The Oxford Handbook of Open Innovation 9780192899811, 9780192899798

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T h e Ox f o r d H a n d b o o k o f

OP E N I N N OVAT ION

the oxford handbook of

OPEN INNOVATION Edited by

HENRY CHESBROUGH, AGNIESZKA RADZIWON, WIM VANHAVERBEKE, and

JOEL WEST

Great Clarendon Street, Oxford, ox2 6dp, United Kingdom Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries © Oxford University Press 2024 The moral rights of the authors‌have been asserted First Edition published in 2024 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, by licence or under terms agreed with the appropriate reprographics rights organization. Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above You must not circulate this work in any other form and you must impose this same condition on any acquirer Published in the United States of America by Oxford University Press 198 Madison Avenue, New York, NY 10016, United States of America British Library Cataloguing in Publication Data Data available Library of Congress Control Number: 2023939949 ISBN 978–​0–​19–​289979–​8 DOI: 10.1093/​oxfordhb/​9780192899798.001.0001 Printed and bound by CPI Group (UK) Ltd, Croydon, cr0 4yy Links to third party websites are provided by Oxford in good faith and for information only. Oxford disclaims any responsibility for the materials contained in any third party website referenced in this work.

Contents

List of Figures  List of Tables  Preface  Acknowledgments  List of Contributors 

xi xv xvii xix xxi

PA RT I :   OP E N I N N OVAT ION PA S T, P R E SE N T, A N D F U T U R E 1. A Reconsideration of Open Innovation After 20 Years  Henry Chesbrough

3

2. Open Innovation as a Field of Knowledge  Agnieszka Radziwon and Henry Chesbrough

19

3. The Evolving Craft of Innovation  John Bessant

37

4. Opening up Open Innovation: Drawing the Boundaries  Wim Vanhaverbeke and Victor Gilsing

51

5. A Multi-​level Framework for Selecting and Implementing Innovation Modes  Marcel Bogers and Joel West

65

PA RT I I :   OP E N I N N OVAT ION WITHIN FIRMS 6. The Graft and Craft of Individual-​Level Open Innovation  Ammon Salter, Anne L. J. Ter Wal, and Paola Criscuolo 7. Open Innovation: Aligning Mechanisms with Project Attributes  Mehdi Bagherzadeh and Andrei Gurca

91 106

vi   Contents

8. Open Innovation in Small and Medium-​Sized Enterprises  Agnieszka Radziwon and Wim Vanhaverbeke

119

9. Open Innovation and the Creation of High-​Growth Ventures  Eva Weissenböck and Marc Gruber

140

10. Open Innovation in Large Companies  Henry Chesbrough

158

11. Designing Openness with Technology and IP  Marcus Holgersson

170

PA RT I I I :   OP E N I N N OVAT ION A M ON G F I R M S 12. The Good, the Bad, the Open: Ethical Considerations in Open Innovation  Ioana Stefan

187

13. Toward Integrating Trust in Open Innovation  Kirsimarja Blomqvist, Pia Hurmelinna-​L aukkanen, and Anne-​L aure Mention

199

14. R&D Alliances and Open Innovation: Review and Opportunities  Hans T. W. Frankort and John Hagedoorn

219

15. Open Innovation and Coopetition: Toward Coopetitive Open Innovation  Sea Matilda Bez and Frédéric Le Roy

237

16. Strategic Acceleration of Open Innovation at Porsche  Andre Marquis and Stefan Dierks

254

17. How Corporate Venturing Adds Value to Open Innovation  Vareska van de Vrande and Corina Kuiper

266

PA RT I V:   N E T WOR K E D F OR M S OF OP E N I N N OVAT ION 18. Open Innovation, Innovation in Ecosystems, and Innovation Beyond the Firm  Joel West and Paul Olk

287

Contents   vii

19. Healthcare as a Sectoral System of Open Innovation  Joel West

308

20. A Typology for Engaging Individuals in Crowdsourcing  Krithika Randhawa

335

21. Extending the Use of Crowds for Innovation? Fund It Yourself!  Lars Frederiksen, Pernille Smith, Carsten Bergenholtz, Susan Hilbolling, Michela Beretta, Oana Vuculescu, Michael Zaggl, and Helle Alsted Søndergaard

357

22. Intermediaries and Platforms for Open Innovation  Kathleen Diener, Frank Piller, and Patrick Pollok

371

23. Driving Open Innovation through Open Platforms  Geoffrey Parker, Georgios Petropoulos, Marshall W. Van Alstyne, and Joel West

387

PA RT V:   I M P L IC AT ION S F OR P U B L IC P OL IC Y 24. Open Innovation in Smart Cities  Esteve Almirall

407

25. Open Innovation in Regional Innovation Clusters and Entrepreneurial Ecosystems  Agnieszka Radziwon

423

26. Dimensions of Openness: Universities’ Strategic Choices for Innovation  Markus Perkmann

438

27. Open Innovation in Science  Marion Poetz, Susanne Beck, Christoph Grimpe, and Henry Sauermann

455

28. Deep Tech, Big Science, and Open Innovation  Jonathan Wareham, Laia Pujol Priego, Angelo Kenneth Romasanta, and Gozal Ahmadova

473

29. Open Innovation Policy: The Outline-​Inspire-​Promote Spinner  Alberto Di Minin and Jacopo Cricchio

487

viii   Contents

PA RT V I :   N E W DE V E L OP M E N T S I N OP E N I N N OVAT ION 30. Open-​Technology Maneuvering in Digital Infrastructures  Erkko Autio, Hervé Legenvre, and Ari-​Pekka Hameri

505

31. Connecting the (Invisible) Dots: When Artificial Intelligence Meets Open Innovation  Xavier Ferràs, Petra Nylund, and Alexander Brem

519

32. Events to Span Knowledge Boundaries for Open Innovation  Paul R. Carlile and Karl-​Emanuel Dionne

533

33. Accelerating the Race to Net-​Zero through Open Innovation  Ann-​Kristin Zobel, Stephen Comello, and Lukas Falcke

549

34. Opening Innovation to Address Grand Challenges  Gabriel Cavalli and Anita M. McGahan

567

PA RT V I I :   OP E N I N N OVAT ION A N D T H E ORY 35. Open Innovation Theories  Yao Sun, Ann Majchrzak, and Arvind Malhotra

593

36. Advancing the Microfoundations of Open Innovation  Nicolai J. Foss and Tianjao Xu

611

37. Leadership Skills for Inbound and Outbound Open Innovation  Stefano Brusoni and Daniella Laureiro Martinez

623

38. Customer-​Centric Open Innovation Guided by Design Strategy  Melissa M. Appleyard and Herb Velazquez

640

39. A Practice Theory Perspective on Open Strategy and Innovation  Richard Whittington

653

40. The Open Innovation–​Business Model Innovation Nexus  Qinli Lu and Christopher L. Tucci

667

41. Effectuation and Open Innovation  Saras Sarasvathy

681

Contents   ix

42. The Changing Nature of Open Innovation  David J. Teece

699

PA RT V I I I :   OP E N I N N OVAT ION I N P R AC T IC E 43. Open R&D in Large Corporations  Bill Roschek Jr and Erica Jones

715

44. Open Source, the Ubiquitous Software Innovation Building Block  James Zemlin

729

45. Measuring the Economic Value of Open Source Software  Hilary Carter

743

46. Cloud Metadata and Interoperability: Open Innovation and Open Source Software Tooling  Param Singh and Jim Spohrer

753

47. How Open Innovation Enabled a Standard for Sovereign Data Exchange  Reinhold Achatz

764

48. Open Innovation in the Context of Digital Ecosystems  Mallik Tatipamula

773

49. Innovability for a Better World (and a New One?)  Ernesto Ciorra, Emanuele Polimanti, and Andrea Canino

786

50. A Practitioner’s View: Three Dimensions of OI Maturity  Marisol Menendez Alvarez

797

PA RT I X :   OP E N I N N OVAT ION A N D T E AC H I N G 51. Teaching Open Innovation in Business Schools  Justyna Dąbrowska and Jonathan Sims

813

52. Teaching Engineers about Open Innovation  Sabine Brunswicker

833

x   Contents

PA RT X :   C HA L L E N G E S , C R I T IQ U E S , A N D SU G G E S T ION S 53. Overcoming Organizational Obstacles to Open Innovation Success  Wim Vanhaverbeke, Henry Chesbrough, Joel West, and Agnieszka Radziwon

849

54. The Use of Open Innovation Metrics  Dieudonnee Cobben and Marcel Bogers

869

55. Failure Cases in Open Innovation  Henry Chesbrough

885

56. Complementarities and Tensions between Appropriability and Open Innovation  Keld Laursen, Ammon Salter, and Deepak Somaya

899

57. The Future of Open Innovation  Agnieszka Radziwon, Henry Chesbrough, Wim Vanhaverbeke, and Joel West

914

Index 

935

Figures

4.1 A classification of different objectives to be pursued through open innovation.

53

4.2 Broadening the open innovation concept.

58

5.1 Locus of innovation creation and commercialization.

68

6.1 Individual roles in inbound open innovation.

94

7.1 Decision framework for the selection of appropriate OI mechanisms based on project attributes.

115

9.1 Enablers of open innovation in new ventures.

142

9.2 Open innovation in the opportunity development process.

146

10.1 A range of open innovation practices at Bayer.

164

10.2 Mapping Bayer’s processes to collaboration objectives.

165

11.1 Example of technology, IP, and contract decisions in open innovation.

177

14.1 Sampled articles over time.

221

14.2 Organizing framework.

223

14.3 Venn diagram of themes in the literature.

224

15.1 The overlap between open innovation between competitors and coopetition literature.

244

16.1 Customer resegmentation in mobility, inspired by Teixeira (2019).

256

16.2 The Bosch innovation funnel.

259

16.3 Validation-​Driven Strategy business process.

261

19.1 Open innovation relationships in healthcare sector.

317

20.1 Typology of crowdsourcing strategies.

338

21.1 Crowd involvement in the innovation process.

359

22.1

The rise of the market of open innovation intermediaries. 376

22.2 Average project cost (in US$) for different service types in 2013 versus 2018. 381 23.1 Normalized smartphone market share of major providers in China between 3Q 2007 and 3Q 2013.

397

24.1 Open innovation in cities.

410

24.2 App: Are you safe?

414

xii   Figures 24.3 App: iLive.at.

415

24.4 Mechanisms of OI in cities and capabilities required.

419

26.1 Universities’ strategic choice options in open innovation.

448

27.1 The Open Innovation in Science (OIS) research framework.

457

27.2 Brain Match project, hosted on Zooniverse.org.

461

27.3 The ODIN model for Open Innovation in Science.

464

28.1 Summary of projects involved in ATTRACT.

479

28.2 ATTRACT motivators.

481

29.1

491

The Outline–​Inspire–​Promote spinner.

30.1 The four maneuvers.

510

31.1 Conceptualization of AI-​enabled OI.

524

33.1a A.P. Moller-​Maersk Group: selected timeline of GHG emission reduction and net zero pledges vs. actual emissions (Scope 1 only: 56% of carbon footprint).

553

33.1b Microsoft: selected timeline of GHG emission reduction and net-​zero pledges vs. actual emissions (Scope 1–​3).

554

33.2 A preliminary framework connecting OI mechanisms with firms’ net-​zero strategies.

561

34.1 The evolution of the open innovation movement.

577

37.1 Inbound and outbound open innovation.

625

37.2 Cognitive skills and OI’s double funnel.

635

38.1 The five dimensions of Gammasonics’s open innovation process.

642

39.1 Relationship between open strategy, open innovation, and open strategizing.

656

40.1 The impacts of the three types of OI on business model systems.

670

40.2 Three-​dimensional OI model with BMI.

672

41.1 The effectual process and its outcomes.

683

41.2 Links between the IAD framework and the effectual process.

692

42.1 The global open innovation organizational logic inspired by Pisano and Teece (1989).

702

43.1 Organizational structure as a spider web vs. an org chart.

716

43.2 Why, What, How framework for strategic design.

719

43.3 What’s Needed, What’s Possible, What’s Required framework for Where to Play.

721

43.4 Successful Minimum Viable Products (MVPs): March 2021 to June 2022.

723

43.5 Nestlé Purina’s approach to Outside-In Open Innovation.

724

Figures   xiii 44.1 Open source software build and package pipeline.

737

45.1 Unique Linux Foundation contributors across all hosted projects, 2013 to 2023.

746

47.1 Data lake versus federated storage of data.

765

47.2 Data space architecture and key components.

767

48.1 1G to 5G evolution.

774

48.2 5G application categories.

777

48.3 Open innovation ecosystem for 5G networks.

780

48.4 Three-​dimensional view of open innovation.

783

49.1 Relationships between the different parties involved in the project.

789

50.1 The Open Innovation Strategy Canvas (Version 2.0).

801

51.1 Word cloud of essential knowledge, theories, and frameworks emphasized in OI courses, based on OI teaching survey, 2022.

817

51.2 Word cloud of essential students’ skills emphasized in OI courses, based on OI teaching survey, 2022.

818

52.1 A cyclical experiential learning framework for teaching engineering students about open innovation.

834

53.1 Outside-​in open innovation modes positioned in the innovation matrix.

851

53.2 Inside-​out open innovation modes positioned in the innovation matrix.

851

55.1 P&G sales history, 2001–​2017.

892

55.2 Change in annual revenues for P&G, 2001–​2017.

894

Tables

3.1 Example perspectives on innovation as a distributed, multi-​actor process

39

3.2 Rothwell’s five generations of innovation models

44

5.1 Key factors that influence innovation creation and commercialization strategies 67 8.1 Differences between SMEs globally

120

10.1 Principles of open Innovation

167

13.1 Insights from trust research relevant to open innovation research

207

14.1 Review synthesis

228

16.1 Acceleration of the disruption of the Fortune 1000

255

17.1 Corporate venturing instruments

271

17.2 Matching corporate venturing instruments to approaches

275

18.1 Open innovation vs. innovation in ecosystems

289

18.2 Open innovation themes identified in prior ecosystem research

295

19.1 Leading sources of biomedical discoveries

313

19.2 Leading sources of biomedical patents

314

19.3 Leading commercializers of biomedical product innovations

315

20.1 Variance in solver interaction and related attributes

339

20.2 Variance in solvers’ technical expertise and related attributes

340

20.3 Future research directions based on the proposed typology and framework

347

21.1 Advantages and disadvantages of leveraging the crowd

366

23.1 Examples of platforms and whether they have the right degree of openness

398

25.1 Similarities and differences between entrepreneurial ecosystems and regional clusters

427

28.1 ATTRACT versus traditional funding types

482

28.2 OI as an enabler of deep tech commercialization

483

31.1 Tasks for AI-​enabled OI with corresponding potential sources of metadata

525

32.1 Summary of digital health stakeholders and their differences

540

32.2 Summary of types of events and their boundary processes

544

xvi   Tables 33.1 Exemplary OI initiatives in the context of net-​zero

557

35.1 Theories and their application in the New Zealand pest management open innovation challenge

595

37.1 Open innovation inbound principles and the corresponding needs in terms of cognition

626

37.2 Open innovation outbound principles and the corresponding needs in terms of cognition

627

49.1 Solar cells efficiency comparison (%)

791

50.1 Strategy variables

802

50.2 Internal organizational variables

803

50.3 Open innovation variables

803

51.1 A selection of OI cases used in teaching

819

52.1 Overview of open innovation modes

836

52.2 Overview of cases

838

53.1 Open innovation strategies based on the three horizon model

850

54.1 Future research directions of OI metrics

876

Preface

The inspiration for this Handbook began just after the inception of the COVID-​19 pandemic in early 2020. While no one knew how long the pandemic would last, it was obvious that academic scholarship needed to continue in the face of this challenge. At the same time, we were cognizant of the upcoming twentieth anniversary of the publication of Chesbrough’s 2003 book, Open Innovation: The New Imperative for Creating and Profiting from Technology. Because of the rich and varied literature that has developed since that initial book, as co-​editors we determined that it would be useful and timely to gather together in one place a wide range of perspectives on Open Innovation in a form of short knowledge-​condensed chapters. The result is what you have before you in this volume. Unlike the earlier 2006 and 2014 academic volumes—​with 14 and 15 standard-​ length chapters respectively—​in this volume, we sought to achieve more comprehensive coverage that would cover the breadth of the past 20 years. We thus solicited more but shorter chapters in a handbook format, with a manuscript length nearly twice that of the previous books. We are gratified that our colleagues at Oxford University Press shared our vision and accepted our submission into their established series of Oxford Handbooks. We are indebted to Adam Swallow, Jodie Keefe, Ryan Morris, and the backstage contributors at OUP for their assistance throughout the development of The Oxford Handbook of Open Innovation. Along with the insights from the Handbook authors, the Handbook builds on the inspiration of the sizable scholarly community that has developed around Open Innovation. The earliest began in 2004 with the first (of now eight) Academy of Management Professional Development Workshops. A more recent resource has been the nine annual editions of the World Open Innovation Conference since 2014, where separate tracks for scholars and practitioners spotlight their respective work in progress and challenges to the study, management, and application of Open Innovation. Last but not least, many of the chapters contained in this Handbook were presented during our weekly Berkeley Open Innovation Seminars, which offered a productive space for discussing work in progress as well as new research ideas and designs. As co-​editors, we were also cognizant of our own limitations. One of the most enduring insights underlying Open Innovation is Joy’s Law, which states that “most of the smart people work somewhere else.” Realizing that the scholarly community might well identify gaps and omissions in our initial outline for the Handbook, after soliciting chapters from leading OI scholars, we announced an open Call for Contributions across various Internet media. From this, we received 86 abstract submissions, and 10 were

xviii   Preface incorporated into this volume. Chapters on topics such as trust and ethics are the direct result of this crowdsourced, open innovation process. Thus, we “walked the walk” to develop a comprehensive perspective on Open Innovation. The result is the compilation of 57 chapters you see before you. We are confident that the chapters in this volume, alongside the aforementioned conferences, workshops, and seminars driven by a growing community of scholars and practitioners, demonstrate the extent to which Open Innovation has grown since the initial publication of Chesbrough’s book in 2003. To supplement the academic perspective, nine of these chapters are from people working in the industry, to provide practical grounding for the Handbook, and to portray open innovation activities in a variety of situations and sectors.

Acknowledgments

This Handbook has been a collective effort involving many contributors, reviewers, and colleagues, whose timely submissions, invaluable feedback, and constant support over the past year made this project a pleasure to work with. Most importantly, this Handbook project would not be possible without our 136 expert contributors. We received many up-​front commitments from top innovation scholars at the proposal stage. Despite working with very tight deadlines the submissions were of very high quality. It’s been a pleasure to read through the chapters and jointly go through the review process. We would like to use this opportunity to thank all the authors of the Handbook chapters for being a delight to work with and for their outstanding contribution to open innovation research. This project received funding in part from the EU’s Horizon2020 research and innovation program.1 We would also like to thank the expert reviewers whose comments were very helpful in revising the chapters. Many of the open innovation experts who reviewed draft chapters were themselves authors of other chapters. The editors would like to thank Alberto Di Minin, Ammon Salter, Anita McGahan, Ann-​Kristin Zobel, Ann Ter Wal, Ari Pekka Hameri, Bill Roschek, Carmelo Cennamo, Chris Tucci, Ernesto Ciorra, Esteve Almirall, Frank Piller, Henry Sauermann, Herb Velazquez, Hervé Legenvre, Ioana Stefan, Jacopo Chroccio, Jim Spohrer, John Bessant, Jonathan Sims, Jonathan Wareham, Justyna Dąbrowska, Karl-​Emanuel Dionne, Keld Laursen, Krithika Randhawa, Laia Pujol Priego, Lukas Falcke, Mallik Tatipamula, Marc Gruber, Marcel Bogers, Marcus Holgersson, Marisol Menendez, Markus Perkmann, Mehdi Bagherzadeh, Melissa Appleyard, Michael Zaggl, Paola Criscuolo, Param Singh, Paul Olk, Paul R. Carlile, Qinli Lu, Reinhold Achatz, Richard Whittington, Stephen Comello, Susanne Beck, Tianjiao Xu, and Xavier Ferràs. To make sure that external perspectives were also taken into consideration in the manuscript development, we were fortunate to receive useful comments and criticisms from a large number of reviewers who did not contribute chapters to the Handbook. We would like to acknowledge their contribution and thank Alberto Bertello, Annabelle Gawer, Asta Pundziane, Carmelo Cennamo, Chad Baum, Ekaterina Albats, Elena Gimenez-​Fernandez, Gary Chapman, Gianlorenzo Meggio, Hans Berends, Ivanka Visnjic, Janet Bercovitz, Jason Li-​Ying, Julia Hautz, Laura Kreiling, Letizia Mortara, 1  The funding was provided under grant no. 956745, EINST4INE: The European Training Network for Industry Digital Transformation across Innovation Ecosystems. The content of this publication does not reflect the official opinion of the European Union.

xx   Acknowledgments Linus Dahlander, Martin Wallin, Mehdi Montakhabi, Nelly Dux, Paavo Ritala, Philip Palermo, Philipp Tuertscher, Pia Hurmelinna-​ Laukkanen, Richard Tee, Siobhan O’Mahony, Stefan Linder, and Yvonne Kirkels. Finally, we would like to thank our colleagues for bearing with us over the past year when our minds were predominantly occupied with editorial work, chapter writing, and endless discussions on the structure, content, and flow of the Handbook. In particular, we would like to thank our colleagues from the Institute for Business Innovation at the University of California Berkeley: Chris Bush, who provided administrative support throughout the editorial process; Adriana Macias, our marketing ninja who promoted both the book and the Open Innovation Seminar; Tristan Gaspi, who always found money to support our ideas; Silvia Farina, who read the first draft of the entire Handbook to apply common standards across all chapters; Mehdi Montakhabi, who, as a co-​organizer of the Berkeley Open Innovation Seminar, made sure that all chapter authors presenting their work received feedback; and Todd Brous, for his passion for sketching, designing, and executing multiple Handbook cover proposals with the use of the latest AI tools. We are grateful for all the efforts behind the scenes of writing, editing, and presenting, and we would not be able to complete this project without their support and friendship. Of course, in an undertaking of this size, there are inevitably errors and omissions that somehow made it through the entire process. We alone are responsible for these, and can only assure intrepid readers that we took many steps to reduce the chances of this occurring. Henry Chesbrough, Agnieszka Radziwon, Wim Vanhaverbeke, Joel West, August, 2023.

Contributors

Reinhold Achatz, Chairman of the Board, International Data Spaces Association (IDSA), Dortmund, Germany Gozal Ahmadova, Research Assistant, Institute for Innovation and Knowledge Management, ESADE Business School, Barcelona, Spain Esteve Almirall, Associate Professor of Innovation and A.I, Department of Innovation, Operations and Data Science, ESADE Business School, Barcelona, Spain Melissa M. Appleyard, Ames Professor in the Management of Innovation and Technology, Portland State University, USA Erkko Autio, Chair Professor of Technology Venturing, Management and Entrepreneurship, Imperial College Business School, London, UK Mehdi Bagherzadeh, Associate Professor of Innovation Management & Head of Department, Strategy & Entrepreneurship Department, NEOMA Business School, France Susanne Beck, Senior Researcher, LBG OIS Center, LBG Open Innovation in Science Center, Vienna, Austria Michela Beretta, Associate Professor, Department of Management, Aarhus University, Denmark Carsten Bergenholtz, Associate Professor, Department of Management, Aarhus University, Denmark John Bessant, Emeritus Professor, University of Exeter, UK Sea Matilda Bez, Associate Professor, University of Montpellier, France Kirsimarja Blomqvist, Professor of Knowledge Management, LUT Business School, Finland Marcel Bogers, Professor of Open & Collaborative Innovation, Eindhoven University of Technology, Netherlands Alexander Brem, Full Professor and Institute Director, University of Stuttgart, Germany Sabine Brunswicker, Director of the Research Center for Open Digital Innovation, Purdue University, USA

xxii   Contributors Stefano Brusoni, Professor of Technology and Innovation Management, ETH Zurich, Switzerland Andrea Canino, Head of Innovation Lab, Enel Green Power SpA, Italy Paul R. Carlile, Professor of Management and Information Systems, Boston University Questrom School of Business, USA Hilary Carter, Senior Vice President, Research and Communications, Linux Foundation, USA Gabriel Cavalli, Ph.D. candidate in Strategic Management, Rotman School of Management, University of Toronto, Canada Henry Chesbrough, Maire Tecnimont Professor of Open Innovation and Sustainability, Luiss University, Rome, Italy; Faculty Director, Garwood Center for Corporate Innovation, Haas School of Business, University of California, Berkeley, USA Ernesto Ciorra, Chief Innovability Officer, Enel Group, Italy Dieudonnee Cobben, Assistant Professor Ecosystem Governance, Department of Strategic Management, Open University, the Netherlands Stephen Comello, Lecturer in Management, Stanford Graduate School of Business, USA Jacopo Cricchio, Ph.D. student of Innovation Management, Institute of Management, Scuola Superiore Sant’Anna, Pisa, Italy Paola Criscuolo, Professor of Innovation Management, Management & Entrepreneurship Department, Imperial College Business School, London, UK Justyna Dąbrowska, Vice Chancellor’s Research Fellow, RMIT University, Melbourne, Australia Kathleen Diener, Professor of Business Informatics and Digital Innovation, Hochschule Niederrhein University of Applied Science, Germany Stefan Dierks, Partner, MHP—​A Porsche Company, Germany Alberto Di Minin, Full Professor of Management, Scuola Superiore Sant’Anna, Pisa, Italy Karl-​Emanuel Dionne, Assistant Professor, Department of Entrepreneurship and Innovation, HEC Montréal, Canada Lukas Falcke, Assistant Professor for Strategy & Innovation, KIN Center for Digital Innovation, School of Business and Economics, Vrije Universiteit, Amsterdam, The Netherlands Xavier Ferràs, Associate Professor, Department of Operations Management, Innovation and Data Sciences, ESADE –​University Ramon Llull, Barcelona, Spain

Contributors   xxiii Nicolai J. Foss, Professor, Department of Strategy and Innovation, Copenhagen Business School, Denmark Hans T. W. Frankort, Professor of Strategy, Bayes Business School (formerly Cass), City University of London, UK Lars Frederiksen, Director of Research, MGMT, BSS, Aarhus University, Denmark Victor Gilsing, Professor of Strategic Entrepreneurship & Organizational Renewal, Vrije Universiteit, Amsterdam, The Netherlands Christoph Grimpe, Professor of Innovation and Entrepreneurship, Copenhagen Business School, Denmark Marc Gruber, Full Professor, College of Management of Technology, Ecole Polytechnique de Lausanne (EPFL), Switzerland Andrei Gurca, Senior Lecturer in Strategy, Queen’s Management School, Queen’s University, Belfast, Northern Ireland John Hagedoorn, Professor of International Business and Strategy, Department of Strategy, International Business and Entrepreneurship, Royal Holloway, University of London, UK Ari-​Pekka Hameri, Professor of Operations Management, HEC Lausanne Susan Hilbolling, Assistant Professor, Department of Management, Aarhus BSS, Aarhus University, Denmark Marcus Holgersson, Associate Professor, Department of Technology Management and Economics, Chalmers University of Technology, Gothenburg, Sweden Pia Hurmelinna-​Laukkanen, Professor, Department of Marketing, Management and International Business, University of Oulu, Oulu Business School, Finland Erica Jones, Education Manager at Youth Haven in Naples, Florida, USA Corina Kuiper, Managing Director, Corporate Venturing Network, Netherlands Keld Laursen, Professor of the Economics and Management of Innovation, Department of Strategy and Innovation, Copenhagen Business School, Denmark Hervé Legenvre, Research Director, Bâtiment Mont-​Blanc, Archamps, France Frédéric Le Roy, Full Professor in Strategic Management, University of Montpellier, Montpellier Management Institute, France Qinli Lu, Postdoc researcher at the INSEAD, France Ann Majchrzak, Professor Emeritus, University of Southern California, USA

xxiv   Contributors Arvind Malhotra, H. Allen Andrew Distinguished Professor and Professor of Strategy & Entrepreneurship, University of North Carolina at Chapel Hill, USA Andre Marquis, Senior Fellow and Lecturer, Haas School of Business, University of California Berkeley, USA Daniella Laureiro Martinez, Associate Professor, Technology and Innovation Management Group, ETH Zurich Anita M. McGahan, George E. Connell Professor of Organizations & Society, University of Toronto, Canada Marisol Menendez Alvarez, CEO & Founder, Bilakatu S.L., Bilbao, Spain Anne-​Laure Mention, Global Business Innovation Enabling Impact Platform Director, RMIT University, Melbourne, Australia Petra Nylund, Researcher, Institute of Entrepreneurship and Innovation Science, University of Stuttgart, Germany Paul Olk, Professor of Management, Department of Management, Daniels College of Business, University of Denver, USA Geoffrey Parker, Charles E. Hutchinson ‘68A Professor of Engineering Innovation, Thayer School of Engineering, Dartmouth College, New Hampshire, USA Markus Perkmann, Professor, Imperial College Business School, Imperial College London, UK Georgios Petropoulos, Research Associate, MIT Sloan School of Management, Massachusetts Institute of Technology, USA Frank Piller, Prof. Dr., RWTH Aachen University, Germany Marion Poetz, Associate Professor, Copenhagen Business School, Denmark Emanuele Polimanti, Executive Assistant to the Chief Innovability Officer -​Innovability, Enel Group, Italy Patrick Pollok, Assistant Professor, RWTH Aachen University, Germany Laia Pujol Priego, Assistant Professor, ESADE Business School, University Ramon Llull, Barcelona, Spain Agnieszka Radziwon, Associate Professor of Innovation Management, Aarhus University, Denmark Krithika Randhawa, Associate Professor of Strategy, Innovation, and Entrepreneurship, The University of Sydney, Australia Angelo Kenneth Romasanta, Postdoctoral Researcher in Innovation Management, Esade Business School, University Ramon Llull, Barcelona, Spain

Contributors   xxv Bill Roschek Jr, Director –​Strategic Innovation, Nestlé Purina PetCare, USA Ammon Salter, Professor, Professor of Technology and Innovation Management, Warwick Business School, University of Warwick Saras Sarasvathy, Paul M. Hammaker Professor of Business Administration, University of Virginia Darden Graduate School of Business, USA Henry Sauermann, Professor of Strategy, ESMT Berlin, Germany Jonathan Sims, Associate Professor of Management, Management Division, Babson College, Wellesley, USA Param Singh, VP Product Management, Salesforce Inc., San Francisco, USA Pernille Smith, Associate Professor, Aarhus University, Denmark Deepak Somaya, Dianne and Steven N. Miller Professor, Department of Business Administration, Gies College of Business, University of Illinois at Urbana-​Champaign, USA Helle Alsted Søndergaard, Associate Professor of Innovation Management, Aarhus University, Denmark Jim Spohrer, Member, Board of Directors, International Society of Service Innovation Professionals Ioana Stefan, Assistant Professor, School of Innovation, Design and Engineering, Mälardalen University, Sweden Yao Sun, Assistant Professor, Department of Social Sciences and Humanities, New Jersey Institute of Technology, USA Mallik Tatipamula, CTO, Ericsson Silicon Valley Labs, USA David J. Teece, Professor of the Graduate School, University of California, Berkeley, USA Anne L. J. Ter Wal, Associate Professor of Technology and Innovation Management, Imperial College Business School, London, UK Christopher L. Tucci, Professor of Digital Strategy & Innovation at Imperial College Business School, London, UK Marshall W. Van Alstyne, Questrom Professor of Management, Boston University, USA Vareska van de Vrande, Professor of Collaborative Innovation and Business Venturing, Department of Strategic Management and Entrepreneurship, Rotterdam School of Management, Erasmus University Rotterdam, The Netherlands Wim Vanhaverbeke, Professor of Digital Strategy and Transformation, University of Antwerp, Belgium

xxvi   Contributors Herb Velazquez, Professor of Practice, Retired, Industrial Design Department, Georgia Institute of Technology, USA Oana Vuculescu, Associate Professor, Department of Management, Aarhus University, BSS, Denmark Jonathan Wareham, Professor of Information Systems, ESADE Business School, University Ramon Llull, Barcelona, Spain Eva Weissenböck, Postdoctoral Research Associate, École Polytechnique Fédérale de Lausanne, Switzerland Joel West, Professor of Entrepreneurship, Hildegard College, USA; Professor Emeritus, Keck Graduate Institute, USA Richard Whittington, Professor of Strategic Management, Said Business School, University of Oxford, UK Tianjao Xu, Postdoc at Department of Strategy and Innovation, Copenhagen Business School, Denmark Michael Zaggl, Associate Professor of Management, Aarhus University, Denmark James Zemlin, Executive Director, Linux Foundation, USA Ann-​Kristin Zobel, Associate Professor of Management, Institute of Management and Strategy, University of St. Gallen, Switzerland

part i

OPEN INNOVATION PAST, PRESENT, AND FUTURE

CHAPTER 1

A REC ONSIDERAT I ON OF OPEN INNOVATI ON A FT E R 20 Y EARS henry chesbrough

Open Innovation first emerged as a concept 20 years ago, with the publication of my book with that title (Chesbrough, 2003). its initial presentation, the evidence used to support this new concept was largely qualitative in nature, with in-​depth case studies of several technology companies. Like other successful books, the timing of this book proved to be fortuitous. The dot-​com bubble had burst, the economy in North America and Europe was recovering, and there was now an opportunity to look forward again. We knew we could not return to the status quo ante, but where and how should companies strive to grow, going forward? Open Innovation was a possible answer. Open Innovation also provided a new insight into some phenomena that were otherwise hard to explain. As Thomas Kuhn (1962) pointed out in his masterpiece, The Structure of Scientific Revolutions, accepted scientific dogma is periodically challenged by an accumulation of anomalies, in which the observed phenomena do not align with the predictions of the currently held theory. Over time, these anomalies accumulate, and understanding them demands a new theory that better explains the evidence. Then, and only then, a new theory emerges that changes our understanding of the theory and the evidence. As I wrote my first book, Open Innovation, I was pondering a series of such anomalies. I had studied Xerox’s Palo Alto Research Center quite closely. This world-​class laboratory had produced many wonderful technologies, but most of their business value had accrued to other firms. I followed Lucent Technologies out of Bell Labs, and watched it bring a powerful lab and a strong patent portfolio to market against Cisco, who had neither of these assets. Yet Cisco is the firm that prevailed. In the life sciences, the Big Pharma firms who organized innovation from the lab to the patient were losing out to a relay race of firms who took new research out of academic medical centers, started up specialty firms, and often licensed successful products to the pharma companies. In my second book, Open Business Models, Procter & Gamble (which had a deep internal

4   henry chesbrough R&D network) was shown to embrace external technology scouting to identify external products that turned into new billion dollar consumer brands. The prevailing wisdom of the time derived from the work of Michael Porter and Alfred Chandler, two very influential Harvard Business School professors. Porter’s work argued that firms would innovate more effectively by creating or increasing entry barriers, to keep other firms out of the industry (Porter, 1980, 1985). Chandler argued that managing R&D was done best by managing internal R&D through vertical integration to create economies of both scale and scope. In both professors’ views, the real action was inside the firm, and the outside world was not fundamentally a part of the innovation process (Chandler, 1990). Yet the anomalies shown in Open Innovation did not fit with Porter’s framework and Chandler’s account. These incumbent firms like Xerox and AT&T had invested heavily in internal R&D, yet each incumbent was having great difficulty responding to the new innovation opportunities they were confronting. As I looked across the case studies, a pattern began to emerge: the internal vertical integration processes of the 20th century were giving way to a more open, more distributed process of innovation. This trend was also noted by other scholars, such as Arora, Fosfuri, and Gambardella’s (2001) work on markets for technology, and Richard Langlois’ (2003) “post-​Chandlerian firm.” Open Innovation became a new theory to explain these otherwise puzzling anomalies, and popularized other academic work that might otherwise have remained obscure. Consider the example of Xerox. I spent a significant amount of time at Xerox and its Palo Alto Research Center. Some of my research there tracked 35 projects that started inside of Xerox’s labs and got to a certain level of development, but then internal funding for all these projects was stopped. I was curious as to what happened to these projects subsequently because in 30 of the 35 cases Xerox proactively encouraged the employees working on them to leave and take them out to the external market. Why? Because once these people left the lab, that budget was freed up to be redeployed in the lab for something that was more strategic and promising for Xerox’s core business. One of the things I discovered was that most of the 35 projects, when they went outside, subsequently failed. And that was what Xerox expected. Since they didn’t see the value of continuing the project, they assumed that there wasn’t much value to be realized. But I also found a fascinating anomaly: 10 of the projects that went outside succeeded brilliantly, and actually became publicly traded companies. If you added up the market value of those publicly traded spinoff entities, it more than exceeded Xerox’s own market value (Chesbrough, 2002). No one inside Xerox had ever expected that result. Back in the day, Michael Porter and Alfred Chandler would have had a very hard time explaining this surprising phenomenon. So that really made me think how to better understand this, and how organizations could innovate more effectively. Could an innovation system be more open? In the example of Xerox, their core innovation processes were doing a good job of commercializing certain technical projects that really fit well with their business model. But there were also these other projects that didn’t fit with the core—​this probably sounds familiar to any manager. In many companies, these projects are simply were

A RECONSIDERATION OF OI AFTER 20 YEARS    5 discontinued internally but, in the Xerox case, when they exited to the outside, they sometimes found different business models that made them much more attractive as standalone entities. I have come to think of these misfit projects as “false negatives”: projects that (at a particular point of time) lacked value in the context of the company’s current business model, but might have significantly more value if commercialized through a different business model. The root of the problem is that innovation involves a substantial degree of both market and technical uncertainty. When evaluating projects under these conditions, managers will exercise their best judgment, but will sometimes commit evaluation errors, derived from a confirmation bias anchored on their dominant business model. These can be “false positives”: projects that looked highly promising, and were launched onto the market, where they promptly failed. Or they can be “false negatives”: projects that were stopped during the innovation process, because they were judged to be unpromising (Chesbrough, 2006). But some of these projects that did manage to continue outside the organization went on to become successful, hence the false negative label. (This concept of false negatives is also something not discussed in the previous research on innovation processes.) OI treats false negatives as a consequence of a mismatch between a potential technology and the company’s business model. This mismatch means that the false negative project needs to be managed through processes that explore alternative business models internally, or to spin off the technology outside the firm, to allow the nascent venture to locate a different business model. These false negatives are at the root of the inside-​out branch of the OI model (Chesbrough & Garman, 2009). A second set of new insights arising from anomalies in Open Innovation lies in the treatment of intellectual property. In the Closed Innovation model, companies historically accumulated intellectual property to provide design freedom to their internal staff. The primary objective was to obtain freedom to operate, and to avoid costly litigation. As a result, most patents are actually worth very little to these companies, and the vast majority are never used by the business that holds them.1 In Open Innovation, intellectual property (IP) represents a new class of assets that can deliver additional revenues to the current business model, and also point the way toward entry into new businesses and new business models. Open Innovation implies that companies should be both active sellers of IP (when it does not fit their own business model) and active buyers of IP (whenever external IP does fit their own business model well). IBM and Philips in the 2000s are examples of this more open attitude toward one’s IP. One empirical way to operationalize this insight is to calculate the patent utilization rate of a focal firm. Start with all the patents that this company owns. Then observe what 1 

While comprehensive evidence of these points is not yet available, some elements are already in the literature. Lemley (2002, pp. 11–​12) cites studies that report a large fraction of patents are neither used, nor licensed by firms. Sakkab (2002) states that less than 10% of Procter & Gamble’s patents were utilized by one of P&G’s businesses. Gruner (2022) states that the majority of US patents are abandoned before expiration.

6   henry chesbrough percentage of these patents is actually used in at least one of the businesses of this company. Often, companies don’t know this percentage, because no one typically asks this question in the Closed Innovation model. One fact is known: about two-​thirds of all the issued patents in Europe are allowed to lapse before their 20-​year expiration date, because the company didn’t want to continue paying the renewal fees to keep the patent in force (Hikkerovaa et al., 2014). In cases where large companies have taken the trouble to analyze their own patent usage, the percentage used is often quite low, between 10% and 30% (Chesbrough, 2006). This means that 70–​90% of a company’s patents are not used. In most companies, these unused patents also are not offered outside for licensing either. With an Open Innovation approach, a company with a low patent utilization rate might benefit from opening up its patents to others for their use. Vanhaverbeke and Gilsing (Chapter 4, this volume) discuss this matter further. However, a lot has changed in Open Innovation since 2003, as Radziwon and Chesbrough (Chapter 2, this volume) discuss at more length. Here, I will highlight certain recent developments and what they mean for industry, innovation, and policy. One theme that will emerge is that Open Innovation requires the right culture, capabilities, and processes, in order to deliver positive business results to the organization (Chesbrough, 2019). A second theme is that Open Innovation has spread well beyond collaborations and partnerships between two organizations (though that remains an important part of its operation) to a much broader canvas of supply chains, networks, ecosystems, and public-​private partnerships (Bogers et al., 2017; West et al., 2006). For Open Innovation to thrive, we need to build physical or digital ecosystems and growth infrastructures to support innovating organizations (Radziwon et al., 2022). In addition, Open Innovation is being extended beyond for-​profit enterprises via user innovation, social innovation, and moonshot initiatives.

Defining Open Innovation Let’s start by defining Open Innovation. In my own work, the Open Innovation paradigm is best understood as the antithesis of the traditional vertical integration model, where internal innovation activities lead to internally developed products and services that are then distributed by the firm. Put into a single sentence: Open Innovation is a distributed innovation process based on purposively managed knowledge flows across organizational boundaries, using pecuniary and non-​pecuniary mechanisms in line with the organization’s business model (Chesbrough & Bogers, 2014, p. 1). This means that innovation is generated by accessing, harnessing, and absorbing flows of knowledge across the boundary of the firm, either flowing in or going out.2

2 

Every theory has its boundaries: Open Innovation is a theory of distributed knowledge flows in innovation, so openness that does not result in innovation is outside the scope of the theory.

A RECONSIDERATION OF OI AFTER 20 YEARS    7 In this definition of Open Innovation, we assume that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as they look to advance their innovations. Open Innovation processes combine internal and external ideas together into platforms, architectures, and systems. Open Innovation processes utilize business models to define the requirements for these architectures and systems. The business model utilizes both external and internal ideas to create value, while defining internal mechanisms to claim some portion of that value. This definition animates all of the chapters in this Handbook, though some of them (e.g., Vanhaverbeke & Gilsing, Chapter 4, this volume) will push the boundary of this definition, as they analyze the innovation activities of today’s firms. There are two important knowledge flows in Open Innovation: outside-​in Open Innovation and inside-​out Open Innovation. The outside-​in part of Open Innovation involves opening up a company’s own innovation processes to many kinds of external knowledge inputs and contributions. It is this aspect of Open Innovation that has received the greatest attention, both in academic research and in industry practice. A lot has been written about technology scouting, about crowdsourcing, about open source technology, and licensing in or acquiring technology. Many scholars and industry people think that is all that Open Innovation is about. But that’s inaccurate. There is a second branch of those knowledge flows that is also a critical part of the concept. Inside-​out Open Innovation requires organizations to allow unused and under-​ utilized knowledge to go outside the organization for others to use in their businesses and business models. This could result in licensing out a technology, or spinning off a new venture, or contributing a project to an open commons, or forming a new joint venture with outside parties. In contrast to the outside-​in branch of Open Innovation, this portion of the model is less well understood, both in academic research and also in industry practice.

The Roots of Open Innovation What’s behind the definition of Open Innovation? Like all research in innovation, Open Innovation stands on the shoulders of previous scholars’ work. The definition of Open Innovation that is based on “purposive inflows and outflows of knowledge” hearkens back to a vibrant economic literature on spillovers that arise from the firm’s investment in research and development. Because firms cannot fully specify the outcomes of this investment in advance, R&D inevitably produces outcomes that were not expected beforehand. These outcomes spill over beyond the ability of the investing firm to benefit from them, hence the term “spillovers.” Alfred Chandler (1990) celebrated the benefits of internal R&D in his magisterial book, Scale and Scope. R&D not only enabled firms to scale their inventions and reduce their costs, but it also revealed adjacent market opportunities to expand the scope of the company’s business. David Mowery (1983) documented the rise of the R&D laboratory

8   henry chesbrough in American manufacturing firms, attributing this rise to the advantageous costs of organizing innovation internally, compared to doing so through the market. Spillovers that occurred in the R&D process were of secondary concern. Economist Richard Nelson observed back in 1959 that basic research generated many such spillovers, and that firms who funded this research had only limited ability to appropriate value from these spillovers (Nelson, 1959). Nobel Laureate Kenneth Arrow (1962) also took note of this spillover problem, recognizing that these spillovers meant that the social return to R&D investment exceeded that of the private return to the firm undertaking the investment. Hence, he reasoned, private firms will underinvest in R&D from a social perspective. Economists Wes Cohen and Dan Levinthal (1990) in turn wrote about the importance of investing in internal research in order to be able to utilize external technology, an ability they termed “absorptive capacity.” Nathan Rosenberg (1990) asked a related question—​why do firms conduct basic research with their own money?—​and answered that this research enhanced the firm’s ability to use external knowledge. It is important to note, however, that the specific mechanisms to enable companies to absorb external knowledge were not identified by these scholars. Nor was there any consideration of companies opting to move unused internal knowledge out into the wider environment, which might enable the firm to obtain additional revenues, or lower their costs of sustaining the technology over time. Adjacent to the economics of R&D literature, there was an empirical thread on strategic alliances (e.g., Hagedoorn, 1993, 2002). There was also a stream of research on corporate entrepreneurship (e.g., Baden-​Fuller, 1995; Covin & Miles, 1999). One contribution of open innovation was to connect these previously disparate research streams. Throughout this earlier literature, spillovers are deemed a cost to the focal firm of doing business in R&D, and are judged to be essentially unmanageable. This is the critical conceptual distinction made by the Open Innovation concept, which proposes that, in the Open Innovation model of R&D, spillovers are transformed into inflows and outflows of knowledge that can be (and should be) purposively managed. Firms can develop processes to seek out and transfer in external knowledge into their own innovation activities. Firms can also create channels to move unutilized internal knowledge from inside the firm out to other organizations in the surrounding environment (Chesbrough & Garman, 2009). Thus, what was unspecified and unmanageable before as a cost of doing business can now be structured and managed in the Open Innovation model and become sources of new opportunity, new cost reduction, a way of sharing risk, or a process to develop new offerings.

The Scope of Open Innovation Open Innovation’s scope extends beyond any single innovation process such as: (1) crowdsourcing, where someone looking for a breakthrough concept or solution submits

A RECONSIDERATION OF OI AFTER 20 YEARS    9 a problem for a group or crowd to solve; (2) managing one’s suppliers better or managing more of them; or (3) open source software or the open source methods inspired by open source software. To be sure, these mechanisms do demonstrate aspects of open innovation, since they involve distributed knowledge flows across organizational boundaries. But Open Innovation’s scope is broader: it is a more general process involving the creation and management of purposive knowledge flows across organizations. This last example deserves more discussion, as it is a very common misconception. The open source approach to Open Innovation often omits the business model and takes no account of the inside-​out half of the Open Innovation model. It also treats intellectual property (IP) as a barrier to innovation, ideally one that should be eliminated. The work of Eric von Hippel, for example, analyzes “open and distributed innovation,” using the example of open source software as the motivating example for his analysis.3,4 West and Gallagher (2006) provide a good analysis of this issue, showing how open source software actually can be harnessed to strengthen one’s business model. Both views of Open Innovation share the insight that being open is a powerful generative mechanism to stimulate a lot of innovation. Von Hippel rightly notes that users are a powerful source of innovation in the early stages of a new product. The differences between “free” and “open” become more apparent once the initial stage of a new innovation is over and that innovation begins to gain traction in the market. At this point, hobbyists give way to companies that come into the market to commercialize these innovations, business models are created, and capital investments have to be made to grow volume sufficiently to spread throughout the society. An example is Linux, one of the most successful open source projects (see Zemlin, Chapter 44, this volume). While Linux was created by Linus Torvalds and a small community of volunteers early on, it is sustained today by the participation of companies like Amazon, IBM, Intel, Google, Microsoft, and Oracle, who have built business models around Linux and have driven its usage in the enterprise.5 This tension in Open Innovation definitions was later resolved by the work of Dahlander and Gann (2010) and Dahlander, Gann, and Wallin (2021). They connected the outside-​in and inside-​out knowledge flows of Open Innovation with both pecuniary and non-​pecuniary reasons for sourcing or sharing that knowledge. The resulting 2x2 incorporates both the Chesbrough and the von Hippel definitions of open innovation. Their 2010 paper was the single most-​cited paper in Research Policy during the decade from 2010 to 2020, indicating its strong embrace by the academic community. 3  Von Hippel (2005) discusses open innovation in quite some detail, but does not cite my 2003 book at all. There also is no discussion of a business model anywhere in his book. 4  Jim Euchner of the data analytics and market research company, IRI, has usefully distinguished open innovation from what he calls “open source innovation,” with the latter corresponding to von Hippel’s treatment of the concept (Euchner, 2010). Dahlander and Gann (2010) and Dahlander, Gann, and Wallin (2021) later successfully resolved this tension. 5  It is worth noting, for example, that the Linux Foundation that governs the Linux kernel these days is comprised of companies like IBM, Intel, Oracle, Dell, Nokia, and others. Membership on the Board requires an investment of $500,000, well beyond the financial capacity of any hobbyist.

10   henry chesbrough

Empirical Validation of Open Innovation Since Open Innovation was first published in 2003, thousands of articles have appeared that explain and expand upon this approach to understanding innovation. A recent search on Google Scholar found more than 160,000 citations to “open innovation.” This considerable literature, in turn, has created problems, as well as insight. There is now so much material published in the open innovation literature that it can be a daunting task to know where to start, what is most important, and where the field is heading. Indeed, this is a primary motivation for developing this Handbook. Nor is interest in open innovation restricted to academic scholars. A recent search on LinkedIn found 665,000 people who had “open innovation” in their job profile (and more than 11,000 job openings with “open innovation” as part of the job description). Open Innovation might not have spread very far, were it not for certain salient research that validated, extended, and updated its ideas. Next to Dahlander, Gann, and Wallin (2021), another important analysis that cleverly measured certain elements of open innovation came from Laursen and Salter (2006) in the Strategic Management Journal (SMJ). Using the Community Innovation Survey (CIS) in the UK, these scholars created count measures of external sources of knowledge, and used this count measure as a proxy for open innovation. Firms with a higher count measure in fact exhibit improved innovation performance. However, past a certain threshold, more sources of knowledge actually showed a negative effect on innovation performance, a curvilinear result. This study operationalized open innovation by examining both search breadth and search depth, and many studies have since replicated its findings—​including the curvilinear pattern—​with CIS data of other countries. This was the first large sample empirical study of open innovation, and powerfully validated the concept. This paper received an award in 2021 from the Strategic Management Society for being one of the most impactful papers published by SMJ in that period.6 Another important empirical study examined open innovation’s influence at the project level. Du, Leten, and Vanhaverbeke (2014) studied projects in a large European manufacturing firm, some of which included significant elements of external knowledge. In this case, the company supplying the data recorded the use or non-​use of external knowledge directly for each project, so no proxy measures needed to be constructed. Incorporating external knowledge helped to either accelerate the time to market for the project, or achieve greater differentiation and value added for the project. Some external partners, such as universities, helped with greater differentiation, while other partners helped with accelerating time to market. This too was a study of

6 

https://​www.bath.ac.uk/​announ​ceme​nts/​profes​sor-​ammon-​sal​ter-​wins-​strate​gic-​man​agem​ent-​ soci​ety-​award/​ (accessed December 6, 2022).

A RECONSIDERATION OF OI AFTER 20 YEARS    11 outside-​in open innovation, and again provided additional empirical support for some of the performance claims of Open Innovation. Other empirical studies have found more mixed support for the performance benefits of open innovation. Huizingh (2011) reviewed a variety of evidence on open innovation effectiveness, and reports mixed results. Laursen and Salter (2014) found a “paradox of openness,” where the benefits of opening up must be balanced against the risks of unwanted expropriation. Cassiman and Valentini (2016) found evidence that open innovation’s inflows and outflows of knowledge were not complementary, because R&D costs to generate knowledge outflows could rise faster than the benefits of those outflows. Brunswicker and Vanhaverbeke (2015) argued that certain Open Innovation practices were not beneficial in enhancing firm innovation performance. Felin and Zenger (2020) claimed that the general advice implied in the open innovation literature lacks specificity, and more nuanced research on firm-​level strategy for open innovation is needed to better understand specific open innovation strategies. Lu and Chesbrough (2022) discuss these mixed empirical findings on Open Innovation’s impact on firm performance.

20 Years After—​What’s Different? Reflecting back on the Open Innovation concept after two decades, several positive developments have arisen. The focus on theoretical anomalies has been quite productive for scholars, and we now have a much richer perspective on innovation than existed in the time of Porter and Chandler. Certain external sources of knowledge, such as startups and universities, are today regarded as being of first-​order importance in a company’s ability to innovate. Relatedly, venture capital and crowdfunding are also more fully included in innovation research these days, alongside government research funding and corporate R&D expenditures. And intellectual property is no longer managed as a specialized task best left to the lawyers. Another positive development has been the emergence of several institutions that have advanced the research on open innovation, and created a strong sense of community among scholars in the process. The Open and User Innovation Conference has been held for two decades now. The World Open Innovation Conference will hold its tenth meeting in the fall of 2023. The weekly Open Innovation Research Seminar at Berkeley has been meeting regularly since 2014. The Academy of Management has hosted annual Professional Development workshops (PDW) on open innovation since 2014, building on an earlier AOM workshop in 2004 that helped to establish open innovation in the academic community.7 Several academic journals have offered multiple special issues 7  This PDW resulted in the academic volume, Open Innovation: Researching a New Paradigm (Chesbrough et al., 2006). This was the first volume to connect Open Innovation concepts with the broader innovation community in academia.

12   henry chesbrough focused on open innovation, creating numerous opportunities for scholars to present their work to an interested audience. There are now chairs in Open Innovation at several universities: the Maire Tecnimont Chair in Open Innovation and Sustainability in Luiss University in Rome; a chair in Open Digital Innovation at Purdue University, USA, and a chair in Open and Collaborative Innovation at the Technical University in Eindhoven, in the Netherlands. These institutional supports promise to advance open innovation research even further going forward. However, our understanding of Open Innovation cannot be considered anywhere near complete, despite 20 years of research. Far too little of the burgeoning literature on the topic takes a critical approach. Open Innovation doesn’t always work, yet its costs, barriers, and limitations have not received adequate attention. We have dedicated a whole Part of this Handbook to Challenges and Critiques of Open Innovation (see Part X), but I will mention a few here as well. A well-​known phenomenon in R&D that can impair Open Innovation’s effectiveness is the Not Invented Here (NIH) syndrome (Antons & Piller, 2015; Antons et al., 2017; Hannen et al., 2019). Organizations with strong technical histories often feature R&D personnel who are convinced that if they didn’t invent it themselves, it must not be important or must not be very good. This hubris arises typically in organizations with a solid history of good technical results, and non-​technical people are not able to evaluate the capability of internal R&D very effectively on their own. Open innovation depends on internal R&D staff at many critical stages of the innovation process, and so an organization with a strong NIH culture might find many ways to subvert the open innovation process. A second source of limitations comes from the organization of knowledge flows inside organizations (see Vanhaverbeke et al., Chapter 53, this volume). Internal siloes inside large companies often frustrate open innovation, because useful internal knowledge gets trapped in specific functions, or is hoarded by defensive managers. It can be a challenge to break through these siloes, because busy managers sometimes lack an incentive—​as well as the knowledge—​to change their ways of working to accommodate new innovations. A third concern comes from opening up a previously vertically-​integrated innovation process. In the Closed Innovation model, an internal R&D unit supplies innovations to one or more downstream business units. Essentially, the R&D unit is a monopoly (from the perspective of the business units) while the business units are a monopsony (from the perspective of the R&D unit). Opening up this internal stack injects competition between internal and external actors. Upstream in the R&D unit, external sources of knowledge sometimes vie with internal projects for the chance to meet the needs of the business units. Downstream, opening up might mean that if the business unit does not embrace a new innovation from the R&D unit, that project might be allowed to go outside the company for use by others, even potentially competitors. Injecting these kinds of competition inside organizations creates new frictions and tensions between these units. Twenty years on, it must also be said that there has not been nearly as much attention paid to inside-​out open innovation as has been received by outside-​in open innovation.

A RECONSIDERATION OF OI AFTER 20 YEARS    13 The very concept of a false negative project evaluation has not been much considered, in theory or in practice. Many companies under-​utilize their library of patents and other know-​how assets. Most internal R&D projects that don’t fit with the dominant business model are left on the shelf (Chesbrough, 2006). It is quite possible that some shelved internal projects might find new life in a new market, if they were allowed the chance to pursue that opportunity outside the company (one example of this is repurposing abandoned pharmaceutical compounds, as in Chesbrough & Chen, 2013). Yet there are real barriers to doing this, not because of the fear that those exploring the opportunity would be wasting the company’s time or resources. The real barrier is the fear that those explorers may succeed in finding a new market. Instead of celebrating this success, those inside the company who previously shelved the technology now look bad. As long as the project stays shelved, no one is at risk of being embarrassed. But allowing the project out of the organization invites the possibility of appearing to look foolish (Bez & Chesbrough, 2020). This behavioral response might be termed Fear of Looking Foolish, or FOLF (with a nod to the millennial notion of Fear of Missing Out, or FOMO). Vanhaverbeke and Gilsing (2023) will look at this question from another perspective as well in Chapter 4.

Open Innovation After 20 Years of Practice My goal in writing the book Open Innovation 20 years ago was to describe a new approach to innovation, with the expectation that companies would readily adopt it, once they understood it. This has proven to be a bit naïve. After watching companies implement Open Innovation, or try to, it is clearer now to me that simply describing this new approach is not enough to motivate its successful adoption and use. Open Innovation touches many parts of an innovating organization, and each of these parts must adapt its ways of working before open innovation will yield any meaningful results. I clearly underestimated the extent of internal change needed, in order to practice Open Innovation effectively. In this Handbook, there are eight chapters that record the experience of companies trying to innovate with Open Innovation. Behind each of these chapters is an organization that has had to change many of its business processes and practices, in order to use Open Innovation well. In each of the eight chapters, the end result was a tangible success. But readers should realize that this success was hard won, and emerged only after a period of internal struggle. A related discovery 20 years on is that there will never be a single set of Best Practices to implement Open Innovation. Recent research based on the Russell 3000 companies in the US demonstrates that specific open innovation practices work differently in different sectors of the economy (Lu & Chesbrough, 2022). Practices, such as joint

14   henry chesbrough ventures, that are commonly employed in real estate and finance in the US, for example, are less prevalent in healthcare. Collaborations with universities, which are very important in healthcare, are seldom observed in real estate and finance in the US. So caveat emptor: companies must be thoughtful in choosing which Open Innovation practices to use to advance innovation in their own context. One size will not fit all. By incorporating eight fresh examples of Open Innovation employed in industry in this Handbook, we hope to stimulate other managers to broaden their perspective beyond a single set of Best Practices, to figure out how they might best implement Open Innovation in their own context.

Conclusion Open Innovation began as a series of case studies that examined collaborations between two organizations to open up the internal innovation process of the focal firm. Today, though, we see many instances in which the concept is being used to orchestrate a significant number of players across multiple roles in the innovation process, in some cases, to address urgent grand challenges (see Cavalli & McGahan, Chapter 34, this volume). Put simply, designing and managing innovation communities, platforms, and ecosystems are going to become increasingly important to Open Innovation’s future. Several chapters in this Handbook articulate this development at length. Because so much has now been written on Open Innovation, the four editors of this Handbook thought it timely and necessary to compile a series of chapters on various aspects of Open Innovation, so that there could be a definitive reference available to both scholars and managers. This Handbook features 57 chapters in total, covering most of the key developments in the Open Innovation literature that have emerged in the past 20 years. These chapters collectively demonstrate the many ways that research has extended, updated, and advanced the concept of Open Innovation. Several Parts organize these developments:

• • • • • • • • •

Open Innovation Within Firms (Part II) Open Innovation Among Firms (Part III) Networked Forms of Open Innovation (Part IV) Implications for Public Policy (Part V) New Developments in Open Innovation (Part VI) Open Innovation and Theory (Part VII) Open Innovation in Practice (Part VIII) Open Innovation and Teaching (Part IX) Challenges, Critiques, and Suggestions (Part X)

A RECONSIDERATION OF OI AFTER 20 YEARS    15 Taken together, the chapters in this Handbook show us the future of Open Innovation, a future that will be more extensive, more collaborative, and more engaging with a wider variety of participants. Just as no man is an island, no firm will be successful in an Open Innovation world if they restrict themselves to the prescriptions of Porter and Chandler. Instead, these companies must embrace the bountiful useful knowledge that exists all around them, and find ways to identify, harness, and deploy that knowledge to advance their business—​before their competitors do so. And both scholars and managers need to understand the limits and costs of Open Innovation, as they strive to benefit from it in the future. The innovation capabilities of organizations in an Open Innovation world no longer stop at the boundaries of one’s own organization. Instead, an organization’s Open Innovation practices, capabilities, and culture will extend to suppliers, customers, partners, complementors, third parties, and the general community as a whole. As one R&D manager explained to me, “It used to be that the lab was our world; with Open Innovation, the world is now our lab.”

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16   henry chesbrough Cavalli, G., & McGahan, A. M. (2023). Opening innovation to address grand challenges. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 34, pp. 567–589). Oxford University Press. Cassiman, B., & Valentini, G. (2016). Open innovation: are inbound and outbound knowledge flows really complementary?. Strategic Management Journal, 37(6), 1034–​1046. Chandler, A. (1990). Scale and scope: The dynamics of industrial capitalism. Harvard University Press. Chesbrough, H. (2002). Graceful exits and missed opportunities: Xerox’s management of its technology spin-​off organizations. Business History Review, 76(4), 803–​837. Chesbrough, H. (2003). Open innovation: The new imperative for creating and profiting from technology. Harvard Business School Press. Chesbrough, H. (2006). Open business models: How to thrive in the new innovation landscape. Harvard Business School Press. Chesbrough, H. (2019). Open innovation results: Going beyond the hype and getting down to business. Oxford University Press. Chesbrough, H., & Bogers, M. (2014). Explicating open innovation: Clarifying an emerging paradigm for understanding innovation. In H. Chesbrough, W., Vanhaverbeke, & J. West (Eds.), New frontiers in open innovation (pp. 3–​28). Oxford University Press. Chesbrough, H., & Chen, E. L. (2013). Recovering abandoned compounds through expanded external IP licensing. California Management Review, 55(4), 83–​101. Chesbrough, H., & Garman, A. R. (2009). How open innovation can help you cope in lean times. Harvard Business Review, 87(12), 68–​76. Chesbrough, H., Vanhaverbeke, W., & West, J. (Eds.). (2006). Open innovation: Researching a new paradigm. Oxford University Press. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–​152. Covin, J. G., & Miles, M. P. (1999). Corporate entrepreneurship and the pursuit of competitive advantage. Entrepreneurship Theory and Practice, 23(3), 47–​63. Dahlander, L., & Gann, D. M. (2010). How open is innovation? Research Policy, 39(6), 699–​709. Dahlander, L., Gann, D. M., & Wallin, M. W. (2021). How open is innovation? A retrospective and ideas forward. Research Policy, 50(4), 104218. Du, J., Leten, B., & Vanhaverbeke, W. (2014). Managing open innovation projects with science-​ based and market-​based partners. Research Policy, 43(5), 828–​840. Euchner, J. A. (2010). Two flavors of open innovation. Research Technology Management, 53(4), 7–​8. Felin, T., & Zenger, T. R. (2020). Open innovation: A theory-​based view. Strategic Management Review, 1(2), 223–​232. Gruner, R. (2022). Does anybody see what I see?: Abandoned patents and their impacts on technology development. NYU Journal of Intellectual Property and Entertainment Law, 11(2). Online. Hagedoorn, J. (1993). Understanding the rationale of strategic technology partnering: Interorganizational modes of cooperation and sectoral differences. Strategic Management Journal, 14(5), 371–​385. Hagedoorn, J. (2002). Inter-​firm R&D partnerships: An overview of major trends and patterns since 1960. Research Policy, 31(4), 477–​492.

A RECONSIDERATION OF OI AFTER 20 YEARS    17 Hannen, J., Antons, D., Piller, F., Salge, T. O., Coltman, T., & Devinney, T. M. (2019). Containing the Not-​Invented-​Here Syndrome in external knowledge absorption and open innovation: The role of indirect countermeasures. Research Policy, 48(9), 103822. Hikkerovaa, L., Kammoun, N., & Lantz, J-​S. (2014). Patent life cycle: New evidence. Technology Forecasting and Social Change, 88, 313–​324. Huizingh, E. K. (2011). Open innovation: State of the art and future perspectives. Technovation, 31(1), 2–​9. Kuhn, T. (1962). The structure of scientific revolutions. University of Chicago Press. Langlois, R. N. (2003). The vanishing hand: The changing dynamics of industrial capitalism. Industrial and Corporate Change, 12(2), 351–​385. Laursen, K., & Salter, A. (2006). Open for innovation: The role of openness in explaining innovation performance among UK manufacturing firms. Strategic Management Journal, 27(2), 131–​150. Laursen, K., & Salter, A. J. (2014). The paradox of openness: Appropriability, external search and collaboration. Research Policy, 43(5), 867–​878. Lemley, M. A. (2002). Intellectual property rights and standard-​ setting organizations. California Law Review, 90, 1889. Lu, Q., & Chesbrough, H. (2022). Measuring open innovation practices through topic modelling: Revisiting their impact on firm financial performance. Technovation, 114, 102434. Mowery, D. C. (1983). The relationship between intrafirm and contractual forms of industrial research in American manufacturing, 1900–​1940. Explorations in Economic History, 20(4), 351–​374. Nelson, R. R. (1959). The simple economics of basic scientific research. Journal of Political Economy, 67(3), 297–​306. Porter, M. (1980). Competitive strategy. Harvard Business School Press. Porter, M. (1985). Competitive advantage. Harvard Business School Press. Radziwon, A., Bogers, M. L., Chesbrough, H., & Minssen, T. (2022). Ecosystem effectuation: Creating new value through open innovation during a pandemic. R&D Management, 52(2), 376–​390. Radziwon, A., & Chesbrough, H. (2023). Open innovation as a field of knowledge. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 2, pp. 19–36). Oxford University Press. Rosenberg, N. (1990). Why do firms do research (with their own money)? Research Policy, 19, 165–​174. Sakkab, N. Y. (2002). Connect & develop complements research & develop at P&G. Research-​ Technology Management, 45(2), 38–​45. Vanhaverbeke, W., Chesbrough, H., West, J., & Radziwon, A. (2023). Overcoming organizational obstacles to open innovation success. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 53, pp. 849–868). Oxford University Press. Vanhaverbeke, W., & Gilsing, V. (2023). Opening up open innovation and drawing the boundaries. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 4, pp. 51–64). Oxford University Press. von Hippel, E. (2005). Democratizing innovation. MIT Press.

18   henry chesbrough West, J., & Gallagher, S. (2006). Patterns of open innovation in open source software. In H. Chesbrough, W. Vanhaverbeke, & J. West (Eds.), Open innovation: Researching a new paradigm (pp. 82–​106). Oxford University Press. West, J., Vanhaverbeke, W., & Chesbrough, H. (2006). Open innovation: A research agenda. In H. Chesbrough, W. Vanhaverbeke, & J. West (Eds.), Open innovation: Researching a new paradigm (pp. 285–​307). Oxford University Press. Zemlin, J. (2023). Ubiquitous software innovation building block: Open source. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 44, pp. 729–742). Oxford University Press.

CHAPTER 2

OPEN INNOVAT I ON AS A F I ELD OF KNOW L E D G E agnieszka radziwon and henry chesbrough

Introduction Open Innovation (OI) has been growing rapidly over the past two decades (Chesbrough, 2003a; Dahlander et al., 2021; West & Bogers, 2014). It gave practitioners a voice and a language to discuss the increasingly dispersed nature of R&D and identified the importance of controlled knowledge spillovers in the innovation process. OI has become more institutionalized in recent years, and scholars not only have studied successful innovation communities but also have enacted the concept by building a dedicated one of their own. This community meets every week during the Spring and Fall semesters at the (virtual) Berkeley Open Innovation Seminar. The community hosts one of the Academy of Management’s biggest Professional Development Workshops every summer and attends an Annual World Open Innovation Conference in the late fall. Moreover, every major academic management conference has OI tracks, top journals host OI special issues with regularity, and Harvard and Berkeley offer respectively MBA and PhD courses on the topic. At this writing, there are three chairs that have been awarded in OI. Finally, practitioners around the world organize and join dedicated OI events, and the EU offers lots of attention to policies and directives following OI principles (Bogers et al., 2017). The impact of these events and the overall scholarship is visible in collaborative academic publications (Beck et al., 2020; Bogers et al., 2017; Dąbrowska et al., 2022) White Papers (Darwin et al., 2019; Darwin et al., 2020; Darwin et al., 2021), teaching cases1 (see Chesbrough & Radziwon, 2021;2 Lakhani, 2008;3 1 For a better overview on Open Innovation cases, visit Bessant (Chapter 3, this volume) and Dąbrowska and Sims (Chapter 51, this volume) as well as https://​www.exnov​ate.org/​teach​ing-​cases-​on-​oi 2  https://​hbsp.harv​ard.edu/​prod​uct/​B5983-​PDF-​ENG?Ntt=​ 3  https://​hbsp.harv​ard.edu/​prod​uct/​608​170-​PDF-​ENG?Ntt=​

20    agnieszka radziwon and henry chesbrough Vanhaverbeke, 20174), and even OI-​inspired movements (see, e.g., the Smart Village Movement described in Chesbrough, 2020a). OI has evolved not only as a concept but also s a field of study and knowledge. That is why, in this chapter, we will review the main OI themes, frameworks, and levels of analysis and provide an overview of how the upcoming Handbook chapters fit and extend the current research landscape. More specifically, we will provide a brief historical perspective, appraise the “state of the art,” and put forward a framework that draws on the Handbook’s assessment of the current knowledge of OI. We then outline the key themes discussed in the OI literature over the last two decades by complementing and further extending the discussion of Dahlander et al. (2021). Our intention is to whet your appetite for a more in-​depth exploration of the individual chapters of this Handbook while offering an overview of the past two decades of OI research.

The Structure of the Handbook The selection of themes and organization of these Handbook chapters is well grounded in the cumulative experiences of publishing several major OI books and edited volumes. Therefore, we wanted to offer a somewhat different experience this time—​short chapters offering a comprehensive discussion through the key theoretical insights with illustrative cases, combined with practitioners’ chapters offering their firsthand managerial experience of OI adoption. In Part I of the Handbook, after the brief walk through the OI definitions (Chesbrough, Chapter 1, this volume), the impact of OI on the innovation management field (Bessant, Chapter 3, this volume), the cost-​benefits depending on the open or closed approach to innovation (Vanhaverbeke & Gilsen, Chapter 4, this volume), clearly distinguishing what OI is not, along with discussing the four main OI modes (West & Bogers, Chapter 5, this volume), we move on to the theme-​specific topics that comprise the bulk of the work in the Handbook. Here we combine the structure of Bogers et al. (2017) with the latest trends proposed by Dahlander et al. (2021) and (in our view) upcoming themes. These cover Within, Among, and Networked forms of OI perspectives along with special attention to regional aspects, new developments, and new themes. Afterwards, we propose a dedicated section outlining the link between OI and the most prominent innovation, strategy, and entrepreneurship theories, written by the founding fathers (and mothers) of these theories. We conclude the book by offering teaching guidance—​ featuring teaching scenarios, practices, and cases, along with critical reflections on the future of OI research. 4 

https://​hbsp.harv​ard.edu/​prod​uct/​W17​559-​PDF-​ENG

OPEN INNOVATION AS A FIELD OF KNOWLEDGE    21

The Evolution of the Field The roots of OI lie in economic spillovers of knowledge from internal R&D, that go back as far as the 1960s—​the analyses of Nelson (1962) and Arrow (1962). Chesbrough (2003a) offers empirical evidence showing that a well-​ established approach to spillovers largely considered as unintended and unmanageable externalities is no longer the one and only way to see knowledge flows. More specifically, it shows that spillovers can be converted into structured and manageable knowledge flows—​both from the outside in and from the inside out. Since the publication of the first OI book by Henry Chesbrough (2003a) and the directly following practitioner-​focused articles (Chesbrough, 2003a, 2003b), we have observed the waves of: (1) systematically published edited volumes (Chesbrough et al., 2006, 2014), (2) special issues with very influential editorials (Huizingh, 2011; West et al., 2014), and (3) state-​of-​the-​art papers (Bogers et al., 2017; Dahlander & Gann, 2010; West & Bogers, 2014). These all together not only outlined excitement about the topic through the development of exploratory cases, measurement instruments, and managerial toolboxes, but also nurtured the field by keeping lively discussions supplied by new empirical findings from different company sizes, industries, and regions.

Early-​Stage Findings and Results OI, labeled as “one of the hottest topics in innovation management” (Huizingh, 2011), was not only a new, timely but also a very rich concept, which offered opportunities but also created certain challenges in its early implementation stages (Dahlander & Gann, 2010; Di Benedetto, 2010). Building a coherent body of knowledge sparked more discussions on open vs. closed innovation (Dahlander & Gann, 2010), innovation process, and outcome (Huizingh, 2011), along with the OI Modes (Gassmann & Enkel, 2004). Initially, those were inspired by the OI definition proposed by Chesbrough (2003a), referring to the combination of internal and external ideas and paths to market to advance the development of new technologies. This definition was further clarified, formalized, and better connected to previous academic work by Chesbrough (2006), where OI was defined as “the use of purposive inflows and outflows of knowledge to accelerate internal innovation and expand the markets for external use of innovation, respectively.” Since the very beginning, the biggest OI interest and most prevalent mode of adoption were outside-​in (inbound) OI, which deals with leveraging external sources of knowledge (and technology) to accelerate innovation. Until today, nearly all scholars have been calling for more inside-​out (inbound) OI studies, which deal with profiting from ideas and assets, which are unused or underutilized internally, by giving them new life outside of organizational boundaries (Chesbrough & Garman, 2009; Dahlander &

22    agnieszka radziwon and henry chesbrough Gann, 2010; Huizingh, 2011).5 Interestingly the latter knowledge’s flow path is one of the novel contributions to the spillover literature, inspired by the idea of false negative evaluation errors in project evaluation inside firms, a cognitive bias derived from the dominant logic of the organization’s business model. This biased evaluations against projects that require a different business model to become commercially attractive and may prevent companies from probing for alternative business models with corporate venture capital (CVC), business partners, or collectively as part of their ecosystems (Chesbrough & Garman, 2009). In the early stage of the field, the firm/​organizational level of the analysis was the most examined—​mainly through inductive, exploratory case studies on early OI adopters—​ often large multinational corporations. Significant attention was also dedicated to networks as well as communities (Füller et al., 2012; West & Gallagher, 2006; West & Lakhani, 2008). Altogether, these studies offered great insights and an excellent overview of the overall OI practices and their benefits (Spithoven et al., 2013). Later, more nuanced knowledge coming from small and medium-​sizes enterprises (SMEs), startups, and intermediaries started to emerge. These studies concerned the motives for opening the innovation process and the importance of timing (early vs late adopters) in this process. There was also a lot of focus on new product development, with a growing curiosity about process innovation (West & Gallagher, 2006). Naturally, more quantitative studies allowed us to determine the relative importance of the qualitatively identified factors, to better understand the chain of effects and both short-​and long-​term strategic consequences, and to test for context dependencies. That’s where we started to learn more about the costs and effectiveness and risks of OI adoption. Vanhaverbeke et al. (2008) observed that OI works differently in SMEs than in large firms. This highlighted that both sizes of firms could benefit from OI, but these benefits may be different. The same authors also advocated for studying the disadvantages, failures, and costs of OI (Faems et al., 2010). Last, but not least Laursen and Salter operationalized the quantitative analysis of OI through the Community Innovation Surveys (2006) and introduced new measures of external search: breadth and depth. This study, along with the follow-​ up research by Belderbos et al. (2010) and Salge et al. (2013), discussed optimal openness and showed how external knowledge can be better used to achieve the highest level of innovation performance (and harm performance if used beyond this point). Less well known but just as important is the idea of search pattern (Grimpe & Sofka, 2009) or search orientation (Chen et al., 2016). Search breadth and depth do not distinguish between different external sources of knowledge, yet the extent that the value of OI depends on the actual combination of different external sources is significant, as there might also be contradictions and complementarities in the use of knowledge. Specific combinations of external partners may be beneficial for the innovating firm,

5  Newcomers to open innovation may have noticed the contributions to outside-​in open innovation provided in the first decade of open innovation. Lichtenthaler’s impact has since been reduced after 16 open innovation papers were retracted over allegations of self-​plagiarism in 2012.

OPEN INNOVATION AS A FIELD OF KNOWLEDGE    23 while others are not. In other words, OI performance depends not only on the breadth and depth of OI but also on the specific mixture of different external knowledge sources.

Wrapping up the First Decade of OI Studies A decade of research on OI offered enough insights to further strengthen the OI definition. This happened by offering more clarity and development to its conceptualization in the edited volume by Chesbrough et al. (2014). That’s when we moved from the combination of internal and external ideas and paths to market (Chesbrough, 2003a) through inflows and outflows of knowledge to accelerate internal innovation and expand the markets for external use of innovation (Chesbrough, 2006) to knowledge flows across organizational boundaries and inbound, outbound, and coupled forms of OI (Chesbrough & Bogers, 2014). That’s where OI was specifically denoted as a distributed innovation process—​knowledge flowing across permeable organizational boundaries and embraced by Chesbrough’s (2006) aspect of purposefulness—​thus control over the flow of knowledge in contrast to barely managed spillovers. The 2014 definition also highlighted both pecuniary and non-​pecuniary mechanisms, inspired by Dahlander and Gann’s work (2010). The inclusion of the non-​pecuniary mechanisms moved OI conceptually closer to User Innovation, proposed by Erik von Hippel (2005), which largely focuses on individuals, who are much more than just customers. However, it is worth mentioning that, unlike Baldwin and von Hippel (2011), “Open” in OI, as a firm-​centric approach, does not denote all information related to the innovation as a public good. Thus, appropriation of the returns to the invention is one of the fault lines between OI and User Innovation scholars, which shows the inspiration of the David Teece appropriation school of thought developed at Berkeley, in contrast to the one proposed by Erik von Hippel and further developed by Carliss Baldwin and Karim Lakhani at Harvard. Nevertheless, the shared lines of thoughts concerning the strong encouragement of firms to source ideas and assets from outside of their boundaries for innovation purposes connect the scholars from both of these communities, which makes them mutually beneficial. Lastly, the part of Chesbrough’s (2003a) OI definition focusing on advancing the development of new technologies was no longer the main highlight. Those differences in the conceptualization of OI may be important when comparing the early findings with the more recent ones. There have been no further changes to the OI definition since 2014, and this is the definition employed by the chapters in this volume. A great way to review the first decade of OI research was offered by West and Bogers (2014). More specifically they provided new insights on outside-​n OI through a four-​ phase model consisting of: (1) obtaining, (2) integrating, and (3) commercializing external innovations—​along with (4) interaction (between the focal firm and its collaborators). While exploring the process of leveraging external sources of innovation, West and Bogers (2014) highlighted the importance of alignment between innovations and a firm’s business models—​sourcing innovations (value creation) is important but

24    agnieszka radziwon and henry chesbrough profiting from innovation (value capture) should not be overlooked (Teece, 1986, 2007). Despite the expansion of OI research to other domains beyond high-​tech industries and products—​such as low-​tech industries and services—​there have still been very few researchers who have examined the coupled form of OI (Burcharth et al., 2014). However, the three OI Modes can only be understood properly while looking into two dimensions: (1) inbound and outbound, and (2) the level of co-​innovation between firms. While pure inbound or outbound are almost transactional (licensing IP, for instance), coupled OI is often based on strong collaboration throughout the innovation process.

The Second Decade of OI During the last decade, OI maintained the position of a hot research topic (Antons et al., 2016) but also started to be seen as an umbrella term for scholars previously studying, networks, alliances, and idea evaluations (Stanko et al., 2017). This prompted the development of (even more) review papers of a different nature (systematic, biometric, and highly collaborative (e.g. Kovács et al., 2015)). We got much better at measuring OI via customers (Dahlander & Piezunka, 2014), co-​patenting (Belderbos et al., 2010), and firm decisions (Mina et al., 2014). There’s been also a substantial contribution to the appropriability discussion, which extended our understanding of the formal and informal appropriability strategies (Laursen & Salter, 2014) and selective revealing and the way industry shocks impact openness (Henkel et al., 2014). The highly collaborative expert review conducted under the leadership of Marcel Bogers and Ann Kristin Zobel with 21 co-​authors (Bogers et al., 2017) offered a holistic view of the OI landscape. Preceded by the open process and discussions at the Academy of Management Professional Development Workshops in 2014 and 2015, the authors adapted and further extended the levels of analysis and possible research objects proposed by Chesbrough and Bogers (2014). Bogers et al. (2017) nicely show how the second decade of OI research inspired exploration of yet overlooked levels of analysis, such as the intra-​organizational level where new research objects are individuals (Dahlander et al., 2016; Salter et al., 2014, 2015), teams (Du Chatenier et al., 2009), and projects (Du et al., 2014; Felin & Zenger, 2014; Lopez-​Vega et al., 2016), at the inter-​organizational level where new research objects are platforms and ecosystems (Nambisan et al., 2018; Radziwon & Bogers, 2018; West, 2014), as well as industry, regional innovation system, and society levels of analysis. In this latter context, the new study objects are nations, cities, and citizens (Almirall et al., 2014; Huff et al., 2013; Matzler et al., 2014). Besides new levels of analysis and new research objects, there have also been many new perspectives to studying OI, such as business models, ecosystems, and platforms (Eckhardt & Shane, 2003; Nambisan et al., 2018; Radziwon et al., 2017), crowdsourcing (Piller & West, 2014), spatial organizations as well as public management started to

OPEN INNOVATION AS A FIELD OF KNOWLEDGE    25 become increasingly popular. There has also been greater attention to the business results of OI, and situations where OI has failed to generate the results expected of it (Chesbrough, 2020a).

OI during the Global Pandemic Besides the two decades of development, during the past two years of the global COVID-​19 pandemic it has proven to be even more important in rethinking, restructuring, and recombining business resources (Bertello et al., 2022; Dahlander & Wallin, 2020; Radziwon et al., 2022), scientific knowledge production process and discoveries, and geopolitics, which shape our collaboration strategies (Beck et al., 2021; Chesbrough, 2020b; Dąbrowska et al., 2022).

Topics in OI Research and the Handbook Framework OI has been having a tremendous impact on scholarly research and managerial practice. Each Part of the Handbook past the introductory section focusing on the past, present, and future of OI introduction focuses on one of the research areas where a separate body of knowledge has been developed.

Established OI Levels of Analysis The Parts of the Handbook largely follow the levels of analysis proposed by Bogers et al. (2017) and are: Part II OI Within Firms Part III OI Among Firms Part IV Networked Forms of OI Part V Implications for Public Policy Part VI New Developments in OI Part VII OI and Theory Part VIII OI in Practice Part IX OI and Teaching Part X Challenges, Critiques, and Suggestions. We now examine these Parts and offer their key highlights.

26    agnieszka radziwon and henry chesbrough

OI Within Firms For many years, OI has been regarded as an organizational-​level phenomenon largely focusing on a firm’s for-​profit activities. This has changed over the years, but despite 20 years of research we still do not know all the answers. To connect the past and present of OI, Part II focuses on individual roles and network practices (Salter et al., Chapter 6, this volume), projects (Bagherzadeh & Gurca, Chapter 7, this volume), small and medium-​sized enterprises (Radziwon & Vanhaverbeke, Chapter 8, this volume), high growth ventures (Weissenböck & Gruber, Chapter 9, this volume), large companies (Chesbrough, Chapter 10, this volume), and designing openness with technology and IP (Holgersson, Chapter 11, this volume).

OI Among Firms Beyond the firm level of analysis, OI has been practiced at the intersection of organizational boundaries among firms. The two opening chapters of Part III offer insights into ethics (Stefan, Chapter 12, this volume) and trust (Blomqvist et al., Chapter 13, this volume). Next, we offer insights into many years of research on alliances (Frankort & Hagedoorn, Chapter 14, this volume), coopetition (Bez & Le Roy, Chapter 15, this volume), as well as accelerators (Marquis & Dierks, Chapter 16, this volume) and corporate venturing (van de Vrande & Kuiper, Chapter 17, this volume).

Networked Forms of OI In recent years, a lot of attention has been paid to OI outside of the organizational boundaries and its networked forms. That is why Part IV offers different perspectives on ecosystems (West & Olk, Chapter 18, this volume), sectors and industries (West, Chapter 19, this volume), crowdsourcing (Randhawa, Chapter 20, this volume), communities (Frederiksen et al., Chapter 21, this volume), intermediaries (Diener et al., Chapter 22, this volume), and open platforms (Parker et al., Chapter 23, this volume).

OI Implications for Public Policy OI has been a very important tool for governments and policymakers—​at both the national and international levels. That is why Part V offers special attention to OI and Smart Cities (Almirall, Chapter 24, this volume), clusters, and entrepreneurship ecosystems (Radziwon, Chapter 25, this volume), university-​industry relations (Perkmann, Chapter 26, this volume), science (Poetz et al., Chapter 27, this volume), deep tech and big research infrastructures (Wareham et al., Chapter 28, this volume), and policymakers and regulators (Di Minin & Cricchio, Chapter 29, this volume).

OPEN INNOVATION AS A FIELD OF KNOWLEDGE    27

New Developments in OI From its inception, OI has been informed by close observation of industry innovation activities, followed by abductive reasoning when those activities do not conform to the predictions of the extant theory. In Part VI on New Developments, we examine several new innovation developments, some of which might cause us to again alter our understanding of innovation and OI. Thus, we will focus on digital technologies and infrastructures (Autio et al., Chapter 30, this volume), Artificial Intelligence enabling innovation (Ferràs, Chapter 31, this volume), digital health (Carlile & Dionne, Chapter 32, this volume), energy transition and decarbonization (Zobel et al., Chapter 33, this volume), and other grand challenges (Cavalli & McGahan, Chapter 34, this volume).

OI and Theory While the concept of OI was developed originally in the context of economic spillovers from internal R&D, there are other ways to conceptualize it. In Part VII on Theory, the OI concept is reframed in several ways. The opening chapter of this section written by Sun et al. (Sun et al., Chapter 35, this volume) offers an excellent opening of the discussions of OI theories while reviewing these from the perspectives of collaboration dynamics, socio-​ technical affordances, and governance approaches. Part VII will also propose to look at OI from the view of micro-​foundations (Foss and Xu, Chapter 36, this volume), cognition (Brusoni & Martinez, Chapter 37, this volume), design (Appleyard & Velazquez, Chapter 38, this volume), open strategy (Whittington, Chapter 39, this volume), business model innovation (Lu & Tucci, Chapter 40, this volume), effectuation (Sarasvathi, Chapter 41, this volume), and the geopolitical context (Teece, Chapter 42, this volume).

OI in Practice Unlike many other Handbooks, which exclusively focus on interesting phenomena, theory development, and both managerial and policy implications, we also invited practitioners to share their experiences and reflections on OI in use. Part VIII of the Handbook features contributions from Nestlé, the Linux Foundation, the International Data Space Association, Ericsson, ENEL, BBVA, IBM, and Salesforce. These chapters are written by managers, executives, and the chairman of the board, who share both their challenges and success stories to help other practitioners to implement OI more efficiently. These chapters are often shorter, and do not include a formal literature review, but instead focus on a managerial situation. One can find chapters focusing on open R&D in large corporations by from Nestlé (Roschek & Jones, Chapter 43, this volume), open source innovation and value measurement from the Linux Foundation (Zemlin, Chapters 44, this volume and Carter, Chapter 45, this volume), data models

28    agnieszka radziwon and henry chesbrough for cloud computing from Salesforce and IBM (Singh & Spohrer, Chapter 46, this volume), data exchange standards from IDSA (Achatz, Chapter 47, this volume), 5G technologies (Tatipamula, Chapter 48, this volume), and energy delivery to the future lunar base of the European Space Agency from ENEL (Ciorra et al., Chapter 49, this volume). Part VIII concludes with a hands-​on model on OI maturity developed and tested on hundreds of firms by former OI manager at BBVA (Alvarez, Chapter 50, this volume).

OI and Teaching OI was first taught as an elective in several MBA programs. Over time, however, it has moved into certain undergraduate and one-​year master’s programs. It has also taken root in several Engineering schools as a course of study. That’s why we offer a dedicated section focusing on teaching, both in business schools and outside of the business context. Part IX features a contribution (Dąbrowska & Sims, Chapter 51, this volume) on teaching OI in business, management, and economics, and (Brunswicker, Chapter 52, this volume) on teaching OI in engineering.

Challenges, Critiques, and Suggestions Like other theories of innovation, OI needs to be challenged and critiqued as new evidence and new theories come to light. In Part X, we invite several leading innovation scholars to subject OI to this criticism. These scholars offer several suggestions to update and revise the concept, in light of new evidence and theory. One challenge in doing so is to retain conceptual integrity for the idea of OI so that it doesn’t degenerate into something that means different things to different people. Part X starts with the discussion of the limitations of OI (Vanhaverbeke, et al., Chapter 53, this volume), then we proceed into measurement (Cobben & Bogers, Chapter 54, this volume), failure cases (Chesbrough, Chapter 55, this volume), and appropriability tensions (Laursen, et al., Chapter 56, this volume). The concluding chapter (Radziwon et al., Chapter 57, this volume) focuses on the future of OI and proposes the future research agenda, which features emerging theories, phenomena, and methodologies, which will move the field forward.

Acknowledgments The authors would like to thank Joel West and Wim Vanhaverbeke for their feedback on the early versions of this chapter.

OPEN INNOVATION AS A FIELD OF KNOWLEDGE    29

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32    agnieszka radziwon and henry chesbrough Dahlander, L., O’Mahony, S., & Gann, D. M. (2016). One foot in, one foot out: How does individuals’ external search breadth affect innovation outcomes? Strategic Management Journal, 37(2), 280–​302. https://​doi.org/​10.1002/​smj.2342 Dahlander, L., & Piezunka, H. (2014). Open to suggestions: How organizations elicit suggestions through proactive and reactive attention. Research Policy, 43(5), 812–​827. https://​ doi.org/​10.1016/​J.RES​POL.2013.06.006 Dahlander, L., & Wallin, M. (2020). Why now is the time for “open innovation.” Harvard Business Review. Online. https://​hbr.org/​2020/​06/​why-​now-​is-​the-​time-​for-​open-​inn​ ovat​ion Darwin, S., Chesbrough, H., Bez, S., Cobben, D., & Radziwon, A. (2020). Moving forward together in uncertain times. Paper presented at Berkeley Innovation Forum, Spring. Darwin, S., Chesbrough, H., & Cobben, D. (2019). Learnings from the past to navigate the future. Paper presented a Berkeley Innovation Forum, University of California, Berkeley, Fall. Darwin, S., Chesbrough, H., Cobben, D., Radziwon, A., & Fischer, W. (2021). Transforming & transitioning your firm during geo-​political unrest through open innovation. White Paper presented a Berkeley Innovation Forum, University of California, Berkeley, April. Di Benedetto, A. (2010). Comment on ‘Is open innovation a field of study or a communication barrier to theory development?’ Technovation, 30(11), 557. Diener, K., Piller, F., & Pollok, P. (2023). Intermediaries and platforms for open innovation. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 22, pp. 371–386). Oxford University Press. Di Minin, A., & Cricchio, J. (2023). Open innovation policy: The outline-​inspire-​promote spinner. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 29, pp. 487–502). Oxford University Press. Du, J., Leten, B., & Vanhaverbeke, W. (2014). Managing open innovation projects with science-​ based and market-​based partners. Research Policy, 43(5), 828–​840. Du Chatenier, E., Verstegen, J. A. A. M., Biemans, H. J. A., Mulder, M., & Omta, O. (2009). The challenges of collaborative knowledge creation in open innovation teams. Human Resource Development Review, 8(3), 350–​381. Eckhardt, J. T., & Shane, S. A. (2003). Opportunities and entrepreneurship. Journal of Management, 29(3), 333–​349. Faems, D., Janssens, M., & Van Looy, B. (2010). Managing the co-​operation—­​­competition dilemma in R&D alliances: A multiple case study in the advanced materials industry. Creativity and Innovation Management, 19(1), 3–​22. Felin, T., & Zenger, T. R. (2014). Closed or open innovation? Problem solving and the governance choice. Research Policy, 43(5), 914–​925. https://​doi.org/​10.1016/​j.res​pol.2013.09.006 Ferràs, X., Nylund, P. A., & Brem, A. (2023). Connecting the (invisible) dots: When artificial intelligence meets open innovation. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 31, pp. 519–532). Oxford University Press. Foss, N., & Xu, T. (2023). Advancing the microfoundations of open innovation. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 36, pp. 611–622). Oxford University Press. Frankfort, H., & Hagedoorn, J. (2023). R&D alliances and open innovation: Review and opportunities. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 14, pp. 219–236). Oxford University Press.

OPEN INNOVATION AS A FIELD OF KNOWLEDGE    33 Frederiksen, L., Smith, P., Bergenholtz, C., Hilbolling, S., Beretta, M., Vuculescu, I., Zaggl, M., & Søndergaard, H. A. (2023). Extending the use of the crowds for innovation? Fund it yourself! In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 21, pp. 357–370). Oxford University Press. Füller, J., Matzler, K., Hutter, K., & Hautz, J. (2012). Consumers’ creative talent: Which characteristics qualify consumers for open innovation projects? An exploration of asymmetrical effects. Creativity and Innovation Management, 21(3), 247–​262. https://​doi.org/​ 10.1111/​J.1467-​8691.2012.00650.X Gassmann, O., & Enkel, E. (2004). Towards a theory of open innovation: Three core process archetypes. R&D Management Conference, 6, 1–​18. Grimpe, C., & Sofka, W. (2009). Search patterns and absorptive capacity: Low-​and high-​ technology sectors in European countries. Research Policy, 38(3), 495–​506. https://​doi.org/​ 10.1016/​J.RES​POL.2008.10.006 Henkel, J., Schöberl, S., & Alexy, O. (2014). The emergence of openness: How and why firms adopt selective revealing in open innovation. Research Policy, 43(5), 879–​890. https://​doi. org/​10.1016/​j.res​pol.2013.08.014 Holgersson, M., (2023). Designing openness with technology and intellectual property. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 11, pp. 170–184). Oxford University Press. Huff, A., Möslein, K., & Reichwald, R. (2013). Leading open innovation. MIT Press. Huizingh, E. K. R. E. (2011). Open innovation: State of the art and future perspectives. Technovation, 31(1), 2–​9. Kovács, A., Van Looy, B., & Cassiman, B. (2015). Exploring the scope of open innovation: A bibliometric review of a decade of research. Scientometrics, 104(3), 951–​983. https://​doi.org/​ 10.1007/​S11​192-​015-​1628-​0 Laursen, K., & Salter, A. J. (2014). The paradox of openness: Appropriability, external search and collaboration. Research Policy, 43(5), 867–​878. Laursen, K., Salter, A., & Somaya, D. (2023). Complementarities and tensions between appropriability and open innovation. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 56, pp. 899–913). Oxford University Press. Lopez-​Vega, H., Tell, F., & Vanhaverbeke, W. (2016). Where and how to search? Search paths in open innovation. Research Policy, 45(1), 125–​136. https://​doi.org/​10.1016/​j.res​pol.2015.08.003 Lu, Q., & Tucci, C. (2023). The open innovation–​business model innovation nexus. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 40, pp. 667–680). Oxford University Press. Marquis, A., & Dierks, S. (2023). Strategic acceleration of open innovation at Porsche. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 16, pp. 254–265). Oxford University Press. Matzler, K., Uzelac, B., & Bauer, F. (2014). Intuition: The missing ingredient for good managerial decision-​making. Journal of Business Strategy, 35(6), 31–​40. https://​doi.org/​10.1108/​ jbs-​12-​2012-​0077 Menendez Alvarez, M. (2023). A practitioner’s view: Three dimensions of OI maturity. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 50, pp. 797–810). Oxford University Press. Mina, A., Bascavusoglu-​Moreau, E., & Hughes, A. (2014). Open service innovation and the firm’s search for external knowledge. Research Policy, 43(5), 853–​866.

34    agnieszka radziwon and henry chesbrough Nambisan, S., Siegel, D., & Kenney, M. (2018). On open innovation, platforms, and entrepreneurship. Strategic Entrepreneurship Journal, 12(3), 354–​368. https://​doi.org/​10.1002/​ SEJ.1300 Nelson, R. R. (1962). The link between science and invention: The case of the transistor. In Universities-​National Bureau Committee for Economic Research & Committee on Economic Growth of the Social Science Research Council (Ed.), The rate and direction of inventive activity: Economic and social factors (pp. 549–​584). Princeton University Press. Parker, G., Petropoulos, G., Van Alstyne, M., & West, J. (2023). Driving open innovation through open platforms. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 23, pp. 387–404). Oxford University Press. Perkmann, M. (2023). Dimensions of openness: Universities’ strategic choices for innovation. In H. Chesbrough, A. Radziwon, W. Vanhaverbek, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 26, pp. 438–454). Oxford University Press. Piller, F., & West, J. (2014). Firms, users, and innovation. In H. Chesbrough, W. Vanhaverbeke, & J. West (Eds.), New frontiers in open innovation (pp. 29–​49). Oxford University Press. https://​doi.org/​10.1093/​acp​rof:oso/​978019​9682​461.003.0002 Poetz, M., Beck, S., Grimpe, C., & Sauermann, H. (2023). Open innovation in science. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 27, pp. 455–472). Oxford University Press. Radziwon, A. (2023). Open innovation in regional innovation clusters and entrepreneurship ecosystems. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 25, pp. 423–437). Oxford University Press. Radziwon, A., & Bogers, M. (2018). Managing SMEs’ collaboration across organizational boundaries with a regional business ecosystem. In W. Vanhaverbeke, F. Frattini, N. Roijakkers, & M. Usman (Eds.), Researching open innovation in SMEs (pp. 213–​248). World Scientific. https://​doi.org/​10.1142/​978​9813​2309​72_​0​007 Radziwon, A., Bogers, M., & Bilberg, A. (2017). Creating and capturing value in a regional innovation ecosystem: A study of how manufacturing SMEs develop collaborative solutions. International Journal of Technology Management, 75(1/​2/​3/​4), 73. https://​doi.org/​10.1504/​ ijtm.2017.10006​145 Radziwon, A., Bogers, M. L. A. M., Chesbrough, H., & Minssen, T. (2022). Ecosystem effectuation: Creating new value through open innovation during a pandemic. R&D Management, 52(2), 376–​390. https://​doi.org/​10.1111/​RADM.12512 Radziwon, A., Chesbrough, H., West, J., & Vanhaverbeke, W. (2023). The future of open innovation. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 57, pp. 914–934). Oxford University Press. Radziwon, A., & Vanhaverbeke, W. (2023). Open innovation in small and medium-​sized enterprises. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 8, pp. 119–139). Oxford University Press. Randhawa, K. (2023). A typology for engaging individuals in crowdsourcing. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 20, pp. 335–356). Oxford University Press. Roschek Jr, B., & Jones, E. (2023). Open R&D in large corporations. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 43, pp. 715–728). Oxford University Press.

OPEN INNOVATION AS A FIELD OF KNOWLEDGE    35 Salge, T. O., Farchi, T., Barrett, M. I., & Dopson, S. (2013). When does search openness really matter? A contingency study of health-​care innovation projects. Journal of Product Innovation Management, 30(4), 659–​676. Salter, A., Criscuolo, P., & Ter Wal, A. L. J. (2014). Coping with open innovation: Responding to the challenges of external engagement in R&D. California Management Review, 56(2), 77–​ 94. https://​doi.org/​10.1525/​cmr.2014.56.2.77 Salter, A., Ter Wal, A., & Criscuolo, P. (2023). The graft and craft of individual-​level open innovation. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 6, pp. 91–105). Oxford University Press. Salter, A., Ter Wal, A. L. J., Criscuolo, P., & Alexy, O. (2015). Open for ideation: Individual-​level openness and idea generation in R&D. Journal of Product Innovation Management, 32(4), 488–​504. https://​doi.org/​10.1111/​jpim.12214 Sarasvathy, S. (2023). Effectuation and open innovation. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 41, pp. 681–698). Oxford University Press. Singh, P., & Spohrer, J. (2023). Cloud metadata and interoperability: Open innovation and open-​source software tooling. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 46, pp. 753–763). Oxford University Press. Spithoven, A., Vanhaverbeke, W., & Roijakkers, N. (2013). Open innovation practices in SMEs and large enterprises. Small Business Economics, 41(3), 1–​26. https://​doi.org/​10.1007/​s11​ 187-​012-​9453-​9 Stanko, M. A., Fisher, G. J., & Bogers, M. (2017). Under the wide umbrella of open innovation. Journal of Product Innovation Management, 34(4), 543–​558. https://​doi.org/​10.1111/​ jpim.12392. Stefan, I. (2023). The good, the bad, the open: Ethical considerations in open innovation. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 12, pp. 187–198). Oxford University Press. Sun, Y., Majchrzak, A., & Malhotra, A. (2023). Open innovation theories. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 35, pp. 593–610). Oxford University Press. Tatipamula, M. (2023). Open innovation in the context of digital ecosystems. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 48, pp. 773–785). Oxford University Press. Teece, D. J. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15(6), 285–​305. https://​doi.org/​ 10.1016/​0048-​7333(86)90027-​2 Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–​1350. https://​ doi.org/​10.1002/​smj.640. Teece, D. (2023). The changing nature of open innovation. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 42, pp. 699–712). Oxford University Press. Van de Vrande, V., & Kuiper, C. (2023). How corporate venturing adds value to open innovation. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 17, pp. 266–284). Oxford University Press.

36    agnieszka radziwon and henry chesbrough Vanhaverbeke, W., Chesbrough, H., West, J., & Radziwon, A. (2023). Overcoming organizational obstacles to open innovation success. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 53, pp. 849–868). Oxford University Press. Vanhaverbeke, W., & Gilsing, V. (2023). Opening up open innovation and drawing the boundaries. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 4, pp. 51–64). Oxford University Press. Vanhaverbeke, W., Van de Vrande, V., & Chesbrough, H. (2008). Understanding the advantages of open innovation practices in corporate venturing in terms of real options. Creativity and Innovation Management, 17(4), 251–​258. https://​doi.org/​10.1111/​j.1467-​8691.2008.00499.x von Hippel, E. (2005). Democratizing innovation. MIT Press. Wareham, J., Pujol Priego, L., Romasanta A., & Ahmadov, G. (2023). Deep tech, big science, and open innovation. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 28, pp. 473–486). Oxford University Press. Weissenböck, E., & Gruber, M. (2023). Open innovation and the creation of high-​growth ventures. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 9, pp. 140–157). Oxford University Press. West, J. (2014). Challenges of funding open innovation platforms: Lessons from Symbian Ltd. In W. Vanhaverbeker, J. West, & H. Chesbrough (Eds.), New frontiers in open innovation (pp. 29–​49). Oxford University Press. West, J. (2023). Sectoral systems of open innovation: The healthcare sector. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 19, pp. 308–334). Oxford University Press. West, J., & Bogers, M. (2014). Leveraging external sources of innovation: A review of research on open innovation. Journal of Product Innovation Management, 31(7), 814–​831. https://​doi. org/​10.1111/​jpim.12125 West, J., & Olk, P. (2023). Innovation beyond the firm: Open innovation and innovation in ecosystems. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 18, pp. 287–307). Oxford University Press. West, J., & Gallagher, S. (2006). Challenges of open innovation: The paradox of firm investment in open-​source software. R&D Management, 36(3), 319–​331. https://​doi.org/​10.1111/​ j.1467-​9310.2006.00436.x West, J., & Lakhani, K. R. (2008). Getting clear about communities in open innovation. Industry and Innovation, 15(2), 223–​231. https://​doi.org/​10.1080/​136627​1080​2033​734 West, J., Salter, A., Vanhaverbeke, W., & Chesbrough, H. (2014). Open innovation: The next decade. Research Policy, 43(5), 805–​811. Whittington, R. (2023). Open strategy and innovation: A practice theory perspective. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 39, pp. 653–666). Oxford University Press. Zemlin, J. (2023). Ubiquitous software innovation building block: open source. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 44, pp. 729–742). Oxford University Press. Zobel, A, K., Comello, S., & Falcke, L. (2023). Accelerating the race to net-​zero through open innovation. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 33, pp. 549–566). Oxford University Press.

CHAPTER 3

THE EVOLVIN G C RA FT OF INNOVAT I ON john bessant

Introduction Helping individuals and organizations understand and work with innovation is not a new field; understanding the components of the craft and finding ways to deliver this knowledge goes back a long way. The process of identifying relevant skills and practices (through a mixture of research and reflective practice) and of codifying this knowledge into courses and training programs dates back at least a century (Gillis et al., 1991). However, for much of its early life, innovation management (IM) provision involved a collection of disciplinary and partial perspectives; it only emerged as a coherent area of study in the 1980s. It was the province of engineering schools or business schools and what was taught translated into practice in distinct and often siloed functional areas of business—​marketing, R&D, operations, etc. Much of what was taught drew on a limited interactive model which placed emphasis on key partners, suppliers, and customers. The emergence of “open innovation” (OI) as a field has brought new perspectives in learning the IM craft, especially in terms of the theoretical underpinning, with emphasis shifting toward a systems approach (see also Chesbrough, Chapter 1, this volume). This chapter looks at the evolution of IM as a body of knowledge and the way in which open innovation (OI) has changed what is taught at a conceptual level and introduces the tools and techniques through which these concepts can be translated into management practice.

38   john bessant

Changing the Lens on the Innovation Microscope Open innovation is also not a new idea. Innovation studies dating back to the early 20th century highlight the importance of connectivity, and case examples from earlier centuries support this view. Innovation has always been a multi-​player game and its effectiveness depends on building and managing links between people and across organizations. For example, the pioneering study by Carter and Williams of “technically progressive” firms in the UK back in the 1950s identified that the degree of “cosmopolitan” orientation (as opposed to “parochial”) was a significant determinant of innovation success. In other words, those organizations with rich networks of connections were more likely to be successful innovators (Carter & Williams, 1957). This theme emerged in the many major studies of innovation throughout the 1960s and 1970s, for example, Project SAPPHO stressed linkages as a critical factor while the Manchester “Wealth from knowledge” research provided extensive case examples of award-​ winning innovators who shared a common external orientation (Langrish et al., 1972; Rothwell, 1977). As Table 3.1 suggests, innovation researchers from many different backgrounds have been working for over half a century on the theoretical development of models which recognize the shifting boundaries and the engagement of an increasingly diverse number of players. However, much of the early and emerging discussion took place among different communities, often with a different disciplinary hinterland, for example, economic geographers’ concerns with clustering as a phenomenon or operations management researchers working on supply chains. The result was a series of “tribes” talking extensively with one another but with little boundary-​spanning or integration of core messages.

Implications for Innovation Management Much of the discourse outlined above was strongly firm-​centered; the primary concern was in identifying key relationships with major customers, suppliers, or key partners but all viewed in a kind of “solar system” fashion, putting the enterprise at the center and seeing interactions rather like orbital patterns of planets moving around the sun. This research perspective mirrored a world of practice where the firm was at the center and its relationships to the outside world were explored and managed from that perspective. Strategy was explored in terms of positioning and alliances; marketing was built on

THE EVOLVING CRAFT OF INNOVATION    39 Table 3.1 Example perspectives on innovation as a distributed, multi-​actor process Key focal themes

Example references

Distributed multi-​actor innovation processes

(Howells, 1996)

Innovation systems, especially national systems but latterly regional innovation systems

(Freeman, 1991; Metcalfe & Miles, 1999; Lundvall, 1990)

User-​led innovation and the role of communities and lead users

(Herstatt & von Hippel, 1992; Von Hippel, 1988)

Globalization and the emergence of global value chains

(Gereffi, 1994; Kaplinsky et al., 2003)

High involvement innovation and the role of internal ideation

(Bessant, 2003; Boer et al., 1999; Schroeder & Robinson, 1991)

Complex product systems and the role of multi-​player stakeholder management

(Davies & Hobday, 2005; Gann & Salter, 2000)

“Recombinant innovation” bridging different knowledge worlds and importance of brokerage

(Hargadon & Sutton, 1997)

Communities of practice sharing knowledge across boundaries

(Brown & Duguid, 2001; Morris et al., 2006; Wenger, 1999)

Clusters and innovation as a source of collective efficiency

(Best, 2001; Piore & Sabel, 1982)

Interactive value creation, early models of co-​creation

(Reichwald & Piller, 2006)

(Asheim et al., 2006; Cooke, 2001)

strengthening close ties with key customers, operations all about value chain management with suppliers being only one link in the chain. And it was at the heart of the way innovation management (IM) was taught, presenting core concepts from the point of view of the enterprise as focal and only gradually abandoning linear models in favor of “coupling” and interactive models based on a small set of key players. This has changed significantly in recent years but before we focus on those changes, it will be useful to explore briefly how the field of IM teaching has evolved.

The Evolution of Innovation Management Teaching The early history of teaching IM was one of fragmentation and gradual convergence. To use a metaphor, we can think of a river system which begins with small springs

40   john bessant and sources which accelerate their way down different mountain slopes, growing in volume to become significant tributaries and eventually converging into a powerful mainstream. The origins of IM teaching can be found in several places but three major tributaries were involved. One emerged from work in engineering schools where concerns were around technological change aimed at productivity improvements and also the deployment of major process innovations. Lessons learned here were codified and taught in fields such as operations management and industrial engineering. In parallel, a second tributary could be found within business schools where the concerns were around key functional areas such as marketing and product innovation, R&D strategy as a subset of business strategy, and the role of finance in accounting for and providing the risk capital to enable innovation. Human resource management was also involved in pockets, for example, around how to manage technical professionals or how to stimulate creativity; early studies in the “human relations” movement like the Hawthorne work fed an important strand around employee engagement and productivity. And organizational behavior looked a key themes such as functional organization and handling differentiation and integration. A third and, for a long time, much smaller tributary could be found in the entrepreneurship field which began with concern for small business foundation and growth. The key role which entrepreneurs play and the potential for startup-​led growth only emerged during the 1990s. Other “rivulets” can also be plotted on our river system map. For example, the growth in the 1960s of a movement concerned with the social impacts of science and technology (STS) drew together researchers from many different disciplinary backgrounds concerned to articulate and understand the process of technological change and how it could be harnessed in a responsible fashion (Owen et al., 2013). Applied research centers began emerging with a concern for action, primarily in policymaking at the national level but with some interest in the internal mechanics and decision-​making within firms. One of their most important contributions was as focusing devices, bringing together what was known from different disciplinary directions and creating a core curriculum, often offering specialist masters-​level courses or short-​courses targeted at increasing awareness and understanding in governments. A good example would be the Science Policy Research Unit (SPRU) at the University of Sussex in the UK, which was founded in 1966 by Chris Freeman and colleagues who shared a vision based on the “economics of hope,” aiming to help direct technological change toward more sustainable and inclusive futures. It drew together economists, psychologists, engineers, and others to research major STS themes and provide guidance for policymakers and practicing managers (Freeman et al., 1973). Their work included developing and running various training programs and a specialist MSc program on Science Policy, soon to be followed by another on Innovation Management. Significantly, one of the earliest books targeted at a lay audience rather than at fellow economists was Freeman’s The Economics of Industrial Innovation, which, despite the title, offered many insights for practicing managers (Freeman, 1974).

THE EVOLVING CRAFT OF INNOVATION    41 While there was interaction between these tributary fields, the process of convergence was slow until the 1980s. It was accelerated by the significant explosion of concern focused around key technologies with potentially enormous transformative power—​ particularly microelectronics and biotechnology. These had moved beyond the lab to the point where widespread application in products and processes became possible—​ but needed situating in a strategic framework (Bessant & Cole, 1985). (Social concerns about the employment implications particularly of microelectronics drove STS-​related research and created a demand side of firms and policymakers wanting to understand how knowledge translated into practice and how this could be effectively managed.) The growing interest in how we might effectively manage innovation found expression in an influential report published in 1987 by the US National Research Council, which talked about “the hidden competitive advantage” which effective management of innovation could contribute (National Research Council, 1987). They used the term “management of technology” (MOT) to cover linking “engineering, science and management disciplines to plan, develop and implement technological capabilities to shape and accomplish the strategic and operational objectives of an organisation.” It came at an opportune moment; the external environment was shifting dramatically; some of the key characteristics highlighted by the study were: • R&D expenditure was increasing, generating increasing technological opportunities. • The proportion of “knowledge workers” was growing, with more involvement in development and use of technology. • The mean time between new generations of technology was being reduced. • Hitherto unrelated technologies were being integrated into new products, making product technologies more complex. • The increasing demands for high product and service quality had extended R&D work over the company boundary to include suppliers as well. • The innovation process was seen as taking place in global or regional technology systems. • The dependency of firms on external sourcing of technology was growing. • New manufacturing technologies were available and were being used globally, often at a faster rate than prior technologies used to be. • Technological protectionism was increasing. The report highlighted concern that the existing provision of management training was not well equipped to help provide the particular set of skills combining understanding of science, engineering, business, and social science across a wide range which would be needed to help deal with these challenges. The report commented that while huge resources were being devoted to developing technology—​for example, the US spent around $97bn/​year on projects involving over 1 million scientists and engineers at that time—​there was little formal education or training available which might guide managers in the process.

42   john bessant The implied call to action was somewhat exaggerated but it did serve to accelerate activity in the field. Isolated courses had existed for many years (e.g., a University of Miami survey identified several engineering management courses which date back to the 1930s, while the MIT Industrial Management program was established in 1913). But the late 1980s saw rapid expansion; for example, an IEEE survey in 1989 found 184 specialist MOT courses and programs reported in over 20 countries, including both industrialized and developing countries. Previous surveys in 1976 and 1982 revealed the existence of 32 and 84 programs respectively. By 1992, there had been significant growth in the provision both in terms of sheer numbers of courses and also in their variety and geographical distribution. In that year, a review suggested most growth has been in post-​graduate or post-​experience courses; there were over 300 courses in over 30 countries worldwide, in addition to many short courses and in-​company programs. That acceleration continued and the field began to mature in terms of a subject being widely taught and with a growing number of faculty building on their own research work and evolving into teachers of innovation management. There are currently well over 1,000 courses available in IM at higher education institutions and many other short courses are on offer from consultancies, research institutes, etc. There has also (particularly as a result of pressures imposed by COVID-​19) been a significant expansion in the number of online courses available, varying in length from short MOOCs to full-​scale M-​Level provision. Perhaps of particular relevance has been the huge growth in entrepreneurship as a complementary field, dealing not only with the startup perspective but increasingly with internal entrepreneurship inside larger organizations and with entrepreneurship as a core “life skill” which is being taught from school level upwards. The difference has primarily been in perspective; much entrepreneurship teaching focuses on individual skills and capabilities whereas IM has tended to look at organizational routines and capabilities. That line is increasingly blurred, as is the definition of “entrepreneur.” Peter Drucker perhaps captured the artificial nature of the separation, commenting that “innovation is what entrepreneurs do”; the main message these days is that they do it in many different contexts (Drucker, 1985). That growth was paralleled by the expansion in communities of practice for teachers to share experiences and ideas; within all the major management academies there is now strong representation of innovation and entrepreneurship and much of this focuses on the teaching challenge. This has led to a growing suite of resources to support IM teaching—​cases, toolkits, and increasingly video and audio material. Significantly until the mid-​1990s, there was no textbook covering the field of IM in integrated fashion; instead, it was left to teachers to assemble their offerings from a mixture of sources and articles reflecting different disciplines and tools. All of this means that there is an increasing need to articulate and present an integrated body of knowledge representing the key concepts of IM. This lies at the heart of the International Organization for Standardization work on establishing a standard for IM and it is a model which has been significantly influenced by the OI perspective,

THE EVOLVING CRAFT OF INNOVATION    43 privileging a systems and interactive approach rather than an enterprise-​centered one (Hyland et al., 2022).

A Change in Perspective As we saw, innovation as a multi-​player game had been recognized for a long time; however, the focus on systems and interactions was less well developed. The evolution of the language and discourse around “open innovation” (OI) falls rather neatly into this millennium; it wasn’t really until the turn of the century (and the publication of key influential articles and books with the catchy “open innovation” label) that things really took off. Chesbrough’s labeling in 2003 drew our attention to several aspects of the challenge which were novel (Chesbrough, 2003; see also Chapter 1, this volume). He wrote at a time when the external knowledge environment was clearly changing fast and having a significant impact on the way in which innovation might be organized and managed. Multiple trends were accelerating and converging to create a “knowledge-​rich” world with a massive increase in the range and volume of potential trigger signals for innovation (Bessant & Venables, 2008). In particular, three big changes were forcing a shift in perspective and requiring changes in the lens through which we observed and worked with innovation: • The explosion of external knowledge availability and the recognition that (in Bill Joy’s memorable phrase) “not all the smart people work for you” in even the largest enterprise. • The simultaneous expansion of mechanisms and tools which enabled working with rich external networks in both physical and virtual space—​meaning that proximity became less important. • The recognition that creation, accumulation, and ownership of knowledge were less important than being able to manage knowledge flows in and out of the enterprise in a strategic fashion. The Procter & Gamble “autobiography” of the radical (for its time) changes in their innovation model outside offers a good illustration of the dramatic shift in perspective triggered by these moves. Their well-​documented shift from an internally focused R&D-​driven model to “Connect and Develop” (with the ambitious target of sourcing 50% of innovations from outside the company) shows how even a long-​established 200-​ year-​old business needed to change (Huston & Sakkab, 2006; Lafley & Charan, 2008). This wasn’t simply a case of relabeling old wine in new bottles. What was going on involved a change in lenses, opening up a new way of looking at innovation. To borrow a concept originated by the philosopher of science, Thomas Kuhn, it was a “paradigm shift” (Kuhn, 1962). Paradigms can be described as “a set of assumptions, concepts,

44   john bessant values, and practices that constitutes a way of viewing reality for the community that shares them, especially in an intellectual discipline” (American Heritage Dictionary, 5th edition). Paradigms are like mental spectacles, and changing them changes the way we view the world. The problems we see and the solutions which become available to us shift and the new perspective offers rich inspiration for research and practice. The importance of viewing innovation in this fashion is clear; our understanding shapes the way in which we try and manage it. Put simply, our mental models shape our actions—​the things we pay attention to, allocate resources to, and take decisions about.

Innovation Model Innovation Looking back in this way at the underlying theory of innovation (and by extension the concepts and methods taught about how to organize and manage it), we can see the importance of dominant models in shaping research and practice. Such models bring some problems into focus and move others to the background; tools and methods for operationalizing innovation follow from this viewpoint, focusing on the problems which are in focus. And changing them involves more than a substitution of one with another; it is essentially “innovation model innovation” and qualifies as a Kuhnian paradigm shift. One of the first writers to explore this “innovation model” view was Roy Rothwell, who published an important review piece about emerging challenges for the field in 1992 (Rothwell, 1992). In this, he provided a useful historical perspective on innovation, suggesting that our appreciation of the nature of the innovation process has been evolving from simple linear models (characteristic of the 1960s) through to increasingly complex interactive models. This five-​generation model (see Table 3.2) neatly captured the emerging discourse and, as a seasoned teacher, he also laid out the framework within

Table 3.2 Rothwell’s five generations of innovation models Generation

Key features

First/​second

Simple linear models—​need pull, technology push

Third

Coupling model, recognizing interaction between different elements and feedback loops between them

Fourth

Parallel model, integration within the company, upstream with key suppliers and downstream with demanding and active customers, emphasis on linkages and alliances

Fifth

Systems integration and extensive networking, flexible and customized response, continuous innovation

THE EVOLVING CRAFT OF INNOVATION    45 which the subject was conceptually framed and taught. His models were also research-​ led, new information eventually forcing a significant move in thinking along the lines of the “paradigm shift” suggested above. It’s worth recognizing that this was a visionary article; writing it in 1992 his “fifth-​ generation” concept saw innovation as a multi-​actor process, which would require high levels of integration at both intra-​and inter-​firm levels. It would also be increasingly facilitated by IT-​based networking, although at the time he had no real picture of what “digital” innovation or the enabling digital toolkit might look like. The internet as we know it today was a skeletal affair then, linking a handful of computers via unreliable dial-​up modems, and was the province of isolated communities sharing information severely limited by bandwidth and speed issues. Yet his picture of a highly connected world running innovation across virtual networks is remarkably true to what we actually have today. And it offered perhaps the earliest snapshot of what has become formalized as “open innovation” as an innovation model. Current models (such as that being promoted by the ISO) draw on extensive work with practitioners and researchers who elaborate Rothwell’s early blueprint and offer a normative framework to help innovation management in practice (Hyland et al., 2022). Importantly they embed a systems approach, seeing the focus not as an individual enterprise but as one element in a wider system. Interaction among these elements shapes the outcome; the possibility exists for emergent properties where the whole becomes greater than the sum of the parts (and sub-​optimal behavior where connectivity fails, and the system performs less well than its component elements might). Open innovation as an innovation lens is at the heart of this model.

Elaborating the OI Model The emergence of OI was not a single event but the beginning of a transformation process in which organizations learned to master new routines and revised or abandoned some of their older ones. New tools, particularly enabled by the rapid rise of the internet, meant that crowdsourcing and innovation marketplaces became a common addition to the portfolio of search tools. This period—​which extends to the present day—​has been one of learning and adaptation, elaborating the new model of OI in practice. And it continues to be fed with new research illuminating challenges and offering new insights, particularly those raised at the inter-​organizational level and around the concept of knowledge flow and trading rather than accumulation and deployment as the driving force. Key themes (explored elsewhere in this book, and see Bogers et al. (2018) for a major review of the field) include exploring issues such as: • managing knowledge flows rather than focusing on creation and accumulation • enabling knowledge trading via different mechanisms

46   john bessant

• • • • • •

IP management issues raised by such trading managing asymmetrical relationships based on knowledge power finding, forming, and enabling performing networks elaborating and enabling absorptive capacity in an OI landscape creating and governing ecosystems creating platform business models and their organization and management.

Evolving IM Course Content to Include OI As we saw, the development of IM courses has mirrored the growth of the field as a whole. From a content point of view, early perspectives drew heavily on versions of the linear model, giving way to those involving limited engagement with external networks. Key themes included: • Innovation as involving “knowledge value chains,” for example, the shift to looking at suppliers as partners often able to offer complementary knowledge (Lamming et al., 2015). • Strategic collaboration to support globalization strategies (Doz et al., 2000). • Growing emphasis on complementary assets, especially in helping scale innovation globally (Teece, 1998). • Emergence of research and then teaching around complex knowledge-​based product/​project systems, which drew together networks of players and required alternative governance models (Davies & Hobday, 2005). • Increasing focus on user innovation as a consistent and significant source of early stage innovation and support for downstream diffusion. This opened up questions about when and how external players could be incorporated, for example, in lead user networks (Herstatt & von Hippel, 1992). • Extensive elaboration of intellectual property (IP) management in terms of modes and options, supporting a growing variety of “knowledge trading” activities. With the paradigm shift in thinking which open innovation represented has come a significant expansion in course provision and an accelerating emphasis on how knowledge might flow in support of the organization’s innovation efforts, both from inside out and from outside in. Taking more of a multi-​player, multi-​stakeholder approach amplified attention to themes such as absorptive capacity and technology transfer (Phelps et al., 2007; Saad, 2000), which are concerned with how organizations could make use of multiple sources of knowledge and how they would need to adapt in order to do so.

THE EVOLVING CRAFT OF INNOVATION    47 “Knowledge management,” which was a popular theme during the 1990s but was primarily concerned with building and operating databases, came back into focus with concerns about the strategic role which knowledge trading could play, informed by important developments in thinking and practice concerning appropriability and intellectual property management. Of particular value has been the thinking around business models and their use as a way of articulating the architecture through which innovation converts knowledge into value. The power of tools such as the Business Model Canvas is in part the way it forces a systems-​level conversation in elaborating and developing innovation plans; not surprisingly they have become an indispensable feature of most IM programs (Osterwalder & Pigneur, 2010). In addition, IM teaching has had to embrace new tools and techniques, especially those enabled by the explosion in digitally-​enabled knowledge management (Reichwald et al., 2013). For example, the search space is now heavily influenced by the ability to crowdsource quickly around innovation challenges and to broadcast a search to a wide audience and work with the “long tail” of potential contributors (Jeppesen & Lakhani, 2010). Collaboration platforms have not only opened up the world of employee involvement, updating the old suggestion box idea and making it viable at scale, but they also provide ways of connecting and exchanging knowledge with suppliers and customers. Importantly, the field of OI teaching has expanded not just in the theoretical content being delivered (the “what?” question) but also in exploring different ways in which OI can be mobilized in different contingencies. OI strategies and supporting analytical tools are playing an increasingly important role and a good example can be found in Brunswicker (Chapter 52, this volume), which explores how engineering students can learn and practice different OI strategies. Another significant development has been the growing presence of practitioners and consultants as “transmission belts” for OI learning. Many of the tools (such as the Business Model Canvas) have emerged from consulting practice and have subsequently found themselves in classrooms throughout the world, reflecting an “open” approach to the challenge of developing and sharing learning materials. For example, the consultancy 100% Open, which was founded early on as an offshoot of a research initiative in the early days of OI, now has a rich and growing toolkit and case examples of its use (100%Open, 2022). There has also been considerable expansion of provision within the world of practice through internal courses and training programs. Of particular significance is the way in which this has spread beyond the commercial sector to public and not-​for-​profit organizations. For organizations working in fields such as development or humanitarian aid, the whole mode of working depends on collaboration and networking, configuring ecosystems which allow scaling of valuable innovation. Significantly, many players now offer extensive support materials for capacity-​building around open innovation concepts, for example, NESTA (NESTA, 2010, 2022), the Humanitarian Innovation Fund (ELRHA HIF, 2022), and various branches of the United Nations (United Nations Innovation Network, 2022).

48   john bessant

Where Next? This brief review of the ways in which IM teaching has changed in response to the paradigm shift which OI represents suggests that a great deal has already been achieved (see also Brunswicker, Chapter 52, this volume; Dąbrowska & Sims, Chapter 51, this volume). Most IM courses explicitly deal with OI and there is an increasingly rich library of resources to support its teaching, notably some excellent case studies from around the world, covering different sectors and organization size. There is also a growing toolkit of tools and techniques with which to work on OI—​and much of this is freely available. What we still lack—​partly because it is still the subject of exploratory research—​is material to support our understanding and teaching of concepts around ecosystems and platform innovation. These have become “hot topics” in the recent OI research literature and there are some useful reference works (e.g., Cusumano et al., 2019) but there is still a lag in translating such insights into useful and tractable concepts and a gap in our toolkit to work with them. Perhaps one of the biggest opportunities opened up in the OI teaching space has come courtesy of the difficulties imposed by the COVID-​19 pandemic. With many institutions forced to close and deliver teaching online, the possibility of getting external speakers to contribute to and enrich classes opened up. Such speakers could share their perspectives and engage in discussion around their experiences as a way of enriching the student exploration of the OI theme. They mirror the idea of using external knowledge and experience to enhance the offering of an organization and represent a way of using OI principles to improve OI practice, at least in the classroom.

References 100% Open. (2022). Open innovation toolkit. Online. https://​www.100o​pen.com/​tool​kit/​ Abate, F. (2013). The American Heritage Dictionary of the English Language, ed. by Joseph Pickett and Steve Kleinedler. Dictionaries: Journal of the Dictionary Society of North America, 34(1). Asheim, B., Cooke, P., & Martin, R. (2006). Clusters and regional development: Critical reflections and explorations. Routledge. Bessant, J. (2003). High involvement innovation. John Wiley & Sons, Inc. Bessant, J., & Cole, S. (1985). Stacking the chips: Information technology and the global distribution of income. Frances Pinter. Bessant, J., & Venables, T. (2008). Creating wealth from knowledge: Meeting the innovation challenge. Edward Elgar. Best, M. (2001). The new competitive advantage. Oxford University Press. Boer, H., Berger, A., Chapman, R., & Gertsen, F. (1999). CI changes: From suggestion box to the learning organisation. Ashgate. Bogers, M., Chesbrough, H., & Moedas, C. (2018). Open innovation: Research, practices, and policies. California Management Review, 60(2), 5–​16.

THE EVOLVING CRAFT OF INNOVATION    49 Brown, J. S., & Duguid, P. (2001). Knowledge and organization: A social-​practice perspective. Organization Science, 12(2), 198. Brunswicker, S. (2023). Teaching engineers about open innovation. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 52, pp. 833–846). Oxford University Press. Carter, C., & Williams, B. (1957). Industry and technical progress. Oxford University Press. Chesbrough, H. (2003). Open innovation: The new imperative for creating and profiting from technology. Harvard Business School Press. Chesbrough, H. (2023). Open innovation: A reconsideration 20 years later. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 1, pp. 3–18). Oxford University Press. Cooke, P. (2001). Regional innovation systems, clusters and the knowledge economy. Industrial and Corporate Change, 10(4), 945–​974. Cusumano, M., Gawer, A., & Yoffie, D. (2019). The business of platforms. MIT Press. Dąbrowska, J., & Sims, J. (2023). Teaching open innovation in business schools. In H. Chesbrough, A. Radziwon, W. Vanhaverbeke, & J. West (Eds.), The Oxford handbook of open innovation (Chapter 51, pp. 813–832). Oxford University Press. Davies, A., & Hobday, M. (2005). The business of projects: Managing innovation in complex products and systems. Cambridge University Press. Doz, Y., Olk, P., & Ring, P. (2000). Formation processes of R&D consortia. Strategic Management Journal, 21, 239–​266. Drucker, P. (1985). Innovation and entrepreneurship. Harper & Row. ELRHA HIF. (2022). Humanitarian innovation guide. Online. https://​www.alnap.org/​help-​libr​ ary/​human​itar​ian-​inn​ovat​ion-​guide Freeman, C. (1974). The economics of industrial innovation. Penguin. Freeman, C. (1991). Networks of innovators. Research Policy, 20(5), 499–​514. Freeman, C., Jahoda, M., Cole, H., & Pavitt, K. (1973). Thinking about the future: A critique of the limits to growth. Universe Books. Gann, D., & Salter, A. (2000). Innovation in project-​based, service-​enhanced firms: The construction of complex products and systems. Research Policy, 29, 955–​972. Gereffi, G. (1994). The organisation of buyer-​ driven global commodity chains: How U.S. retailers shape overseas production networks. In G. Gereffi & P. Korzeniewicz (Eds.), Commodity chains and global capitalism (pp. 95–​123). Praeger. Gillis, A., Macrossan, W., & Miller, C. (1991). Management of technology: An emergent discipline?. Industry and Higher Education, 5(3), 179–​186. Hargadon, A., & Sutton, R. (1997). Technology brokering and innovation in a product development firm. Administrative Science Quarterly, 42, 716–​749. Herstatt, C., & von Hippel, E. (1992). Developing new product concepts via the lead user method. Journal of Product Innovation Management, 9(3), 213–​221. Howells, J. (1996). Tacit knowledge, innovation and technology transfer. Technology Analysis and Strategic Management, 8(2), 91–​105. Huston, L., & Sakkab, N. (2006). Connect and develop: Inside Procter & Gamble’s new model for innovation. Harvard Business Review, March, 58–​66. Hyland, J., Karlsson, M., Kihlander, I., Bessant, J., Magnusson, M., & Kristiansen, J. (2022). Changing the dynamics and impact of innovation management. World Scientific. Jeppesen, L., & Lakhani, K. (2010). Marginality and problem solving effectiveness in broadcast search. Organization Science, 21(5), 1016–​1033.

50   john bessant Kaplinsky, R., Morris, M., & Readman, J. (2003). The globalisation of product markets and immiserising growth: Lessons from the South African furniture industry. World Development, 30(7), 1159–​1178. Kuhn, T. (1962). The structure of scientific revolutions. University of Chicago Press. Lafley, A., & Charan, R. (2008). The game-​changer. Profile. Lamming, R., Bessant, J., & Trifilova, A. (2015). New wine or new bottles? What purchasing and supply managers need to know about open innovation. Journal of Supply Excellence, 4(1), 34–​40. Langrish, J., Gibbons, M., Evans, W., & Jevons, F. (1972). Wealth from knowledge. Macmillan. Lundvall, B. (1990). National systems of innovation: Towards a theory of innovation and interactive learning. Frances Pinter. Metcalfe, S., & Miles, I. (1999). Innovation systems in the service economy. Kluwer. Morris, M., Bessant, J., & Barnes, J. (2006). Using learning networks to enable industrial development: Case studies from South Africa. International Journal of Operations and Production Management, 26(5), 557–​568. National Research Council. (1987). Management of technology: The hidden competitive advantage. National Research Council. NESTA. (2010). Open innovation. NESTA. NESTA. (2022). Innovation toolkit. NESTA. https://​www.nesta.org.uk/​tool​kit/​diy-​tool​kit/​ Osterwalder, A., & Pigneur, Y. (2010). Business model generation: A handbook for visionaries, game changers, and challengers. John Wiley & Sons, Inc. Owen, R., Bessant, J., & Heintz, M. (2013). Responsible innovation. John Wiley & Sons, Inc. Phelps, R., Adams, R. J., & Bessant, J. (2007). Models of organizational growth: A review with implications for knowledge and learning. International Journal of Management Reviews, 9(1), 53–​80. Piore, M., & Sabel, C. (1982). The second industrial divide. Basic Books. Reichwald, R., Huff, A., & Moeslein, K. (2013). Leading open innovation. MIT Press. Reichwald, R., & Piller, F. (2006). Interaktive Wertschopfung. Gabler. Rothwell, R. (1977). The characteristics of successful innovators and technically progressive firms. R&D Management, 7(3), 191–​206. Rothwell, R. (1992). Successful industrial innovation: Critical success factors for the 1990s. R&D Management, 22(3), 221–​239. Saad, M. (2000). Development through technology transfer. Intellect Publishers. Schroeder, D., & Robinson, A. (1991). America’s most successful export to Japan—​continuous improvement programmes. Sloan Management Review, 32(3), 67–​81. Teece, D. (1998). Capturing value from knowledge assets: The new economy, markets for know-​ how, and intangible assets. California Management Review, 40(3), 55–​79. United Nations Innovation Network. (2022). Innovation toolkit. Online. https://​www.uninn​ ovat​ion.netw​ork/​un-​inn​ovat​ion-​tool​kit von Hippel, E. (1988). The sources of innovation. MIT Press. Wenger, E. (1999). Communities of practice: Learning, meaning, and identity. Cambridge University Press.

CHAPTER 4

OPENING U P OPEN INNOVAT I ON Drawing the Boundaries wim vanhaverbeke and victor gilsing

Introduction There have been many attempts by innovation management researchers to classify different open innovation modes (see, among others, Chesbrough, 2003a, 2003b, 2020; Chesbrough & Brunswicker, 2014; Dahlander & Gann, 2010; Enkel et al., 2009; West, 2020). Those classifications led to a general understanding of open innovation modes along two dimensions (Dahlander & Gann, 2010). The first is outside-​in versus inside-​ out innovation. The second dimension is related to the type of the rewards: pecuniary versus non-​pecuniary. Most open innovation agreements are based on a financial compensation for the innovating companies. Non-​pecuniary implies that there is no direct financial reward and compensation associated with the knowledge flows (Greul et al. 2018; Henkel, 2006; Henkel et al., 2014). Although this ruling classification has proven to be useful in structuring our ideas about open innovation, we probe in this chapter whether it is also restricting our understanding of innovation practices and the role of open innovation in companies’ strategies. Recently, Gutman et al. (2021) have developed a different classification, opening up the debate again of what open innovation is all about. In this chapter we attempt to broaden the scope of open innovation while correcting potential biases in the perception of the past. We are interested in introducing two ideas that may extend our view on open innovation. First, inside-​out open innovation has predominantly been used by organizations as a mechanism to allow unused and underutilized ideas to go outside the organization for others to use in their businesses (Chesbrough, 2003a, 2020). We intend to broaden the scope of open innovation by indicating how inside-​ out open innovation has not only a financial but also an important strategic role, while

52    wim vanhaverbeke and victor gilsing outside-​in open innovation has not only a strategic role but also can be used predominantly for better financial outcomes. In this way, we go for a more balanced appreciation of outside-​in and inside-​out innovation. Second, open innovation might be applied more widely in very useful ways if it is decoupled from new product innovation (NPD). Traditionally, open innovation was applied to new product or service innovation—​ with the permeable innovation funnel as main visualization (Chesbrough, 2003a). In contrast, if we start with firms’ strategic objectives—​NPD being only one of the strategic objectives—​new ways to conceptualize open innovation practices emerge. These open innovation practices differ from the classical open innovation examples, they involve other management challenges, they are usually accomplished with more partners in an ecosystem setting, and as the initiator primarily wants to achieve a strategic objective, it is not trivial who will drive the development and commercialization of the innovation.

Classifying Types of Open Innovation There are different ways to source and leverage external sources of innovation (West & Bogers, 2014). Despite this variety of open innovation modes, it is a common practice among innovation management scholars to classify modes into outside-​in and inside-​ out open innovation.1 Furthermore, by following the definition of open innovation—​ “a distributed innovation process based on purposively managed knowledge flows across organizational boundaries, using pecuniary and non-​pecuniary mechanisms in line with each organization’s business model” (Chesbrough & Bogers, 2014, p. 17)—​ outside-​in open innovation is in most cases considered to be of strategic importance, while inside-​out open innovation is predominantly described as a tool to monetize internal knowledge which is no longer of strategic importance to the company. Outside-​ in open innovation is assumed to fit with the strategic logic of the dominant business of the firm. Inside-​out open innovation arises when internal R&D generates results that do not fit with this logic. In that case, a new and different business model has to be developed before the innovation can be commercialized. As those innovations struggle to be commercialized internally because of the dominant logic of the firm, they end up being licensed or sold to other companies. If another company has a business model that fits well with the innovation, the innovating firm might license or sell that innovation to them. Given this approach of outside-​in and inside-​out open innovation, it is no surprise that outside-​in open innovation has received much more attention from both

1  Enkel et al. (2009) also introduced coupled open innovation as a third category. We chose to focus only on outside-​in and inside-​out open innovation in order to keep the analysis tractable—​much along the same lines as Dahlander and Gann (2010).

OPENING UP OI: DRAWING THE BOUNDARIES    53

Strategic return

Reason to start open innovation Financial return

Developing your new products & services

Enable your (potential) partners to innovate

Improving your operations (process innovations)

Monetizing your internal technology

• Collaboration with technology & market partners / crowds • Building the infrastructure • Incentivize others to share innovations

• Factory 4.0 labs • Suppliers • Digital transformation

• Selective revealing • Opening background IP to parners (co-development) • Spinoffs (reacquisition) • Incentivize others to innovate

• Licensing • Spinoffs

Operational input - terminology

Outside-in

Open innovation mode

Inside-out

figure 4.1  A classification of different objectives to be pursued through open innovation.

practitioners and academics. We suggest that both outside-​in and inside-​out open innovation can be practiced predominantly for a strategic return as well as for a financial or monetary return.2 By juxtaposing the two types of open innovation with the two types of return in Figure 4.1, we distinguish four different scenarios. The upper left and lower right cells represent the classical interpretations of outside-​in and inside-​out open innovation. In the lower left cell, we discuss a few ways how a company can benefit primarily in financial terms from open innovation while it’s not engaged in a new product development project. The upper right cell represents cases where inside-​out open innovation is practiced mainly for strategic reasons.

Outside-​In Open Innovation for Strategic Return Let’s first focus on the classical interpretations of outside-​in open innovation. In the upper left cell we find examples of outside-​in innovation practices which management initiates to get a strategic return. Outside-​in innovation has focused on the use of ideas and knowledge from outside an organization in the development of products and services and is usually represented within the context of the open innovation funnel (Chesbrough, 2003a). However, over the last two decades, many companies have extended this original scope: they use open innovation not only to develop new products effectively but also to create the best possible environment to maximize the benefits of outside-​in open innovation. 2  These terms were also used by Chesbrough (2002) for the analysis of corporate venture capital investments. Financial and strategic benefits are not mutually exclusive. In most cases, open innovation project have both strategic and financial benefits. Here we follow Chesbrough (2002) in asking which one is more important in deciding.

54    wim vanhaverbeke and victor gilsing J&J in Beerse in Belgium is a good example of a company that broadened its open innovation practices into a more general approach to create a vibrant environment for the company’s R&D activities. J&J’s approach is based on the search for talent, development of expertise, access to state-​of-​the-​art R&D infrastructure, various sources of external funding and influencing local innovation policies in Flanders. Open innovation is not only about outside-​in knowledge flows to strengthen the innovation engine of the company, but the company is actively shaping the environment and turning it into a vibrant regional innovation ecosystem which, in turn, is beefing up the potential for J&J’s outside-​in open innovation. Let’s take talent and infrastructure as examples. First, for J&J Beerse to be a top location for innovative activities, it has to be able to attract top talent from around the world. To reach that target it should ensure competitive salaries, flexible immigration and labor policies, the availability of top universities and research labs. Most of these factors are beyond J&J’s control and therefore the firm’s relationships with public authorities and universities are important. Complementary activities undertaken by the company to attract and foster talent include summer courses, internships, skill-​building in partnership with universities. Second, state-​of-​the-​art technology is very expensive and sharing R&D infrastructure with other organizations is imperative. For instance, J&J Beerse did not purchase a supercomputer because it is a prohibitively expensive investment, but it reached an agreement with the Belgian universities to share computing time on their supercomputer. Bayer Healthcare (BHC) offers another example of how outside-​in open innovation can be transformed into a complete open innovation toolkit, leading to more complete innovation portfolios and strategies. Open innovation currently reaches beyond outsourcing, licensing, or alliances and is rapidly changing, connecting to new potential partners (cross-​industry, venture capitalists (VCs), user groups), new technological opportunities, such as virtual R&D through bioinformatics (see Rai, 2005) or internet-​based crowdsourcing (see Lessl & Asadullah, 2011). Tamoschus et al. (2015) created four different archetypes based on the intentions and goals for more open forms of outside-​in innovation. First, firms want to gain insight into the innovation landscape, into trends or in order to identify new partners. Second, open innovation is deployed to extend the workbench in terms of complementary tasks, that is to let a partner conduct the process in order to make one’s own organization more flexible or because the expertise is not available internally. Third, open innovation helps to get access to certain institutions, partners, networks or accessing a new idea pool. Fourth, open innovation is used to facilitate joint development—​which overlaps to a great extent with the classical approach to outside-​in open innovation. These patterns of open innovation—​insight, workbench, access, and development—​differ considerably concerning the level of organizational involvement, levels of risk, need for professional management involvement, and metrics that have to be used (Robaczewska et al., 2019).

OPENING UP OI: DRAWING THE BOUNDARIES    55

Inside-​Out Open Innovation for Financial Return Inside-​out innovation has been described originally as knowledge “seeping out” of the firm through vehicles such as employees setting up a start-​up company or external licensing (Agarwal et al., 2009; Chesbrough, 2003a; Ganco et al., 2015). In both cases, firms want to monetize on technology or knowledge that is no longer strategically interesting for them. Reasons for this loss in interest are diverse: the technology requires a different business model which cannot be aligned with the dominant logic of the firm; the technology may not perform as well as originally expected in the research phase; competing technologies may emerge during the R&D process; a competitor may be the first to launch an innovative product, reducing commercial benefits for late entrants; or a company may shift its corporate strategy divesting the technology it has been developing. Inside-​out open innovation was usually framed as an instrument to gain a financial return from technology that would be shelved anyway. Therefore, it’s no surprise that it has never received the same attention as outside-​in open innovation among managers and scholars. Selling or licensing technology may generate a substantial part of the overall revenues: for most years since 1996, IBM generated $1 billion or more in intellectual property revenue, $27 billion in total between 1996 and 2021 (IP Closeup, 2021). Philips is the owner of 62,000 patent rights, 37,000 trademarks, and 104,000 design rights: it has a long tradition of licensing or selling IP, know-​how, and technology that no longer strategically fits with its portfolio. The traditional approach has narrowed inside-​out open innovation to the search by companies for a financial return on technologies that no longer fit with their strategy. This might have blind-​sighted open innovation scholars to explore the possibilities of using inside-​out innovation as a vehicle to gain a strategic return. We will explore this topic in the section “Inside-​out open innovation for strategic return.”

Outside-​In Open Innovation for Financial Return Most publications focus on outside-​in open innovation for strategic reasons. Very few focus on outside-​in open innovations activities that are initiated with the aim of gaining a financial return—​or what we referred to in Figure 4.1 as improving operational benefits. Process innovations represent an interesting field where firms engage in outside-​ in open innovation for financial reasons (Madrid‐Guijarro et al., 2021; Theyel, 2012; Trantopoulos et al. 2017; Tsinopoulos et al., 2018; Von Krogh et al., 2018). Process innovations (usually in combination with product innovations) can be a source of competitive advantage and can therefore be categorized as open innovation for a strategic return. However, many process innovations primarily focus on productivity improvements and cost reductions. The primary drive for open innovation in most

56    wim vanhaverbeke and victor gilsing process innovations is a financial one. Tsinopoulos et al. (2018) and Madrid-​Guijarro et al. (2021) find a positive impact of outside-​in open innovation on process innovation performance. The research by Von Krogh et al. (2018) suggests that for many manufacturers—​even industry leaders—​walling off process innovations from the outside world can be a losing strategy and that open process innovation rather than secrecy is leading to improved process innovations. The authors suggest that companies can increase productivity faster if they open up to the outside, improve their ability to absorb and implement ideas from external sources, exploit data-​based technologies, and use unconventional sources of knowledge. Shifting the focus to open process innovation and its impact on productivity improvement has some major implications for open innovation research. First, it requires a shift in open innovation research from companies that excel in new product innovation to companies with strong internal process innovations, such as Foxconn and Zara, to name a couple. Those companies can engage in open innovation to improve their productivity. Second, open innovation has to consider new product research and development as one (important) area where open innovation can be applied. Process innovations force open innovation scholars to focus increasingly on operations, production, logistics, customer interaction, and value chain innovations. This, in turn, will require a closer collaboration with scholars in these different fields. Third, the organizational requirements for collaboration in process innovations will be different than those for product innovation. We still know very little about how those requirements differ from open innovation in product innovation.

Inside-​Out Open Innovation for Strategic Return Inside-​out open innovation is usually associated with the objective of gaining financially. By selling or licensing out technology, firms may fully capitalize on their internal knowledge. However, there are several strategic objectives that may drive firms to engage in inside-​out open innovation. Classical examples are establishing new industry standards or getting access to complementary technology through cross-​licensing agreements (Grindley & Teece, 1997; Lichtenthaler, 2010; West, 2003). More recently, Leten et al. (2013) illustrated how IMEC, the world leading research institute in nanoelectronics, applies both background and foreground IP-​sharing in industry-​wide, collaborative research programs to develop technologies for core business activity. Closely related is the free revealing of internally developed technology as a strategy to elicit collaboration from other actors and users (Alexy et al., 2013, 2018; Foege et al., 2019; Greul et al., 2018; Henkel, 2006; Henkel et al., 2014; Jeppesen & Lakhani, 2010; Nagle, 2018). Still others have illustrated how firms can team up with other actors to spur ecosystem-​related innovations in order to accelerate the development and commercialization of complementary technology (Chesbrough & Garman, 2009). We can now extend the strategic use of inside-​out open innovation to situations where a company has strong technological capabilities but lacks the incentive to own the newly

OPENING UP OI: DRAWING THE BOUNDARIES    57 developed innovations because they don’t belong to the core area of expertise of that company. Masucci et al. (2020) offer interesting examples of technologies developed by major oil companies who depend on the technological expertise of specialized service companies for their productivity and competitiveness. Yet, specialized firms may do great business with the oil companies based on their current technological solutions and have therefore little incentive to invest in developing novel technologies. This situation undermines the oil companies’ competitive position when the specialized firms’ lack of innovativeness becomes a critical bottleneck. Masucci et al. (2020) describe how oil companies, in addressing these bottlenecks, may leverage inside-​out open innovation—​ that is, they develop technological solutions internally and exploit them commercially outside their boundaries—​to induce specialized services firms to accelerate technological progress, thereby resolving the technological bottlenecks of the major oil companies. Those service companies will not automatically take ownership of these technologies, they have to have an incentive to do so. In other words, how can a focal company use inside-​out open innovation to induce firms with complementary activities to adopt novel technological solutions? According to Masucci et al. (2020), a focal firm (oil company) has to: (1) actively engage with specialized actors that possess the skills needed to further develop and take ownership of the internally developed technology, and (2) align the incentives of the specialized firms that should ultimately deploy those technology in the field. In this respect, it is important to secure control over the technology’s IP and to identify technologies that have the potential to broaden the service companies’ offerings, thereby enhancing their revenue potential. When service companies provide services based on the novel deployed technologies, the focal company improves its value creation potential by successfully applying inside-​out open innovation in a strategic way.

Classifying Open Innovation in a Broader Framework The example of the major oil companies and specialized services companies shows that firms may have compelling strategic reasons to initiate new technological innovations even though they don’t intend to deploy the innovation internally. Can we go one step further, assuming that a focal firm envisions a strategic change but has, in contrast with the oil companies, no or poorly developed technological capabilities to initiate the process? In order to answer that question, we developed Figure 4.2. which summarizes different strategic uses of open innovation. The two dimensions represent whether the firm has the required technology in-​house and whether it intends to deploy (or own) the technology. The lower left cell represents closed innovation, the left upper cell represents inside-​out open innovation and the lower-​right one outside-​in open innovation.

58    wim vanhaverbeke and victor gilsing

Does the firm intend to deploy the technology?

(Strategic use of) inside-out open innovation No

• Selective revealing • Opening background IP

to partners (co-development)

• Standard-setting • Incentivize others to innovate

Closed innovation

Outside-out open innovation • Sustainable OI in non-core

business

• Digitalization of non-core assets

(e.g., digital drugs packaging)

• Technology and market

development in complementrary, non-core products (e.g., SkyNRG)

Outside-in open innovation

• Traditional outside-in OI • New technological capabiilty

Yes

building (e.g., data-driven business model, digital transformation)

Yes

Does the firm have the required technology in-house?

No

figure 4.2  Broadening the open innovation concept.

Strategic use of inside-​out open innovation was explained in the previous section. The firm invites others to jointly commercialize the technology, as in the case of standard-​ setting, selective revealing of technology, and opening up the background IP for joint technology projects. In the case of inside-​out open innovation applied by oil companies, they incentivize service companies to take ownership of the technology and explore further innovations. The right upper cell in Figure 4.2 represents “outside-​out” open innovation. It is an attempt to capture new phenomena in the area of collaborative innovation and at the same time to extend the open innovation concept. We first provide two examples to illustrate outside-​out open innovation: The first one is the Green Fiber Bottle, the world’s first 100% biodegradable bottle that is used by the Danish brewer Carlsberg to address sustainability issues in line with its strategy (Bogers et al., 2018). The second one illustrates how KLM is dealing with its commitment to CO2 emission norms through the establishment of SkyNRG together with several venture capitalists. Both examples represent a situation where the firm has a strategic incentive to invest in a technological solution but lacks the competencies to develop that technology internally while it has no incentive to take ownership of that technology. The Green Fiber Bottle is a major project of Carlsberg to become a more sustainable company. Since packaging was responsible for 40% of its total environmental impact, the company decided in 2015 to consider this as a key area of improvement for sustainability at the company. However, Carlsberg is a beer producer and distributor, not a packaging company. Although it has a strong strategic incentive to develop a sustainable bottle, it has no expertise in bottle production, let alone in sustainable bottle production. The technology for the Green Fiber Bottle came from ecoXpac, a startup in the package solution business, which it developed in collaboration with DTU, a university in Denmark. The startup lacked capital to scale the technology and there was not yet a clear market application for the technology. Carlsberg offered both, but the

OPENING UP OI: DRAWING THE BOUNDARIES    59 Danish beer producer were not interested in getting involved in packaging and it would rather partner with a supplier to further develop and commercialize the technology. As Carlsberg was not interested in taking ownership of the new technology, it had to align incentives among its partners: since ecoXpac was eager to expand its business and as Carlsberg was not entering the packaging business, it did not constrain ecoXpac from offering its novel packaging solutions to other companies. Similarly, Carlsberg wants a potential supplier to take ownership of the technology and will therefore not hinder the supplier in its attempt to sell the new bottle to other customers. Eventually, this may be done in a stepwise way, first, serving customers outside the beverage industry, later on non-​alcoholic beverage producers, and finally competitors in the beer industry. The search for ownership of the technology is illustrative of such radically new technologies. While Carlsberg and ecoXpac were collaborating on a first paper bottle prototype, BillerudKorsnäs, a Danish paper packaging material developer, was also involved in the process of exploring the possibilities of another paper bottle product. The companies had assumed a similar challenge, which soon led to a joint innovation platform. EcoXpac had competence in pulp molding and tool manufacturing, but needed support on fiber technology, stock preparation, paper-​making, barriers, upscaling, market opportunities, etc. As the project progressed, BillerudKorsnäs became a majority shareholder in ecoXpac. A partnership discussion with bottle manufacturer Alpla was initiated, which ultimately led to the decision in 2019 to continue the project in a joint venture called Paboco, that currently offers different bottles available for commercial use (Paboco, 2022). The Green Fiber Bottle case is interesting to illustrate “outside-​out” open innovation.3 Carlsberg had a strong incentive to initiate the open innovation project with ecoXpac. As a potential customer aiming to work in a sustainable way, Carlsberg was an instigator and potential key customer, but since this project was not within its field of expertise, it didn’t play a role as innovator and it didn’t want to take ownership of the technology. In other words, “outside-​out” open innovation implies that a firm is strategically motivated to drive an innovation project, by stimulating partners with the right technological competences to develop the technology and take ownership of it. SkyNRG represents a similar case. The startup was founded in 2009 to help make the market for Sustainable Aviation Fuel (SAF), which at that time did not exist. Founding partners were Air France KLM Group, North Sea Group (a group of companies delivering products and services to the oil market), and Spring Associates (a strategy consulting firm helping companies become more competitive through sustainable products and solutions). SkyNRG’s mission was to build sustainable aviation fuel (SAF) capacity for the aviation industry to meet its 2050 net zero commitment with IATA. The startup facilitates the commercialization and scaling-​up of the supply of aviation biofuels and set up an ecosystem of strategic partners from different industries to

3 

This term was recently introduced by Gutmann et al. (2021) within the context of open innovation in CVC investments. In this chapter, we focus on outside-​out open innovation on a different phenomenon.

60    wim vanhaverbeke and victor gilsing introduce biofuels as an alternative source of energy. In other words, SkyNRG can be considered a global market maker in sustainable jet fuel. Let’s focus on the role of KLM in establishing SkyNRG. Why was KLM interested in being one of the founders? KLM, as a leading company of the Skyteam Airline Alliance, is one of the (potentially many) airline companies that can profit from SAF to achieve the target of net zero carbon emission by 2050. KLM is an airline company and has no ambition to develop and produce SAF, but it has (together with the other Skyteam partners) a major strategic interest as a customer of aviation biofuel to guarantee a stable and competitively priced supply of SAF. With the establishment of SkyNRG, KLM was setting up an ecosystem including different types of partners that are required to switch gradually from petroleum-​based fuel to SAF. From the KLM perspective, the SkyNRG initiative illustrates how a company can tackle a major strategic challenge (dependence on petroleum-​based fuel) by setting up a hub company that develops an ecosystem to guarantee a steady growth in supply of SAF. The required technical innovations (such as the development of second-​generation feedstock for fuel production) are developed by the biofuel industry and specialized technology centers. However, technology development is only one determinant of the mainstream adoption of SAF. An industry-​wide switch can only be achieved by combining expertise in the fields of regulations (standard setting), effective sustainability criteria, product knowledge, and air transport. SkyNRG needs to bring together a diverse set of stakeholders, such as potential SAF producers, who will invest a considerable amount in new technologies to scale the production and sell SAF profitably at affordable prices, regulators, and VCs, port and airport authorities, and airline companies to guarantee a steady increase in demand over time. KLM established SkyNRG to accelerate the technological progress in the biofuel value chain, guaranteeing, in this way, its strategic objective, i.e., the steady supply of aviation biofuel. In other words, KLM realized its strategic objective through the establishment of an ecosystem orchestrated by a dedicated organization (SkyNRG) that crafts an ecosystem between organizations to build SAF capacity for aviation to meet its net zero commitment. KLM is using outside-​out open innovation: it reaches its strategic objective by investing in and setting up a new organization, whose task is to develop the SAF industry from scratch. SAF producers develop and take ownership of the innovations to scale the business but could not do that without the guaranteed demand from the airline industry, and the coordinated actions with regulators, VCs, and infrastructural players. KLM is the instigator, SkyNRG the orchestrator, and the SAF producers the innovators. There are a number of differences between outside-​out open innovation and the traditional open innovation approach. First, the traditional focus of open innovation on new product—​services development can be broadened to any strategic objectives a firm intends to achieve: new product development is only one way to improve the competitiveness of a company. Achieving the net zero emission norm is a strategic objective for KLM and Carlsberg’s objective was to become a more sustainable company. Second, there is a need to introduce more roles: in the classical open innovation there is an innovator and a benefactor. In outside-​out open innovation, there is an instigator (e.g.,

OPENING UP OI: DRAWING THE BOUNDARIES    61 Carlsberg, KLM), innovators (e.g., ecoXpac, SAF producers), and an orchestrator (e.g., Carlsberg and later Paboco). Third, who takes ownership of the technology becomes a crucial variable: Both KLM and Carlsberg have a strong strategic stake in the development of SAF or the Green Fiber Bottle, but they are in a different business and have no interest in driving the development and commercialization of the technology: they benefit strategically from SAF supply and using a sustainable bottle respectively, but they want others to take ownership of further technological developments and production as those products are complements and are not related to the business of the airline company or the beer producer. Fourth, while open innovation is usually considered a direct collaboration between a few organizations based on contractual agreements, examples such as Carlsberg’s biodegradable bottle and KLM’s SkyNRG initiative indicate that an ecosystem approach is required to understand and manage the complexity of radical changes, such as the introduction of sustainable bottles or aviation fuel. Our call for combining research on open innovation and innovation ecosystems is not new. Nambisan (2018) and Curley and Salmelin (2013) already considered ecosystems as a venue for open innovation initiatives. West and Wood (2008) and Rohrbeck et al. (2009) are among the first to describe cases of firms developing such an ecosystem. Finally, outside-​out open innovation is particularly interesting when firms see strategic opportunities based on technologies outside their core competencies and need to team up with partners that have those skills. Outside-​out open innovation is the way to go when bottlenecks in the development of complements or components have to be resolved and where the risks and costs of acquiring those competencies would be too high.

Conclusion In this chapter we focused on two different ways to broaden the applicability of open innovation. First, we started from the observation that, in the literature, outside-​in open innovation usually plays a strategic role, while inside-​out open innovation has predominantly a financial benefit. We explored the underdeveloped roles of both innovation modes—​that is, outside-​in open innovation can generate primarily a financial return and inside-​out open innovation can be applied for predominantly strategic reasons. Our examples show that this may lead to a more balanced appreciation of all modes of open innovation. Second, we explored the possibilities of using open innovation strategically when we are no longer limiting open innovation practices to only the strategic objective of new product or service development. We deliberately chose a broader setting of strategic objectives (and not only new product development) as drivers to initiate open innovation activities. In this approach, it is not only imperative to know whether or not a company has in-​house capabilities to deal with the technological complexities to achieve its strategic objective, but also whether it has an incentive to take ownership of the technology to further develop and commercialize it. It

62    wim vanhaverbeke and victor gilsing becomes interesting when a firm has a compelling strategic reason to start an innovation, but has neither the competencies in-​house, nor the incentive to take ownership of the technology. We introduced the concept of “outside-​out” open innovation to describe this situation: It is a new application area for open innovation and it allows us to apply open innovation in a wider variety of contexts, with more and more diverse partners and with governance challenges that are in line with those described in the innovation ecosystem literature (Nambisan, 2018).

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64    wim vanhaverbeke and victor gilsing Rai, A. K. (2005). Open and collaborative research: A new model for biomedicine. In R. W. Hahn (Ed.), Intellectual property rights in frontier industries: Software and biotechnology (pp. 131–​158). AEI-​Brookings Joint Center. Robaczewska, J., Vanhaverbeke, W., & Lorenz, A. (2019). Applying open innovation strategies in the context of a regional innovation ecosystem: The case of Janssen Pharmaceuticals. Global Transitions, 1, 120–​131. https://​doi.org/​10.1016/​j.glt.2019.05.001 Rohrbeck, R., Hölze, K., & Gemünden, H. G. (2009). Opening up for competitive advantage: How Deutsche Telekom creates an open innovation ecosystem. R&D Management, 39(4), 420–​430. Tamoschus, D., Hienerth, C., & Lessl. M. (2015). Developing a framework to manage a pharmaceutical innovation ecosystem: Collaboration archetypes, open innovation tools, and strategies. Paper submitted to the 2nd World Open Innovation Conference, Santa Clara, Silicon Valley. Theyel, N. (2012). Extending open innovation throughout the value chain by small and medium-​sized manufacturers. International Small Business Journal, 31(3), 256–​274. https://​ doi.org/​10.1177/​02662​4261​2458​517 Trantopoulos, K., Von Krogh, G., Wallin, M., & Wörter, M. (2017). External knowledge and information technology: Implications for process innovation performance. Management Information Systems Quarterly, 41(1), 287–​300. Tsinopoulos, C., Sousa, C., & Yan, J. (2018). Process innovation: Open innovation and the moderating role of the motivation to achieve legitimacy. Journal of Product Innovation Management, 35(1), 27–​48. https://​doi.org/​10.1111/​jpim.12374 Von Krogh, G., Netland, T., & Wörter, M. (2018). Winning with open process innovation. MIT Sloan Management Review, 59(2), 53–​56. West, J. (2003). How open is open enough?: Melding proprietary and open source platform strategies. Research Policy, 32 (7), 1259–​1285. https://​doi.org/​10.1016/​S0048-​7333(03)00052-​0 West, J. (2020). Localized knowledge flows and asymmetric motivations in open innovation. Journal of Innovation Economics and Management, 32(2), 181–​196. https://​doi.org/​10.3917/​ jie.032.0181 West, J., & Bogers, M. (2014). Leveraging external sources of innovation: A review of research on open innovation. Journal of Product Innovation Management, 31(4), 814–​831. https://​doi. org/​10.1111/​jpim.12125 West, J., & Wood, D. (2008). Creating and evolving an open innovation ecosystem: Lessons from Symbian Ltd. SSRN. http://​dx.doi.org/​10.2139/​ssrn.1532​926

CHAPTER 5

A MULTI- L​ EVEL FRA MEWORK FOR SELEC T I NG AND IM PLEM E NT I NG INNOVATION MODE S marcel bogers and joel west

Introduction As a distributed innovation process, open innovation involves knowledge flows across organizational boundaries that entail a separation between creating an innovation and its commercialization (Chesbrough & Bogers, 2014; West & Bogers, 2014). While these two steps were identified long ago, they were assumed to be integrated within a firm during the long period when the success of the modern industrial corporation was defined by its ability to develop and commercialize its own innovations (Chandler, 1977, 1990). Decades of research identified how firms develop technical inventions into technological innovations, and then commercialize these innovations through an internal process of R&D, production, and distribution (Freeman, 1982; Tornatzky & Fleischer, 1990). More recently, the paradigm of open innovation has uncoupled these two steps, by suggesting that successful firm should equally consider both internal and external paths for creation and commercialization (Chesbrough, 2003, 2006a). Here we build on nearly twenty years of open innovation research—​as well as earlier research on external innovation and commercialization—​to identify and summarize four distinct modes for the open innovation-​enabled firm: inside-​in, inside-​out, outside-​in and outside-​out. We discuss the environmental, firm and innovation-​specific factors that cause firms to select each such mode.

66    marcel bogers and joel west We begin by contrasting processes for the creation and commercialization of innovations both inside and outside the firm. We then review how a combination of the creation and commercialization dimensions creates four possible innovation modes, and the process by which firms select a mode for a given innovation. We conclude with a discussion of the implications of this framework for open innovation research and practice, including opportunities for future research.

Openness in Creating and Disseminating Innovations Innovation fundamentally comprises the two steps of (1) creating and (2) commer­ cializing technical inventions—​whether integrated within a firm (Chandler, 1977; Freeman, 1982; Schumpeter, 1934) or distributed across economic actors (Arora et al., 2004; Chesbrough & Bogers, 2014; von Hippel, 2016). Table 5.1 summarizes the factors that will influence the choice of the form to conduct each activity internally or externally. For both creation and commercialization, the viability of the internal alternative will be largely under the firm’s control, but firms have less control (and visibility) for the external one. The relative attractiveness of the internal and external choice thus is a function of both endogenous and exogenous forces. The creation of an innovation may begin with a scientific discovery made through a firm’s own basic research, or may leverage publicly funded research, typically at a university, nonprofit organization or a government laboratory (Rosenberg, 1990). This technical invention activity may be captured through a patent or another form of intellectual property (IP); such patent outputs correlate well with the degree of R&D inputs within a given industry (Griliches, 1990). Factors that determine whether the most likely source of innovation creation would be internal or external will range from knowledge dispersion and the technical environment more generally (Arora et al., 2004; Teece, 1986) to availability of R&D capabilities and other prior knowledge and resources required to create or integrate the innovation (Cohen & Levinthal, 1990; Freeman, 1982). The commercialization of an innovation may then also take place internal or external to the firm. Differences in the ability of firms to commercialize innovations have most often been attributed to characteristics of the firm, groups of firms, or the national economic or legal environment (e.g., Nelson, 1993; Teece 1986). Considering whether the most likely locus of innovation creation would be internal or external will rely on factors ranging from the strength of the appropriability regime and the institutional framework more generally (Murray & O’Mahony, 2007; Teece, 1986) to the availability of complementary assets and fit with the business model (Chesbrough & Rosenbloom, 2002; Zott et al., 2011).

Commercialization

Internal to the firm

Creation

External to the firm

Internal to the firm

External to the firm

Choice

Activity

Availability of technology markets (Arora et al., 2004)

Strong appropriability regime (West, 2006)

Industry structure (e.g., firm concentration) (Dittrich & Duysters, 2007)

Lack of external commercialization channels (Lee et al., 2010)

Geographical proximity (Radziwon & Bogers, 2019)

Distributed nature of knowledge (von Hippel, 2016)

Life cycle/​dominant design (Christensen et al., 2005)

Lack of external markets for sourcing innovation (Arora et al., 2004)

Environment-​level factors

Openness of business model (Saebi & Foss, 2015)

Strong complementary assets (Rothaermel, 2001)

Lack of R&D resources (Bogers, Chesbrough, et al., 2019)

Strong R&D capabilities (Bogers, Chesbrough, et al., 2019) Scale economies of R&D (Arora et al., 2004)

Firm-​level factors

Table 5.1 Key factors that influence innovation creation and commercialization strategies

Low distribution cost (Murray & O’Mahony, 2007)

Strategic importance (Bogers & West, 2012)

To form barriers to imitation (West, 2006)

Fit with business model (Lu & Tucci, Chapter 40, this volume)

Spillover-​inducing codification (Bogers & West, 2012)

Lack of internal support (incentives) (Dahlander & Gann, 2010)

Innovation output (IP) (Holgersson & Granstrand, 2022)

Innovation input (R&D, science) (Chesbrough, 2006b)

Innovation-​level factors

68    marcel bogers and joel west Commercialization Inside focal firm

Outside focal firm

Inside-in

Inside-out

Outside focal firm

Outside-in

Outside-out

Creation

Inside focal firm

figure 5.1  Locus of innovation creation and commercialization.

Available Innovation Modes While a firm that is fully ready for open innovation is equally ready to manage knowledge inflows and outflows (see Chesbrough, Chapter 1, this volume), not every firm will have such options. Instead, as noted earlier, the available internal and external paths for innovation creation and commercialization will depend on both exogenous and endogenous factors. Building upon the simplified two-​phase model in the previous section, we identify two key dimensions: the locus of innovation creation, and the locus of innovation commercialization.1 With the locus of each being either internal or external to the focal firm, this means that for its innovations, a firm has the potential to select from one of four distinct innovation modes: inside-​in, inside-​out, outside-​in, and outside-​ out (Figure 5.1).2 In this section, we examine the external-​and firm-​level factors that influence the feasibility of the firm using each mode for any innovation. In the next section, we consider how a firm decides which available mode to use for a specific innovation.

1 

For consistency with prior innovation research, we use “commercialization” to refer to the process by which innovations are brought into use, but (as discussed below) explicitly include the non-​ commercial diffusion to other parties discussed earlier. 2  Figure 4.2 of Chapter 4 parallels our Figure 5.1, except that it is rotated 90° clockwise and uses slightly different definitions of the two dimensions. Both typologies use similar definitions of inside-​ out and outside-​in, anchored in the previously accepted definitions in open innovation research (e.g., Chesbrough & Bogers, 2014).

MULTI-LEVEL FRAMEWORK FOR INNOVATION MODES    69

Inside-​In Innovation: Created Internally, Commercialized Internally The inside-​in innovation mode corresponds to the traditional vertically integrated model. This innovation mode is based on internally developing new ideas, developing those ideas into new technologies, and commercializing them as products to the firm’s customers. Thus, both creation and commercialization are entirely internalized and controlled by the focal firm (Chandler, 1977, 1990; Teece, 1986). While open innovation is often juxtaposed in opposition to the Chandlerian model (Bogers & West, 2012), in fact, the open innovation paradigm has always assumed that a firm would pursue this vertically integrated alternative when it was the most effective innovation strategy (Chesbrough, 2003, 2006a).

Environment-​Level Factors An important environmental condition is the appropriability regime. While strong appropriability will encourage firms to innovate, whether through formal IP or trade secrets embedded in tacit knowledge and skills (Winter, 1987). Conversely, as Teece (1986) famously demonstrated, weak appropriability can drive firms to commercialize their own innovations because it is impractical to partner with others for commercialization (West, 2006).

Firm-​Level Factors Creation in this mode usually relies on strong capabilities, such as R&D labs to conduct R&D that leads to technical inventions and IP (Chesbrough, 2003; Narin et al., 1997). An example of this would be Dupont’s research into organic chemistry in the 1920s and 1930s, that both discovered and developed high-​volume manufacturing processes for products such as cellulose, Freon, Teflon, acrylic plastics (Lucite), and lacquer-​based automotive paints (Chandler, 1990). To be successful at both creation and commercialization, the innovating firm requires strong organizational routines based on tacit knowledge and skills (Arora & Gambardella, 1994; Nelson & Winter, 1982) and is further motivated by the potential to create economies of scale and scope (Chandler, 1990). This innovation mode also implies internal and external conditions that favor vertical integration over disintegrated organization of innovation activities (Williamson, 1985). Firms also require marketing capabilities specifically adapted to their industry’s demand and competitive conditions (Chandler, 1990). At the same time, recent research has identified conditions under which different organizational units within a large firm may not share knowledge openly, and instead hoard that knowledge. These internal siloes can impede the effective commercialization of internal research—​to the extent that the challenges of crossing internal silos parallel those of open innovation crossing firm boundaries—​and thus large firms need to

70    marcel bogers and joel west recognize and overcome such challenges (Chesbrough, 2019, Chapter 1, this volume; Seran & Bez, 2021).

Inside-​Out Innovation: Created Internally, Commercialized Externally The inside-​out mode (often termed “outbound” in open innovation) considers how innovations created internally are commercialized or otherwise disseminated externally, the subject of an important stream of open innovation research (Chesbrough, 2006a; Chesbrough & Winter, 2014; Enkel et al., 2009; Masucci et al., 2020). Even before this, a sizable literature existed as to the conditions under which inventors will license their (often patented) technology (e.g., Arora, 1997; Arrow, 1962; Teece, 1986) or when they will collaborate with others to find successful commercialization opportunities (Granstrand & Sjölander, 1990; Rothaermel, 2001). Finally, some firms freely reveal innovations without financial consideration (de Jong & von Hippel, 2009; Harhoff et al., 2003).

Environment-​Level Factors The out-​licensing strategy depends on strong appropriability (i.e., patents) to collaborate with external commercialization partners (Arrow, 1962; Teece, 1986; West, 2006). It also depends on the availability of a market for the specific technology (Arora et al., 2004; Arora & Gambardella, 1994, 2010). The shift over decades toward such outbound licensing in an industry is highly dependent on scale economies, suggesting a fairly narrow range of conditions under which such licensing occurs (e.g., Arora, 1997; Lieberman, 1989). Common reasons for seeking such partners include decreasing lead times or reaching new markets (Enkel et al., 2009). Such capabilities are often found in existing firms, including (rarely) market competitors.3 However, if such partners do not exist, licenses may be assigned to spinoff companies formed by the firm (Chesbrough, 2003) or university (Perkmann, Chapter 26, this volume).

Firm-​Level Factors The creation of innovation within the inside-​out mode essentially relies on a strong R&D capability at the level of the firm, although there may be varying motivations for a focal firm to create an innovation that is ultimately commercialized externally. In some cases, a firm creates an innovation for its own use—​typically as a part of

3  In the 1990s, IBM licensed hard disc drives to competing laptop makers (Chesbrough 2003a), but potential rivalry disappeared when IBM sold its global disk drive operations in 2002 to Hitachi (not a laptop competitor), and then in 2005 sold its laptop division to Lenovo. The relative scarcity of such examples suggests the difficulty of aligning such competing interests, even in an era of open innovation.

MULTI-LEVEL FRAMEWORK FOR INNOVATION MODES    71 their value-​adding activities (de Jong & von Hippel, 2009) or new offerings to their customers (Chesbrough, 2003)—​that may also find its way to other firms, whether intentionally or otherwise. In other cases, the innovation may be created but not used internally, whether because R&D is intended to support external commercialization or because it produces innovations that are not aligned to the firm’s actual business model (Chesbrough, 2006b; Chesbrough & Chen, 2013; Chesbrough & Rosenbloom, 2002). Finally, as with universities, inside-​out licensing is long known as the only approach for new or small firms that lack credible commercialization capabilities (Teece, 1986). Whether big or small, firms using the inside-​out mode require outwardly facing capabilities to be able to identify and work with external commercialization partners (Chesbrough & Winter, 2014).

Outside-​In Innovation: Created Externally, Commercialized Internally The outside-​in mode (often termed “inbound” open innovation) considers how firms commercialize innovations that are created outside the firm. It corresponds to the well-​established inbound part of the open innovation paradigm (Dahlander & Gann, 2010; Enkel et al., 2009), which is the largest area of research in open innovation (West & Bogers, 2014), including research on crowdsourcing (Majchrzak & Malhotra, 2013; Randhawa, Chapter 20, this volume). It is also the subject of how firms leverage user-​ created innovations (Bogers et al., 2010; Keinz et al., 2012). A separate coupled mode combines inside-​out and outside-​in modes, either through discrete bidirectional knowledge flows between two partners, or as part of an interactive collaboration to jointly create knowledge valuable to both parties (Enkel et al., 2009; Piller & West, 2014).

Environment-​Level Factors Researchers have identified both pecuniary and non-​pecuniary motives for partners creating external innovations that are later commercialized by firms (Dahlander & Gann, 2010). Such external sources will be more valued when industry changes force existing firms to access new competencies (Pinarello et al., 2022). The pecuniary motives correspond to the earlier inside-​out firms, and thus (like their partners) the outside-​in firms will benefit from appropriability mechanisms and technology markets. Potential innovation creators include suppliers (Schiele, 2010), competitors (Lim et al., 2010), universities (Perkmann & West, 2015), and even startups (Weiblen & Chesbrough, 2015). Non-​pecuniary innovators are often individuals, an important potential source for innovations (Harhoff et al., 2003; von Hippel, 2005). Whether as solitary individuals (Jeppesen & Lakhani, 2010) or in communities (Dahlander & Wallin, 2006; West & Lakhani, 2008), their innovative potential is increasingly being facilitated by the increasing mobility of skilled labor (Chesbrough, 2003; Fleming & Marx, 2006) and the

72    marcel bogers and joel west growing availability and effectiveness of the Internet, ICTs, and toolkits more generally (Afuah & Tucci, 2012; Dodgson et al., 2006; Franke & von Hippel, 2003). The most-​often studied way for firms to harness individuals to address firm-​requested problems is via crowdsourcing (Randhawa, Chapter 20, this volume), which in turn may require the availability of crowdsourcing intermediaries (Diener et al., Chapter 22, this volume).

Firm-​Level Factors Typical approaches to commercialize externally created innovation include sourcing external innovations (Laursen & Salter, 2006; Rothaermel & Alexandre, 2009), combined with internal processes for integrating external innovation, including absorptive capacity (Zobel, 2017) and overcoming the “not-​invented-​here” idea (Antons & Piller, 2015; Hannen et al., 2019). Outside-​in studies generally assume (but rarely test) that the paths for commercializing an external innovation are the same as for an internally generated one (see West & Bogers, 2014). The mechanisms of getting external innovations into the firm consist of processes such as searching, enabling, and filtering such externally sourced innovations (West & Bogers, 2014). Firm-​ level characteristics that affect this process include R&D capabilities and complementary assets (Ceccagnoli et al., 2010; Teirlinck et al., 2010). Support organizations within the firm, such as Purchasing, Legal, HR, and Finance, are also required, and these functions may have to adapt their processes to incorporate external technologies effectively (Chesbrough, 2019). To leverage non-​pecuniary motives, firms have the option of organizing their own communities to bring in ideas and other technologies (Di Gangi & Wasko, 2009; Jensen et al., 2014; Parmentier & Mangematin, 2014). Whether from individuals or communities, firms need specific skills to organize and harvest innovations from non-​ pecuniary sources (Fredberg & Piller, 2011; Keinz et al., 2012).

Outside-​Out Innovation: Created Externally, Commercialized Externally While formal definitions of open innovation have assumed knowledge flows crossing firm boundaries (e.g., Chesbrough & Bogers, 2014; West et al., 2014), firms can also benefit from the creation and commercialization of an innovation without the firm’s involvement. We thus highlight the pattern of outside-​ out innovation, which we define as “innovations created and commercialized outside a firm that support a firm’s innovation strategy and business model.”4 Consistent with the previous definitions of open 4 The definition of “outside-​ out” by Vanhaverbeke and Gilsing (this volume) is when “a firm is strategically motivated to drive an innovation project, by stimulating partners with the right technological competences to develop the technology and take ownership of it.” While this fits the purpose of their typology, we believe that ours is a more general economic definition and a superset of their usage.

MULTI-LEVEL FRAMEWORK FOR INNOVATION MODES    73 innovation, we exclude activities that do not involve knowledge flows or do not help the focal firm’s innovation efforts.5 In the outside-​out innovation mode, both the creation and commercialization of the innovation take place without (or despite) the firm’s lack of involvement, but these innovations benefit the focal firm. By definition, the outside-​out mode means that actors other than the firm are driving the creation and commercialization of the innovation. Outside-​out has similarities but key differences from the outside-​in mode. In the case of Apple, outside-​out would be when Apple incorporates open source software into its operating system (West & Gallagher, 2006), while outside-​out would include an outside firm or community both developing and distributing an operating system (such as Linux) that Apple would never distribute (Steidler-​Dennison, 2009).

Environment-​Level Factors As with outside-​in, firms have the potential to leverage two types of outside-​out external activity: those with economic (pecuniary) vs. those with non-​economic (non-​ pecuniary) motives (Dahlander & Gann, 2010). In the former case, the focal firm plays an economic role that is complementary to that of the outside-​out innovators. For example, many platforms and other ecosystems are both united and governed by the complementarity of their value-​creating efforts (Kapoor, 2013; Thomas et al., 2014), but also applies to communities and other forms of multilateral collaboration (Shaikh & Levina, 2019; West & Kuk, 2016). This complementary relationship applies both to when the firm sponsors the collaboration and when it does not (West & Olk, Chapter 18, this volume). For firm-​sponsored collaborations, we traditionally think of ecosystem sponsors providing technology to an ecosystem and its complementors (Gawer & Henderson, 2007), or creating an app store market for selling their complements (West & Mace, 2010). However, a platform with well-​defined public interfaces can allow complementors to independently create and sell their innovations, whether 20th-​century computer platforms (Bresnahan & Greenstein, 1999) or stereo systems (Robertson & Langlois, 1995), mobile applications before app stores (Boudreau, 2010; West & Wood, 2013) or even auto parts (Adner & Lieberman, 2021). Even if there is little or no business relationship, Teece (1986, 2006, 2018) reminds us that innovating complements makes the core product more valuable—​and vice versa (Gawer & Cusumano, 2014; West & Gallagher, 2006)6—​even when third-​party complements are developed independently of the focal firm. Even without with complementors, a firm benefits if other firms create and commercialize complementary goods and services (Gallaugher & Wang, 2002; Suarez, 2005). 5 

Such actions might include the vertically integrated Chandlerian rivals (inside-​in), or dyadic open innovation partnerships between competitors and their partners (whether inside-​out or outside-​in). 6 In the original open innovation research, a large platform sponsor would be the focal firm, benefitting from external complementors. But a platform complementor benefits from the inside-​in efforts of the platform sponsor—​which are outside-​out from the perspective of the complementor.

74    marcel bogers and joel west Meanwhile, firms can also harness outside communities with noneconomic motives, as when an individual innovates and solves his or her own problem, as when game users modify existing games (Jeppesen & Molin, 2003; West & Gallagher, 2006). User communities make it easier for such individuals to share their solutions (Franke et al., 2006). In some cases, firms can sponsor communities that give away complements that benefit their core products (Jeppesen & Frederiksen, 2006). In the long run, firms can also utilize inbound flows from outside-​out communities to bring external knowledge into the firm (West & Lakhani, 2008). Finally, a firm may benefit from cumulative innovation within a given industry. In such cases, firms or individuals freely build upon each other’s innovations as they pursue their own interests in creating and commercializing innovations. There are two distinct patterns on such sharing: a collaborative approach enabled through voluntary spillovers, and competitive rivalry driven by the infeasibility of blocking spillovers due to weak appropriability (Allen, 1983; Bogers & West, 2012; Meyer, 2006; Murray & O’Mahony, 2007). If the firm actively participates in creating and harnessing such spillovers, this corresponds to the afore-​mentioned coupled mode.

Matching a Specific Innovation and Mode Inherent to the idea of open innovation is that the firm harnesses purposive flows of knowledge and is neutral in the choice of internal and external paths (Chesbrough, 2006a, Chapter 1, this volume). Firms thus have the option of selecting the internal or external path on an innovation-​by-​innovation basis, whether in obtaining technologies to commercialize or profiting from innovations that the firm itself has created. At the same time, they need to balance the tension of using multiple modes at the same time, each requiring different competencies. Thus, a range of distinct capabilities are required for a firm to implement processes that are neutral in practice. If firms can pick and choose the different innovation modes for a given innovation—​ or combine them within the same innovation—​then this implies additional activities beyond those required for each mode individually. Selecting the appropriate modes is one such activity; managing the necessary inbound or outbound knowledge flows is a second. A third is managing the conflicts between internal and external choices in the same division or technology, whether that be “not invented here” (internal vs. external creation) or the threat of cannibalization via out-​licensing (internal vs. external commercialization). Thus, to simultaneously manage multiple creation or commercialization paths, a firm must have ambidextrous open innovation capabilities. This is analogous to the original definition of organizational ambidexterity (Tushman & O’Reilly, 1996), although their organizational change processes are more likely to be sequential, while these open

MULTI-LEVEL FRAMEWORK FOR INNOVATION MODES    75 innovation modes are often used simultaneously. At the same time, such ambidexterity in open innovation appears to be a firm-​level dynamic capability that parallels other previously identified forms of organizational ambidexterity—​whether between radical and incremental change or exploration and exploitation (O’Reilly & Tushman, 2008; Raisch et al., 2009).

Commercialization-​Driven Search A firm that successfully uses open innovation to support its commercialization capabilities will (as advocated by Chesbrough, 2006a) become agnostic to the source of necessary innovation, whether internally (inside-​in) or externally (outside-​in). While the latter is the most commonly studied form of open innovation, it is rarely studied in combination with other modes (Burcharth et al., 2014). Ultimately, if the internal locus of commercialization is a given, then the question is, which conditions would favor internal versus external technology sourcing? A handful of studies examine this choice, sometimes referred to as a “make vs. buy” decision—​most recently with research on specific open innovation projects (Bagherzadeh et al., 2021; Du et al., 2014). Brunswicker and Chesbrough (2018) use a survey of large multinationals to identify the processes firms use to manage outside-​in projects, while Vanhaverbeke et al. (2014) suggest factors that may lead to the success of such projects. In one of the most widely cited studies of outside-​in innovation, Laursen and Salter (2006) use the breadth and depth of search for external innovations to predict innovation performance. While inside-​in innovation would essentially assume strong internal commercialization capabilities in line with the vertically integrated firm (Chandler, 1977; Freeman, 1982), the process of outside-​in innovation would be characterized by a number of firm characteristics that would make this mode more or less likely to succeed. West and Bogers (2014) describe the requirements as having capabilities for obtaining and for integrating external technologies, and commercialization capabilities that would ultimately determine its success. A firm’s choice for preferring internally or externally created innovation will be influenced by, for example, its access to external networks or knowledge brokers, its ability to filter and select external knowledge, its existing knowledge base that serves as absorptive capacity, and the extent to which its culture favors the use of external knowledge sources.

Creation-​Driven Search The converse case is where the firm invents something and tries to commercialize it. This ties to one of the core questions of innovation studies: once a new technology is invented, how do the inventors profit from innovation (Freeman & Soete, 1997; Teece,

76    marcel bogers and joel west 1986)? Such an approach assumes that the value of an innovation remains latent until it is successfully commercialized (Chesbrough & Rosenbloom, 2002). For a given innovation, open innovation posits that the fundamental issue driving the choice of an external commercialization strategy is the alignment of that innovation to a firm’s business model (Chesbrough, 2003, 2006c; Chesbrough & Rosenbloom, 2002; Lu & Tucci, Chapter 40, this volume). At the same time, firms will seek to control their innovation outputs (and avoid external commercialization) if they believe a technology to be highly strategic and thus seek to create barriers to entry and imitation. The overall dispersal of innovative activity across individual and organizational boundaries also depends on the communication costs (Baldwin & von Hippel, 2011). Open innovation is fundamentally about purposively capturing knowledge spillovers that otherwise would not be monetized (Chesbrough & Bogers, 2014). Taking an internally created technology as point of departure, inside-​out innovation could then involve both pecuniary and non-​pecuniary mechanisms, or paid or free revealing (Dahlander & Gann, 2010). Free revealing would be more likely when the business model relies on a firm’s complementary assets to capture the value that would be created in such mode. Besides selling an innovation, for an internal process, firms may instead protect the innovation as a trade secret and use it to improve the production of goods or services for sale—​as with manufacturing process innovations that provide an efficiency advantage (Pisano, 1997). Conversely, firms may decide for a dual revenue strategy—​of using it for its own products and also licensing it to other firms. In some cases, firms may cross-​ license to similarly situated firms who in turn cross-​license their IP to the focal firm (Nagaoka & Kwon, 2006).

Appreciating Commercialization “Failure” One of the earliest contributions of open innovation was the insight that even if a firm couldn’t commercialize its own innovation, external paths might be able to do so. The prevalence of a large number of non-​commercialized scientific inventions (and other innovations) was cited as evidence of this problem,7 and thus the need for the inside-​out mode to overcome a “Type II” error, i.e., a false negative evaluation of an innovation’s commercial potential (Chesbrough, 2003, 2006a, 2006b). Since then, open innovation researchers and managers have focused on eliminating this risk of orphaning valuable innovations. But focusing on spectacular cases where an innovation was dead inside a firm but a huge success outside—​as with Adobe’s PostScript laser printer software—​runs the potential risk of sampling on the dependent variable. Even given firm ambidexterity and persistence, some innovations actually do fail. Here we suggest that this negative

7 

Even before open innovation, the phrase “Rembrandts in the attic” (Rivette & Klein, 2000) was used to call attention to (if not exaggerate) the commercial potential of underutilized innovations.

MULTI-LEVEL FRAMEWORK FOR INNOVATION MODES    77 signal—​a firm’s decision not to internally commercialize its innovation—​has four possible resolutions.

Doesn’t Work for the Firm’s Business Model As represented by the canonical example of the Xerox PARC spinoffs (Chesbrough, 2003; Chesbrough & Rosenbloom, 2002), this is the case of the firm-​business model misfit, that is solved by licensing or spinout to other firms that can commercialize the technology more effectively (see also Lu & Tucci, Chapter 40, this volume).

Doesn’t Work for the Original Purpose Sometimes an invention will fail to deliver the intended function but provide some other function. An example of this is drug repurposing, addressing the problem that approximately 30% of new pharmaceuticals do not prove to be effective for their initial indication (Kola & Landis, 2004). For those drugs proven to be safe, updated techniques allow researchers to identify potential new therapeutic indications to investigate (Pushpakom et al., 2019). Thus, with a paradigm change between both the owner of the failed drug and the emergence of new commercialization partners, new models of drug repurposing have allowed previously failed drugs to be successfully commercialized (Chesbrough & Chen, 2013, 2015).

Doesn’t Work Well Enough to Commercialize In some cases, a technology works, but a firm accurately concludes that it lacks a large enough market to justify formal commercialization efforts. Open source software provides many examples of this, whether it is the professional software developer who wrote smaller pieces of code and gave it away for free (Lakhani & Wolf, 2005) or when Netscape Communications Corporation donated its browser source code to a new open source project because it could not compete with Microsoft’s free browser (Lerner & Tirole, 2002). Meanwhile, because revenues are too low for a for-​profit entity, major pharmaceutical companies have donated all rights for potential malaria treatment drugs to Medicines for Malaria Venture, a public-​private partnership (Casper, 2011). Such non-​ pecuniary dissemination can increase social welfare, even if its impact is not measurable through standard measures of economic output (Benkler, 2006; von Hippel, 2005).

Doesn’t Work At All Some technologies just don’t work. Many technologies are patented before they are fully developed. For example, due to FDA-​mandated disclosures, pharmaceuticals are patented long before they are ever tested in humans: an estimated 30% of human therapeutics are abandoned after Phase I trials demonstrate they don’t meet minimum safety requirements (Kola & Landis, 2004). Thus, of these four cases of non-​commercialized technology, open innovation only addresses the first two cases. The third fits what Eric von Hippel (2016) calls “free innovation”—​which explicitly does not involve a successful business model—​while the final failure path is a true failure of technology rather than commercialization.

78    marcel bogers and joel west

Discussion This chapter makes four main contributions. First, it identifies four modes possible for open innovation strategies, and identifies the conditions under which they are available to a firm, and how firms select innovation mode(s) to use for a specific innovation—​ considering both innovation creation and commercialization. Second, it considers how managing the implementation of these modes ties back to organizational capabilities and ambidexterity. Third, it provides a new definition for outside-​out mode, an approach for commercializing outside the firm that is distinct from the inside-​out and outside-​in modes. Finally, it provides a richer understanding of how and when innovations are not commercialized.

Selecting an Innovation Mode In the past, research on adoption of open innovation practices has considered when and how firms adopt a range of such practices. However, not all firms have the option to use all practices. Instead, we suggest that researchers should (as do firms) separately examine commercialization-​driven search (selecting between inside-​in and outside-​in) and creation-​driven search (selecting between inside-​in and outside-​out), at the level of a technology rather than a firm. We don’t know much about how this decision is made for specific technologies, although (as noted earlier) we have some insights from research at the level of the R&D project. As with other open innovation research, this research has emphasized the commercialization-​centric case—​even though one study of large firms (Brunswicker & Chesbrough, 2018) suggested that about a third of firms used external sourcing and a third leveraged controlled outbound knowledge flows. Thus, consistent with previous calls, we need more research that focuses on the decisions regarding a specific innovation. Our framework suggests that such decisions can only be fully understood by considering factors at all three levels of analysis—​innovation, firm, and environment. As such, we not only support calls for additional open innovation research at levels of analysis beyond the firm and integrating multiple levels (Bogers et al., 2017; Dahlander et al., 2021; West et al., 2006), but suggest how specific factors at each level might predict the innovation-​ level decision. Future research could also explore the path-​dependent legacy of how innovation modes are used over time, both for the firm and also for a specific technology or product line. A dynamic model could examine the relationship between innovation creation or commercialization choices over time, within a firm, product line, specific product, or technology (cf. Aylen, 2010; Christensen et al., 2005; Pattit et al., 2012).

MULTI-LEVEL FRAMEWORK FOR INNOVATION MODES    79

Capabilities for Ambidextrous Combination of Modes Previous research has acknowledged that firms might be simultaneously using multiple modes for different projects (e.g., Laursen & Salter, 2006). If mode selection is at the level of innovation—​and multiple modes are possible for a single innovation—​then this requires a specific set of capabilities to practice open innovation ambidexterity. Such ambidexterity would appear to fit the definition of dynamic capability. Considering open innovation ambidexterity as a dynamic capability (cf. Bogers, Chesbrough, et al., 2019) would allow open innovation scholars to study linkages between firm-​level capabilities and environmental change (cf. Eisenhardt & Martin, 2000; Teece et al., 1997). At the same time, such studies have the potential to extend our understanding of the application and limits of dynamic capabilities, thus providing (as advocated by Radziwon et al., Chapter 57, this volume) a way for open innovation to provide new insights that inform mainstream management theories.

Outside-​Out Innovation The outside-​out mode of open innovation has not received as much attention as the better-​ known outside-​ in and inside-​ out of Chesbrough’s (2003) original concept. Our definition of outside-​out—​defined in terms of innovation creation, control, and complementarity—​both sets boundaries on the category, and highlights the central role this mode plays in explaining differences in how firms benefit from open innovations with ecosystems and platforms (cf. West and Bogers, 2014). Unlike outside-​in, the monetization of outside-​out innovation is primarily through the complementarity of the external innovations that make the firm’s innovations more valuable. As such, this suggests new opportunities for open innovation scholars to link to research on complementarity in ecosystems (Bogers, Sims, et al., 2019; West & Olk, Chapter 18, this volume) and platforms (Jacobides et al., 2018; Parker et al., Chapter 23, this volume).

Realism for Non-​Commercialized Innovations A major goal of the open innovation paradigm was to enable firms to find new paths for commercializing innovations that could not be commercialized internally, as a way of overcoming the risk of a potential “Type II” error (Chesbrough, 2006a). Here, instead of this simplified internal vs. external path, we offer a more robust framework for considering commercialization of an innovation, including repurposing, donating, and a true positive of failure. Pharmaceutical discovery would appear to provide an opportunity for a more systematic examination of these categories and the choices for each. Whether successful or not, such discoveries are almost always patented—​allowing a way to identify the full sample

80    marcel bogers and joel west of inventions. These patents allow identification of both when the commercialization is performed by another firm (inside-​out) or for a completely different purpose (requiring a new method of use patent). Finally, because patents are matched to commercialized products (such as in DrugBank, FDA Orange Book, or PubChem), there is a mechanism to identify inventions that are not commercialized.

Acknowledgments We thank editors Henry Chesbrough and Wim Vanhaverbeke for pushing us to improve the rigor and relevance of this chapter. We are grateful for the constructive suggestions of many friends and conference attendees since our first presentation at the 2010 Academy of Management conference, but of course absolve them of any responsibility for the final result. The earliest version of this chapter was supported by a grant from the Lucas Graduate School of Business at San Jose State University. The two authors contributed equally.

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PA RT I I

OP E N I N N OVAT ION WITHIN FIRMS

CHAPTER 6

THE GRAFT AND C RA FT OF INDI VIDUAL-​LEV E L OPE N INNOVAT I ON ammon salter, anne l. j. ter wal, and paola criscuolo

Introduction The literature on open innovation (OI) has recently increased its attention to the role of individuals, focusing on how companies’ OI strategies and practices are delivered on the ground (Dahlander et al., 2016; Monteiro & Birkinshaw, 2017; Salter et al., 2015). This shift toward the microfoundations of open innovation (see Foss & Xu, Chapter 36, this volume) builds on and extends research on the role of individuals in the wider innovation process (Bogers et al., 2018; Foss & Pedersen, 2016; Van de Ven, 1999). As part of this effort, open innovation scholars have sought to revisit and extend individual role characterizations in the innovation process to reflect how individuals go about trying to make open innovation happen in organizations. The attempt to understand open innovation at the individual level builds on insights and perspectives in the prior innovation literature, in particular, those rooted in the concept of absorptive capacity (Cohen & Levinthal, 1990). Cohen and Levinthal’s description of organizational absorptive capacity highlighted the roles of individuals acting as absorbing agents of the organization: conducting external search, integrating, and utilizing this knowledge in the firm. In fact, they stated that the absorptive capacity of the firm was not simply the aggregation of individuals’ efforts and experience bringing in external knowledge, it was also a product of organizational routines and structures that enabled and channeled such efforts. Crucially, the effectiveness of these organizational practices to absorb external knowledge depended on “the intensity of effort” of key externally-​facing individuals.

92    AMMON SALTER ET AL. This chapter seeks to map the emerging literature on the different types of roles involved in the graft and craft of open innovation. That is, we present a description of various roles that enable open innovation by laying out the effort (“graft”) individuals put into building the knowledge and networks needed, and the skills and work practices (“craft”) needed to succeed in the role. The focus of this chapter primarily is on the roles of individuals in inbound open innovation. We conclude with future extensions to our understanding of the microfoundations of open innovation, including the study of outbound OI roles, and a discussion of managerial challenges.

R&D Work in Closed Innovation Models At the core of the original formulation of the closed model of innovation was the idea that the R&D unit was often separate from the external environment and that individuals working in this role were primarily tasked with finding innovative ideas that could be exploited by the firm (Chesbrough, 2003). The corporate R&D lab, which had emerged in the late 1900s in the German chemical industry, became the dominant form of organizing innovation in the middle of the 20th century (Freeman & Soete, 1997). A paradigm example of the corporate R&D lab was the Bell Labs, where leading scientists and technologists were recruited to work at a specialist and somewhat isolated institute. These scientists and engineers were given resources to experiment and to collaborate with each other, finding new products and solutions for AT&T (Gertner, 2012). The post-​war Bell Labs team was incredibly successful, generating a wide range of radical technologies. Part of the strength of this arrangement was the close interaction of scientists and engineers from different disciplines working on common problems. R&D units, like the Bell Labs, often had their own culture and human resource practices. These facilities mirrored universities with a “campus” model. R&D staff were given high levels of autonomy in terms of which problems they could work on—​defined as strategic autonomy—​and how they could go about solving these problems—​defined as operational autonomy (Bailyn, 1985). To facilitate R&D careers, companies developed specific human resource practices for scientists and technologists, creating a dual career track system, allowing technical specialists to concentrate on their innovative efforts and those with a more managerial approach to focus on organizing and delivering innovation projects (Cardador, 2017; Katz & Tushman, 1981; Katz et al., 1995; Ter Wal et al., 2020). R&D scientists and technologists were often recruited to corporate R&D labs straight from university and then promoted from within. They were rewarded and advanced for their innovative achievements, often measured in terms of inputs into the innovation process, such as patents and technical reports, or for their contributions to the development of new products, processes, or services. It was not unusual for an R&D unit to have many long-​serving staff, with deep technical expertise. The projects that they worked on were selected by committees of business and technical leaders, trying to align the efforts of the R&D team to the strategic goals of the firm. The

THE GRAFT AND CRAFT OF INDIVIDUAL-LEVEL OI    93 timescale of projects was often medium to long term in orientation, with two-​to-​five-​ year expectations before a project would come to fruition (Van de Ven, 1999). Companies operating these R&D labs often placed extreme restrictions on scientists and technologists’ ability to interact with external actors (Liebeskind, 1997). For example, Procter & Gamble did not allow its R&D staff to attend academic conferences (Liebeskind, 1997). In part, this was due to the concerns of the firm to keep secret its projects to gain an advantage over its competitors, and to ensure that intellectual property was secured for these ideas. Although most R&D labs were placed close to corporate headquarters, it was also the case that firms would seek to create labs close to major universities or in innovation ecosystems where they aspired to build connections or benefit from agglomerations of skilled labor (Saxenian, 1991). For example, Xerox had its main corporate lab in Rochester, New York, close to their corporate headquarters. However, they created the Palo Alto Research Center (PARC) in the early 1970s to facilitate access to new technologies developing in the emerging Silicon Valley and Stanford University, and to attract and recruit new and different technical experts (Chesbrough & Rosenbloom, 2002). As the PARC example attests, the R&D labs of the closed innovation model were never truly closed. Indeed, as Allen (1984) stated, “R&D is, by its nature, an open system.” R&D staff often had strong ties to universities, which were an important source of new ideas as well as help on existing projects (Cohen et al., 2002). Moreover, even in the closed model, large R&D-​intensive firms turned to technical support services and external inventors for help on their R&D efforts, which required their R&D staff to engage with external suppliers and inventors (Mowery, 1984, 2009). R&D staff worked closely with suppliers and customers to try to develop new products, processes, and services. They may have also engaged in informal knowledge trading with competitors, outside the purview of their managers (Appleyard, 1996; von Hippel, 1988). However, these attempts to identify, assimilate, and utilize external knowledge often emerged “bottom-​ up,” mostly because of autonomous strategic actions by individuals and teams rather than due to a strategic direction from the organization to engage in open innovation.

Individual Roles in Inbound Open Innovation As organizations have increasingly adopted and embraced the principles of open innovation in their strategies (see Chesbrough, Chapter 10, this volume), the roles of individuals in the innovation process have changed (Salter et al., 2014). Whereas initially individuals would have mostly self-​selected into OI-​focused roles and operated as jack-​of-​all-​trades on a broad spectrum of OI-​related activities, in recent years, OI roles have become more institutionalized and a division of labor with more clearly defined and demarcated roles has started to emerge.

94    AMMON SALTER ET AL. In the open innovation model, the R&D unit is no longer the primary source of innovative ideas and firms should have no preference for internal or external ideas (Chesbrough, 2003; Katz & Allen, 1982; Lifshitz-​Assaf, 2018). Indeed, the model suggests that firms need to look outwards because there are always greater external knowledge and skills outside the firm than inside. At the same time, we have seen many firms retreat from funding more long-​term R&D, turning instead to more incremental and near-​market development efforts (Arora et al., 2018). Many long-​term dedicated R&D units have been closed or operate on a much smaller scale than previously, such as Bell Labs, now run by Nokia. Even in pharmaceuticals, where the level of R&D spending has remained high, we have seen firms restructuring their R&D labs to try to create work environments akin to those in small biotechnology firms, changing to units of 300 researchers rather than 3,000 (Schuhmacher et al., 2013). Career models, which focused on internal innovation efforts, have been revised to acknowledge individuals’ contributions to create external partnerships and collaborations. This is a break from the conventional model of R&D careers being determined by contributions simply to internal innovative inputs and outcomes (Salter et al., 2014). To capture the range of roles of individuals, Figure 6.1 depicts common individual roles in inbound open innovation, organized around stages that broadly map onto Cohen and Levinthal’s stages of absorbing external knowledge. The first stage focuses on the identification of external knowledge, often involving scouts and rich external networks. The second stage

figure 6.1  Individual roles in inbound open innovation. Note: The placing of the boxes indicates the positioning of OI roles in the external knowledge absorption process. In the network graphs beside each role, the black node represents ego (the focal individual), white nodes denote internal stakeholders, while grey nodes depict external partners. The location of nodes inside the triangle is indicative of relative seniority.

THE GRAFT AND CRAFT OF INDIVIDUAL-LEVEL OI    95 involves integrating external knowledge within a firm’s existing expertise, operations, and strategies. The third stage consists of gaining influence and support for external knowledge implementation into a firm’s novel products, processes, or services. While some roles are contained within a single stage of the absorption process, others span multiple stages.

Technology Scouts The first role as identified in prior research and given new emphasis by open innovation scholars is that of technology scouts (Allen, 1977; Birkinshaw & Monteiro, 2007; Dahlander et al., 2016; Katz & Tushman, 1981). The role of a scout is to identify valuable external knowledge, involving considerable environmental scanning and search of the external environment for potentially useful technologies and market trends (Howell & Shea, 2001). Scouts often work closely with university partners, acting as liaison officers with key university partners (see Perkmann, Chapter 26, this volume). They also visit trade fairs, conferences, and other external events seeking to find new technologies and potential partners for the organization. As a consequence, they often develop broad external networks. They may also read patents and technical and scientific journals to keep themselves and their colleagues abreast of the latest developments in different areas. To be effective in these scouting roles, individuals often need broad technological knowledge (Dahlander et al., 2016; Salter et al., 2015) in a bid to identify valuable inputs and ideas for the full range of firms’ internal R&D activities. They also need to develop the political skill to know what to disclose to outsider parties to pique their interest, while carefully guarding information that may be of a sensitive or strategic nature. Originally, scouts may have been structurally distinct in the R&D function, housed in scouting departments. However, as the open innovation logic has diffused across the R&D function, scouting roles are now often played by a wide range of actors and many scouting departments were wound up. A notable example of a scouting department was BT’s attempt to formalize this role and to build its capability to spot latest-​generation technologies and incorporate these into BT’s platform (Monteiro & Decreton, 2018). More recently, the scouting role has morphed into new and changing roles aimed at identifying new leads for valuable external knowledge. For example, companies have created externally facing business development roles. Individuals in these roles are responsible for finding commercializable external knowledge, building relationships with these external actors, and creating compelling cases for the use and development of this knowledge for their internal colleagues (Huston & Sakkab, 2006). As part of the open innovation effort, companies have created corporate incubators, staffed with individuals with knowledge and expertise from within the organization but focused on generating innovative business ideas in partnership with external organizations (Van de Vrande et al., 2009). We have even seen new specialist roles emerge around the design and management of externally facing innovation competitions (Terwiesch & Ulrich, 2009), and in the use of innovation intermediaries, such as InnoCentive, NineSigma, and Yet2.com

96    AMMON SALTER ET AL. (Jeppesen & Lakhani, 2010; Seig et al., 2010)—​see also van de Vrande & Kuiper, Chapter 17, this volume; Randhawa, Chapter 20, this volume; and Frederiksen et al., Chapter 21, this volume. These roles are in addition to the more traditional roles that had a strong external focus, such as university liaison or technology scouts.

Gatekeepers and Assimilators The second core role is that of the gatekeeper, an external knowledge scout who not only takes it upon themselves to identify valuable external knowledge but also makes extensive efforts to diffuse this knowledge internally and align it with internal needs and capabilities (Allen, 1977; Ter Wal et al., 2017). Allen’s seminal work in the 1970s, focusing on the sources of information in the innovation process, emphasized that technology gatekeepers had the dual task of identifying external knowledge and disseminating this knowledge internally among their colleagues. To be an effective technology gatekeeper, an individual required broad technology knowledge and deep knowledge of the organization (Harada, 2003; Macdonald & Williams, 1994). Technology gatekeepers were identified as communication stars with extensive internal and external networks (Allen, 1977). Later studies substantiated this point, demonstrating that effective gatekeepers often had “one foot inside and one foot outside,” with extensive networks with internal colleagues to allow them to find fertile ground for external collaborations with their internal colleagues (Dahlander et al., 2016). Further research also showed the presence of synergies between individual efforts to identify external knowledge and their efforts to assimilate it into a firm’s internal capabilities and categories, such that individuals combining the two were rated by their organization as making more valuable contributions to the firm’s innovation output (Ter Wal et al., 2017). In Allen’s (1977) original formulation, the technological gatekeeper was not a formal position, but reflected informal patterns of engagement. Research showed that having connections to a technology gatekeeper was also beneficial (Katz et al., 1995), suggesting a degree of second-​hand social capital associated with the role (Galunic et al., 2012). Although the diffusion and translation aspects of the gatekeeper role were present in Allen’s original study, as researchers probed more deeply into individual roles in open innovation, they turned increasing attention to the challenge of assimilating external knowledge within the firm. It is one thing to find useful external technology, knowledge, or ideas; it is another to get the firm to use it. The difficulty of assimilation starts with potential fears of Not-​Invented-​Here (Antons & Piller, 2015; Katz & Allen, 1982) and perceived threats of the use of external knowledge to R&D workers’ professional identity (Lifshitz-​Assaf, 2018). Against this background (see Carlile & Dionne, Chapter 32, this volume), it is critical to transform external knowledge into the categories and templates of the firm and align this knowledge with internal routines and strategies (Cohen & Levinthal, 1990; Lane & Lubatkin, 1998). Some individuals—​assimilators—​may not be engaged in much external scouting but may nonetheless play a pivotal role in the translation of external knowledge into forms

THE GRAFT AND CRAFT OF INDIVIDUAL-LEVEL OI    97 better understood internally and diffuse it to those who may find it of use (Monteiro & Birkinshaw, 2017; Ter Wal et al., 2017). To be an effective assimilator, individuals need to have sufficient knowledge of the technology, combined with deep knowledge of the home organization. The labor of assimilators is often repackaging knowledge to make it look more like internal knowledge and building awareness about the potential benefits of the technology among potentially skeptical internal actors. As illustrated in Figure 6.1, assimilators typically need strong internal networks, that often span different divisions or units in the organization (Ter Wal et al., 2017).

Champions, Shepherds, and Ambassadors Long before the growth in interest in open innovation, it has been understood that some individuals play a key role in championing innovations (Howell & Higgins, 1990; Schon, 1963). In this literature, this championing role has been extended to the utilization of external knowledge. Henkel (2009) shows that, within firms, some individuals champion the use of Open Source Software (OSS), acting as champions of openness. These individuals convince others in the firm to use this external knowledge, displaying both a passion and commitment to ensure that it overcomes the various organizational barriers. Champions also act as promoters of external knowledge with key internal stakeholders, shaping wider opinions and attitudes within the organization about the legitimacy of external knowledge. To be an effective champion of external knowledge, individuals need some degree of understanding of the technology itself, as well as deep organizational knowledge. Critically, these individuals need to have the political skills to be able to mobilize coalitions of actors to support the take-​up and use of the external knowledge (Ferris et al., 2007; Howell & Shea, 2001). As such, they often have broad and rich internal networks, with—​as depicted in Figure 6.1—​contacts in senior positions in the organization. As an extension of the idea of the gatekeeper who combines the identification and assimilation of external knowledge, Ter Wal et al. (2017) suggest that some individuals take up the assimilation and championing of external knowledge, in a role they refer to as shepherds. These shepherds need to have deep technical knowledge as well as strong organizational and business knowledge, to help disseminate, align, and promote external knowledge inside the organization. Shepherds may require networks that reach deeply across the firm, as well as to external actors to help understand and transform the technology or knowledge to make it more compatible with and complementary to internal categories, routines, and templates. An important part of this process is convincing risk-​ averse managers (Berg, 2016). Alongside these roles in absorbing external knowledge, we have seen a greater appreciation of the role of individuals in shaping the external environment of the firm itself. Open innovation requires greater awareness of motivations and goals of external actors, but also the coordination of internal efforts to these external actors. In a bid to pave the ground for future open innovation activities, ambassadors might seek

98    AMMON SALTER ET AL. to influence policymakers and industry associations in ways that align with a firm’s core strategic objectives. For example, ambassadors might sit on standard-​setting committees, representing the firm’s interests and trying to steer the technology roadmap of the industry (Leiponen, 2008; Rosenkopf et al., 2001). They also help in regulatory development and compliance, acting as a contributor of the external rules and codes that govern the industry and the firm. In doing so, they may also seek to raise awareness and understanding of potential regulatory changes to their colleagues. Open innovation has also created new ambassadorial roles for individuals to try to orchestrate and influence the design, operation, and direction of industry consortia, many of which involve a mixture of public and private funding. These industry consortia might provide important resources to enable innovation in the firm by supporting mutual research efforts, and by enabling resource sharing (Piller et al., Chapter 22, this volume). A notable example of such hybrid models includes GreenTouch, an industry-​wide attempt to significantly improve the environmental performance of telephone switching systems, or the Structural Geonomics Consortium, which operated as a third party to enable research on the structure of proteins for various large pharmaceutical firms (Perkmann & Schildt, 2015).

The Wider Implications of New Roles for Individuals and Organizations The adoption of open innovation has important implications for traditional career models of innovation. New models of careers suggest more fluid and dynamic career pathways, with individuals taking a series of externally facing roles mixed with some internal responsibilities. Rather than simply rewarding and promoting those with strong achievements with respect to the generation of innovations, individuals may be credited with developing new external relationships. This would mark a shift away from the individual contributor model of career progression that has dominated R&D toward a system of rewards and promotion related to the extraction of value from knowledge and ideas generated outside the firm (Salter et al., 2014). Individuals will be expected to follow the opportunities rather than the organizational career ladder, allowing them to move across firm boundaries to take up roles outside the organization, such as in joint ventures and collaborations (DeFillippi & Arthur, 1994). Open innovation also has important implications for those individuals working in other departments of the firm, groups that previously were relatively immune from the requirement to find ways to develop and exploit external knowledge. For instance, open innovation may force individuals in the legal and intellectual property (IP) department of the firm to rethink their approach to the claiming of legal rights as well as leading them to consider how to incorporate the IP of others into the firm’s IP position. This can be an organizational challenge, as legal departments might have limited technical knowledge and tend to be defensive in orientation (Liebeskind, 1997). For example, large firms often

THE GRAFT AND CRAFT OF INDIVIDUAL-LEVEL OI    99 seek to impose extreme restrictions on collaboration partners, with claims to ownership of IP arising from a collaboration (see Frankort & Hagedoorn, Chapter 14, this volume, and Laursen et al., Chapter 56, this volume). However, this approach may deter collaboration with many externals, what Alexy et al. (2009) call the “Medusa effect.” To respond to the need to find new ways of working with external partners, individuals working in IP departments have had to redraft standard contracts, creating less strict and swifter forms of collaboration. This may be reflected in the use of model contracts that enable external actors and the organization to work together more quickly and effectively, as opposed to being bogged down in conflicts over legal terms in the contract. Cross-​functional teams that combine legal and technical knowledge may also help in creating more effective contract structures (Stefan et al., 2021). In effect, individuals working on appropriability issues around OI need to be facilitators of open innovation by creating the conditions to allow for inter-​organizational collaboration rather than putting up barriers.

Future Research Although the literature on the role of individuals in open innovation has started to delineate some of the key roles and challenges, there remains much we do not understand. First, while the emphasis in most organizations—​and of scholarly effort to study it—​has been on inbound open innovation roles, companies have also responded by creating new roles of individuals in outbound open innovation. Licensing managers had traditionally been in the legal department and operated somewhat distantly from the innovation function of the organization (Arora et al., 2001). However, with attempts to create viable pathways for internal technologies to be exploited by externals, licensing managers now play a more proactive role. They may be responsible not only for negotiating licensing contracts, but also for repackaging internal knowledge and technology for external use, actively searching for partners, and showcasing and broadcasting these technologies. Future research is needed to better document and understand the evolving role of the licensing manager. Second, as the scale of open innovation activities increases, we see the emergence of a more complex division of innovative labor (Arora & Gambardella, 1994), with individuals taking on specialist tasks and roles that previously might have been subsumed into another role. New professional categories within firms might emerge that capture the challenges, skills, and activities of different types of open innovators. They may also generate new organizational units to house these individuals, operating with their own set of norms, routines, and rewards. Indeed, it could be that these open innovation roles become segmented into a specific department, similar to how Human Resources emerged in the 1950s and R&D in the 1930s. We currently lack insights into the demarcation of different roles, and the social networks and expertise that each of these roles requires. We have some evidence that even the most outward-​facing roles still require significant internal knowledge. However, we lack clarity on the combinations

100    AMMON SALTER ET AL. of technical, business, legal, and organizational knowledge required by individuals in these different roles. Third, we also have only a partial understanding of the networks associated with success in these roles. The current research stream has tended to look for associations between network positions and innovation output more generally (Tortoriello & Krackhardt, 2010), as opposed to the effectiveness of individuals and their work practices in open innovation roles (see Lingo & O’Mahony, 2010, for an exception). Given that open innovation roles do not easily translate into the conventional metrics of innovative outputs, such as patents, new products etc., our understanding of effectiveness needs further conceptual and empirical development. Individuals working in open innovation roles do not operate in a vacuum, and their ability to succeed in these roles is partly a function of the attitudes and behaviors of other members of the organization. Our understanding of the tensions between people in these roles and other members of the firm is relatively modest. For example, we have some appreciation of the Not-​Invented-​ Here syndrome (Antons & Piller, 2015; Katz & Allen, 1982), but we do not understand how individuals can successfully navigate around these concerns through proactive and skillful organizational actions (see Lifshitz-​Assaf, 2018, for a recent exception). Fourth, attitudes toward risk also play a key role in shaping the ability of open innovators to assimilate and utilize external knowledge. Research has shown that managers tend to be risk-​averse, preferring ideas with lower novelty than creators (Berg, 2016). This risk aversion may spill over to attitudes to external knowledge, which likely poorly maps onto existing internal knowledge and categories, and may lack legitimacy (Bunduchi, 2017). As it stands, we do not know how open innovators can effectively overcome such risk aversion and legitimacy constraints by reframing, reshaping, and repackaging external knowledge to make it more palatable for their managers. Detailed processual studies of how this process unfolds or field experiments that analyze how individuals mobilize their internal knowledge, networks, and political skills would be highly welcomed. Fifth, future research also needs to develop a more career-​based approach to understanding OI roles. To date, much of the literature focuses on people in these roles and what they do as part of this job role, but how they got there and what happens to them after they take up these roles are poorly understood. Research from LinkedIn profiles suggests that open innovators tend to come from a variety of departments and have long tenure in the organization (Vanhaverbeke et al., 2017). It would be useful to know if taking up an OI role has a beneficial effect on individuals in subsequent stages of their career. It is possible that working in an OI role exposes an individual to a rich set of diverse knowledge from outside the organization, allowing them to build their industry-​specific knowledge. In doing so, they may gain insights into how to recombine knowledge from inside and outside the firm, that may be useful for them in future internally-​facing roles. Indeed, early OI experience might play an imprinting role in careers, with skills learnt operating across organizational boundaries in the innovation process providing a lasting advantage. It may also make them more knowledgeable and aware of outside opportunities, leading to them to depart the firm (Simeth &

THE GRAFT AND CRAFT OF INDIVIDUAL-LEVEL OI    101 Mohammadi, 2022). At the same time, individuals in open innovation roles may lose out from internal opportunities, as they risk becoming unmoored from the organization and its routines, knowledge, and categories. In addition, open innovation roles may be poorly defined, measured, and rewarded, which may make them less attractive to the most talented individuals. Finally, research should also account for the factors that lead organizations to choose some people rather than others for these OI roles. Laursen and Salter (2020) suggest that firms might consider probity concerns when considering who to put in OI roles, as these individuals might have greater bargaining power through increased outside options for employment. They suggest that firms trade off expertise for probity in cases where appropriability concerns are great. Consistent with this suggestion, Palomeras and Wehrheim (2021) show that, to work on external alliances, firms deploy inventors who have strong patents rather than those with weaker patents. This suggests that appropriation concerns shape decisions about who in the firm is suitable to be externally facing. These two studies point to the need for further work on matching between individuals and their organizations in terms of OI roles. Taken together, research on the roles of individuals in open innovation has opened up a new window into how managerial practices and organizational structures are enacted and how individuals navigate the opportunities and challenges that they face in these roles. Blending insights from prior work on the role of individuals in innovation, this burgeoning research area heralds great promise to contribute to the development of the micro-​foundations of open innovation. It will also inform how organizations can better support individuals as they perform the graft of open innovation and master its craft.

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CHAPTER 7

OPEN INNOVAT I ON Aligning Mechanisms with Project Attributes mehdi bagherzadeh and andrei gurca

Introduction Open innovation (OI) is a distributed innovation process based on purposively managed knowledge flows across organizational boundaries (Chesbrough, Chapter 1, this volume; Chesbrough & Bogers, 2014). In an ever-​more competitive business environment, firms are increasingly facing the need to embrace OI to complete their innovation projects (e.g., Chesbrough & Brunswicker, 2014; Markovic et al., 2020). Both systematic research and anecdotal evidence show that OI has a multitude of benefits, such as access to a wide variety of external knowledge, reduced costs, shared risk, and improved time-​to-​market (e.g., Chesbrough, 2003; Markovic & Bagherzadeh, 2018). According to a recent study of firms in the US and Europe, firms are increasingly engaging in OI by increasing their financial and managerial resources investments in OI activities (Brunswicker & Chesbrough, 2018). Along the same lines, Majchrzak et al. (2016) report that more than 80% of the surveyed American firms engage in sourcing from outsiders, including suppliers, competitors, and startups, to complete their innovation projects. The literature has identified various OI mechanisms to tap into the knowledge of outsiders. Most of these mechanisms fit within two broad concepts: (1) crowdsourcing, defined as the act of openly broadcasting a problem that an organization is experiencing to a large pool of potentially interested people, out of which some self-​ select to participate in the development of innovative solutions (e.g., Dahlander et al., 2019; Majchrzak & Malhotra, 2020); and (2) partnerships, which include cooperative interorganizational relationships within dyads or networks of external partners, such as universities, suppliers, and competitors (e.g., non-​equity alliances, joint ventures,

Aligning OI Mechanisms with Project Attributes    107 mergers and acquisitions, corporate venture capital investments and minority holdings, consortia, etc.) (Majchrzak et al., 2015). In addition, the licensing of intellectual property (IP) to and from external partners is a straightforward OI mechanism particularly appropriate for simple projects, when the required knowledge is readily available and held by a source which is known in advance (Bagherzadeh et al., 2022).1 Each OI mechanism, whether crowdsourcing or partnerships, offers benefits (e.g., providing a rich communication channel between solution seekers and partners) and generates costs (e.g., set-​up costs) (Bagherzadeh et al., 2022; Felin & Zenger, 2014). Thus, once firms have decided to embrace OI, the choice of OI mechanism is the subsequent important decision for successful OI management. This brings us to the key point of this chapter: how to select among alternative OI mechanisms. Generally, firms use OI to serve the needs of different innovation projects, such as developing commercial aircraft, electric vehicles, or drugs (e.g., Bagherzadeh et al., 2022). In a multiple case study, Brunswicker et al. (2016) reported different projects are managed through different OI mechanisms, even within the same firm. The selection of appropriate OI mechanisms requires careful consideration of project attributes (e.g., project complexity) (Afuah & Tucci, 2012; Felin & Zenger, 2014; Pisano & Verganti, 2008). For example, Felin and Zenger (2014) argue that partnerships (e.g., non-​equity alliances) could be an appropriate mechanism to leverage the knowledge of outsiders when addressing complex projects. Similarly, recent empirical studies have illustrated how firms choose specific OI mechanisms depending on each project’s attributes (Bagherzadeh et al., 2022; Haeussler & Vieth, 2022; Lee et al., 2019). For example, Lee et al. (2019) show that crowdsourcing is applied mainly when firms engage in projects with low levels of complexity. Although studies on the topic remain rare, the extant OI literature highlights project attributes as an essential driver for the selection of suitable OI mechanisms. Against this backdrop, in this chapter we first explain why certain OI mechanisms seem to address, better than others, the needs of projects with specific attributes. We then develop a decision framework that aligns OI mechanisms with project attributes, thereby helping managers to select the appropriate OI mechanism for their innovation projects. We use several case studies (i.e., OI projects) to better illustrate our decision framework.

1 Research has shown that crowdsourcing is often used in relation to IP licensing, either as a supplement or a complement (Bagherzadeh et al., 2022). For example, before deciding to license readily available IP, firms may try to crowdsource alternative solutions to ensure they access the optimal solution. By engaging in crowdsourcing to identify alternative solutions, firms may also seek to improve their bargaining power in relation to the IP holders. The complementarity side of the relationship is visible once solutions emerge from crowdsourcing, as the newly generated knowledge is often acquired through IP licensing agreements.

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OI Mechanisms Selection: Why a Project-​Level Approach? Considering project attributes is essential for the selection of appropriate OI mechanisms. This is because, in practice, firms manage their innovation activities through a set of innovation projects (Hobday, 2000) and, consequently, many critical decisions are taken at project rather than firm level (Bagherzadeh et al., 2021; Du et al., 2014, for an overview). Kim et al. (2015, p. 412) argue that “openness at the company level is determined by the openness of individual R&D projects, which justifies the necessity of studying openness at the project level.” Innovation projects are different in several aspects, such as complexity, technological intensity, strategic importance, and innovation level (e.g., radical vs. incremental), and require different ways of managing OI. Therefore, the OI literature stands to be enriched by examining how project attributes impact firms’ openness to outsiders. For example, drawing on the problem-​solving perspective, Bagherzadeh et al. (2021) show that when firms deal with complex projects, they tend to involve more diverse partners and engage in extensive knowledge-​sharing during the project. Moreover, using aggregated data at the firm level is likely to lead to a loss of valuable information, mainly due to heterogeneity in projects. As argued by Dahlander et al. (2021, p. 10), “research at the organizational level masks important differences within companies.” Thus, this chapter emphasizes the importance of considering project attributes when selecting the right OI mechanism and highlights the importance of moving beyond firm-​level factors by shifting the level of analysis from firm to project level to explore the preferred OI mechanism for each project separately (see Felin et al., 2015). The project-​level approach in this chapter can provide more stable and detailed explanations for the selection of OI mechanisms than firm-​level studies, which overlook important differences between projects in the same firm (Felin et al., 2015; also see Foss & Xu, Chapter 36, this volume).

Crowdsourcing vs. Partnerships: Project Complexity Among different project attributes (e.g., strategic importance, the novelty of needed knowledge, etc.), project complexity is recognized as a key attribute impacting OI management, especially OI mechanism selection (e.g., Felin & Zenger, 2014; Pisano & Verganti, 2008). Complexity is determined by the number of constitutive elements (i.e., knowledge sets, systems, components, etc.) needed for the completion of an innovation project, as well as the interdependencies between these elements (Simon, 1962).

Aligning OI Mechanisms with Project Attributes    109 Complex projects consist of many highly interdependent elements impacting the final solution. As complexity increases, the interdependencies between elements are poorly understood, unexpected, or unknown (Felin & Zenger, 2014), implying that finding an optimal solution for complex projects is difficult since one element’s performance can substantially impact the other elements’ performance (Ethiraj & Levinthal, 2004). These interdependencies demand deep interaction and extensive knowledge-​sharing among the focal firm (i.e., the solution-​seeker) and its external partners to ensure the successful completion of the project (Gavetti & Levinthal, 2000). Thus, for complex projects, solution-​seekers need to select an OI mechanism that supports deep interaction with external partners. While partnerships (e.g., non-​equity alliances, joint ventures, consortia, minority holdings, and corporate venture capital investments, etc.) provide a rich communication channel, enabling solution-​seekers to interact deeply with external partners, crowdsourcing cannot offer such a rich communication channel (Bagherzadeh et al., 2022). We, thus, argue that partnerships are far more appropriate OI mechanisms for complex projects than crowdsourcing. Conversely, finding an optimal solution for a simple or less complex project is less difficult, mainly because the project’s constitutive elements are not highly interdependent and the project can be described and translated into clear independent elements, sub-​ systems, or modules, reducing the difficulty of the solution search (Felin & Zenger, 2014; Gavetti & Levinthal, 2000). Therefore, a deep communication channel for extensive knowledge exchange between solution seekers and external partners (i.e., the main benefit of partnerships) is not necessary for simple problems (Felin & Zenger, 2014). Thus, we argue that partnerships are not an appropriate OI mechanism for simple projects since they incur considerable costs, such as set-​up costs (i.e., the costs of identifying potential partners and negotiating the terms of the collaboration) and coordination costs (Bagherzadeh et al., 2022). Coordination costs refer to the costs of decomposing tasks among partners along with ongoing coordination of activities to be completed jointly or individually across organizational boundaries and the related extent of communication and decisions that would be necessary (Gulati & Singh, 1998). At the same time, the main benefit provided by partnerships (i.e., a rich communication channel) is not relevant for simple projects. For simple projects, crowdsourcing seems an appropriate OI mechanism enabling solution-​seekers to access a wide variety of otherwise hidden solutions, which can generate novel and out-​of-​the-​box solutions, particularly from solution providers whose knowledge sets may not be apparently related to the innovation project (Jeppesen & Lakhani, 2010). Moreover, given the low interdependencies between elements of simple problems, testing many solutions, even from numerous untrusted and unqualified solution providers in the crowd, does not require considerable costs, cognitive effort, and time allocation from the solution-​seekers (Afuah & Tucci, 2012; Pisano & Verganti, 2008). In summary, we suggest that, as project complexity increases, partnerships (e.g., non-​equity alliances, joint ventures, mergers and acquisitions, corporate venture capital investments and minority holdings, consortia, etc.) seem an effective OI mechanism for the successful completion of the project. Similarly, crowdsourcing seems

110    mehdi bagherzadeh and andrei gurca more appropriate for simple projects. As different types of crowdsourcing (open call for solution providers vs. approaching solution providers directly) (Haeussler & Vieth, 2022) and partnerships (non-​equity vs. equity) (Majchrzak et al., 2015) are available, the next critical decision is to choose the appropriate type of crowdsourcing and partnership. In the following two sections, we argue that the crowdsourcing and partnership type needs to be aligned with the nature of knowledge (as another key project attribute) required to complete the project.

Crowdsourcing and Knowledge Pervasiveness As argued earlier, crowdsourcing is adequate as an OI mechanism for simple projects. In the most pervasive type of crowdsourcing, which we metaphorically label as “fishing,” solution-​seekers launch an open call to invite solution-​providers and then wait for them to self-​select and share solutions, from which the solution-​seekers select the most appropriate (Dahlander et al., 2019; Gurca et al., 2022; Majchrzak & Malhotra, 2020). In an alternative, recently emerging type of crowdsourcing, which we metaphorically label as “hunting,” solution-​seekers proactively leverage technology (e.g., AI-​powered data mining algorithms) to explore vast amounts of open-​access data and identify pools of qualified solution providers, who are subsequently contacted directly and invited to participate in the crowdsourcing exercise, out of which some self-​select to participate2 (see Di Fiore & Schneider, 2017; Gurca et al., 2022; Kokshagina et al., 2017). This approach contrasts substantially with “fishing” crowdsourcing, which expects solution-​providers to search for and find relevant open calls by themselves. Although the “hunting” type of crowdsourcing is still in its infancy and is not as popular as the typical “fishing” crowdsourcing, we expect that further technological developments and increasing availability of open-​access data will contribute to the “hunting” type of crowdsourcing gaining traction. Previous studies considered the pervasiveness of the required knowledge in the crowd as a key attribute impacting the crowdsourcing process (Afuah & Tucci, 2012; Gurca et al., 2022; Kokshagina et al., 2017). Afuah and Tucci (2012, p. 366) argue that “whether a member of the crowd that self-​selects to solve a problem is likely to have what it takes to actually solve the problem is a function of the pervasiveness of problem-​ solving knowhow in the crowd.” As the pervasiveness of knowledge required to complete the project decreases in the crowd, potential solution-​providers with the relevant

2  We would like to thank Jean-​Louis Lievin, CEO and founder of ideXlab, and Razi Rima, ideXlab Business Director, who engaged with us in discussions regarding crowdsourcing and its future. The “hunting” and “fishing” labels have emerged from our conversations with Razi Rima and Jean-​Louis Lievin.

Aligning OI Mechanisms with Project Attributes    111 knowledge become very rare. Such specialists holding domain-​specific knowledge are usually in short supply and high demand. Thus, they tend to be busy and less likely to spend time actively searching for open calls, which is a prerequisite for the “fishing” type of crowdsourcing. The main benefit provided by “fishing” type of crowdsourcing (i.e., access to a large pool of potential problem-​solvers) cannot be leveraged when the pervasiveness of the required knowledge in the crowd is low. Therefore, the typical “fishing” type of crowdsourcing does not seem appropriate to find and attract the best solution-​providers for projects requiring knowledge with a low level of pervasiveness in the crowd. Conversely, “fishing” crowdsourcing can be very effective for projects that do not demand rare specialist knowledge but, instead, require more generalist knowledge with a higher level of pervasiveness within the crowd. In such cases, numerous people may have the relevant knowledge and are more likely to possess the time and willingness to engage in active search for open calls. To illustrate our point, we draw on Pfizer’s pre-​filled syringes project,3 which aimed to design a tech-​enabled locker for pre-​filled syringes. The locker was intended to ensure that people taking medication by use of syringe were adhering to prescribed protocol doses. The design of the syringe locker was a relatively simple problem, as it was well defined and did not require highly specialized knowledge. Still, it required fresh solution concepts as the optimal solution eluded Pfizer. Although the pharmaceutical giant had tried several designs, none of the in-​house designs proved an effective solution. The solution for this project finally emerged through an OI platform, which advertised the innovation challenge through an open call. For this project, Pfizer decided to work with IdeaConnection, an OI intermediary that delivers timely and cost-​effective solutions to their clients. The “hunting” type of crowdsourcing enables solution-​seekers to identify potential solution-​providers holding rare specialist knowledge, who are likely to be outside the reach of the “fishing” type of crowdsourcing due to their low availability to actively search for crowdsourcing open calls. An eloquent example involves Alstom,4 a French multinational company operating worldwide in the rail transport industry. Alstom relied on an OI intermediary that specialized in “hunting” experts to address the problem of dead leaves falling on train tracks in the autumn. The physiochemical reaction of dead leaves on the rails caused trains to lose adherence on the tracks, causing financial losses for train operators and delays for passengers. The OI intermediary used AI-​powered search algorithms to identify and rank several hundred experts based on objective criteria, such as the number of patents, publications, co-​authors, journal metrics, etc. Of these, dozens were contacted and invited to participate in the crowdsourcing effort. The solution was finally proposed by scholars from two universities in England and the Netherlands, who held the relevant scientific knowledge. This targeted “hunting” allowed Alstom to go beyond its usual ecosystem and leverage knowledge that initially seemed outside its reach.

3 

The case is adapted from Brunswicker et al. (2016) and Bagherzadeh et al. (2022). Based on our interview with Jean-​Louis Lievin, founder and CEO of ideXlab, on November 6, 2020 and https://​www.idex​lab.com/​open-​inn​ovat​ion-​rail​way/​ (accessed May 24, 2022). 4 

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Partnership and Knowledge Hiddenness We also argued above that non-​equity and equity partnerships are potential OI mechanisms when dealing with a complex project. While solution-​seekers in a non-​ equity partnership (e.g., an alliance) engage in a cooperative relationship with external partners without involving equity transactions, an equity-​based partnership (e.g., joint venture, merger, acquisition, minority holding) involves equity transactions (Santoro & McGill, 2005). Non-​equity partnerships potentially provide access to a larger number of potential partners than equity-​based partnerships, but also provide less effective communication channels for knowledge-​sharing compared to equity-​based partnerships. However, although equity partnerships pose lower coordination and opportunism costs than non-​equity partnerships, mainly due to equity control via a joint board, equity-​ based partnerships involve higher set-​up costs because they require substantial initial capital investments. The cost-​benefit profiles of equity and non-​equity partnerships (see Bagherzadeh et al., 2022, for a detailed comparative analysis of the cost-​benefit profile of equity and non-​equity partnerships) emphasize the importance of selecting the appropriate type of partnership. The literature has argued that, when solution-​seekers face a complex innovation project, dividing the project into sub-​projects with clear conceptual boundaries for each sub-​project is a key step before engaging in OI to leverage cost reductions and time-​ to-​market benefits (Fernandes & Simon, 1999; Gurca et al., 2021; also see Simon, 1962). Gurca et al. (2021) show that the efficacy of OI depends not only on the ability to decompose the project, but also on the extent to which the sources or locations of required knowledge to complete the project are known to solution-​seekers (i.e., the hiddenness of the required knowledge). Overall, we argue that project decomposition, which is critical for leveraging the knowledge of external partners through non-​equity partnerships, is meaningful and feasible only when solution-​seekers are aware of the location of the required knowledge. This highlights the importance of considering knowledge hiddenness as a key attribute driving the choice between equity and non-​equity partnerships (Bagherzadeh et al., 2022). When solution-​seekers face complex problems for which they can readily identify partners with relevant knowledge (i.e., low knowledge hiddenness), a non-​equity partnership seems more appropriate than equity-​based partnerships. First, a non-​ equity partnership enables solution-​seekers to tap into the knowledge of a large variety of external partners, such as suppliers, clients, competitors, universities, etc., thereby reducing project costs and time-​to-​market.5 Second, non-​equity partnerships

5 Despite the advantages of non-​ equity partnerships for complex projects with low levels of knowledge hiddenness, the decomposition of such problems might lead to some integration difficulties. However, these problems can be addressed by system integration work, i.e., integrating sub-​projects with the designs of other sub-​projects by mutual adjustment (Fujimoto, 2007).

Aligning OI Mechanisms with Project Attributes    113 do not require solution-​seekers to make a heavy initial investment compared to equity-​ based partnerships, which involve substantial set-​up costs. An example in this sense is provided by Boeing’s 787 Dreamliner project.6 As the Dreamliner, a mid-​size aircraft, was expected to cover distances exceeding 15,000 km, the same as the Airbus A380 superjumbo, Boeing had to make it lighter than any plane of comparable size. With this objective in mind, more than 50% of the aircraft had to be constructed from lightweight carbon fiber composite. Boeing did not have any knowledge about composite materials, as its previous airliners had been constructed exclusively from aluminum. Consequently, the American aircraft manufacturer entered non-​equity partnerships with Japan’s Mitsubishi and Kawasaki and Italy’s Alenia for the development and manufacturing of the composite wings and most of the fuselage. Moreover, it was UK’s Rolls Royce that developed and produced the thrust engines, which were designed to be 20% more fuel-​efficient than previous generation engines. Similarly, other companies such as Sweden’s Saab, France’s Latecoere, Korea’s KAL-​ASD, UK’s Messier–​Dowty, and American Goodrich and Spirit were among the key partners in the project. Some 65% of the Dreamliner’s components were designed, developed, and manufactured by external partners. Thus, despite the complexity of the project, Boeing decided to divide the project into sub-​systems (e.g., wings, thrust engines, tail part of the fuselage, etc.) matching the knowledge and expertise of its key partners. It is worth mentioning that Boeing could easily identify strategic partners possessing relevant knowledge as it had prior working relationships with most of them. In contrast, when the locations of relevant knowledge are unknown to solution-​ seekers, project decomposition, which is a necessary step for leveraging the benefits of non-​equity partnerships (e.g., cost reduction), becomes unfeasible. Moreover, when the relevant knowledge is unavailable or nonexistent, non-​equity partnerships can generate considerable coordination and opportunism costs due to difficulties of specifying a complete contract with potential partners. Therefore, non-​equity partnerships do not seem feasible for complex projects with high levels of knowledge hiddenness. To illustrate this point, we build on an example involving a new lithography technology required for the manufacturing of next generation computer chips.7 At the turn of the 20th century, the computer industry used the deep ultraviolet (DUV) lithography technology (involving ultraviolet light at 243-​nanometer wavelengths) to project integrated circuit patterns onto semiconductor wafers to manufacture computer microchips. Most companies in the industry were aware that the DUV technology would soon reach its limitations, as it did not allow for further substantial size reductions of the circuit patterns. At the time, several lithography technologies showed promise in the race for the next generation of microchips. Extreme ultraviolet (EUV) was the technology favored by Intel. Although promising, the EUV technology, which involved ultraviolet light at wavelengths around 13 nanometers,

6  7 

The case is adapted from Lawrence and Thornton (2017) and Wagner and Norris (2009). The case is adapted from Chappell (2001) and Dorsch (1998).

114    mehdi bagherzadeh and andrei gurca was in its infancy and extensive bodies of new knowledge were required to make the technology a viable solution for microchip manufacturing. In this case, the relevant knowledge was clearly hidden as it was yet to be developed. Moreover, due to high knowledge hiddenness, it was impossible to decompose the project into sub-​projects for outsourcing to external partners. Therefore, to advance the EUV technology, Intel initiated EUV Limited Liability Company (LLC), an equity partnership in the form of a consortium, in which Advanced Micro Devices and Motorola were the other original partners. Other actors, such as IBM and Infineon Technologies, subsequently joined the consortium. The aim of EUV LLC was to demonstrate the viability of the EUV technology and, in doing so, to set the next technology standard for the microchip industry. Thus, for projects involving high complexity and high knowledge hiddenness, forming equity-​based partnerships with elite industry players seems to be the appropriate OI mechanism, as such partnerships afford effective communication channels and reduce coordination efforts.

Selection of the Right Open Innovation Mechanism: A Two-​Phase Decision Framework As argued above, some project attributes are a vital driver in determining a solution-​seeker’s choice to source solutions via crowdsourcing or partnerships. Moreover, other project attributes impact the selection of the most appropriate types of crowdsourcing or partnership. Based on our argument, above, we propose a decision-​making framework for selecting appropriate OI mechanisms based on project attributes (Figure 7.1). Our decision framework includes two phases. The first phase seeks to align the two main OI mechanisms, crowdsourcing and partnerships, with the project complexity level. Our insights show that while partnerships are the preferred OI mechanism when projects are complex, crowdsourcing seems more appropriate for simple projects. The second phase involves selecting the crowdsourcing types (“fishing” vs. “hunting”) depending on the pervasiveness of knowledge relevant for simple projects. Respectively, for complex projects, the appropriate partnership type (non-​equity vs. equity) should be selected based on the hiddenness of the relevant knowledge. Our insights regarding simple problems show that the “hunting” type of crowdsourcing is superior when the pervasiveness of the relevant knowledge in the crowd is low, and “fishing” is preferable when knowledge pervasiveness is high within the crowd. Similarly, we argue that non-​equity partnerships are more appropriate for solving complex problems when the relevant knowledge is known to solution-​seekers, while equity partnerships are preferable when the relevant knowledge is hidden.

Aligning OI Mechanisms with Project Attributes    115 Project with specific attributes

Complex project

Phase 1

Simple project

Crowdsourcing

Phase 2

Low K pervasiveness

Hunting

Partnership

High K pervasiveness

Fishing

Low K hiddenness

Non-equity

High K hiddenness

Equity

figure 7.1  Decision framework for the selection of appropriate OI mechanisms based on project attributes.

Conclusion Our central message in this chapter is that the OI mechanism must be aligned with key project attributes for successful solution development. To achieve this alignment, we need to shift the level of analysis from firms to projects. This highlights the importance of collecting detailed project-​level data. Although the number of project-​level studies has recently increased (e.g., Bagherzadeh et al., 2021, 2022; Gurca et al., 2021; Gurca et al., 2022; Haeussler & Vieth, 2022; Lee et al., 2019; also see Markovic et al., 2021, for an overview) and Markovic, Bagherzadeh, Vanhaverbeke, and Bogers co-​edited a special issue on OI at project level of the Industrial Marketing Management Journal in 2021, there is a need for more empirical studies. Considering the literature on OI mechanisms and our proposed decision-​making framework, we conclude this chapter by suggesting some directions for future research. In this chapter, we argue that project attributes drive the selection of OI through their interaction (e.g., complexity and knowledge pervasiveness, complexity and knowledge hiddenness). Moreover, in a recent study, Bagherzadeh et al. (2021) show that project attributes can affect each other. Therefore, considering each project attribute separately and ignoring the potential interaction between these attributes may negatively impact the alignment between projects and OI mechanisms. This calls for a potentially fruitful future line of research on studying the interaction effects between project attributes, and how such interactions impact the OI governance selection. While our primary focus in this chapter is on project attributes, project-​and firm-​level factors can also impact each other (see Barbic et al., 2021). Therefore, future research could advance our decision-​ making framework by studying interactions between project attributes and firm-​level

116    mehdi bagherzadeh and andrei gurca characteristics (i.e., cross-​level interaction). Each stage of an innovation project (e.g., problem formulation, ideation, development, and launch) has different attributes and needs different knowledge sets, thereby potentially calling for different OI mechanisms. Thus, studying the alignment between OI mechanisms and project attributes at different stages in the project presents itself as a potentially fruitful line of research. Finally, evaluating the problem attributes is a critical step in aligning OI mechanisms and project attributes. Evaluating some attributes, such as project complexity, knowledge pervasiveness in the crowd, and knowledge hiddenness, can be challenging since solution-​seekers may not have the required meta-​knowledge (i.e., knowledge about the knowledge used to enable identification, evaluation, and use of knowledge) to identify the relevant knowledge for solution development. Thus, it may be relevant for future studies to explore how solution-​seekers can overcome such cognitive limitations to ensure the accurate evaluation of project attributes.

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CHAPTER 8

OPEN INNOVAT I ON IN SMALL A ND MEDIUM -​SIZED ENT E RPRI SE S agnieszka radziwon and wim vanhaverbeke

Introduction Open Innovation is widely acknowledged to be an important innovation management practice (Bogers et al., 2017; Chesbrough, 2003; Dahlander & Gann, 2010). That being said, there is a certain bias toward large, R&D-​intensive firms, which aim to maintain their position as industry leader (Dahlander et al., 2021). Because of this bias, during the first decade of open innovation, a large number of studies successfully addressed OI practices in large companies (Radziwon & Chesbrough, Chapter 2, this volume). The main reasoning behind these insightful studies was to provide knowledge, which could be generalized to all enterprises, no matter the size or industry. Although many findings were very relevant, scholars started to discover that small and medium-​sized enterprises (SMEs) engage and benefit from open innovation differently than large multinational corporations such as IBM, Lucent, and Intel (Chesbrough, 2003). It is not possible to extrapolate the successful open innovation practices from multinational corporations to small firms and a different approach has to be adopted to guarantee success with OI practices in SMEs (Vanhaverbeke, 2017). In this chapter, we will examine the main OI practices at SMEs that demonstrate how, despite their unique characteristics, SMEs can benefit from open innovation. We will also discuss the main challenges and risks related to open innovation implementation and management (Vanhaverbeke, 2017; Vanhaverbeke et al., 2018). The chapter concludes by presenting examples of good practices of OI management and their policy implications.

120    agnieszka radziwon and wim vanhaverbeke Table 8.1 Differences between SMEs globally Area

SMEs in the Job creation for the economy (%) economy (%)

No of employees

Annual turnover

USA

99.91

62

100–​15002

$1 million to over $40 million3

90

604

< 250