:1), pp.  155–75. (https://​doi​.org/​10​.25300/​MISQ/​ 2015/​39​.1​.07). Mäkinen, S. J., and Dedehayir, O. 2012. ‘Business Ecosystem Evolution and Strategic Considerations: A Literature Review’, 2012 18th International Conference on Engineering, Technology and Innovation, ICE 2012 - Conference Proceedings, pp.  1–10. (https://​doi​.org/​10​.1109/​ICE​.2012​.6297653). Mei, L. et al. 2019. ‘Exploring the Effects of Inter-Firm Linkages on SMEs’ Open Innovation from an Ecosystem Perspective: An Empirical Study of Chinese Manufacturing SMEs’, Technological Forecasting and Social Change (144:March 2018), Elsevier, pp. 118–28. (https://​doi​.org/​10​.1016/​j​ .techfore​.2019​.04​.010). Michalke, S. 2022. ‘Design Knowledge for Digital Business Ecosystems: Towards Design Principles for Digital Engagement Platforms’, in Handbook on Digital Business Ecosystems, Edward Elgar Publishing, pp.  161–76. (https://​doi​.org/​10​.4337/​9781839107191​.00019). Mikalef, P. et al. 2020. ‘Exploring the Relationship between Big Data Analytics Capability and Competitive Performance: The Mediating Roles of Dynamic and Operational Capabilities’, Information & Management (57:2), Elsevier B.V., p.  103169. (https://​doi​.org/​10​.1016/​j​.im​.2019​.05​ .004). Minchala, L. I. et al. 2020. ‘An Approach to Industrial Automation Based on Low-Cost Embedded Platforms and Open Software’, Applied Sciences (10:14), p.  4696. (https://​doi​.org/​10​.3390/​ app10144696). Moerchel, A. et al. 2020. ‘Identifying Crisis-Critical Intellectual Property Challenges during the Covid-19 Pandemic: A Scenario Analysis and Conceptual Extrapolation of Innovation Ecosystem Dynamics Using a Visual Mapping Approach’, SSRN Electronic Journal (1), Centre for Technology Management Working Paper Series. (https://​doi​.org/​10​.17863/​CAM​.58372). Moore, G. 2014. Crossing the Chasm Is – Marketing and Selling Disruptive Products to Mainstream Customers, (3rd ed.), HarperBusiness. Mudambi, S. M., and Tallman, S. 2010. ‘Make, Buy or Ally? Theoretical Perspectives on Knowledge Process Outsourcing through Alliances’, Journal of Management Studies (47:8), pp. 1434–56. (https://​doi​.org/​10​.1111/​j​.1467​-6486​.2010​.00944​.x). Nambisan, S. et al. 2017. ‘Digital Innovation Management: Reinventing Innovation Management Research in a Digital World’, MIS Quarterly (41:1), pp.  223–38. (https://​doi​.org/​10​.25300/​MISQ/​2017/​41:​1​.03). Obradović, T. et al. 2021. ‘Open Innovation in the Manufacturing Industry: A Review and Research Agenda’, Technovation (102), p.  102221. (https://​doi​.org/​10​.1016/​j​.technovation​.2021​.102221). Osterwalder, A. et al. 2014. Value Proposition Design: How to Create Products and Services Customers Want, (1st ed.), Wiley. (http://​marefateadyan​.nashriyat​.ir/​node/​150). Otto, B., and Aier, S. 2013. ‘Business Models in the Data Economy: A Case Study from the Business Partner Data Domain’, Wirtschaftsinformatik Proceedings 2013 (March), pp. 475–89. (http://​aisel​ .aisnet​.org/​wi2013/​30​%5Cnhttp://​aisel​.aisnet​.org/​wi2013/​30/​). Oyekanlu, E. 2017. ‘Predictive Edge Computing for Time Series of Industrial IoT and Large Scale Critical Infrastructure Based on Open-Source Software Analytic of Big Data’, in 2017 IEEE International Conference on Big Data (Big Data) (Vol. 2018-Janua), IEEE, December, pp. 1663–69. (https://​doi​.org/​10​.1109/​BigData​.2017​.8258103). Pappas, I. O. et al. 2018. ‘Big Data and Business Analytics Ecosystems: Paving the Way towards Digital Transformation and Sustainable Societies’, Information Systems and E-Business Management (16:3), Springer Berlin Heidelberg, pp.  479–91. (https://​doi​.org/​10​.1007/​s10257​-018​-0377​-z). Parker, G. G., and Van Alstyne, M. W. 2005. ‘Two-Sided Network Effects: A Theory of Information Product Design’, Management Science (51:10), pp.  1494–1504. (https://​doi​.org/​10​.1287/​mnsc​.1050​ .0400). Pauli, T. et al. 2020. ‘Leveraging Industrial IoT Platform Ecosystems: Insights from the Complementors’ Perspective’, Twenty-Eighth European Conference on Information Systems (ECIS) (May), pp. 1–18. (https://​aisel​.aisnet​.org/​ecis2020​_rp/​77).

124  Handbook on digital platforms and business ecosystems in manufacturing Peltola, T. et al. 2016. ‘Value Capture in Business Ecosystems for Municipal Solid Waste Management: Comparison between Two Local Environments’, Journal of Cleaner Production (137), Elsevier Ltd, pp.  1270–79. (https://​doi​.org/​10​.1016/​j​.jclepro​.2016​.07​.168). Pidun, U. et al. 2020. ‘How Do You ‘Design’ a Business Ecosystem?’, BCG Hendersen Institute, pp. 1–15. Piller, F. T. et al. 2022. Forecasting Next Generation Manufacturing, Contributions to Management Science, (F. T. Piller et al., eds.), Cham: Springer International Publishing. (https://​doi​.org/​10​.1007/​ 978​-3​-031​-07734​-0). Rao, B., and Jimenez, B. 2011. ‘A Comparative Analysis of Digital Innovation Ecosystems’, PICMET: Portland International Center for Management of Engineering and Technology, Proceedings, IEEE. Rehm, S. V. et al. 2017. ‘Visualizing Platform Hubs of Smart City Mobility Business Ecosystems’, ICIS 2017: Transforming Society with Digital Innovation, pp. 0–10. Rong, K., and Shi, Y. 2015. Business Ecosystems – Constructs, Configurations, and the Nurturing Process, London: Palgrave Macmillan UK. (https://​doi​.org/​10​.1057/​9781137405920). Sauer, C. et al. 2020. ‘Current Industry 4.0 Platforms – An Overview’. (https://​doi​.org/​10​.5281/​zenodo​ .4485756). Shipilov, A., and Gawer, A. 2020. ‘Integrating Research on Interorganizational Networks and Ecosystems’, Academy of Management Annals (14:1), pp.  92–121. (https://​doi​.org/​10​.5465/​annals​ .2018​.0121). Sjödin, D. et al. 2022. ‘How Can Large Manufacturers Digitalize Their Business Models? A Framework for Orchestrating Industrial Ecosystems’, California Management Review (64:3), pp. 49–77. (https://​ doi​.org/​10​.1177/​00081256211059140). Stahl, F. et al. 2016. ‘A Classification Framework for Data Marketplaces’, Vietnam Journal of Computer Science (3:3), Springer Berlin Heidelberg, pp.  137–43. (https://​doi​.org/​10​.1007/​s40595​-016​-0064​-2). Stichweh, H. et al. 2021. ‘IIP-Ecosphere Platform Requirements (Usage View)’. (https://​doi​.org/​10​.5281/​ zenodo​.4485801). Talmar, M. et al. 2020. ‘Mapping, Analyzing and Designing Innovation Ecosystems: The Ecosystem Pie Model’, Long Range Planning (53:4), p.  101850. (https://​doi​.org/​10​.1016/​j​.lrp​.2018​.09​.002). Täuscher, K., and Laudien, S. M. 2018. ‘Understanding Platform Business Models: A Mixed Methods Study of Marketplaces’, European Management Journal (36:3), pp.  319–29. (https://​doi​.org/​10​.1016/​ j​.emj​.2017​.06​.005). Teece, D. J. 2014. ‘Business Ecosystem’, The Palgrave Encyclopedia of Strategic Management, (M. Augier and D. J. Teece, eds.), London: Palgrave Macmillan UK. (https://​doi​.org/​10​.1057/​978​-1​-349​ -94848​-2). Teece, D. J. 2018. ‘Profiting from Innovation in the Digital Economy: Enabling Technologies, Standards, and Licensing Models in the Wireless World’, Research Policy (47:8), Elsevier B.V., pp. 1367–87. (https://​doi​.org/​10​.1016/​j​.respol​.2017​.01​.015). Thoring, K. et al. 2020. ‘Workshops as a Research Method: Guidelines for Designing and Evaluating Artifacts Through Workshops’, Proceedings of the 53rd Hawaii International Conference on System Sciences, pp.  5036–45. (https://​doi​.org/​10​.24251/​hicss​.2020​.620). Tsujimoto, M. et al. 2015. ‘Designing the Coherent Ecosystem: Review of the Ecosystem Concept in Strategic Management’, in Portland International Conference on Management of Engineering and Technology (Vol. 2015-Sept), pp.  53–63. (https://​doi​.org/​10​.1109/​PICMET​.2015​.7273192). van der Meer, R. J. et al. 2021. ‘Innovation Labs: A Taxonomy of Four Different Types’, in 2021 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), IEEE, June 21, pp.  1–9. (https://​doi​.org/​10​.1109/​ICE/​ITMC52061​.2021​.9570259). Visnjic, I. et al. 2016. ‘Governing the City: Unleashing ValUe from the Business Ecosystem’, California Management Review (59:1), pp.  109–40. (https://​doi​.org/​10​.1177/​0008125616683955). Visscher, K. et al. 2021. ‘Innovation Ecosystem Strategies of Industrial Firms: A Multilayered Approach to Alignment and Strategic Positioning’, Creativity and Innovation Management (30:3), pp. 619–31. (https://​doi​.org/​10​.1111/​caim​.12429). Walrave, B. et al. 2018. ‘A Multi-Level Perspective on Innovation Ecosystems for Path-Breaking Innovation’, Technological Forecasting and Social Change (136:December 2016), Elsevier, pp.  103–13. (https://​doi​.org/​10​.1016/​j​.techfore​.2017​.04​.011).

Designing innovation ecosystems with functional roles  125 Walter, A. et al. 2011. ‘Championship Behaviors and Innovations Success: An Empirical Investigation of University Spin-Offs’, Journal of Product Innovation Management (28:4), pp. 586–98. (https://​doi​ .org/​10​.1111/​j​.1540​-5885​.2011​.00826​.x). Wolpert, S., and Mengual, M. P. 2020. ‘9 Professional Leadership as Key to Innovation Projects in Open Laboratories’, in Innovating in the Open Lab, pp.  103–12. (https://​doi​.org/​10​.1515/​9783110633665​ -009).

9. Changing the role of a focal firm: the transition of a B2B SME to ecosystem leadership in manufacturing industry Lukas Budde, Leonardo Laglia, Thomas Friedli and Roman Hänggi

INTRODUCTION A combination of forces, namely accelerating globalization and rapidly advancing technological development, have significantly transformed the nature of business-to-business relationships in the 21st century. With ever-increasing interdependence between manufacturing industry players and new disruptive capabilities to collect and leverage data, the ideation of more integrated value creation approaches has rapidly gained traction – both in literature and in practice. Traditional approaches to the creation of value, based on linear models of vertically integrated supply chains, are rapidly making way for reconfigured approaches relying on interfirm co-creation (Pagani, 2013). The concept of the ‘business ecosystem’, as first coined by Moore (1993), has emerged in the literature as a conceptual lens through which to understand this new way of delivering value to customers, one focused on shared fates, increased collaboration and value co-creation (Iansiti and Levien, 2004; Rong et al., 2015; Aarikka-Stenroos and Ritala, 2017; Jacobides et al., 2018). Since Moore’s initial introduction of the concept, research on ecosystems has sprawled in a variety of different directions and uses, from ‘innovation ecosystems’ (Adner and Kapoor, 2010; Dedehayir et al., 2018; Benitez et al., 2020) to ‘service ecosystems’ (Cenamor et al., 2017) and ‘platform ecosystems’ (Ceccagnoli et al., 2012; Gawer and Cusumano, 2014; Hein et al., 2019). Given the conceptual heterogeneity of the term, and the related confusion it can sometimes elicit, this study embraces a minimal, yet cross-field, definition as developed by Aarikka-Stenroos and Ritala (2017): ‘a business ecosystem is a co-evolutionary business system of actors, technologies, and institutions’. This definition allows for the focalization onto the principal elements of importance when inspecting ecosystems, the relevant individuals (or groups of individuals), the enabling technologies and the constraining rules of the game – or the logic by which such actors operate. This understanding of business ecosystems paves the way for the consideration of a related construct, the digital platform. As a technological artefact which allows for the intermediation of different ecosystem actors, channels resources and establishes the groundwork rules of engagement for value-creation, the digital platform appears as a strong mediator of business ecosystems. Accordingly, in the study, the development of a business ecosystem, and the associate role changes of a focal firm, will be explored through the development of a digital platform, with ecosystem roles changing in line with the firm’s core positioning within the platform ecosystem. While existing research has provided insights into how ecosystems may emerge and how they ought to be managed (Moore, 1993; Iansiti and Levien, 2004; Jacobides et al., 2018; 126

Changing the role of a focal firm  127 Stonig et al., 2022), research tends to maintain snapshot views on studied ecosystems, focusing on static samples to analyze (Rong et al., 2015). In this regard, the question of how roles change within ecosystems as the network expands and relationships evolve is an underexplored phenomenon. Research has defined the typology of roles which can emerge (Ikävalko et al., 2018), the specifications for each role (Moore, 1993; Iansiti and Levien, 2004) and the way these roles interact to bring about a value proposition (Adner and Kapoor, 2010; Lusch and Nambisan, 2015; Adner, 2017). Yet, insights on how focal firms transition into new roles as they herald the development of an ecosystem are sparse. Gawer and Phillips (2013) present an insightful theoretical lens through which to study the phenomenon, relying on the concept of institutional work (Lawrence and Suddaby, 2006). Yet, their findings are highly dependent on the case analyzed, particularly its nature as a large and ambidextrous multinational corporation. The application of this similar lens to the case of an SME could yield different results. The relationship between business ecosystems and SMEs is one that the academic literature has explored to a considerable extent. On the one hand, ecosystems, and their related technological interfaces, digital platforms, can offer substantial competitive advantages for SMEs given the latter’s inherent orientation towards networking and reliance on external capabilities (Pérez and Cambra-Fierro, 2015; Cenamor et al., 2019). A high network capability can increase an SME’s integration of outside knowledge and resources, increasing the potential of discovering new opportunities for value creation (Shu et al., 2018). At the same time, however, the high start-up costs for ecosystem intermediation platforms pose a problem for SMEs given their liability of smallness (Giotopoulos et al., 2017). Taking a leading position in an ecosystem development initiative will impose high costs, but potentially provide unique rewards for small and medium-size companies. This paradoxical relationship between the two constructs raises the need for a further exploration on how SMEs can build ecosystems and transition into different roles accordingly. Our study aims to address the conceptual and empirical gap presented above, by examining the case of an industrial recycling firm in the process of building an industry digital manufacturing platform. Focusing on the organization as the unit of analysis, we build off the work of Gawer and Phillips (2013), applying the concept of institutional work as key to ecosystem development, to understand how an SME focal firm navigates such a reinvention. Accordingly, close attention is paid to the strategies implemented to create and legitimize the new collective identity pushed for the network (ecosystem identity) together with the company’s own organizational identity as a platform-oriented business (ecosystem leader). By exploring these dynamics in the SME B2B context, we build upon existing literature and illustrate how previously defined strategies for coping with role change in ecosystems (Gawer and Phillips, 2013) may not apply in a context of scarce resources. Hence, the chapter addresses the following research question: How does an SME focal firm actively change its ecosystem role throughout the stages of ecosystem development? To answer this question, we conducted a longitudinal research approach with a recycling SME as a single case study. We developed a model outlining the evolutionary steps of a company changing its ecosystem role during the birth of a digital platform. The chapter starts with a literature overview about ecosystem theory and institutional work theory to build the foundation for the analysis. Followed by the methodological approach in which we describe the empirical data and the way of data analysis. In the results section we map the findings from the case study on the internal and external institutional work theory to set the basis to derive our ecosystem development model in the discussion section.

128  Handbook on digital platforms and business ecosystems in manufacturing

LITERATURE REVIEW Ecosystems Theory The business ecosystem construct emerged in management and economics literature to better understand the changing nature of business relationships in a context of ever-increasing interconnectedness within and across industries. Framed as a new ‘ecology of competition’ (Moore, 1993), the ecosystem perspective rejects the traditional linear understanding of value creation and adopts instead a broader conceptualization based upon networked dynamics of interdependence, collaboration and value creation. In the majority of instances, the ecosystem spawns around a focal firm (Kapoor, 2018). With its ‘architectural’ work this firm – also referred to as the ‘keystone’ – leads the ecosystem by ‘connecting network participants, providing key resources, and ensuring the stability and health of the ecosystem’(Iansiti and Levien, 2004). Alternatively, adjacent actors – key suppliers, technology providers, firms offering complementary products or services, customers and even competitors – connect in different ways to contribute to the ecosystem’s core value proposition (Iansiti and Levien, 2004). The latter (value proposition) is crucial in delineating the spatial and functional boundaries of the ecosystem, as it highlights the needed actors, technologies and institutions for the ecosystem to achieve its shared mission (Adner and Kapoor, 2010; Aarikka-Stenroos and Ritala, 2017). Accordingly, Adner (2017), in his proposed approach of ‘ecosystem-as-structure’, maps business ecosystems as ‘the alignment structure of the multilateral set of partners that need to interact for a focal value proposition to materialize’. Such an approach presents a useful analytical approach to the study of ecosystems as it allows for a clear characterization of the precise contributions relevant stakeholders in a value chain make towards value creation. In this way, the definition of roles within the network can be derived based upon the kind of activities each actor is contributing. The concept of collaborative value creation in an ecosystem can be paralleled with the work of Lusch and Nambisan (2015) in their exploration of the new ways actors pursue innovation in value delivery, from a goods-dominant to a service-dominant logic. With service-dominant logic, the ‘tangible-intangible and producer-consumer divides’ are transcended in favor of an understanding of service innovation in which interdependent actors in service ecosystems engage in value co-creation enabled by service platforms (Lusch and Nambisan, 2015). In this value co-creation relationship, actors pertain to different roles. The main beneficiary of the service interaction – usually the customer – is classified as the ‘ideator’. This role is where a specific need is articulated, information is volunteered to guide service provision (data for instance), and the service is consumed (Lusch and Nambisan, 2015). The ‘designer’ is tasked with leveraging the data and other resources made available in the ecosystem to develop and deliver the commercial service (Lusch and Nambisan, 2015). Finally, the ‘integrator’ assumes a coordination role aligning activities, enabling access and controlling rules of the ecosystem (and platform if material interface is present) (Lusch and Nambisan, 2015). These S-D logic roles can be a useful guide to characterizing how different members of a value chain operate within an ecosystem. The successful delivery depends on the contributions of all roles to the ecosystem value proposition. As the ecosystem’s size, scope and nature evolve, with it, so will the individual actors’ subscription to the different roles. As the core value proposition changes, the contributions made to it by different members will change too and roles will be reshuffled (Reisinger and Lehner,

Changing the role of a focal firm  129 2022). Within this context, the role of the focal firm is one the most susceptible to change. While business ecosystems are typically built on the ‘architectural’ work of a focal firm, whereby a leading firm takes the responsibility to deliver the initial core value proposition, subsequent growth phases may dictate a shifting of responsibilities for the keystone as new players emerge within the ecosystem and responsibilities are realigned. The stepwise approach suggested by empirical literature in building platform ecosystems (Zhu and Furr, 2016; Dell’Era et al., 2021) highlights how focal firms pivot into different roles as the ecosystem develops. In early birth phases, as firm networks and cooperation are still being consolidated, the focal firm needs to provide the needed leadership and drive ecosystem success. Here the firm assumes a delivery role. Customers (ideators) communicate an unsolved industry wide problem and the keystone works to address it (designer). In subsequent phases, however, with a consolidated core value proposition, and a potential opening of the platform supply-side, focal firms shift into the integrator role, focusing less on service delivery and more on ecosystem management. Such a step is what ultimately unleashes the collaborative innovation potential of platform ecosystems, with the focal firm transitioning more into a coordination and governing role, ensuring value is generated and shared at all levels in the value chain, exploiting the true ‘ecosystem edge’ (Iansiti and Levien, 2004). Change in Focal Firm Role as Institutional Work Importantly, the establishment of an ecosystem, and its subsequent evolutionary steps, does not occur in a vacuum. Scholars have outlined the need for research to provide further insights on the dynamics behind ecosystem emergence and evolution (Rong et al., 2015; de Reuver et al., 2018). Zhu and Furr (2016) suggest practical guidelines for firms willing to make the transition to an ecosystem mediated by a platform, emphasizing above all else a defensible product, an established mass of customers and a stepwise approach, with Dell’Era et al. (2021) confirming such findings with empirical support in B2C. Few contributions, however, delve into the specific actions focal firms take to kick-start ecosystem organization and development. Stonig et al. (2022) present a novel contribution with their process model on ecosystem development through mutual adaptation of product and organizational dimensions. Yet, specific insights on role change within transformation are underexplored. An interesting perspective is provided by Gawer and Phillips (2013), with an application of new-institutionalist lenses to explore the evolution of the Intel Corporation into ecosystem leadership. Their analysis of ecosystem building as a type of institutional work – the ‘purposive action of organizations aimed at creating, maintaining or disrupting institutions’ (Lawrence and Suddaby, 2006) – allows for the differentiation of two categories of actions to drive ecosystem development – and hence role evolution. On the one hand, focal firms need to channel efforts towards externally oriented work, to instill in related firms a collective identity of an ecosystem. Being perceived as an ecosystem leader automatically necessitates the preliminary condition of a loosely connected network of firms collectively identifying as an ecosystem. On the other hand, as supported by other contributions (Stonig et al., 2022), the redefinition of the logic dictating industry identity needs to be complemented with simultaneous internal changes to organizational identity. The explanatory potential of this theoretical lens motivates its application to additional cases of role change in ecosystem developments. Accordingly, the two dimensions of institutional work are explored below.

130  Handbook on digital platforms and business ecosystems in manufacturing Outside-in perspective as collective identity The externally oriented institutional work a firm engages in to drive ecosystem development revolves around how different industry players view the industry and their relation to it. Perspectives on identity in organizational studies literature have developed in focus beyond just intrafirm dynamics and relationships to include consideration for how identities can be constructed at the interfirm level (Wry et al., 2011). In this regard, the concept of collective identity, as developed in new institutionalist perspectives on organizational science (Cornelissen et al., 2007), emerges as a promising lens through which the members of a given organizational field can come to understand the identity of the network of business partners they belong to. This shared understanding of the ‘collective self’ underpins ‘a group of actors that can be strategically constructed and fluid, organized around a shared purpose and similar outputs’ (Wry et al., 2011). The contribution to a value proposition broader than just firm level spurs the development of a way to see your company’s operations as part of a greater network. Accordingly, collective identity is fundamental in constructing the categories by which those included in the collective are defined and differentiated (Khaire and Wadhwani, 2010). Members of the collective operate according to ‘categorical meanings’, or the social, symbolic, but also functional prescriptions, which collectively define the production of goods and services within a given category (Khaire and Wadhwani, 2010). Drawing back to ecosystems theory, the presence of a collective identity tying together the various ecosystems members, would represent the formalization of this new mentality for value creation in industry. Instilling the ecosystem mentality in firm networks is a core component of successful platform ecosystem leadership (Zhu and Furr, 2016). Relatedly, particularities of such a collective identity will also influence the definition of the different roles within it. A platform leader can be defined as a platform leader only insofar as the ecosystem that it is claiming to lead accepts its leadership. Scholarly attention has focused in detail on how collective identities are constructed. Given their essential nature to the structuring of activities across organizations, the question of how collective identities are built is better framed as how old ones are disrupted and new ones instilled. As constructs which are built around shared perceptions of value, actions and purpose, the creation of new collective identities will entail the disruption of established categories, in order then to propose new ones. At times, emergence of new collective identities occurs together with the development of new industries (Navis and Glynn, 2011). In these cases, discursive claims clarifying this new inter-organizational form’s identity – ‘who we are’ and ‘what we do’ – solidifies the industry’s sense of category. Alternatively, in already established industries going through an identity shift, the role of key individuals is emphasized (Khaire and Wadhwani, 2010). Institutional agents benefitting from ‘position-specific opportunities’ – standard setting in industry, design of core value propositions, judgement of value – can play a major role in ‘exploiting tensions, contradictions, and inconsistencies’ and propose new categories to structure inter-organizational cooperation (Khaire and Wadhwani, 2010). In these ways organizations looking to influence the direction of collective identification in a particular industry can engage in targeted types of institutional work to instill a new ecosystem level inter-organizational identity. Internal perspective as organizational identity While during the creation of a shared identification of ecosystems, thinking across organizations is imperative to its proper development, dynamics internal to the focal firm also play

Changing the role of a focal firm  131 a key role in successfully transitioning to an ecosystem leadership role. As core activities of the focal firm change, through ecosystem development, from delivery to integration, the key activities within the firm also change. Given that individuals make sense of the daily practices within their organization ‘through the prism of identity’, institutional work aimed at updating organizational identity together with the role of the firm will become fundamental (Gawer and Phillips, 2013). Traditional understandings of individual and organizational identity assume a defined and given ‘identity’ for both the individual (who is ‘I’) and for the organization (who are ‘we’) (Sveningsson and Alvesson, 2003). The overlap between these two sets of identities can be used as a metric for employee belonging and identification with organization (Dutton et al., 1994). Yet, in ever increasing periods of change and uncertainty, identity (both individual and organizational) are rarely fixed and unchanging constructs (Sveningsson and Alvesson, 2003). Instead, they evolve and adapt continuously according to developments in their respective fields, mutually influencing each other. In this light, the measurement of employee acceptance of new tasks and broader organizational change ought to be conducted repeatedly, at different intervals of change. Relevant to this ever-changing nature of organizational identity is the concept of identity work. This form of institutional work can be defined as work that ‘refers to people being engaged in forming, repairing, maintaining, strengthening, or revisiting the constructions that are productive of a sense of coherence and distinctiveness’ (Sveningsson and Alvesson, 2003). In periods of transition – as in the case of introducing a new business model – organizations need to actively pursue identity work to ensure that organizational and individual identity remain aligned. Accordingly, old organizational identities need to be reworked according to the new logic of doing business and the activities it entails. Identity work can be traced along meters such as organizational discourse – ways of reasoning which determine priorities, courses of action, optimal solutions to problems, or identity claims made alongside the introduction of new practices. More generally, looking at roles, expectations, coordinating efforts and management of knowledge can provide insights as to which levers organizational identity is constructed and reconstructed with. The deployment of this type of institutional work will be a pivotal part of a focal firm’s role changes through the development of an ecosystem. In particular, in cases of internal resistance to activity and identity change, specific identity-targeting interventions can help ease the transition (Gawer and Phillips, 2013). Hence, the purpose of this study is to further explore on how different variants of institutional work – externally and internally-orientated – can be leveraged to understand how a focal firm transitions through different roles, in line with ecosystem development. The application of this theoretical lens in previous studies (Gawer and Phillips, 2013) yielded some important insights. Yet, these remain limited by their case-context. In order to better understand the dynamics of focal firm evolution, especially for more resource-constrained SMEs, this study applies this theoretical lens to a single-case study of a Swiss B2B SME.

METHODOLOGY To address the above stated research question, we carry out a longitudinal single case study of a recycling company over a period of five years. We seek to understand the specific actions taken by the case company to drive ecosystem development and progress through different

132  Handbook on digital platforms and business ecosystems in manufacturing roles. The research design is justified by the explorative nature of the study, as well as its attempt to build upon existing theory (Eisenhardt and Graebner, 2007). While multiple-case study methodologies are usually deemed as more useful for the creation of high-quality research insights, single-case studies can make important contributions in specific cases (Yin, 2018). Among these instances are cases which are unusually informative, exemplary cases or cases that represent a rare or exceptional approach. The longitudinal nature of the study allowed us to break down the transformation of this focal firm through distinct stages of ecosystems development. Accordingly, an evolutionary perspective is presented. The company, referred to as ReValue from now on, is a regional manufacturing and recycling SME operating in Switzerland. Given the changing nature of competition in the industry, ReValue felt the need to innovate its value proposition to maintain competitiveness. By envisioning, first, a digitalization of its service offer and subsequently the development of a multisided platform, the company embarked on a transformation of its role in the manufacturing and recycling industry. Data Collection In line with the longitudinal approach, the case data was collected over a period of five years. We studied the case company in its transition from an established B2B service provider towards a reimagined ecosystem orchestration role and mapped the latter across three distinct ecosystem steps: 1) established business, 2) introduction of digital service, and 3) preparation for multi-sided platform. Across these phases, the data collection was structured according to the two dimensions of institutional work as explained in the previous section (see Tables 9.1 and 9.2). To capture the externally oriented institutional work, the focus of data collection was aimed towards the main industry partners for the case company. Semi-structured interviews were conducted with customers of the recycling firm to capture their perceptions of ReValue, and of the manufacturing and recycling industry as a whole. Here, the focus was on understanding the established logics operating in the B2B recycling field. In the same lens, the case company’s orientation towards their industry partners – in communication, actions, responsibilities – was also explored. In this way, we aimed to capture the discursive and actional relationships and how these changed as the ecosystem developed. On the other hand, the internally oriented institutional work focused on dynamics within the organization. Semi-structured interviews were conducted with employees across the organization, and a perspective was obtained on how the firm managed the disruption to organizational identity brought about by the new ecosystems thinking. Here, emphasis was placed on gaining firsthand perspectives on how roles, expectations and logics changed within the organization. To this end, secondary materials such as internal company events and workshop documents were also included in the data analysis. Data Analysis The collected primary and secondary data was analyzed following the prescriptions of Gioia et al. (2013) for conducting inductive research. This method suggests systematically organizing the qualitative data into first-order themes, second-order categories and aggregated dimensions. First, in a process similar to that prescribed by Strauss (1990) of ‘open coding’, the raw data is closely examined for concepts which accurately define key points. These

Changing the role of a focal firm  133 Table 9.1

Data collected for external institutional work

Actors

Duration (h)

Time period

Customers (e.g. Manufacturer)

29 Interviews: 36h

Oct 2018, Mar–May 2019, Dec 2021–Jan 2022

Complementors (recycling firm, software company, logistics 5 Interviews: 5h

Aug 2021–Dec 2021

company)

Table 9.2

Data collected for internal institutional work

Actors

Duration (h)

Time period

Management Board

8 Interviews: 8h

Sep 2017–Sep 2018, August 2021

Project Leader

7 Interviews: 13h

Jan 2019–Feb 2019, Aug 2021–Jan

Management Board

8 Workshops: 82h

Project Team

5 Workshops: 62.5h

Management Board and

5 Workshops: 17.5h

Aug 2019–Oct 2020

2 Interviews: 6h

Aug 2021–Jan 2022

2022, Oct 2022 Sep 2017–Aug 2019, Aug 2021–Jan 2022 Sep 2018–Mar 2019, Nov 2021–Dec 2021 Project Leader Operations

are direct examples of kinds of actions (institutional work) that were taken throughout the ecosystem development which illustrate attempts to drive a role change. These first-order concepts were formulated according to the interviewees’ own expressions, to maintain a close understanding of the case context. In the subsequent phase, the first-order concepts were categorized according to conceptual similarity into second-order themes. These are abstractions made from the case data to begin building different particularities of the theoretical model. In iteration between the theoretical perspectives and the case data, a theoretical understanding of the various dynamics of role change during ecosystem development emerged (Langley, 1999). This process resulted in the definition of the relevant aggregate dimensions. These are presented in data structures and organized by phase. The insights gained on how both kinds of institutional work then led to focal firm role evolution is presented in the discussion section.

RESULTS In our analysis we apply the lens of institutional work to understand how a focal firm can change its role and drive ecosystem development. Following the insights of Gawer and Phillips (2013) we differentiate between two types of this work, externally and internally oriented. We outline the different types of works ReValue engaged in and structure this according to three distinct phases of ecosystem development. The focal firm initiated this process by introducing the possibility of value co-creation with customers and building internal capacity for driving the digitalization effort. In subsequent rounds, the external work focused on consolidating the new value creation approach through a digital service and triggering new roles for customers as key actors in the process. Additionally, internal identity work attempted to win over employees and drive acceptance of new activities. Finally, in a preparation phase

134  Handbook on digital platforms and business ecosystems in manufacturing to fully roll-out the digital platform, the case company focused on building trust, devising new roles for competitors as potential collaborators, as well as implementing changes to its organizational structure to ease the pressures caused by multiple identities in the organization. The specificities of the key actions taken by ReValue to transition from one phase to the next are presented below. Transition from Traditional B2B Service Provider to an Ecosystem Designer As elucidated earlier, the creation of an ecosystem centers around a new value proposition (Moore, 1993; Adner, 2017). Before the beginning of the transformation of its role towards ecosystem leadership, ReValue was a B2B service provider operating with a traditional linear business model. The relationship with customers was mediated through personal and informal ties with sales employees and the company’s main focus was to deliver high-quality manufacturing and recycling services. Given a trigger for generating new sources of growth and competitiveness for the company, the CEO began thinking about the potential of rethinking the established logic of the manufacturing and recycling industry, as well as ReValue’s role within it. Through a series of top management consultations, a new strategy was defined to embrace digital technologies and innovate the value proposition to provide greater customer value with the added potential afforded by greater transparency and inter-organizational visibility. The first round of this new value proposition was to take shape as a customer portal through which their recycling services could be registered and handled digitally. Further development plans sketched a vision for the customer portal to be transformed into a multisided platform, with the onboarding of not just customers (e.g. manufacturers) but also competing recyclers and logistic companies. This approach to innovation implied a broader transformation to the established logic of doing business in the recycling industry. To best prepare for the launching of the digital service, ReValue engaged in externally oriented institutional work to trigger the creation of new relationships and dynamics of collaboration with the customer, needed for the new value proposition. The major transformation in this regard was to involve the customer in the ideation process of the new digital services. Interviews were carried out with selected customers to better understand their pains, gains and jobs to be done, and customer profiles were built to best design the digital service. A major concern cited by customers (all quotations from the project leader of ReValue, extracted from the interview data) regarded the lack of consideration of industry value by recycling firms: With traders it is sometimes difficult to build a long-term relationship, because they always try to get the maximum advantage for themselves.

Including the customers in a discussion on how the digital service could best be built in order to maximize their value, introduced a new dynamic of closer organizational collaboration between industry players, and laid the basis for the encouragement of the customer to take an ideator role – or the actor in an ecosystem that communicates needs, provides data and benefits from the value proposition (Lusch and Nambisan, 2015). This purposeful inclusion of the customer in value proposition design triggered a shifting of expectations in the recycling industry, establishing a first instance of co-creation and ecosystems thinking. In this way, the building blocks for the stakeholder’s collective identity as an ecosystem of interdependent partners were established.

Changing the role of a focal firm  135 This introduction of a digital approach to value creation also implied some changes within the organization. Understanding customer needs, integrating them into design, and maintaining this new communication link required a reallocation of resources and a redefinition of responsibilities for some employees. In preparation for the rollout of the digital service, top-level management defined a new digital ‘outlook’, outlining the strategic organizational priorities for the company going forward. A key component of this strategic redirection was the development of an internal digitalization project, to build the back-end technical infrastructure needed for the digitally enabled value proposition. As evidenced in this quote: The platform idea is reinforced by the projects launched internally.

To avoid disrupting the daily business of different functions, a head of digitalization was appointed to oversee cross-functional digitalization projects. In this way, ReValue created new internal roles through which the new digital logic could be mediated throughout the organization. As a key intermediary between the top-management vision and department-level business, this new function was pivotal to drive ecosystem activities. Thus, through this combination of externally and internally oriented institutional work, the case company was able to initiate the initial industry evolution into a basic actor ecosystem, building the basis for value co-creation with newly defined roles for customers (see Figure 9.1). Consolidating the Digital Service and Building Ecosystems Thinking Given the decision to digitalize its service offering, and triggering the role change of the customers to encourage a closer collaborative relationship, ReValue found itself in a transition phase of having both its established service business logic and the new digital designer logic embedded in the organization. This tension was observed both from the outside, where certain customers did not see the need or value of a digitalized service offer, and from the inside, where employees were not so willing to let go of the company’s old identity as a traditional recycling business. The actions taken to mitigate these tensions and continue building the ecosystem mindset both within and without were as follows. Looking at the industry-level tensions and opposition to this proposed shift in value creation in the manufacturing and recycling industry, ReValue continued to approach customers for a greater collaboration in finding solutions which would generate more value for them. While the established analogue recycling service was comfortable and valued for its personal approach, the reality was that processes were characterized by severe inefficiencies attributable to human errors. By making clear the current shortfalls in the recycling industry and offering a clear basis to explain how the transition to digital processes would address these, the company began influencing the broader perspective of digitally enabled recycling. As mentioned by the Head of Digitalization: A lot of time and effort is put into advising customers: If I can present to customers the information about the exact disposal, clear, simple and concise, you would save a lot of time.

Acting as a key organizational agent with positional power, ReValue was able to outline the inconsistency of the old model and propose the new digital offer as a signifier of improved processes and performance. The data-informed and clear communication of the potential of the

136  Handbook on digital platforms and business ecosystems in manufacturing

Figure 9.1

Institutional work to trigger role change to designer

customer portal, and more broadly a digitalization of the manufacturing industry, redefined the standard understanding of how value could be generated in the industry. Through free trials of the portal with established customers and direct channels for feedback, user acceptance of the platform was fostered, and steps were made towards the creation of a new recycling logic. While customer and industry opposition to the portal posed significant barriers to the successful evolution into a designer role for ReValue, the tension coming from within the organization presented an even thornier challenge. Alongside the ideation and development of the digital service, the traditional recycler service business was still ongoing for the company. This meant that certain individuals, especially client-facing lower-level sales employees, did not see the value and purpose of the digitalization drive. Veteran employees valued the personal contact with the customers and struggled to accept the processes associated with digitalizing the service offer – e.g. selling a subscription to a customer portal as opposed to a traditional sale. Some employees were scared they would lose their jobs, or not be able to work with new systems and applications.

Changing the role of a focal firm  137 Accordingly, employees started feeling like they had double roles within the organization, and identificatory confusion arose. To address such resistance, ReValue engaged in institutional work targeting work practices. Employees were trained extensively on new processes and the potential of digitalization was thoroughly communicated. In a one-off company event, the ‘digital night’, top management explained the importance of the new direction ReValue was taking and afforded some time for a productive confrontation between dissenting organizational members. By offering an open space for communication and allowing employees to interact with a mock-up version of the customer portal, the complex entity of the ‘digital’ was demystified and made more understandable. In addition, employees who were previously not involved in project work started to have a say as to how best to develop the digital solutions. As shown in this quote: The digital night helped the organization communicate that these digitalization solutions were not magic. On top of that, you now had lower-level employees, for example in data entry, that could now work on project implementation and voice their opinion.

Yet, some employees still struggled to adopt a wholehearted enthusiasm for the digitalization wave of ReValue. To prevent this from posing immobilizing danger, ReValue engaged in another internal institutional work strategy, the segmentation and differentiation of organizational identity. The key actors made responsible for pushing forward this industry digitalization initiative were ultimately reduced to top management and the vital interlocutor role of the head of digitalization. Identity claims and implementations were pushed through these selected actors while lower-level employees focused on daily business activities. Through the mediating role of the head of digitalization, and its cross-functional role, the consolidation and growth of the digital service was propelled forward. Translating expectations and requirements from top-management to functional heads, and subsequently from functional heads to all employees, allowed ReValue to offload some of this identificatory ambiguity and pressure from its employees. However, under this constellation, the employees upon which the new organizational identity was to be focused on needed to be fully onboard. With the presence of some dissenters within the top ranks of the organization too, the CEO pushed for a reorganization of the company’s leadership to replace traditional technical and industry-based expertise with innovation-focused management. The appointment of a new head of marketing with a strong background in digitalization was cited as a key enabler of ReValue’s journey. Recycling grown top managers were replaced with businessmen with a strong digitalization and business background.

Thus, through further institutional work, progress was made to normalize the digital service in industry and establish the technology upon which further ecosystem integration should occur. The importance of ReValue‘s identity work – externally and internally – is evidenced by the successful roll out of the digital service, and the foundational work done in preparation of a multisided platform (see Figure 9.2).

138  Handbook on digital platforms and business ecosystems in manufacturing

Figure 9.2

Institutional Work to trigger role change to designer + integrator

Preparing the Multisided Platform and Stepping into Ecosystem Leadership Once the digital service was consolidated, with the associated changes elicited above targeted at instilling new co-creation relationships with customers and an updated internal mission, the management board began thinking about how to trigger the next stage of ecosystem development. If, through the creation of the digital service, ReValue stepped into the designer role, delivering the commercial service for ecosystem benefit, the next objective was a further transformation into an integration role. With the plans to launch a multisided manufacturing industry platform, ReValue aimed to become the mediating and orchestrating hub for B2B recycling interactions, opening up the service delivery to additional recycling firms. As with

Changing the role of a focal firm  139 previous attempts to change the industry logic, the preparation phase of the multisided platform was met with external and internal resistance. The external opposition was rooted in the lack of trust competing recycling firms had towards ReValue and its proposition to mediate their recycling transactions through an industry platform. The digital platform was seen as a competitive threat for other recycling firms, who worried carrying out their recycling business in this way would endanger their existing business. Hence, to drive the industry-level acceptance of this new role for ReValue, external institutional work aimed to build trust amongst potential future value proposition delivery partners. Mimicking the approach chosen for the integration of the customer side of the platform, the initial partners approached for collaboration in service delivery were strategically chosen, operating in regions outside of ReValue’s main business. Relying on the established success of the digital recycling service, the company focused on communicating the added value presented by the digitally integrated approach, highlighting specifically how complementor companies could elevate their value proposition with ReValue’s technology. The pursuit of objectives beyond more than just a solid business case, such as the potential afforded by the digital transparency for sustainability implementations, allowed the company to position its digital proposal as one focused on broader ecosystem value, not just focal firm value. The platform may be going in the right direction, especially when the platform ecosystem contributes to a higher sustainability of each company.

Including these strategically selected recycling firms in design discussion on how the multisided platform ought to look like and operate, ReValue began to trigger new identities and new roles (in terms of contributions to the core value proposition) in the industry. To emphasize this focus on ecosystems thinking, a new company was founded to spearhead the development and operation of the multisided platform, with a strong emphasis on embodying industry values such as professionality, regionality and honesty. In this way, an attempt to foster ecosystem trust was made by presenting the heralding organization of this new structuring of recycling transactions as a neutral broker, concerned with the effectiveness of the platform as an enabler of the entire value chain, not just one recycling business. To this end, a pricing structure encouraging complementors to join the platform and offer their own recycling services was also established. These attempts to generate trust and change industry perceptions about ReValue’s motivations to start an industry platform are characterizable as externally oriented institutional work aimed at triggering new roles and new relationships in value creation. If the digital service enabled the co-creation with the customer, here similar bases were laid for a comparable relationship with service complementors. The internally oriented institutional work needed to enable this transition to an intermediary role continued to address internal misfits and opposition to the new direction the company was taking. While the creation of a separate business mentioned above facilitated the generation of trust and legitimized the claims ReValue was making regarding its focus on ecosystems value, the decision was motivated also by dynamics internal to the organization. As the business model for the multisided platform was being concretized more and more, the tensions between established service business employees and new activities dictated by the envisioned new orchestration role became too salient. The double identity and mission of the company was seen as too contradicting for some employees, to the point that the work done to trigger the new orchestration function was seen as going directly against that of the service

140  Handbook on digital platforms and business ecosystems in manufacturing delivery. Under this context of deep identity conflict, the decision to split the business, and operate the two components of ReValue’s future role (service delivery and orchestration) separately, allowed a sufficient partitioning of activities. The business was split according to the two distinct business models and contributions to the industry value proposition. While the veteran employees continued to operate ReValue’s service business – albeit now digitally in some form – the new organization focused entirely on creating its own role as an intermediary integrator of different recycling offers and recycling demand. To support investment and lower risks, this new organization was started as a joint venture together with a software company. The two entities were managed independently, and different identity claims were made within them to foster employee acceptance. Accordingly, through the combination of external and internal institutional work, ReValue continued its transformation towards an ecosystem leadership role – see Figure 9.3. Importantly, the pressures brought about internally by this transformation pushed the focal firm to establish a new entity in order continue this evolution.

Figure 9.3

Institutional Work to trigger role change to integrator

Changing the role of a focal firm  141

DISCUSSION The transition of a focal firm through the different roles dictated by ecosystem development requires actors external and internal to the firm to accept this transition and legitimize new roles. Through a combination of institutional work, aimed at stakeholders outside the firm and employees within the firm, we illustrate how ReValue was able to navigate the different roles of ecosystem value creation. The model shown in Figure 9.4 outlines the three key phases of ecosystem development, as triggered by the purposive actions of ReValue. In its transition, the firm maintained its traditional service delivery function alongside the new innovations to value creation. Once the shift to a pure multisided platform integration role came, however, the internal tensions caused by conflicting identities led to the creation of a separate business. The major theoretical findings are explored below. The Centrality of Practice Work to Acquire Industry Legitimacy in B2B Given the case study, an interesting contribution can be made to how focal firms can trigger a transformation in industry roles through external institutional work. In particular, the case company deployed concrete practice work to instill a new vision in the industry as to how value could be created more collaboratively. By approaching customers directly, and clearly understanding their priorities, the company actively encouraged the creation of new roles for co-creation and communicated the role of the customer as a key designer of the new value proposition. By highlighting itself how the established service was error-prone and in need of optimization, ReValue leveraged its position as a key source of expertise within the industry to evidence inefficiencies and needs for innovation. This external institutional work of how actors with ‘positional power’ can influence collective identities in industry supports previous theory by Khaire and Wadhwani (2010). Importantly, this institutional work centered mainly around the concrete solution envisioned by ReValue. The role of the customers was successfully changed because the case company had a working and innovative technology to base this upon. Similarly, when the time came to approach potential service complementors, the value of the digital service was a key driver for collaboration. These findings imply that the creation of new collective identity legitimization and role validation can be tied to practical and concrete contributions to value creation, not only discourse or relational work, as suggested in previous literature (Wry et al., 2011). This finding is attributable to the B2B context of the case, where the establishment of trust revolves more around expertise and performance. By including manufacturing industry partners in co-creation and by delivering on promises made for the new horizons of value creation in the recycling industry, ReValue was able to trigger role evolution towards ecosystem thinking. Importance of Institutional Entrepreneurs in SME to Push Role Change Looking to the internal institutional work performed by ReValue, some interesting observations with regards to the role of institutional actors as facilitators of ecosystem development and role change emerge. Previous research on institutional work as a way to update practices according to new logics emphasizes the role of individual identity as the prism through which new practices are accepted by individuals (Gawer and Phillips, 2013). Without a parallel shift in the identity of individual employees, new practices will not be accepted as necessary or

Figure 9.4

Transformation of focal firm role

142  Handbook on digital platforms and business ecosystems in manufacturing

Changing the role of a focal firm  143 legitimate, and new logics will fail to take hold. In the case of ReValue, however, the parallel evolution of organizational identity in line with new roles of value creation was conducted only through specific individuals. Even though lower-level employees remained central to the delivery of the updated value proposition, the organizational discourse did not change dramatically for them. Instead, the transformation and related activities were mediated through key organizational players, such as the CEO and the Head of Digitalization (King, 2022). In doing so, the initial innovation of the company’s business model did not create any identificational confusion for veteran employees. These findings question the strong emphasis institutionalist perspectives place on the need for a broad-based acceptance of new organizational identities in changing organizations. While it is important for individuals to identify with their daily practices, when practices change gradually and are not accompanied by a serious structural reorganization, employees can continue to operate with preexisting logics and a company can elude the pitfalls of organizational change. Treating the alignment between individual and organizational identities as a construct in perpetual motion, different employees will identify with new organizational roles and discourses at different periods. The transformation of the role was taken up as a strategic long-term plan for ReValue’s top management and changes to the company’s organization were taken gradually. This finding can be related back to the context of the case company. As a resource-constrained SME, the efforts to drive digitalization and envision a new approach to value creation had to be taken exclusively by select employees, as lower-level employees were still needed to drive the established business. Accordingly, in constrained contexts the role of key institutional players within the organization to drive role change is fundamental, echoing the findings of Li et al. (2016). Dealing with Organizational Identity Conflicts in Late Ecosystem Development A third key finding from the study concerns the way in which organizations deal with the eventual confrontation between new and old identities and business models within the company. While the identificatory separation worked for ReValue in the earlier stages of ecosystem development, i.e. approaching the customer for co-creation and rolling out the digital service, the introduction of a radically new approach to value creation, the potential inclusion of additional recycling companies as complementors, caused greater organizational tensions. While previous research has demonstrated that in certain contexts this tension between new and old business can be managed internally (Gawer and Phillips, 2013; Stonig et al., 2022), the reality for ReValue was different. Given its smaller workforce, and greater attachment to practices as dictators of identity, the eventual evolution towards a role which differed significantly from the organization’s established nature, from service delivery to intermediation, caused too many tensions. Hence, to manage this transition and role evolution, the company opted for a split in the organization, with distinct entities focusing on the established recycling business and the new intermediation platform business accordingly. In this way, the identity of employees could once again be closely aligned with their respective activities and business models, with both companies still belonging to a shared group. This strategy suggests a way forward for smaller companies looking to lead innovation and step into a new role, when managing a double identity within one firm is not possible. The creation of a separate entity also facilitated the fostering of trust from other ecosystem partners, helping to establish the new entity as a neutral broker of recycling transactions.

144  Handbook on digital platforms and business ecosystems in manufacturing

CONCLUSION Through our study of this manufacturing and recycling B2B SME, we provided a map for the journey of a focal firm in its efforts to instill an ecosystem collective identity onto its industry and carve out its own role as the mediating leader of the ecosystem. By leveraging its positional power, as a key contributor to the definition of what industry value is, or could be, organizations can influence this development. We rely on the institutional approach deployed by Gawer and Phillips (2013) of treating the triggers of this logic change as forms of institutional work of the focal firm – oriented externally and internally. Analyzing the phenomenon in this way allows for a comprehensive understanding of the interrelated sets of actions that are needed to spur the development towards an ecosystem in manufacturing. While ReValue was attempting to create its new role as an ecosystem leader, it was also attempting to evoke new roles for industry stakeholders – e.g. ideation roles for customers. Accordingly, the changing of a focal firm’s role is highly interrelated with the triggering of the right priorities and external roles as the value proposition evolves. Based on our case study, we identified three key findings underlined by the respective internal and external institutional works. First, our findings reveal that the combination of the involvement of partners and the shared added value shapes a new collective identity and enables a company to foster the role evolution towards ecosystem thinking. Second, our results showed the importance of institutional entrepreneurs to push role change in a company and that the alignment of individual and organizational identities can be time delayed. Third, our results provided ways how to deal with organizational identify conflicts to foster role evolution. The case-context of the study highlights a particular area of novelty of the study as insights on ecosystem leadership for SMEs are few, despite their great potential (Cenamor et al., 2019). In particular, the opportunities presented by the decision to split the organization into two distinct entities – according to the old and new business model – offer valuable insights for resource-constrained organizations looking to engage in similar transformations. Nonetheless, the study is somewhat limited by its single-case methodology and by the specific industrial SME context. Due to the research design of this study, we cannot evaluate the effectiveness of the different internal and external work practices. Quantitative studies are needed to generalize the effectiveness of practices to foster the role change towards ecosystem leadership. For a broader generalization of theory on how focal firms can change their role and drive ecosystem development, more research is necessary in this promising field.

REFERENCES Aarikka-Stenroos, L., and Ritala, P. (2017). Network management in the era of ecosystems: systematic review and management framework. Industrial Marketing Management, 67, 23–36. https://​doi​.org/​10​ .1016/​j​.indmarman​.2017​.08​.010. Adner, R. (2017). Ecosystem as structure: an actionable construct for strategy. Journal of Management, 43(1), 39–58. https://​doi​.org/​10​.1177/​0149206316678451. Adner, R., and Kapoor, R. (2010). Value creation in innovation ecosystems: how the structure of technological interdependence affects firm performance in new technology generations. Strategic Management Journal, 31(3), 306–33. https://​doi​.org/​10​.1002/​smj​.821. Benitez, G. B., Ayala, N. F., and Frank, A. G. (2020). Industry 4.0 innovation ecosystems: an evolutionary perspective on value cocreation. International Journal of Production Economics, 228, 107735. https://​doi​.org/​10​.1016/​j​.ijpe​.2020​.107735.

Changing the role of a focal firm  145 Ceccagnoli, M., Forman, C., Huang, P., and Wu, D. J. (2012). Cocreation of value in a platform ecosystem! The case of enterprise software. MIS Quarterly, 36(1), 263–90. https://​doi​.org/​10​.2307/​ 41410417. Cenamor, J., Parida, V., and Wincent, J. (2019). How entrepreneurial SMEs compete through digital platforms: the roles of digital platform capability, network capability and ambidexterity. Journal of Business Research, 100, 196–206. https://​doi​.org/​10​.1016/​j​.jbusres​.2019​.03​.035. Cenamor, J., Rönnberg Sjödin, D., and Parida, V. (2017). Adopting a platform approach in servitization: leveraging the value of digitalization. International Journal of Production Economics, 192, 54–65. https://​doi​.org/​10​.1016/​j​.ijpe​.2016​.12​.033. Cornelissen, J. P., Haslam, S. A., and Balmer, J. M. T. (2007). Social Identity, Organizational Identity and Corporate Identity: Towards an Integrated Understanding of Processes, Patternings and Products. British Journal of Management, 18(s1), S1–S16. https://​doi​.org/​10​.1111/​j​.1467​-8551​.2007​.00522​.x de Reuver, M., Sørensen, C., and Basole, R. C. (2018). The digital platform: a research agenda. Journal of Information Technology, 33(2), 124–35. https://​doi​.org/​10​.1057/​s41265​-016​-0033​-3. Dedehayir, O., Mäkinen, S. J., and Roland Ortt, J. (2018). Roles during innovation ecosystem genesis: a literature review. Technological Forecasting and Social Change, 136, 18–29. https://​doi​.org/​10​ .1016/​j​.techfore​.2016​.11​.028. Dell’Era, C., Trabucchi, D., and Magistretti, S. (2021). Exploiting incumbents’ potentialities: from linear value chains to multisided platforms. Creativity and Innovation Management, 30(1), 31–46. https://​ doi​.org/​10​.1111/​caim​.12413. Dutton, J. E., Dukerich, J. M., and Harquail, C. V. (1994). Organizational images and member identification. Administrative Science Quarterly, 39(2), 239–63. https://​doi​.org/​10​.2307/​2393235. Eisenhardt, K. M., and Graebner, M. E. (2007). Theory building from cases: opportunities and challenges. Academy of Management Journal, 50(1), 25–32. https://​doi​.org/​10​.5465/​AMJ​.2007​.24160888. Gawer, A., and Cusumano, M. A. (2014). Industry platforms and ecosystem innovation. Journal of Product Innovation Management, 31(3), 417–33. https://​doi​.org/​10​.1111/​jpim​.12105. Gawer, A., and Phillips, N. (2013). Institutional work as logics shift: the case of Intel’s transformation to platform leader. Organization Studies, 34(8), 1035–71. https://​doi​.org/​10​.1177/​0170840613492071. Gioia, D. A., Corley, K. G., and Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: notes on the Gioia methodology. Organizational Research Methods, 16(1), 15–31. https://​doi​.org/​10​ .1177/​1094428112452151. Giotopoulos, I., Kontolaimou, A., Korra, E., and Tsakanikas, A. (2017). What drives ICT adoption by SMEs? Evidence from a large-scale survey in Greece. Journal of Business Research, 81, 60–9. https://​ doi​.org/​10​.1016/​j​.jbusres​.2017​.08​.007. Hein, A., Weking, J., Schreieck, M., Wiesche, M., Böhm, M., and Krcmar, H. (2019). Value co-creation practices in business-to-business platform ecosystems. Electronic Markets, 29(3), 503–18. https://​doi​ .org/​10​.1007/​s12525​-019​-00337​-y. Iansiti, M., and Levien, R. (2004). Strategy as ecology. Harvard Business Review, 82(3), 68–78, 126. Ikävalko, H., Turkama, P., and Smedlund, A. (2018). Value creation in the Internet of Things: mapping business models and ecosystem roles. Technology Innovation Management Review, 8(3), 5–15. https://​doi​.org/​10​.22215/​timreview/​1142. Jacobides, M. G., Cennamo, C., and Gawer, A. (2018). Towards a theory of ecosystems. Strategic Management Journal, 39(8), 2255–76. https://​doi​.org/​10​.1002/​smj​.2904. Kapoor, R. (2018). Ecosystems: broadening the locus of value creation. Journal of Organization Design, 7(1), 12. https://​doi​.org/​10​.1186/​s41469​-018​-0035​-4. Khaire, M., and Wadhwani, R. D. (2010). Changing landscapes: the construction of meaning and value in a new market category – modern Indian art. Academy of Management Journal, 53(6), 1281–304. https://​doi​.org/​10​.5465/​amj​.2010​.57317861. King, D. R. (2022). Strategies for bridging traditional and digital ecosystems. In S. Baumann (ed.), Handbook on Digital Business Ecosystems (pp. 25–39). Edward Elgar Publishing. https://​www​ .elgaronline​.com/​display/​edcoll/​9781839107184/​9781839107184​.00008​.xml. Langley, A. (1999). Strategies for theorizing from process data. The Academy of Management Review, 24(4), 691. https://​doi​.org/​10​.2307/​259349.

146  Handbook on digital platforms and business ecosystems in manufacturing Lawrence, T. B., and Suddaby, R. (2006). Institutions and institutional work. In S. Clegg, C. Hardy, T. Lawrence, and W. Nord, The SAGE Handbook of Organization Studies (pp. 215–54). SAGE Publications Ltd. https://​doi​.org/​10​.4135/​9781848608030​.n7. Li, W., Liu, K., Belitski, M., Ghobadian, A., and O’Regan, N. (2016). E-leadership through strategic alignment: an empirical study of small- and medium-sized enterprises in the digital age. Journal of Information Technology, 31(2), 185–206. https://​doi​.org/​10​.1057/​jit​.2016​.10. Lusch, R. F., and Nambisan, S. (2015). Service innovation: a service-dominant logic perspective. MIS Quarterly, 39(1), 155–75. Moore, J. F. (1993). Predators and prey: a new ecology of competition. Harvard Business Review, May 1. https://​hbr​.org/​1993/​05/​predators​-and​-prey​-a​-new​-ecology​-of​-competition. Navis, C., and Glynn, M. A. (2011). Legitimate distinctiveness and the entrepreneurial identity: influence on investor judgments of new venture plausibility. The Academy of Management Review, 36(3), 479–99. Pagani, M. (2013). Digital business strategy and value creation: framing the dynamic cycle of control points. MIS Quarterly, 37(2), 617–32. Pérez, L., and Cambra-Fierro, J. (2015). Value generation in B2B contexts: the SMEs’ perspective. European Business Review, 27(3), 297–317. https://​doi​.org/​10​.1108/​EBR​-05​-2014​-0045. Reisinger, S., and Lehner, J. M. (2022). Dealing with strategic tensions in digital business ecosystems. In S. Baumann (ed.), Handbook on Digital Business Ecosystems (pp. 63–79). Edward Elgar Publishing. https://​www​.elgaronline​.com/​display/​edcoll/​9781839107184/​9781839107184​.00011​.xml. Rong, K., Hu, G., Lin, Y., Shi, Y., and Guo, L. (2015). Understanding business ecosystem using a 6C framework in Internet-of-Things-based sectors. International Journal of Production Economics, 159, 41–55. https://​doi​.org/​10​.1016/​j​.ijpe​.2014​.09​.003. Shu, R., Ren, S., and Zheng, Y. (2018). Building networks into discovery: the link between entrepreneur network capability and entrepreneurial opportunity discovery. Journal of Business Research, 85, 197–208. https://​doi​.org/​10​.1016/​j​.jbusres​.2017​.12​.048. Stonig, J., Schmid, T., and Müller-Stewens, G. (2022). From product system to ecosystem: how firms adapt to provide an integrated value proposition. Strategic Management Journal, 43(9), 1927–57. https://​doi​.org/​10​.1002/​smj​.3390. Strauss, A. L. (1990). Systematic coding in qualitative research. BMS: Bulletin of Sociological Methodology / Bulletin de Méthodologie Sociologique, 27, 52–62. Sveningsson, S., and Alvesson, M. (2003). Managing managerial identities: organizational fragmentation, discourse and identity struggle. Human Relations, 56(10), 1163–93. https://​doi​.org/​10​.1177/​ 00187267035610001. Wry, T., Lounsbury, M., and Glynn, M. A. (2011). Legitimating nascent collective identities: coordinating cultural entrepreneurship. Organization Science, 22(2), 449–63. Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (Sixth edition). SAGE. Zhu, F., and Furr, N. (2016). Products to platforms: making the leap. Harvard Business Review, April 1. https://​hbr​.org/​2016/​04/​products​-to​-platforms​-making​-the​-leap.

10. Establishing platform-based ecosystems: how automotive manufacturers adapt their value creation to a digital end Nicolas Böhm, Laura Marie-Luise Watkowski and Christoph Buck

INTRODUCTION TO ESTABLISHING PLATFORM-BASED ECOSYSTEMS Digitalization is leading to new products and service offerings that in turn require companies to adapt value creation. In many cases, companies will no longer follow a classical supply chain logic, but instead need to establish a platform ecosystem approach. This trend affects a variety of industries, among which the automotive industry can be found. The industry is of special interest, as it has traditionally been a prime example of supply chain processes. It is currently at the beginning of experiencing a shift towards digital offerings, yet has huge potential attributed to it. Customers expect a vast variety of different digital offerings to be available and continuously extended, especially considering their experiences using smartphones. A study by BearingPoint (2022) quantifies these changing customer needs by stating that 93 percent of all car owners across the US, China and Germany are interested in such offerings. Gao et al. (2016) further predict revenues of up to USD 1.5 trillion in 2030 for digital offerings in and around the vehicle, emphasizing the enormous profit potential for automotive original equipment manufacturers (OEM). Besides, the chance arises to gain shares in an enormously large market, possibly rearranging its dominant structures when following disruptive innovations (Ferràs-Hernández et al., 2017). To take advantage of this change, OEMs nevertheless cannot simply transfer their established supply chain logic to the digital context, as it lacks the dynamics to keep up with the agile collaboration approaches central in the software context, both in speed and volume. This is stressed by the case that most OEMs miss the digital capabilities to create such offerings (Mikusz et al., 2017). To overcome these challenges, i.e., creating additional offerings with high frequency and in large numbers without excessive internal capabilities, establishing platform-based ecosystems (PBE) could be extremely valuable. In distinction to digital platform ecosystems (e.g. Apple’s iOS), the term PBE refers to industries where products are central. Software is added onto hardware products, creating ‘an extensible codebase, which entails the product’s core software functionality and access to the sensor-based product platform’ (Buck and Watkowski, 2023, p. 5). Compared to traditional supply chains, PBEs greatly simplify cooperation with external actors. They primarily rely on external complementors that contribute to the PBE upon availability and by rules set by the platform owner, instead of contractual relationships proven with each supplier individually. Thus, value creation is not solely driven by the OEM and its traditional few suppliers but by a large variety of external complementors. Most interesting, when executed, seemingly this approach potentially results 147

148  Handbook on digital platforms and business ecosystems in manufacturing in a higher amount of new digital offerings and higher publication frequency of digital offerings by making use of increased innovativeness ascribed to external contribution (Hildebrandt et al., 2015). Creating and maintaining a PBE cannot be achieved through a one-fits-all solution. There are fundamentals on which platform ecosystems in general are built up on, yet every manager entrusted with this task should consider the particularities of the industry, as well as the individual needs and expectations of each company. In this context, the automotive industry stands out regarding its extraordinary safety requirements that offerings, physical or digital, need to meet. Besides, its increased complexity, as many offerings must be compatible with different hardware components, like lighting, air conditioning or the braking system, is demanding. Both aspects obviously have consequences on how to integrate contributors. Due to the described relevance for practitioners, and the unique circumstances of the industry, this contribution aims to analyze the most important design choices that must be made by OEMs to establish a PBE in the automotive industry and compare it to current approaches applied by OEMs. First, it is essential to specify which actors are involved (Actors Involved in Platform-Based Ecosystems in the Automotive Industry), what their purpose is within the ecosystem, and what the relationship among the different actors must look like. Next, it must be examined how value creation within the chosen structure is ensured while balancing coherent aspects of governance (Balancing Value Creation and Governance in Automotive Platform-Based Ecosystems). OEMs who aim to establish PBEs need to stimulate contributions while not giving up too much control, otherwise they might lose the traditional position of power they had in their established supply chain. Lastly, it must be discussed how the created value is monetized, i.e. which parties capture the value and in what proportions (How OEMs Capture the Value Created). All the design decisions that must be made lead to an overall strategy for the OEM. The implementation of these design choices in practice is analyzed. By doing so, specific patterns are observed. In the end, from the observations, core strategies are derived that classify current approaches of OEMs (Findings: Platform-Based Ecosystem Strategies across Automotive OEMs).

SPOTLIGHT ON PLATFORM-BASED ECOSYSTEMS AND METHODOLOGICAL PROCEDURE Spotlight on Platform-Based Ecosystems The term ‘platform ecosystem’ has emerged as a salient concept across diverse fields, reflecting its growing importance in shaping modern businesses. This includes the research disciplines of strategic management (e.g. Adner, 2017), production management (e.g. Letaifa, 2014), innovation management (e.g. Eaton et al., 2015), organizational management (e.g. Boudreau, 2012) and information systems (IS) management (e.g. Hein et al., 2020). Given the pervasive use of terms like ‘(digital) platform ecosystem’ or ‘platform-based ecosystem’ in information systems and management literature, researchers have inevitably applied these terms with nuances, underscoring the critical importance of precisely defining our understanding of PBE. Ecosystems in general can be defined as ‘a set of actors with varying degrees of multilateral, nongeneric complementarities that are not fully hierarchically controlled’ (Jacobides et

Establishing platform-based ecosystems  149 al., 2018, p. 2264). Adding onto this definition, platforms typically serve as the hub for the creation of ecosystems. Hence, platform ecosystems follow a modular architecture (Baldwin and Clark, 2000), consisting of a core and complements (Baldwin and Woodard, 2009). The core is the platform provided by the platform owner, while complements refer to additional offerings produced by complementors (Baldwin and Woodard, 2009). Platform ecosystems can then be distinguished by the role digital components play, differentiating digital platform ecosystems (Cozzolino et al., 2021) and PBE. Hence, the former, as the name suggests, exists quasi-entirely in the digital dimension, whereas with the latter, PBE, hardware components remain an important part, to which digital technology is added on top. This leads to the creation of ‘an extensible codebase, which entails the product’s core software functionality and access to the sensor-based product platform’ (Buck and Watkowski, 2023, p. 5), which complementors can leverage to produce additional offerings. To clarify our understanding further, Figure 10.1 depicts the concept. The digital core is added onto the vehicle, i.e. the hardware that is manufactured in the traditional manner. Contribution of additional digital offerings by internal and external developers (or in some cases even suppliers) is in this way enabled, although regulated by the platform owner. The customer compensates the offering financially or through data. The depicted concepts are further clarified in the following chapters. Yet, for the automotive industry, two important observations already arise from this structure. First, a well-coordinated interplay between digital and hardware is crucial (Hanelt et al., 2015; Bohnsack et al., 2021), as it directly affects the driving experience. With high engineering complexity, the technical integration of complementors is advanced. Second, safety concerns are of enormous relevance. Bayer et al. (2016) stress the importance of safety requirements in the interplay between digital and hardware components, highlighting that any failure or IT security gap can have severe consequences. Methodological Procedure As we aim to analyze design opportunities in all important dimensions for PBE of OEM while considering the particularities of the industry, we chose a two-fold approach. First, we reviewed literature, both on platform ecosystems in general and industry-related in specific. Second, we took into consideration a sample of 20 real-world objects. With this approach, we combine theoretical and practical input to be able to address the topic in depth, while simultaneously keeping up with the newest developments in this fast-moving field. For the systematic literature review (SLR) (Webster and Watson, 2002; Kitchenham, 2004), we chose to use the Web of Science (WoS) Core Collection as our database for literature search because it is widely acknowledged by researchers across different fields (Li et al., 2018) and is a comprehensive, multidisciplinary source that covers a wide range of publishers (Clarivate, 2022). To narrow down the results, we apply specific inclusion and exclusion criteria and thoroughly examine the content. Subsequently, we expand the pool of literature using forward and backward searches, as Webster and Watson (2002) suggest. In a first step this procedure is conducted to identify relevant general platform ecosystem literature, returning 12 results (Ghazawneh and Henfridsson, 2013; Gawer, 2014; Eaton et al., 2015; Parker et al., 2016; van Angeren et al., 2016; Adner, 2017; Helfat and Raubitschek, 2018; Hein et al., 2019; Alaimo et al., 2020; Hein et al., 2020; Engert et al., 2022; Tsai et al., 2022). As a second step, it is repeated to identify automotive specific literature, returning nine results (Riasanow et al. 2017; Piccinini et al., 2015; Coppola and Morisio, 2016; Mikusz et

Figure 10.1

Overview on platform-based ecosystems in the automotive industry

150  Handbook on digital platforms and business ecosystems in manufacturing

Establishing platform-based ecosystems  151 al., 2017; Svahn et al., 2017; Svangren et al., 2017; Weiss et al., 2020; Bohnsack et al., 2021; Llopis-Albert et al., 2021). For the analysis of the real-world objects, a sample was selected aiming to be comprehensive, allowing us to depict as many deviating design approaches as possible. Hence, we chose objects differentiated by the scope of their digital offerings, in their founding years and in their country of origin. Thirteen of these firms can be classified as established OEMs, while the remaining seven are newcomers with significant differences in their founding years compared to the other OEMs. The sample includes five OEMs from Europe, three from North America and 12 from Asia. Although the sample selection is limited in some cases due to lack of publicly available information, including conglomerates with multiple brands helps to depict a significant share of the market under the assumption that design approaches are similar across brands.

PLATFORM-BASED ECOSYSTEMS IN THE AUTOMOTIVE INDUSTRY – DESIGN AND PARTICULARITIES Actors Involved in Platform-Based Ecosystems in the Automotive Industry The first important design decision is on the involved actors. Three (groups of) actors are central to PBEs. Namely, these are the platform owner, the complementors and the customers. Platform owner Most OEMs aim to take in the position of the platform owner. This is the entity that architects the platform (Helfat and Raubitschek, 2018) and has the decisive power about the design decisions that are discussed in the following. Nevertheless, they must subordinate themselves to various external factors, like competition (Parker et al., 2016) or size and bargaining power (Adner, 2017). This in turn does not allow decisions without restrictions. There are three options that differentiate the form of ownership: (i) single owner, (ii) consortium (Hein et al., 2020) and (iii) preference-dependent ownership. In single ownership (i), all complementary digital offerings are directly integrated into the owner’s self-developed platform without relying on any partner ownership-wise (e.g. Tesla and Xiaopeng). This approach gives the owner greater control over the platform and more decisive freedom. Additionally, if the OEM takes this position, data privacy can be better ensured. Yet, to implement this approach, the OEM must possess substantial digital capabilities. In the consortium (ii) form, OEM integrate digital platforms into their vehicle that are developed by software companies (e.g. Volvo uses Android Automotive OS, a complete operating system for cars, developed by Google). The OEM acts mostly as hardware provider. In this way, OEMs can leverage the expertise of software companies to quickly establish a well-functioning, user-friendly platform. However, the OEM has less control and is greatly dependent on the software company, possibly reducing their ability to capture value. The preference-dependent ownership (iii) form displays a choice between single ownership and ownership by a consortium and is of special interest in the automotive context. Many OEMs have developed their own platform for their customers to access digital offerings, yet still offer optional integration of Apple CarPlay or Android Automotive. Hence, it depends on the preferences of the customer whether the platform owner relies on a single ownership, i.e.

152  Handbook on digital platforms and business ecosystems in manufacturing the OEM itself, or wants to profit from a consortium of owners. Hence, the approach provides great flexibility and enables the user to choose the ownership model that suits their needs best. However, there is a possibility that the user may not find all the desired offerings on a single platform, leading to the need for complex solutions to utilize both platforms simultaneously. This third option is widely spread among the industry and can be observed at Mercedes-Benz, Cadillac or Volkswagen, but also at many other OEMs. This perfectly depicts how software companies have already established a relevant position in the industry. In the case of Google, more OEMs, like Ford for instance, have announced a switch to the full operating system Android Automotive OS instead of remaining with the optional (and more limited) solution Android Auto (Korosec, 2021). This step should be carefully considered, as these OEMs give up power and hence decrease their ability to capture value in a high-potential market. Complementors Next to the platform owners, the complementors are of interest in PBEs (e.g., Gawer, 2014). They provide additional products or services (Helfat and Raubitschek, 2018) that can be accessed via the platform core and are therefore highly responsible for the value created through the PBE. Complementors in the automotive industry come from various backgrounds and are highly heterogeneous. Thus, we at first differentiate internal and external complementors. The former refers to in-house development (i), the latter is divided into (ii) individuals or groups of individuals, and (iii) companies. Establishing a PBE does not rule out the possibility of in-house development (i) (Helfat and Raubitschek, 2018). Although in-house developers cannot be considered as entirely autonomous, the principle of core and complements still holds. In the case described by Weiss et al. (2020), such a company-internal ecosystem logic is applied. Externally, first, individuals or groups of individuals (ii) can contribute software to the PBE (van Angeren et al., 2016). However, as the requirements for contribution are rather high in the automotive industry in comparison to other industries due to safety concerns and high product complexity, (groups of) individuals are not a primary source of contribution. Therefore, most likely of greatest relevance, there is a large range of companies (iii) that aim to contribute to the PBEs of OEMs. These companies can be subdivided into groups, following two distinct characteristics. They are either (a) digital natives or (b) digital immigrants, and they were either (1) previously involved in the automotive industry or (2) they were not. Digital immigrants that were already involved previously (iii-b1) mainly describes tier 1 to tier n suppliers that can contribute to the PBE as Llopis-Albert et al. (2021) already identified. Typically, they originate from a non-digital background, but like OEMs themselves, they adapt to provide the demanded functions. Examples are the cases of Bosch, who work with BMW to control smart home devices via the voice assistant of the car; or TomTom, who contribute to Peugeot’s 3D Connected Navigation. In contrast, there are companies who do not have any prior relation to the automotive industry but are digital natives with the required competencies (iii-a2). An example for this is Spotify, where in the case of BYD (but also many others) an application is directly available via the infotainment system of the car. The same is the case for applications created by Tencent for LiAuto. Lastly, this description also includes most applications that are available via Apple CarPlay or Android Auto. However, these were not specifically created for individual OEMs but are an adapted version of the ones available via the corresponding stores outside the vehicle.

Establishing platform-based ecosystems  153 Arising from the ongoing digitalization of the automotive industry, there are companies that can be described as digital natives that are related to the industry (iii-a1). These are start-ups who utilize vehicle data to provide additional digital offerings. A popular example is car insurance offerings based on usage data of the car. The fourth possible combination (iii-b2), i.e. companies that neither have native digital competencies nor experience in the industry, are unlikely to occur as complementors in an automotive PBE. Customers The last group of relevant actors involved in automotive PBEs are customers. Here we can differentiate between business-to-business (B2B) and business-to-consumer (B2C) approaches (Hein et al., 2019; Tsai et al., 2022). Popular platform ecosystems outside the automotive industry that focus primarily on B2C are Apple’s App Store and Uber. Opposite to that, an example of nonautomotive PBEs that focus on B2B are IBM’s enterprise services. In the automotive industry, digital offerings for the B2C market strongly dominate. Nevertheless, there are some OEMs that also aim to serve the B2B market with digital offerings. Examples are Volvo’s Dynafleet, although precisely it aims at trucks rather than cars, Ford’s Commercial Solutions, or We Connect Fleet by Volkswagen. These offerings, however, are provided by the OEMs themselves, presumably in cooperation with selected suppliers as complementors, resulting in a strongly restricted ecosystem. Balancing Value Creation and Governance in Automotive Platform-Based Ecosystems The fundamental objective to be achieved by establishing PBEs in the automotive industry lies in creating a more attractive offering and hence additional value for the customers with high frequency and on a large scale by leveraging external digital capabilities. As a platform owner, the OEM is in the position to design value creation mechanisms that serve this purpose and incentivize complementors. However, at the same time, OEMs must control the contributions and establish rules for the PBE, on the one hand, to secure their own position of power, and on the other hand, to ensure order and safety of the PBE. This issue is referred to as governance. Balancing value creation and governance is the central challenge for platform owners in any PBE. In this context, the term boundary resources is introduced. Boundary resources describe ‘the software tools and regulations that serve as the interface for the arm’s length relationship between the owner and the application developer’ (Ghazawneh and Henfridsson, 2013, p. 174). The concept was made popular by Ghazawneh and Henfridsson (2013) and is established among ecosystem researchers (e.g. Eaton et al., 2015; Hein et al., 2019; Tsai et al., 2022), also already specifically for the automotive industry (e.g. Svangren et al., 2017; Weiss et al., 2020; Bohnsack et al., 2021). It is important to notice that Ghazawneh and Henfridsson (2013) include within the term ‘applications’ not only applications in the narrow sense but also other services and systems that are developed to complement the platform core. The idea of boundary resources acknowledges the duality of value creation and governance, as boundary resources serve both a resourcing and a securing function (Ghazawneh and Henfridsson, 2013). Regarding the resourcing, i.e. value creation function, boundary resources can be separated into three types, following Engert et al. (2022), based on Dal Bianco et al. (2014) and

154  Handbook on digital platforms and business ecosystems in manufacturing Petrik and Herzwurm (2020): (i) application boundary resources, (ii) development boundary resources, and (iii) social boundary resources. Application boundary resources (i) (Engert et al., 2022) are used to make the connection between the core platform and complements technically feasible. They are mainly application programming interfaces (APIs). Next, development boundary resources (ii) (Engert et al., 2022) describe resources that are provided to complementors to facilitate the actual process of development. In practice, they can commonly be found as (parts of) software development kits (SDK). More precisely, development boundary resources can be code libraries or templates, development environments, performance assessment tools or tools for debugging, for instance. Lastly, social boundary resources (iii) (Engert et al., 2022) aim to improve communication between the different parties. They help platform owners to clarify how specific application or development boundary resources can be utilized, for example via documentation and how-to guides. But social boundary resources also provide channels in the opposing direction. Complementors are provided with support contacts or developer forums, where they receive help during the development process. Regarding these resources, PBEs in the automotive industry do not differ greatly from those in other branches of industry. More interesting is the specific selection of boundary resources that an OEM offers to external complementors. Examples can be found on the Ford Developer Marketplace, the Toyota Developer Portal or the developer platform by Mercedes. With these kinds of boundary resources, the hurdles of contributing digital offerings to the core product should be lowered as much as possible. Complementors should be able to create offerings in a highly efficient manner, decreasing time and money spent on the development process. Thus, platform owners benefit from a higher frequency of developments and a higher number of complementors, motivated by the favorable conditions. This contrasts with boundary resources that serve a securing function. In a supply chain logic, OEMs usually are in the position of power to implement a top-down governance structure, i.e. strict hierarchies. Within PBEs, the approach needs to be adapted. Too strict governance mechanisms jeopardize the potential of PBEs for value creation. Also in this function, there are different types of boundary resources: (i) qualitative, (ii) financial, (iii) legal and (iv) technical boundary resources. Qualitative boundary resources (i) refer to quality checks that digital offerings need to pass. These tests either evaluate the content of the offering (van Angeren et al., 2016) or the performance of the offering (Weiss et al., 2020). In the automotive industry, General Motors has established an approval and certification process, whereas Toyota clearly defines preferences for offerings developed by complementors already beforehand, steering contribution in this way. Apart from that, in some PBEs complementors can only participate if they pay an access fee (van Angeren et al., 2016), representing financial boundary resources (ii). In the automotive industry, OEMs sometimes charge fees for the data accessible via APIs, as seen at Mercedes and BMW. Next, boundary resources that imply a governance function can be of legal nature (iii). First, there are legal restrictions that are voluntarily imposed by the platform owner to achieve control over complementors. These include standard licensing agreements, terms and conditions agreements, proprietary right agreements, privacy policy statements or individual contracts. Second, there are legal restrictions imposed by law, which OEMs need to integrate

Establishing platform-based ecosystems  155 or complement adequately with self-imposed boundary resources. For the automotive industry these legal boundary resources are of crucial interest, as responsibilities should be clearly defined at places where safety is essential in and around a vehicle. The last category, technical boundary resources (iv) are similar to the application boundary resources mentioned in the context of value creation and mainly refer to APIs. In this double functionality, they not only make the integration feasible but at the same time exclude offerings that do not fit the technical requirements. At many OEMs to access APIs further registration is needed, which can also be seen as a technical boundary resource. With these technical boundary resources, platform owners limit contribution, yet they also ensure technological security (Piccinini et al., 2015) to avoid getting hacked (Svangren et al., 2017), and hence contribute to the safety of the PBE. Again, this supports the central need for security that is of unique interest in the automotive industry. From an individual mixture of resourcing and securing boundary resources within a PBE, additional digital offerings are developed that ultimately create value for customers (Svangren et al., 2017; Bohnsack et al., 2021). These offerings can be categorized into three groups: (i) digital stand-alone offerings, (ii) product-based digital extension (inside the car) and (iii) product-based digital extension (outside the car). Digital stand-alone offerings (i), refer to a variety of offerings that are entirely digital, most like those known from smartphones. They only require an operating system (OS) to run on. The OS can be an infotainment system in the car, on which the offerings are directly installed, as in the case of Volkswagen or LiAuto. Some OEMs even integrate (some of) the offerings directly into the OS without the necessity of additional downloads; this can be seen at Tesla or Nio. Alternatively, a detour via a smartphone can be taken. In this case, offerings are installed on the mobile device, which is connected to the car. The output is then displayed on the infotainment system and can be controlled by the buttons of the vehicle. This concept is made possible by Apple’s CarPlay and Google’s Android Auto. Due to the relatively high similarity to existing PBEs for these offerings, not much industry-specific knowledge is required from a technical perspective. This is also noticeable in the selection of complementors. Common are news and radio applications, music streaming services or navigation applications, but also Tesla’s karaoke function or Nio’s voice assistant fall into this category. Next, additional offerings can be partially digital and product-based, meaning they rely on interaction with a vehicle’s hardware. On the one side, these offerings can create value inside the car (ii). Popular offerings are over-the-air (OTA) updates that unlock additional hardware functionalities. At BMW, it is possible to unlock a heated steering wheel, while Mercedes optionally offers to increase the steering angle for models with rear-axle steering. On the other side, there are offerings that create value in the environment, i.e. outside the car (iii). Here, apps for remote controlling are popular among OEMs. Further, smart home connectivity is increasing in relevance for the industry, use-based car insurance policies emerging, and many more offerings that use data and interfaces provided by the vehicle. B2B customers benefit from similar new offerings, too. Based on telematics, fleet management systems like Volvo’s Dynafleet can help to optimize fuel use, maintenance, or support planning functions. As seen, product-based offerings, both inside and outside the car, strongly contribute to the uniqueness of PBEs in the automotive industry. Here, OEMs can differentiate their offerings and use their product knowledge as an advantage compared to their competitors with a software background. Hence, it is crucial to make optimum use of these competencies to create maximum value for the customer.

156  Handbook on digital platforms and business ecosystems in manufacturing How OEMs Capture the Value Created By establishing a platform ecosystem logic new to the industry and in this way changing value creation mechanisms, OEMs inevitably need to rethink how they capture this value. Traditionally, following a supply chain logic, customers purchase a finished product by making a one-time payment. In addition, over the lifetime of the vehicle, service and repair creates additional revenue. With digital offerings the compensation logic changes. In terms of (i) financial compensation (Riasanow et al., 2017; Hein et al., 2020), we therefore differentiate (i-a) one-time payment and (i-b) subscription models, based on Bohnsack et al. (2021). Besides that, as digital offerings create the opportunity to gather valuable customer data, we further differentiate the concept of (ii) data-based compensation, as recognized for example by Alaimo et al. (2020), Piccinini et al. (2015) and Llopis-Albert et al. (2021). One-time payments are rarely used to compensate digital offerings by OEMs. One of the few cases is, for instance, the opportunity to buy single OTA-updates at Tesla. More popular is the use of subscription models (i-b), as recurring revenue streams can be created. Within this strategy, some OEMs follow the concept of offering separate subscriptions for separate functions (e.g. BMW), some offer one subscription that includes all additional offerings available (e.g. Hyundai) and others are in between, creating a variety of packages of certain offerings (e.g. General Motors with OnStar). However, currently, OEMs also include these functions at no additional costs or gift a free subscription phase when purchasing a vehicle. In the latter case, it remains to be seen whether this is a long-term strategy or only supports ambitions to quickly gain market share in this new segment. Data-based compensation (ii) moves into the focus of OEMs and the complementors of the PBE, as it helps to optimize user experience and create individual and more suitable offerings, resulting in increased revenues in the future. Data can either replace financial compensation or complement it. Popular use cases for the collected data are live-traffic navigation, fleet management in the B2B context or use-based car insurance. Furthermore, developments in autonomous driving heavily rely on such data. Apart from that, developers, especially of in-car applications, collect non-car-related data and hence treat the vehicle as an additional channel for user interaction. In some rare cases, there is no data gathered, and no financial compensation must be paid by the user. Alternative motives for creating such offerings could be an underlying market entrance strategy or the goal to establish an audience that can be monetized for advertising. Obviously, other ways of capturing value are thinkable. So far, the focus was mostly on the compensation transferred between the customer and OEM or complementor. Yet also the interaction between OEMs and complementors demands to be examined due to the changing logic created by PBE. Precisely, this refers to the (i) sharing of compensation that is taking place among the parties or (ii) that is not taking place (Mikusz et al., 2017; Svahn et al., 2017). When following the supply chain logic, suppliers receive a previously negotiated payment in exchange for the product or service they provide. In contrast, in PBEs, usually complementors are not paid directly in exchange for their developed offering by the platform owner; instead, they get a share of the revenue the offering generates over time (i). Especially free digital offerings further rely on individual revenue streams designed by the complementor, like music streaming subscriptions. In such cases, how much compensation the platform owner receives depends on the individual situation. Nevertheless, it cannot be ruled

Establishing platform-based ecosystems  157 out that the OEM does not receive compensation at all (ii). The benefit lies then solely in the increased attractiveness of the OEM’s vehicle due to the increased number of offerings. Besides financial compensation, data can be shared as well. Complementors can access data via APIs. In many cases, however, complementors must pay the OEM to receive the data necessary to create their offering, in contrast to the usual approach in supply chains where suppliers are paid for their contribution. This concerns mostly offerings outside the car. Due to the unique heterogeneity of the offerings in the automotive industry, so far little standardization regarding compensation sharing can be observed. In addition, information on this issue is rarely publicly accessible, increasing the difficulty of analysis even more. As a reference from another industry, it is known that Apple offers a 70/30 split to its third-party developers (Eaton et al., 2015). Such standards cannot be expected in the automotive industry in the near future yet could be developed in a maturing process of the business model in the industry.

FINDINGS: PLATFORM-BASED ECOSYSTEM STRATEGIES ACROSS AUTOMOTIVE OEMS As seen, there are numerous choices for OEMs to design a PBE that fits their needs and expectations. Despite the levels available to customize PBE, in practice, more distinct strategies can be observed within 20 analyzed OEMs. These can be distinguished into four distinct strategic approaches, i.e. premium strategist, cost-value strategist, digital native strategist and hybrid strategist. In the following, we will outline the four strategies that comprise most OEMs and their design choices to establish PBEs. Premium Strategist In this case, platform owners aim to provide customers with a selection of high-quality digital offerings, enabling a differentiating experience inside and outside the car to be made (e.g. Mercedes, BMW, Cadillac). The wide range of offerings is therefore essential, as these OEMs want to meet their customers’ needs as comprehensively as possible. Consequently, all three of the previously described types of offerings can be observed in this strategy. Regarding product-based digital offerings inside the car, Mercedes, for example, offers to unlock an increased steering angle for rear axle steering or a beginner’s driving mode with reduced performance. At BMW, steering wheel heating or a parking assistant can be found as additional offerings, and Cadillac offers Super Cruise Driver Assistance, which allows hands-free driving. This is the dimension of digital offerings, where premium strategists have the highest chance for differentiation, as many other OEMs do not have comparable offerings at all (although Cadillac as premium strategist deviates from this approach). For product-based digital offerings outside the car, this opportunity is limited. Here, the customer experience and functions available when using the remote app is decisive and again the number of available offerings adds value to the core product, although in comparison to the offerings inside the car, the scope is decreased. In this dimension Cadillac uniquely positions itself with multiple safety-related features via their OnStar services. Lastly, concerning established digital offerings, the possibility for differentiation is restricted. Offerings around radio and music streaming or navigation are available at nearly every OEM. Differentiation is only created through

158  Handbook on digital platforms and business ecosystems in manufacturing an outstanding execution of these offerings or in rare cases by providing uncommon offerings like Mercedes’ office package. Apart from the B2B offerings, all named OEMs also focus on B2C customers, emphasizing their position to create a fully comprehensive offering. To realize these digital offerings, premium strategists most likely rely on a great number of complementors. This is due to the strong heterogeneity given when creating such a variety of offerings. Nevertheless, contribution seems rather restricted, meaning that these OEMs have a strong interest in ensuring that only selected complementors are involved in the ecosystem. Most of the digital offerings at premium strategists are available as subscription to a package or individual offering. At all three OEMs mentioned, there is an extensive online store where these offerings can be bought. As their customers are typically highly concerned with the quality and value high functionality, they have a higher willingness to pay for these offerings. This pricing strategy aligns with the pricing strategy for the core product, i.e. the vehicle itself for the OEMs examined. Cost-Value Strategist Here, the goal is to provide digital offerings with the utmost value, while keeping costs low compared to the premium strategists (e.g. Hyundai, Ford, Peugeot and Mazda). It aims at customers whose needs are less demanding, but who are more price sensitive. Therefore, the scope of offerings is less extensive. Product-based digital offerings inside the car are not available at the observed OEMs. Some of the functions that can be subscribed to at the premium strategists can obviously be purchased and built in during production, if desired. The flexibility of subscribing and unsubscribing to these features, however, is not given in this traditional approach. Regarding product-based digital offerings outside the car, all OEMs offer an app. This offering seems central to cost-value strategists, as the apps include a rather broad variety of functions. Entirely digital offerings are mostly provided relying on Apple CarPlay and Android Auto as interfaces to mirror smartphone applications onto the infotainment system of the car. Besides that, cost-value strategists focus on navigation, where information on live traffic, weather, parking, or fuel and charging stations is provided. The number of complementors is lower than in the case of the premium strategists due to the reduced number of offerings, yet external competencies are integrated. Lastly, cost-value strategists stand out by their pricing strategy. Basic functions like smartphone integration are available for free, whereas additional offerings can be subscribed to. At the observed OEMs the customer, however, only needs to purchase one single subscription to unlock all available features. On top of that, with the purchase of the vehicle, in many cases, a subscription is gifted for up to three years. Digital Native Strategist These OEMs do not look back on a great history of engineering but were founded in more recent times. Therefore, from the beginning, they were put in an increasingly connected and digitalized environment, which is why they can be described as digital native OEMs (e.g. Tesla, Nio, LiAuto and Xiaopeng). They understand themselves rather as software companies than manufacturing entities. Their brief existence is their biggest advantage. Unlike OEMs that have been operating for the last 50 years or longer, digital native strategists could fully focus on setting up their company from the beginning in the right way to be optimally prepared to take on the currently emerging challenges. Therefore, they have a timely advantage over

Establishing platform-based ecosystems  159 OEMs that need to redesign fundamental organizational structures to provide the required dynamics. Further, digital native OEMs can create much of their offerings in-house, as they possess the digital competence. Therefore, the PBE in this case can be characterized as rather closed. The modular approach is nevertheless of importance. The OEMs examined here, do not offer product-based digital offerings in the sense of premium strategists. The hardware configuration needs to be determined before purchase. Product-based digital offerings outside the car are like the remaining OEMs integrated into an app. What strongly differentiates digital native strategists is the way established digital offerings are provided. All examined OEMs run their own rather sophisticated operating system that is equipped with various functions and in-car apps that are either available via an in-car app store or directly in the operating system. The offerings cover similar dimensions as with other OEMs, i.e. music streaming or live-traffic navigation, but also include more rare offerings, like video streaming or karaoke apps. The user experience is complemented by OTA updates that improve the operating system regularly. As digital native strategists understand the car as a software product, almost any offering is accessible for free. Only in the case of Tesla is there a premium functionality subscription that adds additional offerings. Hybrid Strategist This strategy emerged during the analysis when examining the case of Volvo. This OEM does not fit any of the so-far discussed strategies yet shows elements of all three, i.e. the premium strategist, the cost-value strategist and the digital native strategist. Thus, the OEM is labeled hybrid strategist. The main differentiation is Volvo’s cooperation with Google. Since the OEMs do not develop an own operating or infotainment system, but use a third-party software, they recreate the approach of the digital native strategists by leveraging the competencies of a software company. There is a great variety of in-car applications accessible via the in-car Google Play Store without the necessity of a phone. Product-based digital offerings inside the car are also not available, like the digital native strategists. In contrast, product-based digital offerings outside the car include an app (as with all other OEMs observed), but also smart home integration, a rather rare feature. After all, the scope of features available is more extensive than with cost-value strategists, yet not as versatile as with premium strategists. What still reminds of the premium strategists, is the availability of a B2B offering and its scope. Volvo’s Dynafleet provides various offerings, like optimizing fuel consumption, routing, or the integration of a fleet into a company’s IT-infrastructure that can be purchased individually, much like the concept of the premium strategists. In their pricing strategy Volvo follows a similar concept to the cost-value strategists. As the Google Services are fully included with the purchase of the vehicle for four years, after that time they must be renewed via a subscription model, hence one subscription is sufficient to use all digital offerings available.

DISCUSSION Our research contribution is fourfold and adds specific knowledge on the design of PBE by automotive OEMs. First, we foster transparency on the design space of PBE in the automotive context derived from literature. By extracting and classifying relevant insights, we capture the vibrant research strand that evolved around the topic and foster its tangibility for practitioners.

160  Handbook on digital platforms and business ecosystems in manufacturing Second, the design space is practice-evaluated and thus it promotes high relevance to serve as a design guide for automotive OEMs. Automotive OEMs benefit from our classification as it depicts the ground rules for orchestrating interactions in a PBE. The strategic rationale for releasing resources is of particular importance here. For example, before releasing resources, platform owners must evaluate cause-and-effect relationships and weigh the extent to which tapping new value creation potential by integrating third parties outweighs the risks associated with opening internal resources. Third, as stated above, the market dynamics require agile reactions to its demands. Literature, as well real-world objects, reveal the dynamics that underlie the continuous management of a PBE. This in turn forces OEMs to change their strategy regarding the made design choices, i.e. iterative strategic decision making. Thereby, value co-creation must be dynamically managed to stay competitive. Our overview assists practitioners to better adjust their strategy, depicting dependencies in current approaches. Fourth, we condensed knowledge and practices into specific strategies that are applied and sustain as our main contribution. The four strategies, i.e. premium, cost-value, digital native, hybrid, are common but not exclusive. Thus, automotive OEMs can define their own strategy within the design choices, while some dependencies (e.g. remaining wholly governance power while choosing an external system) must be considered. The relevance of our research is stressed by further movements in the market. Today, software companies like Apple and Google sense their opportunity in how digital services altered the automotive industry. Specifically, they leverage their expertise and push into a highly lucrative market. From the perspective of the OEMs, this burdens additional complexity to the challenge they face in establishing a PBE. They can either enter competition with all risks and opportunities or instead cooperate, although this most certainly includes a loss in power, and subsequently, revenue. Regardless of the further pressure and its outcome, creating and maintaining a PBE is not a one-size-fits-all solution. Instead, we identified different approaches in practice and their underlying strategies. Practitioners can benefit from this chapter in terms of analysis, structuring and applicability.

CONCLUSION Summarizing, we state that this complex and heavily interrelated research topic, touching various disciplines, gained structure through our analysis. We derived four strategic approaches that OEMs currently follow, when establishing PBEs. The strategic approaches vary in terms of involved actors, how value is created, how value is captured and how governance is applied. Overall, OEMs see a promising avenue in establishing PBE to enhance their business model. We further discuss our results within three key-takeaways. First, in literature, PBEs are analyzed regarding the type of involved actors, how value is created by involving actors, how a platform owner captures value with PBE and the role of governance as a strategic mechanism. These four categories enable researchers and practitioners to better analyze and compare strategic approaches taken by OEMs. Thus, we build the foundation to conduct a more rigorous analysis. Second, in the scholarly discourse, we recognize that platform ecosystems are increasingly addressed. This applies to various research disciplines such as information systems, strategic management, organization management and production management. Nevertheless, the focus of most research lies primarily on software-based companies that act as digital natives

Establishing platform-based ecosystems  161 regarding PBE. Product-based companies instead are burdened by the challenge to enhance their physical product-base with digital offerings. Thus, known approaches from digital native companies such as Apple with IOS and Google with Android do not adequately address the needs of product-based companies. Third, by analyzing 20 real-world examples we can derive a holistic picture on current strategic approaches including real-world examples from Asia, America and Europe. Further, the analyzed examples showed various levels of digital maturity. Thus, our overview on strategic approaches can act as a guideline for OEMS starting to establish PBEs and for existing PBEs and platform owners to challenge their current model regarding actors, value creation, value capture and governance. Summarizing, the topic reveals heterogeneity that comes to light when analyzing real-world examples. As scholarly literature, on the contrary, persists in a primarily homogenous analysis of software centered PBEs, we need to adapt the focus of analysis. This, in turn, will contribute to increasing significance of theoretical findings for practical application.

LIMITATIONS AND FURTHER RESEARCH This paper does not claim to have dealt with the subject conclusively. There are some limitations to which this research is subject. For some real-world examples only limited information on PBE was accessible. This specifically narrowed the analysis on how actors are involved (boundary resources) and what compensation lies behind their involvement. Moreover, the sample objects are, as the name suggests, only a selection of OEMs. Although the sample contains a great variety of companies from different countries, of different ages and with varying product portfolios, a residual chance remains that an OEM with a strongly different PBE design was not considered. However, these circumstances provide various opportunities for further valuable research. This research only depicts a current state and motivates the analysis of how PBEs are changing, either by taking into consideration how the whole industry develops or how certain OEMs adapt over time. Furthermore, this research lays out the design decisions that a platform owner faces and describes which options are chosen by a selection of OEMs. However, the research is less concerned with evaluating these decisions, as this would exceed its scope. When widening the scope and not only considering the automotive industry, one could also apply a similar approach to other branches of industry. PBEs are a trend that did not only find its way into the automotive industry but many other branches as well, strongly interrelated with the emergence of the Internet of Things. To conclude, as seen in these suggestions, the topic of PBEs in general and regarding the automotive industry in specific, is far from exhausted, which is why future research in the field is welcomed.

REFERENCES Adner, R. (2017) Ecosystem as structure: An actionable construct for strategy. Journal of Management, 43(1), 39–58. Alaimo, C., Kallinikos, J. and Valderrama, E. (2020) Platforms as service ecosystems: Lessons from social media. Journal of Information Technology, 35(1), 25–48. Baldwin, C.Y. and Clark, K.B. (2000) Design rules: The power of modularity. MIT press.

162  Handbook on digital platforms and business ecosystems in manufacturing Baldwin, C.Y. and Woodard, C.J. (2009) The architecture of platforms: A unified view. Platforms, markets and innovation, 32, 19–44. Bayer, S., Enderle, T., Oka, D.-K. and Wolf, M. (2016) Automotive security testing – the digital crash test. In: Energy Consumption and Autonomous Driving. Springer, pp. 13–22. BearingPoint (2022) Digital services promise enormous revenue growth for automotive manufacturers, personal communication. 14 July. Available from: https://​www​.bearingpoint​.com/​en/​about​-us/​ news​-and​-media/​press​-releases/​digital​-services​-promise​-enormous​-revenue​-growth​-for​-automotive​ -manufacturers/​[Accessed 17 April 2023]. Bohnsack, R., Kurtz, H. and Hanelt, A. (2021) Re-examining path dependence in the digital age: The evolution of connected car business models. Research Policy, 50(9), 104328. Boudreau, K. J. (2012) Let a thousand flowers bloom? An early look at large numbers of software app developers and patterns of innovation. Organization Science, 23(5), 1409–27. Buck C. and Watkowski L. (2023) How automotive OEMs can lever platform-based ecosystems through the strategic use of boundary resources. International Journal of Automotive Technology and Management, in publication. Clarivate (2022) Trusted publisher-independent citation database. Available from: https://​clarivate​.com/​ webofsciencegroup/​solutions/​web​-of​-science/​. Coppola, R. and Morisio, M. (2016) Connected car: technologies, issues, future trends. ACM Computing Surveys (CSUR), 49(3), 1–36. Cozzolino, A., Corbo, L. and Aversa, P. (2021) Digital platform-based ecosystems: The evolution of collaboration and competition between incumbent producers and entrant platforms. Journal of Business Research, 126, 385–400. Dal Bianco, V., Myllärniemi, V. and Komssi, M. (2014) The Role of Platform Boundary Resources in Software Ecosystems: A Case Study. In: 2014 IEEE/IFIP Conference on Software Architecture. Eaton, B., Elaluf-Calderwood, S., Sørensen, C. and Yoo, Y. (2015) Distributed tuning of boundary resources. MIS quarterly, 39(1), 217–44. Engert, M., Evers, J., Hein, A. and Krcmar, H. (2022) The Engagement of Complementors and the Role of Platform Boundary Resources in e-Commerce Platform Ecosystems. Information Systems Frontiers, 24, 2007–25. Ferràs-Hernández, X., Tarrats-Pons, E. and Arimany-Serrat, N. (2017) Disruption in the automotive industry: A Cambrian moment. Business horizons, 60(6), 855–63. Gao, P., Kaas, H. W., Mohr, D., and Wee, D. (2016). Automotive revolution-perspective towards 2030: How the convergence of disruptive technology-driven trends could transform the auto industry. Advanced Industries, McKinsey & Company. Gawer, A. (2014) Bridging differing perspectives on technological platforms: Toward an integrative framework. Research Policy, 43(7), 1239–49. Ghazawneh, A. and Henfridsson, O. (2013) Balancing platform control and external contribution in third‐party development: the boundary resources model. Information Systems Journal, 23(2), 173–92. Hanelt, A., Piccinini, E., Gregory, R. W., Hildebrandt, B. and Kolbe, L. M. (2015) Digital transformation of primarily physical industries – exploring the impact of digital trends on business models of automobile manufacturers. In: Wirtschaftsinformatik Proceedings 2015. Hein, A., Weking, J., Schreieck, M., Wiesche, M., Böhm, M. and Krcmar, H. (2019) Value co-creation practices in business-to-business platform ecosystems. Electronic Markets, 29(3), 503–18. Hein, A., Schreieck, M., Riasanow, T., Setzke, D. S., Wiesche, M. and Böhm, M. et al. (2020) Digital platform ecosystems. Electronic Markets, 30(1), 87–98. Helfat, C. E. and Raubitschek, R. S. (2018) Dynamic and integrative capabilities for profiting from innovation in digital platform-based ecosystems. Research Policy, 47(8), 1391–99. Hildebrandt, B., Hanelt, A., Firk, S. and Kolbe, L. (2015) Entering the digital era – the impact of digital technology-related m&as on business model innovations of automobile OEMs. In: ICIS Proceedings 2015. Jacobides, M. G., Cennamo, C. and Gawer, A. (2018) Towards a theory of ecosystems. Strategic Management Journal, 39(8), 2255–76. Kitchenham, B. (2004) Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1–26.

Establishing platform-based ecosystems  163 Korosec, K. (2021) Ford vehicles will be powered by Google’s Android operating system. Available from: https://​tcrn​.ch/​2MeTjMT [Accessed 27 November 2022]. Letaifa, S. B. (2014) The uneasy transition from supply chains to ecosystems: The value-creation/ value-capture dilemma. Management Decision, 52(2), 278–95. Li, K., Rollins, J. and Yan, E. (2018) Web of Science use in published research and review papers 1997–2017: A selective, dynamic, cross-domain, content-based analysis. Scientometrics, 115(1), 1–20. Llopis-Albert, C., Rubio, F. and Valero, F. (2021) Impact of digital transformation on the automotive industry. Technological Forecasting and Social Change, 162, 120343. Mikusz, M., Schäfer, T., Taraba, T. and Jud, C. (2017) Transforming the connected car into a business model innovation. IEEE 19th Conference on Business Informatics, 247–56. Parker, G., van Alstyne, M. W. and Jiang, X. (2016) Platform ecosystems: How developers invert the firm. Boston University Questrom School of Business Research Paper, (2861574). Petrik, D. and Herzwurm, G. (2020) Boundary Resources for IIoT Platforms – a Complementor Satisfaction Study. In: ICIS 2020 Proceedings. Piccinini, E., Hanelt, A., Gregory, R. and Kolbe, L. (2015) Transforming industrial business: the impact of digital transformation on automotive organizations. In: ICIS Proceedings 2015. Riasanow, T., Galic, G. and Böhm, M. (2017) Digital Transformation in the Automotive Industry: Towards a Generic Value Network. In: Proceedings of the 25th European Conference on Information Systems (ECIS), 3191-3201. Svahn, F., Mathiassen, L. and Lindgren, R. (2017) Embracing Digital Innovation in Incumbent Firms: How Volvo Cars Managed Competing Concerns. MIS quarterly, 41, 239–53. Svangren, M. K., Skov, M. B. and Kjeldskov, J. (2017) The connected car: an empirical study of electric cars as mobile digital devices. In: Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services. Association for Computing Machinery: New York, NY, USA, pp. 1–12. Tsai, C.-L., Ahn, J. M. and Mortara, L. (2022) Managing platform-based ecosystems in B2B markets – out-bound open innovation perspective. International Journal of Technology Management, 89(3-4), 139–62. van Angeren, J., Alves, C. and Jansen, S. (2016) Can we ask you to collaborate? Analyzing app developer relationships in commercial platform ecosystems. Journal of Systems and Software, 113, 430–45. Webster, J. and Watson, R. T. (2002) Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS quarterly, 26, xiii–xxiii. Weiss, N., Wiesche, M., Schreieck, M. and Krcmar, H. (2020) Learning to be a platform owner: how BMW enhances app development for cars. IEEE Transactions on Engineering Management, 69(6), 4019–35.

11. Connecting the ecosystem: enabling interaction between manufacturing companies Jonas Kallisch

INTRODUCTION Business ecosystems are already an integral part of many companies. The trend of developing business networks that interact with each other in a variety of ways, reminiscent of natural ecosystems, can be seen in many industries. Ecosystems are most common in IT-related sectors, such as telecommunications, games, smartphones or social media. Jacobides et al. define a Business Ecosystem (BE) as ‘a set of actors with varying degrees of multilateral, non-generic complementarities that are not fully hierarchically controlled’ (Jacobides et al., 2018, p. 2264). Their research has shown how ecosystems emerge and evolve and what distinguishes them from other forms of collaboration, including markets, alliances or supply chains. According to their definition, an important difference between ecosystems and supply chains is that, in a supply chain (SC), ‘the hub (OEM, or buying firm) has hierarchical control – not by owning its suppliers, but by fully determining what is supplied and at what cost’ (Jacobides et al., 2018, p. 2265). Accordingly, one of the main differences is the degree of control a single participating firm has over joint value creation, as the participating firms involved work together detachedly and do not imperatively require hierarchical coordination relationships. The ways companies gain value from such a complex environment have been shown by Shipilov and Gawer (2020), while others, like Adner, have provided concepts and frameworks to orchestrate ecosystems (Adner, 2017). However, even with this mindset, the control inside a BE differs significantly from the orchestration provided by an OEM in an SC. This difference indicates why manufacturing companies have yet to adopt the transition to BEs widely but remain in rigid, centrally controlled SCs. The following section presents the research questions posed in this chapter.

RESEARCH QUESTION The difference between a BE and an SC is the degree of control over the collaboration among stakeholders (Jacobides et al., 2018). Based on the perspective presented in this chapter, it can be observed that many manufacturing companies continue to operate in SCs in their production and have not moved to BEs. At the same time, their products gradually become part of ecosystems. This observation raises the question: Why are these companies staying in their SCs and possibly taking disadvantages by not improving their processes and production activities? This chapter attempts to answer the following questions: 1. What are the obstacles and barriers to the cooperation of companies within BEs? 2. What opportunities can BEs offer to enterprises in the manufacturing sector? 3. What approaches can be used to realize these opportunities? 164

Connecting the ecosystem  165 To answer these exploratory questions, this chapter will first examine the state of the linkage between companies in the manufacturing sector. This state will show that manufacturing companies are lagging behind other sectors in the evolution towards ecosystems. The chapter will then attempt to identify this state’s causes, obstacles and barriers and show how manufacturing companies differ from other companies that have already moved into BEs. Next, the chapter will show what technical requirements these companies have. Therefore, the chapter will use existing ecosystem examples to illustrate the cooperation limits between manufacturing companies. These examples of SCs that evolved into industrial BE are also described to show the benefits compared to the current state of most manufacturing SCs. Finally, the chapter describes existing approaches and technical implementations for transforming SCs into BEs. The concepts, models and applications identified are explained, as is their suitability for use in industrial BEs.

METHODOLOGY The research design combines a systematic literature review and expert interviews to evaluate the current state of industrial SC and determine if the observation that manufacturing companies keep in their SCs is supported. A systematic literature review has been used to identify the relevant literature on evolving BEs from existing SCs, especially in the manufacturing sector. The portals ScienceDirect and Google Scholar have been used to identify the relevant papers. ScienceDirect has been chosen because of its extensive research collection, the most fully accessible papers, and extensive manufacturing and logistics management literature base. Google Scholar allows more publishers to access papers, books and journals, since it lists a wide range of sources, including academic publishers, universities and institutional repositories. This extensive coverage increases the likelihood of finding relevant research materials. For the research, the search terms ‘data analytics’ and ‘manufacturing’ were used, in combination with the terms ‘supply chain’, ‘value chain’, ‘business ecosystem’ and ‘transition’. This search was supplemented with ‘data exchange’, ‘data chain’ and a combination of the terms ‘manufacturing network’, ‘ecosystem’, ‘intercompany’ and ‘third party’. We linked the terms individually with ‘AND’. In a second block, the search terms were ‘manufacturing supply chain’ and ‘business ecosystem’ in combination with ‘barriers’, ‘challenge’ and ‘example’. We included a contribution if it was a document-type article, conference paper, or book chapter and if the contribution established a connection to the topic of manufacturing BEs and/ or SC transition. The review identified a total of 141 sources. Contributions were excluded if not written in English or German, had no named author, consisted only of an abstract or had not been peer-reviewed, leaving 36 papers found to be relevant. Of these, 21 papers were used in this chapter. In addition to the literature review, experts were surveyed to answer the research question. This was conducted in two series of interviews and covered different groups of nine companies in the die-casting industry, as well as various representatives of NGOs, including the Chamber of Industry and Commerce in Germany. The results were intended to complement and, at best, confirm the findings of the literature analysis. The core of the survey was the interactions of the companies with other companies, the nature of the exchange relationships, whether and, if so, what data is exchanged, as well as how the exchange is organized and technology used.

166  Handbook on digital platforms and business ecosystems in manufacturing

STATE OF SUPPLY CHAIN CONNECTION The results of the literature review show that SCs can be seen as a precursor of BEs and share many characteristics (Faber et al., 2019). However, as mentioned in the introduction, the systems differ in the degree of control of the participants, or in that in a BE, none of the participants performs a coordinating function. In contrast, in an SC, the participants are firmly connected, and the nature of cooperation is usually organized in detail and contractually (Holweg et al., 2005). The controlling organization, usually a company, defines the rights and obligations of the respective participating ones and is responsible for integrating new partners. The coordinating function is thus the key element of an SC. The difference between SC and BE is particularly apparent in platform systems, which involve many partners who do not have direct interactions (Hein et al., 2018). An excellent example of this are apps on smartphones. Many of these apps only provide the value they do to the customer through the existence of other, independent apps, and not in exchange with them. The collaboration between the companies providing the services takes place indirectly through the platform. Digital communication systems also enable a significant part of this collaboration in an ecosystem (Boley and Chang, 2007). These systems are often designed to be very open and require little effort to join an ecosystem. In the example of the app, the company only needs to operate the interface of the platform operator and can offer services to customers. It can be concluded that extensive (digital) networking is a fundamental requirement for an ecosystem (Bogers et al., 2019). Manufacturing companies would therefore have to operate this to further develop an SC into an ecosystem. To clarify whether this is the case and, thus, whether the operation of ecosystems between manufacturing companies has already begun, we look at the results of the literature review and the expert interviews regarding the exchange relationships of manufacturing companies. According to the literature and supported by the industrial experts in the interview series, the exchange between companies interacting with each other can be divided into two layers, the information flow and the flow of goods, as presented in Figure 11.1 (Melo et al., 2009). The experts in the interviews stated that their SC management is managed at the business layer with support of enterprise resource planning (ERP) systems. The decisions influence the flow of goods by creating orders at the operations planning level. Therefore, the usage of automated business connections has changed how companies work together. Nevertheless, the cooperation is still managed and controlled by a member of the SC. Therefore, the companies are still stuck in an SC and have yet to evolve their cooperation to an ecosystem with more options for the individual members and fewer costs for the controlling organization. Nevertheless, approaches to the further development of SCs into BEs exist in the company’s existing connections. A largely standardized environment creates the basis for simple and efficient communication between companies, even outside the established SCs, particularly at the ERP level of manufacturing companies (Bytniewski et al., 2020). On the other hand, there are no similar (data) structures at the manufacturing system level. Even in companies with identical or successive manufacturing infrastructures, the (IT) systems used sometimes differ significantly. Thus, linking this manufacturing level is associated with hurdles. Efficient order placement in an ecosystem is only possible with an efficient way of transmitting data on the product to be manufactured. The ability to communicate quickly and easily with potential suppliers generates significant benefits for ecosystems.

Figure 11.1

Coordination in a supply chain (based on Kallisch and Wunck, 2022)

Connecting the ecosystem  167

168  Handbook on digital platforms and business ecosystems in manufacturing Another result of the literature analysis is that manufacturing companies are often already involved in ecosystems. However, this does not refer to the manufacturing systems or the cooperation with customers and suppliers but rather to the products manufactured by companies (Wirth and Thiesse, 2014; Weking et al., 2020). These are increasingly part of ecosystems. This was independent of the type of customer – private or business – that they were supplying. For example, modern machines provide extensive communication systems for interaction with production control systems and are thus part of the ‘shop floor’ ecosystem. Services such as predictive maintenance by the manufacturer, networking with in-house logistics and thus more efficient material flows within the company, or even the simple reporting of energy consumption and the resulting increased cost transparency for the buyer of a system are just a few examples of the ecosystems in which the products of manufacturing companies are integrated (Schmidt and Wang, 2018). However, these products of the companies are produced in a controlled SC. Only the final products are introduced into an ecosystem. To understand this distinction between product and production system, we can consider an example from the literature review. An example is the company Shapeways, which is a part of the overall 3D-printing ecosystem and is, at its core, a manufacturer providing infrastructure. Therefore, the company is a compelling example to show the observed distinction. Figure 11.2 shows the ecosystem of the company Shapeways (Shapeways, 2022) and the SC operated by Shapeways. The company provides 3D printing services to business and residential customers who can order customized products through a simple ordering system (Wirth and Thiesse, 2014). The basis of this ordering system is the standardization of data formats for 3D objects. The Shapeways ecosystem integrates designers, logistics service providers, portals for 3D objects, or even payment service providers. Thus, the company’s customers benefit from services not controlled by this provider. A simple example of this is ordering a 3D object that a designer offers as a file. However, as can also be seen in the lower part of Figure 11.2, Shapeways operates an SC to produce the products distributed in an ecosystem, which the company regularly controls (Quantitative Insights, 2021). Roughly speaking, this SC ranges from the supply of various raw materials to the selection of the appropriate equipment, and from the decision made beforehand to purchase this equipment to the quality inspection by a service provider. All these instances are controlled and managed by Shapeways. It is, therefore, clearly an SC, as there are no options for independent entry into the structure. Although Shapeways also integrates 3D printing service providers that expand their own capacities, their integration, unlike that of designers of 3D objects, is not possible for every company. Instead, individual agreements are made. On the one hand, this approach makes sense from the company’s point of view, as it ensures the quality of the products, among other things. However, it also shows that, even with uniform production processes, ecosystems are not used, at least in this example. Now that the example has shown the disconnect between a company’s manufacturing system and its ecosystem in relation to its product, the chapter will look at the possible causes for this state of affairs. The first approach is to consider what distinguishes manufacturing systems from systems in which ecosystems have already been established.

Connecting the ecosystem  169

Figure 11.2

Ecosystem of Shapeways (own illustration)

SPECIFICS OF ECOSYSTEMS WITH MANUFACTURING COMPANIES Now that the chapter has provided evidence that many companies remain in their SCs, the research question of why they remain in the established SCs and do not use the more agile and less planning-intensive structure of BEs can be discussed. To answer this question, a comparison between the characteristics of manufacturing companies and companies whose core is an ecosystem might be helpful. Furthermore, by comparing the literature on existing ecosystems, some conclusions can be drawn as to what characteristics companies need in order to integrate their products and/or their production system into an ecosystem. The first difference identified in the literature between companies or parts of companies located in ecosystems can be identified with regard to their range of products (Zhang and

170  Handbook on digital platforms and business ecosystems in manufacturing Fan, 2010). Thus, with regard to existing ecosystems, they seem to be based primarily on digital products or products with high digital value-added content. Here, using the example of Shapeways, it can be shown that the digital value-added share consists of the design and digital order acceptance and inspection of the 3D objects. This share is implemented in an ecosystem where, for example, the designers of 3D objects can provide services without effort and collaborate with other portals or service providers. The part of the physical service, i.e. the manufacturing of the 3D component, from the selection of the manufacturing site, the process and the material, is handled in an SC, into which providers of 3D printing services cannot join without complications. There are many possible reasons for this difference, but some differences can be identified between the distribution system in the ecosystem and the production system in an SC. Therefore, a significant point suggested by experts and supported by literature is that the physical production system is not scalable at will (Pidun et al., 2019). While the digital ecosystem can accommodate and show new designs and designers to prospective customers at any time, a production system is subject to certain limitations. The provider of a production system cannot scale the production volume at will. Service production requires time and resources, which require lead times and planning. The necessary materials and/or raw materials are subject to these limits likewise, and with some raw materials, the number of possible sources of supply is small. Coordination of the procurement is thus compellingly necessary. In the case of digital services, such as the design of 3D objects, the required resources are generally within the supplier’s reach. In addition, scaling of the output quantity is possible with almost no effort, since a file created once can be used and duplicated as often as desired. In addition to this difference, another identifiable difference is that production systems are investment-intensive, as the experts from the die-casting companies mention that their infrastructure has high value. Digital products typically have a cost of production, but no capital commitment is required to offer and deliver the service. In the Shapeways example, the production system requires 3D printing systems, testing equipment, storage systems for various materials, floor space and factories. The cost of these manufacturing systems also represents a difference because the equipment is designed for specific purposes and interacts with specific systems (Suuronen et al., 2022); therefore, their costs must be at least covered by the output quantity. Thus, entering such a production system represents a greater financial risk than entering a digital value-added system. In addition, the profit of a production system generally increases only linearly with the output quantity, while digital products with low unit costs have high-profit potential. In the expert interviews with company employees, another difference became apparent, covered by the literature: the fact that manufacturing systems, particularly for products with a high level of vertical integration, often have highly heterogeneous machine parts, and their communication is significantly more complex than that in most (digital) ecosystems (Lin and Solberg, 1992; Liu and Jiang, 2016). As a result, the machines often have to be set up at great expense and, in some cases, are incapable of automated order processing. Here, the digital representations, for example, the data structures of the orders, can also vary within an industry. In addition, direct communication of shop floor data between companies reveals sensitive information about production that could, under certain circumstances, reveal intellectual property. Therefore, many companies have little opportunity to develop an ecosystem based on the shop floor.

Connecting the ecosystem  171 Despite the possible causes of a lack of transition from SCs to BEs identified in this section, further developments to such systems are conceivable. The research question posed for this purpose is what benefits such implementations can bring and, thus, whether the effort will pay off for the companies. The following section will describe the benefits of an ecosystem between manufacturing companies.

OPPORTUNITIES OF ECOSYSTEMS FOR MANUFACTURING COMPANIES The second research question in this chapter concerns the benefits of ecosystems for manufacturing companies. This question can best be answered with a view to the possibilities for integration and with a view to existing ecosystems. First, it can be mentioned that a BE might deliver the same benefits to a manufacturing company that it would create in any other company. Suuronen et al. (2022) found that these benefits are as follows: ● New business opportunities: Attracting new customers with a complete continuum of products and services. ● Value co-creation: Intensity of collaboration will rise if enough companies join a data platform and share their information. ● Innovation increase: They suggest that the use of collaboration and data platforms leads to a reduction of time to market and increases the innovatively of companies involved. ● Gain competitive advantages: The paper found that firms in alliance relationships are more likely to survive than others. To do this, they compare the manufacturing sector with that of software providers and app stores. ● Resources and knowledge increase: Companies involved in an ecosystem can rely on common goals and therefore act to reach this. By that, a company can control some resources and knowledge to act in a changing market. ● New venture potential: They describe that platforms deliver opportunities for new ventures, which may threaten established members but create advantages for the whole system. ● Cost and risk management: The paper describes that the structure of an ecosystem delivers risks to companies that cannot adapt fast. The opportunity is that companies involved can quickly switch between different suppliers and customers to adapt to market changes. ● Provides modularity to fulfill customer needs: Modular architectures allow more customers’ individual product development. The combination of different products in an ecosystem can adapt to customer demands. The example of the paper is the personal computer, which is a good example of a manufacturing system combined with an ecosystem of software developers using the personal computer’s platform. This analysis has predominantly reviewed the product and sales-related areas of the companies. However, for manufacturing companies, other specific advantages can result from an ecosystem, additional to those described by the study. The first such advantage results from the possibility of better linking of the internal information systems (Mantravadi et al., 2022). As there are some (digital) gaps between digital orders and the processing of production orders on the shop floor, the automation of communication between these levels of manufacturing may result in an overall increase in efficiency (Helo et al., 2014). For example, direct transfer of order-related data from the customer to the suppli-

172  Handbook on digital platforms and business ecosystems in manufacturing er’s manufacturing systems is not possible for most companies. Eliminating the processing of technical order data can lead to significant savings and increase customer satisfaction through the resulting decrease in processing time. In addition to this advantage, comprehensive networking and standardization of order interfaces can enable companies to form virtual business organizations (Minutoli et al., 2009). Such organizations could jointly fulfill large orders or orders that need an extensive vertical range of manufacturing activities, which smaller companies cannot provide alone. The coordination required for this would be eliminated in an ecosystem, as the platform manages the joint production of services. Thus, the problem of scalability described in the previous section could also be addressed. Furthermore, direct data exchange between manufacturing companies can create enormous added value in terms of the sustainability of their manufacturing systems (Hussein, 2019). Defective parts, causes of defects, and process optimizations can be identified faster and better by sharing data than in individual companies. In particular, causes of defects that are not within a company’s manufacturing infrastructure can be very difficult, or impossible, to identify without sharing with other companies.

OPTIONS TO EVOLVE MANUFACTURING SUPPLY CHAINS TO ECOSYSTEMS Now that the previous sections have shown that manufacturing companies have yet to interact in ecosystems, described possible causes of this state of affairs and described potential added values, we will address the final research question. This concerns the question of what options exist for developing SCs into BEs. This question was partly answered by interviewing NGO experts and through the structured literature review. A first and comparatively simple option for building manufacturing ecosystems would be to extend and standardize the existing interfaces of ERP systems (Schmidt and Wang, 2018). Companies could thus automate the process of placing orders to such an extent that even small transactions between manufacturing companies can take place. One problem here may be specific production requirements, which may mean that not all production processes can be mapped in such a process. Larger order volumes, deadline requirements, and other special manufacturing features may also not be mapped or mapped insufficiently by such processes. However, this is comparable to digital ecosystems, in which individual requirements – in the example of Shapeways, a specific 3D object that is individually designed for a customer – cannot be processed via a standardized order function. Nevertheless, the automated order processing of production orders can provide companies with a high degree of flexibility and thus drive the solution from singular SCs. A manufacturing company can then distribute its service to numerous, even competing, customers. From a technical perspective, there has been some progress in developing these standards in the ERP transactional environment. In particular, cloud-based ERP offerings from vendors such as SAP or Microsoft Dynamics increasingly feature collaborative capabilities. Although these are, at present, limited to their own platform, they may be expanded in the future. For users of the systems, this would offer the advantage that no new skills need to be brought into the company to participate in the ecosystem.

Connecting the ecosystem  173 Another option suggested by the literature, and which could further develop manufacturing SCs, is the use of platforms to connect the processes between these companies. There are many approaches to creating such platforms for industrial use cases. One such platform system is GAIA-X (GAIA-X Foundation, 2023), with various subconcepts such as Catena-X (Catena-X Automotive Network e.V., 2023). These provide ways of networking, sharing and analyzing data across organizations; consequently, they provide an anchor point through which BEs can emerge, enabling rapid integration into, and thus participation in, ecosystems. Data can be easily shared through these platforms, making it accessible to all authorized organization members without tight control. However, for the manufacturing sector, platform ecosystems have two problems. Firstly, the data to be shared on the platform poses a risk to the partner’s intellectual property (Södergren and Cartling Wallén, 2022). In this respect, we found, particularly in the interviews, that companies are very cautious about sharing data. The fear of commercial disadvantages must be taken into account here, which is why creating platforms for data exchange and creating BEs must offer companies the certainty that their trade secrets will not be passed on to third parties and cannot be used against them by malicious partners. In addition to this first issue, integration with both ERP and platform systems poses the problem that only some companies can manage their manufacturing infrastructure with the agility of a BE. To enable manufacturing companies to create and actively participate in such an ecosystem, in addition to efficient and automated communication with customers and suppliers, improved, flexible and, where possible, digitized control of order processing is required (Szaller et al., 2020). As already mentioned, the heterogeneous infrastructure on the shop floor is a particular obstacle, as these systems cannot always communicate with each other or with other companies. In addition to the need for more networking and the question of how order data is preprocessed and transferred to the production area, the production system must be able to guarantee uniform data exchange and present the possibilities or capabilities of the infrastructure to the outside world. Standardization of the communication interfaces of this infrastructure can therefore be identified as a necessary entry point into a BE, enabling orders to be routed directly to the production systems. With a view to the existing literature, this chapter presents a concept that can represent the infrastructure in a unified way, thus enabling communication within the production system and other production systems. A technical way to create these direct exchange interfaces is the asset administration shell (AAS) concept. An AAS provides a unified description of a machine, its characteristics and its functions to the outside world (Tantik and Anderl, 2017). Internally, the AAS contains a machine-specific interface that converts the requests coming from outside into machine-specific instructions. The various characteristics of the machine are mapped in different submodels, such as position, orientation or energy consumption, which allow the machine to be mapped in detail. By that, the AAS is able to control its connected asset, for example, a machine or vehicle. An AAS uses different submodels to implement and provide particular services. A submodel could either be a data package or an executable application. By that, a data model could provide information about the represented asset. The other kind of submodel contains an executable representing a defined ability of the machine or other asset (VDI, 2015). As a result, the submodels represent the entire machine with all its abilities and features. The structure of an AAS is shown in Figure 11.3. If each element of a production system has an AAS, it is possible to have the entire production communicate in a unified way, as if it were a homogeneous infrastructure. Another

174  Handbook on digital platforms and business ecosystems in manufacturing

Figure 11.3

Example of an asset administration shell (own illustration)

interesting possibility of using an AAS is the encapsulation of different production machines or even entire production systems. Thus, it is possible to map different workstation machines, each with their own AAS, in a common AAS. In this way, the AAS can close the gap between a manufacturing ecosystem established at the business level and its manufacturing plants by allowing all manufacturing plants and their products to be addressed via a single homogeneous interface. Strictly speaking, in order to create an actual manufacturing ecosystem, it is not the companies’ production systems that need to be made accessible but rather their services. The use of an AAS can only take over the function of communication, rather than that of presenting the services of the company or its production area. Compared to an existing ecosystem, these service descriptions could best be compared to metadata, such as categories or keywords. Such descriptions need to provide orientation. One concept to enable this is the class number (Eclass, 2022). These numbers uniquely describe a manufacturing process, services and goods and thus can describe both the purpose of equipment and the need for products. In combination with the AAS, an e-class number can thus list a kind of catalog of services for networks of companies (Bouter et al., 2021). Tenders from manufacturing companies could thus specify which manufacturing services are needed and select appropriate suppliers based on unique characteristics. In an ecosystem, companies with different capabilities could now jointly bid for contracts with specified services. This would also enable smaller companies to fulfill larger orders without building up a complex SC.

Connecting the ecosystem  175

CONCLUSION This chapter described the hypothesis that manufacturing companies have not yet followed the general trend toward BEs in the broad sense. This hypothesis was confirmed by looking at the literature and interviewing experts. The chapter also showed an obvious disconnect between establishing ecosystems in manufacturing companies and their products. To confirm this, the example of the 3D printing portal Shapeways was used. This company distributes its products in an ecosystem, while the production of the 3D objects takes place in a classic SC. The first research question arising from this was the causes of this persistence in the SC. To this end, the chapter compared the characteristics of manufacturing companies, or the manufacturing parts of companies, with companies already working in ecosystems. It was identified that the heterogeneous structure of the manufacturing systems, the output quantity that cannot be scaled as desired and the investment intensity of the manufacturing infrastructure are the main differences between the comparison groups. A particular challenge, however, is the complexity of communication between manufacturing companies. Here, there are lead times and planning times, but also the challenge of preparing orders for manufacturing infrastructure, as activities need to be specified and adapted to the respective manufacturing control. The second research question was answered following the first question, which related to the added values resulting from a manufacturing ecosystem. This question was answered to the effect that an ecosystem for manufacturing companies offers similar advantages to existing systems. These include more flexible planning, improved communication with suppliers and customers, added value for customers and the expansion of aftersales services. Finally, the research question was addressed as to what (technical) options exist for establishing an ecosystem between manufacturing companies. Concepts were identified for the various levels that make this possible. One promising solution is the use of the concept of the asset administration shell. This can be seen as a mediator between a production control system and the machine-specific control unit. Combined with implementing a standard for describing goods and services, such as eClass, communication with manufacturing infrastructure could be standardized and automated. Thus, product requirements could be clearly exchanged between companies and efficiently transferred to manufacturing infrastructure. This would create a manufacturing ecosystem in which companies can manufacture products with any supplier and exchange them efficiently without having to establish control over these suppliers.

REFERENCES Adner, R. (2017) Ecosystem as structure: An actionable construct for strategy. Journal of management, 43(1), 39–58. Bogers, M., Sims, J., and West, J. (2019) What is an ecosystem? Incorporating 25 years of ecosystem research. SSRN Electronic Journal. Available from https://​doi​.org/​10​.2139/​ssrn​.3437014. Boley, H. and Chang, E. (2007) Digital ecosystems: principles and semantics. In 2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference. IEEE. Bouter, C., Pourjafarian, M., Simar, L., and Wilterdink, R. (2021) Towards a comprehensive methodology for modelling submodels in the Industry 4.0 asset administration shell. In 2021 IEEE 23rd Conference on Business Informatics (CBI). IEEE. Bytniewski, A., Matouk, K., Rot, A., Hernes, M., and Kozina, A. (2020) Towards Industry 4.0: functional and technological basis for ERP 4.0 systems. In Towards Industry 4.0 – Current Challenges in Information Systems. Springer, Cham, pp. 3–19.

176  Handbook on digital platforms and business ecosystems in manufacturing Catena-X Automotive Network e.V. (2023) Catena-X General Presentation. Available from https://​ catena​-x​.net/​fileadmin/​user​_upload/​Vereinsdokumente/​Catena​-X​_general​_presentation​.pdf (accessed 30 March 2023). Eclass (2022) Startseite – ECLASS. Available from https://​eclass​.eu (accessed 27 November 2022). Faber, A., Riemhofer, M., Rehm, S.-V., and Bodel, G. (2019) A systematic mapping study on business ecosystem types. In: Proceedings of the 25th Americas Conference on Information Systems. Can-cun: Mexico. GAIA-X Foundation (2023) Architecture document – Gaia-x – DRAFT version 1702083. Available from https://​gaia​-x​.eu/​wp​-content/​uploads/​2022/​06/​Gaia​-x​-Architecture​-Document​-22​.04​-Release​ .pdf (accessed 30 March 2023). Hein, A., Scheiber, M., Böhm, M., Weking, J., Rocznik, D., and Krcmar, H. (2018) Toward a design framework for service-platform ecosystems. In 26th European Conference on Information Systems. Helo, P., Suorsa, M., Hao, Y., and Anussornnitisarn, P. (2014) Toward a cloud-based manufacturing execution system for distributed manufacturing. Computers in Industry, 65(4), 646–56. Available from https://​doi​.org/​10​.1016/​j​.compind​.2014​.01​.015. Holweg, M., Disney, S., Holmström, J., and Småros, J. (2005) Supply chain collaboration. European Management Journal, 23(2), 170–81. Available from https://​doi​.org/​10​.1016/​j​.emj​.2005​.02​.008. Hussein, A.H. (2019) Internet of Things (IOT): research challenges and future applications. International Journal of Advanced Computer Science and Applications, 10(6). Available from https://​doi​.org/​10​ .14569/​ijacsa​.2019​.0100611. Jacobides, M.G., Cennamo, C., and Gawer, A. (2018) Towards a theory of ecosystems. Strategic Management Journal, 39(8), 2255–76. Available from https://​doi​.org/​10​.1002/​smj​.2904. Kallisch, J. and Wunck, C. (2022) Options for Connecting Decentralized Data Infrastructure to Improve Supply-Chain Decision Making without Giving Up Individual Data Property. Available from https://​dsi​ -​annualconf​erence2022​.exordo​.com/​files/​papers/​289/​final​_draft/​DSI​_22​_Kallisch​_Wunck​_final​_​.pdf. Lin, G.Y.-J. and Solberg, J.J. (1992) Integrated shop floor control using autonomous agents. Iie Transactions, 24(3), 57–71. Available from https://​doi​.org/​10​.1080/​07408179208964224. Liu, C. and Jiang, P. (2016) A cyber-physical system architecture in shop floor for intelligent manufacturing. Procedia CIRP, 56, 372–7. Available from https://​doi​.org/​10​.1016/​j​.procir​.2016​.10​.059. Mantravadi, S., Møller, C., Li, C., and Schnyder, R. (2022) Design choices for next-generation IIoT-connected MES/MOM: an empirical study on smart factories. Robotics and Computer-Integrated Manufacturing, 73, 102225. Available from https://​doi​.org/​10​.1016/​j​.rcim​.2021​.102225. Melo, M.T., Nickel, S., and Saldanha-da-Gama, F. (2009) Facility location and supply chain management – a review. European Journal of Operational Research, 196(2), 401–12. Available from https://​ doi​.org/​10​.1016/​j​.ejor​.2008​.05​.007. Minutoli, G., Fazio, M., Paone, M., and Puliafito, A. (2009) Virtual business networks with cloud computing and virtual machines. In B. Sokolov (ed.), International Conference on Ultra Modern Telecommunications & Workshops, 2009: ICUMT ‘09 ; 12–14 Oct. 2009, St. Petersburg, Russia, Workshops (ICUMT), St. Petersburg, Russia. IEEE: pp. 1–6. Pidun, U., Reeves, M., and Schüssler, M. (2019) Do You Need a Business Ecosystem? BCG Henderson Institute, 11. Quantitative Insights (2021) Unpacking Shapeways: Cathie Wood’s most recent 3D printing investment. Seeking Alpha, 30 April. Available from https://​seekingalpha​.com/​article/​4422764​-unpacking​ -shapeways​-cathie​-woods​-recent​-3d​-printing​-investment (accessed 27 November 2022). Schmidt, B. and Wang, L. (2018) Cloud-enhanced predictive maintenance. The International Journal of Advanced Manufacturing Technology, 99(1–4), 5–13. Available from https://​doi​.org/​10​.1007/​s00170​ -016​-8983​-8. Shapeways (2022) 3D Printing Service Online. Available from https://​www​.shapeways​.com (accessed 27 November 2022). Shipilov, A. and Gawer, A. (2020) Integrating research on interorganizational networks and ecosystems. Academy of Management Annals, 14(1), 92–121. Available from https://​doi​.org/​10​.5465/​annals​.2018​ .0121. Södergren, F. and Cartling Wallén, M. (2022) Creating Value through Information Sharing: Exploring the Transition Towards a Digital Supply Chain. (Dissertation). Retrieved from https://​urn​.kb​.se/​ resolve​?urn​=​urn:​nbn:​se:​umu:​diva​-194154.

Connecting the ecosystem  177 Suuronen, S., Ukko, J., Eskola, R., Semken, R.S., and Rantanen, H. (2022) A systematic literature review for digital business ecosystems in the manufacturing industry: prerequisites, challenges, and benefits. CIRP Journal of Manufacturing Science and Technology, 37, 414–26. Available from https://​doi​.org/​ 10​.1016/​j​.cirpj​.2022​.02​.016. Szaller, Á., Egri, P., and Kádár, B. (2020) Trust-based resource sharing mechanism in distributed manufacturing. International Journal of Computer Integrated Manufacturing, 33(1), 1–21. Available from https://​doi​.org/​10​.1080/​0951192X​.2019​.1699257. Tantik, E. and Anderl, R. (2017) Integrated data model and structure for the asset administration shell in Industrie 4.0. Procedia CIRP, 60, 86–91. Available from https://​doi​.org/​10​.1016/​j​.procir​.2017​.01​.048. VDI (2015) Status Report-Reference Architecture Model Industrie 4.0 (Rami4.0). Available from: https://​www​.zvei​.org/​presse​-medien/​publikationen/​statusreport​-rami​-40. Weking, J., Stöcker, M., Kowalkiewicz, M., Böhm, M., and Krcmar, H. (2020) Leveraging industry 4.0 – a business model pattern framework. International Journal of Production Economics, 225, 107588. Available from https://​doi​.org/​10​.1016/​j​.ijpe​.2019​.107588. Wirth, M. and Thiesse, F. (2014) Shapeways and the 3D printing revolution. In: ECIS 2014 Proceedings—22nd European Conference on Information Systems. Zhang, J. and Fan, Y. (2010) Current state and research trends on business ecosystem. In 2010 IEEE International Conference on Service-Oriented Computing and Applications (SOCA). IEEE.

12. Relevance of technology and focal product for collaboration: exploring additive manufacturing ecosystems Dominik Morar, Simon Hiller and Dimitri Petrik

COLLABORATIVE VALUE CONTRIBUTION IN MANUFACTURING ECOSYSTEMS Digital companies such as Apple, Google and Facebook are impressively demonstrating the dynamics of collaboration in ecosystems. Many firms follow this approach and try to build ecosystems by orchestrating the complementary value creation of third parties (Iansiti and Levien, 2004; Cusumano et al., 2019, pp. 12–14). A key driver for collaboration is digitization. By increasing interoperability (Hodapp and Hanelt, 2022), digitization can lower initiation and transaction costs, enabling the effective procurement of external resources and skills and contributing to efficient value creation (Autio and Thomas, 2018). These affordances can also be achieved in the manufacturing domain. Accordingly, manufacturing companies try to gain the advantages of digitization and create manufacturing ecosystems based on additional sources of competitive advantage related to producing physical products. In doing so, the companies aim to achieve numerous benefits, such as improving the workload – and thus becoming more profitable – or escaping the commoditization of their products (Petrik et al., 2020; Brekke et al., 2023). The underlying understanding of a manufacturing ecosystem utilizes the principles of collaboration between partners from the manufacturing domain as well as service and technology providers to offer tangible goods to the ecosystem customer. Additional nonmanufacturing partners have the chance to enter these manufacturing ecosystems by offering digital solutions or analytics services (Culot, 2022; Hiller et al., 2022; Pfähler et al., 2022). Therefore, the convergence of manufacturing technology and information technology (IT) is a specific feature of a manufacturing ecosystem. Hence, manufacturing ecosystems stem from Adner’s (2017) ecosystem-as-a-structure perspective, which distinguishes an ecosystem by the inter-organizational collaboration of aligned partners to materialize focal value. Prior research noted that ecosystems are often realized through the use of digital platforms as infrastructure for collaboration and innovation (Selander et al., 2013; Cusumano et al., 2019, pp. 124–32). In platform-based ecosystems, the platform companies are considered dominant players and are referred to as ‘keystones’. To create ecosystem dynamics and foster interfirm collaboration, platform providers must successfully master the trade-offs between heterogeneous dimensions of openness and control (Staub et al., 2023). This necessity originates from software ecosystems, where software applications can be connected, exchanged and recombined via the digital interfaces designed by keystones (Sandberg et al., 2020). However, the transferability of software-based platform ecosystem principles to manufacturing ecosystems with the scope to co-create tangible goods is not necessarily a given (Nischak 178

Relevance of technology and focal product for collaboration  179 and Hanelt, 2019). First, the use of specific manufacturing technologies and the focal product have a major impact on the roles of individual value-added actors in manufacturing ecosystems (Culot, 2022; Hiller et al., 2022). Second, manufacturing ecosystems become increasingly underpinned by digital technology stacks, which can be provided by, for instance, by digital platforms (Culot, 2022). Accordingly, manufacturing ecosystems are dependent on IT (Selander et al., 2013; Sandberg et al., 2020). These two determinants, technology and the focal product, impact the strategic decisions of individual companies about positioning in manufacturing ecosystems. This impact can be explained with path dependencies, which enable organizations to perform better over time by routinizing activities and developing specific capabilities (Vergne and Durand, 2011). Technological leaders in manufacturing domains are known to strive toward the manifestation of ecosystems (Suuronen et al., 2022). Nevertheless, existing research emphasizes that participation in ecosystems, which are characterized by environmental dynamism and higher uncertainty compared to value chains, can result in losses and even endanger the financial sustainability of a firm (Valkokari et al., 2017). Therefore, we ask: How important are technology adaption and contribution to the focal product to determine the value contribution of different actors in manufacturing ecosystems? Considering the aforementioned issues, this study analyzes the ecosystemic value creation in manufacturing ecosystems. The objective is to examine the importance of the determinants (1) technology and (2) focal product for collaboration to (3) support the strategic positioning of the different actors in manufacturing ecosystems. The study relies on additive manufacturing (AM) as an exemplary domain for manufacturing ecosystems. AM, also known as 3D printing, is regarded as a potential paradigmatic change in manufacturing. It is considered a suitable technology to economically produce small batch sizes. Using AM makes the place and time of production flexible (Gebhardt and Hötter, 2016, pp. 1–19). Accordingly, the adaption of AM is characterized by significant annual growth (Wohlers et al., 2021, pp. 104–12). Rong et al. (2020) show that the value proposition of AM is of ecosystemic nature. Therefore, existing (sequential) manufacturing structures are affected by the integration dynamics of new AM-oriented actors. At the same time, the value proposition of AM aims to manufacture tangible goods, making AM suitable for exploring the roles and collaboration determinants in manufacturing ecosystems. Drawing from a systematic literature review and 15 interviews with different actors within AM ecosystems, this paper presents the existing roles in AM ecosystems and their positioning options based on two collaboration determinants: focal product and technology. Combined, the roles and determinants can be considered by decision-makers during the strategic positioning in manufacturing ecosystems to contribute non-generic value propositions in ecosystems (Jacobides et al., 2018). Understanding focal product and technology as substantial contributions to the focal product provide ecosystem actors a foundation to align or even orchestrate a manufacturing ecosystem. Consequently, this paper analyzes the positioning options for ecosystem actors to make more informed positioning decisions in manufacturing ecosystems, according to the role and value proposition, based on the two identified collaboration determinants. The findings are based on an empirical study on the collaboration of different roles in the AM ecosystem, which is discussed in the following section.

180  Handbook on digital platforms and business ecosystems in manufacturing

ADDITIVE MANUFACTURING ECOSYSTEMS AND THEIR ACTORS From a generic viewpoint, an AM ecosystem has general strategic principles in common with other ecosystems. However, AM ecosystems have unique properties based on the specifics of AM technology. This section first gives a general understanding of ecosystems and then addresses strategic positioning in ecosystems based on a resource-based view. Finally, it describes the characteristics of AM ecosystems utilizing different roles of essential AM actors. Conceptualizing Ecosystems for Digital Manufacturing Two main properties typify ecosystems: network effects and multilateral relationships between network actors. Indirect network effects influence the value of an ecosystem by self-reinforcing the growth of user numbers. Following this logic, the value of the ecosystem increases (e.g. the wider variety of services) for one actor group with the number of providers within another actor group, and vice versa. Multilateral relationships describe a structure of n:m relationships between ecosystem actors. For the emergence of a value proposition, ecosystem actors cooperate and thereby enrich the focal value with complementary services or innovation. For example, several manufacturers can provide different components and, together with the software development companies and analytics specialists, create intelligent product-service systems by aligning their contributions to the provision of a focal value proposition (Adner, 2017). A key aspect of this collaboration is that the contribution of each ecosystem participant is non-generic, which means others cannot easily replace or imitate it. Consequently, superior value creation can be reached by gaining access to external resources and knowledge of best-in-class technologies or submodules of other ecosystem actors. Thus far, manufacturing ecosystem cases covered by scientific investigation focus particularly on cooperation in providing digital services for tangible products (Autio and Thomas, 2018; Jacobides et al., 2018). Compared to value chains, ecosystems are exemplified by higher dynamics and external uncertainty for the organizations involved. Ecosystem actors can simultaneously cooperate and compete with one another at the level of individual technologies, modules, products or services. This so-called coopetition significantly affects the process of production-oriented value creation, which has so far been predominantly arranged sequentially and linearly (Petrik and Schüler, 2021). To achieve a beneficial position in such a dynamic network environment, strategic positioning in the ecosystem is of great importance (Weiller and Neely, 2013; van Dyck et al., 2021). However, the positioning of companies in ecosystems by assuming specific roles and aligning their development with ecosystem objectives is a common source of conflicts of interest. Such conflicts often result from a lack of understanding of ecosystem roles and development perspectives and can endanger positive ecosystem development (Cusumano et al., 2019, pp. 55–60; Heimburg and Wiesche, 2022). On the contrary, the alignment of ecosystem actors is needed to achieve sustainability in ecosystems (Wormald et al., 2022). Previous research has demonstrated that a strategy focused on the unrestricted growth of ecosystem actors lowers the incentives for ecosystem actors to innovate (Cennamo and Santalo, 2013). Manufacturing firms, in particular, can experience losses from ecosystem dynamism (Adner, 2006; Valkokari et al., 2017) as they were previously embedded in value chains or nondynamic value networks.

Relevance of technology and focal product for collaboration  181 Hence, they experience difficulties positioning themselves in more uncertain environments such as manufacturing ecosystems (Humbeck et al., 2020; Walleser et al., 2022). Strategic Positioning in Ecosystems Manufacturing companies are finding it increasingly challenging to act in a company-centric manner and source all value creation internally. For this reason, identifying and integrating third-party competencies into value creation gains further relevance (Selander et al., 2013). Previous research on digital innovation and service innovation overwhelmingly agrees that a shift toward digital ecosystems is necessary to integrate the resources and capabilities that one company does not possess internally (El Sawy et al., 2010; Anke et al., 2020). Within business management research, competitive advantage was initially defined as the possession of resources that a company can combine to gain a competitive advantage (Barney, 1991). Later, the resource-based perspective was extended to include the capabilities that companies need to flexibly deploy resources and respond to market conditions (Teece et al., 1997). Compared to static value networks and supply chains, ecosystems have relatively permeable entry boundaries (e.g. secured by standardized entry routines), so complementary value creation partners can enter ecosystems relatively easily (Petrik and Schüler, 2021). Although such openness can provide superior customer benefits (West, 2003), it creates uncertainty for ecosystem actors. When joining an ecosystem, the strategy must be defined but the focal value proposition may be unknown ex ante, thereby promoting uncertainty among value-added partners (Wormald et al., 2022). This situation makes the strategic positioning of one’s own value contribution in an ecosystem even more important (Weiller and Neely, 2013). A precise strategic positioning also supports the orchestration of the value-added partners (alignment and optimization of value creation), which ultimately determines the financial success of an ecosystem (Adner, 2017). Without an orchestrating focal enterprise, the alignment of value-added partners is even more complex. The limited research on strategic positioning (Dattée et al., 2018) further fuels the uncertainty of manufacturing companies entering or launching ecosystems. Against this background, ecosystem research distinguishes two basic directions. First, there is the positioning as ‘coordinator’, which is known as an ‘orchestrator’ in a platform-oriented ecosystem. In reality, a coordinator may be the provider of a critical technology (e.g. platform); however, there could also be other company profiles that take on this coordinating role. Second, a company could strive for a position as a ‘specialist’. The specialization is up to those partners that provide a significant and non-generic value proposition (e.g. a technology or module) at one or more layers of the digital technology architecture (Jacobides et al., 2018; Herterich et al., 2022). AM Ecosystem and Roles The characteristics and roles of an AM ecosystem depend on AM technology and AM-specific activities. AM processes are common in creating 3D components through an automated coating of material layer-by-layer. Small batch sizes or complex geometries can usually be realized with AM without additional costs (Gebhardt and Hötter, 2016, pp. 1–19). In conclusion, these potentials create value in terms of AM application. For example, it is not without reason that lightweight components in aerospace were an early area of application for AM

182  Handbook on digital platforms and business ecosystems in manufacturing in industrial use, as large savings often offset the higher production cost during a lifecycle (Wohlers et al., 2021, pp. 39–42). In addition, AM seems predestined for collaborative value creation (Petrick and Simpson, 2013). The potentials presented above (economical production of small batch sizes and freedom of design) favor the specialization of individual actors. For instance, a high degree of freedom in design leads to the development of complementing, simulation-based design services. Another driver of AM-specific collaboration is the manifold possibilities for customer participation (co-creation) regarding the individualization of components (Gibson et al., 2015, pp. 475–85). The realization of the potentials mentioned above is twofold. On the one hand, the digital-related phases of the AM process, specifically design (output: digital product model) and preprocessing (output: build job), offer data-driven approaches such as computer-generated geometry, digital inventory, digital packing of the build chamber, or production planning. On the other hand, the physical-related phases of the AM process, namely, processing and postprocessing, are characterized by the high flexibility of the AM machinery to produce any geometry only restricted by the dimension of the build chamber (Gebhardt and Hötter, 2016, pp. 20–6). The AM process and its digital and physical artifacts are depicted in Figure 12.1.

Figure 12.1

AM process and key artifacts (based on Gibson et al., 2015, pp. 4–6; Gebhardt and Hötter, 2016, p. 14)

Various roles are established around the AM process and the key artifacts to contribute added value. The identified roles in AM ecosystems (see list below) are based on 15 interviews with AM users, service providers, platform providers, IT providers and suppliers. The objective of this qualitative-empirical research design was to identify the value-added contributions of each interviewed firm and understand their cooperation with other value-added partners to identify the value flows in AM ecosystems. Initially, specific value-creation structures and a generic AM ecosystem could be modeled on this basis. Finally, the results were evaluated by four experts from science and practice in terms of their completeness, traceability and added value (Hiller et al., 2022). The roles and their contribution to value creation in the AM ecosystem are described in Figure 12.2 using an exemplary AM component (ventilation grille made of plastic) from the national railway company of Germany (Deutsche Bahn, 2022).

Relevance of technology and focal product for collaboration  183 The AM Consultant provides AM-specific knowledge for other roles within the AM ecosystem. In the example of the ventilation grille, this role contributes to the identification of the spare part’s AM potential out of a pool of parts to consider. The AM Designer & Producer creates added value by designing digital product models and manufacturing tangible AM components. AM service providers are predestined for this role, as the digital product model is analyzed and, if necessary, revised before production. In the example, the contributions are a preproduction design check and the production of the ventilation grille using powder-based laser melting (plastic). The AM IT Solution Provider develops IT solutions that support the value-added activities of various actors. Accordingly, these IT solutions vary widely (from classic stand-alone software to web-based services or system-integrated software). In the example, simulation tools for the AM Value Creator and the AM Designer & Producer represent the contribution of this role. The AM Material Supplier produces different raw materials that are used in the AM manufacturing system. In the given example, plastic powder is used. The AM Material Purchaser reprocesses unused material for use or reuse in other industries (e.g. as granulate for injection molding). The AM Refiner combines activities of postprocessing and assembly of AM parts into components or products. Highly specialized companies (AM service providers or conventional manufacturing providers) with conventional manufacturing processes execute this postprocessing of AM components. In the given example, a surface finishing of the ventilation grille is necessary. The AM Researcher differs from the AM Consultant mentioned above based on their standardization and certification tasks. In AM application areas such as medicine or transport, certified AM products and processes are required in terms of liability during the usage of AM parts. The AM Sales Platform Provider combines sales and AM-specific IT solutions that digitize and automate order placement to match the demand for existing manufacturing capabilities. This intermediary role cooperates with various AM production service providers that receive component designs or possible production orders from AM customers via the platform. In the example, the AM Sales Platform Provider arranges a suitable service provider (AM Designer & Producer) with available production capacities. The AM System Provider develops and sells AM manufacturing systems. Accordingly, the role combines activities in sales, production and service. The AM manufacturing systems and related services are sourced through the AM Designer & Producer role. The AM Value Creator utilizes the AM potential of a product. Since AM production cannot compete in terms of throughput (in comparison to serial production processes), it is crucial to create additional value through the product use (e.g. bionic lightweight structures or individualization) or the process-related benefits (e.g. decentralized production of spare parts). Therefore, this role combines the value-added activities of engineering, consulting, sales and construction. In the example, the contribution of the AM Value Creator is an AM-compliant reconstruction of the ventilation grille. Note that the roles are not exclusive and can overlap in terms of their value contributions. Different actors can also take on multiple roles in an AM ecosystem. Depending on the application, not all roles are necessary to achieve the value-added contribution. The AM user (end customer) can also occupy one or more roles (e.g. co-creator) or just be the recipient of an AM focal product.

184  Handbook on digital platforms and business ecosystems in manufacturing

Figure 12.2

Value contribution of roles in AM ecosystems with example (own presentation)

THE IMPORTANCE OF FOCAL PRODUCT AND TECHNOLOGY IN COLLABORATION The role descriptions already show that attributes of the focal product (e.g. geometry, material, or degree of individualization) and the application of technology are of outstanding importance for collaboration in the AM ecosystem. The technological capabilities of different ecosystem partners are a prominent influencing factor for the quality of the AM component to be manufactured. According to ecosystems theory, each processing stage can be characterized by creating a non-generic complement (Jacobides et al., 2018) to achieve a final added value (focal product). In the following, the focal product and technology in the AM ecosystem are examined and then classified against the background of the different roles. This chapter focuses on the tangible focal product in terms of both variants as a stand-alone AM part and as a component of higher-level focal products. Therefore, the focal product is the targeted outcome of collaborative value creation in the AM ecosystem and is produced through industrial manufacturing processes (Kemper and Lasi, 2015; Corsten and Gössinger, 2016, p. 169). An AM ecosystem is characterized by the following: ● In product development, the cooperation between users (end customers), product developers and producers yields the subsequent product benefit (e.g. requirements assessment and function-oriented design in the AM Value Creator role). Furthermore, the manufacturing costs are largely determined during product development (e.g. by determining batch sizes, manufacturing technology and materials). The AM-ready redesign of the ventilation grille (cf. Section 2) forms a special case, since it enhances a given product specification. ● In production, most AM processes provide a semifinished product as output. Hence, postprocessing is an essential step in AM value creation (Wohlers et al., 2021, pp. 187–8). The

Relevance of technology and focal product for collaboration  185 requirements for the focal product (e.g. visible component, interim component, prototype) define the stages of postprocessing and the actors to be involved. Referring to the ventilation grille, postprocessing depends on the visibility of the grille. If the grille is not visible to passengers, then dispensing with postprocessing might be an option. ● The distribution (and usage) of AM components is indicated by the potential of AM production to be independent of time and location. In addition to conventional distribution structures (e.g. centralized stockage of the spare part), the AM ecosystem allows customer-oriented production (e.g. by decentralized production service providers or on-site production) (Gebhardt and Hötter, 2016, pp. 424–9). This phase is important in terms of added value of the ventilation grille. By using AM, it is possible to manufacture a spare part on demand and reduce storage and transport costs. ● Regarding recycling, AM components are characterized by the fact that, in addition to conventional material recycling, the reprocessing of wearing parts creates additional benefits (Gebhardt and Hötter, 2016, pp. 407–8). Previous research sees the use of technology as crucial to AM-based value creation (Petrick and Simpson, 2013; Moisa, 2020, pp. 169–79) and hence impacts the interaction in the AM ecosystem regardless of the specific focal product. Accordingly, three fields of technology can be distinguished in the AM ecosystem. The manufacturing process field includes the areas of production technology and automation technology (Moisa, 2020, pp. 177–9; Wohlers et al., 2021, p. 60). Mastering individual techniques in these areas leads to different role characteristics – even within the same role. The AM System Provider distinguishes itself based on the range of AM processes offered (e.g. powder-based laser melting), whereas the AM Designer & Producer specializes by integrating the AM processes into operations (e.g. automated process chain from the digital product model to the tangible AM part). In the AM Refiner role, manufacturing technology contains conventional manufacturing processes for postprocessing (or finishing) of AM semifinished products. The material field reflects the importance of materials for AM application. This is due to the fact that AM processes alter material properties during the process (Wohlers et al., 2021, p. 60). For instance, the material properties of metal powder and molten solid material are entirely different. Furthermore, the diversity of materials is high due to hybrid material connections (e.g. the gradual connection of two materials – conductive/non-conductive or hard/soft). In many cases, certain AM machine parameters exist for different materials. Accordingly, the role of AM System Provider can also be defined by the material processed. The AM Material Supplier is responsible for the development and production of the raw material (both with/ without the dependency of the AM System Provider), while the AM Material Purchaser is responsible for material disposal or recycling. The information and communication field encompasses various aspects of integration of information processing, such as process-related, application-related or data-related (Mertens, 2013, pp. 13–15; Moisa, 2020, pp. 177–9). The process-related linking of IT applications with a direct value-added attribute can be found in the role of the AM Value Creator, which can create original value in product development (e.g. generative design, AM knowledge database, manufacturing process simulation, etc.). The AM Sales Platform Provider also links IT applications procedurally, but with the goal of increasing efficiency in order placement or production planning with several dependent partners. Finally, the AM IT Solution Provider role develops and operates the individual IT applications and addresses data/information levels and

186  Handbook on digital platforms and business ecosystems in manufacturing functional level in addition to process level; this also includes hardware-related applications (e.g. drivers/interfaces for AM machines).

DECISION SUPPORT FOR POSITIONING IN AM ECOSYSTEMS For companies that already participate in an AM ecosystem or intend to do so, options for positioning within the AM ecosystem can be derived based on the findings presented in this paper. The importance of the technological dimension in general and IT in particular is concluded in the dependence of the exchange relationships on the individual roles. The research design includes 15 interviews with actors within AM ecosystems, resulting in an already published generic ecosystem model for AM. A detailed model of the value exchange between the roles (at the activity level) can be found in Hiller et al. (2022). The model was first created based on surveys of actors in the AM domain on the roles and cooperation in AM and then evaluated with independent experts. The role-specific consideration of the determinants of focal product and technology – manufacturing process and material as well as IT (c.f., Section 3) – provides a structure to identify possible strategic positionings in AM ecosystems. These determinants are subdivided in terms of the value exchanges and related strategic positionings of the researched AM actors (c.f., Section 2.2). These strategic positionings of the individual roles help to assess the value-added contribution of the AM ecosystem and highlight additional roles to integrate (for cooperation or further development of one’s role). The following examples should illustrate the results (c.f., Table 12.1): 1. Product performance: Both AM Value Creator (contributes AM-specific knowhow) and AM IT Solution Provider (contributes IT-specific knowhow) can take on a specialization positioning in customizing the ventilation grille with an online tool for parameterized individualization of the grille. In this case the strategic positioning relates to the focal product determinant, since its value contribution is enhanced product performance, that is, better fulfillment of customer requirements through individualization. 2. AM technology and manufacturing engineering: The AM System Provider could take a coordinating position in orchestrating suppliers for material and processing techniques to provide a specialized AM system for an on-demand spare parts provision of the ventilation grilles. This AM system could be operated by customers to quickly replace heavily stressed ventilation grilles. In this case, the strategic positioning relates to AM technology and manufacturing engineering, since its value contribution is the provision of a specialized manufacturing system. 3. IT integration of AM production systems: The AM System Provider could also take a coordinating position by providing production planning services for the ventilation grille on the basis of the real-time capacities of AM service providers. In this case, the strategic positioning relates to the IT integration of AM production systems, since its value contribution is based on data integration from different AM systems of different partners. Table 12.1 details the positionings within the AM ecosystem in relation to the role and the determinants focal product and technology. In this analysis, the role of AM Consultant is an exceptional case because of its manifold value exchange with all AM ecosystem actors. Therefore, AM Consultant is not a subject of the following analysis.

Relevance of technology and focal product for collaboration  187 As the comparison of the positioning in the AM ecosystem points out, using the determinants of focal product and technology is feasible to identify central positioning patterns in the ecosystem. It remains to be clarified if a specialization or a coordination position is of strategic advantage for the respective roles, as the AM ecosystems tend to form around specific industry domains. Thus, both positionings constitute viable options for the decision-makers to choose. It could be stated that the roles close to the end customer with the need for the focal product tend toward a coordination positioning. Such would be the AM Value Creator or AM Design & Producer, which enables the full potential of AM. The AM Sales Platform Provider takes on the coordination of multiple actors, and the AM IT Solution Provider provides the backbone of the coordinating position. The specialization positioning can be seen across all roles, which is evidenced by the dynamic and disruptive market of AM. Furthermore, the applications of AM in the focal products have a wide range of industry domains that lead to a niche market and specialization. In addition to the positioning of the individual roles, coopetition (an essential property of ecosystems) is also important in the AM ecosystem. This behavior can be identified in the following constellations: ● Different AM Designer & Producer instances favor competing behavior as they compete for orders as AM production service providers. However, in cases of high demand, the digital-enabled transferability of AM production data could lead to cooperation to increase manufacturing capacities (e.g. outsourcing of AM production jobs). ● The dependency in AM design on knowhow about AM processes means that consulting services for AM design are dependent on a cooperative exchange with AM production. The value proposition of an AM part is mainly determined in the design phase, since AM is highly competitive in terms of performance of parts (not in terms of production throughput). To realize high-performance parts (e.g. lightweight structure), a designer needs to know the specific capabilities of each AM process in the option. Finally, this knowledge transfer could increase the overall value-added potential of the AM ecosystem and lead to further network effects. ● AM Designer & Producer and AM Refiner also tend to cooperate. On the one hand, AM production service providers insource as many refinement levels as possible to map the added value of the entire focal product. As a result, AM production service providers compete for customers by providing unique AM processes and postprocessing. On the other hand, the abundance of (conventional) postprocessing processes leads to specialization in certain postprocessing activities and to cooperation with competitors for postprocessing processes that are less in demand (higher cost-effectiveness with better utilization).

FUTURE OPPORTUNITIES The findings on positioning options in AM ecosystems and cooperation in the AM ecosystem are also subject to certain limitations. The high dynamics in AM ecosystems (technological advancement, consolidation expectations) can limit the expressiveness of the findings. The vresults in this paper would also benefit from further plausibility checks, since the roles presented in particular were derived from a few specific companies.

Focal Product

AM Value Creator

aerospace or medical products) Market research for product-related material requirements

production lines

 

 

 

Brokering of manufacturing orders

  Product certification for domain-specific requirements (e.g.

 

Identify product optimization potentials for customer

of technology), and AM Designer & Producer

customers, AM Consultant (independent selection

AM System Provider

AM Material Supplier

Value Creator

AM Researcher; AM

Provider

AM Sales Platform

Producer

AM Designer &

Creator

 

Collaborative requirements engineering with

Provider; AM Value

AM IT Solution

Role(s)

individualization)

 

Coordination

product performance (e.g. a tool for parameter-based

Development of product-specific IT solutions to optimize

Product-related development of AM Focus on specific industries/markets/products

qualification

Product-specific material

Industry-specific certification

Proof of manufacturability

Product performance

Specialization

Strategic Positioning

Positioning in AM ecosystems for different roles depending on focal product and technology

Determinant and sub-determinant

Table 12.1

188  Handbook on digital platforms and business ecosystems in manufacturing

IT

Technology: Production & Material

Interface for application systems in product development (e.g. shared database for certified AM process parameters)

planning, etc.)

 

co-creation)

of AM-specific application systems Refiner, AM Value Creator) and customer interface

production planning) Alignment to specific areas of application (e.g.

(e.g. order workflow, production

AM System Provider

Producer; AM Refiner;

AM Designer &

AM IT Solution Provider

AM Material Supplier

Provider

AM Sales Platform

Interfaces to AM-specific application systems (e.g. AM System Provider

IT-Platform technology, integration Integration of key roles (AM Designer & Producer, AM

 

automation (sensors, actuators)

application systems)

Increase in automation (efficiency) by IT-supported process

systems (sensors, actuators,

 

Development of individual IT solutions for AM users (e.g. in Integrate third-party services design and production)

IT integration of AM production

AM-specific application systems

AM IT Solution Provider)

Internal integration of AM machinery (in cooperation with

AM IT Solution Provider

Provider

planning  

AM Sales Platform

with AM Designer & Producer)

cooperation with AM IT Solution Provider), e.g. capacity

AM Material Purchaser

Diversification of AM processes (in cooperation

Integration of production processes External (3rd party) integration of AM machinery (in

AM Material Supplier;

Creator

information

Provider; AM Value

Producer and AM Refiner)

production

AM IT Solution

Customer interface for partner-integrated value creation (in cooperation with AM Designer &

Supplier), process engineering, postprocessing

AM System Provider

AM Refiner

Gathering and provision of material-related

Application tools for monitoring AM production processes

Orchestrating interdisciplinary suppliers such as

  material supply (in cooperation with AM Material

 

Complementing AM postprocesses

Producer

AM Designer &

AM Refiner)

Role(s)

Specialization Complementing further AM processes and post-processing

Coordination Third-party postprocessing (in cooperation with

Strategic Positioning

Process engineering for raw material Material recycling

AM-specific knowledge

engineering

AM technology and manufacturing

Determinant and sub-determinant

Relevance of technology and focal product for collaboration  189

190  Handbook on digital platforms and business ecosystems in manufacturing Specific roles in ecosystems could be limited in their options for strategic positioning in ecosystems, whereas others might be predetermined for a dominant position (Reisinger and Lehner, 2022). Therefore, it remains to be seen to what extent companies can position themselves as coordinating keystone companies in AM ecosystems and establish themselves permanently, as in software or platform ecosystems. Based on the determinants of focal product and technology, the recommendations presented in this paper could help identify critical value-added components for a potential keystone function in the AM ecosystem. According to our findings, AM platforms (AM Sales Platform Provider), AM-specific software developers (AM IT Solution Provider) and AM machine manufacturers (AM System Provider) have the highest keystone potentials. In these cases, IT can be understood as an enabler for the development of coordinating functions. It is also possible that the roles in the AM ecosystem remain unstable. In this case, an AM ecosystem could be understood as an open ecosystem without a keystone (Heimburg and Wiesche, 2022). Further research should address this question and identify factors for and against the establishment of keystones in AM ecosystems. Our results complement the existing literature around digital manufacturing ecosystems (Rong et al., 2020) and AM capabilities (Moisa, 2020) at the nexus. Considering the path dependencies of different roles in AM ecosystems, we propose directions for strategic ecosystem positioning that aim to support the alignment of ecosystem actors (Adner, 2017). In addition, identifying the determinants provides a first orientation for classifying the AM ecosystems. If the collaborative value creation is concentrated along the determinant of the focal product, then the AM ecosystem would rather be classified as a business ecosystem. On the other hand, a concentration along the technology determinant indicates an innovation ecosystem. Classification as an AM service ecosystem is also possible by focusing value creation by manufacturing processes as a service. The positioning options of the AM Sales Platform Provider show that platform-based AM ecosystems are conceivable, with transaction platforms forming the digital infrastructure of the ecosystem and the value-added organization provided along the determinants of focal product and technology (Guggenberger et al., 2020). Even if the results can only be transferred to the ecosystems of other domains to a limited extent, our results support decision-makers beyond the AM domain. Regarding manufacturing ecosystems, the identified determinants and the procedure of ecosystem analysis and establishment are transferrable. For instance, ecosystem analysis can be used when positioning a business model in an ecosystem. Positioning in ecosystems can also be used to analyze the potential of achieving a keystone position.

REFERENCES Adner, R. (2006). Match your innovation strategy to your innovation ecosystem. Harvard business review, 84(4), 98. Adner, R. (2017). Ecosystem as structure. Journal of Management 43(1): 39–58. https://​doi​.org/​10​.1177/​ 0149206316678451. Anke, J., Poeppelbuss, J. and Alt, R. (2020). It takes more than two to tango: identifying roles and patterns in multi-actor smart service innovation. Schmalenbach Business Review 72(4): 599–634. https://​ doi​.org/​10​.1007/​s41464​-020​-00101​-2. Autio, E. and Thomas, L.D.W. (2018). Ecosystem value co-creation. Academy of Management Proceedings 2018(1): 15913. https://​doi​.org/​10​.5465/​AMBPP​.2018​.15913abstract. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management 17(1): 99–120. https://​doi​.org/​10​.1177/​014920639101700108.

Relevance of technology and focal product for collaboration  191 Brekke, T., Lenka, S., Kohtamäki, M., et al. (2023). Overcoming barriers to transformation in manufacturing firms. A path-dependence perspective of digital servitization. Review of Managerial Science. https://​doi​.org/​10​.1007/​s11846​-023​-00641​-0. Cennamo, C. and Santalo, J. (2013). Platform competition: strategic trade-offs in platform markets. Strategic Management Journal 34(11): 1331–50. https://​doi​.org/​10​.1002/​smj​.2066. Corsten, H. and Gössinger, R. (2016). Produktionswirtschaft: Einführung in das industrielle Produktionsmanagement, 14th edition. Berlin: De Gruyter Oldenbourg. Culot, G. (2022). Digital ecosystems in manufacturing: emerging models for Industry 4.0. In Handbook on Digital Business Ecosystems (ed. S. Baumann). 727–42. Cheltenham: Edward Elgar Publishing. https://​doi​.org/​10​.4337/​9781839107191. Cusumano, M.A., Gawer, A. and Yoffie, D.B. (2019). The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power. New York: Harper Business. Dattée, B., Alexy, O. and Autio, E. (2018). Maneuvering in poor visibility: how firms play the ecosystem game when uncertainty is high. Academy of Management Journal 61(2): 466–98. https://​doi​.org/​10​ .5465/​amj​.2015​.0869. Deutsche Bahn (2022). 3D Printing as a Digital Manufacturing Technology. https://​www​.db​-fzi​.com/​ fa​hrzeuginst​andhaltung​-en/​Individual​-Solutions/​3D​-druck​-7314184​# (accessed 24 November 2022). El Sawy, O.A., Malhotra, A., Park, Y., et al. (2010). Research commentary – seeking the configurations of digital ecodynamics: it takes three to tango. Information Systems Research 21(4): 835–48. https://​ doi​.org/​10​.1287/​isre​.1100​.0326. Gebhardt, A. and Hötter, J.-S. (2016). Additive Manufacturing: 3D Printing for Prototyping and Manufacturing. München: Carl Hanser Verlag. Gibson, I., Rosen, D. and Stucker, B. (2015). Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing, 2nd edition. New York: Springer. Guggenberger, T.M., Möller, F., Haarhaus, T., et al. (2020). Ecosystem types in information systems. Proceedings of the 28th European Conference on Information Systems (ECIS). https://​aisel​.aisnet​.org/​ ecis2020​_rp/​45. Heimburg, V. and Wiesche, M. (2022). Relations between actors in digital platform ecosystems: a literature review. Proceedings of the 30th European Conference on Information Systems (ECIS). https://​ aisel​.aisnet​.org/​ecis2022​_rp/​93/​. Herterich, M.M., Dremel, C., Wulf, J., et al. (2022). The emergence of smart service ecosystems – the role of socio-technical antecedents and affordances. Information Systems Journal. https://​doi​.org/​10​ .1111/​ISJ​.12412. Hiller, S., Morar, D., and Petrik, D. (2022). Creating value in additive manufacturing – modeling of ecosystem determinants. Proceedings of the 30th European Conference on Information Systems (ECIS). https://​aisel​.aisnet​.org/​ecis2022​_rp/​78. Hodapp, D. and Hanelt, A. (2022). Interoperability in the era of digital innovation: An information systems research agenda. Journal of Information Technology, 37(4), 407–27. https://​doi​.org/​10​.1177/​ 02683962211064304. Humbeck, P., Jaeckle, J.P., Duwe, J., et al. (2020). The business ecosystem management canvas. In 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 249–54. IEEE, Singapore (14–17 December 2020). Iansiti, M. and Levien, R. (2004). Strategy as ecology. Harvard Business Review 82(3): 68–78. Jacobides, M.G., Cennamo, C. and Gawer, A. (2018). Towards a theory of ecosystems. Strategic Management Journal 39(8): 2255–76. https://​doi​.org/​10​.1002/​smj​.2904. Kemper, H.-G. and Lasi, H. (2015). Industrie 4.0 – definitorische Einordnung und Ausblick: Neues Einsatzfeld für BI-Konzepte [Industry 4.0 – definitional classification and outlook: new field of application for BI concepts]. BI-SPEKTRUM (2): 11–13. https://​webreader​.bispektrum​.de/​de/​profiles/​ d3aebfa037f6​-bi​-spektrum/​editions/​bi​-spektrum​-02​-2015. Mertens, P. (2013). Integrierte Informationsverarbeitung 1: Operative Systeme in der Industrie [Integrated Information Processing: Operating Systems in Industry], 18th edition. Wiesbaden: Springer Gabler.

192  Handbook on digital platforms and business ecosystems in manufacturing Moisa, M. (2020). Additive Manufacturing (AM): Entwicklung eines Capability-Konzepts zur Gestaltung AM-basierter Geschäftsmodelle [Additive Manufacturing: Development of a Capability Concept for Designing AM-based Business Models]. Siegburg: Josef Eul Verlag. Nischak, F. and Hanelt, A. (2019). Ecosystem change in the era of digital innovation – a longitudinal analysis and visualization of the automotive ecosystem. Proceedings of the 50th International Conference on Information Systems (ICIS) 2019. https://​aisel​.aisnet​.org/​icis2019/​is​_heart​_of​ _innovation​_ecosystems/​innovation​_ecosystems/​5/​. Petrick, I.J. and Simpson, T.W. (2013). 3D printing disrupts manufacturing: how economies of one create new rules of competition. Research-Technology Management 56(6): 12–16. Petrik, D. and Schüler, F. (2021). Einfluss plattformbasierter Ökosysteme auf unternehmensübergreifende integrierte Wertschöpfungsnetzwerke [Influence of platform-based ecosystems on cross-company integrated value creation networks]. In Industrie 4.0 – Wertschöpfungsnetzwerke mit digitalisierten Dienstleistungen etablieren: Mit Lean-Service-Zyklus und Entwicklung digitaler Dienstleistungssysteme zum Erfolg (ed. T. Schulz). Berlin, Wien, Zürich: Beuth Verlag GmbH. Petrik, D., Straub, D. and Herzwurm, D. (2020). Potentials of platforms for the realization of digital services for component manufacturers. Procedia CIRP 93: 652–7. https://​doi​.org/​10​.1016/​j​.procir​ .2020​.04​.096. Pfähler, K., Baars, H., Hiller, S., et al. (2022). Data analytics services for additive manufacturing ecosystems. AMCIS 2022 Proceedings. https://​aisel​.aisnet​.org/​amcis2022/​DataEcoSys/​DataEcoSys/​2. Reisinger, S. and Lehner, J.M. (2022). Dealing with strategic tensions in digital business ecosystems. In Handbook on Digital Business Ecosystems (ed. S. Baumann), 727–42. Cheltenham: Edward Elgar Publishing. https://​doi​.org/​10​.4337/​9781839107191. Rong, K., Lin, Y., Yu, J. and Zhang, Y. (2020). Manufacturing strategies for the ecosystem-based manufacturing system in the context of 3D printing. International Journal of Production Research, 58(8), 2315–334. https://​doi​.org/​10​.1080/​00207543​.2019​.1627436. Sandberg, J., Holmstrom, J. and Lyytinen, K. (2020). Digitization and phase transitions in platform organizing logics: evidence from the process automation industry. MIS Quarterly 44(1): 129–53. https://​doi​.org/​10​.25300/​MISQ/​2020/​14520. Selander, L., Henfridsson, O. and Svahn, F. (2013). Capability search and redeem across digital ecosystems. Journal of Information Technology 28(3): 183–97. https://​doi​.org/​10​.1057/​jit​.2013​.14. Staub, N., Haki, K., Aier, S., et al. (2023). Governance mechanisms in digital platform ecosystems: addressing the generativity-control tension. Communications of the Association for Information Systems (52). https://​aisel​.aisnet​.org/​cais/​vol52/​iss1/​9 (accessed 28 November 2022). Suuronen, S., Ukko, J., Eskola, R., et al. (2022). A systematic literature review for digital business ecosystems in the manufacturing industry: prerequisites, challenges, and benefits. CIRP Journal of Manufacturing Science and Technology 37: 414–26. https://​doi​.org/​10​.1016/​j​.cirpj​.2022​.02​.016. Teece, D.J., Pisano, G. and Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal 18(7): 509–33. https://​doi​.org/​10​.1002/​(SICI)1097​-0266(199708)18:​7​%3C509:​ :​AID​-SMJ882​%3E3​.0​.CO;2​-Z. Valkokari, K., Seppänen, M., Mäntylä, M., et al. (2017). Orchestrating innovation ecosystems: a qualitative analysis of ecosystem positioning strategies. Technology Innovation Management Review 7(3): 12–24. van Dyck, M., Lüttgens, D., Piller, F., et al. (2021). Positioning strategies in emerging industrial ecosystems for Industry 4.0. In Proceedings of the 54th Hawaii International Conference on System Sciences: 6153–62. Vergne, J.P., and Durand, R. (2011). The path of most persistence: an evolutionary perspective on path dependence and dynamic capabilities. Organization Studies 32(3): 365–82. http://​oss​.sagepub​.com/​ content/​32/​3/​365. Walleser, N., Schmiedle, L., Petrik, D., et al. (2022). Herausforderungen in der Gestaltung der interorganisationalen Zusammenarbeit in IIoT-Ökosystemen – Eine Fallstudie im Maschinen- und Anlagenbau [Challenges in the design of interorganizational cooperation in IIoT ecosystems – a case study in mechanical and plant engineering]. HMD Praxis der Wirtschaftsinformatik 59(5): 1340–52. https://​doi​.org/​10​.1365/​s40702​-022​-00896​-3.

Relevance of technology and focal product for collaboration  193 Weiller, C. and Neely, A. (2013). Business model design in an ecosystem context. University of Cambridge, Cambridge Service Alliance 1–21. West, J. (2003). How open is open enough? Research Policy 32(7): 1259–85. https://​doi​.org/​10​.1016/​ S0048​-7333(03)00052​-0. Wohlers, T., Campbell, R.I., Diegel, et al. (2021). Wohlers Report 2021: 3D Printing and Additive Manufacturing: Global State of the Industry. Fort Collins, Colorado: Wohlers Associates. Wormald, A., Shah, S.K., Braguinsky, S., et al. (2022). Pioneering digital platform ecosystems: the role of aligned capabilities and motives in shaping key choices and performance outcomes. Strategic Management Journal 24(12): 1187–208. https://​doi​.org/​10​.1002/​smj​.3475.

13. Enabling digital business ecosystems: an empirical analysis of the impact of smart manufacturing technologies on firms’ financial performance Francesco Arcidiacono, Alessandro Ancarani, Carmela Di Mauro and Florian Schupp

INTRODUCTION Over the last decade, the emergence of the smart manufacturing (SM) paradigm has attracted increasing attention from both practice and academia (Kagermann et al., 2013; Culot et al., 2020). SM lies within the broader concept of Industry 4.0 and is characterized by the widespread application in manufacturing contexts of advanced technologies related to digitalization, automation and connectivity (Frank et al., 2019). SM enables real time availability of data and intercompany connectivity (Müller et al., 2018) and, in this respect, paves the way for collaborative networks of organizations that co-create value by means of shared digital platforms (Senyo et al., 2019; Culot, 2022). These networks are commonly referred to as digital business ecosystems and are considered crucial to successfully compete in complex, dynamic and fast changing markets (Baumann and Leerhoff, 2022; Maijanen, 2022). Within the broad concept of digital business ecosystems, digital supply chains denote business partners that leverage advanced technologies to pursue enhanced integration and data exchange (Cui and Singh, 2022). One of the requirements for the creation of digital supply chains is that business partners are aligned in respect of the adoption of SM technologies (Kretschmer et al., 2022). As an example, the deployment of intercompany scheduling systems, aimed at optimizing production costs and enhancing flexibility, requires that all involved business partners have adopted Internet of Things solutions and are familiar with big data and analytics (Hofmann and Rüsch, 2017). However, as of today, adoption of SM technologies along supply chains is often fragmented. In fact, while larger firms are at the forefront of the digital transformation, their suppliers often lag behind (Raj et al., 2020). This state of things is especially marked in some industries, such as the automotive, where nearly half of smaller suppliers have reported struggling with their digitalization initiatives (Capgemini, 2019). In this respect, operations management research has recognized the importance of investigating factors that impact on firms’ adoption of SM technologies (Horváth and Szabó, 2019; Benitez et al., 2020; Lorenz et al., 2020). Empirical investigations have highlighted that firms’ SM adoption can be hampered by technological factors including technological legacy, lack of digital expertise and interoperability issues (Kamble et al., 2018). Organizational factors in the form of weak coordination mechanisms, lack of dedicated roles and avoidance of relationships with external partners 194

Enabling digital business ecosystems  195 have been found to be equally impacting (Horváth and Szabó, 2019; Arcidiacono et al., 2022). Extant research has also pointed out that one relevant barrier to SM adoption is constituted by the insufficient empirical evidence on the performance-related benefits it generates (Kamble et al., 2018; Galati and Bigliardi, 2019). In particular, while studies have suggested that SM technologies positively influence firms’ operational performance (Büchi et al., 2020; Lorenz et al., 2020; Tortorella et al., 2020; Buer et al., 2021, among others), evidence on the impact that these technologies have on firms’ financial performance is still inconclusive (Dalenogare et al., 2018; Somohano-Rodríguez et al., 2022). In turn, this might deter smaller firms from investing in digital technologies, since these organizations usually rely on more limited financial resources and have a short-term strategic focus (Moeuf et al., 2020). With the goal to address this research gap, this study seeks to answer the following research question: What is the impact of SM technologies on firms’ financial performance? In order to answer this question, survey data have been matched to firms’ balance sheet data to test an empirical model linking SM adoption to firms’ financial performance, using improvements in operational performance as mediator. Case firms operate as automotive component suppliers. This setting is especially suited to our investigation. In fact, automotive firms have embraced these technologies at an early stage and their investments have usually been larger than other manufacturers (Arcidiacono et al., 2023). In this respect, they are especially aware of the performance benefits stemming from the use of these technologies. This study contributes to operations management research by providing evidence on performance benefits of SM adoption. In doing so, the study also offers insights to business leaders who are pondering the adoption of digital technologies or are called upon to solve the fragmented adoption of these technologies along their upstream supply chain. The remainder of this chapter is organized as follows. The next section introduces the background for this study, while the third section introduces the methodology followed. Section four discusses findings and implications for research and practice, while the final section concludes with limitations.

BACKGROUND Smart Manufacturing as Enabler of Digital Supply Chains Digital supply chains lie within the broader concept of digital business ecosystems and denote networks of business partners that leverage advanced technologies to achieve optimal integration and enhance upstream and downstream efficiency (Cui and Singh, 2022). SM technologies constitute the technology infrastructure that lies at the root of digital supply chains (Culot, 2022), since they enable digitalization of physical objects on the shop floor and pave the way for intercompany data sharing (Frank et al., 2019). Within the heterogeneous and fast evolving SM technological landscape, four base technologies (i.e., cloud, the Internet of Things (IoT), big data and analytics) support all SM applications (Frank et al., 2019). In particular, cloud services provide access to a shared pool of resources and enable remote data storage. The IoT gives rise to a distributed network of interconnected physical assets that communicate among them (Ancarani et al., 2020). Big data and analytics enable intelligent manufacturing systems by exposing underlying trends in production data (Chen et al., 2015).

196  Handbook on digital platforms and business ecosystems in manufacturing SM technologies are closely intertwined and complementary in their application (Arcidiacono et al., 2022). Therefore, unlike previous technological shifts, progression in SM calls firms to add and interconnect different blocks of technologies, rather than replacing one with another (Culot et al., 2020). Firms’ adoption of SM technologies follows a defined pattern (Frank et al., 2019). In fact, easy-to-implement technologies are introduced first, followed by technologies that exhibit higher complexity with respect to the modifications to production processes, plants’ layout and employees’ competencies they require. Based on the implementation of stable blocks of base technologies, Frank et al. (2019) have empirically defined three stages of SM adoption. Stage 1 encompasses use of cloud applications. Organizations in Stage 2 combine cloud and IoT to collect and store data arising from production systems. Finally, Stage 3 involves the integration of cloud, IoT, big data and analytics to extract insights from real-time data and optimize decision making (Wamba et al., 2017). Smart Manufacturing and Operational Performance Progression in SM is expected to support firms’ competitiveness (Dalenogare et al., 2018). In particular, the investigation of the relation between SM and firms’ operational performance (OP) has attracted growing attention over recent years. Efforts in this field have been motivated by the need to complement with empirical evidence initial claims arising from conceptual papers that identified SM as a means to enhance firms’ cost, quality, delivery and flexibility performance (Alexopoulos et al., 2016; Fatorachian and Kazemi, 2018). In this direction, both qualitative and quantitative studies have been conducted. To exemplify, using case study analysis, Kiel et al. (2017) find that increasing autonomy of production processes results in greater productivity, lower scrap rates and shorter set-up times. Based on survey data, Ji-fan Ren et al. (2017) and Ma et al. (2021) suggest that SM-enabled optimization capabilities contribute to a more efficient use of factors. Lorenz et al. (2020) link maturity in SM to volume flexibility performance. Buer et al. (2021) extend these results by offering quantitative evidence concerning the positive impact of SM on an aggregate construct mirroring the four dimensions of cost, quality, delivery and flexibility performance. Overall, empirical studies provide support for a positive impact of SM on OP. However, these studies have mostly analysed the relation between SM and OP conceptualized as a single latent construct (e.g. Buer et al., 2021; Ji-fan Ren et al., 2017) or have focused on the impact that SM has on selected dimensions of OP, such as cost or flexibility (e.g. Lorenz et al., 2020; Ma et al., 2021). In this respect, an analysis that simultaneously evaluates the impact of SM on the four dimensions of cost, quality, delivery and flexibility performance can contribute to extending knowledge on benefits tied to SM.

SMART MANUFACTURING AND FINANCIAL PERFORMANCE Investigation of the influence that SM has on firms’ financial performance (FP) is still an emerging topic in operations management research and empirical studies often provide contradictory evidence. To exemplify, Peng and Tao (2022) conclude that the digital transformation has a positive impact on FP in terms of return on assets (ROA) and return on equity (ROE). Eslami et al. (2021) find that SM technologies contribute to reinforcing the positive effect that supply chain agility has on FP by facilitating real-time information provision among

Enabling digital business ecosystems  197 supply chain partners. Similarly, Yu et al. (2021) suggest that SM technologies, such as flexible manufacturing, lay a solid foundation to implement green management, which, in turn, results in enhanced FP. In contrast to these results, Horváth and Szabó (2019) suggest that SM investments often entail uncertain returns. Along the same lines, Sony (2020) claims that investments required to implement SM require significant capital expenditure, which might hinder FP, especially in the short term. Finally, Somohano-Rodríguez et al. (2022) do not find empirical evidence supporting a positive effect of the use of SM technologies, including cybersecurity, cloud computing and the Internet of Things, on firms’ financial results. Inconclusive evidence offered by extant literature suggests that the relation between SM and firms’ profitability is complex and could be subject to different influences. On the one hand, investment required to introduce SM technologies could negatively impact on FP in the short term. On the other hand, literature suggests that the way production systems are designed determines how a factory performs, which in turn affects business profitability (Skinner, 1969). Therefore, improvements in OP enabled by SM could have positive repercussions on FP. As an example, higher levels of quality performance enabled by SM might increase firms’ pricing power, thus potentially giving rise to higher profitability (O’Neill et al., 2016). Similarly, adoption of SM technologies might result in superior cost performance, which could lead to higher profit margins (Agarwal and Brem, 2015). If taken together, the above arguments lead to envisaging a model in which the relation between SM and FP is partially mediated by improvements in OP (Figure 13.1). To contribute to clarifying the link between SM and FP, empirical data are used to test the model. In what follows, the methodological steps followed in the analysis are discussed.

Figure 13.1

Model

METHODS Data Collection Selection of participants to this study followed a nonrandom approach (Smith, 1983). In particular, firms operating as automotive component suppliers and that had adopted SM technologies were targeted. Data collection was organized in two phases. At first, between March and April 2021, an invitation letter with a link to an online questionnaire investigating firms’ SM adoption and related improvements in OP was sent to CEOs of 569 automotive component suppliers. Complete responses received were 234, with a response rate of 41 per cent. In the second step, the research team collected data concerning firms’ FP. To this end, the Amadeus

198  Handbook on digital platforms and business ecosystems in manufacturing database from Bureau van Dijik was consulted in September and October 2022. Financial ratios were retrieved for 54 firms, which constitute the final sample size. For the most part, case firms are large (63 per cent) or medium (35 per cent) sized and are mainly located in Europe, with Germany being the most represented country (67 per cent), followed by Italy (19 per cent). As for the industry sector in which the case companies operate, most of the firms operate in the manufacture of basic metals (NACE code: C24; 35 per cent), followed by the manufacture of rubber and plastic components (C22; 20 per cent) and the manufacture of fabricated metal products (C25; 15 per cent). Measurement The questionnaire developed by the research team consisted of multiple sections. Following Tortorella et al. (2020) SM adoption was measured via the four SM base technologies (i.e. cloud, IoT, big data and analytics), which constitute the technology infrastructure enabling digital supply chains (Cui and Singh, 2022). In particular, respondents were asked to rate the adoption of the SM base technologies through a binary scale (0=‘non-adopted’, 1=‘adopted’). SM adoption was then defined following Frank et al., (2019). Therefore, Stage 1 encompasses sole use of cloud; Stage 2 additionally includes use of IoT; Stage 3 involves use of all SM base technologies. Improvements in OP experienced as a result of SM adoption were measured via the scale validated by Devaraj et al., (2007), adapted with the introduction of items from the scale developed by Naor et al. (2008). Therefore, cost items measured improvements in production cost, lead time and inventory turnover. Items for quality performance investigated improvements in the percentage of nonconforming products detected in production or returned by customers. Finally, delivery items focused on improvements in delivery speed and in the number of product features offered, while flexibility items assessed volume and mix flexibility. Respondents were asked to rate their perception of performance improvements over a five-point Likert scale, with 1 indicating ‘no improvement’ and 5 ‘very strong improvement’. As for the measure for firms’ FP, the profit margin averaged over two years (2019 and 2020) was used. Constructs’ Validation Partial least square path modelling (PLS-SEM) with SmartPLS 4 was used to explore the relations between firms’ stage of SM adoption, improvements in OP and FP. As a first step in the analysis, validity and reliability of constructs were verified. As for the latter, Cronbach’s alphas and composite reliability (CR) indices for the four constructs mirroring improvements in cost (OP_C), quality (OP_Q), delivery (OP_D) and flexibility (OP_F) performance were evaluated (Table 13.1). Values were found to be higher than the recommended minimum threshold of 0.7 (Hair et al., 2019). Validity of the measurement model was assessed by evaluating both convergent and discriminant validity. Average variance extracted (AVE) for all constructs was found to be higher than the 0.5 minimum recommended value (Hair et al., 2019). Discriminant validity was evaluated using the Fornell–Larcker criterion and the heterotrait-monotrait ratio of correlations (HTMT). As for the former, the square root of the AVE for each construct was higher than correlations (Table 13.2). Concerning HTMT, all values were lower than the recommended 0.85 threshold (Hair et al., 2019). Therefore, results of the analyses performed showed that the measurement model exhibits satisfactory reliability and validity.

Enabling digital business ecosystems  199 Table 13.1

Validity and reliability

Construct

Mean

SD

Cα

CR

AVE

Item

Loading

OP_C

3.046

0.734

0.784

0.782

0.545

IPC1

0.802

 

 

 

 

 

 

IPC2

0.874

 

 

 

 

 

 

IPC3

0.831

OP_Q

3.345

0.868

0.718

0.841

0.706

IPQ1

0.795

 

 

 

 

 

 

IPQ2

0.934

 

 

 

 

 

 

IPQ3

0.925

OP_D

3.15

0.975

0.919

0.919

0.752

IPD1

0.959

 

 

 

 

 

 

IPD2

0.772

OP_F

2.654

0.992

0.865

0.874

0.851

IPF1

0.96

 

 

 

 

 

 

IPF2

0.964

Table 13.2

AVE, correlations and HTMT values

Constructs

1

2

3

4

5

6

1

SM adoption

1

0.367

0.345

0.17

0.192

2

OP_C

0.315

0.314 0.738

0.818

0.636

0.25

3

OP_Q

0.367

0.708

0.832 0.841

0.482

0.103

4

OP_D

0.301

0.609

0.507

0.715 0.867

0.197

5

OP_F

0.17

0.633

0.461

0.359

0.459 0.922

6

FP

-0.192

0.251

-0.008

0.208

0.229

0.229 1

Note:  AVE in bold on the diagonal. Correlations between constructs below the diagonal. HTMT values above the diagonal.

Model Results Bootstrapping with 5000 re-samplings was used to explore the relations between constructs. Figure 13.2 reports standardized path coefficients. With respect to the link between SM and operational performance, a positive and significant association was observed between SM adoption and OP_C (0.279, p < 0.05), OP_Q (0.350, p < 0.01) and OP_D (0.310, p < 0.05), while the link between SM adoption and OP_F was nonsignificant (0.163 p >0.05). Next, SM has a negative and statistically significant relation with FP (-0.266, p < 0.05). Finally, as for the relation between improvements in operational performance stemming from SM and financial performance, results show that the impact of OP_C (0.284, p >0.05), OP_Q (-0.286, p >0.05), OP_D (0.229, p >0.05) and OP_F (0.150, p >0.05) is statistically nonsignificant. A mediation analysis was conducted to explore the impact SM adoption might have on FP via improvements in operational performance. Results reveal a nonsignificant impact via all four dimensions of operational performance. Goodness-of-fit for the model was evaluated through the coefficient of determination (R2), the Stone-Geisser’s Q² value and the fit indices. The adjusted R2 values for the model (Figure 13.2) were greater than the minimum 0.1 threshold, which implies that variance explained is adequate (Falk and Miller, 1992). All Q2 values were higher than 0, thus confirming predictive relevance for the model (Hair et al., 2019). Fit indices were acceptable (Schermelleh-Engel et al., 2003): SRMR = 0.091, d_ULS = 0.653, d_G = 0.371, chi-square = 122.497, NFI = 0.700.

200  Handbook on digital platforms and business ecosystems in manufacturing

Figure 13.2 Path coefficients

DISCUSSION AND IMPLICATIONS This chapter has empirically explored the links between adoption of SM technologies and FP. This section discusses the main findings and the contribution of this research in light of previous studies. Results point to a negative direct impact of SM adoption on FP. This finding can be explained in light of the significant capital expenditure required to implement SM and empirically confirms claims from previous non-quantitative studies (Horváth and Szabó, 2019; Sony, 2020). Relevant implementation costs for SM stem from both the cost of technological equipment and the cost of the skilled labour force needed in the deployment phase of SM technologies (Sony, 2020), and might constitute a barrier especially for small and medium-sized firms, which often have to rely on more limited financial resources and usually have a short-term strategic focus (Moeuf et al., 2020). Interesting elements emerged from the analysis of the path linking SM adoption, improvements in OP and FP. As for the direct link between SM and OP, results point to SM adoption

Enabling digital business ecosystems  201 being positively related to improvements in terms of cost, quality and delivery performance, while a nonsignificant effect was observed on improvements in flexibility performance. These results might stem from the peculiar characteristics of the automotive supply chain. In fact, automotive firms place a high emphasis on ensuring cost, quality and delivery performance, while flexibility is still a less pressing strategic driver (Arcidiacono et al., 2023). This strategic orientation influences SM applications adopted within automotive digital supply chains and, therefore, has an impact on resulting performance improvements. To exemplify, while SM applications that can support automotive firms to compete in terms of cost, quality and delivery have become an industry standard (e.g. intercompany, cloud-based platforms aimed at sharing data on live demand, parts’ quality and delivery schedules), implementation of SM technologies aimed at supporting flexibility performance (e.g. additive manufacturing) is still limited (Delic and Eyers, 2020). Prior literature has focused on expected performance benefits (Büchi et al., 2020) or on perceived opportunities stemming from SM adoption (Dalenogare et al., 2018). Contributions that have investigated actual impact of SM on firms’ performance have combined a variety of indicators for operational performance into a single dependent variable (Ji-fan Ren et al., 2017; Buer et al., 2021) or have focused on specific performance dimensions, such as cost or flexibility (e.g. Lorenz et al., 2020). In this respect, this study contributes to the literature by providing a more nuanced analysis of benefits in terms of operational performance stemming from SM. Results do not offer evidence for a positive impact of improvements in OP enabled by SM on firms’ profitability. In fact, neither the direct links between improvements in OP and FP nor the mediated paths are statistically significant. This finding lends support to previous research suggesting that SM technologies do not influence firms’ FP (Somohano-Rodríguez et al. 2022) and paves the way for a seemingly troublesome question: If SM adoption results in improved OP, why does this not result in enhanced profitability? One possible answer is that SM technologies constitute a tool that firms use to meet increasingly stringent standards and preserve profitability. As an example, over recent years, firms operating in the automotive industry have been facing increasing pressures to reduce production costs due to intensified competition and the need to finance ever increasing R&D investments tied to the transition to electric mobility (Capgemini, 2019). In this respect, firms might look at SM as a tool to optimize production costs and maintain their competitive position in the market. However, SM adoption might not be sufficient to generate higher margins. This study responds to calls for additional empirical research on SM and on digital supply chains (Koh et al., 2019) and contributes to both theory and practice. In particular, findings enrich operations management literature analysing performance-related benefits stemming from SM (Lorenz et al., 2020; Sony, 2020; Tortorella et al., 2020; Buer et al., 2021; Somohano-Rodríguez et al. 2022, among others) by investigating the impact that SM has on firms’ financial performance. Results also have implications for business leaders called to solve the fragmented adoption of SM technologies, which prevents the creation of digital supply chains. Several car makers and original equipment manufacturers encourage SM implementation among their smaller suppliers by exerting pressure or offering technological support. Findings from this study suggest that this approach might give rise to little benefit if not contextually matched by strategies aimed at mitigating the financial burden tied to the introduction of these technologies. In fact, in the short-term, improvements in OP ensured by SM are not sufficient to offset the relevant capital expenditures required for SM implementation.

202  Handbook on digital platforms and business ecosystems in manufacturing

CONCLUSION AND LIMITATIONS Digital supply chains are considered crucial to successfully compete in a period of rapid technological change (Cui and Singh, 2022). One of the requirements for their development is that SM technologies are uniformly applied among business partners (Kretschmer et al., 2022). However, inconclusive evidence concerning the impact of SM on firms’ FP limits smaller firms’ willingness to invest in SM (Horváth and Szabó, 2019). This chapter has aimed at contributing to solving this gap by investigating the relation between SM adoption and FP. Improvements in OP have been envisaged to partially mediate this link. Results point to a direct negative impact of SM on FP, which might result from the relevant capital expenditure stemming from SM implementation. Further, improvements in OP arising from SM adoption are not found to significantly influence firms’ FP, suggesting that SM could constitute a means to simply preserve profitability in the face of increasing competitive pressures. Limitations of this study must be acknowledged. First, constructs mirroring improvements in cost and quality performance exhibit low Cronbach’s alphas values, although they are still above the 0.7 recommended cut-off (Hair et al., 2019). Second, secondary data on financial performance were retrieved for just 54 firms. Therefore, future studies should verify the robustness of our findings using a larger sample. Future studies could also benefit from analysing a longer time series of profitability data, in order to shed light on the long-term impact of SM. Further, while this study has focused on profitability at firm level, future research should analyse profitability of the specific plants/product lines that have introduced SM technologies. Finally, it might be valuable to investigate if the link between SM adoption and FP is influenced by firms’ characteristics, including size, location and industry.

REFERENCES Agarwal, N., and Brem, A. (2015) Strategic business transformation through technology convergence: implications from General Electric’s industrial internet initiative. International Journal of Technology Management, 67(2/3/4), 196–214. Alexopoulos, K., Makris, S., Xanthakis, V., Sipsas, K., and Chryssolouris, G. (2016) A concept for context-aware computing in manufacturing: the white goods case. International Journal of Computer Integrated Manufacturing, 29(8), 839–49. Ancarani, A., Di Mauro, C., Legenvre, H., and Cardella, M. S. (2020) Internet of things adoption: a typology of projects. International Journal of Operations & Production Management, 40(6), 849–72. Arcidiacono, F., Ancarani A., Di Mauro C., and Schupp F. (2022) The role of absorptive capacity in the adoption of smart manufacturing. International Journal of Operations & Production Management, 42(6), 773–96. Arcidiacono, F., Ancarani, A., Di Mauro, C., and Schupp, F. (2023) Linking competitive priorities, smart manufacturing advancement and organizational microfoundations. International Journal of Operations & Production Management, (ahead of print). Available from: https://​doi​.org/​10​.1108/​ IJOPM​-06​-2022​-0355 Baumann, S., and Leerhoff, M. (2022) Networks, platforms, and digital business ecosystems: mapping the development of a field. In: Baumann, S. (Ed.) Handbook on Digital Business Ecosystems. Cheltenham: Edward Elgar Publishing, pp. 11–24. Benitez, G. B., Ayala, N. F., and Frank, A. G. (2020) Industry 4.0 innovation ecosystems: An evolutionary perspective on value cocreation. International Journal of Production Economics, 228, 107735. Büchi, G., Cugno, M., and Castagnoli, R. (2020) Smart factory performance and Industry 4.0. Technological Forecasting and Social Change, 150, 119790.

Enabling digital business ecosystems  203 Buer, S. V., Semini, M., Strandhagen, J. O., and Sgarbossa, F. (2021) The complementary effect of lean manufacturing and digitalisation on operational performance. International Journal of Production Research, 59(7), 1976–92. Capgemini (2019). Putting automotive manufacturers in the digital industrial revolution driving seat. Available at: https://​www​.capgemini​.com/​it​-it/​resources/​automotive​-smart​-factories​-putting​-auto​ -manufacturers​-in​-the​-digital​-industrial​-revolution​-driving​-seat/​ [Accessed 1 December 2022]. Chen, D. Q., Preston D. S., and Swink M. (2015) How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4–39. Cui, Y., and Singh, P. J. (2022) Blockchain-based supply chain architecture adapted to digital business ecosystems. In: Baumann, S. (Ed.) Handbook on Digital Business Ecosystems. Cheltenham: Edward Elgar Publishing, pp. 387–404. Culot, G. (2022) Digital ecosystems in manufacturing: emerging models for Industry 4.0. In: Baumann, S. (Ed.) Handbook on Digital Business Ecosystems. Cheltenham: Edward Elgar Publishing, pp. 727–42. Culot, G., Nassimbeni G., Orzes G., and Sartor M. (2020) Behind the definition of Industry 4.0: Analysis and open questions. International Journal of Production Economics 226, 107617. Dalenogare, L. S., Benitez, G. B., Ayala, N. F., and Frank, A. G. (2018) The expected contribution of Industry 4.0 technologies for industrial performance, International Journal of Production Economics, 204, 383–94. Delic, M., and Eyers, D. R. (2020) The effect of additive manufacturing adoption on supply chain flexibility and performance: An empirical analysis from the automotive industry. International Journal of Production Economics, 228, 107689. Devaraj, S., Krajewski, L., and Wei, J. C. (2007) Impact of eBusiness technologies on operational performance: the role of production information integration in the supply chain. Journal of operations management, 25(6), 1199–216. Eslami, M. H., Jafari, H., Achtenhagen, L., Carlbäck, J., and Wong, A. (2021) Financial performance and supply chain dynamic capabilities: the Moderating Role of Industry 4.0 technologies. International Journal of Production Research, 1–18. Falk, R. F., and Miller, N. B. (1992) A primer for soft modeling. University of Akron Press. Fatorachian, H., and Kazemi, H. (2018) A critical investigation of Industry 4.0 in manufacturing: theoretical operationalisation framework. Production Planning & Control, 29(8), 633–44. Frank, A .G., Dalenogare L. S., and Ayala N. S. (2019) Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics 210, 15–26. Galati, F., and Bigliardi, B. (2019) Industry 4.0: Emerging themes and future research avenues using a text mining approach. Computers in Industry, 109, 100–13. Hair, J. F., Risher, J. J., Sarstedt, M., and Ringle, C. M. (2019) When to use and how to report the results of PLS-SEM. European business review, 31(1), 2–24. Hofmann, E., and Rüsch, M. (2017) Industry 4.0 and the current status as well as future prospects on logistics. Computers in industry, 89, 23–34. Horváth, D., and Szabó, R. (2019) Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities? Technological Forecasting and Social Change, 146, 119–32. Ji-fan Ren, S., Fosso Wamba S., Akter S., Dubey R., and Childe S. J. (2017) Modelling quality dynamics, business value and firm performance in a big data analytics environment. International Journal of Production Research, 55(17), 5011–26. Kagermann, H., Wahlster W., and Helbig J. (2013) Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final Report of the Industrie 4.0 Working Group. Available at: https://​ en​.acatech​.de/​publication/​recommendations​-for​-implementing​-the​-strategic​-initiative​-industrie​-4​-0​ -final​-report​-of​-the​-industrie​-4​-0​-working​-group/​ [Accessed 30 September 2022]. Kamble, S. S., Gunasekaran, A., and Sharma, R., (2018) Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry. Computers in Industry, 101, 107–19. Kiel, D., Muller J., Arnold C., and Voigt K. I. (2017) Sustainable industrial value creation: Benefits and challenges of Industry 4.0. International Journal of Innovation Management, 21(8), 1740015. Koh, L., Orzes G., and Jia F. (2019) The fourth industrial revolution (Industry 4.0) technologies’ disruption on operations and supply chain management. International Journal of Operations and Production Management, 39(6/7/8), 817–28.

204  Handbook on digital platforms and business ecosystems in manufacturing Kretschmer, T., Leiponen, A., Schilling, M., Vasudeva, G. (2022) Platform ecosystems as meta‐ organizations: Implications for platform strategies. Strategic Management Journal, 43(3), 405–24. Lorenz, R., Benninghaus, C., Friedli, T., and Netland, T. H. (2020) Digitization of manufacturing: the role of external search. International Journal of Operations & Production Management, 40(7/8), 1129–52. Ma, S., Zhang Y., Ren S., Yang H., and Zhu Z. (2021) A case-practice-theory-based method of implementing energy management in a manufacturing factory. International Journal of Computer Integrated Manufacturing, 34(7–8), 829–43. Maijanen, P. (2022) Digital business ecosystems and dynamic capabilities. In: Baumann, S. (Ed.) Handbook on Digital Business Ecosystems. Cheltenham: Edward Elgar Publishing, pp. 50–62. Moeuf, A., Lamouri S., Pellerin R., Tamayo-Giraldo S., Tobon-Valencia E., and Eburdy R. (2020) Identification of critical success factors, risks and opportunities of Industry 4.0 in SMEs. International Journal of Production Research, 58(5), 1384–1400. Müller, J. M., Buliga, O., and Voigt, K. I. (2018) Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0. Technological Forecasting and Social Change, 132, 2–17. Naor, M., Goldstein, S. M., Linderman, K. W., and Schroeder, R. G. (2008) The role of culture as driver of quality management and performance: infrastructure versus core quality practices. Decision sciences, 39(4), 671–702. O’Neill, P., Sohal, A., and Teng, C. W. (2016) Quality management approaches and their impact on firms’ financial performance – An Australian study. International Journal of Production Economics, 171, 381–93. Peng, Y., and Tao, C. (2022) Can digital transformation promote enterprise performance? – From the perspective of public policy and innovation. Journal of Innovation & Knowledge, 7(3), 100198. Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A .B. L., and Rajak, S. (2020) Barriers to the adoption of Industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective. International Journal of Production Economics, 224, 107546. Schermelleh-Engel, K., Moosbrugger, H., and Müller, H. (2003) Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of psychological research online, 8(2), 23–74. Senyo, P. K., Liu, K., and Effah, J. (2019) Digital business ecosystem: Literature review and a framework for future research. International journal of information management, 47, 52–64. Skinner, W. (1969) Manufacturing – missing link in corporate strategy. Harvard Business Review, 50(3), 136–45. Smith, T. M. F. (1983) On the validity of inferences from non‐random samples. Journal of the Royal Statistical Society: Series A (General), 146(4), 394–403. Somohano-Rodríguez, F. M., and Madrid-Guijarro, A. (2022) Do Industry 4.0 technologies improve Cantabrian manufacturing SMEs performance? The role played by industry competition. Technology in Society, 70, 102019. Sony, M. (2020) Pros and cons of implementing Industry 4.0 for the organizations: a review and synthesis of evidence. Production & Manufacturing Research, 8(1), 244–72. Tortorella, G. L., Vergara, A. M. C., Garza-Reyes, J. A., and Sawhney, R., (2020) Organizational learning paths based upon industry 4.0 adoption: An empirical study with Brazilian manufacturers. International Journal of Production Economics, 219, 284–94. Yu, Y., Zhang, J. Z., Cao, Y., and Kazancoglu, Y. (2021) Intelligent transformation of the manufacturing industry for Industry 4.0: Seizing financial benefits from supply chain relationship capital through enterprise green management. Technological Forecasting and Social Change, 172, 120999. Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., and Childe, S. J. (2017) Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–65.

PART III SUSTAINABILITY AND CIRCULAR ECONOMY

14. Profitable sustainability: the tribrid business model for environmental, social and economic value creation in digital platforms Susanne Royer, Sabine Baumann and Paweł Głodek

INTRODUCTION: DIGITAL PLATFORMS AND SUSTAINABILITY The growing importance of facilitating interactions across diverse market segments has engendered significant research focus on platforms in the field of business (Rietveld and Schilling, 2021). Indeed, the emergence of platforms and their management implications have become widely discussed topics among scholars (Gawer, 2009; Dietl, 2010; van Alstyne and Parker, 2017; Constantinides et al., 2018; Fehrer et al., 2018; Ikeda and Marshall, 2019). Platforms can facilitate the sharing of houses, cars or power tools, connect job seekers and employers or individuals in search of potential partners, and bring together sellers and buyers of second-hand items. Platforms make it possible to realize both demand-side and supply-side economies of scale (van Alstyne and Parker, 2017). More specifically, bundling certain activities for whole countries or beyond lead to scale economies for providers of platforms, for instance with regard to IT-related development costs on the supply side. In addition, on the demand side, the attractiveness of a platform to users often increases as the number of other actors increases. For example, an auction platform becomes more attractive to buyers and sellers when a relevant number of participants populate it. This case illustrates the relevance of demand side economies of scale in platform markets. Indeed, access to resources and resource orchestration, as well as access to networks, appears to have become more central than ownership and control of these resources in such markets (van Alstyne and Parker, 2017, p. 26). Resources (and capabilities) are here understood as the material and immaterial assets that managers and entrepreneurs utilize to generate strategic advantages (Barney et al., 2021). In addition to these ongoing discussions about platforms, management researchers have increasingly answered the call to investigate sustainability as a relevant purpose of modern business models and strategy (George et al., 2021; Knyphausen-Aufseß et al., 2021). In our understanding, sustainability refers to strategies that encompass economic, environmental and social elements (see Hart and Dowell (2011, p. 1466) for a similar understanding that also employs resources as a point of reference). The relevance of sustainability has become even more obvious in light of the global Covid-19 pandemic (Arora and Mishra, 2020; Goffman, 2020), which itself may be a consequence of past environmental damages (Arora and Mishra, 2020, p. 118). Since the so-called platform economy (Rietveld and Schilling, 2021) has established a rather poor reputation in terms of its environmental impact, the relevancy of a trifold approach to sustainability for platform organizations has increased. With regard to dealing with the environmental paradox, Reuter (2022) argues that managers of platform organizations have to cope 206

Profitable sustainability: the tribrid business model  207 with tensions between different goals and need guidance. Risi and Pronzato (2021) illustrate the social impact of platforms by exploring the negative consequences of online platforms on remote workers during the Covid-19 pandemic. In addition, concerns have arisen about the increasing market power of some players (Murillo et al., 2017; Bamberger and Lobe, 2018). These negative impacts can be found in several examples from the platform economy, including Amazon, which has developed a powerful bargaining position in the retail sector (e.g. Palmer and Novet, 2020; Lomas, 2022). Indeed, the increase in online shopping has led to a decrease of traditional retailers and given incentives for over-consumption to customers (Bocken and Short, 2021). Moreover, Amazon has become notorious for destroying and throwing away reshipments (e.g. Abelvik-Lawson, 2021; Hassan, 2021), instead of bringing them back into the business cycle. At the same time, companies like Uber are not only disrupting the traditional taxi business and introducing new pricing policies such as surge pricing into the market place, but are also accused of exacerbating inner-city traffic (Hawkins, 2019; European Environment Agency, 2020). As these examples illustrate, platforms can negatively impact the natural environment and lead to new types of jobs with low social and economic standards. Also noteworthy are the potential negative impacts that platforms can have on social discourse. For instance, as a prominent social media networking site, Facebook has not only built up significant market power (see, e.g. McGee, 2023), but it has also established communication channels and space for otherwise dispersed right wing groups and anti-vaxxers. Consequently, it has played an important role in political campaigns and the support for or alienation of politicians. Moreover, its lack of data security has allowed for the undermining of political debate with false information (e.g. BBC News, 2020; Dwoskin, 2021). This brief discussion indicates that platformization and its adherent business models can have negative consequences for many individuals and societies around the world (e.g. Curtis and Lehner, 2019; Bocken and Short, 2021; Risi and Pronzato, 2021). Indeed, this dark side of platformization calls for finer-grained investigations of the facets of different platform-based business models in terms of their sustainability-orientation – i.e. their potential to combine social, environmental and economic value creation. In this research, a business model is understood as consisting of activities provided by a focal actor embedded into activities of partners, as well as the linkages among these activities (Amit and Zott, 2015). The climate crisis and social inequality (see, e.g. Grusky (2014) for an overview) indicate the need for sustainability-oriented business models that are also economically viable. The primary challenge is that economic, environmental and social goals usually cannot all be reached to the same extent. Therefore, the literature rightly questions whether economic, environmental and social aims can be convincingly combined and brought into balance (Quairel-Lanoizelee, 2016; Fish and Wood, 2017). Trade-offs among economic, social and environmental aims (Hahn et al., 2010) can be illustrated by the case of Uber. A person using an Uber to get from A to B enjoys easy access to mobility. However, at the same time, the social security of the self-employed Uber driver is low. In addition, its services replace more environmentally sustainable public transport. There are not only different market contexts in which platform-based businesses emerge, but the size of the market can also play a role: In addition to large global platforms, platforms can exist on the local level – e.g. in the form of a digital platform in one city that brings together retailers who cannot sell all their food with those who are willing to pay a certain

208  Handbook on digital platforms and business ecosystems in manufacturing price for the leftovers. Another example comes in the form of platforms that bundle the sales of organic produce from several farms in certain regions. In sum, a digital platform, as such, is neither ‘good’ nor ‘bad’; instead, as with other types of value creation structures and most technologies, it depends! More specifically, it depends upon the market task fulfilled, as well as the extent of social, environmental and economic impacts. To further qualify this dependency requires a better understanding of economic, environmental and social concerns regarding the different types of platform business models. One important point of reference is that there may be perceived trade-offs between economic and social and environmental concerns. Moreover, taking environmental concerns into account does not automatically lead to social concerns being considered – and vice versa. Thus, the goal of this chapter is to investigate the possibilities of using digital platforms in combination with sustainability-oriented business models. Underlying is the understanding that platforms are not all the same but can, for example, be differentiated by industry and market context (Ikeda and Marshall, 2019, p. 30) and by type of platform (Gawer and Cusumano, 2014). As platforms can reach different degrees of sustainability, a more fine-grained analysis is needed. Accordingly, we follow the call of Kolk and Ciulli (2020) for research into how business models have to be adapted to sustainability requirements in different market contexts. We aim at a holistic investigation of social, environmental and economic value creation on the basis of digital platforms. Thus, we are interested in what we call ‘tribrid business models‘ with the consideration that there are potential trade-offs between social and environmental and economic value creation activities that, however, may be brought into balance. This leads to the following questions, which have already partly been addressed in this introduction: What are the effects of the increase of digital platforms in the economy on financial, environmental and social value creation, and where can possible trade-offs and paradoxes be identified? Insights draw on previous research and build a foundation for examining the challenges, as well as positive and negative effects, for digital business models. To systematize the answers to the latter question, we address the question: How can TRIBRID business models for digital platforms be conceptualized so that they can cope with economic, environmental and social concerns? To do so, we use a qualitative approach by conceptualizing a framework that captures the elements of tribrid business models based on existing research on platforms, digital business ecosystems and sustainable business models. The conceptualization of tribrid business models for digital platform providers is then reflected in the context of illustrating examples. The paper concludes with a summary and outlook.

BALANCING OUT THE ECONOMIC, ENVIRONMENTAL AND SOCIAL AIMS OF DIGITAL PLATFORMS Several recent studies have dealt with topics related to the sustainability of digital platforms, such as sharing-oriented business models aiming for sustainability (Pouri and Hilty, 2018, 2020) or sustainable innovation of small and medium-sized enterprises (Yousaf et al., 2021). Ecosystems reflect the fact that several companies contribute products and services in order to achieve a ‘coherent solution’ (Hannah and Eisenhardt, 2018, p. 3164) in a marketplace. The digital entrepreneurial ecosystem has been examined by various authors who suggest digital multisided platforms as one relevant facet in this context (Hayter et al., 2018; Spigel and

Profitable sustainability: the tribrid business model  209 Harrison, 2018; Srinivasan and Venkatraman, 2018; Song, 2019, p. 576; Baumann, 2022) – one that is also linked to sustainability (Song, 2019, p. 583). More specifically, Reuter (2022) investigates the challenges of sharing platforms in terms of potential trade-offs between environmental and financial value creation. She focuses on environmental and economic value creation, investigating ‘hybrid business models‘ to better understand how multiple goals may be achieved in platform organizations. Her focus lies on environmental and economic (financial) aspects. Organizations with hybrid business models aiming for profit and other purposes must cope with the challenge that ‘[a]dressing one domain may result in insufficiency addressing another one’ (Reuter, 2022, p. 604). This leads to the question of how to balance environmental and financial purposes (Reuter, 2022). With regard to the economic profit aim, Reuter (2022) highlights the specificities of platform markets in terms of the necessity to achieve a critical mass of customers to get a platform business model going and, in terms of the terminology used above, to profit from demand-side economies of scale. This is conceptualized by Reuter (2022) as ‘accessibility design theme‘ (p. 609). The more users attracted to a digital platform, the better it is in terms of its profitability due to the high initial fixed cost of establishing a platform combined with the almost nonexistent marginal cost for including further users. Furthermore, this increases the benefits for the user community by contributing to positive network effects. However, this reasoning assumes that all users provide the same benefit at zero cost, which is not necessarily the case. For example, a user posting inappropriate content could easily become a burden, instead of a benefit. Regarding environmental aims, Reuter (2022) suggests a focus on exploiting underutilized resources and discusses this under the heading of ‘redistribution design theme‘ (p. 610). While demand-side economies of scale can lead to economic value creation, environmental value creation is conceptualized as a result of a reduction of resource consumption with complementarities existing between both accessibility and redistribution design themes. According to Reuter (2022), this should be the focus rather than the paradoxes. Building on identified themes of relevance, Reuter (2022) explores potential managerial drivers to facilitate the mentioned integration by establishing ‘strategic synergies‘ (p. 611) and ‘dynamic coupling‘ (p. 611). The “age of digital entrepreneurship” (Sahut et al., 2021) invites the study of business models that are oriented towards environmental and social sustainability (George et al., 2021) and micro-businesses (Mukhoryanova et al., 2021). Risi and Pronzato (2021) and Bricka and Schroeder (2022) investigate working conditions in platform businesses. Risi and Pronzato (2021) also explore the social and economic aspects of digital platforms in their study of remote workers. More specifically, they investigate ‘the dark side of platformization’ with regard to social consequences for the workforce. Here, trade-offs between the economic (financial) in terms of for-profit and the social outcome in terms of for-purpose become obvious.

CONCEPTUALIZING TRIBRID BUSINESS MODELS We aim at bringing together economic, environmental and social purposes under the roof of one business model for a digital platform through the conceptualization of what we call ‘tribrid business models‘. In line with the terminology introduced above, the perspective taken is that of a focal player that provides certain activities on the basis of underlying resources and capa-

210  Handbook on digital platforms and business ecosystems in manufacturing bilities. Value is created by engaging further actors to contribute activities in a cooperative fashion in order to build an intermediating platform between different market sides. The focal actor can appropriate a certain share of the value created jointly. This phase of value capture, accordingly, is characterized by competition among the connected activity providers. The value created is not limited to financial value, but rather includes environmental and social value. This, however, only implies higher value capture in a monetary sense, if customers are willing to pay a higher price for environmental and social sustainability or when regulation requires it. In our understanding, tribrid business models are characterized by being underpinned by the concept of a ‘corporate purpose’ (e.g. Mayer, 2021) of the focal actor – one that aims at economic, environmental and social value creation. We aim to illustrate tribrid1 business models that reflect the challenges inherent to striving for three purposes, namely economic, environmental and social. This conceptualization of a tribrid, instead of a hybrid, business model increases complexity. However, it is more realistic with regard to the requirement of a truly sustainable business model and more useful for a better understanding of how different elements work together. Here, inspired by Di Stefano et al. (2014, p. 318), we use the metaphor of a bicycle drivetrain2 to bring together economic, environmental and social value creation in the business models of digital platforms that we conceptualize. The metaphor we use is a classical three-speed hub drivetrain that was very common in bicycles until the 1980s and is known for robustness and longevity. Depending on the challenges lying ahead of the cyclist, the three-gear shift is simultaneously a simple and robust technology to change between ‘normal gear’ (second), ‘mountain gear’ (first) and ‘speed gear’ (third). In our metaphor, second gear is the gear that aims at economic value creation, while the first and the third gear stand for environmental and social value creation, respectively. We use the metaphor of the classical three-speed hub gearshift as a high-functioning system to cope with different terrains and challenges. Just using the second gear would make having a gearshift unnecessary. Indeed, the other two gears allow for the possibility to adapt, as illustrated in Figure 14.1. With this metaphor, we also want to evolve a framework of thought that focuses on highlighting trade-offs and on business models that balance out a set of goals in a ‘closed’ drivetrain in which certain constellations can be chosen to cope with different terrains. All three gears are relevant to outcompete others in a terrain (the firm environment) that is not just flat, but also contains uphill and downhill slopes. The three-speed hub comes with extra weight and some complexity but adds value in its ability to cope with different terrains. Where environmental impacts of firm activities are concerned, it is possible to climb uphill reasonably well in first gear (the environmental gear). However, climbing uphill taking environmental concerns into account comes with slower speed. On the other hand, third gear (the social gear) allows for sufficient downhill speed, with more effort by the cyclist (i.e., the company in focus). Finally, second gear allows for coping with normal road conditions and, in our conceptualization, highlights the relevance of achieving economic goals, along with environmental and social ones. As many firm environments are characterized by a dynamic competitive landscape, as well as environmental and social challenges, the ability to change gear depending on the current driving conditions is of the utmost importance.

Profitable sustainability: the tribrid business model  211

Source: Own Figure inspired by three-gear bicycle drivetrain illustrated on https://kreisverbaende.adfc-nrw.de/ adfc-im-neanderland/ortsgruppen/erkrath/technik/drei-gang-nabe.html, last accessed 24 November 2022.

Figure 14.1

Tribrid business model conceptualization as a three-speed hub drivetrain

Figure 14.2 brings together the elements of tribrid business models in order to specify the added value of a digital platform in terms of corporate purpose (i.e. social, environmental and economic). In this context, it is relevant to understand both value creation and value capture. As mentioned above, while the creation of value depends upon cooperation, competitive elements are reflected in value capture (Hannah and Eisenhardt, 2018). This highlights the fact that a case-based investigation of TRIBRID business models is useful and can contribute detailed insights into the understanding of each of the elements enabling value creation and capture. To better understand the potential of the tribrid business model conceptualization, there follows an explorative investigation of digital platform examples.

ILLUSTRATION OF THE CONCEPT OF TRIBRID BUSINESS MODELS We want to provide examples of the embeddedness of economic, environmental and social elements for different types of sustainability-oriented digital platform providers in order to develop a conceptual foundation for future research. The research strategy chosen, therefore, represents an illustrative case study approach. Digital business platforms using a tribrid platform business model are emerging in several sectors of the economy. The authors identified areas of emergence and development of this

212  Handbook on digital platforms and business ecosystems in manufacturing

Figure 14.2

Value creation with TRIBRID digital platform business models Own compilation

type of enterprise using data from the portfolio of venture capitalists (VCs) who implement impact investments. Impact investors, in addition to their emphasis on financial return, require portfolio companies to generate social and/or ecological impacts. However, the requirement for commercial impact distinguishes impact investments from venture philanthropy or responsible investing (Glencross, et al. 2017). VC impact investors represent an attractive investor group for tribrid digital platforms that are at the start-up or early development stage, as conceptualized above – to whom they can present business projects and from whom they can raise commercial financing (Volk, 2021). Moreover, portfolio analysis of impact VC investors enables an overview of companies that have already received at least one investment from an external investor. It thus narrows the search to a group of companies that have undergone a detailed pre-investment analysis and received approval for at least one VC investment and that are implementing the business concept in practice. The assumption is, therefore, that we are dealing with companies that have good market prospects and are focused on rapid growth. This corresponds to achieving economic effects – or ‘economic gearing’ in our drive train model. In this study, the portfolios of 19 impact VC investors were first analyzed. Their total portfolio amounted to 610 companies. The analysis was carried out in such a way that the companies in question fulfilled criteria relating to (1) operating as digital platforms, (2) pursuing social objectives, and (3) pursuing environmental objectives. These criteria allowed for the use of our theoretical reasoning as a pattern to better understand how the business models fit with what we have conceptualized as tribrid business models. The aim is to illustrate how useful this perspective is to understand the three gears of business that aim for economic, environmental and social targets. When analyzing the data, we make use of the fact that some impact VC funds make their own summaries of reports relating to the fulfilment of conditions 2 and 3 available on their websites. After the initial scanning, in a second step, 14 companies were identified that met the relevant criteria. They are a diverse group from different countries and sectors, and are oriented

Profitable sustainability: the tribrid business model  213 towards diverse customer groups. To illustrate their structure, they have been grouped based on similarities in scope of activities and customer characteristics. A division into five groups was conducted, with the first (and largest) group representing a composite of subgroups to better illustrate the diversity of products. The first group contains business concepts related to the circular economy, the second group business models from the sharing economy. The identified business concept in the third group related to the exchange of information regarding social and environmental elements. Finally, the fourth group is located in the food trade. The findings for all groups are summarized in Table 14.1. Table 14.2 specifies examples for business concepts related to the circular economy for different sectors to go into further depth for illustrative purposes. Table 14.2

Group #01: more specification of subgroups

Business concepts related to the circular Description

Examples

economy in different sectors Mobility equipment

Fashion

This includes market platforms for

One example is BikeFair (The Netherlands),

used sustainable modes of transport, in

which offers a marketplace for verified,

particular, bicycles.

second-hand bikes.

This includes market platforms for

One example is pool.berlin (Berlin,

second-hand clothes. Sometimes, they are Germany), which offers fashion as a service, specialized in one type of customer, like

where users rent and swap menswear

women’s, men’s or children’s clothing.

garments for a monthly subscription fee. Other examples include Thrift+ (London, UK) and beebs.com (France).

Renewable energy equipment

This includes market platforms for new

One example is Milk the Sun (Berlin,

and used equipment for renewable energy Germany), which operates a marketplace for generation.

purchasing and selling photovoltaic systems, a professional online management tool, and access to related services.

Raw materials market

This includes concepts based on

One example is Vanilla Steel (Berlin,

supporting the sale/purchase of

Germany), an independent digital platform

materials whose manufacture is less

for metals in Europe, with focus on nonprime

environmentally damaging than prime

steel. The company points out that the sale

materials and can be reused.

of nonprime steel generates CO2 savings of between 70% and 96% as compared to the same tonnage of prime steel.

Source:  Own compilation.

CONCLUDING SUMMARY AND OUTLOOK The Covid-19 pandemic has made the need for sustainability more obvious (Arora and Mishra, 2020; Goffman, 2020). At the same time, however, it has pushed digitization (Amankwah-Amoah et al., 2021) and thus, potentially, the emergence of new platform-driven business models by actors motivated not only by the new possibilities and knowledge gained during the pandemic, but also by new customer necessities and demands. Current political and

One example is GoParity (Portugal), which provides crowd lending and platform financing for social and environmental projects using its community of investors,

Environmental objectives are achieved indirectly through the implementation of projects that receive crowdfunding.

Social objectives are achieved indirectly through the implementation of projects that receive crowdfunding.

Financial instruments/crowd This includes

fundraising (crowd

financing) concepts

financing

financial impact. (Singapore) and Ulule (France).

Other similar examples include Milaap

who are focused on sustainable and

projects.

geographies.

transport gaps in disproportionately poor

local taxis, minibuses and coaches) to fill

transport solutions (shared transport,

environmentally relevant

relating to socially and

Another example is Tandem (UK),

products/materials.

which focuses on supporting flexible

instruments and much more.

for newly manufactured

as electronic equipment, drones, musical

a number of equipment categories such

2. Indirectly reducing demand

has created a platform (Rnters) for renting

goods.

solutions.

One example is Flecto (Portugal), which

#03

generation.

Indirectly reducing waste

products/materials.

1. Increasing the intensity of

3.

for newly manufactured

use of already manufactured

Increasing the social availability of

and raw materials markets.

Indirectly reducing demand

using second-hand products. 2.

equipment, renewable energy equipment,

manufactured products by

based on short-term rental products at accessible prices.

This includes concepts

prices.

Extending the useful life of

Sharing economy

used products.

Examples Examples can be found in the mobility

Environmental objectives 1.

#02

Social objectives Increasing the social availability of

include the circulation of products: used products at accessible

Business models that

Business concept related to: Description

Circular economy

Group

Identified groups with illustrations

#01

Table 14.1

214  Handbook on digital platforms and business ecosystems in manufacturing

Source:  Own compilation.

In particular, this involves the exchange of knowledge among innovators, scientists and business professionals.

tive social impact. In particular, this involves the exchange of knowledge among innovators, scientists and business professionals. Supporting objectives related to the

Strengthening local food producers by

small, local producers.

standards, or goods from

that meet ecological

market and new distribution channels.

relating to trade in goods providing them with access to broader

This includes concepts

their products.

ordering process for stores and suppliers.

to independent retail by digitizing the

farms and expanding the market for which enables more suppliers to sell

One example is Hier (Berlin, Germany),

a positive environmental impact.

and products that can have a posi-

Popularizing organic standards on

and products that can have

innovative projects, technologies

the exchange process.

Food trade

innovators and professionals.

innovative projects, technologies

dissemination of knowledge about

elements are included in

#05

food and food tech newsletter for

dissemination of knowledge about

indirectly through the exchange and

environmentally useful

Triple Helix concept.

indirectly through the exchange and Switzerland), which publishes a weekly

Social objectives are achieved

2.

Examples One example is FoodHack (Lausanne,

Environmental objectives Ecological objectives are achieved

Social objectives 1.

Socially and

Business concept related to: Description

Exchange of information

Group

#04

Profitable sustainability: the tribrid business model  215

216  Handbook on digital platforms and business ecosystems in manufacturing economic crises very clearly indicate the relevance of financial goals next to environmental and social ones. Emerging types of business models come with different aims and purposes. In this chapter, we focused on what we call tribrid business models that come with the challenge to balance economic, social and environmental goals. Here, we use the understanding of business models by Amit and Zott (2015), namely that platform providers are focal actors who establish activities in the form of a platform that connects activities on two market sides. By using the metaphor of the three-gear speed hub drivetrain, we offer a simple conceptualization that clarifies the challenges, as well as opportunities, of coping with three aims. In this context, it becomes clear that digital platforms fulfill certain functions and that they are characterized by certain specificities that have to be included to gain an understanding of the underlying value creation and capture possibilities. After illustrative examples to give first insights into cases that reflect this reasoning, more in-depth cases are now to be undertaken to further develop both theory and pragmatic tools for businesses. Tables 14.1 and 14.2 give an overview of cases that may be investigated in future research. When exploring cases, the role and impact of location-specificities depending on the identified sector-specificities must be considered, e.g. the phase of the lifecycle. Such qualitative and exploratory investigations of the specificities of different platforms and adequate business models are a valuable contribution to the literature because they account for specificities of location and time, as well as platform markets (Kolk and Ciulli, 2020). Future research could explore cases from different sectors that make use of digital platforms, so as to come to a better understanding of ‘national idiosyncrasies’ (Kolk and Ciulli 2020). In sum, we have created the tribrid business model to suggest a useful conceptualization for digital platform providers – one that includes a better systematization of the value created and captured due to the achievement of social, environmental and economic goals. One intended contribution of this study is a focus, not just on trade-offs and challenges, but also on potential. Along the same line, it is relevant for us that – even though platformization has a dark side – it also has a bright side. Designing tribrid business models goes hand in hand with flexibility and agility regarding the achievement of a bundle of targets with underlying paradoxes and trade-offs. However, the current environment and its many uncertainties about future technologies (such as whether, in the heavy truck sector, the cleaner heavy-duty trucks of the future will be powered with green hydrogen or battery-electric) demand flexibility and agility. One central element of uncertainty lies in the willingness of customers to pay for sustainable products and services (in a social and environmental sense). Environmental legislation (e.g. regarding CO2 reduction) and social legislation (e.g. regarding supply chain governance) also plays a role here. Therefore, we have made a first attempt to characterize the use of the tribrid business model conceptualization in entrepreneurial practice. The aim is to lay the foundation for in-depth, follow-up case studies and future research to fill the framework with more content and better adapt it to the realities of platform-based digital businesses and the underlying diverse strengths and weaknesses connected to the ‘mix of vampire, werewolf and witch’ characteristics that must be kept in balance.

Profitable sustainability: the tribrid business model  217

NOTES 1. In the Vampire Diaries, tribrids are referred to as ‘a cross-breed of three different supernatural species’, with the term used to describe characters who are a mix of vampire, werewolf and witch. These characters come with ‘both the strengths and some of the weaknesses of their parent races, along with powerful attributes unique to themselves alone due to their combined heritage’ (taken from https://​the​-legacy​-of​-to​-tvd​.fandom​.com/​wiki/​Tribrid, last access 25-08-2022). Tribrid business models, in our understanding, mix three species in terms of their orientation towards achieving environmental, social and economic goals. 2. Di Stefano et al., 2014, suggest the organizational drivetrain, as a useful theoretical model to conceptualize and integrate different (partly competing) views and perspectives on dynamic capabilities. This inspired us to use the metaphor of the drivetrain and adapt it to our context, where we want to integrate economic, environmental and social elements in the investigation of digital platform business models.

REFERENCES Abelvik-Lawson, Helle (2021): Why Amazon is throwing away millions of unused products – and how we can stop them. Available online at https://​www​.greenpeace​.org​.uk/​news/​stop​-amazon​-throwing​ -away​-millions​-unused​-products/​, checked on 17 May 2023. Amankwah-Amoah, Joseph; Khan, Zaheer; Wood, Geoffrey; Knight, Gary (2021): COVID-19 and digitalization: The great acceleration. In Journal of Business Research 136, pp. 602–11. DOI: 10.1016/j. jbusres.2021.08.011. Amit, Raphael; Zott, Christoph (2015): Crafting business architecture: the antecedents of business model design. In Strategic Entrepreneurship Journal 9 (4), pp. 331–50. DOI: 10.1002/sej.1200. Arora, Naveen Kumar; Mishra, Jitendra (2020): COVID-19 and importance of environmental sustainability. In Environmental Sustainability 3 (2), pp. 117–19. DOI: 10.1007/s42398-020-00107-z. Bamberger, Kenneth A.; Lobe, Orly (2018): Platform market power. In Berkeley Technology Law Journal 32, pp. 1051–92. DOI: 10.15779/Z38N00ZT38. Barney, Jay B.; Ketchen, David J.; Wright, Mike (2021): Resource-based theory and the value creation framework. In Journal of Management 47 (7), pp. 1936–55. DOI: 10.1177/01492063211021655. Baumann, Sabine (2022): Introduction to the handbook on digital business ecosystems: strategies, platforms, technologies, governance and societal challenges. In Sabine Baumann (Ed.): Handbook on digital business ecosystems. Strategies, platforms, technologies, governance and societal challenges. Cheltenham: Edward Elgar Publishing Ltd (Research handbooks in business and management), pp. 1–9. BBC News (2020): Facebook: Privacy scandals take toll on profits. Available online at https://​www​.bbc​ .com/​news/​business​-51304516, checked on 17 May 2023. Bocken, Nancy M. P.; Short, Samuel W. (2021): Unsustainable business models – recognising and resolving institutionalised social and environmental harm. In Journal of Cleaner Production 312, p. 127828. DOI: 10.1016/j.jclepro.2021.127828. Bricka, Traci M.; Schroeder, Amber N. (2022): Having a platform to stand on: examining modern challenges faced by workers in platform-facilitated multi-sided markets. In Sabine Baumann (Ed.): Handbook on digital business ecosystems. Strategies, platforms, technologies, governance and societal challenges. Cheltenham: Edward Elgar Publishing Ltd (Research handbooks in business and management), pp. 439–56. Constantinides, Panos; Henfridsson, Ola; Parker, Geoffrey G. (2018): Introduction – platforms and infrastructures in the digital age. In Information Systems Research 29 (2), pp. 381–400. DOI: 10.1287/ isre.2018.0794. Curtis, Steven Kane; Lehner, Matthias (2019): Defining the sharing economy for sustainability. In Sustainability 11 (3), p. 567. DOI: 10.3390/su11030567.

218  Handbook on digital platforms and business ecosystems in manufacturing Di Stefano, Giada; Peteraf, Margaret; Verona, Gianmario (2014): The organizational drivetrain: a road to integration of dynamic capabilities research. In AMP 28 (4), pp. 307–27. DOI: 10.5465/ amp.2013.0100. Dietl, Helmut (2010): Erfolgsstrategien im Plattformwettbewerb. In Schmalenbachs Z betriebswirtsch Forsch 62 (S62), pp. 63–83. DOI: 10.1007/BF03373747. Dwoskin, Elisabeth (2021): Misinformation on Facebook beats factual news when it comes to clicks, study finds. Available online at https://​www​.washingtonpost​.com/​technology/​2021/​09/​03/​facebook​ -misinformation​-nyu​-study/​, checked on 17 May 2023. European Environment Agency (2020): Walking, cycling and public transport in cities remain greener mobility options than electric scooters or car ride-hailing. Available online at https://​www​.eea​.europa​ .eu/​highlights/​walking​-cycling​-and​-public​-transport, checked on 17 May 2023. Fehrer, Julia A.; Woratschek, Herbert; Brodie, Roderick J. (2018): A systemic logic for platform business models. In JOSM 29 (4), pp. 546–68. DOI: 10.1108/JOSM-02-2017-0036. Fish, Alan J.; Wood, Jack (2017): Promoting a strategic business focus to balance competitive advantage and corporate social responsibility – missing elements. In SRJ 13 (1), pp. 78–94. DOI: 10.1108/ SRJ-04-2016-0054. Gawer, Annabelle (2009): Platform dynamics and strategies: from products to services. In Annabelle Gawer (Ed.): Platforms, Markets and Innovation: Edward Elgar Publishing. Gawer, Annabelle; Cusumano, Michael A. (2014): Industry Platforms and Ecosystem Innovation. In J Prod Innov Manag 31 (3), pp. 417–33. DOI: 10.1111/jpim.12105. George, Gerard; Merrill, Ryan K.; Schillebeeckx, Simon J. D. (2021): Digital sustainability and entrepreneurship: how digital innovations are helping tackle climate change and sustainable development. In Entrepreneurship Theory and Practice 45 (5), pp. 999–1027. DOI: 10.1177/1042258719899425. Glencross, J.; Bogglid, L.; Bishop, M.; Manwaring, S.; Harrison, J. P.; Leytes M.; Plante, B. (2017): The Impact Investing Guidebook for Foundations. Philanthropic Foundations Canada, Community Foundations of Canada, Purpose Capital. Available online at https://​ missioninvestors​ .org/​ sites/​ default/​files/​resources/​Impact​-Investing​-Guidebook​-Foundations​-v16​-Linked​.pdf, checked on 12 October 2022. Goffman, Ethan (2020): In the wake of COVID-19, is glocalization our sustainability future? In Sustainability: Science, Practice and Policy 16 (1), pp. 48–52. DOI: 10.1080/15487733.2020.1765678. Grusky, David (2014): Social Stratification. Class, Race, and Gender in Sociological Perspective, Fourth Edition, New York: Routledge. Hahn, Tobias; Figge, Frank; Pinkse, Jonatan; Preuss, Lutz (2010): Trade-offs in corporate sustainability: you can’t have your cake and eat it. In Bus. Strat. Env. 19 (4), pp. 217–29. DOI: 10.1002/bse.674. Hannah, Douglas P.; Eisenhardt, Kathleen M. (2018): How firms navigate cooperation and competition in nascent ecosystems. In Strat Mgmt J 39 (12), pp. 3163–92. DOI: 10.1002/smj.2750. Hart, Stuart L.; Dowell, Glen (2011): Invited Editorial: A Natural-Resource-Based View of the Firm. In Journal of Management 37 (5), pp. 1464–79. DOI: 10.1177/0149206310390219. Hassan, Jennifer (2021): Footage of Amazon destroying thousands of unsold items in Britain prompts calls for official investigation. Available online at https://​www​.washingtonpost​.com/​world/​2021/​06/​ 23/​amazon​-uk​-warehouses​-destroy​-items/​, checked on 17 May 2023. Hawkins, Andrew J. (2019): Uber and Lyft are the ‘biggest contributors’ to San Francisco’s traffic congestion, study says. Available online at https://​www​.theverge​.com/​2019/​5/​8/​18535627/​uber​-lyft​ -sf​-traffic​-congestion​-increase​-study, checked on 17 May 2023. Hayter, Christopher S.; Nelson, Andrew J.; Zayed, Stephanie; O’Connor, Alan C. (2018): Conceptualizing academic entrepreneurship ecosystems: a review, analysis and extension of the literature. In The Journal of Technology Transfer 43 (4), pp. 1039–82. DOI: 10.1007/s10961-018-9657-5. Ikeda, Kazuaki; Marshall, Anthony (2019): Strategies for competing in markets enabled by digital platforms. In SL 47 (1), pp. 30–36. DOI: 10.1108/SL-10-2018-0097.

Profitable sustainability: the tribrid business model  219 Knyphausen-Aufseß, Dodo zu; Kunisch, Sven; Nippa, Michael (2021): Zur Rolle der BWL in Zeiten großer gesellschaftlicher und ökologischer Herausforderungen. In Die Unternehmung 75 (2), pp. 188–97. DOI: 10.5771/0042-059X-2021-2-188. Kolk, Ans; Ciulli, Francesca (2020): The potential of sustainability-oriented digital platform multinationals: A comment on the transitions research agenda. In Environmental Innovation and Societal Transitions 34, pp. 355–8. DOI: 10.1016/j.eist.2019.12.008. Lomas, Natascha (2022): Germany widens antitrust probes of Amazon to loop in special abuse controls. Available online at https://​techcrunch​.com/​2022/​11/​15/​amazon​-antitrust​-german​-fco​-special​-abuse​ -controls/​?guccounter​=​1​&​guce​_referrer​=​aHR0cHM6Ly​93d3cuZ29v​Z2xlLmRlLw​&​guce​_referrer​ _sig​=​AQAAA​GM15tT0GWC​aDq3hsMAZf​BCvTo3a5tt​h0ptZ6CiGk​RVIEhwxGcl​Fj6XIDqQQ7​ _rj​K5maAaNsSZ​rhzTqfF6mq​pu3A3y6R2i​Bd8a71ogwH​_iXWkVnHVLrc7Mv​-XkpA​_Prjwu​ -hlV​LUWiNhlnM6UMMV98iHtr​-0dTMkbZcUmSSkEfi4, checked on 17 May 2023. Mayer, Colin (2021): The future of the corporation and the economics of purpose. In J. Manage. Stud. 58 (3), pp. 887–901. DOI: 10.1111/joms.12660. McGee, Patrick (2023): Dina Srinivasan: exposing the monopoly power of Facebook and Google. Yale academic was among the first to show that tech groups’ ‘free’ products could be anti-competitive. Available online at https://​www​.ft​.com/​content/​d9a9af52​-4cb5​-4bfb​-92ed​-55e301343ff5, checked on 17 May 2023. Mukhoryanova, Oksana; Kuleshova, Larisa; Rusakova, Nina; Mirgorodskaya, Olga (2021): Sustainability of micro-enterprises in the digital economy. In E3S Web Conf. 250, p. 6008. DOI: 10.1051/ e3sconf/202125006008. Murillo, David; Buckland, Heloise; Val, Esther (2017): When the sharing economy becomes neoliberalism on steroids: Unravelling the controversies. In Technological Forecasting and Social Change 125, pp. 66–76. DOI: 10.1016/j.techfore.2017.05.024. Palmer, Annie; Novet, Jordan (2020): Amazon bullies partners and vendors, says antitrust subcommittee. Available online at https://​www​.cnbc​.com/​2020/​10/​06/​amazon​-bullies​-partners​-and​-vendors​ -says​-antitrust​-subcommittee​.html, updated on 17 May 2023. Pouri, Maria J.; Hilty, Lorenz M. (2018): Conceptualizing the digital sharing economy in the context of sustainability. In Sustainability 10 (12), p. 4453. DOI: 10.3390/su10124453. Pouri, Maria J.; Hilty, Lorenz M. (2020): The relevance of digital sharing business models for sustainability. In Ruzanna Chitchyan, Daniel Schien, Ana Moreira, Benoit Combemale (Eds.): Proceedings of the 7th International Conference on ICT for Sustainability. ICT4S2020: 7th International Conference on ICT for Sustainability. Bristol United Kingdom, 21 06 2020 26 06 2020. New York, NY, USA: ACM, pp. 77–87. Quairel-Lanoizelee, Françoise (2016): Are competition and corporate social responsibility compatible? In SBR 11 (2), pp. 130–54. DOI: 10.1108/SBR-04-2016-0026. Reuter, Emmanuelle (2022): Hybrid business models in the sharing economy: The role of business model design for managing the environmental paradox. In Bus Strat Env 31 (2), pp. 603–18. DOI: 10.1002/ bse.2939. Rietveld, Joost; Schilling, Melissa A. (2021): Platform competition: a systematic and interdisciplinary review of the literature. In Journal of Management 47 (6), pp. 1528–63. DOI: 10.1177/0149206320969791. Risi, Elisabetta; Pronzato, Riccardo (2021): Smart working is not so smart: Always-on lives and the dark side of platformisation. In Work Organisation, Labour & Globalisation 15 (1). DOI: 10.13169/ workorgalaboglob.15.1.0107. Sahut, Jean-Michel; Iandoli, Luca; Teulon, Frédéric (2021): The age of digital entrepreneurship. In Small Bus Econ 56 (3), pp. 1159–69. DOI: 10.1007/s11187-019-00260-8. Song, Abraham K. (2019): The Digital Entrepreneurial Ecosystem – a critique and reconfiguration. In Small Bus Econ 53 (3), pp. 569–90. DOI: 10.1007/s11187-019-00232-y. Spigel, Ben; Harrison, Richard (2018): Toward a process theory of entrepreneurial ecosystems. In Strategic Entrepreneurship Journal 12 (1), pp. 151–68. DOI: 10.1002/sej.1268.

220  Handbook on digital platforms and business ecosystems in manufacturing Srinivasan, Arati; Venkatraman, N. (2018): Entrepreneurship in digital platforms. A network-centric view. In Strategic Entrepreneurship Journal 12 (1), pp. 54–71. DOI: 10.1002/sej.1272. van Alstyne, Marshall; Parker, Geoffrey (2017): Platform Business: From Resources to Relationships. In NIM Marketing Intelligence Review 9 (1), pp. 24–9. DOI: 10.1515/gfkmir-2017-0004. Volk, Ariane (2021): Investing for Impact. The Global Impact Investing Market 2020. International Finance Corporation. Available online at https://​www​.ifc​.org/​wps/​wcm/​connect/​365d09e3​-e8d6​ -4da4​-badb​-741933e76f3b/​2021​-Investing+​for+​Impact​_FIN2​.pdf​?MOD​=​AJPERES​&​CVID​=​ nL5SF6G, checked on 12 October 2022. Yousaf, Zahid; Radulescu, Magdalena; Sinisi, Crenguta Ileana; Serbanescu, Luminita; Păunescu, Loredana Maria (2021): Towards sustainable digital innovation of SMEs from the developing countries in the context of the digital economy and frugal environment. In Sustainability 13 (10), p. 5715. DOI: 10.3390/su13105715.

15. Design thinking in the circular economy: augmenting digital ecosystems and platforms with local entrepreneurial ecosystems Philip T. Roundy

INTRODUCTION The circular economy (CE) movement in manufacturing is focused on ‘replac[ing] the “end-of-life” concept with reducing, alternatively reusing, recycling and recovering materials in production/distribution and consumption processes’ (Kirchherr et al., 2017: 224; Suchek et al., 2022). Making manufacturing more circular requires novel approaches, such as the use of digital sharing platforms (Schwanholz and Leipold, 2020), which can be perceived by industry incumbents as radical deviations from status quo business models. However, entrepreneurs play a unique role in the CE because their alertness to new opportunities, innovativeness and lack of organizational inertia enable them to pursue disruptive business models (Henry et al., 2020). At the foundation of CE entrepreneurship is a desire for sustainable development, which is achieved by moving away from the predominant ‘take-make-use-dispose’ linear supply chain model (Suchek et al., 2022) and towards closed-loop, regenerative manufacturing processes (e.g. Tapia et al., 2021). Because of the complexity and interdependencies in their business models, CE entrepreneurs often create and rely upon digital ecosystems and platforms to reach consumers, coordinate stakeholders, integrate supply chains, redesign production and ultimately, improve the sustainability of manufacturing and consumption (Konietzko et al., 2019; Pizzi et al., 2021). For instance, J. J. Chuan, the entrepreneur responsible for founding the CE venture ‘Rehyphen’, has created an innovative upcycling technique whereby discarded audio cassette tapes are collected and manufactured into consumer products, such as handbags and artwork, as a means of reducing e-waste. Rehyphen has partnered with several digital platforms, such as One Good Thing, Indiegogo and Airbnb to reach consumers. Although digital ecosystems and platforms can be helpful to CE entrepreneurs and, in some cases, are essential to the value propositions of CE ventures, identifying and implementing innovative manufacturing processes that succeed in creating value for consumers is challenging, particularly for early-stage entrepreneurs facing resource constraints (Gauthier et al., 2021). To understand how CE entrepreneurs overcome their resource challenges, scholars and practitioners have sought to identify the strategies that entrepreneurs use (Hull et al., 2021; Le et al., 2022; Suchek et al., 2022). A problem-solving strategy that has received extensive academic and practitioner attention is design thinking (e.g. Andrews, 2015; Elsbach and Stigliani, 2018). Design thinking is a human-centered approach to innovation that adopts the mindset and practices of designers, such as interviewing, ethnographic observation and prototyping, to integrate a deep understanding of human needs with the requirements for business success (Martin, 2009; Dorst, 2011). 221

222  Handbook on digital platforms and business ecosystems in manufacturing Using design thinking, CE entrepreneurs gather the data needed to improve manufacturing processes, increase supply chain efficiency, create value for their stakeholders and avoid ineffective solutions that do not reduce waste, reclaim materials and increase sustainability. However, the design thinking approach is not without costs and may require resources that CE entrepreneurs do not possess. Although there is mounting practitioner and academic evidence that design thinking and CE entrepreneurship are synergistic (e.g. Hannon et al., 2016; Santa-Maria et al., 2022) because both activities are focused on creating innovative solutions tailored to the specific problems of stakeholders (Kurek et al., 2023), it is not clear from extant research how entrepreneurs acquire the resources needed for the design thinking process. To address this challenge, this chapter explores the following question: How do circular economy entrepreneurs leverage design thinking and their local entrepreneurial ecosystems to augment digital ecosystems and platforms in sustainable manufacturing? In pursuing this question, a conceptual framework is proposed to explain how CE entrepreneurs utilize entrepreneurial ecosystems – the interconnected actors and forces in local communities that support entrepreneurship (Stam, 2015). The concept of ‘entrepreneurial ecosystem-enhanced design thinking’ is developed to delineate how CE entrepreneurs can overcome resource limitations by leveraging their local startup communities to acquire resources, enrich the design thinking process and complement their use of digital ecosystems and platforms. The main insight of the chapter is that CE entrepreneurs who embrace the assistance of their local ecosystems supplement their personal and digital resources with place-based resources, which enables them to create business models, processes and products that contribute to the circular economy. Specifically, the chapter explains how CE entrepreneurs leverage the place-based social, cultural and material attributes of entrepreneurial ecosystems (Spigel, 2017), such as local networks, events, values and resource providers, to improve their needfinding, idea generation and idea testing activities, and, in turn, the circularity of their manufacturing (Geissdoerfer et al., 2016; Elsbach and Stigliani, 2018). Entrepreneurial ecosystem-enhanced design thinking is an approach CE entrepreneurs can use in conjunction with digital ecosystems and platforms to fine-tune and refine their sustainable manufacturing processes. Although entrepreneurs are increasingly utilizing digital meta-organizational arrangements (cf. Konietzko et al., 2019; Baumann, 2022; Kretschmer et al., 2022), such as digital ecosystems and platforms, these meta-organizations are often globally distributed, consist of mostly online interactions, and are not location-specific. Further, the digital ecosystems and platforms used in CE tend to focus on facilitating economic transactions, rather than other forms of value co-creation, such as vicarious learning (Henry et al., 2020). Yet, the ‘…circular economy does not occur in isolation. It is a localized interaction among stakeholders, including government, embedded in various network relationships’ (Hull et al., 2021: 1–2). Scholars are increasingly studying the functioning of digital ecosystems in organizations’ business models and value propositions; however, the interactions between digital ecosystems and other forms of ecosystems, such as local entrepreneurial ecosystems, have received significantly less attention. Thus, while digital ecosystems and platforms are increasingly central to manufacturing (Culot, 2022) and the circular economy (Khatami et al., 2023), entrepreneurs’ local ecosystems are also consequential (Roundy and Lyons, 2023) and, as described in later sections, complement the unique role that digital ecosystems and platforms play in supporting CE entrepreneurship and sustainable manufacturing.

Design thinking in the circular economy  223 This chapter addresses key omissions in the CE and ecosystems literatures and responds to calls for more research on how CE entrepreneurs utilize their local communities and collaborate with other actors (Ferreira and Dabic, 2022; Suchek et al., 2022). Specifically, introducing the concept of ‘entrepreneurial ecosystem-enabled design thinking’ makes several contributions. First, by drawing attention to the role of entrepreneurial ecosystems in CE entrepreneurship and design thinking, the conceptual framework identifies one potential source of the resources that entrepreneurs need to create effective solutions to unsustainable manufacturing problems – entrepreneurial ecosystems. The framework highlights a unique set of mechanisms that explain how CE entrepreneurs overcome resource constraints and improve sustainability by not only utilizing digital ecosystems and platforms but leveraging extra-organizational ties to their local communities in the earliest phases of the venture lifecycle. The framework also contributes by exploring previously unexamined pathways through which the micro-level activities of entrepreneurs (i.e. entrepreneurs’ design processes and activities) interact with the contextual, macro-level factors in their external local environments. By identifying these mechanisms, the framework contributes to entrepreneurial ecosystems research by explaining the specific ways in which the structural characteristics of place-based ecosystems influence CE entrepreneurship processes (Spigel and Harrison, 2018). The framework also contributes to the burgeoning stream of research on CE entrepreneurs and their unique ecosystem dynamics by revealing the connection points between entrepreneurs’ pursuit of distinct activities (e.g. shifting manufacturing from linear to regenerative models) and their local communities. The next section reviews the foundations of the conceptual arguments and describes the opportunities at the interface of CE entrepreneurship, design thinking and ecosystems that are addressed by the concept of entrepreneurial ecosystem-enhanced design thinking. A framework is then developed to explain how entrepreneurial ecosystems enable design thinking in CE entrepreneurship. The arguments are organized around three propositions, which correspond to the main phases in the design thinking process and map onto key aspects of CE entrepreneurship. Finally, the chapter concludes with a discussion of the contributions to research on the circular economy and ecosystems, the implications for CE entrepreneurs, and an agenda of research opportunities at the intersection of manufacturing, design and ecosystems.

THEORETICAL FOUNDATIONS Design Thinking as a Tool for Innovation in Manufacturing Design thinking is a toolkit that manufacturers can use to develop solutions to consumer problems (Whitehead et al., 2019). Design thinking is grounded in ‘the logics and practices associated with designers’ (Beverland et al., 2015: 589) and employs ‘tools such as rapid prototyping, user observation, visualization of ideas, and brainstorming’ (Elsbach and Stigliani, 2018: 2275). Design thinking consists of distinct, but reinforcing, phases. Although different labels are used to describe the phases (e.g. ‘inspiration, ideation and implementation’; ‘understanding, exploration and materialization’; ‘empathizing, ideating and refining’) (Razzouk and Shute, 2012), the design thinking process is comprised of three iterative sets of practices: ‘needfinding’, ‘idea generation’ and ‘idea testing’ (Elsbach and Stigliani, 2018). Needfinding involves discovering and defining problems and is focused on learning about the people who are affected by problems and will use designers’ proposed solutions (Seidel

224  Handbook on digital platforms and business ecosystems in manufacturing and Fixson, 2013). Needfinding adopts a user-centric focus aimed at developing empathy for the feelings and experiences of a product’s intended users (Elsbach and Stigliani, 2018; Seidel and Fixson, 2013). Idea generation is focused on brainstorming potential solutions to problems and exploring possibilities (Elsbach and Stigliani, 2018). During idea generation, designers formulate multiple ways to tackle problems by engaging in creative exercises to produce radical and nonobvious solutions. The prospective users of a designer’s products are often included in ideation to ‘co-create’ possible solutions (Deserti and Rizzo, 2013) and help the designer brainstorm as many potential solutions as possible – an approach based on the belief that optimal solutions oftentimes are not obvious and initially may seem far-fetched or ‘outside the box’ (Wylant, 2008). Finally, idea testing involves activities such as rapid prototyping, hypothesis testing and iterative experimentation, and has the goal of ‘test[ing] ideas on a small scale to determine their desirability, technical feasibility, and business viability’ (Elsbach and Stigliani, 2018: 2280). In idea testing, designers build quick, often low-cost, prototypes that allow them to get immediate feedback on their ideas, primarily from prospective users of their solutions (Boland et al., 2008). The learning and insights gained from user feedback allow designers to test, evaluate and refine their ideas by failing quickly (Stackowiak and Kelly, 2020), pivoting to different ideas and then assessing the feasibility of the new ideas. Design Thinking and Circular Economy Entrepreneurship The circular economy is an economic system in which ‘the value of products, materials and resources is maintained in the economy for as long as possible, and the generation of waste minimized’ (Ferreira and Dubic, 2022: 1). Although most types of entrepreneurs create value by solving problems, there is growing evidence that CE entrepreneurs are a unique type of entrepreneur that seeks to solve distinct problems (Suchek et al., 2022). CE entrepreneurs differ from conventional (non-CE) entrepreneurs in important ways. Like mission-focused social entrepreneurs, CE entrepreneurs are explicitly focused on the environmental and social effects of their products. For instance, CE entrepreneurs consider what happens to their products after manufacturing and after end-use and try to prolong the use of their products. Thus, CE entrepreneurs are more concerned with the sustainability of the manufacturing process than are conventional entrepreneurs. CE entrepreneurs gain particular benefit from adopting design thinking because the approach is well-suited for tackling ‘wicked’ problems (i.e. problems that are ‘complex, unpredictable, open ended, or intractable’) (Head and Alford, 2015: 712) – precisely the type of manufacturing problems addressed by CE entrepreneurs (e.g. Salvia et al., 2021). Creating effective solutions to wicked manufacturing problems requires not only developing a deep understanding of the problems but also gaining insights about stakeholders’ experiences and needs, generating multiple impact models and then prototyping these possible solutions with the help of a product’s prospective users to observe behaviors and fine-tune optimal outcomes (Kolko, 2012). Because wicked problems, like the manufacturing problems tackled by CE entrepreneurs, are often highly contextualized (Jordan et al., 2014), design activities require becoming highly embedded in the contexts in which problems occur and in which products are used so that CE entrepreneurs can gain the nuanced, and often tacit, knowledge needed to develop products according to the main tenets of CE production and consumption (i.e. reduce, reuse, recycle recover (Kirchherr et al., 2017)). However, prior work on design thinking and CE entrepreneurship focuses on how design thinking can be used to create CE business models

Design thinking in the circular economy  225 (e.g. Santa-Maria et al., 2022) and does not shed light on how entrepreneurs become embedded in local contexts and acquire the resources needed to use design thinking. In the next section, research is reviewed that focuses on entrepreneurs’ local contexts and two specific strategies for gaining resources: leveraging (1) digital ecosystems and (2) local entrepreneurial ecosystems. Digital Ecosystems and the Circular Economy A growing stream of research focuses on how digital platforms and ecosystems can enable circular economy ventures (e.g. Culot, 2022; Elghaish et al., 2022; Khatami et al., 2023). For instance, Blackburn et al. (2023) analyze digital platforms as a type of meta-organization and, in a study of ten European digital platforms, find that the focal firm in the platform plays a key role in orchestrating value creation in CE business models. Likewise, Schwanholz and Leipold (2020) analyzed how digital platforms enable CE business models by studying 73 German digital platforms. They found variety in the connections between digital sharing platforms and CE business models and that the goals, aims and business models pursued by sharing platforms revealed to what extent the platforms viewed themselves as contributing to the CE. In general, digital ecosystems can contribute to the circular economy as technologies that enable entrepreneurs to design manufacturing and consumption systems that maintain ‘inputs – including products, materials or other resources – for as long as possible’ (Wilson et al., 2022: 9). For instance, Del Vecchio et al. (2021) contend that digital platforms can serve as innovation systems that catalyze knowledge creation and dissemination and ‘fertilize the entrepreneurship development process by creating socio-economic value’ (p. 5) (also cf. Braun et al., 2021). Although it is becoming increasingly clear how entrepreneurs can use digital ecosystems and platforms to support their CE ventures and how these ecosystems connect CE entrepreneurs to stakeholders across the globe, entrepreneurs have access to another type of ecosystem – entrepreneurial ecosystems – which are tied to their local environments. The Key Attributes of Entrepreneurial Ecosystems Scholars are increasingly acknowledging the usefulness of a ‘holistic approach’ to studying CE that includes, among other domains, ‘different geographic regions’ (Ferreira and Dabic, 2022: 1). Entrepreneurial ecosystems are the focus of a strand of entrepreneurship research that explicitly considers the implications of entrepreneurship taking place in different geographic regions. The heightened interest in entrepreneurial ecosystems and circular economy is, in part, a product of the ‘contextual turn’ in entrepreneurship research (Welter et al., 2019), which has extended to CE entrepreneurship (Oliveira et al., 2021; Pizzi et al., 2021; Wilson et al., 2022). Research on entrepreneurial ecosystems has developed in tandem with work on digital ecosystems (Sussan and Acs, 2017). An intuitive framework for organizing entrepreneurial ecosystem attributes was identified and developed by Spigel (2017) who proposed three categories: social, cultural and material attributes. Subsequent studies have confirmed and refined the social-cultural-material attributes framework (Loots et al., 2021). Although digital ecosystems may also have these general attributes (e.g. a digital ecosystem is comprised of social relationships), as described next, the way these attributes manifest in physical, location-specific, geographic ecosystems is unique.

226  Handbook on digital platforms and business ecosystems in manufacturing Social Attributes An entrepreneurial ecosystem’s social attributes create relational resources from the interdependence of ecosystem participants (Spigel, 2017). Entrepreneurs accrue relational resources by becoming embedded in an ecosystem’s networks (Neumeyer and Santos, 2018; Pittz et al., 2021). For instance, within an entrepreneurial ecosystem, entrepreneurs may be connected to other manufacturers, support organization staff, university members and ancillary ecosystem participants (e.g. attorneys and legal professionals who specialize in working with entrepreneurial ventures) (Roundy, 2017a). Unlike a digital ecosystem, in an entrepreneurial ecosystem many of these relationships involve individuals and organizations who are co-located in a city or region. The social networks of entrepreneurial ecosystems are comprised of both formal and informal relationships in local startup communities (Sperber and Linder, 2019). Cultural Attributes Entrepreneurial ecosystem culture is a community’s ‘collective commonality of perspectives’ about entrepreneurship and how ecosystem participants should act (Walsh and Winsor, 2019; Donaldson, 2021: 289). An ecosystem’s culture is comprised of shared values, norms, simple rules, institutions, logics and stories. Culture is produced by the interactions among community members, which reinforce their shared beliefs. Culture is also influenced by a region’s formal and informal institutions, which set the legal and unofficial ‘rules of the game’ for how entrepreneurial ecosystem participants interact (Stam and van de Ven, 2021). The cultures of thriving entrepreneurial ecosystems promote an entrepreneurial logic (Cunningham et al., 2002) focused on value creation, the pursuit of opportunities despite resource scarcity, innovativeness and being open to trial-and-error learning processes, and a community logic focused on cooperation, community development, trust, helping others and being socially responsible (Roundy, 2017b). These values manifest as actions such as sharing information, making introductions to other ecosystem participants, and providing mentorship. Material Attributes The third category of ecosystem characteristics is material attributes, which have a physical presence in entrepreneurial ecosystems. Material attributes include universities, support organizations, co-working spaces and the customers who comprise local markets (Spigel, 2017). Unlike an ecosystem’s social and cultural attributes, and unlike the attributes of digital ecosystems, an entrepreneurial ecosystem’s material attributes are tangible, such as meeting spaces, incubators and small business development centers. An ecosystem’s material attributes produce place-specific resources that are valuable, in part because they enable the other attributes of entrepreneurial ecosystems (Spigel, 2017). For example, entrepreneurs engage in social interactions and learn ecosystem culture by interacting with other ecosystem participants at event spaces. In sum, research has made strides in identifying how digital platforms and ecosystems can enable CE entrepreneurship, however, studies are only beginning to understand how CE entrepreneurs access and use the local resources produced by an entrepreneurial ecosystem’s attributes (Wurth et al., 2022). Indeed, as Aarikka-Stenroos et al. (2021: 260) contend, ‘[...] “ecosystems” are increasingly invoked for improvement of environmental sustainability and

Design thinking in the circular economy  227 the circular economy (CE), but there is conceptual as well as empirical ambiguity regarding their role, composition, and nature’. Moreover, research contends that design thinking is integral to the entrepreneurship process (e.g. Kickul and Lyons, 2020), yet the process by which CE entrepreneurs acquire the resources needed for design thinking activities remains undertheorized. In the next section, a theoretical framework is developed that addresses these voids in the literature and explains how CE entrepreneurs might overcome resource challenges by using local entrepreneurial ecosystem attributes to enhance design thinking and augment digital ecosystems.

CONCEPTUAL DEVELOPMENT CE entrepreneurs can utilize the social, cultural and material attributes of their entrepreneurial ecosystems during each phase of the design thinking process: needfinding, idea generation and idea testing (Elsbach and Stigliani, 2018). Leveraging Entrepreneurial Ecosystems to Enhance Needfinding The needfinding phase begins when entrepreneurs discover a CE opportunity (i.e. an opportunity to minimize resource waste or increase resource efficiency) and are motivated to pursue it. CE opportunities involve shifting a manufacturing and/or consumption process from linear to (more) circular by addressing unsustainable processes, such as the planned obsolescence of products that causes materials to be prematurely discarded. For instance, the entrepreneurs who created the CE venture Retrospekt explain that their mission is to restore and repurpose vintage electronics because: [t]hese products, that still prove to be so useful and enjoyable, shouldn’t be haphazardly discarded. Left to decay (or never decay, because plastic) in some landfill. We give them a new life. (Retrospekt, 2023)

The entrepreneurs have relied on a number of digital platforms, including Etsy, eBay and Instagram, as channels to communicate their message and sell their products. However, defining the parameters of circular economy problems, like the ones addressed by Retrospekt, is difficult because such problems are often complex, highly contextualized and influenced by the idiosyncratic characteristics of the places where they manifest (e.g. the reasons why consumers do not ‘upcycle’ products or their willingness to purchase upcycled products may differ based on region) (Kummitha, 2018). When CE entrepreneurs are inspired to tackle a manufacturing problem, if they are following a design thinking approach, their first step is to increase their empathy for the primary groups affected by the problem (Iida et al., 2021). Empathizing involves CE entrepreneurs seeking to understand the emotions, perceptions and experiences of stakeholders who are influenced by the manufacturing process. Most entrepreneurs engage in some degree of needfinding; however, CE entrepreneurs are faced with the unique challenge of empathizing not only with the needs of the consumers who purchase a venture’s products and services but also the needs of the larger community (including the natural environment) which align with the venture’s mission to make manufacturing and consumption processes more sustainable. To empathize with their stakeholder groups, CE entrepreneurs gather insights and information

228  Handbook on digital platforms and business ecosystems in manufacturing from them, which is a relational process. CE entrepreneurs are better able to understand the complex needs of customers and other stakeholders if they can form connections with them that allow for personal interactions and knowledge sharing, which help them to learn how to make a manufacturing process more sustainable (cf. Busch and Barkema, 2019). CE entrepreneurs with a design thinking mindset can leverage the social attributes of their entrepreneurial ecosystems to build relationships with stakeholders and acquire the information and experiences needed to empathize with customers and community members. The network ties in an entrepreneurial ecosystem, which connect CE entrepreneurs to mentors, other manufacturers, charity organizations and support services, can be used to gather data, learn what knowledge is available in the ecosystem and understand who holds the knowledge (Stam and Van de Ven, 2021). Such connections are valuable because at least three types of circular economy-related knowledge exist in entrepreneurial ecosystems: differentiated knowledge (knowledge about CE entrepreneurship not held by most participants in an ecosystem), shared knowledge (knowledge that entrepreneurial ecosystem participants have in common based on overlapping knowledge bases), and meta-knowledge (knowledge about ‘who knows what’ in the ecosystem – i.e. knowledge about other ecosystem participants’ knowledge) (Roundy, 2020). During needfinding, CE entrepreneurs can use the three types of knowledge that ‘spillover’ from an ecosystem’s social networks (Cao and Shi, 2021) to define the manufacturing problem they are addressing, uncover unmet customer and community needs, and gain insights about their stakeholders and the production-consumption process. Network relationships can help CE entrepreneurs locate and make connections to prospective customers. These connections allow CE entrepreneurs to gather data directly from their key, demand-side stakeholders (e.g. prospective users of the products they manufacture) through interviews, observation and other qualitative techniques that produce rich insights about people’s perceptions, desires and needs (Glen et al., 2014). A CE entrepreneur’s network ties to other local actors can provide entrepreneurs with the local legitimacy (Muñoz and Kibler, 2016) needed for customers and other stakeholders (e.g. manufacturing partners and suppliers) to trust entrepreneurs and be willing to share the information needed to define a problem. Through their ecosystems, CE entrepreneurs can also be connected to other types of partners and support organizations, such as environmental nonprofits (van Rijnsoever, 2020), who may already be working on the manufacturing problem and can help entrepreneurs gain important contextualized knowledge to more accurately frame possible solutions. The knowledge that flows to CE entrepreneurs through an entrepreneurial ecosystem’s networks helps the entrepreneurs begin to understand the manufacturing problem, the consumer need and the potential opportunities to introduce new solutions that are more efficient and create less waste. Thus, CE entrepreneurs who become ‘plugged into’ (Stephens et al., 2019) their entrepreneurial ecosystem networks benefit by expanding the knowledge they can access. The material attributes of an entrepreneurial ecosystem also assist in needfinding. For instance, local customers enable needfinding by helping CE entrepreneurs to more accurately define and frame the manufacturing problems and opportunities they are pursuing. The physical infrastructure of the entrepreneurial ecosystem (Spigel, 2017), such as its meeting and event spaces, is another tangible aspect of ecosystems that assists entrepreneurs by allowing them to experience planned and unplanned interactions with other ecosystem participants; such spontaneous ecosystem interactions (or ‘collisions’; Nylund, and Cohen, 2017) can provide valuable early sources of inspiration and understanding about manufacturing prob-

Design thinking in the circular economy  229 lems. Local universities and support organizations also reinforce the importance of needfinding by teaching design methods. An entrepreneurial ecosystem’s cultural attributes influence the needfinding phase of design thinking in several ways. Entrepreneurial ecosystem culture can enhance needfinding by promoting values and norms that support and encourage entrepreneurs to engage in customer discovery, put themselves in the ‘shoes’ of stakeholders and increase their empathy towards these groups. Entrepreneurial ecosystem culture is also supportive of ecosystem participants working together by placing a high value on cooperation, collaboration and prosocial (helping) behaviors (Theodoraki et al., 2020; Roundy, 2021). If values that prioritize community building are prevalent in an ecosystem, participants will be more likely to trust one another and share information, make introductions to other entrepreneurial ecosystem participants and provide feedback and mentorship – all of which can help CE entrepreneurs to better empathize with customers and other stakeholders and more accurately define their manufacturing problem and business opportunity (Muldoon et al., 2018; Walsh and Winsor, 2019). An entrepreneurial ecosystem’s culture is further comprised of the stories of a region’s residents (Hubner et al., 2022). Such narratives contain valuable information about customers and encapsulate additional learning opportunities for CE entrepreneurs (Donaldson, 2021). Stories can also celebrate entrepreneurs who have been successful in making their manufacturing processes more circular and have excelled in taking a design thinking approach to stakeholder needs. The combined effects of an entrepreneurial ecosystem’s social, material and cultural attributes on CE entrepreneurs’ design thinking suggest: Proposition 1: Circular economy entrepreneurs can leverage the resources in their local entrepreneurial ecosystems to enhance needfinding in the manufacturing process. Leveraging Entrepreneurial Ecosystems to Enhance Idea Generation As entrepreneurs begin to understand the nuances of the manufacturing problems and CE opportunities they are addressing, the attributes of their entrepreneurial ecosystems can assist in another aspect of design thinking: idea generation (Elsbach and Stigliani, 2018). In generating creative ideas, CE entrepreneurs are limited by their personal experiences, mindsets and assumptions. However, the variety of perspectives of entrepreneurial ecosystem participants can serve as a diverse reservoir of community knowledge that can supplement entrepreneurs’ personal knowledge. Entrepreneurs access community knowledge through entrepreneurial ecosystem networks. During idea generation, CE entrepreneurs brainstorm potential solutions to the manufacturing problems they address and formulate possible business models to support their missions to reduce waste, increase efficiency and improve sustainability. If CE entrepreneurs are embedded in an ecosystem’s networks they are positioned in a community’s ‘flow of information’, and both strong and weak network ties can fuel idea generation by exposing entrepreneurs to novel information, skills and manufacturing processes (Walsh, 2019; McKague et al., 2023). In this function, CE entrepreneurs can use entrepreneurial ecosystem networks as community transactive memory systems, or ‘meta-expert directories’ (Bachrach et al., 2019), that allow entrepreneurs to leverage the collective knowledge contained in entrepreneurial ecosystems to enhance their creativity and generate manufacturing innovations. CE entrepreneurs may also improve their ability to create or identify ideas by using their local networks to observe and

230  Handbook on digital platforms and business ecosystems in manufacturing interact with the downstream users of their products and services, who play a key role in their manufacturing and consumption models. Culturally, entrepreneurial ecosystems can promote values that support idea generation and experimentation and discourage entrepreneurs from path dependence, which aligns with advocates of design thinking who contend that entrepreneurs should avoid getting locked into their initial ideas without exploring other possible solutions (Goswami et al., 2018; Marineau and Nordstrom, 2020). CE entrepreneurs can ‘crowdsource’ manufacturing innovations from their local ecosystem by attending events, like ecosystem ‘meet-ups’ (Motoyama et al., 2014), which are aimed at bringing together entrepreneurial ecosystem participants. In addition to having informal conversations with other ecosystem participants at events, CE entrepreneurs can spur creative ideas using more deliberate methods of idea generation to generate context-specific insights, such as formal interviews with local customers (Dalton and Kahute, 2016). CE entrepreneurs can also leverage their local communities during ideation by participating in events explicitly aimed at brainstorming new technological and social solutions (e.g. sustainability-oriented ‘hackathons’; cf. Rys, 2023). In sum, an entrepreneurial ecosystem’s social, cultural and material resources help CE entrepreneurs to generate and explore diverse ideas to address CE opportunities and develop viable business models to support their sustainability. Proposition 2: Circular economy entrepreneurs can leverage the resources in their local entrepreneurial ecosystems to enhance idea generation in the manufacturing process. Leveraging Entrepreneurial Ecosystems to Enhance Idea Testing An entrepreneurial ecosystem’s attributes can assist in the third phase of design thinking: testing and refining new ideas (Elsbach and Stigliani, 2018). If CE entrepreneurs are embedded in an ecosystem’s social networks, they can utilize their community ties to access resource providers, either within the ecosystem or with connections to it (cf. Ratten, 2020a; van Rijnsoever, 2020). These resources can be used for experimenting with manufacturing innovations and/or prototyping solutions (e.g. using 3D printers provided by makerspaces) (Beltagui et al., 2021). Entrepreneurial ecosystem networks also connect CE entrepreneurs to mentors, serial entrepreneurs and other ecosystem participants, who can help entrepreneurs assess and refine the quality of their ideas (cf. Goswami et al., 2018). Entrepreneurial ecosystem organizations, including universities, incubators, accelerators and small business development centers, often hold formal events, such as pitch competitions (Price, 2021), that CE entrepreneurs use to gain additional feedback on early iterations of their ideas and test the effectiveness of CE innovations. Entrepreneurs can also participate in other types of entrepreneurial ecosystem events held by support organizations, such as demo-days, which provide customer reactions to prototypes (Pauwels et al., 2016). Through these formal events and during informal ecosystem interactions, entrepreneurs solicit the feedback of local stakeholders to improve the early versions of their products. In contrast to digital ecosystem interactions, in-person ecosystem interactions, facilitate direct questioning and observation of prospective customers, which can generate rich feedback for CE entrepreneurs. Through the promotion of simple rules, such as ‘fail fast, but learn quickly’ (e.g. Khanna et al., 2016), an entrepreneurial ecosystem’s culture supports idea testing by not stigmatizing failure and by encouraging CE entrepreneurs to experiment and adopt a hypothesis-testing

Design thinking in the circular economy  231 and trial-and-error mindset (DiVito and Ingen-Housz, 2021). In thriving entrepreneurial ecosystems (Adams, 2021), a common belief is that entrepreneurs should repeatedly test and validate ideas before attempting full implementation rather than assuming that initial ideas are the ‘best’ solution. As described, the culture of entrepreneurial ecosystems is built on a community logic, focused on collaboration, which encourages ecosystem participants to help CE entrepreneurs iterate, update and refine their ideas. Entrepreneurial ecosystem culture also consists of an entrepreneurial logic (Cunningham et al., 2002), which involves being open to experimenting with new ideas. The influences of an ecosystem’s social and cultural attributes on idea testing are bolstered by its material attributes (Spigel, 2017). For instance, local investors who reside in a regional entrepreneurial ecosystem provide the financial resources that CE entrepreneurs need to transform ideas into product offerings. An ecosystem’s local customers also assist entrepreneurs in idea testing by providing feedback on prototypes and successively advanced versions of their manufacturing innovations. These arguments suggest the following proposition about the relationship between entrepreneurial ecosystem attributes and CE entrepreneurs’ idea testing processes: Proposition 3: Circular economy entrepreneurs can leverage the resources in their local entrepreneurial ecosystems to enhance idea testing in the manufacturing process. Figure 15.1 summarizes the theoretical arguments.

DISCUSSION Academics and practitioners are increasingly encouraging CE entrepreneurs to leverage digital ecosystems and platforms and adopt a design thinking approach to sustainable manufacturing (Kummitha, 2018; Kickul and Lyons, 2020). However, these strategies are not without costs for CE entrepreneurs. Creating and developing digital ecosystems and platforms requires technological and financial capital that CE entrepreneurs often lack. Likewise, while the design thinking process involves a shift in mindset rather than major financial investments, design thinking does require resources that early-stage CE entrepreneurs may not have, such as connections to consumers who provide rich feedback on manufacturing processes and products. The concept of entrepreneurial ecosystem-enhanced design thinking explains how CE entrepreneurs can address their resource limitations by utilizing their local communities for support. The sections that follow unpack how the conceptual framework developed in this chapter informs research on CE entrepreneurship and creates insights for entrepreneurs. Contributions to Theory In addition to taking stock of their personal characteristics and resources, CE entrepreneurs are increasingly encouraged to consider the ecosystem of external forces that influence manufacturing problems (Pizzi et al., 2021; Wilson et al., 2022). However, the specific strategies and behaviors that CE entrepreneurs engage in when interacting with their ecosystems are untheorized. The conceptual framework developed in this chapter contributes to research on CE entrepreneurs by explaining how they can address complex manufacturing problems

232  Handbook on digital platforms and business ecosystems in manufacturing

Figure 15.1

A theory of entrepreneurial ecosystem-enhanced design thinking and circular economy entrepreneurship

by relying on extra-organizational resources in their local communities to enhance design thinking. Most CE research that has taken an ecosystems approach has focused on digital ecosystems, such as digital sharing platforms; however, digital ecosystems require resources to build and maintain. Digital ecosystems also provide resources that are different from the resources provided by entrepreneurial ecosystems. The proposed conceptual framework contributes to ecosystems research by explaining the ways in which a unique type of ecosystem – entrepreneurial ecosystems – influences the CE entrepreneurship process, which addresses calls for research that is clearer about the mechanisms through which ecosystems influence entrepreneurs’ activities and decisions (Perugini, 2023). The framework clarifies a pathway by which the micro-level activities of CE entrepreneurs (entrepreneurs’ characteristics and behaviors) interact with the macro-level factors in entrepreneurs’ physical rather than digital environments (Roundy and Lyons, 2023). Specifically, the framework contributes to the burgeoning stream of research focused on the dynamics of CE entrepreneurs’ local contexts (‘CE entrepreneurial ecosystems’) (Aarikka-Stenroos et al., 2021) by explicating the connections between

Design thinking in the circular economy  233 CE entrepreneurs’ design activities and the actors and forces in their local communities. The framework suggests that entrepreneurial ecosystems represent an alternative (or supplemental) means by which CE entrepreneurs formulate solutions to manufacturing problems. Implications for Entrepreneurs and Ecosystem Practitioners The concept of entrepreneurial ecosystem-enhanced design thinking has implications for entrepreneurs and ecosystem practitioners. Although most types of entrepreneurs create value by solving problems and thus can benefit from design thinking, there is growing evidence that CE entrepreneurs gain particular benefit from adopting a design thinking approach (Santa-Maria et al., 2022). The proposed framework suggests that CE entrepreneurs who want to improve their design thinking practices should evaluate and map their entrepreneurial ecosystems (e.g. using an entrepreneurial ecosystem ‘canvas’; Founder Institute, 2022) to understand how the resources available in their local communities might augment the resources gained from digital ecosystems and platforms. Further, the findings suggest that important variation may exist in the extent that CE entrepreneurs either use their entrepreneurial ecosystems, alone or in tandem with digital ecosystems, to enhance their design thinking or take a ‘go-it-alone’ approach. CE entrepreneurs who tap into their ecosystems will be more able to translate their design practices into sustainable impact than entrepreneurs without an ecosystem ‘mindset’. For entrepreneurial ecosystem practitioners, and particularly those who build and lead ecosystems, the role that ecosystems play in facilitating the design thinking process underscores the importance of building vibrant communities to support entrepreneurs. If entrepreneurial ecosystems lack key attributes, then CE entrepreneurs will not experience the full benefits of their local ecosystems, which may cause entrepreneurs to forego using design thinking and produce suboptimal solutions to manufacturing problems. Thus, in addition to the critical role that CE entrepreneurs play in addressing unsustainable manufacturing, entrepreneurial ecosystem leaders have an important function in building local environments to support CE entrepreneurs. Opportunities for Future Research The conceptual framework suggests several directions for future research at the interface of CE entrepreneurship, digital and entrepreneurial ecosystems, and design. First, to understand the contexts in which the theory has explanatory power, it is important to explore the framework’s boundary conditions and identify constraints on generalizability (Shepherd and Williams, 2023). Research is needed, for instance, to ascertain the characteristics of entrepreneurial ecosystems that can increase (or decrease) the impact of startup ecosystems on CE entrepreneurs’ use of design thinking. For example, an implicit assumption in the theorizing is that local ecosystems are ‘vibrant’ and have all, or most, of the attributes needed to support CE entrepreneurship (i.e. the full complement of social, cultural and material resources). However, like entrepreneurs, entrepreneurial ecosystems may also be resource-constrained, which may attenuate or alter the enhancing effects of an entrepreneurial ecosystem on design thinking. CE entrepreneurs in unmunificent ecosystems may need to leverage entrepreneurial ecosystems differently or rely more heavily on digital ecosystems for design-enabling resources. It is also important for future research to study if other aspects of CE entrepreneurship, beyond design thinking and the use of digital ecosystems, can be enhanced (or hindered) by

234  Handbook on digital platforms and business ecosystems in manufacturing entrepreneurial ecosystems. For example, it is unclear if entrepreneurial ecosystems influence other activities, such as CE entrepreneurs’ ongoing efforts to balance sustainability with financial viability (i.e. balancing environmental and economic logics; Luiz et al., 2019). Research is also needed to understand if the framework and its mechanisms apply to conventional entrepreneurs, who do not create ventures with explicit environmental and social missions, but who apply design thinking to traditional consumer problems and who use digital platforms and ecosystems to enable non-CE business models. The proposed conceptual framework focuses on entrepreneurs’ use of design thinking; however, in addition to CE entrepreneurs, other ecosystem actors may benefit from adopting design practices. For instance, scholars could examine if there are meaningful differences in entrepreneurial ecosystem leaders (e.g. Porras-Paez and Schmutzler, 2019) in their use of design thinking and explore how ecosystem leaders engage in needfinding, idea generation and idea testing in their efforts to improve startup communities. Such research could explore if community leaders who take a design approach create entrepreneurial ecosystems that are better tailored to meet the needs of CE entrepreneurs. Finally, there is growing evidence that entrepreneurs can benefit from both design thinking and having an ecosystem mindset (Klenner et al., 2022; Ratten, 2020b). CE entrepreneurs utilize digital ecosystems and platforms to facilitate value co-creation and the delivery of their value propositions. However, the conceptual framework developed in this chapter explains how circular economy entrepreneurs can also use their entrepreneurial ecosystems to enhance the effectiveness of design thinking. This framework will hopefully spur further study of how entrepreneurs leverage the support provided by the place-based assets and individuals in their local communities in conjunction with digital platforms and ecosystems to recover materials, reduce waste and make manufacturing more sustainable.

REFERENCES Aarikka-Stenroos, L., Ritala, P., and Thomas, L. D. (2021) Circular economy ecosystems: A typology, definitions, and implications. In: Teerikangas, S., Onkila, T., Koistinen, K., and Mäkelä, M. (Eds.) Research Handbook of Sustainability Agency, London: Edward Elgar, pp. 260–76. Adams, S. B. (2021) From orchards to chips: Silicon Valley’s evolving entrepreneurial ecosystem, Entrepreneurship & Regional Development, 33(1–2), 15–35. Andrews, D. (2015) The circular economy, design thinking and education for sustainability. Local Economy, 30(3), 305–15. Bachrach, D. G., Lewis, K., Kim, Y., Patel, P. C., Campion, M. C., and Thatcher, S. (2019) Transactive memory systems in context: A meta-analytic examination of contextual factors in transactive memory systems development and team performance. Journal of Applied Psychology, 104(3), 464. Baumann, S. (2022). Introduction to the handbook on digital business ecosystems: strategies, platforms, technologies, governance and societal challenges. In Handbook on Digital Business Ecosystems, Edward Elgar Publishing, pp. 1–9. Beltagui, A., Sesis, A., and Stylos, N. (2021) A bricolage perspective on democratising innovation: the case of 3D printing in makerspaces. Technological Forecasting and Social Change, 163, 120453. Beverland, M. B., Wilner, S. J., and Micheli, P. (2015) Reconciling the tension between consistency and relevance: design thinking as a mechanism for brand ambidexterity. Journal of the Academy of Marketing Science, 43(5), 589–609. Blackburn, O., Ritala, P., and Keränen, J. (2023). Digital platforms for the circular economy: Exploring meta-organizational orchestration mechanisms. Organization & Environment, 36(2), 253–281. Boland Jr, R. J., Collopy, F., Lyytinen, K., and Yoo, Y. (2008) Managing as designing: lessons for organization leaders from the design practice of Frank O. Gehry. Design Issues, 24(1), 10–25.

Design thinking in the circular economy  235 Braun, A. T., Schöllhammer, O., and Rosenkranz, B. (2021). Adaptation of the business model canvas template to develop business models for the circular economy. Procedia Cirp, 99, 698–702. Busch C., and Barkema, H.G. (2019) Social entrepreneurs as network orchestrators. In: George, G., Tracey, P., Baker, T., Havovi, J. (Eds.) Handbook of inclusive innovation. London: Edward Elgar, pp. 464–85. Cao, Z., and Shi, X. (2021) A systematic literature review of entrepreneurial ecosystems in advanced and emerging economies. Small Business Economics, 57(1), 75–110. Culot, G. (2022). Digital ecosystems in manufacturing: emerging models for Industry 4.0. In S. Baumann (Ed.), Handbook on Digital Business Ecosystems (pp. 727–42). Edward Elgar Publishing. Cunningham, J. B., Gerrard, P., Schoch, H., and Hong, C. L. (2002) An entrepreneurial logic for the new economy. Management Decision, 40(8), 734–44 Dalton, J., and Kahute, T. (2016) Why empathy and customer closeness is crucial for design thinking. Design Management Review, 27(2), 20-27. Del Vecchio, P., Passiante, G., Barberio, G., and Innella, C. (2021). Digital innovation ecosystems for circular economy: The case of ICESP, the Italian circular economy stakeholder platform. International Journal of Innovation and Technology Management, 18(01), 2050053. Deserti, A., and Rizzo, F. (2013) Design and the cultures of enterprises. Design Issues, 30, 36–56. DiVito, L., and Ingen-Housz, Z. (2021) From individual sustainability orientations to collective sustainability innovation and sustainable entrepreneurial ecosystems. Small Business Economics, 56(3), 1057–72. Donaldson, C. (2021) Culture in the entrepreneurial ecosystem: A conceptual framing. International Entrepreneurship and Management Journal, 17(1), 289–319. Dorst, K. (2011) The core of ‘design thinking’ and its application. Design Studies, 32(6), 521–32. Elghaish, F., Matarneh, S. T., Edwards, D. J., Pour Rahimian, F., El-Gohary, H., and Ejohwomu, O. (2022). Applications of Industry 4.0 digital technologies towards a construction circular economy: gap analysis and conceptual framework. Construction Innovation, 22(3), 647–70. Elsbach, K. D., and Stigliani, I. (2018) Design thinking and organizational culture: A review and framework for future research. Journal of Management, 44(6), 2274–306. Ferreira, J. J., and Dabic, M. (2022) Guest editorial: Circular economy and entrepreneurial ecosystems: a missing link? Management of Environmental Quality: An International Journal, 33(1), 1–8. Founder Institute (2022) Startup ecosystem canvas. https://​fi​.co/​canvas (accessed 2 March 23). Gauthier, J., Cohen, D., and Meyer, C. R. (2021) Entrepreneurial orientation, externalities and social entrepreneurship, Society and Business Review, 16(3), 476–89. Geissdoerfer, M., Bocken, N. M., and Hultink, E. J. (2016) Design thinking to enhance the sustainable business modelling process – A workshop based on a value mapping process. Journal of Cleaner Production, 135, 1218–32. Glen, R., Suciu, C., and Baughn, C. (2014) The need for design thinking in business schools. Academy of Management Learning & Education, 13(4), 653–67. Goswami, K., Mitchell, J. R., and Bhagavatula, S. (2018) Accelerator expertise: Understanding the intermediary role of accelerators in the development of the Bangalore entrepreneurial ecosystem. Strategic Entrepreneurship Journal, 12(1), 117–50. Hannon, E., Kuhlmann, M., and Thaidigsmann, B. (2016). Developing products for a circular economy. In C. Murphy and J. Rosenfield (Eds.), The circular economy: Moving from theory to practice (pp. 22–25). McKinsey & Company. Head, B. W., and Alford, J. (2015) Wicked problems: Implications for public policy and management. Administration & Society, 47(6), 711–39. Henry, M., Bauwens, T., Hekkert, M., and Kirchherr, J. (2020) A typology of circular start-ups: An analysis of 128 circular business models. Journal of Cleaner Production, 245, 118528. Hubner, S., Most, F., Wirtz, J., and Auer, C. (2022) Narratives in entrepreneurial ecosystems: drivers of effectuation versus causation. Small Business Economics, 59(1), 211–42. Hull, C. E., Millette, S., and Williams, E. (2021) Challenges and opportunities in building circular-economy incubators: Stakeholder perspectives in Trinidad and Tobago. Journal of Cleaner Production, 296, 126412.

236  Handbook on digital platforms and business ecosystems in manufacturing Iida, H., Ishibashi, K., Inoue, A., Sawatani, Y. (2021) Empathy-based CE strategy to tackle complex challenges. In: Leitner, C., Ganz, W., Satterfield, D., Bassano, C. (Eds) Advances in the human side of service engineering. AHFE 2021. Lecture Notes in Networks and Systems, Vol 266. Springer, Cham. Khanna, R., Guler, I., and Nerkar, A. (2016) Fail often, fail big, and fail fast? Learning from small failures and R&D performance in the pharmaceutical industry. Academy of Management Journal, 59(2), 436–59. Khatami, F., Vilamová, Š., Cagno, E., De Bernardi, P., Neri, A., and Cantino, V. (2023). Efficiency of consumer behaviour and digital ecosystem in the generation of the plastic waste toward the circular economy. Journal of Environmental Management, 325, 116555. Kickul, J., and Lyons, T.S. (2020) Understanding social entrepreneurship: The relentless pursuit of mission in an ever changing world. Routledge. Kirchherr, J., Reike, D., and Hekkert, M. (2017) Conceptualizing the circular economy: An analysis of 114 definitions. Resources, Conservation and Recycling, 127, 221–32. Klenner, N. F., Gemser, G., and Karpen, I. O. (2022) Entrepreneurial ways of designing and designerly ways of entrepreneuring: Exploring the relationship between design thinking and effectuation theory. Journal of Product Innovation Management, 39(1): 66–94. Kolko, J. (2012) Wicked problems: Problems worth solving. Austin Center for Design: Ac4d. Konietzko, J., Bocken, N. M. P., and Hultink, E. J. (2019) Online platforms and the circular economy. In N. M. P. Bocken, P. Ritala, L. Albareda, and R. Verburg (Eds.), Innovation for sustainability: Business transformations towards a better world, Cham: Springer, pp. 435–50. Kretschmer, T., Leiponen, A., Schilling, M., and Vasudeva, G. (2022) Platform ecosystems as meta‐ organizations: Implications for platform strategies. Strategic Management Journal, 43(3), 405–24. Kummitha, R. K. R. (2018) Institutionalising design thinking in social entrepreneurship: A contextual analysis into social and organizational processes. Social Enterprise Journal, 14(1), 92–107. Kurek, J., Brandli, L. L., Leite Frandoloso, M. A., Lange Salvia, A., and Mazutti, J. (2023). Sustainable business models innovation and design thinking: A bibliometric analysis and systematic review of literature. Sustainability, 15(2), 988. Jordan, M. E., Kleinsasser, R. C., and Roe, M. F. (2014) Wicked problems: Inescapable wickedity. Journal of Education for Teaching, 40(4), 415–30. Le, T. T., Behl, A., and Pereira, V. (2022) Establishing linkages between circular economy practices and sustainable performance: the moderating role of circular economy entrepreneurship. Management Decision, (ahead-of-print). Loots, E., Neiva, M., Carvalho, L., and Lavanga, M. (2021) The entrepreneurial ecosystem of cultural and creative industries in Porto: A sub‐ecosystem approach. Growth and Change, 52(2), 641–62. Luiz, J. M., Kachika, K., and Kudzurunga, T. (2019). Negotiating new institutional logics: Market access amongst smallholder farmers in Zambia and Malawi. Society and Business Review, 14(4), 360–77. Marineau, J. E., and Nordstrom, O. (2020) Uncovering the cultural beliefs in a nascent entrepreneurial ecosystem. Entrepreneurship Research Journal, 13(1), 131–162. Martin, R. L. (2009) The design of business: Why design thinking is the next competitive advantage, Harvard Business Press. McKague, K., Saunders, C., Gilbert, S., and Farrell, E. (2023) Strong ties, information seeking, and ecosystem brokerage among actors in the Atlantic Canadian entrepreneurial ecosystem. Journal of Small Business & Entrepreneurship, 35(6), 871–889. Motoyama, Y., Konczal, J., Bell-Masterson, J., and Morelix, A. (2014) Think locally, act locally: building a robust entrepreneurial ecosystem. Kauffman Foundation. Muldoon, J., Bauman, A., and Lucy, C. (2018) Entrepreneurial ecosystem: do you trust or distrust? Journal of Enterprising Communities: People and Places in the Global Economy, 12(2), 158–77. Muñoz, P., and Kibler, E. (2016) Institutional complexity and social entrepreneurship: A fuzzy-set approach. Journal of Business Research, 69(4), 1314–18. Oliveira, J. C., Lopes, J. M., Farinha, L., Silva, S., and Luízio, M. (2021). Orchestrating entrepreneurial ecosystems in circular economy: The new paradigm of sustainable competitiveness. Management of Environmental Quality: An International Journal, 33(1), 103–23. Neumeyer, X., and Santos, S. C. (2018) Sustainable business models, venture typologies, and entrepreneurial ecosystems: A social network perspective. Journal of Cleaner Production, 172, 4565–79.

Design thinking in the circular economy  237 Nylund, P. A. and Cohen B. (2017) Collision density: driving growth in urban entrepreneurial ecosystems. International Entrepreneurship and Management Journal, 13(3): 757–76. Pauwels, C., Clarysse, B., Wright, M., and Van Hove, J. (2016) Understanding a new generation incubation model: The accelerator. Technovation, 50, 13–24. Perugini, F. (2023). Space–time analysis of entrepreneurial ecosystems. The Journal of Technology Transfer, 48(1), 240–91. Pittz, T.G., White, R., and Zoller, T. (2021) Entrepreneurial ecosystems and social network centrality: The power of regional dealmakers. Small Business Economics, 56(4), 1273–86. Pizzi, S., Leopizzi, R., and Caputo, A. (2021) The enablers in the relationship between entrepreneurial ecosystems and the circular economy: the case of circularity.com. Management of Environmental Quality: An International Journal, 33(1), 26–43. Porras-Paez, A., and Schmutzler, J. (2019). Orchestrating an entrepreneurial ecosystem in an emerging country: The lead actor’s role from a social capital perspective. Local Economy, 34(8), 767–86. Price, D. P. (2021) Introducing University Pitch Competitions: An Analysis of the First Five Years. Journal of Higher Education Theory and Practice, 21(9), 149–61. Ratten, V. (2020a) Coronavirus and international business: An entrepreneurial ecosystem perspective. Thunderbird International Business Review, 62(5), 629–34. Ratten, V. (2020b) Entrepreneurial ecosystems: future research trends. Thunderbird International Business Review, 62(5): 623–8. Razzouk, R., and Shute, V. (2012) What is design thinking and why is it important? Review of Educational Research, 82(3), 330–48. Retrospekt. (2023). About us. https://​retrospekt​.com/​pages/​our​-story (accessed 2 March 23). Roundy, P. T. (2017a) Social entrepreneurship and entrepreneurial ecosystems: complementary or disjoint phenomena? International Journal of Social Economics, 44(9), 1252–67. Roundy, P. T. (2017b) Hybrid organizations and the logics of entrepreneurial ecosystems. International Entrepreneurship and Management Journal, 13(4), 1221–37. Roundy, P. T. (2020) The wisdom of ecosystems: A transactive memory theory of knowledge management in entrepreneurial ecosystems. Knowledge and Process Management, 27(3), 234–47. Roundy, P. T. (2021) Leadership in startup communities: How incubator leaders develop a regional entrepreneurial ecosystem. Journal of Management Development, 40(3), 190–208. Roundy, P. T., and Lyons, T. S. (2023) Where are the entrepreneurs? A call to theorize the micro-foundations and strategic organization of entrepreneurial ecosystems. Strategic Organization, 21(2). 447–459. Rys, M. (2023). Invention Development. The Hackathon Method. Knowledge Management Research & Practice, 21(3), 499–511. Salvia, G., Zimmermann, N., Willan, C., Hale, J., Gitau, H., Muindi, K., Guchana, E. and Davies, M. (2021) The wicked problem of waste management: An attention-based analysis of stakeholder behaviours. Journal of Cleaner Production, 326, 129200. Santa-Maria, T., Vermeulen, W. J., and Baumgartner, R. J. (2022) The circular sprint: Circular business model innovation through design thinking. Journal of Cleaner Production, 362, 132323. Schwanholz, J., and Leipold, S. (2020) Sharing for a circular economy? An analysis of digital sharing platforms’ principles and business models. Journal of Cleaner Production, 269, 122327. Seidel, V. P., and Fixson, S. K. (2013) Adopting design thinking in novice multidisciplinary teams: The application and limits of design methods and reflexive practices. Journal of Product Innovation Management, 30, 19–33. Shepherd, D. A., and Williams, T. A. (2023). Does it need to be broader or deeper? Trade-offs in entrepreneurship theorizing. Entrepreneurship Theory and Practice, 47(4), 1003–1030. Sperber, S., and Linder, C. (2019) Gender-specifics in start-up strategies and the role of the entrepreneurial ecosystem. Small Business Economics, 53(2), 533–46. Spigel, B. (2017) The relational organization of entrepreneurial ecosystems, Entrepreneurship Theory and Practice, 41(1), 49–72. Spigel, B., and Harrison, R. (2018) Toward a process theory of entrepreneurial ecosystems, Strategic Entrepreneurship Journal, 12(1), 151–68. Stackowiak, R. and Kelly, T. (2020) Design thinking overview and history, in Design Thinking in Software and AI Projects. Berkeley, CA: Springer, pp. 1–16.

238  Handbook on digital platforms and business ecosystems in manufacturing Stam, E. (2015) Entrepreneurial ecosystems and regional policy: a sympathetic critique. European Planning Studies, 23(9), 1759–69. Stam, E., and Van de Ven, A. (2021) Entrepreneurial ecosystem elements. Small Business Economics, 56(2), 809–32. Stephens, B., Butler, J. S., Garg, R., and Gibson, D. V. (2019) Austin, Boston, Silicon Valley, and New York: Case studies in the location choices of entrepreneurs in maintaining the Technopolis. Technological Forecasting and Social Change, 146, 267–80. Suchek, N., Ferreira, J. J., and Fernandes, P. O. (2022) A review of entrepreneurship and circular economy research: State of the art and future directions. Business Strategy and the Environment, 31(5): 2256–83. Sussan, F., and Acs, Z. J. (2017). The digital entrepreneurial ecosystem. Small Business Economics, 49, 55–73. Tapia, C., Bianchi, M., Pallaske, G., and Bassi, A. M. (2021) Towards a territorial definition of a circular economy: exploring the role of territorial factors in closed-loop systems. European Planning Studies, 29(8), 1438–57. Theodoraki, C., Messeghem, K., and Audretsch, D. B. (2020) The effectiveness of incubators’ co-opetition strategy in the entrepreneurial ecosystem: Empirical evidence from France. IEEE Transactions on Engineering Management, 69(4): 1781–94. van Rijnsoever, F. J. (2020) Meeting, mating, and intermediating: How incubators can overcome weak network problems in entrepreneurial ecosystems. Research Policy, 49(1), 103884. Walsh, K. (2019) Regional capability emergence in an entrepreneurial ecosystem. Journal of Entrepreneurship and Public Policy, 8(3), 359–83. Walsh, J., and Winsor, B. (2019) Socio-cultural barriers to developing a regional entrepreneurial ecosystem. Journal of Enterprising Communities: People and Places in the Global Economy, 13(3), 263–82. Welter, F., Baker, T., and Wirsching, K. (2019) Three waves and counting: The rising tide of contextualization in entrepreneurship research. Small Business Economics, 52(2), 319–30. Whitehead, T., Evans, M., and Bingham, G. A. (2019). Local or global? Approaches for new product development in low income countries. The Design Journal, 22(5), 707–23. Wilson, M., Paschen, J., and Pitt, L. (2022) The circular economy meets artificial intelligence (AI): Understanding the opportunities of AI for reverse logistics. Management of Environmental Quality: An International Journal, 33(1), 9–25. Wurth, B., Stam, E., and Spigel, B. (2022) Toward an entrepreneurial ecosystem research program. Entrepreneurship Theory and Practice, 46(3), 729–78. Wylant, B. (2008) Design thinking and the experience of innovation. Design Issues, 24(2), 3–14.

16. Evaluation of sustainability of smart-circular product-service ecosystems using the example of 3D printing Verena Luisa Aufderheide

INTRODUCTION The data-based 3D printing process revolutionizes the manufacture of components. Precise-fit printing can reduce material consumption and increase resource efficiency (Bierdel et al., 2019). Although various studies predict high market growth for 3D printing (e.g. PwC Strategy&, 2018), it is still not widespread (Hofmann and Oettmeier, 2016). Especially in the context of the current sustainability debate, the potential of a more sustainable process should be advanced by changing the manufacturing process of components. 3D printers are a smart proposition that, in principle, can also be harnessed for the circular economy, so that 3D printers are considered as a smart-circular product-service-system (SCPSS). If SCPSSs are offered in a network, they are referred to as a smart-circular product-service-ecosystem (SCPSE). SCPSEs have the potential to increase customer benefits of SCPSS through lower costs, longer period of use and better recyclability of products (Alcayaga et al., 2019; Aufderheide et al., 2022). However, it is questionable whether these advantages help to increase the attractiveness of 3D printers for more frequent use. Moreover, even if the attractiveness could be increased, another question arises: whether the offer within SCPSE is more sustainable than traditional offers. Overall, this chapter addresses the following research questions (RQs): ● RQ 1: How can the SCPSE overcome the challenges of offers of 3D printers to contribute to a higher attractiveness of 3D printers by using digital platforms? ● RQ 2: How can the sustainability of SCPSE be evaluated? What KPIs are relevant to measure sustainability of SCPSE? ● RQ 3: How can digital platforms be used to increase the sustainability of 3D printers in SCPSE? To answer these questions, the chapter is divided into five sections. First, the manufacturing process of 3D printers is fundamentally presented in order to classify the offer of 3D printers in the concept of SCPSS. Then it is explained how a SCPSE can overcome the challenges of offering 3D printers in order to increase their attractiveness by using digital platforms. The third section answers RQ 2 and is the main part of the chapter, wherein a new quantitative sustainability index is developed and adapted for the concept of the SCPSE. Section four presents the example and analyzes how digital platforms can be useful to increase the sustainability of 3D printers. Finally, the chapter concludes with a summary and outlook.

239

240  Handbook on digital platforms and business ecosystems in manufacturing

3D PRINTING Customers demand individual products at favorable prices and with short delivery times. Innovations are required ever more quickly. Therefore, innovation cycles must also be short (Bückner et al., 2020). To meet these requirements, innovative manufacturing processes with a high degree of precision perfection are necessary, which can be easily adapted to new conditions. These requirements have led to the development of 3D printing (Gebhardt et al., 2019). 3D printing creates components by applying substances in three dimensions. For production, a computer-aided design (CAD) model is created and applied to a substrate using a nozzle. In this process, the starting materials are molded together layer by layer to form a component. The 3D printer’s robot follows the path specified by the CAD model, so that the nozzle can dispense the material at the right place (Gebhardt et al., 2019). 3D printing can be used in various fields. Common areas of application are medicine, aircraft construction and the automotive industry (Feldmann et al., 2019). 3D printing has numerous potentials for products and supply chains (Fastermann, 2016; Gebhardt et al., 2019), particularly: ● Complexity: 3D printing allows the printing of complex structures that are difficult or impossible to produce using other methods. ● Lightweight construction: Lightweight materials and filigree structures can be used for products where weight is important. ● Lot size reduction and individualization: CAD models can be quickly modified, so small lot sizes as well as individual products can be produced efficiently and cost-effectively. ● Resource conservation: 3D printing generates less waste and conserves resources, which is an important sustainability goal. Although various studies predict high market growth for 3D printing (e.g. PwC Strategy&, 2018), there are some challenges in the various life cycle stages (Fastermann, 2016; Kantaros et al., 2022): ● Beginning-of-life (BOL): Selection of information about 3D printers, high investment costs for the customer, long delivery times. ● Middle-of-life (MOL): Insufficient knowledge in use, long printing times, disposal of excess material, maintenance and repair. ● End-of-life (EOL): Disposal of 3D printers.

SMART-CIRCULAR PRODUCT-SERVICE-ECOSYSTEMS FOR 3D PRINTERS The combination of digitization, sustainability and the concept of product-service-systems (PSS) leads to a new concept: smart-circular product-service-systems (SCPSS), which change the way business is done. To achieve the sustainability goals of SCPSS, ecosystems are necessary (Aufderheide et al., 2022). The following section first describes smart-circular strategies, which are then used to explain SCPSS using the example of 3D printers. After this, the ecosystem for SCPSS is introduced, which can overcome the challenges of 3D printers.

Sustainability of smart-circular product-service-ecosystems  241 Enabling Smart-Circular Strategies for 3D Printers The main goal of the Circular Economy (CE) is to recycle and reuse resources (Kirchherr et al., 2017). Corresponding activities are reuse, maintenance and repair, remanufacturing and recycling (Romero and Rossi, 2017). All these processes support circularity and thus tend to increase sustainability; however, implementation may be difficult. Producers or providers of products often do not have knowledge of the quality, location and condition of the products (Alcayaga and Hansen, 2022). More comprehensive digitization not only of products but also of processes can reinforce circular strategies. Resulting digital circular strategies are (Alcayaga and Hansen, 2022): ● Smart use: for example, digital updates to keep pace with ongoing digitization. ● Smart reuse: for example, the distribution via sharing offers and data-based feedback to help improve customers’ care for the SCPSS. ● Smart maintenance and repair: for example, remote control and condition monitoring. ● Smart remanufacturing: for example, monitoring-based takeback decisions and data-based technological upgrading. ● Smart recycling: for example, IoT-enabled closed-loop product cycles as the basis for recycling. A digital platform is helpful to implement these strategies. The digital platform is the interface where different actors can exchange data with each other (Sendler, 2016). For example, a service provider can provide an update through the platform to follow the smart use strategy. The customer can download and install this update independently. 3D Printers as Smart-Circular Product-Service Systems SCPSS integrate smart products with digital services and digital circular strategies to a new offering (Chowdhury et al., 2018) to increase the customer benefit (e.g. Meier and Uhlmann, 2012; Annarelli et al., 2016; Alcayaga et al., 2019). 3D printers use smart technologies and thus are smart products. Furthermore, 3D printers can be offered as SCPSS if additional services are integrated into the offering and the materials are circulated. Corresponding to the classification of PSS (e.g. Tukker and Tischner, 2006), there are three types of SCPSS: ● Product-oriented SCPSS: The value driver of this offer is the smart product. The smart product is sold, and the customer becomes the owner. The period of use is extended by additional services such as maintenance and repairs (Van Ostaeyen et al., 2013). Services can be executed digitally or locally. Offering 3D printers as product-oriented SCPSSs includes activities that extend the period of use, such as necessary maintenance and repairs, which can be requested online or detected via sensors. ● Use-oriented SCPSS: Value is created by the combination of a smart product and (digital) services. Either the provider or the customer can own the smart product. Examples of this SCPSS type are leasing, renting or sharing (Bressanelli et al., 2017). For example, a 3D printer is rented out for use at the customer’s site. The customer pays for the period of use, before the 3D printer is returned to the provider for reprocessing or disposal.

242  Handbook on digital platforms and business ecosystems in manufacturing ● Result-oriented SCPSS: The smart product is relegated to the background, and the services to achieve the desired result become the value driver of this offer. The ownership of the smart product is not transferred to the customer. The customer merely buys the result (D’Agostin et al., 2020). For example, the customer pays only for the components manufactured by the 3D printers. The provider is responsible for the printing process, including printing material and CAD models. To simplify the collection and return, 3D printers should be offered as a use- or result-oriented SCPSS, as this allows the provider greater influence over the processes. Smart-Circular Product-Service Ecosystems The supply chain of a SCPSS comprises different actors: product-service provider, customer, supplier, producer, disposer, recycler, remanufacturer and platform operator. Areas of responsibility may overlap; for example, the producer may also be the provider of the SCPSS. The supply chain of a SCPSS is a circular system due to the integration of circular strategies of the CE (see Figure 16.1).

Figure 16.1

Supply chain of smart-circular product-service-ecosystems (based on Aufderheide et al., 2022)

In the ecosystem perspective, all actors are interconnected (Wuest and Wellsandt, 2016). Digital business ecosystems build the basis for SCPSEs. They are defined by their characteristics (Aufderheide et al., 2022): ● Value co-creation: At the center of SCPSE is a shared value proposition. To create value from this, various actors work together. ● Defined role archetypes: The SCPSE includes a defined set of activities, which are performed by specific actors.

Sustainability of smart-circular product-service-ecosystems  243 ● Cooperation and competition: All actors work together cooperatively, yet competitively, to achieve the value proposition of satisfying consumer needs and ultimately generating innovation. ● Complementarities and interdependencies: At the economic level, the autonomous actors of the ecosystem are characterized by complementarities; at the structural level, by interdependencies. ● Digital technology infrastructure: The collaborative value proposition is enabled by digital technology like platforms. Facing the Challenges of 3D Printers To address the challenges facing 3D printers at BOL, MOL, and EOL, the SCPSE leverages its strengths of diverse competencies and collaboration. Although any type of SCPSS can be offered in an SCPSE, there are particular advantages to offering a result-oriented SCPSS because the ownership of the 3D printer remains with the provider, who thus retains more codetermination rights. Therefore, the following aspects are elaborated for offering the 3D printer as a result-oriented SCPSS. Challenges at the beginning-of-life At the BOL of 3D printers, the biggest challenges are gathering necessary information and managing the high investment costs for customers. The SCPSE can overcome this challenge by redistributing the investment costs to the provider. The basis of payment for a result-oriented SCPSS is the volume of results. Thus, there are no initial investment costs to pay for the customer. To gather the information about the 3D printer, the printing method and other necessary information, a platform can be used. The supplier can provide information and the customer can read it at any time. In addition, automated assistance can be provided with the help of chat bots to give initial answers to possible customer questions. This simplifies the process up to printing. This solves the second challenge of the SCPSE (Aufderheide et al., 2022). However, the problem of long delivery times persists, which cannot yet be adequately solved via a network like SCPSE. The only advantage is that the exchange of information from the offer to the implementation can be accelerated via the platform, which can mean at least a small time advantage in terms of delivery times. Challenges at the middle-of-life While using 3D printers, customers face several challenges, such as reordering printing material, disposing of excess material and the creating of CAD models. In SCPSE, the customer is only partially responsible for these processes. Cyber-physical systems and artificial intelligence allow an automated ordering process of printing material. A digital platform can enable exchange between machines and actors. Additional service providers within SCPSE are responsible for disposal of excess materials, and digital platforms with connections to support teams can help with the creation of CAD models. By incorporating sensors into 3D printers, cyber-physical systems can also enable a request for maintenance and repair work, which is carried out remotely and without any indication from the customer.

244  Handbook on digital platforms and business ecosystems in manufacturing Challenges at the end-of-life At the EOL of 3D printers, the customer usually has to take care of the disposal activities, which can be a major challenge. If the 3D printer is disposed of improperly, the potential for reuse cycles and secondary raw materials is lost. Usually, customers lack a comprehensive knowledge of disposal activities. In SCPSE, after the period of use, the customer returns the 3D printer to the provider, who remanufactures it for further cycles of the CE and becomes responsible for disposal activities. As the knowledge is bundled in one place and no customer is individually responsible for the disposal activities, a higher recycling rate can be achieved. In order to select the appropriate process and achieve the highest possible recycling rate, digital platforms that store the 3D printer as a digital twin with all complementary information are once again suitable.

EVALUATING SUSTAINABLE PRODUCTIVITY FOR SMART-CIRCULAR PRODUCT-SERVICE-ECOSYSTEMS Sustainability means the equal consideration of the three dimensions: economy, ecology, and social issues (Elkington, 1998). There are numerous approaches to assessing sustainability, but they are mostly qualitatively based or do not take all sustainability dimensions into account. One approach that considers at least two dimensions quantitatively is the Green Productivity Index (GPI). The GPI combines various key performance indicators (KPIs) (Hur et al., 2004). In the following section, a new approach is developed from this basis. First, the basic calculation is presented and adapted for SCPSE. This is followed by a selection of KPIs for the individual dimensions, which are derived from an analysis of sustainability reports of the German DAX-40 companies. From Green to Sustainable Productivity The GPI relates the economic impact of a product to its environmental impact, with two objectives: maximum economic performance and minimum environmental impact. The GPI, according to Hur et al., is calculated as in Equation (1) (2004). productivity environmental impact

​GPI  ​=  ________________ ​    ​    

​ (16.1) ​

The GPI can be further split. The productivity is the quotient of output and input (Mohanty and Deshmukh, 1999). The environmental impact takes into account the produced environmental pollution resulting from the transformation of resources into output (Hur et al., 2004), such as waste, effluents and emissions, and relates this to the environmental impacts used as input. These include the reuse and recycling of raw materials, such as wastewater as a coolant in raw material extraction (Aufderheide and Steven, 2021), see Equation (2). In order to consider the different physical quantities, the GPI is determined on the value level.

Sustainability of smart-circular product-service-ecosystems  245

 ​​(_ ​ ​  ​ ​​    ​ ​    ​ input ) ​  ​         ​ ​    ​ ______________________ GPI  = ​ ​ ​    ​    produced environmental pollution   ​ ​ ​    ​ ​  ​ (__________________    ​     ​ ​)  ​         ​   ​​ ​  output     ​ 

 ​(16.2) ​ ​

used environmental impact

According to the current sustainability debate and the public pressure on companies to make a social commitment, this index is no longer adequate. To consider all three sustainability dimensions equally, the quotient of the GPI is transformed into a product and extended by the ‘social impact’ factor. The resulting sustainable productivity index (SPI) is calculated as in Equation (3). output input

used environmental impact   produced environmental pollution

​SPI  ​=  SPI  = ​  _ ​   ​    ∙  ​________________________          ​  ∙ social impact

​ (16.3) ​

In companies, scaling is necessary so that the value of the SPI is meaningful and relevant for deriving actions. Since the SPI is to be maximized, the following transformation is made: each dimension is normalized separately, with the best value set to one and all other values assigned a correspondingly lower value. Thus, the value of each dimension is in the range [0;1]. The best alternative within a dimension can be determined by means of benchmarking, time comparison or target specifications. In Equation (3), all dimensions are equally weighted. This can lead to a situation where high productivity offsets high environmental pollution and low social impact. To reduce this compensation, exponents are integrated into the calculation. All exponents are greater than or equal to one, so that there is a penalty on the deviation to the reference value. These exponents follow the idea of the triple bottom line. As environmental problems are currently considered to be the greatest challenge, the exponent of environmental impacts ​β​ is chosen to be larger than the exponent of social impacts ​γ​. The economic dimension has the fewest problems, so ​α​ is the smallest to choose; see Equations (4)–(5) (Aufderheide and Steven, 2021). This order ensures that it is more valuable to improve the environmental impacts before improving the other two sustainability dimensions. ________________________ ​SPI  ​=  ​  _  ​  ​     ) ​  ​​  ​ ∙ ​​ (social impact) ​γ​  (​  ) ​​  ​ ∙ ​​ (   

​ (16.4) ​​

​with β  >  γ  >  α  ≥  1

​ (16.5) ​

output input

α

used environmental impact   produced environmental pollution

β

By using the exponents and multiplying all sustainability dimensions, the range of [0;1] is preserved for the SPI (Aufderheide and Steven, 2021).

246  Handbook on digital platforms and business ecosystems in manufacturing Sustainable Productivity for SCPSE The concept of SCPSE impacts sustainable productivity (SP) at several points and leads to a change of the SPI. In particular, when considering the SP within the SCPSE, the input and output can be further divided. Integration of cycles in the output The largest share of the monetary output is generated by the sale of the SCPSS offered within the SCPSE. Some further value can be created by recycling, so that the output in the SPI is composed as in Equation (6): ​output  = ​   product oriented output  +   recycled resourcesoutput  = ​   product oriented output   +   recycled resources

​ (16.6) ​

● The first summand represents the fee for the SCPSS to be paid by the customer. Added to this are the values generated during reuse and after remanufacturing. Thus, each usage cycle by a different customer has a positive impact on the product-oriented output. ● The incentive for recycling the product is created by the second summand of the equation. Thus, the sale of recycled resources from the product can generate additional value. Primary vs. secondary input Raw materials can be divided into primary and secondary raw materials. While primary raw materials have to be newly mined and therefore reduce global resource stocks, secondary raw materials are obtained by recycling products for reuse (Huber, 2016). Therefore, the use of primary raw materials is more harmful to the environment and is penalized in the SPI by a weighting factor µ >1 (see Equation (7)): ​input  = ​    μ ∙ primary input  +   secondary inputinput  = ​    μ ∙ primary input  +   secondary input ​ (16.7) ​

Impact of the SCPSE on SP A circular economy (CE) can save resources and is therefore more environmentally friendly than a linear supply chain (Corona et al., 2019). Collaboration in SCPSEs creates more linkages between actors, and SCPSS are described as sustainability-oriented (Alcayaga et al., 2019). However, it is debatable how all these aspects affect SP. The following changes can be observed by offering an SCPSS in an SCPSE as opposed to traditional product-service-systems. ● Product-oriented output: The output increases through additional use cycles by reuse and remanufacturing. Additional digital services are a purchase incentive and increase the SP (Aufderheide et al., 2022). A facilitated recycling through digital sharing offer (Aufderheide, 2020) and a better data basis and assistance for customers for a sustainable use can increase the output even more. ● Recycled resources: This SPI part is made possible by the CE. Moreover, it increases due to a better data basis and assistance for customers for recycling. More materials can be recycled through collaboration between various actors.

Sustainability of smart-circular product-service-ecosystems  247 ● Input: Although remanufacturing requires new input, it tends to be lower than the production of new products, so there is a disproportionate increase of input (Friedemann and Schumann, 2010). This increase can be related to primary or secondary input. However, more certain planning and more accurate determination of capacity reduces the need for input (Alcayaga and Hansen, 2022). Additionally, secondary input has a rising potential due to better trackability within SCPSE. Increasing secondary input instead of primary input leads to a better SP. ● Used environmental impact: Within linear supply chains, various environmental impacts can be used in each phase, such as the reuse of materials. These environmental impact reduction techniques can also be used within a circular SC. Since circular SC processes are run multiple times, environmental impacts can also be used multiple times. This leads to an overall increasing trend of used environmental impacts. ● Produced environmental pollution: Although a longer period of use increases the value in terms of reuse (Sander et al., 2019), a reduction of produced environmental pollution can be achieved through recycling and new production capabilities within the SCPSE (Oettmeier and Hofmann, 2019). In addition, digital services assist in more sustainable use, so there is potential for reduction in the long term. ● Social impact: Social impact summarizes actions that affect human conditions (Blum et al., 2020). Stronger collaboration within the SCPSE means that social guidelines are better enforced in the supply chain. These guidelines affect more people, leading to an increase in value. Key Performance Indicators Selection The SPI is an index based on different KPIs. While the selection of economic KPIs is not problematic, the selection of environmental and social KPIs is much more difficult. Therefore, the economic KPIs are discussed first, before an analysis is conducted for the selection of the nonfinancial KPIs. Selection of economic key performance indicators The economic dimension is traditionally measured by financial ratios (Wördenweber, 2017). The SPI therefore also uses financial ratios to fill the economic dimension. Revenues from sales, reuse and remanufacturing are used to determine the product-oriented output. For the valuation of recycled resources, the revenues generated from the sale of these are used. The input is divided into primary and secondary input in the SPI. For both types, the respective incurred life-cycle costs of the SCPSS are used as the calculation basis for the SPI. Selection of nonfinancial key performance indicators The basis for the selection of nonfinancial KPIs is an analysis of the sustainability reports of the German DAX-40 companies. The analysis was conducted in the first half of 2022. The most recent published report of each company was used. All published indicators were recorded and sorted by frequency. The most frequently published KPIs are listed in Table 16.1. This forms the basis for the selection of KPIs for the calculation of the SPI. In addition, the table already gives an indication of whether a KPI takes place in the SPI, and the reason for exclusion or additional information, which is presented in more detail below.

248  Handbook on digital platforms and business ecosystems in manufacturing Table 16.1  

Statistics on analysis of sustainability reports  

Quantity

Relevant for

Justification

SPI Environmental KPIs,

Share of renewable energies

25

Yes

 

positive impact

Energy savings

18

Yes

 

Water savings

17

Yes

 

Waste reduction

17

No

Integrated in recycling

Recycling rate

16

Yes

rate  

Environmental KPIs, negative impact

Social KPIs

CO2 emissions

36

Yes

 

Water consumption

25

Yes

 

Energy consumption

24

Yes

 

Waste management

19

Yes

 

Employee training (+ investments in

31 (+5)

Yes

 

Gender equity

34

Yes

 

Health and safety

29

Yes

 

Diversity

26

No

Specification was too

Compliance

25

No

Only a yes-or-no question

Age structure

21

No

Often indicated under the

Key figures on employees by region and

20

No

Donations

17

Yes

 

Human rights

17

No

Only a yes-or-no question

education)

different

umbrella topic ‘diversity’ division

Specification was too different

Since many KPIs, especially in the environmental and social dimension, are particularly relevant for the achievement of sustainability goals but cannot be quantified in monetary terms, classes are defined that allow the indexing of the respective KPI and thus enable a quantitative representation. The more detailed the classification, the more concrete strategies and measures can be derived. In the following, five classes per environmental and social KPI are referenced and are thus assigned points 1 to 5. Environmental key performance indicators Many different KPIs for the environmental dimension can be found in the sustainability reports. After reviewing all 40 reports, the eight KPIs mentioned most frequently were selected (see Table 16.1). Four of these are to be increased, and four decreased. The used environmental impact is formed by the aggregate of four KPIs that have a maximization target, so a higher rating leads to a higher SPI (see Table 16.2). ● Recycling rate: An improvement in the waste situation can be achieved through a reduction in waste volumes and higher recycling rate. Since recycled raw materials additionally conserve primary input, reference is made to this variable below. Recycling rates of over 85 percent are possible, resulting in the classification as shown in Table 16.2 (Hauke and Pibler-Maslo, 2016). ● Share of renewable energies: Due to the finite natural resources for energy production, renewable energies have a special status. Although there are already many possibilities for the use of renewable energies, many nonrenewable energies such as oil and natural gas,

Sustainability of smart-circular product-service-ecosystems  249 Table 16.2

Example for the classification of environmental KPIs used for environmental impacts

 

1 point

2 points

3 points

4 points

5 points

Recycling rate

Less than 30%

30%–45%

46%–60%

61%–85%

Greater than 85%

Share of renewable Less than 50% of the 50%–70% of the

71%–85% of the

86%–95% of the

More than 95% of

energies

used energy

used energy

used energy

used energy

the used energy

Energy savings

Less than 5%

5%–20% compared

21%–40% compared 41%–60% compared More than 60%

compared to the

to the previous year

to the previous year

Less than 5%

5%–20% compared

21%–40% compared 41%–60% compared More than 60%

compared to the

to the previous year

to the previous year

to the previous year

Water savings

to the previous year

Example for the classification of environmental KPIs for produced environmental pollution

 

1 point

CO2-emissions

Less than 50% of the 50%–99% of the average

2 points average

Water consumption Less than 50% of the 50%–99% of the average Energy

compared to the previous year

previous year

Table 16.3

compared to the previous year

previous year

average

Less than 50% of the 50%–99% of the

3 points

4 points

5 points

Same level as the

101%–200% of the

More than twice the

average

average

average

Same level as the

101%–200% of the

More than twice the

average

average

average

Same level as the

101%–200% of the

More than twice the

consumption

average

average

average

average

average

Amount of waste

Less than 10% of

11%–20% of total

21%–30% of total

31%–50% of total

More than 50% of

disposed

total waste amount

waste amount

waste amount

waste amount

total waste amount

are still used on a large scale (Hartmann, 2013). The goal for 2030 in Germany is to use 80 percent renewable energy, but at least 50 percent should be required. The divisions of the classes are made accordingly. ● Energy savings: Changing energy sources is an important step toward more sustainability. At the same time, it is also important to reduce the quantities of energy used. Energy savings can be up to 40 percent compared to a reference value (Bertoldi and Huld, 2006). ● Water savings: Water and energy savings are closely linked, especially concerning warm water consumption. If too much groundwater is extracted, it can have a regional impact on water quality and the quantity of drinking water. It is therefore important for companies to reduce water extraction or, conversely, to save more water. The amount of water savings that can be achieved varies in the literature, ranging from 46 percent (Blount et al., 2021) to 75 percent (Seckler, 1996) compared to a reference value. The produced environmental pollution is formed from the aggregate of four KPIs that have a minimization target. A higher score results in a lower SPI because the score is included in the denominator of Equation (4), (see Table 16.3). ● CO2 emissions: It is quite difficult to identify classes of CO2 emissions based on the amount released. Therefore, a ratio is required. The average of previous years or a sector average can be used for the classification. If the average value is approximately reached, a medium class is achieved. The other classes are adjusted accordingly. Lower points are achieved for a lower release of CO2 emissions, and correspondingly higher points

250  Handbook on digital platforms and business ecosystems in manufacturing Table 16.4

Example for the classification of social KPIs

 

1 point

2 points

3 points

4 points

5 points

Investments in

Less than 5% of

5%–10% of profit

10%–15% of profit

15%–20% of profit

More than 20% of

20%–30% or

30%–40% or

40%–45% or

45%–55%

education

profit

Gender equity /

Below 20% or

profit

Female quota

over 80%

70%–80%

60%–70%

55%–60%

Health and safety

More than 25 days

20–25 days per

15–20 days per

10–15 days per

per employee

employee

employee

employee

per employee

Less than 5% of

5%–10% of profit

10%–15% of profit

15%–20% of profit

More than 20% of

Donations

profit

Fewer than 10 days

profit

for a higher release. Alternatively, the classes can be identified by specific release limits (McKinnon and Piecyk, 2009). ● Water consumption: Similar to CO2 emissions, an assessment of water consumption by absolute numbers is not possible in a generally valid way. The classes can be determined by individual consumption limits or in comparison to the average of previous years or to the sector average. Table 16.3 contains an example of a comparison with the sector average. ● Energy consumption: The problem with CO2 emissions and water consumption also exists with energy consumption, which must therefore also be established as a comparison or by means of individual consumption limits. ● Amount of waste disposed: If waste is finally disposed of, on the one hand there is a high environmental impact and on the other hand the materials can no longer be recycled and used again. Therefore, systematic waste management has to be implemented to reduce the amount of waste disposed. The lower the amount of waste disposed, the better the rating (see Table 16.3). Social key performance indicators Many performance measurement systems that quantitatively assess sustainability predominantly consider only economic and environmental aspects. However, social aspects have a high relevance. According to the quantitative evaluation of the sustainability reports, it can be seen that within the reports there is an effort to publish social indicators. In the following, the most frequent ones are presented, and classes are defined to enable the evaluation by means of SPI for the SCPSE. Most social KPIs from Table 16.1 had to be excluded because the interpretation within the reports was too different or the KPI is only a yes-or-no question. An example for the classification of social KPIs is shown in Table 16.4; the higher the score, the higher the SPI. ● Investments in education: Education and employee training are important to enable progress and an increase of revenues and thus secure jobs. This, in turn, has an impact on employee satisfaction. The investment amount in education can be used as a KPI. To create comparability between companies, a calculation is made on the basis of profit. The higher the proportion of profit spent on education, the better the rating (see Table 16.4). ● Gender equity: Gender equity is a social issue that is currently under discussion. There are two ways in which equity can be addressed in the company. A comparison between the number of male and female employees can be used with the aim of achieving equal staffing

Sustainability of smart-circular product-service-ecosystems  251 as far as possible. This comparison can be specified as a women’s quota. The problem is that there is an imbalance in one of the two directions in various industries without the company having much influence on it. Thus, some companies would be rated poorly without having any control over it. Another option is to evaluate equal pay, also known as the gender pay gap. The smaller the gender pay gap, the more gender equity exists in the SCPSE. However, since more companies publish the female quota, this variable is used to evaluate gender equity despite the aforementioned problem. ● Health and safety: Health and safety at work is a crucial factor for the social sustainability dimension (Badura et al., 2022). However, it is difficult to measure this factor. Companies evaluate different KPIs under this umbrella term, such as the absenteeism rate or sick days in the company or the number of accidents at work. Since accidents at work often lead to sick days, the measurement of sick days is taken as a decisive variable for health and safety. Sick days are to be reported as an average per employee for the SCPSE to provide a comparable measure. Statistics for the last 20 years show values ranging from 12 sick days to over 21 (Badura et al., 2022). Therefore, an evaluation of the sick days can be made as in Table 16.4. A higher score is achieved for fewer sick days in order to pursue the goal of maximizing the SPI. ● Donations: Through donations, SCPSE can also improve the social situation outside its own network. The higher the share of profit used for donations, the better the rating (see Table 16.4).

SPI FOR 3D PRINTERS WITHIN SCPSE: POTENTIALS OF DIGITAL PLATFORMS The last section calculates the relevant KPIs for evaluating 3D printers within SCPSE. For this purpose, the offer of 3D printers is regarded as a result-oriented SCPSS, which means that not only the printer itself is to be evaluated, but also the printing materials and services within the SCPSE are considered to identify which have the greatest effect on the SPI. A comparison is made with traditional manufacturing processes (TMP). It will be shown how digital platforms can be used to achieve a higher SPI. Economic Value of 3D Printers Productivity is the ratio of output to input over the lifecycle of the SCPSS, which is divided into the following parts: ● Product-oriented output: 3D printers have the same output potential as TMP, but they generate less waste. This makes production using 3D printers more attractive to sustainable-oriented customers. Thus, they are willing to pay a higher price, which increases the product-oriented output. At the same time, the offer as SCPSS is extended with additional services, so that another increase in output can be expected. A higher product-oriented output can be generated through reuse. The average period of use of a 3D printer is five years (Lindemann et al., 2012). However, it is the nozzle that is most sensitive component, and therefore, it has the biggest impact on this time. By remanufacturing the nozzle, further use cycles are possible. Because 3D printers are oth-

252  Handbook on digital platforms and business ecosystems in manufacturing erwise robustly built, reuse is possible for significantly longer periods, so overall sales can be greatly increased. If digital platforms are used on which digital twins of the printers are created, information about the quality and condition of the printer can be collected. Thus, the nozzle can be replaced in time without causing downtime, which increases the value of the printer. ● Recycled resources: Basically, it is possible to recycle, for example, the robot of the 3D printer. In terms of printing material, there is great potential for recycled materials when printing with reusable materials like metal. The digital platform assists in collecting and storing information about the 3D printer, so that a closed-loop process can be selected quickly and efficiently. ● Input: Although it is difficult to make a comparative statement about the input for 3D printers, distinct trends can be identified for the production of components with 3D printers compared to TMP. There, the inputs decrease overall due to lower raw material consumption of 3D printers. - Primary input: In terms of the 3D printer itself, one change can be noticed. The demand for input factors is changing with the use of digital technologies in 3D printers. For example, there is an increase in use of rare earths (Haque et al., 2014), which affects the SPI negatively. - Secondary input: 3D printers offer an advantage when it comes to using secondary input. The need for secondary input for printing material can be increased as there are opportunities to use plastic waste as printing material (Fastermann, 2016). Digital platforms can be used to identify secondary input more easily. The value of the economic dimension in the SPI of a 3D printer as a result-oriented SCPSS can be classified as higher than the production of components using TMP, due to the better economic valuation of the construction substances or printing substances. The use of digital platforms can further increase SPI by simplifying, accelerating and better monitoring processes. Environmental Value of 3D Printers The focus is more on the potentials in manufacturing by means of 3D printing processes and less on specific data on the environmental impact, as this is very individual, depending on the component to be printed. In addition, various KPIs can be evaluated only within the entire SCPSE. Therefore, no trends are given below for the KPIs water savings, share of renewable energies and water consumption, which have an impact on SCPSE but cannot be quantified without concrete data. In addition, there are no significant differences in terms of TMP when they are also offered in networks. ● Recycling rate: 3D printers have the potential to recycle and reuse printing material (Fastermann, 2016). This affects the recycling rate of 3D printers in the SCPSE. The features of 3D printers, such as robots, can also be recycled. Overall, the recycling rate increases compared to TMP, and thus waste management can be rated highly especially when digital platforms are used that allow materials to be found quickly. ● Energy savings: Various use cases show significant energy saving potentials of 3D printers compared to TMP. Savings of up to 90 percent have already been achieved (Fastermann, 2016). However, these savings relate only to the use of 3D printers.

Sustainability of smart-circular product-service-ecosystems  253 ● CO2 emissions: Once the 3D printer is set up at a production location or at the customer’s site, there are significantly fewer transports. Components can be printed on site and no longer have to be procured anew. The printing material is also lighter and has a lower transport volume. In addition, numerous different components can be printed with just one device, and new models are available online. This all leads to lower CO2 emissions compared to TMP (Fastermann, 2016). Furthermore, helpful tips for reducing emissions can be passed on to customers via the digital platform when the printer is in use. ● Energy consumption: Even though high energy saving potentials are predicted, no concrete data exist on the energy consumption of 3D printers, due to the variety of possible applications. ● Amount of waste disposed: As the amount of waste generated by using a 3D printer decreases compared to TMP, the overall amount of waste also reduces. In addition, bioplastics can be used for printing, which are compostable and thus constitute waste, but they are less harmful to the environment than non-compostable waste (Fastermann, 2016). The value of the environmental dimension in the SPI of 3D printers as result-oriented SCPSSs can be classified as higher compared to TMP, due to a higher recycling rate, less harmful printing materials and lower transport volumes and distances. Social Value of 3D Printers A social value often relates to a company or a network and is less directly influenced by a product or offer. In specific, the 3D printer can only be assessed in terms of the KPI health and Safety. With regard to all other KPIs, an increase can be expected due to the network structure in the SCPSE. However, this increase needs to be further quantified using concrete data. ● Health and safety: 3D printing is possible without harmful chemical fumes and particles (Fastermann, 2016). This leads to an improvement in the health situation in the SCPSE. However, it is unknown whether this improvement has a direct impact on sick days in the SCPSE. The value of the social dimension in the SPI of 3D printers as result-oriented SCPSS can be estimated to be higher compared to TMP, assuming that TMPs are not offered in a network. Otherwise, it is difficult to find an exact trend. The SCPSE, in particular, represents a potential increase in social value.

DISCUSSION OF THE SPI FOR 3D PRINTERS AND FUTURE WORK A sustainable productivity index (SPI) is an assessment tool to evaluate the sustainability of products, companies or networks. For this purpose, the three sustainability dimensions are aggregated and weighted using selected key performance indicators. The SPI serves as a basis for controlling, monitoring and managing sustainability in companies and can be adapted to specific use cases. Future work should therefore focus on the application with specific data. Furthermore, in this chapter, only the application was presented but no concrete recommenda-

254  Handbook on digital platforms and business ecosystems in manufacturing tions for action have been derived so far in order to achieve an increase of the SPI. This must be made up for in the future. 3D printers are seen as a promising technology to enforce sustainability and digitization. However, the offering faces numerous challenges. Therefore, in this chapter, a digital business ecosystem has been presented and adapted to the 3D printer offering to overcome these challenges, called the smart-circular product-service-ecosystems (SCPSE). To test whether this SCPSE and the 3D printer are more sustainable than traditional manufacturing processes, the SPI was adjusted and estimated for 3D printers within SCPSE. However, only tendencies could be derived here, and no concrete data could be used. Even though a clear increase potential of the SPI and thus of sustainability was identified, this has to be confirmed by further investigations. A high potential for increasing sustainability can be seen in the use of the digital platform, which is used as a means of communication between the individual actors of the SCPSE. The possible applications can be summarized as follows: ● ● ● ● ● ●

Fast communication between the actors First contact with customers and answering of their questions. Fast and simplified information transfer during order processing. Simplified customer management and support for sustainable use. Monitoring of the SCPSS and more efficient closed-loop processes. Storage of data for the collection of materials.

REFERENCES Alcayaga, A. and Hansen, E. G. (2022). Internet of Things Enabling the Circular Economy: An Expert Study of Digitalisation Practices in B2B Firms. IQD Research 2022 No.1, Institute for Integrated Quality Design (IQD), Johannes Kepler University Linz (JKU). Alcayaga, A., Wiener, M. and Hansen, E. (2019). Towards a framework of smart-circular systems: An integrative literature review. Journal of Cleaner Production, 221, 622–34. Annarelli, A., Battistella, C. and Nonino, F. (2016). Product service system: A conceputal framework from a systematic review. Journal of Cleaner Production, 139, 1011–32. Aufderheide, V. L. (2020). Sharing Economy – kurzfristiger Hype oder langfristige Marktchance? In: Steven, M. and Grandjean, L. (Eds.) Marktchancen 4.0. Stuttgart, pp. 63–84. Aufderheide, V. L. and Steven, M. (2021). Gewichtung von umweltbelastungen im green productivity index. In: Wellbrock, W., Ludin, D. (Eds.): Nachhaltiger Konsum. Wiesbaden, pp. 107–25. Aufderheide, V. L., Montag, L. and Steven, M. (2022). Conceptualizing smart-circular product-service ecosystems for the photovoltaic power industry. In: Baumann, S. (Eds.) Handbook on Digital Business Ecosystems. Cheltenham, pp. 475–91. Badura, B., Ducki, A., Meyer, M. and Schröder, H. (Eds.) (2022). Fehlzeiten-Report 2022, Schwerpunkt: Verantwortung und Gesundheit. Berlin: Springer-Verlag. Bertoldi, P. and Huld, T. (2006). Tradable certificates for renewable electricity and energy savings, Energy Policy, 34 (2), 212–22. Bierdel, M., Pfaff, A., Kilchert, S., Köhler, A. R., Baron, Y. and Bulach, W. (2019). Studie: Ökologische und ökonomische Bewertung des Ressourcenaufwands – Additive Fertigungsverfahren in der industriellen Produktion. Berlin: VDI Zentrum Ressourceneffizienz GmbH. Blount, K., Abdi, R., Panos, C. L., Ajami, N. K. and Hogue, T. S. (2021). Building to conserve: Quantifying the outdoor water savings of residential redevelopment in Denver, Colorado. Landscape and Urban Planning, 214, 104178. Blum, N. U., Haupt, M. and Bening, C. R. (2020). Why ‘circular’ doesn’t always mean ‘sustainable’. Resources, Conservation and Recycling, 162, 105042.

Sustainability of smart-circular product-service-ecosystems  255 Bressanelli, G., Perona, M. and Saccani, N. (2017). Reshaping the washing machine industry through circular economy and product-service system business models. Procedia CIRP, 64, 43–8. Bückner, F., Leyens C., López, E. and Wilsnack, C. (2020). Qualitätssicherung in der additiven Fertigung. In: Steven, M., Dörseln, J. N. (Eds.) Smart Factory – Einsatzfaktoren – Technologie – Produkte, Stuttgart: Kohlhammer-Verlag, pp. 142–65. Chowdhury, S., Haftor, D. and Pashkevich, N. (2018). Smart product-service systems (smart PSS) in industrial firms: a literature review. Propedia CIRP, 73, 26–31. Corona, B., Shen, L., Reike, D., Carreón, J. R. and Worrell, E. (2019). Towards sustainable development through the circular economy – A review and critical assessment on current circularity metrics. Resources, Conservation and Recycling, 151, 104498. D’Agostin, A., Medeiros, J. de, Vidor, G., Zulpo, M. and Moretto, C. (2020). Drivers and barriers for the adoption of use-oriented product-service systems: A study with young consumers in medium and small cities. Sustainable Production and Consumption, 21, 92–103. Elkington, J. (1998). Partnerships from cannibals with forks: the triple bottom line of 21st-century business. Environmental Quality Management, 8 (1), 37–51. Fastermann, P. (2016). 3D-Drucken – Wie die generative Fertigungstechnik funktioniert. 2. Auflage, Berlin/Heidelberg: Springer Vieweg. Feldmann, C., Schulz, C. and Fernströning, S. (2019). Digitale Geschäftsmodell-Innovationen mit 3D-Druck, Wiesbaden: Springer Gabler-Verlag. Friedemann, S. and Schumann, M. (2010). Der Umgang mit Unsicherheit in der Produktion bei der Nutzung von nachwachsenden Rohstoffen – State of the Art. Arbeitsbericht des Instituts für Wirtschaftsinformatik Nr. 1/2010. Gebhardt, A., Kessler, J. and Schwarz, A. (2019) Produktgestaltung für die Additive Fertigung, München: Carl Hanser-Verlag. Haque N., Hughes A., Lim S. and Vernon C. (2014). Rare Earth Elements: Overview of Mining, Mineralogy, Uses, Sustainability and Environmental Impact. Resources. 3 (4), 614–35. Hartmann, N. (2013). Rolle und Bedeutung der Stromspeicher bei hohen Anteilen erneuerbaren Energien in Deutschland, accepted PhD thesis, Universität Stuttgart, available from: https://​elib​.uni​ -stuttgart​.de/​handle/​11682/​2167 [accessed 21 November 2022]. Hauke, H. and Piber-Maslo, M. (2016). Durch verantwortung zu dauerhaftem ökonomischen erfolg: Austria glas recycling. In: Schram, B., Schmidpeter, R. (Eds.) CSR und Organisationsentwicklung. Management-Reihe Corporate Social Responsibility. Berlin, Heidelberg: Springer Gabler-Verlag, pp. 355–66. Hofmann, E. and Oettmeier, K. (2016). 3D-druck: wie additive fertigungsverfahren die wirtschaft und deren supply chains revolutionieren. Zeitschrift Führung + Organisation, 85 (2), 84–90. Huber, R. (2016). Weiterentwicklung optischer Sortiertechnik und Erweiterung der Einsatzmöglichkeiten in der Aufbereitung primärer und sekundärer Rohstoffe, PhD thesis, Leoben. Hur, T., Kim, I. and Yamamoto, R. (2004). Measurement of green productivity and its improvement. Journal of Cleaner Production, 12, 673–83. Kantaros, A., Diegel, O., Piromalis, D., Tsaramirsis, G., Khadidos, A. O., Khan, F.Q. and Jan, S. (2022). 3D printing: Making an innovative technology widely accessible through makerspaces and outsourced services. Materials Today: Proceedings, 49 (Part 7), 2712–23. Kirchherr, J., Reike, D. and Hekkert, M. (2017). Conceptualizing the circular economy: an analysis of 114 definitions. Resources, Conservation, Recycling, 127, 221–32. Lindemann, C., Jahnke, U., Moi, M. and Koch, R. (2012). Analyzing product lifecycle costs for a better understanding of cost drivers in additive manufacturing. International Solid Freeform Fabrication Symposium, 177–88. McKinnon, A. C. and Piecyk, M. I. (2009). Measurement of CO2 emissions from road freight transport: A review of UK experience. Energy Policy, 37 (10), 3733–42. Meier, H. and Uhlmann, E. (2012). Hybride leistungsbündel – ein neues produktverständnis. In: Meier, H., Uhlmann, E. (Eds.) Integrierte Industrielle Sach- und Dienstleistungen, Berlin/Heidelberg: Springer Vieweg, pp. 1–22. Mohanty, R. P. and Deshmukh, S. G. (1999). Managing green productivity: a case study. Work Study, 48 (5), 165–69.

256  Handbook on digital platforms and business ecosystems in manufacturing Oettmeier, K. and Hofmann, E. (2019). Additive fertigung und deren auswirkungen auf supply chains. In: Obermaier, R. (Eds.): Handbuch Industrie 4.0 und Digitale Transformation, Wiesbaden: Springer-Gabler-Verlag, pp. 235–46. Romero, D. and Rossi, M. (2017). Towards Circular Lean Product-Service Systems. Procedia CIRP, 64, 13–18. Sander, K, Wagner, L., Jepsen, D. and Zimmermann, T. (2019). Gesamtkonzept zum Umgang mit Elektro(alt)geräten – Vorbereitung zur Wiederverwendung. Umweltbundesamt. Report number: 17. URL: https://​www​.umweltbundesamt​.de/​publikationen/​gesamtkonzept​-umgang​-elektroaltgeraeten. Seckler, D. (1996). The New Era of Water Resources Management: From ‘Dry’ to ‘Wet’ Water Savings. International Irrigation Management Institutre (IIMI), Research Report number: 1. Sendler, U. (2016). Die Grundlagen, Sendler, U. (Hrsg.), Industrie 4.0 grenzenlos, Springer Vieweg, Berlin Heidelberg, pp. 17–40. PwC Strategy& (Part of the PwC network) (2018). Pressemitteilung: Marktvolumen für gedruckte Produkte steigt bis 2030 auf 22,6 Milliarden Euro. Available at: https://​www​.strategyand​.pwc​.com/​ de/​de/​presse/​2018/​3d​-druck​.html [Accessed 13 July 2022]. Tukker, A. and Tischner, U. (2006). Product-services as a research field: past, present and future. Reflections from a decade of research. Journal of Cleaner Production, 14 (17), 1552–56. Van Ostaeyen, J., Van Horenbeek, A., Pintelon, L. and Duflou, J. R. (2013). A refined typology of product-service systems based on functional hierarchy modeling. Journal of Cleaner Production, 51, 261–76. Wördenweber, M. (2017). Nachhaltigkeitsmanagement – Grundlagen und Praxis unternehmerischen Handelns, Stuttgart: Schäffer-Poeschel-Verlag. Wuest, T. and Wellsandt, S. (2016). Design and development of product service systems (PSS)-impact on product lifecycle perspective. Procedia Technology, 26, 152–61.

17. The role of digital platforms as circularity broker: an updated SCOR perspective on circular supply chain performance Tom Pettau and Laura Montag

INTRODUCTION By 2030, the EU aims to achieve a reduction in greenhouse gas emissions of at least 55 percent (compared to 1990); by 2050, Europe should even become climate neutral and have competitive economic growth decoupled from resource consumption (European Commission, 2019). One of the enablers for this European Green Deal is the transition from a linear to a circular economy (CE). In such a circular consumption and production model, waste itself becomes a resource by applying circular strategies such as reusing, repairing, refurbishing and recycling (Kirchherr et al., 2017). The ultimate goal is to keep materials and products in use for as long as possible, and thus, not only minimize waste but also (virgin) material extraction and input, enhancing raw material supply security, increasing competitiveness and creating new job opportunities (EU Commission 2022). Although the CE is seen as a promising solution to current environmental and social challenges, its implementation faces major challenges – most notably a lack of information, data and knowledge exchange between (potential and often cross-sectoral) circularity partners (Pedone et al., 2021). Digitization and Industry 4.0, as well as the associated new digital technologies (DT), are understood as enablers for the realization of the CE by overcoming information- and data-related barriers and thus promoting the implementation of circular strategies and practices (Bressanelli et al., 2022). The digital platform (DP), in particular, is perceived as a key technical enabler to bridge so-called circularity holes and information gaps through providing a virtual marketplace for actors to create circular value together (Berg and Wilts, 2019; Ciulli et al., 2020). The convergence of DT and CE and its potentials are still underinvestigated. In addition to that, CE needs to be understood in a broader ecosystem perspective, in which heterogeneous but interdependent actors collectively create circular value to the user (Trevisan et al., 2022). To this point, the adoption of the circularity from an ecosystem perspective is also largely unexplored and offers research opportunities (Trevisan et al., 2022). The aim of this study is to take a step towards filling the elaborated research gaps in the field of CE and, therefore, it examines the DP as an enabler of CE in the context of circular ecosystems. This study is structured as follows: In the second section, the theoretical background of the CE, circular supply chains and circular ecosystems is explained, followed by details on DT and, in particular, the DP. Section three first presents the essential DP configurations that need to be considered when applied in a circular ecosystem context. Following that, an adapted performance measurement framework for platform-based circular ecosystem is presented that serves not only as a guidance in assessing the ecosystem’s circular, but also economic, 257

258  Handbook on digital platforms and business ecosystems in manufacturing environmental and social performance and also integrates digital measures on the platform’s performance. In the final section, we summarize the implications of our research and the contributions to literature, as well as give a short outlook for future research.

THEORETICAL BACKGROUND Circular Economy, Circular Supply Chains, Circular Ecosystems Within sustainability science the CE has emerged as a promising concept to tackle the environmental challenges of the current production and consumption system. By moving away from the traditional linear economy based on the assumption that resources are easily accessible, and thus on the take-make-waste approach, the CE replaces the end-of-life concept (EoL) by systematically applying R-strategies, such as reducing, reusing, recycling and recovering, and thus slowing, closing and narrowing material and energy loops (Geissdoerfer et al., 2017; Kirchherr et al., 2017). Especially during the last five years, scientific research on CE and related topics has increased immensely and therefore created momentum for progressing its theory (Nobre and Tavares, 2021; Montag, 2022). For example, CE-related publications from 2021 and 2022 account for more than 50 percent of all CE publications in the Scopus database, highlighting the highly active but still developing field of research. One recurring problem that the research field has struggled with since its inception, however, is clear definition and conceptualization, especially regarding the relationship and interdependence between CE and sustainability (Geissdoerfer et al., 2017; Kirchherr et al., 2017; Korhonen et al., 2018). In the context of this research article, the understanding of the CE follows the conceptualizations that it is a sustainable development initiative that aims to achieve economic prosperity, environmental quality and social equity (the ‘triple bottom line’) (see, for example, Kirchherr et al. (2017), Montag et al. (2021), Montag (2022)). However, CE initiatives are not necessarily more sustainable if they are pursued without considering impacts at other levels. For the CE to achieve greater sustainability, four dimensions – circular, economic, environmental and social – must be assessed and considered (Montag and Pettau, 2022). Given the urgent need for a paradigm shift and the potential of the CE, interest in it and its implementation has grown not only in the scientific community, but also among policymakers, businesses, and nongovernmental organizations – such as the Ellen MacArthur Foundation as one of the flagships of CE (Ellen MacArthur Foundation, 2015). At a national level, there are initiatives in the US, Japan, China, and also in the aforementioned EU (Walden et al., 2021). For the latter, for example, the EU Commission adopted a CE action plan in 2015 (and an updated plan in 2020) to close the loop and set clear targets for waste management (European Commission, 2022). A main objective is a successful industrial symbiosis in which waste and discarded by-products from one industry are used as input resources in another industry (Ellen MacArthur Foundation, 2015). Since the CE takes a systemic perspective, no single actor can drive the change towards circularity alone (Fehrer and Wieland, 2021). Given that the global economy is only about 9 percent circular at the moment (Circle Economy, 2022), a shift in the unit of analysis – from a micro-perspective individual business view to a macro-perspective ecosystem focus – is required (Geissdoerfer et al., 2020; Fehrer and Wieland, 2021) to accelerate global circularity and contribute to an ecologically safe and socially just space (Circle Economy, 2022).

The role of digital platforms as circularity broker  259 From the (single) business perspective, the efficient integration of circular principles and strategies into traditional business processes and supply chains is a major challenge. To support this transition and enable the implementation of a circular strategy, the performance of supply chains and ecosystems in the CE era must be measured accordingly (Vegter et al., 2021). To achieve this, the traditional processes and corresponding performance measurement systems defined under the premise of linearity must be adapted to the characteristics of the CE. Vegter et al. (2020) first added the processes of use and recovery to the traditional SCOR model to better represent the key activities. On this basis, they argued that performance measurement can only be holistic if not only circular but also economic, environmental and social performance metrics are reported and monitored (Vegter et al., 2021). The integration of circularity into the (business) ecosystem – bridging the two literature streams of CE and business ecosystem to circular ecosystems – is a fast growing field of interest and has been recently addressed from different perspectives. Trevisan et al. (2022) developed an early systematic literature review on the circular ecosystem concept and proposed a definition of the, often ambiguously used, term. In their conceptualization, a circular ecosystem is comprised of ‘interdependent and heterogeneous actors’ (p. 292) who collaboratively aim for the common interest of a circular value proposition (Trevisan et al., 2022). The CE, circular supply chain and circular ecosystem research fields have all been recently influenced by developments in digitization and DT (e.g. Bressanelli et al., 2022). DT like IoT, blockchain or DP, are seen as enablers of the CE because they may overcome key barriers that hinder its implementation (e.g. lack of transparency, data sharing, circularity holes) (Ciulli et al., 2020; Da Silva et al., 2020; Walden et al., 2021). However, existing literature is still sparse and holistic approaches are lacking. In the following section, the main DT with relevance to enabling the CE will shortly be presented and discussed. Digital Technologies and Digital Platforms The prevailing opinion in academia is that the fourth industrial revolution, and DT in particular, play a key role in the transition from a linear to a circular economy (Pagoropoulos et al., 2017). In a manufacturing ecosystem, for example, the use of these DTs could enable better control and monitoring of production processes, providing a way to achieve the transparency needed for a CE (Walden et al., 2021). They can also help identify best practices for the application of R-strategies, such as remanufacturing or recycling, and strengthen waste recovery through higher visibility along the entire supply chain and over the whole ecosystem (Chauhan et al., 2022). The potential opportunities of DTs as enablers of the CE are manifold, yet these opportunities need to be realized and considered by the stakeholders and actors involved, especially through efficient networking, collaboration and co-creation (Chauhan et al., 2022). Industry 4.0 encompasses various different technologies, some of which (e.g. blockchain or DP) are more important for closing circularity holes than others (such as additive manufacturing). The following section provides an overview of different DTs that are relevant in the context of DP and CE. Digital platform In the broadest sense, a DP can be defined as a digital (market) place where different actors of a digital-centric ecosystem come together, interact and exchange information (Berg and Wilts, 2019). Derave et al. (2021) use the term software-enabled ‘service offering’ (p. 421)

260  Handbook on digital platforms and business ecosystems in manufacturing to define the DP as an intermediary for different types of interactions, involving not only the transfer and exchange of information, but also the frame for transactions of goods and/or services (Ciulli et al., 2020; Derave et al., 2021). In the context of the CE, the DP plays a key role because it can bring together diverse actors from different sectors, enable the exchange of information and knowledge, and match (previously) unknown actors in supply and demand (Ciulli et al., 2020). Section three discusses the DP in the CE context in more detail. Blockchain technology and smart contracts Blockchain technology allows data to be stored in an encrypted, tamper-proof and decentralized manner (Da Silva et al., 2020; Esmaeilian et al., 2020). The blockchain usually contains information on at least five key product dimensions: nature (what), quality (how), location (where), quantity (how much) and ownership (who) (Saberi et al., 2019). This eliminates the need for a trusted central organization to operate and maintain the system (Saberi et al., 2019). Important data can be shared on a peer-to-peer network, and each node on the network has a copy of the ledger where the data is stored (Esmaeilian et al., 2020). Depending on the blockchain design, read and write permissions vary by actor (Da Silva et al., 2020). In the case of decentralization, blockchain can be used as an extension to central cloud storage (Zutshi et al., 2021). Smart contracts are predefined rules for how individual assets should behave and interact autonomously (Rajala et al., 2018). They control the interactions of actors in an ecosystem when certain conditions or events occur (Saberi et al., 2019; Mastos et al., 2021). They can even enable transactions between actors without the need for a third party to ensure that the actors involved trust each other (Rajala et al., 2018; Saberi et al., 2019). Digital twin and digital product passport The digital twin is an exact virtual image of the physical product (Grieves and Vickers, 2017). It contains all the relevant information about the product, including the material list (list of all current and previous components), the process list (list of all operations performed during production, including all measurement and test results), the service list (list of all services performed and components replaced) and the disposal list (Grieves and Vickers, 2017). In addition, other measured data and information can be included to further describe the condition of the product (Grieves and Vickers, 2017). The data is then transmitted to the DP and can be used, for example, for remanufacturing during the EoL phase of the product (Chen and Huang, 2021; Scime et al., 2022). The digital twin can develop its full value when it contains the data of the entire product lifecycle (Scime et al., 2022). A variant and example of the use of the digital twin is the digital product passport (DPP). The DPP is a set of data that stores and shares all relevant product-related information originating from all product lifecycle phases (BMUV), with access to the data being regulated according to actor-specific access rights (Götz et al., 2021). It contains information on origin, composition, repair and disassembly options, recyclability and proper disposal, as well as so-called master data (product, manufacturer, composition, substances of concern, toxicity and origin) and new data (use, modification, maintenance and wear) (BMUV; Götz et al., 2021). The DPP also provides information on the product environmental and social impacts (BMUV). Access to this reliable and consistent information makes it easier for actors of the ecosystem to compare products before they buy and/or use (Götz et al., 2021).

The role of digital platforms as circularity broker  261 Internet of Things The Internet of Things (IoT) can be characterized as an infrastructure that interconnects physical objects equipped with sensors to enable data collection, data exchange and also data mining (Dorsemaine et al., 2015). By sharing information within a network, it connects all actors in a value chain or ecosystem in quasi-real time and allows all devices to be represented and present on the internet (Pagoropoulos et al., 2017; Mouha, 2021). Cyber-physical systems A common definition of CPS is: ‘Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa’ (Lee, 2008, p. 363). IoT is based on these embedded systems that have an internet connection (Kopetz and Steiner, 2022). CPS or embedded systems form smart objects when they consist of a physical unit and a computing unit that processes sensor data and relay the information over the internet to a remote computer system that manages big data (Kopetz and Steiner, 2022). IoT sensors IoT sensors record and transmit physical, chemical or biological information and also include RFID (radio-frequency identification) (Dorsemaine et al., 2015; Mouha, 2021; Kopetz and Steiner, 2022). RFID consists of tags and readers. The tag stores the unique electronic product code of the respective object. The reader acts as a gateway to the internet and transmits the captured information (Kopetz and Steiner, 2022). The sensors enable remote monitoring of the performance and the environment (Berg and Wilts, 2019). Big data Within the Industry 4.0 literature, big data is characterized by a different number of Vs. Some define it according to the three Vs (3V): volume, variety and velocity (Emmanuel and Stanier, 2016). Volume describes the large amount of data that is collected by different sensors from different sources and communicated via the IoT (McKinsey Global Institute, 2011; Bedi et al., 2014); velocity describes the fast rate at which the data is collected and processed (Bedi et al., 2014; Emmanuel and Stanier, 2016); and variety comprises the different types of data (structured vs. unstructured) coming from different sources (Bedi et al., 2014; Ishwarappa and Anuradha, 2015). Other conceptualizations include four Vs (3V + veracity) (Pagoropoulos et al., 2017; Steven, 2019), five Vs (3V + veracity + value) (Ishwarappa and Anuradha, 2015) or even more (Emmanuel and Stanier, 2016). Veracity and value consider the different quality of the data and the value that can be extracted from it (Ishwarappa and Anuradha, 2015). Cloud computing The remote use of storage services, computing power and software services over the internet is commonly known as cloud computing (Steven, 2019). It enables the remote access to on-demand computational resources to process, manage and store data (Steven, 2019; Da Silva et al., 2020). Cloud technology can further be used to facilitate the collaboration between factories and industries (Steven, 2019; Da Silva et al., 2020).

262  Handbook on digital platforms and business ecosystems in manufacturing Digital Technology Overview and Systematization DT can be divided into specific categories according to their functions and data processes (see e.g. Pagoropoulos et al., 2017; Steven, 2019; Esmaeilian et al., 2020). To better reflect the functions of the technologies and to emphasize the importance of DT for information sharing and collaboration among ecosystem actors in the context of CE, the above technologies are divided into four categories: ● Data collection: - Data is collected for objects and machines in various places and by different actors of the ecosystem. This includes, for example, information about the operations performed, the material processed, temperature, wear, energy consumption or usage. - CPS, IoT sensors, digital twin, DPP. ● Data exchange and collection: - The previously collected data is exchanged between the actors and supplemented with further (collected) information. This happens on the micro level, when information is exchanged between different machines along the production process, and on the macro level, when information is exchanged between different actors. The group of actors includes everyone having the ability to exchange information. The read and write permissions for the transmitted data can be restricted on an actor-specific basis. - IoT, blockchain, digital twin, DPP, DP, CPS, cloud computing, big data. ● Data processing and usage: - The data collected and exchanged is used to create added value. For example, consumers can identify delivery delays, manufacturers can use consumer data to improve their products, and machine maintenance can be better planned. - Big data, digital twin, DPP, DP, CPS, cloud computing. ● Data communication and publication: - The data is made available to the actors of the ecosystem – particularly for the target group – for usage along the different lifecycle phases. However, the access rights to the data and its visualization differ depending on the target group. An end consumer, for example, has different requirements for the information provided than a recycling or remanufacturing company. - DP, IoT, digital twin, DPP, cloud computing.

DIGITAL PLATFORMS AS CIRCULARITY BROKER Platform Configuration Many scholars agree that circularity can only be understood as a goal of a whole system and not as a feature of a specific product or service (Fehrer and Wieland, 2021; Trevisan et al., 2022). The transition to a CE is highly dependent on successful cross-sectoral industrial symbioses in which the waste and by-products of one actor becomes the input for another. There are several reasons why the emergence of these symbiotic ecosystems (Asgari and Asgari, 2021) is hindered, but many of them are related to information asymmetries (Berg and Wilts, 2019). The lack of product data and information exchange between different actors not only

The role of digital platforms as circularity broker  263 constrains direct waste recovery, since the value is neither recognized by the generator nor by the potential receiver (Ciulli et al., 2020), but also obstructs the necessary identification of collaboration opportunities with (un)known actors in the ecosystem (Akrivou et al., 2021). Other informational barriers which hamper the successful industrial symbiosis are the unawareness of its principles and benefits, the lack of information-sharing mechanisms and infrastructure, unwillingness and inefficiencies (not only for information but also knowledge sharing) (Kosmol and Leyh, 2019) and the lack of certification data as well as protocols for secure transfers (Pedone et al., 2021). The latter in particular leads to further reservations about the exchange of data – especially business-critical data and intellectual property – because of fears that they could be leaked and misused by competitors (Liu et al., 2022). Ciulli et al. (2020) are among the few scientists who have addressed the missing links between actors that hinder further symbioses and cause so-called circularity holes. The conceptualization of these circularity holes builds on the theories of structural holes and grounds its distinction on the fact that the residual value of a discarded product, and thus unvalued waste, is lost due to the lack of connection between a waste producer and a potential recipient – and unlike structural holes, cannot be recovered or salvaged (Ciulli et al. 2020). Whether industrial symbiosis and thus a higher degree of circularity is inhibited by information gaps or circularity holes plays only a minor role in overcoming them for the time being, because an efficient and effective information and knowledge exchange can bridge both and bring the ecosystem closer to the goal of circularity. Since direct cross-sectoral information exchange from one actor to another is often not possible in a multi-actor ecosystem, a broker must be brought in to enable indirect information exchange through the collection, processing, and analysis of information and data (Kosmol and Leyh, 2019). This broker connects previously known and unknown ecosystem actors to promote coordination, collaboration, and achievement of the common goal of closing the loop (Ciulli et al., 2020). The broker not only favors the circulation of goods, but also provides access to knowledge and helps initiate contacts (Kosmol and Leyh, 2019; Ciulli et al., 2020). To truly identify and leverage potential synergies the broker facilitates the correct identification and required specification of the technical characteristics of the requested or delivered resource stream and enables the supply/ demand matchmaking (Akrivou et al., 2021). One of the technology that can take on such a role as a circularity and information broker, is the DP (Berg and Wilts, 2019; Ciulli et al., 2020). Ciulli et al. (2020) conceptualize the different roles a broker plays in the context of the circular brokerage as: connect, inform, protect, mobilize, integrate, and measure. These roles illustrate the versatility of the DP, which goes beyond matchmaking and bridging of circular holes. The DP is also able to overcome further information- and data-related shortcomings – such as missing data security, lack of trust, insufficient data authenticity as well as high transaction and search costs (Berg and Wilts, 2019). The blockchain technology already mentioned can play a crucial role for enabling the circular ecosystem, as a blockchain-based DP is able to guarantee data traceability, security and privacy through decentralized data infrastructure and builds trust in a trust-free environment (Zutshi et al., 2021). For the specific context of CE, the use of blockchain technology improves the monitoring of product and material data throughout the product lifecycle and enables a seamless data foundation for the application of R-strategies (Soldatos et al., 2021). (Blockchain-based) DPs can differ in their specific design and structure. Although they vary in literature, the following dimensions need to be considered when a DP is to be established:

264  Handbook on digital platforms and business ecosystems in manufacturing (1) Platform ownership: The platform owner defines its governance, i.e. the rules to which all actors of the DP ecosystem must adhere (Chen et al., 2022). A common distinction is made through the distribution of power within the DP. In a centralized platform, one single owner has the main power over governance mechanisms. In a consortium, the power is distributed among a group of actors who cooperatively agree on governance rules and establish and sustain the platform. In a decentralized platform, a peer-to-peer network would take control and all actors could directly influence the DP. This would be a use case for (a fully decentralized) blockchain (Hein et al., 2020). (2) Platform security: If the DP is blockchain-based, then data security is ensured through the decentralized data infrastructure, which is cryptographically secured and integrated (Zutshi et al., 2021). However, blockchain also offers the option of continuing to store the data centrally via cloud storage, using the blockchain as a decentralized supplement in which metadata is stored (Zutshi et al., 2021). (3) Accessibility and openness: The platform owner must then determine the openness and accessibility of the DP (Chen et al., 2022). For a permissioned blockchain (and a closed ecosystem), access rules for participation of interested actors need to be defined (Soldatos et al., 2021; Zutshi et al., 2021). For a permissionless blockchain (and an open ecosystem), there is no need for access rules since any actor can participate (Zutshi et al., 2021), but in this case, computationally intensive consensus mechanisms are required to confirm the authenticity of the data (Soldatos et al., 2021). In both cases, all actors always have the latest encrypted transaction data on the blockchain (Kofos et al., 2022). (4) Actor autonomy: It is important to define the degree of freedom actors have within the ecosystem, i.e. which specific data should be available to which actor and to what extent, based on their role within the ecosystem (Hein et al., 2020). (5) Platform database: For the platform to fulfill its role as an informant, all necessary information must be available to all relevant actors (Akrivou et al., 2021; Pedone et al., 2021). This includes information and data about the material, processes and services. To find synergies between actors, it is necessary to also provide relevant information (needs and requirements) from the demand side (Akrivou et al., 2021). Since relevant product and material information varies by actor, it might be useful to introduce a DPP that contains the appropriate database (Götz et al., 2021). (6) Decision support: A final component of the DP is the level of decision support and transaction facilitation. The platform acts as a marketplace and brings supply and demand together. Depending on the level of decision support, this ranges from proposals to fully automated transactions based on predefined rules via smart contracts (Hein et al., 2020; Mastos et al., 2021). The information and knowledge exchanged over the platform can also be used for buying-decisions or to facilitate better refurbishment and recycling of products (Hakanen and Rajala, 2018; Soldatos et al., 2021). In Figure 17.1, the above-described elements of the DP are synthesized and graphically illustrated as a platform-based circular ecosystem. Each actor plays a unique role regarding the circular value proposition of the ecosystem (Bressanelli et al., 2022) and the corresponding circular processes that take place. Actor roles can be, but are not limited to: raw materials suppliers, parts suppliers, product manufacturers, distributors, users, and EoL-collectors as well as EoL-recoverers, ranging from activities throughout the entire product lifecycle. Key elements need to be considered and managed to align diverse, cross-sector stakeholders toward circular

Figure 17.1

Source: Authors.

Platform-based circular ecosystem

The role of digital platforms as circularity broker  265

266  Handbook on digital platforms and business ecosystems in manufacturing value creation. Trevisan et al. (2022) summarize that it is crucial for a circular ecosystem that – due to the heterogeneity and interdependency of the actors – common interests are aligned, the actors’ reliability is ensured and that there is an appropriate balance between the actors to fulfill the different required circular activities within the ecosystem. The latter in particular is represented by the depiction of two boxes for each actor, implying that many (more) actors (from different sectors and also different ecosystems) are part of this specific one. With regard to the various flows within the ecosystem, a distinction is made between data, information and knowledge flows (dashed lines between the ecosystem actors via the platform), and material and goods flows (solid lines). The nonlinear flows of goods and materials are enabled by the preceding information flows over the DP. The DP itself – as the core of the ecosystem – provides the virtual marketplace through its main function of matchmaking, enabled by data, information and knowledge exchange and processing. As conceptualized by Ciulli et al. (2020), the use of DT, especially DPs and blockchain, serves several functions and roles that can now be used for objectives that go beyond the circularity brokerage. The measuring role in particular makes it possible to track and collect valuable information and data on the consequences of the actors’ actions – not only from the circular perspective, but also from an economic, environmental and social viewpoint (Ciulli et al., 2020), creating thus a holistic dataset for further assessment on the ecosystem’s performance, which will be the focus of the following section. Process Performance of Digital Platform Actors The platform-based circular ecosystem has clear advantages: transparency, traceability and visibility. However, they increase pressure for better performance as the impact of individual actors and the entire ecosystem becomes observable through tracking and measurement (Saberi et al., 2019). Customers, for example, can more easily understand whether a product contains pollutants, was produced under questionable working conditions, or how well it is suited for R-processes. Considering that platform-based circular ecosystems are a rapidly growing but relatively new phenomenon (Trevisan et al., 2022), performance measurement systems that address the process capabilities and impacts of a digitization-based CE (Chauhan et al., 2022) have not been adequately explored and therefore represent an interesting research gap (Asgari and Asgari, 2021). The relationship between the CE and sustainable business is multilayered and complex, because integrating more CE does not necessarily mean improved performance in terms of an environmental or social sustainability perspective (Vegter et al., 2021; Montag and Pettau, 2022). Companies, therefore, need orientation in order to better pursue holistic sustainability. The following performance measurement framework can serve as orientation. However, such a sustainable guide can also be used by customers to check how a company acts in the context of sustainability. The conceptual framework proposed in Table 17.1 builds on previous work on performance measurement in a circular era (Montag and Pettau, 2022) and thus considers a circular, economic, environmental and social perspective within the eight circular processes of plan, procure, make, deliver, use, recycle and enable. The framework aims to offer support for a sustainable process performance measurement system by providing holistic guidance for possible measures in all four sustainability dimensions and along the entire product lifecycle. In the context of the platform-based circular ecosystem, the conceptual framework has now been adapted and extended to meet the new digital conditions. Due to the possibilities offered by DT, they can be included as a complementary and supporting component. However, they do not form an independent sustainability dimension. As support, they enable the better and more precise collection of data and information and thus the discovery of synergy potentials for

The role of digital platforms as circularity broker  267 industrial symbiosis. Nevertheless, since the ecosystem relies on a DP, the digital performance needs to be evaluated as well. The framework is to be understood as a conceptual basis for performance measurement. The measures developed represent approaches to performance evaluation and need to be adapted to by decision-makers in specific use cases. Some of the measures will be more applicable than other, more specific measures. Selected digital measures are described below. ● Plan: - Platform operating cost: This measure looks at the cost that arises from the operation of the platform and the data collection. This primarily includes energy, but also costs related to the maintenance of sensors, if necessary. ● Source: - Execution of unscheduled audits: The provenance of a resource can be well tracked with the use of blockchain. Whether promised working conditions (such as no child labor) are met cannot be monitored by IoT sensors. Unannounced audits by independent auditors can verify the claims and confirm them with a time-limited certificate. ● Make: - Share of digitized processes: Measures the share of manufacturing processes that are digitally captured. The share is low if not every machine has IoT sensors, nor communicates this data, or if data has to be captured manually and transmitted to the platform. - Availability and usage of real-time data: Is real-time data used and does it influence the production process? For example, the production speed can be adjusted if the location of the means of transport indicates that input materials will be delayed. ● Deliver: - Share of digitally tracked material/components: A delivery can be digitally tracked and its location or storage temperature transmitted to the customer in real time. The share of parts so tracked measures this metric. However, working conditions can also be recorded, such as the travel time of truck drivers. - Precision of data collection: includes here whether, for example, the location of a good is recorded precisely (e.g. in a traffic jam), or whether only the transport step (e.g. delivery from the port of departure to the port of destination) is recorded. ● Use: - Ease of use + actor-related suitability of information: When the user is provided with appropriate and easy-to-understand information on how to use the product in the most environmentally friendly way or for the longest possible life. ● Return: - Communication gaps: Are all returned goods recorded along with the information collected so far or are there gaps in the communication of goods-related information. ● Recover: - Ease of data access + Ease of use: A product has a defect and is to be repaired. The two indicators look at how easy it is for the repairer to access the necessary knowledge (repair instructions and notes) and how comprehensible and usable the knowledge and information are. Whether this information is used at all by the actors is captured by means of ‘usage of available information’. ● Enable: - Data storage capacity: How much data can be stored and processed (‘data process capability’) without noticeable loss of speed; e.g. can only metadata be stored on the blockchain due to the volume of data, and other information stored in a cloud storage facility? - Geographic platform scale: Does the platform only consider actors in geographical proximity, country-wide, international (e.g. EU level) or worldwide?

Digital

Source

Digital

equipment

Acquisition of long-lasting and durable

safety; Work-life-balanced schedule

toxic/non-biodegradable raw materials;

recyclable inputs; Amount of secondary inputs; sourcing/primary sourcing costs; Input

Amount of by-products

supply (primary + secondary)

of redundant collaborative partnerships; Scalability; Data scope

capability; Communication gaps; ICT technologies; Existence of autonomous operations/processes; Usage of available information; Data processing time; Holistic data sets; No.

characterization; Availability of up-to-date technologies; Usage rate of DP; Platform operating costs; Discovery of new business collaborators; Data timeliness; Data process

autonomous transactions; Data format; Precision of data collection; Availability and usage real-time data; Sensor durability; Actor participation; Energy consumption; Material

Share of digitally tracked material/components; Share of digitalized processes; Level of actor’s digitalization; Net job generation; Execution of unscheduled audits; Share of

Access to platform data; Actor-specific data generation rights; Ease of data access; Ease of use; Actor-related suitability of information; Information validity; Initial trust audits;

Amount of received packaging materials; suppliers

availability; Reliability and quality of

of integrative suppliers; Transparent

Fraction of local suppliers; Fraction

emissions of transportation; Fraction of

inputs + products; Fraction secondary

of renewable/bio-based inputs; Amount of

Quality of secondary inputs

Fraction of ethical suppliers (e.g. labels);

Sourcing costs; Sourcing costs secondary CO2e emissions of sourcing; CO2e

Fraction of secondary/primary inputs; Amount

time; Holistic data sets; No. of redundant collaborative partnerships; Scalability; Data scope

timeliness; Data process capability; Communication gaps; ICT technologies; Existence of autonomous operations/processes; Usage of available information; Data processing

Sensor durability; Actor participation; Interactive visualization; Availability of up-to-date technologies; Platform operating costs; Discovery of new business collaborators; Data

transactions; Data format; Degree of data standardization; Precision of data collection; Data storage capacity; Platform induced adaptivity; Availability and usage real-time data;

tracked material/components; Share of digitalized processes; Level of actor’s digitalization; Net job generation; Execution of unscheduled audits; Share of autonomous

Access to platform data; Actor-specific data generation rights; Ease of data access; Ease of use; Actor-related suitability of information; Information validity; Share of digitally

changes; Capacity utilization rate

Amount of social gaps; Work quality and

Recovery capacity

No. of socially engaged processes;

equipment; Possibility of ad hoc decision/ of clean technologies/clean equipment;

Social

Acquisition of second-hand equipment;

Environmental

Process reliability; Availability of assets/ Energy self-sufficiency; Acquisition

No. of circular processes / circularity gaps;

Economic

Circular

Plan

Performance measurement framework

SCOR Process

Table 17.1

268  Handbook on digital platforms and business ecosystems in manufacturing

efficiency; Water use/water depletion;

+ product); Flexibility of material input; Substitution ability of inputs;

recycled waste

Digital

Deliver

Digital

by-products;CO2e emissions; Other emissions; Energy use; Production

Standardized production (process

and use of waste and by-products; Fraction of of production site

Routing costs; Packaging costs; scheduling

Amount of empty trips

durability; Job creation

of health and safety standards; Sensor

accidents in deliveries; Implementation

CO2e emission; Environmentally friendly Unhealthy working; Conditions; No. of packaging; Shared use of infrastructure;

of redundant collaborative partnerships; Scalability; Data scope

capability; Communication gaps; ICT technologies; Existence of autonomous operations/processes; Usage of available information; Data processing time; Holistic data sets; No.

characterization; Availability of up-to-date technologies; Usage rate of DP; Platform operating costs; Discovery of new business collaborators; Data timeliness; Data process

autonomous transactions; Data format; Precision of data collection; Availability and usage real-time data; Sensor durability; Actor participation; Energy consumption; Material

of digitally tracked material/components; Share of digitalized processes; Level of actor’s digitalization; Net job generation; Execution of unscheduled audits; Share of

Access to platform data; Actor-specific data generation rights; Ease of data access; Ease of use; Actor-related suitability of information; Information validity; Share

packaging

Reusable packaging take-backs; Use of reusable Delivery time/delay; Flexibility of order

Amount of deliver-take-backs-combinations;

of redundant collaborative partnerships; Scalability; Data scope

capability; Communication gaps; ICT technologies; Existence of autonomous operations/processes; Usage of available information; Data processing time; Holistic data sets; No.

characterization; Availability of up-to-date technologies; Usage rate of DP; Platform operating costs; Discovery of new business collaborators; Data timeliness; Data process

autonomous transactions; Data format; Precision of data collection; Availability and usage real-time data; Sensor durability; Actor participation; Energy consumption; Material

of digitally tracked material/components; Share of digitalized processes; Level of actor’s digitalization; Net job generation; Execution of unscheduled audits; Share of

Access to platform data; Actor-specific data generation rights; Ease of data access; Ease of use; Actor-related suitability of information; Information validity; Share

quality level; Profits (net present value)

Dependency on products/inputs; Product Soil use/soil depletion; Land use

accidents/incidents; Implementation of

Amount of (hazardous) waste +

Production costs; Production time;

Fraction of renewable energy used; Recovery

Make

health and safety standards; Transparency

Social Unhealthy working conditions; No. of

Environmental

Economic

Circular

SCOR Process

The role of digital platforms as circularity broker  269

Digital

Return

Digital

Time of product use; Durability/reliability of

Use

of environmentally friendly use

After-sales-service costs

Accessibility of repairs/upgrading; Incentives

recovery; Firm incentives for recovery

time; Holistic data sets; No. of redundant collaborative partnerships; Scalability; Data scope

timeliness; Data process capability; Communication gaps; ICT technologies; Existence of autonomous operations/processes; Usage of available information; Data processing

consumption; Material characterization; Availability of up-to-date technologies; Usage rate of DP; Platform operating costs; Discovery of new business collaborators; Data

transactions; Data format; Precision of data collection; Implementation of a reward system; Availability and usage real-time data; Sensor durability; Actor participation; Energy

tracked material/components; Share of digitalized processes; Level of actor’s digitalization; Net job generation; Execution of unscheduled audits; Share of autonomous

Access to platform data; Actor-specific data generation rights; Ease of data access; Ease of use; Actor-related suitability of information; Information validity; Share of digitally

to return products

+ variable); Customer incentives for

accidents in returns; Job creation (fixed

extra packaging

centers/ease of returns; Reusable packaging

returns/returnability; Incentives for customers

Unhealthy working conditions; No. of

backs; Costs for extra routing; Costs for

of returns from distributor; No. of collection

use; Support for correct disposal

Costs for return flows; Cost for extra take CO2e emissions; Other emissions; Land

Fraction of returns from customers; Fraction

of redundant collaborative partnerships; Scalability; Data scope

capability; Communication gaps; ICT technologies; Existence of autonomous operations/processes; Usage of available information; Data processing time; Holistic data sets; No.

characterization; Availability of up-to-date technologies; Usage rate of DP; Platform operating costs; Discovery of new business collaborators; Data timeliness; Data process

autonomous transactions; Data format; Precision of data collection; Availability and usage real-time data; Sensor durability; Actor participation; Energy consumption; Material

of digitally tracked material/components; Share of digitalized processes; Level of actor’s digitalization; Net job generation; Execution of unscheduled audits; Share of

Access to platform data; Actor-specific data generation rights; Ease of data access; Ease of use; Actor-related suitability of information; Information validity; Share

repairability of products; Usage data collection

for customers to repair/maintain Upgradability/

of environmental instructions; Possibility loyalty; No. of participative workshops

and maintenance revenues;

of product warranty + technical support;

of consumer complaints; Customer

CO2e emissions; Product wear + tear;

Amount of needed repairs; Warranty Environmental restrictions; Availability

Social No. of usage accidents; No. of incidents

Environmental

Economic

product; No. of customer per product; Provision replacement costs; Repair, upgrade

Circular

SCOR Process

270  Handbook on digital platforms and business ecosystems in manufacturing

efficiency; Water use /water depletion; Soil use/soil depletion; Land use; Use of

treatment costs + profits; Waste discharge/disposal fees

secondary inputs from recycling; Amount

of refurbished products; Amount of reusable

Digital

returned products; Waste and by-product emissions; Energy use; Recovery process accidents/incidents in recoveries; Fraction

of recovered energy from waste; Amount of

of redundant collaborative partnerships; Scalability; Data scope

capability; Communication gaps; ICT technologies; Existence of autonomous operations/processes; Usage of available information; Data processing time; Holistic data sets; No.

characterization; Availability of up-to-date technologies; Usage rate of DP; Platform operating costs; Discovery of new business collaborators; Data timeliness; Data process

autonomous transactions; Data format; Precision of data collection; Availability and usage real-time data; Sensor durability; Actor participation; Energy consumption; Material

of digitally tracked material/components; Share of digitalized processes; Level of actor’s digitalization; Net job generation; Execution of unscheduled audits; Share of

Access to platform data; Actor-specific data generation rights; Ease of data access; Ease of use; Actor-related suitability of information; Information validity; Share

State-of-the-art recover technologies

toxic/corrosive chemicals

by-products; CO2e emissions; Other

Generated profits/revenues; Quality of

separation/disassembly/ recycling; Amount

products; Amount of regenerated waste;

creation (fixed + variable); No. of

Amount of (hazardous) waste +

Recovery costs; Energy costs

Efficiency of recovery processes; Ease of

Recover

of domestic value recovery

Social Unhealthy working conditions; Job

Environmental

Economic

Circular

SCOR Process

The role of digital platforms as circularity broker  271

social performance barriers; CSC transparency and communication; Credible labels and certifications; R&D for social innovations; Job creation in local communities; Fair living wages;

Environmentally friendly product design R&D for new, environmentally compatible inputs; Fraction of compensated emissions

economic performance barriers; Risk management (economic + political); Prospective analysis of future trends;

barriers; Circular product design; R&D for

further regeneration/restoration; Fraction of

shared SC assets; Technology application/IoT

turnover; Employee satisfaction and

through cooperation

deposit /take-back systems

sets; No. of redundant collaborative partnerships; Scalability; Data scope

process capability; Communication gaps; ICT technologies; Existence of autonomous operations/processes; Usage of available information; Data processing time; Holistic data

platform scale; Critical mass of DP; Availability of up-to-date technologies; Data format; Degree of data standardization; Precision of data collection; Data timeliness; Data

Data security; Data storage capacity; Platform induced adaptivity; Availability and usage real-time data; Platform interoperability; Initial ICT infrastructure costs; Geographic

access rights; Information validity; Initial trust audits; Share of digitalized processes; Level of actor’s digitalization; Net job generation; Share of autonomous transactions;

Degree of platform openness; Access to platform data; Actor-specific data generation rights; Ease of platform participation; Ease of data access; Ease of use; Actor-specific

Donations to local communities

Gender ratio in board positions;

on CE; Integrative employment policies;

training; Amount of training provided

participation; Amount of safety-related

Additional employee benefits; Employee

supplier contracts; Cost reduction

of waste and by-products; Implementation of

implementation readiness; Increase of usability Investment risks; No. of long-term

performance goals; Identification of

environmental performance barriers;

performance goals; Identification of

goals; Identification of circular performance

Identification of explicit social

Social

performance goals; Identification of

Source: Own illustration.

Digital

Environmental

Circular business model implementation; Identification of explicit environmental

Economic Identification of explicit economic

Corporate Circular Reporting (Y/N);

Enable

Identification of explicit circular performance

Circular

SCOR Process

272  Handbook on digital platforms and business ecosystems in manufacturing

The role of digital platforms as circularity broker  273

DISCUSSION This study explored the DP in the role of a CE broker to address circularity holes and information gaps that hinder the successful implementation of the CE within an ecosystem of actors. In doing so, we responded to several calls from academics to study the CE from an ecosystem perspective rather than from a single-firm-centric view (Bressanelli et al., 2022; Trevisan et al., 2022). In addition to that, we have taken a first step to respond to the call for performance measurement systems for digitization-based circular ecosystems (Chauhan et al., 2022) by proposing a conceptual framework that goes beyond the isolated perspective of the CE. It enables the measurement and evaluation of circular performance, but also of economic, environmental, social and, furthermore, digital performance. This study makes several contributions to the scientific community as well as for decision-makers in management positions. First, a major achievement of this study is the successful nexus of key research streams that currently dominate the scientific community. Linking CE, as the main sustainability related research track of the last five to ten years, with a digitized ecosystem perspective bridges a research gap that is of great importance for the further transition towards circularity. In doing so, this study is an early attempt to advance interdisciplinary research for the CE. Moreover, the developed framework for ecosystem performance manages to link and assess not only circular performance but also economic, environmental, social and finally digital performance. This led to the development of a performance measurement set that holistically depicts the digitization-based circular ecosystem for all involved actors and included processes. In linking CE-related research with digital technologies, especially with the DP, this study furthermore creates groundwork for future research in this field. As circularity holes and circularity brokerage are so far understudied fields of research – despite their importance in the successful transition from linearity to circularity – the presented framework is an early attempt to provide combined research and guidance for the circular ecosystem in a digital era. From a managerial perspective, the study offers guidance on which aspects need to be sufficiently considered when implementing and monitoring a DP to enhance the degree of circularity. Decision-makers can use the information provided within this research – such as the relevant information on key technologies for data collection, processing and so on – to install and enable a cross-sectoral ecosystems. One goal for the management perspective was also to create awareness of the interconnectedness of potential players in a circular and digitally based ecosystem to better manage the challenges that come with it. As mentioned earlier, the performance framework for now is to be understood as a basic construct providing a potential pool of key measures to assess the overall performance of the ecosystem. The details of a specific performance measurement system created for a particular ecosystem then depend on the circumstances and must be determined by the decision makers. This research is subject to some limitations that have not yet been adequately addressed and discussed. For one – as the conceptualized framework is still on a theoretical basis – it lacks reference to practice and specific testing of the proposed measures in a real-world ecosystem. In this context, for example, interdependencies between actors, processes, dimensions and specific measures could become clear, allowing for further conclusions to be drawn about the performance framework. Another limitation is the rather narrowed management perspective that was taken here. Thus, there is a lack of in-depth information from the field of data science, especially with regard to the concrete design of the DP. Future research should therefore be interdisciplinary in order to do justice to the interlocking disciplines and their focal points. The CE and its implementation need DT to develop their full potential and live up to the ascribed potential for sustainable development.

274  Handbook on digital platforms and business ecosystems in manufacturing

CONCLUSION Within this study, the authors aimed to answer several calls from the research community to align CE and DT, and here especially the DP. The study was guided by the goal to investigate the DP as enabler of the CE in order to fill circularity and information gaps in a circular and digital ecosystem. In linking several underexplored but relevant research fields, the conceptualization of a performance measurement framework is added to the literature and provides new perspectives as well as potential starting points for further research. The proposed framework is an early attempt to link CE and DPs from an ecosystem perspective, providing a holistic view of the performance from a circular, economic, social and digital perspective. First, we presented a selection of relevant DT to facilitate CE and their categorization by function and data processing. Then, we contributed to the literature on DP and blockchain and their role as circular and information broker. Third, we developed a performance measurement framework that uses the DP as an enabler for digital, circular, and thus holistically sustainable performance measurement in four dimensions, which can serve as sustainability guidance for decision-makers and stakeholders. Particularly promising, but only briefly mentioned in this chapter, are the possible applications of the DPP, which contains all the information collected via the platform and processes it in a way that is suitable for each target group. Another aspect that requires further scientific consideration is the concrete technical and legal implementation of such DPs and further technical improvements of the technologies, such as the storage capacity and the processing possibilities of blockchain. Further future research opportunities lie in the subject-specific design of the presented framework, especially against the background of the interdisciplinary character. As mentioned in the Discussion, the link between digitalization and CE needs to be strengthened in order to overcome major barriers in the CE implementation.

REFERENCES Akrivou, C., Łȩkawska-Andrinopoulou, L., Tsimiklis, G. and Amditis, A. (2021) Industrial symbiosis platforms for synergy identification and their most important data points: a systematic review. Open Research Europe, 1, 101. Available from: https://​doi​.org/​10​.12688/​openreseurope​.13893​.2. Asgari, A. and Asgari, R. (2021) How circular economy transforms business models in a transition towards circular ecosystem: the barriers and incentives. Sustainable Production and Consumption, 28, 566–79. Available from: https://​doi​.org/​10​.1016/​j​.spc​.2021​.06​.020. Bedi, P., Jindal, V. and Gautam, A. (2014) Beginning with big data simplified. In: 2014 International Conference on Data Mining and Intelligent Computing (ICDMIC), 2014 International Conference on Data Mining and Intelligent Computing (ICDMIC), 5–6 September, Delhi, India. IEEE, pp. 1–7. Berg, H. and Wilts, H. (2019) Digital platforms as market places for the circular economy – requirements and challenges. NachhaltigkeitsManagementForum | Sustainability Management Forum, 27(1), 1–9. Available from: https://​doi​.org/​10​.1007/​s00550​-018​-0468​-9. BMUV Umweltpolitische Digitalagenda: Digitaler Produktpass. https://​www​.bmuv​.de/​FQ220. Accessed 2 November 2022. Bressanelli, G., Adrodegari, F., Pigosso, D. C. A. and Parida, V. (2022) Towards the smart circular economy paradigm: a definition, conceptualization, and research agenda. Sustainability, 14(9), 4960. Available from: https://​doi​.org/​10​.3390/​su14094960.

The role of digital platforms as circularity broker  275 Chauhan, C., Parida, V. and Dhir, A. (2022) Linking circular economy and digitalisation technologies: A systematic literature review of past achievements and future promises. Technological Forecasting and Social Change, 177, 121508. Available from: https://​doi​.org/​10​.1016/​j​.techfore​.2022​.121508. Chen, L., Tong, T. W., Tang, S. and Han, N. (2022) Governance and design of digital platforms: a review and future research directions on a meta-organization. Journal of Management, 48(1), 147–84. Available from: https://​doi​.org/​10​.1177/​01492063211045023. Chen, Z. and Huang, L. (2021) Digital twins for information-sharing in remanufacturing supply chain: A review. Energy, 220, 119712. Available from: https://​doi​.org/​10​.1016/​j​.energy​.2020​.119712. Circle economy (2022) The Circularity Gap Report 2022, pp. 1–64, Amsterdam: Circle Economy. Available from: https://​circularity​-gap​.world/​2022 [Accessed 6 November 2022]. Ciulli, F., Kolk, A. and Boe-Lillegraven, S. (2020) Circularity brokers: digital platform organizations and waste recovery in food supply chains. Journal of Business Ethics, 167(2), 299–331. Available from: https://​doi​.org/​10​.1007/​s10551​-019​-04160​-5. Da Silva, T. B., Morais, E. S. de, Almeida, L. F. F. de, Da Rosa Righi, R. and Alberti, A. M. (2020) Blockchain and Industry 4.0: Overview, convergence, and analysis. In: Da Rosa Righi, R., Alberti, A. M. and Singh, M. (Eds.) Blockchain Technology for Industry 4.0. Springer Singapore: Singapore, pp. 27–58. Derave, T., Prince Sales, T., Gailly, F. and Poels, G. (2021) Comparing digital platform types in the platform economy. In: La Rosa, M., Sadiq, S. and Teniente, E. (Eds.) Advanced Information Systems Engineering. Springer International Publishing: Cham, pp. 417–31. Dorsemaine, B., Gaulier, J.-P., Wary, J.-P., Kheir, N. and Urien, P. (2015) Internet of things: a definition & taxonomy. In: 9th International Conference on Next Generation Mobile Applications, Services and Technologies, 9th International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST), 9–11 September, Cambridge, United Kingdom. IEEE, pp. 72–77. Ellen MacArthur Foundation (2015) Towards a circular economy: business rationale for an accelerated transition. Available from: https://​www​.elle​nmacarthur​foundation​.org/​publications/​towards​-a​ -circular​-economy​-business​-rationale​-for​-an​-accelerated​-transition [Accessed 6 July 20]. Emmanuel, I. and Stanier, C. (2016) Defining big data. In: Boubiche, D. E., Hamdan, H. and Bounceur, A. (Eds.) Proceedings of the International Conference on Big Data and Advanced Wireless Technologies – BDAW ’16, the International Conference, 10–11 November, Blagoevgrad, Bulgaria. ACM Press: New York, New York, USA, pp. 1–6. Esmaeilian, B., Sarkis, J., Lewis, K. and Behdad, S. (2020) Blockchain for the future of sustainable supply chain management in Industry 4.0. Resources, Conservation and Recycling, 163, 105064. Available from: https://​doi​.org/​10​.1016/​j​.resconrec​.2020​.105064. European Commission (2019) Communication from the Commission: The European Green Deal. Available from: https://​eur​-lex​.europa​.eu/​legal​-content/​EN/​TXT/​?qid​=​1576150542719​&​uri​=​COM​ %3A2019​%3A640​%3AFIN [Accessed 6 November 2022]. European Commission (2022) Circular economy action plan. Available from: https://​environment​.ec​ .europa​.eu/​strategy/​circular​-economy​-action​-plan​_en [Accessed 6 November 2022]. Fehrer, J. A. and Wieland, H. (2021) A systemic logic for circular business models. Journal of Business Research, 125, 609–20. Available from: https://​doi​.org/​10​.1016/​j​.jbusres​.2020​.02​.010. Geissdoerfer, M., Pieroni, M. P., Pigosso, D. C. and Soufani, K. (2020) Circular business models: A review. Journal of Cleaner Production, 277, 123741. Available from: https://​doi​.org/​10​.1016/​j​ .jclepro​.2020​.123741. Geissdoerfer, M., Savaget, P., Bocken, N. M. and Hultink, E. J. (2017) The Circular Economy – A new sustainability paradigm? Journal of Cleaner Production, 143, 757–68. Available from: https://​doi​.org/​ 10​.1016/​j​.jclepro​.2016​.12​.048. Götz, T., Adisorn, T. and Tholen, L. (2021) Der Digitale produktpass als politik-konzept: kurzstudie im rhamen der umweltpolitischen digitalagenda des bundesministeriums für umwelt, naturschutz und nukleare sicherheit (BMU). Wuppertal Report (20), 1–44. Grieves, M. and Vickers, J. (2017) Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen, F.-J., Flumerfelt, S. and Alves, A. (Eds.) Transdisciplinary Perspectives on Complex Systems. Springer International Publishing: Cham, pp. 85–113.

276  Handbook on digital platforms and business ecosystems in manufacturing Hakanen, E. and Rajala, R. (2018) Material intelligence as a driver for value creation in IoT-enabled business ecosystems. Journal of Business & Industrial Marketing, 33(6), 857–67. Available from: https://​doi​.org/​10​.1108/​JBIM​-11​-2015​-0217. Hein, A., Schreieck, M., Riasanow, T., Setzke, D. S., Wiesche, M. and Böhm, M. et al. (2020) Digital platform ecosystems. Electronic Markets, 30(1), 87–98. Available from: https://​doi​.org/​10​.1007/​ s12525​-019​-00377​-4. Ishwarappa and Anuradha, J. (2015) A brief introduction on big data 5Vs characteristics and hadoop technology. Procedia Computer Science, 48, 319–24. Available from: https://​doi​.org/​10​.1016/​j​.procs​ .2015​.04​.188. Kirchherr, J., Reike, D. and Hekkert, M. (2017) Conceptualizing the circular economy: An analysis of 114 definitions. Resources, Conservation and Recycling, 127, 221–32. Available from: https://​doi​ .org/​10​.1016/​j​.resconrec​.2017​.09​.005. Kofos, A., Ubacht, J., Rukanova, B., Korevaar, G., Kouwenhoven, N. and Tan, Y.-H. (2022) Circular economy visibility evaluation framework. Journal of Responsible Technology, 10, 100026. Available from: https://​doi​.org/​10​.1016/​j​.jrt​.2022​.100026. Kopetz, H. and Steiner, W. (2022) Internet of things. In: Kopetz, H. and Steiner, W. (Eds.) Real-Time Systems: Design Principles for Distributed Embedded Applications, 3rd edition. Springer International Publishing; Imprint Springer: Cham, pp. 325–41. Korhonen, J., Honkasalo, A. and Seppälä, J. (2018) Circular economy: the concept and its limitations. Ecological Economics, 143, 37–46. Available from: https://​doi​.org/​10​.1016/​j​.ecolecon​.2017​.06​.041. Kosmol, L. and Leyh, C. (2019) ICT Usage in industrial symbiosis: problem identification and study design. In: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, 2019 Federated Conference on Computer Science and Information Systems, 1–4 September. IEEE, pp. 685–92. Lee, E. A. (2008) Cyber physical systems: design challenges. 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing (ISORC), 363–69. Available from: https://​doi​.org/​10​.1109/​ISORC​ .2008​.25. Liu, Q., Trevisan, A. H., Yang, M. and Mascarenhas, J. (2022) A framework of digital technologies for the circular economy: Digital functions and mechanisms. Business Strategy and the Environment, 31(5), 2171–92. Available from: https://​doi​.org/​10​.1002/​bse​.3015. Mastos, T. D., Nizamis, A., Terzi, S., Gkortzis, D., Papadopoulos, A. and Tsagkalidis, N. et al. (2021) Introducing an application of an industry 4.0 solution for circular supply chain management. Journal of Cleaner Production, 300, 126886. Available from: https://​doi​.org/​10​.1016/​j​.jclepro​.2021​.126886. McKinsey Global Institute (2011) Big data: The next frontier for innovation, competition, and productivity. Available from: https://​www​.mckinsey​.com/​~/​media/​mckinsey/​business​%20functions/​mckinsey​ %20digital/​our​%20insights/​big​%20data​%20the​%20next​%20frontier​%20for​%20innovation/​mgi​_big​ _data​_exec​_summary​.pdf [Accessed 2 November 2022]. Montag, L. (2022) Circular economy and supply chains: definitions, conceptualizations, and research agenda of the circular supply chain framework. Circular Economy and Sustainability. Available from: https://​doi​.org/​10​.1007/​s43615​-022​-00172​-y. Montag, L., Klünder, T. and Steven, M. (2021) Paving the way for circular supply chains: conceptualization of a circular supply chain maturity framework. Frontiers in Sustainability, 2. Available from: https://​doi​.org/​10​.3389/​frsus​.2021​.781978. Montag, L. and Pettau, T. (2022) Process performance measurement framework for circular supply chains: An updated SCOR perspective. Circular Economy. Available from: https://​doi​.org/​10​.55845/​ KAIZ3670. Mouha, R. A. (2021) Internet of things (IoT). Journal of Data Analysis and Information Processing, 09(02), 77–101. Available from: https://​doi​.org/​10​.4236/​jdaip​.2021​.92006. Nobre, G. C. and Tavares, E. (2021) The quest for a circular economy final definition: A scientific perspective. Journal of Cleaner Production, 314, 127973. Available from: https://​doi​.org/​10​.1016/​j​ .jclepro​.2021​.127973. Pagoropoulos, A., Pigosso, D. C. and McAloone, T. C. (2017) The emergent role of digital technologies in the circular economy: A review. Procedia CIRP, 64, 19–24. Available from: https://​doi​.org/​10​ .1016/​j​.procir​.2017​.02​.047.

The role of digital platforms as circularity broker  277 Pedone, G., Beregi, R., Kis, K. B. and Colledani, M. (2021) Enabling cross-sectorial, circular economy transition in SME via digital platform integrated operational services. Procedia Manufacturing, 54, 70–5. Available from: https://​doi​.org/​10​.1016/​j​.promfg​.2021​.07​.048. Rajala, R., Hakanen, E., Mattila, J., Seppälä, T. and Westerlund, M. (2018) How do intelligent goods shape closed-loop systems? California Management Review, 60(3), 20–44. Available from: https://​doi​ .org/​10​.1177/​0008125618759685. Saberi, S., Kouhizadeh, M., Sarkis, J. and Shen, L. (2019) Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117–35. Available from: https://​doi​.org/​10​.1080/​00207543​.2018​.1533261. Scime, L., Singh, A. and Paquit, V. (2022) A scalable digital platform for the use of digital twins in additive manufacturing. Manufacturing Letters, 31, 28–32. Available from: https://​doi​.org/​10​.1016/​j​ .mfglet​.2021​.05​.007. Soldatos, J., Kefalakis, N., Despotopoulou, A.-M., Bodin, U., Musumeci, A. and Scandura, A. et al. (2021) A digital platform for cross-sector collaborative value networks in the circular economy. Procedia Manufacturing, 54, 64–9. Available from: https://​doi​.org/​10​.1016/​j​.promfg​.2021​.07​.011. Steven, M. (2019) Industrie 4.0: Grundlagen – Teilbereiche – Perspektiven. Verlag W. Kohlhammer: Stuttgart. Trevisan, A. H., Castro, C. G., Gomes, L. and Mascarenhas, J. (2022) Unlocking the circular ecosystem concept: Evolution, current research, and future directions. Sustainable Production and Consumption, 29, 286–98. Available from: https://​doi​.org/​10​.1016/​j​.spc​.2021​.10​.020. Vegter, D., van Hillegersberg, J. and Olthaar, M. (2020) Supply chains in circular business models: processes and performance objectives. Resources, Conservation and Recycling, 162, 105046. Available from: https://​doi​.org/​10​.1016/​j​.resconrec​.2020​.105046. Vegter, D., van Hillegersberg, J. and Olthaar, M. (2021) Performance measurement systems for circular supply chain management: Current state of development. Sustainability, 13(21), 12082. Available from: https://​doi​.org/​10​.3390/​su132112082. Walden, J., Steinbrecher, A. and Marinkovic, M. (2021) Digital product passports as enabler of the circular economy. Chemie Ingenieur Technik, 93(11), 1717–27. Available from: https://​doi​.org/​10​ .1002/​cite​.202100121. Zutshi, A., Grilo, A. and Nodehi, T. (2021) The value proposition of blockchain technologies and its impact on digital platforms. Computers & Industrial Engineering, 155, 107187. Available from: https://​doi​.org/​10​.1016/​j​.cie​.2021​.107187.

18. New work in manufacturing: current and future implications to the paradigm shift in global manufacturing work Seth Powless and Ashley Church

DEFINING DIGITAL BUSINESS ECOSYSTEMS AND MANUFACTURING Digital business ecosystems (DBE), as discussed in previous chapters, work in a partly or fully digital environment to create and deliver a product or service through a network of organizations. These organizations can range from manufacturers and competitors, all the way to government agencies (Baumann, 2022). DBEs have been breaking through the traditional barriers of modern-day industries, creating new growth opportunities worldwide. Due to the success of DBEs, businesses are being forced to change how they work and start evaluating the threats and opportunities DBEs pose to their companies (Ives et al., 2019). This wave of change in the way we are working is happening all around the world and we can define this idea as digital disruption, and the impact is being felt far and wide. Currently, older companies with legacy systems are starting to feel the hit more than newer companies that are willing to take risks. However, DBEs are forcing all companies to think more broadly about how they market their products and how they can change the tail end of their value chain to access every customer. Although the future of DBE may already be here, it’s not evenly distributed throughout modern-day industry (Weill and Woerner, 2015). To better understand DBEs, it is essential to break down the prerequisites of a DBE: business ecosystems, digital ecosystems and digital platforms (Suuronen et al., 2022). The first prerequisite is a business ecosystem that consists of multiple interacting entities, including organizations and individuals, that create a system of growth and capability through dependence on each other, leading to the overall drive of performance and survival. A digital ecosystem is the second prerequisite of a DBE, which enables companies to have advanced digital capabilities and plays an essential role in a DBE in manufacturing. Without a robust digital ecosystem, there is little utilization of technology. The final prerequisite to a DBE is an effective digital platform. A DBE cannot exist only with a digital and business ecosystem; a digital platform is crucial to the ‘technological infrastructure’ because big data promotes a methodical product and service development (Schumacher et al., 2016). The Drivers of Digital Business Ecosystems Next, we will discuss digital ecosystems’ primary drivers and how each leads to successful progress (Sebastian et al., 2020). The first driver is the constant evolution of information and communication technology. As these technologies evolve, they change business models and reconfigure existing value chains, creating new boundaries and competitive environments for 278

New work in manufacturing  279 businesses to break through. The second driver of digital ecosystems is a customer-centric needs transformation. A customer-centric needs transformation insists that businesses must operate in multiple forms to fill the complex needs of customers. Their needs drive business model evolution, which drives the digital ecosystem data. The final driver of digital ecosystems is the progress of industry convergence (Spena et al., 2021). Industry collaboration is key to DBE as well as the growth and profit of a business, and these collaborations can change the hierarchical layers of an industry (Kim et al., 2008). Digital Business Ecosystem Operations We’re quickly moving away from value chains to fully operating within business ecosystems. The first step to understanding a business ecosystem is determining how much control companies have in their value chain and how companies are operating within the business ecosystem (Weill and Woerner, 2015). There a four ways companies can operate within a business ecosystem; they can be: ● ● ● ●

Omnichannel businesses; Ecosystem drivers; Modular producers; or Suppliers.

First, businesses can operate as omnichannel businesses within the ecosystem, providing customer access across various physical and digital channels. It’s crucial to understand that this type of operation is more sustainable because of the data and knowledge that can be collected, utilized and implemented in a way where businesses can constantly create beneficial consumer experiences with improved products over time (Weill and Woerner, 2015). Our next model of operation is the ecosystem driver model. In this model, companies partner with other providers to build a consumer base and eliminate competition. Think about this model as a business driver, building a platform for innovators and companies to conduct business. It attracts web traffic and increases both companies’ brand awareness, creating a mutualistic relationship. Next is the modular producer model; these can be described as ‘plug-and-play’ companies. A few industry examples could be PayPal and Afterpay. A modular producer must be two things, they must be able to operate in practically any ecosystem, and they must be the best of the best in their market to survive and be sustainable. Therefore, the model is quite limiting, and businesses that use modular business models are very limited in the raw consumer data, making it difficult to market themselves (Tsai and Zdravkovic, 2020). Our last model is the supplier model, which is becoming less effective over time with the increase in digitization. Suppliers are beginning to experience flat growth. A few examples of these models could be broker services or insurance providers. Currently, suppliers only have a partial understanding of their end customers, which is part of the reason they are not sustainable in their current state. Research predicts that, eventually, if suppliers do not move from the traditional supplier model to a modern-day omnichannel model, they will simultaneously lose power within the industry and be forced to reduce prices (Weill and Woerner, 2015).

280  Handbook on digital platforms and business ecosystems in manufacturing

LEAN MANUFACTURING AND THE EIGHT KINDS OF WASTE The lean manufacturing system, also known as the Toyota Production System (TPS), was first introduced to the automotive industry in Japan in the 1950s. Slowly, industries worldwide saw the positive effects lean manufacturing had and started implementing the practices into their production systems. Lean manufacturing is a solution that stems from value stream mapping (VSM) to eliminate waste and efficiently speed up processes and activities. This system came about due to the need to manufacture a wide variety of product models in a minimal quantity (Santos et al., 2021). To reach a greater understanding of lean manufacturing, it is crucial to understand the eight kinds of waste that lean manufacturing is attempting to measure, target and eradicate. The word ‘lean’ refers precisely to what one might think. It’s about production processes that use less of everything. Researchers claim that when companies implement lean manufacturing practices, they use half the human effort needed in the manufacturing process, half of the manufacturing space, half of the tools and machines, and finally, half the required engineering hours to develop and create a new product (Paramawardhani and Amar, 2020). These techniques have been described as both powerful and significant, especially seeing the result of lean manufacturing in the success of the Toyota Production System (TPS), as discussed in the previous section. Continuing, lean manufacturing focuses on eliminating eight kinds of waste in the production process (Wahab et al., 2013): ● ● ● ● ● ● ● ●

Overproduction; Time on hand; Transportation; Underutilization of workers’ creativity; Processes itself; Excess inventory; Unnecessary movement; and Defective product.

This chapter will evaluate the practices of ‘new work’ and lean manufacturing using the eight types of waste to determine the proper practices that should be implemented to address each waste. Overall, we will discuss how workflow within the production process becomes more predictable when fewer materials are used, and fewer investments are demanded. In turn, this reduces precariousness and establishes trust and confidence in all those involved in the production process. Supplier-client relationships are also much easier to manage and organize. In one study, researchers found that implementing a lean manufacturing system is important to all production systems (Santos et al., 2021). Waste of Overproduction The first type of waste we will discuss is the waste of overproduction. Overproduction occurs when too much is made too soon; some companies look at this through a ‘just-in-case’ lens. Researchers believe this is the most severe type of waste because it can cause various issues and other waste factors (Chen et al., 2019).

New work in manufacturing  281 Waste of Waiting The next waste is the waste of waiting. The waste of waiting has been proven to correlate directly to workflow and processes. This waste could be in terms of how both goods and workers are not moving around efficiently. Researchers say this waste heavily impacts lead times, which impacts a company’s competitiveness and customer satisfaction (Wahab et al., 2013). For this section, understanding VSM is essential. A value stream is the required actions needed to bring a product through all necessary streams to complete manufacture. VSM helps employees understand the complete stream of materials and products manufactured. VSM shows the whole process from start to end using simple visual representations; it can also show us where some loss may occur. When operators are moving throughout the space on the production floor, this can be defined as operation and loss. This loss happens when an activity is performed that does not contribute to the operations, such as waiting, organization of parts, reloading machines, walking from place to place, etc. (Santos et al., 2021). It’s important to identify where waste is occurring using VSM in a way that is understandable to all employees and enhance those processes to be more efficient for future VSM. This mapping allows for the elimination of waste by reducing operations and loss (Valmohammadi and Dadashnejad, 2021). Waste of Transportation Waste of transportation is another vital waste to study in manufacturing. Essentially, the movement of materials and what researchers call ‘double handling’ plays a significant role in this waste, leading to productivity and quality issues (Wahab et al., 2013). Waste of Underutilized People The next type of waste in manufacturing is the waste of underutilized people. This waste can look like too many workers on one job, not involving employees in implementing processes, or simply not engaging with or listening to employees in general (Klein et al., 2023). Waste like this can lead to the absence of brilliant, positive changes that make the workplace more efficient and workers safer. This section will discuss the idea of ‘new work’, how it is relevant, and how it can relieve this specific waste. With the new phase of modern work post-pandemic, expectations of employees and employers are shifting worldwide, changing how organizations work within the job system. These new systems are framing themselves around the ideas and practices of a concept called ‘new work’, redefining ‘old work’ as a practice of the past and bringing forth ‘new work’ as a practice of the future. The Austro-American philosopher and anthropologist Frithjof Bergmann was one of the first to introduce the concept of ‘new work’ (Bergmann (2019)). The problem with the current system: ‘old work’ Before discussing how to make the workplace a better place for all, it is important to acknowledge what isn’t working. Employees are burned out, overworked and no longer find satisfac-

282  Handbook on digital platforms and business ecosystems in manufacturing tion in the workplace. Organizations are still stuck in ‘old work’ practices; these practices include (Bergmann (2019)): ● ● ● ● ● ● ●

Unappreciated and overworked employees; Lack of communication between employees and employers; Poor management; Rigid work schedule; Lack of choice regarding virtual work; Instilling fear to produce results; and Working solely for shareholder benefit.

These issues are what inspired Bergmann to develop the concept of new work as he navigated the concept of emphasizing fulfillment and enjoyment of work. A modern-day concept: ‘new work’ New work is a political and social idea that is transformative. Jobs are ever-changing, and current jobs at various companies are not satisfactory. Employees have gotten so used to working to live rather than living to work, there is no encouragement of passion within organizations; this needs to change. Once someone finds something they are passionate about, Bergmann believes it is essential to professionalize their enthusiasm towards their specific interests. He suggests there must be more flexibility within this work and that passions themselves deserve flexibility within companies (Thoma et al., 2020). But an important distinction must be made. Bergmann is not preaching about changing specific jobs around the world to align with his theories; he wants to change the way society views work and implements working people. Society is currently going through a big transition in the way we work. The current work system is going under, and new ways of working are arising. With rising automation, organizations require more talent, ingenuity and innovation, and Bergmann argues we must prepare people to be innovative and imaginative. This is precisely why new work is viewed as a transformative social idea. New work is constantly changing and developing, and the overall goal is to prepare people for the next stage of technology and manufacturing (Aroles et al., 2021). To implement the practices of new work, it is crucial to understand that work can be something that strengthens employees and the general population. New technologies are being developed daily that constantly shift how work can and should be done. These new technologies empower employees and employers to work, think and organize themselves differently. Some characteristics of new work include (Aroles et al., 2021): ● ● ● ● ● ● ● ● ●

Flexible work schedules, online and in-person; Availability of part-time work; Viewing employees as the biggest asset rather than the biggest risk; Open communication and honesty; Employees doing what they love; Generous employee benefits; Constructive criticism at all levels; Appreciation, praise and empowerment for all employees; and Enriched lives

New work in manufacturing  283 The ‘new ways of work’ practices Flexible work and a distributed workforce are vital components of ‘new ways of work’ (NWW) practices and teleworking, a work arrangement that almost entirely relies on employees’ access to information and communication technologies (ICT). For this next section, we will discuss how new work practices can correlate with technologies to create a workspace where workers are valued (Müller, 2022). Before getting into the association between ICT, advanced manufacturing technologies (AMT), and new work practices, it’s important to analyze and define each concept. ICT helps companies create a network of interactions between employees, allowing firms to exploit information and knowledge (Li et al., 2022). AMT boosts automation and the proper, efficient implementation of manufacturing processes. These technologies increase organization within the company as well as job interdependence, where employees must rely on and trust one another. These technologies, paired with implementing new work practices, can positively transform an organization. Understanding the processes behind adopting these ideas and the differences they make in job performance is essential. This section will discuss these associations and how they can improve job breadth, job autonomy, team collaboration and employee involvement (Bayo-Moriones et al., 2017). AMT and ICT can improve job breadth but in different ways. ICT allows employees to incorporate multiple tasks at once into production jobs. They can also reduce the costs associated with organizational attempts to gather information to improve processes. Many researchers in the field of ICT have stated that incorporating ICT into the regular functions of a firm can increase employee group dynamics and job complexity, as new tasks are constantly being created and new skills always need to be applied (Li et al., 2022). Others have also argued that this adoption can eradicate communication boundaries between firms, their supply and business partners, and even between different departments within the firm. With AMT, job breadth is still improved, but differently. AMT consists of more computer-based technological advances, such as manufacturing automation. Studies have shown that AMT allows firms to increase their product variety without additional costs even at low production volumes (Florén et al., 2021). AMT pushes workers to perform newer, different tasks related to the maintenance and creation of systems and programs, expanding their knowledge of the machines they are working with. Related to AMT and new work practices, researchers have stated that organizations would see positive changes in job characteristics if they were to adopt AMT while introducing new work practices (Bayo-Moriones et al., 2017). Going further, ICT and AMT have also been linked to an increase in job autonomy. Job autonomy is when employees feel both freedom and independence when scheduling and determining their schedules and work procedures. It has been stated that ICT promotes this idea because it limits the need for hierarchy and encourages decentralization within the company and its decision-making processes. In this way, workers would decide their own work pace and use their predetermined procedures to complete the tasks given to them. In terms of AMTs’ impact on job autonomy, in an environment that is actively implementing AMT, tasks and jobs are likely to be more advanced and complex (Florén et al., 2021). This encourages employees to work on more technical and analytical tasks, pushing them to gain a broader range of information and knowledge. Implementing AMT can also sometimes result in a need for shorter decision times, because malfunctioning technologies often require fast action. This means decision-making authority will have to fall on the frontline manufacturing employees, giving them greater responsibilities and, in turn, job autonomy (Bayo-Moriones et al., 2017).

284  Handbook on digital platforms and business ecosystems in manufacturing Behind every great company is a great team. Researchers argue that ICT can help promote more independent, efficient and productive work within teams and departments through information and knowledge exchange. ICT allows teammates to share knowledge and coordinate to generate and manage new ideas while simultaneously increasing rapid feedback and job interdependencies, promoting teams to work together cohesively. ICT has also been known to help companies spread and communicate their organizational goals and objectives to employees, reducing assumptions and boosting teamwork overall. As discussed in the previous paragraph, the adoption of AMT specifically creates more complex tasks for employees (Díaz-Reza et al., 2019). Therefore, tasks or general issues will often need to be addressed by more than one person, encouraging people to work in teams rather than as individuals. These new teams and groups will learn to coordinate and manage with the same goals in mind and equal accountability (Bayo-Moriones et al., 2017). The final beneficial factor in implementing ICT is the promotion of employee involvement practices. These practices include open communication between managers and employees, increased participation, and proper feedback and suggestion systems development. A key goal of employee involvement practices is simply the open, effective sharing of information between every level of a company. Using ICT to create an easily digital, accessible, 24-hour ‘suggestion box’ reduces the cost of implementing physical suggestion systems and creates flexibility for employees and managers in how information is shared and digested. Implementing AMT leads to overall higher interdependencies throughout the firm. To deal with this increase, researchers suggest that bottom-up information flows are crucial in how workers engage with these advanced technologies and their managers. Employees play a crucial role in the resolution of arising problems. Therefore, properly implementing a suggestion system is crucial in how managers are aware, listen to, and utilize employee opinions (Bayo-Moriones et al., 2017).

Figure 18.1

The waste of underutilized people

New work in manufacturing  285 Waste of Manufacturing Processes The next type of waste is the waste of manufacturing processes themselves. Two key issues here are the over-processing of goods and over-complexities regarding procedures that are supposed to be simple. Overly complex solutions can lead to poor layouts, excessive transport and poor communication for those on the manufacturing floor. The biggest takeaway is that, to prevent this type of waste, machines and their processes must be ‘quality-capable’ (Wahab et al., 2013). It could be helpful for companies to study the theory of constraints (TOC) when attempting to improve their manufacturing processes. The TOC is a management philosophy that focuses on and fixes the weak points within a system to improve overall system performance (Şimşit et al., 2014). A constraint is any policy or idea that prevents a system or organization from achieving its goals internally, externally, or both. There are lots of different types of constraints, some of which include (Akdeniz, 2016): ● Equipment: the system in place has a limited ability to produce more goods. ● People: people working within the system lack the skills to complete what is asked of them. ● Policy: organizational policy, both written and unwritten, prevents the system from producing more goods or internally changing processes. For example, according to TOC, when there is a weak link in a project team, the weakest should be seen as a representation of the whole group. TOC encourages people to view this issue as a constraint and to alleviate this constraint by using resources within the company. TOC, in this case, offers the thought process that all employees must be stronger together. Frequently, constraints in manufacturing specifically are measured using three tools. The first tool is throughput, the second is operational expenses and the third is inventory. There are several benefits to successfully implementing TOC, some of which include (Wu et al., 2020): ● ● ● ● ●

Decrease costs within production and manufacturing; Solve workplace issues faster with more efficiency; Boost the number of products being manufactured; Meet project and delivery deadlines; and Reduce glitches in the manufacturing system.

With the TOC comes a five-step implementation process. First, identify an issue or constraint within the workplace. Find the bottlenecks in the process. Is something blocking the company or its employees from reaching their organizational goals? Once the obstacle has been identified, the next step is to exploit the obstacle using existing resources that are available to alleviate this obstacle before trying to find resources outside of the organization (Nechitaylo, 2020). After that, it is important to subordinate and review other activities related to the obstacle at hand and ensure each supports the newly implemented improvement; if not, reassessment may be needed. The next step in this process is to elevate. Even though the constraint has been improved, it may not have been eliminated, so it is crucial to devise a plan to keep the constraint at bay. Lastly, related to subordination, this process can repeat with other constraints that may arise. Constraints continuously evolve, especially when a process is changed for improvement. It is essential to acknowledge and fix constraints to reach organizational goals (Akdeniz, 2016).

286  Handbook on digital platforms and business ecosystems in manufacturing The theory of constraints can be found in every corner of business, and because of this, it can coexist with the theories offered by new work. New work is about offering flexible work and finding something professionals are passionate about. Like constraints, the interests of employees are constantly evolving, and with every project can come new sparked interests in employees (Bergmann (2019)). It is already known that when employees are happy and doing something they want to do, they are efficient, productive and produce their best work. What if we were to apply the implementation processes of TOC alongside the theories of new work? After all, the constraints of the old workplace barricaded employees and employers from thinking openly about how work can be done differently and more efficiently. This section discusses how to synchronously and effectively implement TOC and new work practices to combat unemployment challenges. TOC and new work practices together could potentially reduce the current labor shortage in the United States. TOC does not only have to apply to the workplace; it can apply to people’s lives. This is where the five-step implementation of TOC is advantageous (Nechitaylo, 2020). The current issue being discussed is the labor shortage; people do not want to return to work. What are the constraints holding the unemployed population back from getting a job? While the constraints are endless, some main reasons include the current public health risks due to the pandemic and the caregiving responsibilities of children and/or elderly family members. In a recognizable effort to exploit this issue, organizations have already started to apply new work practices creating flexible, hybrid work, offering higher pay and stipends to those joining the team, and offering hefty benefits to both full- and part-time employees (Aroles et al., 2021). After this, the organization must consider the other activities and challenges related to the obstacle. One big obstacle relating to the idea of new work is that, once people are working again, will they be passionate about what they do? How would an organization keep someone from quitting and becoming chronically unemployed? Once these other issues are recognized, a company elevates, keeping constraints at bay. The answer here is retention. Employee retention is a critical piece to solving the labor shortage. The expectations employees have of the workplace have changed drastically. As more and more companies turn to new work practices, expectations will only get higher. It is crucial to consistently reward a job well done. Employees, especially those new and reentering the workforce, must feel appreciated by their organization. When companies allow employees to do what they love and receive verbal appreciation for what they do for the organization, this will lead to satisfied and fulfilled employees, driving employee retention (Bergmann (2019)). Waste of Inventory Another waste lean manufacturing attempts to eradicate is waste in inventory. Inventory can include raw materials, work-in-progress goods and end items. Usually, a larger inventory, like the waste of waiting, can increase lead times (Mistry and Shah, 2020). Having too much inventory can also increase the number of spaces workers have to work in and transport around while decreasing the time it takes for employees and managers to identify problems. It is becoming increasingly clear how correlated each of these wastes is to each other and how they impact one another. This section will discuss the introduction of digitization in manufacturing systems. The introduction of digitalization has allowed consumers and companies to access a wider array of benefits (Weill and Woerner, 2015). For example, let us look at Walmart’s and

New work in manufacturing  287 Amazon’s business models. They are similar in that they are one-stop shops that provide various products from different companies worldwide. However, how they distribute those products and evaluate their end customers is entirely different. For decades, Walmart was primarily growing through its brick-and-mortar locations, until 2013, when they started to feel the pressure of online shopping competitors like Amazon. Walmart could not truly get to know its end customer through its physical locations. They could not access extensive data about names, addresses, demographics, purchase histories, etc. The key benefit to Amazon’s DBE is data access and analytics. The data Amazon has stored about their end customer goes as far as major life events like holidays customers celebrate, birthdays, and even weddings and anniversaries. Aside from Amazon’s ability to track consumer activity, this data enables them to innovate faster and sustain products longer. The ‘feedback hub’ offers an excellent way for customers to feel trust and true freedom of choice from Amazon (Weill and Woerner, 2015). While digitization appears to offer plenty of benefits to keep companies afloat, there are downsides to the digitization movement. Challenges do not end with digitization. When there are threats within this new type of system, they are often real and happen quickly, sometimes easily going unnoticed (Cantamessa et al., 2020). A research study tells us that two-thirds of companies claimed they are experiencing real threats from other companies who have established end-customer relationships and already have competing products and services, saying these competing companies could disrupt their businesses. Further, companies expressed that alternative digital offerings in the market present the highest threat to the core of their businesses (Weill and Woerner, 2015). Digital business ecosystems are relatively new compared to the long-standing history of general business ecosystems. Manufacturers engaged in DBEs must learn to traverse the elaborate landscape they create and become experts in knowing the relationship between competition and collaboration. In DBEs, there is no room to be weak and unknowing of the landscape compared to competitors if a company wants to survive. A company must maintain its health and sustainability. Strong leaders can drive these factors within a company, and leaders must maintain control and consistency when leading a team in a DBE in manufacturing. For digital platforms specifically, without control, they can become fragmented and, in turn, less useful for developers in manufacturing. Further, excessive discrepancies in a digital platform make it difficult to capture the effectiveness and value of different innovations and additions to the platform over time (Schumacher et al., 2016). Waste of Unnecessary Movement This section will discuss the waste of unnecessary movement. As discussed previously, unnecessary movement can also directly impact the flow of the manufacturing floor layout and the workers who navigate it. This waste can refer to the motions workers go through to move goods from one place to another. This includes reaching, stretching, bending over and picking up goods. which can lead to exhaustion, poor productivity and sometimes even product quality issues. Overall, motion waste can be a health and safety issue and should be addressed and studied in every company (Kumar et al., 2022). In this section, we will discuss risk technologies. One of the biggest challenges to this new shift towards flexible and integrated manufacturing is the safety of workers and the hazards that come with design and human errors. Designating a system that fosters effective communication and risk mitigation is crucial. Advanced risk communication systems (ARCS) help manufacturers implement, nav-

288  Handbook on digital platforms and business ecosystems in manufacturing igate and maintain safety protocols using advanced technology to drive processes compatible with existing work systems. To understand the developmental and design processes of ARCS, it’s important to acknowledge the challenges these systems address (Chu and Zhao, 2021). The first challenge ARCS addresses is a constant presence in the marketing/informational space for targeted audiences in the immediate work environment; information is almost unavoidable. The second is constant salience, where the importance of interfaces does not fade over time. Adaptation is the third challenge ARCS addresses, especially in advanced manufacturing environments, through flexibility to fit in ever-changing work environments and human variation. The last challenge addressed is customization. Traditional risk communication systems are difficult to customize without added costs, while ARCS use advanced technologies and can customize risk communications based on worker profiles (Méndez-Vázquez and Nembhard, 2019). ARCS is creating a smart workplace with smart systems. These systems can monitor users in working environments, utilize and expand databases and user information, and, finally, customize the information presented to users. (Smith-Jackson and Wogalter, 2004). While ARCS may sound well and good for those engaging in a manufacturing environment, often, once operators start trusting these systems, they start over-relying on them. This overdependence on ARCS can lead to the reduction of good training programs and safety monitoring for employees, which could result in more accidents in the workplace. As long as employers are aware of this consequence and ensure proper measures are still implemented regardless of if ARCS are present, risk will be lowered overall (Hipsher and Duffy, 2021).

Figure 18.2

The Waste of Unnecessary Movement

Waste of Defective Products After the waste of inventory comes the waste of defective products. Defects in goods and services can lead to an increase in manufacturing delays, repairs and costs for a company (Sarker et al., 2022).

New work in manufacturing  289

THE FUTURE AND THE USE OF FLEXIBLE BUSINESS STRATEGIES IN MANUFACTURING After an understanding of the eight wastes in manufacturing has been created, it’s important to offer and expand on the modern-day solution companies could use. When the pandemic began in 2020, it forced all companies worldwide to either shut down or completely change how they work, forcing companies to familiarize themselves quickly with flexible business practices and strategies. Researchers argue that this fast-paced change led to an adapted resilience in the manufacturing supply chain industry. Manufacturing supply chain flexibility was described by Upton as ‘the ability to change or react with little penalties in time, effort, cost, or performance’ (Rajesh, 2021). If implemented correctly, these flexible business strategies can enable existing supply chains to rapidly shift when responding to market fluctuations using only their supply of existing resources. Additionally, risks are much easier to manage and mitigate if this concept of flexibility is brought into manufacturing supply chain operations and strategy (Umam and Sommanawat, 2019). A few different frameworks will be discussed within the realm of flexible business strategies that lead to resilience. These include (Rajesh, 2021): ● ● ● ● ●

Flexible supply strategy via multiple suppliers (FSM); Flexible supply strategy via flexible supply contracts (FSC); Flexible process strategy via flexible manufacturing processes (FPM); Flexible product strategy via postponements (FPP); and Flexible pricing strategy via responsive pricing (FRP).

All these frameworks contribute to successfully building manufacturing supply chain resilience. First, we will discuss flexible supply strategy via multiple suppliers and look specifically at upstream supply chains. A company’s upstream supply chain network must be secure to combat unprecedented problems and fluctuating market demands. Single-sourcing raw materials could be a recipe for disaster if unprecedented problems were to occur. Researchers recommend multiple sourcing to avoid supply chain disruptions overall (Mehrjerdi and Shafiee, 2021). The next framework is a flexible supply strategy via flexible supply contracts. To build resilience in this case, companies need to have flexible agreements that address supply periods, delivery schedules and payment options. This way, if suppliers do not fulfill the needs outlined by the company they serve in the contract, the company could be compensated for the suppliers’ mistakes (Rajesh, 2021). After flexible supply contracts, we have the framework of a flexible process strategy via flexible manufacturing processes. For this piece, researchers suggest implementing practices such as total quality management (TQM) or lean manufacturing, as discussed in the previous sections. Implementing flexible manufacturing systems (FMS) can also allow for quick, permissible changes in a company’s supply chain strategies so they can respond promptly to an ever-changing market (Cronin et al., 2019). The next framework is a flexible product strategy via postponements. Postponement is a flexible time strategy companies use to delay manufacturing because they actively wait until all customer information/orders are available. This allows for less product waste and more customization of consumer products and services. The final framework outlined by researchers is a flexible pricing strategy via responsive pricing. To meet an uncertain market’s needs and demands, it is essential to use effective pricing mech-

290  Handbook on digital platforms and business ecosystems in manufacturing anisms through techniques such as price promotions. Determining the proper timing could be based on possible surveys or evaluations of the target market (Rajesh, 2021). If traditional business ecosystems do not change, they will no longer be able to support the vision of a healthy manufacturing business. As discussed, companies can operate as four key business ecosystems: suppliers, omnichannel businesses, ecosystem drivers or modular producers (Weill and Woerner, 2015). There are also three different drivers for these systems: the constant evolution of information and communication technologies, customer needs transformations, and the progress of industry convergence (Spena et al., 2021). Digitalization and a robust digital platform are necessary to keep up with the evolving trends of DBE in manufacturing and play a crucial role in the waste of inventory in lean manufacturing. Overall, the key to avoiding a slow company death is acting fast and efficiently. To prepare for the future, companies must start utilizing the digital capabilities available to them and use them to become more knowledgeable about their end customers. Through these digital capabilities, companies can also begin developing an ‘integrated, multiproduct channel customer experience’ (Weill and Woerner, 2015). It is crucial to amplify the consumer’s wants and needs, and companies must start relying less on gut instincts and more on big data collection from DBEs to begin the processes of evidence-based decision-making. The key takeaways concerning DBEs are as follows: create service-enabled platforms that others can use with ease, become great at building partnerships, and become the first choice in your key market space (Weill and Woerner, 2015). This chapter also discussed lean manufacturing and the eight kinds of waste this system aims to target and eliminate. We touched on all eight kinds of waste and some major takeaways were raised in specific areas, especially the section on the waste of underutilized people. VSM is important to any company looking to understand the simple manufacturing processes of their product from start to end and can alert companies where a loss might occur operationally. A crucial piece of this chapter was the waste of underutilized people. This is where the concept of new work was introduced and how the systems and processes of ‘old work’ can affect productivity and happiness across a whole employee base (Bergmann (2019)). A key takeaway here is for companies to ensure they are valuing their employees’ time and suggestions. Implementing new work practices can hold positive, transformative impacts on how a company works with its partners and employees. As we reflect on digitization, some technologies enable this process, and these technologies are ICT and AMT, which promote more independent, efficient and productive work within teams and departments (Bayo-Moriones et al., 2017). We touched on the theory of constraints for the waste of inefficient manufacturing processes and how it coincides with new work practices. As a reminder, TOC is a management philosophy that focuses on and fixes the weak points within a system to improve overall system performance (Şimşit et al., 2014). Some constraints include equipment, people and policy, which all play a role in the system’s functionality. Implementing TOC practices can aid companies in cutting costs, having efficient project management teams, and reducing manufacturing system glitches overall. Putting the practices of TOC and new work together allows employers to combat issues such as labor shortages, the chronically unemployed and low employee retention (Aroles et al., 2021). Lastly, for the wastes, we touched on the waste of unnecessary movement, how it can contribute to employee bodily harm, and how the advancement of risk technologies dramatically reduces the risk of employee injuries, in turn saving companies time and money. But it is important to not rely too much on these technologies as the sole

New work in manufacturing  291 safety prevention factors and instead use them to aid in workplace safety (Smith-Jackson and Wogalter, 2004). Finally, we wrapped the chapter up with a summary of potential flexible business strategies companies can use in their manufacturing systems. These strategies allow businesses to enable existing supply chains to shift rapidly when responding to market fluctuations using only their supply of existing resources. The subjects of these strategies include multiple suppliers, flexible supply contracts, manufacturing processes, postponement and responsive pricing (Rajesh, 2021). This chapter has given a glimpse into the potential of our current manufacturing/work systems and has shown how much these individual departments intertwine with one another. It is imperative in the ever-changing digital business ecosystem and the manufacturing world for companies to look at change as not a ‘when will they occur’ but rather a ‘how will they occur’ because the time for change is not only in the future but in the present.

FUTURE CONSIDERATIONS FOR NEW WORK ADOPTERS As we move forward in this paradigm shift in how work is valued, created, assessed, implemented and modified, there remain numerous considerations within the parameters of new work ideals. If remote work is to become an option in the job creation toolkit, how are risks mitigated concerning regulatory, safety, health and representation concerns? If flexible scheduling is to become the primary approach for an organization, how do upstream and downstream logistics pivot their respective supply chains of human capital, including labor? Finally, if the principles of ‘lean’ are to be further emphasized at the possible expense of either just-in-time or just-in-case inventory systems, how do global citizens navigate the myriad issues by adopting all attempts at waste elimination without compromising creativity? One such consideration would be the home piece-mail manufacturing model that existed as one of the models of manufacturing in the 1800’s, though implementation could be an interesting yet fascinating exploration. Regardless of looking to the past or the present or even amalgamating both, the future of work in all segments of the global economy is going to look vastly different by 2030 and this is the challenge new work adopters must embrace.

REFERENCES Akdeniz, C. (2016) Theory of Constraints. IntroBooks. https://​www​.everand​.com/​book/​437529923/​ Theory​-of​-Constraints. Aroles, J., de Vaujany, F. X., and Dale, K. (Eds.). (2021). Experiencing the new world of work. Cambridge University Press. Baumann, S. (2022). Introduction to the handbook on digital business ecosystems: strategies, platforms, technologies, governance and societal challenges. Handbook on Digital Business Ecosystems: Strategies, Platforms, Technologies, Governance and Societal Challenges, (1st ed.) 1–9. Edward Elgar. Bayo-Moriones A., Billon M. and Lera-López F. (2017) Are new work practices applied together with ICT and AMT? The International Journal of Human Resource Management, 28:4, 553–80, DOI: 10.1080/09585192.2015.1116453 Bergmann, F. (2019) New Work, New Culture: Work We Want and a Culture That Strengthens US. Winchester, UK: Zero Books. Cantamessa, M., Montagna, F., Altavilla, S., and Casagrande-Seretti, A. (2020). Data-driven design: the new challenges of digitalization on product design and development. Design Science, 6, e27.

292  Handbook on digital platforms and business ecosystems in manufacturing Chen, C. K., Palma, F., and Reyes, L. (2019). Reducing global supply chains’ waste of overproduction by using lean principles: a conceptual approach. International Journal of Quality and Service Sciences, 11(4), 441–54. Chu, Y., and Zhao, C. (2021, July). The world-class manufacturing innovation ecosystem: toward an integrated conceptual framework. In: The 2021 12th International Conference on E-business, Management and Economics (pp. 456–65). Cronin, C., Conway, A., and Walsh, J. (2019). Flexible manufacturing systems using IIoT in the automotive sector. Procedia Manufacturing, 38, 1652–9. Díaz-Reza, J. R., Mendoza-Fong, J. R., Blanco-Fernández, J., Marmolejo-Saucedo, J. A., and García-Alcaraz, J. L. (2019). The role of advanced manufacturing technologies in production process performance: a causal model. Applied sciences, 9(18), 3741. Florén, H., Barth, H., Gullbrand, J., and Holmén, M. (2021). Additive manufacturing technologies and business models – a systematic literature review. Journal of Manufacturing Technology Management, 32(1), 136–55. Hipsher, T. A., and Duffy, V. G. (2021). Integration of internet of things devices in manufacturing workspaces: a systematic literature review. In HCI International 2021-Late Breaking Papers: HCI Applications in Health, Transport, and Industry: 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021 Proceedings 23 (pp. 476–93). Springer International Publishing. Ives, B., Cossick, K., and Adams, D. (2019). Amazon Go: disrupting retail? Journal of Information Technology Teaching Cases, 9(1), 2–12. Kim, Y., Lee, Y., Kong, G., Yun, H., and Chang, S. (2008) A new framework for designing business models in digital ecosystems. ResearchGates, 281–87. Klein, L. L., Vieira, K. M., Alves, A. C., and Pissutti, M. (2023). Demystifying the eighth lean waste: a knowledge waste scale. International Journal of Quality & Reliability Management, 40 (2). Kumar, N., Hasan, S. S., Srivastava, K., Akhtar, R., Yadav, R. K., and Choubey, V. K. (2022). Lean manufacturing techniques and its implementation: A review. Materials Today: Proceedings, 62, 1188–92. Li, D., Chen, Y., and Miao, J. (2022). Does ICT create a new driving force for manufacturing? – Evidence from Chinese manufacturing firms. Telecommunications Policy, 46(1), 102229. Mehrjerdi, Y. Z., and Shafiee, M. (2021). A resilient and sustainable closed-loop supply chain using multiple sourcing and information sharing strategies. Journal of Cleaner Production, 289, 125141. Méndez-Vázquez, Y. M., and Nembhard, D. A. (2019). Worker-cell assignment: The impact of organizational factors on performance in cellular manufacturing systems. Computers & industrial engineering, 127, 1101–14. Mistry, D., and Shah, V. (2020). Application of lean manufacturing tools and techniques: a case study in a manufacturing Industry. International Journal for Research in Applied Science and Engineering Technology, 8(6), 1260–7. Müller, K. (2022). Organizational socialization in the light of digitalization and new ways of work: virtual onboarding/author Kim Müller, BSc. Johannes Kepler University Linz. Nechitaylo, I. A. (2020). The five focusing steps of the theory of constraints: problems of application in strategic analysis. Administrative Consulting, 2020(6): 97–109. Paramawardhani, H., and Amar, K. (2020). Waste identification in production process using lean manufacturing: A case study. Journal of Industrial Engineering and Halal Industries, 1(1), 39–46. Rajesh, R. (2021) Flexible business strategies to enhance resilience in manufacturing supply chains: an empirical study. Journal of Manufacturing Systems, vol. 60, pp.  903–19., https://​doi​.org/​10​.1016/​j​ .jmsy​.2020​.10​.010. Santos, D. M. C., Santos, B. K., and Santos, C. G. (2021) Implementation of a standard work routine using lean manufacturing tools: A case Study. Gestão & Produção, 28(1), https://​doi​.org/​10​.1590/​ 0104​-530X4823​-20. Sarkar, B., Joo, J., Kim, Y., Park, H., and Sarkar, M. (2022). Controlling defective items in a complex multi-phase manufacturing system. RAIRO – Operations Research, 56(2), 871–889. https://​doi​.org/​ 10​.1051/​ro/​2022019 Schumacher, A., Sihn, W., and Erol, S. (2016) Automation, digitization and digitalization and their implications for manufacturing processes. Innovation and Sustainability, 52, 61–166. Sebastian, I. M., Weill, P., and Woerner, S. L. (2020). Driving growth in digital ecosystems. MIT Sloan Management Review, 62(1), 58–62.

New work in manufacturing  293 Şimşit, Z. T., Günay, N. S., and Vayvay Ö. (2014) Theory of Constraints: A Literature Review. Procedia – Social and Behavioral Sciences 150: 930–36. https://​doi​.org/​10​.1016/​j​.sbspro. 2014.09.104. Smith-Jackson, T. L. and Wogalter, M. S. (2004) Potential uses of technology to communicate risk in manufacturing. Human Factors and Ergonomics in Manufacturing, vol. 14, pp. 1–14. Spena, T. R., Tregua, M., and Bifulco, F. (2021). Future internet and digital ecosystems. Digital Transformation in the Cultural Heritage Sector: Challenges to Marketing in the New Digital Era, 17–38. Suuronen, S., Ukko, J., Eskola, R., Semken, R. S., and Rantanen, H. (2022). A systematic literature review for digital business ecosystems in the manufacturing industry: Prerequisites, challenges, and benefits. CIRP Journal of Manufacturing Science and Technology, 37, 414–26. Thoma, G., Weisz, I., and Becker, S. (2020). ‘Frithjof Bergmann.’ Frithjof Bergmann – New Work New Culture. NWNC, December 21, 2020. https://​newwork​-newculture​.dev/​frithjofbergm ann/ [Accessed 15 September 2022]. Tsai, C. H., and Zdravkovic, J. (2020). A survey of roles and responsibilities in digital business ecosystems. In 13th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling (PoEM 2020), Riga, Latvia (virtual), November 25–27, 2020 (pp. 44–53). RWTH Aachen University. Umam, R., and Sommanawat, K. (2019). Strategic flexibility, manufacturing flexibility, and firm performance under the presence of an agile supply chain: A case of strategic management in fashion industry. Polish Journal of Management Studies, 19(2). Valmohammadi, C., and Dadashnejad, A. A. (2021). Value stream mapping implementation: an operational view. International Journal of Productivity and Quality Management, 32(3), 307–26. Wahab A. N., Mukhtar M., and Sulaiman R. (2013) A conceptual model of lean manufacturing dimensions, Procedia Technology, vol. 11, 2013, pp.  1292–8. https://​doi​.org/​10​.1016/​j​.protcy​.2013​.12​.327. Weill, P., and Woerner, S. L. (2015) Thriving in an Increasingly Digital Ecosystem. MITSloan Management Review, 56(5), 27–34. Wu, K., Zheng, M., and Shen, Y. (2020). A generalization of the Theory of Constraints: Choosing the optimal improvement option with consideration of variability and costs. IISE Transactions, 52(3), 276–87.

PART IV INDUSTRY APPLICATIONS AND CASE STUDIES

19. Digital business ecosystems for digital spare parts Sabine Baumann and Marcel Leerhoff

INTRODUCTION Digital platforms like iTunes or Spotify have revolutionized digital business ecosystems by connecting producers and consumers in electronic marketplaces (Zutshi and Grilo, 2019; Kretschmer et al., 2020). Similarly, in German-speaking countries, the B2B platform ‘Wer liefert was’ (German for ‘Who delivers what’) allows SMEs to showcase their goods and services to other companies. The concept of digital business ecosystems has also driven innovation in additive manufacturing (Baumann and Leerhoff, 2022), where platforms like Thingiverse enable consumers to share product designs and create objects at home (Zutshi and Grilo, 2019). Manufacturers can also leverage digital platform ecosystems to provide customers with access to spare parts through additive manufacturing service providers. Compared to traditional manufacturing, additive manufacturing enables localized on-demand production at the point of use (Walter et al., 2004). The concept of ‘digital spare parts’ (DSP) aims to decentralize spare parts manufacturing by digitally storing and transmitting design and manufacturing files. This digitization of formerly physical inventories and reduction of delivery times (Knofius et al., 2016) is made possible through DSP. Digital platforms for trading licenses can facilitate the implementation of DSP and exploit the resource value of information (Wunck and Baumann, 2017). Existing research on DSP has primarily focused on individual supply chain configurations and spare parts design (e.g. Ballardini et al., 2018; Chekurov et al., 2018; González-Varona et al., 2020), lacking a comprehensive understanding of DSP platform ecosystems. Practical applications of DSP are still emerging. This chapter addresses this gap by analyzing the literature on digital business ecosystems in additive manufacturing of spare parts, aiming to develop a framework that captures their scope. Expert interviews have been conducted to evaluate the framework and identify challenges in implementing DSP within the supply chain. The chapter seeks to answer the following research questions: (1) What are the relevant categories that define the scope of digital business ecosystems for additive manufacturing of spare parts? (2) What are the practical challenges associated with implementing digital business ecosystems for additively manufactured spare parts? The remainder of the chapter is organized as follows. After introducing the research questions, we describe the research design, followed by a systematic literature review. The section on digital business ecosystems for DSP presents a condensed framework and explores different supply chain models. The findings from the literature and expert interviews are discussed in detail to capture the scope of platform ecosystems for additive manufacturing of spare parts. The chapter concludes with a summary of the results and addresses the limitations of the study. 295

296  Handbook on digital platforms and business ecosystems in manufacturing

METHODS The research design of this study integrates a systematic literature review and expert interviews to develop a framework with both theoretical and managerial contributions for implementing the DSP concept (see Figure 19.1). The study begins by establishing a theoretical foundation to define and describe the terminology necessary for the systematic literature review. This foundation serves as the basis for selecting case studies and developing an interview guide to structure the semi-structured interviews. The systematic literature review then examines relevant existing literature, focusing on supply chain models and topics related to describing the digital business ecosystem. The empirical section of the study presents case studies that describe models of digital business ecosystems in the context of additive spare parts supply and the practical implementation of the DSP concept. A discussion section compares the results and addresses any deviations observed. Overall, this research design allows for the synthesis of knowledge from the literature review and the insights gained from the case studies and expert interviews, contributing to a comprehensive understanding of implementing the DSP concept.

Figure 19.1

Methodology

To identify relevant literature on digital platform ecosystems for DSP, a systematic literature review was conducted. Nine databases were searched, namely Scopus, ACM Digital Library, IEEE Xplore, Web of Science, EBSCO, Wiley Online Library, ProQuest, Emerald Insight and WISO. These databases were selected based on their suitability and importance for the research question, as previous manual searches had yielded suitable literature from these sources. By searching these databases comprehensively, the review aimed to include a wide range of relevant studies in the field. The search terms for the systematic literature review were derived from the previously identified relevant literature. The terms were then divided into two blocks. The first block focused on the topics of spare parts and additive manufacturing. We searched various combinations of the term ‘spare part’ with terms such as ‘digital’, ‘additive manufacturing’, ‘rapid manufacturing’ and ‘3D printing’. Additionally, terms like ‘rapid repair’ and ‘additive repair’

Digital business ecosystems for digital spare parts  297 Table 19.1 Expert Interviews Interview No.

DSP Supply Chain Model

Position of Expert

Date of Interview

Duration

1

Original equipment

Department Manager Business

14 September 2021

34 minutes

manufacturer

Support

2

Maintenance service

Manager of the Additive

29 September 2021

38 minutes

provider

Manufacturing Business Unit Industrial Engineer in Marketing and 16 September 2021

29 minutes

Development 3

Platform operator

Communication

were included. The term ‘spare part management’ was also combined with ‘additive manufacturing’, ‘rapid manufacturing’ and ‘3D printing’ to further expand the search. The second block pertained to the digital business ecosystem. Terms such as ‘business model’, ‘business environment’, ‘digital ecosystem’, ‘supply network’, ‘distributed manufacturing’, ‘sustainable manufacturing’, ‘supply chain’ and ‘inventory’ were included in this block. Within each block, the terms were linked using the OR operator, while the blocks themselves were linked using the AND operator. This approach ensured a comprehensive search by covering a wide range of relevant keywords related to the research topic. To ensure the relevance and quality of the selected contributions, specific inclusion and exclusion criteria were applied. Contributions were included if they were document-type articles, conference papers or book chapters, and established a connection to the topic of additive manufactured spare parts and (digital) business ecosystems or a related topic such as supply networks or distributed manufacturing. Contributions were excluded if they were not written in English or German, lacked a named author, consisted solely of an abstract or presentation, were not peer-reviewed or focused solely on technical aspects of additively manufactured spare parts. From an initial set of 304 articles, a total of 77 papers remained after applying the inclusion and exclusion criteria. Content analysis of these articles was conducted to derive categories for a framework that captures the scope of platform ecosystems for additive manufacturing of spare parts. To further assess and expand the framework, an interview guide was created, and semi-structured interviews were conducted with experts from original equipment manufacturers (OEMs), maintenance service providers and platform operators. Table 19.1 provides background information on the interview settings. The semi-structured interviews, along with additional materials such as emails and company flyers, provided the basis for presenting various case studies on the use of additive manufacturing processes for supplying spare parts in companies. Empirical data for the case studies was collected through these three semi-structured interviews with industry experts.

FINDINGS OF THE SYSTEMATIC LITERATURE REVIEW This section presents the results of the systematic literature review, organized into five categories that emerged from identifying the central topics: (1) supply chain models and design, (2) strategic challenges and implementation, (3) product identification and design, (4) intellectual property, data transmission and security, (5) social and ecological aspects. These categories

298  Handbook on digital platforms and business ecosystems in manufacturing provide a structured overview of the key findings and themes identified in the reviewed literature. (1) Supply Chain Models and Design In the category focused on the fundamental design of the supply chain for additive manufacturing of spare parts, scholars initially explored the distinction between centralized and decentralized approaches (e.g. Walter et al., 2004; Holmström et al., 2010; Liu et al., 2014; Khajavi et al., 2014). Later contributions delved into scenario analyses of various supply chain configurations, considering different degrees of decentralization and involvement of actors such as OEMs, repair stores and spare parts distributors (Chekurov and Salmi, 2017; Ma et al., 2018). One paper identified outside of the literature review presented a framework with five supply chain configurations (Salmi et al., 2018): OEM-centric, maintenance service provider-centric, platform-centric, additive manufacturing service provider-centric, and end customer-centric. These configurations span from centralized (OEM) to decentralized (end customer) manufacturing approaches. Platforms play a crucial role in implementing decentralized additive manufacturing, as highlighted by various contributions describing them as centers of activity for additive manufactured spare parts, providing end-to-end solutions and digital storage options and acting as gateways to networks of additive manufacturing service providers. The selection of the aforementioned paper was based on its development within a project involving 175 participants from multiple companies, which adds credibility to the five identified supply chain configurations displayed in Figure 19.2 (on the left side). The results from the literature review, divided into five categories (displayed on the right), serve as the foundational aspects of these configurations rather than being tied to a specific configuration. Furthermore, platforms play a crucial role in facilitating the implementation of decentralized additive manufacturing. Numerous contributions highlight platforms as central hubs for activities related to additive manufactured spare parts. These platforms serve as providers of end-to-end (E2E) solutions, offer digital storage options, such as digital warehouses, and act as gateways to networks of diverse additive manufacturing service providers. Examples of these contributions include the works of Hasan and Rennie (2008), Frank et al. (2018), and González-Varona et al., (2020). (2) Strategic Challenges and Implementation Contributions assigned to the second category address strategic challenges and the implementation of additive manufacturing capacities. One aspect focuses on the implementation of additive manufacturing processes, encompassing technology selection, production planning, integration of additive manufacturing capacities within the supply chain and the role of additive manufacturing as an additional or alternative source of spare parts alongside conventional manufacturing processes (e.g. Meisel et al., 2016; Manco et al., 2019; Knofius et al., 2021; Sgarbossa et al., 2021). Furthermore, authors explore the design and optimization of business processes related to additive manufacturing of spare parts. This includes the development of processes for introducing additive manufacturing at various independence levels and the management of print jobs (e.g. Wits et al., 2016; Durão et al., 2016; Durão et al., 2017; Özceylan et al., 2018; Darwish et al., 2020).

Digital business ecosystems for digital spare parts  299

Figure 19.2

Summary of findings

Cost models represent another significant topic area, with authors investigating the profitability of purely digital business models, the economic advantages of additive manufacturing compared to conventional processes and the determination of optimal locations for implement-

300  Handbook on digital platforms and business ecosystems in manufacturing ing additive manufacturing capacities within the supply chain to minimize overall costs (e.g. Ivan and Yin, 2017; de Brito et al., 2019, Totin and Connor, 2019; Cardeal et al., 2021). The contributions in this topic area should be seen as complementary to the rest of the topic areas. For example, one contribution in addition to topic area three deals with the creation of a cost model that can evaluate the cost benefits of redesigning a spare part for additive manufacturing (Minguella-Canela et al., 2018). The final topic area examines the impact of additive manufacturing on other business areas. Contributions in this area explore the influence of additive manufacturing on customer satisfaction and identify barriers to the introduction and implementation of the digital spare parts (DSP) concept (e.g. Chekurov et al., 2018; Muir and Haddud, 2018). These various topic areas provide complementary insights into different aspects of additive manufacturing of spare parts, enhancing our understanding of its strategic implications and practical considerations. (3) Product Identification and Design The third topic area we identified in the additive manufacturing of spare parts focuses on product identification and product design. This entails two main aspects. Firstly, it involves identifying suitable spare parts for additive manufacturing based on considerations of technological feasibility and economic efficiency. Several contributions delve into this area, including the works of Knofius et al. (2016), Chaudhuri et al. (2021) and Marek et al. (2020). Secondly, contributions in this category address product design aspects such as using photogrammetry and reconstruction techniques for spare parts or adapting existing 3D models to optimize them for additive manufacturing processes. Noteworthy contributions in this area include the studies by Bacciaglia et al. (2020) and Montero et al. (2020). (4) Intellectual Property, Data Transmission, and Security The fourth topic area we examined focuses on intellectual property in the context of DSP, data transmission and data security. In terms of licensing law, our literature review yielded only one relevant paper, by Ballardini et al. (2018), which specifically investigates the impact of additive manufacturing processes on the European business and legal framework. The study highlights the need for new regulations to address the handling of DSP or CAD files, as existing patent laws primarily pertain to physical goods rather than their digital representations. Furthermore, a significant number of contributions explore the role of blockchain technology in data transmission and security. Researchers discuss the concept of ‘smart contracts’ as well as various opportunities, challenges, effects and barriers associated with utilizing blockchain in the context of additive manufacturing processes and DSP. Notable contributions in this area include the works of Hasan et al. (2020) and Kurpjuweit et al. (2021). (5) Social and Ecological Aspects The fifth and final topic area in our analysis focuses on the social and ecological aspects related to additive manufacturing of spare parts. Contributions in this category examine sustainability considerations associated with additive manufacturing processes and business models. Researchers investigate aspects such as the extension of product life cycles, reduction of overproduction and minimization of material waste in the context of additive manufactured

Digital business ecosystems for digital spare parts  301 spare parts. Notable contributions in this area comprise the works of Murmura et al. (2021), Cardeal et al. (2020) and Ponis et al. (2021). Additionally, some researchers delve into the broader examination of health and environmental impacts resulting from additive manufacturing processes and materials. Noteworthy studies in this regard include the works of Huang et al. (2013) and Holmström and Gutowski (2017).

SUPPLY CHAIN CONFIGURATIONS FOR DIGITAL SPARE PARTS Based on the insights gained from our interviews with an OEM, a maintenance service provider and a platform operator, we conducted an analysis of their respective supply chain configurations. By augmenting the information obtained from these companies, we developed detailed case studies that showcase real-world examples of additive manufacturing of spare parts. In the following sections, we present these case studies, providing valuable insights into the implementation and operation of digital supply chain configurations in practice. OEM-Centric Supply Chain Model The first case study focuses on a German rail vehicle manufacturer that adopts an OEM-centric supply chain configuration (refer to Figure 19.3). This company serves customers from the rail sector, including state railroads, as well as streetcar and metro operators. The manufacturing processes employed by the company for additive manufacturing of spare parts include fused deposition modeling (FDM), selective laser melting (SLM) and wire arc additive manufacturing (WAAM). In addition to engaging in the spare parts business, the company utilizes additive manufacturing processes for maintenance purposes of rail vehicles. As an OEM, the company is responsible for manufacturing its own spare parts and also third-party spare parts through additive manufacturing, assuming the role of a maintenance service provider in the latter case. In this configuration, the OEM retains the intellectual property and manages the digital inventory of manufacturing files for the spare parts. Apart from additive manufacturing, the OEM is also responsible for quality assurance and post-processing of the spare parts. While the company possesses its own additive manufacturing capacities, it also leverages the manufacturing and reworking capabilities of external service providers. Nonetheless, as an OEM, it holds a central position in the overall supply chain. Maintenance Service Provider-Centric Supply Chain Model The second case study focuses on a maintenance service provider-centric supply chain model (refer to Figure 19.4). This company operates as a maintenance service provider and collaborates with its customers to manufacture prototypes, small series and spare parts on their behalf. Additionally, the company offers consulting services to help customers decide whether conventional or additive manufacturing is the most suitable approach. Its customer base spans various industries, including rail and automotive. The company utilizes selective laser melting (SLM), fused deposition modeling (FDM) and multi-jet fusion (MJF) processes for manufac-

302  Handbook on digital platforms and business ecosystems in manufacturing

Figure 19.3

OEM-centric supply chain model

turing. In cases where the company’s own manufacturing capacities are insufficient, external service providers are engaged. In the maintenance service provider-centric model, the customers typically own the intellectual property rights. If a spare part is newly designed for a customer, the company ensures that it obtains the necessary license. However, if the direct customer does not hold the license for a spare part, the original equipment manufacturer (OEM) of the original part is typically not involved in the process. This is because the maintenance service provider often performs part of the new design and component optimization, resulting in changes to the design or utility model of the spare part. Platform-Centric Supply Chain Model In the platform operator-centric model, the platform operator collaborates with the spare part manufacturer, who provides the design and 3D models and holds the license for the spare part. The manufacturer may also offer the spare parts directly to end customers through its own webshop. The platform operator offers a range of services to both spare part manufacturers and end customers, including digital warehousing, consulting services, end-to-end implementation and quality control for additively manufactured spare parts (refer to Figure 19.5). To fulfill orders, a network of additive manufacturing service providers is employed. These service providers are responsible for on-demand manufacturing, post-processing, quality control and distribution of the additively manufactured spare parts. The platform operator’s

Digital business ecosystems for digital spare parts  303

Figure 19.4 Maintenance service provider-centric supply chain model

Figure 19.5 Platform-centric supply chain model

304  Handbook on digital platforms and business ecosystems in manufacturing objective is to onboard a wide range of service providers into the network, ensuring that when an order is placed, a service provider geographically close to the customer can be selected for production. The introduction and implementation of the DSP concept face practical challenges; which we categorized into technological aspects and the overall ecosystem in our interview guide. In terms of technological challenges, respondents highlighted the lack of 3D models for additively manufactured spare parts, especially for parts with outdated designs that may date back several decades. This necessitates the creation of 3D models, either from technical drawings, if available, or through 3D scanning. This manual effort increases the complexity and cost of manufacturing spare parts additively. Respondents also mentioned the difficulty of identifying suitable spare parts for additive manufacturing, indicating a lack of automation in the process. Furthermore, insufficient materials and manufacturing processes pose additional obstacles to additive manufacturing of spare parts. Within the broader digital business ecosystem, respondents identified a lack of mindset change as a significant challenge. Some actors, including customers, have been hesitant to embrace additive manufacturing processes as a viable alternative or complement to conventional manufacturing methods. This resistance has hindered the implementation of a digital supply chain across the entire ecosystem. Respondents also noted that the additive manufacturing market is perceived as fragmented, with a wide range of technologies, processes and materials available. This fragmentation poses barriers for new entrants seeking to navigate the additive manufacturing ecosystem. Platform operators play a crucial role in driving digitization and ensuring compatibility for data exchange on DSP (digital spare parts). Moreover, protocols for print job management are essential for quality assurance and liability issues. Addressing these challenges requires coordinated efforts from various stakeholders, including legislators, to resolve liability concerns related to additively manufactured parts and the cross-border distribution of data. Figure 19.6 provides a simplified visualization of the relationships between actors in the three supply chain models. The formatting of the boxes and dashes represents the relationships specific to each model. This visual representation helps to illustrate the distinct characteristics and connections within each supply chain configuration.

DISCUSSION This chapter examined the application of the DSP (digital spare part) concept in additive manufacturing of spare parts. The DSP concept involves digitally transmitting the required production information and using it to additively manufacture the spare part. Research Question 1 The systematic literature review revealed five main categories that emerged from the articles related to the research question. These categories include supply chain models and design, strategic challenges and implementation, product identification and design, intellectual property, data transmission and security and social and ecological aspects. The literature primarily focuses on the implementation of additive manufacturing capacities and the associated strategic challenges. It provides decision-making support for process selec-

Digital business ecosystems for digital spare parts  305

Figure 19.6

Connections between the supply chain models

tion, cost modeling for comparing different manufacturing methods and guidance for designing efficient processes. The product identification and design category addresses the identification of suitable spare parts for additive manufacturing and explores techniques such as decision matrices and automated identification software. Intellectual property, data transmission and security are discussed in the context of using blockchain technology to secure print files and enable traceability. The impact of additive manufacturing on intellectual property and licensing law is also explored. The social and ecological aspects category investigates sustainability, CO2 emissions and the development of sustainable processes and business models. These topics are also evident in the case studies, particularly in terms of intellectual property protection, data security, product identification and design and social and ecological considerations. Interestingly, these areas were not heavily emphasized in the systematic literature review but emerged as significant challenges during the interviews. While digital platforms represent only one type of supply chain configuration, the interviews and the practical example of a platform operator demonstrate that the challenges identified across the categories are applicable regardless of the specific configuration. Respondents in the interviews often mentioned the challenges identified in the literature, indicating their relevance in practical implementation. Overall, these findings confirm the importance of the identified categories in defining the scope of digital platform ecosystems and provide a foundation for further development. Research Question 2 The practical challenges of introducing digital business ecosystems and the DSP concept go beyond the technological hurdles identified earlier. One significant challenge is the need for a mindset shift among stakeholders involved in the ecosystem. Customers, in particular,

306  Handbook on digital platforms and business ecosystems in manufacturing may be hesitant to embrace additive manufacturing as a viable alternative to conventional processes. Convincing them to join the ecosystem and adopt additive manufacturing can be difficult. To address these challenges, platform operators are taking steps to align their service offerings. They are expanding their core business beyond additive spare parts manufacturing or the storage and transfer of DSP. This expansion includes the provision of consulting services and end-to-end (E2E) implementation support. By offering these additional services, platform operators aim to counteract the obstacles associated with mindset shift and facilitate the adoption of additive manufacturing within the ecosystem. Overall, the challenges related to mindset shift and customer acceptance represent important considerations for the successful implementation of digital ecosystems and the DSP concept. Platform operators are actively adapting their services to address these challenges and promote the adoption of additive manufacturing processes. Implications for Ongoing Supply Chain Disruptions The DSP concept and additive manufacturing of spare parts have the potential to address current supply chain problems and disruptions in several ways. One significant advantage is the ability to produce required parts locally, independent of transnational shipping blockades or other logistical challenges. This is achieved by digitally transferring the manufacturing files, allowing additive manufacturing to take place at or near the point of use. The COVID-19 pandemic serves as a notable example of how additive manufacturing capabilities were utilized to meet sudden demands for personal protective equipment. Private individuals and other entities with additive manufacturing capabilities were able to contribute to the production of protective equipment, ensuring that production was as local as possible to the point of use. This approach helped overcome the manufacturing and delivery difficulties faced by conventional manufacturing processes during the crisis. However, it is important to critically note that additive manufacturing is not immune to supply difficulties. Although the manufacturing files can be digitally transferred, the availability of materials, machines and spare parts required for additive manufacturing can still be affected by supply chain disruptions. Therefore, while additive manufacturing and the DSP concept offer advantages in terms of local production and flexibility, they are not without their own challenges related to material and equipment availability.

CONCLUSION In conclusion, this study has provided insights into the development and current state of digital business ecosystems for additive manufacturing of spare parts. Through a systematic literature review and expert interviews, five central categories were identified: supply chain models and design, strategic challenges and implementation, product identification and design, intellectual property, data transmission, and security and social and ecological aspects. These categories capture the key aspects of the topic and provide a framework for future research and practical implementation. One key finding from the interviews is the fragmentation of the additive manufacturing market, which poses a significant barrier to entry for new market actors. This fragmenta-

Digital business ecosystems for digital spare parts  307 tion can be attributed to the variety of technologies, manufacturing processes and materials available, making it challenging for new players to navigate and establish themselves in the ecosystem. Another observation is that many manufacturers are reluctant to facilitate direct interactions between customers and additive manufacturing service providers. When such interactions occur, they are often limited in scope. This lack of interaction hinders innovation and the development of the ecosystem as it restricts the flow of information, collaboration and feedback between customers and service providers. To overcome these challenges, it is crucial for stakeholders in the additive manufacturing ecosystem to foster closer collaboration and communication among all actors involved. This can help drive innovation, create more integrated and efficient supply chains and enhance the overall development and adoption of additive manufacturing for spare parts. However, there are limitations to this study. The number of articles on certain topics, such as licensing law or data transmission, was relatively low, indicating potential gaps in the literature search strategy. Additionally, the sample size of the expert interviews was small, limiting the diversity of perspectives and potentially missing out on important insights from different supply chain configurations and business ecosystems. Despite these limitations, the findings of this study can guide future research and provide valuable insights for practitioners looking to conceptualize and implement platform ecosystems for additive manufacturing of spare parts. The identified challenges, such as the need for partnerships and overcoming skepticism, highlight areas for improvement and innovation in the field.

REFERENCES Bacciaglia, A., Ceruti, A., and Liverani, A. (2020). Photogrammetry and additive manufacturing based methodology for decentralized spare part production in automotive industry, in: Ahram, Tareq et al. (ed.). Intelligent Human Systems Integration 2020, Cham: Springer Nature, 796–802. Ballardini, R. M., Ituarte, I. F., and Pei, E. (2018). Printing spare parts through additive manufacturing: legal and digital business challenges. Journal of Manufacturing Technology Management, 29(6), 958–82. Baumann, S. and Leerhoff, M. (2022). Networks, platforms, and digital business ecosystems: mapping the development of a field, in: Baumann, S. (Ed.) (2022) Digital Business Ecosystems: Handbook on Digital Business Ecosystems: Strategies, Platforms, Technologies, Governance and Societal Challenges, Edward Elgar Publishing, Cheltenham (UK), 11–24. Cardeal, G., Höse, K., Ribeiro, I., and Götze, U. (2020). Sustainable business models – canvas for sustainability, evaluation method, and their application to additive manufacturing in aircraft maintenance. Sustainability, 12(21), 1–22. Cardeal, G., Sequeira, D., Mendonca, J., Leite, M., and Ines, R. (2021). Additive manufacturing in the process industry: a process-based cost model to study life cycle cost and the viability of additive manufacturing spare parts, in: Procedia CIRP 98, 211–16. Chaudhuri, A., Gerlich, H. A., Jayaram, J., Ghadge, A., Shack, J., Brix, H., Hoffbeck, L. H., and Ulriksen, N. (2021). Selecting spare parts suitable for additive manufacturing: a design science approach. Production Planning and Control, 32(8), 670–87. Chekurov, S. and Salmi, M. (2017). Additive manufacturing in offsite repair of consumer electronics. Physics Procedia, 89, 23–30. Chekurov, S., Metsä-Kortelainen, S., Salmi, M., Roda, I., and Jussila, A. (2018). The perceived value of additively manufactured digital spare parts in industry: an empirical investigation, in: International Journal of Production Economics, 205, 87–97.

308  Handbook on digital platforms and business ecosystems in manufacturing Darwish, L. R., Farag, M. M., and El-Wakad, M. T. (2020). Towards reinforcing healthcare 4.0: A green real-time IIoT scheduling and nesting architecture for COVID-19 large-scale 3D printing tasks. IEEE Access, 8, 213916–27. de Brito, F. M., da Cruz Junior, G., Frazzon, E. M., Basto, J. P., and Alcala, S. G. S. (2019). An optimization model for the design of additive manufacturing supply chains, in: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 881–5. Durão, L. F. C. S., Christ, A., Anderl, R., Schützer, K., and Zancul, E. (2016). Distributed manufacturing of spare parts based on additive manufacturing: use cases and technical aspects. Procedia CIRP 57, 704–9. Durão, L. F. C. S., Christ, A., Zancul, E., Anderl, R., and Schützer, K. (2017). Additive manufacturing scenarios for distributed production of spare parts. International Journal of Advanced Manufacturing Technology, 93, 869-880. Frank, J., Jussen, P./Holst, L., and Meyring, T. (2018). Scenarios for the development of platform-based networks for additive spare part production, in: IFIP International Conference on Advances in Production Management Systems (APMS), 213–21. González-Varona, J. M., Poza, D., Acebes, F., Villafáñez, F., Pajares, J., and Lopez-Paredes, A. (2020). New business models for sustainable spare parts logistics: a case study. Sustainability, 12, 3071, 1–16. Hasan, H. R., Salah, K., Jayaraman, R., Ahmad, R. W., Yaqoob, I., and Omar, M. (2020). Blockchain-based solution for the traceability of spare parts in manufacturing. IEEE Access, 8, 100308–22. Hasan, S. and Rennie, A. (2008). The application of rapid manufacturing technologies in the spare parts industry, in: Nineteenth Annual International Solid Freeform Fabrication (SFF) Symposium, 584–90. Holmström, J., Partanen, J., Tuomi, J., and Walter, M. (2010). Rapid manufacturing in the spare parts supply chain: alternative approaches to capacity deployment. Journal of Manufacturing Technology Management, 21(6), 687–97. Holmström, J. and Gutowski, T. (2017). Additive manufacturing in operations and supply chain management: no sustainability benefit or virtuous knock-on opportunities? Journal of Ecology, 21, S1, S21–S24. Huang, S. H., Liu, P., Mokasdar, A., and Hou, L. (2013). Additive manufacturing and its societal impact: a literature review. The International Journal of Advanced Manufacturing Technology, 67, 5–8, 1191–203. Ivan, S. and Yin, Y. (2017). Additive manufacturing impact for supply chain - Two cases, in: Proceedings of the 2017 IEEE IEEM, 450-454. Khajavi, S. H., Partanen, J., and Holmström, J. (2014). Additive manufacturing in the spare parts supply chain: a case study of the F-18E/f. Computers in Industry, 65(1), 50–63. Knofius, N., Heijden, M. van der, and Zijm, H. (2016). Selecting parts for additive manufacturing in service logistics. Journal of Manufacturing Technology Management, 27(7), 915–31. Knofius, N., Heijden, M. van der, Sleptchenko, A., and Zijm, H. (2021). Improving effectiveness of spare parts supply by additive manufacturing as dual sourcing option. OR Spectrum, 43(1), 189–221. Kurpjuweit, S., Schmidt, C. G., Klöckner, M., and Wagner, S. M. (2021). Blockchain in additive manufacturing and its impact on supply chains. Journal of Business Logistics, 42(1), 46–70. Kretschmer, T., Leiponen, A., Schilling, M., and Vasudeva, G. (2020)., Platform ecosystems as meta-organizations: Implications for platform strategies, Strategic Management Journal, 1–20. Liu, P., Huang, S. H., Mokasdar, A., Zhou, H., and Hou, L. (2014). The impact of additive manufacturing in the aircraft spare parts supply chain: supply chain operation reference (scor) model based analysis. Production Planning and Control, 25, 13–14, 1169–81. Ma, S., Tan, M. K. J., and Chen, S. (2018). Supply chain perspective on additive manufacturing, in: Proceedings of the 3rd International Conference on Progress in Additive Manufacturing (Pro-AM 2018), 353–7. Manco, P., Macchiaroli, R., Maresca, P., and Fera, M. (2019). The additive manufacturing operations management maturity: a closed or an open issue? Procedia Manufacturing, 41, 98–105. Marek, S., Pause, D., and Stich, V. (2020). Software tool for the selection of 3D print service providers in the context of spare parts logistics, in: 2020 IEEE Technology and Engineering Management Conference, TEMSCON 2020, 161863, 1–6.

Digital business ecosystems for digital spare parts  309 Meisel, N. A., Williams, C. B., Ellis, K. P.; and Taylor, D. (2016). Decision support for additive manufacturing deployment in remote or austere environments. Journal of Manufacturing Technology Management, 27(7), 898–914. Minguella-Canela, J., Planas, S. M., Ayats, J. R. G., and De los Santos-López, M. A. (2018). Assessment of the potential economic impact of the use of AM technologies in the cost levels of manufacturing and stocking of spare part products. Materials, 11(8), 1–26. Montero, J., Weber, S., Bleckmann, M., and Paetzold, K. (2020). A methodology for the decentralised design and production of additive manufactured spare parts. Production & Manufacturing Research, 8(1), 313–34. Muir, M. and Haddud, A. (2018). Additive manufacturing in the mechanical engineering and medical industries spare parts supply chain. Journal of Manufacturing Technology Management, 29(2), 372–97. Murmura, F., Bravi, L., and Santos, G. (2021). Sustainable process and product innovation in the eyewear sector: the role of Industry 4.0 enabling technologies. Sustainability, 13(1), 1–16. Özceylan, E., Cetinkaya, C., Demirel, N., and Sabirlioglu, O. (2018). Impacts of additive manufacturing on supply chain flow: a simulation approach in healthcare industry. Logistics, 2(1), 1–20. Ponis, S., Aretoulaki, E., Maroutas, T. N., Plakas, G., and Dimigiorgi, K. (2021). A systematic literature review on additive manufacturing in the context of circular economy. Sustainability, 13, 1–28. Salmi, M., Partanen, J., Tuomi, J., Chekurov, S., Björkstrand, R., Huotilainen, E., Kukko, K., Kretzschmar, N., Akmal, J., Jalava, K., Koivisto, S., Vartiainen, M., Metsä-Kortelainen, S., Puukko, P., Jussila, A., Riipinen, T., Reijonen, J., Tanner, H., and Mikkola, M. (2018). Digital spare parts, Aalto University, Technical Report. Online accessible: https://​aaltodoc​.aalto​.fi/​handle/​123456789/​30189 [last access: 01 April 22]. Sgarbossa, F., Peron, M., Lolli, F., and Balugani, E. (2021). Conventional or additive manufacturing for spare parts management: an extensive comparison for Poisson demand. International Journal of Production Economics, 233, 107993, 1–16. Totin, A. N. and Connor, B. P. (2019). Evaluating business models enabling organic additive manufacturing for maintenance and sustainment. Defense Acquisition Research Journal, 26(4), 380–417. Walter, M., Holmström, J., and Yrjölä, H. (2004). Rapid manufacturing and its impact on supply chain management, in: Logistics Research Network Annual Conference, 2004, 1–12. Wits, W. W., García, J. R. R., and Becker, J. M. J. (2016). How additive manufacturing enables more sustainable end-user maintenance, repair and overhaul (MRO) strategies, in: Procedia CIRP 40, 2016, 693–8. Wunck, C. and Baumann, S. (2017). Towards a Process Reference Model for the Information Value Chain in IoT Applications, in: Proceedings of the 2017 IEEE European Technology and Engineering Management Summit (E-TEMS) ‘Digital Transformation for Advanced Manufacturing – Managing Technological Challenges’, Munich (Germany), 1–6. IEEE Xplore. Zutshi, A. and Grilo, A. (2019). The emergence of digital platforms: a conceptual platform architecture and impact on industrial engineering, Computers & Industrial Engineering, 136, 546–55.

20. Space digital platforms: empirical evidence from a case study Daniele Binci, Andrea Appolloni and Wenjuan Cheng

INTRODUCTION The space sector is undergoing a radical transition (Vidmar et al., 2020) in terms of industrial structure, competition forces, innovation management, market demand and public-private relationships (Moranta and Donati, 2020). Also known as ‘Space 4.0’, due to the strong interconnection with Industry 4.0 and digital transformation (DT), the sector is opening new trends in the entire space economy value chain and, especially, within the Earth observation (EO) segment that has become relevant for many industrial sectors (i.e. transport, assurance and the agricultural sector) including environmental practices (i.e. Agenda 2030). EO is the practice of collecting data on the Earth by using remote sensing, with the goal of improving organizational effectiveness and efficiency including environmental sustainability. It is enabling new service-oriented business models (Johannsson et al., 2015) in which digital platforms play a key role. However, management research has barely explored digital platforms within the EO downstream sector due to the newness and the complexity of the topic. The existing studies are mainly focused on new business models enabled by the space economy value chain (Aloini et al., 2021), but show a substantial gap in the understanding of the space digital platforms’ peculiarities and dynamics. Even though digital platforms have been analyzed mainly as separated fields of research, both from a technical perspective more focused on the technological components and from an economic perspective more focused on the business model, recent literature has started to analyze a more integrated approach that considers a hybrid perspective (Rolland et al., 2018; Bonina et al., 2021; Floetgen et al., 2021; Baumann and Leerhoff, 2022). In line with such literature, this chapter combines the two perspectives into a unique framework through a sociotechnical approach, with the aim of providing a more comprehensive understanding of the digital platform phenomenon by overcoming the limits related to the separate views. Accordingly, we adopted a qualitative method based on both a theory-driven approach together with an empirical case study approach. Therefore, our research question, which is exploratory in nature (Blaikie and Priest, 2019), moves around the specificities of the sociotechnical perspective with the aim of building a comprehensive framework to explore the input, processes and output (IPO) of digital platforms in the downstream sector, and is as follows: What are the main sociotechnical variables of the digital platform?; and particularly: In what ways are they combined into an input, processes, and output model? After a description of the research background and the main approaches in the platforms’ literature (section two), we focus on the sociotechnical approach as a comprehensive approach for platform understanding (section three). Then we build a case study, particularly on the 310

Space digital platforms: empirical evidence  311 ‘downstream’ sector, based on a real, downstream space agency, which will be used to analyze the digital platforms’ features of the downstream (section four). Finally, we describe the conclusions and future direction (section five).

DIGITAL PLATFORMS: THE RESEARCH BACKGROUND Research on digital platforms has grown over recent years (McIntyre and Srinivasan, 2017) due to the pervasiveness of digital transformation, becoming increasingly relevant for scholars and managers (Abbate et al., 2019). Platforms are marketplaces that radically change the way in which the digital business ecosystem, meaning the network of organizations such as manufacturers, suppliers, distributors, customers, competitors and government agencies (Baumann, 2022), create, deliver and appropriate value. Platforms make market transactions effective and efficient by allowing the matching of supply side complementors, third-party collaborations and applications developers with the demand side of end-users’ and customer groups’ needs (Hein et al., 2020). Digital platforms‘ literature has focused mainly on two approaches, classified as technical and economic perspectives, which have been considered, predominantly, as separate research fields (Bonina et al., 2021). The technical perspective conceptualizes the idea of the digital platforms mainly from the technological side. Accordingly, platforms are ‘the extensible codebase of a software-based system that provides core functionality shared by the modules (software subsystem that connects to the platform to add functionality to it) that interoperate with it and the interfaces through which they interoperate’ (Tiwana et al., 2010: p. 675). The level of analysis is the technical design, the modular architecture, the arrangements, and the role of technical components as the devices, content, services and networks (Tiwanaet al., 2010; Yoo et al., 2010; Rolland et al., 2018). Platforms are conceptualized as technological components, complements and interfaces that bring out a specific valuable outcome (Rolland et al., 2018; Blaschke et al., 2019; Hein et al., 2019), where: 1. Components: the platforms’ software; 2. Complements: the apps or the add-ons that make the platform more usable and attractive; 3. Interfaces: the application program interfaces (APIs) and software development kits (SDKs) that allow the interoperability between components and complements. Within this perspective, platforms are investigated to understand and design consistent digital architectures and applications including the tasks that allow platforms to achieve the expected functions, goals and requirements of different stakeholders (Kapoor et al., 2021). However, the assumption that platforms are considered as stable technologies allows scholars to overlook their inherent complexity and dynamicity, which includes the need for continuous changing requirements due to stakeholders’ evolving needs. As such, the technical perspective does not account for how the digital platforms evolve, with limits on the understanding of the crucial characteristics that, besides the technical architectures and systems, also involve organizational processes (De Reuver et al., 2018) and strategies (Boudreau, 2010). The economic perspective has mainly examined platforms regarding the business models and how they have emerged and evolved over time (Bonina et al., 2021). Accordingly, the focus is on the role of multisided markets and the interaction between consumers and providers

312  Handbook on digital platforms and business ecosystems in manufacturing (De Reuver et al., 2018; Halckenhaeusser et al., 2020). Platforms are disruptive innovations of traditional markets that facilitate efficient interactions among the stakeholders (Koh and Fichman, 2014; Song et al., 2018). Platforms basically satisfy users’ needs by allowing effective economic transactions through the network effects exploitation among the multisided markets (Parker et al., 2016). The simplified matchmaking process (Wenbo Guo et al., 2021) usually lead to costs reduction for the platform stakeholders (as owners, final users and complementors) (Heimburg and Wiesche, 2022). The economic perspective, despite the vital contribution of analyzing the platforms’ business models and explaining how they create value through underlying strategies and best practices, mainly underscores the relationships within the intertwined technologies; the development of software applications and modules is, basically, the core component for platform effectiveness (Rolland et al., 2018). Despite existing research having concentrated on the separate view (Tiwana et al., 2010), recent literature has started to analyze a hybrid perspective (Rolland et al., 2018; Bonina et al., 2021; Floetgen et al., 2021; Grisold et al., 2022), also called the sociotechnical model. The sociotechnical perspective stems from the idea that digital platform research should analyze the all-encompassing phenomenon of platforms and how they operate in reality, within the transaction and innovation process (Blaschke et al., 2019), by considering the complex actor-technologies interactions and interdependencies (Kapoor et al., 2021) of the social (platform stakeholders) and the technical (technologies and processes) systems. The focus is on the recursive relationship, reciprocal influences, co-interaction and coevolution of technical infrastructure, tasks, IT artifacts and social variables (Kapoor et al., 2021), their processes, and actors’ needs (including the network effects). Studies that have analyzed the main dimensions of the sociotechnical approach connect managerial studies with information systems literature by describing the relations between digital platforms’ owners and processes (Hallerstede, 2013; Kapoor et al., 2021) and how such interrelations create value, which also include governance, network orchestration, incentives and competitive strategies (Kapoor et al., 2021). However, to the best of our knowledge, there is a lack evidence in terms of how the technical and social variables of the digital platform are connected, from their inputs to their outputs, and how such interrelations run. To pursue our aim, we analyze both the sociotechnical perspective and the sociotechnical variables of the digital platforms to understand in what different ways the platform components are related to each other. We use an input–process–output (IPO) approach (Kozlowski and Ilgen, 2006; Kohli and Melville, 2019) for steering the mechanism of the digital platform in terms of the social as well as technical variables, where: (1) the inputs are the identification of the triggers, also enabled by the digital technology; (2) the processes can be considered as activities and technologies available for exploiting digital platforms; and (3) the outputs consist of the expected results of its adoption in terms of users’ value-appropriation.

Space digital platforms: empirical evidence  313

DIGITAL PLATFORMS: A SOCIOTECHNICAL PERSPECTIVE The Platform Input: Network Effects In an IPO model, the economic perspective on digital platforms is relevant as it highlights how digital platforms create value (both with transaction and innovation processes) through the concept of network effects, which relies on the critical mass of the demand and supply side of the platform that needs to be matched and satisfied. Accordingly, platform value and use depend on the number of active users (the critical mass) that generate the network effects (Von Briel and Davidsson, 2019). Network effects, which refer to the increase in the platform value due to the increasing number of platform stakeholders (Ozman, 2011), enable the platforms to attract, retain and allow consumers, suppliers and the ecosystem partners their interactions and transactions (Cusumano et al., 2019). They generate the platform demand-side scale economies according to Metcalfe’s law (Metcalfe, 2013) by enabling the matchmaking process between the searching and finding activities, thus reducing the research and transaction costs of products or services for the users. In the platform business model, the platform value depends on the two different platform sides, which are interdependent. In fact, both users and complementors contribute to network effects (Parker et al., 2016). They could be direct, when an increasing number in the user groups directly increase the value of the platform due to an increase in its use, or indirect, when the increase in the use of the platform from the user-side increases the value of the platform from the producer-side (Parker et al., 2016). For instance, on software applications platforms, a greater amount of application providers will expand the availability of a wide variety of applications, which, in turn, will attract an increasing number of users to the platform due to the huge variety of applications. Again, the presence of more users will attract more applications developers due to a better opportunity to sell their applications, which, in turn, will attract more users (Chou and Shy, 1990; Church and Gandal, 1992; Hagiu, 2009). When these inputs of platforms are taken together, the basis of value creation can be identified as facilitating the exchange of services and information between different parties of a multisided market. However, network effects could also have negative impacts for the platform business model, making the platform less valuable and attractive as more people join in (Thies et al., 2018). In fact, in the transaction processes, the lower search costs of information associated to a product or a service depend on the proper ratio between the number of users and providers (Acs et al., 2021). Too many users and/or providers can cause negative network effects that can result in a drop-off of quality or in an increase of the platform costs. More providers usually increase the value for the users, since the latter have more information, options and choice. At the same time, large-scale information associated to a product or a service needs to be continuously monitored and evaluated in terms of consistency and quality; as in the case of the user-generated content (i.e. users’ feedback), which may require the platform owner having more skilled people for quality control, and therefore an increase in the costs. This is due to information quality evaluation and monitoring through which users will decide on their purchase. Nevertheless, the huge computing power and AI automatically reduce the impact of low-quality user-generated content by targeting the most consistent and crucial information, benefits, features and user-friendly automated search results, making the search costs lower and more effective (Acs et al., 2021). For instance, predictive algorithms can recognize valua-

314  Handbook on digital platforms and business ecosystems in manufacturing ble information and arrange it to attract information-seeking users (as in the case of the users’ searching process); the search combination of information and the offering content attributes can automatically identify and offer customer-specific needs (Lo and Fang, 2018; Lee et al., 2021). Although the network effects are relevant for digital platform running and understanding, alone they are not sufficient to describe the platform dynamics. In fact, network effects are only the inputs for the value creation process; the critical mass also should be able to interact with the underlying platform processes (i.e. inbound and outbound open innovation) and technologies (i.e. platforms’ interfaces and applications). Accordingly, network effects enable and are enabled by the technical aspects of digital technologies and open innovation processes. The Platform Processes: Digital Technologies and Open Innovation Network effects should be conceived as more than a social issue. They are the triggers of the intertwined technological and processual issues of the digital platforms (Figure 20.1a), which, at the same time, rely on a high degree of behavioral complexity, like the collaboration (on value creation) and the competition (on value appropriation) dynamics within and between the critical mass of users and complementors (Brunswicker and Schecter, 2019; Cennamo et al., 2020). The platform’s input (network effects) is inherently connected to the technical aspects of the platform processes. The mass of users should be able to interact together by producing direct or indirect effects due to the platform’s processes as well as its technologies. Studies have highlighted how the underlying processes of digital platforms, enabled by the advanced technologies, allow new ways to compete and collaborate with external users and partners to acquire and develop new outputs as ideas, technologies and knowledge (Hossain and Lassen, 2017). Accordingly, platforms are the marketplace in which final users and contributors create value through the open innovation processes (Cavallo et al., 2022), which, in turn, are dependent on the advanced digital technologies. In fact, digital platforms basically rely on a set of mechanisms and processes; the technical-organizational variables that, in turn, are related to the social variables and enable them to reach the platform outcomes. Such mechanisms rely on the technical components, functionalities and modules of the software subsystem, which allow users and complementors to interoperate with the platform processes (Tiwana et al., 2010). The digital components, complements and interfaces are enacted by the critical mass for producing a specific outcome. 1. Digital technologies: Cloud computing, artificial intelligence, big data, IoT, and 5G constitute the backbone of digital platforms. Digital technologies facilitate new business models’ adoption and digital solutions’ offerings (Aloini et al., 2021). In fact, technologies digitize and integrate the platform business processes by increasing automation through the adoption of smart components and informed data to produce more efficient and effective digital services. The adoption of 5G, IoT, big data, and more generally ‘smart’ objects (machines and products), fosters platform business models, impacting on the overall value chain (Fonseca, 2018). Particularly relevant is blockchain technology, which has different applications, especially for smart contracts. Blockchain automates the actions that would otherwise be completed by the parties in the agreement. The contract’s rules are implemented automatically

Space digital platforms: empirical evidence  315 without human manipulation or intervention from a third-party, allowing each side of a transaction to complete its part (Pranto et al., 2021), removing the need for both parties to trust each other. Moreover, digital technologies enable platform development through the interoperability between physical and cyber systems, decentralization, real-time data analytics, service orientation and modularity. Therefore, implementation platforms facilitate the enterprise systems integration and collaboration across the value chain, self-adaptation of production systems and agile responses, lowering collaboration barriers (Figure 20.1b). As illustrated in Figure 20.1, there is a reciprocal interaction between the social elements of the critical mass of platform users (and contributors) and the technical variables of the digital technologies and open innovation-oriented processes, through which users and developers are able to interact and create value within the platform. 2. Open innovation-oriented processes: These processes are the core module, the platform itself, as well as the peripheral modules as extendable applications based on the open innovation (OI) processes that favor interaction matching and connection of customer groups, third-party collaborations and developer communities to enable them to create value and, consequently, to quickly develop applications and new business models (Figure 20.1b). Unlike traditional innovation processes, delineated by a close and linear approach, vertical integration and defensive strategies, related to a strong focus on internal R&D (Herzog, 2011), the sociotechnical approach of the digital platform is very closed to the OI paradigm, in which users can and should use the network effects of external as well as internal ideas, and internal and external paths to market through circular, open and collaborative strategies within the entire ecosystem (Teece, 2006; Reichstein and Salter, 2006). Such inflows and outflows of knowledge (Chesbrough, 2006) basically accelerate the internal innovation and expand the markets for external use of innovation, respectively. Open innovation can be summarized into two main approaches. The first is the ‘outbound’; an inside-out process that triggers the opening-up of the innovation to external knowledge exploitation (Mortara and Minshall, 2011) that is transferred to other companies and business processes, such as out-licensing. The other, which is also the dominant practice, is the ‘inbound’; the use of resources that come from the ecosystem to create value-added offers and accelerate and empower organizations entering the markets. The main ways in which inbound open innovation processes are implemented are user innovation, strategic alliances with technology partners and the integration of suppliers in product development. Companies increasingly use the inbound approach for leveraging external ideas and resources to create value-added offers and penetrate new markets by maximizing the benefits of collaboration and, at the same time, minimizing the conflicts of value appropriation. From the sociotechnical perspective, open innovation processes and digital technologies interconnected to input are an important carrier for searching external knowledge (Hossain and Lassen, 2017) and, therefore, for activating new combinations of knowledge and creating novel and useful solutions to innovation problems (Abbate et al., 2019). The Platform Output: Products and Rewards Despite recent research on digital platforms having predominantly aimed to explain how the stakeholders (users, developers and owners) create value (with mechanisms for transaction

316  Handbook on digital platforms and business ecosystems in manufacturing and innovation), the question of how the value is captured by the platform stakeholders in terms of material and nonmaterial exchanges (Gandia and Parmentier, 2017) remains mostly unanswered (Helfat and Raubitschek, 2018). Material exchanges can be divided into monetary and nonmonetary rewards. Monetary exchanges are the material rewards, which are the direct payments the developers receive for the product or service exchanged (in terms of money and royalties) and transactions fees that the platform owners receive. Nonmonetary exchanges are the material exchanges that come from inbound processes; these comprise ideas, prototypes and licenses that companies take from the market and assimilate into their business models, as well as the outbound processes through which companies bring the product or service (but also intermediate output as ideas, prototypes and licenses) to the market. Immaterial exchanges are nonmonetary rewards that users receive from participation with the platform. Passion is an important nonmonetary reward, associated with an intrinsic positive aspect of a task, for both the opportunities it provides to socialize with others and because it makes contributors feel they are participating in an important cause. Peer recognition and self-realization are other important immaterial incentives. For instance, the programmers in open-source software communities are motivated by the desire to be recognized by peers for their innovative contributions (Malone et al., 2009) and such recognition leads to self-realization. The final users capture values through the match-making process and by finding and buying the outputs they are searching for (with a fee charged for the platform associated with the transaction). Platform contributors, on the other hand, capture values as they are incentivized to produce outputs through monetary (remunerations that contributors received for the outputs exchanged in the platform) (Kapoor et al., 2021) and nonmonetary (community feedback, reputation among peers, or self-realization) incentives. The sociotechnical approach helps in understanding the main output and ways that platform stakeholders capture value by looking at the output as well as at the interconnected technical variables. In fact, in an IPO model, the exchange processes of the platform outputs should be conceived as more than a social issue. They are the consequences of the intertwined technological and processual issues of the digital platforms (Figure 20.1c), which depend both on the social aspect of the platform stakeholders as well as on the technical aspects of the open innovation processes (Table 20.1). Such processes are adaptive and allow platform stakeholders to coevolve together, both with the platform development (functionalities and technologies) and with the platform output by defining its degrees of elaboration, which varies from ideas and concepts to prototypes and finished solutions (that could be bought or sold) (Boudreau, 2012), such as product and services or combined knowledge that is reused (as generativity power of platforms). According to the social variables, both material and immaterial exchanges can influence the performance of digital platforms themselves.

Space digital platforms: empirical evidence  317

Figure 20.1

Sociotechnical framework of digital platforms

Table 20.1

Digital platform outputs, rewards and processes

Open innovation

Material Exchange

processes

Nonmonetary

Monetary

Nonmonetary

Inbound

● Ideas

● Money

● Passion

● Prototypes

● Transaction fees

● Peer recognition

● Licenses

● Royalties

● Self-realization

Outbound

Immaterial Exchange

● Final output ● Intermediate output

DIGITAL PLATFORMS AND THE DOWNSTREAM PERSPECTIVE: SPACEDGE™ The Company Planetek is a benefit company based in Italy and Greece, established in 1994, which employs more than 100 people who are skilled in the disciplines of geoinformatics, space solutions, and Earth science. Planetek provides solutions in both national and international markets. The business is focused on data lifecycle management of geospatial data, from the acquisition, storage and management up to data analysis and sharing. The company operates in many application areas ranging from environmental and land monitoring to open-government and smart cities, including defense and security, as well as space exploration and EO satellite missions. The main strategic business unit (SBU) is focused on three different markets: 1. Government and security: the SBU specializes in the application solutions and services for the public administration and defense market. It delivers geospatial solutions to European agencies (ESA) and institutions such as the European Environment Agency, the European Defence Agency and the European Union. 2. Space stream: the SBU specializes in hardware and software infrastructures for the acquisition, processing and distribution of remotely sensed data along the entire value chain: from deep space to Earth observation; from the space segment to the ground and user segment.

318  Handbook on digital platforms and business ecosystems in manufacturing 3. Business to business: the SBU specializes in offerings based on Earth observation data, which ranges from systems for business intelligence on geographical data to the creation of value-added geoinformation products for the oil and gas, renewable energies and transport (railways, roads) sectors. EO is the main segment in which the company competes, with the following applications: 1. Satellite, aerial and drone data processing for cartography and geoinformation production. 2. Continuous monitoring with satellite data of the Earth’s surface, infrastructures, work sites, urban dynamics or marine coastal areas in support of decision making and operational activities. 3. Design and development of spatial data infrastructures (SDI) for geospatial data archive, management and sharing. 4. Design and development of real-time geolocation-based solutions through positioning systems such as GPS/Galileo/GNSS and indoor location systems. 5. Development of software for satellite on-board data and image processing and for ground segment infrastructures. Within the EO domain, Spacedge™ is an innovative platform that provides on-demand applications based on an end-to-end process of the in-orbit and raw satellite data; applications are available as a service (satellite as a service). Spacedge™: The Platform Overview The space mission’s scenario, based on EO, is rapidly evolving by opening new market opportunities characterized by the commercialization of a large amount of open and distributed data, in which ground-based services improve data processing bottlenecks and lower barriers to space (Denis et al., 2017). Many components of the EO value chain are shifted from ground to space to transform data from remote sensoring into actionable insight that should be available in near real-time to organizations and community marketplaces. Such an evolving scenario is called ‘new space’; a new way of conceiving the space economy where the aerospace methods and business have been challenged by the private sector for exploiting the latest commercial-off-the-shelf technologies (Sweeting, 2018). It needs new approaches that should combine the shortening lifecycle of technology development with the increasing needs for real-time EO data. According to these emerging needs, Spacedge™, Planetek’s platform (built around Aix, a consortium consisting of Planetek, D-orbit and AIKO), provides technologies that impact on data reactivity, responsiveness and latency, as satellite assets as-a-service. This is done by means of EO applications based on on-board artificial intelligence (machine learning and convolutional neural networks) with dedicated processing units (VPU and array processors), and blockchain technologies in real operational scenarios of EO. Spacedge™ (Figure 20.2) is an open innovation-based space ecosystem that provides on-demand service by managing the end-to-end process of the in-orbit and raw satellite data; from data acquisition, processing and selection to extraction and utilization, such data are converted into useful information and insights. It runs through the satellites and the ground marketplace, and all the ground and in-orbit resources are made available to the users through the blockchain-based mechanisms. The on-board applications, which are available as a service (satellite as a service), allow end-users to access data at the right time and in

Space digital platforms: empirical evidence  319 the right place. Basically, users (both final users and app developers) can upload or download the application they need from an app-store and configure and run it on the satellite already in orbit. We describe the platform model according to the theoretical IPO framework as follows. The Input Spacedge™ is a multisided market that relies on the network effects, in which the more users animate the platform, the more developers will try to sell their apps on it by creating a platform competitive advantage. As described in the previous section of this chapter, Spacedge™ value depends on the number of linked nodes; basically, the platform users and developers who contribute to indirect network effects (Parkeret al., 2016). Spacedge™ users and developers are attracted by the platform according to the potentiality of the platform itself (Figure 20.2a). Users access a catalogue of apps and basic building blocks and services workflow that should be configured for each specific application scenario. Developers use an SDK with a software toolchain, the programming interface, the simulation and validation tools and the machine learning framework (i.e. TensorFlow). The Processes and Technologies From the technological point of view, the Spacedge™ ecosystem leverages an integrated artificial intelligence framework based on the Orbiting platform, high-performance computing and the Spacedge™ app-store (Figure 20.2b). The Orbiting platform (D-Orbit’s ION Carrier) is a technological framework composed of space services on-demand, in-orbit and ground facilities, based on blockchain’s technologies and smart contract. The Orbiting platform includes the software framework infrastructure that provides an abstraction layer towards sensors and on-board resources for implementing services (and to help users to find innovative approaches for planning, tasking, data processing and communications). High-performance computing libraries, like standard imaging libraries or artificial intelligence libraries, are available. Finally, there is the app-store catalog, a cloud collecting application that can be deployed on board directly in space on the satellites, allowing for smart contracts and autonomous management of machine-to-machine interfaces. The sociotechnical approach of the digital platform is very close to the OI paradigm. From the case point of view, the Spacedge™ app-store is open innovation oriented. Spacedge™ delivers applications to users through the open innovation bundle from developers and takes a percentage of its sales; it provides specific types of information available through time on technologies that have not yet been commercialized. The Spacedge™ app-store is an open window for applications developers that use the software development tools provided by Spacedge™ and sell the applications first-hand. In the Spacedge™ app store, user-based open innovation is activated through the general developers’ active participation (Yun et al., 2016). This active participation in development activities by developers sustains the production, and supply of quite diverse applications becomes possible. Finally, the core of the Spacedge™ app-store is that the consumers of applications are the basic base of development of app-store applications. This brings about the direct connection between the app-store’s consumers and developers. As such, the app-store also proves open innovation processes (Joseph Yun and Cho, 2014).

320  Handbook on digital platforms and business ecosystems in manufacturing

THE OUTPUT As described in the previous section of this chapter, the Spacedge™ output is based on apps development and sales. Users and developers are attracted by the platform according to the potentiality of the platform itself. From one side, the final users generate revenues through platform subscription and from the Spacedge™ app-store by contributing to defending the platform business. From the other side, the app developers, through app development, attract final users to sustain a solid ecosystem. Users generate revenues by purchasing the apps and are motivated to interact with the platform due to the significant app variety they find in the store. App developers are motivated, due to different economic and intrinsic reasons, to developing the apps for the platform. Another line of income for the platform is represented by the sales of on-board devices that enable access to the Spacedge™ ecosystem Moreover, the Spacedge™ model also incorporates another side of the business in rewarding, through value-sharing contracts, all the participants to the app development (such as computing cloud provider, in-orbit comm provider, involved sensor as a service provider for the used app) (Figure 20.2c).

Figure 20.2

Spacedge™ and IPO framework

DISCUSSION AND FUTURE DIRECTION This research has strived to determine the main sociotechnical factors of digital platforms through a case application based on the space economy domain. Starting from our RQs, which are: What are the main sociotechnical variables of the digital platform?; and particularly: In

Space digital platforms: empirical evidence  321 what ways are they combined into an input, processes, and output model?, we move around the specificities of the sociotechnical perspective with the aim of building a comprehensive framework to explore the input, processes and output (IPO) of the digital platforms in the EO downstream sector. Despite research on platforms steadily growing over recent years (McIntyre and Srinivasan, 2017), focus has been largely on a silo view (Kapoor et al., 2021), with the economic approach (Koh and Fichman, 2014; Song et al., 2018), for the most part, being isolated from the technical one (Eloranta and Turunen, 2016; Goldbach et al., 2018; Costa et al., 2020). Our contribution, instead, in line with a hybrid literature, links the two perspectives into a unique integrated perspective. According to our research, we empirically highlight that digital platforms are sociotechnical assemblages encompassing the technical elements (of software and hardware) and associated organizational processes and standards (De Reuver et al., 2018). Regardless of the idea that network effects have been hugely analyzed in terms of value creation for the platform business model (Meyer and Lehnerd, 1997; McIntyre and Srinivasan, 2017; Chae, 2019), little attention has been dedicated to its interconnections with the technical and processual variables (Bonina et al., 2021). Accordingly, our paper points out that the network effects trigger, as an input, the critical mass (the app developers community and the end-users like public institutions and SMEs) to interact with the platform through the digital technologies and processes (Tilson et al., 2010; Kallinikos, 2012) by relying on the technical side of the platform. App developers build their offerings using SDK software toolchain, the API, simulation and validation tools, and the machine learning framework (i.e. Tensorflow) (Ghazawneh and Henfridsson, 2013). The end-users acquire such offerings and leverage on an integrated artificial intelligence framework based on distributed ledger technologies (i.e. blockchain for smart contracts) together with high-performance computing and the on-demand space services (in-orbit and ground facilities for accessing data and standard artificial imaging intelligence libraries for managing EO information). In line with previous studies (Hossain and Lassen, 2017), we highlighted also the importance of the open innovation processes to allow effective stakeholder interactions (Tilson et al., 2010) to obtain new outputs, ideas and knowledge by exploiting the external resources to create value-added offers (space apps) (Cavallo et al., 2022). The app-store serves as an open innovation window that facilitates the active participation of app developers and end-users by connecting them directly (Joseph Yun and Cho, 2014). The applications developers use the software development tools provided by Spacedge™ and sell the applications, taking a percentage of sales from the end-users. Such processes are adaptive and allow platforms stakeholders to coevolve both with the platform development (functionalities and technologies) as well as with the platform output (Boudreau, 2012). Our sociotechnical approach helps also in the understanding of the main platform outputs and the ways in which stakeholders capture value from them. The specific outputs available in the platform are, in fact, commercialized and exchanged according to inbound and outbound processes within the platform’s final users and complementors. The case shows, from one side, that the final users generate revenues through platform subscription and from the space app-store. They generate revenues by purchasing the apps and are stimulated to use the platform due to the app constellation available in the store. From the other side, the app developers attract final users with the huge variety of applications developed.

322  Handbook on digital platforms and business ecosystems in manufacturing They are attracted due to the economic reasons as Spacedge™ incorporates rewarding through value-sharing contracts to all the participating actors (also including, besides the app developers, the service provider for the used app – the computing cloud provider, in-orbit communication providers and the involved sensor) and the revenue from the sales of on-board devices that enable access to the Spacedge™ ecosystem (Boudreau, 2012; Gandia and Parmentier, 2017; Helfat and Raubitschek, 2018). Finally, we also believe our paper makes several contributions to digital platforms research regarding the downstream space economy. First, it provides an original framework regarding the existing platform literature, which differentiates and integrates social and technical variables, including the interconnections among the components, for understanding how they interact and allow the value creation and value capture processes. Our sociotechnical model highlights the actors and processes of the digital platforms that trigger the critical mass of users (app developers, end-users community, public institutions as well as SMEs) to interact with the platform environment through the digital technologies as, for instance, the APIs and the SDKs (that enable complementors to build their offerings) and the AI and blockchain (that, in turn, enable end-users to buy and use such offerings) (Ghazawneh and Henfridsson, 2013). Second, our model highlights the processes that enable integration of such different variables. We highlight that a digital platform can be considered as a holistic structure that considers a unique framework, for both social and technical components, by pointing out the reciprocal importance for the platform’s effectiveness. Each component is necessary for the input–process–output flow. The sociotechnical dimensions of the platform provide a sharp view of the phenomenon, in which specific inputs (network effects) are enabled and, in turn, trigger specific open innovation processes supported by digital technologies. Such a flow ends with the value capture process, in which outputs (in different forms) are exchanged among the platform stakeholders. Finally, this study does not come without limitations. The first is related to the limited possibility of generalizing the findings due to the qualitative case study approach. We build an empirical framework where the results are, by definition, deeply rooted in the context and depend on the researchers’ interpretation. Although it is not sufficient for generalizing the results, it allows us to analytically satisfy our research questions by providing a satisfactory picture of the main sociotechnical platforms’ components and processes. Second, there is a limitation of validity. Since we derived the key platform dimensions by building them through a sociotechnical framework, additional cases may be useful to validate or enrich the classification. Digital space economy studies are currently in their infancy; therefore, more explorative studies may be useful for defining the related models and concepts to improve the understanding of such a topic. Accordingly, given the explorative nature of this chapter, we suggest future research to advance space-digital platform knowledge by also deepening and comparing different digital platform frameworks as well as their specificities and analogies with the IPO model.

Space digital platforms: empirical evidence  323

REFERENCES Abbate, T., Codini, A. P. and Aquilani, B. (2019) ‘Knowledge co-creation in Open Innovation Digital Platforms: processes, tools and services’, Journal of Business & Industrial Marketing, 34(7), pp.  1434–47. Available at: https://​doi​.org/​10​.1108/​JBIM​-09​-2018​-0276. Acs, Z. J., Song, A. K., Szerb, L., Audretsch, D. B. and Komlosi, E. (2021) ‘The evolution of the global digital platform economy: 1971–2021’, Small Business Economics, 57(4), pp. 1629–59. Available at: https://​doi​.org/​10​.1007/​s11187​-021​-00561​-x. Aloini, D., Latronico, L. and Pellegrini, L. (2021) ‘The impact of digital technologies on business models. Insights from the space industry’, Measuring Business Excellence, (ahead-of-print). Available at: https://​doi​.org/​10​.1108/​MBE​-12​-2020​-0161. Baumann, S. (2022) ‘Introduction to the Handbook on Digital Business Ecosystems: Strategies, Platforms, Technologies, Governance and Societal Challenges’, in Handbook on Digital Business Ecosystems. Edward Elgar Publishing, pp. 1–9. Available at: https://​www​.elgaronline​.com/​view/​ edcoll/​9781839107184/​9781839107184​.00005​.xml. Baumann, S. and Leerhoff, M. (2022) ‘Networks, platforms, and digital business ecosystems: mapping the development of a field’, in Handbook on Digital Business Ecosystems: Strategies, Platforms, Technologies, Governance and Societal Challenges. Edward Elgar Publishing, pp. 11–24. Blaikie, N. and Priest, J. (2019) Designing Social Research: The Logic of Anticipation. John Wiley & Sons. Blaschke, M. et al. (2019) ‘Taxonomy of Digital Platforms: A Platform Architecture Perspective’, Wirtschaftsinformatik 2019 Proceedings [Preprint]. Available at: https://​ aisel​ .aisnet​ .org/​ wi2019/​ track06/​papers/​3. Bonina, C. et al. (2021) ‘Digital platforms for development: Foundations and research agenda’, Information Systems Journal, 31(6), pp. 869–902. Available at: https://​doi​.org/​10​.1111/​isj​.12326. Boudreau, K. (2010) ‘Open Platform Strategies and Innovation: Granting Access vs. Devolving Control’, Management Science, 56(10), pp.  1849–72. Available at: https://​doi​.org/​10​.1287/​mnsc​.1100​.1215. Boudreau, K. (2012) ‘Let a Thousand Flowers Bloom? An Early Look at Large Numbers of Software App Developers and Patterns of Innovation’, Organization Science, 23(5), pp. 1409–27. Available at: https://​doi​.org/​10​.1287/​orsc​.1110​.0678. Brunswicker, S. and Schecter, A. (2019) ‘Coherence or flexibility? The paradox of change for developers’ digital innovation trajectory on open platforms’, Research Policy, 48(8), p. 103771. Available at: https://​doi​.org/​10​.1016/​j​.respol​.2019​.03​.016. Cavallo, A., Burgers, H., Ghezzi, A. and Van de Vrande, V. (2022) ‘The evolving nature of open innovation governance: A study of a digital platform development in collaboration with a big science centre’, Technovation, 116, p.  102370. Available at: https://​doi​.org/​10​.1016/​j​.technovation​.2021​.102370. Cennamo, C. et al. (2020) ‘Managing Digital Transformation: Scope of Transformation and Modalities of Value Co-Generation and Delivery’, California Management Review, 62(4), pp. 5–16. Available at: https://​doi​.org/​10​.1177/​0008125620942136. Chae, B. (Kevin) (2019) ‘A General framework for studying the evolution of the digital innovation ecosystem: The case of big data’, International Journal of Information Management, 45, pp. 83–94. Available at: https://​doi​.org/​10​.1016/​j​.ijinfomgt​.2018​.10​.023. Chesbrough, H. W. (2006) Open Innovation: Researching a New Paradigm. Oxford University Press. Chou, C. and Shy, O. (1990) ‘Network effects without network externalities’, International Journal of Industrial Organization, 8(2), pp.  259–270. Available at: https://​doi​.org/​10​.1016/​0167​-7187(90)90019​-W. Church, J. and Gandal, N. (1992) ‘Network Effects, Software Provision, and Standardization’, The Journal of Industrial Economics, 40(1), pp. 85–103. Available at: https://​doi​.org/​10​.2307/​2950628. Costa, E., Soares, A. L. and de Sousa, J. P. (2020) ‘Industrial business associations improving the internationalisation of SMEs with digital platforms: A design science research approach’, International Journal of Information Management, 53, p. 102070. Cusumano, M. A., Yoffie, D. B. and Gawer, A. (2019) ‘The Business of Platforms: Strategy in the Age of Digital Competition’, Innovation, and Power. HarperCollins Publishers. De Reuver, M., Sørensen, C. and Basole, R. C. (2018) ‘The digital platform: A research agenda’, Journal of Information Technology, 33(2), pp. 124–35. Available at: https://​doi​.org/​10​.1057/​s41265​-016​ -0033​-3.

324  Handbook on digital platforms and business ecosystems in manufacturing Denis, G. et al. (2017) ‘Towards disruptions in Earth observation? New Earth Observation systems and markets evolution: Possible scenarios and impacts’, Acta Astronautica, 137, pp. 415–33. Eloranta, V. and Turunen, T. (2016) ‘Platforms in service-driven manufacturing: Leveraging complexity by connecting, sharing, and integrating’, Industrial Marketing Management, 55, pp. 178–86. Floetgen, R.J. et al. (2021) ‘Introducing platform ecosystem resilience: leveraging mobility platforms and their ecosystems for the new normal during COVID-19’, European Journal of Information Systems, 30(3), pp.  304–21. Available at: https://​doi​.org/​10​.1080/​0960085X​.2021​.1884009. Fonseca, L. M. (2018) ‘Industry 4.0 and the digital society: concepts, dimensions and envisioned benefits’, Proceedings of the International Conference on Business Excellence, 12(1), pp. 386–97. Available at: https://​doi​.org/​10​.2478/​picbe​-2018​-0034. Gandia, R. and Parmentier, G. (2017) ‘Optimizing value creation and value capture with a digital multi-sided business model’, Strategic Change, 26(4), pp. 323–31. Available at: https://​doi​.org/​10​ .1002/​jsc​.2134. Ghazawneh, A. and Henfridsson, O. (2013) ‘Balancing platform control and external contribution in third-party development: the boundary resources model’, Information Systems Journal, 23(2), pp. 173–92. Goldbach, K., Rotaru, A. M., Reichert, S., Stiff, G. and Gölz, S. (2018) ‘Which digital energy services improve energy efficiency? A multi-criteria investigation with European experts’, Energy Policy, 115, pp.  239–48. Available at: https://​doi​.org/​10​.1016/​j​.enpol​.2017​.12​.036. Grisold, T., Kremser, W., Mendling, J., Recker, J., Brocke, J. V. and Wurm, B. (2022) ‘Keeping pace with the digital age: Envisioning information systems research as a platform’, Journal of Information Technology, 38(1), pp. 60–61. Available at: https://​doi​.org/​10​.1177/​02683962221130429. Hagiu, A. (2009) ‘Two-Sided Platforms: Product Variety and Pricing Structures’, Journal of Economics & Management Strategy, 18(4), pp.  1011–43. Available at: https://​doi​.org/​10​.1111/​j​.1530​-9134​.2009​ .00236​.x. Halckenhaeusser, A., Heinzl, A. and Spohrer, K. (2020) ‘Gravitation of SaaS-centric cloud platforms’, in The Routledge Companion to Managing Digital Outsourcing. Routledge. Hallerstede, S. H. (2013) ‘Open innovation platforms’, in S. H. Hallerstede (ed.) Managing the Lifecycle of Open Innovation Platforms. Wiesbaden: Springer Fachmedien (Markt- und Unternehmensentwicklung / Markets and Organisations), pp.  18–34. Available at: https://​doi​.org/​10​.1007/​978​-3​-658​-02508​-3​_5. Heimburg, V. and Wiesche, M. (2022) Relations Between Actors in Digital Platform Ecosystems: A Literature Review. In Proceedings of 30th European Conference on Information Systems-New Horizons in Digitally United Societies, Timișoara, (18–24 June). Hein, A. et al. (2019) ‘The influence of digital affordances and generativity on digital platform leadership’, in Int. Conf. Inf. Syst., ICIS. Association for Information Systems. Available at: https://​ www​.scopus​.com/​inward/​record​.uri​?eid​=​2​-s2​.0​-85097709831​&​partnerID​=​40​&​md5​=​4a​18fdfb5535​ a72ec8d46a45de02806b. Hein, A. et al. (2020) ‘Digital platform ecosystems’, Electronic Markets, 30(1), pp. 87–98. Available at: https://​doi​.org/​10​.1007/​s12525​-019​-00377​-4. Helfat, C. E. and Raubitschek, R. S. (2018) ‘Dynamic and integrative capabilities for profiting from innovation in digital platform-based ecosystems’, Research Policy, 47(8), pp. 1391–99. Available at: https://​doi​.org/​10​.1016/​j​.respol​.2018​.01​.019. Herzog, P. (2011) ‘Open and Closed Innovation: Different Cultures for Different Strategies’. Springer. Hossain, M. and Lassen, A. H. (2017) ‘How Do Digital Platforms for Ideas, Technologies, and Knowledge Transfer Act as Enablers for Digital Transformation?’. Rochester, NY. Available at: https://​papers​.ssrn​.com/​abstract​=​3045012. Johannsson, M. et al. (2015) ‘Space and Open Innovation: Potential, limitations and conditions of success’, Acta Astronautica, 115, pp. 173–84. Available at: https://​doi​.org/​10​.1016/​j​.actaastro​.2015​ .05​.023. Joseph Yun, J. and Cho, B.-J. (2014) ‘An exploratory study of the economic effect of open innovation’, Journal of Science and Technology Policy Management, 5(1), pp. 24–40. Kallinikos, J. (2012) ‘Form, function, and matter: Crossing the border of materiality’, in Materiality and organizing: Social interaction in a technological world, pp. 67–87. Kapoor, K., Bigdeli, A. Z., Dwivedi, Y. K., Schroeder, A., Beltagui, A. and Baines, T. (2021) ‘A socio-technical view of platform ecosystems: Systematic review and research agenda’, Journal of Business Research, 128, pp.  94–108. Available at: https://​doi​.org/​10​.1016/​j​.jbusres​.2021​.01​.060.

Space digital platforms: empirical evidence  325 Koh, T. K. and Fichman, M. (2014) ‘Multihoming Users’ Preferences for Two-Sided Exchange Networks’, MIS Quarterly, 38(4), pp. 977–96. Available at: https://​www​.jstor​.org/​stable/​26627958 . Kohli, R. and Melville, N. P. (2019) ‘Digital innovation: A review and synthesis’, Information Systems Journal, 29(1), pp.  200–23. Available at: https://​doi​.org/​10​.1111/​isj​.12193. Kozlowski, S. W. J. and Ilgen, D. R. (2006) ‘Enhancing the Effectiveness of Work Groups and Teams’, Psychological Science in the Public Interest (Wiley-Blackwell), 7(3), pp. 77–124. Lee, M., Kwon, W. and Back, K.-J. (2021) ‘Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making’, International Journal of Contemporary Hospitality Management, 33(6), pp. 2117–36. Lo, Y. C. and Fang, C.-Y. (2018) ‘Facebook marketing campaign benchmarking for a franchised hotel’, International Journal of Contemporary Hospitality Management, 30, pp. 1705–23. Available at: https://​doi​.org/​10​.1108/​IJCHM​-04​-2017​-0206. Malone, T. W., Laubacher, R. and Dellarocas, C. (2009) ‘Harnessing Crowds: Mapping the Genome of Collective Intelligence’. McIntyre, D. P. and Srinivasan, A. (2017) ‘Networks, platforms, and strategy: Emerging views and next steps’, Strategic Management Journal, 38(1), pp. 141–60. Available at: https://​doi​.org/​10​.1002/​smj​ .2596. Metcalfe, B. (2013) ‘Metcalfe’s Law after 40 Years of Ethernet’, Computer, 46(12), pp. 26–31. Available at: https://​doi​.org/​10​.1109/​MC​.2013​.374. Meyer, M. H. and Lehnerd, A.P. (1997) The power of product platforms. Simon and Schuster. Moranta, S. and Donati, A. (2020) ‘Space Ventures Europe 2018 – Entrepreneurship and Private Investment in the European Space Sector’, New Space, 8(1), pp. 7–17. Available at: https://​doi​.org/​ 10​.1089/​space​.2019​.0020. Mortara, L. and Minshall, T. (2011) ‘How do large multinational companies implement open innovation?’, Technovation, 31(10), pp.  586–97. Available at: https://​doi​.org/​10​.1016/​j​.technovation​.2011​ .05​.002. Ozman, M. (2011) ‘Modularity, Industry Life Cycle and Open Innovation’, Journal of Technology Management & Innovation, 6(1), pp. 26–34. Parker, G., Van Alstyne, M. W. and Jiang, X. (2016) ‘Platform Ecosystems: How Developers Invert the Firm’. Rochester, NY. Available at: https://​doi​.org/​10​.2139/​ssrn​.2861574. Pranto, T. H., Noman, A. A., Mahmud, A. and Haque, A. B. (2021) ‘Blockchain and smart contract for IoT enabled smart agriculture’, PeerJ Computer Science, 7, p. e407. Available at: https://​doi​.org/​10​ .7717/​peerj​-cs​.407. Rolland, K. H., Mathiassen, L. and Rai, A. (2018) ‘Managing Digital Platforms in User Organizations: The Interactions Between Digital Options and Digital Debt’, Information Systems Research, 29(2), pp.  419–43. Available at: https://​doi​.org/​10​.1287/​isre​.2018​.0788. Song, P., Xue, L., Rai, A. and Zhang, C. (2018) ‘The Ecosystem of Software Platform: A Study of Asymmetric Cross-Side Network Effects and Platform Governance’, MIS Quarterly, 42(1), pp.  121–42. Available at: https://​search​.ebscohost​.com/​login​.aspx​?direct​=​true​&​db​=​bth​&​AN​=​ 127748825​&​site​=​ehost​-live​&​scope​=​site. Sweeting, M. N. (2018) ‘Modern Small Satellites-Changing the Economics of Space’, Proceedings of the IEEE, 106(3), pp.  343–61. Available at: https://​doi​.org/​10​.1109/​JPROC​.2018​.2806218. Teece, D. J. (2006) ‘Reflections on “Profiting from innovation”’, Research Policy, 35(8), pp. 1131–46. Thies, F., Wessel, M. and Benlian, A. (2018) ‘Network effects on crowdfunding platforms: Exploring the implications of relaxing input control’, Information Systems Journal, 28(6), pp. 1239–62. Tilson, D., Lyytinen, K. and Sørensen, C. (2010) ‘Research Commentary – Digital Infrastructures: The Missing IS Research Agenda’, Information Systems Research, 21(4), pp. 748–59. Available at: https://​doi​.org/​10​.1287/​isre​.1100​.0318. Tiwana, A., Konsynski, B. and Bush, A. A. (2010) ‘Research Commentary – Platform Evolution: Coevolution of Platform Architecture, Governance, and Environmental Dynamics’, Information Systems Research, 21(4), pp.  675–87. Available at: https://​doi​.org/​10​.1287/​isre​.1100​.0323. Toke Reichstein and Salter, A. (2006) ‘Investigating the sources of process innovation among UK manufacturing firms’, Industrial and Corporate Change, 15(4), pp. 653–82. Vidmar, M., Rosiello, A., Vermeulen, N., Williams, R. and Dines, J. (2020) ‘New Space and Agile Innovation: Understanding transition to open innovation by examining innovation networks and

326  Handbook on digital platforms and business ecosystems in manufacturing moments’, Acta Astronautica, 167, pp. 122–34. Available at: https://​doi​.org/​10​.1016/​j​.actaastro​.2019​ .09​.029. Von Briel, F. and Davidsson, P. (2019) Digital Platforms and Network Effects: Using Digital Nudges for Growth Hacking. Fortieth International Conference on Information Systems (ICIS). Munich (15–18 December). Wenbo Guo, Straub, D., Zhang, P. and Cai, Z. (2021) ‘How Trust Leads to Commitment on Microsourcing Platforms: Unraveling the Effects of Governance and Third-Party Mechanisms on Triadic Microsourcing Relationships’, MIS Quarterly, 45(3), pp. 1309–48. Available at: https://​doi​ .org/​10​.25300/​MISQ/​2021/​14349. Yoo, Y., Henfridsson, O. and Lyytinen, K. (2010) ‘Research Commentary—The New Organizing Logic of Digital Innovation: An Agenda for Information Systems Research’, Information Systems Research, 21(4), pp.  724–35. Available at: https://​doi​.org/​10​.1287/​isre​.1100​.0322. Yun, J. J., Won, D. and Park, K. (2016) ‘Dynamics from open innovation to evolutionary change’, Journal of Open Innovation: Technology, Market, and Complexity, 2(1), p. 7. Available at: https://​doi​ .org/​10​.1186/​s40852​-016​-0033​-0.

21. Maintenance 4.0: applying IoT technologies to increase uptime and efficiency of critical infrastructures Alexander Herzfeldt, Christoph Ertl and Sebastian Floerecke

INTRODUCTION Due to challenged profit margins combined with an increasing shortage of skilled workers, critical infrastructure operators providing and running power plants, power distribution networks, water treatment facilities or airports, need to do ‘more with less’. They must increase capacity, reliability and resilience, but work with noticeably tighter budgets (Spiegel et al., 2018; Tauterat, 2018; Bukhsh et al., 2019). To avoid serious short- and long-term financial business risks, the efficient and effective execution of maintenance processes thus becomes more important. Maintenance of infrastructure and technical systems includes visual and functional checks, servicing and repairing or replacing of necessary devices, equipment, machinery or other physical assets. In most cases, maintenance is performed by the in-house maintenance organization, but it is also not uncommon to be outsourced to third party service providers or original equipment manufacturers (OEM) (Jardine and Tsang, 2005). Furthermore, infrastructure operators face two major external drivers affecting their business model and their surrounding business ecosystem: digitalization and sustainability (Jasiulewicz-Kaczmarek et al., 2020). Digitalization makes it possible to monitor physical processes, set up a digital twin of physical systems and make better decisions by real-time communication and cooperation between humans, machines and sensors (Wang et al., 2016; Leyh et al., 2017). Implementing sustainability into manufacturing seeks to ensure that production considers resources use and social standards (Bonvoisin et al., 2017). To support infrastructure operators in achieving business goals, including cost reduction and improved service quality, as well as sustainability goals, including low resource consumption and closed material cycles, the use of modern maintenance approaches such as Maintenance 4.0 is seen as one of the prevailing solutions both in science and practice (Franciosi et al., 2018; Jasiulewicz-Kaczmarek and Gola, 2019). Maintenance 4.0 is a machine-assisted approach that uses a holistic set of data sources (including sensor data measuring equipment). Maintenance 4.0 techniques correlate data and apply artificial intelligence/machine learning algorithms to determine and schedule suitable actions (Susto et al., 2015; Chebel-Morello et al., 2017; Skog et al., 2017). The approach is combined with advanced Internet of Things (IoT) endpoints like, for example, sensors, robots, or drones (Chui et al., 2019). What has been missing in scientific literature so far is an insight into the added value of IoT solutions regarding the possibilities of Maintenance 4.0 in already existing infrastructures. Many studies deal with the technological basics, architectural foundations or experimental implementations (Jasiulewicz-Kaczmarek et al., 2020; Silvestri et al., 2020; Achouch et al., 327

328  Handbook on digital platforms and business ecosystems in manufacturing 2022; Cachada et al., 2018). However, a holistic practical implementation and assessment approach for already operating factories and processes cannot be found. As representative types of technical facilities regarding utility, risk and expense aspects of predictive maintenance, this book chapter outlines the use of retrofit IoT sensors on existing ventilation systems as well as elevators, preconditioned-air (pca) units and deicing vehicles. This study will therefore address the following research question: What technological capabilities are required to evaluate and implement Maintenance 4.0 technologies? Based on theoretical and practical insights gathered via three case studies at one of Europe’s largest airport operators, a key capabilities framework for Maintenance 4.0 was developed. The framework distinguishes between technological capabilities on the one hand and technologies required to provide these capabilities on the other. Infrastructure operators who master them are more likely to achieve their business and sustainability goals. The premise is that the capabilities and technologies are supported by adequate business processes and managed properly. The framework is especially useful for infrastructure and utility companies trying to start building up Maintenance 4.0 capabilities in order to offer Maintenance 4.0 services internally or within their business ecosystem in the future. Likewise, companies delivering Maintenance 4.0 services can evaluate themselves against the framework and derive potential ways for improvements. The remainder of this book chapter is organized as follows: In section two, we describe and define the term maintenance and analyze different maintenance concepts with focus on Maintenance 4.0. In section three, we present useful and crucial technologies for implementing Maintenance 4.0 based on Industry 4.0 technologies and those encompassed in the much-cited Maintenance 4.0 system architecture by Cachada et al., (2018). In section four, we provide two examples of Maintenance 4.0 in the form of case studies. We derive and depict the Maintenance 4.0 key capabilities framework in section five and close the chapter by discussing benefits and remaining issues of Maintenance 4.0 in general and our framework in particular as well as with a conclusion and outlook of this research field.

FROM MANUAL TO DIGITAL MAINTENANCE This section will provide an overview of the concepts and evolutionary stages of maintenance management. Building on reactive maintenance – repairing assets to standard operating conditions after poor performance or breakdown is observed – concepts such as predictive and preventive maintenance emerged. The infrastructures that operators and manufacturers have deployed vary greatly and are constantly evolving these new concepts (Vogl et al., 2016). Today, the term Maintenance 4.0 as part of Industry 4.0 is becoming more important as a technological approach to develop towards smart and sustainable manufacturing processes (Jasiulewicz-Kaczmarek et al., 2020). Definition of the Term Maintenance The European Standard EN 13306:2017 describes maintenance management as a set of activities ‘[…] that determine the maintenance objectives, strategies and responsibilities, and implementation of them by such means as maintenance planning, maintenance control, and the improvement of maintenance activities and economics’. This guarantees an optimum

Maintenance 4.0: applying IoT technologies to critical infrastructures  329 balance between uptime and cost for inspections and repair – according to an organization’s priorities and agreed service levels (Jardine and Tsang, 2005). It also offers a set of possible maintenance strategies to guarantee satisfactory equipment operation and prevent major issues (Silvestri et al., 2020). In our work we define maintenance as the process of preserving a condition or situation, or the state of being preserved. Maintenance strategies as means of maintenance management are planned ways to upkeep devices, presenting diverse patterns of maintenance times and costs. Maintenance Strategies Towards Maintenance 4.0 Maintenance strategies can be classified via three different dimensions: (1) proactive – reactive (Sambrekar et al., 2018), (2) corrective – preventive (Legát et al., 2017) and (3) planned – unplanned (Arslankaya and Atay, 2015). A single strategy can be assigned either to one or more of these dimensions. For example, predictive maintenance is categorized as a proactive (1), preventive (2) and planned (3) maintenance strategy, whereas condition-based maintenance is a solely categorized as a preventive (2) maintenance strategy. Most organizations these days use a combination of maintenance strategies of all three dimensions aiming to maintain their equipment and physical assets (Bokrantz et al., 2017). Each strategy has specific effects regarding cost, uptime and human involvement (Jin et al., 2016; Vogl et al., 2016). The first (1) category encompasses reactive and proactive maintenance strategies. A reactive strategy is the simplest maintenance paradigm and means a corrective action will be performed after a failure has occurred. It leads to low investment costs and unscheduled equipment downtimes (Jovane et al., 2003). On the other side, proactive strategies offer various options to execute maintenance activities in advance of a failure (Sambrekar et al., 2018). They differ regarding their complexity in activity planning (e.g. simply scheduled vs. predictive) and scope of the maintenance activity (e.g. single components in risk-based maintenance or modification of the machine in design-out maintenance). In the second category (2) corrective and preventive strategies are located. Corrective strategies involve deferred (actions undertaken with planned delay after a failure has occurred) and immediate activities (actions carried out immediately after failure), whereas preventive strategies pursue routine inspection of equipment with the goal of ‘noticing small problems and fixing them before major ones develop’ (Decourcy Hinds, 1985). Preventive strategies are considered as innovative methods to enhance the effectiveness of machines (Gackowiec, 2019). The last category (3) contains planned as well as unplanned strategies. Although it is not a real strategy in a narrow sense, the unplanned strategy is related to breakdown maintenance. On the other side, planned strategy follows regular and routine actions taken on equipment aiming to prevent its breakdown. It means that the repair, replacement or servicing of equipment is conducted after predetermined time intervals or operation count-based cycles (Wood, 2003). This approach may lead to over-maintained equipment and thus to avoidable costs. When studying the three dimensions, predictive maintenance can be identified as a special strategy as it can be found in each of the three dimensions. It is a right-on-time maintenance approach considering the actual condition of equipment, typically captured from sensors. It then determines the optimal maintenance time based on experience from the past (Lee et al., 2014). This is seen as the most advanced and efficient maintenance concept today. A recent survey by BearingPoint (2021) of 203 companies from mechanical engineering, chemical/

330  Handbook on digital platforms and business ecosystems in manufacturing pharmaceutical and automotive industries showed that 75 percent of the companies are actively involved in the field of predictive maintenance. Half of those surveyed have already successfully implemented predictive maintenance (pilot) projects. Today, reactive maintenance (Maintenance 1.0) is very common in manufacturing. However, companies have struggled for years to abandon this approach because it often results in significant unplanned downtime and increased operational costs (Achouch et al., 2022; de Faria et al., 2015). Aiming to reduce the number of reactive maintenance actions, periodic checks and replacement of worn parts were introduced (Maintenance 2.0) (Jasiulewicz-Kaczmarek et al., 2020). Predetermined time intervals based on checklists of OEM recommendations build the base for this preventive maintenance idea. To further increase the productivity of machines, new ways to execute maintenance by considering the operational state of assets emerged (Cachada et al., 2018). These condition-based maintenance approaches (Maintenance 3.0) apply data analysis techniques to the information produced in the shop floor processes to detect anomalies in the assets’ behavior (Garg and Deshmukh, 2006). Extending the possibilities of automation concepts, Maintenance 4.0: ‘[…] describes a set of techniques to monitor the current condition of machines with the goal to predict upcoming machine failure by using automated (near) real-time analytics and supervised or unsupervised machine learning, and to prescribe optimal course of action in real time, analyses potential decisions and interaction between them’ (Jasiulewicz-Kaczmarek et al., 2020).

Predictive maintenance, as one of the central Maintenance 4.0 components, is the superior maintenance approach in most cases, compared to time-based maintenance. In continuation, prescriptive maintenance applies advanced data analytics methods combined with libraries of standard maintenance tasks to prescribe recommendations in order to avoid potential machine breakdowns and optimize maintenance activities (Ansari et al., 2019; Jasiulewicz-Kaczmarek et al., 2020). The holistic use of historical and real-time data on the state of the machine together is changing the paradigm from planned preventive towards proactive and smart maintenance operation (Matyas et al., 2017). However, there are only a handful of research papers on the implementation of prescriptive maintenance in practice (Silvestri et al., 2020). Beyond this background, our aim with Maintenance 4.0 is to build on a predictive maintenance strategy and offset limitations by (I) capturing more and qualitatively better data, including data from ambient sensors and digital imagery analysis, (II) taking better decisions using data analytics and machine learning on this much broader data pool and (III) communicating and connecting efficiently with intelligent IoT endpoints.

KEY TECHNOLOGIES UNDERLYING MAINTENANCE 4.0 One of the most significant preconditions to reduce equipment downtime and costs is the necessity of collecting and storing machine data as well as their automated processing and advanced analysis (Jasiulewicz-Kaczmarek et al., 2020). Numerous studies (e.g. Cristians and Methven, 2017; Pierdicca et al., 2017; Vaidya et al., 2018; Jones et al., 2019; Silvestri et al., 2020) have consistently identified the areas that serve to achieve technological improvements of Maintenance 4.0. In this section, some key technological pillars enabling this data-focused precondition are described.

Maintenance 4.0: applying IoT technologies to critical infrastructures  331 First of all, an Industrial Internet of Things (IIoT) concept hast to be implemented as the basic technology of cyber-physical systems (Lee et al, 2015). This enables a machine-to-machine interaction without human involvement (Shi et al., 2016) by connecting physical objects, sensors and other operational data sources with a central platform using standard internet protocols. In this way, complex and heterogeneous systems are integrated to lever performance and reliability of equipment operation (Morgan and O’Donnell, 2015). With the data collected via the IIoT and the existing IT systems, e.g. an enterprise resource planning system, the basis for big data and analytics is created (Vaidya et al., 2018). Through integrating these data sources into an industrial network and controlling the data flows between the integrated systems establishes a smart factory (Erboz, 2017). The targeted use of various instruments, such as trend analysis, process monitoring, quality controls, error diagnosis and error classification, constitute the functionalities of a smart factory (Ge et al., 2017). Besides the important basis of a horizontal integration, e.g. at shopfloor level, across multiple production facilities or the entire supply chain, the vertical integration gains a crucial spot in the Maintenance 4.0 concept (Peres et al., 2018). Vertical integration encompasses all logical layers within an organization from production and operation, through R&D, quality assurance, product management, IT, sales and marketing (Schuldenfrei, 2019). Both dimensions are relevant when attaining a full connection between all actors in a highly dynamic system (Silvestri et al., 2020). As a result, not only operational but also strategic decisions can be made in the smart factory. Simulation and cybersecurity are two additional technological pillars in setting up Maintenance 4.0 management. Simulation as a tool can help to optimize the existing maintenance activities by using data from the value network to improve the design and operation of the production system (Chong et al., 2018). Cybersecurity encompasses all aspects of protecting shared information and preventing or minimizing the occurrence of incidents and corporate data breaches (Corallo et al, 2022). With the growing importance of data flows in a smart factory in today’s business environment, the need to increase cybersecurity awareness has become more crucial. Thus, companies have to implement consistent concepts to ensure cybersecurity awareness at every enterprise level and to establish techniques to raise company awareness of cybersecurity (e.g. serious games and online questionnaires) (Corallo et al., 2022). With these pillars, an infrastructure provider can transform its production and maintenance processes from an automated level to a smart factory (Zolotová et al., 2020). In a Maintenance 4.0 environment, valuable data sources are provided by the operator, maintainer, enterprise information systems, OEM and suppliers. Data collection is executed automatically via (IoT) sensors and stored on cloud platforms. Revealing data analysis is performed via neural networks, machine learning or fuzzy logic. In doing this, infrastructure operators can start to benefit from a profound understanding of their machines, processes and services (Jasiulewicz-Kaczmarek et al., 2020). Figure 21.1 summarizes the technological pillars in an architecture overview. The starting point of the architecture is represented by the data collection module, which records data from various sources (e.g. IoT sensors, machine data, operational data). From this component the knowledge generation module, as well as the dynamic prediction module, is supplied. Combined with advanced historical and enriched real time data, the forecasting module can derive corrective, preventive and predictive maintenance procedures and pass them on to the

332  Handbook on digital platforms and business ecosystems in manufacturing maintenance resource scheduling program. The maintenance loop is closed with forwarding upcoming or necessary maintenance activities to the corresponding technicians.

DEEP-DIVE CASES: MAINTENANCE 4.0 In this section, some of the technological pillars enabling Maintenance 4.0 are presented to assess their current level of maturity in practice. For this purpose, we performed a metaanalysis on three recent practice-oriented works of Freiberger et al., (2021), Rott et al., (2021), and Rott et al., (2022), who examined the possibilities of data collection via IoT as well as existing data sources and qualities at a leading European airport operator. At least one author of the present chapter was involved in each of these studies. Each of the three papers uses a proper methodology, e.g., case study design according to Yin (2018) and Benbasat et al., (1987) or design science research according to Peffers et al. (2007). Our metaanalysis summarizes and connects the core findings of these three studies. We start the evaluation, as shown in Figure 21.1, with IoT technology as central source for the data collection module. IoT platforms are highly complex systems embedded in a business ecosystem and can vary significantly in terms of their range of functions, their ability to integrate into the enterprise architecture and their license models (Freiberger et al., 2021). In addition, the requirements on IoT platforms – beyond e.g. Maintenance 4.0 use cases – can be quite heterogeneous. Nonetheless, Freiberger et al., (2021) determined the following key aspects form their single case study regarding the usage of IoT platforms at critical infrastructure operators: ● Compare potential IoT solutions with the existing enterprise architecture and define mandatory systems and their required interfaces. ● Consider the scaling properties of IoT platforms, which can positively influence the unit costs and thus the overall economy of the system. ● Create an adequate catalog of requirements to avoid the risk of overlooking important ones or consider requirements that are ultimately irrelevant. ● Determine central knockout criteria (minimum conditions). ● Identify (relevant) Maintenance 4.0 use cases that exceed the level of manufacturing or operating optimizations with comparatively small monetary benefits but are capable of gaining valuable process insights and already reduce equipment break downs. ● Conduct small proof of concept projects to determine the actual functionality of selected IoT platforms. ● Building an IoT platform requires extensive knowhow that many, especially SMEs, do not possess. ● Use an agile, iterative approach to evaluate functional as well as nonfunctional criteria in the specific context or industry. Summarizing the findings, all IoT platforms considered generally performed well. This circumstance is probably caused by the fact that only leading IoT platforms were included in the evaluation. Whereas some of the evaluation criteria, such as user authentication and device connectivity, were consistently covered very well, others, such as company integration and device management, were treated heterogeneously. The study of Freiberger et al., (2021) has thus shown that IoT platforms possess a good degree of maturity for use in practice, provided

Figure 21.1

System architecture for Maintenance 4.0. (Own illustration based on Cachada et al., 2018)

Maintenance 4.0: applying IoT technologies to critical infrastructures  333

334  Handbook on digital platforms and business ecosystems in manufacturing Table 21.1

Benefit and risk factors for assessing the suitability of an asset class for predictive maintenance (Rott et al., 2021)

Benefit Factors

Risk Factors

Plant, machine, and component availability

Data and prediction quality

Cost cutting (maintenance and energy costs)

Data security (data manipulation, increased number of interfaces)

Personnel deployment (own and third-party maintenance staff)

The human factor (employee acceptance and misinterpretation of data)

Planning security (downtimes, optimized tour planning)

Costs (predictive maintenance system, loss of warranty claims)

Process and production quality

Risks of the predictive maintenance system (increased susceptibility to errors or faults through additional sensors)

that a detailed evaluation process is carried out and the platform(s) most suitable for use is (are) selected. Another important input factor for the Maintenance 4.0 system architecture is machine and sensor data. Rott et al., (2022) analyzed several technological concepts regarding operational requirements. These requirements were power consumption, localization, data transmission, robustness and security, as well as economy. The authors compared three different combinations of technologies to meet these requirements. They discovered that IoT technology can help to address the requirements and to enable a more flexible and durable use of the assets analyzed in the use case. Moreover, it also had a positive effect on maintenance requirements. In addition, process optimization potentials can be realized based on the knowledge of the assets. Though, the results of Rott et al., (2022) show that the economic and technological requirements can only be met by combining different basic technologies in one overall concept; if accomplished, IoT-based sensors and machine data can build up a smart product service system and establish the basis for the optimization of machine and equipment operations in terms of Maintenance 4.0. At the next level of the Maintenance 4.0 system architecture lies the off-line data knowledge aggregation module. As presented in the previous sections, the data collection module supplied with sensor data from IoT platforms has a good degree of maturity for practical use; now, the data analytics part must be examined. We build our assessment on the work of Rott et al., (2021), who performed a multiple-case study on three technical systems regarding their maturity for diagnostic or predictive maintenance activities from an analytical point of view. Overall, the results of the study show that the usefulness of introducing a predictive maintenance strategy is influenced by many factors, the extent of which varies enormously between asset classes. The advantages should therefore definitely be checked prior to implementation, preferably using the method by Tauterat (2018). While maintenance costs can usually be reduced and system availability increased, new types of risks arise in connection with data security and higher susceptibility to failure from additional sensors. In the case of external maintenance, it is also indispensable to anchor the predictive maintenance strategy in the service contract. This can lead to a limited choice of provider in the context of a tender and entails a risk of acceptance, since the employees could feel controlled by the predictive maintenance system. The benefit and risk factors shown in Table 21.1 can be used to carry out an initial indication of the suitability of predictive maintenance for a company’s systems. These are the factors that were classified as relevant for at least two plants in the case study. Summarizing the practical use of Maintenance 4.0 modules based on the previous described case studies, some core modules (e.g. the IoT network and IoT platform(s) as well as the usage

Maintenance 4.0: applying IoT technologies to critical infrastructures  335 of additional sensors and machine data) show a high level of maturity. Thus, there are some obstacles to overcome in the data analytics and the data monitoring modules including the function of an earlier detection of errors. These essential benefits of Maintenance 4.0 cannot or can hardly be achieved currently, as the implementation of these modules creates new risks that must be managed properly before being applied at an infrastructure operator. In the following section, we will give an outlook on how to address and manage these risks and prepare the way of increasing the usage of the full Maintenance 4.0 potential in practice.

MAINTENANCE 4.0 KEY CAPABILITIES FRAMEWORK Infrastructure operators must enhance the implementation of their technical capabilities to become more efficient and effective. At the same time, current risks arising with the use of Maintenance 4.0 must be managed and overcome before adopting its functions. To this end, we have derived a Maintenance 4.0 key capabilities framework based on theoretical (e.g. Grieco et al., 2014; Lee et al., 2015) and practical insights gathered in one of the Europe’s largest airport operators. The framework is especially useful for infrastructure and utility companies trying to start building up Maintenance 4.0 capabilities and to offer Maintenance 4.0 services internally or within their business ecosystem in the future. Likewise, companies already delivering Maintenance 4.0 services can evaluate themselves against the framework and derive ways for improvements. We propose that successful operators will be able to (I) efficiently capture data, (II) securely transfer and store data, (III) intelligently analyze data, (IV) successfully bridge between physical and digital world, (V) establish a trusted business ecosystem and (VI) holistically understand process and business cases. Those capabilities together with the technologies that need to be mastered are depicted in Figure 21.2 and explained in the following sections. Efficiently Capture Data Identifying suitable sensors is a key step in rolling out IoT solutions. There are always constraints regarding performance, size, connectivity, security and cost. Often, sensors need to be added noninvasively to maintain integrity of existing equipment and processes. Red-green-blue (RGB) and thermal cameras, LIDAR, or radar combined with digital imagery processing can be treated as noninvasive sensors that are able to capture new data. It is quite clear how IoT and digital solutions can enhance maintenance of physical equipment, which is one of the key processes for an infrastructure company. Modern equipment such as machinery may come with embedded sensors and actuators that collect data and share it to backend services, allowing real-time monitoring of its condition and performance. Furthermore, this data fused with external or ambient sensors creates a digital twin, which optimizes planning and operation. It is also possible to retrofit existing equipment with sensors, which under some circumstances can increase capacity without replacing machinery. Having reliable data is essential for Maintenance 4.0, especially when the predictions of the predictive maintenance trigger automated actions, such as the scheduling of a maintenance measure or the ordering of spare parts (Rott et al., 2021). Where to deploy this technology is an important strategic decision (Mainetti et al., 2011).

336  Handbook on digital platforms and business ecosystems in manufacturing

Figure 21.2

Maintenance 4.0 key capabilities framework (Own illustration)

We have identified an enhanced set of data that should ideally be leveraged to get a more comprehensive view on what is happening in the physical world. This data set is depicted in Table 21.2. Table 21.2

Enhanced data set

Data Cluster

Data Content

Internal Equipment Data

● Real-time sensor data form sensors embedded in equipment – such as voltage, pressure, rotational speed sensors, etc. ● Real-time actuator data from actuators embedded in equipment – such as motor, generator, pump controllers, etc.

External / Environmental Sensor Data ● Real-time sensor data from additional sensors retrofitted or added noninvasively to equipment or surrounding environment, capturing context data such as environmental temperature, vibration, sounds, etc. Maintenance History and Plan

● Automatically captured or manually entered data on equipment, stored in backend

Equipment Master Data

● Static data on make, model, serial number and install date.

systems.

Securely Transfer and Store Data As for many IoT solutions, some of the data that is stored and made accessible to people and machines over the internet might be sensitive. It is thus crucial to find suitable solutions for connectivity and storage, including decisions on what (wireless and/or corporate) networks can be used and where data is processed and stored. In the multi-cloud environments of today,

Maintenance 4.0: applying IoT technologies to critical infrastructures  337 it is often a strategic decision which cloud service providers are selected and how much data processing and storage is done within an organization’s firewall (Botta et al., 2016; Herzfeldt et al., 2019; Floerecke et al., 2021). Due to the expansion of interfaces in the existing IT landscape through IoT and the connection of sensor data with the control of operational systems, there is a new risk of manipulation and failure. For this purpose, mechanisms must be developed and established as to how the data from the field can be meaningfully integrated and checked before it is used to derive decisions for the control and maintenance of the systems. This is the only way to reconcile the advantages of additional process automation and information gathering with the requirements for the secure and reliable operation of infrastructures. There are already technically secure concepts for data transmission. However, it is crucial to keep an eye on these, especially against the background of the merging of operational technology with information technology, and to integrate them into the IT security concepts and continuously improve them. Intelligently Analyze Data As the correlation of this data is relatively complex, machine learning algorithms are used to contextualize the data and create actionable insights. Additionally, the fact that data can be shared across organizational boundaries creates a constantly evolving knowledge base about equipment conditions and ways to prevent failures, improving uptime of all connected machines. The outcome of the machine learning algorithms is the identification of maintenance activities according to defined priorities. There can be integration with OEMs backend systems and services for key maintenance activities, such us ordering spare parts, defining a work schedule and replacing critical equipment. Overall, machine learning improves data quality and usability and could even automatically propose and schedule maintenance actions. Machine learning has been suggested and used in numerous different problems and equipment categories: ● Skog et al., (2017) analyzed sensor data from magnetometers embedded in elevator doors using logical regression. ● Bukhsh et al., (2019) used tree-based machine learning algorithms for predictive maintenance of switches. ● Bangalore and Tjernberg (2015) applied neural networks for the analysis of bearings in wind turbines. ● Susto et al., (2015) used a multi-classifier machine learning algorithm in an ion implanter in semiconductor plants. Machine learning is a key technology to estimate the performance state of an asset, predict maintenance needs and determine possible failure modes, as well as actions to correct the risk of malfunction before the fact (Bukhsh et al., 2019). Hence, the underlying data and prediction model must be of high quality (Rott et al., 2021). This quality can be refined with a constant evolving process of learning and (re)adjusting the Maintenance 4.0 algorithms. Another important factor is training the employees and technicians to correctly interpret the results of the algorithm. This not only addresses a lack of knowledge that must be overcome with new training courses, but also poses an acceptance risk, as employees could feel controlled by the Maintenance 4.0 system. It is central to introduce these new technologies into the current

338  Handbook on digital platforms and business ecosystems in manufacturing and usual working methods of the employees and to break down barriers for usage based on missing technological knowledge. The Bridge between the Physical and Digital World Bridging between physical and digital world is a key challenge in IoT systems. Not every asset can be equipped with sensors, and visual inspections cannot be fully neglected (Ertl et al., 2019). However, visual inspections can be automated, as we have shown with the case studies above, to avoid, for example, foot patrol, expensive helicopter flights, or dangerous climbing on distribution towers and power lines. Robots and drones can inspect (remote) locations periodically as well as in case of an incident. Automated inspections can reduce inspection cost and improve safety, and overall help to increase uptime and reliability of infrastructures. Guiding service technicians through the maintenance processes step by step while they are onsite, or even off-site, and providing them with the necessary information at the right time is another capability that infrastructure operators need to develop and possess. However, building up this bridge between the real and digital worlds creates new risks, e.g. increased susceptibility to failure, unmanageable complexity and excessive amounts of data (Rott et al., 2021). These new requirements must be addressed in the processes of the infrastructure operator. Nonetheless, the first implemented prototypes at the examined airport operator show that that large amount of data can be processed in a targeted manner and complex facts can be displayed, and that there is no increased susceptibility to errors from an installation. Establish a Trusted Ecosystem As described above, the machine learning algorithms analyzing current conditions, malfunction or degradation and proposing corrective actions are at their best when they go beyond an organization’s firewall, considering maintenance and equipment data from other operators around the globe. Besides the mere technical capability of growing such a large data set, trust and interaction must be established between all parties in the business ecosystem, including equipment OEMs, maintenance partners, suppliers and customers (Floerecke et al., 2021; Floerecke and Lehner, 2022). Governance rules need to be set up on how to deal with such a constantly growing knowledge base. To establish this ecosystem on a solid basis, the operator as well as the integrated OEM should adapt or at least strive for building a Maintenance 4.0 system architecture conjointly, e.g. considering the suggested architecture concept by Cachada et al., (2018). A standardized common architectural concept not only reduces the number of interfaces and thus potential IT security lacks but also ensures an efficient and economical way to implement a trusted ecosystem. Holistically Understand Process and the Business Case Any IoT solution is embedded in existing business processes. It is key to understand the impact of these technologies on existing processes and thoroughly analyze to what extent a proposed solution addresses the business goals. Business case modeling remains an important capability, but it is becoming even more important to understand technology trends and other changes. Trend radar has proven itself in practice.

Maintenance 4.0: applying IoT technologies to critical infrastructures  339 IoT technologies can sometimes reduce or eliminate travel time and cost as data that can be accessed from remote locations or collaboration between field agents and staff in a control center is more efficient, for example, because of the use of augmented reality (AR). This complete picture must be understood. However, in view of the quite high initial investment costs, it does not have an advantage over predetermined and corrective maintenance for every asset class (Rott et al., 2021). One of the key factors of success for infrastructure developers and operators that digitalization impacts directly is the ability to manage operational cost in an efficient manner. This does not necessarily mean that operational costs must be minimized by any possible way, but rather making business decisions that can provide the greatest benefits in the overall picture. In asset-intensive industries, a thoughtful and proper maintenance plan is a distinctive lever to improve service quality, reduce unplanned repair costs and enhance both safety and security. Powered by IIoT, Maintenance 4.0 is an approach that can bring significant upside to operations and should be considered when developing a comprehensive operation and maintenance strategy.

CONCLUSION AND OUTLOOK Maintenance 4.0 is the approach of preserving a condition or situation using advanced digital technologies. As the name suggests, very similar technologies as in Industry 4.0 are applied. Condition data from internal or external sensors, performance data from equipment and master data and historical data get analyzed using machine learning algorithms, which can generate a health status and suggest corrective actions. Many of these concepts have already proven to work in research projects and field trials. In our work we wanted to identify what technological capabilities are required to evaluate and implement Maintenance 4.0 technologies in an already existing and operating environment. To address this challenge, we have proposed a key capabilities framework for Maintenance 4.0 that draws on a set of crucial technologies. Hitherto, researchers and practitioners have focused on either one specific capability or technology or tried to implement Maintenance 4.0 aspects within a prototype environment. Our framework builds upon six capabilities necessary to implement a full Maintenance 4.0 stack and combines these capabilities with supportive technologies to realize the intended benefits. With our framework, we designed a holistic architecture for rigorous implementation of Maintenance 4.0 based on the requirements and experiences gained from a real and representative operating environment. For researchers, the next steps include refining the understanding of required capabilities and the combination of research insights from different industries, bringing together existing research streams to make best use of knowledge already available. In addition, the evolving Maintenance 4.0 business ecosystem as a whole and the actors involved with their strategies, business models and corresponding success-leading factors are to be investigated in detail. Moreover, it is important to study how the capabilities and technologies can be implemented and used through adequate business processes and managed properly. For practitioners, more experience and application in the field is required, so that standardization in terms of technology bundles can commence and that experiences get played back to research. The framework is especially useful for infrastructure and utility companies trying to start building up Maintenance 4.0 capabilities to offer Maintenance 4.0 services internally or within their

340  Handbook on digital platforms and business ecosystems in manufacturing business ecosystem in the future. Likewise, companies delivering Maintenance 4.0 services can evaluate themselves against the framework and derive potential ways for improvements. In the end, the following remains to be noted: We have given specific examples for maintenance processes of a specific infrastructure operator. However, we believe much of it is transferrable to other companies and industries and will establish a new generation of ‘e-Services’. The next step from our perspective is the wider use of the technologies in practice, together with increased standardization.

REFERENCES Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H. and Adda, M. (2022). On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Applied Sciences, 12(16), 8081–103. Ansari, F., Glawar, R. and Nemeth, T. (2019). PriMa: A Prescriptive Maintenance Model for Cyber-Physical Production Systems. International Journal of Computer Integrated Manufacturing, 32(4–5), 482–503. Arslankaya, S. and Atay, H. (2015). Maintenance Management and Lean Manufacturing Practices in a Firm Which Produces Dairy Products. Procedia-Social and Behavioral Sciences, 207, 214–24. Bangalore, P. and Tjernberg, L. B. (2015). An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings. IEEE Transactions on Smart Grid, 6(2), 980–7. BearingPoint (2021). Predictive Maintenance Study 2021. BearingPoint, Amsterdam, Netherlands. Benbasat, I., Goldstein, D. K. and Mead, M. (1987). The Case Research Strategy in Studies of Information Systems. Management Information Systems Quarterly, 11(3), 369–86. Bokrantz, J., Skoogh, A., Berlin, C. and Stahre, J. (2017). Maintenance in Digitalized Manufacturing: Delphi-based scenarios for 2030. International Journal of Production Economics, 191, 154-169. Bonvoisin, J., Stark, R. and Seliger, G. (2017). Field of Research in Sustainable Manufacturing (pp. 3–20). Springer. Botta, A., de Donato, W., Persico, V. and Pescapé, A. (2016). Integration of Cloud Computing and Internet of Things: A survey. Future Generation Computer Systems, 56, 684–700. Bukhsh, Z. A., Saeed, A., Stipanovic, I. and Doree, A. (2019). Predictive Maintenance Using Tree-Based Classification Techniques: A Case of Railway Switches. Transportation Research Part C: Emerging Technologies, 101, 35–54. Cachada, A., Barbosa, J., Leitño, P., Gcraldcs, C. A., Deusdado, L., Costa, J. and Romero, L. (2018). Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture. Paper presented at the 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Turin, Italy. Chebel-Morello, B., Nicod, J.-M. and Varnier, C. (2017). From Prognostics and Health Systems Management to Predictive Maintenance 2: Knowledge, Reliability and Decision (Vol. 7): John Wiley & Sons. Chong, S., Pan, G.-T., Chin, J., Show, P. L., Yang, T. C. K. and Huang, C.-M. (2018). Integration of 3D Printing and Industry 4.0 into Engineering Teaching. Sustainability, 10(11), 3960–73. Chui, M., May, B., Narayanan, S. and Shah, R. (2019). What Separates Leaders from Laggards in the Internet of Things. URL: https://​www​.mckinsey​.com/​business​-functions/​digital​-mckinsey/​ our​-insights/​what​-separates​-leaders​-from​-laggards​-in​-the​-internet​-of​-things, accessed on 15 January 2023. Corallo, A., Lazoi, M., Lezzi, M. and Luperto, A. (2022). Cybersecurity Awareness in the Context of the Industrial Internet of Things: A Systematic Literature Review. Computers in Industry, 137, 103614. Cristians, A. and Methven, J. M. (2017). Industry 4.0: Fundamentals and a Quantitative Analysis of Benefits through a Discrete Event Simulation. In Challenges for Technology Innovation: An Agenda for the Future (pp. 177–81). CRC Press.

Maintenance 4.0: applying IoT technologies to critical infrastructures  341 de Faria Jr, H., Costa, J. G. S., and Olivas, J. L. M. (2015). A Review of Monitoring Methods for Predictive Maintenance of Electric Power Transformers based on Dissolved Gas Analysis. Renewable and Sustainable Energy Reviews, 46, 201–9. Decourcy Hinds, M. (1985). Preventive Maintenance: A Checklist. URL: https://​www​.nytimes​.com/​ 1985/​02/​17/​realestate/​preventive​-maintenance​-a​-checklist​.html, accessed on 15 January 2023. Erboz, G. (2017). How to Define Industry 4.0: Main Pillars Of Industry 4.0. In Managerial Trends in the Development of Enterprises in Globalization Era. The 7th International Scientific Conference, Nitra, Slovakia, June 1–2, 2017, (pp. 761–67). Slovak University of Agriculture. Ertl, C., Herzfeldt, A., Floerecke, S. and Krcmar, H. (2019). Entwicklung eines Vorgehensmodells zur Einführung mobiler IT-Lösungen bei Instandhaltungsprozessen, HMD Praxis der Wirtschaftsinformatik, 56(3), 647–59. Floerecke, S. and Lehner, F. (2022). Meta-Study of Success-Related Factors of SaaS Providers Based on a Cloud Computing Ecosystem Perspective. In Handbook on Digital Business Ecosystems (pp. 327–48). Edward Elgar Publishing, Cheltenham. Floerecke, S., Lehner, F. and Schweikl, S. (2021). Cloud Computing Ecosystem Model: Evaluation and Role Clusters. Electronic Markets, 31(4), 923–43. Franciosi, C., Lung, B., Miranda, S. and Riemma, S. (2018). Maintenance for Sustainability in the Industry 4.0 Context: A Scoping Literature Review. IFAC-PapersOnLine, 51(11), 903–8. Freiberger, M., Floerecke, S., Ertl, C. and Herzfeldt, A. (2021): Auswahl einer IoT-Plattform für die Integration von operativen Anlagendaten an deutschen Flughäfen: Vorgehen und Lessons-Learned. Paper presented at INFORMATIK 2021, Berlin, Germany. Gackowiec, P. (2019). General Overview of Maintenance Strategies – Concepts and Approaches. Multidisciplinary Aspects of Production Engineering, 2(1), 126–39. Garg, A. and Deshmukh, S. G. (2006). Maintenance Management: Literature Review and Directions. Journal of Quality in Maintenance Engineering, 12(3), 205–38. Ge, Z., Song, Z., Ding, S. X. and Huang, B. (2017). Data Mining and Analytics in the Process Industry: The role of Machine Learning. IEEE Access, 5, 20590–616. Grieco, L., Rizzo, A., Coluccia, S., Sicari, S., Piro, G., Di Paola, D. and Boggia, G. (2014). IoT-Aided Robotics Applications: Technological Implications, Target Domains and Open Issues. Computer Communications, 54, 32–47. Herzfeldt, A., Floerecke, S., Erl, C. and Krcmar, H. (2019). Examining the Antecedents of Cloud Service Profitability. Journal of Cloud Applications and Computing, 9(4), 38–65. Jardine, A. and Tsang, A. (2005). Maintenance, Replacement, and Reliability – Theory and Applications. CRC Press, Boca Raton, USA. Jasiulewicz-Kaczmarek, M. and Gola, A. (2019), Maintenance 4.0 Technologies for Sustainable Manufacturing – An Overview. IFAC PapersOnLine, 52(10), 91–6. Jasiulewicz-Kaczmarek, M., Legutko, S. and Kluk, P. (2020). Maintenance 4.0 Technologies – New Opportunities for Sustainability Driven Maintenance. Management and Production Engineering Review, 11(2), 74–87. Jin, X., Weiss, B., Siegel, D. and Lee J. (2016). Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing. International Journal of Prognostics and Health Management, 7, 1–34. Jones, M., Zarzycki, L. and Murray, G. (2019). Does Industry 4.0 Pose a Challenge for the SME Machine Builder? A Case Study and Reflection of Readiness for a UK SME. In Precision Assembly in the Digital Age: 8th IFIP WG 5.5 International Precision Assembly Seminar, IPAS 2018, Chamonix, France, Revised Selected Papers (pp. 183–97). Springer International Publishing. Jovane, F., Koren, Y. and Boer, C. R. (2003). Present and Future of Flexible Automation: Towards New Paradigms. CIRP Annals, 52(2), 543–60. Lee, J., Kao, H.-A. and Yang, S. (2014). Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment. Procedia CIRP, 16, 3–8. Lee, J., Ardakani, D., Yang, S. and Bagheri, B. (2015). Industrial Big Data Analytics and Cyber-Physical Systems for Future Maintenance & Service Innovation. Procedia CIRP, 38, 3–7.

342  Handbook on digital platforms and business ecosystems in manufacturing Legát, V., MošnA, F., ALeš, Z., and JurčA, V. (2017). Preventive Maintenance Models – Higher Operational Reliability. Eksploatacja i Niezawodność – Maintenance and Reliability, 19(1), 134–41. Leyh C., Martin S. and Schäffer T. (2017). Industry 4.0 and Lean Production – A Matching Relationship? An Analysis of Selected Industry 4.0 Models. Paper presented at the Federated Conference on Computer Science and Information Systems, Prague, Czech Republic. Mainetti, L., Patrono, L. and Vilei, A. (2011). Evolution of Wireless Sensor Networks Towards the Internet of Things: A Survey. Paper presented at the 19. International Conference on Software, Telecommunications and Computer Networks, Split, Croatia. Matyas, K., Nemeth, T., Kovacs, K. and Glawar, R. (2017). A Procedural Approach for Realizing Prescriptive Maintenance Planning in Manufacturing Industries. CIRP Annals, 66(1), 461–4. Morgan, J. and O’Donnell, G. E., (2015). The Cyber Physical Implementation of Cloud Manufacturing Monitoring Systems. Procedia CIRP, 33, 29–34. Peffers, K., Tuunanen, T., Rothenberger, M. A. and Chatterjee, S. (2007). A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems, 24(3), 45–77. Peres, R. S., Dionisio Rocha, A., Leitao, P. and Barata, J., (2018). IDARTS – Towards Intelligent Data Analysis and Real-Time Supervision for Industry 4.0. Computers in Industry, 101, 138–46. Pierdicca, R., Frontoni, E., Pollini, R., Trani, M. and Verdini, L. (2017). The Use of Augmented Reality Glasses for the Application in Industry 4.0. In: Augmented Reality, Virtual Reality, and Computer Graphics: 4th International Conference, AVR 2017, Ugento, Italy, June 12–15, 2017, Proceedings, Part I 4 (pp. 389–401). Springer International Publishing. Rott, J., Floerecke, S., Ertl, C., Herzfeldt, A., Böhm, M. and Krcmar, H. (2021). Ökonomische Analyse dreier technischer Anlagen hinsichtlich deren Eignung für Predictive-Maintenance – Eine multiple Fallstudie am Flughafen München. Informatik Spektrum, 44(3), 201–13. Rott, J., Ariyanayagam, L., Zink, M., Klose, S., Ertl, C. and Harth, A. (2022). Smarte Produkt-Service-Systeme am Beispiel der IoT-basierten Ortung von Dollies – Eine technologische und wirtschaftliche Analyse am Flughafen München. In Entrepreneurship der Zukunft: Digitale Technologien und der Wandel von Geschäftsmodellen (pp. 275–304). Wiesbaden: Springer. Sambrekar, A. A., Vishnu, C. R. and Sridharan, R. (2018). Maintenance Strategies for Realizing Industry 4.0: An Overview. Paper presented at the International Conference in Emerging Trends in Engineering, Science and Technology (ICETEST 2018), Thrissur, Kerala, India. Schuldenfrei, M. (2019). Horizontal and Vertical Integration in Industry 4.0. URL: https://​ www​ .mbtmag​.com/​business​-intelligence/​article/​13251083/​horizontal​-and​-vertical​-integration​-in​-industry​ -40, accessed on 13 April 2023. Shi, W., Cao, J., Zhang, Q., Li, Y. and Xu, L. (2016). Edge Computing: Vision and Challenges. IEEE Internet of Things Journal, 3(5), 637–46. Silvestri, L., Forcina, A., Introna, V., Santolamazza, A. and Cesarotti, V. (2020). Maintenance Transformation through Industry 4.0 Technologies: A Systematic Literature Review. Computers in Industry, 123, 103335. Skog, I., Karagiannis, I., Bergsten, A. B., Härdén, J., Gustafsson, L. and Händel, P. (2017). A Smart Sensor Node for the Internet-of-Elevators – Non-Invasive Condition and Fault Monitoring. IEEE Sensors Journal, 17(16), 5198–208. Spiegel, S., Mueller, F., Weismann, D. and Bird, J. (2018). Cost-Sensitive Learning for Predictive Maintenance. arXiv preprint, 10979, 1–18. Susto, G. A., Schirru, A., Pampuri, S., McLoone, S. and Beghi, A. (2015). Machine Learning for Predictive Maintenance: A Multiple Classifier Approach. IEEE Transactions on Industrial Informatics, 11(3), 812–20. Tauterat, T. (2018). Verfahren zur Bewertung von Predictive Maintenance für Anbieter von Instandhaltungsdienstleistungen. BoD – Books on Demand, Hamburg, Germany. Vaidya, S., Ambad, P. and Bhosle, S. (2018). Industry 4.0 – A Glimpse. Procedia Manufacturing, 20, 233–8.

Maintenance 4.0: applying IoT technologies to critical infrastructures  343 Vogl, G., Weiss, B. and Helu, M. (2016). A Review of Diagnostics and Prognostics Capabilities and Best Practices for Manufacturing. Journal of Intelligent Manufacturing, 30, 79–95. Wang, S., Wan, J., Zhang, D., Li, D. and Zhang, C. (2016). Towards Smart Factory for Industry 4.0: A Self-Organized Multi-Agent System with Big Data Based Feedback and Coordination. Computer Networks, 101, 158–68. Wood, B. (2003). Building Care. Wiley-Blackwell, Oxford, UK. Yin, R. K. (2018). Case Study Research: Design and Methods. Thousand Oaks: Sage Publications. Zolotová, I., Papcun, P., Kajáti, E., Miškuf, M. and Mocnej, J. (2020). Smart and Cognitive Solutions for Operator 4.0: Laboratory H-CPPS Case Studies. Computers & Industrial Engineering, 139, 105471.

22. A digital platform for heterogeneous fleet management in manufacturing intralogistics Nitish Singh, Alp Akçay, Quang-Vinh Dang, Ivo Adan and E. A. Thijssen

INTRODUCTION A ‘lights-out factory’ requires the control of operations on the shop floor without human interference by effectively using advanced intralogistics solutions for material handling. Automated guided vehicles (AGVs) are one of those solutions. AGVs are driverless vehicles that transport goods and materials throughout various areas, e.g. shipping and receiving areas, storage and workstations (Vivaldini et al., 2016). The applications of AGVs have grown enormously because of their benefits, such as flexibility in processes, space utilization, product safety and computer integration and control. More recently, AGV technology has been enhanced with new advances in their flexibility, where each vehicle may have specific capabilities (e.g. one can lift light or heavy loads, while another can tow loads) to perform heterogeneous tasks (Riazi et al. 2019; De Ryck et al., 2020). This type of tasks is prevalent in the high-tech manufacturing sector, and it necessitates the handling of materials with specialized equipment, thus requiring a fleet of AGVs with varying capabilities. An example of a factory requiring different material handling tasks is Brainport Industries Campus (BIC), a new high-tech campus constructed in Eindhoven, The Netherlands, which serves as a home front for far-reaching partnerships between suppliers, specialist companies, and innovative educational and knowledge institutions. The campus is a joint initiative of high-tech suppliers, also called tenants, to coexist and collaborate on multiple fronts. Tenants in BIC are classified as high mix low volume (HMLV) companies, companies that produce custom parts (high mix) for other companies in small quantities (low volume), and material handling in such an environment necessitates use of AGVs with a variety of handling capabilities. Since multiple tenants collectively make use of a pool of AGVs, practitioners are looking for a better understanding of how to manage large AGV fleets with heterogeneous capabilities that can cater to a more diverse set of requirements. Industry feedback indicates that most companies rely on the prepackaged fleet management software that is provided as standard by AGV manufacturers. However, such software is only applicable to a homogeneous AGV fleet with the same capabilities. Therefore, it is of practical importance to have a platform that allows management of AGVs belonging to different fleet managers in an integrated and coordinated manner. In a multisided market with multiple actor-groups, platforms serve as central intermediaries, offering required infrastructure and making it possible for interactions between actor groups (Mosch and Obermaier, 2022). These actor groups, including consumers, suppliers and institutions, form the business ecosystem. Digital platforms and business ecosystems are inherently interconnected in the modern economy. Recent studies (Chica et al., 2019) have shown that moderation of shared resources via an institutional arrangement (mediator 344

Digital platform for heterogeneous fleet management  345 platform) gives rise to a platform economy and these digital ecosystems allow open, flexible and demand-driven collaboration (Aulkemeier et al., 2019). In this chapter, we introduce an intralogistics-as-a-service (ILaaS) digital platform that enables participating tenants to provision their fleet of AGVs for use by other tenants outside their own production environment. Tenants on this platform contribute their AGVs to a central intermediary at the BIC. Similar to how Uber provides an underlying platform where other actors integrate their resources, such as cars and drivers, the central intermediary does so for tenants at the BIC. The resulting platform enables heterogeneous AGV fleet management in manufacturing intralogistics at the BIC. In the smart industrial sphere, engagement platforms based on technologies including big data, cloud computing, and the Internet of Things provide structural support for the exchange and integration of resources and thereby enable value co-creation between individuals and organizations and, in our case, tenants (Breidbach and Brodie 2017; Senyo et al., 2019; Michalke et al., 2022). The proposed ILaaS platform relies on information and communication technologies (ICTs) capable of serving multiple tenants (or actors) sharing a fleet of heterogeneous AGVs, thereby fostering a digital business ecosystem (DBE) where actors contribute a specific resource (AGVs). The rest of the chapter is structured as follows. Relevant literature is discussed in section two. The architecture of the proposed platform is present in section three. In section four we discuss the applicability and the practical relevance of the proposed platform. Finally, we present conclusions in section five.

LITERATURE REVIEW Digital Business Ecosystems The term ‘ecosystem’ is used to describe connectivity and dependency in relationships among actors (Baumann and Leerhoff, 2022). The business ecosystem is recognized as a loose network of actors, including suppliers, distributors, and outsourced companies that collaborate and compete to create new goods (Moore, 1993). Moore goes on to discuss the mutual innovation-driven co-evolution of actors (species) in the business ecosystem later in his works where he highlights that value is produced by a network of companies with many horizontal relationships (Moore, 1996). Modern technologies, globalization and deregulation have led to the development of co-creative ecosystems heavily reliant on digital technologies. In these ecosystems, heterogeneous actors integrate knowledge and resources to their mutual benefit (Edvardsson et al., 2011). Digitalization is disrupting traditional business models and reshaping organizational structures (Verhoef et al., 2021). Combining business and digital ecosystems results in new collaborative organizational networks that are referred to as digital business ecosystems (DBEs) (Senyo et al., 2019). A DBE is an extension of Moore’s business ecosystem in which digital platforms play a dominant role. Technology infrastructures known as digital platforms enable participating businesses to design, set up and provide innovative services quickly and on an unprecedented scale (Franco et al., 2009; Yoo et al., 2012).

346  Handbook on digital platforms and business ecosystems in manufacturing Digital Platforms The most valuable corporations in the world today, for instance, Apple, Microsoft and Amazon, have experienced tremendous growth over the past ten years, by developing and furthering digital platform business models (Cusumano et al., 2020). Lusch and Nambisan (2015) highlight that service exchange in business ecosystems is not very efficient without a digital platform. The platforms allow users (individuals and organizations) to individually choose how much and in what ways they want to contribute, ‘within the platform’s rules and resources’ (Kretschmer et al., 2022). Although the platform follows a set of established norms, members have the freedom to decide their degree of participation, full or partial. Digital Business Ecosystems in Manufacturing DBEs are addressed in various contexts in the literature on manufacturing (Suuronen et al., 2022). Smart manufacturing has been researched as a way to improve conventional manufacturing processes (Kusiak 2018; Liu et al. 2021). In order to satisfy consumer requests, smart manufacturing aims to create fully integrated, collaborative manufacturing systems that can adapt to changing conditions and demands in real time across the supply chain, the factory and the market (Lu et al., 2019; Suuronen et al., 2022). Furthermore, Industry 4.0’s underlying ICT infrastructures make it possible to use more advanced technologies, such as artificial intelligence (AI), for smart manufacturing. When apps and services are made available for physical machines, their value increases, which can start positive feedback loops. There is a plethora of research on industrial internet platforms (IIPs) (Wang, D., et al., 2020; Wang, J., et al., 2020). IIPs control how physical and digital components interact and are essential to how industrial systems work. Numerous companies, such as GE Predix, ABB Ability, Siemens MindSphere and PTC ThingWorx have developed IIPs. The Predix platform functions as a cloud-based operating system for manufacturers, allowing manufacturers to optimize processes with the use of advanced analytics. The ABB Ability platform is a data-driven decision-making platform. A million or more systems and devices can be connected by MindSphere to offer devices predictive maintenance services. PTC ThingWorx is a platform for industrial innovation that focuses on gathering data and giving consumers insightful information (Suuronen et al., 2022). According to Mitchell and Singh (1996), businesses that pursue alliance connections have a higher chance of surviving in the market than those that work in a siloed way, in which buyers and sellers act independently of one another, without interfering with each other; the main reason being that a single company lacks the resources and expertise necessary to continuously explore and exploit continuously shifting markets. However, many manufacturers still lack in developing digital platforms for their internal and external processes and thereby fall behind in leveraging the value from it (Lager, 2017). The biggest impediment to fostering a successful DBE in manufacturing is the lack of its acceptance among participating actors. To create value and exploit a DBE, members must cooperate, which is often harder to achieve between companies that compete in the market against each other. Additionally, a digital platform should be built with a clear purpose. Building a generic digital platform can have a negative impact on industries that it is not properly set up to serve (Greve and Song, 2017; Troisi et al., 2018; Uysal and Mergen, 2021). Platform users should pledge to fairly distribute revenues among themselves. A profit maximizing approach often adversely impacts a platform ecosystem with participating actors.

Digital platform for heterogeneous fleet management  347 Finally, trust issues are prevalent across the manufacturing sector. Sharing sensitive data generated from industrial processes may lead to a number of data issues, including data fusion, synchronization and data security (Tao et al., 2020). Existing Platforms Searching for literature about platforms for a shared capability fleet serving multiple tenants is bounded by a precise definition of the system to be designed. Erol et al. (2012) propose a multi-agent-based system (MAS) for AGVs and machines within a manufacturing environment. The existing system is built, however, for a homogeneous fleet and a single fleet manager, and it does not account for complexities arising due to different fleet types and managers. They define MAS as a decentralized control system where each subsystem is controlled by its own controller based on local information and actions. In their system, every agent contains some form of intelligence and acts autonomously. The term MAS occurs more often in the search for platforms. Mayer et al. (2019) present a framework consisting of a simulation model, an MAS including centralized or decentralized control logic and a data-exchange interface. They define MAS to be a ‘loosely coupled network of problem-solving entities that work together to find answers to problems that are beyond the individual capabilities or knowledge of each entity’. Lohse et al. (2020) discuss the increase of data generation in production environments. They propose a real time reaction (RTR) concept that is constructed for a cyber physical production system. In RTR, decision-making makes use of live production states. Additionally, they build RTR-relevant software that includes a plant visualization to visualize a manufacturing environment (e.g. machines and AGVs), a fleet management system (FMS) for controlling the AGVs, an Internet of Things platform used as middleware for data generated by IoT devices and a manufacturing execution system (MES) to provide the production order, routing and process parameters. This chapter introduces components needed for a modular platform that can be adapted to fit different production environments. This template-like structure is interesting for the BIC case where the fleet size and capability, as well as the participation of more and more tenants, remains uncertain, with a multifold ramp up in production capacity expected in the future. The digital platform should be able to handle real-time data from different actors, databases, and software. The most important factors to be considered are scalability, power consumption and reliability. Naik (2017) provides an assessment of IoT messaging protocols such as MQTT (message queuing telemetry transport) and CoAP (constrained application protocol). MQTT is a lightweight open messaging protocol that gives network clients an easy way to share telemetry data in low-bandwidth settings. The protocol is used for machine-to-machine (M2M) communication and uses a publish/subscribe communication structure. The CoAP is a popular document transmission protocol for resource-constrained, low-power devices. MQTT and CoAP are both suitable as both are designed to work on low bandwidth and resource requirements. CoAP is slightly favored when it comes to power consumption (Naik, 2017; Yi et al., 2016). Khaled and Helal (2019) mentioned that MQTT is scalable with the help of a program called EMQTT, a fully open source and massively scalable MQTT technology. In contrast, Kovatsch, Lanter and Shelby (2014) propose scalable IoT cloud services based on CoAP. Both are highly scalable, however, in terms of reliability, MQTT is superior to CoAP. The reliability is expressed according to the Quality of Service level (QoS) of MQTT. It has three

348  Handbook on digital platforms and business ecosystems in manufacturing stages, 0, 1 and 2, which means a message is delivered at most once, at least once, and exactly once, respectively. In our platform, we use MQTT as the messaging protocol. Finally, we also propose a modular structure for the platform, with each actor maintaining its internal state with intra-module communication being facilitated by the MQTT messaging server.

PROPOSED ILAAS PLATFORM Certain prerequisites need to be met before a digital platform can be put to effective use. While the platform itself should be easy to use and onboard, the platform should also gain acceptance from participating actors. To create value, participating actors must cooperate in the form of data sharing, develop communication standards and be open to resource sharing. A general resource-sharing platform could not be built as customized modules needed to be built for providing intralogistics services to tenants. Building a general digital platform can adversely affect industries that it has not been adequately configured to serve (Greve and Song, 2017). In the factory of the future, multiple tenants housed together can share an AGV fleet that is able to increase productivity and decrease costs substantially. In this section, we present the architecture of our ILaaS platform, enabling the tenants to provide their AGVs for shared usage. In order to establish a working platform, a communication framework between the different components should be established. In this framework, information flows between different modules, and ensuing events within modules are indicated in Figure 22.1. The global communication lines between the different modules of the system are shown. Note that the system is semicentralized, as the scheduler module obtains all necessary information, but the states are maintained individually by each actor as recently popularized by Didden, Dang and Adan (2021). In Figure 22.1, the proposed platform can be seen with interacting modules, namely, tenants, database, central cloud server, messaging server, fleet managers and heterogeneous fleet. Fleet manager software, provided by each AGV manufacturer, provides an interface, known as application programming interface (API), to send and receive information about their resources (availability, battery charge and locations) and transport instruction, respectively (Ofoeda et al., 2019). To enable bidirectional communication between certain modules, an MQTT broker is implemented as a communication protocol. Each component is handled separately and it is explained how they are designed. Tenants The tenants are the end users of the system. Web pages in the form of HTML pages are rendered to keep them user-friendly and easy to use. Forms are implemented for the end user to submit their transport request details, such as pickup location, delivery location and capability requirements (e.g. heavy or light loads). Using HTTP POST and GET methods, data can be exchanged between the tenants and the central cloud server. Complete management of a transport request is done through web pages: submitting, editing, deleting and finding a request. This module is the only part of the system where user input is required. A screenshot of the user interface can be seen in Figure 22.2.

Figure 22.1

System architecture

Digital platform for heterogeneous fleet management  349

350  Handbook on digital platforms and business ecosystems in manufacturing

Figure 22.2

User interface

Central cloud server Smart manufacturing is seen as the cornerstone of the fourth industrial revolution. It defines the current trend in intelligent manufacturing by connecting machines and resources to the internet. Smart factories generate a lot of data from their machines and resources, which are used for optimization purposes. The central cloud server hosts all the necessary intelligence and coordination schemes to facilitate the working of the proposed platform. The intelligence is housed in this component in the form of expert-designed algorithms. The algorithms make use of the real-time fleet state, incoming transport requests and real time shop floor state as input to the designed algorithms for AGV assignment and repositioning decisions. The AGVs’ status with respect to the battery charge and location is of importance to these algorithms and obtained from fleet managers, which maintain that information locally by using MQTT. MQTT An assessment was done on several IoT messaging protocols, and MQTT was chosen for its reliability. Its in-built features allow the delivery of messages with a high degree of reliability, even in case of bandwidth disruption. In this framework, a locally hosted messaging server is used to which all participating modules connect on a specific port. MQTT is a publish and subscribe messaging protocol that utilizes a message mediator (broker), meaning that upon connecting to the broker, a client can subscribe to certain topics and can publish (sending a message) on this topic through the broker; all subscribed clients will then receive this message. An example of such a publish and subscribe scheme and topics is highlighted in Figure 22.3. For instance, a fleet manager is subscribed to the topic ‘/requests’ and receives a message on this topic when the server publishes it. This fleet manager can interpret the message and send its own message on one of the topics it is publishing to. Fleet managers and AGV fleet Each tenant that joins the platform may choose to make their AGVs available for partial or full-service provision among participating tenants. These tenants provide access to their own fleet and, consequently, fleet managers. Fleet managers, while customized for each fleet, have

Figure 22.3

MQTT publish and subscribe protocol with topics

Digital platform for heterogeneous fleet management  351

352  Handbook on digital platforms and business ecosystems in manufacturing basic functionality allowing extracting real time information, via APIs, about AGVs’ locations, battery charge and availability. Similarly, they have methods that allow dispatching AGVs to specific locations on the shop floor. While each fleet manager has a variant of the aforementioned functionality, the post-processing steps at the central cloud server ensure that the dispatch request is interpretable by the assigned fleet manager and, in turn, the assigned AGV.

DISCUSSION The BIC enables the collaboration of manufacturing agents (tenants), and the proposed platform acts as an operational enabler of such an ecosystem. Thus, collaborative manufacturing in a setting such as that of BIC requires the use of cloud manufacturing. Cloud manufacturing is enabled by a number of technologies: (1) the traditional manufacturing process, (2) cloud computing, (3) IoT, (4) virtualization, (5) service-oriented technologies, and (6) advanced computing technologies. Further, in contrast to existing manufacturing models, cloud manufacturing deals with uncertainty in the capacity of available resources as well as demand from customers (Li et al. 2010). In cloud manufacturing, manufacturing resources are virtualized, i.e., mapped from their physical space into their virtual counterpart (by using IoT, cloud computing, and the internet), and made into a resource pool that can be accessed to fulfill demands. AGVs are one such smart resource at the BIC (see Figure 22.4 for showcases of AGVs). The AGVs can differ in travel speed, charging rate and, material handling capabilities. For example, some AGVs are equipped with a robotic arm for flexibility in material handling, some are capable of lifting heavy loads, and some allow towing of loads. We term a resource ‘smart’ when it can be virtualized and controlled through a digital platform (Raff et al., 2020). These cyber-physical systems allow real-time tracking of their states and management via the internet. These capabilities enable a greater degree of internal and external connectivity and interoperability of smart manufacturing enterprises. The proposed ILaaS platform, illustrated in Figure 22.1, is a campus-wide platform that facilitates inter-tenant communication inside the BIC. It is a cloud-based digital platform, an internet-based software, that allows tenants to exchange data and information with each other. It also keeps track of smart resources for their efficient management. Finally, the ILaaS digital platform is the enabler of a sharing economy within the BIC and acts as a mediator service.

Figure 22.4

Heterogeneous fleet at BIC

Digital platform for heterogeneous fleet management  353 The term sharing economy is used to refer to business models built around the on-demand access to products and services mediated by online platforms that match many suppliers to many buyers via peer-to-peer, on-demand service, or on-demand rental models (Benjaafar and Hu, 2020). Businesses, typically, do not collaborate on tasks such as production, logistics and warehousing. However, sharing manufacturing and transportation resources across manufacturing chains will become common practice (Kusiak, 2018). Some industrial applications have already appeared. For instance, the Dutch company ‘Floow2’ established an online community for manufacturers to share their idle assets (e.g. equipment, employees and workspaces) to gain additional revenue. Haier group, a novel household electrical appliance producer in China, plans to create a resource-sharing ecosystem in which the resources involved throughout the company’s supply chain can all be accessible to the outside partners independently. Unlike the classic supply chain settings in which a firm makes inventory and supply decisions, in a sharing economy, supply is crowd-sourced and can be modulated by a platform. The ILaaS platform proposed in section three is a facilitator platform built on the principles of sharing economy, as highlighted in Figure 22.5. The platform presents a modular and scalable solution to easily provision and mediate resource sharing among participating tenants at the BIC. The participating tenants at the BIC are small to medium-sized manufacturing enterprises with a high mix of product variants and a low volume of production quantities. The prospect of sharing resources is particularly appealing for companies that do not plan for under-capacity, since the number of installed machines and equipment cannot be changed on short notice, and companies with temporary over-capacity can offer resources to companies with temporary under-capacity (Freitag et al., 2015). In times of fleet capacity shortage, either due to uncertain demand or other macroeconomic factors, it is cost-effective for tenants to utilize AGVs from their peers rather than investing in capacity expansion. Also, tenants without a fleet of their own can utilize the ILaaS platform instead of setting up expensive AGV infrastructures. Tenants belonging to the ILaaS platform can act as providers or consumers. A consumer tenant submits a request with a proposed budget, quality, time and capability specifications. Then, a provider tenant offers its excess resources for the desired fee. A coordinated allocation process then ensues via one of the algorithms housed in the central cloud server, as shown in Figure 22.1. Advanced algorithms for controlling heterogeneous AGV fleets have been proposed by Singh et al. (2022) and Dang et al. (2021). Thus, the central cloud server, in the presence of this information, should allocate AGVs in such a manner that none of the tenants have an incentive to move away from the proposed assignment (also known as Pareto efficiency). The platform houses advanced algorithms that may consider, but are not limited to, various parameters such as the urgency of a request, the proposed budget and quality, time and capability specifications when allocating resources. These methodologies can be refined and customized based on the specific requirements of the tenants and the nature of the shared resources. Finally, in a collaborative framework such as that of BIC, it is essential to provide reliable services. A lack of commitment can introduce friction between tenants due to a lack of trust, which can ultimately prove detrimental to the platform’s efficiency. The impact of unreliable players in a sharing economy has been studied by Chica et al. (2019), and Szaller, Egri, and Kádár (2020) provide a decision-making framework based on trust and commitment to organize daily operations in a sharing network of companies. Thus, the ILaaS platform highlights the potential of digital platforms, as an enabler of sharing economy in a multi-tenant environment through interdisciplinary integration. Companies can leverage the proposed platform to gain competitive and innovative edge in building, evaluating and scaling automated logistics solutions without hefty upfront investments and by keeping associated risks to a minimum.

354  Handbook on digital platforms and business ecosystems in manufacturing

Figure 22.5

Platform built on sharing economy principles

CONCLUSIONS In this chapter, an intralogistics-as-a-service platform is proposed. The proposed platform can be utilized in any setting where multiple actor groups wish to collaboratively access virtualized manufacturing assets. The proposed platform is modular and scalable, accommodating newer actors as they join an existing business ecosystem. In our study, we showcase how the proposed platform is developed to control a heterogeneous fleet of AGVs for material handling in a high-mix environment, such as that of the BIC. Tenants on this platform may serve dual roles as providers or consumers. The platform also houses advanced algorithms, of platform owner’s choosing, for efficient management of the assets. However, for the platform to be efficient and applicable, it needs to account for differences in operational processes and technological standards among different tenants. Additionally, the proposed platform requires each actor to act rationally, in a manner which is in the collective interest of all participating actors. Despite these limitations, the ILaaS digital platform showcases the transformative potential of such platforms in shaping the future of manufacturing and logistics in a digitally interconnected era.

Digital platform for heterogeneous fleet management  355

REFERENCES Aulkemeier, Fabian, Maria-Eugenia Iacob, and Jos van Hillegersberg. 2019. ‘Platform-Based Collaboration in Digital Ecosystems.’ Electronic Markets 29 (4): 597–608. Baumann, Sabine, and Marcel Leerhoff. 2022. ‘Networks, Platforms, and Digital Business Ecosystems: Mapping the Development of a Field.’ In Handbook on Digital Business Ecosystems. Edward Elgar Publishing. Benjaafar, Saif, and Ming Hu. 2020. ‘Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?’ Manufacturing & Service Operations Management 22 (1): 93–101. Breidbach, Christoph F, and Roderick J Brodie. 2017. ‘Engagement Platforms in the Sharing Economy: Conceptual Foundations and Research Directions.’ Journal of Service Theory and Practice, 27 (4): 761–77. Chica, Manuel, Raymond Chiong, Marc TP Adam, and Timm Teubner. 2019. ‘An Evolutionary Game Model with Punishment and Protection to Promote Trust in the Sharing Economy.’ Scientific Reports 9 (1): 1–12. Cusumano, Michael, David Yoffie, and Annabelle Gawer. 2020. The Future of Platforms. MIT Sloan Management Review. Available at: https://​sloanreview​.mit​.edu/​article/​the​-future​-of​-platforms/​. Dang, Quang-Vinh, Nitish Singh, Ivo Adan, Tugce Martagan, and Dirk van de Sande. 2021. ‘Scheduling Heterogeneous Multi-Load AGVs with Battery Constraints.’ Computers & Operations Research 136: 105517. De Ryck, Matthias, Mark Versteyhe, and Frederik Debrouwere. 2020. ‘Automated Guided Vehicle Systems, State-of-the-Art Control Algorithms and Techniques.’ Journal of Manufacturing Systems 54: 152–73. Didden, Jeroen BHC, Quang-Vinh Dang, and Ivo JBF Adan. 2021. ‘A Semi-Decentralized Control Architecture for High-Mix-Low-Volume Factories in Industry 4.0.’ Manufacturing Letters 30: 11–14. Edvardsson, Bo, Bård Tronvoll, and Thorsten Gruber. 2011. ‘Expanding Understanding of Service Exchange and Value Co-Creation: A Social Construction Approach.’ Journal of the Academy of Marketing Science 39 (2): 327–39. Erol, Rizvan, Cenk Sahin, Adil Baykasoglu, and Vahit Kaplanoglu. 2012. ‘A Multi-Agent Based Approach to Dynamic Scheduling of Machines and Automated Guided Vehicles in Manufacturing Systems.’ Applied Soft Computing 12 (6): 1720–32. Franco, Rubén Darío, Ángel Ortiz Bas, and Francisco Lario Esteban. 2009. ‘Modeling Extended Manufacturing Processes with Service-Oriented Entities.’ Service Business 3 (1): 31–50. Freitag, Michael, Till Becker, and Neil A Duffie. 2015. ‘Dynamics of Resource Sharing in Production Networks.’ CIRP Annals 64 (1): 435–8. Greve, Henrich R, and Seo Yeon Song. 2017. ‘Amazon Warrior: How a Platform Can Restructure Industry Power and Ecology.’ In Entrepreneurship, Innovation, and Platforms. Emerald Publishing Limited. Khaled, Ahmed E, and Sumi Helal. 2019. ‘Interoperable Communication Framework for Bridging RESTful and Topic-Based Communication in IoT.’ Future Generation Computer Systems 92: 628–43. Kovatsch, Matthias, Martin Lanter, and Zach Shelby. 2014. ‘Californium: Scalable Cloud Services for the Internet of Things with Coap.’ In 2014 International Conference on the Internet of Things (IOT), 1–6. IEEE. Kretschmer, Tobias, Aija Leiponen, Melissa Schilling, and Gurneeta Vasudeva. 2022. ‘Platform Ecosystems as Meta-Organizations: Implications for Platform Strategies.’ Strategic Management Journal 43 (3): 405–24. Kusiak, Andrew. 2018. ‘Smart Manufacturing.’ International Journal of Production Research 56 (1–2): 508–17. Lager, Thomas. 2017. ‘A Conceptual Framework for Platform-Based Design of Non-Assembled Products.’ Technovation 68: 20–34. Li, Bo-Hu, Lin Zhang, Shi-Long Wang, Fei Tao, Jun Wei Cao, Xiao Dan Jiang, Xiao Song, and Xu Dong Chai. 2010. ‘Cloud Manufacturing: A New Service-Oriented Networked Manufacturing Model.’ Computer Integrated Manufacturing Systems 16 (1): 1–7.

356  Handbook on digital platforms and business ecosystems in manufacturing Liu, Qiang, Jiewu Leng, Douxi Yan, Ding Zhang, Lijun Wei, Ailin Yu, Rongli Zhao, Hao Zhang, and Xin Chen. 2021. ‘Digital Twin-Based Designing of the Configuration, Motion, Control, and Optimization Model of a Flow-Type Smart Manufacturing System.’ Journal of Manufacturing Systems 58: 52–64. Lohse, Oliver, Stefan Krause, Christopher Saal, and Christian Lipp. 2020. ‘Real Time Reaction Concept for Cyber Physical Production Systems.’ In 2020 3rd International Symposium on Small-Scale Intelligent Manufacturing Systems (SIMS), 1–5. IEEE. Lu, Yuqian, Tao Peng, and Xun Xu. 2019. ‘Energy-Efficient Cyber-Physical Production Network: Architecture and Technologies.’ Computers & Industrial Engineering 129: 56–66. Lusch, Robert F, and Satish Nambisan. 2015. ‘Service Innovation.’ MIS Quarterly 39 (1): 155–76. Mayer, Sebastian, Christian Arnet, Dennis Gankin, and Christian Endisch. 2019. ‘Standardized Framework for Evaluating Centralized and Decentralized Control Systems in Modular Assembly Systems.’ In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 113–19. IEEE. Michalke, Simon, Lisa Lohrenz, Dominik Siemon, Christoph Lattemann, and Susanne Robra-Bissantz. 2022. ‘Design Knowledge for Digital Business Ecosystems: Towards Design Principles for Digital Engagement Platforms.’ In Handbook on Digital Business Ecosystems, 161–76. Edward Elgar Publishing. Mitchell, Will, and Kulwant Singh. 1996. ‘Survival of Businesses Using Collaborative Relationships to Commercialize Complex Goods.’ Strategic Management Journal 17 (3): 169–95. Moore, James F. 1993. ‘Predators and Prey: A New Ecology of Competition.’ Harvard Business Review 71 (3): 75–86. Moore, James F. 1996. ‘The Death of Competition: Leadership and Strategy.’ The Age of Business Ecosystems. New York, NY: HarperCollins. Mosch, Philipp, and Robert Obermaier. 2022. ‘Digital Platforms in the Industrial Sphere: Value Creation Logics and Platform Types.’ In Handbook on Digital Business Ecosystems. Edward Elgar Publishing. Naik, Nitin. 2017. ‘Choice of Effective Messaging Protocols for IoT Systems: MQTT, CoAP, AMQP and HTTP.’ In 2017 IEEE International Systems Engineering Symposium (ISSE), 1–7. IEEE. Ofoeda, Joshua, Richard Boateng, and John Effah. 2019. ‘Application Programming Interface (API) Research: A Review of the Past to Inform the Future.’ International Journal of Enterprise Information Systems (IJEIS) 15 (3): 76–95. Raff, Stefan, Daniel Wentzel, and Nikolaus Obwegeser. 2020. ‘Smart Products: Conceptual Review, Synthesis, and Research Directions.’ Journal of Product Innovation Management 37 (5): 379–404. Riazi, Sarmad, Thomas Diding, Petter Falkman, Kristofer Bengtsson, and Bengt Lennartson. 2019. ‘Scheduling and Routing of AGVs for Large-Scale Flexible Manufacturing Systems.’ In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), 891–96. IEEE. Senyo, Prince Kwame, Kecheng Liu, and John Effah. 2019. ‘Digital Business Ecosystem: Literature Review and a Framework for Future Research.’ International Journal of Information Management 47: 52–64. Singh, Nitish, Quang-Vinh Dang, Alp Akçay, Ivo Adan, and Tugce Martagan. 2022. ‘A Matheuristic for AGV Scheduling with Battery Constraints.’ European Journal of Operational Research 298 (3): 855–73. Suuronen, Sami, Juhani Ukko, Roope Eskola, R Scott Semken, and Hannu Rantanen. 2022. ‘A Systematic Literature Review for Digital Business Ecosystems in the Manufacturing Industry: Prerequisites, Challenges, and Benefits.’ CIRP Journal of Manufacturing Science and Technology 37: 414–26. Szaller, Ádám, Péter Egri, and Botond Kádár. 2020. ‘Trust-Based Resource Sharing Mechanism in Distributed Manufacturing.’ International Journal of Computer Integrated Manufacturing 33 (1): 1–21. Tao, Fei, Yongping Zhang, Ying Cheng, Jiawei Ren, Dongxu Wang, Qinglin Qi, and Pei Li. 2020. ‘Digital Twin and Blockchain Enhanced Smart Manufacturing Service Collaboration and Management.’ Journal of Manufacturing Systems, 62, 903–14. Troisi, Orlando, Mario D’Arco, Francesca Loia, and Gennaro Maione. 2018. ‘Big Data Management: The Case of Mulino Bianco’s Engagement Platform for Value Co-Creation.’ International Journal of Engineering Business Management 10: 1847979018767776. Uysal, Murat Pasa, and A Erhan Mergen. 2021. ‘Smart Manufacturing in Intelligent Digital Mesh: Integration of Enterprise Architecture and Software Product Line Engineering.’ Journal of Industrial Information Integration 22: 100202.

Digital platform for heterogeneous fleet management  357 Verhoef, Peter C, Thijs Broekhuizen, Yakov Bart, Abhi Bhattacharya, John Qi Dong, Nicolai Fabian, and Michael Haenlein. 2021. ‘Digital Transformation: A Multidisciplinary Reflection and Research Agenda.’ Journal of Business Research 122: 889–901. Vivaldini, Kelen, Luı́s F Rocha, Nádia Junqueira Martarelli, Marcelo Becker, and A. Paulo Moreira. 2016. ‘Integrated Tasks Assignment and Routing for the Estimation of the Optimal Number of AGVS.’ The International Journal of Advanced Manufacturing Technology 82 (1): 719–36. Wang, Dongxu, Jiawei Ren, Ying Cheng, and Fei Tao. 2020. ‘An Aggregated-Tasks Oriented Manufacturing Services Scheduling Toward Industrial Internet Platforms.’ In 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), 716–21. IEEE. Wang, Junliang, Chuqiao Xu, Jie Zhang, Jingsong Bao, and Ray Zhong. 2020. ‘A Collaborative Architecture of the Industrial Internet Platform for Manufacturing Systems.’ Robotics and Computer-Integrated Manufacturing 61: 101854. Yi, Ding, Fan Binwen, Kong Xiaoming, and Ma Qianqian. 2016. ‘Design and Implementation of Mobile Health Monitoring System Based on MQTT Protocol.’ In 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 1679–82. IEEE. Yoo, Youngjin, Richard J Boland Jr, Kalle Lyytinen, and Ann Majchrzak. 2012. ‘Organizing for Innovation in the Digitized World.’ Organization Science 23 (5): 1398–408.

23. Data-driven traffic management on the last mile: understanding manufacturing industry in smart city ecosystems Alisa Lorenz and Nils Madeja

INTRODUCTION The German city Wetzlar is renowned for being an important economic node and industry cluster in central Hesse with the production of world leading precision mechanics technology and heavy industry. However, the city will face great challenges soon: a vital set of roads will be closed and reconstructed for several years, putting a strain on the traffic situation in and around the city. To mitigate the negative effects of this construction site, the VLUID project was initiated, which aims to mitigate the negative impact of the construction scenario with data-driven applications for intelligent traffic management. Besides congestions and delays, confusion among traffic participators and increased emissions are also to be expected. Therefore, the goal of the VLUID project is to minimize these impairments to the city with data-driven solutions like data-based traffic routing, intelligent traffic light systems or digital traffic signs. VLUID is an abbreviation for the German description ‘Verkehrslösungen für komplexe Umbauszenarien auf der Grundlage intelligenter Datenauswertung’, meaning ‘traffic management solutions for complex reconstruction scenarios based on intelligent data analysis’. It is an interdisciplinary project led by six project partners consisting of city administrators, construction engineers, traffic engineers, smart city experts and information system specialists. As an industrial ecosystem is often embedded in a larger context of a city ecosystem, the constraints will also have a major impact on local industry. A significant factor is the ‘last mile’, which will be affected by bottlenecks in city traffic due to the construction sites and resulting congestion. Therefore, we aim to estimate the influence of the construction sites in the city on the supply chains of the residing manufacturers and to understand potential for our applications to support them. In this chapter, we explore how industry logistics can be secured on the ‘last mile’ and how data-driven applications can be applied for improving traffic in a smart city. We will answer the following research questions: RQ 1)How does the manufacturing industry operate in the context of a smart city ecosystem? RQ 2) How does their relationship affect application points for data-driven traffic management applications? This chapter will cover a qualitative study in the city of Wetzlar by involving industry representatives to find out how city and industry ecosystems influence each other in regard to traffic. In our research, we apply aspects from the sensitivity model by Frederic Vester, 358

Data-driven traffic management on the last mile  359 which originated in cybernetics, and demonstrate how it can help to determine nodes for the application of data-driven traffic management solutions. We further enhance our model with qualitative insights from interviews with industry representatives. With our findings we show the relevance of understanding connected complex systems in order to apply related digital applications. By deriving a meta model for industry ecosystems in the context of smart city ecosystems, we will contribute to research in the field of ecosystems of ecosystems and industry clusters. We further contribute to practice by describing our approach for determining the relevant variables in such an ecosystem, which could serve as a blueprint for other cities that face similar challenges. With our research we want to promote collaboration between manufacturers and municipalities and show how this could contribute to a better smart traffic management.

BACKGROUND AND RELATED LITERATURE Smart Cities and Smart Mobility Digital transformation has been a megatrend in industry for several years, with companies using the power of emerging technologies to optimize their operations and stay competitive. More recently, this idea has also evolved in cities, with municipalities recognizing the potential of these technologies (Alvarenga et al., 2020). While digital transformation projects in industry often focus on streamlining processes, cities aim to become so-called smart cities, which are more efficient and liveable (al Nuaimi et al., 2015; Jonathan, 2020; van Veldhoven and Vanthienen, 2022). The manufacturing sector forms an important component of urban economies, contributing to the gross domestic product and being a catalyst for growth and development (Suvarna et al., 2020). This is especially relevant in the context of the trend towards smart cities, where transformation processes affect a multitude of stakeholders of a city. There are six main characteristics of a smart city, which also influence each other: smart economy, smart people, smart governance, smart mobility, smart environment and smart living (Giffinger and Haindlmaier, 2010). This categorization points out the range and variety of dimensions of a smart city, which can comprise different degrees regarding competitiveness, social and human capital, participation, transport, natural resources and quality of life. The concept of smart mobility is one building block of a smart city (Paiva et al., 2021). It can be defined as ‘a set of coordinated actions addressed at improving the efficiency, the effectiveness and the environmental sustainability in cities’ which are characterized by using information and communication technology (ICT) in the context of mobility (Benevolo et al., 2016). Other influencing factors include local accessibility, (inter)national accessibility, availability of ICT-infrastructure and sustainable, innovative and safe transport systems, which underline the technology and sustainability aspects (Giffinger and Haindlmaier, 2010). Besides its obvious impact on routing and punctuality, road traffic is also responsible for about 65 percent of CO2 emissions (Chapman, 2007). Due to the construction sites, which will likely cause congestion and necessitate rerouting, these emissions are further likely to increase in Wetzlar, resulting in the need to mitigate these effects with opportunities of smart mobility applications. Since the research field of smart mobility is relatively new and has only evolved in the last ten years, it has high potential for further research.

360  Handbook on digital platforms and business ecosystems in manufacturing Logistics in the City Cities are hubs, where citizens live, work, shop and spend their free time. Many stakeholders, activities and processes come together in cities, leading to an interdependency of subjects in its urban ecosystem. As pointed out, mobility has a particularly high importance since its goal is to interconnect individuals with their destinations. One central part of traffic in cities is freight traffic, which is largely driven by trucks and commercial vehicles. According to recent traffic statistics, 32,000 vehicles use the federal highway B49 in Wetzlar daily, with approximately 3500 vehicles categorized as heavy load traffic. On the other side of these vehicles stand manufacturers that receive goods and ship finished products. Thus, it becomes clear that efficient flow of goods through the city is of high importance for the manufacturers located in the city. The ‘last mile’ in freight transport can be defined as ‘the movement of goods from a transport hub to the final delivery destination’ (European Environment Agency, 2019). It often literally translates to the last few kilometers or miles of a transportation route in relation to freight traffic, or similarly the last part of a travel route for individuals. The remaining number of vehicles crossing the federal highway reflects mostly individual motorcar traffic of citizens or commuters. Besides being citizens or residents, these can also be understood as a workforce, who travel to their place of employment. Despite many automation efforts, manufacturers still rely on a human workforce in many production steps, making citizens a crucial part of the supply chain and manufacturing process as well. Digital Business Ecosystems The term ecosystem is widely used in multiple disciplines. The biological definition of an ecosystem dates back to at least the 1930s, when Tansley described them as a complex of organisms in a physical, natural environment, characterized by constant interchange within a system (Tansley, 1935). Derived from the biological definition, the term was established in further disciplines such as geography and later also in business and information systems. A definition related to urban planning is that ‘urban ecosystems are those in which people live at high densities, or where the built infrastructure covers a large proportion of the land surface’ (Pickett et al., 2001). In business studies, characteristics and definitions from a biological ecosystem can be applied to explain business networks. Business networks are ‘communities of agents with different characteristics and interests, bound together by different mutual relationships as a collective whole’, leading to a dependency of individual actors in such a network (Corallo et al., 2007). Ecosystems in general can therefore be understood as complex systems, which are characterized by ‘probabilism and constantly changing elements, with regard to both their state and – much more fundamentally – their kind and number, a system that is difficult to influence, often only with undesirable side effects, due to its very own dynamics’ (Malik, 2016). Summarizing, ecosystems are defined by the dependency and connectivity among actors (Baumann and Leerhoff, 2022), who are not fully hierarchically or centrally controlled (Jacobides et al., 2018). Digital business ecosystems share several similarities to biological ecosystems and the previous definitions. While there are broad definitions for digital business ecosystems, they are often characterized by information management and data sharing, by development and inter-

Data-driven traffic management on the last mile  361 connected networks and by platforms. Their actors particularly cooperate to achieve common objectives while remaining independent in a constantly evolving ecosystem (Baumann and Leerhoff, 2022). Members of a digital business ecosystem can be diverse and, though connected by a common interest, also stand in competition to each other (Götz et al., 2022). Managing Complex Systems Following the previous insights, we can understand companies themselves are ecosystems, as well as their relation to one another and their role in a bigger, geographical, and political city ecosystem. Further, intelligent transport systems that improve traffic efficiency and safety can themselves be understood as complex systems, including traffic control systems, smart mobility systems and others (Kyamakya et al., 2021). The sensitivity model by Vester originated in cybernetics and provides one approach to model and understand complex systems that has been used for city or environmental planning (Vester, 1988, 2007). This model has been picked up by other researchers and applied in many cases and disciplines, e.g. computational biology (Amaya Moreno et al., 2014), in collaborative system modelling (Liu et al., 2022) or for an interdependency analysis of critical infrastructure (Dietrich et al., 2019). This shows the broad application possibilities and adaptability of this model. Other researchers have used the model specifically in the traffic context to determine interdependencies of stakeholders (Kunze et al., 2016). They realized that there are multiple stakeholders in a city that need to be considered for traffic management and built a model. The related research serves as an inspiration for our approach, which we will cover in the methods section. In the context of the VLUID project, the complexity of a city and industry ecosystem unveils the need to understand the application context of data-driven traffic applications before planning specific initiatives. To establish a digital ecosystem in a real-life ecosystem, a thorough analysis of the real-life system is needed to understand its complexity and the relations and possible impacts within or outside the system. We therefore argue that the first step, before planning a digital ecosystem with a central data platform and data-driven services, is to understand the underlying real-life system. In the following section, we describe how we approached this challenge and which tools we used for modelling both an industry and city ecosystem. The VLUID project The German city of Wetzlar is currently facing a transformation process towards becoming a smart city, with focus on data-driven traffic management as part of a smart mobility initiative and, therefore, serves as application context for our research in this chapter. Located in central Hesse, approximately 53,000 citizens inhabit the city. The city and the surrounding county have emerged as an economic hub with a multitude of medium-sized manufacturers, but also hidden champions and world market leaders. Many of these companies focus on heavy industry or belong to the ‘optical valley’ with expertise in precision mechanics technology. Besides the economic relevance, the city is an important traffic node due to its central location within Hesse, and the federal highway B49 that crosses the city center, connecting it to other geographically strategic points. However, this highway and an additional set of important roads

362  Handbook on digital platforms and business ecosystems in manufacturing will be partially closed and reconstructed from the year 2035, causing a challenging traffic situation in and around the city. Because of this, the ‘VLUID’ project was initiated to mitigate the negative effects of these construction sites. The overall goal of VLUID is to ensure fluid traffic as well as accessibility of destinations in and around Wetzlar in the face of the construction sites, while considering effects on liveability and attractiveness of the city. Over the course of the project, a team of interdisciplinary experts will connect several data sources and integrate them into one central digital platform, the ‘Wetzlar data space’. With this basis, the team will test several prototypes for data-driven traffic management and evaluate potential long-term solutions with the highest effect. Examples for such applications are smart traffic light management, intelligent routing, or smartphone apps for interconnected mobility. According to an internal analysis, over 130 cities in Germany have a size comparable to Wetzlar, some of which might face similar challenges in the future, especially regarding urbanization and shortage of living space. Therefore, other cities in Germany or abroad might profit from our findings and knowledge transfer.

METHODS Methods Overview As pointed out before, we conducted our research in the context of the city of Wetzlar in central Germany as part of the VLUID project. Based on both the needs of the VLUID project and the reviewed literature from the last chapter, we identify the need to first analyze the relevant relations and impact factors in a city and industry environment. We define the sum of these relations and interdependencies as an ecosystem. We recognize that manufacturers are part of an industry ecosystem with interconnected but unique features when compared to a city ecosystem. By conducting an analysis of the interrelated city and industry ecosystems within the context of the critical ‘last mile’, we aim to identify key nodal points for applying our data-driven traffic management solutions. Building upon prior research that applied system thinking to city logistics, we explore the interdependencies of manufacturers and cities in the context of traffic (Kunze et al., 2016). To achieve this, we adopt an approach of Vester’s sensitivity model to determine the relevant nodal points that we can target with data-driven applications. We focus our research on the manufacturing industry and conduct qualitative interviews with representatives from relevant companies. Further, we use this approach to achieve our goal of determining a starting point for our data-driven applications. Moreover, we enrich these theoretical findings with qualitative insights from the interviews, thus providing a deeper understanding of the complex relationship between manufacturers and their urban environment. Our research process consists of several steps, which we summarize in Figure 23.1 and explain in the following. According to Langer, the first steps of software development are assessing requirements and establishing the requirements of the software product (Langer, 2016). Given that VLUID is a publicly funded project aimed at enhancing traffic conditions in the city, it is crucial to understand the stakeholder’s needs. Therefore, we began our research by conducting a thorough stakeholder analysis, employing a power interest grid to identify the most influential actors within the city’s industrial sector (Freeman, 2010). Our goal was to obtain first-hand

Data-driven traffic management on the last mile  363

Figure 23.1

Research process

insights from the key stakeholders in order to tailor our data-driven solutions to their unique requirements. Therefore, following the stakeholder analysis, we opted for an exploratory research approach to gain a deeper understanding of the specific challenges and needs of manufacturers in the face of the future construction site in the city. We decided to conduct qualitative expert interviews with representatives from various companies in the city. To facilitate this process, we created a questionnaire and reached out to the most influential companies according to our stakeholder analysis and successfully scheduled interviews with nine companies from manufacturing, logistics and retail. We chose to expand our scope and include representatives from logistics and retail since they are closely linked to manufacturers.

364  Handbook on digital platforms and business ecosystems in manufacturing Our interviews were conducted between November 2022 and February 2023, enabling us to gain critical insights from diverse perspectives. After gathering the necessary data from our expert interviews, we transcribed them and used the software MaxQDA for qualitative coding to identify the main themes. Our analysis revealed two main directions, which we explored in more detail. First, we identified elements of both the industry and city ecosystems that helped us determine intersections and regulation points that would be critical to improving traffic conditions. We tested these findings against a criteria matrix as part of the sensitivity model approach according to Vester, which we promote as an appropriate method for addressing similar challenges in other projects (Vester, 2007). Second, we used our more detailed qualitative findings to develop an understanding of the specific conditions in Wetzlar. We enriched the identified variables with insights from the interviews to identify potential application points for our data-driven solutions. This step enabled us to tailor our recommendations to the unique needs of the manufacturers, further enhancing the effectiveness of our approach. In the following sections, we will outline our method in more detail and describe how we synthesized our findings in a way that reflects the complex interplay of factors that influence traffic conditions in the city and the effectiveness of manufacturers. Stakeholder Analysis As described in our research process, we started our research by conducting a stakeholder analysis to identify the key parties and actors crucial for our project. We employed Freeman’s power interest approach as a well-known instrument to classify stakeholders in their areas of influence (Freeman, 2010). The advantage of this approach lies in its flexibility and simplicity in use. We performed this analysis within our interdisciplinary project team and deliberated the position of each stakeholder on the grid during multiple sessions. For our conclusion, we considered various factors, such as the stakeholders’ relevance for the project, their location in the city and the individual level of influence of the construction site on them. Figure 23.2 illustrates the industry perspective of the results with individual names anonymized for confidentiality reasons. The results shown do not include stakeholders outside the industry perspective to focus on the scope of this paper. The analysis shows that the resident industry forms a crucial part of the stakeholders. The largest and most important companies were added individually because of their importance and influence; the remaining companies were subsumed under the node ‘companies/employers’. In addition, there are four industry parks, A–D, that hold clusters of companies, which usually have a higher influence since they combine several interests. Building on the results of our stakeholder analysis, we identified the key companies that play a crucial role in the local industry ecosystem and are therefore important to manage more closely for the success of the project. To gain a deeper understanding of their specific logistics challenges, we decided to conduct expert interviews as an exploratory approach with a special emphasis on understanding the importance of the ‘last mile’. We aimed for qualitative in-person interviews for several reasons that align with recommendations from literature (Cresswell and Cresswell, 2018). First, due to the exploratory nature of our research and the aim to determine experiences, we wanted to get into the field

Data-driven traffic management on the last mile  365

Figure 23.2

Power interest grid with industry view on stakeholders in Wetzlar (framework based on Freeman, 2010)

and talk directly to the affected companies and their representatives. Second, this setting also allows for more flexibility through an interview, compared to an anonymous questionnaire. In this setting, researchers are also better able to understand meaning and deduce underlying motives. The advantages further lie especially in studying contexts, gaining deeper insights that might be missed otherwise and uncovering salient issues, making this method suitable for our research question (Tracy, 2013). We were able to schedule meetings with nine of the most important companies in the city, according to our power interest grid, which are either manufacturers, retailers or logistics companies. We chose the companies regardless of their location within the city or their specific industry. Expert Interviews The interviews were conducted in a semi-structured way and were designed for 90 minutes, to have enough time to address a broad set of questions while also respecting the value of participants’ time (Kaiser, 2021). The interviews lasted on average 76 minutes and were mostly held face to face in person with one to four interviewees per session. Most of the interviewees were representatives from middle management, logistics experts or managing directors. The interviews were recorded on audio tape and transcribed later.

366  Handbook on digital platforms and business ecosystems in manufacturing We designed the interview guide to answer questions from six main categories that we identified as relevant for our research questions. ● Logistics: The aim was to get a broad picture of the supply chain of the manufacturers, of the role of logistics and of the ‘last mile’ as well as the characteristics of their logistics processes. We especially focused on capturing indicators such as number of transports, time sensitivity, special characteristics such as weight or length and the importance of roads for the industry traffic as compared to rail traffic or other forms of transport. By combining these insights, we aimed to estimate the traffic load from logistics and special requirements towards possible solutions. ● Construction site in the city: We asked our interviewees to estimate the importance and influence of the future construction sites on their supply chains with a focus on logistics. We wanted to get an impression of the most important roads, the range of the consequences and the timeframes that have critical impact on their production. Furthermore, we aimed to explore potential requirements for our data-driven applications. ● Individual and workforce traffic: Besides the input material that must be delivered to the production site, companies need employees for their production processes. Since all companies in our study each have more than 100 employees, workforce traffic is another influencing factor on the daily traffic on one hand and a crucial part of the manufacturing processes on the other hand. Therefore, we aimed to assess how many employees need to reach the companies in what timeframes, whether the manufacturing process is planned in shifts that are dependent on each other, how employees reach the companies and what influence late arrivals have on the production processes. ● The VLUID project: As this study was started in an early phase of the VLUID project, we wanted to assess possible requirements that are already transparent at this point in time for the manufacturers. More importantly, we wanted to find out the relevance that the manufacturers assign to possible data-driven solutions, which data they would require and whether they would be willing to share their data. With these questions we aimed to assess the willingness for and openness to data sharing and collaboration with other manufacturers. ● Data sharing: To target possible data-driven solutions, we asked the participants what data they would need to better plan their logistics and whether they would be willing to share their data. With these questions we wanted to tackle the topic of a data economy and how realistic a digital ecosystem would be. ● Manufacturing and industry ecosystem: Combining the information from the previous categories, we aimed to ask the manufacturers to place themselves in the industry and city ecosystem and elaborate where they see touchpoints, synergies or conflicts. With the related questions we aimed to derive the ecosystem model and collect additional information that might not have been mentioned before. The interviewees were asked to answer the questions to the best of their knowledge and were encouraged to share additional information that they estimated as important for the project. Most of the interviewees did not stick strictly to the questions and elaborated on further topics that were important to them. The complete interview guide can be found in Table 23.1.

Question guide

How important is logistics for your company (both inbound and outbound)?

Which goods, materials and products play a role in your supply chain?

How is logistics planned in your company?

How far in advance are in- and outbound deliveries planned?

To what extent does the current traffic situation influence your logistic planning?

How time-sensitively are your supply chains planned?

How do you secure your supply chains against disturbances?

Which means of transport do you use as part of your logistics?

How important is road traffic for your logistics?

How many transports do you have daily to/from your company?

Are there any specific characteristics in your supply chain, e.g. length, weight, perishable goods?

How important is the ‘last mile’ in the city for your company and your supply chain?

4

5

6

7

8

9

10

11

12

13

14

What influence do you estimate will the future construction site have on your company?

Which functions of supply chain management do you think will be influenced by the future construction site (functions according to question 1)

Which aspects of traffic are particularly critical in your opinion?

17

18

19

Which types of workers are relevant for your company? (E.g., employees, clients, service workers)

How many employees are currently working for your company on-site?

How far away do your employees usually live?

Which modes of transport do your employees use for their journey to work?

Do your employees work in shifts and if yes, in which departments?

21

22

23

24

25

Part 3: Individual and workforce traffic

After 30 minutes | after 60 minutes | after 120 minutes | other

What effect does an interruption of your supply chain have on your company and when does it become critical for your operating schedule?

Congestions (delays) | rerouting (confusion, delays, unfamiliar streets) | emissions (noise, pollution, vibration) | other

Which construction sites in the past have influenced your company and how?

16

20

Which streets in the city are especially important for your logistics or company in general?

15

Part 2: Construction site in the city

Over which/how many departments are these functions distributed?

3

in (external) warehouses | shipping | customer service

Procurement | Inbound logistic | quality control | goods receiving | supply and demand planning | material and inventory monitoring | order processing | production planning | distribution

Which of the following functions of supply chain management can be found in your company?

2

1

Part 1: Logistics

Question guide for ‘last mile’ manufacturer study

Table 23.1

Data-driven traffic management on the last mile  367

What consequences does it have on your company if employees arrive late to work and when does it become critical for your operating schedule?

27

How do you think could the VLUID project mitigate the negative effects of the construction site with data-driven solutions?

Which specific applications would be beneficial for your company?

Which requirements do you have towards these applications?

Would you be willing to participate in the specification and the testing of these applications?

29

30

31

32

What data would be relevant for your company and for which purposes would you use them?

Would you be willing to (anonymously) share parts of your company data to get access to other data in exchange?

What would be your requirements to be willing to share your (anonymous) company data?

34

35

36

How can the city support your company (especially the logistics) in view of the construction site?

How would you work together with the city regarding the construction site?

Which other intersections do you see between your company and other companies/actors/locations in the city?

Is there anything else that you would like to know or add?

37

38

39

40

Part 6: Manufacturing and industry ecosystem

Would external data help you improving your (logistics) planning? (E.g., traffic forecasts)

33

Part 5: Data sharing

What are your expectations for the VLUID project?

28

Part 4: The VLUID project

After 30 minutes | after 60 minutes | after 120 minutes | other

Do all other employees have core working hours or flexible working hours?

26

Part 1: Logistics

Question guide for ‘last mile’ manufacturer study

368  Handbook on digital platforms and business ecosystems in manufacturing

Data-driven traffic management on the last mile  369 Data Analysis and System Modelling For analysing the interviews, we transcribed the audio and video recordings of the focus groups and analyzed them with the qualitative coding software MaxQDA. We used the transcript-based analysis method where complete recordings are transcribed, which makes it the most detailed and accurate approach though being very time consuming (Onwuegbuzie et al., 2009). We conducted audio material of about 10.5 hours in total, resulting in 117 pages of transcript. Despite the expenditure of time, we opted for the transcript-based analysis because our questionnaire was very broad and regarded various themes. We therefore considered it more suitable for our broad analysis purposes. We developed our qualitative code system beforehand based on the themes of the questionnaire and enhanced it with additional topics mentioned during the interviews. Therefore, our code system evolved throughout the coding process, resulting in 404 individual codes and 1366 coded segments in total. We then reviewed our code system to eliminate duplicates and merge codes when possible and reasonable or divided them into more subcodes. This resulted in 39 code categories with 436 individual codes, which were used 1357 times during the nine interviews. Finally, we extracted the most relevant code themes for deeper qualitative insights. We chose the codes based on the number of times they were mentioned as well as the weight that the interviewees assigned them by mentioning them multiple times. Finally, we brought together the insights from both analysis strands in the recommendation that follows in the results chapter.

RESULTS Development of the Criteria Matrix In the following, we describe the process of how we combined findings from our interviews into the criteria matrix as a model to identify the completeness of an ecosystem. We followed the two-step process as proposed by Vester (Vester, 2007) and executed the following steps: 1. Determine the relevant variables (resulting from coding in MaxQDA). 2. Verify the variables with a criteria matrix. We started our analysis by identifying the major themes that apply to the city and industry ecosystem. Usually, around 20–40 variables are determined in this process. Variables in the context of the sensitivity model are defined as modifiable parameters of a system that will later function as nodes in the effect system. Breaking down topics of a complex system into manageable variables is crucial in order to avoid overwhelming complexity. Without a systematic consideration, there is a risk of including multiple variables that overlap in meaning, leading to inefficiencies. By utilizing the criteria matrix to identify and cover all relevant areas, a holistic understanding can be achieved, helping to ensure that no aspect is overlooked and a unilateral view is avoided. The goal is therefore to ensure that the variables cover all relevant aspects of an ecosystem but don’t include more than necessary to describe the system.

370  Handbook on digital platforms and business ecosystems in manufacturing

Figure 23.3

Interview analysis process

We started with a list of 20 topics that were mentioned the most during our interviews (rows in Table 23.2). We then applied them to a criteria matrix that serves as a filter for the collected variables (Table 23.2). Vester defines four categories with a total of 18 criteria (columns in Table 23.2) which should be represented in the set of variables. Each variable is then to be checked against these criteria and assigned a weight according to the applicability. A rating of 1 stands for ‘fully applicable’ and a rating of 0.5 for ‘partially applicable’. Categories that are not applicable are rated with zero. After the rating, the values within a column are summed to determine whether all categories are sufficiently represented. In our case, there were few categories that were underrepresented, e.g. information (total score = 4), natural balance (total score = 5) or spatial dynamics (total score = 5). However, despite their low score, they apply to several variables and are therefore covered by our set of variables. From this matrix we were able to identify duplicates and overlapping variables to narrow down our set of influence factors. First, we merged emissions (variable 3) and environment (variable 13). Even though they seem to provide two different perspectives, their influence is

1

Location advantage

Communication

19

20

Sum:

Shipping companies

Rescue security

17

18

Construction sites

Employer/jobs

15

16

Environment

Politics and governance

13

14

Other companies

Industry cluster

11

12

Congestions

Public transport

9

10

0.5

Streets/last mile

Traffic volume

7

8

0.5

13

0

1

0

1

1

1

0.5

0

1

1

0.5

0.5

0.5

Inbound logistics

Outbound logistics

5

1

0

1

1

Economy

6

Emissions

Value creation

3

4

Production line

Employees/citizens

1

2

0 = not applicable

0,5 = partially applicable

Spheres of Life

0

Population 5.5

0

1

0

0

1

0

0

0

0.5

0.5

0

0.5

0.5

0

0

0

0

0.5

1

1

Space Utilization 11

0

1

0

1

0

1

0

0

1

1

1

0

1

1

1

1

0

0

0

0

Human ecology 8

0.5

1

1

0

0.5

1

0

1

0

0

0

1

1

0

0

0

0

1

0

0

Natural Balance 5

0

0

0

0

0

1

0

1

0

0

0

1

1

0

0

0

0

1

0

0

Infrastructure 10

0.5

0

1

0.5

0

1

0

0

1

0

1

1

1

1

1

1

0

0

0

0

Rules and Laws 4.5

0

0

0.5

0

0.5

0.5

1

0.5

0

0

0.5

0

0

0.5

0

0

0

0.5

0

Physical Cat.

1

Matter 15

0

0

0.5

1

1

1

0

0.5

1

1

1

1

1

1

1

1

0.5

0.5

1

1

Energy 9

0

0

0

0.5

0

0.5

0

1

0

0

0.5

0.5

0.5

0

1

1

0.5

1

1

0

Information 4

1

0

0

0

0

0

0.5

0

0

0

0.5

0

0

0.5

0.5

0.5

0

0

0.5

Dynamic Cat.

1

Flow quantity 9.5

0.5

0

0.5

0.5

0

0

0

1

0

0

0.5

0.5

1

0

1

1

0.5

1

0.5

quantity

0.5

Structural 9

0

0

0

0.5

0

1

0

0

0.5

1

0.5

1

1

1

0.5

0.5

0

0

1

dynamics

0.5

Temporal 10

0

0

0.5

0

0.5

1

0

1

0

0

0.5

1

1

0.5

1

1

0.5

1

0

0

Spatial dynamics 5

0

0

0

0

0.5

1

0

0

0

0

0.5

1

1

0

0.5

0.5

0

0

0

12

0.5

0.5

0.5

1

0.5

0.5

1

0

1

1

1

1

1

1

0

1

0

0

0.5

0

Opens through

System Relations

input

 

15

0.5

0.5

0.5

1

0.5

0.5

1

0.5

1

1

1

1

1

1

1

1

0.5

1

0.5

0

Opens through

 

1 = fully applicable

output

Criteria matrix for the industry-city ecosystem

inside 11

1

0.5

0.5

0

1

0

0

0.5

0.5

0

0

0

0.5

0.5

1

1

1

1

0.5

1

Influenced from

Table 23.2

Influenced from

0.5

outside 13

1

1

1

1

0

1

1

0

0.5

1

1

1

1

1

0

0.5

0

0

0.5

Data-driven traffic management on the last mile  371

372  Handbook on digital platforms and business ecosystems in manufacturing Table 23.3

Final set of influencing variables

1

Production line

2

Employees/citizens

3

Emissions and environment

4

Value creation

5

Company logistics

6

Traffic volume

7

Congestions

8

Public transport

9

Local industry partners

10

Politics and governance

11

Construction sites

12

Employer/jobs

13

Rescue security

14

Location advantage

15

Communication

quite similar, as the values for each variable show. We therefore created one variable ‘emissions and environment’. Further, we recognized a large overlap between ‘inbound logistics‘ (variable 5) and outbound logistics (variable 6). Again, we merged them into one variable ‘company logistics’ that covers both perspectives. Another similarity can be identified between ‘streets/last mile’ (variable 7) and ‘traffic volume’ (variable 8). Even though those variables do not overlap as much as others, we found that variable 7 was not unique enough and did not have any relevance without variable 8. We therefore eliminated the variable ‘streets/last mile’. Lastly, we analyzed the variables ‘other companies’ (variable 11), ‘industry cluster’ (variable 12) and ‘shipping companies’ (variable 17). We found that there was a high overlap between all three variables. As shipping companies can be understood as part of ‘other companies’, we decided to merge both together. Further, an industry cluster is a union of several companies, therefore also fitting to the same scope and having similar implications. Hence, we created a new variable ‘local industry partners’ and subsumed all three of the previous variables. Our final set of variables therefore included 15 key factors that can be described as node points which influence a city and industry ecosystem (Table 23.3). Manufacturer’s Insights After completing our criteria matrix, we want to elaborate further on the key variables and themes that we identified during the interviews. The matrix provides valuable implications for the strategy to select data-driven applications with the highest impact. As expected, traffic volume, congestion and construction sites play an important role for both the industry and city ecosystem. Seven of the companies said that road traffic was very important for their company; two said that it was vital. All nine companies stated that additional congestion and resulting delays during the construction scenario would have a high impact on their supply chain, followed by the impacts of rerouting (8 companies).

Data-driven traffic management on the last mile  373 However, we did not expect that punctuality of employees would be more important than the punctuality of goods for the production line. Five companies stated that unpunctual employees who work in the production would be fatal, starting from a ten-minute delay, since many production lines are timed. Almost all companies work in shifts (8) and half of them in three shifts (4) around the clock. All companies agreed that strains in workforce punctuality would lead to less output and loss in revenue. In the worst case, the whole production would need to be halted if personnel are missing. Besides the economic factor, three companies highlighted that congestions, delays and longer journeys to work would also have a negative impact on the wellbeing of employees. Four of the companies further mentioned that, if the traffic situation became too strained for a long time, some employees, especially from the surrounding areas, might look for other jobs, leading to fluctuation. Regarding the construction sites, the interviewees explained that the traffic situation was already dense today. One interviewee predicted that ‘If two or three construction sites are added, the situation becomes critical’. The consequences of the construction sites that were mentioned most were that the companies would have time delays (7), have delays in outbound delivery (5), that they might not be able to stick to their time plans (4) and that laws which regulate working times could become critical since employees and deliverers would be required to work more or wait in the traffic jams. All companies stated that communication of the overall plan should be distributed as early as possible. Some mentioned that depending on the impact and strategic relevance, it could be helpful to get these plans at least three years in advance to take related measures. They further asked for frequent updates so that they could adjust to any changes. Depending on the severity of the situation, some companies even stated that they would think about relocating parts of their production, which could affect the industry cluster of the optical valley and heavy industry. We further gained insights regarding data sharing. We were positively surprised that many participants were open to providing data if they are anonymized and if they gain information back from other manufacturers. Summarizing, the employees have a high impact on the production processes, which shows that workforce traffic might be just as important as freight traffic to secure the supply chains. This insight has important implications for our data-driven applications and the requirements of manufacturers. It raises the question whether data-driven applications that specifically target the industry are even needed or whether general recommendations for all forms of transport are sufficient.

IMPLICATIONS AND LIMITATIONS In this chapter, we described an approach to integrate industry stakeholders as crucial part of a city and industry ecosystem into smart mobility initiatives in cities. We further showed how insights from qualitative interviews with these stakeholders can be used to systematically develop a set of variables which helps in gaining an understanding of interdependencies. Our findings provide the basis for the next step in developing data-driven traffic management applications and evaluating their impact on different variables within an ecosystem. We are convinced that our research provides a contribution to understanding the dynamics between the manufacturers themselves with the surrounding urban area in the context of mobility.

374  Handbook on digital platforms and business ecosystems in manufacturing While the strength of this chapter lies in its practical research as well as broad qualitative insights, we recognize that there are limitations to our study that we would like to address. First, we chose an exploratory approach to gain insights for a project that is still in an early phase. While qualitative interviews may be a good instrument to uncover new insights in an unknown field with the ability to directly relate to the answers given, a main point for critique is that insights from qualitative interviews might not be generalizable. Our small sample size contributes to this criticism. However, we conducted our interviews until we found a saturation of information, which was already reached with nine interviews. Furthermore, compared to the size of the city and the importance of our selected interview partners, we are confident that we were able to draw a realistic picture of the industry in the city in general. Another aspect is that the qualitative nature of answers from the participants leaves room for interpretation, as is usual in qualitative research. We aimed for a reflected and thorough coding of our interviews but recognize that there might be aspects that we missed or misinterpreted due to bias. Regarding our sample, we only conducted interviews with the largest companies in the city that also have the highest impact on traffic. We recognize that this excludes smaller companies that could also add up with their logistic processes. In future research, our findings could be complemented by a quantitative study to support our findings or other qualitative approaches that test our insights with a larger sample size. In addition, we could involve companies that are not manufacturers but have a high impact on the city and industry ecosystem in other regards, e.g. due to high numbers of employees who cause individual car traffic, which we recognized as crucial. Further, we could involve smaller companies with less political power to provide a more diverse view. As our study specialized on manufacturers, we only focused on one stakeholder group. We are currently already conducting other studies that involve further stakeholder groups such as citizens in other participatory approaches. In the development process of our criteria matrix, we assigned values based on the results from the interviews, which were qualitative in nature and could therefore lead to bias. Lastly, our study was conducted in only one medium-sized city in central Germany. As our research shows, city and industry ecosystems are very complex and characterized by a multitude of dynamics. The location, size and circumstances of the city might therefore have an influence on the results. We recognize that cities and industry clusters might have different dynamics and that the study could come to different results in other cities. We therefore encourage other researchers to adopt this approach and verify it in similar scenarios. According to Vester, there are further steps in which the impact of the variables can be analyzed further and summarized in a sensitivity model. We focused this study on the first two steps since the determination of variables is the most critical part on which the other steps depend. After understanding the variables in detail, we see potential for future research in which we could approach further steps as proposed by the sensitivity model. We also aim to focus our future research more on citizens as another part of the city that causes high traffic load and gain further insights on the optimization of traffic flows in view of the upcoming construction sites and challenges for the city, people, industry and environment.

SUMMARY AND OUTLOOK In this chapter we addressed the question of how industry logistics can be secured on the ‘last mile’ and how data-driven applications can be applied to improve traffic in a smart city. We

Data-driven traffic management on the last mile  375 first covered relevant literature and outlined our research approach. We then explained our research process in which we conducted qualitative interviews with industry representatives from a German city and applied our findings do determine influencing variables in the city and industry ecosystem. Hence, we answered our research question RQ 1 on how the manufacturing industry operates in the context of a smart city ecosystem. We complemented our model with additional insights from the qualitative interviews to point out the important nodes in the industry-city ecosystem that we derived for the city of Wetzlar. With this, we also answered our research question RQ 2 on how their relationship affects application points for data-driven traffic management applications. With our research we contribute to the area of ecosystems of ecosystems in the context of smart cities and industry clusters. We want to promote collaboration and data sharing between manufacturers and municipalities and look forward to gaining more insights through future studies. We are convinced that the results contribute to the development of data-driven traffic management applications in Wetzlar and could also support initiatives in other cities.

REFERENCES Al Nuaimi, E., Al Neyadi, H., Mohamed, N., and Al-Jaroodi, J. (2015). Applications of big data to smart cities. Journal of Internet Services and Applications, 6(1), 25. https://​doi​.org/​10​.1186/​s13174​-015​ -0041​-5 Alvarenga, A., Matos, F., Godina, R., and C. O. Matias, J. (2020). Digital transformation and knowledge management in the public sector. Sustainability, 12(14). https://​doi​.org/​10​.3390/​su12145824 Amaya Moreno, L., Defterli, O., Fügenschuh, A., and Weber, G.-W. (2014). Vester’s Sensitivity Model for Genetic Networks with Time-Discrete Dynamics (pp.  35–46). https://​doi​.org/​10​.1007/​978​-3​-319​ -07953​-0​_3 Baumann, S., and Leerhoff, M. (2022). Networks, platforms, and digital business ecosystems: mapping the development of a field. In S. Baumann (Ed.), Handbook on Digital Business Ecosystems: Strategies, Platforms, Technologies, Governance and Societal Challenges (1st ed., pp. 11–24). Edward Elgar. Benevolo, C., Dameri, R. P., and D’Auria, B. (2016). Smart Mobility in Smart City (pp. 13–28). https://​ doi​.org/​10​.1007/​978​-3​-319​-23784​-8​_2 Chapman, L. (2007). Transport and climate change: a review. Journal of Transport Geography, 15(5), 354–67. https://​doi​.org/​10​.1016/​j​.jtrangeo​.2006​.11​.008 Corallo, A., Passiante, G., and Prencipe, A. (2007). The Digital Business Ecosystem. Edward Elgar Publishing. https://​doi​.org/​10​.4337/​9781781009925 Cresswell, J. W., and Cresswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). SAGE. Dietrich, A., Tzavella, K., Setiadi, N., Fekete, A., and Neisser, F. (2019). Enhanced Crisis-Preparation of Critical Infrastructures through a Participatory Qualitative-Quantitative Interdependency Analysis Approach. ISCRAM 2019: 16. International Conference on Information Systems for Crisis Response and Management, pp. 1226–44. European Environment Agency. (2019). The first and last mile – the key to sustainable urban transport. Publications Office of the European Union. Freeman, R. E. (2010). Strategic Management. Cambridge University Press. https://​doi​.org/​10​.1017/​ CBO9781139192675 Giffinger, R., and Haindlmaier, G. (2010). Smart cities ranking: an effective instrument for the positioning of the cities? ACE: Architecture, City and Environment, 4(12), 7–26. https://​doi​.org/​10​.5821/​ace​ .v4i12​.2483 Götz, F., Buck, C., Rosemann, M., and Meckl, R. (2022). Digital business ecosystems: a comparison of different industries. In S. Baumann (Ed.), Handbook on Digital Business Ecosystems: Strategies, Platforms, Technologies, Governance and Societal Challenges (1st ed., pp. 530–48).

376  Handbook on digital platforms and business ecosystems in manufacturing Jacobides, M. G., Cennamo, C., and Gawer, A. (2018). Towards a theory of ecosystems. Strategic Management Journal, 39(8), 2255–76. https://​doi​.org/​10​.1002/​smj​.2904 Jonathan, G. M. (2020). Digital Transformation in the Public Sector: Identifying Critical Success Factors (pp.  223–35). https://​doi​.org/​10​.1007/​978​-3​-030​-44322​-1​_17 Kaiser, R. (2021). Qualitative Experteninterviews. Springer Fachmedien Wiesbaden. https://​doi​.org/​10​ .1007/​978​-3​-658​-30255​-9 Kunze, O., Wulfhorst, G., and Minner, S. (2016). Applying systems thinking to city logistics: a qualitative (and quantitative) approach to model interdependencies of decisions by various stakeholders and their impact on city logistics. Transportation Research Procedia, 12, 692–706. https://​doi​.org/​10​ .1016/​j​.trpro​.2016​.02​.022 Kyamakya, K., Chedjou, J., Al-Machot, F., Haj Mosa, A., and Bagula, A. (2021). Intelligent transportation related complex systems and sensors. Sensors, 21(6), 2235. https://​doi​.org/​10​.3390/​s21062235 Langer, A. M. (2016). Guide to Software Development. Springer, London. https://​doi​.org/​10​.1007/​978​ -1​-4471​-6799​-0 Liu, J., Gatzweiler, F., Hodson, S., Harrer-Puchner, G., Sioen, G. B., Thinyane, M., Purian, R., Murray, V., Yi, X., and Camprubi, A. (2022). Co-creating solutions to complex urban problems with collaborative systems modelling – insights from a workshop on health co-benefits of urban green spaces in Guangzhou. Cities & Health, 6(5), 868–77. https://​doi​.org/​10​.1080/​23748834​.2022​.2026694 Malik, F. (2016). Strategy for Managing Complex Systems. Campus, IA. Onwuegbuzie, A. J., Dickinson, W. B., Leech, N. L., and Zoran, A. G. (2009). A qualitative framework for collecting and analyzing data in focus group research. International Journal of Qualitative Methods, 8(3), 1–21. https://​doi​.org/​10​.1177/​160940690900800301 Paiva, S., Ahad, M., Tripathi, G., Feroz, N., and Casalino, G. (2021). Enabling technologies for urban smart mobility: recent trends, opportunities and challenges. Sensors, 21(6), 2143. https://​doi​.org/​10​ .3390/​s21062143 Pickett, S. T. A., Cadenasso, M. L., Grove, J. M., Nilon, C. H., Pouyat, R. V., Zipperer, W. C., and Costanza, R. (2001). Urban ecological systems: linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas. Annual Review of Ecology and Systematics, 32(1), 127–57. https://​doi​.org/​10​.1146/​annurev​.ecolsys​.32​.081501​.114012 Suvarna, M., Büth, L., Hejny, J., Mennenga, M., Li, J., Ng, Y. T., Herrmann, C., and Wang, X. (2020). Smart Manufacturing for Smart Cities – Overview, insights, and future directions. Advanced Intelligent Systems, 2(10), 2000043. https://​doi​.org/​10​.1002/​aisy​.202000043 Tansley, A. G. (1935). The use and abuse of vegetational concepts and terms. Ecology, 16(3), 284–307. https://​doi​.org/​10​.2307/​1930070 Tracy, S. J. (2013). Qualitative Research Methods: Collecting Evidence, Crafting Analysis, Communicating Impact. Wiley-Blackwell, NJ. van Veldhoven, Z., and Vanthienen, J. (2022). Digital transformation as an interaction-driven perspective between business, society, and technology. Electronic Markets, 32(2), 629–44. https://​doi​.org/​10​ .1007/​s12525​-021​-00464​-5 Vester, F. (1988). The biocybernetic approach as a basis for planning our environment. Systems Practice, 1(4), 399–413. https://​doi​.org/​10​.1007/​BF01066582 Vester, F. (2007). The Art of interconnected thinking: Tools and concepts for a new approach to tackling complexity. Mcb Verlag.

24. Digital transformation and the role of platforms in fostering co-creation: the case of the Open Italy program by ELIS consortium Nicola Del Sarto, Alberto Di Minin, Giulio Ferrigno and Asia Mariuzzo

INTRODUCTION During the last half-century, digital technologies have spread out and changed every aspect of our everyday life (Ferrigno et al., 2023). The process of translating a piece of analogic information into a digital term is called digitization (Bloomberg, 2018), while turning entire processes from analogic to digital is usually referred to as digitalization (Reis et al., 2019; Dąbrowska et al., 2022). Digitization and digitalization may be thought of as two micro processes that are carried out in restricted environments and are limited to certain areas of interest (Dąbrowska et al., 2022). What they both bring about at a macro and more pervasive level, though, is called digital transformation, namely the transformative change that involves many – if not all – aspects of the economy, society and politics (Kraus et al., 2022). Digital transformation goes beyond merely changing the technologies that are used, by changing the way businesses are run, relationships are held, services are provided and products are designed (Rachinger et al., 2019). In particular, digital transformation has given rise to new virtual places where different economic agents can interact in modalities that would not be possible otherwise, i.e. platforms. Platforms are places of relationships and exchanges, which are not necessarily limited to the exchange of goods and services, but can also widen the exchange of knowledge and competencies (Gawer, 2011; Gawer and Cusumano, 2014). In some cases, it can even happen that ecosystems emerge around platforms, which are used as meeting places by companies that want to engage in co-creation projects, namely in projects where two or more agents collaborate and share resources for creating higher-value products and services (Cennamo, 2021). However, some scholars have recently advocated that in manufacturing contexts a crucial aspect that needs to be investigated is how platforms foster value co-creation in the digital transformation era (Favoretto et al., 2022). This research aims to address this gap through an in-depth analysis of ELIS, a paradigmatic example of platform where many Italian corporations collaborate with startups for the implementation of manufacturing projects that have digitalization at their core. The number and importance of open innovation cases have grown rapidly (Ferrigno, Del Sarto, et al. 2022), and so has the related literature, which has flourished during the last 20 years, especially after Chesbrough’s pioneering definition (Chesbrough, 2003). Such development can also be explained by the widespread use of digital technologies that provide the tools to both companies and intermediaries to create and run effective platforms. While past research has extensively investigated how platforms act as intermediaries in open innovation initiatives where knowledge already existed (Randhawa et al., 2017), there is a gap in the lit377

378  Handbook on digital platforms and business ecosystems in manufacturing erature regarding the role of platforms in co-creation manufacturing projects, especially in the B2B market. The contribution of this research is twofold. First, it investigates the emergence of new ecosystems triggered and enabled by digital transformation. Second, it provides evidence of open innovation models that lead to economic development and may be imitated as good practice. Beyond academic contributions, this study offers many insights for practitioners. As a matter of fact, it offers a showcase of co-creation opportunities for corporations and startups. Public agencies should look at the phenomenon of platforms that play a proactive role in social and economic development to review their policies and redesign their investment strategies.

THEORETICAL BACKGROUND Digital Transformation ‘The widespread of digital technologies has enabled a notable transformation on the firms’ boundaries, processes, structures, role and interaction’ (Cennamo et al., 2020). Yet, digital transformation is not just a mere IT back-end process: it is a brand-new approach for doing business that affects the organization, the definition of new strategies, entrepreneurial and innovative processes, people and change management (Kraus et al., 2022). The global population lives in a more and more connected world and, in Italy, although in some aspects it tends to be far behind other nations, the data confirm a relentlessly growing ‘connected life’. Another central concept to be underlined is that digital transformation is not driven by IT, but technology and IT are how this change takes place. Therefore, everything has to be digitized: people, products, companies, managers, processes, production, thinking but also attitudes. As Anderson and Lanzolla (2008) pointed out, ‘in the past, [media and technology] industries operated through specialized value chains with clearly defined boundaries […] but new technologies have made it possible to convert different kinds of content into digital data’ (p. 72). The question is to understand why the digital transformation is different from other transformations that have occurred before (Dąbrowska et al., 2022). In particular, talking about businesses, digital transformation ‘is disrupting businesses in every industry by breaking down barriers between people, businesses and things’ and this leads to the creation of ‘new products, services and more efficient ways of doing business’ (Schwertner, 2017; p. 388). Thus, digital transformation is transforming businesses in all their aspects, from manufacturing to sales, from strategy to structure and from individual beliefs to the company’s culture (Lepore et al., 2019). To have a deeper cognition of how and why this transformation is happening, it is therefore necessary to take a step back and start from when the phenomenon was born, why it has spread and to deepen the changes it has brought with it in global socioeconomic life. Platforms as a Tool for Digital Transformation So far, the literature review has only discussed the opportunities that digital technologies provide to organizations. However, digitalization processes and the technologies that enable them also offer new means for collaboration among firms, for sharing information and resources and for the creation of collective output that can offer new or superior value propositions to customers. The impact of digital transformation goes beyond the firm and sector

The role of platforms in fostering co-creation  379 boundaries, affecting the level of complementarities across firms’ activities and products (Crupi et al., 2022). This phenomenon not only changes the logic of value creation but also leads to the expansion of interconnection and interdependence across the set of firms forming the ecosystem. In this light, firms active in an ecosystem need to consider the trade-off between being tied by greater interdependence and gaining more flexibility, autonomy and latitude of action thanks to the implementation of a digital strategy. At the same time, a digital platform economy is emerging (Ferrigno et al., 2023). Companies such as Amazon, Facebook, Google and Uber are creating worldwide technological infrastructures, while their business models open the way for radical changes in how we work, socialize, create value in the economy and compete for the resulting profits (Kenney and Zysman, 2016). Gawer and Cusumano (Cusumano and Gawer, 2002; Gawer, 2011; Gawer and Cusumano, 2014) introduced the term ‘platform economy‘, which encompasses a growing number of activities in business, politics and social interaction, enabled and/or made more easily accessible using platforms. As Kenney and Zysman (2016) argued, ‘we are in the midst of a reorganization of our economy in which the platform owners are seemingly developing power that may be even more formidable than was that of the factory owners in the early industrial revolution‘ (p. 62). Value Co-Creation in Manufacturing Contexts Digital transformation is transforming businesses in all their aspects, from manufacturing to sales, from strategy to structure, and from individual beliefs to the company’s culture (Cennamo et al., 2020). With regards to the manufacturing contexts, Favoretto et al. (2022) show that digitalization creates challenges regarding value co-creation processes. The key challenge deals with the need to adapt the customer relationship processes for co-creating value (Coreynen et al., 2017; Kotarba, 2018; Müller et al., 2018). This can be explained through two key issues. First, product visibility and data availability support the generation and consolidation of customer data (Bressanelli et al., 2018; Vial, 2019). Second, contacts via digital platforms and other digital communication channels enhance the customer experience and proximity (Matthyssens, 2019; Verhoef and Bijmolt, 2019; Vial, 2019). Moreover, Abbate et al. (2019) pointed out that some of today’s challenges related to co-creation processes are: the dichotomy between the low pace of innovation velocity connected with the degree of uncertainty of R&D and the high pace of technology change required by the market; the accessibility of talents to cope with a changing environment and response to emerging capabilities’ necessity; and an inflexible structure and bureaucracy path, which impact consistently on the speed of the decision-making process. These considerations are particularly true in manufacturing contexts (Culot, 2022). As a net result, these arguments lead us to posit that nowadays companies should rethink strategies and business models to keep up with competitiveness, where technologies, processes and relationships change at an ever faster pace (Björkdahl, 2020). The relationship between large manufacturers and the startups they interact with has changed (Coreynen et al., 2017), especially in manufacturing contexts (Favoretto et al., 2022). In this research, we try to address how the relationship between large manufacturers and manufacturing startups can be understood through a paradigmatic example of a platform in which many value co-creation opportunities arise.

380  Handbook on digital platforms and business ecosystems in manufacturing

RESEARCH SETTING AND PLATFORM BACKGROUND As earlier indicated, the focus of this chapter is to explore how platforms can facilitate value co-creation processes in manufacturing contexts affected by digital transformation. In this perspective, we performed an in-depth analysis of a single case (Eisenhardt and Graebner, 2007; Siggelkow, 2007). This choice occurs for several reasons. First, the analysis of a single case study represents a unique and critical case in testing a well-formulated theory (Yin, 2003). Second, since only limited theoretical knowledge exists concerning platform co-creation processes, an inductive research strategy allowing theory to emerge from the data can be a valuable starting point (Siggelkow, 2007). Although some would argue that, by picking a small sample (in this case just one case) and a nonrandom case, the case study may lack persuasiveness and representativeness, according to Siggelkow (2007) it is rather desirable to choose a particular organization. It allows researchers to gain certain insights that other organizations would not be able to deliver. On the other hand, by studying one particular organization, one should be careful with the conclusions that s/he draws, since they should not be too case-specific, but this makes it possible to draw inferences also for other kinds of organizations. One big challenge is to come up with a new conceptual framework. Therefore, starting from the general assumption that nowadays platforms may have an accelerating role in fostering co-innovative digital transformation processes, the aim is to investigate how this happens in practical terms. Third, the investigators have access to a situation previously inaccessible to scientific observation (Yin, 2003). Hence, it seems that a single case study may be considered as a revelatory case in exploring value co-creation processes in manufacturing projects that can be realized through the implementation of platform ecosystems. Theoretical Sampling The selection of the case is based on the principles of theoretical sampling (Glaser and Strauss, 1967; Pettigrew, 1990; Mason, 1996). A handful of important reasons have driven us to study ELIS – a non-profit platform where Italian corporations have the opportunity to meet and collaborate with startups for the implementation of projects that have digitalization at their core. Through this case study, it is possible to draw the fil rouge between the three macro topics, where a platform is an enabling tool (more precisely, a marketplace), co-creation is the modality (i.e. the tactic) utilized and digital transformation is the expected output of companies that are trying to adapt their strategies and processes to an ever-changing environment. The case of ELIS is presented as an illustration, namely additional (but not unique) evidence in favor of the argument. As Siggelkow (2007) pointed out, ‘a paper should allow the reader to see the world, and not just the literature, in a new way’ (p. 23). Given the novelty of the phenomenon to be examined and the inductive nature of the research questions, the whole investigation is carried out by adopting a qualitative case-based method. Qualitative research is an organized method that allows the examination of people’s experiences and feelings through data collection and interpretation, and the exploration of the phenomenon within its context, using a wide range of data sources (Yin, 2003). The qualitative case-based methodology is to be preferred when the main research questions ask ‘how’ or ‘why’ and when the researcher has little control over behavioral events since the boundaries between the phenomenon and the context are not very clear (Yin, 2003). In qualitative research,

The role of platforms in fostering co-creation  381 qualitative primary and secondary information are gathered in the form of non-numerical data. There are different methods to collect the required data, including interviews, observations, focus groups, narratives, notes, reports and reviews of archives (Ghaljaie et al., 2017). In the next subsection, we explain the methodology used to collect case study data. Data Collection and Data Analysis The research question is investigated by using a qualitative methodology, thus extrapolating information from interviews, observations, focus groups and websites. The primary data for this study is extracted from a wide-ranging program of interviews with key informants coming from academia, the industry and the government, grounded in semi-structured questions. Additional information was derived from websites, reports and in-person events and integrated into the primary research, to answer the question by providing a more complete picture. The objective of presenting a nuanced picture of the role of platforms in helping corporations overcome challenges and opportunities that characterize the context of co-creation for the sake of digital transformation called for a qualitative research design. The first step was to explore what are, according to the literature and experts in the field, the micro, meso, and macro elements that enable or hinder digital transformation in a company. In the Appendix we included the list of questions we asked to the interviewees. Eventually, by applying the snowball sampling technique, it was possible to gather evidence from nine experts, coming from universities, big companies, startups and the government. The interviews were held on the phone or via video call and ranged from 19 to 45 minutes in duration, with an average of 30 minutes. They were all recorded and transcribed. We sent a summary of the transcripts to the same interviewees to validate the contents. The list of the people surveyed, their professional fields and their ‘perimeter’ of expertise can be found in Table 24.1. Following Yin (2003), the qualitative data for this study was collected from a variety of sources. The use of many different data sources represents an essential element of the analysis because it ensures the diversity of perspectives required by the constructivist principles on which qualitative analyses are based. The data collected include both primary data (semi-structured interviews) and secondary data (website, internal reports and documents, presentations and other printed materials). The preliminary analysis performed was helpful in the design of the interview protocols used in the collection of primary data and in illustrating the context for the interpretation of the data gathered by interviews (Abbate et al., 2019). Semi-structured interviews were undertaken with key informants from significant stakeholders, including academic experts, participating corporations and members of the ELIS team . These interviews formed a substantial component of the gathered data. Managers and scholars were engaged through conference calls and face-to-face interviews, lasting between 15 and 35 minutes. The interviewees were called on to participate in the study because of their knowledge and experience: eventually, the total number of respondents was sufficient to provide enough evidence to answer the questions at stake. The interview protocol was designed to both address topics relating to the research questions and, at the same time, leave room for the respondent to spread the discussion to unpredicted issues (Yin, 2003). In this way, it was ensured that the respondents were free to interpret each question from their perspectives, as required in this type of research (Abbate et al., 2019).

382  Handbook on digital platforms and business ecosystems in manufacturing Table 24.1

Interviews with managers

Initials

Context

Expertise

When

Duration

A.M.

University

Innovation management, Open Innovation

16 April 2021

25 minutes

N.V.

University and industry

Research and industrial application of

20 April 2021

33 minutes

22 April 2021

24 minutes

and Technology transfer wearable robotics C.S.

University and industry

Bio and industrial application of robotic systems

C.A.A.

University

Robotics and automation, machine learning 23 April 2021

34 minutes

P.P.

Government

Innovation management, Ministry for

29 April 2021

45 minutes

Technological Innovation and Digital Transition A.L.S.V.

Government and industry

Electronic engineering, Computing sciences 1 May 2021

19 minutes

D.M.

Industry

Research in human-machine interface, user

3 May 2021

22 minutes

experience, Internet of Things, and Industry 4.0 L.V.

Industry

Innovation and technology transfer

4 May 2021

17 minutes

M.B.

University and industry

Robotic systems, virtual/augmented/mixed

12 May 2021

23 minutes

reality, human-machine interface

Some questions were added to capture issues that showed up during discussions in the interviews, while other questions were left out if the discussion was taking a different direction. The interviews were recorded to minimize data loss and then immediately transcribed for later analysis. The transcriptions were also synthesized and sent to the respondent in order to obtain contents validation of each interview. The information derived from the interviews has been integrated with secondary data to mitigate the risk of informant bias, control for the subjective judgments of individuals, and thus, increase construct validity. Furthermore, we continually kept in touch with ELIS staff and also some corporate managers during the 12 weeks of implementation of the project to remain well-informed about activities and projects.

FINDINGS Sharing and Building The first phase is called ‘sharing and building’, since it is the phase in which corporations share their problems and build their portfolio of startups that offer themselves as solution providers. In practice, corporations are invited to join a digital platform managed by ELIS and explain the problems they would like to solve and the business area to which they are related. What corporations state is made public among the participants – corporations and startups: this means that they are asked to expose some of their weaknesses even to companies that may be competitors. Still, using this exercise, companies gain a deeper idea of the nature of and the potential solutions to their problem and make it clearer also to ELIS (the platform) and the startups (the other side of the market) that might be willing to engage in a project of co-creation. By analyzing the different and common problems shared by corporations, ELIS can develop a taxonomy to classify them, which helps both companies and startups by giving direction to their research. The components of the categorization are called ‘perimeters’ and

The role of platforms in fostering co-creation  383 they usually correspond to corporate business areas. For the Open Italy 2021 edition, ELIS distinguished eight perimeters: 1) Change management and digital HR; 2) Customer engagement and new sales channels; 3) Operation improvement; 4) Privacy and cybersecurity; 5) Smart health and safety; 6) Sustainability, renewable energies and circular economy; 7) Public government; and 8) Made in Italy 4.0. Of course, since we are talking about big corporations with thousands of employees in Europe and around the world, it could happen that the same company presents more than one challenge for each perimeter. In this way, startups have an easy overview of what are their competence areas and the companies they may work with. After companies have published their challenge on the platform, the startup can apply for a precise challenge in a given area and also provide a score of self-evaluation about the fitting between the challenge and the solution. In other words, in this very preliminary phase in which both corporations and startups –the two sides of the market – introduce themselves and their interests, not only does the Open Italy ecosystem allow them to meet through a digital platform, but it also facilitates the process by developing a taxonomy that benefits both parties in finding information about participants. In total, for the Open Italy 2021 edition, corporations expressed 335 business needs that were faced by 375 startups enrolling in the program. Since one business need can be addressed by many startups, the total number of applications submitted was 1957. As far as the process is automated through digital technologies, the final choice of which business needs to be prioritized and which startup to choose to solve it is left to human judgment. That is why the platform service provided by ELIS during the Open Italy program is not limited to the provision of digital services, but also and especially more human-centered and customized services developed in partnership with other organizations and experts that are part of the ELIS ecosystem. To provide a high-value service to the companies in the consortium, all the applicant startups, after registering on the platforms and applying for a given challenge, are asked to provide a score that expresses a self-evaluation of how much the startup feels its solution is suitable to the corporate needs. Furthermore, startups are judged by a jury composed of ten experts from outside the consortium and particular to each perimeter. The experts can be professionals, scholars or venture capitalists: the variety of their backgrounds and fields of expertise highlights how ELIS can create a proper ecosystem in which different actors and organizations work for the provision of an enhanced form of value. In the end, eight juries of ten experts are engaged and they have to provide a score on the potential of the startup. Each startup is scored and ranked based on the average calculated on a scoring table from one to five that considers five characteristics of the startup: ● The level of innovativeness, namely how innovative are the business idea and the technology used to deliver value to customers; ● The market, in terms of dimensions, scalability, scope and the presence of competitors; ● The validity of the business model, namely how value is created and captured by the startup, which signals both the current stability and the potential of growth; ● The competencies and stability of the team; and ● References, previous grants and prizes, since they add a sense of liability and social recognition of the startup. To be evaluated according to these five dimensions, startups were required to provide relevant information, which includes, but is not limited to, their value proposition in general, the solution they want to provide to the corporation with specific reference to the challenge at

384  Handbook on digital platforms and business ecosystems in manufacturing stake and how they differentiate from potential competitors. Furthermore, startups are asked to allocate the applicability of their offer to not more than two perimeters, so that it is easier for corporations to filter and navigate the range of available startups. Thus, the work of the experts’ jury is to provide an evaluation of the startup and its business idea as it is, and not on the fitness between the corporate challenge and the startup’s proposed solution: this job is carried out by another external actor. Finally, an external consulting company is involved and appointed to give an opinion on the fit between the corporate need and the startup’s proposed solution. Again, the score is expressed from one to five together with qualitative feedback to justify the given score. To sum up, corporations are provided with three evaluations and feedback, different in the element they are judging: the self-evaluation by the startup on the fitness between problem and solution; the score by the jury of experts on the goodness of the startup; and the revised opinion by the consulting group. Thus, according to the evidence collected, ELIS supports corporations in choosing the right startup to work with and the right project to start by collecting information about participants; effectively making information available; providing objective opinions of experts on startups; and providing objective opinions of professionals on startups solution proposals. While the digital platform is the tool through which the comparison between startups and projects happens, the whole method of scoring and feedback is thought to give corporations a valuable service and help them find the right match according to their needs. The final choice, however, is made by the respective innovation managers, who can vote for their favorite startups directly on the platform. Prioritizing The second phase is about ‘prioritizing‘, namely recognizing the critical aspects of a co-creation project in the context of digital transformation. After corporate managers have been made aware of the jury’s and the consultancy company’s feedback and after expressing their preferences, startups can present themselves ‘in person’ to corporations. This can happen in two different ways: either during the Demo Days, where the ten most voted-by-managers startups for a given perimeter are presented; or during one-to-one meetings between the startup and corporate managers. It could happen that none of the ten most voted startups for a given perimeter are voted for by the corporation, which is why two different meeting types are established. Yet, it can also be that a corporation is interested in a startup that did not end up among the 10 most voted ones: during the one-to-one meeting, thus, corporations can get to know startups that did not achieve much visibility but have been deemed to be interesting. Therefore, during Demo Day, great visibility is given to those startups that have received the highest number of votes by corporations: in this way, also, companies that may not be interested in a given perimeter have the chance to explore the range of startups. During the one-to-one meetings, instead, corporations meet those startups that they have expressly chosen, regardless of the scores and feedback provided by the jury and the consulting company: the final word is left to the corporate innovation managers.

The role of platforms in fostering co-creation  385 Co-Innovating The third phase is called ‘co-innovating‘ since it is the phase in which corporations and startups set up the project structure and steps. The process of co-innovation is proposed to be structured in 12 weeks. In ELIS, it is called the 12-week agile methodology, precisely because the principles required are a high level of focus, control and flexibility, together with moments of confrontation, engagement and frequent mutual feedback. The idea of co-innovation allows the creation of an open and innovative mindset, while the ‘sprint’ produced by the very short timing allows accelerated reactions and readiness to change. The twelve weeks are organized in three steps, where: ● From week 1 to 4, all the project phases and activities are defined, together with the responsibilities of the participants; furthermore, a feasibility analysis is performed that considers both stakeholders’ involvement and resources requirement. ● From week 5 to 8, an initial prototype is developed and put into work for testing its performance; the activity is monitored and feedback is provided with regard to the intended KPIs, to realign the project when necessary. ● From week 9 to 12, the final tests for the development of the proof of concept (PoC) are carried out; the definite version of the prototype is implemented and an analysis of the future prospect is drawn. This last analysis allows startups not only to present the final solution to the corporation but also to have a PoC to show to future potential clients or investors. The co-innovation phase, though, is not an easy phase and not to be taken for granted, because not all corporations approach it the same way. During the two Kickoff Days, we had the opportunity to observe different behaviors of corporations, which reflected their conviction or insecurity towards the project, and discuss with them the reasons why they were more or less insecure about what to do and how to do it. This is an important contingency for ELIS to be taken into account since it influences how the collaboration will proceed during the following 12 weeks and the outcome. We also tried to frame corporations’ behavior and described it as follows: ● The ‘committed’ are those corporations that know exactly what they want and how they want to achieve it. After months of one-to-one meetings with startups and effective internal communication for priority definition, these corporations arrived ready and eager to set up the next steps for the actual implementation of the project. ● The ‘laggards’ are those corporations that have decided at the last minute to start the program of co-creation, so they have clear in mind what they want but not how to reach it, since they did not have much time to discuss it earlier with both the startup and the internal business units that are supposed to be involved. ● The ‘unsure’ are those corporations that still need to figure out both the need they want to prioritize and the path they want to follow. Thus, in these cases, it is not clear to them the problem they want to address or the process, product or service to apply. Most of the time, they assume the most suitable technology to apply, and so they contact the startup that can provide it to them.

386  Handbook on digital platforms and business ecosystems in manufacturing Starting from the Kickoff Days and during the whole duration of the Open Italy program, ELIS provides a supporting team to facilitate the corporate and startup referents in the implementation of the project. Precisely, three figures from ELIS are accountable during the co-innovation phase: ● The senior advisor, who advises the team in the planning stages to effectively and efficiently pursue the objectives. Especially during the Kickoff Days, the senior advisor usually comes up with observations and questions about latent aspects, so the corporation and the startup can begin their project with nothing left out. ● The project manager, who monitors project activities, collects all documents and deliverables produced, organizes meetings and shares weekly meeting reports and prepares the project deliverables, together with the startup and junior consultants. The project manager also plays an interesting role during the Kickoff Days, since she/he acts as a human ‘metronome’ that sets the rhythm of work and keeps up with the agenda. ● The junior consultant, who supports the corporate and startup referents in the preparation of documents and deliverables. This figure may be considered also as kind of symbolic, since the junior consultant is usually a university student of the ecosystem nature of ELIS, whose main objective is to create social rather than economic impact. As the 12 weeks of the project are structured with a very precise calendar, precise appointments delimit the activities and tasks during the individual weeks. Every Monday, for example, a work-in-progress call between the corporate, the startup and ELIS is held, to briefly discuss the previous week and coordinate the work of the following one. In addition, if necessary, Wednesdays may include another call with the project manager for the resolution of critical aspects. Eventually, every Friday, weekly deliverables are sent to the project manager and the senior advisor. It is not by chance that individuals and roles within the project team are not limited to the respective corporations’ and startups’ staff. The fact that people from ELIS and from outside the consortium can also participate in the co-innovation is another distinguishing element of the fact that ELIS creates an actual ecosystem.

DISCUSSION AND CONCLUSIONS By studying digital transformation, we understood how it is a disruptive revolution that represents a holistic process in continuous evolution and that is completely overturning the fate of the global economy. Companies must reinterpret their culture and their structures to obtain advantages from the use of digital technologies, then renew their mindset, their strategies, processes and skills with completely new eyes that can embrace innovation. Digital transformation is a real lever, capable of breaking down intermediaries without strategic value and maximizing the relationships underlying the new business models, which require a renewed and even deeper awareness of the new market logic and the impact that digital means for the people involved in business processes. No single recipe can guide all companies toward the effective and efficient digitization process, but rather dynamics that require different approaches to different company functions. Digital transformation is a process of cultural and managerial change, in which the synergy between companies and research becomes an added value for all the stakeholders involved and which needs new paradigms, new skills and new learning paths. The impact of digital transfor-

The role of platforms in fostering co-creation  387 mation goes beyond the firm and sector boundaries, affecting the level of complementarities across firms’ activities and products. This phenomenon not only changes the logic of value creation but also leads to the expansion of interconnection and interdependence across the set of firms forming the ecosystem. In this light, firms active in an ecosystem need to consider the trade-off between being tied by greater interdependence and gaining more flexibility, autonomy and latitude of action thanks to the implementation of a digital strategy. At the same time, a digital platform economy is emerging, where digital giants’ business models enabled by their huge infrastructures open the way for radical changes in how we work, socialize, create value in the economy and compete for the resulting profits (Kenney and Zysman, 2016). In ever-changing environments where traditional markets and technologies are disrupted by new ones, large corporations’ competitive advantage is never to be taken for granted, as they struggle to secure efficiency and long-term sustainability (Andriopoulos and Lewis 2009). Most of them recognize the necessity to be proactive in adapting to disruption and they choose to engage with small firms and startups, adopting an ‘exploit and explore’ strategic approach (Ahuja and Novelli 2016). In this light, it is becoming more common for big corporations to adopt structures and processes to cooperate, rather than compete, with small and young enterprises that would be labeled as potential disruptors otherwise. This is nothing but a form of open innovation that practically finds application in interactive coupled projects, namely co-creation. In the literature, co-creation is part of the wider discussion on open innovation, a term that was first coined by Henry Chesbrough in 2003. The number and importance of open innovation cases have grown rapidly, and so did the related literature. Such development can also be explained by the widespread use of digital technologies that provided the tools to both companies and intermediaries to create and run effective platforms. Thus, while past research has extensively investigated how platforms act as intermediaries in open innovation initiatives where knowledge already existed (Randhawa et al., 2017), this research contributes to the literature in three ways. First, it addresses the gap in the literature regarding the role of platforms in co-creation projects, namely those projects where knowledge is created from scratch. Second, it investigates the emergence of new ecosystems triggered and enabled by digital transformation. Third, it provides evidence of open innovation models that leads to economic development and may be imitated as good practice. In this light, both the academic and the industrial communities can benefit from the insights provided by this work, as it offers a showcase of co-creation opportunities for corporations and startups. Furthermore, public agencies should look at the phenomenon of platforms that play a proactive role in social and economic development to review their policies and redesign their investment strategies. For the sake of this research, the case of ELIS consortium was chosen. ELIS is an Italian platform where Italian corporations have the opportunity to meet and collaborate with startups and begin projects of co-innovation that have digitalization at their core. The corporate-startup collaboration setting provides an opportunity to study the economic performance of agents beyond alliances and acquisitions as well as to explore the linkages between entrepreneurial processes and innovation outcomes – at the startup, firm and ecosystem levels – from a diversity of theoretical perspectives (Del Sarto et al. 2022; Crick et al., 2023). The ELIS case allowed us to investigate how a (digital) platform acts as an intermediary accelerator of the processes of co-creation between large and small firms, following them in the three steps of value creation: value identification, value iteration and value realization (Kostis and Ritala, 2020). However, this study has not investigated how the platform leads to technological experimentation of the manufacturing projects (Ferrigno, Zordan and Di Minin, 2022). Moreover,

388  Handbook on digital platforms and business ecosystems in manufacturing another limitation of the study is that we have not analyzed how the companies that participated in the ELIS platform have involved their employees. This issue is particularly important, according to recent research on the topic (Cucino et al., 2022). Despite some limitations of the study, the ELIS analysis provided evidence of critical aspects and tensions arising from a co-innovation program, setting the grounds of how the same platform could act to smooth them. Co-innovation projects are complex, as they involve intensive interactions among diverse stakeholders. Although uncertainty in industrial relationships has often been associated with concerns about partner opportunism, much of the uncertainty in contemporary industrial co-creation is related to the ambiguity of collective work in industrial projects. Weber and Mayer (2014) define it as interpretive uncertainty, which is the misalignment of views on the process and outcomes of co-creation due to differences in partners’ interests, views and understandings of what should be accomplished and how. These obstacles derive from dissimilarities in industry membership, expectations, core technologies, knowledge bases, perceptions of task complexity and views on the continuity of the relationship (Kostis and Ritala, 2020). The ELIS case study, as well as many other co-creation cases, shows how digital co-creation practices correspond to three processes that Kostis and Ritala (2020) identifies as value identification, value iteration and value realization. Because of the complexity of co-creation projects, to which the complexity of digital technologies is added, an effective, regular and transparent communication between the interacting actors is essential. The creation of an ecosystem, such as the one built by ELIS, is the expression of a new mindset by which the interest of the many is put before the interest of the individual. What ELIS has been able to provide during these years was not only a platform and a meeting point for many organizations that would not otherwise talk to each other but also a training field for CEO, managers, startups and students to train the innovation muscle.

REFERENCES Abbate, T., Codini, A., and Aquilani, B. (2019). Knowledge co-creation in open innovation digital platforms: processes, tools and services. Journal of Business & Industrial Marketing, 34(7), 1434–47. Ahuja, G., and Novelli, E. (2016) Incumbent responses to an entrant with a new business model: Resource co-deployment and resource re-deployment strategies. In ‘Resource redeployment and corporate strategy’, Emerald Group Publishing Limited. Anderson, J. and Lanzolla, G. (2008). Digital transformation. Business Strategy Review, 19(2), 72–6. Andriopoulos, C., and Lewis, M. W. (2009). Exploitation-exploration tensions and organizational ambidexterity: Managing paradoxes of innovation. Organization Science, 20(4), 696–717. Bloomberg, J. (2018). Digitization, digitalization, and digital transformation: confuse them at your peril. Forbes. https://​www​.forbes​.com/​sites/​jasonbloomberg/​2018/​04/​29/​digitization​-digitalization​ -and​-digital​-transformation​-confuse​-them​-at​-your​-peril/​. Retrieved on 14 June 2021. Björkdahl, J. (2020). Strategies for digitalization in manufacturing firms. California Management Review, 62(4), 17–36. Bressanelli, G., Adrodegari, F., Perona, M., and Saccani, N. (2018), Exploring how usage-focused business models enable circular economy through digital technologies, Sustainability, Vol. 10 No. 3, p. 639 Cennamo, C. (2021). Competing in digital markets: A platform-based perspective. Academy of Management Perspectives, 35(2), 265–91. Cennamo, C., Dagnino, G. B., Di Minin, A., and Lanzolla, G. (2020). Managing digital transformation: scope of transformation and modalities of value co-generation and delivery. California Management Review, 62(4), 5–16.

The role of platforms in fostering co-creation  389 Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Harvard Business Press. Coreynen, W., Matthyssens, P., and Van Bockhaven, W. (2017). Boosting servitization through digitization: Pathways and dynamic resource configurations for manufacturers. Industrial marketing management, 60, 42–53. Crick, J. M., Crick, D., and Ferrigno, G. (2023). Coopetition and the marketing/entrepreneurship interface in an international arena. International Journal of Entrepreneurial Behavior & Research, (ahead-of-print). Crupi, A., Del Sarto, N., Di Minin, A., and Kenney, M. F. (2022). Disentangling the importance of digital platforms and absorptive capacity in digital business ecosystems. In Handbook on Digital Business Ecosystems. Edward Elgar Publishing. Cucino, V., Del Sarto, N., Ferrigno, G., Piccaluga, A. M. C., and Di Minin, A. (2022). Not just numbers! Improving TTO performance by balancing the soft sides of the TQM. The TQM Journal, (ahead-of-print). Culot, G. (2022). Digital ecosystems in manufacturing: emerging models for Industry 4.0. In Handbook on Digital Business Ecosystems (pp. 727–42). Edward Elgar Publishing. Cusumano, M. A., and Gawer, A. (2002). The elements of platform leadership. MIT Sloan Management Review, 43(3), 51. Dąbrowska, J., Almpanopoulou, A., Brem, A., Chesbrough, H., Cucino, V., Di Minin, A., ... and Ritala, P. (2022). Digital transformation, for better or worse: a critical multi‐level research agenda. R&D Management, 52(5), 930–54. Del Sarto, N., Ferrigno, G., Parida, V., and Di Minin, A. (2022). Do start-ups benefit from coworking spaces? An empirical analysis of accelerators’ programs. Review of Managerial Science, 1–32. Eisenhardt, K., and Graebner, M. (2007). Theory building from cases: Opportunities and challenges. Academy of Management Journal, 50(1), 25–32. Favoretto, C., Mendes, G. H. D. S., Filho, M. G., Gouvea de Oliveira, M., and Ganga, G. M. D. (2022). Digital transformation of business model in manufacturing companies: challenges and research agenda. Journal of Business & Industrial Marketing, 37(4), 748–67. Ferrigno, G., Del Sarto, N., Cucino, V., and Piccaluga, A. (2022). Connecting organizational learning and open innovation research: an integrative framework and insights from case studies of strategic alliances. The Learning Organization, (ahead-of-print). Ferrigno, G., Zordan, A., and Di Minin, A. (2022). The emergence of dominant design in the early automotive industry: an historical analysis of Ford’s technological experimentation from 1896 to 1906. Technology Analysis & Strategic Management, 1–12. Ferrigno G., Crupi A., Di Minin A., Ritala P. (2023). ‘50 + years of R&D Management: a retrospective synthesis and new research trajectories’. R&D Management, (ahead-of-print). Gawer, A. (Ed.). (2011). Platforms, markets and innovation. Edward Elgar Publishing. Gawer, A., and Cusumano, M. A. (2014). Industry platforms and ecosystem innovation. Journal of Product Innovation Management, 31(3), 417–33. Ghaljaie, F., Naderifar, M., and Goli, H. (2017). Snowball sampling: A purposeful method of sampling in qualitative research. Strides in Development of Medical Education, 14(3). Glaser, B. G., and Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago, IL: Aldine Kenney, M., and Zysman, J. (2016). The rise of the platform economy. Issues in Science and Technology, 32(3), 61. Kostis, A., and Ritala, P. (2020). Digital artifacts in industrial co-creation: How to use VR technology to bridge the provider-customer boundary. California Management Review, 62(4), 125–47. Kotarba, M. (2018). Digital transformation of business models, Foundations of Management, Vol. 10 No. 1, pp. 123–42. Kraus, S., Durst, S., Ferreira, J. J., Veiga, P., Kailer, N., and Weinmann, A. (2022). Digital transformation in business and management research: An overview of the current status quo. International Journal of Information Management, 63, 102466. Lepore, D., Nambisan, S., Tucci, C. L., and Zahra, S. A. (2019, July). Digital transformation & firms’ innovative strategies: capabilities, ecosystems, and business models. In Academy of Management Proceedings (Vol. 2019, No. 1, p. 14623). Briarcliff Manor, NY 10510: Academy of Management.

390  Handbook on digital platforms and business ecosystems in manufacturing Mason, J. (1996). Qualitative researching. Newbury Park, CA: Sage. Matthyssens, P. (2019), ‘Reconceptualizing value innovation for industry 4.0 and the industrial internet of things’, Journal of Business & Industrial Marketing, Vol. 34 No. 6, pp. 1203–9. Müller, J. M., Buliga, O. and Voigt, K. I. (2018), Fortune favors the prepared: how SMEs approach business model innovations in industry 4.0, Technological Forecasting and Social Change, Vol. 132, pp. 2–17 Pettigrew, A. M. (1990). Longitudinal field research on change: Theory and practice. Organization Science, 1(3), 267–92. Rachinger, M., Rauter, R., Müller, C., Vorraber, W., and Schirgi, E. (2019). Digitalization and its influence on business model innovation. Journal of Manufacturing Technology Management, 30(8), 1143–60. Randhawa, K., Josserand, E., Schweitzer, J., and Logue, D. (2017). Knowledge collaboration between organizations and online communities: the role of open innovation intermediaries. Journal of Knowledge Management, 21(6), 1293–1318. Reis, J., Amorim, M., Melão, N., Cohen, Y., and Rodrigues, M. (2019). Digitalization: a literature review and research agenda. In ‘International Joint conference on Industrial Engineering and Operations Management’. Springer Cham, 443–56. Schwertner, K. (2017). Digital transformation of business. Trakia Journal of Sciences, 15(1), 388–93. Siggelkow, N. (2007). Persuasion with case studies. Academy of Management Journal, 50(1), 20–4. Verhoef, P. C., and Bijmolt, T. H. (2019). Marketing perspectives on digital business models: A framework and overview of the special issue. International Journal of Research in Marketing, 36(3), 341–9. Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., and Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of business research, 122, 889–901. Vial, G. (2019), Understanding digital transformation: a review and a research agenda, The Journal of Strategic Information Systems, Vol. 28 No. 2, pp. 118–44. Weber, L., and Mayer, K. (2014). Transaction cost economics and the cognitive perspective: Investigating the sources and governance of interpretive uncertainty. Academy of Management Review, 39(3), 344–63. Yin, R. K. (2003). Case Study Research – Design and Methods. 2nd edition., London Sage Publication, Thousand Oaks.

APPENDIX 1. LIST OF QUESTIONS OF THE INTERVIEWS 1. What is the field you work in? 2. What are the three most relevant capabilities a manager should have to design and implement a successful strategy? 3. What are the three main enabling resources necessary for the implementation of a digital strategy? 4. What are the three environmental elements providing a facilitating context for digital transformation? 5. What are the three main critical aspects of digitalization that should be prioritized? 6. Would you please provide me a couple of names of experts that, like you, are providing significant insights on digital transformation?

25. Digital platforms in the Norwegian food industry: an ecosystem perspective on the nation’s dairy and beef production Victoria Slettli

INTRODUCTION The conventional view of industries, traditionally presented in terms of a linear value chain, is currently being challenged by the advance of digital transformation and the opportunities it brings (Loonam and O’Regan, 2022). Recent developments in IT, technological advancements connected to artificial intelligence, the Internet of Things (IoT), machine learning and big data are changing the business context and structure. These changes refer to the emergence and rise of digital business ecosystems (DBEs) and platforms. DBEs are defined as open ‘socio-technical environments of individuals, organizations and digital technologies’ that interact with each other for the purpose of value creation by means of shared digital platforms (Senyo et al., 2019). Digital platforms can be understood as either purely technical or sociotechnical formations, where the latter refers to ‘technical elements (of software and hardware) and associated organizational processes and standards’ (De Reuver et al., 2018). Digital platforms play a crucial role as they change the basis for offering and capturing value and suggest new opportunities for value proposition and innovation. In their essence, platforms facilitate networking ecosystems, enabling cross-border and cross-sector cooperation for actors operating in different industries, transcending borders and locations (De Reuver et al., 2018). Digital platforms and ecosystems can be found in several industries – including automotive, energy, electronics and automation, agriculture and retail – providing opportunities for interaction, knowledge sharing and creation, innovation and co-evolution. Nambisan et al. (2019) highlights two streams of research on digital platforms and ecosystems. The first stream of literature sets its focus on product development; it conceptualizes platforms as a shared set of assets, components and technologies that facilitate innovation. The second stream of research adopts an industrial economic stance and conceptualizes platforms as a multisided marketplace with a set of rules and architectures that mediate interactions and transactions among entities. Even though these two streams of literature have contributed to the theoretical development of the concepts of digital platforms and ecosystems, the research is very fragmented and fails to provide a holistic understanding of the issues at hand (De Reuver et al., 2018). Further, the scope of the research literature in the area builds mainly on success cases of such ecosystems and platforms, many of which represent cases of share economy (Uber, Airbnb), social media (Twitter, Facebook, LinkedIn), online retailers (Alibaba, Amazon, eBay) or technology giants (John Deere, GE, Sony, Apple, Google and Microsoft) (Nambisan et al., 2019). In addition, whereas the mainstream literature has focused on the consumer Internet of Things platforms, little focus was paid to the Industrial Internet

391

392  Handbook on digital platforms and business ecosystems in manufacturing of Things platforms (IIoTPs) facilitating business-to-business interactions (Mosch and Obermaier, 2022). This chapter aims to contribute to the contextualized understanding of digital platforms and ecosystems by adopting an ecosystem perspective on the national dairy- and beef-producing industry, which has traditionally been presented in terms of a linear pipeline value chain (Ursin et al., 2016). A digital ecosystem perspective on the national food industry highlights the complex, interactive and highly interdependent nature of the relationships among beef and dairy farmers, research organizations, slaughterhouses, food manufacturers and public organizations. Second, the chapter seeks to add to the theoretical and practical knowledge about IIoTPs for business-to-business interaction, and raises the following question: what are the functions and purposes of the digital platforms operating in such an ecosystem? The rest of the chapter is structured as follows. First, the current understanding of digital platforms is outlined in a theoretical section. This is followed by a case examining the Norwegian dairy and beef industry – including the key actors of the ecosystem, the challenges of the industry and examples of digital platforms. The chapter closes with a discussion of the findings and concluding remarks specifying avenues for future research.

DIGITAL PLATFORMS: AN OVERVIEW The conceptualization of industry structure, competition and cooperation is currently under revision. To a large degree, this is happening due to the digital revolution and the appearance of digital platforms – and their associated ecosystems (Loonam and O’Regan, 2022). Digital platforms can be defined as ‘a shared set of technologies, components, services, architecture, and relationships that serve as a common foundation for diverse sets of actors to converge and create value’ (Nambisan et al., 2019). De Reuver et al. (2018) distinguish between digital and non-digital platforms. Non-digital platforms are characterized by a stable core and variable periphery and are used for recombinant innovation through modularization. Within this perspective, one can distinguish between internal firm platforms, supply chain platforms and industry platforms. Digital platforms, on the contrary, are characterized by distributedness, reprogrammability and homogenous data and can be understood as sociotechnical aggregations of technical elements and related organizational processes (Tilson et al., 2010). This chapter adopts a sociotechnical view of digital platforms and ecosystems. The emergence of advanced technology, such as IoT, cloud computing, big data analytics, blockchain, etc., has provided new and numerous opportunities for platform value creation (Loonam and O’Regan, 2022). Organizations seek to employ platform strategies, where ‘complex relationships are formed in which producers, consumers, and the platform itself – connect […] interactions with one another using the resources provided by the platform’ (Parker et al., 2016). According to the ecosystem principle, the company is no longer a member of a single industry but a part of the business ecosystem that operates across industries and allows for cooperation and competition in delivering new products, satisfying customers’ needs and fostering innovation (Moore, 1993). From the industrial economics perspective, platforms can be understood as ‘multisided markets’ that possess network effects, meaning that the benefits of one side participating in a platform are dependent on the size of the other side (Nambisan et al., 2019). These network

Digital platforms in the Norwegian food industry  393 effects may influence the pricing approach and the market competition and are central to platforms’ functioning and success. Adoption of a platform approach requires some fundamental changes in strategic thinking. Instead of focusing on safeguarding unique resources, core competencies and capabilities, organizations should think at the ecosystem level: how can an organization achieve an advantage within its ecosystem by orchestrating resources and capabilities around the platforms’ actors? (Loonam and O’Regan, 2022; Slettli, 2022). According to the traditional strategic literature, competencies and dynamic capabilities are crucial for a firm’s sustainable competitive advantage. In the context of digital platforms, the challenge is to coordinate the competencies and capabilities present in the ecosystem. To achieve value creation in the ecosystem, managing competencies and capabilities should be supported by platform quality, trust between actors, ethics and values, data and security. In this context, platform leaders play a crucial role in designing the ecosystem and orchestrating capabilities and resources (Teece, 2017). Digital platforms vary in the level of openness which signifies ‘the threshold condition of entry’ for suppliers and buyers (Wang et al., 2020). The higher the threshold, the more closed is the platform. The research investigating the connection between the level of openness and platform performance suggests inconsistent results. According to one group of studies, platform openness can boost the variety of products and services, strengthen network effect and, therefore, improve platform performance (Natalicchio et al., 2017). Another group of studies argues that a higher level of platform openness may provoke congested competition and problems in coordination of innovation activities, which may hamper ecological symbiosis and diminish platform performance (Casadesus‐Masanell and Hałaburda, 2014). Further, a varying level of platform openness may require different modes of coordination mechanisms within the ecosystem (Dolata and Schrape, 2022; Hsieh and Vergne, 2023) Industrial digital platforms (IIoTPs) facilitating business-to-business interactions serve two main purposes. First, they provide the basis for transactions between different actors. Second, they enable innovation encompassing third-party complementors (Mosch and Obermaier, 2022). IIoTPs are characterized by a high complexity of value creation and community governance. The data transferred via such platforms is critical for the success and competitive advantage of the individual actors. In addition, the digital services offered by such platforms are very sophisticated and require a ‘deep understanding of the underlying processes’ (ibid., p. 178). Based on the criteria of the basic characteristics, supporting value creation logics and dominant value creation logics, Mosch and Obermaier (2022) have elaborated a typology of the industrial digital platforms. Beginning with the simplest, connectivity platforms organize simple and efficient connection between different systems of a certain manufacturing company. The second type, digital marketplaces are transaction platforms between the providers and customers where the providers often try to integrate into the customers’ procurement processes, to foster dependency and, hence, create a lock-in effect. Whereas, the relevancy of the third-party complementors is low for both types of platforms, the network effect potential of transaction good is low for the connectivity platforms and high for the digital marketplaces. Two other types of platforms – integrated Industrial Internet of Things platforms (IIoTP) and hyperscalers represent more advanced platforms with high relevancy of third-party complementors. The Integrated IIoTP operate in a cloud setting and enable extraction, collection and analysis of big data for process and product innovation. This type of platform provides

394  Handbook on digital platforms and business ecosystems in manufacturing software and platform offerings that are tailored to manufacturing industries, and the network effect potential is typically low. The integrated IIoTP are dependent on hyperscalers in cloud computing, which provide open platform offerings and on-demand storage of data and information via cloud technology. Hyperscalers represent a multisided market in which third-party developers provide digital services for industrial customers (ibid., p. 185). The software solutions built by hyperscalers are usually standardized and scalable – which makes it possible to extract the full benefit from the network effect. It is argued that digital platforms and ecosystems facilitate new ways of building and employing knowledge and relationships (Nambisan et al., 2019). This happens due to multilevel social and economic processes through which knowledge may be obtained, transferred and adopted by the ecosystem actors. Other mechanisms inherent to digital platforms that assist in integrating local knowledge across the ecosystem include a shared set of standards, processes and governance systems. Despite giving essential advantages to their members, digital platforms and ecosystems suggest a number of risks and costs (Nambisan et al., 2019). Some of these risks refer to the dependencies intrinsic to the platforms and networks – such as dependence on the platform leader or partners. Here, one may talk about innovation risk, as well as reputational, operational and legal risks. Also, external shocks can produce negative ripple effects for the actors within the ecosystem. De Reuver et al. (2018) highlight major challenges connected to the studies of digital platforms related to conceptual ambiguity, the scope of the study and methodological issues. Differing units of analyses across studies and different framing of platforms lead to poor comparability between studies. Much knowledge about digital platforms and ecosystems is based on the successful cases, which reveals a certain bias. Digital platforms and ecosystems are large, complex, dynamic and distributed in nature and have long time horizons, which require both longitudinal and large-scale methods. Finally, studies of platforms for application in specific industries and sectors need to produce an understanding of the outcomes of the interweaving between digital platforms and systems and institutions. Hence, this chapter attempts to deal with this challenge by addressing the context of the Norwegian dairy- and beef-producing ecosystem to assist in developing a contextualized theory on digital platforms and ecosystems in manufacturing.

NORWEGIAN DAIRY AND BEEF INDUSTRY: KEY ACTORS IN THE ECOSYSTEM This section describes the key actors within the Norwegian dairy and beef industry that are involved in the interaction on digital platforms and play critical roles in this ecosystem. These actors are characterized by different roles and functions along the manufacturing chain, as well as various forms of ownership – from private to public, and from joint-stock companies to cooperatives. An overview of the central actors in the Norwegian dairy- and beef-producing ecosystem is provided below.

Digital platforms in the Norwegian food industry  395 Norwegian Farmers Farming in Norway is challenging due to the peculiarities of the landscape and climate. Only about 3.5 percent of the whole country’s area is suitable for agriculture and cultivation. Of this land, about two-thirds is mainly used for forage grass, while only one-third is used for cultivating corn (Nafstad, 2021). Despite the lack of cultivating soil, about 45 percent of the country’s area can be used for grazing ruminants. In addition to these challenges, farming in Norway is costly and not very well paid. The public standards and legal requirements for farming are high – to satisfy them, farmers need to put in extra effort and extra work hours. Due to all these factors, the number of farmers in Norway has dramatically decreased. Thus, during the decade between 2010 and 2020, the sector lost about 6000 farmers and 7911 farms were shut down, which represents a 17 percent reduction in the total number of farms in Norway (Tolfsen and Evjen, 2021). Traditionally, Norway housed a lot of small and middle-sized farms. However, during the last several years, it has become more difficult to operate small farms, and farmers normally have another job alongside farming to manage financially. Thus, farming is more like a hobby than an occupation (Slettli and Mei, 2022). Due to this, many small farms have been shut down or acquired by bigger ones. The observed trend in the sector is thus a decline in farm number, yet growth in farm size. To compensate for the challenges of farming, the Norwegian government provides support to farmers, making the sector one of the ‘most subsidized and protected areas of food production in the world’ (Ursin et al., 2016). Despite the described challenges, in 2021, Norwegian dairy and beef farmers were responsible for 98 percent (dairy products) and 93 percent (meat total) of the self-sufficiency production rate in Norway, where the total production of beef corresponded to 87,731 tons. Tine Tine is the largest producer and distributor of dairy products in Norway (Ursin et al., 2016). Its history goes back to 1881, when it was started as an association of Norwegian dairy farmers. At present, Tine is a concern owned by more than 9000 farmers and is organized as an agricultural cooperative. Tine’s particular position in the ecosystem can be explained by its threefold role. First, Tine is assigned the role of market regulator, which means that it receives milk from all farmers for an equal price and supplies all domestic dairy companies with milk at the same price. In addition, Tine takes on a protective role by shielding the Norwegian market from foreign producers – for example, by influencing toll barriers. Second, being a farmers’ cooperative, Tine is called upon to act in the interests of the farmers and hence provide ‘the best possible price for the milk’ (Ursin et al., 2016). For example, in 2020, a Norwegian farmer got paid 5.95 Norwegian kroner per liter of milk (Landbruksdirektoratet, 2022). Such an approach creates an equal position for the farmers in Norway and an equal starting point for all dairy producers competing in the domestic market. In addition, Tine acts as a consultant for farmers in such areas as animal feeding, economy, animal health and welfare, technology and equipment, animal control, etc. Third, being the largest producer of dairy products, Tine seeks to deliver products of competitive price and quality. This third function grants Tine the advantage of influencing and shaping the taste preferences of Norwegian consumers. Performing the three different functions with different goals – which are at times conflicting – requires intricate balancing by the company. This balance is reflected in Tine’s vision

396  Handbook on digital platforms and business ecosystems in manufacturing and commitments towards sustainable value creation and includes focusing on renewable resources and their optimal use, animal welfare and supplying healthy and varied food supplies to the Norwegian population (Tine, 2022). Nortura Nortura is an agricultural concern and cooperative that is owned by 17,100 Norwegian farmers all over the country (Nortura, 2022). This concern operates slaughterhouses and meat and egg processing plants. Any Norwegian cattle farmer can sign up for a membership in Nortura and become a co-owner of the cooperative, which offers several benefits to its members. In total, Nortura owns over 30 production units with about 5000 employees and processes 350,000 tons of meat and eggs annually. The largest raw food resources refer to eggs, turkey, pork, beef, sheep and goats. The cooperative possesses a few brands, of which Gilde (red meat) and Prior (white meat and eggs) are the largest. Nortura being one of the largest producers, its brands are represented in all Norwegian grocery chains, as well as hotels, restaurants, gas stations, etc. The concern possesses several daughter companies that process the raw materials remaining after primary production. Such resource leftovers are used to produce animal feed, medicine, protein supplements and bioenergy. The yearly turnover of the concern is about 26 billion Norwegian kroner. Like Tine, Nortura, performs a triple function of being a cooperative of farmers, a market regulator and a major commercial actor in the Norwegian meat and egg market (Ursin et al., 2016). First, Nortura seeks to set the highest possible price for eggs and meat to be paid to the farmers. Second, the cooperative receives meat and eggs from all farmers, supplies the domestic market and proposes production quotas and toll barriers to keep the market stable. And finally, it provides consumers with produce of high quality and competitive prices. Like Tine, Nortura seeks to balance its conflicting goals by appealing to the principles of sustainability and circularity, paying particular attention to animal welfare, climate issues, food safety and security, food waste and the use of antibiotics and recycled materials (Nortura, 2022). Geno Geno is a research and development organization that is owned by 8100 Norwegian cattle farmers. Being a gen-tech center, Geno is engaged in the breeding and development of the Norwegian red cow population. The company sells cow genetic material in the form of semen and embryos to domestic and foreign cattle producers (Geno, 2022). The turnover of the Geno concern in 2020 was about 400 million Norwegian kroner. The current goal in Geno’s breeding work is to develop cows that are healthier, more resource-effective and more climate-friendly, producing lower levels of emissions. The gen-tech company recognizes the immense role that Norwegian farmers perform by collecting and sharing their farming data, which becomes the big data employed in advanced analysis for further value creation and cutting-edge breeding research (Slettli, 2022). Animalia Animalia is a leading Norwegian organization engaged in developing professional and IT solutions for meat and egg producers. The organization refers to itself as a ‘neutral’ actor that

Digital platforms in the Norwegian food industry  397 provides Norwegian farmers and the whole meat-producing industry with knowledge and competence by means of animal controllers and animal health services; expert systems for operation, research, and development projects; e-learning and courses; and other knowledge communication initiatives (Animalia, 2023). Animalia’s goals include enhancing the industry’s long-term competitiveness, increasing value creation, reducing costs and creating a high level of trust in Norwegian meat producers. Among the strategic priority areas, the organization distinguishes sustainability and digitalization. Sustainability as a focus area refers to sustainable meat and egg production and the central role of animal-based food production in the sustainable Norwegian food system. Digitalization denotes Animalia’s efforts to facilitate the digital transformation of the animal-based food production value chain by developing digital and IT solutions for the whole industry. The new digital solutions are meant to improve user benefits and provide new functionalities, effective documentation and data sharing. Such digital transformation entails simplification, consistent documentation and openness of the business operations and includes all producers and actors of the ecosystem. Mattilsynet The Norwegian Food Safety Authority (NFSA), also known as Mattilsynet, is a Norwegian state agency responsible for safe food – which includes control over the whole food value chain: from the farms, fields and seas to the final consumer. The NFSA aims to promote human, plant, fish and animal health; environmentally friendly production; and ethical animal-keeping. Some of the key functions include the following: proposing, developing and administrating regulatory requirements; carrying out risk-based supervision; information and knowledge sharing; and emergency response. Each year, the NFSA implements several monitoring and mapping programs to get knowledge about the state of animal health and food safety in Norway. The results of the monitoring and control initiatives are presented in the open-access reports available on Mattilsynet’s website (Mattilsynet, 2023). In addition, the NFSA is authorized to provide expert advice to the Norwegian Ministry of Agriculture and Food; the Ministry of Trade, Industry, and Fisheries; and the Ministry of Health and Care Services. Veterinarians Veterinarians are argued to play the key role for the national Animal Welfare Program to be successfully implemented (Nafstad, 2021). The requirements for veterinarians in private practice are many and extensive: they are supposed to possess high competence regarding animal welfare and infection prevention, carry out preventive health work, have knowledge about structure of the Welfare Program and their own role in it, and communicate with farmers. In addition, veterinarians inspecting the farms should meet the requirement of regular professional development and vocational training. Veterinarians are required to have knowledge of how to perform farm inspection, according to the requirements of the Welfare Program, to report the results of the inspection on the corresponding platform, to initiate improvements and notify norm deviations and follow-up incompliances. Further, veterinarians are supposed to supervise farmers to motivate them for improvements (Nafstad, 2021). This extended list of the veterinarians’ responsibilities highlights a much more crucial and central role of the

398  Handbook on digital platforms and business ecosystems in manufacturing veterinarians who previously examined only those animals who were sick or were ready for insemination. In their new, extended roles, veterinarians are acting as experts who are able to connect animal welfare and animal health and serve as ‘consultants’ for the farmers. The Welfare Program thus highlights the necessity of good communication and dialogue between the veterinarians and farmers who can jointly detect problem areas and find solutions for improvements. It is expected that all consultancy, incompliance episodes and improvement measures are well documented by veterinarians on the platform. To equip veterinarians with the necessary knowledge about the requirements of the Welfare Program and their roles in it, Animalia is providing courses in this area.

NORWEGIAN DAIRY- AND BEEF-PRODUCING INDUSTRY: THE CHALLENGE The modern context of the Norwegian dairy- and beef-producing industry is currently characterized by a few challenges. One of the major challenges is a generally critical attitude toward animal-based food production, including the meat, dairy and egg industries (Ruud, 2022). According to Animalia, these challenges can be dealt with by expanding the knowledge of the Norwegian food system as a whole and the meaning of food safety and security. This requires that all actors in the whole value chain be willing and able to change their operation and production approaches and employ the principle of openness and transparency about their processes. Major challenges for the industry in question can be described as follows: ● The Norwegian agricultural sector’s goal is to reduce greenhouse gas emissions by 40 percent by 2030. ● The whole value chain experiences pressure on the economy. ● Meat’s reputation is challenged from the health, sustainability and animal welfare perspectives. ● The high status of animal health and food safety is challenged by increasing travel, international trade, climate change and changing regulatory frameworks. ● Raw products and produce quality essentially depend on expert knowledge about feedstock, meat technology and quality from the value chain perspective. ● The frameworks regulating R&D have changed, setting higher requirements for the responsibility of the industry actors. ● The competency requirements for all actors in the value chain are increasing. In the context characterized by such major challenges, technology development and digital transformation are seen as ways to improve the industry’s effectiveness and strengthen its competitive power.

Digital platforms in the Norwegian food industry  399

EXAMPLES OF DIGITAL PLATFORMS AND THEIR FUNCTIONS IN THE ECOSYSTEM Live Cattle Sales Platform In May 2021, Nortura launched a digital platform for selling/buying live cattle for farmers nationwide. The platform project was tested for half a year before full implementation at the country level. The platform provides farmers with a choice of either direct agreement with the buyer/seller about the transaction and corresponding registration of this purchase on the platform or reporting a wish to sell or buy animals on the platform with further assistance of Nortura to carry out the transaction. While initiating the sales transaction, the farmers are required to provide some minor data about the animals; meanwhile, the rest of the (animal health-related) information is extracted from other animal data registers and data bases. The platform provides participants of the transaction with the updates on the course of the animal purchase and delivery. Before the platform became available, about 80 percent of all live animal sales happened via telephone, which required numerous manual operations connected to searching the animal-related information in the databases (Nortura, 2023). Such animal-related information refers to health certificates and contains data about animal health and the farm of origin. This work was previously performed by 17 Nortura consultants who processed 36,000 inquiries annually. The new platform allows farmers to abandon Excel tables, retrieve animal-related information themselves and register sale-purchase transactions on the platform. The importance of the live cattle sales’ platform is difficult to underestimate, since the annual turnover of live cattle is about 760,000 animals distributed among 8000 sellers and almost as many buyers. In financial terms, this turnover equals 1.4 billion Norwegian kroner. Nortura perceives this platform as equally important for themselves as for the farmers. Facilitating the effective sale and purchase of live cattle for its members is a matter of competitive sustainable advantage for Nortura. For the farmers, it may be crucial to buy a certain animal – for example, a healthy calf for breeding purposes – at the right point in time. Besides getting a digital solution (and mobile application) for the sale of live animals and access to animal-related information, farmers get an estimated price for the animals already in the early phase of the transaction and on-time updates about their order’s status. Nortura calculates a guide price for cattle at least once a year. This price is determined based on the forecasts for the beef price and expenses in connection with beef production. Based on these assumptions, Nortura estimates how much a farmer should pay to feed a calf until it can be slaughtered. The guide price thus has the goal to arrange a just sharing of this expenditure between the seller and the buyer. Animal Welfare Portal The Animal Welfare Portal for cows is a digital platform connected to the national Animal Welfare Program (AWP) which took effect in 2022. The portal was launched as a voluntary response by the industry and ecosystem actors towards societal pressures to continuously improve animal-keeping conditions and welfare – a measure which goes beyond the formal requirements of the regulative framework for the industry. Even though Norwegian society is showing a high level of trust in Norwegian agriculture, many are wondering about the welfare

400  Handbook on digital platforms and business ecosystems in manufacturing and life conditions of the animals, and six out of ten respondents demand stricter rules for animal welfare (Bogerud, 2020). To meet the requirements of Norwegian consumers, the purpose of the AWP is to document animal welfare and corresponding measures beyond the public regulatory framework, as well as to promote and improve animal health on Norwegian cattle farms. The AWP assumes regular veterinarians’ visits to the farms, during which improvement areas should be developed in collaboration with the farm owners. The launching of the AWP was approved by the aggregated Norwegian animal farming industry, including Nortura, Tine, Geno, the National Association of Meat and Poultry Producers, the Norwegian Agrarian Association, the Norwegian Farmers and Smallholders Union and other dairy producers and industry actors. The process of program development has been coordinated by Animalia, and a reference group of farmers and veterinarians has participated as well. All farms housing more than 10 animals (about 10,000 farms) were enrolled in the program by May 2023. Thus, the AWP embraces 99 percent of all cows and 93 percent of all cow farms nationwide. The key element of the AWP is veterinarians’ visits to farms, and during the AWP’s rollout, each farm was supposed to receive its first AWP visit from a veterinarian. The first AWP visit is meant for inspecting the whole farm and documenting the chosen welfare indicators together with the farm owner. Examples of such indicators are cleanliness, animal care/ animal husbandry and lameness. The maximal interval between AWP visits is 16 months, and it is farmers’ responsibility to prepare for such visits and make sure the inspections occur regularly. The program accentuates that veterinarians and farmers should cooperate and have a dialogue-based AWP inspection. The Animal Welfare Portal allows farmers to contact veterinarians to order the first and subsequent AWP inspections. Veterinarians use the portal to approve farmers’ inspection requests, register the AWP visits and close cases when noncompliance has been improved. The final reports and statuses are also available to the farmers. The portal sends out reminders via SMS and e-mail concerning the key deadlines for AWP visits, noncompliance improvements or changes in the agreements with the veterinarians. Failure to improve noncompliance results within 15 days after the deadline results in a 1 kroner per kilo reduction of the raw meat price at slaughter. Further failure to improve the AWP-related fails within 45 days after the deadline will be punished by a 20 percent reduction in the price of milk and beef. The idea of the AWP and the portal is to strengthen collaboration between veterinarians and farmers on the issues of the animal welfare. Veterinarians are acting as hubs of expertise on the animal welfare and health, and farmers are encouraged to make use of this knowledge during the AWP visits to improve general animal-keeping conditions for better animal wellbeing. Additionally, veterinarians perform the role of motivator and supervisor for the farmers, as well as of a controller and reporter of the animal’s welfare status. Since the AWP visits are dialogue-based, and reporting on the platform is done collaboratively by the farmer and the veterinarian, the cooperative element of this dyadic relationship and its benefits may apply.

Digital platforms in the Norwegian food industry  401 Animal Health Portal The Animal Health Portal (AHP) is a digital platform for the registration of animal health data, insemination, health inspections, retrieval of food chain-related information, and a database for effective breeding and genetics research. The portal is a result of cooperation between Animalia, Tine and Geno. The main assumption behind the portal is that animal health-related data should be reported one time in one place and thereafter be available for all those actors who have a legal right to use this data. Therefore, the AHP collects, systematizes and shares animal health data from different sources and for different purposes (Nafstad, 2020). The original need to register animal health data points to the EU’s hygiene package from 2010, which demands openness throughout the production chain and sets requirements for food chain information. The regulatory framework requires that such information as the health status of the animals, eventual restrictions and animal treatment with veterinary medical products be provided to the slaughterhouses. The AHP houses different types of data (Animalia, 2022); some examples of such data are provided below: ● Connection to the slaughterhouses. The AHP shows which slaughterhouses the farmer has delivered his animals to during the last 12 months. ● Information about animal husbandry. Shows if the farm is registered in the home animals register. ● Production subsidy. Shows whether a producer has applied for the production subsidy of dairy cows. ● Restrictions. Shows restrictions imposed by the Norwegian Food Safety Authority (Mattilsynet), where possible restrictions refer to violations of the animal-keeping regulations, health danger and animal sickness. ● Status in the Quality System in Agriculture (QSA – a quality assurance and internal control tool designed for Norwegian farmers). Approved QSA status is given only to producers who have carried out their own internal audits during the last 12 months and have improved eventual noncompliance within the deadline after an external QSA audit. ● Cow health attestation. Shows farms’ status in relation to virus diseases. Shows attestation that documents disease spreading risk and protection in the farm. ● Remarks. Shows if farms had thin animals, high levels of animal disposal, disposals during the last 3 years, etc. ● Other health-related instances on the farm. These may be registered by either veterinarians or producers. Since the AHP houses various types of data, it provides access to various users with different functions and access levels, including farmers, veterinarians, semen technicians, breeders and slaughterhouse and platform administrators. Information about the roles of different actors is provided on the platform with open access. A peculiar feature of the AHP is that it shares data with several actors. The stated purpose of such data sharing is to simplify registration work for the users, improve data quality and data access, as well as report to the officials on behalf of the users. For example, food chain data is being collected in the databases of the slaughterhouses and Mattilsynet for the purposes of further analyses, which could help to uncover failures and deviations from the regulatory frameworks – both single instances, systematic mistakes and unfortunate patterns (Alvseike,

402  Handbook on digital platforms and business ecosystems in manufacturing Table 25.1

Characteristics of the three digital platforms

Platform

Type

Live cattle sales platform Digital Marketplace Animal Welfare Portal

Animal Health Portal

Integrated IIoTP

Integrated IIoTP

Functions

Purpose

Institutional logic

Animal sales

Business operation

Pragmatism

Transaction recording

efficiency improvement

Animal welfare

Enhancing legitimacy

documentation and

and reputation in

improvement

society

Animal health control and Compliance with documentation

Appropriateness

Instrumentality

the formal public regulatory frameworks

2020). The Norwegian Agriculture Agency receives information about veterinarians’ travels to farms for inspection purposes. Further, R&D firms, such as Geno, retrieve semen-related data, which is used for breeding and genetics research. The results of the genetics research and selective breeding carried out by Geno become available for the farmers who may employ the genetics data on individual animals for better breeding results. Therefore, the AHP becomes a data hub where aggregated farming data from individual producers makes up the bulk of the big data employed in animal breeding research, analysis and forecasts, with the final goal of improving the quality of production and enhancing the economic benefits of the ecosystems’ actors.

DISCUSSION The three examples of digital platforms operating in the ecosystem under consideration reveal different characteristics in terms of the scope of the actors involved, type of platform (according to Mosch and Obermaier, 2022), functions and purposes, and types of institutional rationale/logic (Scott, 2013). A summary of the following discussion is presented in Table 25.1. The first example of the platform was developed for live cattle sales between farmers, performed either with or without Nortura’s assistance. In the case of direct animal sale between farmers, the platform serves as a transaction register, of which Nortura has full control. The purpose of this platform is related to business operation efficiency improvement – a distinctive purpose of digitalization, in which manual operations are substituted by SAP-based data processing and information flow. Pragmatism is the type of logic underlying the processes facilitated by the platform. Referring to Mosch and Obermaier’s (2022) platform typology, the live cattle sales platform is an example of a digital marketplace industrial platform used for an efficient connection between buyers and sellers interacting in a dyadic relationship. Such platform provides an effective, partly automated purchase transaction to lessen the complexity of business-to-business (farmers’) relationships. Farmers receive an opportunity to outsource the whole animal buying/selling process to Nortura via the platform, meanwhile enjoying online updates of the transaction status – which might increase their dependency and improve the lock-in effect. Both for Nortura and the farmers, the stated competitive advantage is fostered through the digital business ecosystem embedment and network effects. The second platform – Animal Welfare Portal – has been established with the purpose of obtaining legitimacy for the ecosystem by showing a moral and social obligation towards ‘good’ animal-keeping and documenting such welfare practices. By demonstrating responsi-

Digital platforms in the Norwegian food industry  403 bility for the animal’s wellbeing, the ecosystem actors respond to the external social pressure from consumers to improve the reputation of the industry. The societal concerns about animal welfare make the ecosystem actors critical of their own roles in this situation and choose appropriate behavior, resulting in the establishment of the AWP and the consequent portal. Such behavior can be explained by the institutional logic of appropriateness, which conditions the mandatory participation of the farmers in the AWP and the portal. The third platform – the Animal Health Portal – aims to provide unified aggregated information about animal health to meet the requirements of the formal regulatory frameworks. Besides the primary reporting function, the platform serves as a storage and source of various types of animal-related data, which may be used by different ecosystem actors for multiple purposes – such as control, analysis, forecasts and R&D in animal genetics and selective breeding. Here, some of the actors are functioning as major information providers (farmers, veterinarians), while others perform the role of information consumers (Mattilsynet) and some combine both roles (Geno). The portal was initiated on the basis of the European regulatory framework as well as some national standards and laws, and hence, members of the ecosystem conform to the rules and regulations because they either seek a reward or want to escape sanctions – a behavior which is guided by the institutional logic of instrumentality (Scott, 2013). The Animal Welfare and Health Portals are examples of the integrated IIoTP (according to Mosch and Obermaier’s 2022 typology) since they provide software (data analysis) and platform (development environment, APIs and basic analysis tools) offerings tailored to and integrated specifically with farming and meat production industry. The number of potential customers on such platforms is naturally limited to the number of actors in the industry (or ecosystem). Typically for the integrated IIoTPs, the portals facilitate an open digital business ecosystem via their platform solutions containing different third-party development communities, such as Animalia and Geno. By taking advantage of the animal-related big data accumulated on the portals, ecosystem actors with IT and R&D functions use the platforms to create advantage for the whole ecosystem through research-based selective breeding of the animals and other genetics and animal health-related research. The dominant value creation logics in the case of such platforms is connected to a proper and skilled organizing and governance of the ecosystem community. Management of the platform community may encourage innovation and development in the ecosystem (by means of improved animal health and welfare) and ensure transparency of value distribution (in the form of improved industry reputation, healthier animals and better breeding results). Referring to the discussion presented above, this study contributes to the scope of knowledge about digital platforms in the business-to-business contexts (Mosch and Obermaier, 2022). It extends theoretical understanding of the platform typology, which draws on the market-based value creation logics by adding another component that characterizes digital platforms – from the point of institutional logics. The introduction of the institutional view (Scott, 2013) on the digital platforms and ecosystems enables opening a new understanding of the platforms, which is different from the mainstream market-based literature. Digital platforms can be understood as phenomena that constrain and regulate behavior of the actors in the ecosystem through rule-setting, monitoring and sanctioning. Next, digital platforms may be understood as normative systems that introduce a prescriptive and evaluative dimension through values and norms into the ecosystem. These systems define objectives within the ecosystem and appropriate rules to achieve them. Addressing digital platforms in terms of normative systems may help to explain how they can constrain the behavior of the actors, empower them and enable to action.

404  Handbook on digital platforms and business ecosystems in manufacturing The functions and purposes of the two integrated IIoTP platforms connected to the transparency, control, reporting and documentation of the efforts aimed at improving animal welfare and health suggest an important function that platforms may perform – that of accountability. Accountability is a broad concept and describes a number of functions, such as reducing uncertainty, accompanying legitimization processes and supporting the exercise of power (Konovalenko, 2012). Accountability possesses a relational dimension – that is, to whom accountability is provided. It is also characterized by a technical representation, such as figures and narratives, and justification mechanisms that suggest that organizations and people are functioning properly and legitimately. In the case of digital platforms and ecosystems, information and data from the platforms are intended to be used as internal and external accounts of legitimacy for various interest groups, ecosystem actors and broader society. The study findings highlight a particular role that the ecosystem assigns to veterinarians in the task of farm inspection and reporting results on the platforms. Applying a knowledge management perspective, veterinarians perform an important role as boundary spanners (Tushman, 1977). Through dialogue and cooperative efforts with farmers, veterinarians enable the processes of knowledge socialization and externalization (Nonaka and Takeuchi, 1995), by which the farm owners’ tacit knowledge is conveyed to and processed together with the veterinarians and then becomes articulated and codified on the portal. To sum up, the chapter suggests that digital business ecosystems and platforms assign a high degree of importance to the boundary spanners in the process of knowledge externalization. The digital platforms described in this study provide numerous and varied opportunities for the ecosystem actors. However, they also restrict and govern their behaviors. As we see, failures to improve noncompliance become more visible when (non-)behaviors are registered on the platforms. Hence, the risk of being ‘punished’ by other members of the ecosystem in the case of nonadherence to the standards becomes higher. In this sense, the platforms perform the role of a control tool to stimulate desired behavior among platform actors. In saying that, one may bring up the old discussion of how technology is shaping human behavior and disciplining both organizational members and whole organizations (Law, 1991) in the new context of digital platforms and ecosystems. Turning to practical implications of the study, the pioneering experience, described here, of the digital industrial business-to-business platforms employed in the ecosystem of the Norwegian farmers and food producers nationwide may suggest an interesting and informative example for other industries and business sectors. It may encourage and inform digital business collaboration between different actors of the private and public sector possessing shared values and pursuing complementary goals. In addition, this study informs ecosystem actors about the choice of alternatives when it comes to the functions, purposes and rationale for the launch and use of the digital platforms.

CONCLUSION The agricultural and food-producing industries in Norway are consolidated in one business ecosystem supported using various digital platforms. The digitalization or digital transformation in this ecosystem goes at an accelerated pace, reshaping communication, the quality and quantity of the information flows and the tempo at which business operations take place.

Digital platforms in the Norwegian food industry  405 This chapter has provided an outlook on the advanced digitalization practices of the Norwegian dairy- and beef-producing business ecosystem and related digital platforms. Analysis of the findings suggests that platforms are characterized by a variety of functions, purposes, and underlying institutional logics. Efficiency improvement, transparency, legitimacy and reputation enhancement, as well as compliance with the laws and legal requirements, are some of the rationales for the operation of such digital platforms. The case study reveals how digital platforms and ecosystems can provide opportunities for the key actors of the ecosystem, guide their actions in the desired direction, restrain them and even discipline them for norm deviation. The study is not free from limitations. Due to its descriptive nature and use of the secondary data sources, this work provides a relatively static view of the digital business ecosystem and platforms. The complex organization and functioning of the platforms are represented in a ‘snapshot’ perspective, which lacks the dynamic component. Furthermore, the study lacks an in-depth perspective on how the platforms influence actors’ behaviors and changes interaction between the actors in the ecosystem. Having said that, the study opens new avenues for further research of the platforms’ effects on actors’ evolution and relationships. By adopting various theoretical lenses, one can obtain a comprehensive understanding of digital platforms and their functions in the ecosystem. Thus, a management control perspective allows us to uncover the accountability function of digital platforms. The knowledge management perspective suggests the importance of boundary spanners for the realization of digital platforms’ potential. Finally, a critical perspective calls for a more nuanced view of the power distribution and regulation of digital ecosystems and platforms.

REFERENCES Alvseike, O. (2020) ‘Hva slags nyttig informasjon kan matkjededata gi oss?’, Go´mørning, 02, 15, available: https://​www​.animalia​.no/​contentassets/​119​187080f9e4​22e819e301​c583e8a51/​gm​-2​-2020​_web​ .pdf [accessed 20 October2022]. Animalia, (2023) [online], available: https://​www​.animalia​.no/​[Accessed 05 April 2023]. Animalia (2022) ‘Matkjedeinformasjon’, [online], available: https://​www​.animalia​.no/​no/​Dyr/​dyrehelseportalen/​ matkjedeinformasjon/​[Accessed 20 October 2022]. Bogerud, M. (2020) ‘6 av 10 vil ha strengere regler for dyrevelferd’, Go´mørning, 02, 26–7. Casadesus‐Masanell, R. and Hałaburda, H. (2014) ‘When does a platform create value by limiting choice?’, Journal of Economics & Management Strategy, 23(2), 259–93. De Reuver, M., Sørensen, C. and Basole, R. C. (2018) ‘The digital platform: a research agenda’, Journal of information technology, 33(2), 124–35. Dolata, U. and Schrape, J.-F. (2022) ‘Plattform-Architekturen’, KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie, 74(1), 11-34. Geno (2022) [online], available: https://​www​.geno​.no/​[Accessed 10.10. 2022]. Hsieh, Y. Y. and Vergne, J. P. (2023) ‘The future of the web? The coordination and early‐stage growth of decentralized platforms’, Strategic management journal, 44(3), 829–57. Konovalenko, V. (2012) A ‘coordination kaleidoscope’: The role of a ‘Corporate University’ as a coordinator of knowledge flows in a Russian transnational corporation. , unpublished thesis University of Nordland. www​ .l​ Landbruksdirektoratet (2022) ‘Norsk landbruk – tall og fakta.’, [online], available: https://​ andbruksdi​rektoratet​.no/​nb/​norsk​-landbruk​-tall​-og​-fakta [Accessed 14 October 2022]. Law, J. (1991) A sociology of monsters: Essays on power, technology, and domination., London: Routledge.

406  Handbook on digital platforms and business ecosystems in manufacturing Loonam, J. and O’Regan, N. (2022) ‘Global value chains and digital platforms: Implications for strategy’, Strategic Change, 31(1), 161–77. Mattilsynet (2023) ‘Tertial og årsrapporter for dyrevelferd’, [online], available: https://​www​.mattilsynet​ .no/​dyr/​dyrevelferd/​tertial​-og​-arsrapporter​-for​-dyrevelferd [accessed 01 April 2023]. Moore, J. F. (1993) ‘Predators and prey: a new ecology of competition’, Harvard business review, 71(3), 75–86. Mosch, P. and Obermaier, R. (2022) ‘Digital platforms in the industrial sphere: value creation logics and platform types’ in Handbook on Digital Business Ecosystems, Edward Elgar Publishing, 177–93. Nafstad, O. (2020) ‘Viktig informasjon følger kua fram til slakting’, Go’mørning, 02, 1–2, available: https://​www​.animalia​.no/​contentassets/​119​187080f9e4​22e819e301​c583e8a51/​gm​-2​-2020​_web​.pdf [accessed 17 October 2022]. Nafstad, O. (2021) ‘Systematisk arbeid for bedre velferd’, Go’mørning, (2), available: https://​www​ .animalia​.no/​contentassets/​119​187080f9e4​22e819e301​c583e8a51/​gm​-2​-2021​-web​-1​.pdf [accessed 15 September 2022]. Nambisan, S., Zahra, S. A. and Luo, Y. (2019) ‘Global platforms and ecosystems: Implications for international business theories’, Journal of International Business Studies, 50(9), 1464–86. Natalicchio, A., Petruzzelli, A. M. and Garavelli, A. (2017) ‘Innovation problems and search for solutions in crowdsourcing platforms – A simulation approach’, Technovation, 64, 28–42. Nonaka, I. and Takeuchi, H. (1995) The knowledge-creating company : how Japanese companies create the dynamics of innovation, New York: Oxford University Press. Nortura (2022) [online], available: https://​www​.nortura​.no/​om​-nortura [accessed 15 October 2022]. Nortura (2023) ‘Digital satsing går live i alle regioner’, [online], available: https://​www​.nortura​.no/​ nyhetsartikler/​digital​-satsing​-g​%C3​%A5r​-live​-i​-alle​-regioner [accessed 05 April 2023]. Parker, G. G., Van Alstyne, M. W. and Choudary, S. P. (2016) Platform revolution: How networked markets are transforming the economy and how to make them work for you., WW Norton & Company. Ruud, T. A. (2022) ‘Matsikkerhet i ustabile tider. ‘, Go’mørning, 01, 1–2, available: https://​www​ .animalia​.no/​contentassets/​119​187080f9e4​22e819e301​c583e8a51/​gm​-1​-2022​-​.pdf [accessed 17 October 2022]. Scott, W. R. (2013) Institutions and organizations: Ideas, interests, and identities., Sage publications. Senyo, P. K., Liu, K. and Effah, J. (2019) ‘Digital business ecosystem: Literature review and a framework for future research’, International Journal of Information Management, 47, 52–64. Slettli, V. K. and Mei, X. Y. (2022) ‘Cultural Pursuits and Farm Entrepreneurship in Norway: Embracing the Aesthetics of Peasant Romanticism’ in Ratten, V., ed. Cultural Entrepreneurship, Singapore: Springer, 39–51. Slettli, V. K. (2022) ‘Developing big data analytics capability in a focal firm of a digital business ecosystem: the case of the Norwegian swine breeding industry.’ in Baumann, S., ed. Handbook on Digital Business Ecosystems, Edward Elgar Publishing, 697–713. Teece, D. J. (2017) ‘Dynamic capabilities and (digital) platform lifecycles’. in Entrepreneurship, innovation, and platforms. Emerald Publishing Limited. Tilson, D., Lyytinen, K. and Sørensen, C. (2010) ‘Digital infrastructures: The missing IS research agenda’, Information systems research, 21(4), 748–59. Tine (2022) [online], available: https://​www​.tine​.no/​[Accessed 13 October 2022]. Tolfsen, C. B. and Evjen, A. F. (2021) ‘Færre driver landbruk, men Einar (33) satser på storfe.’, NRK, available: https://​www​.nrk​.no/​osloogviken/​antallet​-landbruk​-totalt​-gar​-ned​-​_​-men​-flere​-velger​-a​ -starte​-med​-kjottproduksjon​-1​.15695283 [accessed 15 October 2022]. Tushman, M. L. (1977) ‘Special boundary roles in the innovation process’, Administrative science quarterly, 587–605. Ursin, L., Myskja, B. K. and Carson, S. G. (2016) ‘Think global, buy national: CSR, cooperatives and consumer concerns in the Norwegian food value chain’, Journal of Agricultural and Environmental Ethics, 29(3), 387–405. Wang, J., Guo, B., Wang, X. and Lou, S. (2020) ‘Closed or open platform? the nature of platform and a qualitative comparative analysis of the performance effect of platform openness’, Electronic Commerce Research and Applications, 44, 101007.

Index

3D printers  239–241, 251 challenges 243 economic value of  251 environmental value of  252 smart-circular product-service-systems 241 strategies for  241 social value  253 3D printing  16, 179, 240, 297 health and safety  253 3D scanning  304 Aarikka-Stenroos, L.  126, 226 AAS see asset administration shell Abbate, T.  379 academic literature  118 academic theory  72 accessibility 264 accessibility design theme  209 accountability 404 actor  28, 57, 264 actor autonomy  264 actor groups  344 actor-related suitability, information  267 actors  1–2, 20, 28, 37–9, 53, 57–8, 89–90, 97, 106–7, 110, 126–8, 133, 137, 141, 148, 151–3, 160, 164, 179–80, 183, 186, 206, 216, 228, 260–66, 361, 364, 402 interdependent and heterogeneous  259 actors’ interdependency  27 Adan, I.  348 additive manufacturing (AM)  15–19, 179, 295–301, 306 actors 180 capabilities 306 capacities 298 consultant 183 Designer & Producer  183 ecosystem  180–82, 187, 190 characterization 184 importance for collaboration in  184 multiple roles in  183 IT Solution Provider  183, 186 Material Purchaser  183 Material Supplier  183 process 182 production systems  186 refiner 183 researcher 183

sales platform provider  183, 190 service providers  302 system provider  183 technology 16 technology and manufacturing engineering 186 value creation  183–6 Adner, R.  107, 109, 111, 128, 178 advanced manufacturing technologies (AMT) 283–4 advanced risk communication systems (ARCS) 287–8 after-sales service  118 age of digital entrepreneurship  209 AGV see automated guided vehicles (AGVs)  AHP  see Animal Health Portal (AHP) AI  see artificial intelligence (AI) AI-based energy optimization solution  118 Alharthi 37 Alpha 15–20 AM see additive manufacturing (AM) Amit, R.  216 AMT  see advanced manufacturing technologies (AMT) AnalytX platform  75 Anderson, J.  378 Animal Health Portal (AHP)  401, 403 animal husbandry  401 Animalia  396, 398 animal welfare  396 Animal Welfare Portal  399–402 Animal Welfare Program (AWP)  397–9 antibiotics 396 APIs  see application programming interface (API) application programming interface (API)  154–5, 322, 348 archetypes 243 roles  108, 113–20 ARCS  see advanced risk communication systems (ARCS) Arend, R.J.  91 artificial intelligence (AI)  16, 106, 112 technologies  52, 110 Ashton, K.  73 asset administration shell (AAS) concept  173–5 automated guided vehicles (AGVs)  344–5, 350–54

407

408  Handbook on digital platforms and business ecosystems in manufacturing automated order processing  172 automotive digital supply chains  201 automotive industry  147–53, 161 automotive OEMs  160 AWP  see Animal Welfare Program (AWP)  400 B2B see business-to-business (B2B) B2C see business-to-consumer (B2C) Ball 92 Ballardini, R.M.  300 Bangalore, P.  337 Barriers of Ecosystem  165 Baumann, S.  27 Bayer, S.  149 BE see business ecosystem (BE) BearingPoint 147 beginning-of-life (BOL)  240 Benbasat 332 BIC see Brainport Industries Campus (BIC)  bi-directional communication  348 big data  261, 393 BioMaterial 16 Blackburn, O.  225 BlastIQ platform  79 blockchain technology  260 BMOs see business model orientations (BMOs) 71 BMUV 260 Boeing 81 BOL see beginning-of-life (BOL) boundary resources  153–4 boundary-spanning resources  74 Brainport Industries Campus (BIC)  344, 352–3 brainstorming potential solutions  224 Bricka 209 Buer, S.V.  196 Bukhsh, Z.  337 business case modeling  338 business ecosystem (BE)  1–2, 27, 73–4, 110, 126,–9, 164–6, 169, 173–5, 278, 307, 345 business models  16–20, 28, 55–8, 65–8, 72, 207–9, 311 concept 72 formation 66 innovation  71–2, 75 theory 72 transformation  11–14, 19–20 business model orientations (BMO)  71, 74–5, 80–83 optimize and automate  79 propose and compare  78 typology 82 visualizations 75 business networks  27, 56 business processes  315

business-to-business  43, 74, 132, 318 contexts 403 industrial world  10 interaction 392 markets 52 models 80 networks 28 platforms 404 recycling interactions  138 relationships 126 sector 90 service provider  134 SME 144 business-to-consumer (B2C)  153 ecosystem 52 markets 43 sectors  10, 42 Cachada, A.  328, 338 CAD see computer-aided design (CAD) model capital expenditures  202 Cardeal, G.  301 CASTER 45–51 machine engineering  47 Catena-X 29 CE see circular economy (CE) Cenamor, J.  13, 89 Cennamo, C.  91 central cloud server  350, 353 challenges  27–30, 33–4, 38–9 Chesbrough, H.W.  377, 387 Chica, M.  353 circular economy (CE)  222, 225, 227, 241, 246, 257–9 business models  224 entrepreneurs 221–34 movement 221 opportunities 227–30 circular ecosystems  258–9 circularity  263, 274, 396 holes  263, 273 circular strategy  259 circular supply chains  258 circular value proposition  264 Ciulli, F.  208, 263, 266 class number  174 clear business model  64 cloud computing  261 cloud manufacturing  352 cloud services  195 cloud technology  261 CO2 emissions  249, 253 CoAP see Constrained Application Protocol (CoAP) co-creation 387

Index  409 examples 97 relationships 138 co-evolutionary business system  126 co-innovation digital transformation  380 phase 385 program 388 collaborative manufacturing  352 collaborative value creation  182, 190 collective identities  130 collective identity  130 communication gaps 267 protocol 50 systems 168 compensation sharing  157 competencies  63, 181, 377 in titanium processing  15 competition 243 competitive advantages  171 competitive thinking  34 complementarities 243 complementors  90, 93, 96, 99, 149–54, 157–8, 314 complex actor-technologies  312 complex systems  361 composite reliability (CR)  198 computer-aided design (CAD) model  240 conceptual heterogeneity  126 connectivity 71–5 connectivity business models  83 Constrained Application Protocol (CoAP)  347 construction sites  366, 373 conventional manufacturing processes  306, 346 Cook, K.S.  56 cooperation 243 co-opetition  101, 180 co-opetitive environment  97 coordination positioning  187 Corbin, J.  29 corrective strategies  329 cost and risk management  171 cost models  299 cost-value strategists  158 ‘could-do’ logic  100 COVID-19 pandemic  213, 306 cow health attestation  401 CPS see cyber-physical systems (CPS) CR see composite reliability (CR) credibility 298 criteria matrix  369, 372 Cronbach’s alphas  202 cross-sectional problem  43 crowdfunding 64 crowd-lending 64

cultural attributes  226 culture 226 Curry, E.  37 customer-centricity 11 customer-centric needs transformation  279 customer engagement  383 customers’ individual product development  171 Cusumano, M.A.  379 cybernetics 361 Cyber Physical Production System  347 cyber-physical systems (CPS)  261 cyber security/cybersecurity  34, 331 dairy- and beef-production ecosystem 394 industry 392 Dal Bianco, V.  153 Dang, Q.-V.  348, 353 Danish Innovation Foundation  64 data analysis  337, 369, 381 analytics 165 collaborative opportunities of  38 collection  59, 92, 132, 197, 262, 267, 331, 381 collection module  331 economy 30 ecosystems 26–39 exchange 165 exchange and collection  262 generation 50 marketplace platform  112 marketplaces  110, 115 network effects  53 ownership rights  33 platform 361 sharing  26, 366, 375 storage capacity  273 transfer and store  336 transparency 38 data-based 3D printing process  239 data-driven business models  39 solutions 106 technologies 106 Dattée, B.  52 DBEs see digital business ecosystems (DBEs) DC see dynamic capabilities (DCs) decision-making  45, 379 decision support  264 Dedehayir, O.  111 deep-dive cases  332 Dell’Era, C.  129 Del Vecchio, P.  225 demand-side economies  206

410  Handbook on digital platforms and business ecosystems in manufacturing Denmark  59–60, 63, 68 Derave, T.  259 De Reuver, M.  392, 394 design process  17 design thinking  221–4, 227, 230 approach  227, 233 and circular economy entrepreneurship  224 process 222–3 Devaraj, S.  198 Didden* 348 die-casting problems  50–51 differentiation 47 digital business environments 38 models 299 platforms 211 digital capabilities 153 communication 379 systems 166 companies 178 competence 159 core technologies  16 disruption 278 economy 72 ecosystems  19, 53, 170, 172, 181, 222, 225, 231–4, 278–9 and circular economy  225 entrepreneurial 208 gaps 171 imagery analysis  330 immigrants 152 industrial platforms  10–20 infrastructures 106 innovation 181 knowledge 19 manufacturing ecosystems  190 marketplaces 393 native strategists  158–9 offerings  147, 151, 156–9 orders 171 performance  267, 273 services  26, 135, 141 servitization  13, 68, 81 skills 17 space economy  322 spaces 11 stand-alone offerings  155 strategy 387 supply chains  194–5, 201–2, 304 technologies  1, 10–21, 149, 257–9, 262, 314, 377–8, 383, 386–8 infrastructure 243 transformation  11, 20, 42, 71, 311, 359, 377–81, 384–7

process 11 projects 359 value-added system  170 digital business ecosystems (DBEs)  1–3, 6, 37, 88–92, 97–102, 194–5, 278, 287, 295–7, 304–6, 345, 360, 391, 405 DBE-agnostic partners  99 DBE-related sensing  92 dynamic capabilities  89 operations 279 socio-technical design of  27 Digital Planner  17 digital platforms (DP)  2, 5–6, 10, 15, 19–20, 28, 71–4, 83, 127, 139, 206–8, 211, 216, 225–7, 234, 241, 257–9, 278, 287, 295, 305, 311–12, 314, 322, 346–8, 391–4, 399–404 actors 266 approach 14 and downstream perspective  317 economic perspective on  313 economy 379 ecosystems  147, 149, 295, 305 giants 2 knowledge 322 phenomenon 310 research  312, 322 socio-technical factors of  320 sustainability of  208 technological maturity assessment of  2 as tool  378 digital product passport (DPP)  259–60 Digital Spare Parts (DSP)  295–6, 301, 304–6 concept 300–304 platform ecosystems  295 digital technology affordance theory  72 Digital Twin (DT) product technologies  15, 19, 260 physical systems  327 Digital Marketplace industrial platform  402 digital-centric ecosystem  259 digitalised business models  56 digitalization  13, 27, 34, 88, 132–7, 147, 194–5, 274, 290, 327, 339, 345, 377–80, 387, 397, 404 digitally enabled recycling  135 digitally integrated approach  139 digitally tracked material/components  267 digital-native companies  10 digitization  213, 241, 259, 287, 377 digitization-based circular ecosystems  273 digitized processes  267 discriminant validity  198 Di Stefano, G.  210 diverse additive manufacturing service  298

Index  411 domain-specific functional roles  111 donations 251 DPP see digital product passport (DPP) DSP see Digital Spare Parts (DSP) DT see Digital Twin (DT) product technologies dynamic capabilities (DCs)  88 dynamic coupling  209 dynamic sensing capabilities  89, 99–100 E2E see end-to-end (E2E) Earth 310 ease of data access  267 EBM see Electron Beam Melting (EBM) technology e-class number  174 economic exchange (EE)  57–60, 65–7 economic key performance indicators  247 perspective 311 transaction 312 value creation  42, 209 ecosystems  27, 38, 42–5, 110, 128–30, 164–8, 173, 180, 228, 345, 359, 375, 404 actors  26, 180, 263 approach, potential of  48 creation 44 decision support for positioning  186 design  107–8, 118–19 development  133, 138, 143, 180 driver model  279 dynamism 180 innovations 50 interactions 228 leadership  129, 131, 138, 140, 144 manufacturing 180 for manufacturing companies  171 with manufacturing companies  169 in manufacturing sector  51–2 manufacturing supply chains  172 modeling 110 orchestration  45, 48, 53 practitioners 233 principle 392 relationships 102 specific roles in  190 strategic positioning in  181 strategy 49–52 theory  128, 130, 184 thinking  134–5, 144 ecosystem-based competition  51 ecosystem-level adaptation  49 education, investments in  250 EE see economic exchange (EE) effective governance  44 Egri, P.  353

Eisenhardt, K.M.  58, 111 Electron Beam Melting (EBM) technology  16 ELIS 380–88 Emerson, R.M.  56 employee retention  286 end-of-life (EOL)  221, 240, 244, 258 end-to-end (E2E) implementation 306 solutions 298 energy consumption  250, 253 energy savings  249, 252 enterprise resource planning (ERP) systems  166, 172 transactional environment  172 entrepreneurial ecosystems  222–3, 226–33 culture  226, 229, 231 enhance idea testing  230 idea generation  229 key attributes of  225 needfinding 227 entrepreneurial ecosystem-enabled design thinking 223 entrepreneurial ecosystem-enhanced design thinking  222, 231–3 entrepreneurial ecosystem organizations  230 entrepreneurs 68 entrepreneurship processes  223 environmental dynamism 179 impact 247 key performance indicators  248 pollution 247 value creation  209 EOL see end-of-life (EOL)  260 Erol, R.  347 ERP see enterprise resource planning (ERP) Eslami, M.H.  196 European digital platforms  225 European Green Deal  257 evolutionary perspective  132 exchange 56–7 networks 59 processes 58 theory 56 expert interviews  365 Facilities and Technology Provider  117 Fadler, M.  37 farming 395–6 FarmSight brand  79 Favoretto, C.  379 FDM see fused deposition modelling (FDM) feasibility analysis  385 financial compensation 156

412  Handbook on digital platforms and business ecosystems in manufacturing performance 196 sustainability 179 first-cycle codes  59 first-cycle macro-level coding  59 fleet management system (FMS)  347 fleet managers  350–2 flexible business strategies  289 flexible product strategy  289 FMS see fleet management system (FMS) focal actors  88, 207, 210, 216 firms  88, 97 adaptation 50 product 184 Ford, D.  58 Fornell–Larcker criterion  198 fourth industrial revolution  259 Frank, A.G.  196, 198 Frank, J.  298 Freiberger, M.  332 Fuel Dashboard  78 functional roles  110–11 Furr, N.  129 fused deposition modeling (FDM)  301 Gaia-X  29, 38, 173 Gao, P.  147 Gawer, A.  127, 129, 133, 144, 379 GE see General Electric (GE) Power Gelhaar, J.  37 Gelhard, C.  90 gender equity  250 Genennig, S.M.  111 General Electric (GE) Power  80 Geno  396, 402 gen-tech center  396 Geographic platform scale  273 German VR market  91, 101 Ghazawneh, A.  153 Gioia, D.A.  132 Gioia method  92 Glaston 78 GlastOnline customer portal  78 global economy  258, 386 González-Varona, J.M.  298 Google Scholar  165 Google Services  159 governance  148, 153–4 government agencies  278 Government & Security  317 GPI see Green Productivity Index (GPI)  greenhouse gas emissions  257 Green Productivity Index (GPI)  244 hand-picked digital services  79

Hasan, H.R.  300 Hasan, S.  298 health and safety, work  251 Helal, S.  347 Henfridsson, O.  153 Herzwurm, G.  154 heterogeneity  158, 161, 266 heterogeneous infrastructure  173 heterotrait-monotrait ratio of correlations (HTMT) 198 high-tech manufacturing sector  344 high-value service  383 Hiller, S.  186 horizontal integration  331 Horváth, D.  197 HTMT see heterotrait-monotrait ratio of correlations (HTMT) hybrid business models  209 hybrid strategist  159 hyperscalers 393–4 Iansiti, M.  110 ICT see information and communications technology (ICT) idea generation  224, 229 idea testing  224 ideator  128, 134 identity claims  137 identity conflict  140 ideological resources  67 IE see innovation ecosystems (IE) IIM see industrial, intelligent manufacturing (IIM) IIP see Industrial Internet Platforms (IIPs) IIoT  see Industrial Internet of Things (IIoT) IIoTPs see Industrial Internet of Things Platforms (IIoTPs) ILaaS see Intralogistics-as-a-Service (ILaaS) digital platform implementation platforms  315 inbound logistics  372 incentive systems  32 individual actors  266 individual identity  131, 141 inductive code development  30 industrial digital platforms  393 ecosystem 358 engineering 2 experimentation lab  117 innovation labs  112, 117 manufacturing 106 partner organizations  117 platforms 18 revolutions  12, 379

Index  413 symbiosis  258, 267 industrial, intelligent manufacturing (IIM)  107–9, 111–12, 119 ecosystem roles for  113 Industrial Internet of Things (IIoT) 108, 112–15 concept 331 Industrial Internet of Things Platforms (IIoTPs)  110–15, 118, 393, 403 Industrial Internet Platforms (IIPs)  346 Industry 4.0  1, 10, 43, 51, 55, 194, 259, 261, 328 industry clusters  359, 374 digital manufacturing platform  127 ecosystems  358–9, 362, 366, 372–5 legitimacy 141 logistics  358, 374 stakeholders 373 information broker  263, 274 information and communication field  185 information and communications technology (ICT)  74, 283–4, 345 information gaps  274 information systems  2 infrastructure operators  328 innovation  117, 141, 403 cycles 240 platforms 110 velocity 379 innovation ecosystems (IE)  107, 118–19, 126 alignment structure of  107 concept 109 Innovation Lab Operator  117 innovative technology paradigm  72 input–process–output (IPO) approach/ model  312–13, 316 institutional agents 130 entrepreneurs 141 work 131–5 work strategy  137 work theory  127 intangible services  57 integrated Industrial Internet of Things Platforms (IIoTP)  393, 404 integrated value chains  97 integrated value propositions  46–51 integrative approach  3 intellectual property  300, 305 intelligent manufacturing  119 intelligent transport systems  361 interdependencies  243, 266 intermediation 143 intermediation platform business  143 internal identity  133 internal institutional work  141

Internet 73 Internet of Things (IoT)  71–3, 261, 327, 335, 345 affordances 83 paradigm 26 platforms 332–4 sensors  261, 328, 331 solutions  335, 338 technologies  75, 339 Internet of Things platforms (IIoTPs)  392 interoperability 73 interview guide  366 interview protocols  381–2 Intralogistics-as-a-Service (ILaaS) digital platform  345, 348, 352–4 IoT see Internet of Things (IoT) IoT connectivity  71–4, 80–81 affordances  71–5, 80–81 business model orientations (BMOs)  72–4, 81–3 identification and descriptions  74 business models  72–3, 79, 82–3 business model innovation, evolutionary model of  81 IoT-connected business models  81 IoT-enabled connectivity  82 IPO see input–process–output (IPO) approach/ model Jacobides, M.G.  30, 44, 109, 164 JDLink 79 Jeglinsky, V.  37 Ji-fan Ren, S.  196 John Deere  79–80 Johnson, R.E.  58 junior consultant  386 Jürjens, J.  37 Kádár, B.  353 Kalmar 80 Kapoor, R.  109, 111 Kenney, M.  379 key performance indicators (KPIs)  244, 248–53 keystones 178 Khaire, M.  141 Khaled, A.E.  347 Khan, O.  90 Kiel, D.  196 knowledge  29, 171, 316, 366, 377 creation 100 gap 30 management perspective  405 transfer 97 Kolk, A.  208 KONE Care DX  79 Kostis, A.  388

414  Handbook on digital platforms and business ecosystems in manufacturing Kovatsch, m.  347 KPI see key performance indicators (KPIs)  Kurpjuweit, S.  300 Langer, A.M.  362 Lanter, M.  347 Lanzolla, G.  378 lean manufacturing  280, 290 legacy 17–20 Legner, C.  37 Leipold, S.  225 Levien, R.  110 Li, W.  143 Likert scale  198 Linde, L.  90 linear supply chain model  221 live cattle sales platform  399 Llopis-Albert, C.  152 logistics 366 cities 360 Lohse, O.  347 longitudinal problem  43 long-term sustainability  387 Lorenz, R.  196 Lusch, R.F.  128, 346 machine learning (ML)  2, 337 algorithms  17, 337–8 Machine Operators  113–14, 117 Machine Providers  113 machine software code  47 machine-to-machine interaction  331 macro-level coding technique  59 Maijanen, P.  90, 93 Maintenance 4.0  327–8, 330–35, 339 key capabilities framework  335 key technologies underlying  330 Maintenance 4.0 algorithms 337 concept 331 management 331 system  334, 337 maintenance management 328 paradigm 329 positions 96 research 310 service provider-centric supply chain model 301 strategies 329 manufacturer’s insights  372 manufacturing  10–12, 132 digital business ecosystems in  346 flexible business strategies  289 manufacturing and recycling industry  135

manufacturing companies  165–6, 172–5 ecosystems  174–5, 178–9, 190, 259 characteristics 175 collaborative value contribution in  178 value-added actors in  179 innovation lab  112 processes  106, 185 SCs 173 sector 165 startups 379 systems 166–8 manufacturing execution system (MES)  347 market hierarchies  27 marketplaces 109 market power  207 market trends  90 Ma, S.  196 mass customization  12 mass personalization  12 material attributes  226 material field  185 material recycling  185 Mattilsynet  397, 401 maximization target  248 MaxQDA  92, 364, 369 Mayer, C.  347, 388 McDonald, R.M.  58 medical device sector, case study  15 MES see manufacturing execution system (MES) Message Queueing Telemetry Transport (MQTT)  347, 350 broker 348 meta-expert directories  229 Metcalfe law  313 microfoundations 89–92 middle-of-life (MOL)  240 MindSphere 346 minimization target  249 Mitchell, W.  346 ML see machine learning (ML) modern business models  206 modern-day concept  282 modular architectures  171 modular producer model  279 MOL see middle-of-life (MOL) Moore, G.  112 Moore, J.F.  27, 126 Mosch, P.  393, 402 MQTT see Message Queueing Telemetry Transport (MQTT) multi agent-based system (MAS)  347 multi-homing decisions  89 multi-jet fusion (MJF) processes  301 multilateral innovation  28

Index  415 Murmura, F.  301 Naik, N.  347 Nambisan, S.  128, 346, 391 Naor, M.  198 needfinding  223–4, 227–9, 234 network effects  313–14 new business opportunities  171 new market actors  306 New Space  318 ‘New Ways of Work’ practices  283 new work  281–2, 286 adopters 291 practices  283, 286, 290 NFSA see Norwegian Food Safety Authority (NFSA)  non-digital platforms  392 nonfinancial KPIs  247 non-focal actors  88, 101 non-focal firms  96–100 non-monetary exchanges  316 non-profit foundation  63 non-profit platform  380 non-random approach  197 Nortura  396, 399, 402 Norwegian Agriculture Agency  402 Norwegian dairy and beef industry  394 business ecosystem  405 challenge 398 Norwegian farmers  395 Norwegian Food Safety Authority (NFSA)  397 Norwegian organization  396 Norwegian state agency  397 ‘not-invented-here-syndrome’ 37 NVivo software  59 Obermeier, R.  393, 402 OEM see original equipment manufacturers (OEMs)  147, 151, 159–61 OEM-centric supply chain configuration  301 OI see open innovation (OI) ‘old work’  290 practices 282 omnichannel businesses  279 on-board applications  318 Onboarding Consultant role  116 one-time payments  156 OP see operational performance (OP) open innovation (OI)  315, 377 initiatives  378, 387 models 378 processes  314–15, 319, 322 Open Italy program  386 openness  264, 393, 398 operational performance (OP)  195–6

opportunities of manufacturing ecosystems  171 opportunity screening  93, 98 Orbiting platform  319 organizational actors 28 factors 194 forms  43, 51, 53 identities  127, 131–2, 137, 143 conflicts 143 internal perspective as  130 learning  43, 51 process 43 structures 1 visibility 134 organization-related challenges  33 organized systems  78 Orica 79 original equipment manufacturers (OEMs)  111, 147–61, 297, 302 OTA see over-the-air (OTA) updates Otto, B.  37 outbound logistics  372 overproduction 280 waste of  280 over-the-air (OTA) updates  155 own core-competences  58 Pareto efficiency  353 Parida, V.  91 partial least square path modelling (PLS-SEM) 198 partnership scouting  89–93, 98–101 operationalization 90 outside specific ecosystem  97 within specific ecosystem  96 partnership-scouting 98 path dependencies  179 Pauli, T.  12 PBE see platform-based ecosystems (PBEs) peer-to-peer business models  55 marketplace 64 platform-based business models  57 platform economy  57–8 sharing economy  59 platforms  55, 58–9 Peffers, K.  332 Peltola, T.  111 Peng, Y.  196 people-related challenges  30 performance improvements  198 performance measurement  267, 274 personalization 10–20 Petrik, D.  154

416  Handbook on digital platforms and business ecosystems in manufacturing Phillips, N.  127, 129, 133, 144 physical and digital world, bridging  338 physical resources  56 Planetek 317 platform approach 393 business model  313 co-creation 380 configuration 262 database 264 development 316 dynamics 314 economy  206–7, 379 ecosystems  27, 43, 101, 126, 129, 148–9, 153, 160, 295–7, 380 markets 216 openness 393 operating cost  267 operators  88, 101 owners  151, 157, 160 ownership 264 security 264 strategies 392 systems 166 types 120 value 313 platform-based business models  207 businesses 207 innovation strategies  109 platform-based ecosystems (PBEs)  4, 42–3, 147–57, 160–61 circular 264–6 platform-centric supply chain model  302 platform-driven business models  68, 216 platformization  207, 216 platforms  377, 380 PLS-SEM see partial least square path modelling (PLS-SEM) political campaigns  207 political enthusiasm  26 Ponis, S.  301 Ponsse 78 Ponsse Manager  78 Porter, M.E.  42 positioning options  187 positive feedback loop  52 positive network effects  209 precise strategic positioning  181 predictive algorithms  313 Predictive maintenance  330 strategy 334 preference-dependent ownership  151 premium strategist  157 pre-operative planning  20

preventive strategies  329 pricing strategy  158 primary vs. secondary input  246 prioritizing phase  384 private investor  64 proactive partnering approach  100 problem-solving strategy  221 process efficiency  46 companies 161 offerings 155 digital 158–9 differentiation  43–7, 51–2 product-differentiation 46 product-focused activity system  45, 47 product-focused firms  42 product identification  300 production machines  174 production subsidy  401 productive opportunism  99 productivity 251 product lifecycle phases  260 product-oriented output  246–7, 251 product-service-systems (PSS)  240 project manager  386 Pronzato, R.  207, 209 proof of concept (PoC)  385 PSS see product-service-systems (PSS) PTC ThingWorx  346 public agencies  378, 387 ‘pull’ effect  99 QoS see Quality of Service level (QoS) QSA see Quality System in Agriculture (QSA) qualitative approach 27 boundary resources  154 case-based methodology  381 code system  369 methodology 381 research 380 study 28 Quality of Service level (QoS)  347 Quality System in Agriculture (QSA)  401 quantitative studies  144 radical business model innovation  81 radical transformation  51 radio-frequency identification (RFID)  261 rapid manufacturing  297 reactive maintenance  328 real-life ecosystem  361 real-time data  267 Real Time Reaction (RTR) concept  347 real-world objects  151 reciprocity 66–7

Index  417 recursive relationship  312 recycled resources  246–7, 252 recycling industry 141 rate  248, 252 services 139 transactions 139 redistribution design theme  209 regenerative manufacturing  221 regional business actors  59 Rehyphen 221 remuneration 80 renewable energies  248 Rennie, A.  298 research design  165 research gaps  257 resource constraints  99 restrictions 401 Retrospekt 227 return on assets (ROA)  196 return on equity (ROE)  196 return-on-investment assumptions  34 Reuter, E.  206, 209 ReValue 132–44 RFID see radio-frequency identification (RFID) right governance approach  44 Risi, E.  207, 209 Ritala, P.  126, 388 ROA see return on assets (ROA) robust digital platform  290 robust technology  210 ROE see return on equity (ROE) role-making 56–8 role-taking 56–8 Rong, K.  179 Rott, J.  332, 334 Real Time Reaction (RTR) concept  347 RTR see Real Time Reaction (RTR) concept Saldaña, J.  59 satellite assets as-a-service  318 SBU see Strategic Business Unit (SBU) SC see supply chains (SCs) Schroeder, A.N.  209 Schwanholz, J.  225 Schweihoff, J.C.  39 ScienceDirect 165 SCPSE see smart-circular product-service-ecosystems (SCPSEs) SCPSS see smart-circular product-service-systems (SCPSS) SDK see software development kit (SDK) software toolchain S-D logic roles  128 second-cycle coding  59–60

selective laser melting (SLM)  301 self-evaluation 384 semi-structured interviews  29, 91, 132, 297, 381 sensor-based product platform  149 service-centered logics  17 service-dominant logic  128 service ecosystems  126–8 service innovation  128, 181 service offering  259 service production  170 service providers  113 servitization 10–20 Shapeways  168, 170, 172, 175 sharing and building phase  382 sharing sensitive data  347 Shelby, Z.  347 ‘shop floor’ ecosystem  168 Siggelkow, N.  380 simulation 331 Singh, K.  346, 353 single ownership  151 Skog, I.  337 SLM see selective laser melting (SLM) SLR see systematic literature review (SLR) SM see smart manufacturing (SM) small to medium-sized enterprises (SMEs)  33–4, 38, 68, 127, 131, 143–4 smart-circular product-service ecosystems (SCPSEs)  239, 242, 254 sustainable productivity for  244 smart-circular product-service systems (SCPSS)  240, 243 product-oriented 241 result-oriented 242 use-oriented 241 smart cities 358–9 ecosystem 375 contracts 260 factories 350 mobility  359, 373 solutions 71 traffic management  359 Smart Manufacturing (SM)  194, 350 digital supply chains  195 technologies  194–7, 200–202 and financial performance  196 and operational performance  196 SmartPLS 4  198 SME see small to medium-sized enterprises (SMEs) snowball sampling technique  381 social attributes 226 boundary resources  154

418  Handbook on digital platforms and business ecosystems in manufacturing cohesion 60 and ecological aspects  300 and economic exchanges  55 exchange  57–8, 60, 64–7 impact 247 interactions 66 key performance indicators  250 networks 226 sensing activities  100 sensing routines  93 sustainability  209, 266 socio-technical approach 321 dimensions 322 model  312, 322 perspective 312 platforms 322 systems 28 thinking 30 software-based platform ecosystem  178 software development kit (SDK) software toolchain 321 Software Solution Provider  113 Somohano-Rodríguez, F.M.  197 Sony, M.  197 SP see sustainable productivity (SP) Space 4.0  310 Spacedge™ 318–21 space digital platforms  310 space sector  310 Space Stream  317 SPI see sustainable productivity index (SPI)  247, 249 spinoff company (C7)  65 stakeholder analysis  364 stakeholders 363 start-up businesses  55, 58–9, 63, 113, 117 business models  56, 59 entrepreneurs  56–61, 65–6 static value networks  181 Stonig, J.  43, 129 Strategic Business Unit (SBU)  317 strategic challenges 298 hedging 89 management 148 positioning 181 synergies 209 Strauss, A.L.  29, 132 submodels 173 supply chains (SC)  164–72, 181, 259, 295, 298 configurations 301 disruptions 306 efficiency 222

flexibility 289 models 306 transparency 38 supply-side economies  206 surgeon-to-engineer interface platform  17 sustainability  206–9, 213, 221, 258, 396–7 sustainable business model  210 innovation 208 manufacturing 222 productivity 244 sustainable productivity index (SPI)  247, 249, 253 sustainable productivity (SP) for SCPSE  246 Susto, G.A.  337 systematic literature review (SLR)  149, 297 systematization 262 system integration  42–4, 47–50 System Integrators  115, 118–19 strategy 46–9 system modelling  369 Szabó, R.  197 Szaller, Á.  353 Talmar, M.  111–12 Tansley, A.G.  360 Tao, C.  196 Tauterat, T.  334 Tavalaei, M.M.  91 taxonomic differentiation  91 team-based strategic decisions  49 technical boundary resources  155 interoperability 34 systems 327 variables 312 technological artefact 126 changes 42 development 49 infrastructure 278 innovation 44 modularity 52 platforms 108 technology-based business models  72 technology-related challenges  30 Teece, D.J.  58, 88–9, 110 tenants  344–5, 348, 353, 355 Tesla 83 theoretical sampling  380 Theory of Constraints (TOC)  285–6, 290 theory of social and economic exchange  56 three-speed hub  210 Tian, J.  55, 68

Index  419 Tine 395–6 Tjernberg, L.B.  337 TMP see traditional manufacturing processes (TMP) TOC see Theory of Constraints (TOC) TomTom 75 top-down strategy  47 Tortorella, G.L.  198 Toyota Production System (TPS)  280 TPS see Toyota Production System (TPS) trade-offs 207 traditional business ecosystems  290 business models  345 manufacturing 295 capabilities 106 recycling business  135–6 traditional manufacturing processes (TMP)  251 transaction platforms  393 transaction processes  313 transactive memory systems  229 transparency 398 Trevisan, A.H.  259, 266 TRIBRID business model  208, 210–12, 216 case study approach  211 conceptualization 209 digital platforms  212 trusted business ecosystem  335 trusted ecosystem  338 trustworthiness 65 unplanned strategies  329 unscheduled audits  267 urban ecosystems  360 value  75, 210 capture 79 chains  129, 165, 180, 397 creation  26–7, 44, 50, 88, 127–30, 135, 141–3, 153–6, 178–9, 208–11, 313, 396 co-creation  100, 133–5, 160, 171, 242, 377–9 logics  393, 403 identification 71 proposition  15, 19, 27, 45–6, 48, 53, 72, 106, 111, 119, 127–30, 134–5, 141, 144, 179–81, 187, 234, 384 value-added partners  181–2 value-creation mechanisms  17

value-related challenges  33 value-sharing contracts  321 value stream mapping (VSM)  280–81, 290 variables  369–70, 372, 374 Vegter, D.  259 venture potential  171 vertical integration  331 Vester, F.  358, 361, 369, 374 veterinarians 397 virtual modeling  17 virtual reality (VR)  91 companies 97 industry 91 market  88, 98–9, 101 software 89 VLUID project  358, 361–2, 366 Vodafone 75 Volvo 159 VR see virtual reality (VR) VSM see value stream mapping (VSM) Wadhwani, R.D.  141 waste disposed amount  250, 253 waste of defective products  288 inventory 286 manufacturing processes  285 transportation 281 underutilized people  281, 284 unnecessary movement  287 waiting 281 water consumption  250 water savings  249 Watson, R.T.  149 Weber, L.  388 Webster, J.  149 Wetzlar  358–64, 375 data space  362 wicked problems  224 Winkler, H.  37 workforce punctuality  373 workforce traffic  366 Yin, R.K.  332, 381 Yu, Y.  197 Zapadka, P.  91 Zhu, F.  129 Zott, C.  216 Zymans, J.  379