Innovation Economics, Engineering and Management Handbook 2: Special Themes [1 ed.] 1786307014, 9781786307019

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Innovation Economics, Engineering and Management Handbook 2

Innovation Economics, Engineering and Management Handbook 2 Special Themes

Edited by

Dimitri Uzunidis Fedoua Kasmi Laurent Adatto

First published 2021 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address: ISTE Ltd 27-37 St George’s Road London SW19 4EU UK

John Wiley & Sons, Inc. 111 River Street Hoboken, NJ 07030 USA

www.iste.co.uk

www.wiley.com

© ISTE Ltd 2021 The rights of Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988. Library of Congress Control Number: 2021932860 British Library Cataloguing-in-Publication Data A CIP record for this book is available from the British Library ISBN 978-1-78630-701-9

Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xvii

Dimitri UZUNIDIS and Fedoua KASMI Chapter 1. Meaning – The Meaning of Innovation: Theoretical and Practical Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

Joëlle FOREST 1.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 1.2. Conceptions of the meaning of innovation over time . . . 1.3. When innovation, like the phoenix, rises from the ashes . 1.4. In search of lost meaning . . . . . . . . . . . . . . . . . . 1.5. The PSI approach: a philosophy of, and for, action . . . . 1.6. By way of conclusion . . . . . . . . . . . . . . . . . . . . 1.7. References . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 2. Engineering – Innovation Engineering: A Holistic and Operational Approach to the Innovation Process . . . . . . . . . . . . .

19

Laure MOREL and Mauricio CAMARGO 2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Innovation engineering: a field of research that has struggled to structure itself in France . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Practical guide to innovation engineering . . . . . . . . . . . . . . . 2.3.1. First bias: there are no good or bad innovative ideas! . . . . . . 2.3.2. Second bias: any innovation process requires contextualization of the situation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3. Third bias: there is no innovative project management without collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . .

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2.3.4. Fourth bias: a universal innovation process does not exist! . . . . . . . . . 2.3.5. Fifth bias: the importance of materializing and evaluating ideas as early as possible by including users in the process . . . . . . . . . . . . . . . . . . . . . . 2.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chapter 3. Absorption – Technological Absorptive Capacity and Innovation: The Primacy of Knowledge . . . . . . . . . . . . . . . . . . . . . .

35 36 37 38 39

43

Sonia BEN SLIMANE 3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Technological absorptive capacity: a cognitive process . . . . . . . 3.3. The multidimensional nature of absorption capacity and innovation 3.4. Measuring absorptive capacity . . . . . . . . . . . . . . . . . . . . . 3.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 4. Big Data – Artificial Intelligence and Innovation: The Big Data Issue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51

Laurent DUPONT 4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Humans and data: diversity and consensus . . . . . . . . . . . . . . 4.3. Big Data: an interdisciplinary approach to technology and its uses . 4.4. A wide range of applications: promises and fears. . . . . . . . . . . 4.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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51 52 54 55 56 57

Chapter 5. Blockchain – Blockchain and Co-creation within Management Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

59

Eric SEULLIET 5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. The interest of Blockchain in the field of immaterial exchanges 5.3. The limits of the co-creation process . . . . . . . . . . . . . . . . 5.4. Blockchain in mobilizing and organizing co-creation processes 5.5. The promises of Blockchain . . . . . . . . . . . . . . . . . . . . 5.5.1. Intellectual property renewal . . . . . . . . . . . . . . . . . . 5.5.2. “Empowerment” of individuals . . . . . . . . . . . . . . . . 5.5.3. Scaling up . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.4. Collective intelligence . . . . . . . . . . . . . . . . . . . . . 5.5.5. New forms of organization and social impact . . . . . . . . 5.5.6. Necessary developments . . . . . . . . . . . . . . . . . . . .

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5.6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

65 66

Chapter 6. Bricolage – From Improvisation to Innovation: The Key Role of “Bricolage” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

67

Paul BOUVIER-PATRON 6.1. Introduction . . . . . . . . . . . . . . . . . . 6.2. Bricolage: new concept, old practice . . . . . 6.3. Current application of the bricolage concept 6.4. Bricolage and improvisation . . . . . . . . . 6.5. Bricolage and frugal innovation . . . . . . . 6.6. Conclusion . . . . . . . . . . . . . . . . . . . 6.7. References . . . . . . . . . . . . . . . . . . .

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67 67 68 69 70 72 73

Chapter 7. Circularity – The Circular Economy as an Innovative Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

75

Sonia VEYSSIÈRE 7.1. Introduction . . . . . . . . . . . . . . . . . . . . 7.2. The circular economy: a transformative concept 7.3. The circular economy as a source of innovation 7.4. Conclusion . . . . . . . . . . . . . . . . . . . . . 7.5. References . . . . . . . . . . . . . . . . . . . . .

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75 76 77 81 82

Chapter 8. Co-creation – Co-creation and Innovation: Strategic Issues for the Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

85

Paul BOUVIER-PATRON 8.1. Introduction . . . . . . . . . . . . . . . . . . . . . 8.2. Co-creation: a strategic challenge for companies . 8.3. Co-creation, DIY and DIWO . . . . . . . . . . . . 8.4. Co-creation, creativity and innovation . . . . . . . 8.5. Co-creation and intellectual property rights . . . . 8.6. Co-creation and eco-design . . . . . . . . . . . . . 8.7. Conclusion . . . . . . . . . . . . . . . . . . . . . . 8.8. References . . . . . . . . . . . . . . . . . . . . . .

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Chapter 9. Community – Innovative Communities of Practice: What are the Conditions for Implementation and Innovation? . . . . . . . .

93

Diane-Gabrielle TREMBLAY 9.1. Introduction: communities of practice and innovation . . . . . . . . . . . . . .

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9.2. Communities of practices, a definition: group cohesion, complicity and dynamism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3. Work teams and virtual communities . . . . . . . . . . . . . . . . . 9.4. Organizational learning . . . . . . . . . . . . . . . . . . . . . . . . . 9.5. Animation role . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.7. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 10. Craftsman – The Innovative Craftsman: A Historically Permanent Socio-economic Function . . . . . . . . . . . . . .

101

Sophie BOUTILLIER and Claude FOURNIER 10.1. Introduction . . . . . . . . . . . . . . . . . . . 10.2. The craftsman, an ignored innovator . . . . . 10.3. The innovative craftsman of the 21st century 10.4. Conclusion . . . . . . . . . . . . . . . . . . . 10.5. References . . . . . . . . . . . . . . . . . . .

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Chapter 11. Defense – Military Innovation: Networks and Dual-use Technological Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

109

Pierre BARBAROUX 11.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2. Military innovation: main attributes . . . . . . . . . . . . . . . . . . 11.2.1. Military innovation as a knowledge-intensive and dual process . 11.2.2. Military innovation as a technology-driven process . . . . . . . 11.2.3. Military innovation as a demand-oriented process . . . . . . . . 11.3. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 12. Design Thinking – Design Thinking and Strategic Management of Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

115

Bérangère L. SZOSTAK 12.1. Introduction . . . . . . . . . . . . . . . . . . 12.2. The origins of design thinking . . . . . . . 12.3. Design thinking in innovation management 12.4. Conclusion . . . . . . . . . . . . . . . . . . 12.5. References . . . . . . . . . . . . . . . . . .

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Chapter 13. Digital – Digital Entrepreneurship as Innovative Entrepreneurship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Birgit LEICK and Mehtap ALDOGAN EKLUND 13.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2. Definition and characteristics of digital entrepreneurship 13.3. Digital entrepreneurship in the field of innovation studies 13.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5. References . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 14. Entrepreneurship – Social Innovative Entrepreneurship: An Integrated Multi-level Model . . . . . . . . . . . . . . . . . . . . . . . . . . . .

129

Susanne GRETZINGER 14.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2. State-of-the-art: contemporary issues, approaches and levels of analysis . 14.3. Integrated multi-level model of innovative social entrepreneurship . . . . 14.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.5. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 15. Fintech – Technology in Finance: Strategic Risks and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

137

Arvind ASHTA 15.1. Introduction . . . . . . . . . . . . . . 15.2. Evolution of technology in finance . 15.3. Risks of fintech . . . . . . . . . . . . 15.4. Concluding remarks . . . . . . . . . 15.5. References . . . . . . . . . . . . . .

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Chapter 16. Gerontech – Geront’innovations and the Silver Economy . .

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Blandine LAPERCHE 16.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2. The Silver Economy: a new area for innovation . . . . . . . . . . . . . . . . . 16.3. “Gerontechnologies”: the technological dimension of innovations in the Silver Economy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4. Towards “geront’innovation” . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.6. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 17. Greentech – Contributions and Limitations to the Environmental Transition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

153

Smaïl AÏT-EL-HADJ 17.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2. Green technologies, the first technological response to the environmental crisis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2.1. New energies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2.2. Information technologies and green technologies . . . . . . . . . . . . 17.2.3. Biology as a preferred carrier of green technologies . . . . . . . . . . . 17.2.4. Nanotechnologies: cross-technology dimension of green technologies . 17.2.5. New services and organizations: recycling, industrial ecology, the economy of functionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.3. From green technologies to a sustainable technological and socio-economic system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.3.1. Green technologies are a one-off and partial response to the environmental challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.3.2. The shifting of boundaries and environmental problems. . . . . . . . . 17.3.3. The global environmental limit implies responding with a global reconfiguration of the technological system . . . . . . . . . . . . 17.3.4. The global environmental limit implies a societal reconfiguration beyond technology . . . . . . . . . . . . . . . . . . . . . . . . . 17.3.5. The current criticality of the environmental threat implies a massive and rapid transition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.4. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 18. Hacker – Hackerspace as a Space for Creative Exploration .

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Dave MOBHE BOKOKO 18.1. Introduction . . . . . . . . . . . . . . 18.2. The rise of hacker culture . . . . . . 18.3. Cybercrime or creative exploration? 18.4. Conclusion . . . . . . . . . . . . . . 18.5. References . . . . . . . . . . . . . .

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Chapter 19. Health – Telemedicine: Decentralized Medical Innovation . . .

167

Patricia BAUDIER 19.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 19.2. Information technology at the service of medical care 19.3. High-performance medical devices . . . . . . . . . . . 19.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . 19.5. References . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 20. Intellectual Corpus – Inventive Intellectual Corpus: Knowledge-based Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Pierre SAULAIS 20.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.2. Concept of knowledge-based innovation . . . . . . . . . . . . . . . . . . 20.3. Modeling knowledge creation . . . . . . . . . . . . . . . . . . . . . . . . 20.4. Activation of the chaotic inspiration model of knowledge evolution by emergence using the ICAROS® method . . . . . . . . . . . . . . . . . . . . . . 20.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.6. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 21. Imagination – Imagination, Science Fiction, Creativity and Innovation: An Integrated Process . . . . . . . . . . . . . . . . . . . . . . . . . .

181

Thomas MICHAUD 21.1. Introduction . . . . . . . . . . . . . . . . . . 21.2. Tame the imagination in order to innovate . 21.3. Imagination: from creativity to innovation . 21.4. Conclusion . . . . . . . . . . . . . . . . . . 21.5. References . . . . . . . . . . . . . . . . . .

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Chapter 22. Marketing – Marketing of Innovation and University–Industry Collaboration . . . . . . . . . . . . . . . . . . . . . . . . . .

187

Cheikh Abdou Lahad THIAW 22.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2. Innovation marketing and inter-organizational collaboration. 22.3. The cross-functionality of innovation marketing . . . . . . . 22.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.5. References . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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187 188 190 192 192

Chapter 23. Milieu – Innovative Milieu: The Strength of Proximity Ties . .

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Chapter 24. Nanotech – Nanotechnologies: The Future of Innovations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Jean-Louis MONINO 24.1. Introduction . . . . . . . . . . 24.2. Nanotechnology applications 24.3. RFID chips . . . . . . . . . . 24.4. Global potential risks . . . . 24.5. Conclusion and outlook . . . 24.6. References . . . . . . . . . . 24.7. Webography . . . . . . . . .

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Chapter 25. Novelty – Novelty and Innovation: The Nodal Place of Creativity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Laure MOREL 25.1. Introduction . . . . . . . . . . . . . . . . . . 25.2. Innovation and novelty. . . . . . . . . . . . 25.3. Creativity as a prerequisite for innovation . 25.4. Conclusion . . . . . . . . . . . . . . . . . . 25.5. References . . . . . . . . . . . . . . . . . .

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Chapter 26. Open – Open Source and Open Data: Filiation, Analogies and Common Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Laurent ADATTO 26.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.2. Open source and open data: guiding concepts . . . . . . . . . . . . . . 26.3. Open source: process innovation and legal innovation via copyleft . . 26.4. Open data: dynamics of open innovation 2.0 in line with open source 26.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.6. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 27. Personality – The Deviant Personality of the Innovative Actor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Dimitri UZUNIDIS 27.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.2. The actor, the system and the question of the complementarity of roles 27.3. The deviant personality of the innovator . . . . . . . . . . . . . . . . . . 27.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.5. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 28. Real Estate – Business Real Estate and Innovation: A New Profession for New Spaces . . . . . . . . . . . . . . . . . . . . . . . . . .

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Frédéric GOUPIL DE BOUILLÉ 28.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.2. The prevalence of the financial referent, reasoning and industrialist practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.3. Weakness of the human resources paradigm applied to real estate . 28.4. Employees empowered by change management . . . . . . . . . . . . 28.5. Powerful, but inconsistent with regard to use, real estate marketing . 28.6. The real estate market versus the innovative company . . . . . . . . 28.7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.8. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 29. Skills – Innovation and Entrepreneurial Skills. . . . . . . . . . .

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Giovanni ZAZZERINI 29.1. Introduction . . . . . . . . . . . 29.2. Innovation skills . . . . . . . . 29.3. Entrepreneurial competencies . 29.4. Ideas and opportunities . . . . 29.5. Resources . . . . . . . . . . . . 29.6. Into action . . . . . . . . . . . . 29.7. References . . . . . . . . . . .

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Chapter 30. Small Business – Small Business and Innovation: Specificities and Institutional Context . . . . . . . . . . . . . . . . . . . . . . . .

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Son Thi Kim LE 30.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.2. The relation between small business and innovation . . . . . . 30.2.1. What is small business? . . . . . . . . . . . . . . . . . . . . 30.2.2. Small business and innovation . . . . . . . . . . . . . . . . 30.3. The specificity of small business innovation . . . . . . . . . . . 30.3.1. Innovation efforts: external knowledge source rather than in-house R&D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.3.2. Adopting and adapting external knowledge resources . . . 30.4. Government support for small business innovation . . . . . . . 30.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.6. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 31. Spin-off – Research Spin-off: How the University Fosters Innovative Entrepreneurship . . . . . . . . . . . . . . . . . . . . . . . .

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Elisa SALVADOR 31.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2. An overview of the development of research spin-offs . . 31.3. Main perspectives and taxonomies of research spin-offs . 31.4. Fragility and future avenues for improvement . . . . . . . 31.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 31.6. References . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 32. Start-up – Start-ups, Venture Capital (SVC) and the Financial Cycle of the SVC System . . . . . . . . . . . . . . . . . . . . . . . . . .

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Angelo BONOMI 32.1. Introduction . . . . . . . 32.2. Start-ups . . . . . . . . . 32.3. Venture capital . . . . . 32.4. The SVC system cycle . 32.5. Conclusion . . . . . . . 32.6. References . . . . . . .

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Chapter 33. Territory – Territorial Dynamics and Innovative Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Michelle MONGO 33.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.2. Innovation in services: what are we talking about? . . . . . . . . 33.2.1. What does it mean to innovate in services? . . . . . . . . . . 33.2.2. Which service for innovation analysis? . . . . . . . . . . . . 33.3. Geography of innovation in knowledge-intensive business services and territorial impact . . . . . . . . . . . . . . . . . . . . . . . 33.3.1. Stylized facts about the geography of knowledge-intensive business services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.3.2. The contribution of knowledge-intensive business services to territorial innovation dynamics . . . . . . . . . . . . . . . . . . . 33.4. Public innovation policy: historical actions and future prospects 33.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.6. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 34. Well-being – Subjective Well-being and Innovation . . . . . . .

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Francis MUNIER 34.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 34.2. Creative destruction impacts subjective well-being . . . 34.3. A questionable relationship . . . . . . . . . . . . . . . . 34.4. Innovation-care: theoretical approach and applications . 34.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 34.6. References . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Summary of Volume 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Introduction General Presentation

“Innovation is everything in the economy that is either not being done, or not being done again.” - Dimitri UZUNIDIS Agility, flexibility and rapid adaptation to change are becoming the key words for growth and development in our society. Finding new ways of doing things and creating something new out of what already exists remains essential when facing crises (economic, social and environmental). The ability to innovate is therefore the main condition for maintaining the competitiveness and performance of companies, regions and territories in a changing context. Innovative activity has long been considered a driving force for “progress”, but its impact on the transformation of socio-economic systems is greater when a succession of profound changes are introduced on broader scales (organizational, social, environmental, political, behavioral, etc.). Achieving these transformations requires the mobilization of resources, information, knowledge and networks of specific actors in order to guide innovation efforts to respond to more global challenges such as reducing environmental impacts, building resilience, and improving health, safety and people’s well-being. Through what mechanisms and under what conditions does innovation enable more radical changes that progressively and sustainably reorient our modes of development? This is the overall question that this two-volume encyclopedic book answers, by mobilizing a set of interdisciplinary theories and concepts devoted to the study of innovation. Innovation, in fact, consists of the design and marketing of new goods and technologies, the application of new working methods or the conquest of new markets. Today’s knowledge-based economy implies that innovation is the result of greater interaction between businesses, universities, public institutions, consumers Introduction written by Dimitri UZUNIDIS and Fedoua KASMI.

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and citizens. Innovation networks create new knowledge and contribute to the diffusion of new socio-economic and technological models, through new modes of production and distribution. Innovation results from technological, organizational and commercial changes. How do organizations design and manage innovation processes? What strategies and management tools do they apply for the concrete implementation of innovation processes? What role do innovation policies play in driving these processes? How does innovation impact competitiveness and performance? This involves analyzing companies’ technological opportunities, organizational strategies and the integrated management of research and development, marketing and financial projects, etc. This book is dedicated to the study of innovation. Theoretical reminders are associated with the discussion of concepts. Written in a didactic way, the reader will easily be able to situate the current debates around the need for technological and social innovation and the imperative of creating a climate conducive to the launch of large-scale innovation processes, because the current socio-economic stakes are as important as they are global. The book consists of two volumes. The first one is devoted to the presentation of the basic concepts. Its aim is to provide a broad and precise overview of the fundamental issues addressed by economists, historians and engineers specializing in innovation. The second volume contains a set of studies of current concepts and opens the debate on the evolution of the concept of innovation in the years to come. The innovation process has a causal relationship with a problem – technological, economic, social – posed to the market economy and identified consciously or unconsciously by its actors (companies, entrepreneurs, consumers, etc.). Innovation is thus linked to the search for the optimal solution to the problem posed. This presupposes the use of knowledge and information from practice, experience and scientific activity. Innovation is itself a cumulative and historical process defined by six major characteristics highlighted in this book: (a) the impacts of innovation are difficult to predict; (b) the scale of diffusion of innovation is difficult to calculate; (c) innovative activities are asymmetric and staggered in time; (d) the time of learning, execution and diffusion plays a crucial role in the act of innovating; (e) the business environment conditions the time, scale, nature and impacts of innovation; and (f) innovations are interdependent. In new approaches to innovation, the entrepreneur and the company are studied through their skills and their function of resource creation. Gradual or radical innovation thus becomes endogenous and is integrated into a complex process characterized by a lot of feedback and interactions in production and marketing networks: clusters, sectors and territorial or national innovation systems. The innovative organization is presented as a dynamic system composed of specific and

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diversified skills. Through the acquisition, combination and mobilization of these competencies, the innovator (entrepreneur or organization) can create technological resources and evolve the relationships it maintains with its environment. This explains the importance of design, application and development management in the implementation of an innovation process. An innovation system (sectoral, territorial or national) mobilizes a set of knowledge and skills resulting from learning processes and integrated into its memory. This knowledge must be enriched in order to be valorized by technological, organizational and commercial innovation. The survival of the system depends on its capacity to innovate, which enables it to face external aggressions, to transform and endure. External stimuli (competition, product substitutability, innovation policies, etc.) are generated by the economic context and affect the means of selection of entrepreneurs, companies and other public or private institutions. Selection procedures are shaped by the business climate: the nature of the product market, the availability of capital and labor, the pace of innovation, the effects of public policies, etc. They can, therefore, create alternatives to the mode of operation, management and production of a given firm (of an organization or, more generally, of a particular innovation system). It is thus clear that the effectiveness of innovation management is highly dependent on the internal capacity to seize external opportunities. The authors of this book repeatedly stress that innovation is part of the dynamic growth model based on uncertainty, risk and profit. The “flaws” that characterize an economic system are, however, important sources of opportunities for investment, production and the diffusion of innovations. The richness of this book is the result of the reflections developed within the Research Network on Innovation (RNI) and carefully selected to take into account current and historical analyses, the relationship between technological mutations and social change, and the presentation and perspective of management, strategies and innovation policies. The authors are among the most eminent specialists of the Network, whose main objectives are the study of innovation processes in today’s information and knowledge society, the analysis of the intensification of links between the worlds of research and business, and the examination of the modes of appropriation and management of innovation by companies from a global as well as local or sectoral perspective. The Network has more than 1,500 researchers in 36 countries specializing in the multidisciplinary study of innovation: economics, management, engineering, sociology, history, law, epistemology, anthropology and psychology of the innovator. The guiding principle of the studies presented in the two volumes allows us to understand the systemic nature of innovations and to reflect on their potential for dissemination and application, to study how innovations question our categories of thought and challenge the traditional mapping of knowledge… to think about the meaning of innovation.

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This book is the continuation of a set of books dedicated to the study of innovation in the “Innovation in Engineering and Technology” Set published by ISTE and Wiley: – Innovation Engines: Entrepreneurs and Enterprises in a Turbulent World (2017). – Science, Technology and Innovation Culture (2018); – Collective Innovation Processes: Principles and Practices (2018); Divided across two volumes, it is composed of four long chapters on epistemology, economics, management and engineering that trace the contours of the holistic conception of innovation and continues with 81 shorter chapters that present and discuss, according to the sensitivity of their authors, the key notions associated with the studies of innovation. Note that the last chapter of Volume 1 on “X-Innovation” is devoted to highlighting the complexity of the concept in order to open perspectives for future research on innovation. We would like to thank our colleagues Sophie Boutillier (University of the Littoral Opal Coast), Thierry Burger-Helmchen (University of Strasbourg), Vanessa Casadella (University of Picardie), Joëlle Forest (National Institution of Applied Sciences, Lyon), Michaël Laviolette (University of Lyon), Laure Morel (University of Lorraine), Francesco Schiavone (Parthenope University of Naples), Bérangère Szostak (University of Lorraine) and Corinne Tanguy (AgroSup-Dijon) for their contribution to the conception of this book. We express our gratitude to our colleague Laurent Adatto for his contribution to the finalization of this important project. Finally, it is important to mention the contribution of our colleague Blandine Laperche, President of the Research Network on Innovation, to the realization of this project. We express our gratitude and best wishes to her.

1 Meaning – The Meaning of Innovation: Theoretical and Practical Perspectives

1.1. Introduction Who can be against innovation nowadays? Regarding the permanent injunction to innovate associated with contemporary societies – in many fields, if not the whole of society – we would be inclined to say no-one. Nevertheless, the answer is not so obvious in spite of appearances. Indeed, at the same time as it contributed to popularizing the concept of sustainable development (“that meets the needs of the present without compromising the ability of future generations to meet their own needs” (Brundtland 1987, p. 14)), the Brundtland Report initiated numerous publications stigmatizing the negative impacts of anthropogenic activities on the environment, which has gradually established “sustainable development” as a major concern for our societies. The necessarily progressive conception of innovation has tended towards decline. There are several reasons for this. These include the following, without claiming to be exhaustive: – the observation that we have never has so many technologies available to us while inequalities are growing in the world (the poor are getting poorer), that world hunger affects nearly 2 billion people, that half of the world’s population does not have access to basic healthcare according to the WHO, etc.; – the realization that the unbridled development of new technologies goes together with an incessant evolution of skills to use them, and leads to an

Chapter written by Joëlle FOREST. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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accumulation of continuous learning or risk of being overwhelmed and staying on the side lines; – the contribution of innovation to growth and productivity gains that is running out of steam. All these reasons have led to a disenchantment with regard to innovation. In this chapter, however, we will show that such disenchantment is not separate from our way of thinking and of implementing innovation. The epistemology of innovation is indeed a valuable “tool” for studying and questioning the production of knowledge about innovation and, through this, the relationship of models to action. However, one observation must be made at the outset. While there is an abundance of academic literature dedicated to innovation (mainly apologetic, by the way), only a few focus on the links between innovation and society or, to be more precise, question the meaning of innovation. Among the latter, there is a lack of consensus on the meaning of innovation. Indeed, there are many different points of view in the literature. For example, for Benoit Godin, innovation is essentially a political concept. Beginning with a history of the concept of innovation, he points out that it should be recalled that the concept was historically constructed and “those who have challenged innovation for centuries – governments – are the same ones who have de-challenged it, making innovation an instrument of economic policy” (Godin 2014). For the supporters of design thinking, innovation generates meaning for the user. Popularized in the early 2000s under the aegis of Tim Brown, design thinking is presented as a “methodology that imbues the full spectrum of innovation activities with a human-centered design ethos” (Brown 2008, p. 86). The meaning of innovation and its perception by users are then considered as the designer’s main challenges1 in order to avoid a dichotomy in meaning between the designer and the user, and to guarantee the success of the innovation. However, a question remains open, wondering if an innovation that makes sense for the user is necessarily advisable at a societal level? For the promoters of responsible innovation, responsibility appears to be the aim of innovation. However, and as several authors have emphasized (Gossart 2018; Pavie 2018), this forgets that innovation considered responsible for its aim can have catastrophic ecological footprints or can be produced under deplorable working

1 In the artificialist tradition, the designer is the emblematic figure of the design and innovation process. The use of the term designer in the singular does not refer to the idea of an omniscient designer, but rather corresponds more to a typical ideal.

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conditions. It also forgets that the concept of responsible innovation is not separate from the issues of the risk society; this, besdies the fact that it is a revival of the figure of the omniscient actor, leads to a lock-in related to the paradigm of possible control over an uncertain future (Genus and Iskandarova 2017). Despite generating knowledge, these different points of view underrate the “political meaning of innovation” question, as the link between innovation issues and the city2. However, we will show that the circumspect view about innovation evoked above is intimately linked to the loss of the political meaning of innovation. We shall see that while this loss of meaning has led to a questioning of innovation, it has not only contributed to rehabilitating innovation but it has also opened the way to a renewed conception of innovation that acknowledges the fact that innovation must meet the inseparable objectives of creating value for the user and society. Because it rehabilitates the question of the political meaning of innovation, we will thus present the outlines of the Penser le Sens de l’Innovation (PSI) (Thinking about the Meaning of Innovation) approach (Chouteau et al. 2020). We will see, along the way, how this approach is situated in relation to the different points of view mentioned above and how the epistemology of innovation can highlight updated innovation practices, issues in sync with major contemporary challenges. 1.2. Conceptions of the meaning of innovation over time The relationship between society and innovation is emblematic of the history of a tumultuous relationship. Innovation was initially perceived negatively because, as Plato suggests, it calls into question the established order and leads “without anyone noticing, youth (…) to despise what is old and esteem what is new (…) it is the greatest evil that can befall any state” (Plato 2013, pp. 2679–2680)3. As Benoit Godin (2014) points out, this conception of innovation would last for centuries. Can we find a better illustration than the definition given in Diderot and d’Alembert’s Encyclopédie where innovation is defined as a disease “These kinds of innovation are always deformities in the political order” (Joncourt 1751, in Huyghe 2013)? From the 16th century, however, innovation has been symbolic of a break with tradition, a break even more understandable because innovation is based on the idea of progress. From the end of the 16th century, Francis Bacon and René Descartes,

2 The concept of the city refers to that of polis, i.e. a community of citizens. 3 Let us specify that Plato does not use the term innovation, which did not exist at the time, but the notion of change that he associates with instability.

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for example, associated the progress of knowledge with that of technology and the progress of technology with the progressive improvement of living conditions for humankind. Indeed, technological progress is considered by René Descartes as the vector for the conception of a new “Garden of Eden” in which misery, illness and even death can be excluded, building on human genius (1966). This vision of Progress, with a capital letter, we might say, culminated in the Age of Enlightenment, a century that could be considered as the moment of the victory of Progress against retrograde obscurantism. A century in which man would no longer endure the course of history but become the subject of history by taking God’s place in the order of creation and participating in the design of the world in which he lives. A century in which faith in the capacity of humankind to act through reason would prevail, to concretize moral and social ideals in the real world, which would lead to the development of Saint-Simonianism in France. Indeed, this postrevolutionary doctrine initiated by Saint Simon saw in the rise of industry “a true project for society, capable of allowing a policy favorable to the public interest and generating true social peace” (Ménissier 2016), leading him to affirm that the golden age of humanity was before us and not behind us. History seems to agree with such a vision of things because, in France a century later, the Belle Epoque consecrated the advent of a period of prosperity sustained by the greatest wave of discoveries and innovations in history, a time when the sense of innovation continued to be seen through the prism of progress oriented by a political project: the increase in the happiness of humanity. However, the 20th century marked a decisive turning point. At the beginning of the century, the belief in Progress collapsed and led, through the advent of the relationship between innovation and economic progress4, to a shift from a concept of innovation for society to that of innovation for business. The entry into the era of the consumer society opened up a process of “massification of the production of innovations” (Forest 2020). This process is not unrelated to market saturation, combined with exacerbated competition, which today condemns many companies to innovating simply to tread water. This observation may seem trivial, but it is not, as it helps us to understand that innovation, whatever its nature, has changed its status over time. In the majority of cases, it is no longer considered as a project at the service of society, but an end in itself, intended to anticipate the offers of potential or existing competitors; i.e. emphasizing the strategic meaning of innovation for the company and underrating the meaning of these innovations and the relationship we have with them. This observation is even more worrying as the shift from the meaning of innovation to that of a meaning of company-oriented innovation was 4 The latter is presumably not alien to Joseph Schumpeter’s definition of innovation as the introduction of a novelty “into the economic system”.

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combined, on the consumer side, with the idea that the increase in the consumption of innovations is connected to that of our well-being5, even though the correlation established between innovation and happiness is an illusion. 1.3. When innovation, like the phoenix, rises from the ashes It is clear that, with the era of the consumer society, innovation has become dissociated from the idea of Progress and thus from a reflection on the meaning of society that these innovations help to create. This dissociation was even easier as the massification of the production and consumption of innovations seemed to make sense and led the designer to abandon their capacity for reflection and political orientation6 in favor of a process of continuous production of innovations. Taking into account the unsustainable nature of the happiness offered by the consumption of innovations, combined with the fact that a consumer society is now more concerned with stimulating the desire to buy than providing individuals with “useful” consumption, has resulted in a gradual questioning of innovation in a context marked with: – ecological urgency. The recent IPCC report has clearly sounded the alarm about the emergency we now find ourselves facing (IPCC 2018); – an abundance of critical writings on the limits of innovations, since the 18th century, new reflections about the limits of progress beyond which we have as much to lose as to gain (Diderot 1829), and critical reflections on the development of technologies that appeared in the second half of the 20th century (Wiener 1959; Ellul 1990). However, the latter should not be read in a negative way because, in reality, its benefit for innovation itself is substantial. Indeed, initially, this questioning brings to light a narrow vision of innovation that would be essentially technological7. However, this is indicative of a misconception of innovation because it is not only technological (Forest 2017). Secondly, it highlights the fact that contemporary disenchantment with innovation is closely linked to the fact that “innovation does not, as in the past, tie itself to a moral and social purpose” (Ménissier 2011, p. 17), or, to put it another way, that the

5 As if our happiness depended solely on the quantity of objects we possess or experiences we have had. 6 Here understood in the sense of the ability to transform the world on purpose. 7 And preferably new in societies marked by the seal of “youthism”!

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question of the relationship between innovation and society is not asked, since the illusion of the meaning of innovation has led to the eminently political question of knowing what innovations we want for what society being underrated. It is precisely this observation that has an “advantageous” effect on innovation. Advantageous because it allows us to understand that the issues are not related to innovation itself but its lack of meaning. We must therefore be wary of throwing the baby out with the bath water too quickly, especially since, as we are forcefully reminded by the global pandemic caused by the SARS-CoV-2 coronavirus (COVID-19), which first appeared on November 17, 2019 in the city of Wuhan (China) and which we are going through at the time of writing, we will not solve the major contemporary challenges without innovating, whether in the way we produce and consume, in our lifestyles, but also in our ways of thinking, which act as restrictive frameworks for innovation. Indeed, the time we are living through is strangely reminiscent of the apocalyptic paintings that often inspired painters, from the Middle Ages to the 19th century. However, we should not forget that if the word “apocalypse” is today associated mainly with the idea of chaos, the term borrowed from the Latin apocalypsis8 actually means “revelation”. But what the current situation reveals to us is humankind’s extraordinary capacity for innovation to combat this unprecedented crisis, which has resulted in the emergence of new forms of solidarity9 (while others have had to reinvent themselves), the deployment of new pedagogical modalities to implement the distance learning continuity project requested by the French government, an unprecedented organization of collaboration between medical services in the city, in hospitals and private clinics to deal with the wave of severe cases, or the creation of new technologies for the rapid detection of COVID-19 in people suspected of carrying the virus, to name but a few examples. Welcomed innovations are not only technological but also social, organizational and process-related. In his speech on March 12, 2020, French President Emmanuel Macron, for example, hailed caregivers as “formidable innovators and mobilizers”, on March 27, 2020, French Prime Minister Édouard Philippe, for his part, praised teachers for their “remarkable work, imagination and inventiveness” in trying to guarantee the pedagogical continuity called for by the Minister of National Education and Youth.

8 Itself borrowed from the ancient Greek apokálupsis. 9 Solidarity with the most vulnerable people, with hospital staff, but also solidarity between regions and nations (as shown, for example, by the Resilience Project, where Cuban doctors travelled to Italy to help out).

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These innovations largely approved because inaction is not an option in the face of the COVID-19 crisis. A dam has to be built, to use the terms of the French government, and this is reflected in the multiplication of calls for innovative projects, such as the €10 million call for projects launched on March 19, 2020 by the French Ministry of the Armed Forces. This call focused on the search for innovative solutions (which could be “directly mobilized” against the virus) related to the automation of tasks for sampling, room and equipment cleaning, mass production capacity for decontamination solutions and detection of the virus in the environment, or following the example of the call for projects from the “Coalition Innovation Santé – Crise Sanitaire” (“Health Innovation Coalition – Health Crisis”)10, aimed at designing innovative solutions (information, home care, patient monitoring, medical care, etc.) to help to relieve congestion in the healthcare system and enable patients with chronic diseases to continue to receive care. This public acclaim for innovation is far from being unique to France. It is global, as evidenced by the 73 calls for projects launched by States and private players from around the world (United States, Great Britain, Germany, Brazil, India, etc.) to find innovative solutions against COVID-19 over the period from March 20 to May 31, 202011. These were innovations whose mobilizing force12 lies precisely in the fact that they are charged with a political meaning, the term political being understood as we have indicated in its primary meaning of “that which concerns the citizen”. For example, the aim is to ensure the health of all, to take care of the most fragile people, to improve relationships between people or to improve the management of health risks related to the fight against the COVID-19 crisis previously mentioned. This is precisely where the second advantageous effect of questioning innovation lies since it opens up a new conception of innovation that acknowledges that innovation must meet the inseparable objectives of creating value for the user and society13, and advocates that innovation in the 21st century should be a political14 innovation. In this way, this questioning, rather than leading to a fearful retreat that sets up a state of paralysis leading, for example, to inaction among engineering students who are extremely concerned about environmental problems because they fear of contributing further to environmental degradation, gives meaning to action. 10 Initiated by France Biotech, France Digitale, MedTech in France and AstraZeneca, with the participation and support of Assistance Publique-Hôpitaux de Paris (AP-HP) and France Assos Santé, with the support of Bpifrance and EIT Health. 11 Data collected on the site covidtenders.org. 12 More than 1,100 projects were submitted by April 4, 2020 in response to the call for projects from the Ministry of the Armed Forces mentioned above. 13 Putting an end to the disconnect that has developed between the economy, on the one hand, and society and its culture, on the other hand. 14 It is understood that the term “political” here does not refer to a particular field but to its purpose.

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1.4. In search of lost meaning If we accept the idea that the question of the political meaning of innovation needs to be rehabilitated, the question then arises of how to reconfigure ourselves intellectually to achieve this; during the 20th century, we lost our ability to give meaning to our actions, too busy developing and implementing “efficient” design methods aimed at rapidly producing a continuous flow of effective innovations, such as methods of functional analysis, innovative design, innovative project management or even eco-design. Indeed, while the latter makes it possible to reduce the environmental impact of products, it leaves the question of the meaning of innovation in the shadows, as we will see. We defend the thesis that innovation in the 21st century implies getting rid of lazy thinking reduced to the application of methods for innovation, which, while they freed the designer from thinking about the political meaning of innovations, prevented them from demanding and creating real innovations. It is in this perspective that we have developed the above-mentioned Penser le Sens de l’Innovation (PSI) approach (Chouteau et al. 2020). The PSI approach emphasizes that the question of meaning must be considered from the dual point of view of its direction and significance. Direction, which, if one dares to draw an analogy with the development of a tree, corresponds to a branch of the tree. It refers to a principle of a possible solution (concept) and recalls a characteristic of the design process, namely that there is no single solution to an identified problem, but a set of solutions that refers to the idea of fields of possibilities. The deployment of a direction depends on the capacity of the imagined concept to make sense for the user. Does any creation of value for the user automatically make sense for our society? Nothing is less certain! Just take the example of Instagram. It is a tool that allows users to take a photo, edit it and then share it online with their network, and whose social dimension has often been highlighted. But what kind of society are we shaping when we design devices that stimulate the cult of the image and the perfect body and make people feel that they do not live lives as rich and exciting as others do? This is why the PSI approach invites the designer to consider the meaning of each direction at the level of the user, on the one hand, and of society, on the other hand, because, while it is now commonly accepted that an innovation is always the translation of compromises made during the design process (compromises between functions to be in sync with specifications, and compromises between stakeholders), we cannot forget that it is meaningful to the society it helps to design.

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Figure 1.1. Innovation meaning tree (according to Forest 2020)

Rehabilitating the question of the political meaning of innovation is therefore what differentiates the PSI approach from that of design thinking. The latter, precisely because it is user-centered, leaves the question of meaning for society in the shadows, whereas the PSI approach forces the designer to think about the meanings for the user AND society, leading the designer to become aware of the solutions they project and to question the meaning of the society they are helping to create. This leads us to Anthony Masure, who asserts that design thinking has removed design from all political thinking (Masure 2015). However, make no mistake about what we are saying: this is not a question of rejecting any interest in design thinking, but more modestly of emphasizing that the PSI approach is more an extension of design thinking than an opposition to it.

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Re-emphasizing the question of the political meaning of innovation also allows us to distinguish the PSI approach from the “Design-driven innovation” that Roberto Verganti promotes. According to him, individuals do not only buy products and services but also meanings. He advocates moving from a focus on the “what” to a focus on the “why”. The objective is to design an innovation that proposes a new reason for the question of why people use a device, which requires interpreters to collaborate (Dell’Era et al. 2018, p. 388). While Roberto Verganti’s approach puts the question of meaning at the forefront, the fact remains that we are still dealing with the user and not with the question of whether any innovation that makes sense for the user is necessarily “good” or “desirable” for society. The controversy over the French StopCovid application, designed by the National Institute for Research in Digital Science and Technology (Inria), and inspired by the TraceTogether system implemented in Singapore, is emblematic of the issue. In an interview given to the newspaper Le Monde on April 8, 2020, Health Minister Olivier Véran revealed that the government considered the development of the application, with a view to limiting the spread of the virus by identifying chains of transmission and people who have been in contact with a patient who has tested positive. This project generated, from the very next day, a significant amount of opposition. If this application can make sense: – for the user, promising to increase their safety and security, – for many epidemiology and health specialists, who consider the application an indispensable tool to avoid a second health crisis15, – it can question the society that we contribute to design using it. Indeed, beyond the ongoing debate on the effectiveness of this application (according to expert estimates, at least 60% of French people would have to download it for its relevance. Bluetooth technology can produce false positives, and, in an open letter on April 19, 2020, 300 international researchers asked states not to abuse digital tracking technologies, pointing out the security flaws in applications such as StopCovid, etc.), we cannot hide the fact that it raises legitimate questions in terms of respect for individual liberties and privacy protection, and that we must be wary, as indicated by the Secretary of State to the Prime Minister Marlène Schiappa, of letting our anxiety in the face of the crisis lead us to endorse a clear retreat from our rights. These are questions that the PSI approach invites us to ask ourselves, beyond the search for a “why” directed to the user.

15 See https://www.lemonde.fr/idees/article/2020/04/25/tracage-numerique-pour-eviter-uneseconde-crise-sanitaire-il-faut-s-en-donner-les-moyens_6037732_3232.html.

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1.5. The PSI approach: a philosophy of, and for, action The PSI approach is a philosophical approach to action based on “political heuristics”. It is not intended to be an overarching and moralizing discourse that would consist of making an ex post value judgment on this or that innovation. More modestly, it aims to reconnect, through the innovation project, with debates relating to the society we conceive and within which we evolve. In this way, innovation is not, in the words of Benoit Godin, just a political concept, or, to put it another way, the servant of politics, it is also a political project. Nor does the PSI approach aim to integrate fear as part of the innovation process. Indeed, we live in an era marked by an ambient catastrophism of which collapsology is the archetype. The fatalistic conception of collapsology breaks, however, with the words of Jean-Pierre Dupuy who, in his book Pour un catastrophisme éclairé, proposed that the image of a future sufficiently catastrophic was repulsive and sufficiently credible to trigger the actions that would prevent its realization (Dupuy 2002) because this “heuristic of fear” (Jonas 1990) acts as a revealing indicator, in sync with the photographic sense of the term “developer”, of what has incomparable value for us16 and, contrary to the precautionary principle, avoids sticking to a probabilistic management of risks where it would be necessary to anticipate the catastrophe (Dupuy 2002). Political heuristics aspires, through reflection on action, to bring the designer closer to an action that makes sense for society and questions the values we wish to defend. The political heuristic underlying the PSI approach thus appears to be a positive heuristic because it is not a question, contrary to Hans Jonas’ heuristic of fear, of identifying the undesirable in order to lead to prudence and responsibility17, but of exploring the meanings of the different possible directions, which allows for innovation in consciousness. In doing so, the PSI approach is also a philosophy for action. Indeed, the PSI approach claims a practical sense that invites us to think about the meaning of what we conceive during the very process of innovation and has an active scope turned towards the future. The horizon targeted is the same as that of Hans Jonas’ heuristic of fear, for which we need to emancipate ourselves from the concept of responsibility conceived ex post with regard to effective action (of what has been done) in favor of a conception of responsibility that proceeds from the future (from the power to do). However, while the scope of the heuristic of fear is turned towards the question of preserving our humanity (“Act in such a way that the effects of your action are compatible with the permanence of an authentically human 16 In the view of the author, it is in the anticipation of the threat, in the apprehension of the loss, that we discover the value of what we are going to lose. 17 Responsible innovation is, in fact, based on a negative heuristic building on the paradigm of the mastery of technology and, through it, of people.

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life on earth” (Jonas 1990. p. 30), “never must the existence or the essence of man as a whole be put at stake in the gamble of acting” (Jonas 1990, p. 62). The PSI approach invites us to question in each innovation the values we wish to defend, whether they are directly linked to our survival or not18. This means, for example, questioning ourselves on the tension between the right to privacy and the right to security when designing a video surveillance system or questioning ourselves on the contemporary trend of rewards. Some stores, for example, give discount coupons to customers who bring back their plastic bottles. If this seems effective at first in inciting virtuous practices, what kind of society are we building by moving in this direction? Isn’t the implementation of such a system in opposition to altruism, which seems to be a key element of living together? It is clear that the PSI approach is not an apology for innovation geared towards economic growth19, but an approach that invites us to think of a society in which innovation cannot be seen as a producer of gadget innovations linked to an unbridled and unreasonable consumer society, barely created and already outdated, while basic needs remain poorly met. What about inequalities in access to water – one in three people in the world do not have access to safe water and 40% of the world’s population (i.e. 3 billion people) do not have facilities for washing their hands with soap and water at home – at a time when the WHO is reminding us that handwashing is an essential preventive measure in the fight against the COVID-19 pandemic we are experiencing? What about the obligation to stay indoors when some people, including in developed countries such as France, do not have access to housing? Or what about the inequality of access to healthcare, which is reflected in the over-representation of black Americans among COVID patients in the United States? The PSI approach is also the way to question the priorities we define. Let us take the water crisis as an illustration. A lack of water is no longer the prerogative of 18 Xavier Pavie also wonders about the meaning of this “sickly anthropocentrism” because, in his view, the fundamental question is that of the amputation of all forms of life owing to the impact of anthropic activities (2018). 19 If the primary objective is not economic growth, this does not mean that there cannot be economic growth. These new registers of value can, accompanied by adequate communication, lead to economic value creation. Moreover, does the state not have a key role to play in encouraging companies to move in this direction (via the mechanism of public procurement, subsidies, etc.) rather than providing purely technocratic responses to major contemporary challenges? This is precisely the question that can be asked concerning the development of nanotechnologies or artificial intelligence, which are often thought of solely in relation to their technological performance (more speed, more efficiency, more productivity, etc.) and not the social upheavals that they could generate or the symbols or values that they carry (performance society, artificial empathy), in short, their political meaning.

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poor countries and now affects rich countries. In this context, is it not anecdotal to plan to create a device that allows the first two liters of water that arrive cold in the shower and are, most of the time, wasted to be recouped, when we know that, in London, losses and leaks caused by a dilapidated distribution network are estimated to be the equivalent of 300 Olympic swimming pools per day, or that 20% of water is wasted before it even reaches homes (Carrington 2017)? Let us also take the example of masks. Their use has become widespread as part of the fight against the COVID-19 pandemic. However, this is not a good thing; because of their single use, they have a negative impact on the environment, whether we consider their footprint from the point of view of the consumption of materials they represent or the pollution they cause, as deplored by activists from the NGO Oceans Asia in Hong Kong. If the disposable mask materializes the choice of a device that prioritizes access to health for all over environmental preservation, other directions are possible, such as Le Mask français (the French Mask), which has a reusable part that reduces waste but which may have a slightly higher cost. We can see through this example how the PSI approach leads to a reflection on the values that we wish to have, a reflection that allows us to innovate consciously. We can finally make the hypothesis that the PSI approach is a vector for asking ourselves about the problems we are dealing with. We join Navi Radjou for whom “brainstorming to solve problems that do not exist is over. We need to go to the field to solve the real problems around energy, health and education” (Radjou 2020). In the same way, the PSI approach seems capable of leading us to question the current trend that tends to establish a hierarchy between social issues, as if the ecological question were more important and deserved priority treatment over the issues of equity, freedom, the aging population or the inclusion of people with disabilities. Finally, the PSI approach is a way to avoid false good ideas, such as the creation of reusable straws. These straws were born of the realization that every day 1 billion non-recyclable straws are thrown away worldwide, including nearly 9 million in France in fast food restaurants alone. But shouldn’t a real sensitivity to environmental issues invite us to stop drinking from straws in order to stop wasting straws rather than creating recyclable straws? Similarly, the management of the plastic waste that invades our soil, our rivers and our oceans is currently viewed through the prism of recycling, presented as the miracle solution. But as Nathalie Gontard points out, “this mirage overshadows the only real solution: reducing plastic production” (2018). Finally, it should be pointed out that while the PSI approach invites debate on the world we are shaping, this debate is not the prerogative of politicians and scientists, a sort of “republic of experts”, who would be the only ones with the intellectual bases required for debate. Because it takes place during the design process itself, the question of meaning engages the designer and all stakeholders in the design process and cannot be restricted to so-called “responsible” or “social” innovations (Chouteau et al. 2020).

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PSI approach

Criteria

Jonas concept

Innovating with awareness

Basis for action

Principle of responsibility

Political heuristics

Underlying heuristics

Heuristics of fear

Thinking about the meaning of what we create and, through it, our humanity.

Function of heuristics

Ensuring the survival of humanity

During the design process

Questioning time

Ex post observation: the fact that a technique is potentially dangerous must lead to its suspension because the irreversible nature of the consequences “forbids rolling the dice”.

Strength of proposals

Nature of the prescriptions

Force of restrictions or even prohibitions

Technical democracy (designers, users, institutions, etc.)

Key player

An elitism in favor of committees of wise men (a benevolent dictatorship)

Table 1.1. Putting the PSI and Jonas approaches into perspective (according to Forest 2020)

It is clear from the above that the PSI approach implies thinking about innovation beyond the mere question of the potential value for the user by integrating, from the outset, the relationship that the innovation in question has with our society. This approach implies the use of critical thinking, i.e. it invites us to develop a state of mind and practices that allow us to emancipate ourselves from the register of the promises of innovation in the making and to think about the meaning of each projected direction20. It is on this condition that it is possible for us to question ourselves collectively about the choices we make and the directions we favor and thus to innovate consciously. We know, for example, that the construction of a tramway line leads to higher land prices, which can result in socio-spatial segregation. In the Sustainable City Factory project, the PSI approach thus rehabilitates the eminently political question of what we decide to do (building eco-neighborhoods or reducing socio-spatial segregation?) and the place of the human and social sciences in thinking about tomorrow’s innovation.

20 It is not a question of criticizing but of questioning.

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1.6. By way of conclusion Today, our society is faced with unprecedented challenges (access to water, education, health, waste and pollution management, etc.) in a context marked by ecological urgency. However, if the situation seems desperate, it is not necessary to despair of everything. In the above, and after having indicated that the questioning of innovation is in fact not a criticism of innovation but a criticism of its loss of meaning, we have presented the PSI approach, which places the question of the political meaning of innovation in the very process of innovation. The latter supposes that we extend our thinking beyond the point where it takes us today, i.e. beyond the sense of innovation for the user. To mobilize the PSI approach is indeed: – to use a reflexive approach that involves our critical faculties and helps the designer to work within common sense, i.e. in a sense that is accepted by and acceptable to all because it meets the values of our society, values that should not be forgotten and are culturally and historically located; – to work against the dictatorship of “ever more” because our society does not always demand more innovation; it wants the best. Some will no doubt see in the PSI approach a heroic conception of the designer. This is a conception that we willingly accept on the condition that we appreciate this heroism not in the designer’s capacity to transform the world but in their capacity to become philosophers in action and to reintroduce a certain form of wisdom. 1.7. References Brown, T. (2008). Design thinking. Harvard Business Review, 84–92. Brundtland, G.H. (1987). Notre avenir à tous. Rapport de la commission mondiale pour l’environnement et le développement, Fleuve, Montreal. Carrington, D. (2017). Water companies losing vast amounts through leakage. As drought fears rise. The Guardian, 11 May 2017 [Online]. Available at: https://www.theguardian.com/ environment/2017/may/11/water-companies-losing-vast-amounts-through-leakage-raisingdrought-fears. Chouteau, M., Forest, J., Nguyen, C. (2020). Innovations for Society: The P.S.I. Approach. ISTE Ltd and John Wiley & Sons.

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Dell’Era, C., Altuna, N., Verganti, R. (2018). Designing radical innovations of meanings for society: Envisioning new scenarios for smart mobility. Creativity and Innovation Management, 387–400. Descartes (1966). Discours de la méthode, 6th part. Gallimard. Diderot, D. (1829). Correspondance inédite de Grimm et de Diderot, et recueil de lettres, poésies, morceaux et fragments retranchés par la censure impériale en 1812 et 1813. Editions Furne. Dupuy, J.-P. (2002). Pour un catastrophisme éclairé. Quand l’impossible est certain. Editions du Seuil. Ellul, J. (1990). La technique ou l’enjeu du siècle. Economica. Forest, J. (2017). Creative Rationality and Innovation. ISTE Ltd and John Wiley & Sons. Forest, J. (2020). Thinking about the meaning of innovation. In Innovations for Society: The P.S.I. Approach, Chouteau, M., Forest, J., Nguyen, C. (eds). ISTE Ltd and John Wiley & Sons. Genus, A. and Iskandarova, M. (2018). Responsible innovation: Its institutionalisation and a critique. Technological Forecasting and Social Change, 128, 1–9. Godin, B. (2014). Une histoire intellectuelle de l’innovation. De l’interdit politique à la politique publique. In Principes d’économie de l’innovation, Boutillier, S., Gallaud, D., Forest, J., Laperche, B., Tanguy, C., Temri, L. (eds). Peter Lang. Gontard, N. (2018). Déchets plastiques : lee recyclage n’est pas la solution. The Conversation [Online]. Available at: https://theconversation.com/dechets-plastiques-la-dangereuse-illusiondu-tout-recyclage-90359. Gossart, C. (2018). Innovation: To be or not to be responsible? IMT Science and Technology News [Online]. Available at: https://blogrecherche.wp.imt.fr/en/2018/10/19/responsibleinnovation/. Huyghe, P.-D. (2013). Cycle de conférences sur l’Innovation de l’ENSCI, Tuesday 8 October. ENSCI [Online]. Available at: http://www.ensci.com/actualites/une-actualite/news/detail/ News/18248/. IPCC (2018). Global warming of 1.5°C. Rapport du GIEC [Online]. Available at: https:// www.ipcc.ch/sr15/. Jonas, H. (1990). Le principe responsabilité. Editions Le Cerf. Masure, A. (2015). L’injonction à la créativité dans le design. Des logiciels de création innovants aux programmes inventifs. In Proceedings de l’injonction à la créativité à sa mise en œuvre : quel parallèle entre monde de l’art et monde productif, Kogan, A.-F. and Andonova, Y. (eds). MSH Ange-Guépin. Ménissier, T. (2011). Philosophie et innovation, ou philosophie de l’innovation? Klesis – revue philosophique, 18, Varia.

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Ménissier, T. (2016). Innovation et histoire. Une critique philosophique. Quaderni, 91 [Online]. Available at: http://journals.openedition.org/quaderni/1009. Pavie, X. (2018). L’innovation à l’épreuve de la philosophie. PUF. Plato (2013). Œuvres complètes. Arvensa Editions, ePub, December. Wiener, N. (1959). Man and the machine (Interview with Norbert Wiener). Challenge: The Magazine of Economic Affairs, 7(37).

2 Engineering – Innovation Engineering: A Holistic and Operational Approach to the Innovation Process

2.1. Introduction The strategic and competitive environment in which organizations must operate has been significantly affected in recent years by globalization, technological and digital revolutions as well as the changing needs and expectations of users and consumers/customers. Today, more than ever, it is urgent to make organizations capable of dealing with change in an innovative and truly agile way. How can more or new value be created? Whether we are talking about products, processes or services, innovation has the mission of bringing something new and original to existing or new markets. Any innovation process is based on the concept of a “real good idea”, one that will add value by going off the beaten track and that will be accepted by the target user/consumer. It happens neither by chance nor due to the sole fact that scientists have such a mission in the R&D department. It can be done by anyone. It is true that some innovations that have received a lot of attention from the media have arrived by accident, or by chance (e.g. the post-it), although these remain marginal. Today, most innovations are the result of a continuous search for opportunities within the firm’s environment and are the result of a systematic approach and a set of planned activities.

Chapter written by Laure MOREL and Mauricio CAMARGO. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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Managing the innovation process therefore requires skills that need to be developed through a specific methodological toolset which will allow us to be aware of global circumstances in order to act locally and which, above all, will make it possible to measure the impacts of decisions made about the ecosystem in which the innovation will/must find its place. In this sense, the objectives of the implemented approach in innovation engineering are: to encourage the emergence of concepts from end-users (bottom-up) by relying on agile-inspired approaches and by using rapid prototyping from the upstream phases (front end) of the innovation process; to develop co-creation and co-design by promoting collaborative and open innovation within a group of extended actors (customers, users, suppliers, partners, etc.); and to have the end-users evaluate the prototypes produced. We therefore assume that it is a process, i.e. a set of interdependent activities that use inputs to produce a result that adds value to the field under study. It is therefore possible to measure the state of each activity and thus to propose ways of improvement. In this sense, innovation engineering cannot be a linear process, but a succession of steps (“gates” in the sense of Cooper (1990)), which can be arranged according to the context of the innovation and reviewed along the way. In this contribution, we first propose tracing the origins of the structuring of this field of research in France, showing the point of view of engineering that deals with the theme of innovation and the major currents of thought that have enabled innovation engineering to be structured as an approach to support the improvement of innovation processes. We have chosen to focus on France because, as actors in this ecosystem, we think we know it well enough to provide a complete, if not exhaustive, view of this field of research. We will nevertheless draw parallels with international authors who, because of their popularity, have had a perceptible impact on the evolution of research, as well as with research communities that have been created and that were at the origin of the first international conferences on innovation, science and technology management. Then, we will present the key concepts and major biases of the transformation of an idea into a useful innovation for society. We also provide some advice to make the engineering for innovation approach accessible to any researcher, trainer or industrialist who wishes to get out of their routine and acquire new practices to carry out their innovation projects. Beyond a method, this is really a new way of acting (mindset) that allows an organization and its individuals to become more efficient. It is not a matter of reproducing a formula learned by heart, but rather of understanding the main principles in order to develop the process best suited to the context and singularities of each situation.

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2.2. Innovation engineering: a field of research that has struggled to structure itself in France Since when have engineers been interested in innovation as a field of research in its own right? Is it possible to find the historical landmarks that have marked the structuring of this field of research and especially the contributing feeder disciplines? Many authors agree that the emergence of this field of research has its roots in two streams of research: industrial engineering and industrial systems engineering, disciplines that were born at the beginning of the century in the United States1, 2. The first definition, established in 1955 and revised in 1985 by the Institute of Industrial Engineers, states that “industrial engineering” is concerned with the design, improvement and implementation of integrated systems of human resources, materials, equipment and energy. It uses knowledge and know-how in mathematics, physics and social sciences, as well as the principles and methods of analysis and design relevant to the art of engineering, in order to predict and evaluate the results that can be expected from such systems. Although the term “design” is used twice in this definition, practice shows that this definition is very production-oriented. As a result, in the United States, we have seen the emergence of “engineering management” from management schools, which focuses more on the technological lifecycle (Cleland et al. 1981). In France, the concept of industrial engineering (IE) arrived in 1975 to face economic pressures and to solve problems of optimizing the organization of production systems in terms of price, quantity, quality and flexibility. More generally, it deals with the topics of production management, product/process design and project management and thus marks a first step in responding to the problem of optimizing industrial processes (design process, manufacturing, inventory management, quality, planning, marketing, etc.). It is an interdisciplinary application of the models and tools of the feeder disciplines to the problems of industrial organization (e.g. the contributions of statistics to quality control, or those of mathematics to production scheduling). In the 1980s, increasing globalized competitive pressure led to the search for decisive competitive advantages, in particular by working on the integrated design 1 Annual meeting of the American Society of Mechanical Engineers, New York, December 6, 1912. 2 The following data are borrowed from an article published in the Revue futurible by Guidat et al. (1998).

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of products, processes, production tools and marketing strategies to reduce lead times and become increasingly responsive to the environment. This is how industrial systems engineering (ISE) was born, integrating the dimensions of “production sciences”, “design sciences” and “project management”, thus combining the approaches of industrial engineering and engineering management. More generally, the idea is to consider a more global vision and to work on enlarged study objects (e.g. moving from flow optimization to optimization of the organization of the industrial system or from product design to activity design). This has led to a change in the level of approach to problems both in their spatio-temporal and cultural dimensions. In addition, the obsolescence or decline of entire areas of industrial and service activity (metallurgy, coal mining, traditional trade) associated with the emergence of new technologies, the evolution of consumption and the internationalization of markets has led to the increasingly acute and rapid need for the creation of new activities. In France, these two schools of thought coexist, and although they originally shared a technological vision of innovation, they separated from it in the mid-1980s, thus marking a differentiating aspect in both disciplines and the first signs of what would later become a common line of research: innovation engineering in an ecosystems perspective. We will come back to this later. In this chapter, we have attempted the perilous exercise of retracing the evolution, both temporal and conceptual, of the vision of the notion of innovation from the moment that IE and the ISE became interested in it. We have also highlighted the contributions of researchers from engineering schools to the structuring of this field of research. Finally, we have chosen to take into consideration certain works carried out by engineers who have developed a dual competence (economics, management, sociology, etc.) that we consider to be important in creating the foundations of research in innovation because they have inspired the work of previous researchers. Finally, we have tried to retrace some international works and international associations that have had an influence on French research. As this list cannot be exhaustive, we apologize in advance for this incompleteness. Our research process was structured as follows: – identification of personalities from the engineering world who supported the introduction of IE and ISE to France; – search in Google Scholar for traces of founding documents linking engineering and innovation for the period 1975–2000; – search for inspirational international authors for the same period of time;

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– search for international associations of scientific and practice communities that were created during the period under review; – our results are referenced in two tables: the first highlights, in chronological order, the inspirational authors and engineers and the themes of their work (Table 2.1); the second highlights, in chronological order, the authors making progress in the engineering sciences and who are recognized as contributors to the development of research in innovation, whether or not resulting from the development of IE and ISE in France (Table 2.2). Themes

Year

Inspirational authors’ seminar work

Information systems 1973

Le Moigne, J.-L. (1973). Les systèmes d’information dans les organisations. Presses universitaires de France, Paris.

Decision systems

1974

Le Moigne, J.-L. (1974). Les systèmes de décision dans les organisations. Presses universitaires de France, Paris.

Prospective/ systemic-holistic

1975

de Rosnay, J. (1975). Le macroscope : vers une vision globale. Éditions du Seuil, Paris.

Systemic-holistic

1979

Mélèse, J. (1979). Approches systémiques des organisations : vers l’entreprise à complexité humaine. Edition Hommes et Techniques, Paris.

Technological excellence

1985

Morin, J. (1985). L’excellence technologique, 1st edition. Editions Jean Picollec, Paris. Morin, J. (1988). L’excellence technologique, 2nd edition. Editions Jean Picollec, Paris.

Management of technological resources

1988

Morin, J., Seurat, R., Marbach, C. (1989). Le management des ressources technologiques. Editions d’Organisation, Paris.

Systemic-holistic

1990

Le Moigne, J.-L. (1990a). La modélisation des systèmes complexes. Dunod, Paris.

Systemic-holistic

1994

Le Moigne, J.-L. (1990b). La théorie du système général : théorie de la modélisation. Presses universitaires de France.

Table 2.1. A non-exhaustive list of inspirational books written by trained engineers

As we have already pointed out, the 1970s clearly appear to be the beginning of a new way of thinking, designing and acting on technological systems. It was during this period that two key notions appeared, which were prospective and systemic through founding works: the macroscope (de Rosnay 1975), information and decision-making systems (Le Moigne 1973, 1974) and the systemic approach to organizations (Mélèse 1979), which was not widely disseminated at the time. It was not until the mid-1980s that this holistic vision appeared in the understanding of industrial systems and organizations, namely, the management of technological resources (Morin 1985; Morin et al. 1989) and the modeling of complex systems

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(Le Moigne 1990a, 1990b). These works were a necessary condition for the emergence of innovation engineering because they inspired the academic community, which was already aware of the importance of taking a new look at engineering research, in particular by introducing the notion of innovation (Table 2.2). Finally, it is interesting to note that the emergence of a field of research on innovation was not an isolated phenomenon in France, since we can see the emergence, this time concomitantly, in the United States, England and Japan, in particular, of a discipline with an “integrated” approach to “value development” under very different qualifiers, as shown in Table 2.2. Themes

Year

Inspirational authors

Competitive advantage

1985

Porter, M.E. (1985). Competitive advantage: Creating and sustaining superior performance. Competitive Advantage, 167, 167–206.

Innovation and lead users

1988

von Hippel, E. (1988). Sources of Innovation. Oxford University Press, New York.

Innovation and learning 1990 organizations

Senge, P. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization. Random House, London.

Technologica 1994 l change

Freeman, C. (1994). The economics of technical change. Cambridge Journal of Economics, 18(5), 463–514.

Critical skills 1994

Hamel, G. and Prahalad, C.K. (1996). Competing for the Future. Harvard Business School Press, Boston, MA

Critical capabilities and technology strategy

1995

Leonard, D. (1995). Wellsprings of Knowledge. Harvard Business School Press, Boston, MA.

Innovation and the learning organization

1995

Nonaka, I. and Takeuchi, H. (1995). The Knowledge-creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, Oxford.

Disruptive innovation

1995

Bower, J.L. and Christensen, C.M. (1995). Disruptive technologies: Catching the wave. Harvard Business Review, January–February, 43–53.

Innovation audit

1996

Chiesa, V., Coughlan, P., Voss, C.A. (1996). Development of a technical innovation audit. Journal of Product Innovation Management: An International Publication of the Product Development & Management Association, 13(2), 105–136.

Learning organization

1996

Argyris, C. and Schön, D.A. (1996). Organizational Learning II: Theory, Methods and Practice. Addison-Wesley, Boston, MA.

Engineering

Themes

Year

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Inspirational authors

Disruptive innovation

1997

Christensen, C. (1997). The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail, 1st edition. Harvard Business Review Press, Cambridge, MA.

Industrial innovation

1997

Freeman, C. and Soete, L. (1997). The Economics of Industrial Innovation, 3rd edition. Routledge, Abingdon.

Technological innovation and change

1999

Mowery, D.C. and Rosenberg, N. (1999). Paths of Innovation: Technological Change in 20th-Century America. Cambridge University Press, Cambrdige.

Technology management

2000

Khalil, T.M. and Shankar, R. (2000). Management of Technology: The Key to Competitiveness and Wealth Creation, 1st edition. McGraw-Hill Science, Boston, MA.

Open innovation

2003

Chesbrough, H.W. (2003). Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business Press, Boston, MA.

Innovation

2003

Shavinina, L.V. (ed.) (2003). The International Handbook of Innovation. Elsevier, Oxford.

2020 vision engineer

2004

Clough, G.W. (2004). The Engineer of 2020: Visions of Engineering in the New Century. The National Academies Press, Washington, DC.

Innovation

2005

Fagerberg, J., Mowery, D.C., Nelson, R.R. (2005). The Oxford Handbook of Innovation. Oxford University Press, Oxford.

Innovation strategy

2005

Mauborgne, R. and Chan, W.K. (2005). Blue Ocean Strategy. Harvard Business Review Press, Boston, MA.

Table 2.2. A non-exhaustive list of significant works in the international community

Origin (US)

Industrial engineering/ engineering management

Disciplines (FR)

Mechanical engineering

Mayor

Concepts

Year

Reference

ISE

Innovative design

1988

Duchamp, R. (1988). La conception de produits nouveaux. Hermes Lavoisier, Hoboken, NJ.

IE

Integrated design

1994

Tichkiewitch, S. (1994). De la CFAO à la conception intégrée. Revue internationale de CFAO et d’infographie, 9(5), 609–621.

IE

Intermediary design objects (IDO)

1995

Mer, S., Jeantet, A., Tichkiewitch, S. (1995). Les objets intermédiaires de la conception : modélisation et communication. Le communicationnel pour concevoir, 21–41.

IE

Technology transfer

1995

Bergeron, J. and Bocquet, J.C. (1995). Introducing new technologies in organisations – Business model perspective. Benchmarking – Theory and Practice. Springer, Boston, MA.

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Origin (US)

Industrial engineering/ engineering management

Disciplines (FR)

Mayor

Concepts

Year

Reference

IE

TRIZ – the theory of inventive problem solving (TIPS)

1997

Cavallucci, D. and Lutz, P. (1997). TRIZ : une nouvelle theorie d’aide à l’innovation industrielle. Revue française de gestion industrielle, 15–28.

ISE

Project management of innovation

2001

Longueville, B., Le Cardinal, J., Bocquet, J.C. (2001). La gestion des connaissances pour les projets de conception de produits innovants. Septième colloque sur la conception mécanique intégrée, PRIMECA.

ISE

Systemic vision

1979

Le Goff, P. (1979). La valeur de l’énergie a-t-elle une base économique, écologique ou technique ? Critère d’optimisation en énergétique industrielle. Revue d’économie industrielle, 8(1), 68–98.

ISE

Technological innovation engineering

1983

Castagne, M., Guidat, C., Voinson, P. (1983). Proposition d’une méthodologie de sélection des procédés valorisant la biomasse lignocellulosique. Revue d’économie industrielle, 26(1), 14–23.

ISE

Technological innovation engineering

1984

Guidat, C. (1984). Contribution méthodologique à la formalisation d’un nouveau métier : l’ingénierie de l’innovation technologique à partir de l’expérience d’une innovation technique dans la filière bois : AGRESTA (Procédé de transformation physico-chimique du bois en isolant pour la construction), HDR, Institut National Polytechnique de Lorraine, Nancy.

ISE

Innovative organizations

1987

Castagne, M. (1987). Le génie des systèmes industriels : une discipline nouvelle. European Journal of Engineering Education, 12(3), 271–276.

Process engineering ISE

Foresight

1987

Boly, V. (1987). Elaboration de scénarios à 10 ans par les méthodes micmac et smic, application à une initiative de développement local. PhD thesis, Institut National Polytechnique de Lorraine, Nancy.

IE

Manufacturing industrial systems

1988

Gousty, Y. and Kieffer, J.P. (1988). Une nouvelle typologie pour les systèmes industriels de production. Revue française de gestion, 104–112.

ISE

Technology watch

1995

Baldit, P., Quoniam, L., Ruiz, J.M., Dou, H. (1995). La gestion de projet et la veille technologique : vers une méthodologie commune. Direction et gestion des entreprises, (155–156), 61–68.

ISE

Risk and project management

1997

Karsenty, P., Zelfani, M., Angot, P., Lacoste, G. (1997). Intégration et évaluation des risques en gestion de projet dans les industries pilotées par la recherche. Congrès international de génie industriel, Albi, France.

ISE

Innovation engineering

1998

Morel, L. (1998). Proposition d’une ingénierie intégrée de l’innovation vue comme un processus permanent de création de valeur. PhD thesis, Institut National Polytechnique de Lorraine, Nancy.

Engineering

Origin (US)

Disciplines (FR)

Mayor

Year

Reference

2000

Boly, V. (2000). Processus d’innovation : contribution à la modélisation et approches méthodologiques. HDR, Institut National Polytechnique de Lorraine, Nancy.

2001

Richir, S., Taravel, B., Samier, H. (2001). Information networks and technological innovation for industrial products. International Journal of Technology Management, 21(3–4), 420–427.

2002

Cordova-Lopez, E., Lacoste G., Le Lann, J.-M. (2002). Use of Altshuller’s matrix for solving slag problems related to steering knuckle (Part I of II) [Online]. Available at: https://triz-journal.com/use-altshullersmatrix-solving-slag-problems-relatedsteering-knuckle-part-ii/.

1995

Vernadat, F. (1995). Modélisation systémique en entreprise : métamodélisation. La modélisation systémique en entreprise, Braesch, C., Haurat, A. (eds). Hermes, Stanmore.

2001

Tomala, F., Senechal, O., Tahon, C. (2001). Modèle de processus d’innovation. MOSIM01’ : actes de la troisième conférence francophone de modélisation et simulation : conception, analyse et gestion des systèmes industriels. Ghent.

Innovation and organization

1985

Agrell, P., Hatchuel, A., van Gigch, J.P. (1985). Innovation as Organizational Intervention. California State University Sacramento, School of Business and Public Administration, Sacramento, CA.

Innovation process

1987

Hatchuel, A., Agrell, P., van Gigch, J.P. (1987). Innovation as system intervention. Systems Research, 4(1), 5–11.

Technological system

1989

Aït-El-Hadj, S. (1989). L’entreprise face à la mutation technologique. Les Editions d’Organisation, Paris.

Management of technological resources

1993

Durand, T. (1993). The dynamics of cognitive technological maps. Implementing Strategic Processes, 165–189.

Management of technology

1998

Durand, T. (1988). Management pour la technologie : de la théorie à la pratique. Revue française de gestion, (71), 5–14.

2001

Hatchuel, A., Le Masson, P., Weil, B. (2001). De la R&D à la RID : de nouveaux principes de management du processus d’innovation. Congrès francophone du management de projet, AFITEP : “Innovation, conception… et projets”, Paris.

2001

Hatchuel, A. (2001). Towards design theory and expandable rationality: The unfinished program of Herbert Simon. Journal of Management and Governance, 5(3/4), 260–273.

ISE

Innovation process

ISE

Data exchange for technological innovation

IE

TRIZ – the theory of inventive problem solving (TIPS)

IE

Enterprise modeling

IE

Innovation process modeling

Automatics/ system engineering

Industrial engineering/ engineering management

Management sciences

Concepts

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Innovation and management of R&D

C-K (concept-knowledge) theory

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Origin (US)

Disciplines (FR)

Mayor

Concepts

Reference

2002

Aït-El-Hadj, S. (2002). Systèmes technologiques et innovation : itinéraire théorique. Editions L’Harmattan, Paris.

Innovation and stakeholders

1988

Akrich, M., Callon, M., Latour, B. (1988). A quoi tient le succès des innovations ? 1 : L’art de l’intéressement ; 2 : Le choix des porte-parole. Gérer et comprendre. Annales des Mines, 4–17 and 14–29.

Technological innovation

1987

Akrich, M. (1987). Comment les innovations réussissent ? Recherche et technologie, 26–34.

Technological innovation

1994

Callon, M. (1994). L’innovation technologique et ses mythes. Gérer et comprendre, 34, 5–17.

Technological system

Sociology

Year

Table 2.3. Emergence of innovation engineering research within the engineering community in France (source: our research)

In France, in particular, Table 2.3 shows that the engineering community interested in innovation has drawn on various disciplines, such as mechanical engineering, production engineering, process engineering, management sciences and sociology. In any case, the work clearly shows that it was product design that originally concentrated research efforts in the mechanical engineering community under the impetus of Gousty and Kieffer (1988), gradually associating with it the notion of innovation under the “design of new products” (Duchamp 1988). Conversely, at the same time, the industrial systems engineering community from process engineering advocated a “systems” vision (Castagne 1987). The notion of technological innovation engineering (Castagne et al. 1983; Guidat 1984) and foresight to generate innovation scenarios (Boly 1987) even explicitly appeared (Castagne et al. 1983; Guidat 1984). This raises the differences in the way of conceiving what prefigures a field of research in innovation: for the former, design is associated with the creation of a product, whereas for the latter, it is a question of designing the processes/processes (the set of unit operations and the process leading to them) to manufacture this product. “It is obvious that it is necessary to abandon, for example, the belief in ‘harvesting’ technological innovation to move on to the concept of ‘cultivated’ innovation” (Castagne 1987). We believe that the precursory genius of Pierre Le Goff, Professor of Process Engineering in Nancy, in the holistic understanding of the world, is not insignificant. Indeed, as early as 1979, he published an article that was a forerunner of what has become a systemic vision of energy recommending the association of ecological, economic and technical points of view (Le Goff 1979).

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In any case, research conducted on innovation is eminently confronted with what (Lemoigne 1984) qualifies as “the paradoxes of the engineer”3: the difficulty of conceiving a complexity arising from realities held to be inconceivable by our reason (paradox of conceiving complexity and complexity of design (action of designing and its result)). It is also important to underline the publishing activity of colleagues, engineers by training, who have acquired a double competence through a doctorate in management sciences and sociology and whose contribution to the development of industrial systems engineering and innovation is undeniable. This is a question of citing the work carried out by A. Hatchuel (Agrell et al. 1985; Hatchuel et al. 1987) which presents innovation as a system of intervention and the work (Aït-El-Hadj 1989) that deals with the notion of innovation and technological systems. Concerning the sociology of innovation, we could not follow the detour in the work carried out by Akrich (1987) and Akrich et al. (1988) in the mid-1980s. In line with these precursors, bearers of a new vision of design and innovation, a series of works have been produced which show an evolution in the concepts mobilized. This evolution is also the result of the introduction in France of a new research theme, once again coming from the United States: technology management. As we presented at the beginning of this section, the United States very early on developed “industrial engineering” in engineering faculties and “engineering management” in business schools. At the end of the 1980s, the latter developed a research axis entitled “Management of Technology” (MoT) (Khalil and Bayraktar 1988), which corresponds to both innovation engineering and innovation management (French version). MoT covers areas of investigation such as industrial strategy, technology transfer, product and technology lifecycles, management of research and development projects, technological innovation processes, risk analysis, cooperation strategies, quality as a development tool and management of technological resources. It is important to note that international associations were formed over the same period, carrying this new vision of the innovation process and allowing a wider dissemination of the work through the organization of international conferences. The list includes, in particular: ISPIM (The International Society for Professional Innovation Management), founded in 1973 by Professor Knut Holt at the University of Science and Technology in Trondheim, Norway, and whose first conference was held in 1983, and subsequently internationally. 3 J.L. Le Moigne bases this research on an illustration of R. Ruyer’s “la méthode des paradoxes” (the paradox method), published in Paradoxes de la conscience, Albin Michel, Paris, 1966.

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IAMOT (The International Association for Management of Technology), founded in 1988 by Professor Tarek Khalil of the University of Miami and whose first conference was held on the same date and subsequently every other year in the United States and around the world. PICMET (Portland International Conference on Management of Engineering and Technology), created in 1989 by Professor Dundar Koaglu in Portland. The first conference was held in 1991 and later chose the same format as IAMOT. ICE (International Conference on Engineering, Technology and Innovation), which was first held in 1994 in France, and every year since then in a new European country. CIGI (Congrès international de génie industriel – International Conference on Industrial Engineering), created in 1995, by researchers, professors and industrialists active in the field of industrial engineering. The first conference was held in 1995 in Montreal, and then every other year in Montreal or in France. As a result, this influence can be found in France in a new generation of researchers. The product vision thus broadens with the notions of integrated design and intermediate design objects, introduced by the teams of S. Tichkiewitch (Tichkiewitch 1994; Mer et al. 1995) and the notions of technology transfer and functional and value analysis by J.C. Bocquet (Yannou 1998; Longueville et al. 2001). The first writings on the concept of inventive design also appeared during this period with the work of Cavallucci and Lutz (1997). Finally, the collective nature of innovation is highlighted by M. Callon as early as 1994 (Callon 1994) and would, in fact, go on to constitute one of the major works on the management of innovative projects and the piloting of innovation. In the same way, the process/process vision is broadened with the notions of technology watch and project risk assessment through the work carried out by teams around J.M. Ruiz (Baldit et al. 1995) and G. Lacoste (Karsenty et al. 1997). Likewise, work on the systemic modeling of the firm and the innovation process in particular is emerging in the production engineering community, notably in the work of F. Vernadat (1995). The same applies to the notions of MoT and the management of technological resources, which Thomas Durand (1988, 1993) has taken up. Finally, in line with the work of C. Guidat and J.C. Bocquet respectively, innovation engineering becomes a process of value creation (Morel 1998; Yannou 1998). We find here one of the major advances in structuring innovation engineering as a field of research, the fact of clearly discerning the act of design from the act of innovation. If design is a result-oriented process that consists of a time-limited rational act endowed with specific resources and for which tools, methods and virtual

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representations of an object are developed (procedural system), then innovation is an essentially irrational act that is built progressively, by breaking the automatisms and routines that an individual or a community has constructed for itself (system of uncertainty). As a result, the process of innovation clearly appears to be a process of broadening and enriching skills in order to build new solutions, as a capacity to find new relationships with an object and to go beyond the boundaries of the system under study. The study of innovation processes is definitively enriched by what will be called “glocal” thinking (or thinking in terms of global/local–local/global circularity). It is also interesting to highlight an initiative led by engineering schools and their associated research laboratories: the creation in 1993 of the CONFERE (Collège d’études et de recherches en design et conception de produits – Association for Study and Research of Product Design) symposium, specific to this community of researchers in product innovation and design. The objective is to participate in the academic recognition of design, product design and innovation as a priority research subject. CONFERE is thus positioned as a symposium in innovation sciences in which researchers from the previously mentioned teams participate4. The community also created its own journal in 1998 in order to disseminate its work on a wider scale: IJODIR, the International Journal of Design and Innovation Research, (formerly Design Recherche, created in 1990). This journal aims to provide a scientific reflection on the act of designing and developing innovative products adapted to the world we live in, assuming a position in the field of engineering design. In our opinion, the 2000s marked a turning point in innovation research. While the historical disciplines that nurtured innovation continue their work, we are also seeing the emergence of research to develop a new vision of the innovation process (Hatchuel et al. 2001; Tomala et al. 2001), which is becoming collaborative (Boujut 2001; Boujut and Blanco 2003), and to confirm the link between the technological system and innovation (Aït-El-Hadj 2002). It is also a period when the first signs of a blurring of disciplinary boundaries in favor of multidisciplinary work can be detected. For example, we will mention those under the impetus of B. Taravel (Richir et al. 2001), founder in 1999 of Laval Virtual, a show on innovation and new technologies such as virtual reality and augmented reality, which remain a reference to this day. This awareness in engineering sciences, that innovation is a matter of integration and negotiation between different points of view, has raised the importance of creating synergies between social, technological and process to contribute effectively to customer satisfaction. This is in part why the work carried out by Hatchuel et al. (2001) has had such an impact on the innovation community. The latter suggests that, in order to innovate continuously, the R&D process must have an intermediate phase called the 4 For more information, see: http://www.ijodir.org.

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Research–Innovation–Development process, thus allowing innovation to take place in less restricted and less structured situations. Therefore, since the 2000s, several laboratories based in engineering or science schools for engineers have carried out research to better understand the upstream phases of the innovation process (front end) and in particular the processes of idea generation and selection before moving on to design. As a result, under the heading of inventive design, theories and methods have been investigated on a massive scale, such as C-K (concept-knowledge) (Hatchuel 2001), TRIZ (Cordova-Lopez et al. 2002) and value creation (Yannou et al. 2002). In addition, innovation is becoming collaborative and open, popularizing research around open innovation. In the same way, taking needs and uses into account becomes a key element in improving the ideation phase and the steering of innovation in general (Boly 2004). Finally, these years saw the emergence of a whole stream of research into the metrology of innovation (Morel and Boly 2004). Although this list is not exhaustive, it does show that a great deal of research has made it possible to consolidate a community around innovation that remains very active in French engineering schools, and contributes to a field of research that can be described as “innovation engineering” and whose key concepts will be presented in the following section. 2.3. Practical guide to innovation engineering Innovation processes have radically changed and must take into account the increasing capacity of industrial developments and the growing complexity of surrounding systems. The question of innovation has therefore emerged over time as a fundamental element that can reconcile two requirements: short-term efficiency in the control of an industrial process and long-term development that will reside in the capacity to create value (both in a human and a productive dimension). These changes in the understanding of the concept of innovation in general have naturally contributed to a new field of research: innovation engineering, whose role is to study the mutual involvement of the disciplines that feed into each other and to obtain knowledge that will become levers of growth for tomorrow’s economy. Following the exercise of inventorying methods and tools in innovation engineering carried out in 2011 by a collective of researchers and practitioners in engineering techniques under the direction of B. Yannou (2011), we have chosen to take this work for granted and to focus on the accepted bases in the piloting of innovative projects. Moreover, starting from the observation that there is a more than sufficient number of definitions of innovation, we will base ourselves on the definition of innovation of the Oslo Manual, version 2018, taken up in the very recent ISO/TC279 standard on innovation management:

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An innovation is a new or improved product or process (or combination thereof) that differs significantly from the organization’s previous products or processes5 and that has been made available to potential users (product) or put into use by the organization (process). 2.3.1. First bias: there are no good or bad innovative ideas! Innovation is a building process. It begins with an intention to create something new because of dissatisfaction, awareness of potential improvements or by surprise/serendipity (i.e. by the involuntary observation of a phenomenon). This is particularly due to the inherent curiosity of every human being and his or her ability to deploy resources to improve living conditions. As De Brabandere (De Brabandere 2014) points out, being creative means taking a different look at an object or a situation, whereas being innovative means achieving the solution. It is therefore essential to develop one’s ability to explore the ecosystem in which one develops, in particular by being able to understand and integrate the major challenges we face in a mode of exploration commonly known as “from glocal to local”. As a result, the current trend of frugal innovation cannot be ignored today when it comes to designing an innovation that is accessible to a larger number of people and nor can we ignore the needs expressed or not expressed by users. The analysis of needs and requirements is one of the major tools in innovation engineering. This ability to better understand and identify the context of the innovation is a prerequisite to any innovation engineering process in order to contextualize the project to be developed. At this stage, it is not a matter of selecting ideas. In fact, it is a mistake to do so because many experiences show that ideas perceived as very attractive at the end of a creativity session turn out to be, in the end, little or not at all accepted by users/consumers. This leads to the interest of integrating the potential users of the innovation upstream of the innovation process, which we will come back to later (second bias). Engineering research in the field of ideation is now looking for ways to make the process more efficient. To do so, methods are more and more systematic and supported by computer-based tools that make them more robust, such as the association of the TRIZ method with AI, or the development of expert systems associated with ontologies to drive the entire creative process. 5 In the Oslo Manual, the term “enterprise” is used. In order to respect the terminology used in the future standard, this has been amended and replaced with the term “organization”.

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Innovation is also the result of confrontation. An idea must go through a phase of enrichment, particularly by confronting the expertise of people with whom a relationship of trust has been built. Managing a network of internal and external experts is a crucial aspect of engineering in the service of innovation. There can be no management of innovative projects without collaboration (third bias). 2.3.2. Second bias: any innovation process requires contextualization of the situation Each innovation context is specific according to the nature of the innovation, the degree of novelty or the field of study. It is therefore essential, before committing further to the project, to carry out a watch in order to determine the variables of the macro-environment that could positively or negatively influence the innovative project. It is therefore common to use a macro-environmental analysis tool, such as PESTEL, to assess the political, economic, socio-cultural, technological, ecological and legal dimensions associated with the field of study. It can be carried out at several scales depending on the case: local, national and international. Moreover, in order to refine the analysis, the use of a SWOT analysis allows us to qualify the PESTEL data as opportunities and/or as threats and to verify, from this preliminary phase, the resources available to the organization to support future innovation. Evaluating an organization’s capacity for innovation is a prerequisite for the implementation of an engineering approach (Boly 2004). Indeed, an organization can find itself in difficulty if the innovative project takes it too far away from its core competencies or requires human and financial resources that it does not have. Finally, a fortiori today, as the business environment has become prone to “VUCA” (volatility, uncertainty, complexity and ambiguity), taking into account trends and weak signals in a market proves to be an undeniable asset to create, in fine, a truly disruptive innovation. This aspect of context analysis and assessment has been facilitated in recent years by the increasing availability of information and data. This also makes it more difficult, in particular because of the availability of tools for data visualization, processing and storage. Work on data mining has found a growing echo in improving the understanding of the project context. It is the same for research on AI. In recent years, this has become an essential tool to help innovative project leaders make the “best” decisions in a complex and uncertain world. Likewise, as we have already pointed out, needs and requirements analysis is one of the major tools of innovation engineering. Based on the concept of “customer pain” and the highlighting of dissatisfactions, the needs analysis makes it possible to refine the first part of an innovation process known as the “front end” or the upstream phase. If, in addition, the needs analysis is combined with a prospective analysis, then the analysis can be refined by looking at implicit and unspoken dimensions, such as trends and new uses. In this way, a clear vision of the needs and

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expectations of the parties involved in the innovation project can be obtained and potential scenarios can be developed. Innovation is therefore systemic. Having this awareness at the beginning of the implementation of an innovation process also means having a perception of the associated project risk. 2.3.3. Third bias: there is no innovative project management without collaboration For several years, the belief that the success of an innovation lays in hiding it and keeping it well controlled within an organization has been transformed into a new way of acting that is more open and requires internal and external collaboration. Individual value is being replaced by collective value. Indeed, research conducted by the engineering community has led to the development of tools and methods to facilitate this collaboration and to support projects integrating multiple stakeholders around the same objective. As a result, research on the interoperability of objects and systems and on collaborative virtual reality and augmented reality has been widely developed to allow the management of distributed, inter-temporal and inter-organizational knowledge and skills. This work has enabled us to move from a theoretical concept of collaboration between individuals and organizations, and open innovation, to a method equipped to manage knowledge remotely. Indeed, the technological push has undeniably accelerated the use of external partnerships, be they academic, research, or technology or technique transfer partnerships. In addition, research on collaborative platforms has multiplied, offering opportunities to include in the management of an innovative project a multi-player, multi-skilled and low-cost perspective. While this considerably increases the creative dimension, it has also created new problems of intellectual protection when the production of ideas is collective. Similarly, research to improve the management of innovation processes has been enriched by the latest work on flexible methods. Indeed, the imperatives of permanent and continuous innovation have shown the limits of traditional project management tools and methods. If these are well adapted for product design/ improvement, it is not the same when it comes to innovative projects. 2.3.4. Fourth bias: a universal innovation process does not exist! The ISO 56002 standard on innovation management correctly places the key moments of an innovation project: identifying opportunities, creating concepts, validating concepts, and developing and deploying solutions. It also specifies that although there is not a universal process model that is applicable for all

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organizations, it is possible to identify five main “phases” that are more or less always present. The ISO 56002:2019 standard represents those “phases”. It is therefore commonly accepted today that there is no ideal process for developing an innovative activity; however, there are margins of freedom to be taken. Although at first glance it seems that innovation is a unique and creative process, and apparently without structure, it has been proven that innovation can benefit from a common framework and standardization (ISO 56002). There is no typical process leading from an idea to an activity anchored in its environment, but rather an agility in adapting and reorganizing itself according to the evolution of the environment. As a result, agile innovation and innovation agility are the foundations of innovation engineering. It is all about learning to free oneself from any typical model by developing the ability to reconfigure steps, to remove or add steps if necessary. Depending on the very nature of the innovation, whether it is incremental, radical or disruptive, engineering must be at the service of the project and not the other way around. It is therefore obvious that if we are looking for incremental innovation, we are not going to immediately start setting up a creativity session. Rather, we will prefer tools such as competitive intelligence or the search for customer dissatisfaction in order to improve the existing product/process or service at a lower cost and in the short term. However, the more we look for radical innovation, the more open innovation approaches will be used to try to pick up weak signals from the markets. Feeling the unspoken needs of users and establishing foresight-related tools are another way to approach this first phase of an innovation process. 2.3.5. Fifth bias: the importance of materializing and evaluating ideas as early as possible by including users in the process Concept validation has gradually become a central theme in the innovation engineering process. Indeed, it is at this stage of the process that ideas begin to take shape and can therefore be improved and amended by a collective. Very early on, researchers realized the importance of integrating potential users and their uses as early as possible in order to co-design future products/services/organizations with them. Indeed, testing and experimenting with concepts as early as possible will allow participants to better visualize the scope of the innovation, to better embody it. In fact, rapid prototyping has become the technical support for validating the project progress as early as possible because it provides a physical (prototype) or virtual

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(3D model) representation of an idea with the possibility, at this stage of the process, of integrating user feedback to improve or even rethink the initial idea. However, although prototyping, because of its iterative nature, is a very interesting way to improve the acceptability of the concept in the upstream phase, it also generates a lot of waste. Indeed, prototyping has been greatly facilitated by the development of low-cost simulation techniques and tools such as 3D scanners and printers. The multiplication of trial-and-error attempts has resulted in a lot of waste from this creative part of the innovation process. We then saw the parallel development of numerous research efforts in order to recycle the materials used during the materialization process for validation by the users of the concepts. This, by integrating the concept of a short circuit (closed loop) in order to consider the entire lifecycle of the product for an innovative and responsible design. Integrating users upstream of the innovation process is not an easy thing either. We have seen the development of a whole research activity, supported by information and digital technologies around the notion of a living lab or laboratory of uses. These are defined by the European Network of Living Labs, ENOLL, as user-led open innovation ecosystems that engage all stakeholders in the form of a public–private–people partnership (PPPP) to co-create products, services, social innovations and more in a real context, whether physically or virtually. In fact, living labs are part of open-innovation and usage-based innovation research trends, and help to explain the explosion of work on these themes in engineering since the mid-2000s. 2.4. Conclusion We have shown in this contribution that, while innovation has long been of interest to the engineering sciences, we note that there was a turning point in the 2000s in terms of production and reflections on the use of engineering in the service of innovation. In an increasingly connected world, where the globalization, acceleration and democratization of technologies are combined, new methods and processes of innovation are needed. The aim is to innovate faster, and to decompartmentalize organizations in order to create more value and reach new markets. Through the design and development of tools (physical and digital platforms, software, etc.), methods (taking user requirements into account when assessing the need for novelty, prospective and dynamic simulation methods for analyzing the impact of an innovation on its environments, diagnosis of the capacity to innovate, collaborative engineering, decision support, etc.) and partnership modes (open innovation approach, etc.), engineering research has shed significant light on the upstream phase of innovation.

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We have thus moved from a strategy of designing products/processes/services “for” to a strategy of designing “for, with and by” users, thereby restoring the credibility of a previously forgotten approach, design thinking. Taking into account the user experience through actual practices of product use, and assessing their needs and desires become the central concepts of current investigations. Indeed, new methodologies, metrics and tools to assess the sustainable value of solutions by jointly evaluating their social, environmental and economic impacts need to be considered. The same is true for new demonstrators and experimental protocols enabling research to be conducted in living lab mode (i.e. involving public–private–people partnerships). Innovation management must adapt: project management methods and tools are becoming “lean” and flexible, and are adopting evaluation techniques that integrate an increased variety and variability of data in the development of prospective scenarios for an innovation. As you will have understood, innovation as seen through engineering sciences still has many fields to investigate in order to provide our industries and communities with methods and tools for sustainable innovation, i.e. to create value for the benefit of all stakeholders. This is where innovation engineering and innovation management stand out: 1) “Innovation management is the implementation of management techniques and systems designed to create the most favorable conditions for the development of concrete innovations”6. It is a managerial process. 2) Innovation engineering enables the design and implementation of tools orchestrated by an ecosystem-based architecture for understanding and operationalizing the innovation process. 2.5. Acknowledgments The authors would particularly like to thank Professor Claudine Guidat for her clarifications and her testimony on the beginnings of industrial engineering and industrial systems engineering in France, disciplines at the origin of work on innovation and its engineering.

6 Wikipedia source (https://en.wikipedia.org/wiki/Innovation) consulted on May 8, 2020.

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2.6. References Agrell, P., Hatchuel, A., van Gigch, J.P. (1985). Innovation as Organizational Intervention. California State University Sacramento, School of Business and Public Administration, Sacramento, CA. Aït-El-Hadj, S. (1989). L’entreprise face à la mutation technologique. Editions d’Organisation, Paris. Aït-El-Hadj, S. (2002). Systèmes technologiques et innovation : itineraire théorique. Editions L’Harmattan, Paris. Akrich, M. (1987). Comment les innovations réussissent ? Recherche et technologie, 4, 26–34. Akrich, M., Callon, M., Latour, B. (1988). A quoi tient le succès des innovations ? 1 : L’art de l’intéressement ; 2 : Le choix des porte-parole. Gérer et comprendre, Annales des Mines, 4–17 and 14–29. Argyris, C. and Schön, D.A. (1996). Organizational Learning II: Theory, Method and Practice, Addison-Wesley, Boston, MA. Baldit, P., Quoniam, L., Ruiz, J., Dou, H. (1995). La gestion de projet et la veille technologique : vers une méthodologie commune. Direction et gestion des entreprises, (155–156), 61–68. Bergeron, J. and Bocquet, J.C. (1995). Introducing new technologies in organisations – Business model perspective. Benchmarking – Theory and Practice, Springer, Boston, MA. Boly, V. (1987). Elaboration de scénarios à 10 ans par les méthodes micmac et smic, application à une initiative de développement local. PhD Thesis, Institut National Polytechnique de Lorraine, Nancy. Boly, V. (2000). Processus d’innovation : contribution à la modélisation et approches méthodologiques. HDR, Institut National Polytechnique de Lorraine, Nancy. Boly, V. (2004). Ingénierie de l’innovation : organisation et méthodologies des entreprises innovantes. Hermes Science Publications, Paris. Boujut, J.-F. (2001). Des outils aux interfaces : pour le développement de processus de conception coopératifs. PhD Thesis, Institut National Polytechnique de Grenoble, Grenoble. Boujut, J.-F. and Blanco, E. (2003). Intermediary objects as a means to foster co-operation in engineering design. Computer Supported Cooperative Work (CSCW), 12(2), 205–219. Bower, J.L. and Christensen, C.M. (1995). Disruptive technologies: Catching the wave. Harvard Business Review, 43–53, January–February. Callon, M. (1994). L’innovation technologique et ses mythes. Gérer et comprendre, 34, 5–17. Castagne, M. (1987). Le genie des systemes industriels : une discipline nouvelle. European Journal of Engineering Education, 12(3), 271–276. Castagne, M., Guidat, C., Voinson, P. (1983). Proposition d’une méthodologie de sélection des procédés valorisant la biomasse lignocellulosique. Revue d’économie industrielle, 26(1), 14–23.

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Cavallucci, D. and Lutz. P. (1997). TRIZ : une nouvelle théorie d’aide à l’innovation industrielle. Revue française de gestion industrielle, 15–28. Chesbrough, H.W. (2003). Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business Press, Boston, MA. Chiesa, V., Coughlan, P., Voss, C.A. (1996). Development of a technical innovation audit. Journal of Product Innovation Management: An International Publication of the Product Development & Management Association, 13(2), 105–136. Christensen, C. (1997). The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail, 1st edition. Harvard Business Review Press, Cambridge, MA. Cleland, D.I., Kocaoglu, D.F., Brown, J., Maisel, J.W. (1981). Engineering Management. McGraw-Hill, New York. Clough, G.W. (2004). The Engineer of 2020: Visions of Engineering in the New Century. National Academy of Engineering, Washington, DC. Cooper, R.G. (1990). Stage-gate systems: A new tool for managing new products. Business Horizons, 33(3), 44–54. Cordova-Lopez, E., Lacoste, G., Le Lann, J.-M. (2002). Use of Altshuller’s matrix for solving slag problems related to steering knuckle (Part I of II) [Online]. Available at: https://trizjournal.com/use-altshullers-matrix-solving-slag-problems-related-steering-knuckle-part-ii/. De Brabandere, L. (2014). Petite philosophie des grandes trouvailles. Editions Eyrolles, Paris. Duchamp, R. (1988). La conception de produits nouveaux. Hermes Lavoisier, Hoboken, NJ. Durand, T. (1988). Management pour la technologie : de la théorie à la pratique. Revue française de gestion, (71), 5–14. Durand, T. (1993). The dynamics of cognitive technological maps. Implementing Strategic Processes, 165–189. Fagerberg, J., Mowery, D.C., Nelson, R.R. (2005). The Oxford Handbook of Innovation. Oxford University Press, Oxford. Freeman, C. (1994). The economics of technical change. Cambridge Journal of Economics, 18(5), 463–514. Freeman, C. and Soete, L. (1997). The Economics of Industrial Innovation, 3rd edition. Routledge, Abingdon. Gousty, Y. and Kieffer, J.-P. (1988). Une nouvelle typologie pour les systèmes industriels de production. Revue française de gestion, 104–112. Guidat, C. (1984). Contribution méthodologique à la formalisation d’un nouveau métier : l’ingénierie de l’innovation technologique à partir de l’expérience d’une innovation technique dans la filière bois : AGRESTA (Procédé de transformation physico-chimique du bois en isolant pour la construction). PhD HDR, Institut National Polytechnique de Lorraine, Nancy. Hamel, G. and Prahalad, C.K. (1996). Competing for the Future. Harvard Business School Press, Boston, MA.

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Hatchuel, A. (2001). Towards design theory and expandable rationality: The unfinished program of Herbert Simon. Journal of Management and Governance, 5(3/4), 260–273. Hatchuel, A., Agrell, P., van Gigch, J.P. (1987). Innovation as system intervention. Systems Research, 4(1), 5–11. Hatchuel, A., Le Masson, P., Weil, B. (2001). De la R&D à la RID : de nouveaux principes de management du processus d’innovation. Congrès francophone du management de projet, AFITEP : “Innovation, conception… et projets”, Paris. von Hippel, E. (1988). The Sources of Innovation. Oxford University, New York. Kasenty, P., Zelfani, M., Angot, P., Lacoste, G. (1997). Intégration et évaluation des risques en gestion de projet dans les industries pilotées par la recherche. Congrès international de génie industriel, Albi, France. Khalil, T.M. and Bayraktar, B. (1988). Challenges and opportunities for research in the management of technology. Report, NSF/University of Miami, Coral Gables, FL. Khalil, T.M. and Shankar, R. (2000). Management of Technology: The Key to Competitiveness and Wealth Creation, 1st edition. McGraw-Hill, Boston, MA. Le Goff, P. (1979). La valeur de l’énergie a-t-elle une base économique, écologique ou technique ? Critère d’optimisation en énérgétique industrielle. Revue d’économie industrielle, 8(1), 68–98. Le Moigne, J.-L. (1973). Les systèmes d’information dans les organisations. Presses universitaires de France, Paris. Le Moigne, J.-L. (1974). Les systèmes de décision dans les organisations. Presses universitaires de France, Paris. Le Moigne, J.-L. (1984). Les paradoxes de l’ingénieur. Centre de recherche sur la culture technique, Neuilly-sur-Seine. Le Moigne, J.L. (1990a). La modélisation des systèmes complexes. Dunod, Paris. Le Moigne, J.-L. (1990b). La théorie du système général : théorie de la modélisation. Presses universitaires de France, Paris. Leonard, D. (1995). Wellsprings of Knowledge. Harvard Business School Press, Boston, MA. Longueville, B., Stal Le Cardinal, J., Bocquet, J.-C. (2001). La gestion des connaissances pour les projets de conception de produits innovants. Septième colloque sur la conception mécanique intégrée. PRIMECA. Mauborgne, R. and Chan, W.K. (2005). Blue Ocean Strategy. Harvard Business Review Press, Boston, MA. Mélèse, J. (1979). Approches systémiques des organisations : vers l’entreprise à complexité humaine. Editions Hommes et Techniques, Paris. Mer, S., Jeantet, A., Tichkiewitch, S. (1995). Les objets intermédiaires de la conception : modélisation et communication. Le communicationnel pour concevoir, 21–41.

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Morel, L. (1998). Proposition d’une ingenierie intégrée de l’innovation vue comme un processus permanent de création de valeur. PhD thesis, Institut National Polytechnique de Lorraine, Nancy. Morel, L. and Boly, V. (2004). Mastering innovativeness potential: The results of an expert consultation. Revista Eletrônica de Administração, 10(6), 165–180. Morin, J. (1985). L’excellence technologique, 1st edition. Publi-Union, Éditions J. Picollec, Paris. Morin, J. (1988). L’excellence technologique, 2nd edition. Editions Jean Picollec, Paris. Morin, J., Seurat, R., Marbach, C. (1989). Le management des ressources technologiques. Editions d’Organisation, Paris. Mowery, D.C. and Rosenberg, N. (1999). Paths of Innovation: Technological Change in 20th-Century America. Cambridge University Press, Cambridge. Nonaka, I. and Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, Oxford. Porter, M.E. (1985). Competitive advantage: Creating and sustaining superior performance. Competitive Advantage, 167, 167–206. Richir, S., Taravel, B., Samier, H. (2001). Information networks and technological innovation for industrial products. International Journal of Technology Management, 21(3–4), 420–427. de Rosnay, J. (1975). Le macroscope, vers une vision globale. Editions du Seuil, Paris. Senge, P. (1990). The Fifth Discipline: The Art and Practice of the Learning Organisation. Random House, London. Shavinina, L.V. (ed.) (2003). The International Handbook on Innovation. Elsevier, Oxford. Tichkiewitch, S. (1994). De la CFAO à la conception intégrée. Revue internationale de CFAO et d’infographie, 9(5), 609–621. Tomala, F., Senechal, O., Tahon, C. (2001). Modèle de processus d’innovation. MOSIM01’ : actes de la troisième conférence francophone de modélisation et simulation : conception, analyse et gestion des systémes industriels. Ghent. Vernadat, F. (1995). Modélisation systémique en entreprise : métamodélisation. La modélisation systémique en entreprise, Braesch, C., Haurat, A. (eds). Hermes, Paris. Yannou, B. (1998). Analyse fonctionnelle et analyse de la valeur. Conception de produits mécaniques. Méthodes, modèles et outils, 77–104. Yannou, B. (2011). Déployer l’innovation : méthodes, outils, pilotage et cas d’étude. Techniques de l’ingénieur, Paris. Yannou, B., Hajsalem, S., Limayem, F. (2002). Comparaison de la méthode SPEC et de l’analyse de la valeur pour l’aide à la conception préliminaire de produits. Mécanique & industries, 3(2), 189–199.

3 Absorption – Technological Absorptive Capacity and Innovation: The Primacy of Knowledge

3.1. Introduction The technological absorptive capacity, initially defined by Cohen and Levinthal (1990) as “the capacity of a firm to recognize the value of new and external information, to assimilate it and to apply it for commercial purposes” (p. 128), has been of interest in literature on organizational and strategic management and industrial economics for 30 years. This concept has specifically enhanced a multitude of theoretical and empirical contributions in the field of innovation and mobilized a variety of concepts, including organizational learning, knowledge management and dynamic capabilities to explore the determinants of innovation capacity, a source of performance at the firm level (Lane et al. 2006; Volberda et al. 2010; Mäkinen and Vilkko 2014; Zou et al. 2018). However, the multidimensional and dynamic nature of this capacity has divided researchers according to the sources of absorptive capacity creation, the weight of its components and its link with the environment. In this chapter, we propose, under an integrative perspective, to shed light on the theoretical and empirical weaknesses that still persist, in order to develop a better understanding and assessment of this concept. 3.2. Technological absorptive capacity: a cognitive process The innovation process requires companies to mobilize resources and capacities to successfully transfer external knowledge with a perspective of creating a new one. Chapter written by Sonia BEN SLIMANE. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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In this context, the concept of technological absorptive capacity, developed by Cohen and Levinthal (1990), is defined as a cognitive process that refers to the knowledge acquired and to be created, and calls upon the organizational dimension with regard to the firm’s capacity to acquire information internally, assimilate it and apply it, with a view to creating new knowledge. The contribution of Zahra and George (2002) was crucial to understanding this concept. Firstly, the addition of a fourth capacity, that of transformation capacity, allows the absorptive capacity to be broken down into two major blocks: potential absorptive capacity, which covers the capacities of acquisition and assimilation, and realized absorption capacity, which includes the capacity to transform and then the capacity to exploit knowledge. These authors have particularly emphasized the combinatorial nature of these capacities, in the way that they must rely on each other to ultimately produce what is called an absorptive capacity. The underlying idea is that a firm can have a high potential absorptive capacity, but this cannot ensure an effective realized absorptive capacity. Zahra and George thus call for a further in-depth analysis of the relationships between these four components of the absorptive capacity, their impacts on the organizational and strategic choices of a company and on the management of knowledge and dynamic capacities. Nevertheless, the multitude of theoretical and empirical works that have followed have neither succeeded in finding a consensus to define the absorptive capacity, nor in specifying the components of absorptive capacity (Ferreira and Ferreira 2017) or in developing an integrative vision of these components (Volberda et al. 2010; Zou et al. 2016). From the conceptual perspective, the dynamic dimension of absorptive capacity implies dynamic and evolving feedback mechanisms and internal adaptations that nourish an innovation process through the link between the components of the absorptive capacity and the efficiency of the whole process. However, substantial research in this area still diverges on the dimensions that compose the technological absorptive capacity and on the importance given to certain capacities in relation to others within this process (Zahra and George 2002; Lane et al. 2006; Chauvet 2014). Much work has focused, for example, on the evaluation of the realized absorptive capacity at the expense of potential absorptive capacity (Zahra and George 2002; Zou et al. 2016, 2018). The potential absorptive capacity is critical and offers flexibility in adapting to the environment, which is specifically the case in small firms. In parallel, having a high level of potential absorptive capacity does not guarantee an efficient absorptive capacity; it could be harmful for the realized absorptive capacity (Ferreira and Ferreira 2017) and could even create internal conflicts when there is a lack of coordination and self-reinforcement between the different capacities composing absorptive capacity.

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Furthermore, Cohen and Levinthal’s (1990) model of knowledge creation follows the Schumpeterian model of innovation, in which innovation is associated with large firms that have experience of the market and invest substantially in R&D activity. From this perspective, maintaining a high level of effort in R&D spending internally provides sufficient absorptive capacity to use and exploit knowledge produced elsewhere. In an extension to this vision, firms without sufficient financial capacity cannot develop their absorptive capacity. Recent work challenges this idea and shows that absorptive capacity is no longer the prerogative of large firms (Zou et al. 2018), but that other organizational mechanisms are also involved in developing absorptive capacity. Exploring new organizational forms, including startups, or open innovation and other forms of networked innovation, will help in rethinking the importance of organizational characteristics (Zou et al. 2018), such as organizational flexibility, coordination and communication mechanisms as sources of absorptive capacity development and their role in today’s changing innovation landscape. Therefore, external absorption through the acquisition of complementary assets, in particular through strategic cooperation and alliances or other forms of inter-organizational cooperation, are of importance as factors of knowledge acquisition, and are particularly suitable for small firms, specifically in terms of reducing acquisition costs (Volberda et al. 2010) and as an opportunity to acquire a diversity of knowledge and skills, as well as the formal and informal relationships (Ben Slimane 2011). Further analysis of these sources of absorption of knowledge can be instructive, particularly according to their potential impact on small organizations. 3.3. The multidimensional nature of absorption capacity and innovation The multidimensional nature of the absorptive capacity is one of the founding frameworks of the concept and refers to the existence of various but complementary dimensions in an innovation process: acquisition, assimilation, transformation and exploitation. Although these dimensions are different because of the diversity of their conceptualizations, as well as of the people, relationships and environments they involve (Chauvet 2014), the combination of these four capacities is a prerequisite in the innovation process (Zahra and George 2002). However, the impact of this combined character on the dynamics of absorption capacity has not been validated (Volberda et al. 2010). Absorptive capacity includes the absorptive capacities of its members, considered individually or within the organizational framework. It therefore implies a prior investment in the development of its individual absorptive capacities, as well as the development of its organizational absorptive capacity on a cumulative basis. Moreover, the organizational literature on absorptive capacity seems to be

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particularly unclear, mainly in considering individual and collective actions, as well as the key role of coordination mechanisms and organizational management of absorptive capacity, while a firm’s absorptive capacity refers to both individual capacity (the capacity and know-how of its members) and organizational capacity (the organization of members), and intra-organizational interactions. At the operational level, the mechanisms of knowledge transfer between individuals and among departments, as well as the communication structure and management of skills within the organization, are crucial in analyzing absorptive capacity. Therefore, the social dimension of knowledge transfer, the impacts of intra-organizational interactions and the management of skills and potential conflicts within firms can be a fruitful starting point for further theoretical and empirical exploration. In this vein, formal and informal managerial incentives, social integration mechanisms, management support and relational capacities can be factors of performance of absorptive capacity in innovation context (Volberda et al. 2010; Zou et al. 2018). Furthermore, learning, as a cumulative process, has been widely analyzed without an explicit emphasis of its impact on absorption capacity in an innovation context. The set of capacities and components of absorptive capacity involves various cumulative learning processes, such as exploratory learning, transformative learning and learning through exploitation (Narula 2003; Lane et al. 2006). This variety of learning dimensions reflects the greater or lesser ease with which certain types of information, whether tacit or codified, can be used and assimilated. These learning mechanisms are favored by a multitude of factors. First, through experience (Cohen and Levinthal 1990; Zahra and George 2002), since companies acquire experience by becoming familiar with particular skills and abilities through accumulative effect. Moreover, the quantity and intensity of knowledge acquired depend on individual interactions and thus, on human capital. As a result, the availability of a large stock of skilled human resources is determinant for monitoring the evolution and assessing the relevance of external knowledge, and for integrating it into innovation activities (Narula 2003). The assessment of human capital, particularly with regard to the management of skills, deserves much more in-depth exploration that could bring together researchers in management and organizational strategy in anchoring the organizational perspective when analyzing the absorptive capacity. 3.4. Measuring absorptive capacity The conceptual weaknesses in analyzing absorptive capacity, particularly in terms of exploring the complexity of cognitive, intra-organizational and inter-organizational mechanisms, have accordingly affected the effectiveness of

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empirical studies that have aimed at evaluating absorptive capacity and its link with innovation. Quantitative research has been inspired by the Schumpeterian framework of innovation strongly associated with innovation activity in large firms and has focused on indicators such as investment in R&D, the number of highly qualified people or their interaction with technicians (Volberda et al. 2010; Ben Slimane 2011; Mäkinen and Vilkko 2014). These indicators are restrictive for small firms that do not have a comparable R&D budget and do not follow patent registration rules (Chauvet 2014; Zou et al. 2018). In this vein, the recent work of Zou et al. (2018) shows that absorptive capacity does not increase with firm size, suggesting that these small firms use other non-tangible resources to achieve absorptive capacity. Moreover, the performance of absorptive capacity in terms of innovation is mainly associated with financial performance indicators (Zou et al. 2016). However, if we consider the type of innovation that can be incremental or radical and that accordingly mobilizes different resources and capacities, it would be necessary to adapt the performance criteria in relation to the type of innovation achieved. The qualitative evaluation of the absorptive capacity remains incomplete. First, there is a lack of convergence on the indirect measurement scales that need to be developed in order to assess organizational learning mechanisms, even though many studies have attempted to develop Likert measurement scales to assess organizational and psychological aspects (Volberda et al. 2010; Ben Slimane 2011). Second, current qualitative studies are often one-dimensional and focus on potential or realized absorptive capacity, but not on both (Chauvet 2014). Therefore, further measurement tools that take the multidimensional nature of absorptive capacity and all of its components into account have yet to be developed, but will support a complete and better understanding of the absorptive capacity effectiveness in the context of innovation. 3.5. Conclusion The relationship between absorptive capacity and innovation has been defined since the early work of Cohen and Levinthal (1990) and Zahra and George (2002). However, the link is still unclear and poorly assessed in empirical studies. This chapter has tried to highlight, at the conceptual level, the chronic lack of consensus on the components of absorptive capacity. Another theoretical bias lies in the basis for the development of absorptive capacity from an innovation perspective, long associated with large firms. However, taking the new forms of firms and the organizational aspects they imply into account, as well as the type of resulting innovation should broaden the analysis of the concept and adapt it to the reality of firms.

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Empirical work raises the difficulty of translating the components of absorptive capacity into indicators, whether quantitative or qualitative, that take their multidimensional and combinatorial nature into account in an innovation perspective. Substantial work on the development of a matrix evaluation grid of absorptive capacity may constitute a solution for the convergence of different disciplinary approaches. Moreover, longitudinal studies allow for observing and assessing the performance of a firm over time (Zou et al. 2016). Finally, the literature has paid little attention to the anchoring of the firm in its environment, which is changing and accordingly affecting absorptive capacity, particularly for developing countries in a perspective of technological catch-up. As Narula (2003) pointed out, firms primarily operate within a national system, in which the institutional environment that promotes innovation in promising technological sectors can support the actions of firms in developing their absorptive capacity from an innovation perspective. Second, firms are not isolated from the external knowledge of firms in other countries. An exploration of the role of these determinants of the macroeconomic environment could be instructive. 3.6. References Ben Slimane, S. (2011). The IJV effects on developing technological capacities: The case of the Tunisian firms. Economics, Management, and Financial Markets, 6(1), 302–324. Chauvet, V. (2014). Absorptive capacity: Scale development and implications for future research. Management International, 19(1), 113–129. Cohen, W.M. and Levinthal, A.D. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. Ferreira, G. and Ferreira, J. (2017). Absorptive capacity: An analysis in the context of Brazilian family firms. Revista de administração mackenzie, 18(1), 174–204. Lane, P.J., Kokan, B.R., Pathak, S. (2006). The reification of absorptive capacity: A critical review and rejuvenation of the construct. The Academy of Management Review, 31(4), 833–863. Mäkinen, S.J. and Vilkko, M.K. (2014). Product portfolio decision-making and absorptive capacity: A simulation study. Journal of Engineering and Technology Management, 32, 60–75. Narula, R. (2003). Understanding absorptive capacities in an “Innovation Systems” context: Consequences for economic and employment growth. DRUID Working Paper, (04–02), 53.

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Volberda, H.W., Foss, N.J., Lyles, M.A. (2010). Absorbing the concept of absorptive capacity: How to realize its potential in the organization field. Organization Science, 21(4), 931–951. Zahra, S. and George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. The Academy of Management, 27(2), 185–203. Zou, T., Ertug, G., George, G. (2018). The capacity to innovate: A meta-analysis of absorptive capacity. Innovation: Management, Policy and Practice, 20(2), 87–121.

4 Big Data – Artificial Intelligence and Innovation: The Big Data Issue

4.1. Introduction Big Data refers to datasets whose scale and complexity require dedicated analytical and statistical technologies, processes and approaches. This is not a recent phenomenon, but the increasing digitization of our society and the increased connectivity of the artifacts that surround us are accelerating and amplifying our confrontation with very large quantities of largely eclectic data. Data comes from machines, with the rise of the Internet of Things (IoT); from organizations and companies that are releasing more and more data as part of “open data” from humans through our human activities, and even from our biological nature, such as the sequencing of our DNA. For Delort (2018), “Big Data is about looking for patterns in low-density data, extracting new facts or new relationships between facts.” The author also states that it is a question of “creating predictive models through exploratory and inductive methods on masses of data with low information density.” The author proposes the Volume x Density matrix to distinguish Big Data from more conventional databases governed by a high density. The thresholds for determining the boundary between dials are scalable (Delort 2018). Lemberger et al. (2015) also consider the boundary to be evolutionary, with Big Data for them being notably conditioned by the reading time of a volume of data. It is therefore not relevant to define Big Data exclusively by a quantitative approach. There are many aspects to consider. The boom in data volumes is the result of a combination of economic and technological factors. It is also a breakthrough in the approach to information analysis (Lemberger et al. 2015).

Chapter written by Laurent DUPONT. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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To understand and characterize this massification and complexification of data is to try to identify a vast ecosystem that combines artificial systems, natural systems and social actors. The origins of the phenomenon and the potential that it represents, as much as the known successes and limitations, also offer a certain insight into innovation at technological, scientific and societal levels. First, we will describe the typologies of Big Data and the acceptances that have a consensus among the different communities. We will then address the issue of interdisciplinarity to deploy a Big Data strategy. Finally, we will return to the questions that remain open and the quests that they fuel. 4.2. Humans and data: diversity and consensus According to the work directed by Bouzeghoub and Mosseri (2017), astrophysicists are considered the first community to have had Big Data. The Sloan Digital Sky Survey, a sky observation program, has, since the year 2000, been collecting such a large amount of data that it cannot be analyzed by a small group of scientists alone. Faced with this challenge, the researchers decided to pool the data rather than keep it for their own use, as was the norm before. The resulting very large databases were structured with complex query protocols. Access and use of the data were offered to a large community of users who were not necessarily laboratory scientists. These users in turn contribute data that they collect themselves, thus contributing to the expansion of the databases. The massification of data is not of the same nature and does not have the same consequences, depending on the domain. In science, scientific instruments produce a priori massive databases that are extremely clean and highly structured, containing elements of very high quality. For the data collected and manipulated by GAFAM for commercial use, Big Data are characterized by their great disparity. The challenge is to cross highly variable and disparate sources of information. That is, there is no specific experimental plan developed a priori. The data is acquired in an opportunistic way by analyzing, for example, the path of a web user, their clicks, their requests, the time spent and so on. Analysts seek to deduce a certain number of behaviors and generate recommendation or preference algorithms. As a result, with similar volumetric challenges, between scientific or “market” oriented communities, the difficulties to manage are not the same. The notions of variability (noise) are posed in a completely different way. At the level of scientific data, the sensor itself can be a source of noise, for which scientists will deploy the appropriate corrective measures and keep only the real data from the experiment. On the other hand, for the processing of web searches, or in the case of social networks, which generate a large volume of data, variability is intrinsic to the generation of

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these data. Companies such as Flickr, Google and Yahoo! have thus drawn some of the contours of Big Data (Babinet 2015). Since then, approaches based on Big Data have been successfully used in many fields, such as climatology and meteorology, as well as in finance and politics. Corpuses of literary works, in which data is more complex than massive, can also benefit from Big Data. Biological research has followed this scientific and technological evolution, known as bioinformatics, making it possible, in particular, to decipher the human genome more and more rapidly. The use of Big Data is also increasingly implemented in the field of health, such as in oncology (Anon 2020). Despite the disparity in the fields of application and the differences that may result, the literature and practices reveal certain invariants. Researchers and practitioners alike describe Big Data according to three fundamental characteristics, the “three Vs”: 1) Volume: exploiting a quantity of data from digital imaging, e-commerce, video surveillance, social networks and so on that requires innovative approaches in terms of both hardware and software (to store, distribute, parallelize, retrieve, etc.). 2) Variety: matching heterogeneous data from very diverse sources. There is no universal approach, and each case requires an adapted study. 3) Velocity: quickly access stored data, or even manage a continuous flow of data, in order to make crucial decisions in real time (e.g. one of the requirements for the advent of the autonomous vehicle) or gain a competitive advantage (e.g. in financial and stock market analysis, marketing strategy, bioinformatics or the design of customized customer services, etc.). Other V characteristics are evoked to describe the masses of data. These secondary specificities are derived from the previous properties. It is nevertheless useful to mention them because they offer an additional insight to better understand the operational issues related to Big Data. 4) Veracity: signifying the need to have correct and usable data. The preparation and processing of the Volume are particularly delicate operations and are essential to ensuring the quality of the data. 5) The huge amount of data underlying Big Data presents a major challenge in terms of Visualization. The representation of data and their analysis call for new and original formats to make possibly abstract, even elusive, phenomena readable and to reveal the meaning of the data as a complement to statistics. Visualization allows human judgment to be used to understand the context of a situation.

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6) Finally, the sometimes long and tedious processing of Big Data questions the Values (societal, environmental, economic) of such operations aiming to crossreference various data. Finally, the three-V description of the Big Data phenomenon outlines the wide variety of innovations that have been necessary to see the emergence of new practices and new businesses around Big Data. 4.3. Big Data: an interdisciplinary approach to technology and its uses Big Data requires an interdisciplinary approach at the least. Disciplines and professions must communicate in order to imagine and design relevant solutions. Computer database specialists and statisticians are forced to adopt processing systems capable of scaling up to a volumetric scale beyond the traditional computer spreadsheets of the 1990s. To the statistician and computer scientist must be added the business expert (i.e. the specialist in the discipline who seeks answers from the analysis of their data) in order to specify the questions to be asked, to provide a business vision, to give the keys to interpreting the data. Technologies are inseparable from Big Data on several levels: their generation (even if it is not systematic) and capture, storage, analysis or “search”, and representation. Reciprocally, this data is part of the growth and progress of these technologies, such as the structure of computers, the massively parallel processing of information storage, the development of database software, asynchronous computing, the production of results by fusion, the emergence of the Cloud. Certain services dedicated to masses of data are also emblematic of this industrial and commercial revolution: MapReduce (from Google) or Hadoop (from The Apache Software Foundation). On the methodological level, the emergence of Big Data leads to a scientific approach linking the exploratory and the confirmatory. In other words, “in a world where data are increasingly numerous, inductive reasoning is taking on a growing role” (Delort 2018). The analysis of a set of collected data, or datamining, therefore makes it possible to discover a relationship, to see correlations and events, hitherto ignored or invisible. The association of two molecules, for example, seems to show a positive effect. The researchers will then try to confirm this hypothesis with a very strict scientific protocol, such as the setting up of a clinical trial, if we take the example of the link between two molecules.

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Machine learning is also a constituent of mass data. This is predictive analysis, an automatic way of learning that is specific to the machine, which relies on the label attached to a large amount of data enabling it to distinguish images or consumer behavior, discover customer preferences and so on, in order to anticipate trends and make decisions accordingly. Nevertheless, this is an area that has been evolving rapidly since the beginning of 2010. While many algorithms are proposed or tested, they do not solve all problems and may have certain limitations, such as “overlearning” which “accurately reproduces learning data while being unable to make accurate extrapolations” (Lemberger et al. 2015). 4.4. A wide range of applications: promises and fears In a style close to that of a science fiction novel, several perspectives for the application of Big Data are widely described in the literature. In the field of public health and individual well-being, Big Data represents a potential that has barely been implemented. For example, moving from a curativebased health system, now largely based on the chemistry of drugs, to a preventive system that can skillfully and ethically exploit a multitude of Big Data to help us live in (good) health and autonomy for as long as possible. The analysis of prescriptions issued by doctors or the cross-referencing of all data from the National Health Insurance Fund would generate tremendous cost reductions for the community, coupled with undeniable individual gains (Babinet 2015; Bouzeghoub and Mosseri 2017). Numerous methodological, legal and industrial obstacles, in particular, make the advent of such scenarios uncertain, or greatly delay their implementation. In agriculture, the environment, cities and even intelligent territories, the deployment of sensors, “open” approaches (open data, open source, open-source hardware) and the associated data offer levers for analysis, management, design, implementation and collective action that raise many hopes. When subjects are exclusively invested in by private companies, expectations are more in the order of economic gain, but when a multitude of actors get along and work together, the expected gains are more in the order of societal and environmental gains (Babinet 2015; Broudoux and Chartron 2015; Bouzeghoub and Mosseri 2017). This dynamic also concerns large public administrations. In this respect, the creation of Etatlab in February 2011 is an example of the French government’s explicit commitment to design and implement its data strategy.

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Charolles (2019) and Dupuy (2019) question and warn of the possible drifts linked to the use of Big Data, pointing in particular to the epistemological weaknesses that tend to confuse causality and correlation. Although the massification of data allows certain links to emerge, it does not explain them. In particular, it is necessary to develop a capacity to detect biases in this mass of information, which, however important it may be, nevertheless remains fragmented in the face of the richness and complexity of the world (either because the data collected is not exhaustive or has been altered, or because it has been necessary to reduce it, i.e. destroy it, in order to store and analyze it). The position of these two authors also offers a response to Chris Anderson’s pessimistic view of the obsolescence of the scientific method (Anderson 2008). Projects and strategies based on Big Data ultimately retain an experimental character (Lemberger et al. 2015), which offers a double opportunity for innovation: this is around the associated technologies and methods, and the deployment of this technology in the different social universes that it transforms or questions. 4.5. Conclusion All of these observations lead us to sketch out certain development perspectives around Big Data and, more broadly, to understand artificial intelligence and the digitalization of all parts of our society. This digitization of the world must involve the advent of a digital world in the service of society and our environment by reducing energy-intensive infrastructures and processes, relying on cyber security that respects privacy, questioning the ownership of data and the ownership of value, and so on. At the European level, the General Data Protection Regulation (GDPR) offers an initial framework that enables everyone to be aware of and active in their digital lives. The exploitation and analysis of Big Data, particularly open data, must also be in the service of the common good and a shared society project. In this sense, France is spearheading the concept of “general interest data” introduced by the Digital Republic Act of October 20161. Transparency in the algorithms used, a wide dissemination of knowledge accessible to the greatest number of people and the generalization of computer language learning could also serve such a project. In a society imbued with artificial intelligence, professions are transforming and new ones are inevitably emerging. Companies, institutions, territories and even citizens will have to acquire new skills and know-how, no doubt at the interface between the management of innovation systems and the management of technological resources. Innovative training courses that combine technical, methodological, economic, organizational, managerial and philosophical aspects are therefore essential to understanding and using Big Data.

1 https://www.legifrance.gouv.fr/eli/loi/2016/10/7/ECFI1524250L/jo/texte.

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4.6. References Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. WIRED [Online]. Available at: https://www.wired.com/2008/06/pb-theory/. Anon (2020). Big hopes for big data. Nature Medicine, 26(1), 1–1 [Online]. Available at: http://www.nature.com/articles/s41591-019-0740-8. Babinet, G. (2015). Big Data, penser l’homme et le monde autrement. Le Passeur Editeur, Paris. Bouzeghoub, M. and Mosseri, R. (2017). Les Big Data à découvert. CNRS Edition [Online]. Available at: https://www.cnrseditions.fr/catalogue/sciences-politiques-et-sociologie/lesbig-data-a-decouvert/. Broudoux, É. and Chartron, G. (2015). Big Data – Open Data : quelles valeurs ? Quels enjeux ? De Boeck Supérieur, Louvain-la-Neuve. Charolles, V. (2019). Illusions et vérités du big data. Le Débat, 207(5), 132. Delort, P. (2018). Le Big Data. PUF, Paris. Dupuy, J.-P. (2019). La nouvelle science des données. Esprit, (5), 89 [Online]. Available at: http://www.cairn.info/revue-esprit-2019-5-page-89.htm?ref=doi. Lemberger, P. (2015). Big Data et Machine Learning – Manuel du data scientist. Dunod, Paris.

5 Blockchain – Blockchain and Co-creation within Management Methods

5.1. Introduction Blockchain1 is a form of data storage technology that makes it possible to make transactions traceable. Initially, Blockchain was dedicated to monetary transactions (bitcoin), but gradually it has come to concern all types of transactions, including intangible transactions. This is why Blockchain can be interestingly applied to open innovation, which is characterized by exchanges of ideas and contributions from innovators and co-creators. The concept of open innovation was first theorized by Henry Chesbrough. It involves an organization innovating outside the box, by opening up its network of traditional partners (suppliers, customers, research centers, academic and institutional partners, etc.) to innovate with them in a collaborative way. The concept of co-creation is a specific type of open innovation. This concept is often associated with “living lab” type approaches in which the individual user is the central actor in the innovation. A living lab can be defined as an open innovation ecosystem that reproduces real life scenarios and allows us to learn how individuals behave and use innovations in new ways. In a living lab, observation and interactions with users thereby provide concrete avenues for innovation. By being positioned as early as possible in the innovation process and throughout it, users drive innovation and can be qualified as co-innovators or even co-creators.

Chapter written by Eric SEULLIET. 1 By convention, the term Blockchain (with a capital B) refers to the underlying technology, while the term blockchain (with a lowercase b) refers to an application-specific blockchain. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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This co-creation has many advantages: – At the ideation stage, it makes it possible to produce more ideas and, above all, to generate a biodiversity of creative ideas from a plurality of actors. The observation of various situations and practices in real life contexts also makes it possible to detect new ways of using innovations. – Then, when it comes to experimenting more creatively, the larger number of actors facilitates faster iterations, generating more relevant results. – In the solution development phase, co-creation creates a constructive climate thanks to the mobilization of the collective intelligence of the actors. However, it is difficult to mobilize co-creators over time owing to a lack of motivation to contribute and difficulties in capitalizing on contributions (Dupont et al. 2019). As a result, the outcomes of traditional co-creation processes are often limited in scope and interest. In this context, Blockchain can be a solution to amplify co-creation and make it more effective. The rest of this chapter first presents Blockchain, as well as a review of the relevant literature on the specific topic of the use of Blockchain in management. We will then attempt to show the limits of co-creation, followed by the ways in which Blockchain (Seulliet 2016) can compensate for these limits. Finally, we will show that Blockchain can bring about radical changes in the broader field of intangible assets, intellectual property and individual creativity. 5.2. The interest of Blockchain in the field of immaterial exchanges Blockchain technology was invented in 2008 by a person or group known as Satoshi Nakamoto, and its first known application is Bitcoin. Blockchain is a technology that solves the problem of double spending usually associated with digital currencies. A Blockchain is a database that contains the history of all exchanges made between its users since its creation. This database is secure and widely distributed: it is shared by its different users, without intermediaries, allowing each user to verify the validity of the chain. Indeed, each transaction generates a new “block” to be added to the existing chain. After validation by the different members of the network, each new block is time-stamped and added to the blockchain. Each new linked transaction must be validated not by a trusted third party but by a form of consensus involving “miners” who must validate the transactions by solving complex cryptographic problems.

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A concept associated with Blockchain is that of “smart contracts”. These are algorithms that automate certain actions when certain predefined conditions are met. Just as Blockchain can secure and trace physical exchanges, it can also be applied to immaterial exchanges within and between organizations. A review of research carried out reveals that the theme of co-creation has been the subject of numerous analyses, particularly within the ecosystem of living labs; similarly, the subject of Blockchain has been studied numerous times, be it through the lens of technology, economics or society. On the other hand, the relevant literature is very incomplete on the topic of Blockchain in the context of co-creation. The most notable sources are listed in the bibliography. 5.3. The limits of the co-creation process For an organization, co-creation consists of involving its stakeholders – and in particular its customers and users – in its innovation processes by involving them in the process as early as possible. Co-creation processes face a number of limitations that reduce their impact: – The first limitation is quantitative, as it is often difficult to gather a large number of contributors. The scope of the innovations produced is also limited by the fact that contributors are generally recruited from a small and homogeneous circle of users. If co-creation is to be truly broadened, many more co-creators with diverse profiles need to be sought out. – A major problem in co-creation processes is sufficiently and sustainably mobilizing participants. Several factors explain such difficulties in mobilizing and involving co-creators. First and foremost, there are psychological difficulties: problems of envy, jealousy and competition between individuals, which tend to lead to a natural tendency of each individual to overvalue their own contribution and to minimize that of others. This can lead to the withholding of information and a reluctance to cooperate. – Another important factor to consider is the issue of intellectual property. The full engagement of participants in a co-creation process will be quickly limited if they feel that they are not recognized as the authors of their contributions. This recognition is a key motivating and stimulating factor in co-creation processes. It is similar to the concept of “nudging” (popularized by Richard H. Thaler, winner of the Nobel Prize in Economics in 2017), which consists of indirectly encouraging

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individuals to adopt new behaviors deemed responsible and beneficial to themselves and to the community. – Co-creation also faces organizational and logistical difficulties: how can co-creation approaches be optimized in order to get the most out of them? How can groups best capitalize on each other’s contributions so as to not “reinvent the wheel”? How can contributions be efficiently sorted and prioritized? How can they be measured and evaluated? How can productive group meetings be organized? What are the most effective methods and strategies? What are the most favorable environments and contexts? In practice, it is complicated to recruit co-creators and it is difficult to motivate them in the long term. In addition, the whole process is rather slow because of the iterations involved. The process also needs to be organized and animated, which requires human resources and tools. 5.4. Blockchain in mobilizing and organizing co-creation processes Although most of the research related to Blockchain since its inception has focused on issues related to Bitcoin, it is essential to note that Blockchain technology has applications that go far beyond cryptography. For example, Blockchain finds a particularly promising field in intangible capital and co-creation. For what is a co-creation process made up of if not transactions of ideas, suggestions, creative and inventive contributions? All the advantages of Blockchain can therefore be applied to co-creation (Duvaut et al. 2018): – Blockchain is a technology with intrinsic virtues of transparency and fairness. It generates a spirit of sharing and collective intelligence, as well as a sense of belonging to a community. These qualities make Blockchain an ideal tool for building trust. – By protecting intangible assets, Blockchain provides security for innovators, which in turn encourages their involvement. – By ensuring the traceability of contributions, Blockchain makes it possible to know who was at the origin of the value creation in a co-creation process, which ensures that the co-creators receive true recognition for their respective contributions. – The capitalization of contributions allows new paths to be built by recombining innovative ideas and approaches. This cross-fertilization is a powerful driver of creativity and inventiveness.

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– Blockchain may also include a scoring and voting mechanism that allows for peer evaluation of participants. Blockchain can also further incentivize contributors by setting up a system of remuneration through “tokens”. – Thanks to the implementation of “smart contracts”, Blockchain can make cocreation processes more efficient by accelerating and optimizing exchanges, notably by eliminating the need for intermediaries. Above all, by enabling the hybridization and entanglement of contributions, Blockchain enables new avenues of innovation to be mapped out. With these characteristics in mind, it is not surprising that Blockchain can be considered the technology with the most potential for advancing the co-creation of innovation. 5.5. The promises of Blockchain Looking ahead, we can predict that Blockchain will open up new horizons in many areas. 5.5.1. Intellectual property renewal By encouraging individuals to become more innovative and creative, Blockchain could revolutionize intellectual property (Duvaut and Seulliet 2018). The adoption of Blockchain should pave the way for a cheaper, faster, automated patenting process whose arbitration would depend on an algorithm rather than a judge. It can also be argued that tokenization via Blockchain prefigures a new form of patent system, which would increase trust between competing companies, stimulate cooperation and, ultimately, further promote open innovation (Duvaut et al. 2019). 5.5.2. “Empowerment” of individuals A major revolution brought about by Blockchain is certainly its ability to give power and autonomy back to everyone. Blockchain is a tremendous driver of inclusiveness: everyone can express themselves, express their ideas, get rewards and even monetize them. This opens up immense possibilities for the extensive deployment of innovation and therefore for much broader value creation that benefits everyone (Shavit and Seulliet 2017).

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5.5.3. Scaling up Blockchain makes it possible to create virtual communities. These can be very large and can extend ubiquitously across the globe. Blockchain can support and multiply this growth in innovation by hugely scaling it up. 5.5.4. Collective intelligence The great strength of Blockchain is that it develops organically, according to the biological process of stigmergy. This principle states that the trace left in the environment by the initial action of an agent stimulates a subsequent action, either by the same agent or a different one. In this way, successive actions tend to reinforce each other, leading to the spontaneous emergence of coherent and systematic activity. Thanks to this process, Blockchain makes it possible to mobilize collective intelligence, introduce a spirit of sharing, mutualize the contributions of community members and generate broader cooperation that is more natural, fairer and more motivational. 5.5.5. New forms of organization and social impact The spirit of Blockchain is fundamentally based on collective values: those of sharing, cooperation and the creation of common ground. It thereby paves the way for a new economy that some call the crypto-economy, based in particular on peer-to-peer transactions without confiscation by intermediaries of the value created (Schenk et al. 2020), with the latter equitably distributed among those who created it. These new schemes will enable the emergence of new decentralized, more democratic and ethical social organizations such as DAOs (Decentralized Autonomous Organizations): these are organizations whose rules of governance are automated and inscribed in an immutable and transparent way within a blockchain. 5.5.6. Necessary developments However, it must be noted that Blockchain as it stands is not free of technological, practical and societal issues. In particular, it will be necessary to resolve the issue of the enormous computing power required for mining operations, as well as those of generating costs, low yields and slow transactions. At the technological level, the challenge for Blockchain is to be able to harness ideas and co-creations and to transcribe them correctly. There is also the challenge of bringing

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together the innovators most suited to collaboration. This is where solutions based on artificial intelligence and smart data come into play. When it comes to developing proof of concept, virtual reality is also a solution that can bring great added value to a co-creation platform. Another issue is the security and reliability of the system. Recent events have shown that Blockchain is not without flaws. Ultimately, provided that we do not chain individuals to technology, however attractive this may seem, and that we are careful to give primacy to human beings and ethics, the paths of Blockchain will certainly deserve to be explored by those who believe in the virtues of co-creation. 5.6. Conclusion As we have seen, co-creation is a recognized approach that makes innovation more relevant. By involving the user, this type of collaborative innovation allows us to better take into account the user’s needs and aspirations and to imagine new ways to meet them. However, co-creation comes up against certain limitations as co-creators are often reluctant to get fully involved and their contributions are not always exploited to the full. Co-creation therefore has a limited impact. Faced with these limitations, Blockchain can provide interesting answers. Indeed, it has two essential advantages in terms of co-creation: on one hand, it creates a climate of trust and transparency that makes co-creators feel secure and, on the other, it generates a type of “nudge” that encourages their commitment. However, Blockchain is not yet a mature technology and must itself overcome certain economic, technological and environmental problems. There is no doubt that viable solutions to these problems will be found, as avenues are already being explored. On the other hand, other “deeptech” technologies that combine with Blockchain, such as artificial intelligence, smart data, virtual reality, cybersecurity, the Internet of Things and so on, are likely to offset and amplify the contributions of Blockchain. In the future, Blockchain could therefore be a formidable driver to stimulate co-creation, thus enabling it to move from the artisanal stage to a larger scale.

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5.7. References Dupont, L., Mastelic, J., Nyffeler, N., Latrille, S., Seulliet E. (2019). Living lab as a support to trust for co-creation of value: Application to the consumer energy market. Journal of Innovation Economics & Management, 53–78. Duvaut, P. and Seulliet, E. (2018). Blockchain, a technology that also protects and promotes your intangible assets [Online]. Available at: https://medium.com/@ericseulliet/Blockchaina-technology-that-also-protects-and-promotes-your-intangible-assets-20fc9154e885. Duvaut, P., Seulliet, E., Shavit, D. (2018). Reinventing co-creation thanks to the Blockchain [Online]. Available at: https://www.linkedin.com/pulse/reinventing-co-creation-thanksBlockchain-eric-seulliet/. Duvaut, P., Joly, L., Seulliet, E., Solani, S. (2019). Libérer la propriété intellectuelle grâce à la Blockchain [Online]. Available at: https://www.hbrfrance.fr/chroniques-experts/2019/ 07/27030-liberer-la-propriete-intellectuelle-grace-a-la-Blockchain. Schenk, E., Schaeffer, V., Pénin, J., Mention, A.-L., Torkkeli, M. (2020). Blockchain and the future of open innovation intermediaries: The case of crowdsourcing platforms. In Managing Digital Open Innovation, Barlatier, P.-J. and Mention, A.-L. (eds). World Scientific Publishing Co. Pte. Ltd., Singapore. Seulliet, E. (2016). Open innovation, co-creation: Why Blockchain is a small revolution [Online]. Available at: https://medium.com/@ericseulliet/open-innovation-co-creation-whyBlockchain-is-a-small-revolution-73e7d0b480d5. Shavit, D. and Seulliet, E. (2017). The empowerment of people thanks to the Blockchain in 7 points [Online]. Available at: https://medium.com/@ericseulliet/the-empowerment-ofpeople-thanks-to-the-Blockchain-in-7-points-e5ccb345905e.

6 Bricolage – From Improvisation to Innovation: The Key Role of “Bricolage”

6.1. Introduction In a basic sense, do-it-yourself (DIY), or bricolage, indicates a practice of creation, creative repair and/or repurposing of use, essentially using the means at hand (tools, methods, spaces, materials) and without an a priori commercial aim. Beyond the experience already acquired through learning as well as through intuition, experimentation and interactivity, bricolage is a practice that favors the resolution of more or less complex problems up to and including the elaboration of objects using tools (generally rudimentary but sometimes sophisticated), through the development of flexible and open knowledge mobilizing significant creativity (and/or co-creativity in a collaborative framework). Alternatively, as with any innovative activity, the creation of something new, here through practice, is incorporated into something existing (for a creation, modification, transformation, functional repair or repurposing of use), either marginally or structurally. To analyze the term bricolage, we will consider it as a new concept, through its current application, and through its link to improvisation and frugal innovation. 6.2. Bricolage: new concept, old practice Bricolage is a practice based on experience, learning, intuition, trial-and-error experimentation (including by chance through serendipity: finding by not looking or not looking for the thing found) and interactivity. It is an adaptive creative approach with strong individual involvement (which can be part of a collaborative framework) Chapter written by Paul BOUVIER-PATRON. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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through the acquisition of skills in a generally agreed and collectively accumulated knowledge base, transmitted by word of mouth and/or through associations and/or by access, via the Internet, to a digitized corpus (fixed or evolving but accessible and interactive) based on exchanges, reciprocity or free unilateral provision. Although it has existed for a long time, bricolage is a new concept that should be explored, as well as (re)placed at its true level of importance. Bricolage was highlighted within HSS by Levi-Strauss (1962) as a key practice in primitive societies, which cleverly mobilize the natural resources at their disposal to best meet their needs. This concept is now finding a renewed presence through the activity of “makers” (Anderson 2012), mobilizing new technologies (Information & Communication (ICT); Digital (DT)) within communities of practice (Brown and Duguid 1991), through creation/co-creation (then conception/co-design) by making new objects, or even repairing or repurposing objects, either individually (DIY: “Do It Yourself”) or collectively (DIWO: “Do It With Others”) in a closed proprietary perspective (with a commercial valuation perspective) as opposed to an “Open Source” perspective (to develop open applications for sharing and/or commercial purposes). 6.3. Current application of the bricolage concept Bricolage is part of a broad spectrum of gradation of the technological means mobilized based on the use of the means available. It is a more or less formalized and organized individual and/or collective logic of sharing, exchange, learning and effective realization. The commercial purpose is not to be excluded when the art of bricolage is used in for-profit organizations, indirectly by innovation communities not exclusively linked to companies or directly when innovation communities function as adjuncts to the service of a company(ies). Innovation communities bring together, around a project that cannot be usefully or efficiently conducted (within a company or in the inter-organizational sphere through the sole coordination of the client–supplier network), complementary knowledge, know-how and interdisciplinary skills, with a more or less technological foundation. They do this by meeting in a physical place (“Makerspace”) called an innovation platform if dedicated to a specific project, generally mobilizing a “fab lab” creation space, and other technological or organizational devices (a co-working space, an incubator for start-ups, an accelerator to accompany the growth of a mature start-up, a “show room” with a test space). Such a place can exist, virtually, through a virtual community of innovation linkage, via the Internet, in an interactive and dynamic way, with individuals or collectives passionate about an idea, a project or an attractive center of interest (e.g. brand) coming together to generate collective intelligence and/or move towards a common realization.

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Bricolage is essential in the process of creating or co-creating objects in order to explore and test the system of constraints, and even to be able to build a scalable (possibly digital) model depending on the complexity to be considered or, in any case, a first, more or less functional, prototype. Bricolage mobilizes available resources without any (immediate) profitability objective, but with the aim of experimenting with solutions and, according to the constraints established (by a reference community or beyond), possible objectives, relevant functionalities, potential uses, costs, means and of working towards an acceptable compromise (“satsficing” as per H. Simon), in the expected results. Alternatively, it at least encourages openness to solutions or unexpected results (serendipity) by inviting cross-disciplinary fields (HSS/Sciences/Engineering) to collaborate and by articulating all areas concerned (design, production, distribution, use), as well as all stakeholders (shareholders, employees, customers, suppliers, associations concerned by the positive/negative impact of the company). 6.4. Bricolage and improvisation A distinction should be made between the concepts of bricolage (where scarcity is essential through the initial mobilization of the means available, without any time constraints except, pragmatically, where required or in the event of imminent danger) and of improvisation (where the execution time of a solution found spontaneously or through research and/or training, in response to a new situation, is essential in a competitive and performance context). In improvisation, the temporal dimension makes it possible to deal as quickly as possible and in a flexible way with any unforeseen event, compared to the usual organization of the activity prescribed or carried out, effectively and largely without the constraints of scarcity. To date, the literature on management has generally been oriented towards this last concept of improvisation (Weick 1993; Cunha et al. 2014). For improvisation, the challenge is to anticipate the adaptation required to succeed in creating (or co-creating) or restoring an appropriate situation (developing or not). Improvisation can benefit from an active preliminary training phase based on instructions, rules, guides or scenarios (from the most to the least likely), according to a likely spectrum of possibilities (without, however, ever being exhaustive). However, the critical case, within the framework of an organizational routine, is precisely the occurrence of an unexpected situation that deviates from what has been foreseen, formalized and/or planned: this critical case requires, in order to respond to it, the art of improvisation (based on training, practice, experience and know-how) and can mobilize, if necessary, the art of bricolage. As for the art of bricolage itself, it fundamentally challenges the concept of managerial efficiency (linking the means used to achieve the result; already opposed to the concept of efficiency where the end justifies the means to achieve the result).

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Indeed, the art of bricolage makes it possible to obtain a result by often using very few means or makeshift methods, whether unexpected or surprising, while reaching the result. We can therefore consider bricolage to be a strong contributor to organizational innovation and sometimes even be a trigger for it. The practices of bricolage and/or improvisation are compatible and can be practiced individually or collectively, in a closed or open space, privately or collectively, and with or without profit. The art of bricolage and the art of improvisation (jointly or separately) contribute to creation and co-creation, then to conception and inventive co-design, through practice based on experience, knowhow, intuition, experimentation, interactivity, exchanges of experience and sharing, while acknowledging the crucial importance in innovation of experimentation and chance (serendipity). 6.5. Bricolage and frugal innovation The art of bricolage and the art of improvisation, useful for creativity and co-creativity, can be fostered by, inter alia, technological means (“fab lab” functioning as a Swiss Army knife that can do almost anything: Gershenfeld 2005) and organization (“MakerSpace”), but the predominant method in the practice (sophisticated “high tech” or frugality). The sophistication versus frugality debate is essential in a context of increasing scarcity of resources and cost rationalization. Frugality itself deserves a closer look in order to clarify its scope, as the term does not refer to the same content for everyone. Frugality must be clearly dissociated from the question of inversion, on the one hand, and from circularity, on the other hand. There is “inversion” of the usual flow (perceived through Vernon’s product lifecycle curve) if the export originates from a BRIC-type country and the recipient is a Western country. However, in the case of offshoring, there is no inversion if the product is re-exported to the West (while benefiting from low or non-existent social and environmental costs). Two economic models are then opposed, the low cost model (mainly for export or re-export) and the BOP model (“Bottom of the Pyramid”: Prahalad 2005), where the aim is to serve first the population base (in its entirety but beginning with the masses). Possessing a logic of inclusiveness. BOP is based on a frugality of Indian origin and occurs in two main modalities: “Gandhian” (more with less for many) and “Jugaad” (more with less for many, but of good quality and desirable/attractive). The frugal innovation approach mobilizes bricolage at the stage of creation (and co-creation) and design (and co-design) of object(s) but uses a successful logic of making a product that mobilizes the least amount of immediately available (and generally scarce) resources. In contrast, sophistication may resort to technological

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bricolage (Gershenfeld 2005; Anderson 2012) at the creation (co-creation) and design (co-design) stages of the proto-industrial stage. Frugal innovation consists of doing what already exists or what does not yet exist but with less: doing it cheaper and better without sophistication while offering at least as many functionalities (often new ones) as well as product design, production quality, relevance (adequacy to real local needs: adapted, affordable, robust, repairable product), effectiveness (ease of use) and development potential. However, there is no circular economy in the sense that the waste generated elsewhere is not necessarily intended to be reused as inputs. The circular economy implies thinking about the production of objects for reuse. This is not in itself environmental (reducing or eliminating environmental impact). CtoC (“Cradle to Cradle”: Braungart and McDonough 2002) can therefore be considered environmentally virtuous depending on whether or not it allows the production of environmentally friendly products with ecological inputs that can be reused indefinitely. The environmental consequences are analyzed throughout the lifecycle, from design, production, sale, use and disposal. Properly conducted eco-design (see co-creation) must anticipate environmental consequences and can draw inspiration from nature (Benyus 1997). This is the purpose of “grassroots” innovation (decentralized, collective and environmentally friendly innovation: Seyfang and Smith 2007), which is lateralized as it is based on skills distributed among peers and induces common sense, bricolage and/or even technological bricolage, as well as individual/collective resourcefulness (System “D”), by taking into account from the outset the environment at all stages of the product’s lifecycle without unnecessary or excessive sophistication (referring to technological sobriety or a satisfactory technological compromise: H. Simon’s “satisficing”). “Grassroots” innovation is a form of frugality that uses bricolage by associating it with the reuse of available resources (resulting from waste constituted by objects at the end of their life for various reasons: defectiveness, non-repairable function, breakage, spare parts that cannot be found). Is this waste the result of mass obsolescence? The term “planned obsolescence” has been used at least since 1932 (through the publication in the USA of a booklet entitled Ending the Depression through Planned Obsolescence by B. London). It is, however, with the industrial designer B. Stevens (Brooks Stevens Design Associates: BSDA), that the concept of aesthetic obsolescence was born in the 1950s, namely to produce goods that are attractive but can be made obsolete quickly by creating a desire for renewal and change. Obsolescence is caused by inculcating in the buyer the dual desire for an ever more

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recent one to be disposed of a little earlier than necessary. A. Sloan, director of General Motors from 1923 to 1956, in addition to implementing the divisional form of which he was the designer and project manager (A. Chandler then theorized it), was also a precursor by promoting, on the basis of the chassis–engine couple, a range of vehicles with bodywork renewed annually by advertising stimulation (by playing on shape, color, accessories) from the 1930s onwards. The obsolescence planned by fashion and/or design is a fact. Contemporary debate on planned obsolescence is based on a conspiracy theory based on suspicions of planning for defects, fragility or a shortened lifespan, independently of the necessary power efficiency–cost trade-offs of a product sold (a product is optimized on functionalities and/or capacities at a given moment but will be overtaken by technological development). A product should, however, be repairable, but this is rarely the case. Most of the products on offer today are still disposable (possibly recyclable, but not really designed for circular reuse). Bricolage users can be mobilized to repair, in whole or in part, an object and/or make use of waste by extracting the spare parts needed for re-assembly, or assemble them differently to make something else. Waste collection centers are essential in making reused materials available: decommissioned stocks feed resource centers, improvised locally or organized by sector and volume (on a large scale and by networks in developed countries). In the least developed countries or poor countries with no health and/or environmental concerns, it still seems difficult to develop recycling channels that respect the health of populations and the environment. However, starting to establish a virtuous circle does not solve the issue of reducing the stock of waste piled up globally, on land, underground or in the seas. 6.6. Conclusion Whether in a frugal approach (in the first instance, technologically simple or, more precisely, mobilizing a satisfactory level or degree of technology), in rudimentary workshops or in sophisticated spaces (intense in technology within a “fab lab” or, more broadly, a “makerspace”), bricolage (basic and/or technological) is essential in both the creation of intellectual and/or artistic works (and co-creation) and the co-design of artifacts. Bricolage has been a central concept in practices from the primitive ages of civilization to modern “makers”, as a solution to problems arising in the development of projects and/or objects in an individual or collective framework. Bricolage is inseparable from experimentation during the course of a practice and calls upon intuition, creativity, learning, interactivity and the mobilization of tricks and tips to develop, repair and divert already existing or invented objects, generally with the means at hand, as well as with sophisticated technological means.

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Bricolage exists in every company, from sole proprietorships to start-ups to very large companies. 6.7. References Anderson, C. (2012). Makers: The New Industrial Revolution. Crown Business, New York. Benyus, J.M. (1997). Biomimicry, Innovation Inspired by Nature. Harper Collins, New York. Braungart, M. and McDonough, W. (2002). Cradle to Cradle: Remaking the Way We Make Things. North Point Press, New York. Brown, J. and Duguid, P. (1991). Organizational learning and communities of practice: Towards a unified view of working, learning and innovation. Organization Science, 2, 40–57. Cunha, M., Rego, A., Oliveira, P., Habib, N. (2014). Product innovation in resource-poor environments: Three research streams. Journal of Product Innovation Management, 31(2), 202–210. Gershenfeld, N. (2005). Fab: The Coming Revolution on Your Desktop – From PC to Personal Fabrication. Basic Books, New York. Levi-Strauss, C. (1962). La pensée sauvage. Plon, Paris. Prahalad, C. (2005). The Fortune at the Base of the Pyramid: Eradicating Poverty through Profits. Wharton School Publishing, Philadelphia. Seyfang, G. and Smith, A. (2007). Grassroots innovations for sustainable development: Towards a new research and policy agenda. Environmental Politics, 16(4), 584–603. Weick, K. (1993). The collapse of sensemaking in organizations: The Mann Gulch disaster. Administrative Science Quarterly, 38, 628–652.

7 Circularity – The Circular Economy as an Innovative Process

7.1. Introduction The circular economy refers to the transition to a new model of production and consumption whose objective is to extend the use of non-renewable resources and avoid or reduce the production of waste (Gallaud and Laperche 2016). Although the concept was popularized in 2012 by the reports from the Ellen MacArthur Foundation (2012, 2013, 2014), its theoretical basis dates back to the 1970s and the work of economists on environmental issues and resource depletion. The circular economy is an “umbrella concept” that is fed by contributions from related concepts: systems and closed-loop thinking (industrial ecology, industrial and territorial metabolism), symbioses between stakeholders (industrial ecology), expansion of the lifespan of products and services (eco-design, lifecycle analysis), and dematerialization of exchanges and goods (economy of functionality). It therefore maintains close links with engineering and management. The issue of optimizing the management of resources and flows, which is the domain of engineers, is thus opened up to management issues following questions about the processes of coordination and organization between stakeholders. The literature on circular business models questions the practical applications of the economic model of the circular economy on the evolution of the organization of companies. Nevertheless, the issue of innovation and innovative processes is still little addressed in the literature on the circular economy (Gallaud and Laperche 2016).

Chapter written by Sonia VEYSSIÈRE. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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The link between the circular economy and innovation is emphasized in the design of new business models or new products; however, the nature of the circular economy as an innovative process is rarely taken into account. We will therefore focus on demonstrating how the circular economy itself represents an innovative process, before returning to the nature of the innovations potentially constituted by the circular economy. 7.2. The circular economy: a transformative concept If we consider the circular economy solely as the introduction of practices aimed at optimizing the use of resources and reducing waste, we may wonder about its radically innovative nature. Indeed, the work of Sabine Barles (2015) presents practices for the recovery of urban waste and excreta dating back to the 19th century (ragpickers and scrap dealers). The circular economy would thus be an attempt to bring these practices up to date in a post-industrial society. The introduction to the special issue of the journal Technology and Innovation, devoted to the circular economy (Boldrini and Entheaume 2019), calls for a more comprehensive and systemic view of the circular economy. It would refer to a transition away from a linear model (take, make, dispose) by reconsidering, within technical systems, the design of products and the choice of their materials, reorganizing sectors and their supply chains, imagining new sustainable business models and creating favorable conditions from a technological, organizational, strategic and legislative point of view (Bocken et al. 2016). The circular economy describes, at this scale, the trajectory of radical and systemic innovation, passing through a stage of emergence and then transformation of the market and productive processes. Similarly, Blomsma and Brennan (2017, p. 603) consider the circular economy to be a framework, where ideas establish particular practices when they concern wastes and resources operating at a territorial scale and implemented in value chains, industries and other networks. The Ellen MacArthur Foundation (2012) refers to the Schumpeterian concept of creative destruction as the scenario for a transition, where the circular economy spreads through pioneering industries and then a phase of wider adoption. However, a review of the literature reveals two major trends. First, the majority of publications on the definition of the circular economy either do not address its relationship to innovation or remain very vague on the subject. The circular economy is assimilated into a new economic model. Greyson (2007) analyzes the transition to the circular economy as a shift from an incremental approach to a preventive approach, motivated by the consideration of economic imperatives at the

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level of governments. Similarly, Ghisellini et al. (2016) contrasts classical economic models with the circular economy, which introduces a paradigm shift on the idea of growth and the infinite availability of resources. Esposito et al. (2017, p. 7) define the circular economy as an emerging paradigm of industrial organization, which engages economic actors so as to review their current models and change their conceptual relationship with and thinking about their markets, clients and resources. It is thus admitted in some publications that the circular economy is a new model and a vector of change; however, this observation is sometimes detached from a reflection on what makes this model new in relation to the logics of the past, the transformations brought about by this new model and their effects. This literature gives little information on what makes up the innovation process. 7.3. The circular economy as a source of innovation Nevertheless, the literature crossing the circular economy and innovation is growing, and allows us to shed light on different aspects of the nature of the innovative process and the character of the innovations generated by the circular economy. On the basis of a search on the international Web of Science database, we have come up with several results (63 selected references). We built a typology in relation to the main keywords of the articles and the abstracts. The articles highlighted concern innovations related to modes of organization, technological innovations related to products or processes and manufacturers, and the emergence of specific forms of innovation related to the circular economy. We note that there are few publications concerning innovations of a technological nature and they mostly concern the design of productive processes related to the looping of flows at the macro (electronic waste, agricultural by-products) or micro (phosphorus) scale. Technological innovation is sometimes an entry point for analyzing more systemic transformations within firms. The literature discusses Product-Service Systems (PSS) combining different forms of innovation (sale of products and services). Based on a review of the literature, Guzzo et al. (2019) establish a typology of PSS related to the circular economy. Barrie et al. (2019) show, in the field of biotechnologies, how product innovations promote coordination between actors in the case of innovation networks. The literature on technological innovations is therefore deeply connected with that on organizational innovations. The latter constitutes the majority of the articles, with a predominance of research on circular business models and the scale of firms. This literature responds to several challenges, establishing the characteristics of circular business models with a direct application focus and studying examples in

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different types of firms (start-ups, SMEs, multinationals). Nussholz (2017) contributes to defining the innovative character of circular business models, since they focus their value creation on strategies that guarantee the preservation of the economic and environmental value of materials. They thus involve organizational reconfigurations, especially in logistics and collaboration between firms and subcontractors. However, this current of literature insists on the non-territorial aspect of innovation, which reinforces the idea defended in particular by the Ellen MacArthur Foundation on the pioneering industries of transition. Other authors, in line with the work on industrial ecology, have focused on demonstrating the role of the territory in the innovative process, particularly in the establishment of symbioses between actors for the exchange of by-products and services. Kasmi et al. (2018) mobilize the theoretical framework on the innovative environment, resulting from the work of Aydalot and GREMI (1987), to link the characteristics of the environment and the creation of innovations of various natures (products and services). Urban and rural territories are also the subject of articles, but more to study small-scale processes (makerspaces, living labs). Finally, the circular economy is linked to the notion of eco-innovation. Eco-innovation refers to the introduction of efficient production practices, and products and services that help to mitigate negative environmental impacts (OECD 2010). It is understood as a lever for the transition to the circular economy (De Jesus et al. 2018) at different levels: at the macro level, eco-innovation accompanies global change dynamics by strengthening cooperation between the public and private sectors and new public policies; at the meso level, eco-innovation fosters collaborations around sustainable products and services within a system; and, at the micro level, eco-innovation is a tool for the eco-design of products and services. The development of eco-innovations also supports indicators of circularity (Smol et al. 2017). From this literature review, we can distinguish several features of circular innovation. It is above all global and complex in scope as it combines several forms of innovation (organizational and product). It pursues objectives related to preserving the value of resources within business models and in products, and seeks to eliminate the negative impacts of growth on the environment. The innovative process is based on coordination at different scales (between the public and private sectors, between different types of stakeholders and activities within a symbiosis and between different companies within innovation networks).

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Type

Example

Authors

Technological innovations

Product

De Los Rios, I.C. and Charnley, F.J. (2017). Skills and capabilities for a sustainable and circular economy: The changing role of design. Journal of Cleaner Production, 160, 109–122. Potting, J., Hekkert, M.P., Worrell, E., Hanemaaijer, A. (2017). Circular Economy: Measuring Innovation in the Product Chain (No. 2544). PBL Publishers. Smol, M., Kulczycka, J., Avdius Chenko, A. (2017). Circular economy indicators in relation to eco-innovation in European regions. Clean Technologies and Environmental Policy, 19(3), 669. Wieser, H. (2016). Beyond planned obsolescence: Product lifespans and the challenges to a circular economy. GAIAEcological Perspectives for Science and Society, 25(3), 156–160.

Organizational innovations

Circular business models

Bocken, N.M., De Pauw, I., Bakker, C., van Der Grinten, B. (2016). Product design and business model strategies for a circular economy. Journal of Industrial and Production Engineering, 33(5), 308–320. Geissdoerfer, M., Morioka, S.N., de Carvalho, M.M., Evans, S. (2018). Business models and supply chains for the circular economy. Journal of Cleaner Production, 190, 712–721. Linder, M. and Williander, M. (2017). Circular business model innovation: Inherent uncertainties. Business Strategy and the Environment, 26(2), 182–196. Nußholz, J. (2017). Circular business models: Defining a concept and framing an emerging research field. Sustainability, 9(10), 1810. Urbinati, A., Chiaroni, D., Chiesa, V. (2017). Towards a new taxonomy of circular economy business models. Journal of Cleaner Production, 168, 487–498.

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Example

Authors

Systemic innovations

Product–service systems

Barrie, J., Zawdie, G., João, E. (2019). Assessing the role of triple helix system intermediaries in nurturing an industrial biotechnology innovation network. Journal of Cleaner Production, 214, 209–223. Guzzo, D., Trevisan, A.H., Echeveste, M., Costa, J.M.H. (2019). Circular innovation framework: Verifying conceptual to practical decisions in sustainability-oriented productservice system cases. Sustainability, 11(12), 3248. McAloone, T.C. and Pigosso, D.C. (2018). Designing product service systems for a circular economy. Designing for the Circular Economy. Routledge. Tukker, A. (2015). Product services for a resource-efficient and circular economy – A review. Journal of Cleaner Production, 97, 76–91. Yang, M., Smart, P., Kumar, M., Jolly, M., Evans, S. (2018). Product-service systems business models for circular supply chains. Production Planning & Control, 29(6), 498–508.

Eco-innovations

Product/organizational, De Jesus, A., Antunes, P., Santos, can be generated by Mendonça, S. (2018). Eco-innovation in transition to a circular economy: the territory analytical literature review. Journal Cleaner Production, 172, 2999–3018.

R., the An of

Demirel, P. and Danisman, G.O. (2019). Eco-innovation and firm growth in the circular economy: Evidence from European small- and medium-sized enterprises. Business Strategy and the Environment, 28(8), 1608–1618. Scarpellini, S., Marín-Vinuesa, L.M., PortilloTarragona, P., Moneva, J.M. (2018). Defining and measuring different dimensions of financial resources for business eco-innovation and the influence of the firms’ capabilities. Journal of Cleaner Production, 204, 258–269.

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Authors Shih, D.H., Lu, C.M., Lee, C.H., Cai, S.Y., Wu, K.J., Tseng, M.L. (2018). Eco-innovation in circular agri-business. Sustainability, 10(4), 1140. Smol, M., Kulczycka, J., Avdiushchenko, A. (2017). Circular economy indicators in relation to eco-innovation in European regions. Clean Technologies and Environmental Policy, 19(3), 669.

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Barrie, J., Zawdie, G., João, E. (2017). Leveraging triple helix and system intermediaries to enhance effectiveness of protected spaces and strategic niche management for transitioning to circular economy. International Journal of Technology Management & Sustainable Development, 16(1), 25–47. Kasmi, F. (2018). Le milieu “écoinnovateur” – Écologie industrielle et diversification de l’économie territoriale. Revue technologie et innovation, (18/3), 1–17.

Table 7.1. Typology of forms of innovation related to the circular economy

7.4. Conclusion The circular economy is in itself an innovation. Beyond its impacts in terms of growth and jobs (Ellen MacArthur Foundation 2012), it calls for a profound change in economic model. From this perspective, it goes beyond the field of production and exchange to other dimensions (societal, political and ecological). Although it introduces a radical break with the linear model, the circular economy is built incrementally, by including various concepts and practices contributing to the valorization and reduction of resource consumption (industrial ecology, economy of functionality, lifecycle analysis). The circular economy is also a source of innovation, mostly multidimensional (product/services). This is part of eco-innovations, which are aimed at sustainability objectives, although other frameworks can be mobilized (sustainable innovation and frugal innovation). Concerning the nature of the innovative process, the debate

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remains ongoing between models where the influence of the entrepreneur and the firm’s internal R&D predominates, and evolutionary and interactionist models where the environment plays a role in the constitution of innovations. However, the systemic and cross-cutting nature of the economy justifies inter-sectoral and multi-actor collaboration, if only because it is based on macro-level transformations. The resolution of obstacles to innovation (Kasmi 2018) (legislation, lack of trust between actors) is based on these coordination processes. One of the challenges remains the visibility of the impacts (economic, social and environmental) of circular innovations to be able to establish different transition scenarios. 7.5. References Barles, S. (2005). L’invention des déchets urbains, France, 1790–1970. Seyssel, Champ Vallon. Barrie, J., Zawdie, G., João, E. (2019). Assessing the role of triple helix system intermediaries in nurturing an industrial biotechnology innovation network. Journal of Cleaner Production, 214, 209–223. Blomsma, F. and Brennan, G. (2017). The emergence of circular economy: A new framing around prolonging resource productivity. Journal of Industrial Ecology, 21(3), 603–614. Bocken, N.M., De Pauw, I., Bakker, C., van Der Grinten, B. (2016). Product design and business model strategies for a circular economy. Journal of Industrial and Production Engineering, 33(5), 308–320. Boldrini, J.C. and Antheaume, N. (2019). Une transition vers quelle économie circulaire ? Revue technologie et innovation, (19/4), 1–6. De Jesus, A., Antunes, P., Santos, R., Mendonça, S. (2018). Eco-innovation in the transition to a circular economy: An analytical literature review. Journal of Cleaner Production, 172, 2999–3018. Ellen MacArthur Foundation (2012). Towards the Circular Economy, Vol. 1: An economic and business rationale for an accelerated transition [Online]. Available at: https://www.ellenmacarthurfoundation.org/publications/towards-the-circular-economy-vol1-an-economic-and-business-rationale-for-an-accelerated-transition. Ellen MacArthur Foundation (2013). Towards the Circular Economy, Vol. 2: Opportunities for the consumer goods sector [Online]. Available at: https://www.ellenmacarthurfoundation.org/ publications/towards-the-circular-economy-vol-2-opportunities-for-the-consumer-goodssector.

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Ellen MacArthur Foundation (2014). Towards the Circular Economy, Vol. 3: Accelerating the scale-up across global supply chains [Online]. Available at: https://www.ellenmacarthurfoundation.org/publications/towards-the-circular-economy-vol3-accelerating-the-scale-up-across-global-supply-chains. Esposito, M., Tse, T., Soufani, K. (2018). Introducing a circular economy: New thinking with new managerial and policy implications. California Management Review, 60(3), 5–19. Gallaud, D. and Laperche, B. (2016). Circular Economy, Industrial Ecology and Short Supply Chain. ISTE Ltd, London and John Wiley & Sons, New York. Ghisellini, P., Cialani, K., Ulgiati, S. (2016). A review on circular economy: The expected transition to a balanced interplay of environmental and economic systems. Journal of Cleaner Production, 114, 11–32. Greyson, J. (2007). An economic instrument for zero waste, economic growth and sustainability. Journal of Cleaner Production, 15(13–14), 1382–1390. Guzzo, D., Trevisan, A.H., Echeveste, M., Costa, J.M.H. (2019). Circular innovation framework: Verifying conceptual to practical decisions in sustainability-oriented product-service system cases. Sustainability, 11(12), 3248. Kasmi, F. (2018). Le milieu “éco-innovateur” – Écologie industrielle et diversification de l’économie territoriale. Revue technologie et innovation, (18/3), 1–17. Nußholz, J. (2017). Circular business models: Defining a concept and framing an emerging research field. Sustainability, 9(10), 1810. Smol, M., Kulczycka, J., Avdiushchenko, A. (2017). Circular economy indicators in relation to eco-innovation in European regions. Clean Technologies and Environmental Policy, 19(3), 669.

8 Co-creation – Co-creation and Innovation: Strategic Issues for the Company

8.1. Introduction Co-creation implies a joint creation process between legally distinct entities (personal or moral: individuals, organizations and companies) and goes through a creation phase based on creativity methods (formalized and codified at the professional stage), which then possibly leads to a design activity (co-design if joint) in order to produce one or more prototypes. Co-creation initially refers to a joint invention, at the level of collaborative ideation (possibly artistic). Intrinsically, creation, first and foremost, involves ideas, intellectual works or craft and artistic practices. Creators can also mobilize inventions or scientific knowledge (theoretical or applied), processes and/or results from engineering sciences, innovations, products or other technological applications. Scientists or engineers, starting from their fields, can develop artistic or Art–Science approaches. The creative activities (creation or co-creation) fall within a broad spectrum, from intellectual or artistic work, arts and crafts and traditional objects, to certain activities within companies (start-ups or tertiary/industrial companies) oriented towards innovation (via R&D), with the design (co-design) of innovative items at the prototype stage and then in pre-series. To facilitate the analysis of the term co-creation, we will define it as: strategic for companies; individual (DIY) and collective (DIWO); and linked to creativity and innovation, intellectual property rights and eco-design.

Chapter written by Paul BOUVIER-PATRON. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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8.2. Co-creation: a strategic challenge for companies Co-creation is a driver of business innovation: innovation, as a managerial process, goes through the company in successive key stages, including creation (co-creation) and design (co-design). The company can be seen as an organizational entity that creates the knowledge (Nonaka 1994) necessary for creation/co-creation: the raison d’être of the company would thus be to continuously create potentially usable knowledge, understood in the broader context of an inter-organizational space where each related company is a creator of knowledge. The company must therefore have an internal capacity for knowledge creation, at least at the level of the critical mass required to absorb external knowledge (absorptive capacity, Cohen and Levinthal 1990), which is absolutely essential for innovation. In the inter-organizational space, open collaborative knowledge, with interactive learning, generates a collective dynamic depending on the quality of internal capacities. The dynamic generated participates in the absorption capacities of external knowledge, and also favors the export of internal knowledge (transfer of idiosyncratic and tacit knowledge, the latter being central to the creation process: Nonaka 1994) throughout the community. The articulation between communities of practice (Brown and Duguid 1991) and epistemic communities allows the codification of widespread knowledge crucial to the dissemination of know-how in the co-creative process. Moreover, the tension between the individual and the community finds a satisfactory balance to promote a dynamic of progress through co-evolution: the individual is at the base of the process, but interactions with others allow for enrichment and the emergence of useful (valuable) solutions. Communities of practice realize projects thanks to a range of more or less sophisticated tools (e.g. 3D printing), and also thanks to real (and/or virtual) coordination in innovative physical locations (“fab lab”, “MakerSpace”), inside or outside of the company. The innovation community associated with a “MakerSpace” (where the “fab lab” is one of a number of tool locations), based on complementary areas of expertise, brings together passionate individuals (from different backgrounds and skill levels), in permanent interaction, physically and/or virtually, to consider and select solutions. An innovation community dedicated to a specific project, or an innovation platform, can only be usefully or efficiently led within a company or in the inter-organizational sphere. The innovation community is a space (real and/or virtual) that is relevant in achieving a collective invention (Allen 1983) and, consequently, making open innovation possible (Chesbrough 2003), generally, within innovative business ecosystems (Moore 1996) where the stake is the collective dimension of innovation in a process of continuous improvement and perpetual questioning (of a common asset that can be valued by each party in its own core business). Open innovation (Chesbrough 2003), through permanent comings and

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goings between legally distinct entities, mainly explores disruptive innovation, which is a revolution that involves users during innovative design (von Hippel 1976). Co-creation is based on this collective logic of collaborative openness, upstream of co-design (aimed at producing artifacts), through inter-organization (sometimes involving competitors for business ecosystems) via networks of customer–supplier relations (each one refocused on its business and performing well in innovation in its area of expertise: “skills”). The formal/informal, commercial/non-commercial, individual/collective resilient intertwining networking – favored by Information & Communication Technologies (ICT) and Digital Technologies (DT: virtualization/dematerialization, then re-materialization by 3D printing) offers concrete places for practices and exchanges (around knowledge, know-how, ideas and projects), but tends to structurally modify the way of thinking–creating–designing– manufacturing–marketing a product (e.g. “MakerSpace”). The dichotomy of complementary versus similar business activities (according to Richardson 1972) classifies the client–supplier relationships essential to inter-organization as complementary: co-creation and then co-design will mainly take on this dimension. Referring to an alliance between rival firms, similarity can lead to specific, more or less strategic cooperation necessary for each party, by co-creating a joint subsidiary (joint ventures) with co-design or commercial agreement. 8.3. Co-creation, DIY and DIWO By freeing ourselves from the ISO approach, we lift the constraints of existing knowledge to mobilize it and make and develop (by mixing existing and future) product concepts in an experimental approach (to test, learn, rectify, modify and improve), before making a final choice for industrial development with a prototype and launching a pre-series (and a short/long series by restoring ISO standardization; 3D printing is also intended for the production of long series). The “fab lab” (Gershenfeld 2005), which makes it possible to do almost anything, is the ideal way to explore the field of possibilities. This path involves hacking practices and technological DIY (bricolage). The “fab lab” is an experimental place for enthusiasts to carry out individual (DIY: “Do It Yourself”) or collective projects (DIWO: “Do It With Others”) by making available, in a limited physical space but an open human environment, tools related to DT (for digital design and 3D printing) and ICT (for the connection of tools between them, their management, the collaborative interaction of actors, dissemination, access or return of experience). The “fab lab” can be institutional, associative or corporate (inside or outside of a company), and more or less selective in its membership (in terms of initial knowledge or know-how). The “fab lab” is the centerpiece of places with different

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purposes (“HackerSpace”, “MakerSpace” or, directly commercial, “TechShop”): it promotes experimentation, bricolage, calm, exchanges, learning, feedback (of experiences, knowledge and know-how) and helps to finalize projects (with commercial or non-commercial purposes). 8.4. Co-creation, creativity and innovation Innovation is promoted by an internal company organization that encourages and stimulates horizontality, decentralization and creativity by offering degrees of freedom. This includes paid working time to enable employees to think about and carry out innovative individual or collective projects, sometimes even to the point of spinning off or encouraging the creation of a start-up, while often benefiting from support and access to resources (provision of high-performance digital tools, human resources, exchanges, various facilities, salaries paid, bonuses, organization of working time, direct financing of the project, etc.). Organizational innovation is also a potential consequence. In a company’s internal organization (since the 1980s), design, which was previously carried out solely within the R&D department, has become an interface process combining R&D, strategic marketing and purchasing. The rise of strategic marketing in product design shapes the need to maximize the value perceived by the customer (a central concept first popularized by Porter and taken up by innovation marketing). As for the purchasing department, its role has increased, owing to the refocusing on the company’s core business, accompanied by outsourcing to a network of suppliers, selected because they are highly competent and innovative (as well as being refocused on their core business). The success lies in the client–supplier network mobilizing the most innovative complementary players in their field of expertise. This necessary collaborative opening to the outside world in the inter-organizational sphere implies that the pivotal company (customer) develops its products together with its vertical partners (first-tier suppliers and other less strategic but necessary ones). The suppliers, in fact, occupy a predominant place in the realization of the products, sometimes by co-design. The co-design can be punctual or durable, depending on the more or less strategic nature of the relationship, the type of sourcing (single, double and multiple), the proximity to the core business of the purchase, the risk/dependency analysis conducted, the frequency of recourse, the quality/quantity/price, the competitive bidding, the call for tenders (generally restricted) and the lifecycle of the goods to be produced. The client–supplier interface of a distributed design results from the division of the complementary skills required and is formalized in a set of specifications (functional then technical; the mastery of both is interpretable in terms of the more or less specific skills acquired and, consequently, of the potential negotiating power and knowledge of whether the client knows their needs and how to express them, with

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several consequences in terms of the balance of the relationship or the domination of one of the parties). The innovative co-creation between companies will generate co-design activity downstream and thus, intrinsically, design activity (AFNOR 1988 gives the following definition): “creative activity which, starting from the needs expressed and the existing knowledge, leads to the definition of a product satisfying these needs and industrially feasible”. Design consists of moving from needs to specifications (product characteristics) and proves to be an integrated collaborative practice with complementary interdisciplinary experts mobilizing knowledge. Design is the cognitive result of collaborative knowledge building. The design of a new artifact is based on components and subsets already used in existing artifacts. More explicitly, Altschuller (1988) establishes, on a broad statistical basis, that most of the innovations referenced are incremental in nature (as opposed to radical: Schumpeter’s typology) and, more importantly, that the innovative process, using his TRIZ method of creativity (Teorija Rezhenija Izobretatel’stich Zadach in Russian, or the theory of inventive problem solving), operates according to the similarity of situations, in order to move towards new solutions by using what already exists. This path is also a way to create knowledge. The TRIZ method of creativity refers above all to an engineer’s approach to innovation and design. The creative method, rather diffused in HSS, is design thinking with the seminal work of Simon (1969, The Science of the Artificial), where design becomes a way of thinking to solve complex problems. As early as the 1970s, Stanford University developed a disciplinary field on human creativity through design (under the aegis of, among others, McKim). The very term design thinking, associated with its practical method, foments creation (in 1991), among others by Kelley and Brown, from the design IDEO agency in Silicon Valley. Design thinking claims innovation through uses while understanding its feasibility and viability in an interdisciplinary approach, where the phases are articulated in an interactive nonlinear way. Creativity methods help creation (or co-creation), as well as design (or co-design). The creative activity is upstream of the design activity. Consequently, upstream of the innovation process, co-creation can concern a creation or an invention made collectively, while deciding on the associated rights in terms of intellectual property. The co-creation leads to the co-design having the right to file a patent, which tends to be used strategically (defensively or offensively) to exchange or block competitors. 8.5. Co-creation and intellectual property rights Intellectual property rights apply, in principle to any creative or co-creative activity in the event of dissemination. This is particularly the case for digital works (e.g. software) with the proprietary alternative copyright versus the free “copyleft”.

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In the first case, Digital Rights Management controls the use made of digital works. A hybrid situation can combine both aspects with a free kernel and proprietary applications: case of digital products derived from Linux. Open-source software is based on the General Public License, known as “copyleft”, which allows access to the source code: anyone can therefore access, use, modify, copy and even distribute software under this license. In 1985, the Free Software Foundation (FSF) enacted the first fundamental freedoms of free software. In 1998, at the instigation of Raymond (1999), the Open-Source Initiative (or “open source”) further extended access rights. Raymond contrasts the commercial model (“Cathedral Model”) with the subversive “Bazaar” model for open software (e.g. Linux), where technological bricolage is a collective key to creativity and innovation. 8.6. Co-creation and eco-design Environmental considerations are the basis of a new type of design that is likely to renovate the practice itself through eco-design. Loosely based on the definition given by ADEME, eco-design is a preventive (referring to the precautionary principle) and innovative approach that makes it possible to reduce the negative impacts of the product on the environment throughout its lifecycle (on all sites and stages), by using non-renewable resources as little as possible, as well as by reusing, repairing and recycling as much as possible and recovering waste (see circular economy). Imposed by necessity by legislation and/or in the name of corporate social and environmental responsibility (CSER), eco-design is optimistically perceived as a solution to the crisis and is put forward as a factor of competitiveness through a very qualitative innovation (with the risk of “greenwashing” for companies wishing only to profit from the market without any real commitment). Environmental pressure (regulation, CSER, associations and consumers) involves, for companies, the search for eco-innovation (or ecological innovation aimed at reducing or eliminating any impact on the environment) through eco-design (based, for example, on bio-mimicry Benuys 1997), and, given the complexity and/or cost of dynamic skills or new materials, the use of co–eco-design. 8.7. Conclusion Co-creation is upstream of co-design. Co-design refers to an innovative process where complementary skills (difficult to substitute and replace) must be combined to create a new item. While creation (co-creation) is at the level of an intellectual ideation, an artistic gesture, an intellectual or artistic work, or design (co-design) refers to the item (developed jointly), often relying on managerial engineering. The

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creation (co-creation) and the concept (co-design) resulting from it refer to a process that is increasingly open to interdisciplinary activity and environmental consideration. The essential link between creation/co-creation and conception/ co-design is creativity. 8.8. References Allen, R. (1983). Collective inventions. Journal of Economic Behaviour and Organization, 4, 1–24. Altshuller, G. (1988). Creativity as an Exact Science. Gordon & Breach, London. Anderson, C. (2012). Makers: The New Industrial Revolution. Crown Business, New York. Benyus, J.M. (1997). Biomimicry, Innovation Inspired by Nature. HarperCollins, New York. Brown, J. and Duguid, P. (1991). Organizational learning and communities of practice: Towards a unified view of working, learning and innovation. Organization Science, 2, 40–57. Chesbrough, H. (2003), Open Innovation: The New Imperative for Creating & Profiting from Technology. Harvard Business School Press, Boston. Cohen, W. and Levinthal, D. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), Special Issue: Technology, Organizations, and Innovation, 128–152. Gershenfeld, N. (2005). Fab: The Coming Revolution on Your Desktop – From PC to Personal Fabrication. Basic Books, New York. von Hippel, E. (1976). The dominant role of users in the scientific instrument innovation process. Research Policy, 5, 212–225. Moore, J. (1996). The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems. HarperCollins, New York. Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14–37. Raymond, E. (1999). The Cathedral and the Bazaar. O’ReillyMedia, Inc, California.

9 Community – Innovative Communities of Practice: What are the Conditions for Implementation and Innovation?

9.1. Introduction: communities of practice and innovation Communities of practice have raised interest over recent years and have multiplied in organizations aiming for more creativity and innovation in their teams. In addition, with Open Innovation theories in the Knowledge Economy (Tremblay 2015, 2017), firms have looked to create inter-organizational communities of practice, or innovative communities, as this theory puts forward the idea that there will be more creativity and innovation in a group which is diverse in terms of organizational or professional identity. This chapter will thus center on the definition of the concept of communities of practice, or innovation communities, and the issues surrounding it. As Open Innovation Theory would suggest inter-organizational innovation communities, this chapter will address the preconditions for implementation as they are documented in the literature on communities of practice and in our research. We will first present the essential elements of the definition of communities of practice in the following section, and then compare them to work teams, and virtual communities of practice. We will then highlight the gains in terms of organizational learning, the importance of the animation role in these communities, before ending with a short conclusion.

Chapter written by Diane-Gabrielle TREMBLAY. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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9.2. Communities of practices, a definition: group cohesion, complicity and dynamism We will first present the definition and meaning of the concept of communities of practice (CoP), and recall the elements highlighted by other researchers as conditions of success and for these communities of practice (CoP), based on our literature review, and our own research on innovative communities of practice (Tremblay 2004a, 2004b). First, let us recall that the term “communities of practice” was first used by Wenger and Lave some 13 years ago (Wenger and Lave 1991). Many different views and definitions have been presented since then, but most, if not all refer to the importance of sharing information within a small group, as well as the value of informal learning for a group and for an organization as a whole. A few definitions of communities of practice are presented in Mitchell (2002): – communities of practice are groups of people who share a concern, a set of problems, or a passion about a topic, and who deepen their knowledge and expertise in this area by interacting on an ongoing basis (Wenger et al. 2002, p. 4, quoted in Mitchell 2002, p. 12); – a group whose members regularly engage in sharing and learning, based on their common interests (Lesser and stork 2001, p. 831, quoted in Mitchell 2002, p. 12). The main elements stressed here are the sharing of a concern, a set of problems, the ongoing interaction between the group, the ongoing sharing and learning. As we will see later, these elements were found in our community and contributed to its success. However, while some more conventional definitions of communities of practice often refer to an informal group, management studies have been more interested in organizational or inter-organizational communities created by firms in order to foster innovation. Indeed, the dozen innovative communities we have studied over the years (from 2001 to 2017) are created and structured by an organization and much more formal. Let us recall a few other definitions in order to highlight differences in types of communities, which may be different according to their objectives, which are essentially innovation, creativity and learning, but sometimes other objectives such as reduction in costs or better service. Here are a few other definitions: – groups of people bound together by shared expertise and passion for a joint enterprise (Wenger and Snyder 2000, p. 139);

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– clusters and networks of employees who work together – sharing knowledge, solving common problems and exchanging insights, stories and frustrations (Lesser and Prusak, in Lesser et al. 2000, p. 831, quoted in Mitchell 2002, pp. 11–12). 9.3. Work teams and virtual communities Over recent years, organizations have shown an interest in work teams that work from a distance, although sharing a project, and more and more, firms have been creating innovative communities of practice that are also working from a distance as well as for different employers or institutions (private firms, public organizations, community, associative or research organizations). It must be stressed that virtual communities of practice, working from a distance, are more than simple teams working from a distance. They are seen as a group that has a common mission, that has a common task and must deliver an innovative product, service or idea, based on the regular exchanges and information sharing within the group, as defined in McDermott (1999). Work teams usually have a predetermined goal and schedule, often very clearly defined tasks and their activity is usually centered on their work tasks, and done during working hours; often, work teams disintegrate once the objective is attained, but in the manufacturing sector, they often remain to assume general work tasks collectively (Tremblay and Rolland 2019). Also, work teams are often characterized by a strong division of labor, whereas communities imply more direct cooperation between the members (Tremblay et al. 2000). Innovative communities of practice are thus seen as having wider and less defined objectives, as not having a specific schedule and dates for attaining the various objectives (contrarily to work tasks), and usually these innovative communities pursue their activities for quite some (often indeterminate) time. As indicated in much of the literature on work teams as well as communities of practice, working “together” as a group usually requires some prerequisites, the main precondition appearing to be trust in other members of the group. This is all the more important in a context of communities of practice, since members of the community are expected to share tacit knowledge, to construct new knowledge collectively and develop innovative ideas, products or services (McDermott 1999, 2001; Wenger and Snyder 2000). It is precisely because of this trust element that many authors recommend that virtual communities of practice (CoP) be developed on the basis of existing informal groups, groups that share values and already trust each other, although this is not always necessary. This is, however, often not possible in firms and it is why many virtual communities of practice are designed without being based on a previously existing informal work group, as research has shown. This, of course, represents an additional challenge for CoPs, i.e. when

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previous acquaintance and trust of members has to be developed within the CoP, all the more so when it is a virtual CoP. Among the other main prerequisites often mentioned in the communities of practice literature (as well as in much of the teamwork literature – see Tremblay and Rolland 2000, 2019; Tremblay et al. 2000) are the importance of the leader or animator of the community, the interest and motivation of individuals in working together as a group, and the support received from the organization: support and legitimization of the group on the part of the immediate superior or higher levels of hierarchy, financial or non-monetary rewards for the participants and the like (Wenger et al. 2002). Available technology and technological support are sometimes mentioned, but most research seems to indicate that the human resources and organizational challenges are more important and that technology plays a more limited role in the success or failure of communities of practice. Some elements of the teamwork literature also appear useful in our consideration of conditions for the successful implementation of CoPs and collaboration within them. According to Letize and Donovan (1990), the leader must proceed through a succession of four functions in order for the team to succeed and, in our view, these elements could be transposed to the context of CoPs and organizational learning. At first, they must consolidate the team (leader, trainer and expert roles). They must then, as an animator and facilitator, oversee the development of the skills of the individuals who make up the group. At the next stage, they put more emphasis on the management of the team’s performance (role of auditor and buffer to protect the team from external attacks). Finally, the supervisor plays the role of external consultant to various teams to help them reach their goals (advisor role). Roy (1999) identifies three types of supervision that form a kind of continuum of team autonomy. When teamwork is first adopted, the role of the supervisor tends to change. The supervisor becomes a facilitator, a resource person or coordinator who helps the team to assume its new responsibilities. A team member may then be chosen as team leader to coordinate and represent the team. They may report to a senior manager or a coordinator who supervises a number of teams. Finally, some firms have chosen to distribute management responsibilities among team members. In this case, each team member so designated becomes the team’s reference person for the particular matter for which they are responsible. Authors recognize the determining influence of the organizational context on the involvement and effectiveness of teams (Guzzo and Shea 1992). Several dimensions of the organizational context are considered – technology, human resource management policy (Guzzo and Shea 1992), and the support and involvement of management and the organizational structure (Tremblay and Rolland 2019). We

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have tested many of these dimensions in our analysis of CoPs and the results concerning the animation roles confirm some elements of the teamwork literature. Having summarized the literature on preconditions for implementation of communities of practice which is pertinent for our purpose here, let us mention a few results concerning objectives, which usually include organizational learning, creativity and innovation. 9.4. Organizational learning Organizational learning is one of the reasons firms and organizations implement communities of practice. OL can be defined as a collective phenomenon of acquisition and development of knowledge, which transforms the management of situations and situations themselves. The collective dimension of organization can be activated by the circulation and diffusion of new knowledge and by the development of relations between existing competencies. Duncan and Weiss (1978) define the organization as a group of individuals who engage in organization and transform directly or indirectly a series of inputs and outputs1. The organization is thus a system of purposeful actions according to Duncan and Weiss (1978). The organizational efficiency is thus determined by the quality of knowledge available beyond individual learning, which brings about changes in individuals. Organizational learning can translate into changes of another nature, and at another level. Information sharing can happen between individuals from different employer organizations as well as within a firm. In our research with over 240 respondents, fostering innovation was clearly among the main objectives, attaining a score of 4.14 out of 5 (maximum score). Respondents also considered that the virtual community project favors excellence and stimulates creativity. 9.5. Animation role The animation role is always important and all the more important in a virtual community of practice, relying totally on electronic exchanges, and it is somewhat more challenging in this context as well. In research, all results indicate that the role of the animator is crucial. While many other factors such as individual motivation and trust of other participants are important, the absence of management support and

1 Ducan, R. and Weiss, A. (1979). Organizational learning: Implications for organizational design. Research in Organizational Behavior, 1, 75–123.

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the absence of any form of monetary or other reward in the workplace make the role of the animator even more crucial. As to the roles which are considered important among the roles identified in the literature, participants mention that an animator must be dynamic, presenting new ideas and tools, leading discussions, supporting individual members, giving expertise on collaboration tools online, measuring progress and informing members. 9.6. Conclusion As was observed by specialists on CoPs (Wenger et al. 2002, p. 4, quoted in Mitchell 2002, p. 12), many people can participate in a community, but not always so actively. Many indicate that they learnt more than they contributed, indicating that there was a certain periphery of participants somewhat less engaged than others, as the literature calls this group of more passive participants, which the animator will try to bring to the core of discussions in the CoP to ensure more creativity and innovation. Although workgroups or teams that work from a distance have been the object of some attention recently, these are generally much more loosely related than members of a community of practice, which shares a common task, often centered on innovation, and participates directly in the same specific task. Some specialists of cooperation and collaboration actually make a distinction between the two concepts in the sense that in one case there is a strong division of labor, which can also apply to international teams working from a distance, while in the other case, none of the participants can go ahead without the others, implying a more direct participation in tasks by participants, as should be the case in an innovation community of practice. As indicated in much of the literature on work teams as well as communities of practice, working “together” as a group usually requires some preconditions, the main condition appearing to be trust in other participants. It is precisely because of this trust element that many authors recommend that virtual communities of practice be developed on the basis of existing informal groups, groups that share values and trust each other. Group cohesion and complicity also appear to be important factors, as identified in the literature on collaborative work (Henri and Lundgren 2001), while rivalries and tensions seem to have been limited in this CoP, which surely favored a successful implementation.

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While group cohesion, complicity and dynamism of the leadership and animation are crucial factors confirmed as essential to successful implementation, it could be recommended that virtual multi-organization CoPs should pay more attention to recognition and support, even though the employer is not directly involved, since this would be useful for participants and may as well provide benefits to the employer. However, let us recall that while part of the competencies developed here – cooperation and information exchange capacity – can be transferred into the regular workplace of the participants, the participants dealt with an issue outside of their work activity, so it is difficult to conclude anything on the impact on performance at work after the experience. 9.7. References Duncan, R. and Weiss, A. (1979). Organizational learning: Implications for organizational design. In Research in Organizational Behavior, Staw, B.M. and Cummings, L.L. Elsevier (eds). 1, 75–123. Guzzo, R.A. and Shea, F.P. (1992). Group performance and intergroup relations in organizations. In Handbook of Industrial and Organizational Psychology, Dunnette, M.D. and Hought, L.M. (eds). Consulting Psychologists Press, Palo Alto. Henri, F., and Lundgren-Cayrol, K. (2001). Apprentissage collaboratif à distance : pour comprendre et concevoir les environnements d’apprentissage virtuels. Presse de l’Université du Québec, Sainte-Foy. McDermott, R. (1999). Nurturing three dimensional communities of practice: How to get the most out of human networks. Knowledge Management Review. Fall edition. McDermott, R. (2000). Knowing in community: Ten critical success factors in building communities of practice [Online]. Available at: http://www.co-i-l.com/coil/iknowledge. Mitchell, J. (2002). The Potential for Communities of Practice. John Mitchell and Associates, Australia. Tremblay, D.-G. (2004a). Virtual communities of practice: Towards a new mode of knowledge sharing and learning? In Key Contexts for Education and Democracy in Globalising Societies, Ruzicka, R., Ballantne, J.H., Ruiz San Roman, J.A. (eds). Accentura M Agency – Charles University, C117–124, Prague. Tremblay, D.-G. (2004b). Les communautés virtuelles de praticiens : vers de nouveaux modes d’apprentissage et de création de connaissances ? Possibles, Numéro spécial sur la formation. Tremblay, D.-G. (2015). Emploi et gestion des ressources humaines dans l’économie du savoir. Presses de l’université du Québec, Quebec. Tremblay, D.-G. (2017). Innovation technologique, organisationnelle et sociale. Presses de l’université du Québec, Quebec.

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Tremblay, D.-G. and Rolland, D. (2000). Labour regime and industrialisation in the knowledge economy: The Japanese model and its possible hybridisation in other countries. Labour and Management in Development Journal, 7. The Australian National University [Online]. Available at: http://www.ncdsnet.anu.edu.au. Tremblay, D.-G. and Rolland, D. (2019). Gestion des ressources humaines ; typologies et comparaisons internationales. Presses de l’université du Québec, Quebec. Tremblay, D.-G., Rolland, D., Davel, E. (2000). Travail en équipe, compétences et pratiques de sélection au Québec. In Actes du XI Congrès de l’Association francophone de gestion des ressources humaines, Cadin, L. (ed.). Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity. Cambridge University Press, Cambridge. Wenger, E. and Lave (1991). Communities of Practice. HBS Press, Boston, MA. Wenger, E. and Snyder, W. (2000). Communities of practice: The organisational frontier. Harvard Business Review, 78(1), 139–145. Wenger, E., McDermott, R., Snyder, W. (2002). Cultivating Communities of Practice: A Guide to Managing Knowledge. Harvard Business School Press, Boston, MA.

10 Craftsman – The Innovative Craftsman: A Historically Permanent Socio-economic Function

10.1. Introduction It is generally accepted that the craftsman is not an innovator. Definitions of craftsman from the oldest dictionaries make this clear. None of these dictionaries emphasize the craftsman’s ability to innovate or to produce new technologies and knowledge. The craftsman is generally related to the poor who often had to make a hard living from their work. However, since the times of the ancient Greeks, manual work has been fundamentally looked down on in favor of intellectual work. In this same context, the first Industrial Revolution had the widespread consequence of relegating the craftsman to the rank of a historically outdated figure, unable to adapt to the shift toward industrial and mechanized modernization. At present, craftsmanship1 is precisely defined in industrial countries, mainly on the basis of five criteria: definition of a list of craft trades (carpentry, bakery, hairdressing, etc.); the production and transformation of goods and services through the know-how of the manager and recognized by their professional qualification; a ceiling on the number of workers (which varies greatly depending on the country); the acquisition and development of know-how through apprenticeship; and the integration of the company into its territory through social responsibility (Boutillier et al. 2009). Far from being negligible, in Europe, 12 million enterprises fall into this category, bringing together 50 million people2.

Chapter written by Sophie BOUTILLIER and Claude FOURNIER. 1 https://smeunited.eu/. 2 Ibid. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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Over time, the craftsman has never stopped innovating, contributing to the advancement of scientific and technical knowledge. From medieval guilds to the present, craftsmen have marked the history of production and techniques, and have evolved alongside the productive system. First, we will show how the role of the craftsman has evolved over the course of history and the active role they played in the production of scientific and technical knowledge. Second, we will explain how this situation has evolved with industrialization, without fundamentally questioning the craftsman’s role as an innovator. 10.2. The craftsman, an ignored innovator Etymologically, the word “artisan” comes from the Latin “artis”, introduced into the French language through the Italian “antigiano” in the 16th century. Its English equivalent, “craftsman” refers to one who works with his hands. The word appears a little earlier, around the 12th–13th century. Generally speaking, a craftsman is designated as one who lives from his art in the service of others. His origin is common to that of the artist, but the latter, unlike the craftsman, has no commercial vocation. Moreover, a survey of French language dictionaries since the 17th century paints a negative picture of the craftsman, related to his trade, which does not allow him to live comfortably. In the Richelet dictionary (1680), the craftsman is “he who has a profession and some trade and who earns his living from the sweat of his face; poor craftsman, vile craftsman”. In the Furetière dictionary (1690), the craftsman is “a worker who earns his living by working in the mechanical arts, as a shoemaker, locksmith, hat maker”. In the first Dictionnaire de l’Académie française (1694), the craftsman is a worker in a mechanical art. He is defined as a man of trade. Today, Larousse3 defines the craftsman as a self-employed worker who has a professional qualification and is registered in the directory of trades to exercise, by his own account, a manual activity. It is also said that a craftsman is a person who practices a trade according to traditional standards. On the other hand, in the English language, in the Cambridge Dictionary4, the craftsman remains summarily a skilled worker who makes something with his hands. The definition seems to have changed little over time as, in the English language, the craftsman was already defined in the 13th century5, according to these terms. How can we explain this historical contempt for the craftsman? The explanation certainly lies in the fact that craftsmen practice mechanical arts, which are opposed to liberal arts. The former represent the branch of practical science relating to the imitation of nature, according to ancient traditions. These are the technical 3 https://www.larousse.fr/dictionnaires/francais/artisan/5579. 4 https://dictionary.cambridge.org/fr/dictionnaire/anglais/craftsman. 5 https://www.etymonline.com/word/craftsman.

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applications implemented on building sites and supervised by religious and temporal authorities. The liberal arts are related to science and education. There is therefore a strict boundary between technology, which is in the realm of practice, and science, which is in the realm of abstract reflection. This observation is not insignificant because many works by historians clearly show that craftsmen have been leading innovators for centuries. Zilsel (2018) highlighted very early on the active participation of craftsmen in the development of scientific and technical knowledge. In essence, he explains that craftsmen were, through their trade, led to produce new knowledge and that they were concerned with the rules of rational operations, with the rational search for causes or with rational physical laws. In short, qualities that are generally attributed to the scientist. The distinction between mechanical and liberal arts was at the origin of a precise classification of trades, differentiating between manual and scientific trades. Surgeons who performed manual activities (operations, dissections) belonged to the same class as barbers, and their social position was similar to that of midwives (Zilsel 2018). Zilsel (2018) adds that craftsmen were placed in a literate position and worked silently to advance techniques. They invented many things such as the marine compass and firearms. They also created blast furnaces and helped to mechanize mining, as introduced during the 16th century. The experimental method was developed long before scientists paid attention to it and used it for their own purposes. The writings of Galileo and Bacon make it very clear that they were inspired by craftsmen (Conner 2011). According to Hall (1983), the roles of craftsmen and scientists in the scientific revolution were complementary. Through their work, the latter provided the raw material from which the former produced scientific knowledge. Using the same logic, Conner (2011) points out that the experimental method developed by Renaissance scholars was of craft origin. He gives the example of Galileo, who visited construction sites and talked with workers to learn their techniques and secrets. From the 18th century onwards, the chemical industry was developed by craft enterprises. At the beginning of the 20th century, craftsmen still played a major role in the first steps of aviation and the spread of the automobile, thanks to the existence of a close network of garage craftsmen (Perrin 2017). 10.3. The innovative craftsman of the 21st century If these historical considerations are correct, it is also easy to understate their importance by referring to the first Industrial Revolution, which clearly marked the decline of craftsmanship, initially with the development of a “putting-out system”, which made it possible to bypass the trade guilds that regulated competition. Moreover, the factory that symbolizes the first Industrial Revolution is endowed with the prodigious power bestowed upon it by the steam engine. Secondly, because the

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process of division of labor that began in the factory rendered obselete the long years of apprenticeship, essential for the craftsman to be able to reach the top of his art. The apprentice had to produce a masterwork and become a journeyman, working for 5–10 years to present an advanced masterwork (Sennett 2008). The craftsman is thus distinguished by his ability to combine design and fabrication. As a result, the question of the division of labor would be non-existent in the workshop, but would have arisen with the increase in the scale of production, via the appearance of manufacturers. The division of labor has been an established fact since antiquity in the construction sites of large buildings (palaces, cathedrals, fortresses, etc.) (Drancourt 2002). In artists’ workshops, the making of a work of art was also the product of what we would today call collaborative work, where the creation of a collective work was valued. The craftsman’s workshop was a true social space in which individuals with varied and complementary knowledge interacted (CassagnesBrouquet 2014). Moreover, despite industrialization (Zarca 1986; Mazaud 2012, 2013; Perrin 2019), the head of a craft business remains a skilled worker in his or her trade. Together with his or her workers, he or she masters the entire production process. At present, in Europe, craft is defined by the exercise of a specific trade, as in the Livre des métiers (“Book of the Guilds”) in which Étienne Boileau – the provost of Paris – had, in 1268, collected the regulations of Parisian trades for the first time. The relationship to the trade still persists, although since the end of the 20th century, the situation has changed as the craftsman has been obliged to devote more time to managing his or her business, in an economy that is increasingly regulated on fiscal, technical and social levels and by an increasingly narrow division of labor between enterprises of all sizes. However, a number of constants remain. Marked by the seal of tradition, the craftsman is rarely described as innovative, even today. It is only relatively recently that innovation by craftsmen has been of interest to researchers. In many of the academic papers that have been published since the 1980s, the focus has been on the difficulties craftsmen face in adopting new information and communication technologies (Ravix 1988). However, since the beginning of the 21st century, the ability of craftsmen to innovate has been recognized, but in a different way from the ability of large firms. Indeed, in craftsmanship, the innovation process is not structured like that of a large enterprise: there is no budget dedicated to research and development (R&D), and no staff specialized in this task or laboratory. Innovation is described as spontaneous (Ravix 1988). However, the debates are open between those in favor of a particular type of innovation that would be specific to the craft industry and those in favor of a more organizational and assembly approach, based on customer demand. It emerges from this observation that, if we want to try to quantify the innovative character of craft

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enterprises, it is necessary to construct means of measurement that allow for the reproducibility of these measures. These means, which, concretely, result in the construction of indicators, require a fairly detailed knowledge of the practices of craftsmen. Whereas for a medium or large enterprise, the number of patents filed can in itself constitute an indicator, this is not the case for craft enterprises, the majority of which do not file patents. This does not mean that they should not be taken into account but illustrates the need to construct indicators that aggregate such primary data. This synthesis work on the construction of indicators has been attempted since 1995, including in 2002 by the Institut supérieur des métiers (ISM). Six synthetic indicators, which take into account the practices of craftsmen, were proposed and tested in the form of surveys carried out on the basis of representative samples (Survey for Technology and Innovation in Crafts and Professions or TIME by its French initials). This work only concerned production companies. Fournier and Constant (2008) then tried to generalize the method to the entire craft industry by defining more general synthetic indicators, assuming that craft innovation is not only an innovation of product or service, but also includes other aspects (enterprise organization, skills management, use of digital technologies, etc.). The results of these surveys may have shown that the assumption that innovation in the craft industry is more of an organizational or service innovation which is rarely technical, is not always true. Craft innovation may correspond to a strategy of differentiation in markets dominated by large department stores or to a strategy of product and service diversification in markets dominated by major brands. In business markets, craft companies rely on technical knowledge and proximity to the trade. When the craft enterprise is a subcontractor of a large one, it may indeed be led to implement a quality approach (ISO 9000, for example), take into account the health risks related to an activity, or continuously improve performance in terms of safety, health and environment (Cognie and Aballéa 2010). In this case, craft innovation is defined more as a capacity to adapt to economic, technical, social or regulatory change and so on. However, while the development of the Internet competes with craft enterprises, they have managed to appropriate this technology. The concept of a home flower delivery service was invented in 1908 by a German craftsman florist and is known today as “Interflora”. However, what makes the craft enterprise specific is that it remains rooted in a territory, local market or proximity (which has varied considerably with the development of the Internet). From this territory, the craftsman creates his or her own symbolic universe based on the trade and the know-how he or she values. In the absence of a dedicated R&D budget, the craftsman’s ability to innovate also relies on a network of relationships with his or her environment (customers, suppliers, employees, professional community) that influences his or her innovation trajectory, since he or she innovates in response to external demands (Boldrini et al. 2011).

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Craft innovation is thus frequently the result of teamwork in an approach managed by the craftsman (sometimes intuitively and informally), which reveals his or her ability to adapt to change. Craft innovation is generally understood as a combination of tradition and modernity. But what tradition? The majority of trades have evolved, including technologically. The knowledge used by today’s craftsmen is far removed from ancient knowledge. What is there in common between those who, not so long ago, handled the adze, the jointer plane, the plane and so on, and those who, with a digitally controlled machine tool, produce parts with a perfect surface finish? 10.4. Conclusion Craft innovation is always a more or less chaotic and spontaneous process, specific to small enterprises. In the context of the end of the 20th century, marked by the knowledge economy, the injunction to innovate has become widespread. Craft enterprises have not escaped it. And yet, they innovate! Beyond conventional wisdom, the craftsman innovates. Most of the time, this is innovation without a budget or dedicated R&D staff. In addition to spontaneous or assembly innovation, which is frequently mentioned when we talk about artisanal innovation, we fail to mention that craft enterprises are also at the origin of radical innovations (the beginning of aviation, the development of the automobile, construction, etc.) (Perrin, 2017, 2019). However, while innovation for large enterprises generally takes the form of a patent application, craftsmen rarely use these (for reasons of cost and lack of information), hence the need to construct appropriate indicators. 10.5. References Boldrini, J.-C., Journé-Michel, H., Chené, E. (2011). L’innovation des entreprises artisanales. Les effets de proximité. Revue française de gestion, 213, 25–41. Boutillier, S., David, M., Fournier, C. (2009). Traité de l’artisanat et de la petite entreprise. Educaweb, Barcelona. Bréchet, J.-P., Journé-Michel, H., Schieb-Bienfait, N. (2008). Figures de la conception et de l’innovation dans l’artisanat. Revue internationale des PME, 21(2), 43–73. Cassagnes-Brouquet, S. (2014). Les ateliers d’artistes au moyen age entre théorie et pratique. Perspectives. Actualité en histoire de l’art, 1, 83–98. Cognie, F. and Aballéa, F. (2010). L’artisanat, figue anticipatrice d’un nouvel entrepreneuriat. Management et avenir, 40, 79–99. Conner, C.D. (2011). Histoire populaire des sciences. Editions l’échappée, Paris.

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Drancourt, M. (2002). Histoire de l’entreprise de l’Antiquité à nos jours. PUF, Paris. Fournier, C. and Constant, O. (2008). Définition d’indicateurs de mesure et d’observation des activités des entreprises artisanales. Study report, Institut supérieur des métiers, Paris. Hall, R. (1983). Revolution in Science 1500–1750. Longman, London. Mazaud, C. (2012). Artisan, de l’homme de métier au gestionnaire ? Travail et emploi, 130, April–June, DARES. Perrin, C. (2017). Petites entreprises dans l’histoire industrielle. Marché & organisations, 30. Perrin, C. (2019). De l’indépendance à l’entrepreneur. Les artisans du bâtiment en France des années 1930 aux années 1970. AEdificare, 5, 193–212. Ravix, A.L. (1988). Les comportements d’innovation dans l’artisanat de production industrielle. Approche régionale et politiques publiques de l’innovation, Revue internationale des PME, 1(3–4), 277–294. Sennett, R. (2008). The Craftman. Yale University Press, London. Zarca, B. (1986). L’artisanat français, du métier traditionnel au groupe social. Economica, Paris. Zilsel, E. (2018). Les racines sociologues de la science. Zilsel, 3, 288–309.

11 Defense – Military Innovation: Networks and Dual-use Technological Development

11.1. Introduction Military leaders consider innovation as a strategy and as a process. As a strategy, its main goal is to provide the armed forces with the full range of capabilities enabling them to support national defense objectives. As a process, innovation relies on a demand-driven, knowledge-intensive and context-dependent approach that is firmly rooted in experimentation and collaborative interactions between academic, industrial and institutional actors (Reppy 2000). The military capabilities resulting from innovation processes are basically shaped by current and future knowledge and practices (i.e. conceptual, cultural, organizational, technological, business-oriented and political practices) which characterize a given socio-economic and geopolitical system. These capabilities are made up of a variety of tangible and intangible resources (e.g. values and norms, organization, equipment, technology, strategy, tactics, training, doctrine and logistics) that are used in combination to support operations and deliver lethal and non-lethal effects on the battlefield. The dynamics of innovation in defense-related sectors have been profoundly altered in response to the growing complexity of military affairs and technology, the consolidation of defense-related industries, the increasing competition between firms and the radical changes affecting defense budgets and policies (Barbaroux 2019). These long-term structural trends have transformed military innovation processes in two ways. On the one hand, they have triggered the intensification of dual-use technological development and increased the contribution of scientific research to military innovation processes. On the other hand, they have modified military innovation processes by reinforcing cooperation between installed players Chapter written by Pierre BARBAROUX. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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(e.g. prime contractors and acquisition agencies) and new entrants coming from high-tech sectors (e.g. biotech and IT companies). As a result, contemporary military innovation processes now exhibit higher levels of complexity in terms of variety and spatial distribution of knowledge sources and contributors. The former led military leaders to revitalize their perception regarding their role and functions as lead users driving much of military innovation processes. 11.2. Military innovation: main attributes 11.2.1. Military innovation as a knowledge-intensive and dual process Military innovation is the outcome of a knowledge-intensive, collaborative process rooted in a combination of scientific and applied research (i.e. Science & Technology, S&T). According to Hägelin (2004), “military innovation implies military exploitation of both domestic and foreign S&T results in support of new ideas and problem solving related to the development of military capabilities” (p. 287). By definition, S&T outcomes are neutral: they can foster both civilian and military applications of knowledge and technology, therefore supporting dual-use technological development (Ayerbe et al. 2014). Basically, a dual-use technology (product or service) means that it can fulfill both military and civilian users’ needs. However, duality is a multidimensional attribute that is likely to address different aspects of a given product, service or technology. For example, duality can be used to describe the knowledge, skills and competences supporting its production (“upstream” duality) as well as its particular utilization by civilian and military customers (“downstream” duality). It can also be applied to characterize public and private organizations’ political orientations (e.g. Boeing is a dual company addressing both civilian and military markets). It can finally be used to typify the focal business of larger innovation systems. Interestingly, the perception of the role played by dual-use technologies within the defense community has changed through time. When the Cold War ended in the early 1990s, duality became positively evaluated by military leaders and company executives as it could enable their respective organizations to mitigate the risks generated by decreasing demands for military products and services. Altogether, perceptions regarding duality mirrored the distinctive properties of A&D industries, with aerospace and electronics sectors exhibiting higher levels of upstream and downstream forms of duality in comparison with weapon systems sectors, naval shipbuilding and land platforms manufacturing1.

1 Segmentation between the civilian and military knowledge domains, products, services and technology is higher in traditional military platform manufacturing sectors.

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In conjunction with duality, the complexity and scientific intensity of knowledge have been identified as central attributes associated with modern military innovation systems. Cognitive complexity has been reinforced by the combined effects of internationalization of R&D, increasing competitive pressures on military markets, and the massive integration of IT into military capabilities. Empirical evidence of the growing complexity of knowledge structures supporting the development of military capabilities has been provided by scholars examining patent citations and authorship in defense-related industries. Investigating the French defense industry’s knowledge structures and practices, Guillou et al. (2009) have shown that “firms in the defense industry have higher patenting intensity than other companies” (p. 178). The former means that defense-related companies’ turnover is strongly protected by patents, the latter being considered as relevant proxies of firms’ knowledge bases. These research efforts have unveiled the networking architectural form supporting military innovations, establishing connections between different technological domains, including biotechnology, nanotechnology, IT, space technology, optics and energy storage. 11.2.2. Military innovation as a technology-driven process The second force affecting military innovation systems is technology itself. Recent advances made in technological domains which are often distant from traditional defense-related core knowledge and practices, opened up a range of threats and opportunities for the military community at large. S&T applications such as artificial intelligence (AI), robotics and quantum technologies currently have a huge impact on military capabilities such as cyber-defense, autonomous systems (e.g. remotely piloted aircraft systems, missiles), command and control (C²) and communication, resource planning and coordination, and intelligence, surveillance and reconnaissance (ISR). Progresses made in digital computing and communication capacities also triggered a deep change in the military workforce’s education and training together with a revolution in logistics and maintenance (Barbaroux 2019). While new technologies, enabled, or enhanced, by current advances made in IT and AI (e.g. Mobile Internet, The Internet of Things (IoT), Cloud computing, Advanced robotics, Autonomous vehicles, 3D printing), are affecting current military capabilities, they do not necessarily involve radical transformations of industrial landscapes, technical capabilities and/or business models, as Clayton Christensen would suggest (Christensen 1997). They rather involve a change in existing mental models used by military users and customers to evaluate the potential benefits of new technologies. This is particularly true for demand-driven industries, like the military, where price/performance trade-offs do not always drive customers’ purchasing policies and acquisition decisions. Within this framework, demand behaviors are dependent upon geopolitical, economic and technological

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factors which, in turn, shape military customers’ motives, investment policies and acquisition strategies. 11.2.3. Military innovation as a demand-oriented process There exists an important literature investigating how demand provides incentives to firms’ investments in technological development, therefore shaping innovation processes and industrial change (Malerba 2007). Within this framework, demand is commonly represented by myriad individual customers who express distinctive preferences regarding price/quality trade-off, the latter impacting the adoption and further diffusion of innovation (Adner 2003). However, like many industries which do not deliver normal goods and services (e.g. space manufacturing industry), military technology does not fit with the predictions made by conventional lifecycle and evolutionary theories. Indeed, military innovations do not emerge during a period of intense experimentations conducted by individual entrepreneurs or experimental users. They rather require massive investments made by governments and public agencies to advance both scientific and technical knowledge, and develop industrial facilities and capabilities. Furthermore, the role played by demand for military technology is not bounded to the selection of technological standards (and successful firms) as it usually occurs during the growth phase in the normal goods industry. In fact, military demand forces (i.e. acquisition agencies and military services) actively participate in the generation of technological competencies and development of dominant designs: they are lead users (von Hippel 1986) as they contribute to fund, develop and operate high-cost, customized technologies. Within this framework, the motives expressed by military customers, together with the effectiveness of their absorptive capacities (Cohen and Levinthal 1990), are likely to play a great role in shaping the dynamics of military innovation. Military customers’ motives are consciously articulated through current and future warfare doctrines, including technical and tactical procedures and concepts of operations (CONOPS). These motives have a direct impact both on the investments made by the military in specific technological domains (e.g. IT, sensors, AI and space technologies), and on industry structures and company strategy. As an illustration, when the Cold War period came to an end, U.S. analysts elaborated on a new warfare model called network-centric warfare (NCW). The novel doctrine aimed at harnessing the full benefits attached to networking communication systems and improving military capabilities, notably sense-making and decision-making, multi-level communication, organization design and deployment, and coordination of heterogeneous and geographically dispersed units. It followed a huge restructuring of industrial facilities through vertical integration and collaborative partnerships, particularly in the U.S. and Europe, with traditional platform manufacturers cooperating with – or being absorbed by – aerospace and electronics

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companies2. More recently, the U.S. military leaders have refined the NCW doctrine so as to improve military capabilities in the context of a joint, networking force. The resulting model was called Multi-Domain Command and Control (MDC²). Despite a strong conceptual proximity, the MDC² philosophy slightly differs from the NCW doctrine on one central point: in MDC² organizational forms, automation should cover activities which traditionally rested on human actions and decision-making. While representing a strong cultural shift for military customers, the former has profound implications for defense-related suppliers. For example, the performance of the MDC² model critically depends upon information availability, accessibility and integrity. Prime contractors therefore must assure military customers that the systems they offer are individually and collectively (i.e. when integrated) cybersecure and cyber-resilient. This requires prime contractors to increase the level of control over extended supply chains. The resulting cross-functional integration, however, is hard to achieve in a distributed, often disaggregated, industrial environment with suppliers’ dispersion (particularly in the electronics and IT sectors) making prime contractors more vulnerable to a variety of cyber-threats and attacks which may negatively affect military customers’ level of trust. 11.3. Conclusion Military innovation is a process and a strategy involving collaboration between government agencies, defense-related companies and research laboratories, to provide the armed forces with effective capabilities to design, plan and conduct current and future military operations. Considered as a demand-driven knowledge intensive process, military innovation is highly dependent upon changes affecting customers’ motives and purchasing behavior, the latter being shaped by geo-political, economic and technological factors. Within this framework, military leaders are currently struggling with the integration of new technology into existing networks and weapon systems. Progresses made in IT and AI, for example, have triggered a deep transformation of warfare models and combat networks’ architecture, on the one hand, and defenserelated industrial structures, on the other hand. The variety of knowledge sources supporting modern military innovations, and the relative distance which separates 2 BAE Systems was created in 1998 by merging UK aerospace (British Aerospace) and electronic facilities (Marconi); EADS – now Airbus Group – was created in 1999 thanks to the merger of French (Aerospatiale-Matra) and German (Daimsler) aeronautics and space assets; Thales sent its naval activities to DCN (Direction des Chantiers Navals) in 2007, giving rise to DCNS, now called Naval Group; BAE Systems acquired United Defense Inc. in 2005 and Armor Holdings in 2007, therefore becoming a world leader in defense-related businesses.

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them from lead users and prime contractors’ core competences, have given rise to a major challenge for the defense community at large: to develop and adapt continuously its creative and absorptive capacities in order to fully benefit from technological advances. The risk for military stakeholders is to undergo innovation forces rather than drive them, by not accurately anticipating the impact of new technologies or political changes on their business models, technical skills, managerial practices, warfare models and policies. To engage with the above challenge and risk, government agencies and defense-related firms must critically examine the organizational rigidities that make them unable to “unlearn” certain established practices, and take actions to set the conditions for developing collective learning, and promote creativity. 11.4. References Adner, R. (2003). When are technologies disruptive? A demand-based view of the emergence of competition. Strategic Management Journal, 23, 667–688. Ayerbe, C., Lazaric, N., Callois, M., Mitkova, L. (2014). The new challenges of organizing intellectual property in complex industries: A discussion based on the case of Thales, Technovation, 34(4), 232–241. Barbaroux, P. (ed.) (2019). Disruptive Technology and Defence Innovation Ecosystems. ISTE Ltd, London and John Wiley & Sons, New York. Christensen, C.M. (1997). The Innovator’s Dilemma. When New Technologies Cause Great Firms to Fail. Harvard Business Review Press, Brighton, MA. Cohen, W.M. and Levinthal, D.A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. Guillou, S., Lazaric, N., Longhi, C., Rocchia, S. (2009). The French Defence industry in the knowledge management era: A historical overview and evidence from empirical data. Research Policy, 38(1), 170–180. Hägelin, B. (2004). Science- and technology-based military innovation: The United States and Europe. Armaments, Disarmaments and International Security SIPRI Yearbook 2004, 9, 285–304. von Hippel, E. (1986). Lead users: A source of novel product concepts. Management Science, 32(7), 4791–805. Malerba, F. (2007). Innovation and the dynamics and evolution of industries: Progress and challenges. International Journal of Industrial Organization, 25, 675–699. Reppy, J. (ed.) (2000). The place of the defense industry in national systems of innovation. Cornell University Peace studies program occasional paper #25, 233.

12 Design Thinking – Design Thinking and Strategic Management of Innovation

12.1. Introduction Design thinking is defined as a pragmatic method of designing products, services and desirable experiences for consumers. It has become a buzzword, a word that has become commonplace in the speech of many managers and in society in general. As empirical evidence, a search engine search shows more than a billion results for the term “design thinking”. However, some major press magazines have said that “design thinking is dead” (Johansson-Sköldberg et al. 2013, p. 123). Designers, such as Natacha Jen from the international freelance design agency Pentagram, have even described design thinking as “bullshit”, as in an article in the newspaper FastCompany in 2018. Researchers, such as Lee Vinsel, associate professor at Virginia Tech, and Natasha Iskander, associate professor of Urban Planning and Public Service at New York University, agree with this critical perception of design thinking. They consider this method to be like a kind of syphilis or the source of innovation failure in organizations, or at least of disruptive innovation in the case of Roberto Verganti (2008, 2011). Natasha Iskander writes more precisely that design thinking is “a strategy to preserve and defend the status quo – and an old strategy at that. Design thinking privileges the designer above the people she serves, and in doing so limits participation in the design process” (Harvard Business Review, 2018, September 5, website). In short, design thinking is just a staging of innovation. These attacks may come as a surprise in view of the development of numerous research studies on the subject (Borja de Mozota 2018), as well as of the massive adoption of design thinking in companies’ methods of innovation and creativity at both the product/service and strategy levels (Brown 2009; Carlgren et al. 2014; Gay Chapter written by Bérangère L. SZOSTAK. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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and Szostak 2019). Why is there such criticism of design thinking at the present time? Our answer is that there are essentially strong omissions or misunderstandings about what design thinking is. This is why this chapter proposes, firstly, going back to the origins of design thinking, secondly, clarifying what the practice of design thinking in innovation management consists of, and thirdly, proposing research perspectives. 12.2. The origins of design thinking In order to understand the criticisms mentioned above, it is first necessary to clarify the origins of what we consider to be a design method that produces meaning essentially for the user, according to the so-called user-centered approach (Verizer and Borja de Mozota 2005). Tim Brown’s name is often considered inseparable from the concept of design thinking, as he has helped this method to become better known, among other things, thanks to his consulting work at IDEO (an American design firm founded in 1991) and his now famous book Change by Design (2009). However, if we go back to the origins of this profession, we can see that this method is not original in itself for designers. Indeed, design really took off with the industrialization of production in the 17th century, and was then formalized little by little by the rise of artistic and cultural movements, such as the Arts and Crafts movement, the Werbund, the Bauhaus and the Ecole de Nancy (Szostak 2006). These different movements then showed the importance of respect for materials, manual know-how and the political project consubstantial to the conception of a product. Post-war mass consumption led designers to seek to satisfy consumer needs with simple, rational and functional products, such as the black and white razor (1969) from the company Braun. Without necessarily replacing this functionalism in design, later on, we can observe that Free Form designers advocate more eclecticism, and thus more marginal and colorful products such as Ettore Sottsass’s Carlton shelf (1981) or Philippe Stark’s Dr Kiss toothbrush (1996). Then, completing these two design logics, there is a third logic much more oriented towards business and consumer needs. This has been used by the Design Management Institute since 1975, and has the support of big companies (3M, IBM, Caterpillar, Kraft Foods, etc.). These three logics still coexist in design today, and we can reaffirm that design is a human activity based on conception that stems from culture and technique (Szostak 2006). The objectives may be industrialization and commercialization, but this is not systematic. Moreover, the purpose (or project) of this activity is design (or creation). In other words, design uses culture and art to transform a technology into a useful object that will generate a commercial transaction. Joe Colombo, an Italian

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designer (1930–1971), sums it up this way: “design is what makes technology enjoyable.” In short, we can retain that design methods are to be understood as structured towards an approach focused either on disruptive innovation, through a renewal of the company project itself or on the user to detect their expectations, including those of which they themselves are not aware (Verizer and Borja de Mozota 2005). Design thinking is part of the second approach. The first is a question of design-driven innovation, in which a breakthrough is sought through a radically new vision of meaning (Verizer and Borja de Mozota 2005). Verganti (2008, 2011) then speaks of “meaningful innovation”, which is based on a subtle understanding of socioeconomic and cultural developments translated into a new product. 12.3. Design thinking in innovation management This return to the sources of design thinking allows for a better understanding of the criticisms mentioned above. Indeed, many innovation managers, and even specialized researchers, reduce design to design thinking, which has a double consequence. On the one hand, design thinking being reduced to a method without history, without value, without roots, does not contribute as much as it could to innovation and creativity thinking. On the other hand, an increasing number of players are no longer even associating designers, whose profession is design, with their project, which often results in a poor use of specific practices. The results expected by companies using design thinking are not achieved, which leads them to fail to grasp its real usefulness. Designers and other specialists, who are aware of these consequences, can only criticize design thinking presented in too technical a manner and with a focus on commercial and financial performance. If the criticisms of these professionals seem legitimate, we must, however, recognize that Tim Brown, in particular through his book, has been able to put the spotlight on this design activity and propose a framework for design thinking that is simple and accessible to managers. This has contributed to the dissemination of this method to companies, as well as to stimulating the interest of innovation management researchers who are not very familiar with design as a discipline. As a result, instead of castigating the clumsy use of design thinking, it seems important to us now to clarify what it actually consists of when its use respects its origins, in this case those of the design discipline. First of all, it should be remembered that design thinking is only one method among others for the designer. For this professional, design thinking is considered too orderly and linear (Liedkta 2018); it makes it difficult to explore, on a conceptual level, a question relating to an object, a situation or a place, which can be

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done, for example, through design fiction or critical design. Next, design thinking offers innovation management actors (Brown 2010) a method in three clearly identified steps: 1) discover the customer’s need, to give it meaning in line with what matters to the customer; 2) generate ideas in line with the criteria defined by the stakeholders, setting aside their assumptions, and discuss each of them as a team to select some of them; 3) iteratively test the ideas retained thanks to basic artifacts, which can be detailed storyboards, prototypes imagined with pieces of string, cardboard, paper, etc., as well as thanks to small-scale, even unfinished, experimental realizations in real situations, which are called proofs of concept. This structured framework reassures those who are not experts in design (Liedkta 2018), who have to manage innovation and creativity. From their point of view, the strength of design thinking then lies in its ability to create meaning formulated according to the needs of users, by cross-referencing each other’s expertise through discussion. Moreover, this work by the actors makes it possible to arrive at a definition of the problem to be solved and the solution to be developed that is common to the entire innovation team. To arrive at this definition, the actors also cultivate curiosity about these needs and can adopt specific practices. For example, empathy allows them to understand the user’s point of view on a cognitive and emotional level, as well as to grasp what the user expects without necessarily verbalizing it, thanks to ethnographic surveys, observation and immersion in the users’ living and working places. This can be done, among other things, through learning expeditions or the practice of coolhunting (or trend spotting). Moreover, if design thinking does not, in essence, permit the development of a political project for society or even a utopia, it remains, however, very relevant when it comes to investigating so-called wicked problems (Ritter and Webber 1973). Examples include promoting access to primary care, decent housing or clean water, making life more pleasant for disabled, sick or elderly people, reducing greenhouse gases or food waste, and so on (Ritter and Webber 1973). These problems are complex to solve, for at least three reasons: (1) the information to understand them is in itself a source of confusion; (2) many actors and decision-makers are involved, even though they have contradictory values; and (3) the ramifications or links between the information and the values of the whole system can confuse the actors themselves. The design thinking method therefore appears to be an avenue to be explored in order to imagine original solutions, by organizations engaged in this kind of problem, such as entreprises à mission in France or B-corps in the United States, or even social and solidarity economy organizations.

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Surveys also show that this use seems more successful if the organization has specific organizational capacities, in this case creative capacities (Parmentier et al. 2017). These are capacities to generate, collect and select new, appropriate and useful ideas and solutions to improve, change and renew both organizational processes and outputs, and the organization itself. These capabilities facilitate, for example, the management of paradoxical configurations: as an illustration, it enables an original and new solution to be proposed, while taking into account strong economic constraints. These creative capabilities are also available in hybrid, open and flexible spaces, such as open-labs and fab labs, which need to be managed and promoted. Research in creativity specifies the conditions favorable to such capacities (characteristics of individuals, teams and the organizational context, role of creativity tools and methods). In addition, work on dynamic capacities allows us to examine in greater depth how the integration, construction and reconfiguration of the organization’s skills enable it to respond quickly to changes in its environment. 12.4. Conclusion The use of design thinking in an organization requires the actors concerned, and, in particular, innovation managers, to make design history and echo the different possible approaches to this design discipline. Furthermore, the reflections on design thinking would benefit from being nourished by work on organizational creativity, which would certainly reduce the criticisms mentioned at the beginning of this chapter. Furthermore, it should not be forgotten that design researchers themselves develop complementary work to that done in management (Borja de Mozota 2018). It would therefore be astute to combine all these forms of scientific expertise in order to go beyond a vision of design thinking reduced to a pragmatic and concrete method, and to show the extent to which design thinking also contributes to the conceptual renewal of innovation management, and even of organizations. 12.5. References Borja De Mozota, B. (2018). Quarante ans de recherche en design management : une revue de littérature et des pistes pour l’avenir. Sciences du design, 7(1), 28–45. Brown, T. (2009). Change by Design. HarperBusiness, New York. Carlgren, L., Rauth, I., Elmquis, M. (2016). Framing design thinking: The concept in idea and enactment. Creativity and Innovation Management, 25(1), 38–57.

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Gay, C. and Szostak, B.L. (2019). Innovation and Creativity in SMEs. ISTE Ltd., London and John Wiley & Sons, New York. Johansson-Sköldberg, U., Woodilla, J., Çetinkay, M. (2013). Design thinking: Past, present and possible futures. Creativity and Innovation Management, 22(2), 121–146. Liedtka, J. (2018). Why design thinking works. Harvard Business Review, 96(5), 72–79. Parmentier, G., Szostak, B.L., Rüling, C.-C. (2017). Créativité organisationnelle : quels enjeux en management stratégique dans un contexte mondialisé ? Management International/International Management/Gestión Internacional, 22(1), 12–18. Rittel, H. and Webber, M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 5, 155–69. Szostak, B.L. (2006). L’impact des facteurs organisationnels sur l’image institutionnelle des organisations. Le cas des agences de design en France. PhD Thesis, Université Lyon 3. Verganti, R. (2008). Design, meanings, and radical innovation: A metamodel and a research agenda. The Journal of Product Innovation Management, 25(5), 436–456. Verganti, R. (2011). Radical design and technology epiphanies: A new focus for research on design management. Journal of Product Innovation Management, 28(3), 384–388. Veryzer, R.W. and Borja De Mozota, B. (2005), The impact of user-oriented design on new product development: An examination of fundamental relationships. Journal of Product Innovation Management, 22(2), 128–143.

13 Digital – Digital Entrepreneurship as Innovative Entrepreneurship

13.1. Introduction The digital business world is shaped by the interplay of new technological opportunities and challenges, as well as ubiquitous societal trends that will drastically alter the economy due to the rise of artificial intelligence (AI), cryptocurrencies and blockchain technologies, Internet of Things, technology-based surveillance and other disruptive innovations. These developments open for a variety of new entrepreneurial opportunities that businesses can exploit and innovate, based on the development and use of digital technologies (Kraus et al. 2019). The abundance of entrepreneurial opportunities is based on digital technological innovation, lower costs and market entry barriers through the usage of digital technologies and new stakeholders involved in entrepreneurial processes, for example, users (Richter et al. 2017; Sussan and Acs 2017). As the phrase “disruptive innovation” alludes, the current and forecasted transformation of the economy, including entrepreneurship, following these trends is, at least partly, the result of necessities forced upon companies to stay competitive, rather than just opportunities responded to, since the forces in the global market environment increasingly push companies across industries, nations and regions to adopt more digital strategies and “digitize” their established business models. Hence, digitization implies a paradigmatic shift in the understanding of entrepreneurship, but this paradigmatic shift has not been fully understood and uncovered. Digital entrepreneurship represents an emerging research topic in innovation and entrepreneurship studies and other disciplines (information sciences, engineering and technology studies). To this aim, the present chapter reviews the Chapter written by Birgit LEICK and Mehtap ALDOGAN EKLUND. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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state-of-the-art understanding of digital entrepreneurship in literature by, first, providing some key definitions and typologies used for the concept (Kraus et al. 2018), second, describing the emerging debates in scholarly literature, and third, giving a brief outlook on open questions in existing research, regarding the impact of digital entrepreneurship on society and policy issues. This chapter is organized as follows: After mapping the definition and characteristics of digital entrepreneurship in the next section, the subsequent section will describe the field in relation to innovation studies and provide ideas about future research topics. 13.2. Definition and characteristics of digital entrepreneurship There are no generally accepted definitions of “digital entrepreneurship”. The term overlaps with related notions, for example, digital business models, digital business ecosystems and digital innovation. At first glance, the notion of “digital entrepreneurship” creates spontaneous associations with the large and globally operating businesses in the digital era, such as Google, Apple, Facebook in social media, and Airbnb, Uber, Etsy regarding the peer-to-peer platform-based sharing economy enterprises. However, digital entrepreneurship also exists outside of these main domains with small, niche-based start-ups, some of which turn into unicorns with the help of digital technology. The German Fintech start-up N26 and the UK-based online app bank Atom 1 are prototypical cases of business start-ups with a value of more than one billion US dollars. Although finance is a core sector attracting digital entrepreneurship, it can be found across high-technology sectors and industries outside the high-tech field. The numerous start-ups in the peer-to-peer platform-based sharing economy bear witness that retail trade (e-commerce), tourism and hospitality services, food economies and agriculture, and even cultural and creative industries can incubate digital entrepreneurs. The variety of entrepreneurial ventures found that can be classified as numerous variants of digital entrepreneurship represents a huge challenge for the goal to arrive at a joint understanding and shared definition of digital entrepreneurship. Authors such as Hull et al. (2007) have emphasized the challenge, as well as the need to establish a typology of digital entrepreneurship. Indeed, as Steininger (2018, p. 363) stated, the “marriage of information systems and entrepreneurship” through the use and development of Information and Communication Technologies (ICT) for entrepreneurship in the digital age, influences the complete entrepreneurship process. ICT solutions may be the outcome of “traditional” entrepreneurship, such as new business ventures in Information Technology (IT) services. ICT technologies may also influence the start-up phase and operational phases of entrepreneurs with ICT technologies in these phases (but not their development). Finally, ICT solutions

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may support or enforce a change in an established business model towards adopting a more digital system. Given the multiple combinations of technology and entrepreneurship with huge socio-economic impact emerging in the digital age, the literature discusses at least two different concepts with distinct sub-variants. – Digital technology entrepreneurship covers the field of technology-based entrepreneurship, taking into account the fact that new goods and services brought to the market are only based on ICT-based technology development, and entrepreneurial activities are related to the generation of ICT- and Internet-based smart devices, for example, smartphones (Giones and Brem 2017). – A distinct sub-type of digital technology entrepreneurship is associated with entrepreneurship in digital platform environments (Srinivasan and Venkatraman 2018), which includes, for example, the peer-to-peer online platforms of the sharing economy. – By contrast, digital entrepreneurship refers to the broader field of entrepreneurship that not only develops, but also uses digital and ICT technologies. Hence, new goods and services are enabled, which are based on the Internet, for example, apps, and where technology is one input factor among others. Hereby, the innovative product or service is typically run in a cloud and uses big data or artificial intelligence (Giones and Brem 2017). Airbnb, Snapchat and Uber and Apps are common examples. – A sub-type of digital entrepreneurship is related to digital business model innovation, where technology is not the critical resource but access to technology, for instance, through cloud services, ICT and peer-to-peer online platforms supports the modification of existing business models towards innovative systems with digital elements (see Standing and Mattson 2018). The literature on digital entrepreneurship (e.g. Giones and Brem 2017; Sussan and Acs 2017; Standing and Mattson 2018) shows that there is a bias towards equating digital entrepreneurship with digital technology entrepreneurship, i.e. innovation, product and process development only because of ICT-based digital technologies (Nambisan 2016; Giones and Brem 2017). However, digital entrepreneurship, as it is understood in the present chapter, is also related to the usage of digital technologies to innovate by means of the technologies. Because of the overlapping variants of digital entrepreneurship found, the classifications of specific activities might be hard: A good case in point is the peer-to-peer platform-based sharing economy, which is characterized by the co-existence of

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technological platform development and usage, both of which lead to various forms of innovation and entrepreneurship. To conclude, entrepreneurship in the age of digitization incorporates various entrepreneurial and innovative business activities, ranging from the creation of new technology-driven entrepreneurial ventures based upon digital technologies, i.e. business ventures and entrepreneurial start-ups, to the – possibly forced – change of established business models on the part of incumbent companies and whole industries, in order to avoid obsolescence in the digital age (Kraus et al. 2019). 13.3. Digital entrepreneurship in the field of innovation studies Innovation studies cover many facets of new product and process development, as well as organizational innovation in both the economy and society. The emerging field of digital entrepreneurship is, thus, connected to research located in innovation studies, because both the creation and use of digital technologies and ICT commonly imply innovation with entrepreneurs and entrepreneurship. However, this link has not been fully explored to date. Regarding the development of literature on digital entrepreneurship in innovation studies, three important observations need to be mentioned: First, this research topic is still in its infancy, respectively in a state of emergence, on one hand, but it faces an explosion of interest by scholars, practitioners and policy-makers, on the other hand. Second, much of the literature is primarily concerned about conceptual issues (Giones and Brem 2017; Richter et al. 2017), whereas empirical studies of digital entrepreneurship are scarce. Finally, Kraus et al. (2018) find that the literature is dominated by research on digital business models, but less empirical evidence is provided on entrepreneurship in the digital age. According to Kraus et al. (2018), emerging debates that need more exploration in future research are: the digital entrepreneurial process; platform strategies; digital entrepreneurial ecosystems; entrepreneurship education in the digital age; and social digital entrepreneurship. In fact, to fully capture the paradigmatic shift that digital entrepreneurship will bring about, the business ventures and innovation in the digital age need to be explored by considering ecosystems in which this entrepreneurship is embedded and governance issues, in order to understand how agents may influence those systems and the entrepreneurs located in the systems. A future research agenda should thus deal with three important topics to further explore the core of digital entrepreneurship.

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First, and conceptually, a taxonomy of digital entrepreneurship is needed that guides scholars in the process of identifying and describing digital entrepreneurs. The taxonomy should provide clarity on, for instance, subsistence entrepreneurs in the digital economy (prominent cases are Uber drivers, who are officially self-employed, yet less entrepreneurial in the Schumpeterian sense, and micro-enterprises using digital channels). The concept of opportunity versus necessity-driven entrepreneurship might offer guidance in these matters. Another important question that will need to be addressed for such a taxonomy, is where the boundary between technology development and technology usage lies for digital entrepreneurs. In addition, it is necessary to explore what distinguishes digital entrepreneurship from “traditional” entrepreneurship in the industrial production era. While most of the general characteristics of the enterprising individuals in the “old” world are the same as digital entrepreneurs (risk-taking attitude, innovative product or service offered on the market, etc.), it is not yet clear what the specific building blocks of a digital entrepreneur are, considering the aforementioned literature and types of entrepreneurs. Second, and empirically, there is a knowledge gap on the various elements of digital entrepreneurs. More case studies are needed to help identify research avenues, for example, by addressing the following open questions: How much technology use within digital economy is acceptable to label entrepreneurship “digital”? Where is the borderline between the application of technology (thereby improving technology, products and services and incrementally innovating) and the development of technology (which is clearly an entrepreneurial task beyond incremental innovation)? Who are entrepreneurs in the digital age, and how can non-corporate entrepreneurship, such as social and institutional entrepreneurship, be integrated into the concept of digital entrepreneurship and its ecosystem? What role is needed for policy and society in fostering digital entrepreneurship, and are the support tools and programs the same than for “traditional entrepreneurship”? Finally, in political respects, digital entrepreneurship needs to be seen as a topic that is embedded in the larger societal trend of the digitization of the economy, which is ubiquitous in that it revolutionizes processes in markets as well as publicly dominated sectors (e.g. health and education). However, little is known about the political and institutional settings for digital entrepreneurship in such public sectors, and policy has only just started to implement regulation for some sectors in the digital economy. Scholarly research may inform policy and support the development of appropriate regulation. Moreover, both the implications of and challenges for public policy and entrepreneurship in the digital era are related to governance questions about the “optimal” organization of the transformative, disruptive processes. For instance, considerations about management governance can bring new approaches of leadership and corporate governance for the digital entrepreneur to the foreground. In a similar vein, the social implications of digital

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entrepreneurship may arouse questions related to how to set up corporate social responsibility frameworks for these activities. Network governance results from the understanding that digital entrepreneurship processes are embedded in ecosystems with multiple stakeholders, including users and public actors (Sussan and Acs 2017). Finally, spatial governance issues are associated with the spatial effects of digital entrepreneurship, something that new concepts for planning such as “Smart Regions” and “Smart City” currently show. 13.4. Conclusion The field of digital entrepreneurship is still in its infancy, and a rising number of scholars across various disciplines are contributing to its development. For innovation studies, the understanding of digital entrepreneurship has not fully been uncovered, given the manifold variants of entrepreneurial behavior, entrepreneurial ventures and innovation in the digital age. Thus, empirical research needs to explore those variants in relation to innovation studies and address their wider societal, institutional, governance and public-policy implications. This chapter has briefly addressed the current state-of-the-art and presented a future research agenda on digital entrepreneurship and digital innovation, such as providing clarity about the confusing typologies used, emerging research questions and practical issues. 13.5. References Giones, F. and Brem, A. (2017). Digital technology entrepreneurship: A definition and research agenda. Technology Innovation Management Review, 7(5), 44–51 [Online]. Available at: https://timreview.ca/article/1076. Hull, C.E., Hung, Y.T.C., Hair, N., Perotti, V., De Martino, R. (2007). Taking advantage of digital opportunities: A typology of digital entrepreneurship. International Journal of Networking and Virtual Organisations, 4(3), 290–303. Kraus, S., Kailer, N., Kallinger, F.L., Spitzer, J. (2018). Digital entrepreneurship: A research agenda on new business models for the twenty-first century. International Journal of Entrepreneurial Behavior & Research, 25(2), 353–375. Kraus, S., Roig-Tierno, N., Bouncken, R.B. (2019). Digital innovation and venturing: An introduction into the digitalization of entrepreneurship. Review of Managerial Science, 13(3), 519–528. Nambisan, S. (2016). Digital entrepreneurship: Toward a digital technology perspective on entrepreneurship. Entrepreneurship Theory & Practice, 41(6), 1029–1055. Richter, C., Kraus, S., Brem, A., Durst, S., Giselbrecht, C. (2017). Digital entrepreneurship: Innovative business models for the sharing economy. Creativity and Innovation Management, 26(3), 300–310.

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Standing, C. and Mattsson, J. (2018). Fake it until you make it: Business model conceptualization in digital entrepreneurship. Journal of Strategic Marketing, 26(5), 385–399. Steininger, D.M. (2018). Linking information systems and entrepreneurship: A review and agenda for IT-associated and digital entrepreneurship research. Information Systems Journal, 29(2), 363–407. Srinivasan, A. and Venkatraman, N. (2018). Entrepreneurship in digital platforms: A network centric view. Strategic Entrepreneurship Journal, 12(3), 54–71. Sussan, F. and Acs, Z.J. (2017). The digital entrepreneurial ecosystem. Small Business Economics, 49(1), 55–73.

14 Entrepreneurship – Social Innovative Entrepreneurship: An Integrated Multi-level Model

14.1. Introduction Over the last two decades, social entrepreneurship has emerged as a new discipline. Scholars are in the midst of various discussions relating to definitions, concepts and theoretical clarity (Short et al. 2009). The communality of the existing definitions is that the actors involved in social entrepreneurship are assumed to be motivated to create social value, rather than just commercial or shareholder-related wealth (Phillips et al. 2015). To this end, it is not only inventing new commercial products and services that is viewed as entrepreneurial action, but also the process of developing new concepts related to issues such as the inclusion of people with disabilities in society. Or, it can be recombining resources so that production and consumption of the product become more environmentally and socially sustainable. The arenas where social entrepreneurship takes place are embedded in both the economic and public domain (Austin et al. 2012). The process of deducing social solutions, with the aim of meeting the needs of the society as a whole, may require the involvement of a broad range of experts, stakeholders and/or citizens. To this end – depending on the specific target – social entrepreneurship makes the participation of citizens or experts, representing various societal groups, necessary. The analysis of the social entrepreneurship phenomenon extends from the individual

Chapter written by Susanne GRETZINGER. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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micro-level to the meso- and macro-levels of entrepreneurial behavior (Montgomery et al. 2012; Saebi et al. 2019). In summary, the main difference between the terms entrepreneurship and social entrepreneurship is that the deduced values are not just of economic relevance but, at the same time, are of social relevance and environmental sustainability (Dacin et al. 2011). But to understand social entrepreneurship, it is not just important to realize the differences between both concepts, it is also vital to understand the communality. In the same way as classical entrepreneurship, social entrepreneurship depends on the innovative use and (re)combination of resources for translating opportunities into a strategy, and finally an action plan (Bacq and Eddleston 2018). The following begins with the presentation of contemporary issues within the discussion on social entrepreneurship and introduces different approaches. The text is organized according to the various levels for analyzing social entrepreneurship, namely the macro-, meso- and micro-levels. Finally, an integrated multi-level model is presented and an overview of current research questions, discussed at the different levels, will be given. 14.2. State-of-the-art: contemporary issues, approaches and levels of analysis The contributions of social entrepreneurship have rapidly grown and are anchored within various disciplines, namely sociology, economics, management and ethics. To date, the discussion has been rather fragmented and the prevailing theories, which are guiding this discussion, cannot be identified at this point. (Saebi et al. 2019). For gaining an overview of social entrepreneurship, Saebi et al. (2019) differentiated the discussion related to the various levels of analysis, namely the micro-, the meso- or the macro-level of analysis. The micro (individual)-level of analysis: social entrepreneurs are assumed to have a prosocial personality and are described as having an enduring tendency to think about welfare and how to cope with societal challenges. Feeling concern and empathy are viewed as typical characteristics for a social entrepreneur. Studies discussing social entrepreneurship on the micro-level typically discuss whether the characteristics of individual social entrepreneurs translate into social-entrepreneurial intentions and activities (Ruskin et al. 2016). But there are some other discussion streams that accentuate aspects like different resource constellations of entrepreneurial milieus. Yet, other studies focus on processual aspects of entrepreneurial activity like identifying, judging and managing entrepreneurial opportunities (Zahra et al. 2008). Factors on the micro-level matter for

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understanding social entrepreneurs as individuals – particularly how these actors behave in the venture creation phase. This perspective bears the risk of overlooking more distal effects of micro-factors and processes located at the meso- and macro-levels (Saebi et al. 2019). Even if single social entrepreneurs deduce good social innovations, while these ideas do not capitalize on political agendas, they will not affect social wealth and/or environmental sustainability. The meso (organizational)-level of analysis: Studies at organizational level focus on a broad and diverging range of variables. Some studies investigate fundraising capabilities of organizations and/or companies while others focus on the embeddedness of actors within social networks (Calic and Mosakowski 2016). Yet another series of investigations works on issues like marketing capabilities and performance of social ventures or other organizations (Liu et al. 2015). The applied range of theories, the institutional theory as well as the organizational identity theory, and social capital approaches can be identified (Saebi et al. 2019). One further promising model is the resource-based view on social entrepreneurship. On the one hand, it draws the assumption that social entrepreneurs – the same as commercial entrepreneurs – effectively exploit resources for translating opportunities into strategy and therewith get rewards in the form of values and/or rents but, on the other hand, this theory internalizes the “scaling” of social entrepreneurship. The scaling of entrepreneurship is defined as improvement of the social impact that an organization deduces for better matching the targeted social values (Day and Jean-Denise 2016). In the resource-based view on social entrepreneurship opportunities as well as resources, constraints are impacted by the interplay of internal and external resources and actors, including their common and/or diverging goals. The impact of external and internal resources and actors on the entrepreneurial scaling is conceptualized based on four different types of capital, namely social capital, human capital as a value creating factor, as well as political and financial capital as a value capturing factor (Day and Jean-Denise 2016). By using these constructs, social as well as societal determinants, such as resources, actors, institutions, networks and domains are also internalized. Even if this theory approaches the organizational level, the impact of institutions on social entrepreneurship is also internalized. Saebi et al. (2019) consolidate the contemporary discussion on the organizational level and point to the fact that the hybrid nature of social ventures would lead to problems as a result of conflicting goals – economic goals pitted against social goals. In conclusion, future research should focus on two essential issues: first, the issue of the various forms of social ventures that exist and second, the different kinds of problems that these organizations are facing, working on and/or causing.

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Based on the resource-based view of social entrepreneurship, the impact of different types of processes on how value is captured in social entrepreneurial processes can be explored. Jean-Denise (2016) highlights that the most promising opportunity for future research would be to gain a better understanding of the distinctive structures of targets, goals, processes and resources within the various contexts. The macro (institutional)-level of analysis: contemporary investigations at the institutional level of social entrepreneurship revealed that unmet social needs and/or social problems which are internalized by the private business sector, as well as governmental failures, are vital predictors of social entrepreneurial activities (Saebi et al. 2019). While economies constrained by scarce resources often cause an abundance of societal problems and an increased demand for social entrepreneurship, societies with strong formal institutions based on property rights, rules and norms lessen the demand for social entrepreneurship (Saebi et al. 2019). Institutions, which can be characterized as the rules of the game, that impact and/or limit the behavior of organizations and companies, are viewed as being central to the macro-level of analysis. An important approach for explaining social entrepreneurship at the macro-level is the institutional entrepreneurship approach. Important issues for future research on the institutional level are questions like: How can various institutions be described in relation to their societies? How can we describe the main types of outcome of social entrepreneurship? (Saebi et al. 2019). 14.3. Integrated multi-level model of innovative social entrepreneurship As the micro-level is not only impacting the meso- and macro-levels of social entrepreneurship, but the macro-level in turn is impacting the meso- and the micro-levels, it is vital to develop approaches based on this multi-level behavior. In doing so, causal structures within and across the various levels can be identified. Multi-level approach of social entrepreneurship: by applying Coleman’s logic of the macro–micro–macro model (1990, Chapter 1) and a differentiated set of mechanisms in line with Hedström and Swedberg (1998) – which are assumed to impact actors and their institutions – Saebi et al. (2019, pp. 82–88) have depicted social entrepreneurship as a multistage, multi-level model. The strength of this model is that it explains social entrepreneurship as a societal process which is impacted by individuals and organizations and/or various other coalitions. In particular, longitudinal research based on the multi-level approach can help in understanding the dynamics of social entrepreneurship.

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Level

Focus

Micro-level

What are the classical characteristics of social entrepreneurs? How can we describe social entrepreneurial intentions? How can we motivate individuals to participate in social entrepreneurial activities?

Meso-level

Which forms and various kinds of social ventures exist? What problems are these organizations working on? How can we describe the resources, capabilities and performances as well as the context of social ventures? How are social ventures embedded in the various domains?

Macro-level

How can we describe the central types of outcome of social entrepreneurship? How can we describe various types of institutions related to the different societies? Based on that various types of institutions could be analyzed comparatively, their impact on social entrepreneurship.

Integrated multi-level

How do institutions impact the meso- and the micro-levels of behavior of social entrepreneurship? How do the micro- and meso-levels impact the macro-level?

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14.4. Conclusion Even though it is still an emerging subject, social entrepreneurship has managed to generate discussion within different disciplines. Depending on the focus of the research within social entrepreneurship, different approaches at the micro-, meso- or macro-level have been developed and future investigations can build on them. In terms of social entrepreneurship as a phenomenon, it is important to improve the discussion at the macro-level with an institutional perspective. Institutions present the rules of a society. When these rules do not orchestrate entrepreneurial activities toward social and environmental sustainability, it is very unlikely that solutions on a global scale will be achieved. But institutions are just one side of the coin. The other side of the coin is innovation. Without new sustainable solutions in areas such as energy production and distribution, transportation and food production, no kind of socially

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entrepreneurial institution will have a significant effect on social and/or environmental sustainability. To this end, it should be recognized that social entrepreneurship research is not just about exploring how social entrepreneurs think, behave and perform. Contemporary investigations on social entrepreneurship should contain a very critical view on institutional settings and the roles public and private actors play in these processes (Dacin et al. 2011). Policies that do not support innovation and the development of effective institutions would harm social entrepreneurship. 14.5. References Austin, J., Stevenson, H., Wei-Skillern, J. (2012). Social and commercial entrepreneurship: Same, different, or both? Revista de Administração, 47(3), 370–384. Calic, G. and Mosakowski, E. (2016). Kicking off social entrepreneurship: How a sustainability orientation influences crowdfunding success. Journal of Management Studies, 53(5), 738–767. Coleman, J.S. (1990). Foundations of Social Theory. University of Chicago Press, Chicago. Dacin, M.T., Dacin, P.A., Tracey, P. (2011). Social entrepreneurship: A critique and future directions. Organization Science, 22, 1203–1213. Day, S.W. and Jean-Denis, H. (2016). Resource based view of social entrepreneurship: Putting the pieces together. Journal of Strategic Innovation and Sustainability, 11(2), 59–69. Hedström, P., Swedberg, R., Hernes, G. (1998). Social Mechanisms: An Analytical Approach to Social Theory. Cambridge University Press, Cambridge. Montgomery, A.W., Dacin, P.A., Dacin, M.T. (2012). Collective social entrepreneurship: Collaboratively shaping social good. Journal of Business Ethics, 111(3), 375–388. Phillips, W., Lee, H., Ghobadian, A., O’Regan, N., James, P. (2015). Social innovation and social entrepreneurship: A systematic review. Group & Organization Management, 40(3), 428–461. Ruskin, J., Seymour, R.G., Webster, C.M. (2016). Why create value for others? An exploration of social entrepreneurial motives. Journal of Small Business Management, 54(4), 1015–1037. Saebi, T., Foss, N.J., Linder, S. (2019). Social entrepreneurship research: Past achievements and future promises. Journal of Management, 45(1), 70–95. Short, J.C., Moss, T.W., Lumpkin, G.T. (2009). Research in social entrepreneurship: Past contributions and future opportunities. Strategic Entrepreneurship Journal, 3(2), 161–194.

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Thompson, J.L. (2002). The world of the social entrepreneur. International Journal of Public Sector Management, 15(5), 412–431. Zahra, S.A., Rawhouser, H.N., Bhawe, N., Neubaum, D.O., Hayton, J.C. (2008). Globalization of social entrepreneurship opportunities. Strategic Entrepreneurship Journal, 2(2), 117–131.

15 Fintech – Technology in Finance: Strategic Risks and Challenges

15.1. Introduction Fintech comes from the amalgam of the words Finance and Technology. It signifies technology that is new and aimed at serving the clients of financial institutions. The modern day fintech bypasses the traditional dividing lines of back office, middle office and front office and is therefore responsible for significant organizational change. There are many examples of Fintech, and different authors and practitioners include different examples. Part of the reason for this is that technology is evolving extremely fast and is causing threats and changes to the system faster than scholars can research, analyze and discuss the phenomena. Some examples of phenomena that are included in fintech are online platforms that include crowdfunding (Assadi et al. 2018) as well as online stockbroking; digital payments that include mobile payments using QR codes, Internet-based banking, remittances using telephones; online and mobile banking including lending, savings and bill discounting; insurance using telematics as well as satellite-based information; blockchain technologies including cryptocurrencies as well as distributed ledgers; and artificial intelligence, including machine learning, use of algorithms, robotics, voice and image recognition. Although the main geographical areas are China, USA and the European Union, different technologies are surfacing in different continents. For example, mobile payments started in Kenya and the Philippines before coming to Europe.

Chapter written by Arvind ASHTA. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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Fintechs are radical innovations (Abernathy 1978). The essential question is whether they are going to be disruptive or sustaining innovations (Christensen et al. 2000; Ashta and Assadi 2010; Uzunidis 2018). The latter are innovations that are implemented by incumbents to change the game and keep challengers out of the market. Disruptive innovations come from challengers and change the game so that incumbents lose out. The incumbents in our case are bankers and financial actors who were, before the crisis, considered as the sector controlling the economy and led to the coining of terms such as financial capitalism. What is consternating to the incumbent bankers and financial actors is the combination of these technologies that may totally disrupt their sector. The first part of this chapter briefly traces the evolution of technology in finance and the response of banks to this. The second part outlines the strategic risks created by banks and the threats and challenges imposed by the entry incumbents. Thereafter, a few concluding remarks are offered. 15.2. Evolution of technology in finance Banks introduced various innovations in the 20th century without much ado, although organizational change accompanied each innovation. These included the use of cheques in the 1940s, ATMs and credit cards in the 1960s; telex and SWIFT transfers in the 1970s; debit cards in the 1980s; and Internet banking in the 1990s (Ashta and Biot-Paquerot 2018). The use of cheques created an additional back-office function for cheque clearing and verification, while the front office could cash the cheques of the customers. It also required an additional clearing house function, usually with the central bank. ATMs changed the role of the front office by reducing the need for customer interaction and it enlarged the need of the back office to ensure cash was available and that accounting was correct. The banks did not absorb the ATM technology. Instead, it collaborated with ATM providers. As the ATM technology itself advanced, the providers changed but the banks continued. Nevertheless, there are some providers who are in the white ATM market, providing cash dispensing services in niches where banks were absent. However, the advent of the telex network resulted in banks purchasing telex for its own organizational communication as well as organizing differently for the use of telex for customer payments. The telex machines were not manufactured by banks who were content to buy the most useful one from different telex manufacturers. This reorganization was at the industry level where banks created a cooperative (SWIFT) to process payments between each other beyond national borders. The use of debit cards can be considered the first innovation where banks were forced to share profits with an external agency: the card providers such as Visa, Mastercard, American Express, and Diners Club card. However, many of these independent companies were started by banks as subsidiaries. For example, the Bank of America

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started Visa and American Express Bank started American Express. Mastercard was started by a consortium of banks to compete with Visa and was therefore a cooperative. The Diners Club Card was started earlier, in the 1950s, and was an independent company, but eventually acquired by Citibank in 1981 before being sold to Discover Financial Services in 2008. Visa is now an independent public held company. The debit cards they introduced in the 1990s did not change the financial service landscape as far as banks were concerned: they just allowed clients to have a new financial offering. The early experiments with international banking in the 1990s did not result in any significant threat to banking as usual, probably because of the digital divide: very few adults owned computers and could use them proficiently. From the above examples in banking history, we can say that with each technological innovation, banks competed and changed their organizations for competitive advantage. When the technology involved manufacturing, the banks preferred to enter into a strategy of collaboration, outsourcing the telex and ATM machines. When the innovation was in services, especially involving a contact with the final customer, the banks competed more directly in owning the service. If necessary, banks cooperated with each other at an industry level to ward off competition. Thus, the banks managed to sustain their competitive advantage in the wider financial services segment. Although the early innovator may have obtained competitive advantages compared to other banks, the banking sector remained vibrant to any attack from technology providers. The rule was collaboration and cooperation (Ashta and Biot-Paquerot 2018; Uzunidis 2018). The single most important change in this century (from about 2000 onwards) was the rise in the use of Internet and the smart phone. This made cheap communication technology and information available at the level of the individual, all over the world (Ashta and Patel 2013). This ability to stay informed and communicate decisions was accompanied by smarter decision-making based on better analytics. This analytic software (including simple software such as excel) was accessible at the micro-entrepreneur level for educated populations. However, even for the illiterate, information channels could now dispense vocal instructions based on the latest satellite-based data collection. All these innovations made people aware of the large spread between savings and credit interest rates and financial operators intervened to reduce them. Often, these financial operators were former employees of banks who were looking for excitement and the opportunity to do good and make money. Finally, as people became comfortable with shopping on the Internet, they became comfortable with online money. Indeed, the biggest use of technology in finance is in the payment markets. This has seen the rise of global giants such as Alibaba offering Alipay, WeChat offering WeChat Pay, Amazon offering Amazon Pay and Apple offering Apple Pay. This is in addition to PayPal. Thus, bigtech operators have entered the payment space in a

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big way. PayPal, Google Pay, Amazon Pay, Apple Pay, Stripe and Pay U operate in many countries. At the same time, interest rates since the financial crisis have been low, allowing both investors and consumers to experiment by taking credit. The huge consumer interest in ICTs provided an opportunity for rapid scaling in many online markets. The financing of all these online markets brought security issues to the forefront and gave an extra fillip to cryptocurrencies and the decentralized blockchain which automated accounting. The distributed ledger technology based on these blockchains permits outsourcing of accounting and back-office work for banks, leading to even more restructuring. The security provided by these technologies may also make micropayments cheap and secure. The volatility of the initial currencies has created a noise effect and 855 cryptocurrency coins are listed on coinmarketcap.com worth $ 171 billion (on November 25, 2019). Of these, the three market leaders (Bitcoin, Ethereum and XRP) had a capitalization of $155 billion). Only 10 cryptocurrencies had a market value of at least $ 1 billion. However, experiments add to the total global knowledge. For example, Ethereum developed smart contracts using blockchain technologies. These smart contracts have in turn boosted financing through initial coin offerings (ICOs). ICOs are meant to launch cryptocurrencies and other decentralized (non-monopoly) projects by giving a token (not shares) in a project (Le Moign 2019). The price of the token is expected to increase if the project is successful. The total number of tokens launched is 1506 with a combined capitalization of $16 billion. Most of these launches are on the Ethereum platform, although the largest circulating token (Tether) was launched on the Omni platform. Today, ICOs are considered the biggest portion of the crowdfunding market. However, their financing is often limited to very technological projects to develop blockchain and other technologies. The erstwhile big crowdfunding platforms (e.g. Kickstarter, Indiegogo) have found their role in projects that have consumer appeal. For example, the biggest projects on Kickstarter are Star Citizen, a video game, and Pebble, a watch. Some of the P2P lenders have gone public and are now independent companies. LendingClub, which is in the P2P lending space, raised $900 million in an IPO in 2014. The biggest crowdfunding market is in China. However, the fast growth of crowdfunding of the Chinese market is often attributed to illegal financial schemes and this has caused the failure of many of these platforms as the regulator starts using technology to audit them. In this fast-moving market place, the landscape of banking services is changing. Banks can adopt a “wait and see” attitude to buy out the technologies that are mature. In the meantime, they can experiment in small ways to obtain organizational

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learning. The major problem today is that fintech operators may be niche players but there are too many niches exposed at the same time. Large banks can therefore buy out the fintechs only if they outgrow the fringe. However, asymmetric information keeps them from knowing which players to buy out because it does not know which players are operating within regulation and outside regulation. Due diligence takes time and in that time the market place has changed (Ashta and Biot-Paquerot 2018). Therefore, instead of buying out the fintech operators, strategic alliances are an option. Small banks can get into strategic alliances with small fintech operators, but large fintech operators partner with all banks. For example, LendingClub operates with big and small banks. However, what banks fear most is bigtech companies. Alibaba started Ant Financial; other big tech companies have started their own payment services. This is what may cause disruptions. 15.3. Risks of fintech The fast pace of fintech innovations coupled with the rapid speed of scaling up that has resulted from the platforms of ICTs had created uncertainty in the marketplace. Traditional banks, conforming to prudential guidelines, are ill equipped to take risks. Besides the disruption that this changing landscape may create for banks as well as for the banking industry, there are some macro- and micro-issues that need to be highlighted. First, banks were effectively controlling the central bank, which alone issues currency. Perhaps, the most radical, disruptive innovation is that with cryptocurrencies such as Bitcoins and Ethereum coming in, the control of central banks is being undermined (Fama et al. 2019). Cryptocurrencies are digital objects used as a medium of payment, backed by strong cryptography to provide security for financial transactions using a distributed ledger technology. This technology means that control is decentralized since the distributed ledger is stored in the cloud and verified by independent operators. While governments have accepted central banks as an independent co-regulator of the economy, they are less likely to accept a multitude of cryptocurrency operators with volatile currencies. The regulator has realized that the control of cryptocurrency is based on who has the encryption key and it wants it to be deposited legally. Private auditors argue that they need this encryption key to verify transactions. Second, online fraud may be reduced by cryptocurrencies and blockchains. Blockchains link records in a manner that if one needs to be changed, the whole chain must be modified, requiring verification of every step. This was meant to deter fraud. However, we have seen that these too can be hacked. Moreover, ICOs being financed are subject to the uncertainty, information asymmetry, Ponzi schemes and

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budgetary management problems common to all project financing (Le Moign 2019). Although the total percentage of projects financed by ICOs remains marginal, there is progress, and, despite the risk of fraud, millions of individuals are using their telephones to transfer money with technologies as basic as SMS. Third, there is a big question on the future of work (Keynes 1931; Ashta 2015). As productivity goes up, fewer workers and fewer bankers will be required. However, it is hoped that they will become employees or owners of fintech firms, conforming to the concept of creative destruction (Schumpeter 1994). Clearly, talented engineers are required by all the fintech and bigtech players. Finding such talented engineers with a flair for banking and finance is a big challenge. Fourth, the cryptocurrency market is still faced with challenges on transaction processing. The massive amounts of energy required to validate transactions have implications on global warming. 15.4. Concluding remarks Innovation is a dialectical process with both incumbents and challengers offering new solutions. If radical innovations such as cryptocurrency can be disruptive, how are central banks fighting back? More and more central banks are studying the possibility of having their own digital currencies (Fama et al. 2019). Yet, bigtech operators such as Facebook are coming with yet another offering: a global cryptocurrency such as Libra with support from a multitude of stakeholders (Taskinsoy 2019). Crowdfunding platforms are being financed by banks. All this raises the question of the need for regulation at a global level. If the digital payment platforms are present in many countries, they would benefit from and be better regulated by international regulations (Le Moign 2019). However, building a uniform regulation takes time and trust between countries. In today’s antagonistic international trade environment, this seems difficult. 15.5. References Abernathy, W.J. (1978). The Productivity Dilemma: Roadblock to Innovation in the Automobile Industry. Johns Hopkins University Press, Baltimore, MD. Ashta, A. (2015). Why does creative destruction no longer work? Proposing actions for a future with reduced employment. Challenge, 58(5), 428–438. Ashta, A. and Assadi, D. (2010). Should online micro-lending be for profit or for philanthropy? DhanaX and Rang De. Journal of Innovation Economics, 2(6), 123–146.

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Ashta, A. and Biot-Paquerot, G. (2018). Fintech evolution: Strategic value management issues in a fast changing industry. Strategic Change: Briefings in Entrepreneurial Finance, 27(4), 301–312. Ashta, A. and Patel, J. (2013). Software as a service: An opportunity for disruptive innovation in the microfinance software market? Journal of Innovation Economics, 11(1), 55–82. Assadi, D., Ashta, A., Jung, A. (2018). A tale of three musketeers of alternative finance: Stagnating microcredit, growing p2p online lending and striving for slow money. Journal of Innovation Economics & Management, 26(2), 13–36. Christensen, C.M., Bohmer, R., Kenagy, J. (2000). Will disruptive innovations cure health care? Harvard Business Review, 78(5), 102–112. Fama, M., Fumagalli, A., Lucarelli, S. (2019). Cryptocurrencies, monetary policy, and new forms of monetary sovereignty. International Journal of Political Economy, 48(2), 174–194. Keynes, J.M. (1931). Economic possibilities for our grandchildren (1930). Essays in Persuasion. Macmillan And Co., Limited, London. Le Moign, C. (2019). ICO françaises : un nouveau mode de financement ? Revue d’économie financière, 135(3), 131–144. Schumpeter, J.A. (1994). Capitalism, Socialism and Democracy. Routledge, London. Taskinsoy, J. (2019). This time is different: Facebook’s libra can improve both financial inclusion and global financial stability as a viable alternative currency to the U.S. Dollar. Journal of Accounting, Finance & Auditing Studies, 5(4), 67–86. Uzunidis, D. (2018). Introduction: Collectives of information and collective information. In Collective Innovation Processes: Principles and Practices, Uzunidis, D. (ed.). ISTE Ltd, London & John Wiley & Sons, New York.

16 Gerontech – Geront’innovations and the Silver Economy

16.1. Introduction The aging of the population is accelerating. In OECD countries, the proportion of people over 65 in the total population rose from 9% to 15% between 1960 and 2010 and, by 2050, it is expected to reach nearly 26% of the total population. The proportion of those aged over 80, which represented 1% of the population in 1950, totals 4% in 2010 and is expected to reach nearly 10% in 2050 (OECD 2011). Numerous studies on the aging of the population concern the evolution of their standard of living and the cost of caring for the elderly, the financing of pension systems and so on. The question of the impacts of aging on economic activity, particularly in terms of the potential for innovation, has until recently been less studied. Since the Schumpeterian analysis, innovation has been perceived both as a factor of destruction (of jobs, companies) and of creation of even more activities and wealth. Can aging be a driving force of innovation and can the Silver Economy be a “new economy”, generating renewed economic growth? If so, what forms of innovation does the Silver Economy generate? To answer this question, we first define the outlines of the Silver Economy and present the potential for innovation that it represents; then, in a second step, we focus on the “gerontechnologies” that have so far caught the attention of specialists. Finally, we propose the term “geront’innovations” to describe the forms of potential innovations, which are not restricted to the technological dimension.

Chapter written by Blandine LAPERCHE. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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16.2. The Silver Economy: a new area for innovation Noting that the elderly constitute a potentially large market (in terms of their purchasing power and numbers), the “silver market” or “graying market” has every chance of becoming a market of the future, a source of investment opportunities for innovative entrepreneurs and for companies present in many sectors of activity. The “Silver Economy” is thus defined not as a separate sector, but as a set of production and consumption activities that cut across the sectors that make up the productive system (Enste et al. 2008). The Silver Economy can in fact be considered to be a set of economic activities oriented towards: a) the spheres of production and distribution of goods and services corresponding to the needs of the elderly; and b) the preparation of a field of well-being for the youngest categories of individuals who will sooner or later enter the aging process. In France, the Silver Economy is defined by the sector contract signed in December 2013 (Ministry of Social Affairs and Health 2013) as a set of economic and industrial activities that benefit seniors in terms of increased social participation, improved quality of life and comfort, reduced loss of autonomy and increased life expectancy. This new sector is also intended to be an economic and industrial opportunity that should result in the creation of new companies and jobs. Eleven key sectors have been identified as being at the heart of this sector (see Box 15.1). The Silver Economy includes goods and services that can be designed in all of the following industrial sectors: – Housing: home automation, urban planning, architecture, collective housing, adapted housing (MAD), energy efficiency, etc.; – Communication: cell phones, touch tablets, Internet access, social networks, etc.; – Transportation: better adapted public transportation, solution to be found at the “last mile”; – E-autonomy: active/passive/evolved telecare, service packages, etc.; – Security: remote monitoring, remote assistance, payment methods, office management, etc.; – Health: e-health, nutrition, telemedicine, m-health, etc.; – Services: personal service, meal delivery, insurance, etc.; – Distribution: adaptation of the consumer’s journey, adaptation of packaging; – Leisure: games, fitness, sensory stimulation, intergenerational; – Work: telecommuting, training and support for family caregivers; – Tourism: tourism for seniors Box 16.1. Excerpt from the Silver Economy sector contract, December 12, 2013

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In October 2018, a new roadmap for the sector was defined by the French National Council and is based on three issues: cities, territories and mobility; adapted housing and the use of digital and technological innovations by establishments and home services1. Indeed, demographic changes could generate the market for the Silver Economy, fostering innovation and economic growth (Kohlbacher and Herstatt 2011). In this sense, Neven (2015) evokes a “triple-win narrative”, where public actors, innovators and the elderly could benefit equally from the development and diffusion of technologies. Grinin et al. (2017) anticipate the formation of a new Kondratieff cycle in the 21st century that will benefit the development of medicine and the improvement of the health status and living conditions of the elderly through the combination of nanotechnologies, biotechnologies, additive technologies, information technology robotics and cognitive technologies. A lot of work has been carried out on technological developments in recent decades to meet the needs of dependent elderly people. Developed by engineers, sociologists and historians of science and technology, they focus mainly on the conditions of use and acceptance of these technologies by dependent elderly people (see, for example, the special issue of the journal Technological Forecasting and Social Change, no. 93, 2015, which traces the results of research in the field of Science–Technology–Society – STS, Peine et al. 2015). The characteristics of the supply side of firms are less well studied. As pointed out by Kohlbacher et al. (2015), research at the intersection of entrepreneurship, innovation management and population aging remains largely unexplored. We are seeking to identify the technologies potentially involved in this offer and the related innovations. Innovation is generally defined as the introduction to the market (or to production), of new products and processes, as well as of new commercial and organizational methods. Are the innovations designed to meet the needs of dependent older people only technological? This is what the concept of “gerontechnologies”, which is the term generally used to describe them, might suggest. 16.3. “Gerontechnologies”: the technological dimension of innovations in the Silver Economy Several terms exist to name technologies dedicated to frail and dependent elderly people. The most popular is “gerontechnology”, invented by J. Graafmans in 1989. “Gerontechnology”, as a scientific discipline, is the study of technology and aging 1 https://www.silvereco.fr/une-nouvelle-feuille-de-route-2019-2020-de-la-filiere-silver-economie/ 31106059.

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for the improvement of the daily lives of the elderly (Bouma and Graafmans 1992). However, the term also refers to technological products based on information and communication technologies (ICT), robotics and home automation and NBIC (nanotechnology, biotechnology, artificial intelligence, cognitive sciences). Other terms are also found in the literature, such as “gerontechological innovation” (Neven 2015), “silver innovation” (Kohlbacher et al. 2015) and “welfare technology” (Ostlund et al. 2015). They all emphasize the technological dimension of innovations. Similarly, Cozza et al. (2018) focus on “wellness technology practices”. Technology thus appears to fundamentally be the answer to the question of aging. Within ICT, the Internet of Things corresponds to a progressive transformation of the Internet into a wide area network linking several billion human beings and tens of billions of objects. These connected objects find applications in the fields of security (connected pendants, fall detectors in the home), mobility (connected wheelchairs) and health and care services (connected pill dispensers). Robotics offers a whole range of service robot prototypes (dedicated to home security and protection, rehabilitation). Assistive social robots can interact with the user and promote participation in certain activities (travel, domestic tasks, monitoring, entertainment, contact with families). In the field of health, technologies are acting on the reduction of capacities with, in particular, cataract surgery, hip and knee arthroplasty and cochlear implants, as well as applications of genetics, biomaterials and biological engineering (artificial retinas, artificial pancreas, artificial hearts, artificial bladders, etc.). NBIC proposes the integration of nanotechnologies into human functions. Brain implants can already control technical assistants (such as wheelchairs), stimulate the muscles of disabled people and govern technical extensions of the body (by exoskeletons). They thus open up considerable prospects in terms of prolonging healthy lifespans. Nanotechnologies make it possible to envisage the manipulation of matter for human purposes on the scale of the molecule, operating atom by atom. Biotechnology has made significant progress in the field of genetic engineering. The increase in computing speeds and the emergence of artificial intelligence make it possible to create automata whose intelligence could eventually surpass that of humans. The cross-fertilization of these fields is therefore promising. However, the innovations developed in the Silver Economy are not just technological in nature. 16.4. Towards “geront’innovation” The identification of technologies developed to support and increase the wellbeing of frail and dependent elderly people has led us to put forward much broader forms of innovation than the simple introduction of new products and technological processes in markets and production processes. In particular, all

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technologies linked to the Internet of Things are associated with measurement, monitoring, alarm services and so on. In fact, product and service systems based on technology, as well as beyond it, are being developed to promote “aging well”. Adapting the OECD definition of innovation (Oslo Manual, OECD 20052), we define innovation as the diffusion of a product (a good or service or a combination of these) or the implementation of a new or substantially improved process, a new method of marketing or a new organizational method for the benefit of frail or dependent older people. A study conducted by the French Research Network on Innovation (Réseau de Recherche sur l’Innovation – RRI 2016; Laperche et al. 2019) included a survey of stakeholders in the Silver Valley in France3 and a literature review on several technologies (robotics, home automation) and application areas (food, mobility, healthcare services). It highlighted various forms of innovation, not limited to technology. In particular, 70.6% of the players surveyed stated that they offer “solutions” combining products, services, new business methods and new organization (Figure 15.1). This is the case for home automation solutions: connected objects in the home are combined with assistance services. This is also the case of care services for the elderly, which can rely on robotics. Figure 15.1 shows, in the form of an “innovation tree”, the scientific fields, key technologies and areas covered by geront’innovations. This representation provides a better understanding of the process that leads to their development, as well as visualizes the areas in which innovations, combining products and services, will “flourish”. The consideration of non-technological forms of innovation per se or those combined with technology is essential in all areas of activity and probably even more so when they are aimed at vulnerable people. The use of the term “geront’innovations” underlines this diversity.

2 The new definition of innovation in the latest edition of the Oslo Manual distinguishes product and business process innovation (OECD 2019). The notion of “business” processes refers to the traditional functions of a company. It brings together the process, organizational and marketing innovations defined in previous editions. 3 The Silver Valley is a cluster bringing together private and public players in the Silver Economy, with the aim of creating favorable conditions for the development of the market dedicated to seniors; see http://www.silvervalley.fr. The survey conducted among Silver Valley players from December 15, 2014 to February 10, 2015 (51 respondents) focused on the profile of companies, the type of activities developed, the resources and innovation strategies implemented, and the strengths and obstacles to the dissemination of “geront’innovations”.

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Figure 16.1. Forms of innovation in the Silver Economy in France (source: RRI 2016)

Figure 16.2. Geront’innovations

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16.5. Conclusion However, the emergence and consolidation of new innovation trajectories based on an aging population depends on a large number of factors related to supply (market structure, investment and business creation incentives, technological performance and interoperability) and demand (consumer support, ease of use, perceived utility, fashion effect). Multiple supply- and demand-side bottlenecks and lock-in factors often hinder the development and diffusion of “geront’innovations”. As a result, the development of the Silver Economy as a new industrial sector capable of generating growth and employment depends, on the one hand, on the removal of these bottlenecks so that geront’innovations can be seen as a new paradigm for companies, and for public and para-public institutions. On the other hand, in the current context marked by the global spread of a virus that particularly affects the elderly, we may wonder whether the innovations that need to be implemented should be more concerned with the conditions of the reception and care of persons. Not only care facilities for the elderly (residential institutions for the dependent elderly), but also hospitals are subject to the standards of the new public management, which place greater emphasis on profitability than on well-being. The consequences are now glaring. This should undoubtedly be the preferred field of geront’innovations in the years to come. 16.6. References Bouma, H. and Graafmans, J.A.M. (1992). Gerontechnology: Studies in Health Technology and Informatics. IOS Press, Amsterdam. Cozza, M., Crevani, L., Hallin, A., Schaeffer, J. (2018). Future ageing: Welfare technology practices for our future older selves. Futures, 27(3), 117–129. Enste, P., Nagele, G., Leven, V. (2008). The discovery and the development of the silver market in Germany. In The Silver Market Phenomenon, Kohlbacher, F. and Herstatt, C. (eds). Springer, Heidelberg. Grinin, L.E., Grinin, A.L., Korotayen, A. (2017). Forthcoming Kondratieff wave, cybernetic revolution, and global ageing. Technology Forecasting and Social Change, 115, 52–68. Kohlbacher, F. and Herstatt, C. (2011). The Silver Market Phenomenon Marketing and Innovation in the Aging Society. Springer, New York. Kohlbacher, F., Hersttat, C., Levsen, N. (2015). Golden opportunities for silver innovation: How demographic changes give rise to entrepreneurial opportunities to meet the needs of older people. Technovation, 39(40), 73–82.

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Laperche, B., Boutillier, S., Djellal, F., Ingham, M., Liu, Z., Picard, F., Reboud, S., Tanguy C., Uzunidis, D. (2019). Innovating for elderly people: The development of geront’innovation in the French silver economy. Technology Analysis & Strategic Management, 31(4), 462–476. Ministère de la santé et des affaires sociales (2013). Contrat de filière Silver économie. Paris. Neven, L. (2015). By any means? Questioning the link between gerontechnological innovation and older people’s wish to live at home. Technological Forecasting and Social Change, 93, 32–43. OECD (2005). Oslo Manual. Guidelines for Collecting and Reporting Data on Research and Experimental Development, 3rd edition. Paris. OECD (2011). Demographic and Labour Force Database. Paris. Ostlund, B., Olander, E., Jonsson, O., Frennert, S. (2015). STS-inspired design to meet the challenges of modern aging. Welfare technology as a tool to promote user-driven innovations or another way to keep older users hostage? Technological Forecasting and Social Change, 93, 82–90. Peine, A., Faulkner, A., Jaeger, B., Moors, E. (2015). Science, technology and the ‘Grand Challenge’ of ageing: Understanding the socio-material constitution of later life. Technological Forecasting and Social Change, 93, 1–9. RRI (2016). Geront’innovations. Peter Lang, Brussels.

17 Greentech – Contributions and Limitations to the Environmental Transition

17.1. Introduction “Green technologies” are a concept that appeared in the 1970s, together with renewable energies, a criterion of “naturalness”, used to designate technologies that are neutral for the environment, non-polluting, non-carbonated, possibly recyclable, energy and non-renewable resource efficient. Today, this type of technological response can be questioned in view of the global, multidimensional and critical nature of the environmental problem (Dolique 2007), which calls for global responses and transformations. 17.2. Green technologies, the first technological response to the environmental crisis The general principle of these technologies is the substitution of a traditional polluting technology with a new, innovative, non-polluting and non-degrading technology for the environment. 17.2.1. New energies The first realizations of these green technologies appeared as early as the 1980s, in the field of energy, with “renewable energies”, such as solar and wind power. They have gradually been developed in most fields of activity and private use and are presented as being able to cover all energy production (UN Environment 2017). Chapter written by Smaïl AÏT-EL-HADJ. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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They are now giving rise to gigantic projects, a continuation in the green economy of the concentrated development models of the industrial economy. These include the DESERTEC project, a project of 20 concentrated solar power plants in the Sahara, representing a production capacity of 700 TW/h, and the “Wind Water Sun” project, which proposes covering the whole world’s energy needs by 2030, using 3.8 million wind turbines and 89,000 solar power plants. Renewable energy concepts are also on the rise, notably wave energy and tidal turbines, as well as energy from biology: biogas, biomass and biofuels. 17.2.2. Information technologies and green technologies These green technologies, which were not yet known as such, took their initial form in the mid-1970s with the “chip” revolution in electronic and computer devices, which was generally supposed to “replace matter and energy by information”. This technological change has been able to, for example, improve the technological performance and therefore the environmental efficiency of the automobile through the aerodynamic refinement of vehicles using CAD and electronic control of engine operation. In the same way, environmental improvement has been achieved in the reduction of material consumption for many structures and containers, thanks to computerized finite element calculation, as well as the general promise of “zero paper” thanks to the generalization of digital generation and remote communication (teleconferencing, telemedicine, teletraining, etc.), leading to a reduction in physical travel. An avatar of these digital devices, presented today as green technology, are “smart grids”, intelligent electrical networks that, thanks to computer technologies, adjust the flow of electricity between suppliers and consumers, and allow the intermittent electrical production of a network of wind and photovoltaic power plants to be optimized. Smart grids are now operating in several places as green technologies, notably in the “smart traffic” project being developed in the field of road transport, optimizing the load and flow of the grid, and thereby minimizing the environmental impact of vehicles. 17.2.3. Biology as a preferred carrier of green technologies The environmental constraint, particularly through the fossil fuel crisis, has led to the development of biofuels and bioenergy, as well as, in the field of food, to GMO integrating, through genetic implantation mechanisms, sought-after resistance traits, thus replacing the use of pesticides and saving water. This recourse to biology will largely affect the chemical field with the production, through biological processes, of intermediate chemical products such as acids, solvents, lubricants and materials, and by opening up a field of biomaterials (biopolymers), avoiding the use

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of hydrocarbons, and eliminating the non-degradable nature of polymers by the use of raw vegetable materials. This is done by fermenting genetically modified cellular organisms in so-called cell factories or genetic biocatalysis. Biology is at the basis of green technologies in agriculture, through its substitution to traditional mechanical and chemical agricultural techniques, in what is beginning to be called organic agriculture. However, biology as a green technology is also involved in the field of health with its investment in the pharmaceutical industry, vaccines and diagnostics, as well as treatments based on gene and molecular therapies. Last but not least, a particularly green use of biology is its use in depollution, the destruction of pollutants by microorganisms, as commonly practiced in the treatment of wastewater and the regeneration of polluted soils. A new green technology is emerging, in the face of the crucial and urgent nature of climate change, and that is CO2 capture technology. Various processes have been developed, but they are only at the project or prototype stage (Bihouix 2014). 17.2.4. Nanotechnologies: cross-technology dimension of green technologies At the other end of the dimensional spectrum, electronic or mechatronic nanotechnologies are also included in green technologies in the sense that they will make certain actions technically possible, particularly in the field of health, or activate certain functions at low energy cost, notably through increasing precision and zero rejection. Green technologies include nanotechnologies in the form of nanomaterials that will include a function, or boost a function at a supposedly negligible environmental cost. 17.2.5. New services and organizations: recycling, industrial ecology, the economy of functionality Other remedies for environmental degradation that are not strictly speaking technological, but rather new types of services, or new forms of industrial organization, include recycling, which seeks to provide a remedy for the depletion of resources as well as for the accumulation of waste. While it is a mature technology in the recycling of common metals and glass, this activity is much more difficult in the recovery of rare metals that are often dispersed in their applications or mixed in composite systems and are therefore irrecoverable. Recycling has developed in the textile industry, with difficulties caused by the manufactured nature of textiles, which often makes them difficult to recycle, or by functions with little value and a level of profitability that hinders their development. Even more difficult is the development of plastic recycling, despite the dramatic accumulation of waste. The

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difficulty mainly comes from the composite and mixed state of manufactured plastic and especially from its dispersion and physical decomposition into micro-particles in the waste state. Industrial ecology consists of building local industrial networks in which the discharge and waste of one company are used as raw materials and resources of neighboring companies to obtain a rejection-free and carbon-neutral whole. The economy of functionality imagines and implements a system of services making the means they traditionally owned available to users. It is a system that promotes the optimal use of the resource, avoiding any over-equipping. 17.3. From green technologies to a sustainable technological and socio-economic system 17.3.1. Green technologies are a one-off and partial response to the environmental challenge Green technologies are often based on a bifurcation, sometimes deep, but almost always timely, of the technological, industrial and usage itinerary of the technologies in our current system. In this respect, each one seeks to correct or reduce a particular environmental nuisance in a particular phase of the lifecycle of the technology and activity in question. In doing so, these green technologies often displace the environmental problem by amplifying another problem in the system. 17.3.2. The shifting of boundaries and environmental problems One of the components of green technologies is based on the use of biotechnology, which is supposed to correct the environmental problems generated by the chemical or mechanical technologies that it replaces by developing, for example, a biochemistry of the plant developing biopolymers and biofuels, in particular. However, all of these green plant technologies compete with other uses of plants, such as food for cultivated products (cereals, sugars, oil seeds), sometimes causing these crops to grow, becoming space-extracting; or by supplying themselves with forest and non-cultivated plants, accelerating deforestation and the destruction of wetlands. These green technologies can generate pollution at other stages of their lifecycle than the one they correct, particularly by using more and more polluting inputs. This is the well-known case of the battery in the engine of an electric car (carrying the carbon equivalent of 140,000 kilometers of combustion engine travel), whose overall carbon content is also dependent on the carbon content of the electricity used to charge the vehicle.

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We are beginning to realize that information technologies, which, as mentioned above, are certainly likely to make strong contributions to the environmental improvement of a multitude of activities, and were long perceived as immaterial technologies, have no material cost and generate a massive use of materials that are sometimes critical for the environment, such as rare metals (Pitron 2018), and are in danger of being in short supply (Pitron 2018). For their operation, they cause massive energy consumption and are, moreover, very large producers of heat (to the point of giving rise to large-scale recovery projects, where we join recycling). Green technologies, generating an often partial correction of a local environmental problem, are therefore not able to provide a global response to an environmental limit that is systemic. 17.3.3. The global environmental limit implies responding with a global reconfiguration of the technological system Green technologies have represented a wave of responses to environmental problems that are perceived as numerous but isolated and can be addressed in a specific way. We have shown the limits of these responses, which have arisen, in particular from the interactions between different environmental problems. The system and synergy effect, problems and degradation in one environmental field generate or amplify environmental problems in other fields (global warming causes glaciers to melt, leading to water shortages). This awareness of the role of ecosystems in the environmental crisis, of which climate change (Touzard 2017) is an illustration, implies a global response in terms of reconsidering and reconfiguring the overall technological system (Diemer 2012). 17.3.4. The global environmental limit implies a societal reconfiguration beyond technology However, the scale and criticality of the environmental crisis means that we need to question not only our technological functions, but also our modes of production and consumption, and the lifespan of our technical resources as objects of daily life, as well as our scales of development and growth regimes. An example of this is the mass and scale effect. It is largely because human activity is carried out on a very large scale and with strong growth that it generates environmental degradation. However, the generation of green technologies was part of a perpetual logic of continuous growth, known as “green growth”. In this way, the environmental gain brought about by green technologies is largely offset by the

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aggravation of the environmental problem caused by the mass and growth effect. The 3.8 billion automobiles worldwide, with a fleet growth rate of 5% per year, are likely to increase the sector’s contribution to the greenhouse effect (even if it has become residual at the individual vehicle level), thus generating new pollution and perpetuating a gigantic pressure on common and rare metals, contributing to maintaining strong pressure on space and the artificialization of soils, and generating a gigantic mass of waste that is only partially recyclable (Magny 2019). It is also probably the suppression of useless polluting activities that would currently be the most powerful means of returning to a sustainable environmental level. 17.3.5. The current criticality of the environmental threat implies a massive and rapid transition One problem that is often ignored by proponents of green technologies is the effect of the possible pace of the transition, which is another action of its mass dimension, acting to slow down transition. Park effects, for example, in the energy field, include Chinese coal-fired power plants built recently en masse to keep up with China’s industrial growth. These then carry a residual value of production capacity over 30 years, and the owners and operators of these units will not agree to dismantle them. However, this mass effect also plays a role in the positive transition. The reasoning on renewable energies, for example, leads to proposals for rapid implementation, which would involve a gigantic use of materials, including rare metals, and the mobilization of gigantic industrial capacities. This acts as an obstacle to a rapid response to an environmental crisis that has become critical. This is the very paradigm that structures “the social production of our existence”, as Karl Marx would have said, i.e. the paradigm of the omnipotence of humans over their natural environment, the logic of continuous progress, the infinite satisfaction of needs, “doing something because we know how to do it” without concern for the consequences (Fressoz 2012), all of which is underpinned by the logic of the “invisible hand”, and all of which we will have to question. 17.4. References Bihouix, P. (2014). L’âge des low tech. Le Seuil, Paris. Diemer, A. (2012). L’écologie au cœur du développement durable : mythe ou réalité ? Innovations. Revue d’économie et de management de l’innovation, 37(1), 73–94 Dolique, L. (2007). Risques globaux et développement durable. L’Harmattan, Paris.

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Fressoz, J.B. (2012). L’apocalypse joyeuse : une histoire du risque technologique. Le Seuil, Paris. Magny, M. (2019). Aux racines de l’anthropocène, une crise écologique reflet d’une crise de l’homme. Editions Le Bord de l’eau, Bordeaux. Pitron, G. (2018). La guerre des métaux rares, la face cachée de la transition énérgétique et numérique. Editions Les Liens qui Libèrent, Paris. Touzard, J.M. (2017). Innover face au changement climatique. Innovations. Revue d’économie et de management de l’innovation, 54(3), 5–13 UN Environment (2017). Green technology choices: The environmental and resource implications of low-carbon technologies. International Resource Panel Report.

18 Hacker – Hackerspace as a Space for Creative Exploration

18.1. Introduction As the influence of the Internet and New Information and Communication Technologies (NICTs) has spread in the daily lives of a wide public, knowledge that was previously difficult to access has also spread. This era of “democratization” of access to information has had the effect of accelerating the dissemination and exchange of know-how. Indeed, although the majority of NICT users – users of the Internet in particular – are passive consumers, a strong technological culture has become increasingly widespread. Well beyond the distribution of “bricolage” guides or the sharing of tips in the form of tutorials on YouTube, this is a question of the exchange and acquisition of technical and technological know-how. From computer programming to 3D printing, from building robots to repurposing objects of all kinds for educational or artistic purposes, it is about the rise of a culture as old as the Internet that is becoming more and more popular: the culture of the “hacker”. It is characterized by an appetite for “creative hacking”, aimed at understanding the internal workings of a system (computer-based, electronic and sometimes mechanical) in order to find alternative designs and uses. Hacking, considered to be the activity of hackers, can be regarded as the set of practices characterized by the repurposing of objects and technical devices from their current use (Levy 2014). Today, the emergence of “third places” (Cléach et al. 2015) has opened the doors of this movement, initially reserved for geeks (a person with a passion for computers, science fiction or video games who is always on the lookout for new things and improvements to digital technologies), and then to a wider public made up of amateurs or laypersons. These physical places, dedicated to Chapter written by Dave MOBHE BOKOKO. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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computer and digital technology enthusiasts, experts to a greater or lesser extent, allowing interactions and exchanges of knowledge and know-how within a community of users, and promoting technological hacking, are called “hackerspaces”. Moreover, the network culture reinforced by the Internet, especially hacking, has also attracted new organizations conducive to collaborative work (Dambron and Xu 2017) that go even beyond the walls of the company. In this respect, hackerspaces provide a space dedicated to the counterculture of the company’s usual reference points, especially hierarchy. The presence of professionals in these dedicated spaces, looking for an “anarchic” environment conducive to freedom for the development of their projects and the exchange of ideas and new know-how, is understandable. A hackerspace is characterized by a deliberate and collaborative social practice, marked by the sharing of knowledge, materials and learning. These spaces provide their members with easy access to specific services and technologies such as 3D printers, laser cutters and other machine tools. Does the “hacking”, this “working for oneself” practiced in the hackerspaces, contribute to stimulating a creative process? Is it an alternative innovation ecosystem? To provide some answers to these various questions, we will first present the characteristics of hackerspaces and the reason for their growth. And, finally, we will look at the extent to which they could be linked to creativity and innovation. 18.2. The rise of hacker culture The term “hackerspace” comes from the combination of two words: “hacker” and “space”. The word hacker represents the “person who hacks” or “builds a hack” (the word “hack” represents the search and exploration of lines of code – software – and not a reference to cybercrime). In 1959, the term hacker was coined in the jargon of the Tech Model Railroad Club (TMR), a student association at the Massachusetts Institute of Technology (MIT). In 1996, the 1983 Request for comments1 (RFC) defined a “hacker” as “a person who enjoys a thorough understanding of the inner workings of a system, particularly computers and computer networks” (Malkin 1996). In this sense, the word hacker refers to creative exploration (tinkering or hacking) to improve the functioning of a system, while the word “space” naturally indicates a physical place. Together, the term “hackerspace” indicates a physical place where hackers meet. The hackers, gathered in hackerspaces, are bound by an ethic of cooperation, a rejection of hierarchy, and a free exchange of information and knowledge (Levy 2014; Dambron and Xu 2017). Propelled by the spirit of “free software” (Broca 1 Requests for comments (RFCs) are a numbered series of official documents describing the technical aspects and specifications of the Internet, or of various computer hardware.

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2013), the culture of hacking makes hackerspaces a third place dedicated to questioning past laws, technical, social or philosophical concepts. There was a time when hackerspaces were very rare, almost legendary entities known only to insiders. Today, these third places centered on computing and digital technologies have a slightly more open audience. To understand the rise of this movement, it is necessary to have a brief flashback to the beginning of the Internet. Note that access to the first computers gave rise to the culture of the “hacker”, driven by the creative exploration of these new modern systems. A fortiori, we would say that the mass arrival of computers in households, as well as the installation of the Web on a global scale, served as a boost for the propagation of this culture, once confined to the workshops of Silicon Valley. Strangely enough, it is in Germany rather than the United States that we must look for the origin of hackerspaces as we know them. In September 1981, the Chaos Computer Club2 (CCC), a vast network of German hackers, appeared in Berlin. This club became hacker legend by succeeding, in November 1984, three years after its creation, in hacking the German Minitel3. Other hackerspaces would appear in the 1990s, such as C-Base in Berlin or C4 in Cologne. Then, in 2006, Metalab in Vienna would lay the foundations for the operation of these third places. It should be noted that Metalab Vienna is a hackerspace that has been a catalyst in the global hackerspace movement and has been the birthplace for several digital startups. In France, the movement was launched in 2007 with the TMP/LAB in Vitry sur Seine, Breizh Entropy in Rennes, Tetalab in Toulouse and others. This movement would be in vogue in the United States from 2007 and 2008, where NYC Resistor in New York, HaCDC in Washington and Noisebridge in San Francisco were born. On a global level, the expansion is evident. Since then, the movement seems to have gained momentum, with the creation of numerous work and gathering spaces. The site wiki.hackerspace.org has 2396 listed hackerspaces, of which 993 are active and 359 are under construction4. 18.3. Cybercrime or creative exploration? While valuing the achievement of creative technical prowess (which can also be translated as “hacks”), hackers are driven by the belief that general access to computers, a global network, and new modes of human–machine interaction can 2 See: https://www.ccc.de/en/. 3 The hacking of the German Minitel was motivated by the search for computer vulnerabilities that could make the system vulnerable to cybercriminal attacks. See: http://www. slate.fr/story/94973/ccc-legende-hacker-minitel-allemand. 4 List consulted on December 23, 2020, see: https://wiki.hackerspaces.org/List_of_Hacker_ Spaces.

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bring about transformations in all spheres of human activity (Delbecque 2016). This anarchist optimism has often run up against legal and ethical controversies, leading to the labeling of hacker practices as “cybercriminal”5. As a result, for much of the general public, greatly influenced by the media, the term “hacker” is taken to mean a “harmful” person, acting under the cover of anonymity for commercial or illicit purposes. Contrary to the semiological shift in meaning that has seen it become confused with a “cracker” or computer “pirate”, the term “hacker” refers to a “code breaker” attitude that defines a hacker rather as an inventor. Hackerspaces have the ability to mobilize the use of the collective intelligence of hackers in the development of new products in the Internet environment (Gu and Zhang 2018). Among the most remarkable initiatives in this area, the “hackathon” is the most well-known manifestation of a hackerspace’s activity. The term “hackathon” comes from the juxtaposition of two words: “hack” and “marathon”, together the term formed indicates “the intense and concentrated effort – as in a marathon – to find a new concept involving the development of a concrete solution (prototype, software, mock-up, proof of concept, etc.)” (Mobhe Bokoko 2020). Originally reserved for hacker challenges, the practice of hackathon hacking has now become more widespread in both technological and non-technological companies. If the hackathon has quickly become a lever for collaborative creativity, initiating the development of future innovations, it is because companies have been able to perfect them through structured methods, and in a more or less supervised manner. Hackerspaces, as part of a creative and exploratory “code breaking” approach, should theoretically lead to the development of many innovations. Indeed, it is by understanding the functioning of a system that the hacker succeeds in improving and/or creating a product, a service, a process, a method, a use and so on. This is an attitude that makes hackerspaces a third place dedicated to innovation. In fact, it is understandable that several digital startups have emerged from these dedicated spaces. In reality, although recognized as spaces of injunction for creative emancipation for “innovative consumers” (von Hippel et al. 2011), several contradictions stand out. Indeed, the culture of hacking is based on anarchy and a non-commercial vision of free access (Levy 2014). This implies a disadvantage in the acceptance of hackerspaces as third places dedicated to innovation. First, hackerspaces are places where hierarchy is absent, all that matters is the ability to hack the most complex systems. Anarchy, advocating this absence of hierarchy and the culture of free activity, is a real contradiction to the structured methods applied during hackathons. 5 Several laws around the world prohibit hacking, for example the STOP Online Privacy Act (SOPA) in the USA and the Digital Economy Act (LCEN) in France.

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In other words, an exploratory yet creative approach cannot lead to the result of an innovation process if it is not structured. The performance of the hackathons practiced by the most innovative companies comes from the fact that they effectively handle the combination of techniques of creative divergence and convergence. Several approaches are also involved, such as design thinking and SCRUM6. Second, hackerspaces are part of the “free” hacker philosophy, which is the opposite of the commercial approach of companies. Hackerspaces should therefore not be considered to be physical places to stimulate innovation. Innovation here is taken in its broad sense as the marketing of a significantly improved or new product (good or service; OECD 2005). 18.4. Conclusion Hackerspaces can be seen as open community labs where hackers can share resources, knowledge and know-how. The culture and inventiveness of hackers have significantly contributed to the development of computing and the Internet. These third places therefore have the particularity of transforming their members into active and committed consumers. They stimulate creative potential and restore the productive capacity of each citizen aware of the digital reality. However, the anarchist culture that reigns there does not guarantee the commitment of a systematic innovation process. In conclusion, in hackerspaces, it is not so much the result (innovation) that counts, because the work of hacking is an end in itself. 18.5. References Broca, S. (2013). Utopie du logiciel libre. Edition Le Passager Clandestin. Cléach, O., Deruelle, V., Metzger, J.-L. (2015). Les “tiers lieux”, des microcultures innovantes ? Recherches sociologiques et anthropologiques, 46(46–2), 67–85. Dambron, P. and Xu, M. (2017). Chapitre 12 – Métamorphose du leadership à l’heure des clusters et des hackerspaces. In Réinventer le leadership. EMS Editions Académie des Sciences de Management de Paris, Caen. Delbecque, Y. (2016). Culture hacker, hacks et création, création politique et politique de la culture. Les territoires de l’art. Art et politique, 15(Winter 2016), 130–144. Gu, Y. and Zhang, S. (2018). A hybrid decision model for heterogeneous schemes in “Internet Plus” hackerspace product development. 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE). 6 Scrum is a lightweight framework that helps people, teams and organizations generate value through adaptive solutions for complex problems, see: https://www.scrumguides.org/scrumguide.html.

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von Hippel, E.A., Ogawa, S., de Jong, J.P.J. (2011). The age of the consumer-innovator. MIT Sloan Management Review, Fall 2011, 53(1), 26–36. Levy, S. (2014). L’Ethique des hackers. Ecole des Loisirs. Malkin, G. (1996). Request for comments: 1983 [Internet User’s Glossary] [Online]. Available at: https://tools.ietf.org/html/rfc1983. Mobhe Bokoko, D. (2020). Développement de la créativité collaborative par la pratique du hackathon. Effet de levier pour le développement des bases des futures innovations. Technologie et Innovation, 20(5). ISTE OpenScience. OECD (2005). Manuel d’Oslo : principes directeurs pour le recueil et l’interprétation des données sur l’innovation. OECD Publishing.

19 Health – Telemedicine: Decentralized Medical Innovation

19.1. Introduction Today’s society faces many challenges in the field of healthcare. Indeed, for several decades now we have been witnessing a concentration of the population in cities and a desertification of the countryside, leading to the closure of hospitals and clinics. Some municipalities are finding it increasingly difficult to attract doctors and other medical personnel, even though they often have an aging population requiring regular care. The function of the country doctor, traveling hundreds of kilometers per week to visit patients in all weathers and at all hours, no longer appeals to practitioners or future doctors and is tending to disappear. Municipalities, departments and regions are therefore faced with medical deserts. It is becoming necessary to rethink the traditional face-to-face patient–doctor relationship by integrating the notion of decentralized medicine, where the patient and the practitioner are no longer physically in the same room, but exchange information via the Internet. This new model of decentralized medicine has been able to develop thanks, in particular, to the progress of new technologies. These remote consultation solutions, although they have many advantages, can only be adopted if users have confidence in the technology offered. In addition, patients expect the same level of quality and attention from healthcare staff as they would expect from a traditional consultation. 19.2. Information technology at the service of medical care The emergence of new “smart” technologies, such as connected objects, could provide solutions to reduce inequality of access to care (Finkelstein et al. 2006). Chapter written by Patricia BAUDIER. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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Indeed, many connected objects are developed around the theme of health or wellbeing, allowing users to monitor the evolution of their health. For example, connected bracelets (Baudier et al. 2019) collect data, analyze it and can signal potential problems, including those relating to heartbeat, blood pressure, blood glucose or oxygen levels in the blood, and can alert either the patient or a healthcare professional. This monitoring, practiced by individuals, can help to quickly identify a problem by avoiding emergency situations or potential complications, and thus contribute to limiting levels of hospitalization (Mittelstadt and Floridi 2015). The information communicated to healthcare professionals allows the remote monitoring of patients. With the advent of the Internet, the penetration rate of computers, tablets and smart phones has enabled the development of telemedicine at home. This solution offers the possibility of remotely following elderly people or people suffering from specific diseases, such as cardiovascular problems or diabetes (Klaassen et al. 2016). Simon (2016) defines telemedicine or teleconsultation solutions as a remote connection between a patient and a doctor who, thanks to the indications provided, can make a diagnosis and propose appropriate treatment. However, telemedicine remains a clinical act, practiced by qualified personnel and should not be equated with e-commerce (Simon and Lucas 2014). Since September 15, 2018, medical consultations carried out by videoconference in France have been reimbursed, under certain conditions, at the same rate as a physical consultation by the Assurance Maladie and the Caisses de Mutuelles. This measure tends to promote the launch of the telemedicine booth, the use of which must be simple if it is to be accepted by individuals. The patient, once inside the booth, is immediately taken care of by the doctor by means of a screen. The practitioner may ask the patient, after listening to their symptoms, to use certain medical devices made available in the booth. The telemedicine booths are equipped with measuring devices to monitor, for example, weight, blood pressure, blood oxygen saturation rate and temperature, as well as devices to facilitate diagnosis, such as stethoscopes. The doctor can also test visual acuity, carry out hearing checks or perform an electrocardiogram. These tools must be systematically checked and disinfected between each patient to ensure a good level of hygiene and safety. This task therefore requires the presence of qualified personnel, who will also be able to prevent any potential problems relating to the degradation of the equipment. After carrying out the necessary examinations, the doctor will be able to make a diagnosis, initiate the printing of a prescription or redirect the patient to a specialist center or hospital. 19.3. High-performance medical devices The main advantages of these solutions are time saving (no travel for patients or physicians; no waiting time), cost reduction, particularly those related to transportation (Kassirer 2001), and easier access to care. Telemedicine booths are

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beginning to be installed in France in health centers, pharmacies, public places and in some companies, with the approval of the Agence Régionale de la Santé (ARS), the Union Nationale des Organismes Complémentaires d’Assurance Maladie (UNOCAM) and the Commission Nationale pour l’Informatique et Libertés (CNIL). The installation of these solutions within companies enables them to respond to the problems of employees who sometimes struggle to find an available slot for a medical consultation outside working hours. Employers, for their part, also benefit from these teleconsulting solutions. Indeed, they can help to maintain or improve the company’s “social image” by demonstrating their interest in the health and well-being of their employees, as well as preventing the possibility of more or longer sick leave owing to the lack of early treatment for a disease. These telemedicine booths or teleconsultation solutions, installed within companies, often accept the “carte vitale” and the “carte de mutuelle” (health insurance cards), in which case the patient does not have to pay any fees in advance. However, given the private nature of the data, individuals may be wary of the risk of information leakage, the lack of confidentiality or potential technological breakdowns, such as a loss of signal, sound or data (Bishop et al. 2002). As a result, individuals will only accept teleconsultation if they trust both the technology and the healthcare provider (the physician). In addition, the perceived ease of use and usefulness are key to the adoption of these new technologies (Papa et al. 2018). Patients often establish a relationship of trust with their referring physician, known as the “family doctor”, who has been following all of the members of their family for several years. In the context of telemedicine solutions, this link is non-existent, since the physician changes and therefore cannot establish a confidential, long-term relationship. However, this relationship does not differ from those established during an emergency consultation with a doctor in a hospital, with the fire department or with paramedics. Individuals who agree to consult remotely must have confidence that the health practitioner will be caring (acting in the best interests of the patient), have integrity and be competent (McKnight et al. 2002). 19.4. Conclusion The advent of information and communication technologies has enabled the development of decentralized medicine, such as the medical booth, which may prove to be a potential solution to meeting current and future challenges of the health system, such as the difficulties of access to medical care in regions suffering from a shortage of doctors. This solution could also make it possible to meet the specific expectations of certain individuals, such as people with reduced mobility or busy people who have difficulty finding time slots compatible with their

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professional activity. Teleconsultation systems could also help to relieve hospital emergency rooms and reduce expenses, such as the cost of transporting certain patients by ambulance. However, in order to increase its level of acceptability and reduce the perception of risk, users must be reassured of the security of their data, guaranteeing them confidentiality of information, especially in the case of telemedicine solutions offered in the workplaces of individuals. Indeed, the link between confidentiality concerns and the notion of risk has been demonstrated in the field of e-health. However, the greater the sense of usefulness, the less concern there is about confidentiality. Patients must also be assured of a good level of quality infrastructure and interpersonal relations with the health practitioner. It can be assumed that the reimbursement of teleconsultation procedures by, for example, the Caisse d’Assurance Maladie, will help to increase the use of these facilities. However, it would be desirable to set up national awareness campaigns via the media to inform individuals and make the concept more widespread. Will the medical booth be the virtual equivalent of neighborhood dispensaries launched decades ago that have today almost disappeared? 19.5. References Baudier, P., Ammi, C., Lecouteux, A. (2019). Employees’ acceptance of healthcare Internet of Things: A source of innovation in corporate human resources policies. Journal of Innovation Economics & Management, 30(3), 89–111. Bishop, J.E., O’Reilly, R.L., Maddox, K., Hutchinson, L.J. (2002). Client satisfaction in a feasibility study comparing face-to-face interviews with telepsychiatry. Journal of Telemedicine and Telecare, 8(4), 217–221. Finkelstein, S.M., Speedie, S.M., Potthoff, S. (2006). Home telehealth improves clinical outcomes at lower cost for home healthcare. Telemedicine and e-Health, 12(2), 128–136. Kassirer, J.P. (2001). Financial conflict of interest: An unresolved ethical frontier. American Journal of Law & Medicine, 27, 149. Klaassen, B., van Beijnum, B.J.F., Hermens, H.J. (2016). Usability in telemedicine systems – A literature survey. International Journal of Medical Informatics, 93, 57–69. McKnight, D.H. and Chervany, N.L. (2002). What trust means in e-commerce customer relationships: An interdisciplinary conceptual typology. International Journal of Electronic Commerce, 6(2), 35–59. McKnight, D.H., Choudhury, V., Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334–359. Mittelstadt, B.D. and Floridi, L. (2015). The ethics of big data: Current and foreseeable issues in biomedical contexts. Science and Engineering Ethics, 22(2), 303–341.

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Papa, A., Mital, M., Pisano, P., Del Giudice, M. (2018). E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation. Technological Forecasting and Social Change, 153, 119226. Simon, P. (2016). Des pratiques de télémédecine pour structurer le projet medical partagé d’un groupement hospitalier du territoire. European Research in Telemedicine, 5, 71–75. Simon, P. and Lucas, J. (2014). La télémédecine n’est pas du e-commerce mais de la médecine clinique. European Research in Telemedicine, 3, 27–34.

20 Intellectual Corpus – Inventive Intellectual Corpus: Knowledge-based Innovation

20.1. Introduction The field of knowledge-based innovation is addressed here in an industrial context. When a creative idea is generated, its transformation into new knowledge depends on a cycle connecting three subsystems and highlights the link between an idea and knowledge: the individual who generated the idea, the knowledge domain that serves as a repository, and the community of knowledge actors who evaluate, select and validate the relevant ideas. The present contribution covers the reversibility of the idea–knowledge link, i.e. the passage from knowledge to the inventive idea, by promoting a dynamic of innovation ab nihilo (from ideation to innovation). In an industrial context, innovation activities in the technical field are schematically based on two dynamics (Saulais and Ermine 2011): – the confrontation with a problem (reduced at a given time to a technical dimension) leading to an innovative solution. This is a process of innovation within a closed framework consisting of the problem to be solved and externally driven (see Figure 20.1). It is the best known and most practiced innovation dynamic, the problem-solving type; – the idea having found a market. This is a process of innovation within an open framework, described from the inside (development of an idea ab nihilo) to the outside (see Figure 20.2). This is the innovation dynamic we are interested in here.

Chapter written by Pierre SAULAIS. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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Figure 20.1. Dynamics of innovation from problem to solution

Figure 20.2. Dynamics of innovation from ideation to innovation

The current chapter describes a method for generating inventive ideas that takes advantage of the deep knowledge of knowledge actors, based on the epistemic link between the structural analysis of the knowledge contained in the inventive intellectual capital and the inventive idea considered to be an inventive generation of knowledge. This method is part of the field of knowledge-based innovation. Section 20.2 is dedicated to the concept of knowledge-based innovation, which broadens the scope of issues and innovation compared to the vision of “design to specification” that is usual in companies. A model of knowledge creation is presented in section 20.3 and its activation is described in section 20.4, before the chapter is summarized in section 20.5. 20.2. Concept of knowledge-based innovation The general KBI (Knowledge-Based Innovation) process is based on the representation of explicit elements of the knowledge capital obtained through a “knowledge drilling” process. This representation is then used as a cognitive stimulus to stimulate the reflection of knowledge actors on the potential evolution of knowledge in several knowledge fields that make up a specific area of their organization.

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Figure 20.3 shows the concept of knowledge creation proposed by Jean-Louis Ermine (2003), according to two stages (analysis of knowledge assets and discovery of the laws of innovation): – Capital analysis: the history of this innovation is the history of the concepts, ideas and even cultural myths that have marked its evolution. This history can be retraced through the lived experiences of past designs, bringing together different successes and failures. It is therefore a question of extracting, from this history, a summary and fruitful vision of the paths of evolution that have been taken in the past, justifying the choices and non-choices made. – Discovery of the laws of innovation: previous work is an opportunity to discover, through analysis and cross-checking, certain avenues that have not been or have been poorly explored. It is an opportunity to point out factors that have prevailed in past innovations, and major laws that have made it possible to innovate. These include assimilation (incorporation of elements from the environment), accommodation (modification of the structure according to changes in the environment), allopatric speciation and adaptation (balance between assimilation and accommodation). The hypothesis made (Ermine 2003) consists of assimilating the process of knowledge creation to the process of evolution of the firm’s knowledge capital, based on the creativity of the knowledge actors, both within the firm and in interaction with their environment.

Figure 20.3. The knowledge creation mechanism

The inventive intellectual capital is illustrated in Figure 20.4. The intellectual creation characteristic of the intellectual mind will be translated into new

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knowledge, which, after formalization, will be added to an intellectual corpus formed of this inventive intellectual capital (Saulais 2013).

Figure 20.4. Inventive intellectual capital

20.3. Modeling knowledge creation The theoretical model identifies a data input in the form of a cognitive stimulus and a data output in the form of a prospective vision. The cognitive stimulus is based on an in-depth analysis of the texture of knowledge structuring the inventive intellectual capital. The instantiation of the theoretical model is carried out through experimentation through the intervention of individuals solicited as knowledge actors (Figure 20.7). As a result, based on the vision of creativity as a process of idea generation, itself interpreted as a process of evolution of the inventive intellectual capital, this theory of “chaotic” evolution by emergence can be adapted to the creation of inventive knowledge (Saulais and Ermine 2011). This theory of “chaotic” evolution is illustrated in Figure 20.5. A system is built on structures that can be transformed under the effect of an energy input, according to a transformation regulated by its confrontation with the environment. These structures have a purpose that is expressed by properties. By transforming themselves, these structures acquire new properties. In the process of evolution, only the properties retained by the stabilization loop are in conformity with the purpose of the system. Those that disappear correspond to the so-called entropy of the system (Heudin 1998).

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Figure 20.5. Chaotic evolutionary process

Since we are dealing with the problem of the evolution of the inventive intellectual capital system, we can adapt the general model of evolution in Figure 20.5 to the problem of creativity, thanks to the theoretical elements already collected, in order to obtain the chaotic inspiration model of the evolution of inventive knowledge by emergence, as illustrated in Figure 20.6.

Figure 20.6. The chaotic inspiration model of knowledge evolution by emergence

Evolution can be well represented by a dynamic, nonlinear and unstable system, whereas this nonlinearity and instability are part of the approach to chaos under the second principle of thermodynamics, which led us to name the model in Figure 20.6 “chaos-inspired”. Emergence, whose model uses the principle, then manifests as the appearance of new qualities that cannot be reduced or deduced from the properties of the initial components. This capacity-building operation is therefore an emerging phenomenon as the result is a complete structured product (possibilities of innovative products) that makes sense for the organization. It corresponds to what biologists call an

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“emerging quality”. The framework described here allows us to set up an operational mechanism for idea generation that is regulated, weighted and then oriented towards the company’s objectives. Chaotic “inspirational” reasoning identifies as the starting point of creativity a dissatisfaction or tension between the desire to evolve towards a higher form of organization (negentropy) and a statement of evolution towards a lower form of organization (entropy). This tension can only be released by identifying a source idea (creativity), structuring it through reasoning into an invention idea, or more generally a work (inventiveness), which must be materialized as an innovative product (Saulais 2013). We believe that the evolutionary framework described in Figure 20.6 will allow the establishment of an operating mechanism for idea generation that is regulated, weighted and then oriented towards the organization’s objectives, which is the basis of our ICAROS® method. 20.4. Activation of the chaotic inspiration model of knowledge evolution by emergence using the ICAROS® method The proposed method of stimulated creativity (analysis of intellectual capital for a reasoned stimulation of creativity or ICAROS® method) aims to activate the chaotic inspiration model of knowledge evolution by emergence, as shown in Figure 20.6, from the creation of a cognitive stimulus. This comes from the process of innovative knowledge creation, as illustrated in Figure 20.3, by reducing the knowledge capital to the inventive intellectual capital and by replacing the current analysis with the confrontation of the analysis of the structure of the inventive knowledge available in the inventive intellectual capital with the cognitive capital of knowledge actors. Indeed, the evolutionary structures of the general model in Figure 20.3 then become inventive knowledge. According to our ICAROS® method, this knowledge is enriched under the effect of a cognitive stimulus resulting from the confrontation of a structured analysis of the concatenation of the inventive intellectual capital with the cognitive patrimony of knowledge actors. These are experts, bearers of reference knowledge on both their external environment (markets, state of the art, etc.) and the internal environment (the company’s own tangible and intangible resources). This cognitive capital is therefore a representation of the firm’s internal and external innovation ecosystem relative to the field under consideration and corresponds to the role that the environment plays in the general model. The result of the confrontation will create variations in the knowledge structures in the form of new ideas likely to make knowledge evolve. These are subjected to a development loop in which a

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reasoned iteration eliminates the variations that are too far from or too close to the reference knowledge, and then to a filter of feasibility of action (skills). Since the organization’s activity corresponds to a productive purpose, the new skills must generate productive capacities. The strategic alignment plays the role of stabilization in the general model and makes it possible to control the conformity of the emerging competencies with the organization’s objectives. Those capabilities considered non-strategic are not retained and correspond to the entropy dissipated by the system. Figure 20.7 provides an overview of the creativity mechanism as applied. The overall mechanism consists of a preliminary part (phase 1) and a main part (phase 2 to phase 4).

Figure 20.7. The knowledge-based creativity mechanism

The approach is centered on the actor of inventive knowledge and not on the organization. Moreover, our reasoning is based on the analysis of the inventive intellectual capital, whose bearer is the creative individual and which cannot be appropriated by the organization. This inventive intellectual capital of a given actor takes its roots not only in the actor’s present and past professional context, but also in the whole environment outside their professional context (Saulais 2013). The new use of the inventive intellectual capital as a dematerialized corpus stems directly from the mental schema we have chosen to support the inventive knowledge that generates intellectual property rights (Figure 20.4).

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20.5. Conclusion This contribution illustrates a deepening of the concept of knowledge-based innovation, by describing a theoretical model of knowledge evolution by emergence and a structured method, ICAROS®, based on the activation of this theoretical model of inventive knowledge creation, able to feed the innovation process. Our approach, based on the dual concept of creation (creativity-inventiveness), suggested that the creators of inventive knowledge should be considered the backbone of the method, after the prior inventory of inventive activity and the structuring of inventive intellectual capital of the knowledge actors. We have shown that the ideation, stimulated by the critical analysis of the knowledge structure found in the inventive intellectual capital of the knowledge actor, can be considered an epistemic mutation, where the source, the process, the results, the corpus and the knowledge actor can be assimilated into a single entity. KBI (knowledge-based innovation) consists of relying on the firm’s existing knowledge capital, which is its “genetic capital”, and in promoting the “laws of evolution” (accommodation, assimilation, mutation, etc.) of this capital in relation to its environment. In a financial context not very favorable to internal R&D investments, in order to stimulate innovation, KBI could be a very effective alternative to open innovation with its strong hypothesis of path dependency: This is the first level of application of our method. The second level of application of our ICAROS® method, based on the dynamic determination of the creative knowledge to be acquired, consists of an epistemic mutation, which represents a very promising line of research for in-depth exploration. 20.6. References Ermine, J.-L. (2003). La Gestion des connaissances. Hermès, Paris, France. Heudin, J.-C. (1998). L’évolution au bord du chaos. Hermès, Paris, France. Saulais, P. (2013). Application de la gestion des connaissances à la créativité des experts et à la planification de la R&T en milieu industriel de haute technologie. Doctoral thesis, Télécom Ecole de Management, Evry, France. Saulais, P. and Ermine, J.-L. (2011). Créativité et gestion des connaissances, GeCSO 2011. Gestion des connaissances pour la societe et les organisations, Clermont-Ferrand, France.

21 Imagination – Imagination, Science Fiction, Creativity and Innovation: An Integrated Process

21.1. Introduction The search for creativity in organizations and companies involves stimulating the imagination. There are many methods for optimizing creativity and they are at the heart of managers’ concerns, aware of the growing strategic importance of fiction in the development of innovative products and technologies. While imagination was long marginalized owing to a philosophical tradition placing it in opposition to rationality, since the 1970s it has become an increasingly attractive element for innovators seeking to surprise markets with attractive and original products. Imagination and fantasy are therefore increasingly encouraged, and companies call upon creative minds to better understand market expectations (market pull), and to surprise them by offering original goods and services (technology push). Imaginations are numerous, and must be mapped and channeled by institutions in order to exploit them for profit and profitability. Science fiction is an example of the imagination particularly appreciated by innovators, who employ authors to develop futuristic visions that prove crucial when launching innovations in consumer markets. NASA, ESA, Microsoft, Orange, Google, Intel and many other institutions are part of an ambivalent collective imagination that is both technophile and technophobic. Imaginary representations accompany the diffusion of innovations. Fiction and the imagination intervene at all levels of the innovation process. In fact, design fiction is a method that appeared at the end of the 2000s, using science fiction to imagine new goods and services. It is seen as a new approach to storytelling,

Chapter written by Thomas MICHAUD. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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conceiving fiction and the imagination as increasingly influential elements in designing scenarios used as strategic narratives in most innovative economic fields. After drawing attention to the reticence of certain actors towards the imaginary, science fiction will be presented as a driving force for creativity and inventiveness in industrialized societies. This artistic genre has been developing since the 19th century in Europe and the United States, before becoming a global culture used by public institutions or companies to develop innovations. Faced with this global success, the question of the ethics of its use and its format is raised. 21.2. Tame the imagination in order to innovate The imaginary is a formalization of the imagination using discursive structures to feed the productive system, in particular with stories and fictions with multiple vocations. The imaginary can legitimize a social, political or economic organization, or criticize it, to the point of developing counter-powers. Control over the imaginary is an issue of power in most societies. The major religions are based on a specific imaginary and fight the influence of narratives, such as mythologies, that could undermine their authority if they were appreciated by an increasing number of followers. The dominant imaginary seeks to justify tradition, and legitimizes the dominant social classes that are its guarantors. However, the imaginary can also be a source of innovation. It channels the imagination of an individual or a population, and creates representations that are sometimes subversive or avant-garde, and often so creative that they arouse curiosity and the desire to see these fantasies come true for individual and collective happiness. This subversive function of the imaginary interests the most innovative economic actors, constantly in search of new concepts or weak signals likely to give them a head start over their competitors. The imaginary has had an increasingly positive reputation since the beginnings of cinema and television. Imagination, long considered “la folle du logis” (“the madwoman of the house”), as Nicolas de Malebranche (1638–1715) put it, was for a long time opposed to reason, and particularly to science. Yet the imaginary is already an effort of rationalization, of domestication of the imagination, with the aim of feeding minds and society with representations favorable to the expression of enlightened conceptions and to novelty. Science fiction is an example of the imaginary that plays an important role in the construction of representations of the future and in the creation of strategic speeches by entrepreneurs, investors and designers of new technologies. Its creation date is traditionally given as 1818, the year of publication of Mary Shelley’s cult novel, Frankenstein. Authors such as Jules Verne and H.G. Wells then laid the foundations of a new artistic genre, which imposed itself on popular culture in Europe at the end of the 19th century, characterized by important changes linked to the Industrial

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Revolution. This imaginary was indeed the product of major changes linked to the emergence of flourishing capitalism. Religion was challenged by the philosophers of the Enlightenment, then by Marxism, giving way to a new imaginary, relating to the evolution of the living conditions of Westerners. The genre then took off in the United States in the 1920s, under the impetus of Hugo Gernsback, creator of numerous scientific journals, in which there were many utopian, imaginary technologies. A true golden age of science fiction saw many authors, mainly American, imagine future societies revolutionized by phantasmagorical technical objects that fed the imagination of industrialized societies. Positivism and science were based on a sometimes violent criticism of religion, in particular Christianity, but the emergence of philosophical and even mystical or religious questions in works of science fiction was gradually taking place – to the point of positing the hypothesis of the creation of a new religion, adapted to the new values of a society in which science and technology became the driving force. Science fiction is not yet a religion, but an imaginary that has become more and more influential in the scientific community since the end of World War II. The Apollo program to send American astronauts to the Moon was considered the realization of Jules Verne’s novel From the Earth to the Moon, because of the many similarities between the novel’s descriptions and the conditions for launching this mission, which was so important to the United States and to humanity. The Star Trek series helped to create a very important popular infatuation for this project and for space exploration in general. Then, at the end of the 1970s, the Star Wars saga became a cult success and brought science fiction into a new dimension, that of planetary mythology. Its director, George Lucas, admits to having been inspired by the theory of the monomyth, by Joseph Campbell, an American anthropologist who determined the structure of all popular myths, meeting several specific criteria. Hollywood cinema was inspired by this research to create the world’s greatest movies. 21.3. Imagination: from creativity to innovation Faced with this growing success, some American institutions called upon science fiction authors to fuel their creative sessions, the latter being conceived as the ability of an individual or a group to imagine or conceptualize new solutions to a problem, or new objects that could be commercialized. These are referred to as innovations. The military and NASA were among the first to claim the services of the most brilliant authors to design futuristic scenarios. An author like Isaac Asimov, for example, was noted for his brilliant predictions of American society a few decades ahead of time. So much so that science fiction was quickly characterized as a

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prophetic genre. Indeed, his stories often take place in the future, and the utopian technologies of multiple works were often realized a few years after their appearance in works of fiction. Gradually, futurologists and futurists dared to cite science fiction sources to illustrate their work on the potential future of humanity, or particular areas such as virtual reality. Cyberpunk was a science fiction trend that appeared in the early 1980s, notably by William Gibson, author of the famous Neuromancer, in which the term “cyberspace” appeared, which fascinated and inspired thousands of computer scientists and engineers who eventually developed the Internet. Cyberpunks published stories set in the near future, and helped fuel the imagination of engineers with representations of the future of the virtual and of a global immersive network still under construction, of which the Internet might be only a pre-structure. Science fiction proposes sectorial myths useful to economic actors in innovating and disseminating their new products. Cyberspace, then Neal Stephenson’s Metaverse (in Snow Crash) and Ernest Cline’s OASIS (Ready Player One) are sources of inspiration for the designers of new virtual technologies. Terraforming is another example of a sectorial myth that appeared in the 1990s, thanks to Kim Stanley Robinson’s Mars Trilogy. It draws attention from thousands of researchers working on projects to send humans to Mars to establish a colony, or even a civilization. Science fiction and “technoscience fiction” (a term coined by Gilbert Hottois) have shown their ability to imagine the future and potentially useful concepts for innovators. This led some researchers to develop methods of creativity based on this imaginary, design fiction and science fiction prototyping. “Design fiction” is a term that was invented by cyberpunk science fiction author Bruce Sterling in 2005. Derived from design thinking, it aims to develop the imaginary within projects in order to create useful prototypes that can later be used to inform the design and improvement of new products. Design thinking is a method of stimulating creativity used in innovative projects and developed by Rolf Faste in the 1980s. This first approach was summarized in seven steps: define, research, imagine, prototype, select, implement and learn. Science fiction prototyping is an approach comparable to design fiction, conceived by Intel futurologist Brian Johnson in 2011. Here again, innovative companies and organizations are invited to organize creative sessions with science fiction authors in order to imagine scenarios and prototypes useful for the internal and external strategic discourse of the group or company. Imaginary science fiction is a particularly favorable manifestation for the emergence of new ideas, and has thus become a central element of innovation policies. Foresight increasingly uses fiction to design new products. Some R&D centers are also studying the technical imaginary of their sector of activity to map the concepts that could be tomorrow’s successes. The imaginary can be

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performative. It tends to become truth under certain conditions. The belief of decision-makers in a vision of the future can lead to the implementation of investment policies in projects aimed at realizing it. It is therefore important to have an ethical approach to the imaginary, especially science fiction. An R&D policy must therefore avoid realizing visions that are often delusional or even dystopian. A phenomenon of ideological filtering must accompany the integration of the imagination into decision-making and innovation processes. The imaginary continues to be criticized by certain actors, who still see it as a danger to a science seeking to define and describe reality. The utopian imaginary remains a unifying and driving discourse in industrialized societies. Technical historian Howard Segal has described how technological utopianism helped to stimulate innovation in the United States in the 19th century. Science fiction took over this trend. It is important to take a critical look at this imaginary in order to avoid technoscience realizing, through obscurantism or blindness, an infernal imaginary vision of the industrial era. Between hell and paradise, between dystopia and utopia, the imaginary propagates ambivalent and influential representations of the future. The imaginary industries propagate these fictions in society, and constitute a soft power, mostly American and Hollywood in origin. Organizations and companies also produce an imaginary, with the aim of spreading a positive image within a system in which storytelling is becoming increasingly important. The use of science fiction authors or artists aims to establish a strategic vision and an attractive outlook for consumers who are increasingly influenced by technical imaginaries. 21.4. Conclusion Imagination has become an important element of innovation policies through methods such as design thinking, science fiction prototyping or design fiction. Science fiction is increasingly used to fuel the thinking of strategists and scientists. Engineers draw from it many original ideas that contribute to their ideation and prototyping processes. While the imaginary is rejected by a certain realistic tradition, it remains valued in many institutions for its creative and performative virtues. Science fiction authors, heirs of utopian thought, show their ability to adapt to new technoscientific and economic challenges by offering stories rich in utopian technologies and new ideas conducive to invention and innovation. 21.5. References Brown, T. (2009). Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation. Harper Business, New York. Dunne, A. and Raby, F. (2013). Speculative Everything: Design, Fiction, and Social Dreaming. MIT Press, London.

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Johnson, B. (2011). Science Fiction for Prototyping: Designing the Future with Science Fiction. Morgan & Claypool Publishers, San Rafael. Michaud, T. (2017). Innovation, Between Science and Science Fiction. ISTE Ltd, London, and John Wiley & Sons, New York. Michaud, T. (2018). La réalité virtuelle, de la science-fiction à l’innovation. L’Harmattan, Paris. Minvielle, N., Wathelet, O., Masson, A. (2016). Jouer avec les futurs : utilisez le design fiction pour faire pivoter votre entreprise. Pearson Education, Paris. Noble, D.F. (1997). The Religion of Technology, the Divinity of Man and the Spirit of Invention. Penguin Books, New York. Scardigli, V. (2013). Imaginaire de chercheurs & innovation technique. Éditions Manucius, Paris. Segal, H.P. (2005). Technological Utopianism in American Culture. Syracuse University Press, New York.

22 Marketing – Marketing of Innovation and University–Industry Collaboration

22.1. Introduction The unstructured global economy has forced the creation of new development support policies based on the promotion of innovation in networks in the form of clusters (Porter 1990, 1998, 2000), technology parks, competitiveness clusters and other types of configurations. These arrangements are characterized by the congregation of actors from different backgrounds, the encouragement of collaborative work mechanisms and a focus on innovation. Thanks to the prospects for innovation they offer, they represent the tools for propelling the success of tomorrow’s economies and have aroused great interest among researchers, practitioners and governments. They promote collaborative relationships between universities and businesses to generate marketable innovations. This is where the concept of collaborative innovation emerged as a result of the forced development of collaborative processes between economic and academic research actors (Thiaw 2013, 2018). However, the literature has shown that while these heterogeneous arrangements are factors that drive innovations, the lack of a marketing and market-oriented approach to collaborative projects limits the scope of the results (dissemination and adoption of innovations). Indeed, in such environments, which are both heterogeneous and inter-organizational, and which require collaboration between actors of different natures and objectives, and which are even contradictory in many respects (companies and universities), the objective of flooding the market with innovations has sometimes seemed utopian. Too strong a focus on technologies

Chapter written by Cheikh Abdou Lahad THIAW. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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produces a “deficit of achievements compared to expectations for easier access to new markets/potential customers” (BPET 20121). This observation allows us to raise the issue of market orientation in these university–industry interaction tools. Since the relationships often focus on the innovation process and collaborative R&D activities, they also raise questions about the dilemmas noted by practitioners relating to the complexity of innovation: should we focus on technological opportunities or on meeting the expectations of the market? Should we push technological innovation rather than the development of new economic models? The theoretical framework concerned refers to the question of the dilemma of market-pull, techno-push innovation. This text therefore explores the perspectives of innovation marketing at the inter-organizational level and questions the “market-pull–techno-push” dilemma. The issue of innovation in companies has been widely discussed (production, processes, R&D), but the issue of innovation marketing focusing on the adoption and commercialization of innovations resulting from university–industry collaboration has not yet received strong interest from researchers. Indeed, most of the existing research and definitions are confined to topics dealing solely with products (i.e. OECD 2005). The difficulties of disseminating and adopting the innovations generated in heterogeneous networks are linked to the diversified strategic intentions of the actors and the lack of shared visions of the “market” aspects of the projects. In addition to the question of the adoption of innovations generated in heterogeneous environments, this research opens up other analytical areas dealing with innovation business models specific to networks and the mechanisms for introducing a market and enterprise vision in universities. Our text focuses on the marketing of innovation, placing emphasis on the concept of market orientation (Day 1990; Kohli and Jaworski 1990; Narver and Slater 1990; Ruekert 1992; Deshpandé et al. 1993), but analyzed in a context of inter-organizational collaboration (Thiaw 2013). 22.2. Innovation marketing and inter-organizational collaboration In a modern economy marked by the constant emergence of innovative products and technologies and a rapid compression of technological lifecycles, innovation is the ideal solution, the source of relevant growth that organizations and states must prioritize. Indeed, focusing on innovation provides companies with market openings, 1 Bearing Point-Erdyn-Technopolis ITD (2012).

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competitive advantages and immeasurable development potential. Similarly, it allows states to propel their development and drive sustainable economic growth. In order to take full advantage of these opportunities and face the hazards of globalization, public authorities have deployed new development support policies based on the promotion of collaborative innovation through interactions between industry (companies) and academia (research laboratories) around R&D activities (Porter 1990, 1998, 2000; Bell 2005; Retour 2009; Chabault 2010; Gosse and Sprimont 2010; Corbel et al. 2011; Thiaw 2013, 2018; Crespin-Mazet 2015; Torre and Zimmerman 2015; Chamaret 2016; Vicente 2016). Innovation marketing refers to the marketing techniques specifically used to adapt to this complex environment, marked by the predominance of innovation. It allows companies to have easier control of actions to disseminate innovations (new products, services) thanks to a better understanding of market expectations and improved facilitation of the adoption of innovations by end customers (consumers/users). In this era of an economy marked by the promotion of innovation in networks of interactions between industries and universities, innovation marketing invites itself to the ongoing debate in the literature to investigate the issue and try to find effective solutions to the problems of the access of innovations to markets (Brem and Viardot 2015). Overall, innovation through R&D involves choosing between two options: prioritizing the market (market-pull or market-driven) or prioritizing technology (technology-push or techno-push). In networks in which innovation is the result of university–enterprise interactions, this dilemma of market orientation and/or focus on technology is even more acute. In these cases, innovation is generated by activities whose scope of intervention is occupied by actors of different natures and strategic intentions. In university research laboratories, researchers develop innovations through the conversion of their scientific knowledge without prior concern for market expectations. Within companies, it is more the users – and not the producers – who are at the origin of innovations (Johnson et al. 2011; Thiaw 2011). In other words, for industrialists, innovation comes, first of all, from satisfying the needs of end-users, i.e. the market. Consequently, companies may be distanced from scientific interests, while university researchers may also be distanced from market-oriented notions. The differentiated strategic intentions of industry and university actors therefore imply disparate behaviors that influence the strategic orientation choices of collaborative innovation projects. Depending on their importance to the projects and the consideration of the weight of their respective representatives, the generation of innovations conducive to market access may be affected.

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In recent years, a lot of work has been dedicated to this kind of collaborative way of working. However, the topics addressed are only oriented towards the interactions of actors and the performance of innovation networks, therefore towards mechanisms for the fluidity and improvement of the work of collaborative innovation actors. They have investigated the phases of joint production of innovation knowledge more, and analyzed less the subsequent maneuvers and postinnovation practices and related less to market diffusion and commercialization of the innovations generated (Thiaw 2013). This explains the failure of the first phase of the French competitiveness clusters (2005–2008), because embedding knowledge and know-how is not enough to allow industry–enterprise relationships to be efficient in terms of innovation potential. At the same time, it is necessary to foster the emergence of the sales skills essential to the success of the innovation process and the adoption of innovations by the market. Innovation is certainly very important and remains indispensable to the sustainability of companies’ activities and their economic development, but it will only be a source of competitive advantage if the innovative company achieves the expected objectives, i.e. commercialization. Indeed, for partnership networks between companies and universities, it is the mastery of the dissemination of innovations that constitutes the fundamental source of performance. For industrialists, collaborative innovation spaces are tools for mobilizing R&D projects from a collective angle, taking advantage of the scientific knowledge of university research entities. In collaborative innovation processes aimed at optimizing the development of new products and services, the role of marketing is even more preponderant. Innovation is understood and considered differently depending on the nature and positioning of the actors (company, university, customer or supplier). The company and its internal actors consider it a means to be used in order to stay in line with market expectations, improve their products and services, and sustain their activities. While through their scientific activities, university researchers hope to discover new technologies and make science evolve. For customers, it is a question of constantly having access to ever more efficient and original products and services. Innovation therefore promotes the development of new markets (Prime and Usunier 2003). 22.3. The cross-functionality of innovation marketing The role of marketing cuts across all phases of the innovation process: design, development, production and distribution. In most cases, it is at the origin of innovative ideas and evaluates their commercial potential. When managing the collaborative innovation process, it is also the role of marketing to make the “customer’s voice” heard by working closely with researchers to ensure that the innovation ideas put forward are in line with market expectations, and that the

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orientation of collaborative innovation projects takes into account the commercialization potential of the technologies implemented. Then, once the opportunity for innovation has been proven, R&D activities are deployed and prototypes are developed; marketing is also responsible for managing customer tests and, ultimately, for developing the conditions for bringing the innovations generated into new products or services to market. In the life of companies, the customer’s need is the filter point for the activities in the innovation process. It occupies all actors in the company involved and those who work for its profitability by proposing products or services that satisfy customer needs. In collaborative innovation spaces that promote interaction between universities and industries, innovation is the result of cooperation and is ultimately the outcome expected by the actors. Therefore, it seems essential to have, in practice, actors involved, with a concrete commitment, and interacting around shared objectives. Admittedly, inter-organizational interests are not mutually exclusive and the actors’ motivations differ, but it is more a contribution to reinforcing the value of university–industry collaboration, provided that there is a cultural position and regulation that encourages the creation of a more open and collaborative environment. This necessarily requires a more comprehensive approach on the part of both parties, bringing them closer together through the market orientation of projects. In concrete terms, universities need to commit to working with industrialists by making their culture more entrepreneurial, and industrialists need to develop services to manage their relationships with universities. Within this framework, the development of involvement indicators (Aliouat 2010) referring to the involvement and participation of members of the collaborative space would make it possible to optimize innovation results. This involvement is measured by the intention to collaborate and the actions concretely carried out by the members that result in their participation in projects, leadership actions and governance bodies (Lallemand 2013). In a synthetic way, innovation appears to be the result of a complex system of interactions between industrial enterprises and universities in collaborative projects, while being driven by disparate objectives, in a complex dynamic of actors/projects. In this context, successful collaborative innovation is based on a hybrid approach that focuses on the technological dimension while integrating market aspects (Jaworski and Kohli 1993; Webster 1994; Slater and Narver 1999; Lambin et al. 2005). The introduction of innovations into markets is always accompanied by uncertainties and a risk of non-adoption by consumers. Consequently, the commercialization of innovations and interesting ideas deserves to be formally framed by marketing in order to ensure success and be a source of value creation for stakeholders.

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From the point of view of socio-semantic objectification, the spaces of industry– university interaction are places of epistemic communities that must involve, on the one hand, a “market” orientation, and, on the other hand, a “technology” orientation (technology standards) shared by partners to positively influence the success of collaborative innovation. 22.4. Conclusion While innovation spaces organized around interactions between industry and university research are continually developing, difficulties remain in accessing innovation markets. This is specifically the case for French competitiveness clusters (Thiaw 2013). Nevertheless, the literature shows little study of activities downstream of the innovation process (dissemination and commercialization). It has focused mainly on upstream activities, i.e. how to optimize inter-organizational collaboration aimed at obtaining and integrating new sources of innovation. As a result, since the success of an innovation is closely linked to its adoption by the market, there is now a renewed interest in commercialization and adoption of innovations. Today, innovation marketing is essential to optimizing the dissemination of innovations resulting from collaborations between research and companies. Innovation networks that master its workings can monitor, in real time, changes in the macroeconomic framework around technologies and the market: consumer needs and expectations, competition, scientific knowledge and so on. In this context, university–industry interaction spaces must consider market orientation as a strategic vector to bring the two players closer together around the common objectives of diffusion and marketing of innovations, minimizing the pitfalls generated by any tensions and differences in perception and interest regarding “market” and “technology” aspects. They must make the effort to understand each other and have knowledge of both technological aspects and market issues. The development of innovations in university–industry interaction spaces has led to the emergence of new concepts at the crossroads between innovation and marketing: open innovation, closed innovation, network innovation, the role of internal and external actors in the success of collaborative innovation and more. 22.5. References Bearing Point-Erdyn-Technopolis ITD (2012). Étude portant sur l’évaluation des pôles de compétitivité. Global Report, 15.

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Brem, A. and Viardot, É. (2015). Adoption of innovation: Balancing internal and external stakeholders in the marketing of innovation. In Adoption of Innovation, Brem, A. and Viardot, É. (eds). Springer, Cham. Le Nagard, E., Manceau, D., Morin-Delerm, S. (2015). Le marketing de l’innovation, post-print halshs-01186378. HAL. OECD (2005). The measurement of scientific and technological activities: Guidelines for collecting and interpreting innovation data: Oslo manual. Third Edition by Working Party of National Experts on Scientific and Technology Indicators. OECD, Paris. Porter, M. (1990). The competitive advantage of nations. Harvard Business Review, March–April. Prime, N. and Usunier, J.C. (2003). Marketing international-développement des marchés et management des hommes. Edition Vuibert, Paris. Salmelin, B. (2013). Innovation in horizon 2020 – Reflections from open innovation 2.0 paradigm. Open Innovation 2.0 Conference, Brussels, 28. Thiaw, C.A.L. (2018). Innovation localisée pour le développement économique du Sénégal. Édition Harmattan, Paris. Torre, A. and Zimmerman J.-B. (2015). Des clusters aux écosystèmes industriels locaux. Revue d’économie industrielle, 4(152), 13–38. Vicente J. (2016). Économie des clusters. Repères économie, 676–128.

23 Milieu – Innovative Milieu: The Strength of Proximity Ties

23.1. Introduction Territorial development is deeply linked to the processes of knowledge creation and its transformation into innovation. It is the capacity for innovation that determines the level of growth and development of regions and territories. Some territories develop easily, while others find it difficult to reinvent themselves. Understanding this unequal distribution of innovation in space is the reason why the European Research Group on Innovative Media (GREMI, from its French name – Groupe de Recherche Européen sur les Milieux Innovateurs) initiated the concept of the innovative milieu in 1985. GREMI’s work examines the factors that encourage the emergence of innovation processes allowing us to cope with crisis situations. It analyzes the mechanisms by which innovation emerges in the “milieus”. In this text, we will first define the concept of an innovative milieu and present the context of its emergence and its evolution over time. We will highlight its characteristics and then explain the mechanisms of its functioning. We will then show that the articulation of the three forms of proximity (geographical, organizational and cognitive) plays a primordial role in the activation of the interaction and learning logics that constitute the systemic character of the milieu. Finally, we will conclude by emphasizing new approaches to the innovative milieu that promote new territorial development trajectories responding to current environmental and social issues.

Chapter written by Fedoua KASMI. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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23.2. Definition and characteristics of an innovative milieu The innovative milieu approach has been developed in parallel with other concepts such as industrial districts, clusters or regional innovation systems, which are mainly inspired by Marshallian work. These works share the same hypothesis that knowledge externalities and agglomeration effects produced locally or regionally play an important role in the development and strengthening of innovation capacities. According to GREMI, the milieu refers to both the context and the actor environment (Tabariés 2005), and constitutes “a set of relationships occurring in a geographical area that brings together, in a coherent whole, a production system, a technical culture and actors” (Maillat et al. 1993, p. 7). This definition has become clearer over time with the multiple research programs carried out by GREMI. This work has made it possible – due to the strong interaction between empirical analysis and theorization – to explain the characteristics of an innovative milieu and the process that allows innovation to emerge in milieus. A total of six surveys were carried out in different European regions on the basis of a single question: “why are some regions more dynamic than others?” The surveys were based on a common methodology and questionnaire to allow for better comparability between the different regions studied (Tabariés 2005): – GREMI 1 studied the role of the milieu and local factors of innovation in relation to factors outside the region in triggering business innovation (Aydalot 1986). – In GREMI 2, the objective was to understand the relationship between the company and the milieu (internal and external) in order to identify the factors that allow the innovation process to be triggered. Emphasis is also placed on the potentially destructuring effects of opening up to external spaces (Maillat 1995). – GREMI 3 has made it possible to understand how the milieu as an organized and territorialized whole is transformed through interactions woven by the different networks that participate in the innovation process (Maillat et al. 1993). – GREMI 4 focused on the study of the development dynamics of milieus and the trajectories of their evolution over the long term. This survey focused on the impact of history on the evolution of innovative milieus. The decisions and constraints of the past, in fact, condition the evolutionary trajectory of territories and thus engage them in situations of path dependency (Maillat 1995). – GREMI 5 and 6 differ from previous surveys in that they study non-industrial urban milieus. GREMI 5 focused on the relationship between the milieu and the city

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“how and in what way does the innovative milieu differ from the city?” (Tabariès 2005). In GREMI 6, the study focused on the heritage, natural and cultural resources of the milieu (Camagni 2004). All of these surveys have made it possible to identify the characteristics of innovative milieus that constitute criteria that meet the need to measure, identify the innovative capacities of these milieus and understand the dynamics that distinguish them (Tabariès 2005). A milieu then becomes conducive to innovation when it is defined by a geographical space integrating a group of actors coordinated by an institutional framework (set of rules, norms, values, etc.) and an organizational logic based on innovation networks allowing the implementation of a learning dynamic (Maillat 1992; Crevoisier 2001): – A geographical space determined by a group of actors and coordinated by an institutional framework: the innovative milieu brings together a set of actors (companies, research and training centers, universities, funding institutes, associations, public administration, etc.) located in a geographical space that presents a certain unity and homogeneity. This group of actors is characterized by behaviors specific to the milieu, notably a shared technical culture (Crevoisier 2001). The relationships between actors are marked by economic coherence and cohesion. Multiple exchanges favor the establishment of cooperative and collaborative relationships, while taking into consideration the competitive aspect and autonomy in the formulation of strategic choices (Maillat 1992). All of these actors benefit from the existence of geographically close material, human, financial, technological and informational resources offered by the milieu (Uzunidis 2010). The behaviors and relationships they maintain are governed by an institutional framework (set of rules, norms, values, etc.) allowing them to be organized and their interactions regulated. – An organizational logic based on networks and learning dynamics: the actors in a milieu cooperate to innovate, and their connection in a network allows the creation of a specific mode of organization, which favors the creation of common projects and the reduction of uncertainties (conflicts, costs related to innovation processes, etc.). The actors inserted in innovation networks develop capacities for the acquisition, production and dissemination of new knowledge. These learning capacities are built up over time and enable the behavior of the actors to be adapted and modified according to the transformations of their milieu (Maillat 1992). They thus guarantee a control of the productive process in the broad sense (technical, commercial or organizational). The learning capacity of actors presents the milieu as a context favorable to innovation (Maillat 1992; Tabariès 2005; Uzunidis 2010). These processes of interaction and learning enable the creation of specific resources that nourish the innovation process.

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The functioning of an innovative milieu is thus based on two logics which, when activated, allow the creation of innovation. A logic of interaction that translates into cooperation and collaboration between actors. A logic of collective learning based on the mobilization of resources specific to the milieu by the actors. The articulation of forms of proximity in the milieu contributes to the implementation and reinforcement of these two logics (Uzunidis 2010; Tanguy and Uzunidis 2016), thus giving the milieu its systemic character. This notion of proximity is today a key notion in the innovation economy. It “is presented as linked to the existence of localized externalities that produce spatial agglomeration effects and territorial dynamics” (Uzunidis 2010, p. 121). In fact, the presence of forms of proximity in the milieu makes it possible to generate agglomeration effects resulting in the external economies that are essential to the genesis of innovation. 23.3. Proximity and territorialized innovation networks In GREMI’s work, territorialized innovation networks are mainly based on relations of geographical proximity (Tanguy and Uzunidis 2016). These networks are formed thanks to the interdependent links woven between a coordinated set of heterogeneous actors, enabling them to build synergetic relationships and strengthen territorial anchoring through better valorization of the resources present in the territory. The location of members of the innovation network close to each other in a defined geographical space has a strong impact on their ability to innovate collectively. This geographical proximity, related to the physical distance separating the actors, is at the origin of positive externalities allowing the members of the innovative milieu to benefit from agglomeration effects. Indeed, collaboration and joint learning allow for the exchange and sharing of inputs, specialized services and infrastructures, a common labor market and an “industrial atmosphere” conducive to the dissemination of knowledge. The companies that are part of this milieu can thus benefit from external economies of scale that are favorable to their competitiveness. However, geographical proximity cannot generate agglomeration effects without the presence of specific organizational modes that facilitate communication and interaction between the different actors. Other forms of proximity, organizational and cognitive, must then be associated with geographical proximity (Uzunidis 2010). Organizational proximity can be defined as the extent to which relationships are shared within an organizational arrangement (within an organization or between organizations). It implies both the intensity of relationships and the degree of autonomy within these organizational arrangements (Boschma 2005). It enables the formation and modification of the relationships and synergies that bind them, thus promoting interaction and collective action. It also integrates an institutional dimension that plays an important role in providing a favorable framework for the

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behavior of economic actors through rules, laws, norms, values and so on (Boschma 2005). The logic of interaction is activated when organizational proximity is strong. While organizational proximity facilitates the organization of interactions between actors, cognitive proximity enables collective learning. It is based on the sharing of the same knowledge base, experiences, know-how, routines and more, allowing actors to engage in common projects (Uzunidis 2010). The learning logic is activated when the cognitive proximity between actors is strong. 23.4. Conclusion The concept of the innovative milieu provides a set of tools to analyze territories and understand their innovation capacities. All GREMI programs have gradually evolved this concept from a “black box” to a stabilized conceptual framework that makes it possible to explain the success factors of developing territories and the failures of blocked territories (Crevoisier 2001). The approach by innovative milieus thus systematizes the main questions relating to spatial economic dynamics. It makes it possible to qualify, on the one hand, the evolution of technology and interactions between actors and, on the other hand, the spatial and temporal forms that these processes take. Recent approaches to the innovative milieu take into account the environmental and social issues and problems faced by territories, a dimension rarely addressed in previous work. Emphasis is placed on bringing the innovative milieu and sustainable development closer together, with the view that the processes underlying them can complement each other (Gallaud and Laperche 2016). The concept of the eco-innovative milieu is thus introduced to explain the mechanisms for the emergence of new innovation dynamics integrating the inclusive and sustainable dimensions of economic growth (Kasmi 2018). This new approach aims to build an analytical framework to guide innovation policies towards the development of new economic models combining competitiveness, innovation and sustainable development, based on circular economy practices. These circular practices make it possible to move from a linear economy, in which resources are considered to be unlimited, to new ways of organizing economic activities inspired by the functioning of the natural ecosystem. The implementation of the circular economy on a territorial scale results in the constitution of networks of actors sharing common territorial projects around the valorization of material and energy flows. This functioning in networks specific to innovative milieus can be at the origin of new eco-innovation or environmental innovation dynamics that participate in the bifurcation of territorial trajectories (Kasmi 2018).

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23.5. References Aydalot, P. (1986). Milieux innovateurs en Europe. GREMI, Paris. Boschma, R.A. (2005). Proximity and innovation: A critical assessment. Regional Studies, 39(1), 61–74. Camagni, R. (2004). Natural and cultural resources and the role of the local milieu: Towards a theoretical interpretation. In Ressources naturelles et culturelles, milieu et développement local, Camagni, R., Maillat, D., Matteaccioli, A. (eds). GREMI, EDES, Neuchâtel. Crevoisier, O. (2001). L’approche par les milieux innovateurs : état des lieux et perspectives. Revue d’économie regionale et urbaine, 1, 153–166. Gallaud, D. and Laperche, B. (2016). Circular Economy, Industrial Ecology and Short Supply Chains. ISTE Ltd, London and John Wiley & Sons, New York. Kasmi, F. (2018). The “Eco-Innovative” milieu: Industrial ecology and diversification of territorial economy. In Collective Innovation Processes, Uzunidis, D. (ed.). ISTE Ltd, London and John Wiley & Sons, New York. Maillat, D. (1992). Milieux et dynamique territoriale de l’innovation. Revue canadienne des sciences régionales, 15(2), 199–218. Maillat, D. (1995). Milieux innovateurs et dynamique territoriale. In Économie industrielle et économie spatiale, Rallet, A. and Torre, A. (eds). Economica, Paris. Maillat, D., Crevoisier, O., Lecoq, B. (1993). Réseaux d’innovation et dynamique territoriale : le cas de l’arc jurassien. In Réseaux d’innovation et milieux innovateurs : un pari pour le développement régional, Maillat, D., Quevit, M., Lanfranco, S. (ed.). GREMI/EDES, Neuchâtel. Tabaries, M. (2005). Les apports du GREMI à l’analyse territoriale de l’innovation ou 20 ans de recherche sur les milieux innovateurs. Cahiers de la Maison des Sciences Économiques, CNRS, 1–22. Tanguy, C. and Uzunidis, D. (2016). Milieu innovateur et entrepreneuriat innovant : la force des proximités et des réseaux. Technologie & Innovation, 1–11. Uzunidis, D. (2010). Innovation et proximité. Entreprises, entrepreneurs et milieux innovateurs. Revue des sciences de gestion, 241, 13–22.

24 Nanotech – Nanotechnologies: The Future of Innovations

24.1. Introduction Nanotechnology is a booming sector. Developed from the middle of the 20th century onwards, nanotechnologies have experienced tremendous growth over the last 20 years. It is necessary to support the development of nanotechnologies by setting up an approach that allows the monitoring and control of information flows and that shows a scale of participation that goes from the provision of data to decision-making (Monino 2016). The term “nanotechnology” was first used by Norio Tanigushi in 1974. With the discovery of the scanning tunneling microscope (STM) and then the atomic force microscope (AFM) in the 1980s, the “nano” world really opened up to all researchers. From that moment on, exploiting the properties of matter at the atomic scale was no longer science fiction! Nanotechnologies are now all around us. Nanoparticles can be described as particles for which at least one of their dimensions is smaller than 100 nanometers (nm). The nanometer is used to designate a measurement of the order of a billionth of a meter (10–9). More concretely, a diameter 30,000 times smaller than the thickness of a human hair. At the nanometer scale, our current technologies are a construction set at the atomic scale. Nanotechnologies are technologies of the infinitely small (of the order of magnitude of a billionth of a meter). This offers significant potential for the future (contactless payment, H1N1 vaccine, labeling, traceability, etc.) as well as significant risks and dangers for Chapter written by Jean-Louis MONINO. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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humanity. The aim will be to trace the various innovations up until today, and then the question will be whether nanotechnologies will have a positive or negative impact in this world. At the annual conference of the American Physical Society, Richard Feynman declared in 1959 that “there’s plenty of room at the bottom”. The American physicist was suggesting to the scientific community that it should explore the universe of the infinitely small. In 1974, the first person to use the term nanotechnology was Taniguchi, who referred to a technology that would design, manufacture and use structures with dimensions of the order of a nanometer. Jean-Marie Lehn, winner of the 1987 Nobel Prize in Chemistry, defined “supramolecular chemistry”. It was a question of understanding and constructing nano-sized assemblies in chemistry. The first experiments were carried out in the 1970s to create drugs. On October 29, 2002, the French Bureau du Sénat referred a request for a study on “Nanosciences and medical progress” to the Parliamentary Office for the Assessment of Scientific and Technological Options (OPECST). Jacques Attali, in his report on “the fourth industrial revolution”, believed that nanotechnologies would be the driving force of the next industrial revolution. They have considerable potential for development and applications, particularly in the field of electronics, as well as in biotechnology, materials and information technology. In 2004, the size of transistors were reduced to under 90 nm in width. At the current scale (32 nm), the laws governing the operation of electronic chips are no longer those of classical physics: new properties appear – the quantum effect. From microelectronics to nanoelectronics: – the electronic transistor is the fundamental active component; it enables the processing of electrical signals, such as amplification, filtering, stabilization, modulation, etc.; – innovation led to ever smaller and more numerous transistors, following Moore’s Law (Moore, the co-founder of Intel, stated as early as 1965 that the number of transistors per circuit of the same size would double, at constant prices, every year). A promising sector is that of healthcare. Scientists have recently succeeded in operating in vitro nanomotors that will soon be able to move through the human body to destroy diseased cells or to deliver molecules such as insulin;

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– the IoE (Internet of Everything) sector: Cisco is of the view that at least 50 billion objects will be connected by 2025 and estimates that 500 billion “connectable” objects will promote the implementation of smart cities. 24.2. Nanotechnology applications Have nanotechnologies already been commercialized? The answer is yes. In fact, there are many examples of their use: – the packaging for Mars bars is made of nano-sized titanium oxide for its sealing properties; it is transparent and it prevents oxygen from coming into contact with the food; – Kraft and Nestlé are working on the taste, color and nutritional properties of their products with the help of nanotechnology-related patent applications published at the USPTO (United States Patent and Trademark Office); – Monsanto and BASF, famous for their genetically-modified seeds and herbicides, are working on nanocides, pesticides created using nanotechnology; – the H1N1 vaccine contains nanotechnology, as do sunscreens and toothpastes. The three main areas of current applications of nanotechnology are: – nanoelectronics: miniaturization of electronic components of computers; – nanomaterials: the smaller the particle size of a material, the greater its resistance; – nanobiotechnologies: today’s medicine represents a significant potential for their use. Patent applications for nanotechnology concern: – all technologies related to energy (batteries, insulating materials, etc.); – the automotive industry (hybrid vehicle batteries, scratch-resistant or anti-corrosion materials, etc.); – optics (scratch-resistant plastics for glasses and lenses, tinted lenses, etc.). Nanomaterials was the first area of nanotechnology to be commercialized. 24.3. RFID chips RFID (Radio Frequency Identification) allows us to store and retrieve data remotely. The system is activated by a transfer of electromagnetic energy between a

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radio tag and an RFID transmitter. The components enable signals to be read and answered simultaneously. With a contactless payment application, the signal range can go up to more than 100 m. At the level of use, RFID is used for the traceability of goods via the creation of labels. RFID can contain more information than a barcode, and enables remote updates to be applied, for example, to price labels in store. RFID chips are very special computers: – discreet, the size of a human hair, with no peripherals or interfaces; – revolutionary for tracing; – impressive information storage capabilities. The bar code has become obsolete with the advent of RFID. The applications of RFID are very numerous and highly varied: – intelligent LEDs (light-emitting diodes); – giant interactive window screens to personalize advertising; – translucent interfaces to see at night or to anticipate the risks of accidents; – “smart” clothing; – “smart” implants, to monitor physical condition and make remote medical diagnoses; – labeling, traceability or toxic and sanitary studies? Of course, not all of these innovations are on the market yet, but they have all passed the test stages. 24.4. Global potential risks Nanotechnology raises the possibility of microscopic recording devices, which would be undetectable. It is possible that nanotechnology could be used for military purposes. In “Armament and Defense”, A. Deixonne (2018) reviews the progress of new technologies in the field of defense and armament. Nanotechnology defies our wildest dreams, such as with the “smart bullet”, whose principle is simple: to allow any shooter to hit the target with a certain hit. These developments could prove to be a godsend for the army, but the consequences would be disastrous according to the users.

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Nanoparticles can have the same dimensions as some biological molecules and can interact with them. In humans and other living organisms, the usual defense mechanisms may not be able to react adequately to these nanoparticles, and they may have characteristics never before encountered. The parameters of nanoparticles that appear relevant for health effects are size, chemical composition, and surface and shape characteristics. Nanoparticles can exist naturally, be unintentionally emitted by industrial or domestic activities, such as cooking, manufacturing or transportation, or be specifically designed for consumer products and advanced technologies. In the liquid phase, manufactured nanoparticles are mainly produced by controlled chemical reactions. Those present naturally are generated by the erosion and degradation of plants, clay and so on. In the gas phase, natural or manufactured nanoparticles are usually created by chemical reactions in which gases are transformed into tiny liquid droplets that condense. They rarely result from the decomposition of larger particles. In rural or urban areas, a liter of air can contain several million nanoparticles. In urban areas, they come mainly from diesel engines or vehicles with cold or defective catalytic converters. In some workplaces, exposure to airborne particles can be a potential health hazard. We do not know precisely where they are present, nor the number of workers actually exposed; however, the sectors most concerned include the manufacture of pharmaceuticals, chemicals, electronics, automotives, textiles, paints, tires and cosmetics. According to data from “statnano” on nanotechnology, the United States has the world’s most active market for nanotechnology innovations. More than 8,900 nanotechnology patents were filed with the USPTO in 2019. The most innovative and patent-holding countries in the fields of nanotechnology and nanoscience are the United States and European countries, with China and South Korea also very active. 24.5. Conclusion and outlook Nanotechnology is not only an industrial revolution that helps in the development of progress, creating value and jobs, but also a path that can solve many of the problems of our world. Nanotechnology has revolutionized some areas of everyday life, such as the nano-objects as we have seen discussed above. As a result, in various fields, such as in medicine, nanotechnologies could lead to monitoring of the individual, thanks to nano-objects injected into the human body or the emergence of a collective intelligence.

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Currently, the areas where there are high societal expectations are mainly health, environment, employment and safety. In this sense, the strategic and collective dimension of economic intelligence resides in the triptych of appropriation, interpretation and action (Moinet 2009). In fact, economic intelligence (Monino and Sedkaoui 2016) will enable companies to anticipate new levers and opportunities for growth, which they will seize by modifying their activities. Concerning the patent applications of nanotechnology in the total number of patent applications, we can make two important remarks: – Iran and Saudi Arabia do not file many patents, but of those they have filed, the proportion relating to nanotechnology is high. Both countries give a high priority to nanotechnology; – in 2019, the United States, South Korea and Japan were the top three countries in terms of the total number of nanotechnology patents. However, many questions arise about nanotechnologies, including their toxicity for humans or the environment. Today, only a small part of the total investment in nanotechnology is devoted to research on toxicity. This is why some scientists advocate uncertainty and the precautionary principle. In France, a health agency report gave an opinion on nanoparticles: “it would be more prudent to declare nanoparticles as an unknown level of danger, and to handle them with the same caution as hazardous materials”. Nanotechnology is already having an impact on products such as medical devices, coatings and high-resolution cinema screens, as well as on global markets for precision engineering, electronics, biomedicine and others. This offers a great potential for the future, as well as significant risks and dangers for humanity. The CNRS “Sagascience” collection on nanomaterials opens up many and varied perspectives for research and industry. The emergence of these new materials and the consideration of ultrafine particles emitted during certain industrial processes raises the question of the risks incurred during occupational exposure. At the scale of nanotechnology, distances are measured in billionths of a meter. Matter acquires new physical, chemical and biological properties, making it possible to manufacture materials with often unprecedented characteristics. From the pharmaceutical industry to telecommunications, from aeronautics to chemistry, the fields of application of nanotechnologies are becoming more numerous every day.

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24.6. References Christian, J. and Plévert, L. (2008). Nanosciences. La révolution invisible. Le Seuil, Paris. Deixonne, A. (2018). Armement et Défense : des nouvelles technologies dignes de Marvel [Online]. Available at: https://toiledefond.net/armement-defense-nouvelles-technologies/. de Kerorguen, Y. (2006). Les nanotechnologies, espoir, menace ou mirage ? Lignes de repère, Clichy. Laurent, L. and Philipon, P. (2007). Les nanos vont-elles changer notre monde ? 82 questions à Louis Laurent Physicien. Specifique Editions. Luzeaux, D. and Puig, T. (2007). A la conquête du nanomonde. Nanotechnologies et microsystèmes. Editions du félin, Paris. Moinet, N. (2009). De l’information utile à la connaissance stratégique : la dimension communicationnelle de l'intelligence économique. Communication & Organisation, 35(1), 215–225. Monino, J.-L. (2016a). Nanotechnologies et intelligence économique. Colloque international sur les nanotechnologies : recherches, innovations et enjeux economiques, Casablanca. Monino, J.-L. (2016b). Data value, big data analytics, and decision-making. Journal of the Knowledge Economy, Springer Science+Business Media, New York. Monino, J.-L. and Sedkaoui, S. (2016). Big Data, Open Data and Data Development. ISTE Ltd, London and John Wiley & Sons, New York. Monino, J.-L., Sedkaoui, S., Matouk, J. (2014). Big data, éthique des données, et entreprises. Les Cahiers du CEDIMES. Economie et gouvernance, Dossier, 8(2). Moret, R. (2006). Nanomonde. Des nanosciences aux nanotechnologies. CNRS Editions, Paris. Samueli, J.-J. (2007). Par-delà les nanosciences et les nanotechnologies. Ellipses, Paris.

24.7. Webography http://sagascience.cnrs.fr/dosnano/accueil.htm. https://statnano.com/. http://archives.lesechos.fr/archives/cercle/2016/10/21/cercle_161754.htm#hUuf23lzwErWH1 Cd.99. http://up-magazine.info/index.php/le-vivant/nanotechnologies/2312-les-defis-du-nano-mondeimpact-economique-industriel-et-societal. http://www.inrs.fr/risques/nanomateriaux/ce-qu-il-faut-retenir.html. http://tout-sur-la-nanotechnologie.e-monsite.com/pages/l-histoire-de-la-nanotechnologie.html. https://www.lesechos.fr/2018/01/preparer-la-quatrieme-revolution-industrielle-982441.

25 Novelty – Novelty and Innovation: The Nodal Place of Creativity

25.1. Introduction Innovate, innovate, innovate! A desire for some, a requirement for survival for others. Innovation has become a major industrial challenge for companies that want to move out of the competitive field of cost reduction. Starting from the observation that the refusal to innovate is a more serious threat than innovation itself, a large part of research has been directed towards the study of phenomena related to innovation and creativity in companies, in order to develop knowledge to improve these two aspects. While some authors insist on the strategic and economic aspect of innovation (for them, innovation is linked to the successful launch of a new product onto its market), others describe innovation as an informational or decision-making process, focusing on the individual study of projects. These visions of innovation can be linked to schemas that consider innovation to be a succession of unitary operations of “processing” an idea, in order to transform it into a new product. On the other hand, some research on cognition addresses innovation by questioning traditional modes of reasoning and the development of new representations of objects. Faced with this multitude of visions, we propose to show how definitions of innovation have been refined over time, placing creativity as one of the possible factors at the origin of an innovation, in the same way as research or monitoring. By creativity, we mean the ability to generate new and original ideas, alone or in a group. A return to the sources seemed necessary to us to position our subject. We will present the evolutions consecutively, both in defining innovation and in conceiving Chapter written by Laure MOREL. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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the associated innovation models. We will insist, in particular, on the place occupied by creativity in the innovation process over time, in order to demonstrate in fine that it has become an essential tool when seeking to innovate. 25.2. Innovation and novelty Cost reduction has long been considered the main factor in the success of a business. It is the key argument historically defended by Taylorism. However, changes in markets and demand have given rise to other competitive factors, such as quality and respect for deadlines. While each was considered independently, the 1980s showed the need to address the cost–time–quality triptych to ensure performance and responsiveness. It was not until the end of the 1990s (Dert 1997) that innovation emerged as the fourth factor needed to meet the new challenges of competitiveness in markets that had become global: quality, responsiveness, innovation and productivity have become essential for bringing innovative, high-quality products to market within set deadlines. The first commonly accepted definition of innovation is attributed to Schumpeter. Innovation was then defined as the result of the establishment of a new production function, a change in the set of possibilities defining what is produced and how it can be produced (Schumpeter 1934). Innovation becomes the realization of new productive “combinations” corresponding to the following five cases: the manufacture of a new good; the introduction of a new production method; the opening of a new outlet; the conquest of a new source of raw materials or semi-finished products; and, lastly, the creation of a new organization. Subsequently, the Oslo Manual (OECD/Eurostat 2005) definition of innovation as the implementation of something new and unprecedented has very frequently been used: “the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations”. Finally, the ISO 56002 standard, published in 2019, defines innovation as “new or changed entity [...] realizing or redistributing value”, specifying that this can be “a product, service, process, [...] model, method, etc.”; and that the notion of value is plural: financial, non-financial, image, strategic advantage, knowledge acquisition, intellectual property, and so on.

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Beyond the need to stabilize the definition of innovation, the confusion between innovation and novelty is often highlighted and therefore deserves attention. For this reason, we will refer to the work of Booz-Allen and Hamilton, developed in 1982, and cited by Cooper (2001). According to them, a product is either new to the company (i.e. it has never offered or developed it in the past) or it is new to the market (i.e. it is the first product of its kind on the market). By playing with positioning combinations in relation to these two levels of novelty, Booz-Allen and Hamilton identified six types of innovation classes (Figure 24.1).

Figure 25.1. Booz-Allen and Hamilton classification of innovations (source: Cooper (2001))

In their approach, they demonstrate that, generally speaking, a company has a product portfolio made up of a mixture of products from these six categories; knowing that, on average, it is estimated that 10% of the products are truly innovative, i.e. new to the world (Cooper 2001). It is therefore crucial to make a clear distinction here between novelty and type of innovation. Garcia and Calantone (2002), in line with previous work, propose three categories of innovation: radical, truly new and incremental: – Radical innovations are those that present technological and marketing discontinuities on a global scale, in the industrial sector (macro-level) and also at company level (micro-level). Radical innovations do not respond to expressed needs. – Truly new innovations are those that, on a macro-level, show technological or marketing discontinuities, but not both. On a micro-level, they present combinations of technological and marketing discontinuities. They generally involve the evolution

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of certain product lines, enrichment of an existing line by a new technology or the conquest of a new market by a new technology. – Incremental innovations are those that exhibit technological or marketing discontinuities only at a micro-level. They are products that introduce new features or improvements to an existing technology in an existing market. An example is disposable, multi-blade vibrating razors. As a result, in order to recognize the type to which a given innovation belongs, it is sufficient to observe the level at which it is located (macro- or micro-level) and its nature (technological or market). Finally, starting from the observation that innovation is not a precise moment of a determined action, but a process, we grant it two main orientations: – Innovation (idea, form, material, method, etc.) that results from an analytical application of some previously established, well-structured, objective knowledge. In this case, the product to be introduced exists before the innovative action. – Innovation that is designed and built according to a complex indeterminate process integrating the cognitive dimension, and that gives rise to a creation or invention. In this case, it is the innovative action that leads to a transferable solution. In the first orientation, innovation is nothing more than a process of adoption and diffusion of new products or new processes designed by a specific entity (research and development department or engineering department). In the second orientation, a more global and integrated vision of the process must necessarily be accessed. The notion of collective creativity underlies this. We can clearly see the evolution in the acceptance of what innovation is, moving from a status of “creative destruction”, mostly internal and attributed to an individual, to a more collective and outwardly open dimension. We find this way of conceiving innovation in the models that will follow Schumpeter’s work, moving from a linear vision of the innovation process to a loop model. It is in this sense that the work of Rothwell (1994) is, in our opinion, very interesting. In a study devoted to the identification of the factors of success of industrial innovation, he highlighted five generations of innovation processes: – 1950–1960: the first generation, Technology-Push, corresponds to a linear and sequential process, where R&D activity is the driving force behind the innovation project.

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– 1960–1975: the second generation processes, Market-Pull, remain linear and sequential but consider the market a potential source of ideas. The value of considering the needs of consumers is added. Although very popular, according to the author, these models have the limitation of being adapted to small incremental innovation-type projects and not at all to major projects leading to radical innovation. – 1975–1985: the third generation processes, the Interactive Model, are a coupling of the two previous ones. They remain sequential, but include the possibility of going back and forth between two consecutive stages. These models were challenged by Cooper in 1983 and by Kline and Rosenberg in 1986, popularizing, respectively, the stage system that would become the Stage Gate System in 1990, and the Chain-linked Model and its famous feedback loops. Moreover, these authors put their finger on two key concepts: experimentation and exploration, which they believe are necessary for innovative activity. Scientific research cannot be and is not the only way to generate innovation. The generation of ideas and therefore the tools and methods of creativity appear as an alternative in fourth generation processes. – 1980–1995, the fourth generation, the Integrated Model, is a complete break with the three previous generations. The sequential model is developed in parallel, allowing, in particular, for a reduction in time to market. It is therefore no longer a process to be followed in stages, but one to adapt to the context in order to respond as quickly as possible by working in an integrated way. – In the 1990s, the fifth generation, the System Integration and Networking Model, appeared. It carries the premise of open innovation, without being so called. Development occurs in an integrated customer/supplier relationship and information technology is used to support these new co-development issues. Cooper’s Stage Gate process is the most widespread model. 25.3. Creativity as a prerequisite for innovation The mid-2000s saw the popularization of the notion of open innovation, thanks in particular to the work of Chesbrough (2003) who insists on the crucial role that the external environment can play in the generation of ideas and innovation. In addition, in 2014, Cooper published an article entitled “What’s next? After Stage-Gate” in which he stressed the importance of flexible methods to accelerate and make the innovation process more adaptable. They both highlight a key aspect of the innovation process: the “fuzzy front-end” or upstream phase, the former by showing the value of drawing inspiration from the outside to feed the innovation

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process, the latter by showing that flexible methods with their creative sprints are totally appropriate to managing creativity. Should we talk about a sixth generation? The question remains open. Keeping a fairly competitive product portfolio provides a definite competitive advantage. However, keeping a constant flow of innovation is not easy and, above all, requires a particular relationship with risk and uncertainty. In any case, innovation projects are now carried out by multidisciplinary teams with little aversion to risk and to whom the company must leave room for maneuver, and a total degree of freedom in the development of their mental representations. It is even this taste for risk that explains why they will be the bearers of ideas with a high degree of novelty that will have to be transformed into innovation. The process of innovation therefore clearly appears to be a process of broadening and enriching skills in order to build new solutions, an ability to find new relationships with an object and “an ability to transgress established rules and, at the same time, to be unpredictable” (Alter 1995). 25.4. Conclusion Innovation – as we know – can no longer be envisaged within the strict framework of organizational boundaries. This external origin of innovations is not limited to identified partners but is also open to a plurality of individuals (the crowd) used as much for its contribution to generating and bringing creative ideas as to supporting the conditions of the realization of projects (financial, social, political, etc.). Innovation thus requires the presence of a diverse set of talents in the company, capable of creativity and cognitive agility, to act within the organization (inside) as well as toward the exterior (outside). In this sense, developing the ability to generate new and original ideas, alone or in a group, is one of the challenges facing organizations today. If creativity is not the only tool for innovation, it is clear that the ease of understanding creativity techniques and tools (see creative input) and their popularity make creativity a must today when we have a problem to solve, even in everyday life. 25.5. References Alter, N. (1995). Peut-on programmer l’innovation ? Revue française de gestion, 103, 78–86. Boly, V., Camargo, M., Morel, L. (2016). Ingénierie de l’innovation, 3rd edition. Lavoisier, Paris.

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Chesbrough, H. (2003). The era of open innovation. MIT Sloan Management Review, 44(1), 35–41. Cooper, R.G. (2001). Winning at New Products. Accelerating the Process From Idea to Launch, 3rd edition. Basic Books, New York. Dert, F. (1997). L’art d’innover ou la conquête de l’incertain. Maxima, Paris. Garcia, R. and Calantone, R. (2002). A critical look at technological innovation typology and innovativeness terminology: A literature review. Journal of Product Innovation Management International, 19(2), 110–132. OECD/Eurostat (2005). Oslo Manual – Guidelines for Collecting and Interpreting Innovation Data, 3rd edition. Manual. OECD Publishing, Paris. Rothwell, R. (1994). Towards the fifth-generation innovation process. International Marketing Review, 11(1), 7–31. Schumpeter, J.A (1934). The Theory of Economic Development. Harvard University Press, Cambridge.

26 Open – Open Source and Open Data: Filiation, Analogies and Common Dynamics

26.1. Introduction While it is possible to study open source and open data separately, this chapter proposes conducting a cross-analysis of these open domains. Their common filial relationships, analogies and dynamics will be highlighted. In particular, it will be shown that the structuring and deployment of open data is based on the constitutive matrix and best practices tested in open source. In fact, while some of the precursor elements of these two movements have already existed, open source has established itself as a vector of mature technical–industrial confluence, at the origin of widely used computer programs, particularly in the areas of software infrastructure and networks. Open data is more recent, it is still in the process of unlocking a very large potential for innovative applications relating to the processing of public digital data. Both open source and open data are leading to the creation of common digital data and related services. In this chapter: – the guiding concepts of these two areas will be defined; – the intrinsic relationship of these movements to innovation will be a dominant thread. It will be explained that open source and open data are stakeholders in the “2.0” extension of open innovation, an evolution linked to the new potential for dissemination and contributory co-development taking place on digital networks, which may involve a large number of remote participants; Chapter written by Laurent ADATTO. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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– we will explain that open source and open data have the characteristic of being based on specific elements of intellectual property rights (IPRs). These innovative IPRs have a key role in regulating the recursive openness of digital devices generated by these two movements, software and digital data, respectively. 26.2. Open source and open data: guiding concepts The movement linking open source and free software is related to the free use, modification, redistribution and access to the source code of computer programs according to the terms of the accompanying user licenses. Since the mid 1990s, the movement has established itself as a proven technical and industrial field, having demonstrated its innovative capacity to generate high-quality developments and to federate the open cooperation of heterogeneous contributors involved in the software engineering and ICT sector. Open data, in its contemporary sense, is based on the combination of the universal disclosure of data (particularly of administrative, academic and research origin) and its digitization, which drastically facilitates its dissemination via ICT networks and its computational processing, leading to the development of dedicated applications that generate a proliferation of innovation. 26.3. Open source: process innovation and legal innovation via copyleft Buoyed by the joint research efforts on electronic technologies integrated into pre-existing automatic computers (including the fundamental conceptual work of Alan Turing in computer science and programming, and the work of John von Neumann, who in 1945 established the architecture still in use in most of today’s computers), the development of pioneering computer science programs, mainly in the United States, was based on the cooperation between public authorities, machine manufacturers and university research centers. After the computer industry evolved by closing down the codes of its programs, the appearance of open source, together with an innovation in software development processes, established itself as a revival of the philosophy of cooperation that prevailed during the pioneering computer science era. The purpose of open source is to enable the open development of computer programs. This means that any potential contributing agent, whether an individual or an organization, can initiate or join an open source project development or even create a “fork”. To facilitate this, the source code of an open source software is freely accessible according to the terms of its user license. These terms may vary with respect to copyleft, a legal innovation and neologism from the Free Software Foundation, which determines the level of recursiveness and

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propagation of openness when the initial source code is attached to a source code framed by a third-party license. The consequence of this innovation in IPRs management is to allow the creation of a variety of open source licenses adaptable to the different strategies and business models of organizations. These range from the guarantee of absolute maintenance of full openness extending to combined modules (strong copyleft, of which the GNU General Public License (GPL) is the standard), to licenses that guarantee the maintenance of the openness of the initial and supplemented code. This is without extending this obligation of openness to juxtaposed modules under other licenses (weak copyleft), or even without copyleft that may allow a change of license of the initial code under conditions of legal notices referring to the source code, and distinguishing open source without copyleft from software in the public domain. The rise of open source was based on the conjunction of this innovative legal matrix determining the modalities of openness, combined with the progress and diffusion of computer technology, both hardware (in particular, the advent of the Internet and the multiplication of connected personal computers) and software (digital tools facilitating remote and asynchronous cooperative development). In addition, the process innovation provided by open source has resulted in the potential for technical excellence based on open access to the source code and scanning by an unlimited number of agents. In this way, the spontaneous detection of anomalies and bugs is made possible, leading to a continuous improvement in reliability, a process that contrasts with the traditional closed development of proprietary software. This technical excellence can be found, in particular, in the flagship open source projects. The Linux operating system and the Apache HTTP (HyperText Transfer Protocol) server are just two of the many examples firmly established in the industry. Indeed, these innovative qualities have imposed open source within the digital sector by combining the contributions of a wide range of players involved in ICT. In particular, the most influential firms, especially in relation to software infrastructure and network development projects, i.e. categories of programs that are best able to federate these companies. Among these firms, IBM has already invested more than a billion dollars in open source. Moreover, in 2016, Microsoft, which had long been reluctant to invest in a field that it initially saw as a competitor, joined the Linux Foundation, a non-profit consortium that oversees the standardization, evolution and protection of Linux. Jim Zemlin, director of the foundation, told the ICT industry trade journal ZDNet: “Microsoft has grown and matured in its use of and contributions to open source technology. The company has become an enthusiastic supporter of Linux and of open source and a very active member of many important projects. Membership is an important step for Microsoft, but also for the open source community at large, which stands to benefit from the company’s expanding range of contributions”.

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As a result, innovations specific to open source, innovations in open co-development processes and legal innovations linked to the concept of copyleft, combined with contextual innovations in computer, hardware and software technology, have enabled the rapid success of the movement. Open source has been able to go beyond the formal constraints of rigidities and imperatives of place and time, which are characteristic of proprietary IT development, to assert itself as a center of excellence and induce the participation of most of the major organizations and firms in the industry. Gassmann and Enkel (2004) consider open source to be “the most prominent example of the revolutionising of the conventional innovation process”, and Jullien and Zimmermann (2009) highlight open source as an extreme case of open innovation. 26.4. Open data: dynamics of open innovation 2.0 in line with open source As indicated in the introduction, open source and open data have a proximity precisely induced by their common inclusion in the field of open innovation, highlighted in the academic sector by the work of Chesbrough (2008). Moreover, open source and open data belong to a singular branch of open innovation specific to the digital sector, the densification of networks and the new processing possibilities of digital devices linked to advances in ICT. In line with the importance of this development, the academic sector of innovation management, as well as recent work by the European Commission on the economic impact of innovation, has characterized it as Open Innovation 2.0 (Jullien and Pénin 2014; Rayna and Striukova 2015). The suffix “2.0” marks the analogy with the Web 2.0, an evolution of the World Wide Web correlated with a significant increase in interactivity, ergonomics and possibilities for digital exchanges and co-creation. Singularly, open data concerns “open” digital data, i.e. freely accessible and exploitable according to the terms of their user licenses. The latter guarantees an unbiased opening in the sense of being non-discriminatory on financial, technical and use purpose grounds. This leads to the centrality of IPRs associated with open data, based on an analogy with open source. This is a relationship highlighted by the Open Knowledge Foundation, a non-profit association that, in 2005, played a pioneering role in shaping open data by drawing inspiration from the principles defining open source. The relevant formatting of open data facilitates further processing. The standardization of open data for normative purposes, like the standardization specific to major open source projects, improves the dynamics of innovation and guarantees that technology is not locked in by the interoperability, compatibility and substitutability of the digital devices concerned. In the field of open source, this has

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been illustrated with the Linux Foundation, which aims to standardize the project through consultation and open cooperation between the contributors involved, in order to ensure its continued evolution. Open standardization applies to open data formats, allowing the automation of processes concerning the same range of open data. For example, a guidance application will be able to use all regional mapping data applying the same open standardized format. This is a new link between open source and open data, where standardization efforts are strengthening these areas. With this in mind, one of the most notable researchers who has contributed to the advent of the contemporary digital age, Sir Tim Berners-Lee, rightly links open source, open data and open standardization. The British engineer, the principal architect of the Web, has perpetuated the evolution of the associated technologies by making them open standards through the consortium he founded specifically for this purpose, the W3C (World Wide Web Consortium), an international non-profit organization for the development of open standards. However, historically, the shaping of telecommunications standards (Russell 2014) has been the matrix of open movements, of which open source and open data are occurrences. The centrality of managing IPRs to maintain openness is found in open standardization, as shown in open source and open data. Berners-Lee has thus ensured that the IPR management of open standards within the W3C consortium enables their implementation as open source programs. In particular, by excluding the possibility that these standards may contain unavoidable implementation elements linked to software patents subject to royalties that are incompatible with development, according to open source. In addition, in 2009, Berners-Lee launched a pioneering call for the opening up of raw data held by administrations, with the aim of promoting open data. The following year, he established a five-point scale to serve as a benchmark for advancing open data strategies. He was then given the task of guiding the UK’s open data policy. He implemented the value of acting in symbiosis with open movements, by associating the transparency of public actions with the opening up of data, the use of open standards and the use of open source software within government agencies. In relation to this movement to open up the data held by administrations, the large potential open data resources resulting from open data policies constitute a considerable source of innovation. Entrepreneurs find a breeding ground that they can leverage through the development of innovative applications and new business models (Zuiderwijk et al. 2014). More and more organizations, including government institutions and beyond, from research to business, have understood the value of open data in enhancing the common good and related services by promoting open data strategies for previously buried or not easily accessible data. In this way, the European Commission is linking economic growth, innovation and policy to promote open data.

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In particular, open data involves disruptive innovations according to the management concept highlighted by Christensen (1997). Such technological innovations are characterized by the low cost of introducing ranges of devices, products and services, which are initially exploratory and not mature, but whose quality will grow to the point of imposing these innovations on the market by creating new uses and business models. At the same time, the entrepreneurial typology of start-ups is particularly adapted to the open data context (Lindman et al. 2014). The strategies of organizations contributing to open data are fundamentally aimed at generating added value through the processing of open data via software processes, and the creation of dedicated services and innovative applications. 26.5. Conclusion This contribution analyzed the constitutive and operative proximities between open source and open data. In particular, analogous legal principles have been developed to regulate the openness of associated digital devices, software and data fields, which by definition circulate outside their initial matrices. It has been analyzed that innovation is at the heart of these movements from their belonging to the 2.0 extension of open innovation, to the innovative management of the IPRs they integrate and to the process innovations they constitute. Finally, it has been shown that they generate breeding grounds for innovation, from which high-quality software for open source and innovative applications for open data are born. Both movements attract contributions from a multiplicity of organizations linked to the ICT sector. Finally, both movements lead to the creation of digital commons. According to all the prospective analyses, these will continue to grow in the future, quantitatively, qualitatively and in terms of the variety of devices and services available. Finally, more and more individuals will have access to these digital commons, which bodes well for enhanced social good and extends the perspective outlined by Elinor Ostrom (Hess and Ostrom 2011). 26.6. References Chesbrough, H. (2008). In Open Innovation: Researching a New Paradigm, Chesbrough, H., Vanhaverbeke, W., West, J. (eds). Oxford University Press, Oxford. Christensen, C.M. (1997). The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail, 1st edition. Harvard Business Review Press, Cambridge. Gassmann, O. and Enkel, E. (2004). Towards a theory of open innovation: Three core process archetypes. R&D Management Conference (RADMA), Lisbon. Hess, C. and Ostrom, E. (2011). Understanding Knowledge as a Commons: From Theory to Practice. MIT Press, Cambridge.

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Jullien, N. and Pénin, J. (2014). Innovation ouverte : vers la génération 2.0. Encyclopédie de la stratégie, 701–714. Jullien, N. and Zimmermann, J.B. (2009). Firms’ contribution to open source software and the dominant user skill. European Management Review, 6(2), 130–139. Lindman, J., Kinnari, T., Rossi, M. (2014). Industrial open data: Case studies of early open data entrepreneurs. 47th Hawaii International Conference on System Sciences, (HICSS), Hawaii, Sprague, R.H.J. (ed.), IEEE Computer Society, 739–748, 6–9. Rayna, T. and Striukova, L. (2015). Open Innovation 2.0: Is co-creation the ultimate challenge? International Journal of Technology Management, 69(1), 38–53. Russell, A.L. (2014). Open Standards and the Digital Age: History, Ideology, and, Networks. Cambridge Studies in the Emergence of Global Enterprise. Cambridge University Press, Cambridge. Zuiderwijk, A., Janssen, M., Davis, C. (2014). Innovation with open data: Essential elements of open data ecosystems. Information Polity, 19(1), 17–33.

27 Personality – The Deviant Personality of the Innovative Actor

27.1. Introduction Defining an innovator requires a clear definition of innovation, which, strangely enough, is not so obvious. We consider innovation to be the introduction of a new technology, a new product, a new industrial or commercial process or a new organization, in the broadest sense of these terms, in order to improve the efficiency (competitiveness) of an organization or the economy as a whole. Innovation is a “societal phenomenon” and requires a collective choice (at the micro-, meso- or macro-economic level) to change a production or consumption model. The innovator assumes the central function of the innovation process. Through their decision-making capacity in modifying the existing organizational model or introducing novelty into the market (by transforming the invention or idea into a commodity), they change the rules of the economic game. In the innovation process, the innovator may be an individual or a multi-skilled team whose objective is the creation, application, improvement or commercialization of a market novelty. However, in theory as well as in economic policies of growth, the individual entrepreneur–innovator holds the central place (Boutillier and Uzunidis 2016). If the entrepreneur is an individual whose objective is to create value, the innovative entrepreneur is the one who creates a new type of value that is measurable by quantity and, above all, by quality. In this chapter, we present the basic characteristics of the innovative entrepreneur’s personality with reference to psycho-economic analysis. We first start by discussing the function and role of the innovator in relation to their Chapter written by Dimitri UZUNIDIS. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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socio-economic background, and then put forward some key ideas on the importance of the “eccentricity” of their character in the initiation of innovation processes. 27.2. The actor, the system and the question of the complementarity of roles Challenging the principle of economic rationality and combining the contributions of sociological, economic and psychological analysis, we can trace the outlines of the personality of the innovative entrepreneur, in other words, the specificities of their character, for a better understanding of their temperament, motivations and behavior and the contribution to economic dynamics. If we consider that personality conditions each individual’s particular way of being, leading them to create specific codes of behavior, its extension to a group as a whole – here the innovator, in general, and the innovative entrepreneur, in particular – is not without risk. To avoid stereotypes, we can consider the characteristics common to a large number of business creators and managers. The individual, through their socialization, is an author and an actor in a system that is constantly changing and evolving. They are “in a situation”, confronted with problems to which they must find an adapted solution (Nicolaï 1999, p. 176). To do this, they must adopt a behavior that allows them to subjectively choose the “right” answers to the problems posed (Lagache 1955). Some individuals, in particular circumstances, stand out, because of their creativity and willingness to change. The pleasure of doing proves to be more powerful than that of undergoing. If we consider capitalism – and under the assumption that the creator of activities guarantees change in the historical continuity of the system – the entrepreneur holds a prominent place in the hierarchy of roles and functions in society. An examination of the nature of the systemic relations that are established between the actor (the innovative entrepreneur) and the system (the market economy) provides us with information about the personality of the entrepreneur and explains their social utility. Many researchers have attempted to define the personality of the entrepreneur. For McClelland (1961), the entrepreneur is, first and foremost, an individual that is motivated by a need for achievement. For Collins and Moore (1970), they are a tough, pragmatic being, driven by the need for independence and achievement. Bird’s (1992) entrepreneur is an individual that is subject to intuitions, intense brain activity and disappointment; they are ingenious, resourceful, clever, opportunistic, creative and sentimental. For Gasse (2005), they are motivated by achievement, success, challenge, autonomy, power and recognition. Their aptitudes are self-confidence, enthusiasm, perseverance, intuition and imagination. The multidisciplinary approach adopted here implies that social behaviors cannot be reduced to economic criteria alone, and even less to profit maximization, for marginal economists, for whom the producer is a kind of fiction without personality, as defined here. In a way, society produces

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antibodies (deviant agents) that guarantee the renewal of its fundamentals and enable it to endure. This is the case of the innovators who, to paraphrase Schumpeter (1911), ensure that the average 21st century consumer has access to a supply of goods that the monarchs of the past could not imagine, despite their power. According to the theory of neoclassical economic rationality, agents act under economic constraint, but the various economic constraints are insufficient to understand the functioning of agents in all their complexity. Marginalists avoid the question of how a system generates personalities that are adequate to the roles that society provides. Extra-economic and economic phenomena can diverge. According to Schumpeter (1911), the innovative temperament can only be explained by extra-economic factors. The search for profit maximization, therefore, in no way justifies entrepreneurial behavior. According to Menger (2011, p. 342), entrepreneurial rationality is subjective and based on psychological factors. The entrepreneur is not rational, in the sense of Homo œconomicus. They create without respite because they cannot do anything else (Schumpeter 1911, p. 134). On the other hand, the system cannot function without taking advantage of personality neuroses. Its rationality of reproduction can make use of the “irrationality” of agents. The system creates different but complementary roles associated with a multiplication and specialization of social functions. The range of specializations can be more or less broad depending on the quantity of roles that the social mechanic generates and imposes on its parties: employee, manager, shareholder, entrepreneur, civil servant, industrialist, merchant, banker, and so on. The differentiation and complementarity of roles is also accompanied by a hierarchy of agents, which has a functional basis – it is not arbitrary. Some social roles are more important than others and so some roles will be placed at a higher level in the social hierarchy. For example, in growing or transitional societies, the group that has the function of investment and innovation will be placed at the top of the hierarchy, even though it only includes few individuals. However, the major fact is that agents can function in complementarity, and that this complementarity optimizes the functioning of the system. The specialization and complementarity of roles comes from the socialization of agents who are trained to fulfill their role. A more or less broad range of behaviors exists. If the complementarity between agents is ensured poorly, mechanisms for forcing them to adopt complementary behaviors are necessary. External social control gradually leads agents to behave according to society’s norms. The agents are, therefore, actors in social relations. The imperfect complementarity of roles can lead to social dysfunctions (e.g. following an economic crisis, entropic movements diminish as socio-economic roles and functions are reinvented). However, social disruption can also result from the behavior of agents who are “outside the norm”.

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27.3. The deviant personality of the innovator Innovative entrepreneurs are deviant agents. They constitute useful margins for the reproduction and sustainability of the socio-economic system. Cantillon (1755) even goes so far as to assert that beggars and thieves are entrepreneurs. Unofficial, clandestine or occasional roles can arise to feed the economy with innovations. Baumol (1990) has the same conception of the opportunistic individual, who may be a harmful entrepreneur, if the institutions guaranteeing the function of the entrepreneur in the dynamics of capitalism are absent, failing or fallible. This was also the case of Smith (1759) who, two centuries earlier, castigated the project-maker whose passion often led them to hijack laws. Smith condemned this type of deviant entrepreneur, preferring the prudent individual and the child of the poor man. The prudent individual is a shrewd calculator; they own a small business, which can be more lucrative than a large one. The child of the poor man sympathizes with the rich and seeks to imitate them. Their desire to become an entrepreneur can be explained by their willingness to assert themselves socially. For the project-maker, the function of the entrepreneur is the goal; for the other two types, it is the means of integration or social inclusion. The entrepreneur only exists when they create their company. For Galbraith (1967), the entrepreneur is like the male aspis meblifera (European honey bee), who performs the act of procreation at the price of his own life. This leads us to the question of the social function of the entrepreneur. The entrepreneur is the one who gives free rein to their deviance (revealed by circumstances and “used” by seizing opportunities for profit, creativity or power) and not the one who locks themselves into their “out of phase normality”, who does not pick up the signals of crisis or entropy emitted by the system. The characteristic traits of a deviant personality are not fixed: they appear, mutate and change according to the context and the resources available to the individual or those they are able to acquire. The domains of possibilities for a polymorphic personality are more numerous and differentiated than for the “normals” (those who conform to the existing social norm) and even more so than for the ultra-conformists, who hinder the realization of new productive combinations and new projects (Nicolaï 1999). The personality of the entrepreneur is, therefore, studied in relation to the socialization of the agent and their role in the reproduction of the system (based on private property and the dynamics of inequalities). In exceptional situations, deviant agents take advantage of the surrounding entropy. They are individuals who have not internalized the social norms in force, but who know that others have done so. They are in a privileged situation: they use the conformity of others to achieve their ends and thus express their egocentricity. The others are fascinated by their personality and these individuals use their charisma to enroll them in their project. This is how Schumpeterian innovators appear (Nicolaï 1999). The entrepreneur (innovator) is a

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deviant. To evolve, society needs conformists and reforming deviants (innovators; provided that the innovation is adopted and digested by the actors and structures of the system). The personality traits of the entrepreneur–innovator deviant are: – the perversity of their character must prevail over neurosis, i.e. be polymorphic, in other words, consider that they are allowed everything from a wide range of choices; – they have not repressed violence (social prohibitions); – they doubt nothing; – they are selfish, capable of living alone; – they are seductive enough to be surrounded and followed (even though they are wrong); – they exhibit chaotic behavior and disturb others. Deviants emerge from different backgrounds, formed in marginal groups. Relationships within these socialization settings (family and community) must be fraught with conflict for their members to resort to clandestine practices to cope. These marginal groups are very sensitive to social contradictions. The individual (the child) is then subjected to a contradictory order, which produces an identification crisis with positive sides (resourcefulness, creativity and innovation) and negative sides (discordance with the reality of social roles). Leaving such a context (family backgrounds form the entrepreneurial spirit (Casson 1982; Gasse 2005)), the entrepreneur seeks to integrate other agents into their strategy and actions in order to recreate a non-contradictory, organized social environment on which they will rely to carry out their project. The inscription on the tombstone of Andrew Carnegie (American textile and then steel entrepreneur, 1835–1911) can be read with the idea that, under his command, a team was created that allowed ordinary people to do extraordinary things. He wrote his own epitaph: “Here lies a man who knew how to enlist in his service better men than himself”. Here, “enlisting” means that other individuals accept the standards set by the innovative entrepreneur before those standards, and the practices that follow, are spread through imitators who are attracted by the innovator’s success. For change to occur, the innovator must be followed. Monitoring is essential, because it creates the conditions for the realization of new institutions of power and contributes to the generalization of new practices. Ultra-conformists are thereby eliminated and those who survive must conform to the new situation. Entrepreneurial success thus depends on the ability of innovators to deal with three kinds of “enemies”, who pose their own constraints: existing competitors, those who are envious (who may become potential competitors) and conformists, i.e. those who take comfort in routine social practices.

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27.4. Conclusion Say (2006) and, much later, Schumpeter (1911) emphasized the particularity of the entrepreneur’s personality: an exceptional being that is endowed with a great “capacity for judgment”, seeking to give impetus to a new economic dynamic, desperate for the prevailing social inertia. Their aspiration to integrate into the most “modern” part of the socially dominant group creates their motivations: performance, creation, innovation, power and enrichment. When these motivations are incompatible with the objective constraints, and with those related to the replication or reproduction of the dominant group, the entrepreneur fails. However, for the reproduction of the social system, what matters is the function that the entrepreneurs perform. The most important thing is that there are individuals who act without reference to a rational calculation. There is, in fact, no basis for rational calculation once one leaves routine behavior. Capitalism generates a pool of deviant agents from which the innovators that are necessary for its survival are drawn. For their part, the innovator is the one who lives their game and very often plays with their own life. 27.5. References Baumol, W.J. (1990). Entrepreneurship: Productive, unproductive, and destructive. Journal of Political Economy, 98(5), 893–921. Bird, B.J. (1992). The operation of intentions in time: The emergence of the new venture. Entrepreneurship: Theory and Practice, 17(1), 11–20. Boutillier, S. and Uzunidis, D. (2016). The Entrepreneur: The Economic Function of Free Enterprise. ISTE Ltd, London and John Wiley & Sons, New York. Cantillon, R. (1755). Essai sur la nature du commerce en général. Institut Coppet, Paris. Casson, M. (1982). The Entrepreneur: An Economic Theory. Rowman & Littlefield, Lanham. Collins, O.F. and Moore, D.G. (1970). The Organisation Makers. Appleton-Century-Crofts, New York. Galbraith, J.K. (1967). The New Industrial State. Houghton Mifflin, Boston. Gasse, Y. (2005). Sensibilisation à l’entrepreneuriat, construction et validation empirique d’un outil pratique. Université Laval, Quebec. Lagache, D. (1955). La psychanalyse. Presses universitaires de France, Paris. McClelland, D.C. (1961). The Achieving Society. Free Press, New York. Menger, C. (2011). Recherches sur la méthode dans les sciences sociales et en économie politique en particulier. Éditions de l’EHESS, Paris. Nicolaï, A. (1999). Comportements économiques et structures sociales. L’Harmattan, Paris.

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Say, J.-B. (2006). Traité d’économie politique. Economica, Paris. Schumpeter, J.A. (1911). Theory of Economic Evolution. Harvard University Press, Cambridge. Smith, A. (1759). Theory of Moral Sentiments, 2nd edition. A. Miller, the Strand, and A. Kincaid and J. Bell, Edinburgh.

28 Real Estate – Business Real Estate and Innovation: A New Profession for New Spaces

28.1. Introduction In 2020 in the tertiary sector, a set of globally homogeneous real estate practices can be observed. With mature professions, a mature industry and smooth transactions, the mechanics of the sector is well established, after nearly 30 years of steady growth in the profession and its market expertise. In this rather stable landscape, sanctioned by seemingly unfailing economic health, the insertion of real estate options into corporate strategies seems easy, and potentially capable of making a significant contribution to the much more complex effort that companies are led to undertake in order to establish the terms of strategies capable of ensuring, at the very least, their survival, and at best their sustainability, in a turbulent context. However, a closer look at the maneuvering of real estate departments reveals a relative poverty in the choice of common practices that always end up densifying and rarefying workstations to reduce the surface area and, consequently, the costs. This could be in the city center or in the suburbs, isolated offices, open spaces or teleworking, assigned or unassigned offices, in touch with nature or not, “like a home from home”. Standards, practices or certifications – such as Sustainable Development, Corporate Social Responsibility and green leases – formulate a narrow range of nuances to bring to a pragmatic type of space planning that counts each square meter as a dubious expense. When you look at it carefully, you leave the manager a rather slim choice of possibilities between an acceptable solution and one

Chapter written by Frédéric GOUPIL DE BOUILLÉ. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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that makes a real difference, and, in any case, nothing that resembles the terms of his or her corporate strategy in his or her real estate strategy. Whether we think of mining villages, Apple’s headquarters, the experiment of “The Camp” or the industrial park, a company knows how it wants to form its own real estate objects as soon as it admits that the use of buildings, being central to the experience that people have of them, is part of the language of the company and a source not only of productivity, but also of the company. It is at this level that real estate is introducing an innovation, often a breakthrough, in the market continuum and offering to participate in a strategic advantage. Are real estate managers free to position themselves in the face of these challenges? How is their position evolving? What are the changes that support them and those that constrain them? What has changed in the real estate manager’s mission? What are the levers of innovation of a real estate policy now? This question deserves investigation, which CRDIA proposes opening in the coming months. To clarify the issue, let us look at the vocabulary available to real estate departments and see how it can be linked to corporate strategy. 28.2. The prevalence of the financial referent, reasoning and industrialist practices In the tertiary sector, real estate costs are often the second largest item of expenditure, behind salaries and ahead of IT. There is therefore a strong temptation to focus on direct productivity through lower costs, which is easier to see immediately, rather than on improving production, which would mobilize productive capacities (intangible assets, ergonomic access to resources, availability of players, commitment of occupants) subject to other contingencies, particularly organizational or managerial, and which are therefore more risky and more complex to implement. Reducing the square meters or the costs associated with square meters are legitimate objectives, but they cannot constitute a policy in the service of a strategy as they do not propose anything that determines the form or use of the building other than its price, nor any value term that refers to the company’s strategic model. However, many “policies” are presented or even defined in this way, in terms of an objective of means. The focus on the “measurable solution” – reducing the square meters – outweighs the end goal: to achieve an increased economic performance on terms that would strengthen the company’s dynamics.

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These discourses and practices also exploit an intellectual background advocating the mobilization of the levers of industrialist productivity: massification, saturation of the means of “LEAN” production (optimization of production by eliminating waste, direct labor intensity, integration of technologies, etc.). We recognize the merits, as well as the limits and effects. The convergence of practices centered on obtaining a result – here a saving of square meters, itself a guarantee of reduced expenses – relays a summary understanding of productivity through the reduction of labor costs. This leads us to formulate a methodological intuition. It is probably companies that “don’t act like others”, that do not manage their working environments like turning off a tap, that have a real estate strategy of their own. Identifying these singular actors is a first point to be documented in a survey. 28.3. Weakness of the human resources paradigm applied to real estate Faced with the financial simplification of the terms of occupancy of buildings, the proposal that would rely on the mobilization of occupants as a resource lacks funds. Human resources directors and staff representative bodies, if they are consulted at all, lose their influence as soon as the question of workspaces is no longer a point of law. Productivity through the ergonomics of the work tool and attractiveness are management strategies that do not translate to or enjoy support in the social field. The skills in the area of human resources do not go as far as translating the design of spaces into physical, cognitive or organizational ergonomic data capable of use in discussions with project teams. They will therefore be unable to support the real estate department on proposals for empowering occupants at work, even though each party shares with the other the conviction that any genuine policy of mobilization towards innovation and creativity requires a complete rereading and reappropriation of workspaces, as a vector for reappropriating work itself. This misalignment of skills blocks alliances that could compete with the financial approach. 28.4. Employees empowered by change management The employees themselves – who are directly concerned – can be involved in the process, but the challenge of a standard quality of working life and the satisfaction of the “occupants”, while almost always the subject of attention, speeches and sometimes opinion polls, does not go beyond the level of maneuvering aimed, ultimately, at avoiding the higher costs of a real adaptation of workspace. Barely a caricature, the question is never “how could you work better?” But rather “do you prefer green or pink chairs?” The visibility of exceptions – often experiments – underlines that the usual project regime is not looking for a breakthrough innovation, but is instead negotiating the acceptance of a pre-established solution.

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28.5. Powerful, but inconsistent with regard to use, real estate marketing The so-called “innovative” marketing of the the use of premises supports the market’s proposal, by displaying the announced solutions as bringers of productivity. Appropriating these levers could constitute a strategy, but while at the core of its business activity the company focuses on the finest level of evidence, in real estate, no one is alarmed that the body of studies justifying these new proposals for the use of workspaces does not stand up to scrutiny. Studies based on the impacts of location or development choices are rare and often truncated or even ambivalent; they do not allow for conclusions to be drawn on either the risks or the promised benefits. Work on teleworking, which has been an individual right since 2014, is also ambivalent. It comes to conclusions, certainly to the satisfaction of the people concerned and the observation of a form of improved productivity, but relies more on short-term statements than on evaluations analyzed over time. The levels of evidence are low. Clusters and open spaces would decompartmentalize and facilitate communication... The model, which goes well beyond the cognitive and sociological domains, is much more complex. The “open” office would make it possible to respond to the new characteristics of increasingly nomadic work and the need for collaborative activities. However, we do not have enough hindsight to assess its effects scientifically. This would correspond to the expectations of the younger generations... Let us wait until they are at least 10 years old to decide what to discuss. This unanimity on “evidence”, sometimes fueled by largely promotional “benchmarks”, is suspect: reality – and this is where the resource generally resides – is always more complex and richer. It contributes, however, to installing a form of intellectual imperialism against a backdrop of self-professed modernity. It should be noted that this is spreading far beyond the real estate sphere. The pseudo-scientific form of these publications is addressed not to the expert, i.e. the real estate manager, but rather to a generalist on the lookout for simplified models that promise quick profits: the decision-makers. Those who doubt, those who ask questions, are suspected of conservatism or technophobia. This is a type of censorship. Faced with a proposal for a consensual decision, supported by an ambient discourse conducive to mimicry, the proposal of a real estate strategy involving complexity and uncertainty, and therefore a gamble – no more and no less than that of the company itself on its core business, however – is unlikely to prevail in the face of the financial shortcut. In this way, it begins to identify the target of the discourse, the decision-maker, and his or her hostage: the real estate manager.

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While business leaders are project managers with a culture of boldness and vision, real estate managers are pragmatists. They will keep it simple. Their ability to distinguish themselves from the mimetic discourse is therefore a marker of the presumption of an authentic strategy and must be identified. 28.6. The real estate market versus the innovative company How much space do real estate managers – who in the end seem to be rather isolated, poorly supported, and even ill-equipped to make their specific contribution – have left to propose a strategy? What support does the market offer them? Buildings are the products of an offer which itself responds to specific economic and business models. Asset managers, property managers, developers and brokers, supported by project management assistants, propose and argue the relevance and qualities of their offers. Their products are all the easier to sell and resell, and are therefore more attractive, if they lack that “special quality”. Functionalities (energy performance, technologies and density) are on the menu, but there is rarely, if ever, a particular “identified” relevance when referring to the search for an economic performance of the particular work that will be “housed” there. The offers argue for a potential for high-density development and organizations, for low refurbishment costs and for the shortest possible commitment period. “Neutral” designs have become the rule. They do away with any search for specificity and adaptation to particular populations, cultures, professions or activities. Even the urban fabric that surrounds them is more often due to a marketing motive than a marker of use. The supply of workspaces, including in their dimension of immaterial use, is thus conceived as the supply of “undifferentiated goods”, even though no two companies are equivalent and real estate is characterized by an almost infinite range of diversity (in terms of location, history, materials and use, etc.). Each time the property is adapted to the business, the value of the exact cost of restoring it to its original state is reduced, undifferentiated. The leases provide for systematic compensation. In other words, what makes the company’s investment is precisely the deviation from the real estate market norm. Real estate in support of innovation should be able to be remodeled infinitely to match the plasticity of work, give the company an architectural image of its uniqueness, provide health and safety to teams totally absorbed by something else, reduce the financial risk on the emerging activity to a minimum, remind people at

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every moment that anything is possible and facilitate cohabitation according to the rhythms of the activity. The real estate market, built to secure the return on assets, by systematically compensating flexibility and adaptability with financial security through higher prices, is directly opposed to innovation, which is at the heart of corporate strategies. Under these conditions, can we seriously choose a building or an occupancy strategy like we pick out a packet of washing powder at the supermarket? Do the transaction cost, the impact on the result, and the volume of investment not require, beyond a purchasing methodology, a strategic alignment of the means committed to the company’s strategy? We sense a threat to the position of the real estate departments in relation to the market, which is typical of the functions that have become support functions, and that, ultimately, are quite similar to the position of human resources managers in relation to their union counterparts, or of IT management in relation to the major IT companies. It is more a question of adapting the company’s demand to the market’s supply than the opposite. A proposal that genuinely conforms to the DNA of the company sounds like a challenge. 28.7. Conclusion While attention to costs (buildings and operations) is unavoidable, our assumption is that it is not the only relevant matter, nor sufficient to constitute a real estate policy. What would it take? A fluidity in the value chain linking the city to the occupant of the buildings, which would free itself from the levy of value confiscated by real estate tactics, and challenging what is essential: to look at the building as a place of work above all else. How does one enrich work? By treating the building as a service made available to work. It is understandable that this proposal makes the supporters of an essentially capitalist activity shudder. Obviously, the ambition to identify and study this contribution to work performance is, and will remain, difficult to demonstrate because of a lack of measures. The contribution to the production of value (economic, utility) from real estate resources is difficult to “value” and/or “monetize” in investment and innovation decisions. However, in an economy that has become service-based, tertiary or industrial buildings are no longer just “buildings” or financial assets. They are service supports

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at the service of work. This proposition of real estate as a service renews the value proposition of real estate strategies. Is this the case? How is it the case? Our study aims to identify companies that have developed a real estate strategy of their own and to examine how it links work and corporate strategy in an original way. From our point of view, these are major resources for innovation, levers for adapting to today’s and tomorrow’s worlds, and for taking initiative, which need to be documented for the benefit of companies and all players, giving back to the real estate mission, perhaps revisited, with the topicality and ambition that is rightfully its own. 28.8. References Donis, C. and Taskin, L. (2017). Résistance par l’espace dans le contexte de mise en œuvre de bureaux partagés, une approche par la territorialité. RIMHE, 26, Spring. JLL (2018). Flex-office : “sans bureaux fixes” désespérés ou collaborateurs libérés ? [Online]. Available at: https://www.jll.fr/fr/etudes-recherche/management/le-flex-office-sans-bureauxfixes-desesperes-ou-collaborateurs-liberes. Traore, F. (2019). Manager sans bureau : ce qui se joue en coulisse. Métis [Online]. Available at: http://www.metiseurope.eu/manager-sans-bureau-ce-qui-se-joue-en-coulisses_fr_70_art_ 30786.html. Wohlers, C. and Hertel, G. (2017). Choosing where to work at work – Towards a theoretical model of benefits and risks of activity-based flexible offices. Ergonomics, 60(4), 467–486 [Online]. Available at: http://dx.doi.org/10.1080/00140139.2016.1188220.

29 Skills – Innovation and Entrepreneurial Skills

29.1. Introduction While the role of the entrepreneur or innovator is undisputed in triggering the process of economic development and technological progress, there are some doubts and uncertainties relating to the possibility of acquiring or developing those characteristics, behaviors, routines and skills that make this role what it is and distinguish it from others. Are innovators born or made? Is it possible to acquire and develop innovation skills? According to trait theorists, those interested in the analysis of traits that determine human personality, the answer is no, since human personality is mostly defined by genetic makeup and rarely by experiences. In other words: innovators are born innovative. On the other hand, social cognitive theorists, who focus on a subject’s surrounding environment, believe that behavior can be shaped by social context. As a consequence, according to these scholars, innovation skills can be learnt. It is possible to become an innovator. Over time, the social cognitive approach has proved itself to be the most convincing: attitudes, which are defined as settled ways of thinking or feelings about something and are core drivers that inform us how to act when dealing with a situation, can be influenced by social processes, in particular by educational context. Eventually, it is possible to work on attitudes in order to become innovators, while still recognizing the importance of genetic makeup. In this chapter, first, innovation skills are identified and then compared with entrepreneurial competencies. Finally, the five discovery skills identified by Jeffrey Chapter written by Giovanni ZAZZERINI. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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Dyer, Hal Gregersen and Clayton Christensen in their book The Innovator’s DNA, Mastering the Five Skills of Disruptive Innovators (Dyer et al. 2011) are presented. 29.2. Innovation skills Some skills have been identified as being fundamental in developing innovation: 1) Creativity, intended as the use of original ideas to create something new. It has a deeper meaning than imagination, which only deals with envisioning an idea in the future. 2) Self-efficacy, which includes self-belief, self-assurance, self-awareness and feelings of empowerment. It means an individual’s confidence in their ability to complete a task or achieve a goal. 3) Energy and enthusiasm, with three main aspects: conation, cognitive and affect, meaning, respectively, that the action is goal-oriented, it involves judgment and it encompasses feelings. 4) Empathy and curiosity, which arise from the desire to learn other people’s point of view, are useful in recognizing that different approaches should be used with different people, being sensitive to the needs of others. 5) Brokering, linked to empathy, enables the management of potential conflicts through the mediation of different visions; therefore, a variety of partners can collaborate despite any differences. 6) Risk-propensity, which is foundational, since the innovation process has an uncertain outcome. The potential innovator should not be risk-averse, frightened of uncertainty, nor a risk-rider, diving blindly into risk. The right approach consists of calculative risk-taking, characterized by management of the risk. 7) Leadership, the art of motivating a group of people to act towards achieving a common goal, is critical in gaining support for new ideas. Effective leadership is based both on communication and management, since the leader is the inspiration for and director of the action. 29.3. Entrepreneurial competencies The above-mentioned skills almost overlap with the entrepreneurial competencies, which are defined as the stock of knowledge, traits, attitudes and skills needed to start and grow a venture (Lackeus 2015).

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Here, the concept of entrepreneurship must be broadened to include the development of certain personal qualities and mindsets, which are fundamental regardless of whether we own a business, have an employer or are self-employed, and which can be summarized in an individual’s ability to turn ideas into action. It includes creativity, innovation and risk-taking, as well as the ability to plan and manage projects in order to achieve objectives. This supports individuals, not only in their everyday lives at home and in society, but also in the workplace in being aware of the context of their work and being able to seize opportunities and is a foundation for more specific skills and knowledge needed by those establishing or contributing to social or commercial activity. (European Commission 2007) Entrepreneurial competencies encompass several aspects. Besides basic business skills, the capacity to spot and recognize opportunities is also considered to be crucial. Personal and interpersonal characteristics also matter. Personal virtues, such as innovativeness and creativity, proactiveness, perseverance, risk tolerance, and being open to experiences as well as interpersonal virtues such as leadership, team-working, managing people and communication are all considered as building blocks of the entrepreneurial set. Entrepreneurial competencies are also detailed in the model by Bagheri and Pihie (2011). The model distinguishes five entrepreneurial competencies which are connected to two basic challenges that entrepreneurs face. Firstly, entrepreneurs face the challenge of envisioning the future and the question of how to make their vision come true (scenario enactment); secondly, they have to influence and inspire people to accomplish their vision of the future (cast enactment). For scenario enactment, entrepreneurs need to be proactive, innovative and willing to take risks, whereas cast enactment requires competencies of commitment building and specifying limitations. Drawing upon the literature mentioned above, the Entrepreneurship Competence Framework (Bacigalupo et al. 2016) offers an overview of the different yet interconnected entrepreneurial competencies. These competencies are grouped into three areas: ideas and opportunities, resources and into action. Each area contains five competencies, and together these make up the 15 competencies that individuals use to discover and act upon opportunities and ideas. 29.4. Ideas and opportunities 1) Spotting opportunities: the ability to identify unmet needs and opportunities to create value in the macroenvironment.

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2) Creativity: the ability to brainstorm and develop ideas, providing original solutions to existing and new challenges, experimenting with and exploring new approaches and methodologies. 3) Vision: the ability to imagine the future, possible scenarios and outcomes and to develop a vision to turn ideas into action. 4) Valuing ideas: to know how to judge what value is in social, cultural and economic terms, and to recognize the potential of an idea. 5) Ethical and sustainable thinking: the ability to assess the impact of ideas and actions on the community, the market, the society and the environment and to act responsibly. 29.5. Resources 1) Self-awareness and self-efficacy: the aptitude to understand your needs, wants and goals in the short, medium and long term, assess your strengths and weaknesses and to believe in your ability to influence the final outcome with your actions and behavior. 2) Motivation and perseverance: the determination to take action to achieve your goal and the resilience to temporary failures and adversities. 3) Mobilizing resources: the competence to get and manage material and non-material resources needed to turn ideas into action. 4) Financial and economic literacy: the skill to estimate the costs and revenues of turning an idea into a value-creating activity and the proper management of financial resources. 5) Mobilizing others: the gift of inspiring and involving relevant stakeholders and getting the support needed with effective communication, persuasion, negotiation and leadership. 29.6. Into action 1) Taking the initiative: start the value creation process and work independently to achieve goals. 2) Planning and management: setting and defining goals and running of the action plan.

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3) Coping with uncertainty, ambiguity and risk: the capability to make decisions when the results of these decisions are uncertain and with incomplete information. 4) Working with others: the ability to work together and co-operate with others to develop ideas and turn them into action. 5) Learning through experience: using any initiative, both successes and failures, as a learning opportunity. This approach is also corroborated by Dyer, Gregersen and Christensen, who point out that “innovative entrepreneurship is not a genetic predisposition, it is an active endeavor”. In their book The Innovator’s DNA, Mastering the Five Skills of Disruptive Innovators (Dyer et al. 2011), they claim that creativity skills are not simply genetic traits endowed at birth, it is not just a matter of being “right-brained”. From several studies on intelligence and creativity conducted on twins, it appeared clear that general intelligence is a genetic matter, but creativity is not; “nurture trumps nature as far as creativity goes”. Moreover, the studies revealed that roughly two-thirds of the innovation skills come through learning, through understanding, practicing and gaining confidence in a skill. In their research, involving hundreds of innovators and thousands of entrepreneurs, managers and executives from around the world, Dyer et al. (2011) identified five skills, mental muscles that can grow stronger over time, which shape “the innovators’ DNA”: 1) Associating: this is the ability to connect unrelated questions or ideas from different fields. Our brain works through connections; in more detail, it does not store information in alphabetical order, like a dictionary, but evokes such information through the association with diverse experiences from our lives. This is why it is fundamental to have a diverse background of experiences in order to favor new associations. 2) Questioning: if most managers ask questions on how to make existing processes work better, innovators constantly ask questions that “question the unquestionable”, spending time thinking on how to change the status quo. In particular, innovative entrepreneurs are much more likely to change the assumptions, asking questions on “why”, “why not” and “what if”, imagining opposites and embracing constraints. 3) Observing: innovative ideas often rise from executives’ capacity to scrutinize the behavior of potential customers. Innovators constantly look out for small details in order to gain insights into new ways of doing things, trying all sorts of techniques to see the world from a different point of view.

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4) Experimenting: like scientists, innovative entrepreneurs experiment new ideas by creating prototypes; they use the world as a laboratory and do not limit themselves to mere observation. Experimenting can take different forms: intellectual exploration, physical tinkering or engagement in new surroundings. Jeff Bezos, for example, has institutionalized experimentation in Amazon; he said: “if we can do a lot of experiments […] we will get a lot more innovation”. 5) Networking: innovative entrepreneurs devote their time to finding new ideas through a network of diverse individuals, who can all contribute with different perspectives. Innovators visit other countries, meet people from other walks of life and attend conferences which draw together artists, entrepreneurs, academics and scientists to extend their own knowledge domains. In conclusion, innovation and entrepreneurial skills and competencies can be acquired, trained and developed over time, hence they can have an extensive impact on a business or even in our personal lives, and distinguish innovative entrepreneurs and leaders from others. 29.7. References Bacigalupo, M., Kampylis, P., Punie, Y., van den Brande, G. (2016). EntreComp: The entrepreneurship competence framework. EUR 27939 EN, Publication Office of the European Union, Luxembourg. Bagheri, A. and Pihie, Z.A.L. (2011). Entrepreneurial leadership: Towards a model for learning and development. Human Resource Development International, 14(4), 447–463. Dyer, J., Gregersen, H., Christensen, C.M. (2011). The Innovator’s DNA, Mastering the Five Skills of Disruptive Innovators. Harvard Business Review Press, Cambridge. European Commission (2007). Key competencies for lifelong learning – A European framework. Office for Official Publications of the European Commission, Luxembourg. Lackeus, M. (2015). Entrepreneurship in education: What, why, when, how. Background Paper, OECD, Paris.

30 Small Business – Small Business and Innovation: Specificities and Institutional Context

30.1. Introduction Innovation is a predominant source of employment, wealth and prosperity of society (Uzunidis 2008). Literature currently refers to innovation as an important engine of competitiveness among firms of different levels (Acs and Audretch 2010). The innovation pathways of large firms are assumed to be different in small enterprises. For instance, large firms have greater financial resources and are thus able to hire high-skilled workers and invest in higher R&D intensity, in order to increase innovation capacities (Schumpeter 1934). Meanwhile, small firms have to cope with inherent resource constraints due to their small scale (Laperche and Liu 2013). Therefore, on average, small firms are less innovative than large enterprises (OECD 2019). However, many small firms in various sectors can be still highly innovative (Acs and Audretch 2010). This is because they tend to pursue innovation strategy in various ways: innovation outcomes are no longer limited to corporate R&D labs, but can now come from assimilating technology acquisition (Hall et al. 2009), or from enhancing absorptive capacity towards open innovation strategies to build up their own knowledge capital (Laperche and Liu 2013; Laperche 2017), and even from the bricolage process by recombining all limited resources “at hand” in an unconventional way (Baker and Nelson 2005), etc.

Chapter written by Son Thi Kim LE. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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However, evidence from previous studies shows that small firms can also be highly innovative. Several scholars have indicated that small firms provide a crucial contribution and become the engine of innovative activity in various industries (Acs and Audretch 2010). However, entrepreneurship literature on innovation describes the controversy concerning the influence of specificities of small firms on innovation activities (Pierre and Fernandez 2018). The specificities of small firms that encourage the innovation process still remain ambiguous to researchers and practitioners. The question addressed concerns how small firms innovate under scarcity conditions. “Are small firms different innovators compared to large firms?” The understanding of the innovation strategies of small firms is a critical question that needs to be studied further to define adapted public policy aimed at fostering the growth of small firms. The remainder of this chapter is organized as follows. Section 30.2 reviews the relation between small business and innovation, while section 30.3 describes the specificities of small business innovation and provides a summary on government support to foster innovation in small enterprises. Finally, the conclusion is discussed in section 30.4. 30.2. The relation between small business and innovation 30.2.1. What is small business? Despite the common use of the term “small business”, the legal definition for small business varies depending on the country and industry. Small businesses can be defined by various factors: number of employees, annual revenues, sales, assets, or annual gross or net profits. According to the World Bank definition: micro-enterprises have up to 10 employees, small-scale enterprises have up to 50 employees and medium-sized enterprises have up to 300 employees. Small enterprises are generally defined by the European Union as having less than 50 employees and either an annual turnover, or a balance sheet total less than EUR 10 million; medium-sized businesses generally have fewer than 250 employees and either an annual turnover of up to EUR 50 million, or a balance sheet total of less than EUR 43 million1. Similarly, the Small Business Administration of the United States specifies small business as having less than 500 employees for manufacturing industries and less than USD 7.5 million in annual receipts for most non-manufacturing businesses; a medium-sized or mid-sized business has less than 500 employees. Different classifications of firms’ size are important in determining whether they are able to benefit from government support (e.g. EU funding 1 Commission Recommendation of May 6, 2003 concerning the definition of micro-, small- and medium-sized enterprises.

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programs to promote SMEs) and qualify for certain preferential policies (e.g. EU funding programs to promote SMEs, SME-specific competition rules in EU, tax policy in the United States which varies by the state and industry). 30.2.2. Small business and innovation Due to the traditional disadvantages linked to size limitations, SMEs rarely have the necessary finance, human resources and appropriate capabilities to develop the formal R&D investments that mainly generate innovation (Hall et al. 2009), or to cope with new opportunities and challenges (Baker and Nelson 2005). Thus, small firms are confronted with managerial and organizational difficulties in following the innovation process: for instance, fewer internal corporate R&D investments, low absorptive capacity (Farace and Mazzotta 2015) and weak strategic management of intellectual property (Pierre and Fernandez 2018). However, Schumpeter (1934) still argues that innovation is an opportunity for small firms to gain rents through the temporary establishment of a monopoly. As SMEs are nimbler than larger companies, they can move faster and obtain these monopoly rents for a longer period of time. Small firms – due to their nature and size – are flexible and can rapidly adapt to changes and adopt new strategies (Acs and Audretsch 2010). Small firms may also stand out from their competition by introducing innovative products, services and/or processes, tailored to attractive niches (Schumpeter 1934; Liu and Laperche 2015). By offering highly innovative products, small firms avoid price competition. Such products can even create new demand and market that, in turn, evidently fosters firm growth. These advantages indicate that small firms can greatly benefit from innovation. The recent developments of technologies and markets create new opportunities for SMEs to innovate. Digitalization accelerates the diffusion of knowledge and facilitates the emergence of new business models that enable firms to scale quickly, with only a few employees and tangible assets. Innovation in SMEs, thus, exhibits various peculiar features that most traditional innovation frameworks do not cover, for instance, innovation with non-R&D investment (Hall et al. 2009) or with all resources “at hand” regardless of whether they are useless or substandard (Baker and Nelson 2005). Due to their scarcity environment and small scale, they tend to engage in various knowledge production activities based on sources other than in-house R&D.

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30.3. The specificity of small business innovation 30.3.1. Innovation efforts: external knowledge source rather than in-house R&D Due to fewer resources for conducting in-house R&D, the innovation efforts of small firms mainly result from informal R&D or non-R&D investment linked to innovation activities (Hall et al. 2009). As a result, SMEs only represent a very small fraction of total business R&D. For instance, the share of GDP devoted to R&D activities of all OECD countries in 2019 was about 2.4%2. Instead of innovating thanks to expensive R&D projects, the innovation of small firms tends to be induced by external knowledge factors (Acs and Audretch 2010). Based on a sample of Italian SMEs, Hall et al. (2009) also recognize the importance of technology sources developed outside for the innovation process, in which external technology acquisition is an important source of innovation in small firms. Similarly, R&D investments are not the only innovation efforts that firms in developing country may dedicate to the innovation process. Firms in developing countries (mainly SMEs) generally have a lower level of human capital and technology compared with other developed countries. Thus, instead of R&D, the innovation efforts of those firms may come from external technology sources embodied in newly acquired machinery (Chudnovsky et al. 2006). 30.3.2. Adopting and adapting external knowledge resources Resource constraints might be a trigger for creative searching processes and sometimes the source of innovative activities (Baker and Nelson 2005). To overcome scarcity, small firms aim to develop and use their internal strategic resources effectively (OECD 2019) and develop collaborations with external partners in innovation networks, in order to search for external knowledge sources (Liu and Laperche 2015; Pierre and Fernandez 2018). Due to strong globalization, small firms become more and more open to collaboration with large companies or other stakeholders in innovation networks (Farace and Mazzotta 2015). These open innovation strategies emphasize the importance of inter-organizational networks, which are referred to as potential knowledge pools. Small firms participating in these networks can benefit by exploring and integrating external knowledge coming from the outside, in order to build up their knowledge capital (Laperche and Liu 2013). 2 The data on Gross domestic spending on R&D is extracted from OECD (2020), Gross domestic spending on R&D (indicator). doi: 10.1787/d8b068b4-en (Accessed on 21 April 2020).

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Innovation process

Large firms

Small firms

Advantages and disadvantages

– Easier access to capital market – High-quality human resources, especially scientific, technical and managerial personnel – Greater absorptive capacity, large knowledge capital, adequate internal technical capacity – Better protection of intellectual assets – Highly structured organization, more bureaucracy and complicated layers of bureaucratic resistance

– Limited financial resources – Less skilled/specialized workers – Weak managerial skills and capabilities – Less effective in terms of absorptive capacity, weak ability to build up a strong knowledge capital – Weak power in enforcing IPRs – Less bureaucratic, informality, more flexibility and ability of move quickly in terms of technology development and commercialization activities

– R&D

– Be R&D intensive (e.g. investment in expensive R&D activities and diversifying R&D projects, having a R&D laboratory/department)

– Using minimum of internal R&D or non-R&D inputs to create innovative products

– Collaboration/ open innovation

– More formal networks such as collaborations with other firms (e.g. customers, distributors, suppliers and potential strategic alliances) and scientific communities (e.g. universities and research centers) and different channels of open innovation

– Use collaboration and the inbound open innovation processes – More informal networks and more cooperation with market sources (e.g. customers and suppliers) and other firms in the production chain, rather than networking with universities, public research centers, government agencies and international partners – Collaboration with large firms to seek new resources and opportunities, to share and constitute new knowledge under the form of partnership, outsourcing

Innovation approach

– Large firms create links with small firms by using combined forms including licensing, collaborative development, R&D or manufacturing subcontracting, joint venture, corporate venture, investment in start-ups (through venture capital)

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Innovation process

Large firms

– Assimilating and exploiting technology embodied in newly acquired machinery – Using internal strategic resources effectively or recombining existing knowledge and resources in an unconventional way (or bricolage process)

– Informal innovation activities

Innovation outputs

Small firms

– Radical innovation, patents, high sale of innovative products

– Incremental innovation in traditional sectors, product innovation in high-technology sectors

Table 30.1. Differences between the innovation processes of small firms and large firms

In addition, prior works on innovation have suggested bricolage as an innovative pathway of small firms (Baker and Nelson 2005). Under scarcity conditions in which the cost of acquiring resources is out of resource-constrained firms’ ability, such firms may choose to mobilize resources by accumulating slack, discarded or undervalued resources. Through recombining existing knowledge and resources in novel and different ways, small firms can create innovative outcomes with very limited resources “at hand” (Baker and Nelson 2005). In summary, the differences between the innovation processes of small firms and large firms are reported in Table 30.1. 30.4. Government support for small business innovation Due to size-inherent disadvantages, small firms actually need more policy protection and support than large firms. To support innovation in SMEs, for example, governments of OECD countries are proposed various programs to foster innovation systems that encourage small business innovation. First, government innovation policies should be appropriate to the way SMEs innovate, for instance, R&D grants that directly focus on small firms or on activities that small firms tend to easily take part in, such as collaborative innovation. The Small Business Innovation Research program that was introduced in the United States in 1982, is a typical example of government support for small business innovation. They provided 2 billion dollars every year to subsidize US companies. The main objective of this program is to enhance R&D efforts of such firms and help them to pursue the commercialization of innovations. Similarly, R&D tax credit is the most typical

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form of innovation policy that was followed by many OECD countries such as Australia, Canada, Korea, Japan, Norway, the United Kingdom and Poland (OECD 2019). These countries have introduced special provisions to promote SMEs using R&D tax credit (i.e. increasing investment tax rates) and supporting the commercialization of innovation. Second, many recent OECD government programs are targeted to improve the managerial skills of small firms and strengthen their operational efficiency. These programs provided management training, consulting and other support related to Information and Communication Technology to boost innovation in both low-tech and high-tech small firms. Governments in OCED countries also set up policies to improve workforce skills in SMEs, such as creating business training groups: Group Training Organizations of Australia, Inter-company Vocational Training in Germany and Training Consortia of Korea (OECD 2019). The main purpose of these policies is to enhance SMEs’ ability to absorb new knowledge sources through collaborations with external partners in the innovation system. Finally, the most important policies to support small business innovation are the development of national innovation systems that are effective in the commercialization of knowledge and innovation for a large scale of SMEs. Such policies mainly foster the collaboration and flows of knowledge/information among the key players in the innovation system, such as enterprises, research institutes, universities and government agencies. 30.5. Conclusion Much evidence has shown that small firms increasingly contribute to the innovation system and greatly support innovation-led growth. Overcoming their “smallness”, small firms develop the innovation process in a very different way. For instance, by relying on a minimum of in-house R&D, small firms introduce innovative products with low R&D inputs, or even without formal R&D inputs. Furthermore, some small enterprises are highly innovative thanks to collaborating to search and benefit from external resources and effective integration with their internal strategic resources. Bricolage is also known as a possible strategy to innovate with limited resources in small enterprises. The explosion of technology and digitalization facilitates the diffusion of knowledge flows, and offers small firms several new innovating opportunities and business models. At the same time, due to the globalization, cross-border collaboration with other main stakeholders in the innovation ecosystem is more and more popular in small firms. The government can foster innovation in small firms by providing a sound business environment, supporting the development of strategic resources at the

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firms’ level, such as managerial skills, finance and labor force. The policies given should create favorable conditions for small firms to be effective in using and developing their internal strategic resources at hand. Last but not least, an innovation ecosystem that facilitates knowledge commercialization for different types of small enterprises needs to be built and tailored for the varying needs of such firms. 30.6. References Acs, Z. and Audretsch, D. (2010). Handbook of Entrepreneurship Research: An Interdisciplinary Survey and Introduction. Springer Science & Business Media, Berlin. Baker, T. and Nelson, R.E. (2005). Creating something from nothing: Resource construction through entrepreneurial bricolage. Administrative Science Quarterly, 50(3), 329–366. Chudnovsky, D., López, A., Pupato, G. (2006). Innovation and productivity in developing countries: A study of Argentine manufacturing firms’ behavior (1992–2001). Research Policy, 35(2), 266–288. Farace, S. and Mazzotta, F. (2015). The effect of human capital and networks on knowledge and innovation in SMEs. Journal of Innovation Economics & Management, (1), 39–71. Hall, B.H., Lotti, F., Mairesse, J. (2009). Innovation and productivity in SMEs: Empirical evidence for Italy. Small Business Economics, 33(1), 13–33. Laperche, B. (2017). Knowledge capital and small businesses. In Enterprise Knowledge Capital, Laperche, B. (ed.). John Wiley & Sons, New York. Laperche, B. and Liu, Z. (2013). SMEs and knowledge-capital formation in innovation networks: A review of literature. Journal of Innovation and Entrepreneurship, 2(1), 21. Liu, Z. and Laperche, B. (2015). The knowledge capital of SMEs: The French paradox. Journal of Innovation Economics & Management, 17(2), 27–48. OECD (2019). Promoting innovation in established SMEs. Strengthening SMEs and Entrepreneurship for Productivity and Inclusive Growth: OECD 2018 Ministerial Conference on SMEs. OECD Publishing, Paris. Pierre, A. and Fernandez, A. (2018). Going deeper into SMEs’ innovation capacity: An empirical exploration of innovation capacity factors. Journal of Innovation Economics & Management, 25(1), 139–181. Schumpeter, J.A. (1934). The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle. Harvard University Press, Cambridge. Uzunidis, D. (2008). The logic of the innovative milieu. In The Genesis of Innovation, Systemic Linkages between Knowledge and the Market, Laperche, B., Uzunidis, D., von Tunzelmann, G.N. (eds). Edward Elgar Publishing, Cheltenham.

31 Spin-off – Research Spin-off: How the University Fosters Innovative Entrepreneurship

31.1. Introduction Research spin-offs best exemplify innovative spin-off companies as they are one of the main tools for the exploitation of university research results. In recent years, interest in research spin-offs has grown increasingly around all of Europe: they are a particular type of company as they come from the scientific research world (Salvador and Rolfo 2011). Since the early 2000s, interest in research spin-offs has continued on an upward trajectory, leading to increased recognition in academic literature. (Chiesa and Piccaluga 2000). Over the last 15 years, on average, the number of research spin-offs has grown all over Europe and, consequently, universities have been forced to issue specific sets of rules for regulating the participation of researchers and professors as research spin-off founders or members (Salvador 2009). Research spin-offs deal on the market with a business purpose, but, at the same time, they represent the commercialization of scientific knowledge developed in universities/research centers. Several debates surrounding the effective success, convincing strategies and real performance of this peculiar kind of start-up exist and raise tricky questions (Salvador 2011b). Investigations about research spin-offs are difficult because it is not easy to collect reliable data: a formal and agreed definition of a research spin-off firm at the national or international level does not exist. Similarly, incubators and science parks do not distinguish between the start-ups and research spin-offs they host (Mariotti and Salvador 2015). Research spin-offs can be formally defined as: Chapter written by Elisa SALVADOR. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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[…] firms established by current or former university/research centre members (e.g., professors, researchers, technical and administrative staff and Ph.D. candidates) for the purpose of exploiting research results, regardless of whether the firm holds a university share or patent (Benghozi and Salvador 2014, p. 51). Generally speaking, on the one hand, research spin-offs are considered to be one of the main strategic tools for the external transmission and valorization of scientific knowledge activity realized in universities: they may contribute to creating highly skilled jobs as an alternative or in addition to university positions. On the other hand, they are characterized by managerial weaknesses because their founders are researchers and not businesspeople. Nonetheless, structures like incubators and science parks could help in filling these gaps, as well as the accreditation from the university/research center of origin, usually known as the parent institute, that plays the role of “brand name” (Salvador 2011a). Research spin-offs are usually described in the literature as small, characterized by modest growth and risk aversion, by a strong tacit and codified knowledge background, as well as by a strong desire for autonomy and independence. The scientific literature has focused on several aspects related to the creation, growth and development of research spin-offs, and several typologies, theories, perspectives and taxonomies have been proposed for research spin-off analysis. Recently, the wide scientific production on the research spin-off phenomenon resulted in systematic literature reviews for identifying research categories or assessing research spin-off development, growth and performance, as well as suggesting a future research agenda (Mathisen and Rasmussen 2019). This chapter is structured as follows: a section dedicated to an overview of the development of research spin-offs will be followed by a summary of the main perspectives and taxonomies of this phenomenon. Then, some considerations about the fragility and future avenues for improvement of these companies will be presented. Some concluding remarks will follow. 31.2. An overview of the development of research spin-offs The establishment of research spin-offs is not new; however, it has become a phenomenon in recent years, not only because they are increasing in number but also because public policies have been introduced to encourage the commercial exploitation of public sector research results through the creation of new firms (Wright et al. 2007). The starting point for this public policy can be retraced to the introduction of the Bayh–Dole Act in the US and the technology policy actions developed at the beginning of the 1980s in many European countries, following the

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single European Treaty. In this context, incubator and science park structures are important tools of policy to support start-ups evaluated as the most promising, which includes research spin-offs. In recent years, the research spin-off phenomenon has intensified considerably, both because of a lack of employment within universities and because of the new entrepreneurial mission of universities and their great autonomy in facilitating the creation of research spin-offs (Chiesa and Piccaluga 2000). Technology transfer and its commercialization are nowadays current activities of universities aside from their traditional mission of teaching and research. Rather than “ivory towers” devoted to the pursuit of knowledge for its own sake, universities are now regarded as instruments for knowledge-based economic development and change. The transfer of technology from universities to the commercial sector has historically been dominated by licenses; however, this is not the only solution available for universities and academics wanting to benefit from the commercialization of university discoveries. Regional policies regard research spin-offs as an important mechanism of development of university–industry relationships and creation of jobs and wealth. Universities also regard research spin-offs as a pivotal tool in valuing research results (Salvador and Benghozi 2015). Notwithstanding plenty of scientific production on research spin-offs, an agreed and precise definition does not exist. The heterogeneity in research spin-off definitions highlights the inherent complexity of the phenomenon; having been investigated from different points of view, there are similar or different conclusions, but these have not resulted in same definition. Research spin-offs are broadly defined in the literature as new firms created to develop knowledge, technology and university research results for commercial use. Research spin-offs can be defined as “innovative firms” if they devote time to research with continuity: research spin-offs are innovative firms that aim to commercialize research results, starting from R&D, then reaching the market and the consumers. They are a typical example of knowledge-based entrepreneurship, with the particularities of scientific knowledge and its mode of transfer. The role of knowledge transfer is clearly central to the innovation process. A known distinction in the knowledge transfer process is drawn between tacit and explicit knowledge. The tacit dimension of knowledge was introduced by Polanyi (1956) as a form of human knowledge, distinct and complementary to the knowledge expressed in conscious cognitive processes. In summary, concrete difficulties prevent the determination of applicable criteria for identifying a firm as a research spin-off. At the core of this problem is the concept of “scientific knowledge”, even though no clear consensus exists in the literature regarding its definition. Research spin-offs are a particular kind of firm,

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small in size with management and finance gaps, but with a very strong scientific knowledge background. 31.3. Main perspectives and taxonomies of research spin-offs The spread of the research spin-off phenomenon since the end of the 1990s and the emerging interest in them, now regarded as “a fashion of the moment” and thus “a good thing”, consequently produced a large quantity of scientific production in academic journals. Recently, many have been theoretical and empirical studies with the aim of investigating different aspects of research spin-offs and many are the disciplines from which these studies originated. Just as a single definition for a research spin-off does not exist, similarly the perspectives from which research spin-offs have been investigated until now are numerous, as are the disciplines from which the studies originate. An overview of the main perspectives and taxonomies suggested in the scientific literature is presented in Table 31.1. Perspectives Resource-based

The resources (technological, human, social and financial), and in particular the scientific and social ones, of the firm may be a differentiator and a predictor of competitive advantage

Business model

Three groups: the “first” one analyses the business model focusing on the activities undertaken by the research spin-off (service or product); a “second” group of studies focuses on the growth orientation of companies by analyzing not only how much these firms grow, but also if and when founders decide to implement a growth strategy. Finally, a “third” group of studies examines how technologies or knowledge can be transformed into commercial value

Institutional

The link with the university, usually referred to as “the parent institute”, and the institutional environment are pivotal. Factors like environment support, local group norms and university culture and institutional framework influence research spin-off’ behavior

Network activities, internal communication and adhocracy

Factors that enable a research spin-off to grow faster and thus to become more successful: network activities and internal communication. The relationship between network activities and company success is also influenced by an organizational culture characterized by flexibility, openness, creativity and dynamism, called “adhocracy”

Lifecycle

A process or set of events that occur through a necessary sequence of defined steps, stages or phases

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Teleological

The purpose or final goal guides the development process. The process develops from constructive action: a repetitive sequence of goal formulation, implementation, evaluation and modification

Dialectic

Embeddedness in a context where environment support, local group norms and university culture affect company behavior. Development processes refer to the balance of power between opposing entities

Evolutionary

The external environment affects the company process by influencing the opportunity, the individuals involved and the university context. Change processes go through a continuous cycle of competitive selection Taxonomies

Venture capitalbacked

Attractive for venture capitalists; scientific credibility, visibility, growth process, international market. Number of these research spinoffs: very limited

Prospector

Attractive for capital from public or private equity funds

Lifestyle

Low-growth oriented at start-up; sometimes high-growth oriented after the start-up phase. Less demanding in terms of human, financial and technological resources

Low selective model Aim: maximize the number of research spin-offs; not very competitive, focused on local and national markets, with a low level of capitalization, and with a weak managerial structure Supportive model

Focus on research spin-offs willing to grow and with average resource intensity. Technology licensing and business plan have a key role. Compared with the previous model, the number of research spin-offs is very limited

Incubator model

Clear plan of development based on a license and a deep knowledge of a specific technology. Venture capitalists are interested in this type of company from the beginning

Finance needs

Research spin-offs need either a minimum or a large amount of finance

Characteristics of research spin-off founders

Desire for autonomy and independence

Table 31.1. Summary of the main perspectives and taxonomies of research spin-offs suggested in recent literature (source: Salvador and Benghozi 2015, pp. 25–26)

31.4. Fragility and future avenues for improvement Over the last few decades, research spin-offs have attracted significant attention. Nevertheless, empirical evidence has shown that just a small percentage of them exhibit solid growth, whereas most of them tend to be stagnant (Salvador et al. 2019).

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The founders of research spin-offs are researchers with strong expertise in the field of scientific research; they are not businesspeople, they do not necessarily have managerial competences: this is one of the main weaknesses highlighted in the literature (Wright et al. 2007). This can also deepen their lack of credibility and the consequences of the liability of newness. Nonetheless, structures like science parks and incubators could help in filling these gaps, as well as the accreditation from the university of origin. The role and importance of structures like incubators and science and technology parks playing the role of “brand name” (Salvador 2011a) can be considered a key possible solution to fill the managerial knowledge gap. The strong (tacit and codified) knowledge background of a research spin-off needs to be linked to a structure that works as a solution for the gaps in these companies. The debate over the effective role of science parks and incubators for particular companies like research spin-offs is still open. Generally speaking, science parks and incubators act as business support and technology transfer initiatives, with working links to a university or other higher education institution. They aim to foster and support the start-up, incubation and development of innovative and knowledge-based businesses with high growth potential. Being young, usually small, ventures with a lack of managerial and business competences (Wright et al. 2007), research spin-offs may have advantages in building partnerships. Industrial partnerships could in fact enable research spin-offs to compensate for weaknesses in key aspects linked to company development and growth. Nonetheless, one also has to take into account that research spin-offs are characterized by a strong individualistic attitude or organizational egocentricity, and they are influenced by universities’ non-commercial cultures that may result in tension and possible misunderstandings. Furthermore, partnerships imply the guarantee trust between partners: research spin-offs hold tacit knowledge, difficult to codify and transfer, that could initiate opportunistic behavior from the receiving firm. In short, considering the common peculiarities of research spin-offs, the embeddedness in a business ecosystem, being physical or digital, can be considered as a useful complement or alternative to traditional industrial partnerships (Benghozi and Salvador 2014). Specific industrial partnerships may be conceived not in the traditional sense, but rather in the form of a set of industrial relations between research spin-offs themselves. This constitutes a business ecosystem, be it physical or digital, an emerging concept analogized from biology, meaning a dynamic structure which consists of an interconnected population of organizations.

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31.5. Conclusion Research spin-offs are important mechanisms for valuing scientific research and technology transfer that have become increasingly more important in Europe since the 1990s (Chiesa and Piccaluga 2000). The US has long had experience in the field of research spin-offs, while in Europe, the phenomenon has only emerged in recent years, attracting more and more attention. There is an increasing focus on the entrepreneurial orientation of universities and their ability to exploit and transfer scientific knowledge to the commercial sector. Governments are becoming increasingly aware of the importance of research spin-offs. The Bayh–Dole Act of 1980 is regarded as an important cornerstone to stimulate the establishment of research spin-offs. Research spin-offs can be a typical example of an “innovative firm” if they continue with research work and if they improve their scientific results and exemplify the relationship between “tacit and codified knowledge”; because research spin-offs develop the “tacit knowledge” residing in the minds of scientists into something that is ready for the market. Research spin-offs are an example of entrepreneurship, but they come from the university sector and are therefore constrained by university rules and procedures. Research spin-offs should be seen as “creative” firms holding specific knowledge, not included in the traditional outputs of intellectual property rights (Salvador et al. 2019). 31.6. References Benghozi, P.-J. and Salvador, E. (2014). Are traditional industrial partnerships so strategic for research spinoff development? Some evidence from the Italian case. Entrepreneurship & Regional Development: An International Journal, 26(1–2), 47–79. Chiesa, V. and Piccaluga, A. (2000). Exploitation and diffusion of public research: The case of academic spin-off companies in Italy. R&D Management, 30(4), 329–340. Mariotti, I. and Salvador, E. (2015). On-park and off-park research spin-offs: Some insights from an empirical investigation on Italy. International Journal of Entrepreneurship and Innovation Management, 19(5/6), 405–422. Mathisen, M.T. and Rasmussen, E. (2019). The development, growth, and performance of university spin-offs: A critical review. The Journal of Technology Transfer, 44(6), 1891–1938. Polanyi, M. (1956). Personal Knowledge: Towards a Post-Critical Philosophy. University of Chicago Press, Chicago. Salvador, E. (2009). Evolution of Italian universities’ rules for spin-offs: The usefulness of formal regulations. Industry and Higher Education, 23(6), 445–462.

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Salvador, E. (2011a). Are science parks and incubators good “brand names” for spin-offs? The case study of Turin. Journal of Technology Transfer, 36(2), 203–232. Salvador, E. (2011b). How effective are research spin-off firms in Italy? Revue d’économie industrielle, 133, 99–122. Salvador, E. and Benghozi, P.-J. (2015). Research spin-off firms: Does the university involvement really matter? Revue Management International, 19(2), 22–39. Salvador, E. and Rolfo, S. (2011). Are incubators and science parks effective for research spin-offs? Evidence from Italy. Science and Public Policy, 38(3), 170–184. Salvador, E., Marullo, C., Piccaluga, A. (2019). Determinants of growth in research spin-offs: A resource-based perspective. Recherches en sciences de gestion, 133, 53–78. Wright, M., Clarysse, B., Mustar, P., Lockett. A. (2007). Academic Entrepreneurship in Europe. Edward Elgar, Cheltenham.

32 Start-up – Start-ups, Venture Capital (SVC) and the Financial Cycle of the SVC System

32.1. Introduction Start-ups financed by venture capital (VC), indicated by the acronym SVC, represent an organizational system to make technological innovations, and may be considered an evolution of the industrially financed R&D project system. Although the first financing organization with the characteristics of modern VC was the American Research and Development created in 1946 in the Boston area (Branscombe et al. 2000), this type of innovation structure has seen its great development only since the 1970s, especially in the Silicon Valley. The SVC system is composed of small companies financed by VC with the objective of reaching an exit consisting of selling the developed technology and associated business to large companies or a collection of industrial capitals in order to transform them into industrial firms. In this trade, VC has a return of investment (ROI) that may be used to refinance new start-ups. An alternative to VC are the so-called business angels, individuals acting with same strategies of VC, often investing with seed capitals representing the initial investments needed for the development of start-ups. Another alternative is crowdfunding which is spreading in particular in Europe and it is of particular interest in countries in which VC is characterized by limited capital availability. In territories in which the presence of start-ups is important, the VC tends to be differentiated by the innovation field of the start-ups, technological, socio-economic, etc., and by the phases of development. In this case, there is a trading activity among VC companies that are involved in the various phases of financing the start-up development, adapting in a certain way the increase of Chapter written by Angelo BONOMI. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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capitalization with the decrease in risk and the increase in potential ROI. In this way, a market of start-ups with list of capitalization values has been formed that in the Silicon Valley are reported in local data banks and of course are associated with a high volatility (Bonomi 2019). In this chapter, we describe the characteristics of both the start-ups and the venture capital followed by a description of the financial cycle of the SVC system and the results of adopted financial strategies. 32.2. Start-ups A start-up is a company of small size that begins its activity having, however, different characteristics from other current small companies that are also beginning their activity, in particular for their interest in new technologies and business model developments. Although a start-up is formally a company, its activity is more similar to a project, consisting of a single non-repetitive enterprise undertaken to reach an objective within a limited time and capital availability. The main difference between the activity of start-ups with technological purposes and the activity of R&D projects consists in the fact that in start-ups, R&D activity is accompanied by business model developments suitable for the developed technology. This fact represents an important difference compared with the system based on R&D as projects. In the latter system, a potential innovation might be rejected and technology development even abandoned depending on the strategies of the firms financing the projects. From a technological point of view, the activity of the start-up with technological objectives follows the same development phases as R&D projects, starting with the generation of the innovative idea, a feasibility phase followed by a development phase and reaching the industrialization phase making the start-up ready for an exit. Start-ups, as R&D projects, generate useful knowledge, not only technical but also in terms of business models, which plays an important role in generating new start-up projects. The development of new business models represents an important aspect of technological and economical innovation in what is called the regime of open innovation (Chesbrough 2003) and consists of the identification of a market segment articulating the created value and structure of the value chain. In business models, the value thus derives from the structure of the situation, rather than from some inherent characteristic of the technology itself, and technical uncertainty is a function of market focus and will vary with the dynamics of change in the marketplace (Branscombe et al. 2000). During the final phases of development, a start-up may have commercial activities that, however, have more the function of showing the validity of the technology than supplying financing to the start-up in form of commercial revenues. There are various types of promoting structures for start-ups following the various stages of development. We have at the beginning co-working structures offering work space in open spaces, meeting rooms. A variant of co-working space is the open lab – this is a space with various equipment such as welding machines, tools and 3D printers useful for making

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prototypes. For start-ups in an advanced phase, there are incubators or accelerators that offer office spaces or even spaces for small productions or laboratory work. For more advanced start-ups, there are scientific or technological parks with the availability of small industrial buildings. 32.3. Venture capital Venture capital refers to an entity financing the development of start-ups with strategies completely different from industrial capital financing innovation through R&D projects. In fact, in a different way to industrial capital, it develops technologies to sell them rather than use them (Bonomi 2019). The strategy of VC financing is conditioned by achievement of a sufficiently high ROI in order to cover not only the cost of development of successful start-ups, but also the capital invested in abandoned start-ups and have possibly margins for an increase of available capital for further investments. Such a constraint puts limits on the conditions in which the VC considers the investment. In particular, VC never invests in scientific research and almost never in proving scientific principles. It rarely invests to develop an enabling technology but often invests in developing the use of a new technology or developing a new product. Very often, it invests in revising and improving a product, broadening a product line and applying a product to another application. When investing, VC considers various elements of risk such as: size of the market and suitability of technology or product to the market needs. In general, its portfolio must be closely scrutinized and cannot take into account too high a number of start-ups, and it is unwilling to invest in very young firms that only require small capital infusions. However, we can observe an undesired increase in the number of start-ups that are being examined. (Branscombe et al. 2000). In fact, it is important to build a system of innovation designed primarily to maximize the probability of success for each project, limiting risk by the selection of people in whom VC have confidence, both in the technical and the business dimensions of the enterprise. In fact, looking at the strategies of VC in territories with large SVC activities and a large number of financed start-ups, accompanied by a high percentage of abandonment, and a very high ROI in the case of success, it seems that, even though each VC company does not follow a portfolio strategy, the total set of VC companies acts in fact as following a portfolio strategy with enough start-ups to statistically obtain a certain number of very successful investments. In fact, there are also particular strategies exploiting in part portfolio statistical advantages by making small investments in a very high number of start-ups followed by the choice of few of them showing more favorable conditions for further larger investments. It shall be noted that in territories with a highly developed SVC system, as in the Silicon Valley, many big firms born as start-ups, and having large amount of capital available, also tend to finance start-ups, entering into competition with VC; on the

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other side, for their development, such big companies tend not to acquire but to invest in companies that are of technological or market interest for them. 32.4. The SVC system cycle The activity of the SVC system may be seen in the form of a cycle, as presented in Figure 32.1, starting with a presentation of start-up projects to VC which then selects financeable start-ups. Following the development, financed start-ups are abandoned or reach an exit. The technology and business of start-ups is sold by VC and the obtained return is in part retained by VC to cover its costs and rewards and the rest reinvested in new start-up developments. This cycle presents a parameter defined by the value of the rate between the total obtained ROI and the total capital invested in start-ups. At equilibrium, this rate should be high enough to cover the initial capital invested in either successful or abandoned start-ups, increased by the operational costs and rewards of VC. If this rate is continuously higher than the value of equilibrium, it will assist to an autocatalytic increase of reinvestment capabilities and, should the number of start-ups projects always be fully available, to a continuous growth of new technologies and rewards. On the contrary, if this rate is lower than that of equilibrium, it will assist to a decrease of available reinvesting capital, a decrease of the generation of new technologies and cycles characterized by a loss. Two types of strategies of VC can be observed concerning the selection criteria: the first one, typically American, that selects start-ups on the basis of their potential ROI and the validity of the start-up team, the second one, typically European, selects start-ups on the basis of the probability of success while often the failure of a team in previous start-ups is considered negatively. This attitude is in fact irrational as the majority of start-ups are abandoned and failures, in fact, bringing an accumulation of useful experience for the management of future start-ups rather than being an indication of the poor management quality of the team. In fact, in Europe, the American-type of strategy is however now increasingly more widespread. The American type strategy is characterized by a rate of failure around 90% of financed start-ups, while for the European-type strategy, the rate is around 70%. In spite of this difference, the American-type strategy is more economically efficient because of the high attainable ROI from successful start-ups. In fact, simulating the SVC cycle mathematically, and running these two types of strategies, it is easy to demonstrate that for financial equilibrium, the American strategy needs a much higher ROI. However, considering the entirety of VC activity, there is with the American-type strategy, a much higher number of financed start-ups exploiting in this way a favorable statistical factor in obtaining highly successful exits. In every case, the success of the American-type strategy may also be attributed to a know-how that American VC has accumulated during the long time of operation in the SVC system and experience in selection and support to the financed start-ups (Bonomi 2019).

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Figure 32.1. Schematic view of the start-up – venture capital cycle

32.5. Conclusion In fact, the major advantage of the SVC system in technology innovation is in the business model development suitable for the technology is not limited in the development of new technologies by the strategies of the firm financing the R&D activity. In fact, it is observed that often companies are biased in making investments in technologies that do not fit with their established business models, while start-ups have the advantage of freedom in establishing the best business model suitable for the developed technology, and it is a common opinion that established firms exhibit a systematic biased underinvestment or overinvestment in commercialization of novel emerging technologies, while start-ups exhibit less of this bias (Branscombe et al. 2000). Furthermore, the frequently observed superior efficiency of start-ups in the R&D apparently reflects the superior quality of their technical personnel, greater cost consciousness, and better understanding and communication of the problem to be solved. The limits of the SVC system are in the necessity to get ROI high enough

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to cover the investments made, and the necessity of financing new technologies with relatively high degree of radicalism, and thus high risk of failure, in order to assure a high ROI. The question about the possibility of the use of public financing for start-ups in compensation for an insufficient territorial availability of VC might be considered negatively in general because of the lack of experience by public authorities in sustaining a successful SVC cycle. However, public aid may be useful in the preliminary phases of development of start-ups looking for VC financing. The question of public governmental financing of start-ups has been, however, the object of a series of recommendations such as building relationships and understanding of the VC industry, considering the narrow technological focus and uneven levels of VC investments, appreciating the need of flexibility in the VC investment process, carefully analyzing the track record of entrepreneurs, and examining the track record of the firms receiving public venture awards (Branscombe et al. 2000). In fact, the problem remains in how to obtain enough ROI to sustain a publicly financed SVC cycle and the question may be also how much public funds might be continuously fed to a public SVC cycle to maintain beneficial general effects for start-up developments. 32.6. References Bonomi, A. (2019). The start-up venture capital innovation system: Comparison with industrially financed R&D projects system. IRCrES Working Paper, 2. Branscombe, L., Morse, K., Roberts, M., Boville, D. (2000), Managing Technical Risk: Understanding Private Sector Decision Making on Early Stage Technology-based Projects. NIST GCR 00-787, US Department of Commerce. Chesbrough, H.W. (2003). Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School Press.

33 Territory – Territorial Dynamics and Innovative Services

33.1. Introduction Innovation increasingly conditions the economic growth prospects of territories in an economic context, and the service sector represents nearly 70% of the GDP of developed countries. The issue of innovation in the service sector is a major challenge for the economic development of territories. However, there is little empirical evidence on the issue of innovation in services and there has been, to date, a limited number of studies characterizing innovation processes in this sector and their implications for public policies in territories. One of the possible explanations for the lack of studies on innovation in this sector is that “services do not innovate, or are content to adopt technological innovations produced in industry” (Gallouj and Gallouj 1996, p. 2). This representation of the economic structure centered on industry and material production relegates services and service functions to the periphery (Gallouj and Gallouj 1996). The strong contribution of higher services to the innovation of industrial firms (Muller and Zenker 2001; Werner and Strambach 2018) nevertheless reinforces the questions concerning the role of these activities in the territorial dynamics of innovation. However, the various studies carried out on the theme of services and/or the geography of innovation (Chalaye and Massard 2012) underline the work that still needs to be done to achieve a better understanding of the mechanisms at work when we talk about innovation in services, and to define indicators to measure these phenomena and their impacts on territories. These various works constitute many elements that justify the realization of this contribution. The objective of this contribution is to propose an analysis to better understand the issues related to Chapter written by Michelle MONGO. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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innovation in the service sector. This will therefore be a question of going back over the definition of innovation in services, the specificities of the mechanisms at work in this framework, the territorial impacts observed and the public policies engaged, and making it possible to promote the local dynamics of innovation of these activities. 33.2. Innovation in services: what are we talking about? 33.2.1. What does it mean to innovate in services? The notion of innovation in services is quite complex since it involves different innovation characteristics than in the industrial sector. In industry, the object of innovation usually materializes in a product (good), whereas in services, the object of innovation is rather vague, as the distinction between product and process is not very clear (Djellal and Gallouj 2000). Similarly, the degree of novelty in industry is mostly materialized by a radical dimension, in comparison with the service sector, whose innovations are rather incremental in nature. In theoretical literature, the perception of innovation in the service sector has evolved according to three main lines of analysis that gradually recognize the existence of innovation dynamics within this sector (Gallouj and Savona 2009). Among these currents, we can distinguish the following approaches: technologist, service and integrative. The technologist approach strictly analyzed innovation in services through a conceptual framework and indicators specific to the industrial sector. This framework assumes that service companies are mere users of the technologies produced within the industry and that they are therefore not very innovative. The service approach, carried out by the University of Lille from the 1990s onwards, underlines the particularities of the product/process within services and demonstrates the complex nature of innovation processes within this sector. This work recognizes that the interactivity of the service activity encourages the implementation of particular forms of innovation based on customer/supplier relationships. These various innovations are widely perceived as being of a non-technological nature. They most often emerge from pure services for which the criteria of immateriality and co-production of output are the most marked. Finally, the integrative approach is considered the most promising in terms of theoretical progress. It is based on the observation that the boundaries between goods and services are shrinking and that a certain number of services tend to be “industrialized”, while the production of certain goods is becoming “tertiarized”. Goods and services evolve towards a good/service continuum in which the product (goods/services) is redefined in terms of functions or characteristics.

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From an empirical point of view, interest in the service sector in econometric studies and, in particular, those that attempt to shed light on innovation processes is still limited and largely focused on technological innovations that are mainly present in the industrial sector. Nevertheless, there are a number of empirical studies that attempt to quantitatively explain the differences in innovation processes between services and industry (Arundel et al. 2007). The latter point out that the specificities of innovation in services lie in low investment in R&D activities, interest in other forms of innovation focused on organizational change, more intense investment in staff training, close relationships with service customers and technology providers, and the existence of intra-sectoral differences linked in part to the nature of the innovations developed. In addition, technological innovation whose development is more strongly determined by a strong commitment to R&D activities seems to benefit more industrial and/or technology-intensive companies (high- and mediumhigh-tech industry and technological services). Conversely, non-technological innovation, which is particularly present in service companies characterized by a low technological level (intellectual services), appears to be strongly determined by staff training, machine acquisitions, privileged customer/supplier partnerships and a local level of cooperation. These empirical results justify the decisions made in literature on the type of service activities that will be taken into account for the empirical analysis of innovation dynamics in services and their effects on territories. These service activities are presented in the rest of this section. 33.2.2. Which service for innovation analysis? The broad scope of activities contained in the services sector has forced specialists to make a trade-off on the type of activities that will be taken into account when analyzing and measuring innovation in services. In view of the results of the literature (Miles et al. 1995; Hertog 2000), knowledge-intensive business services (KIBS1) appear to be the most appropriate for the analysis of innovation phenomena in services. KIBS include “technological services” activities, as well as “intellectual services” activities (Miles et al. 1995; Cordellier 2011). The difference between these so-called “innovative” service activities and the others lies in the degree of knowledge incorporated within these activities (Muller and Doloreux 2009). Indeed, one of the major characteristics of KIBS is that their services are generally based on strong expertise in a specific field, and the workforce is highly qualified.

1 The abbreviation KIBS, or knowledge-intensive business services, is usually used in literature. See Miles et al. (1995).

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Service acvies for innovaon analysis

Technological level

High

Low

Recepon and support services Hotel and restaurant services (P10), Real estate (M01 et M02), Mail services without the post office (N11), and operaonal services

Technological services R&D (N40) IT services (N21) Telecommunicaons (N12) Audiovisual acvies (P21)

Intellectual services Professional services (N22) Market research adversing (N24) Engineering architecture controls (N25)

Level of knowledge

High

Source: Author's table based on Cordellier (2011)

Table 33.1. Source: author’s table based on Cordellier (2011)

33.3. Geography of innovation in knowledge-intensive business services and territorial impact 33.3.1. Stylized facts about the geography of knowledge-intensive business services The stakes involved in locating KIBS are high in view of the spillover effects, in terms of the territorial innovation dynamics they generate (Muller and Zenker 2001). Stylized facts show that most innovation activities are concentrated in a few countries, regions, or even urban areas within these regions (Chalaye and Massard 2012). For KIBS, the concentration effects in Europe are stronger compared to industrial activities. The highest concentrations of KIBS are found in large metropolitan areas. In the latter, interregional disparities are perceptible and are characterized by differences in sectoral specialization in KIBS branches. The concentration of innovative activities is usually explained by the geographical dimension of knowledge externalities, which occurs on a local basis when knowledge is tacit in nature. The latter circulate through different channels arising from inter-firm interactions, private–public research, the concentration of a skilled workforce, high inter-firm mobility, the nature of local structures (diversified or specialized) and the resilient nature of knowledge production (Chalaye and Massard 2012). Some of these factors significantly contribute to reinforcing the localization effects of KIBS, given the innovation mechanisms at work in this sector. The latter also contribute to the territorial dynamics of innovation.

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33.3.2. The contribution of knowledge-intensive business services to territorial innovation dynamics The contribution of KIBS to territorial innovation dynamics lies in both the direct (related to their own innovation activities) and indirect (related to the innovation contribution of industrial firms) effects caused by their innovation activities (Werner and Strambach 2018). Hertog (2000) suggests that KIBS can be seen as: – vectors of innovation: mainly when they contribute to the transfer of existing knowledge between or within organizations, industries or networks, in order to apply it in a new context (e.g. consulting or training activities); – innovation facilitators: mainly when they help an organization in its innovation process (technology service); – sources of innovation: when they are involved in launching and developing innovation activities within client organizations (R&D, design). Werner and Strambach (2018) admit that KIBS activities contribute to increasing the adaptability of urban economies, which also tends to strengthen local innovation dynamics. There are numerous2 examples of empirical studies that confirm these effects, and despite this, innovation policy (more specifically French innovation policy) remains poorly adapted to this sector. 33.4. Public innovation policy: historical actions and future prospects For a number of years, innovation policy in Europe has been oriented towards promoting the competitiveness of territories. The actions carried out are reflected in the implementation of research and innovation framework programs, aimed at promoting the R&D of European research actors through a call for a project mechanism focused on R&D. However, societal changes (user economics, the role of digital technology, the collaborative economy) show that innovation does not only come from R&D. It is driven by uses and business models, it goes through design, and is partly incremental. In this sense, European innovation policy must adapt to these changes and take the major economic, societal and environmental challenges into account (the green economy and the impact of climate change). It is within this framework that the new European policy aims to fight against the effects of global warming (Commission 2 For a review of these studies, the reader is invited to read Muller and Zenker (2001).

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2017). In France, for example, Encaoua (2017) admits that, in view of the digital revolution in French society and its effects on uses, it is essential that the State rethinks its innovation policy through the implementation of support mechanisms for induced societal transformations. Among other things, this would involve support for increased collaboration between digital technology developers and users. As a result, beyond the expected effects in terms of job creation, this new innovation policy would help to integrate users at the heart of the innovation process of companies. 33.5. Conclusion The service sector employs two-thirds of the population of developed countries and its share in their GDP is about 70%. In spite of this, the issue of innovation in services has little empirical evidence and to date there is a limited number of works that allow us to characterize the processes of innovation in this sector, as well as the implications for public policies in the territories. This fact is linked to the specific characteristics of service activities and the modes of innovation inherent to this sector. From this point of view, theoretical work on the “service economy” and “innovation in services” shows the extent to which the diversity of activities within the service sector, the heterogeneous definitions of service activity, the difficulties in applying the distinction between product and process innovation, the forms of innovation characterized by a non-technological dimension and more, are all elements that make it difficult to apprehend and measure innovation in services. These different elements have forced specialists to make a trade-off on the type of activities that will be taken into account for the analysis and measurement of innovation in services. Knowledge-intensive services thus appear to be the most appropriate activities for the analysis of service innovation phenomena. Their innovation processes, which are more focused on the client/provider relationship and intersectoral interactions, help to identify them as vectors, facilitators and territorial sources of innovation. However, public innovation policies are still too largely focused on promoting R&D activities carried out within the industry. These activities are not very present in low-technology services. In short, in order to boost innovation in services, it is essential to rethink innovation policy, by considering the gradual transformation of our societies from property economies to service and use economies. It is within this framework that the positions of the new European policy, which follows on from the European Horizon 2020 program (H2020), are set.

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33.6. References Arundel, A., Kanerva, M., van Cruysen, A., Hollanders, H.J.G.M. (2007). Innovation statistics for the European service sector. Unu-Merit: European Innovation Scoreboard [Online]. Available at: http://www.proinno-europe.eu/admin/uploaded_documents/ Innovation_Indicators_for_the_European_Service_Sector.pdf%5Cnpapers3://publication/ uuid/7F218F1B-7F91-4867-A215-1C63FDA469F7. Chalaye, S. and Massard, N. (2012). Géographie de l’innovation en Europe. Observer la diversité des régions françaises. Datar. Djellal, F. and Gallouj, F. (2000). Le “casse-tête” de la mesure de l’innovation dans les services : enquête sur les enquêtes. Revue d’économie industrielle, 93(1), 7–28. Encaoua, D. (2017). Repenser les politiques d’innovation en France ? Revue française d’économie, XXXII(3), 90. European Commission (2017). Open Innovation Open Science Open to the World. [Online]. Available at: https://ec.europa.eu/digital-single-market/en/news/open-innovation-openscience-open-world-vision-europe. Gallouj, C. and Gallouj, F. (1996). L’innovation dans les services. Economica, Paris. Gallouj, F. and Savona, M. (2009). Innovation in services: A review of the debate and a research agenda. Journal of Evolutionary Economics, 19(2), 149–172. Hertog, P.D. (2000). Knowledge-intensive business services as co-producers of innovation. International Journal of Innovation Management, 4(4), 491–528. Miles, I., Kastrinos, N., Flanagan, K., Bilderbeek, R., Hertog, P.D. (1995). Knowledge intensive business services: Users, carriers and sources of innovation. Publication no. 15, European Innovation Monitoring Service. Muller, E. and Doloreux, D. (2009). What we should know about knowledge-intensive business services. Technology in Society, 31(1), 64–72. Muller, E. and Zenker, A. (2001). Business services as actors of knowledge transformation and diffusion: Some empirical findings of the role of KIBS in regional and national innovation systems. Research Policy, 30, 1501–1516. Werner, P. and Strambach, S. (2018). Policy mobilities, territorial knowledge dynamics and the role of KIBS: Exploring conceptual synergies of formerly discrete approaches. Geoforum, 89, 19–28.

34 Well-being – Subjective Well-being and Innovation

34.1. Introduction Historically, research on innovation has focused on understanding the reasons for the success of the innovation process with either a focus on inputs such as the number of researchers/R&D or on outputs, such as the number of innovations and patents. In addition, research on innovation has shown the impact of innovation on economic growth and productivity as something positive. Innovation should improve people’s lives. The purpose of this chapter is to present outcomes of research on the subject of the impact of innovations on subjective well-being. We also try to understand this by reciprocating the role of subjective well-being in the innovation process. In short, subjective well-being is mostly measured by Life Satisfaction based on questions like “All things considered, how satisfied are you with your life as a whole these days? 1 (dissatisfied) … 10 (very satisfied)”. Only a few researchers have examined this topic of innovation and subjective well-being. In this regard, the works of Binder and Witt (2011), Dolan and Meltcalfe (2012), Binder (2013) and Aghion et al. (2016) are pioneers. They show the value of focusing innovation analysis on subjective well-being, indicating a mitigated impact; most technological innovations create winners and losers. Therefore, we present the main elements and results of these seminal researches, and then focus our text on a recent study analyzing the impact of innovation on the entrepreneur (Liu and Munier 2019). All this research invites us to step back and question the role of innovation and its impact on individuals via the concept of innovation-care (Pavie 2018). Chapter written by Francis MUNIER. Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

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34.2. Creative destruction impacts subjective well-being The economics of happiness can be seen as a recent approach in economics in order to observe, understand and analyze the economic determinants of the subjective well-being of individuals as reported in surveys, in order to interpret some implications for policy design. The economy of happiness can shed new and relevant light on the concept of creative destruction and how this Schumpeterian concept affects subjective well-being. The main findings of Alghion et al. (2016) show that the effect of creative destruction on subjective well-being is positive. The research question is: how can creative destruction impact the subjective well-being of individuals and in what form? Individuals are reluctant to change and as the level of creative destruction increases, individuals’ perceptions of risk also increase. Creative destruction is related to turnover. When turnover is high, it has direct and indirect, positive and negative, effects on subjective well-being. Due to the creation of new companies, the probability of an unemployed person being employed is higher because companies are able to create new jobs, which leads to an increase in subjective well-being. This hiring subsequently allows the creation of new consumer goods and thus the supply is regularly renewed and allows a wider choice of consumer goods for satisfied consumers. This renewal also has an impact on the improvement of productivity, which in turn leads to higher wages and job creation. An externality of growth will be more important and thus lead to an increase in subjective well-being. In order to correlate subjective well-being with creative destruction, Aghion et al. (2016) conducted a study to analyze the importance of the role of creation and destruction. The effect of creative destruction on subjective well-being is positive according to the control it has on the level of unemployment. The effect of job creation and job destruction on subjective well-being are positive and negative, respectively. Job destruction has a less negative effect when unemployment benefits are higher. Job creation has more of a positive impact on future subjective well-being for more “forward-looking” individuals. Creative destruction increases subjective well-being more in States with more generous unemployment benefits. Dolan and Metcalfe (2012) conclude from a survey that innovation has to be considered when determining subjective well-being. The authors show a positive impact of innovation on subjective well-being and conversely, it seems that subjective well-being induces a stronger, innovative and more creative climate. Binder and Witt (2011) and Binder (2013) provide some interesting elements about the question: What are the effects of innovativeness on subjective well-being? Considering the improvement of theoretical and empirical research on happiness, Binder (2013) suggests two evaluation rules, the “life domain evaluation principle”

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and the “welfare dynamics principle” as relevant guides for such a normative assessment. 34.3. A questionable relationship In Binder and Witt (2011), two possible complications are highlighted in the analysis of the relationship between innovation and subjective well-being. A first limitation is the fact that the impact of innovation on economic growth is rather easy to show and observe but it is more difficult to identify this impact at the individual level, a fortiori on a case-by-case logic. On the basis of the concept of the “perennial gale of creative destruction” or the “restless nature of capitalism” (Schumpeter 1942), the mitigated impact of innovation on the subjective well-being is indeed admitted. There are winners as well as losers, particularly in the area of pecuniary externalities. Moreover, due to the novel nature of innovation, it is sometimes impossible to anticipate all the implications and consequences of these innovations. It is also crucial to consider the risks inherent in certain technological innovations which may induce negative externalities in terms of health or ecology. This corresponds to what is also known as the dark side of innovation. A second limitation stems from the fact that the innovation itself can lead to a change in perception or even in the basement of measurement of subjective well-being. It is necessary to go beyond the standard welfare economic view, considering that preferences are not given and are invariable, but rather evolve, in particular, according to innovations. This is more the case when innovation is radical and consumers are forced to learn how to use the new technology. In their article, Liu and Munier (2019) (after conducting a study on the relationships between innovation, job satisfaction and satisfaction with work–life balance) suggest that innovation and the job satisfaction of entrepreneurs are positively linked. Therefore, very innovative entrepreneurs experience a higher satisfaction in their jobs. They argue that if people are happier in their working environments, their happiness will overflow to other areas of their life (Liu and Munier 2019). For all entrepreneurs, the available findings show that innovation benefits life satisfaction. When they sort the samples into opportunity and necessity entrepreneurs, this relationship does not change; however, necessity entrepreneurs’ life satisfaction is more affected by innovation compared to opportunity entrepreneurs. Innovation has a direct impact on satisfaction with work–life balance for all entrepreneurs. For necessity entrepreneurs, innovation has a significant positive impact on life satisfaction; however, opportunity entrepreneurs’ satisfaction with work–life balance is not affected by innovation. Job satisfaction plays a

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mediating role in the relationship between innovation and life satisfaction for all entrepreneurs, for both opportunity and necessity entrepreneurs. Job satisfaction has a complementary mediation effect on the relationship between innovation and life satisfaction for all entrepreneurs and opportunity entrepreneurs, but only an indirect mediation effect on the relationship of necessity entrepreneurs. Innovation has less impact on the satisfaction of work–life balance and job satisfaction than it does on the rest of entrepreneurs. For opportunity entrepreneurs, however, innovation has more impact on job satisfaction than it does for necessity entrepreneurs when compared to the rest of the entrepreneurs. At this stage, it seems that the sometimes blind enthusiasm for technical progress and new technologies must be somewhat nuanced because the effects on subjective well-being are still up for debate, particularly in view of the limits highlighted by Binder and Witt (2011). This is why the concept of innovation-care makes sense in this context (Pavie 2018). 34.4. Innovation-care: theoretical approach and applications Innovation-care does not only focus on the pure essence of innovation, which, according to Schumpeter, is economic performance. Its essential aim is simply to ensure that every individual who comes into contact with an innovation is treated carefully, regardless of the sector, market, product or service. According to Pavie (2018), there must be concern for the other. A commitment that is not made as a response, but as a response to the consequences of innovation on the individual. He or she is at the center of attention, particularly with regard to the consequences that innovation brings. Thus, the concept of innovation-care is projected into the future, with a forecasting of consequences. We must ask ourselves in advance how innovation will evolve in the future and how the individual will be impacted. To this extent, it can be noted that three different notions pave the way for this approach and intertwine in its realization: ethics, innovation-responsibility and care. Returning to the concept of happiness, the aim of innovation-care is to make happiness possible for as many people as possible (Pavie 2018). Caring is understood as the ultimate strategic goal of innovation. In order to achieve this, the innovator, who generally has an interest in benefiting economically from the innovation, must at the same time also see himself/herself as a citizen, an individual who uses innovation. In this way, the subjective well-being of the innovator and that of the citizen can be combined.

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Once people learn how innovation-care can be defined, they often have a number of questions about the process of applying a form of innovation-care. First of all, the relationship between the innovator and the citizen must be questioned in greater depth. Even if the innovator should in theory also see themselves as a citizen who is affected by their innovation, in reality there is still a gap between the two. The innovator, as the creator of the innovation, naturally has a more powerful position than the citizen. They have studied the needs and wishes of the citizen and thus also has the opportunity to influence themselves. Even if it can be assumed that the innovator does not intentionally want to harm the citizen, the question arises as to how they would react if they needed to decide between an innovation that could not be regarded as innovation-care, but which would generate profit, or an innovation that would not be economically worthwhile but would be an asset for the individual. Awareness of this interrelationship can probably make the innovator’s thinking and action clearer. The goal of care is to accept this interdependence while taking care of ourselves and of others, which is surely not an insignificant difficulty for most companies. This shows that an innovation will not always naturally create happiness for citizens. The innovator must think beyond the satisfaction of short-term needs for the citizen. For example, a new high-end mobile phone will certainly make many citizens happy. However, the mass production of these devices requires an environmental impact, as limited resources are needed for production. Therefore, the innovator, if formulated a little exaggeratedly, will contribute to a deterioration in the standard of living of citizens in the long term. Furthermore, to implement a true innovation-care, it is not enough to focus solely on the citizens directly affected by the innovation. It is also necessary to take into account all those who could be indirectly affected by the consequences of the innovation. There is a need to define responsibility for innovation in a different way, taking into account all the players, whatever their role and function. In this way, Pavie (2018) emphasizes that the innovator should be among those who provide citizens with different forms of care. However, care must always be compatible with the economic goals of enterprises to ensure a certain quality and should deal with concrete events in real life. 34.5. Conclusion In conclusion, it seems to us that this new avenue of research is promising in many ways. Rather than taking an overly positive, optimistic and sometimes blind view of the benefits of innovation, it is important to consider the impact on individuals and society and not simply the economic or financial impact on the innovative company. The dark side of innovation is becoming an important theme in research, to which I would add the theme of happiness. For, after all, as we have

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shown, what must come first is that innovation must first improve the quality of life of individuals, particularly through a care-based approach. 34.6. References Aghion, P., Akcikit, U., Deaton, A., Roulet, A. (2016). Creative destruction and subjective well-being. American Economic Review, 106(12), 3869–3897. Binder, M. (2013). Innovativeness and subjective well-being. Social Indicators Research, 111(2), 561–578. Binder, M. and Witt, U. (2011). As innovations drive economic growth, do they also raise well-being? The Papers on Economics and Evolution, 1105, Max Planck Institute of Economics. Dolan, P. and Metcalfe, R. (2012). The relationship between innovation and subjective wellbeing. Research Policy, 41(8), 1489–1498. Liu, J. and Munier, F. (2019). Innovation and entrepreneurs’ subjective well-being: The mediation effect of job satisfaction and satisfaction with work-life balance. Working Paper BETA, 2019-42, Bureau d’économie théorique et appliquée, UDS, Strasbourg. Pavie, X. (2018). L’innovation à l’épreuve de la philosophie. PUF, Paris. Schumpeter, J.A. (1942). Capitalism, Socialism and Democracy. Routledge, London.

List of Authors

Laurent ADATTO CNAM Paris France

Patricia BAUDIER Métis Laboratory Normandy Business School France

Smaïl AÏT-EL-HADJ ITECH University of Lyon France

Sonia BEN SLIMANE ERIM (ESCP Research Institute of Management) ESCP Business School Paris France

Mehtap ALDOGAN EKLUND Accountancy Department University of Wisconsin-La Crosse USA Arvind ASHTA CEREN, EA-7477 Burgundy School of Business University Bourgogne Franche-Comté France Pierre BARBAROUX Centre de Recherche de l’École de l’Air École de l’air Salon-de-Provence France

Angelo BONOMI IRCrES – Istituto di Ricerca sulla Crescita Economica Sostenibile Consiglio Nazionale delle Ricerche Moncalieri Italy Sophie BOUTILLIER ISI Laboratory RII University of the Littoral Opal Coast Dunkirk France Paul BOUVIER-PATRON CERGAM Aix-Marseille University France

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Mauricio CAMARGO ERPI Laboratory University of Lorraine France Laurent DUPONT ERPI Laboratory University of Lorraine France Joëlle FOREST Chaire Saint Gobain “Ingénieurs Ingénieux” INSA Lyon France Claude FOURNIER Institut supérieur des métiers Paris France Frédéric GOUPIL DE BOUILLÉ SNCF Immobilier Paris France Susanne GRETZINGER Department of Entrepreneurship and Relationship Management University of Southern Denmark Odense Denmark Fedoua KASMI ERPI Laboratory University of Lorraine France Blandine LAPERCHE ISI Laboratory RII University of the Littoral Opal Coast Dunkirk France

Son Thi Kim LE ISI Laboratory RII University of the Littoral Opal Coast Dunkirk France Birgit LEICK USN School of Business University of South-Eastern Norway Bø Norway Thomas MICHAUD Independent researcher France Dave MOBHE BOKOKO ISI Laboratory RII University of the Littoral Opal Coast Dunkirk France Michelle MONGO Henri Fayol Institute École nationale supérieure des mines de Saint-Étienne France Jean-Louis MONINO Montpellier recherche en économie laboratory University of Montpellier France Laure MOREL ERPI Laboratory University of Lorraine France

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Francis MUNIER Bureau d’économie théorique et appliquée CNRS University of Strasbourg France

Diane-Gabrielle TREMBLAY École des sciences de l’administration TÉLUQ University Quebec Canada

Elisa SALVADOR ESSCA School of Management Paris France

Dimitri UZUNIDIS ISI Laboratory RII University of the Littoral Opal Coast Dunkirk France and Technical University of Crete Greece

Pierre SAULAIS Institute of Knowledge and Innovation South East Asia Bangkok University Thailand Eric SEULLIET La Fabrique du Futur Paris France Bérangère L. SZOSTAK Bureau d’économie théorique et appliquée CNRS University of Lorraine France Cheikh Abdou Lahad THIAW GREDEG CNRS University of Nice Sophia Antipolis Nice France and École supérieure polytechnique Cheikh Anta Diop University Dakar Senegal

Sonia VEYSSIÈRE ISI Laboratory RII University of the Littoral Opal Coast Dunkirk France Giovanni ZAZZERINI INSME – The International Network for SMEs Rome Italy

Index

3D printing, 86, 87, 111, 161, 162, 264 A, B, C absorption, 43–46, 86 adoption, 63, 76, 112, 115, 169, 187–192, 212 artificial intelligence, 12, 33, 34, 51, 56, 65, 111–113, 121, 123, 137, 148 Arts and Crafts movement, 85, 116 Asimov, Isaac, 183 audit, 24, 96, 140, 141 Bauhaus, 116 benchmark, 25, 221, 236 Berners-Lee, Tim, 221 Big Data, 51–56, 123 bio energy, 154 informatics, 53 technology, 77, 80, 111, 147, 148, 156, 202 Bitcoin, 59, 60, 62, 140, 141 Blockchain, 59–65, 121 breeding ground, 221, 222 Brown, Tim, 2, 116, 117 business ecosystem, 86, 122, 260 C-K (concept-knowledge) theory, 27, 32 Chesbrough, Henry, 25, 59, 86, 213, 220, 264

circular business model, 75, 77 cloud computing, 54, 111, 123 cluster, 95, 149, 187, 190, 192, 196, 236 co-creation, 20, 59–65, 67–72, 85–90, 220 co-design, 20, 36, 68, 70, 72, 85–90 collaboration, 6, 34, 35, 78, 82, 96, 98, 113, 139, 187, 188, 191, 192, 198, 250, 251, 253, 274 commercialization, 116, 188, 190–192, 210, 225, 251–255, 257, 267 common good, 56, 221 community of practice, 68, 86, 93–98 competitive advantage, 21, 24, 53, 139, 189, 190, 214, 258 competitiveness, 90, 187, 190, 192, 198, 199, 210, 225, 247, 273 consortium, 139, 219, 221, 253 contactless payment, 201, 204 cooperation, 29, 45, 63, 64, 78, 87, 95, 98, 99, 109, 139, 162, 191, 198, 218, 221, 271 copyleft, 89, 218, 220 cost (see also price), 13, 35–37, 45, 55, 64, 69, 70, 72, 90, 94, 106, 112, 121, 145, 155, 157, 168, 170, 197, 209, 210, 222, 233–235, 237, 238, 244, 252, 265–267 creative destruction, 76, 142, 212, 278, 279

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creativity, 33, 36, 60, 62, 67, 70, 72, 85, 88–91, 93, 94, 97, 98, 114, 115, 117–119, 162, 164, 175–184, 209, 210, 212–214, 226, 228, 229, 235, 242–245, 258 cross-analysis, 217 crowdfunding, 137, 140, 142, 263 crypto-economics, 64 customer, 12, 19, 20, 31, 34, 36, 53, 55, 59, 61, 69, 87, 88, 104, 105, 110–113, 118, 138, 139, 188, 190, 191, 213, 245, 251, 270, 271 cybersecurity, 56, 65, 113 D, E, F DAO (decentralized autonomous organization), 64 decentralization, 64, 71, 88, 140, 141, 167, 169 decentralized medicine, 167, 169 decision-maker, 23, 112, 118, 139, 185, 201, 209, 225, 236 dematerialization, 75, 87 democratization, 37, 161 design thinking, 2, 9, 38, 89, 115–119, 165, 184, 185 digital commons, 222 devices, 154, 218, 220–222 rights management, 90 digitization, 51, 56, 121, 124, 125, 218 DIY, 67, 68, 85, 87 economic growth, 12, 145, 147, 189, 199, 222, 269, 277, 279 economics, 22, 24, 43, 130, 273, 278 electric car, 156 end customer, 189 user, 20, 65, 189 energy, 13, 21, 28, 56, 111, 133, 142, 146, 153–155, 157, 158, 176, 199, 203, 237 efficiency, 146 performance, 237

engineering, 7, 19–38, 69, 75, 79, 85, 90, 121, 148, 206, 212, 233, 236 entrepreneur, 82, 112, 121–126, 129–134, 139, 146, 147, 182, 191, 221, 222, 225–230, 241–243, 245, 246, 248, 255, 257, 261, 268, 277, 279, 284 entropy, 163, 176, 178, 179, 228 ergonomics, 220, 235 European innovation policy, 273 exoskeletons, 148 externalities, 196, 198, 272, 278, 279 fab lab (fabrication laboratory), 68, 70, 72, 86, 87, 119 factory, 14, 103, 155 Feynman, Richard, 202 financing, 88, 140, 142, 145, 263–265, 267 flexibility, 21, 35, 38, 44, 45, 67, 69, 119, 213, 238, 249, 251, 258, 268 Free Software Foundation (FSF), 90, 218 G, H, I GAFAM (Google, Apple, Facebook, Amazon, Microsoft), 22, 53, 54, 122, 139, 142, 181, 219, 246 gerontechnology, 147 GNU General Public License (GPL), 219 governance, 27, 64, 124–126, 191 green technology, 154, 155 GREMI (Groupe de Recherche Européen sur les Milieux Innovateurs), 78, 195, 196, 198, 199 growth, 2, 12, 32, 54, 64, 68, 77, 78, 80, 81, 112, 140, 151, 157, 158, 162, 188, 195, 201, 206, 225, 233, 248, 249, 253, 256, 258–260, 266, 278 green, 157 hackathon, 164 hackerspace, 162–165 healthcare, 1, 7, 12, 149, 167–169, 202 heterogeneous environment, 188 Homo œconomicus, 227

Index

ICT (information and communication technology), 68, 87, 122–124, 148, 218–220, 222 ideation, 32, 33, 60, 85, 90, 173, 174, 180, 185 imitation of nature, 102 implementation, 12, 21, 34, 35, 38, 55, 63, 93, 96–99, 149, 158, 185, 197–199, 203, 210, 221, 259, 270, 273, 274 industry, 4, 103, 104, 111, 112, 138, 139, 141, 155, 188–192, 203, 206, 211, 218–220, 233, 248, 257, 268–271, 274 innovation agile, 20, 36, 214 artistic, 72, 85, 90, 116, 161, 182 craft, 105, 106 demand, 15, 104, 109, 111–113, 132, 151, 210, 238, 249 disruptive, 34, 87, 115, 117, 121, 222 ecosystem, 59, 162, 178, 253, 254 frugal, 33, 67, 70, 71, 82 geography of, 269, 272 incremental, 36, 125, 212, 213, 252 management, 29, 32, 35, 38, 116–119, 147, 220 network, 77, 78, 80, 190, 197, 198, 250 process, 149, 218, 219, 222, 274 radical, 36, 76, 106, 138, 141, 142, 211, 213, 252 ISO 56002:2019, 35, 36, 210 integrated design, 21, 24, 25, 30, 89, 113, 125, 129, 130, 132, 133, 181, 212, 213, 218 intellectual property (IP), 60, 61, 63, 85, 89, 179, 210, 218, 249, 261 rights (IPRs), 85, 89, 179, 218, 261 inter-organization, 35, 45, 46, 68, 86–88, 93, 94, 187, 188, 191, 192, 250, 272 interaction, 46, 47, 59, 86, 87, 94, 109, 138, 157, 162, 163, 175, 188–192, 195–199, 272, 274 interoperability, 35, 151, 220 invention, 85, 86, 89, 178, 185, 212, 225

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investment, 45, 47, 112, 146, 151, 155, 180, 185, 206, 227, 237, 238, 249–251, 253, 263, 265, 267, 271 IoT (Internet of Things), 51, 65, 111, 121, 148, 149 IT (information technology), 110–113, 122, 147, 167, 202, 213, 220, 234, 238 K, L, M KIBS (knowledge intensive business services), 271–273 know-how, 21, 46, 56, 68–70, 86, 87, 101, 105, 116, 161, 162, 165, 190, 199, 266 knowledge capital, 174, 175, 178, 180, 247, 250, 251 Kondratieff, Nikolaï, 147 lean manufacturing, 38, 235 learning, 2, 6, 24, 36, 43, 46, 47, 55, 56, 67, 68, 72, 86, 88, 93, 94, 96, 97, 114, 118, 137, 141, 162, 195, 197–199, 245 LED (light-emitting diode), 204 legislation, 76, 82, 90 license, 90, 218, 220, 251, 257, 259 lifecycle, 21, 37, 70, 71, 75, 81, 88, 90, 112, 156 Linux Foundation, 219, 221 local, 20, 26, 30, 33, 34, 71, 72, 105, 156, 157, 196, 198, 258, 259, 264, 270–273 maker, 68, 72, 102, 124, 228, 236 makerspace, 68, 70, 72, 78, 86, 88 manager, 38, 46, 56, 69, 72, 86, 90, 96, 101, 114, 115, 117, 119, 181, 226, 227, 233, 234, 236–238, 245, 249, 251, 253, 254, 256, 259, 260 market pull, 181, 188, 189, 213 marketing, 21, 22, 53, 87, 88, 131, 149, 165, 187–192, 210–212, 236, 237 masterwork, 104 mental representations, 214 multidimensional, 43, 45, 47, 48, 81, 110, 153

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multidisciplinary approach, 31, 214, 226 mutation, 27, 180

proximity, 88, 105, 113, 195, 198, 199, 220, 222 PSI approach, 8–15

N, O, P Nakamoto, Satoshi, 60 nanoelectronics, 202, 203 nanotechnology, 12, 111, 147, 148, 155, 201–206 neoclassical economics, 227 von Neumann, John, 218 novelty, 4, 34, 37, 55, 112, 182, 183, 209–211, 214, 225, 252, 267, 270, 279 open data, 51, 55, 56, 217, 218, 220–222 innovation, 20, 25, 32, 35–38, 45, 59, 63, 86, 93, 180, 192, 213, 217, 220, 222, 247, 250, 251, 264 innovation 2.0, 220 source, 55, 68, 90, 217–222 source hardware (OSH), 55 source software (OSS), 218, 221 Open Knowledge Foundation (OKF), 67, 220 Oslo Manual, 32, 149, 210 Ostrom, Elinor, 222 pandemic, 6, 12, 13 partnership networks, 190 patent, 47, 63, 89, 105, 106, 111, 203, 205, 206, 221, 252, 256, 277 pioneering industries, 76, 78 Porter, Michael E., 24, 88, 187, 189 positivism, 183 price (see also cost), 14, 21, 88, 111, 112, 140, 202, 204, 228, 234, 238, 249 product line, 212, 265 productivity, 2, 12, 32, 62, 76, 77, 102, 142, 146, 165, 179, 182, 197, 210, 228, 234–236, 277, 278 progress, 1, 3–5, 23, 30, 36, 54, 86, 98, 111, 113, 142, 148, 158, 167, 202, 204, 205, 219, 270, 280

Q, R, S quality control, 21 R&D (research and development), 19, 27, 29, 31, 45, 47, 82, 85, 88, 104–106, 111, 180, 184, 188–191, 212, 247, 249–253, 257, 263–265, 267, 271, 273, 274, 277 real estate, 233–239 recyclable resource, 13, 72, 153, 158 remote communication, 154 renewable energies, 75, 90, 153, 158 requirements, 32–34, 37, 53 RFID (radio-frequency identification), 203, 204 risk, 2, 3, 7, 11, 26, 29, 30, 35, 88, 90, 105, 110, 114, 125, 131, 137, 138, 141, 142, 169, 170, 191, 201, 204, 206, 214, 226, 236, 237, 242, 243, 245, 256, 264, 265, 268, 278, 279 robotics, 111, 137, 147–149 Schumpeter, Joseph, 4, 89, 142, 210, 212, 227, 230, 247, 249, 280 science fiction, 55, 161, 181–185, 201 scientific knowledge, 85, 103, 189, 190, 192, 255–257, 261 scrum, 165 service approach, 270 Simon, Herbert A., 27, 69, 71, 89 smart data, 65 grids, 154 technology, 123, 139, 168 traffic, 154 software engineering, 218 source code, 90, 218, 219 space program, 112, 181, 183 standardization, 36, 87, 219–221

Index

start-up, 45, 68, 73, 78, 85, 88, 122, 124, 163, 164, 222, 251, 255, 257, 259, 260, 263–267 strategic narratives, 182 supplier, 20, 59, 68, 69, 87, 88, 105, 113, 154, 190, 213, 251, 270, 271 sustainable growth, 1, 14, 38, 76, 78, 79, 81, 129, 133, 156, 158, 189, 199, 233, 244 synergy, 31, 157, 198 T, V, W Taniguchi, Norio, 201, 202 technical democracy, 14 technology push, 188, 189, 212 telemedicine, 146, 154, 167–170 territorialized innovation networks, 198

291

territory, 55, 56, 75, 76, 78, 80, 81, 101, 105, 147, 170, 195, 196, 198, 199, 263, 265, 268, 269, 271–274 theoretical literature, 270 theory of inventive problem solving (TRIZ), 25, 26, 32, 33, 89 transformation, 5, 15, 20, 26, 35, 44, 45, 56, 67, 76, 77, 82, 97, 101, 109, 111, 113, 116, 121, 148, 153, 164, 165, 173, 176, 195–197, 205, 209, 214, 225, 258, 263, 274 Turing, Alan, 218 vaccine, 155, 201, 203 videoconference, 168 virtual reality, 31, 35, 65, 184 workspace, 235–237 World Wide Web Consortium (W3C), 221

Summary of Volume 1

Introduction Dimitri UZUNIDIS and Fedoua KASMI Chapter 1. Economy – Innovation Economics and the Dynamics of Interactions Sophie BOUTILLIER, Vanessa CASADELLA and Blandine LAPERCHE Chapter 2. Management – Managing Innovation According to Space, Time and Matter Bérangère L. SZOSTAK, Michael E. LAVIOLETTE and Thierry BURGER-HELMCHEN Chapter 3. Agriculture – Agricultural and Food Innovations and Agro-ecological Transition Ludovic TEMPLE Chapter 4. Anthropology – Anthropological Aspects of Innovation: Defining Benchmarks Dominique DESJEUX Chapter 5. Business – Business Creation and Innovative Entrepreneurial Ecosystems Sophie BOUTILLIER Chapter 6. Capacity – Innovation Capacities and Learning Dynamics Vanessa CASADELLA

Innovation Economics, Engineering and Management Handbook 2: Special Themes, First Edition. Edited by Dimitri Uzunidis, Fedoua Kasmi and Laurent Adatto. © ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.

Innovation Economics, Engineering and Management Handbook

Chapter 7. Capital – Knowledge Capital and Innovation: Production and Use of Knowledge in Companies Blandine LAPERCHE Chapter 8. Cluster – Innovative Cluster: Geographical and “Virtual” Proximity in the Digital Era Elisa SALVADOR Chapter 9. Collaboration – Collaborative and Open Innovation in Highly Competitive Contexts Camille AOUINAÏT Chapter 10. Creativity – Creativity for Innovation: A Mutually Advantageous Relationship Raphaël BARY Chapter 11. Cycles – The Long Cycles of the Economy and the Question of Innovation Dimitri UZUNIDIS Chapter 12. Design – Innovative Design: The Importance of a Methodical Approach Joëlle FOREST Chapter 13. Diffusion – Diffusion and Adoption Behavior of Innovations Marc BAUDRY Chapter 14. Disruption – Disruptive Innovation and the Evolution of Competitive Relationships Giovanni ZAZZERINI Chapter 15. Ecosystem – Innovation Ecosystem: Generativity, Resilience and Power of Attraction Patrick COHENDET Chapter 16. Entrepreneur – The Innovative Entrepreneur as an Actor of Economic Change Sophie BOUTILLIER Chapter 17. Financing – Financing R&D and Innovation Béatrice DUMONT

Summary of Volume 1

Chapter 18. Frugality – Frugal Innovation as Inclusive Innovation Christian LE BAS and Sana KHAN Chapter 19. Future – The Future of Innovative Technologies: Between Imagination and Technological Ideology Thomas MICHAUD Chapter 20. Hybridization – Hybridization of Tech-Push and Market-Pull Approaches in Innovation Processes Florin PAUN Chapter 21. Incentives – Incentives for Innovation: Diversity and Public–Private Combinations Babacar NDIAYE Chapter 22. Indicators – The Complexity of Innovation Indicators Slim THABET Chapter 23. Information – Information for Innovation: Strategic, Competitive and Technological Intelligence Stéphane GORIA Chapter 24. Invention – Shared Inventions and Competitive Innovations Michel VIGEZZI Chapter 25. Knowledge – Knowledge Management in Learning Innovative Organizations Marcos LIMA Chapter 26. Location – Local Innovation Issues and Priorities for Public Intervention Cheikh Abdou Lahad THIAW Chapter 27. Market – Market Innovation: Opening and Controlling New Markets Béatrice SIADOU-MARTIN Chapter 28. Model – Business Models for Innovation Strategies Marcos LIMA

Innovation Economics, Engineering and Management Handbook

Chapter 29. Network – Networks and Development of Innovation Processes Delphine GALLAUD Chapter 30. Organization – Modern Innovative Organizational Structures Angelo BONOMI Chapter 31. Paradigm – The Techno-scientific Paradigm: The Ethical Control of the Technological Progress Thomas MICHAUD Chapter 32. Pattern – Linear, Interactive and Hybrid Patterns of Innovation Blandine LAPERCHE Chapter 33. Persistence – The Economic Analysis of Persistent Innovation Christian LE BAS Chapter 34. Policy – Reinventing Innovation: From Criticisms of the Traditional Paradigm to Policy Transformation Pierre-Benoit JOLY Chapter 35. Property – Intellectual Property and Innovation Béatrice DUMONT Chapter 36. Proximity – Impacts of Geographic, Organizational and Cognitive Proximities on Innovation Damien TALBOT Chapter 37. Responsibility – Responsible Innovation in Corporate Strategy and Public Policy Leïla TEMRI Chapter 38. Revolution – Innovations and Industrial Revolution Cédric PERRIN Chapter 39. Services – Defining Service Innovation Céline MERLIN-BROGNIART

Summary of Volume 1

Chapter 40. Social – Social Economy and Social Innovation Paul MULLER Chapter 41. Space – Innovation in Urban or Rural Spaces Delphine GALLAUD Chapter 42. Standardization – Standardization and Innovation Management Laure MOREL Chapter 43. Synchronization – Synchronization and Coordination of Innovation Sana ELOUAER-MRIZAK Chapter 44. System – National Innovation System: The Primacy of Interactions Between Economic Actors Vanessa CASADELLA Chapter 45. Tax – Taxation and Innovation: Incentives, Attractiveness and Innovation Policies Olivier ESNEU Chapter 46. Technology – Theoretical Model of Technology for Innovation Angelo BONOMI Chapter 47. Timing – Timing of Innovation: The Central Position of the Innovative Enterprise Babacar NDIAYE Chapter 48. Trajectory – Innovation Trajectories and Dynamic Capabilities Blandine LAPERCHE Chapter 49. User – User Innovation: Interactions Between Users and Firms in Innovation Processes Francesco SCHIAVONE Chapter 50. Value – The Value of Innovations: Specificity and Evaluation Methods of Innovation Marc BAUDRY

Innovation Economics, Engineering and Management Handbook

Chapter 51. Work – Innovative Behavior at Work Audrey BECUWE Chapter 52. X-Innovation – The Polymorphism of Innovation Blandine LAPERCHE

Other titles from

in Innovation, Entrepreneurship and Management

2021 BOBILLIER CHAUMON Marc-Eric Digital Transformations in the Challenge of Activity and Work: Understanding and Supporting Technological Changes (Technological Changes and Human Resources Set – Volume 3)

2020 ACH Yves-Alain, RMADI-SAÏD Sandra Financial Information and Brand Value: Reflections, Challenges and Limitations ANDREOSSO-O’CALLAGHAN Bernadette, DZEVER Sam, JAUSSAUD Jacques, TAYLOR Robert Sustainable Development and Energy Transition in Europe and Asia (Innovation and Technology Set – Volume 9) BEN SLIMANE Sonia, M’HENNI Hatem Entrepreneurship and Development: Realities and Future Prospects (Smart Innovation Set – Volume 30)

CHOUTEAU Marianne, FOREST Joëlle, NGUYEN Céline Innovation for Society: The P.S.I. Approach (Smart Innovation Set – Volume 28) CORON Clotilde Quantifying Human Resources: Uses and Analysis (Technological Changes and Human Resources Set – Volume 2) CORON Clotilde, GILBERT Patrick Technological Change (Technological Changes and Human Resources Set – Volume 1) CERDIN Jean-Luc, PERETTI Jean-Marie The Success of Apprenticeships: Views of Stakeholders on Training and Learning (Human Resources Management Set – Volume 3) DELCHET-COCHET Karen Circular Economy: From Waste Reduction to Value Creation (Economic Growth Set – Volume 2) DIDAY Edwin, GUAN Rong, SAPORTA Gilbert, WANG Huiwen Advances in Data Science (Big Data, Artificial Intelligence and Data Analysis Set – Volume 4) DOS SANTOS PAULINO Victor Innovation Trends in the Space Industry (Smart Innovation Set – Volume 25) GASMI Nacer Corporate Innovation Strategies: Corporate Social Responsibility and Shared Value Creation (Smart Innovation Set – Volume 33) GOGLIN Christian Emotions and Values in Equity Crowdfunding Investment Choices 1: Transdisciplinary Theoretical Approach GUILHON Bernard Venture Capital and the Financing of Innovation (Innovation Between Risk and Reward Set – Volume 6)

LATOUCHE Pascal Open Innovation: Human Set-up (Innovation and Technology Set – Volume 10) LIMA Marcos Entrepreneurship and Innovation Education: Frameworks and Tools (Smart Innovation Set – Volume 32) MACHADO Carolina, DAVIM J. Paulo Sustainable Management for Managers and Engineers MAKRIDES Andreas, KARAGRIGORIOU Alex, SKIADAS Christos H. Data Analysis and Applications 3: Computational, Classification, Financial, Statistical and Stochastic Methods (Big Data, Artificial Intelligence and Data Analysis Set – Volume 5) Data Analysis and Applications 4: Financial Data Analysis and Methods (Big Data, Artificial Intelligence and Data Analysis Set – Volume 6) MASSOTTE Pierre, CORSI Patrick Complex Decision-Making in Economy and Finance MEUNIER François-Xavier Dual Innovation Systems: Concepts, Tools and Methods (Smart Innovation Set – Volume 31) MICHAUD Thomas Science Fiction and Innovation Design (Innovation in Engineering and Technology Set – Volume 6) MONINO Jean-Louis Data Control: Major Challenge for the Digital Society (Smart Innovation Set – Volume 29) MORLAT Clément Sustainable Productive System: Eco-development versus Sustainable Development (Smart Innovation Set – Volume 26)

SAULAIS Pierre, ERMINE Jean-Louis Knowledge Management in Innovative Companies 2: Understanding and Deploying a KM Plan within a Learning Organization (Smart Innovation Set – Volume 27)

2019 AMENDOLA Mario, GAFFARD Jean-Luc Disorder and Public Concern Around Globalization BARBAROUX Pierre Disruptive Technology and Defence Innovation Ecosystems (Innovation in Engineering and Technology Set – Volume 5) DOU Henri, JUILLET Alain, CLERC Philippe Strategic Intelligence for the Future 1: A New Strategic and Operational Approach Strategic Intelligence for the Future 2: A New Information Function Approach FRIKHA Azza Measurement in Marketing: Operationalization of Latent Constructs FRIMOUSSE Soufyane Innovation and Agility in the Digital Age (Human Resources Management Set – Volume 2) GAY Claudine, SZOSTAK Bérangère L. Innovation and Creativity in SMEs: Challenges, Evolutions and Prospects (Smart Innovation Set – Volume 21) GORIA Stéphane, HUMBERT Pierre, ROUSSEL Benoît Information, Knowledge and Agile Creativity (Smart Innovation Set – Volume 22) HELLER David Investment Decision-making Using Optional Models (Economic Growth Set – Volume 2)

HELLER David, DE CHADIRAC Sylvain, HALAOUI Lana, JOUVET Camille The Emergence of Start-ups (Economic Growth Set – Volume 1) HÉRAUD Jean-Alain, KERR Fiona, BURGER-HELMCHEN Thierry Creative Management of Complex Systems (Smart Innovation Set – Volume 19) LATOUCHE Pascal Open Innovation: Corporate Incubator (Innovation and Technology Set – Volume 7) LEHMANN Paul-Jacques The Future of the Euro Currency LEIGNEL Jean-Louis, MÉNAGER Emmanuel, YABLONSKY Serge Sustainable Enterprise Performance: A Comprehensive Evaluation Method LIÈVRE Pascal, AUBRY Monique, GAREL Gilles Management of Extreme Situations: From Polar Expeditions to ExplorationOriented Organizations MILLOT Michel Embarrassment of Product Choices 2: Towards a Society of Well-being N’GOALA Gilles, PEZ-PÉRARD Virginie, PRIM-ALLAZ Isabelle Augmented Customer Strategy: CRM in the Digital Age NIKOLOVA Blagovesta The RRI Challenge: Responsibilization in a State of Tension with Market Regulation (Innovation and Responsibility Set – Volume 3) PELLEGRIN-BOUCHER Estelle, ROY Pierre Innovation in the Cultural and Creative Industries (Innovation and Technology Set – Volume 8) PRIOLON Joël Financial Markets for Commodities QUINIOU Matthieu Blockchain: The Advent of Disintermediation

RAVIX Joël-Thomas, DESCHAMPS Marc Innovation and Industrial Policies (Innovation between Risk and Reward Set – Volume 5) ROGER Alain, VINOT Didier Skills Management: New Applications, New Questions (Human Resources Management Set – Volume 1) SAULAIS Pierre, ERMINE Jean-Louis Knowledge Management in Innovative Companies 1: Understanding and Deploying a KM Plan within a Learning Organization (Smart Innovation Set – Volume 23) SERVAJEAN-HILST Romaric Co-innovation Dynamics: The Management of Client-Supplier Interactions for Open Innovation (Smart Innovation Set – Volume 20) SKIADAS Christos H., BOZEMAN James R. Data Analysis and Applications 1: Clustering and Regression, Modelingestimating, Forecasting and Data Mining (Big Data, Artificial Intelligence and Data Analysis Set – Volume 2) Data Analysis and Applications 2: Utilization of Results in Europe and Other Topics (Big Data, Artificial Intelligence and Data Analysis Set – Volume 3) UZUNIDIS Dimitri Systemic Innovation: Entrepreneurial Strategies and Market Dynamics VIGEZZI Michel World Industrialization: Shared Inventions, Competitive Innovations and Social Dynamics (Smart Innovation Set – Volume 24)

2018 BURKHARDT Kirsten Private Equity Firms: Their Role in the Formation of Strategic Alliances

CALLENS Stéphane Creative Globalization (Smart Innovation Set – Volume 16) CASADELLA Vanessa Innovation Systems in Emerging Economies: MINT – Mexico, Indonesia, Nigeria, Turkey (Smart Innovation Set – Volume 18) CHOUTEAU Marianne, FOREST Joëlle, NGUYEN Céline Science, Technology and Innovation Culture (Innovation in Engineering and Technology Set – Volume 3) CORLOSQUET-HABART Marine, JANSSEN Jacques Big Data for Insurance Companies (Big Data, Artificial Intelligence and Data Analysis Set – Volume 1) CROS Françoise Innovation and Society (Smart Innovation Set – Volume 15) DEBREF Romain Environmental Innovation and Ecodesign: Certainties and Controversies (Smart Innovation Set – Volume 17) DOMINGUEZ Noémie SME Internationalization Strategies: Innovation to Conquer New Markets ERMINE Jean-Louis Knowledge Management: The Creative Loop (Innovation and Technology Set – Volume 5) GILBERT Patrick, BOBADILLA Natalia, GASTALDI Lise, LE BOULAIRE Martine, LELEBINA Olga Innovation, Research and Development Management IBRAHIMI Mohammed Mergers & Acquisitions: Theory, Strategy, Finance LEMAÎTRE Denis Training Engineers for Innovation

LÉVY Aldo, BEN BOUHENI Faten, AMMI Chantal Financial Management: USGAAP and IFRS Standards (Innovation and Technology Set – Volume 6) MILLOT Michel Embarrassment of Product Choices 1: How to Consume Differently PANSERA Mario, OWEN Richard Innovation and Development: The Politics at the Bottom of the Pyramid (Innovation and Responsibility Set – Volume 2) RICHEZ Yves Corporate Talent Detection and Development SACHETTI Philippe, ZUPPINGER Thibaud New Technologies and Branding (Innovation and Technology Set – Volume 4) SAMIER Henri Intuition, Creativity, Innovation TEMPLE Ludovic, COMPAORÉ SAWADOGO Eveline M.F.W. Innovation Processes in Agro-Ecological Transitions in Developing Countries (Innovation in Engineering and Technology Set – Volume 2) UZUNIDIS Dimitri Collective Innovation Processes: Principles and Practices (Innovation in Engineering and Technology Set – Volume 4) VAN HOOREBEKE Delphine

The Management of Living Beings or Emo-management

2017 AÏT-EL-HADJ Smaïl The Ongoing Technological System (Smart Innovation Set – Volume 11)

BAUDRY Marc, DUMONT Béatrice Patents: Prompting or Restricting Innovation? (Smart Innovation Set – Volume 12) BÉRARD Céline, TEYSSIER Christine Risk Management: Lever for SME Development and Stakeholder Value Creation CHALENÇON Ludivine Location Strategies and Value Creation of International Mergers and Acquisitions CHAUVEL Danièle, BORZILLO Stefano The Innovative Company: An Ill-defined Object (Innovation between Risk and Reward Set – Volume 1) CORSI Patrick Going Past Limits To Growth D’ANDRIA Aude, GABARRET

Inés Building 21st Century Entrepreneurship (Innovation and Technology Set – Volume 2) DAIDJ Nabyla Cooperation, Coopetition and Innovation (Innovation and Technology Set – Volume 3) FERNEZ-WALCH Sandrine The Multiple Facets of Innovation Project Management (Innovation between Risk and Reward Set – Volume 4) FOREST Joëlle Creative Rationality and Innovation (Smart Innovation Set – Volume 14) GUILHON Bernard Innovation and Production Ecosystems (Innovation between Risk and Reward Set – Volume 2)

HAMMOUDI Abdelhakim, DAIDJ Nabyla Game Theory Approach to Managerial Strategies and Value Creation (Diverse and Global Perspectives on Value Creation Set – Volume 3) LALLEMENT Rémi Intellectual Property and Innovation Protection: New Practices and New Policy Issues (Innovation between Risk and Reward Set – Volume 3) LAPERCHE Blandine Enterprise Knowledge Capital (Smart Innovation Set – Volume 13) LEBERT Didier, EL YOUNSI Hafida International Specialization Dynamics (Smart Innovation Set – Volume 9) MAESSCHALCK Marc Reflexive Governance for Research and Innovative Knowledge (Responsible Research and Innovation Set – Volume 6) MASSOTTE Pierre Ethics in Social Networking and Business 1: Theory, Practice and Current Recommendations Ethics in Social Networking and Business 2: The Future and Changing Paradigms MASSOTTE Pierre, CORSI Patrick Smart Decisions in Complex Systems MEDINA Mercedes, HERRERO Mónica, URGELLÉS Alicia Current and Emerging Issues in the Audiovisual Industry (Diverse and Global Perspectives on Value Creation Set – Volume 1) MICHAUD Thomas Innovation, Between Science and Science Fiction (Smart Innovation Set – Volume 10)

PELLÉ Sophie Business, Innovation and Responsibility (Responsible Research and Innovation Set – Volume 7) SAVIGNAC Emmanuelle The Gamification of Work: The Use of Games in the Workplace SUGAHARA Satoshi, DAIDJ Nabyla, USHIO Sumitaka Value Creation in Management Accounting and Strategic Management: An Integrated Approach (Diverse and Global Perspectives on Value Creation Set –Volume 2) UZUNIDIS Dimitri, SAULAIS Pierre Innovation Engines: Entrepreneurs and Enterprises in a Turbulent World (Innovation in Engineering and Technology Set – Volume 1)

2016 BARBAROUX Pierre, ATTOUR Amel, SCHENK Eric Knowledge Management and Innovation (Smart Innovation Set – Volume 6) BEN BOUHENI Faten, AMMI Chantal, LEVY Aldo Banking Governance, Performance And Risk-Taking: Conventional Banks Vs Islamic Banks BOUTILLIER Sophie, CARRÉ Denis, LEVRATTO Nadine Entrepreneurial Ecosystems (Smart Innovation Set – Volume 2) BOUTILLIER Sophie, UZUNIDIS Dimitri The Entrepreneur (Smart Innovation Set – Volume 8) BOUVARD Patricia, SUZANNE Hervé Collective Intelligence Development in Business GALLAUD Delphine, LAPERCHE Blandine Circular Economy, Industrial Ecology and Short Supply Chains (Smart Innovation Set – Volume 4)

GUERRIER Claudine Security and Privacy in the Digital Era (Innovation and Technology Set – Volume 1) MEGHOUAR Hicham Corporate Takeover Targets MONINO Jean-Louis, SEDKAOUI Soraya Big Data, Open Data and Data Development (Smart Innovation Set – Volume 3) MOREL Laure, LE ROUX Serge Fab Labs: Innovative User (Smart Innovation Set – Volume 5) PICARD Fabienne, TANGUY Corinne Innovations and Techno-ecological Transition (Smart Innovation Set – Volume 7)

2015 CASADELLA Vanessa, LIU Zeting, DIMITRI Uzunidis Innovation Capabilities and Economic Development in Open Economies (Smart Innovation Set – Volume 1) CORSI Patrick, MORIN Dominique Sequencing Apple’s DNA CORSI Patrick, NEAU Erwan Innovation Capability Maturity Model FAIVRE-TAVIGNOT Bénédicte Social Business and Base of the Pyramid GODÉ Cécile Team Coordination in Extreme Environments MAILLARD Pierre Competitive Quality and Innovation MASSOTTE Pierre, CORSI Patrick Operationalizing Sustainability

MASSOTTE Pierre, CORSI Patrick Sustainability Calling

2014 DUBÉ Jean, LEGROS Diègo Spatial Econometrics Using Microdata LESCA Humbert, LESCA Nicolas Strategic Decisions and Weak Signals

2013 HABART-CORLOSQUET Marine, JANSSEN Jacques, MANCA Raimondo VaR Methodology for Non-Gaussian Finance

2012 DAL PONT Jean-Pierre Process Engineering and Industrial Management MAILLARD Pierre Competitive Quality Strategies POMEROL Jean-Charles Decision-Making and Action SZYLAR Christian UCITS Handbook

2011 LESCA Nicolas Environmental Scanning and Sustainable Development LESCA Nicolas, LESCA Humbert Weak Signals for Strategic Intelligence: Anticipation Tool for Managers MERCIER-LAURENT Eunika Innovation Ecosystems

2010 SZYLAR Christian Risk Management under UCITS III/IV

2009 COHEN Corine Business Intelligence ZANINETTI Jean-Marc Sustainable Development in the USA

2008 CORSI Patrick, DULIEU Mike The Marketing of Technology Intensive Products and Services DZEVER Sam, JAUSSAUD Jacques, ANDREOSSO Bernadette Evolving Corporate Structures and Cultures in Asia: Impact of Globalization

2007 AMMI Chantal Global Consumer Behavior

2006 BOUGHZALA Imed, ERMINE Jean-Louis Trends in Enterprise Knowledge Management CORSI Patrick et al. Innovation Engineering: the Power of Intangible Networks