New Perspectives in Technology Transfer: Theories, Concepts, and Practices in an Age of Complexity (FGF Studies in Small Business and Entrepreneurship) 303061476X, 9783030614768

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FGF Studies in Small Business and Entrepreneurship

Dana Mietzner Christian Schultz   Editors

New Perspectives in Technology Transfer Theories, Concepts, and Practices in an Age of Complexity

FGF Studies in Small Business and Entrepreneurship Editors-in-Chief Joern H. Block, Department of Economics, Trier University, Trier, Rheinland-Pfalz, Germany Andreas Kuckertz, Institute of Marketing & Management, University of Hohenheim, Stuttgart, Germany Editorial Board Dietmar Grichnik, Institute of Technology Management (ITEM), University of St. Gallen, St. Gallen, Switzerland Friederike Welter, University of Siegen, Siegen, Germany Peter Witt, Schumpeter School of Business & Economics, University of Wuppertal, Wuppertal, Germany

More information about this series at http://www.springer.com/series/13382

Dana Mietzner · Christian Schultz Editors

New Perspectives in Technology Transfer Theories, Concepts, and Practices in an Age of Complexity

Editors Dana Mietzner Wildau, Germany

Christian Schultz Berlin, Germany

ISSN 2364-6918 ISSN 2364-6926 (electronic) FGF Studies in Small Business and Entrepreneurship ISBN 978-3-030-61476-8 ISBN 978-3-030-61477-5 (eBook) https://doi.org/10.1007/978-3-030-61477-5 © Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

The Technology Transfer Challenge. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christian Schultz and Dana Mietzner

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Actors Inventions and Their Commercial Exploitation in German Universities: Analyzing Determinants Among Academic Researchers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Teita Bijedi´c, Simone Chlosta, and Arndt Werner

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Do Startups Benefit from Incubation? An Analysis of Startups’ Absorptive Capacity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constantin Schmutzler and André Presse

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Absorptive Capacity Approach to Technology Transfer at Corporate Accelerators: A Systematic Literature Review. . . . . . . . . . . . . Ufuk Gür

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Strategy Processes in Technology Transfer Offices: Antecedents and Consequences. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ciara Fitzgerald, James A. Cunningham, Matthias Menter, and Richard B. Nyuur Role and Impact of Maker Spaces in Universities Third Mission: The ViNN:Lab Case. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dana Mietzner and Markus Lahr

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Does Technology Scouting Impact Spin-Out Generation? An Action Research Study in the Context of an Entrepreneurial University. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Christian Schultz Technology Transfer Through Intersectoral Partnerships: The Case of Digitalization in the German Health Sector. . . . . . . . . . . . . . . . . . . . . 129 Markus Göbel, Hans Dieter Gräfen, and Christian Schultz v

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Processes The Relevance of Technology Transfer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Frank Piller, Dennis Hilgers, and Lisa Schmidthuber Start-Ups as Relevant Supporters and Initiators of Sustainability Attributes in Global Value Chains of the Future. . . . . . . . . . . . . . . . . . . . . . . . 165 Stephanie Rabbe, Christoph von Viebahn, and Marvin Auf der Landwehr Progressive University Technology Transfer of Innovation Capabilities to SMEs: An Active and Modular Educational Partnership. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Mauricio Camargo, Laure Morel, and Pascal Lhoste Facilitating Knowledge and Technology Transfer via a Technology Radar as an Open and Collaborative Tool. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Marko Berndt and Dana Mietzner Using Open Innovation Platforms for Technology Transfer. . . . . . . . . . . . . . 231 Frank Piller, Dennis Hilgers, Christoph Ihl, and Lisa Schmidthuber Toward Systemic Strategy Development: A Contextual Innovation Framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Florian Grote and Eva-Maria Lindig University Technology Transfer as Control Parameter of Complex Entrepreneurial Ecosystems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 Andreas Liening, Jan-Martin Geiger, Tim Haarhaus, and Ronald Kriedel

Editors and Contributors

About the Editors Dana Mietzner is professor for innovation management and regional development and head of the Research Group for Innovation and Regional Development at the Technical University of Applied Sciences Wildau, Germany. She is currently responsible for the university-wide implementation of the maker space ViNN:Lab and head of the project Innovation Hub 13, which is implemented to explore new approaches in knowledge and technology transfer. She also conducts practical foresight exercises and is interested in the further development of foresight methods, with a focus on SMEs and selected regions. Christian Schultz is full professor for business administration at hwtk (University of Applied Sciences) Berlin, Germany and head of the distance learning program since 2015. Christian has led numerous national and international research projects in the fields of entrepreneurship and innovation. His research interests lay in selected areas of entrepreneurship and innovation management, such as financing of technology-oriented companies, the start-up team and the development of management tools for open innovation systems.

Contributors Marvin Auf der Landwehr holds a M.Sc. in Strategic Business Development with further postgraduate studies in digital transformation, urban logistics, business process analysis, simulation development and innovation management. After having worked for several years as Country Manager within an organization specialized in e-commerce and digital business strategies, he possesses relevant knowledge and experiences in designing as well as implementing new business strategies and foster business innovation. Currently, Marvin Auf der Landwehr works as research assistant and Ph.D. candidate at the Hochschule Hannover in Germany, where he particularly vii

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engages in developing, assessing and evaluating innovation models related to urban logistics in general and last-mile fulfillment in particular. Marko Berndt is a researcher and part of the Research Group for Innovation and Regional Development at the Technical University of Applied Sciences Wildau. He received his master’s degree in Business Communication Management from the University of Applied Sciences in Berlin (HTW). During his studies, he founded a fashion tech start-up, which was complemented by several accelerator programs. Based on this experience, his research interests cover technology and knowledge transfer, strategic foresight and entrepreneurship. Teita Bijedi´c is a senior researcher at the Institut für Mittelstandsforschung (IfM) Bonn, Germany and lecturer in Entrepreneurship at the University of Flensburg. She studied psychology at the University of Duesseldorf and attained her doctorate degree in Economics and Social Sciences (Dr. rer. pol.) at the University of Flensburg. Her research interests lie at the intersection of entrepreneurship, psychology and education with a focus on behavioral and institutional aspects of entrepreneurship, innovation, including gender and diversity. Mauricio Camargo is Professor of Management of Technology and Innovation at the ENSGSI of Nancy (School of Industrial Engineering in the Lorraine University), and Director of the ERPI Laboratory (Research Team on Innovative Processes). Ph.D. in Automatics and B.Sc. in Chemical Engineering. His main research interests are: innovation management, new product development, decision making in productprocess design under sustainability dimensions. Simone Chlosta is head of the entrepreneurship department at the RKW Competence Centre in Eschborn, Germany, and professor in entrepreneurship and business psychology at the FOM University of Applied Sciences in Frankfurt. She studied psychology at the University of Frankfurt and attained her doctorate degree in economics and social sciences (Dr. rer. pol.) at the EBS University. Her research interests lie at the intersection of entrepreneurship and psychology focusing on individual and situational influences during the start-up and innovation process. James A. Cunningham is a Professor of Strategic Management at Newcastle Business School, Northumbria University, UK. His research intersects the fields of strategic management, innovation and entrepreneurship. His research focuses on strategy issues with respect to scientists as principal investigators, university technology transfer commercialization, academic, public sector and technology entrepreneurship, entrepreneurial universities and business failure. Ciara Fitzgerald is a faculty member at the Cork University Business School, University College Cork. Her research and teaching interests lie at the intersection of technology innovation, entrepreneurship and strategy. Her research investigates (1) strategies used by universities and firms to manage intellectual property and the

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commercialization process, (2) strategies to engage citizens and policymakers in technology assessment and (3) exploratory and applied research of innovative health information systems. Jan-Martin Geiger is a research assistant at the Chair of Entrepreneurship and Economic Education at the TU Dortmund University and manager of the Junior Entrepreneurship School. His research focuses entrepreneurship education and entrepreneurial learning applying experimental methods. His works have been published in renowned journals and in proceedings of leading entrepreneurship conferences. Markus Göbel is full professor for business administration especially corporate management and corporate theories at Helmut-Schmidt-University Hamburg, Germany. His research interests lay in the fields of strategic management, organization theory and public management. Hans Dieter Gräfen, M.B.A. and MOP (Master of Organizational Psychology) has been using technical solutions for organizational optimization and business model innovation projects since 1992, e.g., at the German Association of Cities, BMF, Ina Schaeffler, Xerox. Most recently, he headed the Digital Accelerator and Digital Campus at Bayer AG. Today, as a senior digital expert, he manages the Digital Innovation Campus Health DICH GmbH and advises automotive companies on their digital strategy. Florian Grote is Professor of Product Management at CODE University of Applied Sciences in Berlin. He has filled design and product roles in the music technology industry, working on innovative instruments for electronic music production. His research focuses on cognitive and systemic perspectives on learning organizations with special attention to resilience. More info and contact: https://fgrote.com. Ufuk Gür is an Assistant Professor of Innovation and Entrepreneurship. Her research interests include technology transfer, entrepreneurial universities, and corporate entrepreneurship. As a young scholar, her research was published in top journals such as Technological Forecasting and Social Change. She was awarded Technology Entrepreneurship Grant by Scientific and Technological Research Council of Turkey-TUBITAK in 2017, and she was placed among finalists for Dr. Fikret Yücel Award in 2019 by Technology Development Foundation of Turkey with her doctoral dissertation. Tim Haarhaus is a research assistant at the Chair of Entrepreneurship and Economic Education at the TU Dortmund University. He pursues research interests in both entrepreneurship and strategic management, studied both empirically and theoretically, with various methods. His works have been published in international journals such as Technological Forecasting & Social Change and in proceedings of leading entrepreneurship and information systems conferences.

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Dennis Hilgers is professor of Public and Nonprofit Management at the Institute for Public and Nonprofit Management of the Johannes Kepler University (JKU) Linz. His research focuses on managing innovation and performance in public administrations. Christoph Ihl is professor of management and head of the Institute of Entrepreneurship at the Technical University of Hamburg (TUHH). His research about entrepreneurship and innovation lies at the intersection of organizational theory, sociology and strategy. He is particularly interested in how actors are influenced by and interact with their social and cultural environments to bring about novelty, e.g., with regard to ideas, teams, products, practices or business models. Ronald Kriedel is CEO of the Center of Entrepreneurship & Transfer (CET) and responsible for the development of university-wide technology transfer. He is also a research assistant at the chair of Entrepreneurship and Economic Education at TU Dortmund University. His research focuses on entrepreneurship and system competence. Markus Lahr is researcher, 3D printing enthusiast and FabLab manager with the Research Group for Innovation and Regional Development at the Technical University of Applied Sciences Wildau, Germany. He is also a Ph.D. candidate with the Finland Futures Research Institute at the University of Turku. His research focus is on the futures of innovation labs, participative futures workshop methods and the role of maker spaces triggering innovations. Pascal Lhoste is Professor at the University of Lorraine—National School of Systems Engineering and Innovation (ENSGSI) since 2004, a school he managed from 2009 to 2019. His research activity deals with Contribution of Knowledge Modelling to Innovation Processes developed at ERPI (Research Team on Innovative Processes), a laboratory he led from 2006 to 2010. He is also co-founder of the Lorraine Fab Living Lab® and of the first mobile FabLab in France. He previously founded a new scientific discipline: the Automation Engineering, which led him to develop new tools and methods to accelerate the design of automated systems. Andreas Liening is head of the Chair of Entrepreneurship and Economic Education and is director of the Centre of Complexity Sciences and Entrepreneurship Education (CCSEE) at TU Dortmund University. He is dean of the Faculty of Business and Economics and member of the executive board of the Center of Entrepreneurship & Transfer (CET). He has authored numerous articles and books about various aspects of entrepreneurship and economic education. Eva-Maria Lindig is Program Coordinator and Lecturer for Digital Product Management at CODE University of Applied Sciences in Berlin. After filling different roles in marketing at XING, she led and consulted several teams in digital product development in fintech, insurance and ad tech companies. Eva Lindig’s

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primary research interests are product discovery and innovation methods. Furthermore, she is interested in approaches to integrate ethics and sustainability in the product development process. Matthias Menter is an Assistant Professor of Business Dynamics, Innovation and Economic Change at the Friedrich Schiller University Jena (Germany). He has further worked at the School of Public and Environmental Affairs (SPEA) at Indiana University—Bloomington (USA) as a visiting scholar and adjunct lecturer. His research focuses on aspects of entrepreneurial and innovative ecosystems, academic entrepreneurship, university-industry collaborations and public policy. Laure Morel is Professor of Innovation and Director of the ENSGSI the “Ecole Nationale en Génie des Systèmes et de l’Innovation" (School of engineering of the Lorraine University). Her research interests span the field of new product development, metrology and Technological Risk management. Dr. Morel is on the Directorate board of the International Association for Management of Technology (IAMOT). Richard B. Nyuur is an Associate Professor (Reader) of Strategic Management and International Business at the Newcastle Business School, Northumbria University, UK. His research interests lie at the intersection of strategy and international business in the broad areas of foreign direct investment (FDI), international business strategy, international human resource management and corporate social responsibility. Frank Piller is professor of management at the Technology & Innovation Management Group of RWTH Aachen University, Germany. His research focuses on value co-creation between businesses and customers/users and the interface between innovation management, operations management and marketing. André Presse is Professor for Entrepreneurship and Executive Director of the Grenke Centre for Entrepreneurial Studies at SRH Berlin University and Adjunct Professor at the University of Waterloo, Ontario, Canada. Prior to that, he was Assistant Professor for Entrepreneurship, Technology-Management and Innovation at the University of Bolzano, Northern Italy, and Visiting Scholar at the Yale Entrepreneurial Institute (YEI), Yale University, CT, U.S. Andre holds and MBA from Leipzig Graduate School of Management (HHL) and a Ph.D. from Karlsruhe Institute of Technology (KIT). Stephanie Rabbe is an economist and social scientist with a Ph.D. in Strategic Sustainability Management and holds the Chair of Entrepreneurship at HAWK University of Applied Sciences and Arts in Hildesheim since 2014. There she is also responsible for the entire academic start-up support. Her research areas include strategy, entrepreneurship, innovation and sustainability, especially in start-ups. She teaches business administration for start-ups interdisciplinary and practice-oriented with a focus on idea and business model development. In 2004, Stephanie Rabbe

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founded a start-up herself and still runs its business today. She is also a partner of Digital Pioniere UG, a management consultancy for digital transformation. Lisa Schmidthuber is post-doctoral researcher at the Institute for Public and Nonprofit Management at Johannes Kepler University (JKU) Linz, Austria. Her research interests include public innovation management, digital transformation and accounting innovation. Constantin Schmutzler is an experienced start-up consultant with a background in Communication Management and Entrepreneurship. He holds a Master’s degree in Entrepreneurship from SRH Berlin University of Applied Sciences with the focus on entrepreneurial education and incubation. During his studies, he founded Berlin Startup School to help the next generation of entrepreneurs building their businesses. Since then he has helped students and employees to build or to improve start-ups. Christoph von Viebahn following positions in IT and logistics consulting (Focus Maritime Supply Chain), Christoph von Viebahn began teaching Business Informatics 2005 at the Applied University of Bremen (School of International Business and Ship Management) and at the FOM University. In 2008, he was appointed Managing Director of the regional consortium SEIS and the assumption of responsibility for the project by the Bertelsmann Stiftung. Since 2012, Christoph von Viebahn works as professor in the field of business informatics at the University of Applied Sciences Hannover where he is responsible for the specialization in Supply Chain Management and the basics of Business Administration. In teaching, he built up the subject of simulation in the field of business informatics. Arndt Werner holds a chair for SME Management and Entrepreneurship at the University of Siegen. His current research focus is on corporate social responsibility (CSR) in family firms, mechanisms of the academic innovation transfer, entrepreneurial well-being and growth paths in SMEs.

The Technology Transfer Challenge Christian Schultz and Dana Mietzner

Abstract The sudden appearance and rapid spread of the coronavirus in the beginning of 2020 has caused the loss of life, immeasurable suffering and it already impacted the world economy and everyone’s personal life. But times of tremendous crises also spark human creativity and innovation behavior. The coronavirus pandemic will be most likely and hopefully soon resolved through technologies, e.g., a drug and, ultimately, a new vaccination, flanked by medical devices and the health system in general. Adequate technology transfer processes as the movement from knowledge, inventions, and innovative technologies from the lab into the private sector can contribute to the efficiency and speed of innovation. These processes are not linear and can be experienced as various interactions between actors and organizations of the science system with other social subsystems and citizens. In this edited volume, we provide insights on technology transfer that are relevant to scientists, technology transfer practitioners, and policymakers. The structure of the edited volume follows the seven dimensions identified in the state-of-the-art literature review by Battistella, De Toni, and Pillon (J Technol Transf 41: 1195–1234, 2016). Keywords Technology transfer · Innovation system · Technology transfer process

1 Why Do We Need to Think About Technology Transfer? The year 2020 will undoubtedly become associated with the crisis due to the worldwide pandemic spread of the coronavirus. In March of 2020 the whole world is in a quasi-shutdown with people staying at home with their children to social distance themselves to slow down the spread of the virus and to prevent a potential national C. Schultz (B) HWTK—University of Applied Sciences, Bernburger Straße 24/26, 10963 Berlin, Germany e-mail: [email protected] D. Mietzner Technical University of Applied Sciences Wildau, Hochschulring 1, 15745 Wildau, Germany e-mail: [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_1

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overflow of patients in hospitals and healthcare facilities. Something which has never been experienced by the bulk of the population during their lifetimes and many thought of only as a theoretical possibility than a realistic measure. Right now, research groups all over the world are feverishly searching for an effective drug in the short term to combat the virus, which dangerously affects the elderly and weakened most severely but also apparently healthy people, and a vaccine to protect the population as a whole in the years to come. While measures to contain the virus, e.g., quarantine and curfews are essentially non-technology-related, humanity is pinning its hopes on bringing an end to the pandemic with the help of drugs, which are developed with biotechnology and supercomputers, who simulate the effectiveness of active substances. A crisis can become a time, where human creativity and its innovation spirit is sparked and enables a push toward new innovative solutions. As sad as it is, violent conflicts from the Punic Wars to the present day have regularly led to new inventions and innovations that have found their way from exclusive military to private use. After World War II, Vannevar Bush made his case for the societal impact of research and science in his work “Science as an Endless Frontier” (Bush, 1945). He compellingly presented the relevance of research and science for societal challenges, e.g., diseases, food production, and overall quality of life. The establishment of the National Science Foundation is mainly attributed to his convincing outline. While the corona pandemic will—in all likelihood and hopefully soon—be resolved by technology, the questions remain how can we prepare more effectively for possible coming crises and how can we prepare our systems to be more resilient in a highly uncertain and dynamic environment? We are positive that systematic technology transfer has the potential to speed up innovation, to enhance interdisciplinary problem solving and knowledge sharing between different groups of actors. This edited volume can provide impulses for different stakeholders.

2 Technology Transfer Models The research on technology transfer and innovation models ranges from the work on national systems of innovation (Lundvall, 1992) and regional innovation systems (Cooke, Uranga, & Etxebarria, 1997) to current approaches like the quadruple helix model (Etzkowitz & Leydesdorff, 1995, 2000) and free innovation (von Hippel, 2017). Without going deeply into sophisticated detail on the diversity and differentiation of each approach, we provide an overview of two fundamental streams in the literature that are important for the understanding of the state of the art in technology transfer research (see also Hartmann & Mietzner, 2019, pp. 7–8). On the one hand, the research and technology-driven innovation approaches and on the other hand the demand- and user-oriented innovation approaches. This background information shall assist the reader to contextualize the contributions correctly before we outline the structure of this edited volume in the final section of this introduction.

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In the beginning, there was the “linear” innovation model, which saw the starting point for innovation processes in research. In this model, it is assumed that inventions are channeled into application-oriented research and inevitably become transformed into innovations that are taken up by companies and finally marketed. This innovation model is highly compatible with the traditional understanding of technology transfer. At its center lays the economic exploitation of technological knowledge, which was built in universities and research institutions and is later picked up by large companies to introduce new product and services. This simplified model without distinct feedback from the market expanded to include perspectives, where innovations are created in non-linear, interactive actor constellations or so-called innovation networks. In the national innovation system approach (Lundvall, 1992), innovations are the results of interactions and learning processes of actors from different institutional spheres of the system. According to Lundvall (2007), these systems are not rigid, mechanistic structures, but rather characterized by complexity, co-evolution, and self-organization. The national innovation system model was supplemented with regional components and gave birth to a new literature stream on regional innovation systems (Cooke, 1992; Cooke, Boekholt, Schall, & Schienstock, 1996; Cooke et al. 1997). In this understanding, regional innovation systems are social systems where innovations arise as a result of interaction between regionally located economic actors. Central elements in this context are universities, non-university research institutions, technology transfer agencies, consulting institutions, financing institutions, and companies. Critics disapprove the sole focus on market logic and that many important aspects of innovation, e.g., sociocultural dynamics, political and social governance as non-economic areas of society, are neglected (Moulaert & Sekia, 2002, p. 300). In contrast to rather research and technology-driven innovation approaches demand- or user-oriented innovation approaches evolved (Arnkil, Järvensivu, Koski, & Piirainen, 2010). Etzkowitz and Leydesdorff (2000) point out that government, academia, and industry need to work together in the so-called triple helix model to reach the full economic technology transfer potential. More recently, the triple helix has been supplemented with “societal-based innovation” as a fourth element to become the research framework of the quadruple helix model (Miller, McAdam, & McAdam, 2018). Starting with the work of Chesbrough (2003), the research on open innovation has played a prominent role in the innovation discussion for nearly two decades. In an open innovation environment, organizational barriers are permeable, which means that innovation can happen inside or outside large businesses, SMEs, universities, or research facilities all over the world with the involvement of users in several ways. More recently, von Hippel goes remarkably far in terms of user involvement with his concept of “free innovation” (von Hippel, 2017), which focuses on innovative end users. In this framework, it is the consumer, in contrast to the traditional manufacturer, who develops new products, processes and services at his own expense without enjoying legal protection and thus makes his ideas available to everyone. In this model, consumers set the innovation impulses and producers support or rather exploit them, e.g., through new manufacturing technologies.

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Based on this short overview the transfer process can be characterized as a continuous interaction between actors and organizations of the science system with other social subsystems and citizens. Major social challenges, like the effects of demographic and climate change or the consequences of the current pandemic, which are highly dynamic, complex and with long-term effects, require corresponding approaches and scientific processing which “(…) goes beyond one-dimensional, unidisciplinary analyses and approaches to solutions and which takes into account the interaction between the disciplines and between science and other social functional systems” (Wissenschaftsrat, 2015, p. 17).

3 Goals and Structure of This Edited Volume The goal of this edited volume is to provide access to research results that are relevant to scientists, practitioners, and policymakers, who engage in knowledge and technology transfer to enhance their efforts. We consider the know-how of transferring inventions, knowledge, and ultimately innovative technologies from the lab into practical use as the main success factor to appropriate societal benefits from public investments. According to the state-of-the-art literature review by Battistella, De Toni, and Pillon (2016, p. 1197) the literature on technology transfer has the following seven dimensions of analysis: 1. Source: Sending entity in the transfer process. 2. Recipient: Receiving entity of the transfer process. 3. Intermediaries: The intermediary (also called broker or gatekeeper) is an actor who may or may not be involved as part of the transfer process. 4. Relationships: Relationships between the actors. 5. Object: Transferred technology/knowledge, their properties and characteristics. 6. Channels and mechanisms: Different transformations of the form (tacit vs. explicit) of knowledge during a transfer. 7. Context: Environments and the contextual embeddedness. Table 1 categorizes the chapters of this edited volume into the dimensions by Battistella et al. (2016). Part I of this edited volume concentrates on the actors in the technology transfer process. Through an extensive quantitative analysis, Bijedi´c, Chlosta, and Werner shed light on transfer behavior of scientists and show that gender differences as well as career and human capital-related factors (e.g., scope of employment, professional experience, and leadership position) play a role in exploiting inventions. Schmutzler and Presse use the multidimensional approach of Flatten et al. (2011), to analyze the potential differences in the absorptive capacity of incubated and non-incubated start-ups. Through data on 66 German start-ups (36 incubated, 30 non-incubated), it becomes clear that incubated start-ups have a significantly higher absorptive capacity than non-incubated start-ups.

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Table 1 Matching of dimensions of analysis according to Battistella et al. (2016) with the chapters of this edited volume No. Dimensions of analysis

Chapter

A

Actors

1

Source

z Teita Bijedi´c, Simone Chlosta, and Arndt Werner: Inventions and their commercial exploitation in German universities: Analyzing determinants among academic researchers

2

Recipient

z Constantin Schmutzler, and André Presse: Do startups benefit from incubation? An analysis of startups‘ absorptive capacity

3

Intermediaries

z Ufuk Gür: Absorptive capacity approach to technology transfer at corporate accelerators: A systematic literature review z Clara Fitzgerald, James A. Cunningham, Matthias Menter, and Richard B. Nyuur: Strategy processes in technology transfer offices: Antecedents and consequences z Dana Mietzner and Markus Lahr: Role and impact of maker spaces in universities third mission: The ViNN: Lab Case z Christian Schultz: Does technology scouting impact spin-out generation? An action research study in the context of an entrepreneurial university

4

Relationship

z Markus Göbel, Hans Dieter Gräfen, and Christian Schultz: Technology transfer through intersectoral partnerships—The case of digitization in the German health sector

B

Process

5

Object

6

Channels and mechanisms z Mauricio Camargo, Laura Morel, and Pascal Lhoste: Progressive university technology transfer of innovation capabilities to SMEs: An active and modular educational partnership z Marko Berndt and Dana Mietzner: Facilitating knowledge and technology transfer via a technology radar as an open and collaborative tool z Frank Piller, Dennis Hilgers, Christoph Ihl, and Lisa Schmidthuber: Using open innovation platforms for technology transfer

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Context

z Frank Piller, Dennis Hilgers, and Lisa Schmidthuber: The relevance of technology transfer z Stephanie Rabbe, Christoph von Viebahn, and Marvin Auf der Landwehr: Start-ups as relevant supporters and initiators of sustainability attributes in global value chains of the future

z Florian Grote and Eva-Maria Lindig: Toward systemic strategy development: A contextual innovation framework z Andreas Liening, Jan-Martin Geiger, Tim Haarhaus, and Ronald Kriedel: University technology transfer as control parameter of complex entrepreneurial ecosystems

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Gür contributes to the knowledge base on corporate accelerators through systematic literature review and delivers an absorptive capacity process model to inform future research and practice. Fitzgerald, Cunningham, Menter, and Nyuur show that the strategic orientation of technology transfer offices is surprisingly diverse. Some have taken different responses to the same environmental stimuli, conducted different forms of benchmarking, and undertook different professional development and agenda setting activities. Mietzner and Lahr explore the role of a universitybased maker spaces as intermediary, broker, and also facilitator for knowledge and technology transfer. Based on a case study of a German maker space they present how a maker space can enhance university-industry interaction, how it attracts new transfer partners, and how it can trigger new ideas for further exploration. Schultz, in the concluding chapter regarding the intermediary category, explores the impact of technology scouting on the spin-out activity in an entrepreneurial university. Main results are that technology scouting is beneficial but start-up activities have not been increased sustainably by technology scouting. Göbel, Gräfen, and Schultz make the case for intersectoral partnerships to find new solutions for game-changing events and new conditions. Part 2 of this edited volume contains 7 chapters which center around the technology transfer process. Piller, Hilgers, and Schmidthuber start the second half of the edited volume and discuss the mechanisms and consequences of new technology transfer instruments and approaches and depict their relevance for a more productive technology transfer. Rabbe, von Viebahn, and Auf der Landwehr demonstrate the role and impact of start-ups as supporters and initiators of sustainability in global value chains, based on an integrated single case study. Camargo, Morel, and Lhoste show that the utilization of an innovation voucher for booking consulting services at a French university has positive impacts on firms’ innovative capabilities, but also fosters the analytical skills and self-directed learning capabilities of students. Berndt and Mietzner provide an overview of knowledge and technology transfer methods and focus on a technology radar as a potential digital tool that facilitates knowledge and technology transfer. The chapter especially delivers insights how a technology radar can be developed. They incorporate aspects of user testing in the developing process in order to gain insights and feedback for the further development of this digital tool. Piller, Hilgers, Ihl, and Schmidthuber outline the use of open innovation platforms in innovation management that marks a rethinking from classical principles of coordination in innovation processes. This procedure offers completely new potential and opportunities for knowledge and technology transfer and gives access to knowledge held by third parties in new ways. Grote and Lindig propose an approach to create alignment between different stakeholders via a contextual innovation model that takes the perspectives of the various participants into account and contrasts them with goals derived from a central objective. Liening, Geiger, Haarhaus, and Kriedel focus on knowledge generation and investigate practices of university-based technology knowledge dissemination by reviewing current approaches and best practices. They propose a micro–macrolevel model in order to provide theoretical as well as practical implications to support the transition from inventions to innovations.

The Technology Transfer Challenge

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These available 14 chapters form a rich and exciting work on the different aspects of knowledge and technology transfer. We thank all authors and reviewers for their excellent contributions, without their commitment to quality this work would not have been possible. Furthermore, we are very thankful to Martina Konieczny, member of the Research Group for Innovation and Regional Development at the Technical University of Applied Sciences Wildau for her great efforts regarding the proofreading process and layout preparation of this edited volume.

References Arnkil, R., Järvensivu, A., Koski, P., & Piirainen, T. (2010). Exploring Quadruple Helix. Outlining user-oriented innovation models. Final Report on Quadruple Helix Research for the CLIQ project, Työraportteja 85/2010 Working Papers. ISBN 978-951-44-8208-3. Battistella, C., De Toni, A. F., & Pillon, R. (2016). Interorganisational technology/knowledge transfer: A framework from critical literature review. The Journal of Technology Transfer, 41, 1195–1234. Bush, V. (1945). Science: The endless frontier. Transactions of the Kansas Academy of Science, 48(3), 231–264. Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiling from Technology. Boston: Harvard Business Press. Cooke, P. (1992). Regional innovation systems: Competitive regulation in the new Europe. Geoform, 23, 365–382. Cooke, P., Boekholt, P., Schall, N., & Schienstock, G. (1996). Regional Innovation Systems: Concepts, Analysis and Typology. Conference paper, EU-RESTPOR Conference “Global Comparison of Regional RTD and Innovation Strategies for Development and Cohesion”, Brussels. Cooke, P., Uranga, M. G., & Etxebarria, G. (1997). Regional innovation systems: Institutional and organisational dimensions. Research Policy, 26(4–5), 475–491. Etzkowitz, H., & Leydesdorff, L. (1995). The Triple Helix—University–industry–government relations: A laboratory for knowledge-based economic development. EASST Review, 14, 14–19. Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: From National Systems and “Mode 2” to a Triple Helix of university–industry–government relations. Research Policy, 29(2), 109–123. Flatten, T. C., Engelen, A., Zahra, S. A., & Brettel, M. (2011). A measure of absorptive capacity: Scale development. European Management Journal, 29, 98–116. Hartmann, F., & Mietzner, D. (2019). Zukunft des Wissens- und Technologietransfers in der Region des Innovation Hub 13 im Jahr 2030: Rückblick und Status Quo (Teil I) Innovation Hub 13 working paper, Wildau. Lundvall, B. A. (1992). National systems of innovation: Towards a theory of innovation and interactive learning. London: Pinter Publisher. Lundvall, B. A. (2007). National innovation systems—Analytical concept and development tool. Industry and Innovation, 14(1), 95–119. Miller, K., McAdam, R., & McAdam, M. (2018). A systematic literature review of university technology transfer from a quadruple helix perspective: Toward a research agenda. R&D Management, 48(1), 7–24. Moulaert, F., & Sekia, F. (2002). Territorial innovation model: A critical survey. Regional Studies, 33(7), 289–302. Von Hippel, E. (2017). Free innovation. Cambridge and London: MIT Press.

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Wissenschaftsrat (Ed.). (2015). Zum wissenschaftspolitischen Diskurs über Große gesellschaftliche Herausforderungen (Positionspapier). https://www.wissenschaftsrat.de/download/archiv/459415.pdf?__blob=publicationFile&v=4, electronically accessed: 31 May 2020.

Actors

Inventions and Their Commercial Exploitation in German Universities: Analyzing Determinants Among Academic Researchers Teita Bijedi´c, Simone Chlosta, and Arndt Werner

Abstract Institutions of higher education are considered to be an important source of innovation and thereby a key driver of economic growth and development. Consequently, efforts are made to facilitate technology transfer from universities into the market. However, technology transfer in German universities does not seem to live up to its full potential: Using a sample of 7317 university scientists from 2013 covering 73 German universities, we find that while 18.5% of our scientists did in fact generate at least one invention, only 4.5% are actually engaged in commercialization activities. Based on this finding, we then analyze how individual, career-related, and institutional factors affect the innovation and knowledge transfer activities of male and female academics to understand why the vast majority of inventions remains commercially unexploited. We show that gender differences as well as career and human capital related factors (e.g., scope of employment, professional experience, and leadership position) affect innovation transfer activities. For example, while women generate fewer inventions than men, full-time employed researchers with professional experience outside of academia holding a leadership position generate

This chapter includes research insights and results presented in an earlier version hosted as a working paper in: Bijedi´c, T., Chlosta, S., & Werner, A. (2016). Inventions and their commercial exploitation in academic institutions: Analysing determinants among academics (Working Paper 04/16). Bonn: IfM Bonn. T. Bijedi´c Institut für Mittelstandsforschung (IfM), Maximilianstr. 20, 53111 Bonn, Germany e-mail: [email protected] S. Chlosta RKW Kompetenzzentrum, Düsseldorfer Straße 40 A, 65760 Eschborn, Germany e-mail: [email protected] A. Werner (B) University of Siegen, Unteres Schloss 3, 57072 Siegen, Germany e-mail: [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_2

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more inventions and show partly higher exploitation activities than the average scientist. We also find positive effects of institutional factors on innovation transfer activities: using the services of patenting agencies, for example, not only leads to the generation of inventions but also to stating intellectual property rights and commercially exploiting these inventions. Keywords Academic entrepreneurship · Gender · Commercial exploitation · Institutional context

1 Introduction Innovations are essential for economic growth and structural change. They are considered a job generator, especially in knowledge-driven societies (Guerrero, Urbano, & Fayolle, 2016). Moreover, much of the commercially utilizable and therefore highly valuable knowledge is created in institutions of higher education as research output (Audretsch, 2014). Therefore, these institutions make great efforts to establish and incorporate services and infrastructure to facilitate the knowledge transfer to the private sector and thus the commercial exploitation of inventions (Algieri, Aquino, & Succurro, 2013; Cunningham & Link, 2015; Meoli & Vismara 2016). Bijedi´c, Maaß, Schröder, and Werner (2014) can show, for example, that 96% of institutions of higher education in Germany have some kind of facilitating infrastructure for knowledge transfer and start-up activities. These efforts predominantly target disciplines like science, mathematics, engineering, and informatics, so-called STEM field, since the research output within these fields is usually associated with patentable and marketable inventions (Abreu & Grinevich, 2013; Czarnitzki, Rammer, & Toole, 2013; Fini & Toschi, 2016). To foster a cultural shift to a more friendly environment for such knowledge transfer activities, Germany has modified the law regarding the ownership of inventions made with federal funding similar to the Bayh-Dole Act in the US. The goal was to facilitate the process for the researchers as well as to find an additional source of revenue for the institutions of higher education (Von Ledebur, Buenstorf, & Hummel, 2009). The statute originally provided university professors with unrestricted rights to use and commercialize inventions. With the mentioned amendment, the property rights of university research results swapped from the individuals to the institutions. From there on, the legally protected (e.g., as patents) and commercially exploited research outputs belong to the institution and the inventor receives 30% of the gross commercialization revenues. In exchange, the institution bears all costs for patenting and acquires potential partners from the industry to facilitate the commercial exploitation of the patentable research output (Cuntz, Dauchert, Meurer, & Philipps, 2012). However, despite a comprehensive infrastructure and this statutory reform for facilitating commercial exploitation of research, it seems that even inventions of great commercial potential still remain unexploited in universities (Bijedi´c et al.,

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13

2016; Cuntz et al., 2012). For example, the amount of patents claimed by institutions of higher education dropped by 25% almost in a decade since the mentioned statutory amendment was launched (Cuntz et al., 2012). Aside from the fact that the patenting potentials of available research may be exhausted, and the financial incentives are decreased for the researchers (Antonioli, Nicolli, Ramaciotti, & Rizzo, 2016; Rizzo, 2015; Schmoch, 2007), high-ranked publications become more important and are often prioritized over knowledge transfer activities (Cuntz et al., 2012; Wright, Piva, Mosey, & Lockett, 2009). Furthermore, only every second academic with a start-up idea and entrepreneurial propensity is willing to advance her/his idea (Bijedi´c et al., 2014) and one out of three nascent entrepreneurs who already started with their entrepreneurial activities do not follow up a year later (Werner, 2011; for similar results Bijedi´c et al., 2016). Based on these findings that innovations and their potential commercialization at German institutions of higher education are not being exploited despite the abovementioned reforms, the chapter at hand tries to identify the gaps and barriers of these untapped potentials. Previous research shows that innovation transfer activities—which—according to our definition—includes the three stages: (a) generating inventions, (b) stating intellectual property rights of these inventions, and (c) commercially exploiting them—is determined by specific internal and external context variables (Kuckertz, Kollmann, Krell, & Stöckmann, 2017; Polkowska, 2013). Especially gender is an often discussed (internal) determinant within innovation research (Alonso-Galicia, Fernández-Pérez, Rodríguez-Ariza, & FuentesFuentes, 2015; Brink, Kriwoluzky, Bijedi´c, Ettl, & Welter, 2014; Fernández-Pérez, Alonso-Galicia, Fuentes-Fuentes, & Rodriguez-Ariza, 2014; Maes, Leroy, & Sels, 2014; Nählinder, Tillmar, & Wigren-Kristoferson, 2012; Pecis, 2016; Shinnar, Hsu, & Powell, 2014; Tonoyan & Strohmeyer, 2006). Gender differences are not only prevalent regarding innovation activity, but also regarding decisions preceding and determining innovation activity, e.g., career choices and preferences for specific fields of study. Women, for example, are underrepresented in disciplines associated with higher innovation activity, like science, informatics, or engineering (Becker, Grebe, & Lübbers, 2011; IWD, 2018; Thebaud & Charles, 2018), as well as pursuing a career in academia or research (Weisgram & Diekman, 2017). Furthermore, when engaging in research, women seem to rarely take part in patenting research results as well as in participating in university start-ups (Bunker-Whittington & Smith-Doerr, 2008; IWD, 2019; Lawton Smith, Henry, Etzkowitz, Meschitti, & Poulovassilis, 2015). Because the majority of studies analyzing effects on innovation activity use singular determinants (e.g., gender, field of study) or apply small samples (e.g., within a certain field of study), we believe there is a need for a broader view on what drives innovation as well as commercialization activities of university researchers in Germany. Thus, in order to close a research gap, our study incorporates individual, career-related as well as institutional determinants of innovation activity in a holistic manner and among a wide range of academic fields incorporating the information of 7317 university scientists from 73 German universities. The chapter is organized as follows: In the next section, we will discuss the relevant research literature and formulate three research questions which may help to explain

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why so many inventions remain commercially unexploited. Section 3 focuses on the empirical analysis of these research questions. Finally, in Sect. 4, we shortly discuss our results and make some concluding remarks.

2 Literature Review and Research Question In the following, we theoretically and empirically derive a hierarchy of steps toward innovation activity from an individual’s perspective. The first and crucial step illustrates “whether to innovate at all” which is considered a step of opportunity recognition (OR) in order to generate an invention. This first step differentiates innovators from non-innovators and is considered to be a cognitive process linked to creative and strategic thinking (Correia Santos, Caetano, Baron, & Curral, 2015; Kuckertz et al., 2017; Shane & Nicolaou, 2015). Thereby it ought to be determined by human capital and career-related factors (e.g., knowledge, occupational history) and jobrelated preferences (Vohora, Wright, & Lockett, 2004). The subsequent two steps demonstrate the process of opportunity exploitation (OE), i.e., the commerciallyoriented interest to protect and further exploit the generated invention. It can be seen as a market-oriented behavior (Davidsson, 2006; Kuckertz et al., 2017). OE is therefore predominantly affected by institutional and market-related factors (e.g., environmental conditions) and individual factors like gender or risk-taking propensity (Bijedi´c et al., 2014; Kuckertz et al., 2017). Figure 1 provides an overview of our research model.

2.1 Individual Factors In general, a lower amount of women remains in the academic sector and pursues an academic career (Svinth, 2006). Furthermore, the majority of studies reveal that women are still underrepresented when it comes to generating an invention (BunkerWhittington & Smith-Doerr, 2008; Colyvas, Snellman, Bercovitz, & Feldman, 2012). Gender differences are also existent regarding the exploitation of inventions. The so-called pipeline leak shows that fewer women register patents than men (BunkerWhittington & Smith-Doerr, 2008; Hahn, Eddleston, & Minola, 2019; IWD, 2019). This is due to several reasons. To generate inventions, the human capital of the scientist is crucial. However, female academics have more interruptions in their work-life history than their male counterparts, which leads to a reduced human capital stock (Polkowska, 2013). The number of inventions produced and registered by universities reflects this (Lawton Smith et al., 2015; Thursby & Thursby, 2005). Another individual factor that also influences human capital endowments and thus affects innovation activities is the age of the potential inventor. With increasing age, the individuals gain more work

Inventions and Their Commercial Exploitation in German …

15

INNOVATION ACTIVITY

Invention First stage

Individual factors Gender Age Nationality Risk taking propensity

Commercial property rights

Commercial exploitation

Second stage

Third stage

Career-related factors Field of study Job title Leadership position Research activity Research focus Occupational history Sidline business Scope of work

Institutional factors Institution of higher education Technology transfer bureau Patenting agency

© IfM Bonn 15 1508 001

Fig. 1 Model of analysis: stages of innovation activity and categories of their determinants (Source Based on Bijedi et al. [2016])

experience and thus a broader and more defined human and social capital basis (Haeussler & Colyvas, 2011; Murray, 2004; Perkmann et al., 2013). Another potential individual driver of innovation activity is the nationality of the innovator. From entrepreneurship research, we know that foreign researchers show higher founding intentions compared to German researchers (Bijedi´c et al., 2014). A similar tendency can be expected for innovation activity. Bijedi´c et al. (2014) for example, can show that founding intentions of researchers are only stated if own inventions were generated before. From innovation research in existing firms, we can learn that companies with an above-average number of migrants generate a higher innovation output (Welter, Bijedi´c, & Hoffmann, 2015). This leads us to the assumption that the nationality of academics affects their innovation activity. Finally, innovation activity is considered a risky behavior which can lead to failure (Caliendo, Fossen, & Kritikos, 2014; Rasmussen, Mosey, & Wright, 2014). The patenting usually occurs at an early stage of the product development where the innovator cannot foresee the successful exploitation and final market entry of her/his invention (Jensen & Thursby, 2001; Jones & Bouncken, 2008; Shneor, Metin Camgöz, & Bayhan Karapinar, 2013). Therefore, the level of risk-taking propensity is expected to have an effect on the opportunity recognition and exploitation processes.

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Overall, for our research model, we include the above-mentioned individual factors: gender, age, nationality, and risk-taking propensity and test their effects on the different steps of innovation activity. In other words, we derive the following explorative research question: Research Question 1: How do the above mentioned individual factors affect the different steps of innovation activity, from opportunity recognition to opportunity exploitation?

2.2 Career-Related Factors With regard to the career-related factors, we focus in this paper on the field of study, research activity as part of the working contract and scope of employment (i.e., contractual and factual working hours) which are expected to have an effect on the researcher’s innovation activity. Moreover, we also test for the following careerrelated effects: job title (e.g., professor), leadership position, research focus (i.e., basic, applied, or multidisciplinary research), occupational history, and potential sideline business. Previous research shows that the innovation activity strongly depends on the particular field of study which partially determines if the research results are patentable and thus can be commercially protected and exploited. Within the STEMfields (science, technology, engineering, and mathematics), patents are looked upon as a valid measure for innovation activity, while the research output in other fields like the humanities often does not meet the requirements of patenting (Hahn et al., 2019; Pohlmann, 2010; Thebaud & Charles, 2018). According to the requirement of “absolute novelty” for patents in Germany the publication of research in academic journals (which is another possible exploitation of research output) eliminates the opportunity of additional patenting of these results (BMBF, 2002). Thus, technologyoriented fields like STEM provide a more suitable environment for the commercial protection and exploitation of research output than other fields of study (Abreu & Grinevich, 2013; Fini & Toschi, 2016). This leads to the assumption that the different fields of study have an effect on the innovation activity. Regarding the distribution of gender within the different fields of study, it stands out that women outweigh men in humanities, arts and cultural sciences, as well as in life sciences. However, men still dominate the STEM-fields (GWK, 2014; IWD, 2019). In the recent past, academics face an area of conflict as they have to juggle teaching activities, research activities, and the exploitation of their research results (Glauber et al., 2015; Hahn et al., 2019; Jain, George, & Maltarich, 2009; Obschonka, Silbereisen, Cantner, & Goethner, 2015). The scope of employment which comes along with a certain amount of time spend at work not only influences the opportunity recognition of the academics but also their opportunities of exploiting research results (Moog, Werner, Houweling, & Backes-Gellner, 2015). Thus, with limited working time the necessity to teach and publish research can hinder or at least postpone innovation activities (Chang & Yang, 2008; Czarnitzki, Rammer, & Toole, 2014; Heller & Eisenberg, 1998; Neves & Franco, 2018).

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17

In a similar vein, we expect that it makes a difference whether research activities are part of the working contract of the academic researchers or not. For creative output not only a certain amount of working time is needed but also the “permission” to use this working time for research and innovation. Summing up, we expect careerrelated factors to have an effect on innovation activity and therefore derive the second explorative research question. Research Question 2: How do the above discussed career-related factors affect the different steps of innovation activity, from opportunity recognition to opportunity exploitation?

2.3 Institutional Factors Besides teaching and research, a “third mission” which stands for transferring knowledge from different research fields to private industry has been integrated into the functions of academic institutions (Visintin & Pittino, 2014). Meanwhile, technology transfer is included in the state law as part of the activities of universities and colleges (Cuntz et al., 2012; Czarnitzki et al., 2013). However, this still resembles a challenge for academic institutions as they have to create an infrastructure which not only facilitates excellent research and teaching but also the exploitation of research results (Chang & Yang, 2008; Hahn et al., 2019; Heller & Eisenberg, 1998). When comparing the different academic institutions, we find that at universities research activities are part of the job profile of scientists while at institutions of applied sciences this is not the case. To actively invest in the infrastructure of technology transfer, many academic institutions installed so-called centers for technology transfer and patent exploitation agencies to assist in the innovation exploitation process. These agencies and support centers help the academics in the process of protecting and commercially exploiting their inventions (Brettel, Mauer, & Walter, 2013; Fini, Fu, Mathisen, Rasmussen, & Wright, 2017). Especially when it comes to patenting, these agencies not only test whether the inventions fulfill the requirements for patenting but also whether or not they have the potential of economic exploitation (Schmoch, 2007; Siegel, Veugelers, & Wright, 2007; Siegel, Waldman, Atwater, & Link, 2004; Sugimoto, Ni, West, & Lariviere, 2015). The above-mentioned support structures positively affect the knowledge and technology transfer in academic institutions and stimulate academics to commercially exploit their research results (Bijedi´c et al., 2014, 2016; Chang, Yang, & Chen, 2009). With the university’s growing experience in the patenting process, an increase in the registration of patents can be found. This is due to the growing ability to recognize promising inventions and research ideas (Foltz, Kwansoo, & Barham, 2003; Glauber et al., 2015; Huelsbeck & Menno, 2007; von Ledebur et al., 2009). Therefore, we derive the third explorative research question. Research Question 3: How does the institutional infrastructure that facilitates knowledge and technology transfer affects the different steps of innovation activity, from opportunity recognition to opportunity exploitation?

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3 Data and Methodology 3.1 Data Set As a database, we use a survey that was conducted among 36,918 academics in November and December 2013 in 73 randomly selected German institutions of higher education from a variety of disciplines. We included all hierarchical levels of academic staff. Our final sample base for consists of 7317 completed questionnaires.

3.2 Variables and Methodology Our dependent variable is innovation activity, which we operationalized as a threestage dichotomous variable including: having generated an invention (first stage), having claimed patenting or other protection of intellectual property (second stage) and, finally, having commercially exploited the invention(s) (third stage). In the following, we refer to all three activity stages as innovation activity. Overall, we found that 18.5% of the sample did in fact generate at least one invention. 12.5% secured property rights of their invention, but only 4.5% commercially exploit these. In order to analyze the various influences on innovation activity, we include the above-discussed three categories of determinants which—based on previous empirical findings stated above—are expected to have an impact on innovation activity of academics. Within the category of individual factors, we include the variables gender, age, nationality, and risk-taking propensity. The variables job title (e.g., professor), leadership position, research activity as part of the working contract and research focus (basic, applied, or multidisciplinary), as well as occupational history, current sideline business and scope of employment (i.e., working hours) constitute the category career-related factors which have a critical impact on human capital. This category also contains the variable field of study, which we subsumed into seven sub-categories: STEM (Science, Technology, Engineering, and Mathematics), life sciences (e.g., medicine, psychology, and health management), economics (e.g., business studies and similar disciplines), architecture, creative studies (e.g., music, arts, design, and communication) and humanities (including social sciences and law). Finally, the category institutional factors consists of the type of academic institution (university vs. school of applied science), and the infrastructure supporting the knowledge transfer within each institution (technology transfer bureau and patenting agency). Table 1 summarizes our dependent and independent variables. We analyze the data using probit regression modeling. Since we regress three stages of innovation activity (invention, commercial protection, and commercial exploitation) on the introduced innovation drivers, we separately estimate three specifications of the empirical model. An overview of all included variables with their pairwise correlations can be found in Table 2.

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19

Table 1 Operationalization of variables and descriptive statistics Mean

(SD)*

Dependent variables Invention

Scientists have realized at least one invention during their tenure at the university: Yes = 1, No = 0

.19

(.39)

Commercial protection

Scientists who have realized at least one invention during their tenure at the university have also commercially protected at least one of these inventions: Yes = 1, No = 0

.13

(.33)

Commercial exploitation

Scientists who have commercially protected at least one invention have also commercially exploited at least one of these inventions: Yes = 1, No = 0

.04

(.21)

Gender

Gender: Female = 1, Male = 0

.32

(.47)

Age

Age of scientist (in years)

37.32

(.13)

Nationality

Nationality: Foreign = 1, German = 0

.10

(.30)

Risk taking

Risk attitude: (very) high = 1, not (very) high = 0

.19

(.39)

Field of study

Field of study: Science, Technology, Engineering, Mathematics (STEM Fields) = 1, Other = 0

.71

(.45)

Position

Vocational position: Professor = 1, Other =0

.18

(.38)

Leadership position

In charge of supervising staff: Yes = 1, No =0

.26

(.01)

Research activity

Research is a contractually stipulated part of the working contract: Yes = 1, No = 0

.76

(.43)

Basic research

Focus on basic research: Yes = 1, No = 0

.41

(.49)

Applied research

Focus on applied research: Yes = 1, No = 0

.56

(.01)

Multidisciplinary research

Focus on multidisciplinary research: Yes = 1, No = 0

.43

(.01)

Occupational history

Prior job experience in and outside of academia: Yes = 1, No = 0

.57

(.01)

Sideline business

Scientist has a second job assignment in addition to the current job at the university: Yes = 1, No = 0

.29

(.01)

Scope of work

Weekly working (in hours)

Independent variables

32.68

(.13) (continued)

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T. Bijedi´c et al.

Table 1 (continued) Mean

(SD)*

Institution of higher education

Type of the institution of higher education: University = 1, Schools/Colleges of Applied Sciences = 0

.80

(.40)

Patenting agency

Used services of a patenting agency: Yes = 1, No = 0

.05

(.22)

TTO

Used services of a technology transfer bureau: Yes = 1, No = 0

.06

(.24)

*SD Standard deviation

We find that the correlation between the explanatory variables is only of moderate size. Thus, multicollinearity should not be an issue in this study. To convey the results in an understandable way, we report the probability of a researchers’ likelihood of realizing one of the three stages of innovation activity compared to those who did not realize the particular stage of innovation activity (average marginal effects).

4 Results The results of the probit regressions are reported in Tables 3 and 4. Our analyses show that gender strongly influences the first step of innovation activity, i.e., women are significantly less likely to generate inventions than men (dF/dx = −.062). However, we find no significant gender effects regarding commercial property rights (dF/dx = −.014) and commercial exploitation of the invention (dF/dx = −.002). In sum, these results partly support our arguments that female scientists are less innovative than their male counterparts. In addition, age has a significant positive effect on the generation of inventions (dF/dx = .003) and their protection (dF/dx = −.007). On average, the probability to generate an invention raises for .03 percentage points and for .07 percentage points to commercially protect the invention, for every year in life. This is an expected outcome considering the growing professional experience and skills conducive to innovation activity. We also find that non-German academics generate significantly more inventions than German scientists (dF/dx = .072), but do not protect or exploit their inventions significantly more than German researchers. This result is partially in line with previous research, which provided evidence that foreign scientists also have higher propensity of founding a new venture, and by doing so, exploiting knowledge from the university environment (see, e.g., Bijedi´c et al., 2014) due to the fact that academic start-ups are predominantly based on a previously generated invention. However, our findings do not provide evidence for higher exploiting activities of inventions. Within our sample, a high risk-taking propensity leads to a significantly higher likelihood to generate inventions (dF/dx = .036): On average, the probability to

Significance level: *(.1); **(.05); ***(.01)

Table 2 Pairwise correlations

Inventions and Their Commercial Exploitation in German … 21

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Table 3 Probit regression results (basic model) Basic Model Invention (Yes) dF/dx

Commercial property rights (Yes)

z-value dF/dx

Commercial exploitation (Yes)

z-value dF/dx

z-value

Individual factors −.42

−.020

−.45

Age (max. 50)

.003***

5.93

.007***

4.69

−.001

−.83

Nationality (Foreigner)

.072***

5.03

.058

1.72

.027

.59

Risk: (very) high

.036***

3.41

−.016

−.55

.053

1.47

.117***

11.70

3.10

.047

.88

.56

−.015

−.34

Gender (female)

−.062*** −6.57

−.014

Career-related factors Field of studies (STEM) Position (Professor)

−.031**

−2.39

.118*** .021

Leadership position

.084***

7.81

.115***

4.06

. 069*

Research activity ([fully] applies)

.046***

3.72

.031

1.89

.78

.067

1.32

Basic research ([fully] applies)

.011

1.17

−.046*

−1.67

−.039

−1.10

Applied research ([fully] applies)

.069***

6.97

.038

1.28

.070*

1.82

Multidisciplinary research ([fully] applies)

.019**

2.10

−.008

−.31

.077**

2.31

Occupational history (Yes)

.016*

1.71

−.064**

−2.37

.112***

3.05

Sideline business (Yes)

.030***

2.92

.052*

1.86

.086**

2.43

Scope of work (full time)

.001***

2.76

−.001

−.52

−.004**

−2.06

.024*

1.78

.053

1.34

−.057

−1.16

Institutional factors Institution of higher education (University) TTO

.053***

2.69

Patenting agency

.361***

13.47

N Log pseudolikelihood Pseudo-R2

7.317 −2.888.4 .176

.078*

1.86

.044

.149***

4.05

.135***

1.355 −768.7 .102

912 −567.6 .073

Note Average marginal effects and z-values; Significance level: *(.1); **(.05); ***(.01)

.94 3.09

Inventions and Their Commercial Exploitation in German …

23

Table 4 Probit regression analyses (extended models) Invention (Yes) dF/dx

Commercial property rights (Yes) z-value dF/dx

Commercial exploitation (Yes)

z-value dF/dx

z-value

Extended model: fields of studies (reference category: STEM) Economics

−.136*** −1.45

−.068

−1.22

−.307*** −3.28

−.040

Architecture

−.097*** −3.94

Life sciences

−.023

−.75

.057

.68

.065

.59

Arts and media

−.117*** −3.99

.037

−.32

.131

.87

N

.119*** −4.52

−.231*

1.93

−.293 −1.62

−.210*

−1.94

−.226 −1.38

7317

1355

Log pseudolikelihood −2878.0 Pseudo-R2

−1.02

−.105*** −3.53

Humanities Other

−3.28

−.50

912

−762.6

.179

−564.6

.120

.078

Extended model: position (reference category: Professor) Post-doctoral position

.045***

2.84

.059

1.41

.013

.25

Pre-doc/assistant

.024*

1.65

−.040

−.92

.019

.35

Other

.027

1.56

−.095*

−1.76

.014

.21

N Log pseudolikelihood Pseudo-R2

7.317 −2.886.9 .177

1.355 −762.8 .109

912 −567.6 .073

Note Average marginal effects and z-values; Significance level: *(.1); **(.05); ***(.01)

generate an invention raises for 3.6 percentage points when the academic has a high risk-taking propensity—all things equal. However, it has no significant impact on the commercial protection and exploitation activities, which we found surprising given the previous findings that opportunity exploitation depends highly on environmental factors and is therefore more affected by the perception, assessment, and tolerance of potentially risky circumstances. We also find significant positive effects of several career-related factors on the innovation activity. Academics engaged in STEM-fields are most likely to generate inventions (dF/dx = .117), followed by life sciences. On average, the probability for scientists in STEM-fields to generate an invention is 11.7 percentage points higher than for academics in other fields of studies. However, STEM-academics are not more likely to commercially protect or exploit these inventions. Furthermore, contractually stipulated research activities have a significant fostering effect on invention activities (dF/dx = .046), as well as the research focus: An applied (dF/dx = .069) or multidisciplinary research focus (dF/dx = .019) leads to significantly more inventions and their commercial exploitation than basic research. Moreover, we also find that a higher scope of work (dF/dx = .001), a postdoctoral (dF/dx = .045) (cf. Table 4) as well as a leadership position (dF/dx = .084) have

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T. Bijedi´c et al.

a positive effect on generating, but not on commercially protecting or exploiting inventions: On average, full-time employed academics are .1 percentage points more likely to generate inventions than their part-time employed colleagues. Furthermore, academics in leadership positions are also about 8.4 percentage points more likely to invent than academics with no leadership responsibilities. Finally, a wider scope of occupational experience, i.e., a previous job experience (dF/dx = .016) as well as a current sideline business outside the university (dF/dx = .030), has a significant positive impact on the innovation activity in general. Finally, we found significant influences of all analyzed institutional factors on the likelihood to generate an invention. Academics working at universities are more likely to generate inventions than those working at institutions of applied sciences (dF/dx = .024). Additionally, academics who use the services of patenting agencies (dF/dx = .053) and technology transfer bureaus (dF/dx = .361) are more likely to engage in invention activities as well as opportunity exploitation activities.

5 Discussion In the presented study, we have analyzed potential drivers and barriers of innovation activity of academics. With our unique and representative sample containing data from over 7300 academics in 73 German institutions of higher education, our study simultaneously has tested for the effects of individual, career-related, and institutional conditions on the different stages of innovation activity of female and male academics. Based on our results, we draw the following profile of a successful innovator: A typical innovator is predominantly male, active within STEM-disciplines, in a postdoctorate position, non-German, in a leadership position and full-time employed. Furthermore, he has a high risk-taking propensity and possesses work experience outside of the university (e.g., a current sideline business or previous job experience). He is also focused on multidisciplinary and applied research rather than basic research. These findings are partly in line with the results of Haeussler and Colyvas (2011), who also found that senior male academics with close entrepreneurial orientation possess sufficient material and social resources and are more likely to engage in various entrepreneurial activities. Our study reveals that women generate significantly less inventions than men, in general as well as within each field of study. The results indicate that these differences between female and male academics cannot only be explained by gender-specific preferences for certain disciplines, for example, an underrepresentation of women in highly innovative STEM-fields but also by additional innovation drivers. Generally, male and female academics are driven by distinct motivations and perceive the support received and challenges encountered differently (Alonso-Galicia et al., 2015; Fernández-Pérez et al., 2014). When inventions already exist, no gender differences regarding commercial protection or commercial exploitation of the inventions can be found. This contrasts

Inventions and Their Commercial Exploitation in German …

25

previous research, where women are claimed to have less propensity to engage in commercialization activities (like, e.g., start-ups) than men (Bijedi´c et al., 2014). A similarly surprising finding relates to the effect of risk-taking propensity on innovation activities. Previous research indicated that commercial protection and exploitation of inventions as a market-related process depend more on environmental factors and personal characteristics compared to the generation of inventions. However, our results indicate that a high risk-taking propensity fosters the first step of invention activity and has no significant impact on opportunity exploitation. In addition, we find positive effects of several career-related factors on the innovation activity. An applied or multidisciplinary research focus as well as a leadership position has a significant fostering effect on creating invention(s). Moreover, a wide range of career experiences, in form of previous employments or a current sideline business outside the university, have an impact on innovation activities in general—regarding opportunity recognition as well as opportunity exploitation. These results emphasize the importance of outer-university professional experience on inner-university innovation activity—especially concerning the commercial exploitation activities. This leads to more market-related experience as well as a wider social network crucial to exploitation activities (Fernández-Pérez et al., 2014; Hayter, 2016; Jensen & Thursby, 2001). When comparing the different academic positions, researchers in post-doc positions are most likely to generate inventions. However, this advantage disappears for the commercial protection and exploitation of inventions. Since requirements for tenured positions at universities focus mainly on highly ranked publications, which are considered the main criteria for research excellence, post-doctorate researchers, mostly short-term appointed, feel the pressure to engage in publishing rather than in knowledge transfer into the market. Once the research results are published, they are not patentable anymore, that implies that in our current system academic publishing prevents patenting and commercialization. Using the services of technology transfer bureaus or patenting agencies goes along with a high innovation activity, which makes sense, given that in order to use these services, the researchers need to have generated patentable or market-relevant invention(s). While the positive effect of using services of technology transfer bureaus decreases on the next stages, the effect of patenting agencies remains strong on all stages of the innovation transfer activities. Last but not least, our results indicate that academics at institutions of applied sciences, where they have a higher teaching load as integral part of their employment contracts, are less likely to engage in innovation activity. Several implications could be drawn based on our findings. Firstly, considering male and female academics are driven by distinct motives and perceive support and challenges differently, it is necessary for university administrators to treat them differently. We recommend encouraging female academics early in their career to engage in research activities across disciplines as well as making research activities a contractually stipulated part of their job profile regardless of the kind of institution. For universities attempting to increase innovation and entrepreneurial involvement

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among female academics, “gender sensitive programming” should be developed (Díaz-García & Jiménez-Moreno, 2010). Secondly, with regard to tenure and promotion policies, academics’ promotion and tenure assessments are still primarily based on scientific productivity and quality such as publications. Such orientation constrains the entrepreneurship involvement of academics, particularly those who are younger and non-tenured. Hence, to encourage academics to participate in commercialization activities, university administrators should reconsider the existing promotion policies and consider adjusting reward systems by including more entrepreneurial accomplishments as measurable indicators for promotion and tenure (Clarysse, Tartari, & Salter, 2011; Huyghe & Knockaert, 2015). Thus, we recommend partially revising and broadening the requirements of tenure positions within universities, e.g., by acknowledging achievements in knowledge transfer and market-relevant experience in addition to the number of publications. Junior as well as foreign researchers need to increase their awareness of opportunities for patenting and knowledge transfer. Patenting agencies as well as technology transfer bureaus provide support for commercial protection and exploitation of technology-based innovations. In order to recognize and exploit more opportunities for knowledge transfer, these agencies might offer services for a wider range of innovations. Therefore, the knowledge transfer practice in German institutions of higher education might profit from a broader and more current definition of innovation, as already perpetuated in the contemporary academic discussion. As always, also this study is not without limitations. The central limitation is that the data is of cross-sectional nature and that the researchers self-report on the innovation steps taken. Therefore, future research could investigate the innovation steps with focus on using longitudinal data. Moreover, the questions raised need to be embedded more strongly into a theoretical framework and should also be tested across different institutional and cultural environments (e.g., other regions in Europe). Thus, we encourage future research to study the process of inventions and their commercial exploitation at universities by focusing on these limitations. Acknowledgements Thanks to Xiangyu Chen for his helpful support. This study is based on the Survey on Potential Drivers of Entrepreneurial Activities of Academics in Germany (Hochschulbefragung des IfM Bonn) from the Institut für Mittelstandsforschung (IfM) Bonn. All results have been reviewed to ensure that no confidential information is disclosed. The authors’ program codes will be provided upon request. Any errors are our own. This paper reflects the opinions of the authors and not necessarily those of the Institut für Mittelstandsforschung (IfM) Bonn.

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Do Startups Benefit from Incubation? An Analysis of Startups’ Absorptive Capacity Constantin Schmutzler and André Presse

Abstract The German startup ecosystem and the number of support programs continue to grow. Differences in innovative power and adaptability between incubated and non-incubated startups have been found, but so far no comparison has focused on the impact of incubation on their absorptive capacity (ACAP). This study uses the multidimensional approach of Flatten, Engelen, Zahra, and Brettel (EMJ 29: 98–116, 2011), to analyze and compare the ACAP of incubated and non-incubated startups. By means of a quantitative analysis, data has been collected through an online questionnaire from a sample of 66 German startups (36 incubated, 30 non-incubated) and their (co-)founders or Chief Executive Officers (CEOs). The results indicate that incubated startups have a significantly higher ACAP than non-incubated startups. This study provides an insight into how ACAP in general as well as its components differ between incubated and non-incubated startups. Based on the results, we conclude that support programs have a positive impact on startups. Keywords Absorptive capacity · Incubation · Startups · Entrepreneurship · Ecosystems

1 Introduction and Literature Review 1.1 Problem Statement and Research Goal The number of startup incubators in Germany is growing: In 2018, the German Federal Ministry of Economics and Energy identified 56 incubators in Germany, most of them located in Berlin (37% of all incubators), Hamburg and Munich (Zinke et al., 2018). Previous research in this field provides a range of different analyses of how incubators can operate successfully (Bergek & Norrmann, 2008; O’Neal, 2015) and what kind of impact they have on the development of startups (Mian, Lamine, & Fayolle, 2016): C. Schmutzler · A. Presse (B) SRH Hochschule Berlin, Ernst-Reuter-Platz 10, 10587 Berlin, Germany e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_3

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C. Schmutzler and A. Presse […] incubated firms have a more educated workforce [and] a significantly greater probability of adopting technological innovations. The greater educational achievements and professional skills of both entrepreneurs and employees of incubated start-ups clearly increase the “absorptive capacity” (Cohen, and Levinthal, 1990) of these firms. (Colombo & Delmastro, 2002, p. 1117)

Some research indicates that incubated startups have an increased Absorptive capacity (ACAP). Patton (2013) conducted a research on ACAP of incubated firms and concluded: “Future research needs to concentrate on these dynamic practices that enable founders to combine new and existing knowledge […]” (ibid., p. 913). Moreover, existing analyses are limited to unidimensional approaches or smalland middle-sized companies. A comparison between incubated and non-incubated startups on a multidimensional level of ACAP has not been executed so far. This study focuses on absorptive capacity of startups in different environments. By analyzing the ACAP of incubated and non-incubated startups, the comparison shows that incubation improves ACAP in startups.

1.2 The German Startup Ecosystem The German startup ecosystem has developed rapidly in recent decades and the number and size of investments have considerably increased over the last years (Ernst & Young GmbH, 2019b). The ecosystem as a whole, as well as in the individual federal states, is now regarded as one of the strongest in Europe (European Startup Initiative, 2018). For this study, the definition for startups by the yearly recurring German Startup Monitor (Kollmann, Hensellek, Jung, & Kleine-Stegemann, 2018) is referred to: ● A Startup is maximum 10 years old, ● has (planned) employee/sales growth ● and/or has (highly) innovative products/services, business models and/or technologies. A similar definition was applied for a major study about the support landscape for startups on behalf of the German Federal Ministry of Economics and Energy and thus underlines the successful applicability (Zinke et al., 2018). Zinke et al. (2018) provide a more accurate evaluation. They have identified more than 1130 offers to support startups in Germany in their latest study. Among others, they include 56 incubators, 121 accelerators, and 309 Technology and Founders Centers (TFC). Depending on the program, they support startups at different stages of their lifecycle (Fig. 1 includes the three most prominent support systems). Figure 1 outlines the similarity of the different support programs. Incubators focus on the first three stages (pre-seed, seed, and startup phase), accelerators focus on the second up to the fourth (development) stage. Technology and Founders Centers have similar characteristics to Incubators (ibid.). In the further course of the work, it will

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Incubators (n= 56), Accelerators (n= 121), Technology and Founders Centers (n= 309) Fig. 1 Support programs in the startup lifecycle (based on Zinke et al. [2018])

therefore be discussed whether the study should include other support systems in addition to incubators.

1.3 Models of Incubation There are several approaches to the origin of incubation. For example, Hausberg and Korreck (2018) offer a wide range of definitions by analyzing papers from the last 30 years. The European Commission describes them as follows: “A business incubator is an organization that accelerates and systematizes the process of creating successful enterprises by providing them with a comprehensive and integrated range of support […]” (European Commission, 2002, p. 3). The words “incubator” and “accelerator” are sometimes confused. In this chapter, we use “Incubation” for the sum of all support programs for startups. As a result, this study does not consider incubators and accelerators. Whether other programs should also be considered will emerge in the course of further elaboration.

1.4 Impact of Incubation on Startups Mian et al. (2016) offer a systematic literature review from papers written between 1985 and 2014 and conclude a variety of approaches showing the multi-disciplinarity

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of the research field. The results are inconclusive as they support both claims: a significant impact as well as no (long-term) impact of incubation on startups (Barbero, Casillas, Ramos, & Guitar, 2012; Hansen, Chesbrough, Sull, & Nohria, 2000; Mian, 1997; Mian et al., 2016; Schwartz, 2010; Westhead, 1997). Soetanto and Jack (2016) have analyzed spin-offs from university-based incubators across the UK, Netherlands, and Norway resulting in the evidence that an ambidextrous strategy (exploiting technologies while exploring a new market to increase revenue) has a positive impact on performance. In addition, the study found that networking strengthens the relationship between innovation and performance. According to the authors, a strategy to exploit the market and the inherent technologies has a more positive (and stronger) effect on the performance of spin-offs. Barbero et al. (2012), on the other hand, claimed that startups from private incubators perform significantly better than others. Cohen and Levinthal (1989, 1990) analyzed the impact of ACAP on innovation and learning found that employees of incubated firms increase the absorptive capacity of their firms. This is one of the major connections between the theoretical framework and the impact of incubation on startups. Later, Colombo and Delmastro (2002) claimed interrelations to Cohen and Levinthal (1989, 1990) as mentioned above (see Sect. 1) by stating that higher educated employees of incubated startups increase their startups’ ACAP. Moreover, they confirm that there are differences between incubated and non-incubated startups: They analyzed the effectiveness of incubators in Italy and concluded that incubated startups tend to cooperate more, are more innovative (e.g., in the introduction of new products and services; cf. Barbero et al. [2012] and Díez-Vial and Montoro-Sánchez [2016]) and are more likely to adopt information. In summary, ACAP can significantly differ between companies. The following outline gives an insight into the historical development and how it became a muchdiscussed theory in the research about incubation.

1.5 Absorptive Capacity The concept of absorptive capacity has developed over the last three decades from a unidimensional approach to a multidimensional theory. The starting point was in the 1990s (Cohen & Levinthal, 1989, 1990). With increasing requirements and diversity in the scientific field, the theory evolved at the beginning of the new millennium. The latest studies covered scale development and validation of ACAP. Its history shows how the theory has developed over the last decades from an elusive concept to detailed operationalization.

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1.6 Early Definitions The term “absorptive capacity” is mostly cited from the research of Cohen and Levinthal (1989, 1990) in the context of Research & Development (R&D). Both authors argued that R&D not only generates innovations but is also responsible for a company’s ability to identify, assimilate and exploit knowledge from external sources. Moreover, they highlighted that ACAP is directly related to a firm’s innovative capability and that a well-developed absorptive and innovative capability can be a major advantage in an increasingly competitive environment (Cohen & Levinthal, 1990). During the 1990s, other researchers like Mowery and Oxley (1995) also offered definitions of absorptive capacity. Based on these definitions, a number of studies have shown the impact of ACAP on, for example, the business performance (Ahuja & Katila, 2001), level of innovation (Tsai, 2001), productivity (Cockburn & Henderson, 1998), inter-organizational learning (Lane & Lubatkin, 1998; Lane, Salk, & Lyles, 2001) as well as inter-organizational transfer of knowledge (Gupta & Govindarajan, 2000). Flatten, Engelen, Zahra, and Brettel (2011) have also addressed these concepts in their paper about ACAP. Even though the impact of ACAP can be versatile, studies seem to have one object of investigation in common: the quality and processing of knowledge. Due to several co-existing theories, scientists had the opportunity to choose the most suitable for their studies according to the different dimensions. A dilemma that Zahra and George (2002) evaluated in their study.

1.7 Reconceptualization Zahra and George (ibid.) analyzed several studies on ACAP and concluded a confusion in conceptualization and operationalization. As a result, they reconceptualized the definition of ACAP on four dimensions: The acquisition, assimilation, transformation, and exploitation of external knowledge. ● Acquisition denotes a company’s ability to recognize and acquire external knowledge; ● Assimilation describes the routines when understanding and processing information from external sources; ● Transformation refers to the capability to improve and refine the procedures when facilitating the combination of existing and newly acquired (assimilated) knowledge; ● Exploitation stands for developing, increasing, and leveraging existing or new capabilities by processing acquired and transformed knowledge into its operations (ibid.).

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Fig. 2 Model of potential and realized absorptive capacity (based on Zahra and George [2002])

[…] ACAP is viewed as a dynamic capability embedded in a firm’s routines and processes, making it possible to analyze the stocks and flows of a firm’s knowledge and relate these variables to the creation and sustainability of competitive advantage. […] this definition suggests that the four capabilities that make up ACAP are combinative in nature and build upon each other to produce a dynamic organizational capability. (ibid, p. 188)

To make ACAP a dynamic capability, Zahra and George (ibid.) complemented their theory by assigning the four dimensions to two different levels: Knowledge acquisition and knowledge assimilation form potential absorptive capacity (PACAP), knowledge transformation and knowledge exploitation are summarized in realized absorptive capacity (RACAP). According to the authors, both subcategories of ACAP have complementary but separate roles to improve a company’s performance. Figure 2 illustrates the model of ACAP as suggested by the authors and explains both roles of PACAP and RACAP when processing information. In their model, companies acquire knowledge from different sources while experiences influence the capacity to acquire and assimilate knowledge. Internal (e.g., performance failure) and external events (e.g., technological or policy changes) can trigger absorptive capacity. Depending on the intensity of the event, companies are more or less likely open to (actively search for or) acquire external knowledge (ibid.). To sum up, a company’s experience, the source of knowledge and the intensity of activation are in relation to each other and affect PACAP. Social integration mechanisms (e.g., sharing knowledge among employees) increase the efficiency of transforming and exploiting knowledge (RACAP). Besides, well-developed social mechanisms within a company reduce the gap between PACAP and RACAP. As a result, a well-developed ACAP contributes to competitive advantage and performance through strategic flexibility and innovation. Regimes of appropriability (referring to the dynamics which influence the ability to protect advantages) can affect competitive advantage (ibid.). Zahra and George (ibid.) have paved the way for further research on the topic of ACAP. Lane, Koka, and Pathak (2006) in contrast have built on Cohen and Levinthal (1990), describing it as a process-oriented perspective and sequential process through

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(1) recognizing and understanding valuable knowledge (exploratory learning) (2) assimilating knowledge (transformative learning) and (3) applying new knowledge (exploitative learning) to result in commercial outputs. In the following years, the model of Zahra and George (2002) was validated and operationalized by a large number of studies (e.g., Camisón & Forés, 2010; Jiménez-Barrionuevo, García-Morales, & Molina, 2011), while demonstrating that external inflows of knowledge are related to ACAP and innovation (Kostopoulosa, Papalexandri, Papchroni, & Ioannou, 2011). Besides the established studies about absorptive capacity, Qian and Acs (2011) developed a theory about entrepreneurial ACAP “[…] that allows entrepreneurs to understand new knowledge, recognize its value, and commercialize it by creating a firm” (ibid., p. 1). The paper argues that the exchange of knowledge depends mainly on the (entrepreneurial) capacity to absorb it. Building on prior literature, Flatten et al. (2011) came to the conclusion that existing analyses about absorptive capacity are mostly limited to Research and Development (R&D): “The use of these proxies may have contributed to conflicting and misleading findings about the nature and contributions of ACAP” (ibid., p. 99). They argue that this applies to several papers that have tried to operationalize ACAP before (e.g., weakness in validity). Processing existing knowledge as well as gaining and sustaining a competitive advantage has stayed in the center of attention until today. The chapter is mainly based on the concept suggested by Flatten et al. (2011).

1.8 Scale Development Flatten et al. (ibid.) highlighted that R&D proxies still lacked a consistent and valid measurement. This included most of the studies outlined in the previous section. They have built on the notion of Zahra and George (2002) to develop a multidimensional operationalization for ACAP. Quantitative, as well as qualitative methods, made them benefit from combined effects. As a first step of the scale development process, Flatten et al. (ibid.) analyzed 269 studies of which 33 included measurements with relevance to ACAP. Thus, the authors matched the related research to a specific dimension of ACAP. The matching process concluded in 52 items of ACAP. The final scale of Flatten et al. (ibid) resulted in 14 items to measure ACAP. Every item consists of a measurement that was originally derived from the literature item pool. The table with all ACAP items and the corresponding scale can be found in Appendix 1. According to Flatten et al. (ibid.), all items cover the range of ACAP. Depending on the dimension, the number of items varies, but was statistically proven by the scale refinement. The evaluation from Flatten et al. (ibid.) made it possible to analyze and compare companies’ absorptive capacity on a multidimensional level. In addition to this specific approach, the analyses of ACAP in startups also evolved over the last decade.

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1.9 Absorptive Capacity in Startups Quantitative research about ACAP in startups is rather young compared to the general evolution of the model. As quoted in the introduction, incubated startups can be assumed to have a better way of processing knowledge (similar to assimilating knowledge) and applying knowledge (comparable to the exploitation of knowledge) to their means. Patton (2013) combined the development of the ACAP theory with incubator studies. “[…] to successfully develop a business from these innovations, firm founders must absorb but also appropriately exercise managerial knowledge and expertise” (ibid., p. 912). This implies that absorptive capacity differs between startups. In the same year, Hess and Siegwart (2013) have shown that technology alliances between established ventures and academic spin-offs can be a way to develop technologies efficiently. In their model, ACAP plays a major role in the early cooperation phase. To conclude, although differences were found between different companies, incubated and non-incubated startups were not yet compared with respect to their ACAP. In this regard, there have only been assertions or unconfirmed hypotheses so far. This chapter builds on the theoretical and practical implications of ACAP and adds another level to it.

2 Research Questions and Hypotheses Prior analyses have shown that incubation can have a positive impact on startups and their ACAP. The preliminary research question is based on the theoretical framework and the research gap as elaborated in the previous sections: What are the differences between incubated and non-incubated startups in their absorptive capacity? The literature review results in hypotheses reflecting the four dimensions of ACAP and their 14 items according to Flatten et al. (2011): knowledge acquisition, knowledge assimilation, knowledge transformation, and knowledge exploitation. Moreover, they are clustered in accordance with Zahra and George (2002) on the level of PACAP and RACAP. By assigning the individual dimensions to their superordinate levels, we even enhance the multidimensional approach of Flatten et al. (2011). The hypotheses are complemented by comparing the mean of all ACAP items to make a general statement about the ACAP of incubated and non-incubated startups. Figure 3 summarizes the development of the hypotheses in the context of the literature review: Derived from the concept of Absorptive capacity, the hypotheses are based on the elaborations of Zahra and George (2002) as well as Flatten et al. (2011). Based on the literature review, a total of seven hypotheses have been elaborated. This systemization summarizes the theoretical background for our hypotheses (see Table 1).

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Fig. 3 Development of hypotheses

Table 1 Overview of Hypotheses

Hypothesis 1: Incubated startups have a higher absorptive capacity than non-incubated startups Hypothesis 2: Incubated startups have a higher potential absorptive capacity than non-incubated startups Hypothesis 2.1: Incubated startups have a higher knowledge acquisition rate than non-incubated startups Hypothesis 2.2: Incubated startups have a higher knowledge assimilation rate than non-incubated startups Hypothesis 3: Incubated startups have a higher realized absorptive capacity than non-incubated startups Hypothesis 3.1: Incubated startups have a higher knowledge transformation rate than non-incubated startups Hypothesis 3.2: Incubated startups have a higher knowledge exploitation rate than non-incubated startups

The hypotheses follow a top-down approach: While the first hypothesis compares ACAP as a whole (with the mean of all ACAP items), the subsequent hypotheses (hypotheses 2 and 3) focus on the individual dimensions (PACAP and RACAP). Each collection of items is assigned to the respective level of PACAP (hypotheses 2.1 and 2.2) and RACAP (hypotheses 3.1 and 3.2).

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3 Methodology The research method used to analyze the differences between incubated and nonincubated startups in their absorptive capacity will be a quantitative analysis conducted by a questionnaire. Operationalizing absorptive capacity based on its original theory, Flatten et al. (2011) analyzed the status quo of research to define a variety of items for each dimension. Their statistically validated findings are used as a basis for this study. It will help to analyze absorptive capacity on different levels to confirm or reject the hypotheses. 14 ACAP items can also be applied to startups, all of them were transferred into the questionnaire. The survey itself was created with Google forms and later analyzed with the tool SPSS from IBM.

3.1 Pre-testing Following Diekmann’s (2008) recommendations (Harris, 2014), the questionnaire was refined through pre-testing. The first draft questionnaire was sent to a test group of five (co-)founders and Chief Executive Officers (CEOs) of different startups. The profile of the startups resembled the profile of the targeted startups from whom the data would be collected by the final version. The questionnaire was revised relying on the feedback from the test group. All pre-test participants completed the survey, resulting in some minor changes: ● The team size was explained in detail with the help of “full-time equivalents” (FTEs). ● The predetermined questions for ACAP by Flatten et al. (2011) ask for the behavior of the management toward their employees. Since some of the startups did not have employees yet, a note was added to the questionnaire (rather than changing the questions/items which might affect the reliability of the result): “The following sections will ask you to what extent you agree with certain statements. If you do not yet have any employees, please indicate to what extent the statements apply to you/your founding team.” A second test group, again consisting of (co-)founders and CEOs of five different startups, completed the questionnaire without any remarks, resulting in the final version.

3.2 Questionnaire The questionnaire followed the model of Harris (2014) from general to more specific questions resulting in five different sections (followed by an introductory statement).

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To make sure that the participants/startups fit into the target group, it included a screening section: 1. Executives are considered to know the company’s operations best, thus, the study only included (co-)founders and CEOs of startups 2. The startup should be already operating by being officially registered as a company founded in Germany Answering one of the three questions of the first section with “No” resulted in a direct termination of the questionnaire because the startup would not be part of the target group. Since the survey was specifically sent to startup founders, the screening process did not include a specific question asking for the innovative capability or the sales/employee growth. It was assumed that the respondents fulfill at least one of these two requirements. It was more important that the (co-)founder/CEO identified the business as a startup. Therefore, the exclusion criterion remained that the startup must have been founded a maximum of 10 years ago (the same definition applied for a recurring study about the European startup ecosystem by Ernst & Young GmbH [2019a]). Moreover, this study included all kinds of support programs from incubators to accelerators and others. The mentioned sections were programmed with skip patterns: Only if a startup would be joining a support program, the type and length of residency would be asked. The same logic applied to the section asking for the past support programs. The questionnaire was also asking for the dimensions of ACAP as mentioned at the beginning of this section. All items referred to ACAP could be scored on a 5-point Likert-type scale, ranging from “strongly disagree” (1) to “strongly agree” (5). Due to the fact that startups might neither disagree nor agree with the questions and to measure answers more precisely, the questionnaire offered them a midpoint.

3.3 Sample Selection and Scope The screening process should make sure that the sample would only contain answers from startup (co-)founders or the CEOs (they are considered to know about the company’s operations best in order to rate the answers in the questionnaire as reliable as possible; Flatten et al. [2011]). The quantitative analysis should be conducted by a survey of at least 50 startups of which around half should have been incubated. The participants received a link to the questionnaire (except for the startup event, where the founders would directly open the questionnaire at the site) also containing information about the objectives of the study. Due to absences and in order to increase the response rate, reminder emails were sent to all those who had not yet taken part in the survey. Furthermore, to increase the response rate, an incentive was offered: Every participant could join a raffle to

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win a voucher for advertising materials. The survey officially started on the 2nd of July 2019 and ended one month later on the 2nd of August 2019.

4 Results The data obtained through the survey was prepared for further processing by correcting (e.g., spelling mistakes), coding and quantifying, and transferring it in the form of variables.

4.1 Sample Description In order to reach the minimum number of startups, almost 370 startups have been contacted over a period of one month. The number of participants was 73 which results in a total response rate of nearly 20%. This engagement rate is higher than for a normal web-based survey (Klassen & Jacobs, 2001). Out of the 73 participants, 66 valid entries were identified. With the help of the screening process, seven startups were excluded from the sample. They were not part of the target group as they were either not yet founded (five), or the participant was not the CEO or (co-)founder (two).

4.2 Incubated vs. Non-incubated Startups Out of the 66 valid entries, 36 incubated and 30 non-incubated startups could be identified. Out of the 36 incubated startups, 18 startups were joining a support program (>1 week) when they filled out and the other 18 had received support only in the past. 36 incubated startups made use of 42 support programs in total (this also takes also into account whether the incubated startups had already joined a support program in the past). Accelerators are the most chosen type of support program, followed by incubators and others (such as the German “EXIST” program). The evaluation of the hypotheses comes to the conclusion that there are significant differences between incubated and non-incubated startups (Table 2). Five of seven hypotheses were confirmed. Incubated startups have a significantly higher ACAP than non-incubated startups (compare hypothesis 1). On the dimensions as defined by Zahra and George (2002; hypotheses 2 and 3), the analysis detected major differences between the subjects of investigation. Both dimensions of potential absorptive capacity (PACAP) and realized absorptive capacity (RACAP) are similarly distinctive in comparison: PACAP and RACAP of incubated startups are more developed.

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Table 2 Evaluation of Hypotheses Confirmed or rejected Hypothesis 1: Incubated startups have a higher absorptive capacity than non-incubated startups t = 0.324; df = 64; p = 0.003 (significant)

Confirmed

Hypothesis 2: Incubated startups have a higher potential absorptive capacity than non-incubated startups t = 2.817; df = 64; p = 0.006 (significant); Cronbach’s α = 0.72

Confirmed

Hypothesis 2.1: Incubated startups have a higher knowledge Rejected acquisition rate than non-incubated startups t = 1.926; df = 64; p = 0.059 (not significant); Cronbach’s α = 0.517 Hypothesis 2.2: Incubated startups have a higher knowledge assimilation rate than non-incubated startups t = 2.657; df = 64; p = 0.010 (significant); Cronbach’s α = 0.768

Confirmed

Hypothesis 3: Incubated startups have a higher realized absorptive capacity than non-incubated startups t = 2,437; df = 64; p = 0.018 (significant); Cronbach’s α = 0,73

Confirmed

Hypothesis 3.1: Incubated startups have a higher knowledge Rejected transformation rate than non-incubated startups t = 1.473; df = 64; p = 0.146 (not significant); Cronbach’s α = 0.701 Hypothesis 3.2: Incubated startups have a higher knowledge exploitation rate than non-incubated startups t = 2.342; df = 64; p = 0.022 (significant); Cronbach’s α = 0.734

Confirmed

4.3 Discussion On the multidimensional level of Flatten et al. (2011; hypotheses 2.1, 2.2 and 3.1, 3.2), the results are to be viewed in a more differentiated way. Taking all four dimensions into consideration, only two of them are significantly different: Incubated startups have a higher knowledge assimilation and higher knowledge exploitation rate than non-incubated startups. With regard to the individual items, however, a much more specific conclusion can be drawn. From the 14 established items to measure ACAP, five show a significant difference (no significant difference was found among the transformation items). The following five statements (according to the items of Flatten et al. (2011)) therefore apply more to incubated startups than non-incubated startups: ● Our management motivates the employees to use information sources within our industry (Acquisition #2). ● In our company ideas and concepts are communicated cross-departmental (Assimilation #1). ● Our management demands periodical cross-departmental meetings to interchange new developments, problems, and achievements (Assimilation #4). ● Our management supports the development of prototypes (Exploitation #1).

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● Our company has the ability to work more effective by adopting new technologies (Exploitation #3). In summary, specific knowledge acquisition, knowledge assimilation, and knowledge transformation processes are more developed in incubated startups. In particular, the acquisition of external knowledge, cross-departmental communication, periodical cross-departmental meetings, the development of prototypes and the adoption of new technologies are more likely to be found in incubated startups. The latter also confirms the findings as in Soetanto and Jack (2016). Overall, the results confirm previous studies (compare, e.g., Patton [2013] for improved knowledge management, and Colombo and Delmastro [2002], regarding the greater adoption of technological innovations by incubated startups). As a result, the analyses come to a differentiated conclusion: Considering the mean values of the four ACAP dimensions (measured by means of the respective items), 50% significant differences can be found. Looking at the ACAP items in detail, the differences between incubated and non-incubated startups are less explicit (36% of the items show significant differences). On the one hand, the findings indicate that a multidimensional approach has the advantage of capturing and evaluating a dimension through several differentiated items. On the other hand, it can also result in contradictions: Contrary to the mean of the complete dimension of knowledge acquisition, significant differences were found for one respective item. However, the analysis of each individual item provides much clearer information about the characteristics of the subjects. Although not all hypotheses could be confirmed, incubated startups have a higher mean for the overall ACAP. This indicates that support programs contribute to improving the ACAP of startups. This statement reflects the basic focus of this research: The items in the survey enabled this study to evaluate the differences in startups’ ACAP. In addition, there are many other research opportunities, e.g., analyzing the reasons for differences in ACAP.

4.4 Conclusion According to the results of the study with 66 participants, startups seem to benefit from incubation. As the analyses of the questionnaire have revealed, the statement can be made that incubated startups have a higher ACAP than non-incubated startups. However, the findings had to be examined in a more differentiated way. Based on the operationalization by Flatten et al. (2011), it was possible to distinguish more clearly between the different ACAP items. For this study, it can be argued that the multidimensional approach was particularly beneficial. As shown by the distinction between dimensions and individual items, there are subtle differences in absorptive capacity between incubated and non-incubated startups. Although a dimension as a whole had a significant difference, individual items did not differ (and vice versa).

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In conclusion, it can be said that the study builds on the findings of various analyses of ACAP in startups. This also applies to the operationalization of Flatten et al. (2011), which was successfully applied in this study.

4.5 Future Research Several aspects of the results could be used for subsequent research: Statistics such as team size could be used for further research in relation to the startups’ ACAP and to examine which institutional or demographic characteristics have an influence on its development. In this context, Cohen and Levinthal (1990) and Colombo and Delmastro (2002) could also be taken up by investigating the influence of employees on a startups’ ACAP. Future studies could re-examine whether the operationalization is also applicable to startups or whether it needs to focus on other characteristics that are more suitable for this stage of a company. Consideration could also be given to carrying out a qualitative study on incubated and non-incubated startups. It would be possible to evaluate individual dimensions in this context: For example, whether a high value of exploitation has a more positive effect on the performance (Soetanto & Jack, 2016). Another avenue for future research is an analysis which factors of support programs have the strongest impact on the ACAP of startups. This might not only emphasize the importance of absorptive capacity for startups, it might also give a hint if the concept of support is more or less important in the development of a startup. By interrogating individual characteristics of the support programs, it might be possible to find out more correlations. Results of a comparison could be used to investigate the question of what makes support programs particularly successful (O’Neal, 2015). For this study, the author has determined that the impact of incubation takes effect after joining a support program for at least one week. Future studies should investigate when an effect on ACAP actually occurs. It would be useful to carry out a study over a longer period of time and to accompany the startups in their development phase. The influence of ACAP on the innovative capacity and performance of the respective startups could also be analyzed (Barbero et al., 2012; Díez-Vial & Montoro-Sánchez, 2016; Rothaermel & Thursby, 2005). In addition to the mentioned potential research avenues, the investigation could be continued in other directions: According to Hess and Siegwart (2013) and Fernández et al. (2015), co-operations have an influence on ACAP. This would also be in line with Martín-de Castro (2015, p. 1): “Firms should rely on external relationships and networks in order to complement its knowledge domains, and then, develop better and faster innovations.” The differences between incubated and non-incubated startups in their ACAP were proven by the results of this study. Startups are therefore recommended to use an incubator or a similar support program during their development phase. An investigation into the causes and effects of ACAP on performance can provide further evidence.

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Since the number of support programs is increasing, they might be interested in not only having the best possible reputation, but also executing a well-designed program with a lasting impact on their startups. As a result, the recommendation of this paper can also be implemented in practice, e.g., by improving the supply side of support programs to develop startups’ absorptive capacity.

4.6 Limitations For further analyses, the sample should be significantly enlarged to obtain even more valid evidence on the aspects researched. In general, additional data should be gathered to provide a more differentiated insight into the effects of incubation. This study has not investigated whether and in which criteria startups are successful and sustainable and whether ACAP affects their performance. Therefore, we cannot make any statements about the relationship between ACAP and other variables. Furthermore, it has neglected serial founders. One could have explicitly asked about the founding experience (since it might have an effect on ACAP). There is room for the investigation of entrepreneurial ACAP (compare Qian & Acs, 2011). There are most probably other elements that can influence startups’ ACAP: the personality of the founder(s), the level of education, age, work experience or family background, skills, the economic situation, etc. (Albort-Morant & Oghazi, 2016; Colombo & Delmastro, 2002; Klofsten, 2005). Another line of future research could focus on personal components of the founder. In this research, we identified differences in ACAP between incubated and nonincubated startups. Our research however did not analyze why the rates were different and if the differences were caused by incubation or other factors.

Appendix 1 14 items to measure ACAP by Flatten et al. (2011) (used with permission from Elsevier). Dimension

Item

Acquisition #1

The search for relevant information concerning our industry is everyday business in our company

#2

Our management motivates the employees to use information sources within our industry

#3

Our management expects that the employees deal with information beyond our industry (continued)

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(continued) Dimension

Item

Assimilation #1

In our company, ideas and concepts are communicated cross-departmental

#2

Our management emphasizes cross-departmental support to solve problems

#3

In our company, there is a quick information flow, e.g., if a business unit obtains important information it communicates this information promptly to all other business units or departments

#4

Our management demands periodical cross-departmental meetings to interchange new developments, problems, and achievements

Transformation #1

Our employees have the ability to structure and to use collected knowledge

#2

Our employees are used to absorb new knowledge as well as to prepare it for further purposes and to make it available

#3

Our employees successfully link existing knowledge with new insights

#4

Our employees are able to apply new knowledge in their practical work

Exploitation #1

Our management supports the development of prototypes

#2

Our company regularly reconsiders technologies and adapts them accordant to new knowledge

#3

Our company has the ability to work more effective by adopting new technologies

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Absorptive Capacity Approach to Technology Transfer at Corporate Accelerators: A Systematic Literature Review Ufuk Gür

Abstract Corporate accelerators are the specific programs designed by incumbent companies to scan, select, and accelerate the growth of promising technology startups for definite mutually beneficial objectives such as gaining access to disruptive innovation of the startup or outreaching to incumbents’ customers for fast time to market. Based on the theoretical background, this study contributes to literature by adopting systematic literature review of “corporate accelerators” as the methodology to deliver cumulative insights and a final absorptive capacity process model was built based on examining prior single or multiple case studies which applied to their context as a limitation. Absorptive capacity theory enhances the understanding of technology transfer’s modus operandi at corporate acceleration settings. As a result of the systematic literature review, the author offers a conceptual model to inform future research and practice. Keywords Absorptive capacity · Corporate accelerators · Technology transfer · Corporate acceleration · Startup accelerators

1 Introduction Corporate accelerators are the specific programs designed by incumbent companies to scan, select, and accelerate the growth of promising technology startups for definite mutually beneficial objectives such as gaining access to disruptive innovation of the startup or outreaching to incumbents’ customers for fast time to market. Among the innovation management initiatives of corporations, corporate accelerators are recognized as more feasible than internal R&D activities (Gutmann, Kanbach, & Seltman, 2019), since a well-designed program may trigger the technology transfer function efficiently in an open innovation landscape. In corporate accelerator studies, the methodology is dominated by single or multiple case studies which imply that limited conclusions can be drawn for a widely U. Gür (B) Faculty of Business Administration, Duzce University, 81100 Duzce, Turkey e-mail: [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_4

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prevalent phenomenon. As a responsive initiative, this study aims to deliver a pattern of successful corporate acceleration practice through the systematic review of the most relevant research in the domain. Moreover, in a mutually beneficial collaborative activity, research has focused more on the startups’ outcomes, creating a research need on corporate side processes. Despite the recognition of corporate accelerators as an open innovation mechanism for technology transfer, most real-life program implications did not turn out to provide the expected benefits neither to startups nor incumbents ending up as failures and terminated programs (Moschner, Fink, Kurpjuweit, Wagner, & Herstatt, 2019). This challenge was led mainly by the different organizational cultures, the time horizon for market reach, and misaligned objectives between the startup and the incumbent. Weiblen and Chesbrough (2015) differentiated corporation-startup interaction into two types of programs: one with dominating corporation interests, where access to technology is obtained, and the other with dominating startup interests, where the startup company can scale through the technical platform of the incumbent. The Siemens case represents how an incumbent can bring the technology and let it develop with its original inventors inside the corporation consistent with business units’ requirements and concluding as a market launch (Weiblen & Chesbrough, 2015). A highly relevant example of the second program can be found in the SAP case, where the startups capitalized on the benefits of fast market access and product development (Gutmann et al., 2019). To avoid implementation pitfalls, the scholars base their studies on single and multiple case studies yet still call for a systematic understanding of key performance indicators and success metrics (Mahmoud-Jouini, Duvert, & Esquirol, 2018). This paper has been driven by the question of whether a fundamental approach to examine the program success or failure can be based on the “Absorptive Capacity” of the incumbent translating into a process-based view of an acceleration mechanism rather than the categorization of different program specifications. As the research field is recognized as infant by scholars (Connolly, Turner, & Potocki, 2018; Drori & Wright, 2018; Gutmann et al., 2019), the corporate acceleration phenomenon needs examination by different theories and different methodologies to build a theory development path separating from independent accelerators or business incubation literature and focusing on acceleration for technology transfer performance (Cris, an, Salant, a˘ , Beleiu, Bordean, & Bunduchi, 2019). This paper has adopted systematic literature review of “corporate accelerators” as the methodology to deliver cumulative insights and a final absorptive capacity process model was built based on examining prior single or multiple case studies that were limited to their context (Corbin & Strauss, 2014).

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2 Theoretical Background Today’s organizations must adapt to rapidly changing environments to survive and respond to continuously shifting customer needs and market conditions. Knowledge is defined as the applied version of information combined with experience, context, and insight; it is the critical element of organizational learning. Organizations possess ambidextrous structures that were initially coined by Duncan (1976), coping with exploration and exploitation of knowledge at the same time. March (1991) elaborated this activity of combined effort for finding an appropriate balance between knowledge activities in an organizational setting, furtherly claiming that firms both need to exploit core knowledge and explore new ones for competitive innovation. Tushman and O’Reilly III (1996) approached the concept in organizational design perspective and nominated ambidexterity as the “ability to simultaneously pursue both incremental and discontinuous innovation and change that result from hosting multiple contradictory structures, processes, and cultures within the same firm” suggesting that innovation units of an organization focus on incremental and radical innovation separately for either short-term efficiency or long-term innovation, respectively (Damanpour & Aravind, 2012). Differentiation between explicit and tacit knowledge was first introduced by Polanyi (1966), approaching knowledge management as knowledge creation and extraction process later elaborated by Nonaka, Byosiere, Borucki, and Konno (1994). Explicit (codified) knowledge is codified through systematic language, whereas tacit knowledge is embedded in deeper roots of cognition in the human mind, thus harder to explore and articulate. Nonaka adopted the view that knowledge is the catalyzer of innovation, and knowledge-creating organizations must evolve from mainstream pregiven information processing structures to knowledge-creating ones. Organizational knowledge creation theory later expanded with the discussion of “knowledge conversion,” which is the interaction between explicit and tacit knowledge examining the conditions for improving innovation and learning. Nonaka, Von Krogh, and Voelpel (2006) addressed the Japanese philosophical concept of “Ba” creating shared spaces in virtual, physical, or mental means in organizations to foster knowledge conversion and innovation.

2.1 Knowledge and Organizations Today’s organizations must adapt to rapidly changing environments to survive and respond to continuously shifting customer needs and market conditions. Learning and creative organizations provide an innovative climate and culture for its members to learn and improve their skills and abilities because organizations learn through its members who are capable of learning. Senge (2006) addressed the presence of five disciplines within an organization to be a learning one:

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1. Learning must take place in teams by thinking together and offering collective insights which are not attainable by individuals. 2. Individuals who maintain personal mastery strive for a lifetime learning mode, which in turn helps the organization learn. 3. Mental models that shape the personal understanding of the world and the behavior must be exposed to the outer world with the ability to carry on meaningful conversations, including inquiry and advocacy. 4. A shared vision of the future encourages experimentation and innovation. 5. Systems thinking focuses on the interactions between individuals and between organizations providing a full understanding of constituents of the system. Discussions regarding ambidextrous organizations for innovation management have dominated research in recent years (Cantarello, Martini, & Nosella, 2012). Damanpour and Wischnevsky (2006) manifested a new perspective by distinguishing organizational units either as supply/produce innovations or as the units that consume/use those innovations. Another dimension of organizational knowledge is the potential result deriving from the combination of explicit and tacit knowledge in a team of members, namely as “embedded knowledge” (Badaracco & Badaracco, 1991; Madhavan & Grover, 1998). Embedded knowledge creation is critical to new product development at organizations.

2.2 Organizational Learning Organizations must maintain a knowledge-building culture to stay ahead of the competition in terms of innovativeness. This culture is elaborated around the concept of organizational learning, which can be traced back to the 1960s. Cyert and March (1963) discussed that routines’ association with experienced success in an organization which includes “the forms, rules, procedures, conventions, strategies, and technologies around which organizations are constructed and through which they operate” (Levitt & March, 1988), affects the likelihood of its use. Argyris and Schön (1997) approached the learning process as the presence of substantial change efforts employing single-loop or double-loop learning, which are both present in the organization. When the organization takes corrective actions toward mistakes in terms of rethinking policies and objectives, it initiates double-loop learning. In contrast, single-loop learning is limited to carrying on present policies and objectives. Dutton and Thomas (1984) addressed four major factors causing progress in organizations. The first one is the effects of technological change based on Arrow’s (1962) Learning by Doing concept, which is the introduction of improved capital goods changes the production environment resulting in progress. Secondly, improvement may also derive from the Horndal (labor learning) effect that took place in the Horndal iron production facility in Sweden addressing the productivity increase caused by the fact that direct labor learning improved the performance of output per man. As a

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third category, local industry and firms characteristics also affect the progress with regard to the degree of mechanization, the ratio of the assembly to the machining, the length of cycle times, and the type of manufacturing process and fourthly the effect of scale economies resulting in explaining progress. These four categories form the basis for the endogenous learning environment in a firm’s progress. The source of a firm’s progress may also be settled in exogenous learning when the firm acquires information from external sources such as suppliers, competitors, customers, and government. As a means of progress environment, induced learning occurs when the firm enriches resources and investments to provide a learning environment, and autonomous learning occurs by automatic improvements led by sustained production over time.

2.3 Absorptive Capacity As an excellent example of Nonaka’s “Ba” space organizations, corporate accelerators emerged as an interface between incumbent companies and innovative startups, which is mutually beneficial. Innovative startups obtain networking, fast track to market, human capital benefits, whereas corporate accelerators claim rights on promising startups’ technological innovations. Recent studies on corporate accelerators called for more systemic research on corporate accelerators based on different theoretical lenses such as the absorptive capacity theory (Bauer, Obwegeser, & Avdagic, 2016). Cohen and Levinthal (2000) originated the term “Absorptive Capacity” addressing the ability of the firm to “recognize the value of new, external information, assimilate it, and apply it to commercial ends” as a function of firm’s level of prior knowledge. Organizational absorptive capacity is dependent on leveraging the individual capability by building an active network of internal and external actors. Being active in a network also reinforces the readiness of the firm in times of competence-destroying technical change (Schumpeter, 1942; Tushman & Anderson, 1986) by being exposed to new development in their fields in time. In this sense, Cohen and Levinthal (2000) withdraw the limited insight of self-reinforcing past performance concept of March and Simon (1958), concluding that opportunities are recognized and realized as innovation by both experience and absorptive capacity. Absorptive capacity has also implications in the adoption and diffusion of innovations through a preexisting knowledge base of prospective users. As an example, it has been claimed that personal computers have been adopted by consumers and firms more rapidly who were experienced in mainframes or minicomputers priorly. The absorptive capacity theory was reconceptualized and extended by many scholars since the 1990s, and it has been firmly attached to the knowledge transfer domain (Lane, Koka, & Pathak, 2006; Lane & Lubatkin, 1998; Lichtenthaler, 2007, 2009; Zahra & George, 2002). Nominating the incumbent company as the recipient, one can claim that its absorptive capacity will determine how the technology will be obtained, processed, utilized, and retained. Lack of absorptive capacity is the primary

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reason for failure within the context of knowledge transfer between different organizations (Lichtenthaler & Lichtenthaler, 2010). In corporate acceleration literature, Bauer et al. (2016) called for the application of absorptive capacity theory to the research context beyond other approaches such as open innovation theory (Weiblen & Chesbrough, 2015). The absorptive capacity theory is closely related to different organizational theories, which makes it rich and complex in practice regarding the various theoretical development paths. Many studies have examined the theoretical multidimensionality in ACAP theory, including Apriliyanti and Alon’s (2017) study.

3 Problem and Research Question As research studies show, many accelerator programs fail and lead to loss of time and resources for both parties. In this research study, the author would like to explore the relevant literature about how absorptive capacity theory is related to the technology transfer process from innovative startups to the incumbent company through corporate acceleration with a critical approach to performance enablers and blockers evident in the literature.

4 Methodology The study aims to develop the existing body of knowledge about the technology transfer between incumbents and innovative startups through corporate acceleration by systematically reviewing the literature about corporate accelerators within the logical structure of absorptive capacity theory. Webster and Watson (2002) addressed that a successful literature review builds questions and answers in a cumulative approach based on observed patterns in the past studies and informs the future studies for closing critical knowledge gaps. They emphasized that the ultimate goal of the review is sensemaking, which can be enhanced by logically structured ideation delivering key findings and relationships. Following the methodological guidelines of Webster and Watson (2002), such as in the work of Bauer et al. (2016), the research scope was limited starting from 2005 when the first accelerator was founded. As comprehensive databases, the Web of Science (WoS), and Scopus databases was used between July 2019 and October 2019 to determine relevant research for “corporate accelerators” using search terms “corporate accelerators” and “corporate accelerator” in titles, obtaining an initial group of 10 full-text sources for in-depth review. Following the first group of publications’ backward and forward citations as suggested by Webster and Watson (2002), more articles were obtained for composing a list of further reading for the audience (see Table 1).

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Table 1 Reading list Articles

Journal

Publishing year

A systematic literature review on accelerators

The Journal of Technology Transfer

2019

Understanding a new generation incubation model: The accelerator. Technovation

Technovation

2016

Accelerating entrepreneurs and ecosystems: The seed accelerator model

Innovation Policy and the Economy

2016

Accelerators and ecosystems

Science

2015

Innovation accelerators: defining characteristics among startup assistance organizations

Small Business Administration

2014

Open accelerators for startups success: European Journal of Innovation A case study Management

2017

Open innovation with digital startups ZPB Zeitschrift für Politikberatung 2017 using corporate accelerators: A review of the current state of research Where do accelerators fit in the venture creation pipeline? Different values brought by different types of accelerators

Entrepreneurship Research Journal

2018

Coding protocol has been developed according to the summary of various elements of ACAP theory, where agreements and disagreements about the model components still exist between significant studies of the domain (see Table 2) (Gao, Yeoh, Wong, & Scheepers, 2017). The progress of the absorptive capacity theory is not the subject of this study; thus, variations were provided as a complete set of items regarding the typology. Gao et al. (2017) categorized ACAP Model components as antecedents, ACAP components, contingent factors, and outcomes, leveraging codification, and categorization of the themes in reviewed articles. The induction process functioned under four themes: antecedents of corporate acceleration which indicate the prior conditions for the absorptive capacity of the incumbent for a successful acceleration, ACAP components which form a set of incumbent absorptive capacity strategies in action for acceleration, contingent factors which are likely to effect the success or failure potential of an acceleration program, and outcomes which represent the possible results likely to be obtained from corporate accelerator programs. According to the analysis, review resulted in a conceptual model for absorptive capacity approach to technology transfer at corporate accelerators (see Sect. 5.3). Exclusion criteria placed in the protocol are as following; ● Research that is mainly concerned with business incubation; ● Research that is exhaustive for “corporate intrapreneurship-corporate venturinginternal acceleration” (Shankar & Shepherd, 2019);

Knowledge source Complementarity Prior knowledge Intra-organizational antecedents Managerial antecedents Learning relationships/individual development Environmental conditions and incentives Perceived absorptive capacity Realized absorptive capacity Recognizing the value Acquire Assimilate Transform Apply/exploit Regimes of appropriability Activation triggers Social interaction mechanisms Environmental conditions Organizational mental models Organizational strategies Organizational structures and processes Power relationships

Antecedents

ACAP components

Contingent factors

Model components

Table 2 Elements of ACAP theory (adopted from Gao, Yeoh, Wong, and Scheepers [2017], used with permission from Elsevier)

(continued)

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Outcomes

Model components

Table 2 (continued) Innovation Innovative performance Knowledge outputs Exploitation/exploration Commercial outputs Flexibility Performance Competitive advantage

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IDENTIFICATION

EXTRACTING& CODING SCREENING

A7: Extracting 10 articles for coding

A1: Keywords «Corporate Accelerator» «Corporate Accelerators»

A5: Search July 2019 October 2019

A2: Database WoS and Scopus

R1: 10 Initial Articles

CC1: Antecedents

A3: Search Constraints as Journal Articles, in Titles

A6: Backward and Forward Citation Search

CC2: ACAP Components

R2: Reading List of 8

A8: Data Coding

CC3: Outcomes CC4: Contingent Factors

A4: Exclusion Criteria

A9: Reporting

A5: Coding Criteria

R3: Conceptual Model

A: Activity R: Result CC: Coding Criteria

Fig. 1 Research process flow

● Research that focuses on technology transfer between universities and corporations; ● Research that is mainly concerned with independent accelerators (powered or consortium accelerators) (Moschner et al., 2019) other than corporate sponsorship; ● Degree Theses, Working Papers, Books, Book Chapters, and Proceedings; ● Unavailable sources in full text. The research process flow is presented in Fig. 1, adopting the systematic literature review process of Cris, an et al. (2019).

5 Results 5.1 Results of Database Search See Table 3.

5.2 Results of Citation Search Provided as Reading List See Table 1.

“Corporate accelerators”

2018

Key factors in building a corporate accelerator capability

2017

2017

Outsourcing creativity: An Creativity and abductive study of open Innovation innovation using corporate Management accelerators International Journal of Innovation Management

Business Horizons

Situational Logic: An Analysis Of Open Innovation Using Corporate Accelerators

Corporate accelerators: Building bridges between corporations and startups

2016

2019

Accelerating strategic fit Journal of Business or venture emergence: Venturing Different paths adopted by corporate accelerators

Research-Technology Management

2019

Toward a better Business Horizons understanding of corporate accelerator models

“Corporate accelerator”

Year published

WoS

Journal

Articles

Search term in titles

Source

Table 3 Results of database search

Multiple Case Study

Multiple Case Study

Multiple Case Study

Multiple Case Study

Single Case Study

Secondary Data Analysis and Multiple Case Study

Methodology

(continued)

They identified common patterns for designing corporate accelerators. Proposition, Process, People, Place

They raised questions about the power relationships, project speed, radicalization of the future, personal risk taking, tolerance for conflict, bureaucratization of communication, orientation to fast failure or success results.

They identified seven components of successful open innovation using corporate accelerators: Strategy, Resources, Procedures, Structure, Roles, Environment, Metrics and Outcomes

They found out that “the corporations’ strategic posture and investment time horizon influences their use of one of two corporate acceleration processes—accelerating strategic fit or accelerating venture emergence”

They identified two key challenges: “attracting startups in a highly competitive accelerator market and ensuring access to the internal resources needed to manage the interaction with startups and enable a convergence of interests”

They recommend that “companies develop a holistic strategy for startup collaboration and choose the accelerator program that fits this strategy best”

Key findings

Absorptive Capacity Approach to Technology Transfer … 61

Search term in titles

“corporate accelerator” and/or “corporate accelerators”

Source

Scopus

Table 3 (continued)

2016

Journal of Applied Business Research

International Food and 2018 Agribusiness Management Review

Corporate accelerators as recent form of startup engagement: The what, the why, and the how

IGNITE your corporate innovation: insights from setting up an ag-tech startup accelerator

2019

Year published

2017

Problems and Perspectives in Management

Journal

Corporate accelerators: Journal of Business fostering innovation while Strategy bringing together startups and large firms

Exploring the benefits of corporate accelerators: Investigating the SAP Industry 4.0 Startup program

Articles

Single Case Study

Multiple Case Study

Single Case Study

Single Case Study

Methodology

They formed the acronym IGNITE regarding, Intention, Group, Neighborhood, Independence, Transparency, Expertise

They identified “four distinct types of corporate accelerators specific to the primary objectives and the program configuration. Three types, (1) listening post, (2) value chain investor, and (3) test laboratory, are based on mainly strategic rationale. The fourth, (4) unicorn hunter, is based on mainly financial rationale, and, therefore, applies the business model of an independent accelerator within the corporate context”

They have seen “five major success factors for corporate accelerator programs: transparent and aligned goals, an independent team of startup advocates, a large and committed external network, top-management backing, long-term objectives and performance indicators”

They claimed that corporate accelerators “aim to increase the competitiveness of established companies running such programs by developing a product ecosystem and the brand, infusing startup culture into the organization and developing customer relationships”

Key findings

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Fig. 2 A conceptual model for the absorptive capacity approach to technology transfer at corporate accelerator

5.3 A Conceptual Model for Absorptive Capacity Approach to Technology Transfer at Corporate Accelerators See Fig. 2.

5.3.1

Antecedents

Weiblen and Chesbrough (2015) addressed that corporations’ successful engagement would be dependent on setting clear corporate objectives, targeting right fit startups within a growing startup ecosystem, and differentiating the accelerator program from other substitution mechanisms such as venture capitals. These objectives may be based on internal business unit challenges or market growth, leading to further choices for prioritizing those objectives for resource allocation (Moschner et al., 2019). Leveraging resources strengthens the value proposition to startups and the level of attraction and retention for adequate absorption of new knowledge, concluding that a critical consideration for the corporate is matching startups’ solutions with the right business units (Mahmoud-Jouini et al., 2018). Establishing goals frames the scope and the style of the relationship between the incumbent and the startup (Kohler, 2016). Richter, Jackson, and Schildhauer (2018) further elaborated on the strategic objectives from the corporate side as attracting creative staff in the startups supporting Kupp, Marval, and Borchers’ (2017) study. Kupp et al. (2017) have claimed that the best talent is attracted to startups where they can find a fast and demanding environment with increased responsibility and offering incentives such as share options. Other objectives are the contribution to innovation ecosystems as a social good, positioning the company brand as a creative and dynamic one, triggering outside-in

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innovation, proactively identifying competitive challenges, and creating new business models (Richter et al., 2018). Financial objectives such as equity investment are not prioritized in most programs (Gutmann et al., 2019; Richter et al., 2018), yet program sustainability might depend on financial returns obtained directly or indirectly (Kanbach & Stubner, 2016). Setting objectives and aligning them with corporate strategy are executed by top management; thus, the involvement of the executive team in the process and embracement of startup engagement in a supportive way enhances the chance of absorption of technology in a corporate accelerator program (Kupp et al., 2017). Ownership of top management supports the legitimacy of the program, encouraging dedication of employees and company-wide acceptance (Kanbach & Stubner, 2016).

5.3.2

Components

Richter et al. (2018) claimed that corporate accelerator programs reduce the chance of likely failures of the innovation process by careful selection of startups in the cohort with strict assessment criteria and control over the startup aligned with firm interests. Innovation scouting for current business challenges may be triggered by carefully examining the problems faced by the specific business units, and those challenges are translated into targeting suitable startups for providing solutions with innovative technologies (Moschner et al., 2019). It is critical to identify those startups with necessary effort based on a viable screening and evaluation process (Gutmann et al., 2019). Targeting startups with a track record of the developed product, past funding success, existing revenue stream, media coverage, and previous successful experience at another incubator or accelerator program are other failure filters observed in the studies (Mahmoud-Jouini et al., 2018; Moschner et al., 2019). Control orientation on the corporate side must be balanced against startup’s need for flexibility, and startups must be provided with resource and communication access to business units (Mahmoud-Jouini et al., 2018; Richter et al., 2018). Startups need to be protected from corporate complexity because the objective of obtaining a productcorporate fit may stifle the process of product-market fit, which prevents startups from building a scalable business. (Kohler, 2016) defined it as over-protection of corporate backing, which increases the likelihood of later failure for the startup when they are not exposed to market forces and valuable feedbacks to adapt. To attract more promising startups (Kanbach & Stubner, 2016), incumbents should design corporate accelerators for absorbing technology as well as using desorptive capacity (Dell’Anno & Del Giudice, 2015) to scale startups. Shankar and Shepherd (2019) reported that when corporate executives act as a mediation and lobbying unit, the acceleration process is wholly utilized for successful technology transfer. Sufficient absorptive capacity can be obtained by assigning experienced mentors, experts, and investors who are skilled in business planning, entrepreneurship, law, technology, and marketing into the organization and execution of the program (Richter et al., 2018). The assignment can be achieved by already investing in leveraging networks and partnering with external accelerators before building an insider

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unit may help define the right successful metrics and key performance indicators for tracking progress (Kohler, 2016). Getting feedback from all participating stakeholders’ evaluation would also help develop the program design with constructive insights (Connolly et al., 2018).

5.3.3

Contingent Factors

The major contingency regarding technology transfer at corporate accelerators is the time horizon of technology to market in different technological domains. Kohler (2016) urged program designers to consider sample cases in hardware and health technology and innovations which require more extended time frames. Gutmann et al. (2019) furtherly illustrated different industries as a critical factor for the success of accelerator programs as well as distinct geographic regions, different objectives, and various offerings. The geographical proximity between incumbent and startup contributes to program success by facilitating face-to-face regular communication and collaboration. As startups are more attracted to well-known corporations with a global footprint (Mahmoud-Jouini et al., 2018), medium-sized and less-known companies may opt for the consortium model where other companies are involved in startup acceleration to share investment costs and benefit from the reputation of each other (Moschner et al., 2019). The selection of the cohort from the startups in similar vertical expertise enriches the collaboration through the synergy among teams (Kohler, 2016). The expertise and quality of the mentor and investor networks also affect the successful execution of the program (Kupp et al., 2017).

5.3.4

Outcomes

Moschner et al. (2019) noted that corporate accelerators turn into paying customers after completion of the program based on successful exploitation supporting Richter et al. (2018), and Kohler (2016) claims about obtaining future suppliers and partners as the result of acceleration. Startups stimulate early recognition of a competitive environment and enhance entrepreneurial spirit at the organization (Mahmoud-Jouini et al., 2018; Richter et al., 2018; Shankar & Shepherd, 2019). The time horizon of the corporate accelerator with strategic fit orientation is shorter to absorb innovation and obtain immediate results at the incumbent (Shankar & Shepherd, 2019). As a reflection of absorbed technology, customers of the incumbent also benefit from the enrichment of product offerings (Gutmann et al., 2019). Kupp et al. (2017) emphasized that accelerating the startup’s growth can also work for the incumbent’s business scaling especially valid for platform-based acceleration in the form of channel partnerships which may lead to consideration for investment in or acquisition of the startup which implies a technology transfer strategy (Kohler, 2016). Corporate accelerator programs represent a positioning strategy as a byproduct when it is used for publicity in attracting potential talented employees (Kanbach &

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Stubner, 2016). However, this strategy brings out the risks associated with corporate reputation when disclosure of failures and problems of the startups arise (Moschner et al., 2019; Connolly et al., 2018).

6 Contributions and Conclusion This study contributed to the literature about technology transfer at corporate accelerators by addressing the process in Absorptive Capacity theory with its dimensions that possess variations in the theoretical development path. Shankar and Shepherd (2019) cast doubt on the scope and depth of the corporate acceleration literature, which has been limited to the descriptive approach rather than inductive processes. The absorptive capacity theory provides a guide for practitioners for the configuration of those programs, bridging the gap between theory and practice, as discussed by Kanbach and Stubner (2016). Absorptive Capacity theory enhances the understanding of technology transfer’s modus operandi at corporate acceleration settings. As a result of the systematic literature review, the author offers a conceptual framework to inform future research and practice. To change an inevitable failure to a success story both for startups and corporations, one can claim that the Absorptive Capacity theory provides necessary guidance and metrics to reach the ultimate objective which is to create and develop innovative technologies in an open innovation landscape. As a managerial implication, firstly author points out that strategic fit-oriented companies (Shankar & Shepherd, 2019) may be more suitable for technology absorption from external partners since the principal objective is to solve business challenges rather than accessing potential unicorns (Kanbach & Stubner, 2016). Secondly, interest in access to equity and IP control may lead to negative consequences when startups try to survive under corporate pressure. The outcome of the accelerator program is highly dependent on the program objectives and strategy. Thirdly, the venture emergence (Shankar & Shepherd, 2019) direction may lead to another dynamic capability, namely as “desorptive capacity” (Dell’Anno & Del Giudice, 2015) of the incumbent. Desorptive capacity is highly relevant in platform-based acceleration, such as in the case of SAP, yet “the design characteristics of a corporate accelerator program need to be tailor-made to the corporate’s needs, focus and resource-base” (Gutmann et al., 2019). In terms of research implications, the author envisions a great potential in corporate volunteering research at startup settings combined with corporate acceleration literature. Corporate volunteering at a startup is rare, however, in Germany, which also dominates corporate acceleration literature (Selig, Gasser, & Baltes, 2018), innovative programs might be in action promising a positive change in organizational learning at the incumbent as an antecedent of absorption of technology. According to Moschner et al. (2019), employees who take place in settings facilitating the exchange of information with startups tend to be intrapreneurs and change agents at their companies. The critical research question might be based on investigating

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those mechanisms already in place, such as employee mentoring at corporate accelerators (Connolly et al., 2018; Kohler, 2016) and how corporate volunteering can be attached to those mechanisms to create a company-wide cultural shift. Another research opportunity lies in training incumbents for successful accelerator program management (Connolly et al., 2018; Richter et al., 2018), leading more valid performance indicators and success definitions as an outcome. A critical research question arose as a result of this study, observing that “lean” methods are being used in corporate acceleration programs to test wrong ideas early (Richter et al., 2018). Is “lean” the right method to decide on the destiny of startup solutions when the case under consideration has a longer time horizon such as in healthcare? Public policy implications are critical to the success of corporate acceleration programs as political contexts affect entrepreneurial ecosystems (Cris, an et al., 2019) and represent regimes of appropriability and activation triggers. Corporate acceleration is a tool for incumbents to disrupt themselves rather than leave the opportunity to others (Kupp et al., 2017; Richter et al., 2018), while the ineffective public policy might lead to ill-defined market regulations resulting in time and investment costs. Regional public authorities can also cooperate with corporate acceleration programs to keep successful startups in the region for regional economic development as governmental sponsors represent one of the key stakeholders in such programs (Cohen, Fehder, Hochberg, & Murray, 2019).

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Strategy Processes in Technology Transfer Offices: Antecedents and Consequences Ciara Fitzgerald, James A. Cunningham, Matthias Menter, and Richard B. Nyuur

Abstract The purpose of this paper is to examine antecedent factors that contribute to strategy processes in technology transfer offices (TTOs) and underpin the formulation of strategy. Set in an Irish context using multiple cases, we find that TTOs have taken different responses to the same environmental stimuli, conducted different forms of benchmarking and undertook different professional development and agenda setting activities. Given the paucity of research on strategy dimensions of TTOs, we conclude our chapter by outlining some future avenues of research. Keywords Technology transfer offices · Strategy processes · University-industry collaborations · Universities · Strategy

1 Introduction Technology transfer offices (TTOs) are significant intermediary actors in successfully commercialising university intellectual property. Academics, governments and state agencies worldwide place huge effort in understanding the nature of universityindustry engagement and in finding means to encourage closer university-industry linkages (Lehmann & Menter, 2016). Within the literature, there have been growing appeals for the TTO to be considered as a strategic subject, empirically and in practice, as Phan and Siegel (2006, p. 26) suggest: “For technology transfer to succeed, it is critical for university administrators to think strategically about the process”. How strategy is undertaken by TTOs is not well understood and needs significant research (Markman, Siegel, & Wright, 2008; Sanders & Miller, 2010; Siegel, Wright, Chapple, & Lockett, 2008). To this end, Derrick (2015) encourages investigations of TTOs C. Fitzgerald University College Cork, Cork, Ireland J. A. Cunningham · R. B. Nyuur Northumbria University, Newcastle upon Tyne, UK M. Menter (B) Friedrich Schiller University Jena, Jena, Germany e-mail: [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_5

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from a strategic lens. The purpose of this chapter is to address such calls by examining antecedent factors that contribute to strategy processes in TTOs.

2 Literature Review 2.1 Universities and Strategy Strategy is a much investigated concept in management studies, while it has somewhat been disregarded in higher education studies. While strategic planning is common in the private industry, with the onset of managerialism in the higher education sector, scholars have begun to investigate the nature of strategy formulation in a university setting (Denman, 2005; Ferlie, 1992; Slaughter & Rhoades, 2004). Universities have been required to develop and extend their mission and tasks and there have been some studies beginning to address the nature of strategy in universities (Hardy, 1991; Jarzabkowski & Wilson, 2002). Among these, the request to formulate their own strategy is paramount, whether it has been characterised as strategic planning or defining a mission. Similarly, Bryson (2018) highlights the contribution strategic planning can make to any organisation’s self-concept of sustainability, allowing them to reinvent themselves by envisioning a future beyond today’s status quo. Normative stances on appropriate processes of strategy making in universities have been developed (Duderstadt, 2009; Keller, 1983). These include applying rational logic to decision making of universities as outlined by Delucchi (1997, p. 417) who describes strategic planning in universities as a normative necessity: “Processes which organisations engage into illustrate that they understand the rules of the game and which confer certain legitimacy upon the organisation”. However, there is a differentiation between “planning for show” because it looks good rather than because it is good (Mintzberg, 1994). What he means by this is informing important outsiders like financiers, suppliers and government agencies about the substance of the plans, so that they can help the respective organisations to realise them. For TTOs, such engagement is crucial as their performance and realisation of strategic objectives are dependent on other actors such as scientists in the principal investigator role (Menter, 2016), who interact with different actors within their local ecosystem (Cunningham, Menter, & Wirsching, 2019) and co-create value (Cunningham, Menter, & O’Kane, 2018).

2.2 Technology Transfer Offices The core role of a university TTO is to identify, manage, protect and market its intellectual property to other parties for further development (Cunningham, Romano, & Nicotra, 2020). A TTO is recognised as typically centring on commercialising

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applied research, but often providing a wide range of complementary services such as supporting spin-outs, entrepreneurship training and industry outreach programmes. In an effort to be called a boundary spanning object, it is important to recognise that a boundary spanning object straddles two or more communities of practice (Sanders & Miller, 2010). The TTO literature has developed rapidly in recent years, reflecting the increase in the number of TTOs and as such an increased academic interest in them (Albats, Fiegenbaum, & Cunningham, 2018; Fitzgerald & Cunningham, 2016; Rothaermel, Agung, & Jiang, 2007). Previous studies have suggested that size (Chapple, Lockett, Siegel, & Wright, 2005; Macho-Stadler, Pérez-Castrillo, & Veugelers, 2007; Markman et al., 2008; Siegel, Waldman, & Link, 2003), age or experience of TTOs (Carlsson & Fridh, 2002; Friedman & Silberman, 2003; Lockett, Wright, & Franklin, 2003; Powers & McDougall, 2005), skills and capabilities of TTOs (Lockett et al., 2003; O’Kane, Mangematin, Geoghegan, & Fitzgerald, 2015; Perkman & Walsh, 2007; Powers & McDougall, 2005) and incentives for TTOs are all significant to the performance of the TTO (Hülsbeck, Lehmann, & Starnecker, 2013; Jensen, Thursby, & Thursby, 2003; Link, Siegel, & Bozeman, 2017; Markman et al., 2008). However, not withstanding the economic and social rationale for TTOs, TTO practitioners also need to be aware of the critics refuting the legitimacy of TTOs in creating and even facilitating university-industry linkages (Kenney & Patton, 2009). Such criticisms focus on the TTO as a monopoly gatekeeper (Litan, Mitchell, & Reedy, 2007), redundant entitlements (Colyvas et al., 2002) and metrics that do no capture the social benefits (Grimaldi, Kenney, Siegel, & Wright, 2011). However, there is a dearth of studies that focus on strategy, particularly how TTOs strategise and set up their strategy processes.

2.3 Strategy Process Research Within the strategy field, process research has been defined as research primarily focused on actions that lead to and support strategy. Research in this area has prescriptive and descriptive work on planning methods and decision making with attention directed towards the effectiveness of alternative means for generating and implementing strategy. The great bulk of contributions of process scholars to strategic management have been in the study of choice and change processes as summed up by Van de Ven (1992, p. 172): “Strategy process is diverse and cannot be contained within any single paradigm […] Need to be clear about the meaning of process in our research […] Need to be explicit about the theory of process we draw on […] Design process research is a way that is consistent without definition and theory of process”. Strategy process research is influenced by the studies of Sztompka (1991) who argues that social reality is not a steady state, but rather a dynamic process. He argues that social reality occurs rather than exists. This is further supported by Pettigrew (1992, p. 8) when he discusses the purpose of process analysis: “The studies of sequences of events are crucial in any process analysis. However the purpose of

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process analysis is not just to describe the sequence or tell the story, but to identify patterns in the process often across several carefully chosen cases”.

2.4 The Need to Examine Strategy Processes in TTOs Within the TTO literature, there has been an absence of focus on examining the strategic dimensions of TTOs. There is a real need to understand this given onset of managerialism in the public sector. According to Ferlie, Fitzgerald, and Pettigrew (1996), process-based models of strategy have analytic resonance in many public sector settings such as universities which display characteristics such as a high degree of politicisation and of political behaviour, multiple stakeholders that engage in bargaining behaviour to form dominant coalitions, vague and multiple objectives that are reinterpreted at a local level, the lack of strong market pressures that can drive change and limited power of top echelons to impose direction. Moreover, there is increased pressure for universities to deliver on their third mission. TTOs are boundary spanning organisations responsible for delivering on the third mission of commercialising university IP and so are emerging on the strategic agenda both at an institutional level and a policy level (Cunningham, Lehmann, Menter, & Seitz, 2019). Thus, it is critical to think of them in a strategic nature (Cunningham & Harney, 2006; Cunningham, Harney, & Fitzgerald, 2020; Phan & Siegel, 2006). Furthermore, processual models of strategy have been usefully applied within empirical studies within public sector professional bureaucracies such as universities and hospitals (Mintzberg & McHugh, 1985). Several studies such as those by Burgelman (1983a, 1983b) as well as Lovas and Ghoshal (2000) have provided the field with profound insights into the actual strategy process in organisations, thereby offering an organic perspective. TTOs are organisations that require to be understood in their own histories and contexts. No model of strategy process will transfer to them without dissonance; rather it is required that TTOs acquire models and systems of strategic awareness, management and process that recognise issues, context and processes which actually shape strategic change (Hardy, Palmer, & Phillips, 2000). Crucially, context is used analytically not just as a stimulus environment, but also as a nested arrangement of structures and processes in which the subjective interpretations of actors perceiving, learning and remembering help to shape processes (Weick, 1979). A process vocabulary is needed to best capture the process. At the most general level, process questioning involves the interrogation of the phenomena over time using the language of what, who, where, why, when and how in relation to uncovering the link from processes to outcomes. Consequently, against this background and gap, we examine the antecedent factors that contribute to strategy processes in TTOs to address this deficit in research focus.

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3 Methodology For the purpose of this study, we adopted a multiple case design as this allows for cross-case analysis and comparison and the investigation of a particular phenomenon in diverse settings (Cunningham, Menter, & Young, 2017). Our study is set in the Irish university context, whereby we analysed the full population of seven university TTOs, all of whom received funding through the Technology Transfer Strengthening Initiative (TTSI), an Irish government policy implemented by Enterprise Ireland—a state agency—to support the development of Irish TTOs. Table 1 outlines the case selection.

3.1 Data Sources and Collection With respect to data sources, Yin (2003) outlines six sources of evidence: (1) documentation, (2) archival records, (3) interviews, (4) directs observation, (5) participant observation and (6) physical artefacts. Table 2 outlines the type of data collected for each method for this study. Our data collection of primary data consisted of two phases. The first phase involved building trust with TTOs and we did this through a variety of mechanisms as outlined in Table 3. Our second phase involved conducting semi-structured in-depth interviews with 36 key individuals throughout all areas of the TTOs, in seven Irish universities focusing on the strategy of the office, policies and processes. We also conducted interviews with non-TTO members such as government agencies and other key stakeholders (see Table 4). The audio-taped interviews were of 60-90 min duration and transcribed after the interview. In total, 296 pages of interview transcriptions were analysed. Standard questions were utilised to warrant validity and consistency. Also, Table 1 Selected TTO case studies University

TTO

Number of students (approx.)

Number of staff (approx.)

Research income

Age

Full-time equivalents

Alpha

−20,000

2000+

e50 m+

5 years+

10+

Beta

−20,000

−2000

e50 m+

−5 years

−10

Charlie

20,000+

2000+

e50 m+

5 years+

10+

Delta

−20,000

2000+

e50 m+

−5 years

−10

Echo

−20,000

−2000

−e50 m

−5 years

10+

Foxtrot

−20,000

−2000

−e50 m

−5 years

−10

Golf

−20,000

−2000

−e50 m

5 years+

−10

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Table 2 Sources of evidence Case study sources of evidence Examples used in data collection Documentation

TTO websites, IP contracts, Mission statements, Planning documents, University strategic plans, Government documents

Archival records

Business plan template from the TTSI; Policy document outlining the need for TTOs

Interviews

TTO directors, Commercialisation specialists, Industry liaison roles, Operation managers, TTO managers, Government actors

Direct observation

Yes

Participant observation

No

Physical artefacts

Signage around campus, reporting lines of TTO

Table 3 Mechanisms to engage with case participants Mechanisms used to engage with case participants • Informal chats with former members of TTOs, academics who had interacted with TTOs • Joined LinkedIn TTO discussion groups and read archival and current conversations which highlighted some of the issues TTOs were dealing with (e.g. Spinout and Technology Transfer Ireland) • Attending TTO presentations to industry • Researched TTO websites analysing communication of activities, team members, strategic focus • Researched university strategic plans, university quality reviews and university annual reports to gauge the level of third mission activity within each university. In particular, looking to ascertain of third mission seemed central to activities or mandated by government intervention • Researched newspaper headlines of various success stories issued by the TTOs. These were in the Sunday Business Post, the Examiner and on the Finfacts website (www.finfact.ie) Table 4 Overview of interviewees, by role

Distribution of persons interviewed, by role Role

Quantity

TTO TTO directors

7

Commercialisation specialists

12

Business incubation managers

5

Office managers

2

Industry liaison officers

4

Government agencies Enterprise Ireland

4

FORFÁS

1

Department of Enterprise, Trade and Employment 1 Total

36

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during location visits, we requested a copy of supporting organisational documentation, such as organisational charts, strategy documents and reports. After each interview, we created a Daily Interpretive Analysis (Silverman, 2019) which consisted of observations of the interview and overall interpretations, intuitive thoughts as well as the look and feel of the interview.

3.2 Data Analysis Following Eisenhardt (1989), the data was analysed by first conducting single-case analysis for each case to find themes emerging in the analysis, followed by cross-case analysis to make comparisons and find patterns. Triangulation with secondary data was also conducted to increase the validity of the study. Such secondary data was analysed according to Garfinkel’s (1967) idea of “documentary method of interpretation”. This is a process in which a set of appearances may be objects, events, persons or symbols are taken as evidence of some underlying patterns. He suggests that the documentary method of interpretation is a feature of common sense whereby the researcher engages in under-covering processual activities of an organisation. Two qualitative data techniques were used: (1) coding literature-based taxonomy and (2) pattern searching via maps. The analysis progressively moved from very broad categories to key themes and constructs (Miles & Huberman, 1994). Data was coded using the NVivo software package. Multiple sources facilitated us to interpret a reasonably holistic representation of TTO processes within their context (Pettigrew, 1990). Finally, to appreciate the contextual implications of the university sector, during the time of study, and more specifically, how such a context was interpreted by the members of TTOs, secondary data in terms of policy documents and government initiated inquiries and reports were considered. Using writing as an analytic device, we categorised and re-categorised coded sections to identify thematically coherent interpretations of the work of technology transfer professionals. We adopted a reflexive approach to the empirical data that draws on pre-existing theories (i.e. strategy formulation to guide analysis). Table 5 presents a summary of the main steps in our data collection and analysis process. Following initial coding, the analysis of findings became directed and more conceptually motivated whereby we chose analytic categories of interest, based on our study.

3.3 Study Limitations Our study is not without limitations. Firstly, our study focuses only on universities and not on institutions of technology as knowledge creating organisations in an Irish context. Secondly, there is a relatively small sample of cases in this study and their location in a single economy may be problematic. However, this was overcome by the

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Table 5 Summary of main steps in data collection and analysis process Steps in data collection

Activities

Mapping the national context and the universities

National level: Attending policy and practitioner conferences, conversations with leading experts, examining policy documents University level: Visits to TTOs, conversation with university personnel

Case selection

Identified TTOs based on network, websites and general information search Identified case contacts through well-informed persons and networks

Initial case investigation

Internet search and informal conversations

Document collection

Obtained plans, presentations from interviews Searched the Internet for webpages, press releases, news reports

Data transcription

Transcribed the interviews from tape, focus on revealing process

Mapping the central events over time

Writing narratives about the strategy formulation process, and making tables describing time, actors and events

Matching theoretical concepts

Working with theory and data in an iterative process

following activities: attending the Association of University Technology Managers (AUTM) Annual Meeting, the leading association in technology transfer as well as the Technology Transfer Society Annual Conference to discuss the findings of the research with leaders in the academic and practitioner fields as well as participating in discussions in an international LinkedIn group dedicated to addressing TTO issues debates. Also, our study was validated by using the full population of seven Irish university TTOs.

4 Findings From our analysis, we identified the following antecedent factors that contributed to TTOs’ strategy processes within this study, namely differing organisational response variations, environmental scanning, professional development activities and agenda setting activities. We found that in responding to TTSI, each of the TTOs in our study responded differently to this policy initiative. In all seven cases, we found that the TTO directors engaged in formal planning when the TTSI was being introduced by the government agency, Enterprise Ireland, in 2007. Each TTO director submitted a business plan, outlining resource allocation intentions for the individual offices. The business plans were formulated outlining resources requested and the associated metrics expected. These were prescriptive as there was a template dictating the structure as required by

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the funding agency, Enterprise Ireland. All seven TTO directors outlined a proposed budget including details of the costs associated with the recruitment and employment of additional technology transfer staff, costs associated with essential administrative staff, costs associated with purchase/development of procedures software and training costs for staff. The desirable metrics were subdivided into primary and secondary metrics by the government agency. These consisted of primary metrics: technology transfer licences, collaborative R&D agreements, researchers moving to industry as a result of technology transfer agreements, number of disclosures and number of spin-offs as well as secondary metrics: the number of patents filed and granted.

4.1 Organisational Response Variation While the stimulus was the same, the TTOs responded in different ways. The TTO director of case Delta explained how a focus on getting funding to hire the right people was the objective of their business plan for Enterprise Ireland, “part of it is getting the right people…each university had to fulfil certain criteria to get the funding” (TTO-I-D5). While six of the seven cases created the plan for the purpose of the TTSI, one of the cases was less directed by the Enterprise Ireland call and this TTO had its own strategic plans in place prior to the government call. The TTO of case Charlie had been involved with commercialising IP long before the TTSI. The TTSI funded the offices based on the research income of the universities in return for expected metrics such as invention disclosures, patents and spin-outs. The TTO of case Charlie drew up its plan for the government based on its own strategic planning while the rest was shaped by the TTSI: “We had our own plans in place before the TTSI” (TTO-I-C1). The funding structure of the offices provided insights into the planning approach to TTSI by the TTOs. In five of the TTOs, over half of the office was funded by TTSI and therefore it can be concluded that those TTOs were heavily reliant on their influence for human capital costs. The TTO of case Charlie had less than 50% of staff reliant on funding and was therefore less reliant on their influence. Table 6 presents an overview of the planning approaches by all TTOs to TTSI. Besides the TTSI, there were other influences, which affected how the TTOs planned. The Innovation Taskforce and the Forfás AD Little Report played a role in determining what the TTOs focused on and this has been recognised by the TTO director of case Beta: “The AD Little report and the innovation taskforce report will all determine what we do” (TTO-I-B1).

80 Table 6 Overview of planning approaches to TTSI

C. Fitzgerald et al. Case

% funded by TTSI Planning approach

Alpha

Over 50

Reactive to TTSI No strategic plans in place prior to TTSI

Beta

Over 50

Reactive to TTSI No strategic plans in place prior to TTSI

Charlie Less than 50

Own plan Strategic plans in place prior to TTSI

Delta

Over 50

Reactive to TTSI No strategic plans in place prior to TTSI

Echo

Over 50

Reactive to TTSI No strategic plans in place prior to TTSI

Foxtrot

Over 50

Reactive to TTSI No strategic plans in place prior to TTSI

Golf

Over 50

Reactive to TTSI No strategic plans in place prior to TTSI

4.2 Environmental Scanning Strategic planning in higher education institutions can be defined as a process by which the institutions assess its current state and the likely future condition of its environment (Ferlie, 1992). One way to achieve this is by assessing best practice among similar institutions. We found a number of activities in which the TTOs sought best practice when preparing for their TTSI application. This gives an insight into the strategy formulation processes of the offices by examining the TTO activities benchmarking and engaging in professional development activities (see Table 7). In Table 7 Benchmarking activities: pre and post TTSI

Pre TTSI

Post TTSI

Alpha

◯

Beta

◯

Charlie

◯

Delta

◯

Echo

◯

Foxtrot

◯

Golf

◯

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seeking best practice, the TTOs engaged in benchmarking against national and international TTOs as they were: “[…] conscious of benchmarking against best practice” (TTO-I-A1). The TTOs engaged in benchmarking pre and post TTSI. Pre TTSI: One of the TTOs benchmarked themselves against European and US TTOs pre the implementation of TTSI. The TTO director of case Charlie discussed how he benchmarked the office on an international bearing point and how they positioned themselves as a European TTO as they looked to the UK and the USA most often as the constituencies to benchmark against: “We set our targets comparing ourselves to the UK and the US using reports from AUTM and the like to measure invention disclosures and spin-outs. We used it as a yardstick – this is where we want to be and we have made them” (TTO-I-C1). Also, the TTO of case Charlie participated in international TTO surveys, shaping their identity as a European TTO. Therefore, benchmarking prior to TTSI has shaped their formulation of strategy to become a European TTO. Post TTSI: All of the other TTOs discussed the significant role of Enterprise Ireland in benchmarking post the implementation of the TTSI. It is common practice in Enterprise Ireland where meetings are held regularly with TTO directors and the outcomes for each office are reported to compare their performance against the performance of other national TTOs. This post TTSI benchmarking activity represents the TTOs’ strategy formulation processes being influenced by their national standards, rather than positioning themselves as a European TTO.

4.3 Professional Development Activities Besides benchmarking, using Enterprise Ireland and AUTM data, the TTO professionals attended courses to learn of best practice. We observed PraxisAuril (formerly known as PraxisUnico) training folders on display in some of the offices (TTO—A, B, C, D, E, F). TTO directors and commercialisation specialists all referred to similar types of training or displayed training folders from such technology transfer professional organisations as AUTM and ProTon Europe, thus keeping up with international best practice. While the courses did not focus on the strategic practices of a TTO, their attendance and public display of the training materials contributed to legitimising their presence on campus. As well as attending PraxisAuril and AUTM training courses to keep up to date with operational efficiencies, the management team of case Beta sourced best practice from technology transfer journals such as the Journal of Technology Transfer and Research Policy, referring to empirical work from leading technology transfer academics such as Donald Siegel and Mike Wright. Furthermore, the technology transfer professionals in each of the cases participated in external training courses (see Table 8). The courses typically were focused on building up core skills of the TTO professionals such as negotiation skills and patenting courses. Three of the cases discussed attending international training events. In case Beta, they attended courses held by PraxisAuril, the educational not-for-profit organisation set up to support innovation and commercialisation of public sector and charity

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Table 8 Professional development activities TTO

Approach

Professional development activities

Alpha

Predominantly internal

Attended international training courses Attended national training course

Beta

Predominantly internal

Researched best practice from academic journals Attended International Training Courses

Charlie

Predominantly external

attended international training courses Participate in European surveys

Delta

Recognise the value of context

Attended international training courses

Echo

Predominantly internal

Attended international training courses

Foxtrot

Predominantly internal

Attended international training courses

Golf

Predominantly internal

Attended international training courses

research for social and economic impact: “I’ve been on 10-15 courses. Praxis is very good […] The benefit is networking, realising everyone has the same problems as us” (TTO-I-B4). However, in spite of the TTO professionals expressing the benefit of such sessions, the content of the courses was heavily focused on the operational side of the technology transfer process, and little on the strategic side. Furthermore, two of the TTOs focused more on the Enterprise Ireland delivered training courses: “EI puts on training courses 1–2 times a year, put on a 2 day event, you go and focus on core area. I’ve been on negotiation courses that you just pick out yourself. It’s up to each individual to see where their weaknesses are and to up skill. The more you do the better you get” (TTO-I-A2). This is evidence of the internal focus of the TTO in attending internal delivered training. However, in spite of seeking best practice, two of the seven TTOs were aware of the contextual differences of each university setting as case Echo recognised that the heterogeneity of university contexts played a key role in how the TTO engaged in benchmarking activities: “It is very hard to compare […] we have to do things in a slightly different way so there is no model out there for us, but we have looked at the way different people do things in different ways. Like companies, all universities have advantages” (TTO-I-E2). Furthermore, the TTO professionals in case Golf emphasised the importance of the institutions to differentiate the TTOs: “It’s the same, you are all doing the same sort of stuff. What makes you unique is the university” (TTO-I-G1).

4.4 Agenda Setting Activities As part of the strategy formulation process, the TTOs engaged in agenda setting activities (see Table 9). There was evidence of different types of agenda setting activities, which ranged from operational focus when allocating resources to alignment to university, which embraced a stakeholder orientation. Core agenda setting activities centred on appropriating and allocating resources and aligning themselves to the

Strategy Processes in Technology Transfer Offices … Table 9 Appropriating and allocating resources

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Case

Core competences

Approach

Alpha

People skills

Planned competences

Beta

Industry or TTO experience

Planned competences

Charlie

Learning on the job

Laissez faire

Delta

People skills

Planned competences

Echo

PhD

Planned competences

Foxtrot

Learning on the job

Laissez faire

Golf

Learning on the job

Laissez faire

university. Our findings show variance in activities pre and post TTSI. With respect to appropriating and allocating, resources emerged as significant to the strategy formulation process in TTOs. The TTO director of case Charlie recognised how universities were all very different therefore deciding on what skills the TTO needs was different in each university: “A lot of tech transfer you learn it on the job. Every university is different; culture will make it different” (TTO-I-C3). TTO directors from case Foxtrot and case Golf echoed this sentiment: “You can’t teach this, you can’t bring someone in and say this is your training and you all walk out a tech transfer expert. A lot of is learning as you go along” (TTO-I-F3) and “It’s almost like learning law, case by case. The analogy of case manager, every case is different, you learn something and you apply it to the next case. Something else will come up that you haven’t come across before” (TTO-I-G2). Recognising learning on the job as a skill for the office lends itself a laissez faire process. However, while three of the seven cases placed high value on learning on the job, two TTO directors had a more planned approach, specifically aspiring for firm criteria for their TTO staff. For example, the TTO director of case Beta stipulated: “Industry experience, or have spent a number of years in a technology transfer office” (TTO-I-B1). The TTO director of case Echo stressed the need for a PhD in the area when hiring his case managers. The reason for requiring a PhD was very interesting and revealed the agenda setting intentions of the particular office: “Otherwise they won’t have creditability with academics: it’s like badge of honour, otherwise they would be seen as administrator. Also some of PIs that we deal with now, don’t have PhDs themselves, so it is almost like a reverse situation” (TTO-I-E2). Critically, they looked for a PhD to add to the reputation of the TTO, thus seeking to acquire legitimacy from the principal investigators. Furthermore, the TTO director from case Alpha stressed that he looked for people skills with less importance on knowledge of the technology. He emphasised the negotiation and selling necessities of the role: “The most important thing is people skills: it would be nice if you know a bit about technology, patents, and start-ups. But the most important thing is people skills, you cannot have a stick to beat the academic with, you cannot say you must do that, you must convince them, you have to have people skills, it is the most important thing” (TTO-I-A1). Commercialisation specialists also discussed the need for credibility among the academic community. One of the case managers from case Delta reiterated this: “You have to be able to have credibility and

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sensitivity towards the academic mindset and also you have to have an understanding of commercial drivers of both sides of equation […] You need to be able to manage the two different worlds” (TTO-I-D2). A clear tension emerged between the different approaches of a laissez-faire-led process where the TTOs valued learning on the job and a planned competences approach where the TTOs had a clear plan for what type of core competences the office needed. Interestingly, the justification behind the core competences was to gain commitment and acquire legitimacy from the key stakeholders, such as the academics, particularly those in the principal investigator role.

5 Discussion and Conclusions The purpose of this chapter was to explore the antecedent factors that contribute to strategy processes within TTOs, addressing calls within the literature to explore how TTOs formulate strategy (Sanders & Miller, 2010; Siegel, Veugelers, & Wright, 2007). In six of the cases, the planning was an example of planned strategy formulation in response to government agency demands or planning as a normative activity. The strategic intentionality is brought into question, as six of the cases were responding to demands of stakeholder, here as opposed to planning strategically themselves. The outlier being case Charlie, as they had plans in place before the TTSI, which fed into their plans for the TTSI. The embryonic nature of the system was stressed as a reason to why planning is a normative activity. According to the TTO director of case Delta, “it takes time to grow and mature. You can do different things to speed it up but you can’t say I am going to plant a seed and want an apple tree next year and I want loads and loads of apples, which is the position EI is putting us in, and the government are putting them in that position” (TTO-I-D1). Furthermore, there was variance in how the TTOs engaged in agenda setting and assessing best practice activities as part of strategy formulation. The typical activities were benchmarking, attending courses, meetings and allocating resources. Five of the seven TTOs pursed agenda setting activities at local and national level, were operationally focused and internally driven. The outlier focused on international benchmarking and positioning themselves as a European TTO. Resource allocation was based on understanding the academic mindset. Other TTO directors mandated the TTO team to have PhDs, in order to acquire legitimacy from the academics involved in the project. How strategy was formulated in the TTO appeared to be more emergent in nature as TTOs reacted to the serendipitous nature of their environment as they were heavily reliant on volume and quality of academic IP. Also, they were reliant on government funding to continue the process. These contextual challenges of the system drove most of the TTOs to be passive and reactive. However, the TTO of case Charlie engaged in strategic thinking as it recognised the need to be more creative with the IP as the funding dried up. From the analysis of the findings, our study shows that the antecedents for planning were homogenous for all TTOs. However, from the analysis of the pattern of activities, leading to strategic outcomes, there was variance

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between TTOs in our study. Our study affirms the need for researchers to examine the strategic dimensions of TTOs. Future research needs to investigate how TTOs strategise and how external performance measures influence their strategic plans and ambitions. There is a need to examine the role of individuals within the TTOs and how they contribute to strategy formulation, particularly TTO directors—the chief strategy officer role among many roles that they have to fulfil. How do TTO directors influence and shape the TTO’s strategic plans? Finally, there is a need to better understand the analytical tools, business models and the artefacts that TTOs use to strategise and in doing so how they engage their stakeholders in this process.

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Role and Impact of Maker Spaces in Universities Third Mission: The ViNN:Lab Case Dana Mietzner and Markus Lahr

Abstract The maker movement is a technology-based and collaborative creative movement that uses rapid prototyping technologies to create objects and products in innovative ways. It focuses on the collaborative, creative development and realization of innovative ideas and their implementation in publicly accessible spaces. The steady growth of this movement has been facilitated by recent technological progress and the many new digital manufacturing possibilities that support low-threshold access to technology. This development is increasingly attracting the interest of universities. In this study, we present a framework for the different forms and formats of knowledge and technology transfer, respectively the Third Mission, as curated by a selected university-based maker space: the ViNN:Lab (Venture Innovation Lab) in Germany. We derived the framework from the exploration of different forms and formats of transfer activities in the maker space and the development of the number of users and user groups within a time frame of 6 years (2014–2019). Besides developing insights into the development of involved users and forms of usage, in this study, we also examine the following activities: (a) outreach activities, (b) practical interactions, and (c) research and development. As part of this case study, we briefly discuss how the ViNN:Lab enhances university–industry interaction, attracts new and even unexpected partners beside company partners, and how the ViNN:Lab acts as a space for the development of new ideas. Keywords Maker movement · Maker space · University–industry interaction · Open lab day · Innovation camp · Knowledge and technology transfer · Third Mission

D. Mietzner (B) · M. Lahr Technical University of Applied Sciences Wildau, Hochschulring 1, 15745 Wildau, Germany e-mail: [email protected] M. Lahr e-mail: [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_6

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1 Maker Movement and Maker Spaces as Universities The maker movement is a technology-based and collaborative creative movement that uses innovative ways to create objects and products using rapid prototyping technologies. It focuses on collaborative, creative development and the realization of innovative ideas and their implementation in publicly accessible workshops (Hartmann & Mietzner, 2017, p. 10; Peppler, Halverson, & Kafai 2016). Based on a media content analysis (Hartmann & Mietzner, 2017), which was based on United States, British, and German media articles and which focused on the maker movement in 2015/2016, the predominant general opinions regarding the maker movement could be summarized through the concept of a modern, democratic culture of innovation. According to the media content analysis, the maker movement builds on the open availability of digital manufacturing technologies (e.g., 3D printers, laser cutters, CNC routers, software tools, scanners), including software that empowers the public to create new products and further develop and manufacture existing designs. The use of digital technologies should be relatively easy and applicable by most people and available in workshops or during “open lab days” (OLDs) hosted by maker spaces (Hartmann & Mietzner, 2017, p. 10). The steady growth of this movement has been facilitated by technological progress and the many new digital manufacturing possibilities that support the low-threshold access to technology. In addition, globally networked online communities allow the exchange of working techniques and knowledge, allowing people to work together on problem-solving strategies (Papavlasopoulou, Giannakos, & Jaccheri 2017). Although the maker movement in the United States first developed through interfaces with institutionalized education, such as in schools, colleges, and universities, to promote creativity and innovation (Barrett et al., 2015), many different spatial forms, such as fab labs, tech shops, or hackerspaces, have been developed. This development is increasingly and rapidly attracting the interest of universities. Meanwhile, examples of university-based maker spaces (especially fab labs) in Germany are manifold (e.g., the fab lab at RWTH Aachen, the first fab lab in Germany, which began operations in 2009; the HRW Fab Lab at the University of Applied Sciences, Ruhr West; the maker space as part of UnternehmerTUM (startup incubator) associated with the Technical University Munich; the fab lab at Rhein-Waal University of Applied Sciences; and the ViNN:Lab at the University of Applied Sciences Wildau). At universities, many activities involving interaction with companies are an integral part and often the focus of applied research. This is particularly true for universities of applied sciences. Cooperation with companies, contract research, and joint development projects—usually using laboratories—have been carried out by universities for many years. Diverse activities, such as promoting start-ups from within universities, have also become an approach to knowledge and technology transfer that universities have pursued for some years (e.g., the entrepreneurial activities of the German universities in www.gruendungsradar.de). A strong business orientation can also be observed at the student level. Thus, especially at the universities of applied sciences, many degree programs are practice-oriented, and many students write their

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theses in cooperation with companies or other organizations, and the modules are increasingly integrating projects or “challenges” from the practice. The activities of the universities, which are subsumed under the term Third Mission, even go one step further and focus on linking universities with civil society. These include social and regional commitments, highlighting the benefits for society, social innovations, continuing education, and diverse cooperation with civil society partners on an equal footing with corporate partners (Roessler, Duong, & Hachmeister, 2015, p. 4). The universities operating in the maker movement offer a variety of activities within their maker spaces that can be linked to knowledge and technology transfer or the Third Mission. This can range from experimenting and prototyping with digital technologies, research projects, student projects, and collaborative projects with the civil society, schools, or public bodies. Many maker spaces open up for the public on “open lab days” with further linkages to companies and other organizations. Fab labs as an expression of the maker movement at universities are also the object of investigation within the research project Fab101 of the University of Siegen, RWTH Aachen, the University of Bremen, and the Folkwang University of Art, funded by the German Research Ministry (03/2017–02/2020; see also https://fab101.de for further information). The current activities indicate that maker spaces may have the potential to act as facilitators for knowledge and technology transfer (the Third Mission), especially due to the increased engagement with civil society, public bodies, and companies.

2 Research Question and Methodological Approach The aim of this study is to explore the role and impact a university-based maker space has on knowledge and technology transfer, or, in other words, how does a university-based maker space act as a facilitator for knowledge and technology transfer, its Third Mission? To answer the research question, we decided conducting a case study (Yin, 2009) on a selected maker space is a suitable approach. According to Yin (2009), “A case study is an empirical inquiry that investigates a contemporary phenomena within its real life context, especially when the boundaries between phenomenon and context are not clearly evident” (p. 18). In this study, we explore a single case, a selected university-based maker space, handled as a typical case (Yin, 2009, p. 48), which serves as a rationale for a single case. Furthermore, the selected case met the characteristics of a longitudinal study (Yin, 2009, p. 49), where the same case is studied at different points in time (Yin, 2009, p. 49). In our case study, we investigate the development of the number of users of the selected maker space and the implemented activities on an annual basis over a period of 6 years. According to Yin (2009, pp. 18, 102), there are different possible sources of evidence within case studies (e.g., documents, direct observation, participant observation, interviews, physical artifacts). Nevertheless, even quantitative data often become part of the database within case studies, but qualitative data dominate (Patton & Appelbaum, 2003, p. 60).

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Table 1 Data and information sources for the case study Research purpose

Data source

Data description

Type of data and information investigation

Development of number of users over time (code 1)

User statistics of OLDs (2014–2019)

Number of users during 316 OLDs (2014–2019)

Quantitative data analysis (descriptive)

Background of users (user groups) (code 2) (student, pupil, professional, others)

User short questionnaires on the background of users at OLDs (2014–2019)

1088 short questionnaires with background information of a randomly selected set of users during OLDs (2014–2019)

Quantitative data analysis (descriptive)

Exploration of user insights User (code 3) questionnaires on z User’s professional user insights background during OLDs (2017–2019) z Age and gender of the user z Purpose for using the maker space z Duration of stay z Current project z Working style (autonomously/collaboratively) z Staff interactions

726 questionnaires Quantitative data from lab users (client analysis insights) (2017–2019) (descriptive)

(continued)

Being aware of the limitations a single case study may have, we built up a database with different sources (triangulation) and form the fundamentals for discussion and the wrapping up of initial results as a starting point for further investigations. For the ViNN:Lab case study, the authors of this chapter, associated with the ViNN:Lab and in a continuous interaction with maker space users, had the opportunity to use data and information collected over a period of 6 years (2014–2019). Table 1 delivers an overview of the sources. For further references, we refer to Sect. 3 to the different sources by using code 1 till code 4.

3 ViNN:Lab In the following sections, we (a) deliver a short overview of the configuration and technical infrastructure of the ViNN:Lab, (b) present the numbers of users and user groups as well as insights about typical projects that users bring into the lab, and (c) summarize the key activities associated with knowledge and technology transfer,

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Table 1 (continued) Research purpose

Data source

Data description

Exploration of knowledge and technology transfer activities (2014–2019) (code 4)

Implemented z 9 innovation camps activities within (course of the camp, the ViNN:Lab participants, without OLDs outcome) (code 4.1) listed in the annual z On average 2–4 company visits per ViNN:Lab month (company schedule inquiries) (code 4.2) (2014–2019) z 215 workshops with pupils and kids (2015–2019) (course of the workshop) (code 4.3) z 19 workshops with companies, intermediaries, researchers, society (course of the workshop, composition of participants, outcome) (code 4.4) z On average 3–5 direct company contacts/inquiries per week (code 4.5) z 2 third party funded research projects (code 4.6) z Participation on 42 fairs, events or information events/days hosted by others (code 4.7)

Type of data and information investigation Qualitative description of different forms of interaction with transfer partners and society and pattern building

triggered and implemented in the ViNN:Lab based on the data and information sources (see Table 1).

3.1 Configuration and Equipment Drafted in 2013 and officially opened in early 2014, the ViNN:Lab (Venture Innovation Lab) at the Technical University of Applied Sciences Wildau (Germany) was the first maker space/fab lab in the greater metropolitan area of Berlin and Brandenburg.

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The University of Applied Sciences Wildau was founded in 1991 and offers currently 15 bachelor and 15 master programs for 3646 students (number of students in the winter term 2018/2019) (TH Wildau, 2020). With no major funding for staff, the maker space started in a small 70 m2 space and later moved to the current 140 m2 sized room. As of 2019, two full-time employees share the management of the maker space. There are also 6–10 student employees who are trained to operate the machines and feed the social media channels. Drawn from the concepts of enabling spaces (Peschl & Fundneider, 2014) and knowledge brokering (Feller, Finnegan, Hayes, & O’Reilly, 2010), the maker space was intended to serve as a platform for technology and knowledge exchange, besides being an open innovation space for the maker movement. Although open for any user, the maker space was not considered a central facility of the university in the early stages. In fact, it was implemented and is still operated by the Research Group for Innovation and Regional Development associated with the professor for innovation management and regional development, but it has since become part of the central unit for research and transfer. This bottom-up process of implementing maker spaces in higher education seems to be a common approach, yet it is due to underfunding. It can be observed that the establishment of a maker space often depends on the initiative and commitment of a professor, interested in the fab lab approach and digital technologies rather than on a top-down approach of the university or a respective faculty. As is commonly known, maker spaces provide access to rapid prototyping machines such as 3D printers, laser cutters, CNC-milling machines, and 3D scanners. The machines are capable of rapidly producing lateral and vertical prototypes for almost any application (Grønbæk, 1989; Nielsen, 1989). Table 2 provides an overview of the ViNN:Lab equipment and infrastructure. Table 2 Technical equipment of the ViNN:Lab, Technical University of Applied Sciences Wildau (Status: April, 2020) Additive manufacturing

Subtractive manufacturing

z FDMa large-scale z 3-axis portal milling machine printer z 3-axis spindle z SLSb printer molder z SLAc printer

= Fused deposition modeling = Selective laser sintering c SLA = Stereo lithography a FDM b SLS

Cutting

Imaging techniques Others

z Laser cutter z Vinyl cutter

z Structured light scanner

z VR cave z AR devices z Workstations z Photo booths z Tools

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3.2 Users and Usage in the ViNN:Lab In accordance with the Fab Charter (Massachusetts Institute of Technology, 2012) and established fab lab policies at the RWTH Aachen (Germany) or Fab Lab Copenhagen (Denmark), so-called open lab days (OLDs) were introduced as a first step to opening the space to the public. OLDs are events where anybody can utilize the lab and infrastructure in a controlled supervised manner. OLDs are important for the process of fostering knowledge creation and transfer because they facilitate the exchange of knowledge between different users and support the process by recombining existing knowledge to create innovative solutions. Based on the user statistics (code 1, Table 1), collected by the lab personal, since 2014, the frequency of OLDs at the ViNN:Lab remained basically the same, but the numbers of users changed in quantity and composition (code 2, Table 1). As can be seen in Fig. 1, OLDs started with 460 users in 2014. This number quickly more than tripled to 1122 users in 2016. The fewer number of users in 2014 can probably be attributed to the novelty of the maker space concept, it being relatively unknown, and the capacity constraints due to the lack of equipment, space, and reservations. Each user is only counted once on each OLD, but they are counted repeatedly over the course of different OLDs when they participated in several days. Since 2016, the numbers have remained at around 1000 users a year (see Fig. 1), which could indicate that the maker space has reached saturation within its catchment area. Besides the number of users at OLDs, the background of a randomly selected set of users (user groups) at OLDs was also surveyed by the lab personal (code 2, Table 1). As a university-based maker space, the number of users with university affiliation is quite high, yet this proportion has been declining, year by year until 2017. In 2014, 72% of all users were students, and an additional 7% were pupils from surrounding schools. In 2015, the share of pupils rose to 13%; meanwhile, the share of students declined to 67%. In 2019, there was a variance in the ratio between User per year (OLD) 1200

1122

1080 1035

1000 909

813

800 600 400

460

200 0 2014

2015

2016

Fig. 1 Open lab day user (2014–2019)

2017

2018

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students and pupils, with a sharp rise in student numbers from 2018 on. Mostly unchanged, the share of people with a professional background has remained the same, around 20–25% of users (Fig. 2). Those professionals mostly comprise artists, designers, engineers, natural scientists, and teachers. For research purposes and to better understand the motivations and rationales of the users since 2017, a broad variety of data (see Table 1) was collected via user questionnaires (code 3, Table 1). As seen in other maker spaces, the share of male users (66%) is much higher than those of female users (34%). The average user age is 27, and visitors tend to spend 3.65 h in the ViNN:Lab. One-third of the users (33%) build prototypes while at the ViNN:Lab, and roughly 30% use the space for coworking. The machines used most often are the laser cutter (39%) followed by 3D printers (24%) and workstations (19%). Additionally, 55% of the projects carried out in the maker space are of a private nature, 30% are educational, 10% are professional, and 2% are entrepreneurial projects. The share of professional users accounts for the most innovative and complex projects. A significant number complete work-related projects for their employers, hence using the ViNN:Lab as an outsourced innovation lab. A number of those projects have been translated into larger research projects or provided inspiration for research activities in areas such as materials research or collaborative innovation approaches. Overall, user projects tend to vary greatly in effort and complexity. An estimated 70–80% of users work along predefined pathways (e.g., downloading 3D models from online 3D printing databases). Very common is using the laser cutter to customize products such as mobile phone cases, cutting boards, and the like. Although these projects seem trivial, they often serve as low-threshold gateways to learn about how to utilize rapid prototyping machinery, hence paving the way to more complex usage scenarios. The remaining 20–30% of projects tend to be more complex. These projects include the use of various machines in combination User groups 2014 (n=214)

2015 (n=281)

2016* (n=39)

2017 (n=72)

2018 (n=297)

2019 (n=185)

80% 70%

72%

69%

67%

63%

59%

60% 50%

37%

36%

40%

36%

30% 20%

20%

10%

7%

0%

1%

27% 20% 13%

24% 13% 18%

13%

2% 3%

Students

Professionals

Pupils

Others

Fig. 2 User groups in the ViNN:Lab (2014–2019) (*Survey data 2016 only available for January– May)

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with micro-computers, different sensors, and novel approaches to using or creating materials and parts.

4 Knowledge and Technology Transfer (KTT) in the Maker Space For the ViNN:Lab, knowledge and technology transfer activities can be differentiated into three different but equally important pillars (code 4, Table 1): (a) outreach activities, (b) practical interaction, and (c) research and development. Being a maker space, practical interactions constitute the majority of activities with the “open lab day,” and they are key concepts for user and company interactions.

4.1 Outreach Activities The outreach activities of the ViNN:Lab include acting as an information platform for trade fairs, science days, or university-based events and conducting “show and tell” activities, where external actors request information on maker space-specific topics. Because the ViNN:Lab is not conducting many continuous marketing measures outside the university, besides social media activities, participating in trade fairs has been essential for coming into contact with potential users, project partners, and companies. Although the maker movement is still niche, exhibitions such as MakerFaire or Fabfestivals attract a broad audience from business, research, and civil society. On a regular basis, the ViNN:Lab attends MakerFaire Berlin, FabFestival Toulouse, and FabConference. Activities at trade fairs usually include participative workshops, exhibitions of project results, and scientific presentations. The ViNN:Lab conducts “show and tell” events to inform participants about topics such as the maker movement, 3D printing applications, or how to set up a maker space but with decreasing frequency (code 4.4, Table 1). This is due to a wide range of available and accessible knowledge, the use of 3D printing applications in many areas, and the corresponding lower level of novelty of this technology in many fields of application. The maker spaces were relatively novel for many actors, and publicly available knowledge was relatively limited during these years. On a company level, firms mostly requested information on how to integrate 3D printing into their production routines and which 3D printing technologies were most suitable for their specific needs. Contact was mostly made via regional trade chambers and industry associations, which acted as intermediaries between the ViNN:Lab and companies. “Show and Tell” events were almost exclusively attended by regional SMEs and often resulted in temporary cooperation—to the point where SMEs had built up their own 3D printing capabilities or identified suitable service providers.

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The number of participants and the duration of the “show and tell” events differ with respect to the chosen format and the in-depth knowledge requested. The numbers of participants vary between 10 and 200, and the event duration varies between 2 hours and 2.5 days. Most of the events were conducted between 2014 and 2017 and examined topics such as rapid prototyping (code 4, Table 1). A number of enquiries came from public libraries, universities, and schools. Between 2015 and 2017, the ViNN:Lab conducted three large in-house workshops to explain to public libraries how to set up maker spaces and how to operate the necessary machines (code 4, Table 1). In the same timeframe, multiple universities around Germany reached out to the University of Applied Sciences Wildau to request help with setting up a maker space. The establishment and operation of maker spaces within universities can be a challenging task confronted with many barriers. As a consequence, the ViNN:Lab initiated the national Fab:UNIverse Network in 2017. Since that, representatives of such spaces have been exchanging ideas at the annual Fab:UNIverse events (see Fab:UNIverse activities conducted by Fab101 in https://fab101.de/fabuni verse/). The Fab:UNIverse network serves as a platform for university-based maker spaces to exchange knowledge on maker space management, research projects, and cooperation.

4.2 Practical Interactions Being a fab lab, the ViNN:Lab follows the Fab Charter (Massachusetts Institute of Technology, 2012), which requires every space to be open at least once a week to the public, without needing any membership or pay wall. From this precondition, and considering the lab as a nonprofit organization within the framework of the university, an “open lab day” was introduced from the very beginning of lab operations. OLDs are one of the main pillars of the lab concept and generate most of its users. During an OLD, which in the case of the ViNN:Lab is every Wednesday and every first Saturday of the month (ViNN: Lab, 2020a), the space is open to anybody to collect information, talk to staff, and most importantly to work on the machines. Users usually take a one-time safety instruction to familiarize themselves with the lab rules, understand the basics of the maker movement, clarify the handling of intellectual property issues, and learn about the different types of machines that are available (ViNN:Lab, 2020a). Because the users come from very different backgrounds (code 2, code 3, Table 1), they draw their knowledge from different levels of experience in using and operating machines such as 3D printers or laser cutters. To accommodate these differences, OLDs require a high staff assignment. On a common OLD, that means having two full-time employers in the lab at all times, supported by 8–10 student employees, who work in shifts. The main tasks are helping users understand the requirements of their projects, introducing them to CAD (Computer Aided Design) software, and giving hands-on lessons to operate the machines. After one or two projects, users are knowledgeable enough to work on their own and teach other users. Staff

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is encouraged to motivate users to support other users, which helps build a user community that interacts freely, exchanges knowledge, develops new projects, and collaborates outside the usual peer groups. Many of the maker space users utilize the lab for professional projects at first (code 3, Table 1). Students and pupils work mostly in groups on their course-specific projects; meanwhile, professionals work in the lab individually and seek more interaction with staff. A noticeable finding is that, afterward, users return to the maker space to work on personal/private projects (code 3, Table 1). During a regular OLD, between 20 and 30 users work in the space (code 1, Table 1). The length of stay differs significantly and is related to the complexity of the project. Most commonly, users either stay 15–20 min to, for example, start a 3D printing project and pick it up later, or they spend around 3 hours as the project requires (e.g., the use of specific software) (code 3, Table 1). Usage peaks are regularly just before exam periods and bank holidays, such as Christmas and Easter (code 1, Table 1). The ViNN:Lab also received numerous requests to perform workshops for pupils and children soon after opening, which resulted in 215 workshops for pupils within a time frame of six years (code 4.3, Table 1). Workshops for pupils were sought on more general topics such as 3D printing or using Arduino micro-computers, as well as more course-specific topics around natural sciences, such as physics or chemistry, and labeled under the headline KiVi:Lab (KidsVenture Innovation Lab). Requests for workshops covered all age groups between 6 and 18 + and come from primary as well as secondary and vocational schools (see Fig. 3). By 2016, lab management had developed a set of workshops for schools with different complexities for all age groups (ViNN:Lab, 2020b). As can be seen in the graph (see Fig. 3), the absolute number of participants from schools rose significantly—from 106 in 2015 to 526 in 2016 to 1224 in 2018. By the end of 2018, the number of workshops had to be reduced because conducting the workshops for schools tied up too many resources. It turned out that workshops for schools were an ideal tool to promote the ViNN:Lab beyond university users (code 4.3, Table 1). Most workshops are designed to deliver a “take home” result, so participants had an artifact to show around at home and in school. This supposedly helped make the concept more understandable to nonusers and showed in a low threshold the capabilities of the maker space. Although these workshops can be considered as Third Number of participants 1500

1224 975

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500 0 58 48

106

53 132 109

232

351 92

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454 72

45

197

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0 2015 Kindergartens

2016 Primary Schools

2017 Secondary Schools

2018 Mixed Groups

Fig. 3 Number of participants in workshops for kindergartens and schools

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Mission activities (Roper & Hirth, 2010), workshops for schools played a significant role in acquiring third party funded projects, such as Horizon2020 Phablabs 4.0 (see: www.phablabs.eu) and brought the ViNN:Lab in contact with a number of institutions that in the long term acted as project partners. Using the environment and infrastructure of the ViNN:Lab, a special format, the innovation camp, was implemented several times during 2014–2019 (code 4.1, Table 1). Innovation camps can be characterized as an action-learning approach that allows the integration of different groups of stakeholders in a collaborative way. Normally, innovation camps address a limited number of students from different disciplines and take place in a physical facility such as a maker space. In this kind of setting, participants work intensely in cross-disciplinary groups within a limited time frame. In these camps, participants work across the problem-to-idea-to-concept stages (Bager, 2011, pp. 280 et seq.). Innovation camps enhance creativity and support the development of creative solutions for innovation management issues. This general approach allows the integration of students with different backgrounds, experts, and corporate managers. Furthermore, potential customers can participate in various ways to give valuable early feedback on new concepts. During the innovation camps, different methods from innovation, project, and technology management (e.g., creativity techniques, design thinking, graphic recording, rapid product development, agile project management methods, user tests) are implemented. The innovation camps offer the opportunity to invite externals, such as people representing civil society for tests and feedbacks of early prototypes developed in the ViNN:Lab. The innovation camps address current innovation challenges, mainly from SMEs and start-ups. The challenges are given to a group of students, who develop solutions by using innovation and technology management methods. In this sense, the innovation camps work as a knowledge transfer approach from both sides (students and companies). However, the organization of innovations camps requires high resources from teachers, facilitators, and corporations, in terms of preparation, briefings, and debriefings and builds on well-grounded methodological knowledge. The ViNN:Lab environment thus offers the infrastructure to promote an open innovation culture and support creative interactions.

4.3 Research and Development The ViNN:Lab’s research and development efforts include prototyping as well as testing and evaluating the feasibility of new products and services. R&D projects mostly mean collaborating with outside actors, which takes place within predefined project frameworks. Producing prototypes is the most common reason companies contact the ViNN:Lab (code 4.5, Table 1). Although this happens on a regular basis, the complexity and duration of collaborations vary. First, companies differentiate between horizontal (low fidelity) and vertical (high fidelity) prototypes (Böhmer et al., 2017, pp. 84 et seq.; Budde & Zullighoven, 1990, p. 421; Isa & Liem, 2020, pp. 2 et seq.). Horizontal low-fidelity prototypes, which are less detailed and have

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a reduced functionality, are used in early phases of product development cycles to support development teams to, e.g., define features and functions of a product (Pizana, Valdez, & Hernandez, 2018, pp. 172 et seq.). Vertical prototypes are used in later stages of the development process and are more technical and complex in nature and simulate in-depth functions of individual components or the complete prototype (Buskirk & Moroney, 2003, pp. 613 et seq.; Jones, 2007, pp. 128 et seq.). Due to its limited resources, the ViNN:Lab is more specialized to help with early stage— horizontal—prototypes, which require less staff involvement. Customers either seek one-off prototypes (e.g., for fitting purposes) or collaborate within an iterative process that requires frequent modifications and production. Typical examples for iterative processes are the development of lamps, components for mobility purposes, artworks, or textile applications. Customers who seek one-off prototypes use those for casting, reverse engineering shapes or fitting prototypes. Similar to prototyping, testing and feedback take place within a third party funded project in the area of 3D printing (code 4.6, Table 1). Next to printing materials (filaments) that are tested on a regular basis for their printability and possible applications, requests come from research institutions that want to, for example, test the conductibility of 3D printing materials. Feedback assessments also include public participation. Assessments are usually organized as user tests, where specific target groups are asked to interact with prototyped products or services. In a more complex project, the ViNN:Lab is part of the process for developing a new type of resin in combination with a fast printing DUP (direct UV-printing) printer. In this case, the ViNN:Lab will invite its users to work with the new machine and printing materials and systematically collect feedback by using eye-tracking technology or testing surveys. Another important contribution involves the testing of hardware components on machines themselves. The ViNN:Lab is tasked with experimenting with new types of adhesive tapes and nozzles or more rarely to beta test complete machines and give feedback on usability and reliability for usage in maker spaces.

5 Discussion and Results The presented case study exemplifies the KTT activities carried out by the ViNN:Lab. Those activities might differ distinctively from other university-based maker spaces due to the nontechnical background of its personal and the fact it is attached to a professorship of business management rather than engineering or informatics. Although this seems problematic in terms of researching new modes of production or developing new complex machines and software solutions, it also holds the opportunity to open up maker spaces in universities to more business strategy-focused KTT activities, as well as acting on a consultancy basis. Furthermore, it provokes strong collaboration with the engineering faculty and encourages interdisciplinary initiatives. Nevertheless, as shown in the previous section, the ViNN:Lab has engaged in a number of “nontypical” maker space activities, such as innovation camps or library

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workshops, which are more management focused and require less engineering and expert informatics knowledge. In general, university-based maker spaces profit from a number of circumstances that, in combination with their open and collaborative approach, create a certain attractiveness toward company cooperations. Being embedded in a university means having access to many areas of knowledge from different faculties, which can be called in case of more complex project requirements. Although maker spaces are still relatively novel concepts for many companies, the work in the university setting is providing the notion of a “trusted partner,” which helps with creating new projects. Another important point is the involvement of students in maker space activities. This includes operation and support as well as student projects being carried out in the maker spaces. By not allowing NDAs and other forms of confidentiality agreements, maker spaces incorporate an open approach that also allows for very fast response times to company requests. Although waiving secrecy agreements is a challenge for a number of companies, the expectations of the value of the results seem to outweigh the legal concerns. Novel approaches to dealing with customer demands and maker spaces acting like lean organized companies are probably the key factor for their continuing success. Given these preconditions, and the fact that the ViNN:Lab has operated for 6 years, this allows us to draw a number of conclusions about the suitability of maker spaces as a new approach for knowledge and technology transfer. 1. Enhancing university-industry interactions Compared to traditional research activities, maker spaces attract a high amount of demand from SMEs. Mostly driven by the sophisticated technical equipment and to a lesser extent through the availability of methodological knowledge, smalland medium-sized businesses from different branches of industry have made contact with the ViNN:Lab. Although there is no specific pattern to the number of contacts over time, in general between three to four companies reach out to the maker space every month. 2. Attracting new and unexpected partners By providing state-of-the-art machinery and knowledge, universities act as partners for companies seeking support in their R&D efforts. For purposeful collaborations, companies usually look for partners who strategically contribute to reaching the goal of the research and development projects. In the case of the Technical University of Applied Sciences Wildau, those partners usually come from industries such as biosciences, optical technologies, or informatics. By providing the means for prototyping and experimental materials handling for actors from arts and culture, visual artists have become a new target group for collaborative projects. Next to visual artists, archaeological institutions and museums are exhibiting increased demand for maker space competencies and seeking cooperation. Considering the spatial proximity of the Technical University of Applied Sciences Wildau to Berlin and its thriving creative cluster, this

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development might be fostered by spillover effects and thus not representative of university-based maker spaces in smaller cities. 3. Acting as a lab for new ideas Maker spaces are places for knowledge creation and interdisciplinary knowledge exchange. They provide many insights into new social practices, such as do-it-yourself production, the circular economy, and interdisciplinary collaboration. Although users experiment with new modes of production and develop new products or find novel ways to collaborate, maker spaces provide a safe environment where failure is always an option and learning from mistakes is a common and welcome practice. People tend to use maker spaces to experiment and work on new ideas. By monitoring these activities, researchers and companies alike can obtain an early understanding of novel ideas. Additionally, by using maker spaces, small- and medium-sized companies have the chance to get in contact with open innovation approaches that can open them up for future cooperation. They also learn about new work practices, such as coworking, of which smaller companies often are not accustomed.

6 Conclusions In this case study, we discuss the role and impact of a selected university-based maker space. Maker spaces are an expression of the maker movement and are of increasing interest for universities in Germany. The presented insights, based on data and information, collected over a period of 6 years, can be of interest to other universities operating under similar or at least comparable preconditions as the Technical University of Applied Sciences Wildau. We presented how outreach activities can become part of the maker space activities, how practical interactions and especially opening up for the public can be carried out, and how R&D activities can be conducted. The results indicate that the ViNN:Lab strongly supports interactions with civil society, as shown by the appearance and number of workshops with schools or even kindergartens, or the openness for the public on OLDs and during fairs and civil exhibitions. The low-threshold access to the maker space distinguishes the maker space strongly from traditional university labs, which are not as easily accessible. Besides civil society attraction, the maker space can also act as a door opener for collaborative projects with companies and other partners to conduct joint research projects. As seen in the ViNN:Lab case, the maker space attracts new partners (e.g., artists), who are normally not in the focus of the university but have the potential to bring in new research and development perspectives. As with many maker spaces, the ViNN:Lab started as an initiative of a rather smaller unit within the university, but since the activities are touching many aspects with regard to the university’s Third Mission, as well as many disciplines and departments within the university, its organizational belonging to the center for research and transfer, as a university-wide acting entity, is unfolding the maker space’s full visibility and potential with respect to the university’s Third Mission.

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Even with many similar activities (e.g., OLDs, workshops), university-based maker spaces still need to customize their facilities and activities according to the respective university and its activities and goals for knowledge and technology transfer. There is no “one size fits all” approach. For future studies, researchers should extend the number of cases to learn more about the diverse activities of different types of university maker spaces with regard to the Third Mission and to effect stronger validation and pattern building. Furthermore, it is important to assess how university-based maker spaces are operating over the long term to determine standards. It is also of interest to investigate how the informal learning setting is curated by maker spaces and what are the impacts on teaching and learning in Higher Education Institutions.

References Bager, T. (2011). The camp model for entrepreneurship teaching. International Entrepreneurship Journal, 7, 279–296. Barrett, T., Pizzico, M., Levy, B. D., Nagel, R. L., Linsey, J. S., Talley, K. G., … Newstetter, W. C. (2015). A review of university maker spaces (Georgia Institute of Technology, Paper ID #13209). https://smartech.gatech.edu/bitstream/handle/1853/53813/a_review_of_uni versity_maker_spaces.pdf. Accessed 14 May 2020. Böhmer, A. I., Schweigert, S., Devecka, J., Grauvogl, C., Becerril, L., Bahrouni, Z., & Lindemann, U. (2017, June 27–29). Towards agile development of physical products a startup case study. 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 78–85). https://doi.org/10.1109/ice.2017.8279872. Budde, R., & Zullighoven, H. (1990, May 8–10). Prototyping revisited. COMPEURO’90: Proceedings of the 1990 IEEE International Conference on Computer Systems and Software Engineering—Systems Engineering Aspects of Complex Computerized Systems (pp. 418–427). https://doi.org/10.1109/cmpeur.1990.113653. Buskirk, R. V., & Moroney, B. W. (2003). Extending prototyping. IBM Systems Journal, 42, 613– 623. https://doi.org/10.1147/sj.424.0613. Feller, J, Finnegan, P., Hayes, J., & O’Reilly, P. (2010). Leveraging ‘the crowd’: An exploration of how solver brokerages enhance knowledge mobility. ECIS 2010 Proceedings. 16, 1–13. http:// aisel.aisnet.org/ecis2010/16. Accessed 14 May 2020. Grønbæk, K. (1989). Rapid prototyping with fourth generation systems—An empirical study. Office Technology and People, 5(2), 105–125. https://doi.org/10.1108/EUM0000000003530. Hartmann, F., & Mietzner, D. (2017, June 18–21). The maker movement triggering collaborative innovation? A qualitative media analysis. Full paper accepted for presentation at XXVIII ISPIM Conference, Vienna. Isa, S. S., & Liem, A. (2020). Exploring the role of physical prototypes during co-creation activities at LEGO company using case study validation. CoDesign, 1–25. https://doi.org/10.1080/157 10882.2020.1715443. Jones, M. C. (2007). Patchwork prototyping with open source software. In S. A. Kirk & S. Brian (Eds.), Handbook of research on open source software: Technological, economic, and social perspectives. Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-59140-999-1.ch011. Massachusetts Institute of Technology. (2012). The fab charter. http://fab.cba.mit.edu/about/cha rter/. Accessed 22 April 2020.

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Nielsen, J. (1989). Usability engineering at a discount. Proceedings of the Third International Conference on Human-Computer Interaction on Designing and Using Human-Computer Interfaces and Knowledge Based Systems (2nd ed., pp. 394–401). Boston, MA: Elsevier Science Inc. Papavlasopoulou, S., Giannakos, M. N., & Jaccheri, L. (2017). Empirical studies on the Maker Movement, a promising approach to learning: A literature review. Entertainment Computing, 18, 57–78. https://doi.org/10.1016/j.entcom.2016.09.002. Patton, E., & Appelbaum, S. H. (2003). The case for case studies in management research. Management Research News. Peppler, K., Halverson, E., & Kafai, Y. B. (2016). Makeology: Makerspaces as learning environments (Vol. 1). New York: Routledge. Peschl, M. F., & Fundneider, T. (2014). Why space matters for collaborative innovation networks: On designing enabling spaces for collaborative knowledge creation. International Journal of Organisational Design and Engineering, 3, 358–391. http://www.inderscience.com/offer.php? id=65072. Pizana, J. E. P., Valdez, S. O. & Hernandez, L. M. B. (2018). Prototyping: Applications of Conscious Innovation in Organizations. Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-5225-40236.ch005. Roessler, I., Duong, S., & Hachmeister, C. D. (2015). Welche Missionen haben Hochschulen?: Third Mission als Leistung der Fachhochschulen für die und mit der Gesellschaft. Centrum für Hochschulentwicklung gGmbH. Roper, C. D., & Hirth, M. A. (2010). A history of change in the third mission of higher education: The evolution of one-way service to interactive engagement. Journal of Higher Education Outreach and Engagement, 10, 3–21. TH Wildau. (2020). Die TH Wildau im Profil .www.th-wildau.de/hochschule/ueber-uns/profil/. Accessed 24 May 2020. ViNN:Lab. (2020a). Makerspace der TH Wildau - offen für alle. www.th-wildau.de/vinnlab. Accessed 12 January 2020. ViNN:Lab. (2020b). KiVi:Lab. www.th-wildau.de/kivilab. Accessed 12 January 2020. Yin, R. K. (2009). Case study research: Design and methods (4th ed., Vol. 5). Applied Social Research Methods Series. Thousand Oaks: Sage.

Does Technology Scouting Impact Spin-Out Generation? An Action Research Study in the Context of an Entrepreneurial University Christian Schultz

Abstract This study sheds light on the benefits, challenges, and shortcomings of a new technology scouting program to increase spin-out company creation at an entrepreneurial university. Through a canonical action research approach, it becomes clear that a technology scouting instrument such as a customized technology radar is advantageous to discover and analyze technologies with transfer potential. However, it is not sufficient to sustainably increase spin-out activity. After initial success, the “supply” of technologies and motivated scientists is exhausted. Consequently, the spin-out volume drops sharply. It becomes clear that the widespread lack of genuine entrepreneurial motivation among scientists is a massive hindrance in transferring technologies from the lab into the private sector through spin-out companies. In the medium term, a holistic approach to technology transfer support that complements technology scouting with a structured team matching process might be able to connect technologies with motivated teams and raise the level of spin-out activity again. In the long term, other factors contribute to the development of an entrepreneurial university, such as an adequate incentive system or a focused recruiting strategy. Keywords Technology scouting · Technology transfer process · Spin-out companies · Technology radar

1 Introduction Universities play an instrumental role in transferring technology out of the research lab and into the private sector. The main technology transfer channels include licensing, selling, or supporting spin-out company creation. In addition to direct technology transfer as the means to move technology from different organizational contexts into new institutions (Roessner, 2000), universities also enable technology transfer indirectly by training and educating their students for future jobs in different industries (Carayannis, Rogers, Kurihara, & Allbritton, 1998). C. Schultz (B) HWTK—University of Applied Sciences, Bernburger Straße 24/26, 10963 Berlin, Germany e-mail: [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_7

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The traditional way of transferring technology from the public to the private sector used to be through licensing (Siegel, Waldman, & Link, 2003). From the point of view of the university, licensing has the advantage of creating an additional revenue stream that can be channeled into other promising research projects without major upfront investments. This situation explains the finding by Markman, Phan, Balkin, and Gianiodis (2005) that university technology transfer offices overemphasize royalty income. Licensing as a technology transfer mechanism inevitably has different disadvantages. First, many technological advancements are not patentable; therefore, generating royalties is impossible. Second, universities might only be able to claim a small share of the overall commercial benefit by licensing. Through a spin-out company, knowledge and commercial benefits remain partially inside the university. Further, long-term potential technology enhancement or even the development of related technology might materialize, which leads to additional value for the organization. Consequently, Bray and Lee (2000) find that possessing equity in a spin-out company has a larger return than licensing. This fact contributes to the finding by Feldman, Feller, Bercovitz, and Burton (2002) that taking an equity share in a company became increasingly popular in US universities through the 1990s. This study concentrates on the technology transfer channel of spin-out companies and analyzes the impact of additional resources, in particular, the implementation of a technology scouting program at an entrepreneurial university in Germany. The guiding research question is as follows: How does the implementation of an institution-wide technology scouting program contribute to spin-out formation? The benefit of this study is twofold. First, readers can follow the implementation of a new technology scouting program in the context of the University of Potsdam, a medium-sized entrepreneurial university in Germany. Second, the specified findings regarding technology scouting help researchers and practitioners analyze benefits and shortcomings to refine technology transfer support designs. This chapter is structured as follows. Next, I discuss the theoretical background of this research study, especially the rise of the entrepreneurial university and technology scouting. In Sect. 3, I outline the canonical action research approach that structures the research process as well as its results and specified findings. In the conclusion, I summarize the main points and identify limitations.

2 Theoretical Background In his seminal paper, Etzkowitz (2003) identifies the evolution of the university’s mission from a sole focus on teaching to a mission where economic and social developments are an integral part. Multiple research groups who work in different departments conduct research inside the university. The group’s researchers need to compete for resources, especially funding from external sources, to keep the group “alive” to continue with their research endeavors. The competitive environment facilitates the development of firm-like qualities and processes, which ultimately leads to

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research groups as quasi-firms. Therefore, the research university on the meso-level resembles a start-up even before it directly engages in entrepreneurial activities. Markman et al. (2005) make the important point that the development toward the entrepreneurial university in the USA was strongly facilitated by different legislation, such as the 1980 Bayh–Dole Act, the 1980 Stevenson–Wydler Act, and the 1985 Federal Technology Transfer Act. These laws enabled universities to enact technology transfer mechanisms, which have led to tremendous growth in the volume of patents and licensing royalties since the 1990s (Mowery & Shane, 2002). In addition to new legislation, different changes in the business landscape contributed (Bercovitz & Feldman, 2006). Chesbrough (2006) identifies the openness of companies to work with different actors collaboratively, and the increase in available venture capital provides the necessary capital for entrepreneurial activities. Etzkowitz (2003) draws his conclusions mainly from the US experience, especially institutions such as Stanford and MIT. A European role model of an entrepreneurial university is the University of Twente in the Netherlands (Lazzeretti & Tavoletti, 2005). In terms of methodology, it is appropriate to use handpicked examples to exemplify a point. However, not all of the conclusions might hold true for average universities, emergent entrepreneurial universities, or even non-US universities. All of the mentioned examples show that an institution-wide entrepreneurial climate, where entrepreneurial thinking and acting exist on all levels, is a main factor in fostering the creation of spin-out companies. When individuals act entrepreneurially, they pursue opportunities without regard to resources they currently control (Stevenson & Jarillo, 1990). Successful universities that cannot necessarily rely on decades of entrepreneurial tradition, like MIT can, have a clear strategy regarding how they want to keep the institution-wide entrepreneurial spirit alive and how they plan to create spin-out companies (Lockett, Wright, & Franklin, 2003). O’Shea, Allen, Chevalier, and Roche (2005), in their empirical study on US universities, find that the success of frequently cited leading institutions in spin-out creation is due to the path-dependent configuration of different capabilities. These capabilities are built by dedicating additional resources to the creation of an entrepreneurial climate (Siegel, Waldman, Atwater, & Link, 2004). In general, technology scouting is practiced in large companies that compete in dynamic technological environments and are dependent on not missing out on technological developments. Rohrbeck (2010) describes the technology scouting cases of three telecommunication multinationals—Deutsche Telekom, Teléfonica, and British Telecom. In a company setting, technology scouting is the search for new technological fields (technology monitoring) or new technological opportunities (technology scanning) in sectors that are currently not covered by the company. Normally, a team of technology scouts identifies and assesses new technological developments and reports its findings to high-level management. The scouts’ abilities in creating and using a network of experts and technology holders are of paramount importance for success in the data gathering process. Then, the multiple data sources are evaluated through the so-called technology radar (some authors suggest the name “innovation radar”). This tool visualizes different technological fields and sectors by using customized selection criteria. The primary benefit of the technology radar for

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companies is the structured and high-quality intelligence on the maturity of technologies that might become relevant for their future business activities. Golovatchev et al. opine that the technology radar is a good strategic tool for early-stage identification and rough prioritization of what technology to focus on. During the companywide planning process, the technology radar can act as a high-level summary of a sophisticated prioritization process. The proactive and real-time information of this instrument is its main benefit in comparison with other mechanisms, such as patent analysis, which relies on time-lagged publication by the patent office and is not as rich in information as reports by dedicated technology scouts in the detection of technological developments. At the time of this study, published cases of technology radars came predominantly from the private sector. I present the planned design of the technology radar in Sect. 3.3 by a public institution.

3 Research Methodology Action research (AR) is a special variant of the case study method (Baker & Jayaraman, 2012). Generally, case study research has advantages in answering the how and why questions over other methodologies in social sciences, especially quantitative research designs (Yin, 2003). In management science and related fields, the studies of Eisenhardt (1989) and Eisenhardt and Graebner (2007) on the methodological issues and practices of case study research are widespread and have become a quasi-standard. While their case study practice stems from the methodological foundation of grounded theory, which was mainly developed by Glaser, and Strauss (1999) and Strauss and Corbin (1997), AR has different and older origins, stemming from the research of the famous psychologist Kurt Lewin (1946). The major difference between these two methodological approaches, in addition to deeper philosophical issues, is that the researcher is not limited to an observer’s role but is actively involved with the research subject. Contrary to case study research as proposed by Eisenhardt (1989), there is no dominant practice or standard for the practice of AR. This limits its application because researchers are unsure of which protocol to follow for valid results that are accepted by their peers. Furthermore, it is not simple for researchers to educate themselves in the application of AR due to definitional confusion: The action research literature is rather imprecise in its basic terminology. The term “action research” is itself used, on the one hand, to refer both to a general class of methods in social enquiry and on the other hand, to a specific sub-class of those methods as distinguished from “action science”, “action learning”, “participatory action research.” (Baskerville, 1999, p. 6)

In the development of action research, different methodological approaches have been put forward (Checkland & Holwell, 2007), e.g., action design research (Sein, Henfridsson, Purao, Rossi, & Lindgren, 2011), innovation AR (Kaplan, 1998), canonical AR (Davison, Martinsons, & Kock, 2004), normative AR (Babüroglu & Ravn, 1992), or, more recently, statistical AR (Durcikova, Lee, & Brown, 2018). Chiasson,

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Germonprez, and Mathiassen (2008) opine that over a dozen distinct methods of AR exist. This study adheres to the principles of canonical AR in accordance with Davison, Martinsons, & Ou (2012).

3.1 The Action Research Cycle The AR strategy has the potential to contribute new research results, wherever change and dynamic effects lay at the core of the research interest. Those areas include technology transfer (Mietzner & Schultz, 2014), social entrepreneurship (Gedajlovic, Honig, Moore, Payne, & Wright, 2013), or the growth decisions of technology startups (Zhang, Levenson, & Corssley, 2015). According to Argyris, Putnam, & McLainSmith (1982), the following are significant elements in such an approach to research: ● It is a collaborative process between the researcher and the people in a social situation. ● The research is critical and reflective. ● The focus is on social practice. ● It is a process that explicitly relies on reflective learning. Susman and Evered (1978) define different phases for the conduct of an action research study. This general process is still state of the art and is fit to guide the research process. AR is a cyclical process that starts with the diagnosis of the problem and continues with action planning, action taking, evaluation, and specifying learning. The cyclicality of the research ensures that the results have a preliminary character and can be improved over the course of the process (Riel, 2010). AR is a collaborative research approach that involves collaboration between researchers and social actors from the area of the research subject. Table 1 provides an overview of the cyclical AR process applied to this study. There is a methodological dispute about the role of theory in the AR process. McKay and Marshall (2001) opine that theory is a necessity for AR. Other researchers such as Bunning (1995) and McTaggart (1991) argue that theory application before the start of the AR project might hinder valuable results, and Cunningham (1993) makes the point that it is highly unlikely that the researcher could identify the adequate guiding theory beforehand. This study uses the resource-based view of the firm (Peteraf, 1993) as its theoretical guidance. Resources are an organization’s bundle of tangible and intangible assets and capabilities (Amit & Schoemaker, 1993; Barney, Wright, & Ketchen, 2001), which are fundamental to firm growth (Penrose, 1959; Wernerfelt, 1984), competitive advantage (Barney, 1991; Peteraf, 1993), and performance (Crook, Ketchen, Combs, & Todd, 2008; Newbert, 2008; Rumelt, 1984). Typical resources and capabilities include management skills, organizational processes, and information. Due to the ubiquitous role of resources in organizational processes and outcomes, scholars focus on several issues that relate to resources and resource usage, such as identifying which resource characteristics are critical (Barney, 1991;

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Table 1 Overview of the study’s AR research process (following the steps of Susman and Evered [1978, p. 588]) No.

Step

Description

1

Diagnosing (Identifying or defining a problem)

• See Sect. 3.2 • A thorough analysis in the grant application program “EXIST IV Start-up Culture” comes to the following conclusions: – The University of Potsdam is successful in winning research grants, especially in the science departments, but technology transfer and especially entrepreneurial activity is rather low – There is no systematic technology scouting that enables the management of the technology transfer process. To establish a systematic technology transfer process, a technology scouting program is essential

2

Action planning (Considering alternative courses of action for solving a problem)

• See Sect. 3.3 • The plans for a technology scouting process with different instruments, e.g., the technology radar and the transformation, are presented

3

Action taking (Selecting a course of action)

• See Sect. 3.4. • Technology transfer managers gather data and form a network inside the natural sciences faculty of the university • The data are visualized through different instruments such as the technology and the transformation radar • Different shortcomings of the original plans and challenges arise during the course of the project

4

Evaluating (Studying the consequences of an action)

• See Sect. 3.5 • The impact of the newly established instruments on spin-out activity is analyzed and different advantages and challenges are described

5

Specifying learning (Identifying general findings)

• See Sect. 3.6 • Different findings with regard to the impact of technology scouting, technology scouts, and the mayor challenges and barriers are presented

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Dierickx & Cool, 1989; Rasche & Wolfrum, 1994) or in which way they are allocated (Conlon & Garland, 1993) and constructed efficiently (Baker & Nelson, 2005). The underlying hypothesis of this study is that additional valuable resources, through the implementation of a technology scouting program, will have a positive effect on the desired outcome. Ergo, a newly funded technology transfer support program, will result in a significant increase in spin-out activity.

3.2 Diagnosing In Germany, various initiatives have aimed to increase the creation of spin-out companies during the last three decades. Public efforts are bundled in the EXIST (in German: Existenzgründungen aus der Wissenschaft; in English: income substitution/start-ups from science) program. In addition to the direct impact of funding schemes, where capital and expertise are available for spin-outs, e.g., the EXIST research transfer program, the “EXIST IV Start-up Culture” program aims to establish a sustainable entrepreneurial culture at selected German universities. The University of Potsdam regularly achieved above-average ratings in various university rankings on startup friendliness (Schmude, Heumann, & Wagner, 2009). In 2010, the University of Potsdam had more than 15,000 students, and its research facilities were part of a network of different high-class external research institutes funded by different reputed research entities such as the Fraunhofer or Leibniz association. The University of Potsdam is the major public research institution in the federal state of Brandenburg. A strong indicator for the excellence of its researchers, especially in the science department, is the increasing volume of competitive research grants received over time (see Fig. 1). Despite its research excellence and the accompanying development of technologies, institution-wide technology transfer success remained low. At that time, technology transfer was driven by some exceptional scientists, who explicitly asked the university’s administrators for help in patenting inventions. The university’s administration realized that the lack of a systematic technology transfer program was a major hindrance to becoming a true entrepreneurial university. In 2011, the University of Potsdam won a grant of approximately 3 million e in the publicly funded “EXIST IV Start-up Culture” program to establish an institutionwide technology transfer support program. The main reasoning for the grant application was to lower the disparity between high research achievement, as indicated by success in competitive research grants, and low level of technology transfer activities, especially spin-out creation. The overall project consisted of three subprojects (entrepreneurship academy, technology scouting, and entrepreneurial culture) and two project phases (phase A: 10/2011–09/2014, phase B: 10/2014–10/2016). The mission of the entrepreneurship academy was to establish an extracurricular education for potential entrepreneurs while the entrepreneurship culture subproject was to strengthen the institution-wide entrepreneurial spirit. This study concentrates on the implementation and the effects

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60 58,818 €

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Fig. 1 Research grants (overall) and research grants (Science Department) of the University of Potsdam from 2008 to 2018 in Mio. e (based on: University of Potsdam (Ed.), 2019)

of the technology scouting subproject. The author was the head of the project team that implemented the entrepreneurship academy program and worked collaboratively with the technology scouting project team from 2011 to 2014 to harmonize all project steps and to enhance the impact of the overall project. I used different data sources in the course of the research process. Interviews, informal talks with different team members, and observations were recorded in a diary-like fashion. Upcoming issues were regularly discussed with team members, and I made different research notes. Furthermore, over the course of 36 project months (10/2011–10/2014), different documents ranging from emails, presentations, and official project records (EXIST IV start-up culture grant application, progress reports, memos) were analyzed and evaluated.

3.3 Action Planning Table 2 is an excerpt of the timetable and deliverables of the EXIST IV project to foster technology transfer. Major rescheduling did not take place in the first 3 years. In 2014 and 2015, the deliverables were readjusted in advance because it became clear that the target deliverables were too optimistic.

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Table 2 Excerpt of project timetable in 2010 (used with permission from Dana Mietzner, based on: Universität Potsdam—Potsdam Transfer [2010, pp. 31–32]) Milestone Name

Time

Deliverable

Overall project MS 1.5

Start-up monitor Year 1

Year 1, after 12 months • 30 spin-outs, 10 technology-based

MS 1.6

Start-up monitor Year 2

Year 2, after 24 months • 40 spin-outs, 20 technology-based

MS 1.7

Start-up monitor Year 3

Year 3, after 36 months • 50 spin-outs, 25 technology-based

MS 1.8

Start-up monitor Year 4

Year 4, after 48 months • 55 spin-outs, 30 technology-based

MS 1.9

Start-up monitor Year 5

Year 5, after 60 months • 60 spin-outs, 35 technology based.

Subproject technology scouting MS 2.1

Detailed concept

Year 1, after 12 months • Detailed concept technology radar • Templates and checklist for technology scouts • Update project schedule

MS 2.2

Technology radar software

Year 2, after 24 months • Technology radar software beta version

MS 2.3

Mid-project evaluation

Year 3, after 36 months • Evaluation of all subprojects´ benefits • Re-scheduling of the remaining tasks

MS 2.4

Monitoring technology key Year 4, after 48 months • Network of 30 research stakeholders 1 institutes

MS 2.5

Monitoring technology key Year 5, after 60 months • Network of 60 research stakeholders 2 institutes

In Sect. 2 of this study, I outline that the technology radar originates from usage in private companies to detect relevant technological developments. For a public institution, e.g., a large public research institute or a university, no published precedent existed at the time the project started. Therefore, the project team planned the development of the technology radar by following the underlying logic of examples in large private companies (Rohrbeck, 2010). The technology radar’s mission for the project was to visualize the state of a technology in regard to its transfer potential via a spin-out company. According to the project plan, the technology scouts’ primary objective was to establish contacts with research group leaders and to maintain them over time (see Table 3). In parallel, they established a network with large private companies, SMEs, and additional research institutions regionally and internationally. Technologies were scouted through interviews with research group heads via structured interviews and group discussions in the so-called “Clusterworkshops.” During these workshops, the

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Table 3 Overview of planned targets and tasks for technology scouts (used with permission from Dana Mietzner, based on: Universität Potsdam—Potsdam Transfer [2010, p. 13]) No.

Activities

Deliverables

Internal scouting • Identification of • Monitoring and Technology technologies with evaluation of R&D strategy manager spin-off potential projects • Initiation of (instruments: interdisciplinary technology profiles cooperation and technology • Increasing the radar) number of • Classification of technology and R&D projects in the knowledge intensive technology radar and start-ups creation of • Increase attention transformation for start-up activities profiles • Assessment of start-up options and alternatives (e.g., cooperation, patent strategies) • Scanning third-party funding applications, publications, patent applications • Creation of profiles of inventors/scientists (e.g., video pods) • Reports for deans, research group heads, and other stakeholders

Description • Workshops at institutes and scouting meetings to identify R&D projects (instruments: technology profiles and technology radar) • First contact person at the research institutes for transfer activities (“office hours” in the institutes) • Implementation of seminars and events (e.g., high-tech Starter Lounge, Market of Technology/Market of Ideas, Technology Entrepreneurship Summit) • Consultations for researchers for technology transfer activities • Welcome meetings with new scientists (Welcome package)

(continued)

central tool to gather data in a systemized way was the technology profile. Afterward, all information was stored in a database, which was regularly screened and further supplemented. In the second year of the project, the data were visualized via a newly developed IT tool. In the following paragraph, the different planned instruments of technology scouting are described in more detail: ● Clusterworkshops Clusterworkshops were a platform to discuss and examine technological research results with scientists. The workshops started with two to three informative, technology-specific short presentations by the scientists. The scouts created technology profiles via a group discussion in interdisciplinary groups. Finally, the scientist received concrete recommendations for further actions in the transfer process. ● Technology profile

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Table 3 (continued) No.

Activities

External scouting • Identification of application areas for Technology new technologies marketing • Establishing manager relationships with partners, SMEs, and research institutions in the region, nationwide and internationally • Increasing network capability and the level of awareness for the UP as an entrepreneurial university

Deliverables

Description

• Scanning the • scientific/technological environment to identify possible fields of application • Targeted search for complementary technologies, cooperation partners, • potential customers, and fields of application • Assessment, storage and preparation of information • Provision of industry and market-related background knowledge (trends, technology, market, competition, and competitors) and fields of application and complementary research projects and technologies • Creation of profiles of inventors/scientists (e.g., video pods) • Reports for deans, research group heads, and other stakeholders

Targeted search for complementary technologies, cooperation partners, potential customers, and fields of application in the frame of interviews and personal talks First contact person for companies and their scouts, who are looking for joint R&D projects

The technology profile was the result of a standardized analysis of a technology and its potential with regard to the current status of development, potential application, and market opportunities. To evaluate the technology’s development stage, the scouts summarized a brief verbal description of the technology and assessed the application potential in the long-, medium-, or near-term. Another sector of the technology profile was the overview of competitive technologies, including their advantages/disadvantages. Additionally, complementary technologies were listed and described briefly. The scouts assessed the development time until a demonstrator or prototype was likely to be ready and whether the protection of intellectual property rights was possible. Then, the scouts summarized influencing factors that could hinder or promote development. After a comprehensive description of the technology’s development, the scouts examined the partner network of

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the technology owner, either the scientist or the involved working group. The aim was to determine who was a potential key partner in further technology enhancement. On this basis, the scout defined utilization options, fields of application, or already possible products or services. Then, the scouts systematically determined the need for further support to address different transfer channels. The technology profile is supplemented with literature analysis or expert interviews. ● Technology and transformation radar After analyzing the literature on technology radars, especially the experience reports at Deutsche Telekom (see Golovatchev, Buddeund, & Kellmereit, 2010; Rohrbeck, Arnold, & Heuer, 2007), the project team designed a prototype for a technology radar that visualized technologies with high spin-out potential. At the core of the technology radar (see Fig. 2) lay the collected data of the technology profile. The radar clarified (1) the assignment to the respective research discipline and its research areas, (2) the advised time to market of the developed applications, (3) the last contact between scientist and scout with a traffic light system, and (4) the state of the property right proceedings. A semantic search made connections between research projects visible. Another instrument to monitor progress in the spin-out process was the transformation radar (see Fig. 3), which systematically recorded the potential implementation of the technology in a business model. The transformation profile collected indicators that enabled the description of the business potential. The results of the transformation profiles were visualized in the transformation radar, which formed the basis for the derivation of innovative business models. Based on the Computer Science Agricultural Sciences

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Fig. 2 Technology radar sketch (used with permission by Dana Mietzner, based on: Universität Potsdam—Potsdam Transfer [2010, p. 10])

Does Technology Scouting Impact Spin-Out Generation? … Business Potential

Expected Growth Potential

119 Type of Innovation and State of Research

Patentability of the Technology

Market Impact

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(potential market size, disruptive potential, cost reduction)

(novelty, improvement, extension, new development)

Process Innovation

Differentiation Potential

(novelty, improvement, extension, new development)

Customer Integration in Poduct Development

Service Innovation (novelty, improvement, extension, new development)

(scope and orientation of the product to market and customer requirements)

Crossover-project Existing Contacts to Potential Customers

(dual use, Plattform Innovation)

Technological Feasibility / Technical Risk (complexity,

Cooperation with Research Institutes Embedding in current research trends or complementary projects

Network

implementation risk, costs)

Founding Team (completeness and organisation of the company)

Complementary Technologies

Potential for Implementation and Applicability

Fig. 3 Transformation radar sketch (used with permission from Dana Mietzner, based on: Universität Potsdam—Potsdam Transfer [2010, p. 11])

representation in the transformation radar, transformation measures for the further development of the technologies could be derived, and cooperation partners could also be gained to close skills gaps. Further measures such as special workshops for detailed business model development could then be planned and implemented.

3.4 Action Taking The project was implemented as it was planned in the grant application. Several adjustments were necessary during the course of the project, as team members generated more know-how about the technology scouting process. From a project management standpoint, the project was rather hard to manage efficiently, mainly for the following reasons: 1. Lack of experienced personnel: While the project’s administration had a plan regarding how to develop and implement technology scouting, no team member had executed a similar undertaking before. In general, qualified personnel, especially for the technology scout positions, were scarce, and the hiring process proved to be a real challenge. Highly qualified candidates with relevant job experience were not interested in a meager public salary with a time-limited contract for 3 years, which was the duration of project phase A. Normally, the hired technology scouts had no job experience, and therefore, the period of vocational adjustment was long, which delayed the project’s timetable regularly. The

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necessary catch-up management measures put the whole project team under considerable performance stress. 2. Public spending rules: Satisfying the university’s processes and standards regarding hiring, purchasing, and furnishing offices took considerable time and management resources. These resources were desperately needed in training new technology scouts. This circumstance put even more stress on the management team to perform. 3. Negative group dynamics: The coordination between the three subprojects was challenging as well, as only a small part of the team had worked collaboratively together before. The three subprojects—technology scouting, entrepreneurship academy, and entrepreneurship culture—were very different regarding their missions and goals. After the first project year, this led to signs of groupthink and opportunistic behavior, as every team cared only about its own deliverables and did not spare time to help other teams. Therefore, the project’s management was constantly alert to take countermeasures against these destructive tendencies, such as regular project meetings and individual conversations. In general, the project team practiced an open communication atmosphere, where everybody was encouraged to articulate his or her improvement ideas without regard to hierarchical position. The Clusterworkshop was tested and served primarily as an instrument to form a network with motivated scientists and to gather input data for the technology radar. In 2013, a total of 36 scientists participated with 22 potential spinout projects in 8 Clusterworkshops. The Clusterworkshop centered around a moderated group work process, where confidential information and different ideas were shared openly. The Clusterworkshop was complemented with the newly developed Transformationworkshop, which was aimed primarily at deepening the connections to technology holders. In this workshop, the participants outlined a business model and discussed the next steps to initiate a spin-out company. The experience of the first 2 years of the project showed that the implementation of the Transformationworkshop was essential to achieve results regarding spin-out activity.

3.5 Evaluating In 2014, when project phase A came to a close after 3 years, the project’s management analyzed the overall progress of the project and its output indicators. All findings were summarized in a large report to the project’s execution organization, which included different analyses of the subprojects and an application to execute phase B of the project for another 2 years to achieve additional results. The author contributed to this report either by writing parts or providing information to the project’s administration. This report is a main data source for the evaluation section. Other sources include memos, meeting protocols, and informal talks with the involved project’s employees and administrators. The progress report (Universität Potsdam—Potsdam Transfer, 2014) stated that the planned technology scouting approach was practiced

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successfully, and different improvements regarding the original plans were continuously tested and refined. At the end of the second project year, the Cluster- and Transformationworkshop had surpassed the prototype stage and were used regularly. Furthermore, the software package to visualize the technology and transformation radar worked. The spin-out activity increased considerably during the project’s implementation phase (Universität Potsdam—Potsdam Transfer, 2018). Out of the 40 spin-out companies that were generated in 2013, the majority were knowledge and technology based (see Fig. 4). The expanding network resulted in a strong demand by 76 scientists for support services to apply for start-up grants. In addition to the success of the technology scouting program, the progress report also outlined shortcomings. The propensity to initiate a spin-out company, especially among scientists, was rather lower than expected. This resulted in a severe lack of entrepreneurial teams that were willing to start an entrepreneurial process that would lead to the formation of a spin-out company. The project’s administration aimed at attenuating this bottleneck by establishing team matching formats, where motivated technology holders had the opportunity to network with entrepreneurial-minded people to discuss a potential joint formation of a spin-out company. It proved to be challenging throughout the project to hire technology scouts who were able to adequately fill their role. On the one hand, technology scouts needed to be competent in their assigned technological area, and on the other hand, they needed to have at least a basic understanding of business activities such as opportunity recognition or business planning. 55 55

45 40

35 35

25 20 16

15 9 10

5 2012

2013

2014

2015

2016

2017

2018

Fig. 4 Spin-out activity from 2012 to 2018 at the University of Potsdam (based on: Universität Potsdam—Potsdam Transfer [2018, p. 14])

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The project team did not necessarily look for team members who had a science and a business degree, but scouts needed to demonstrate entrepreneurial orientation either by having worked in a business development division of an existing company or by having participated in an entrepreneurial team. Despite these problems, in 2014, the project organization committee approved the application for project phase B. Retrospectively, the growth in spin-out activity persisted in 2014, but in 2015, the spin-out activity dropped to the lowest levels seen (see Fig. 4). Surprisingly, this occurred in the second project phase, where professional technology scouting instruments were available. However, this resource improvement did not necessarily translate into sustainable long-term high spin-out activity. After 2014 and the significant rise in spin-out activity, the supply by scientists for spin-out activities seemed to be simply exhausted for the following years and only started to increase again in 2017 at a very low level. The main explanation for this development lays in the fact that the project team miscalculated the motivation of the scientists to engage in spin-out activities. Some professors and research group heads were very open to spinning out technologies through a company and had already done that before. Their main concern rather was that they could not spare the necessary time to manage a new operation and that they needed additional external capital funding. The majority of scientists were not sincerely interested in founding a company. This observation corresponded with a survey of different interest groups at the University of Potsdam, where it became clear that the bundle of motivational factors by scientists were not well suited to promote spin-out activities (Schultz & Bröker, 2015, p. 43). A scientist does not necessarily need to participate in a spin-out company to use his or her knowledge or to find a challenge. To form entrepreneurial teams exclusively out of scientists proved to be hard, mainly because of their lack of genuine motivation to act entrepreneurially. Adding a business-oriented team member to form a functional entrepreneurial team proved to work occasionally but was infeasible most of the time. The first and foremost reason for this situation was the lack of commitment to entrepreneurial activity by the main technology holders who did not want to give up the intellectual property rights to their technology.

3.6 Specifying Findings I summarize the following specific findings of this study, which build on the evaluation in the section before: ● Technology scouting is beneficial The implementation of technology scouting is beneficial in different areas. First, it provides transparent management information to assign resources more efficiently. Second, through systematic technology scouting activities, the technology scouts form a network with technology holders that pays off through follow-up consultations for technology transfer support grants.

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● The technology scouts’ quality and motivation are the dominant success factors While well-grounded technology scouting instruments help to structure the technology scouting process and visualize its results, the quality and motivation of the technology scouts are the dominant success factors. Their highest impact activity is to form personal relationships with entrepreneurial-minded scientists who work in applied research areas. However, as mentioned before, recruiting efficient technology scouts proved to be a real challenge. ● Technology scouting does not raise spin-out volume sustainably The expectation that technology scouting can raise spin-out activity sustainably on its own inevitably overburdens this management method. Other bottlenecks, especially the lack of genuinely motivated scientists to engage in spin-out activity, limit the potential success of all technology transfer measures that aim to increase spin-out activity. ● Technology scouting needs to be incorporated into a holistic technology transfer program Technology scouting may realize its full benefits if it is incorporated in a holistic program that addresses the lack of potential entrepreneurial team members as the main bottleneck for spin-out activity. Those supplementing measures shall aim at matching a potential entrepreneurial team with the technology holder. A spinout company requires interdisciplinary teams who want to take the plunge into entrepreneurship. ● Scientists’ motivational structure does not favor entrepreneurial activity The motivational structure of scientists is not necessarily aligned with entrepreneurial activity. To convince scientists who were not motivated toward entrepreneurship proved to be a time-consuming and rather unsuccessful endeavor. Technology scouts need to understand the motivational factors of scientists to offer some form of adequate technology transfer solution beyond a spin-out.

4 Conclusion The guiding research question of this study is how does the development of a university-wide technology scouting program contribute to spin-out formation? In a first step, I summarize the theoretical and practical implications and conclude with the limitations of this study. Theoretical considerations As predicted by the resource-based view of the firm, technology scouting had a positive impact on spin-out activity. The project’s targets regarding spin-out company volume were met until 2014, which indicates that the analysis of the original EXIST

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IV start-up culture grant application, that the University of Potsdam possessed unrealized technology transfer potential, was somewhat right. After the 3-year phase A of the project, the spin-out number dropped to a hitherto unseen lower level. Despite the advances in the technology scouting program, which had fully developed instruments and processes at its disposal since 2014, the output shrank considerably. This surprising development seems to be contradictory to the resource-based view that predicts a continuous rise in output as the effectiveness of the program and its resources improve. There are two major explanations for this development. First, key personnel left the project, which inevitably results in a knowledge and experience drain that deteriorates the resources and weakens the effectiveness of the overall program. This explanation is in line with the resource-based view of the firm. Second, the supply of technologies or rather the reservoir of scientists who were interested in spin-out activities was exhausted after 3 years. After 2014, quick wins in spin-out activity had been realized, and the reservoir of available technologies and motivated scientists was exhausted. The technology scout reported that the barriers and challenges in motivating scientists to engage in technology transfer activity were underestimated. This result corresponds to the finding of Sassmannshausen (2011), who stated that after multiple years of start-up support programs at the University of Wuppertal, many senior scientists did not show any sign of entrepreneurial intention at all. Practical implications This study shows that a contingent approach to technology transfer programs in the entrepreneurial university context is the most suitable. Role model entrepreneurial universities such as the MIT benefit from a tradition of entrepreneurial activity and technology transfer. It is important to note that MIT does not necessarily “make” its scientists more interested in technology transfer, but the institution attracts motivated personnel, which in turn contributes to an entrepreneurial atmosphere. This magnet effect and resulting self-feeding mechanism is mainly the result of decades of excellence in research, advantageous administrative measures, and embeddedness in the private sector with a large proportion of technology companies. The University of Potsdam was founded in 1991 and did not take major steps toward becoming an entrepreneurial university until the mid-2000s. Although external rankings evaluate the institution as a premier place for entrepreneurship in Germany, the entrepreneurial tradition does not run nearly as deep as at MIT or European role models such as Twente University in The Netherlands. To establish a sustainable high level of spin-out activity, it is essential to consider other factors contributing to spin-out potential. In an underdeveloped entrepreneurial context, the university has to take a more active role in transferring technologies from the research lab into the private sector. This finding is in line with the results of Breznitz, O’Shea, and Allen (2008), who opine that a university with an underdeveloped entrepreneurial context needs to take a proactive role in supporting entrepreneurship by providing services and facilities such as incubators to foster technology transfer. A major practical implication of this study is that administrators should

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realize that the establishment of a single support infrastructure system such as technology scouting has positive near- and medium-term effects but is not sufficient to guarantee a high spin-out output in the long term. After the initial technology transfer supply is exhausted, long-term contributing factors need to be addressed, such as reward systems, staffing practices in the technology transfer office, flexible policies to facilitate technology transfer and reducing the cultural and informational barriers to technology transfer (Siegel et al., 2004). Another important point is that support infrastructure such as the technology scouting program needs to be incorporated into a holistic approach that covers all important areas of technology transfer. Scientists at the University of Potsdam have a different mindset and are motivated by a variety of different factors. A missing relevant personal network is a central barrier for many university researchers with an intention to start an entrepreneurial activity (Mosey & Wright, 2007). This deficit is due on the one hand to the fact that social contact between the respective specialist areas appears to be insufficient, and on the other hand, there is a lack of communication with external experts. The best way to accomplish effective entrepreneurial teams is a systematic team matching process (Paglialonga & Schultz, 2020). Outlook and limitations This study provides first-hand results from a project aimed to install a technology scouting program at an entrepreneurial university. Canonical action research is the appropriate research methodology to derive original results, but internal and external validity is often questioned. Given the multiple data sources, the closeness of the researcher to the project team, and its research experience, the case for internal validity is strong. To evaluate external validity and the representativeness of these findings for other institutions, we need to take into account to what extent this case and its context corresponds to the typical situation at a medium-sized university. There are many resemblances between the University of Potsdam and other medium-sized universities all over the world. There are specialties, such as the prominent position of the institution inside the federal East German state of Brandenburg, the rather challenging business environment and its short history. The project of installing a technology scouting program is in itself an exception, and the results of a similar undertaking have not been published to the best of the author’s knowledge. Overall, given these facts, the representativeness of all of the study’s findings is not as strong as the internal validity. Future studies of similar activities might yield results corresponding to this study’s findings. It is important to share these results, as researchers and practitioners can learn through best practices and stop devoting resources to low-quality technology transfer support measures.

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Technology Transfer Through Intersectoral Partnerships: The Case of Digitalization in the German Health Sector Markus Göbel, Hans Dieter Gräfen, and Christian Schultz

Abstract The various players in the health sector, such as health insurance funds, hospitals, and doctors, are aware of the challenges they face due to digitalization. But, it is ambiguous how changes will enfold and how they can be dealt with strategically. Due to the sector-specific high level of regulation in recent decades, which has predetermined the actors’ activities within narrow limits, they are used to ground their actions in causation logic. Now, decision-makers are confronted with a revolution that no longer allows them to use their beloved routines and processes. But how can we test technologies in the health sector and derive strategic implications? Based on various research results, this conceptual study shows that in an increasing dynamic and unpredictable environment the decision logic of effectuation is better suited for decision-makers. It becomes clear, that in order to cope with the revolutionary effects of digitalization the involved stakeholders need to work in intersectoral partnerships that live up to the challenge. The Digital Innovation Campus Health DICH GmbH, an institutionalized, intersectoral partnership, is a promising approach to enable digital technology transfer into the German health sector. Keywords Effectuation · Digitalization · Intersectoral partnerships · Digital Innovation Campus Health

This chapter draws on the working paper: Der digitale Elefant: Organisation und Führung in intersektoralen Partnerschaften, by Markus Göbel and Hans Dieter Gräfen, published by HelmutSchmidt-Universität/Universität der Bundeswehr in 01/2020. This work was supported by Deutsche Forschungsgemeinschaft (DFG; German Research Foundation), Key Number GO2778/2-1 M. Göbel (B) Helmut-Schmidt-Universität/Universität der Bundeswehr, Holstenhofweg 85, 22043 Hamburg, Germany e-mail: [email protected] H. D. Gräfen Odenthal Street 113a, 51061 Cologne, Germany C. Schultz HWTK—University of Applied Sciences, Bernburger Straße 24/26, 10963 Berlin, Germany e-mail: [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_8

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1 Introduction: The Digital Elephant or What Does Digitalization Mean for the German Health Sector? “The next decade”, opines Christian Böllhoff—managing director of the consultancy firm Prognos—“will be marked by two big D’s” (Bartz et al., 2019, p. 54) demography and digitalization. The latter in particular confronts the economy and society with far-reaching developments, as sociologist Armin Nassehi recently made clear: It should be cautiously pointed out that the West has much to lose – not only economically and in terms of power, but also in terms of the achievement of institutional arrangements. The fact that the danger is primarily visible in the role of digitalization is no coincidence, but can be explained by that option enhancing form of digitalization that is directly linked to the option-enhancing possibilities of functional systems. The traditional institutions have little to counter this at present. (Nassehi, 2019, p. 186)

There are few societal sectors, where Nassei’s prophecy seems to fit as well as in the German health sector. According to Behm and Klenk (2019, p. 5), the complex constellation of actors in this sector has so far made it “one of the fields that is largely characterized by reform blockades and status quo policies.” According to this year’s Hessian Entrepreneurs’ Day (Becker-Mohr, 2019, p. 1), a “medical revolution thanks to digitalization” is imminent. According to its chairman, “digitalization offers so many opportunities that its extent cannot yet be estimated” (Becker-Mohr, 2019, p. 1). In a recent study, the premier management consultancy McKinsey comes to the conclusion that digitalization measures of the healthcare system “make services cheaper and improve quality” (McKinsey & Company, 2018, p. 2). Their analysis shows: “The potential benefits in the German health care system from digitalization amount up to EUR 34 billion” (McKinsey & Company, 2018, p. 6). In the original and narrow sense, digitalization is the digital representation of physical objects, events or otherwise analogue media. However, the commonly used meaning is much broader, as the following quotation from a publication of the Federal Ministry of Economics and Technology of Germany in 2015 demonstrates: Digitalization stands for the comprehensive networking of all areas of the economy and society and the ability to collect, analyze and translate relevant information into action. The changes bring advantages and opportunities, but they also create completely new challenges. (BMWi, 2015, p. 3)

According to surveys, local doctors expect the ongoing digitalization to provide “a solution for the growing bureaucratic burden” (Reiche, 2019, p. 2) and 70% of German people hope that “digital technologies will lead to better diagnosis and treatment of diseases” (PWC, 2018a). However, the advocates of increasing digitalization are confronted with many concerns by critics. For example, patients fear that their free choice of a physician is at risk through increased control of health insurance funds (Waschinski, 2019, p. 1). Some are concerned that “Google, Amazon, Facebook, Apple & Co. take over and Europe-wide sovereignty over patient data is lost” (Evans, 2019, p. 2). The

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majority of physicians surveyed in a representative survey (PWC, 2018a) expect a deterioration of the physician-patient relationship through telemedicine. In addition, physicians who refuse to participate in digital care might be threatened with sanctions like fee reductions. Finally, digitalization implies overall social risks to the extent that “hospitals as critical infrastructures could become the target of cyberattacks” (Behm & Klenk, 2019, p. 3). Digitalization—so it seems—is connoted by some stakeholders either as magic potion or devil’s stuff. Depending on profession, function, and organization, every observer has a different perspective on the implications of digitalization. This situation is reminiscent of the metaphor of the elephant: Six blind men bump into an elephant. One of them grabs the tusk and thinks that the shape of the elephant must be that of a spear. Another gropes the elephant from the side and claims that it looks more like a wall. The third one feels a leg and announces that the elephant bears a great resemblance to a tree. The fourth seizes the trunk and believes that the elephant resembles a snake. The fifth takes hold of an ear and compares the elephant with a fan, and the sixth, who takes hold of the tail, contradicts and thinks that the elephant is something like a thick rope. (Kieser, 1995, p. 1)

Similar to the elephant metaphor, the power to cope with digitalization in the health sector is distributed among many organizational actors, like the service providers— hospitals, professional associations, etc.—and the health insurance funds (Behm & Klenk, 2019). This study’s guiding management and organizational theory-driven questions are: 1. Which individual decision logic seems promising in regard to the severe uncertainty in the digitalization process? 2. Which organizational arrangement will structurally support this decision logic best? 3. How can managers set up an organizational arrangement in a cost-efficient and success-oriented manner? The structure of this study is as follows. In Sect. 2, we differentiate the two individual decision logics of causation and effectuation, which deal with the problem of fundamental future uncertainty in an alternative way. We start Sect. 3 with a presentation of the intersectoral partnership as an organizational arrangement that has proven to be promising in theory and practice for solving challenges facing society as a whole. Then the challenges that arise from the individual benefit calculations for cooperative work within the framework of intersectoral partnerships are specified from an organizational theory perspective. Section 3 ends with the description of reciprocity, a social science concept that is constitutive for any cooperative relationship. We conclude this study with an outline of the Health Campus Wildau as a cost-efficient and success-oriented design of an institutionalized intersectoral partnerships.

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2 Theoretical Background: Organizational Decision-Makers Between Causation and Effectuation The diagnosed situation faced by the organizations operating in the health sector (companies, administrations, NGOs, etc.) and their decision-makers in regard to increasing digitalization is characterized by three features (Mauer & Grichnik, 2011): 1. Knight’s uncertainty (Knight,1964): The decision-makers are confronted with complete uncertainty, i.e., they are neither aware of the possible events nor of their probability of occurrence. 2. Target ambiguity: The information on which the present decision situation is based, makes it impossible for decision-makers to specify clear targets. Therefore, they can only generate targets retrospectively. 3. Isotropy: With regard to potential developments and rational decisions, the relevant environment gives ambiguous signals to decision-makers. In general, the central players in the health sector—health insurance companies, ministries, hospitals, pharmaceutical companies—possess pronounced hierarchization of the organizational structure, a high formalization of work and communication processes and a strong differentiation of work tasks. In short, these organizations are either machine bureaucracies, professional bureaucracies, or sectoral organizations (Mintzberg, 1992). They regard strategic control as a clearly defined planning task that relies on an analytical penetration of all relevant problems. Starting from a target value as pre-defined organizational goals, the strategy is derived with the resulting plans. Following a strategically oriented review of the current situation, which on the one hand analyses the company’s own resource and capabilities and on the other hand forecasts future developments in the relevant environmental segments, the strategic problem for the organization is defined clearly. The organization reviews all strategy alternatives and their effects in detail and finally evaluates strategic success according to the criteria derived from the organizational objectives. The strategic plan resulting from this conceptual process forms the basis for the genesis of tactical and operational planning, as well as budgets and control measures, which are intended to link the actions of individual organizational members directly to the long-term organizational goals (Schreyögg, 1998). The basis for such a plan-driven understanding of strategy is a decision logic known as causation (Sarasvathy, 2001), which attempts to transform market, technical or social changes and uncertainties into predictable risks. If decision-makers consider the situation to be predictable and measurable, they will systematically collect and analyze information. In their eyes, the future is predictable and consequently controllable (Sarasvathy, 2001, p. 250). In this school of thought, entrepreneurial opportunities are market imperfections, which can be discovered in the course of a systematic information search and analysis. The search for opportunities and their subsequent exploitation is a rational, analytical, and goal-oriented behavior (Frese, 2014). Causation logic is, in the sense

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of Mintzberg (1978), a planned strategy that makes use of market studies, trend analyses, technical forecasts, etc. and is found, for example, in the preparation of business plans. However, if decision-makers in the health sector consider the market’s technical and societal changes through digitalization to be contradictory and unpredictable, they will tend to try to gather information relevant to decision-making through experiments and iterative learning techniques (Frese, 2014). Sarasvathy (2001) calls this decision logic effectuation. Based on the available resources, decision-makers who follow the effectuation logic are on the lookout for effects and goals that can be achieved with the available means. The targets are therefore not set ex ante (Frese, 2014). According to Sarasvathy (2001), the constitutive resources on an individual level are differentiated into three categories: 1. the characteristics, preferences, and competencies of the decision-maker, 2. the know-how of the decision-maker, 3. the social network of the decision-maker. Central to the functioning of the effectuation logic are the decision-makers and the interaction with actors they consider relevant. “They provide the basis for deriving possible effects from the combination of existing resources and for reacting to unforeseen changes in the environment of these actors” (Frese, 2014, p. 23). In other words, the decision-makers do not even try to predict what they consider to be unforeseeable. Rather, their behavior is aimed at “understanding uncertainty as an advantage and establishing control by actively helping to shape the various development opportunities that exist” (Küpper, 2010, p. 44). Or as Sarasvathy (2001, p. 250) emphasizes: “to the extent that we can control the future, we do not need to predict it.” With regard to the strategy typology of Mintzberg (1978), it is rather an emergent strategy, “… within which the selection of alternatives is based on experimentation and flexibility and against the background of a potentially affordable loss” (Frese, 2014, p. 23). Effectuation’s decision-making process possesses different characteristics that are central to its success (Küpper, 2010, p. 46): 1. 2. 3. 4.

Selection of possible effects, that are achievable with the given resources. Selection criteria depend on possible loss or acceptable risk. Effects depend on decision-making preferences and individual skills. Selection criteria depend on individual affordable loss, or acceptable risk.

Consequently, effectuation focuses on controllable aspects of an indefinite future to the extent that we are able to design the future we do not need to predict. If Sarasvathy (2001) sees effectuation logic primarily represented in the entrepreneurship community, current studies (Brettel, Mauer, Engelen, & Küpper, 2012; Küpper, 2010) show that effectuation as a relevant decision-making logic can also be found in working contexts of large organizations when they deal with innovation. The effectuation approach was successfully implemented within the Digital Acceleration Program 2016 at Bayer Group (see Fig. 1). As part of a systematic

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Fig. 1 Process and method of a digital accelerator

expansion of the available resources via a partner program with main organizations in the field of digitalization such as the Fraunhofer Institute, SAP, Microsoft, ATOS, and Henkel a Digital Campus was created jointly in Leverkusen. The digital accelerator acted in “dual speed” internally according to effectuation methods with simultaneous coupling to the decision-making structures in the line organization through an innovation process with quality gates. However, the campus concept, which was perceived as very successful by stakeholders from politics and business, fell victim to micro-political processes in the course of a group-wide reorganization and was discontinued by the responsible decision-making level in favor of a procedure-oriented toward causation logic. While the application of effectuation logic is suitable in dynamic, non-linear markets (Sarasvathy, 2001), the use of causation logic is appropriate in a relatively static market context. An analysis of the German health sector shows that up to now it has been a prime example of a regulated market. The functions and roles of the organizational actors—health insurance funds, ministries, associations of statutory health insurance physicians, hospitals, pharmaceutical companies—were more or less fixed by law, as were the institutionalized regulatory systems that govern the actions of the actors. In this respect, it is not surprising that the health sector is one of the policy areas “which is characterized to a large extent by reform blockades and status quo policies” (Behm & Klenk, 2019, p. 5). In view of these characteristics of the German health sector, it is plausible that most actors have assessed previous system changes as predictable and consequently plannable and have therefore followed a causation logic in their decisions. However, this certainty seems increasingly deceptive, because “ … technological change is transforming the health sector forever” (Behm & Klenk, 2019, p. 2). The

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German health sector—as Matusiewics and Behm (2017, p. 67) state—“is in a transformation process with an uncertain outcome. Change is the norm, with the speed of development and the intervals between changes […] having increased in recent years.” In view of this fundamental shift, a change in the decision-making logic at the management levels of the involved organizations is inevitable. While causation logic, which has dominated up to now, aims to preserve market share in existing markets through competitive strategies, the effectuation logic focuses on the penetration of new markets through proactive design and cooperative action (Küpper, 2010). We propose the network-like integration of organizational actors in order to use their complementary skills, resources, and competencies for the active shaping of the health sector. In the following section, we characterize different organizational modes of intersectoral partnership and related important concepts.

3 Intersectoral Partnerships: Manifestations, Thematic Priorities, and Theoretical Approaches Intersectoral partnerships are collaborative arrangements, where actors from different sectors of society (government, business, civil society) share resources and information in order to work on social problems with common goals (Hodge & Greve, 2007; Selsky & Parker, 2005). The number and variety of these forms of governance have increased significantly in recent decades. A key driver of this development is the assumption that partners with different sectoral backgrounds possess diverse resources and capabilities and that social problems can be solved more effectively if complementarities are used with the aim of creating economic, social, and ecological value (Selsky & Parker, 2005). Extensive research on intersectoral partnerships has emerged, which is briefly outlined below along its thematic priorities and disciplinary approaches (Vogel, Göbel, Grewe-Salfeld, Herbert, Matsuo & Weber, 2020). ● Collaborative governance refers to a paradigm shift in policy, administration and management research (Amsler, 2016) according to which the change from “government” to “governance” and thus from vertical to horizontal coordination within and between social sectors is observed. The discussion is increasingly distancing itself from a purely efficiency-driven optimization of the state hierarchy, oriented on the model of the private enterprise (Göbel & Vogel, 2010), and is instead focusing on networked arrangements of co-production of public goods and services for the creation of social value (Klijn & Koppenjan, 2016). The authors refer, among other things, to organizational sociology and network theory in order to shed light on the emergence, design, and impact of interorganizational forms of governance at the interfaces of state, business, and civil society (e.g., Crosby & Bryson, 2010). The interest of this branch of research focuses on

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new forms of network management (Markovic, 2017), but also on how collaborative forms of control place new demands on classical management functions and roles (Torfing & Ansell, 2017). ● Public-private partnership is a variant of collaborative forms of control that is practiced and discussed for some time (Song, Zhang, & Dong, 2016). The research combines political and administrative science from the public perspective and management and economics from the private perspective. Since a large number of PPPs are located in the infrastructure sector, there are also many interdisciplinary links with more technical disciplines such as engineering, transport, and environmental sciences. Dominant topics of this research stream are the selection of suitable partners (Zhang, 2005), the institutional and contractual design of PPPs (e.g., Demirel, Leendertse, Volker, & Hertogh, 2017), risk allocation between partners (e.g., Zhang, Zhang, Gao, & Ding, 2016), the measurement and control of success (e.g., Atmo, Duffield, Zhang, & Wilson, 2017) and success factors of the partnership (e.g., Osei-Kyei, Chan, Javed, & Ameyaw, 2017). ● Nonprofit business partnership is a form of intersectoral partnership which occurs when profit and nonprofit organizations cooperate to create economic, social, or environmental value (Austin & Seitanidi, 2012a, 2012b). This cooperation takes place without the direct participation of state actors. For companies, the main focus of their cooperation with civil society organizations is usually not a direct profit-making interest, but rather a commitment to social responsibility (Sakarya, Bodur, Yildirim-Oktem, & Selekler-Goksen, 2012) and social innovation (Shier & Handy, 2016). This applies in particular to multinational companies (Marano & Tashman, 2012). It is of interest why and how such social alliances are formed (Seitanidi, Koufopoulos, & Palmer, 2010), how they are structured and managed (Hahn & Gold, 2014), which factors determine their joint success (Weidner, Weber, & Göbel, 2016), how this success contributes in turn to the individual achievement of the partners’ goals (Schuster & Holtbrugge, 2014) and what social impact the partnership has (van Tulder, Seitanidi, Crane, & Brammer, 2016). This research field is highly fragmented and very interdisciplinary. A common feature of many studies is the positive connotation of intersectoral partnerships. Especially in the literature on nonprofit business partnerships, intersectoral partnerships are often presented as a “kind of magic bullet capable of providing solutions to diverse development problems across a variety of settings through win-win situations where all stakeholders benefit” (Rein & Stott, 2009, p. 80). With regard to the establishment and management of intersectoral partnerships in institutionalized and politicized actor constellations, as is the case in the health sector, the potential benefits of such partnerships can also be offset by considerable costs, which originate from the different institutional logics of the actors (Vogel et al. 2020). If the market sector aims for the greatest possible efficiency, the political-administrative sector focuses on legitimacy under the rule of law on the basis of rule-based coordination and control processes. Additionally, the civil society sector aims to form consensual, fair and socially acceptable solutions on the basis of communicative argumentation (Hilpert, 2011). On an interorganizational level,

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the different logics result in phenomena known as conflicts of identity and goals (Simpson, Lefroy, & Tsarenko, 2011), communication barriers (Koschmann, 2016), and “clash of cultures” (Marschollek & Beck, 2012). It is therefore necessary to reconcile different, often paradoxical frameworks for orientation (Le Ber & Branzei, 2010). Otherwise, the interaction costs between the involved actors can escalate in such a way that positive socio-economic added value of the partnership is in jeopardy for internal and external stakeholders (Weidner et al., 2016). Examples of this kind of situations in the German context are infrastructural projects like Stuttgart 21 or Berlin-Brandenburg International Airport, who have become synonymous for failure in the public eye. In the case of the German health sector, the plans for the introduction of the health card have similar negative public relation potential. Organizational and management research now offers a rich portfolio of concepts and instruments for creating added value in organizational partnerships, as we will show in the next section.

3.1 Network Commons as Forms of Added Value In theory and practice, intersectoral partnerships are regarded as a “success model” for solving social challenges. On the one hand, the solutions developed across sectors are enjoying increased acceptance in business, government, and civil society (Weidner et al., 2016). On the other hand, the solutions found across organizational and sectoral boundaries are based on collective bundles of resources and knowledge that emerged in the course of the network partners’ collective action (Göbel, Weber, & Vogel, 2015). In economics, collective action refers to “the cooperation of individuals, organizational units or social groups in pursuit of overriding community goals from which the community benefits” (Frost, Morner, Vogel, & Queißer, 2010a). By its very nature, collective action generates costs only for those network partners who make a contribution (Olson, 2004). From an individualistic perspective, it would therefore make sense not to participate, since ultimately all actors benefit from the overall result (Frost et al., 2010a). This well-known free-riding phenomenon can lead to no or only suboptimal problem solving, as not enough network partners are committed to make a contribution (Olson, 2004). Consequently, the rationality of the individual network partner does not lead to the collective rationality of the intersectoral partnership (Frost, Morner, Vogel, & Queißer, 2010b). In contrast to private are public goods, which we call network commons. They possess the following main traits: 1. Non-excludability: Actors cannot be excluded or can only be excluded at high cost from the use of the service. Once a network common is produced in the framework of a trisectoral partnership, those network partners who have not made a contribution they cannot be expelled from consumption (Olson, 2004). 2. Non-rivalry: Network commons can be simultaneously consumed by individuals without being worn out by use (Olson, 2004).

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If one focuses on the development of societal solutions to problems on the basis of complex bundles of knowledge and resources as a central function of trisectoral partnerships, it becomes clear that neither pool nor club resources are decisive for the success of trisectoral partnerships. Rather, network-specific public resources are of central relevance. Their value unfolds only from the complementary interaction of the knowledge contributions by the various network partners (Frost et al., 2010a). In the course of the reciprocal linking, a mutual appreciation of the respective knowledge contributions takes place (Göbel et al., 2015). In this respect, “complementarities also conceal added value potentials in the form of super-additive cooperation rents” (Frost et al., 2010a, p. 180) as they are realized in networks through a strategy of synergy management. However, the latter must provide a sustainable solution to the problem of potential undersupply. Because non-excludability, in conjunction with non-rivalry in consumption, encourages individual network organizations to free ride and ultimately leads to too few contributions being made available for the production of public goods (Frost et al., 2010a).

3.2 Cooperative Relations Between Utility and Morality Interorganizational design variables influence the steering relationship between the partner organizations, the used resources and actions (Frost et al., 2010a). In addition to the already known variables of collective good characteristics (degree of exclusivity and degree of rivalry in consumption), it is above all, the output measurability that is relevant for the selection of the appropriate form of control. As long as the performance contribution of the individual partner is clearly measurable with manageable effort, it is possible to assign all consequences of his actions to him by means of performance-related remuneration. If, however, individual performance contributions—as in team production—is no longer clearly attributable massive remuneration and control problems arise, which result in exorbitant management and control costs (Picot, Dietl, Franck, Fiedler, & Royer, 2015). The value of the partnership exclusively unfolds from the complementary interaction of the knowledge contributions of the various network partners (Frost et al., 2010a). In addition to specialist knowledge (publications, documentation, etc.), the provided knowledge is above all implicit knowledge, i.e., skills and abilities of the network partners that have manifested over many years of market activity. The value of implicit knowledge can neither be quantified ex ante nor clearly attributed in terms of its contribution to the overall result. These measurement problems pose particular challenges for the management of a cross-sectoral network. Due to the non-attributability of the individual knowledge contributions to the overall result, it is neither possible to draw up clear contracts with specifiable services and contributions, nor do elaborate monitoring and sanction mechanisms work. Simply because the share of each partner organization in the overall success of the intersectoral partnership cannot be determined precisely (Frost et al., 2010b). Therefore, the generation of network-specific public resources via forms of contractual or hierarchical control

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is of little practical use. For the design and management of intersectoral (knowledge) networks, forms of self-organization that rely on the steering effect of reciprocity standards are promising. The following studies show how the danger of opportunistic behavior in knowledge-intensive networks can be reduced, despite extensive scope for action and decision-making, and how cooperative interaction contexts can be created. In their study on the establishment of an R&D consortium in the semiconductor industry, Browning, Beyer, and Shetler (1995) examine the question of how cooperation is created and how it achieves persistence in a highly competitive environment. The initiation of such a cooperation is various forms of unconditional giving, which individual or cooperative actors voluntarily undertake. For example, the performance of Charlie Sporck, a prominent founder of SEMATECH, was “a pure gift because it conferred benefit on others, imposed a cost on him, and was voluntary” (Browning et al., 1995, p. 130). This form of unconditional giving became a self-reinforcing process insofar as it provided the impetus for a “moral community” and was the starting point for the formation of a structure, which started other structures. With regard to Toyota’s supplier network, Dyer and Nobeoka (2000) asked how to solve the problem of free-riding in knowledge sharing in learning networks. The formation of a network identity controlled by a reciprocity norm proved to be constitutive for the solution of the problem. Just as with SEMATECH, the voluntary gift of an actor was the trigger for the reciprocal cooperation relationship. In the course of their identification with the network, a network of mutual obligations emerged, which was always based on moral motives for action in addition to the individual maximization of benefits. In his study of complex exchange systems in Silicon Valley, Ferrary (2003) points to the fundamental relevance of a gift system as a form of reciprocal interaction: “It is the nature of the goods exchanged as well as the density of social networks, which make gift exchanges the principal explanation of the circulation of goods” (Ferrary 2003, p. 120). According to the allocation logic of the gift system, cooperation is not universally motivated by utilitarianism. Rather, a controlling morality is discernible, which turns a working relationship into a community of values. In experimental economic research (e.g., Falk, 2003; Fehr & Gächter, 2000; Fehr & List, 2004), the reciprocal behavior presented in the case studies is neither limited to the abovementioned work contexts nor is it an exception in economic life in general (Göbel, Ortmann, & Weber, 2007). Rather, it seems that, in addition to considerations of benefit, moral influences in the form of reciprocity norms always determine our actions. “The existence of reciprocal behavior has been demonstrated in dozens of experiments under varying experimental conditions and in different cultures” (Falk, 2003, p. 154). The involved actors believe that: ● the mutual exchange of information, goods, or services will balance each other out in the long term; ● that certain penalties will be imposed on those who refuse to help those who have helped them; and ● that those whom they have helped can be expected to help them in turn.

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The relevance of reciprocal behavior in the much-cited sharing economy becomes obvious. Regardless, if we observe online forums, digital file-sharing networks or open innovation communities, reciprocal behavior is constitutive for the genesis and persistence of sharing systems (Göbel, Vogel, & Weber, 2013).

3.3 Design and Management of Intersectoral Partnerships: Initial Considerations and Possible Scenarios With references to the results of an expired research project on the governance of interorganizational relations in the venture capital industry (Weber & Göbel, 2006, 2010) as well as a currently ongoing DFG-funded research project on the legitimization of intersectoral partnerships, we provide initial considerations on the design and governance of intersectoral partnerships. Characteristic for intersectoral partnerships is usually a multitude of different organizations, which differ, among other things, in size, age, sector affiliation, target system, degree of formalization, and innovation capacity. The involved organizations are connected to each other on the interaction level via a complex gift system. In this gift system, reciprocity presents itself as a generalized norm in the sense of a moral imperative of social life to which all network partners feel fundamentally committed in their actions (Göbel & Weber, 2007). Reciprocity is sometimes only evident in the long term and balance results from a factually, socially and temporally complex structure of transactions, “in which every resource provider is also a resource taker at the same time” (Göbel & Weber, 2007). Such exchange systems gain their permanence by reproducing the moral order that controls them in the course of their successful functioning (Weber & Göbel, 2010). As a compelling consequence, “exchange systems must offer opportunities for moral action if they want to maintain a corresponding exchange morality” (Kappelhoff, 1995, p. 11). The obligation to give does not run directly between resource recipients and providers, but through the gift system itself and its members (Weber & Göbel, 2010). The risk for the individual remains limited because the systemic gift cycles allow indirect, typically time-delayed compensation (Göbel & Weber, 2007). Nevertheless, the willingness to subordinate individual benefit calculations in principle to a morally controlled system of gifts is a prerequisite. In addition to trust in the integrity of the partner, this primarily involves trust in the functioning of the gift system. Trust in the system and the moral order that governs it becomes a central form of coordination (Weber & Göbel, 2006). Beyond formal contracts, this exchange system is dominated by informal cooperation in the sense of Smith, Carroll, and Ashford (1995, p. 5), “which involves adaptable arrangements in which behavioral norms rather than contractual obligations determine the contributions of parties”. Compliance with norms is controlled by forms of social control (Göbel & Weber, 2007). All intersectoral partners monitor compliance with the standards in this gift system and jointly sanction deficiencies. Since control and sanctioning power is

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not individualized but systemic, the transaction costs arising from formalized control procedures are eliminated (Weber & Göbel, 2006). What are the implications of such a gift system in terms of potential results? Two scenarios are conceivable here, depending on the degree of institutionalization and regulation density: ● Scenario 1: If the gift system is controlled by an exogenous moral order with a high degree of regulation and institutionalization, i.e., if system and morality are closely linked, a symbiosis of system trust and power often develops (Weber & Göbel, 2006). “This form of power is not indifferent to the individual interests of the parties to the exchange, but it does appear extremely difficult to use or abuse for opportunistic strategies” (Weber & Göbel, 2006, p. 324). The high degree of normative institutionalization coupled with the power-confidence symbiosis implies a system-specific sociality that prevents innovative—because it does not conform to the system—behavior. This sinister alliance can, at worst, drive the gift system into a “lock in,” which results in a low degree of innovation. ● Scenario 2: If the density of regulation and the degree of institutionalization of the exogenous moral order is comparably low, i.e., if the system of gifts and morality are loosely coupled, trust and power present themselves as discrete coordination alternatives (Weber & Göbel, 2006). “Supported by system trust and system power, or both, members’ decisions favor a mechanism of coordination that always orient towards the specific exchange partners” (Weber & Göbel, 2006, p. 324). Although the interactions are controlled by a set of rules, the utilitarian calculations are not superseded. The gift is a game with mixed motives, in which on the one hand strategic action is evident, so that the gift system is endogenously dynamized. On the other hand, the entrepreneurial character of the individual interaction remains embedded in the system’s specific sociality. Since, such innovative action is only loosely coupled with the controlling moral order in the course of purpose-rational calculations, coordinated action is safe from disappointment and thus control costs are reduced, but at the same time the degree of innovation is not minimized. In this way, innovation dynamics are combined with cost efficiency and lead to above-average value creation.

4 Conclusion The digital elephant will occupy the healthcare system intensively in the coming decade. The future will reveal if digitalization will lead to a “medical revolution.” However, it is already clear today that profound change in the structures of the health market will take place. According to a recent study by the management consultancy PWC (2018b), the boundaries between sectors will increasingly dissolve. In the future, so-called mega-clusters—areas in which companies of different specialization are active—will replace the existing industries. The new mega-cluster “New Health” is emerging in the health sector. In addition to the health and pharmaceutical

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industries, it is likely that companies from different technology sectors will become particularly active in this new health cluster. With permeable sector boundaries, the establishment of new market participants and the development of comprehensive mega-clusters, a decade-long design and planning security for the hitherto dominant players in the German health sector is coming to an irretrievable end. Systemic confusion and uncertainty replaced this lack of dynamism. This new situation necessitates a change in the decision-making and planning logic at the management levels of the involved organizations. Forms of plan-determined control, which rely on a competition-oriented corporate policy according to a causation logic, are supplemented by a meta-control (Schreyögg, 1998), which is open to diverse suggestions, impulses, and strategy formulations—also from outside the organizational boundaries—in the sense of an effectuation logic. As we have been able to show, this openness to complementary perspectives, knowledge, and resources across borders and, where appropriate, across sectors is not a new phenomenon, but rather a proven practice for tackling of complex problems in different areas of society. Nevertheless, value-added genesis in an intersectoral partnership is a prerequisite. Only if the individual rationality of the individual network partner is transformed into a collective rationality of the overall system via rule-based self-regulation the intersectoral partnership is able to ensure a higher than average benefit for the individual organization and the system as a whole. With reference to past and current research projects, the implementation of a cross-organizational and cross-sectoral gift system appears to be the most promising approach. This system must not only offer benefit-maximizing calculations of action but also the opportunity for moral action, which is an essential characteristic of a successful intersectoral partnership. Such a self-organizing system is controlled by the universal norm of reciprocity (Gouldner, 1960). The Digital Innovation Campus Health DICH GmbH is a platform for intersectoral partnership. This institution takes up the presented ideas and offers a common transformation platform for organizations from the political, legal, academic, and economic spheres of the health sector. Digital solutions developed within this organizational platform will then be tested for functionality and security in cooperation with cities and municipalities. Special attention is paid to the development and promotion of solutions that focus on health maintenance as a social responsibility at the municipal level. Future research endeavors can deepen the above-mentioned concepts for the design and management of intersectoral partnerships through the action research methodology. The greatest challenge in tackling digitalization in the health sector is overcoming the established but inadequate thought and power structures. Intersectoral partnerships such as the Digital Innovation Campus Health make an important contribution to transforming such institutionalized thinking and power structures to improve the economy and society sustainably.

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Processes

The Relevance of Technology Transfer Frank Piller, Dennis Hilgers, and Lisa Schmidthuber

Abstract The economic strength of nations worldwide is increasingly dependent on its research systems to sustain innovation and new product and service development. Besides research, education and entrepreneurial innovation are seen as core factors to promote a dynamic and competitive modern economy and nearly all governments in the world pursue research and technological development programs to fund research activities especially at universities, research laboratories, and companies, intending to constantly advance their economy. How can knowledge from basic research be used for industrial practice at an early stage? What are the different channels of knowledge transfer? And above all, how can this process be accelerated? In this chapter, we will discuss the mechanisms and consequences of new technology transfer instruments and approaches and depict their relevance for a more productive technology transfer. We end with an outlook on the potential role of artificial intelligence and machine learning for the future of technology transfer. Keywords Technology transfer · Open innovation · R&D · Innovation policy

1 Introduction The European countries are leaders of knowledge-driven research in many areas. Such basic research is essential because it provides the basis for developing new technologies and opening up markets. However, in a society based on the division This chapter draws on an earlier publication by the authors in Piller and Hilgers (2013). F. Piller RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany e-mail: [email protected] D. Hilgers (B) Johannes Kepler University Linz, Altenberger Straße 69, 4040 Linz, Austria e-mail: [email protected] L. Schmidthuber Vienna University of Economics and Business, Welthandelsplatz 1, 1020 Vienna, Austria e-mail: [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_9

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of labor, basic research only leads to economic prosperity and growth if the research findings successfully reach the next level of application. In most cases, this requires a transfer of knowledge to another actor, who then combines this knowledge with other (often preexisting) technological knowledge and applies the knowledge in practice. However, there are many hurdles and challenges that can hinder the efficient and targeted transfer of knowledge (Katila & Ahuja, 2002). For example, in Germany, the largest economy in Europe, the number of research and development jobs rose from 2005 to 2016 by 20% to 600,000. In 2016, the R&D spending of businesses reached around e84 billion (nearly 50% increase from 2005). The German government has also massively increased its research investments from e9 to e15.8 billion in 2016—an increase of more than 80% (BMBF, 2018). Even though this only accounts for just fewer than 3% (2.92% in 2016) of the Gross Domestic Product (GDP) and is therefore comparable to international levels, never so much money had been spent on research and development in Germany before. Increasing investments in the academic system, e.g., with globally recognized excellence initiatives, the professionalization of university management, and university autonomy, are expected to contribute to better and faster commercialization of research. In innovation management research, various studies have investigated specific aspects and “best practices” of such a transfer and have indicated the general importance of external knowledge from universities and basic research institutions for the industry (Bruneel, d’Este, & Salter, 2010; Laursen, Reichstein, & Salter, 2011; Lee, 1996; Steinmo & Rasmussen, 2018). In the following, a number of methods are described that are applied in transfer processes and are supposed to make the transfer from academic research to practice more efficient and effective.

2 What Are Technology Transfer and Knowledge Transfer? Human knowledge doubles every five years, but about half of it becomes obsolete again within around another three years. A new chemical formula is developed every minute, a new physical relation is found every three minutes, and a new medical discovery is made every five minutes. The idea that innovation consists of individual contributions by ingenious inventors working alone has long been debunked as a myth. Instead, innovation arises by combining knowledge of different types from different sources in new ways. In turn, the interaction between knowledge carriers, namely the communication between them, plays a decisive role. Today, scholars agree that successful innovation arises from network-oriented interaction between different actors (Bercovitz & Feldman, 2011; Rajalo & Vadi, 2017). The technology providers among these actors are universities, applied universities, various large research institutions, and companies. Companies are often considered the primary technology recipients, but any of these actors can also be the recipient of new (technical) knowledge and innovative processes and products.

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This generates a variety of interactions between academia and companies. Communication and exchanges between the actors involved in an innovation are therefore the key factor of success. These actors must possess interaction competence or acquire it over the course of the innovation process to successfully transfer knowledge to their communication partners. Besides knowing about other actors which possess relevant knowledge, this requires to be motivated to actively incorporate the knowledge into the own domain of knowledge (at either an individual or organizational level). Thereby, incentives for the transfer or incorporation of knowledge play an important role. Technology and knowledge transfer from research institutions into industrial applications forge a link between academia and business with the objective of putting academic research results into practice. The terms “knowledge transfer” and “technology transfer” are often used interchangeably (Gopalakrishnan & Santoro, 2004). In principle, the concept of “knowledge transfer” can be understood in a broader sense, for example, including knowledge from research in the humanities or social sciences in addition to the transfer of technology from academic research into industrial applications. The concept of “technology transfer” refers to a transmission process of a specific technology (often theoretically patentable) from a technology provider to a technology recipient. It can therefore be seen as a subset of knowledge transfer but usually remains the focus of public discussion and research on the topic. In the following, we shall use both terms synonymously, even though we focus predominantly on product and process technologies, namely traditional technology transfer. Technology transfer typically unfolds relations between universities, research institutions, and companies, but also within multinational companies, departments, and governments (e.g., Kavusan, Noorderhaven, & Duysters, 2016). “Intraorganizational transfer” means that technology transfer occurs within an organization, for example, when the research department delivers technological solutions to the manufacturing department (Van Wijk, Jansen, & Lyles, 2008). The transfer of any technology or findings is ultimately a knowledge transfer, i.e., a transfer of (new) knowledge from a provider to a recipient. Technology transfer therefore refers to the diffusion or dissemination of technology to make it commercially viable for third parties. This technology transfer can take place as an iterative process at every level of the innovation process but is distinct from the concept of innovation, since it also encompasses the implementation of existing technologies and transfer from one domain to another. The transfer objects of the technology transfer are “any material and immaterial manifestations of product and process technologies.” Since technology transfer is shaped by the active interaction between the parties involved in the transfer and their structural properties, it describes “value-oriented, planned, and time-limited exchange processes between organizations that aim to transfer technologies from an academic basis into economic applications” (Walter, 2005). Figure 1 shows that technology transfer can be understood as a planned and therefore influenceable exchange process with the objective of realizing economic benefits for the transfer partners. In general, the transfer process can be divided

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Fig. 1 Phases of the technology transfer process

into several phases (selection phase, acquisition phase, market phase), often with feedback loops between the different phases or overlaps. Each phase involves key activities (identification, selection, contracts). The value creation process of an innovation begins with the generation of knowledge, i.e., a new discovery by a “knowledge” actor (individual or institutional actor). This discovery is then acquired by various “intermediate” actors, extended, and passed on, until the extended discovery ultimately leads to a product or process improvement and is offered on the market. Before marketability, the production of the innovation involves costs. The lower these costs, the lower the risk associated with placing the innovation on the market. From a business perspective, the expected revenue from the product must be greater than the manufacturing costs, including the development costs and risk costs, and must generate a significant profit. In this regard, innovations driven by a specific purpose based on market demand (“demand pull”) have an advantage over “technology push” innovations: the existing market demand increases the willingness of economic actors to invest in the product development process, since the risk associated with selling the product on the market is lower. By contrast, technology push innovations must first open up an area of application and thus market potential. Nevertheless, since the degree of novelty is often higher, they also have higher innovation potential.

3 Traditional Channels of Technology Transfer There are a variety of transfer mechanisms for organizing the exchange of knowledge. Usually, academic publications are first screened. This allows researchers to share their knowledge. In general, this so-called dissemination of research results is realized by publishing and presenting results at conferences. However, presenting the findings publicly does not yet guarantee that they will be publicly applied by other actors for technology transfer and economic exploitation, which would require special expertise by the recipient. Knowledge about the product and the market is also necessary to

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estimate the economic potential of a transfer. Finally, in addition to the knowledge itself, the company must have the resources to apply it. Accordingly, there can be intermediaries between knowledge providers and knowledge recipients to facilitate the knowledge transfer. Examples include technology brokers, university transfer offices, and other similar intermediaries. Studies on the various transfer mechanisms distinguish between direct technology transfer, where “knowledge itself or knowledge concretized as technology” is transferred, and indirect technology transfer, where the “knowledge carriers” are transferred rather than the technology itself. Another categorization of technology transfer can also be made according to institutional background by distinguishing between interorganizational and intraorganizational technology transfer. Additionally, different directions of technology transfer, horizontal or vertical, and according to the extent of the interaction between the transfer parties, active or passive can be distinguished. But where and how does technology transfer take place? There are various possible channels of technology transfer: 1.

2.

3.

4.

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“Transfer via heads”: The concept of personnel mobility describes the changeover of employees from academic research institutions to a company. This allows academic findings and, in some cases, academic working practices to be “imported” into the industry. The new employees often remain in contact with their former academic institutions, so that collaboration between the university and the company is facilitated. For example, the German Research Foundation (DFG) explicitly supports transfer via heads with their transfer and start-up projects. Consulting: Experts advise companies on how to solve a technological problem. This service is usually billed according to fixed daily rates and extends over a limited number of days. Contract research: Companies commission research institutions (or in-house research departments) to perform specific orders with (contractually) predetermined parameters. The research results are owned and exploited by the contracting company. Sponsored projects: In publicly funded research projects, multiple project partners, typically from research and academia, collaborate to solve a shared problem. Depending on the type of funding, the research results and/or development work might be usable by all project partners, might belong to the participating companies, and/or might be made partially available to the public. Patents: Patents are intended to be granted for inventions that are new, derived from creative activities, and commercially applicable. Patent holders are entitled to prohibit others from using their inventions after they have disclosed the new knowledge. This disclosure documents the new knowledge and makes it accessible to all market actors (even though it can only be exploited after a certain period has expired). In academic and technical fields, patent applications are seen as an indicator of the performance and application-oriented nature of

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science and research institutions. Patents can be exploited in various ways, for example, by selling or licensing. 6. Publications: Publishing findings in specialized journals, conference proceedings, or project reports is an essential means of disseminating knowledge. This type of disclosure typically means that the results are no longer eligible for patenting. The goal is a broad and rapid transfer of knowledge to other academics or companies. 7. Licensing: In this case, a company acquires the rights to exploit the research findings economically. In industry and commerce, license agreements play an important role in granting third parties the rights to use industrial property (patents, utility models, registered trademark) subject to predetermined conditions. 8. Diploma/seminar papers: Research and development work can be performed in the course of a diploma or student thesis. Besides these findings, large companies often use this type of work to recruit young academics for their own research and development departments. 9. Spin-offs: Spin-offs have proven to be another successful tool of technology transfer. When founding a spin-off, researchers do not have to wait for an existing company to recognize and “absorb” their findings. The individual recognizes the marketability of the technology and creates a new company to exploit its applications. 10. Contacts and informal exchange: Another important transfer instrument is given by personal exchanges between academics and companies at conferences and seminars. In many cases, this initiates a technology transfer in one of the other forms listed above.

4 The Challenge of Knowledge Transfer Although there are various channels for knowledge transfer, individuals or organizations struggle transferring technology successfully. A reason why technology transfer is failing is the necessary conditions that must be met for a successful transfer of knowledge from academia to industry, especially when complex technology is involved. To transform research results into innovations, namely marketable and successful products and services, a range of different people and organizations are involved. These are the actors of an innovation network consisting of researchers and their research institutions, technology transfer offices, research funding institutions, authorities, specialized organizations, and other stakeholders, as well as parties to sell the products on the market, namely preexisting companies or new companies founded for exactly this purpose. According to famous Austrian economist Schumpeter in the twentieth century, companies are the actors that place inventions (or new knowledge) on the market and establish them to gain a competitive advantage. They rely upon their ability to recognize, adopt, and process technical knowledge in the form of inventions or new

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findings from research projects and bring this knowledge to the market to establish a new combination of resources. In management research, this ability is described as the “absorption capacity” of an organization (Cohen & Levinthal, 1990; Zahra & George, 2002). Weak technology transfer and low volumes of high-quality, innovative products on the market could therefore stem from the fact that the absorption capacity of European companies is lower than their American or Asian counterparts. The group of small- and medium-sized enterprises (SMEs) is especially problematic. To increase innovation capacity and the intensified exploitation of research results, however, the participation of SMEs is indispensable. The transfer of new knowledge into the industry is a highly complex process that requires a wide range of specialized expertise and the capacity to recognize application and implementation potentials—from plant engineering to dental technology. State universities, research institutions, and funding institutions can massively contribute to optimizing technology and knowledge transfer with active moderating measures. In order to stimulate knowledge transfer, universities need a kind of transfer capacity to stimulate and manage the diffusion of new knowledge into (industrial) practice.

5 Transfer Activity and Transfer Potential In the following, we describe four strategies for technology transfer. Therefore, we distinguish between the dimensions of “transfer potential” and “transfer activity” (Pechmann, Piller, & Schumacher, 2010). We consider a research project as an example. The transfer potential of this project describes the technological features that can be exploited. Evaluating and operationalizing the transfer potential is not straightforward. One possible approach for the project managers is to perform an assessment. However, any assessment of the exploitability necessarily depends on estimates of every possible application scenario, which are only incompletely known to any given person, so that such an assessment cannot be objective and can hardly be considered valid. One possible alternative is an expert survey, i.e., a survey of a suitable group of people to whom the research results of the relevant projects are presented. This would require the experts to understand and evaluate the research results, but they also need to be familiar with the results’ relevance in potential areas of application (products, production processes, markets, etc.). Despite these challenges, communication between a transfer office employee and the project managers can facilitate to identify the transfer potential. The second dimension is the transfer activity, which describes any actions that contribute to technology transfer from the technology provider to the technology recipient. This dimension therefore encompasses the activities performed by the research center to exploit the results. Researchers might provide information about the processes that have already been implemented. Figure 2 relates the two dimensions and creates a square matrix with four fields. The first field (bottom left) corresponds to both low transfer activity and low transfer

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Fig. 2 Strategies for balancing the transfer potential and the transfer activity

potential. Since the low potential means that any transfer efforts would not (yet) result in a transfer, the activity of these projects can in fact be considered efficient. The projects in the top left field have high potential but low activity. Thus, transfer has not yet taken place, despite being desirable—from the perspective of the transfer, these projects are (currently) ineffective. The third field (top right) lists projects with both high transfer potential and high transfer activity. From the perspective of exploitation, this combination is desirable, since usable results have been achieved, then transferred and exploited. The fourth field refers to projects with high activity and low potential. This combination must be considered inefficient, since the efforts of this activity are wasted due to the low transfer potential. Classifying projects into these four fields enables the development of strategies of active technology transfer management, which provide recommendations on how the activities of a research project should be prioritized to advance a project to the next phase (see paths A–D). The first field relates to projects that contain a technology that is not (yet) sufficiently mature to be transferred to the industry. Generally, the research of these projects should be continued, if only because the future application potential cannot yet be fully assessed or, in some cases, even identified at such an early stage. If the application potential is already recognizable, subsequent projects can focus on further developing the technology toward the target applications and increasing the transfer potential (strategy A). Research topics for which future applications have not yet been identified require a different path. One approach to handling these types of research topics is to fund additional knowledge-oriented basic research projects devoted to questions that were not yet answered by previous projects or that were newly raised by these projects for the first time. This can lead to new application ideas, especially if the results of

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these projects are communicated in a way that makes them accessible to actors in subsequent stages of the exploitation and transfer chain, for example, in applicationoriented research. If this allows new application potentials to be discovered, they can be pursued using strategy A as described above. This process of additional knowledge-oriented research, communicated as broadly as possible to the relevant actors in the subsequent stages of the transfer project, can be described as “seeding.” It can be repeated cyclically for as long as interesting research questions remain open. A small number of projects in phase one of this model generate many more projects in later phases of technological development. One possible explanation is that basic findings can generate many different ideas about the direction of future research. Another possible explanation relates to the method used to select the study aims, which considered the presence of development goals as one of its criteria, so that projects with a higher transfer potential were more likely to be included in the sample. If transfer potential is available, the technology should ideally be transferred by advancing to phase three as quickly as possible (strategy B). But the existence of this potential needs to be established first, for example, by industrial interest in research collaborations and inquiries from potential users. Nevertheless, phase two should be kept as brief as possible, and transfer activities should be initiated as quickly as possible if potential is available (strategy C). This can, for example, be accomplished by a lively public presentation of the relevant projects to allow any emerging transfer potential to be noticed more quickly from external reactions.

6 Novel Paths for the Management of the Transfer Process In the following, we will identify a series of new ideas and channels for technology and knowledge transfer. The transfer potential was proposed as an additional metric that is primarily determined by the maturity of the transfer object. It is influenced by various properties of the transfer object and gives insight into the exploitability of the technology. The higher the transfer potential, the more application scenarios are available for the technology, and the more specific they are. There is a clear positive correlation between the transfer activity and the transfer potential. For technologies whose applications are still distant, generating exploitable ideas is the priority. This could, for example, be realized with a public presentation targeting practitioners. Having these considerations in mind, a series of instruments and approaches are investigated that can support the exploitation strategies presented above with practical methods (see Fig. 2). Below, we describe these new methods with the aim of increasing the transfer capacity of technology providers in university environments.

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6.1 Open Innovation Platforms from an Academic Perspective By crowdsourcing innovation tasks, companies can advertise specific problems to an undefined, generally large group of individuals by leveraging online platforms (if necessary, against compensation or remuneration) and motivate researchers to screen problems and propose solutions (Bogers et al., 2017; Diener & Piller, 2019; Piller, Möslein, Ihl, & Reichwald, 2017).

6.2 Problem Conferences: A New Approach from Market Places to Problem Places This method turns the traditional approach of academic conferencing upside down: Instead of presenting solutions and academic research, the participants are called upon to collaboratively solve problems presented by a company. This method is derived from the general open innovation paradigm, when problem solvers and problem seekers meet and match online. This method combines the open innovation concept with a traditional conference approach. However, rather than new products or research, problems that cannot be solved by companies are presented. Like a stock exchange, the conference principle is transferred to the problem-solving potential of conference participants that are openly called for participation. Direct contact with local motivated individuals facilitates to identify their problem-solving competencies from different disciplines. This approach is particularly interesting for the regional search for knowledge and ideas, since an offline event typically attracts a limited number of people from the surrounding area to participate and participants are able to extend their network, next to access problem-solving competencies.

6.3 Matching Panel: Improving Networking at Events The idea of the match panel method is to improve networking at events such as trade fairs and academic conferences. Individuals who have never met before are matched together based on specialized criteria collected beforehand. For this purpose, the participants create user profiles in an online tool, in which individual data, skills, and interests are stored. The user profiles can either be created online in advance of the event or during the event at special user terminals. To organize the matching process particularly efficiently, experts develop appropriate selection criteria and questions that are tailored to the topic of the respective event. An algorithm or manual allocation based on defined criteria then decides on the exact matching based on the information provided by the user. The simple act of making contact is facilitated through various

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media (e.g., Facebook or WhatsApp Groups), enabling the participants to meet the people suggested to them over the course of the event.

6.4 Crowdfunding for Start-Ups and Seeding Phases for Young Companies Crowdfunding is a new type of financing where donors selected over the Internet provide funding for a transfer project or a new product idea with micro-contributions (Mollick, 2014). While in the past mostly expert committees decided on the granting of financing in culture and start-up support, crowdfunding enables new decisionmaking processes and structures. If transfer projects want to use the new sources of funding, they have to attract attention and support much earlier than when applying traditional methods. Thereby, universities have a critical role because they are able to support transfer projects in crowdsourcing and—investigating due to their reputation and their network of researchers and alumni. For this purpose, universities might not limit their support in technology transfer to the content-related development of a business plan, etc., but also support the professional presentation and communication of the project. In terms of technology transfer, crowdfunding is particularly suitable for fully developed end-consumer products and crowdinvesting for business models that are currently being set up.

6.5 Opportunity Recognition Workshops The academic idea of “Opportunity Recognition” describes a strategy to bring unsatisfied market needs in line with solutions to meet these needs (Shane & Venkataraman, 2000). For this purpose, transition processes are an important factor for the emergence of new market opportunities. This kind of processes can be of technological, political, regulatory, social, or demographic origin. In research, Opportunity Recognition is mainly used to observe business formations and focuses mostly on the personality of the entrepreneur and his or her social network. However, academic business literature offers only limited gains of knowledge about the exploitation of knowledge in research institutions, especially regarding Opportunity Recognition in non-acquisitive entities. To support knowledge transfer from research to practice, moderated Opportunity Recognition Workshops should be held regularly to give researchers the opportunity to discuss their findings and possible applicability with practitioners and other academics. The method’s idea is to help the workshop participants recognize exploitation ideas by drawing a link between opportunities arising from unfulfilled market needs and potentially available (often technical) solutions.

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6.6 Map Sets: Roadmapping and Network Analysis The effectiveness of the knowledge and technology transfer depends heavily on a fast, economic, and extensive transfer of new findings and solutions into marketable products, processes, and services that prevail in international competition. Particularly during the early stage of knowledge transformation, it is crucial to create bilateral bonds, based on expert knowledge, between researchers and the industry. These bonds enable the harmonization of assumptions and perceptions about future needs for research and their implementation possibilities and obstacles. To support this essential stage in the transfer process, it seems reasonable to make use of roadmapping as a method to visualize and conceptualize developments in companies or research institutions, especially to support the medium and long-term planning of products, technologies, services, and business models. In practice, this can be realized by topic mapping on the one hand and by means of a network analysis on the other hand to link these topics with actors (actor mapping). This concept thus links a content-related mapping of future topics using the roadmap with an institutionally related mapping of the relevant actors in research and business.

7 Outlook: An AI-Based Future of Technology Transfer Existing approaches of technology transfer are generally based on the documentation and active transfer of existing knowledge. This process is often executed by researchers in commercial enterprises by means of a “transfer via heads” from one domain (university) to another (typically enterprises). The storage of research results in databases by researchers and the subsequent search of databases on the basis of an application problem is a second method of classical and prominent transfer which turns out to be less successful. In both cases, a restriction with respect to an exclusive focus on local paradigms impedes the effectiveness of knowledge transfer. In this chapter, we presented new approaches addressing to resolve these problems and overcoming the problem of local search and of not being willing to leave the own domain of knowledge. The purpose of this chapter was to reflect classical ways and present new innovative, open coordination and motivational principles to transfer external knowledge from universities to the entrepreneurial innovation process. But what is next? Research and development has always relied on state-of-the-art technologies to push the boundaries of innovation. Today, we observe another generation of digital technologies and data-driven applications transforming the nature of the process of innovation (Nambisan, Wright, & Feldman, 2019): Methodologies based on artificial intelligence (AI) and machine learning (ML) are utilizing vast, connected repositories of connected data for innovation. These technologies may open up the next range of opportunities to increase the efficiency of technology transfer. The term AI refers to machines performing the cognitive functions typically associated with humans, including perceiving, reasoning, learning, interacting,

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or creating (Rai, Constantinides, & Sarker, 2019). Only recently, the disposability of big, connected sources of data and rapid improvements in computing power to run efficient algorithms processing and analyzing these data have placed AI in center of attention among managers in all industries (Rindfleisch, O’Hern, & Sachdev, 2017). Artificial intelligence could become the new open innovation mechanism to boost technology transfer. AI may complement this perspective, fundamentally expanding the role of information technology in the innovation process by “elevating computers from mere servants to partners, empowering us to express our human strengths even further” (Kakatkar, Bilgram, & Füller, 2018, p. 1). In other words, the machine could become the next generation of partners for the innovation process. Potentially, AI provides firms with unprecedented opportunities for increasing R&D productivity into new dimensions (Bloom, Jones, Van Reenen, & Webb, 2019; Furman & Seamans, 2019). Consider, for example, material sciences, a basic technology field with large impact for many applications. Breakthroughs in the discovery of new materials have become harder and more expensive to attain as the field became dramatically more complex and saturated with data—a typical technology transfer problem where potential users of existing research could not any longer manage the complexity and scope of the existing (and steadily) growing body of knowledge in the material sciences. But the complexity that has slowed progress in these fields is where deep learning, a core method of AI, excels: Searching through a multidimensional space to come up with valuable predictions is one of AI’s core competences (Rotman, 2019). AI-driven innovation processes in this field are potentially much faster and cheaper by orders of magnitude. In other words, AI’s “killer application” might not be the autonomous car, fake images, or even voice-enabled home automation, but its capacity to empower the innovation process itself and solve the technology transfer crises. AI-powered system can perform deeper analyses of data (e.g., better-recognizing patterns in data, deriving latent variables, or spotting anomalies than humans) and support decision-making under uncertainty (e.g., generating predictions, dealing with information asymmetry, overcoming human biases). Descriptive and diagnostic analyses may be especially useful in the exploration phases, since these methods can help technology managers to gain an information-rich view of the problem and solution spaces by screening large amounts of technical literature, patents, or also “grey” literature like project reports or press releases. These applications are, however, not without constraints. One of the most important issues with AI methods is that they are generally concerned with establishing correlational relationships between variables, not causal ones (Kakatkar et al., 2018). Hence, also claims that with AI, prior theorizing and hypotheses development are not necessary and that results should be fully and solely driven by what was found in the collected data (a view popularized as “the fourth scientific paradigm” by Gray [2009]), are probably wrong (Chai & Shih, 2017). A future of solely data-driven and algorithm-based “autonomous technology transfer” seems unlikely. AI-driven methods and approaches will be supplemental to existing transfer methods—and will reimaging these and the innovation processes they are embedded in. This opens fascinating questions and perspective for the future of technology transfer.

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Start-Ups as Relevant Supporters and Initiators of Sustainability Attributes in Global Value Chains of the Future Stephanie Rabbe, Christoph von Viebahn, and Marvin Auf der Landwehr

Abstract This chapter examines the impact and role of start-ups on sustainable value creation across global supply chains. Within their highly innovative, disruptive, and often cooperative activities, start-ups can exert a significant influence on the business processes and practices of their supply chain members. Consequently, asymmetric partnerships between start-ups and SMEs are common in industrial practice and can support the overall value creation process in various business contexts. The main objective of this paper is to operationalize the individual role of start-ups in creating sustainability attributes along its given supply chain. Therefore, a theoretical background on the strategic concept of cooperative awareness as well as its practical implications is given. Moreover, to examine how start-ups act as supporters and initiators of sustainability in global value chains, an integrated single case study is conducted. By substantively reviewing and analyzing the case study object in terms of its value chain set-up, operations and interdependencies, it is shown that, depending on supply chain architecture and nature of cooperation partners, start-ups can have a major influence on sustainable supply chains. Keywords Sustainability · Cooperative awareness · Start-ups · Value chain · Case study

S. Rabbe HAWK Hochschule Für Angewandte Wissenschaft und Kunst, Goschentor 1, 31134 Hildesheim, Germany e-mail: [email protected] C. von Viebahn · M. Auf der Landwehr (B) Hochschule Hannover (University of Applied Sciences), Ricklinger Stadtweg 120, 30459 Hannover, Germany e-mail: [email protected] C. von Viebahn e-mail: [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_10

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1 Introduction The economic relevance of start-ups is increasing. They invent new services and innovative business models, disrupt established markets as well as industry sectors, boost the digitalization and influence cooperation in global value chains or rather supply chains. This chapter focuses on start-ups with a sustainable entrepreneurial mindset and exploits their intended as well as their actual effects on value chains or rather supply chains. In terms of goals, intentions, results, and impacts, sustainable startups are equally oriented toward social, ecological, and economic attributes. These attributes determine the development and implementation of innovations. Their suppliers and logistics partners must cope with their high expectations to support their sustainable business activities. The chapter aims at developing approaches to operationalize these expectations by examining the ecological dimension (e.g., reduction of emissions), the economic dimension (e.g., scalability of solutions, purchasing decisions) and the social dimension (e.g., forms of cooperation, coopetition) of sustainable start-ups on value chains or rather supply chains using case studies or conceptual considerations. The objective of this chapter is to gain insight into the question “How will sustainable start-ups have a noticeable influence on global value chains or rather supply chains in the future?” A special context is the observation that companies, especially small and mediumsized enterprises (SMEs), cooperate within their sector in established value chains and that their ability to act jointly with asymmetric partners outside their own sector can be regarded as a key competence in the future. Interviews and case studies show that science and business notice a striking cooperation gap in the operational design of cooperation, the origin of which seems to stem from the factual and perceived asymmetry of the partnerships. It is therefore important to discuss the extent to which sustainable strategies of established companies and start-ups are based on cooperation with each other.

2 Framework Established companies are faced with the challenge of opening up their innovation processes to external impulses in order to actively use their corporate environment to increase their own innovation potential (ZEW, 2016). According to the Community Innovation Survey, 63.7% of German companies pursued innovation activities between 2014 and 2016. Of these, a large proportion (60.8%) implemented innovations of a technological or non-technological nature and are regarded as so-called innovative companies. As the number of employees increases, the proportion of innovative companies rises from 54.9% for smaller companies to 74.2% for medium-sized companies and 89.6% for large companies. In particular, SMEs focus on either technological or non-technological innovation activities (IfM, 2019). Som (2015) states that, for example, SMEs cooperate in established value chains within their sector

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and anticipates their ability to cooperate with asymmetric partners outside their own sector as a future key competence. In the case study with Runamics, this contribution therefore focuses on a sustainable start-up as an asymmetric partner outside the industry for cooperation and innovation for SMEs (Deloitte, 2017; Wallisch & Hemeda, 2018). According to prevailing opinion, start-ups act as technology input for value chains and disruptors of traditional business models. They are active drivers of crosscutting issues such as sustainability or digitization and act as transfer channels from science and universities (Plöger, 2016). 67.1% of start-ups cooperate with established companies (Kollmann, Hensellek, Jung, & Kleine-Stegemann, 2018). Their goals are simplified market and customer access, proof of concept as well as image gains and technology expertise (Deloitte, 2017). Their motives for cooperation are technical (joint research and development), economic (cost reduction), or organizational (exchange of experience) (Deloitte, 2017). Cooperation takes place in pilot projects (50.3%), in marketing (47.9%), and in research and development (44.5%) (Kollmann et al., 2018). In Kühmayer’s Leadership Report 2016, start-ups are still regarded as “innovation pioneers” (Zukunftsinstitut, 2019) but in his Report 2019, disillusionment sets in and the focus is once again on the “heroes of medium-sized businesses” (Kühmayer, 2018). The state of the art is also divided into two fragments: Optimistic contributions define cooperation between SMEs and start-ups as a promising success factor based on suitable cooperation models, trust and balanced expectations (Wrobel, Schildhauer, & Preiß, 2017). Critical studies counter this fear of cooperation with a lack of experience (Investitionsbank Berlin and Creditreform Berlin, 2018) and untapped potential (Kollmann, Stöckmann, Hensellek, & Kensbock, 2016). Problems include the identification of the right partners, a “generation conflict” (Kuckertz & Allmendinger, 2017), different corporate structures and culture (Löher, Paschke, & Schröder, 2017), a lack of commitment on the part of management or unwanted dependence on third parties (KMU-Report Berlin, 2018). For some SMEs, cooperation with start-ups represents an examination of their attractiveness as a participation or purchase option, which leads to a one-sided view from the investment perspective (Wallisch & Hemeda, 2018).

3 Objective 3.1 Creating Awareness of Cooperation A solution for dealing with the imbalance can be the creation of cooperative awareness as a success factor of strategy formation. Here, the chapter contributes to future research of asymmetric partnerships (Wrobel et al., 2017) and offers a first conceptual attempt of explanation with the assumption of an insufficiently pronounced awareness of cooperation and approaches to its improvement. The frame of reference is

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CA

CN

CC

CO

CQ

Fig. 1 Cooperative awareness (own illustration)

the so-called strategic awareness (Gibb & Scott, 1985), the inadequately developed strategic awareness, which is used as an explanation of the planning gap that is assumed to exist in SMEs (Rabbe & Schulz, 2007). This is the reason for the high complexity of the problem, because sustainability is an important strategic issue for sustainable start-ups that they can only position appropriately in cooperation if it has a similarly high strategic significance for their partners. For example, a distinctive strategic competence together with an appropriate cooperative competence on both sides of a partnership can promote the influence of sustainable start-ups on the value chains of the future. The conceptual considerations on cooperative awareness are followed by an explanation of the methodological approach and the Runamics case study.

3.2 Cooperative Awareness Gibb and Scott (1985) describe strategic awareness as the sum of strategic necessity, strategic ability, environmental awareness and management time (Gibb & Scott, 1985). The assumed planning gap is considered a consequence of a lack of strategic awareness. If one considers quantitative and qualitative deficiencies in the cooperation behavior between stabilized companies and start-ups as reasons for the cooperation gap, an analogous analysis would be appropriate. We define cooperative awareness (CA) as the sum of the recognized necessity of SMEs and start-ups to cooperate with each other (cooperative necessity, CN), their cooperative capabilities (CC), the perception of cooperation opportunities (CO) and the communication quality (CQ) for initiating and realizing cooperation (Fig. 1).

3.3 Implications In order to close the cooperation gap and create cooperative awareness, initial implications can be formulated for the positive effect of the individual components and for the development of a cooperative awareness.

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Cooperative Necessity

To raise awareness of the need for cooperation, hub initiatives, for example, aim to be an interface for innovation partners and to inform about synergies between established companies and start-ups. They collect contacts and create docking points to relevant networks. However, there is a lack of low-threshold, local or regional formats and suitable channels. Original marketing campaigns or targeted project funding could contribute to visibility.

3.3.2

Cooperative Capabilities

The development of modern teaching and training formats should aim to ensure that the participants come together in teams and develop their own qualification needs based on their cooperation project (Tiimiakatemia, 2019). Therefore, they use various modules, lecturers act as coaches and have the task of moderating the processes in a target-oriented and accompanying them in an inspiring way. Thus, the foundations are laid for the exchange of experience and hierarchy-free learning from one another, and stabilizing competences are specifically trained. Wrobel et al. (2017) refer, for example, to the cooperation phases “Learn, Match and Partners,” which take into account the various levels and depths of cooperation. These different phases require different skills. In order to enable both partners to cooperate, an analogous solution to Heyse and Erpenbeck’s (2009) competence atlas is conceivable, which extends this approach and considers different cooperation competences as well as their learnability.

3.3.3

Cooperation Opportunities

In order to create cooperation opportunities for start-ups, hub initiatives facilitate access to a network of investors or customers, enable a professional increase in reach for one’s own idea and often offer online contact with experts. For companies, they open up access to start-ups and their agile innovation potential. In order to exploit this potential, network formats with a focus on joint innovation projects and active exchange are conceivable. Research and university-related projects offer the first digital and analog platforms to initiate this exchange.

3.3.4

Communication Quality

The starting point for promising cooperation is the consistent and sensitive management of expectations throughout the phases of cooperation.

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Currently, a wide variety of innovation platforms are being developed, which, with the help of digital technologies and communication methods, attempt to stimulate and accelerate creative solution processes, but thus only address performanceoriented success factors. However, there is no target-oriented moderation and effective accompaniment of joint innovation processes. The stability-oriented perspective of the success factors requires an intensive examination of aspects such as trust, culture, similarity, common goal formulation, commitment, or flexible organization. A solution approach includes systematic consulting concepts to support upcoming cooperation as well as collaborations that are yet to be implemented; e.g., role plays, team building on partner level or also the joint strategy development, in order to understand the culturally conditioned, diverging viewpoints of the counterpart. The sustainability of this first conceptual explanatory approach and the effectiveness of the implications are now being examined with the help of the case study. Within this context, we define corporate sustainability as “meeting the needs of the direct and indirect stakeholders [], without compromising the ability to meet the needs of future stakeholders as well. Towards this goal, firms have to maintain and grow their economic, social and environmental capital base while actively contributing to sustainability in the political domain” (Dyllick & Hockerts, 2002, pp. 131f.).

4 Methodology In general, it is difficult to clearly distinguish sustainability-related problems, because they neither have a precisely defined target state nor provide any information about potential challenges and obstacles between the actual and the target state. Such problems, also referred to as “ill-defined problems,” serve as a typical starting point of the case study approach and require knowledge at three levels—understanding, comprehension, explanation (Scholz & Tietje, 2002, pp. 26ff.). Hence, to identify the impact of start-ups on sustainability attributes within global value chains and to discover the relevance of cooperation awareness on both sides of an (asymmetric) partnership, we pursue a transdisciplinary research approach by means of a case study. In the context of this study, we employ the definition of Robert K. Yin (2009, pp. 17f.), which suggests that the decision analysis and evaluation is a central element of the case study approach, where, based on several sources of evidence whose data converge in a triangular manner, a contemporary phenomenon is empirically studied in its real context. In general, a distinction can be made between an integrated case study design and a holistic case study design (Scholz & Tietje, 2002, p. 9f.). While the latter is characterized by a qualitative research approach, integrated case studies encompass more than one unit of analysis and support both qualitative and quantitative evaluations (Bortz & Döring, 1995). To answer our research hypothesis, we investigate the start-up Runamics and its value chain architecture, which was selected as case study object in the course of a theoretical sampling based on content-related criteria for sustainability-oriented

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Fig. 2 Methodical process model regarding the holistic case study development (own illustration)

value creation. Due to its unique organizational and holistic focus on sustainable value creation, the start-up provides an idle framework for our research and promises unrivaled insights into the initiation of sustainability attributes along global value chains. After all, case study-based research is not about representative population samples, but about entities whose characteristics are likely to provide comprehensive insights into the subject of the research (Dul & Hak, 2007). In line with the priory presented definition, the examination of the company’s impact on the sustainability of its stakeholders constitutes an “ill-defined-problem.” Additionally, Runamics can be regarded as representative study object for investigating the impact of start-ups on sustainability attributes in global value chains, so that the complexity of the problem can be considered in appropriate substantive depth (Yin, 2009, p. 47). Hence, we have chosen an integrated single case approach. Our methodological process model in relation to design and implementation of the case study and the interactions of the individual phases is shown in Fig. 2 and derived from the principles given by Scholz and Tietje (2002), Zaugg (2007) as well as Yin (2009). To analyze the specific impact of Runamics on sustainability attributes, we use the traditional value chain architecture of Porter (1985). Since young start-up companies mainly focus on primary business activities (Raith, 2014), within the framework of our study, the following business areas are considered: Inbound Logistics, Operations, Marketing and Sales, Outbound Logistics, and Customer Service. In contrast, supporting activities such as human resources, technology development and enterprise infrastructure play a subordinate role as they are usually not, or only to a very limited extent, present in start-up organizations and are therefore not examined in this case study. In order to collect the required information regarding the affected value chain elements in the case study, a 2-hour expert interview was conducted with the founder of the start-up Runamics. While the company and its general context provide the framework for this study, a total of five subordinate cases have been identified, namely the primary businesses activities presented by Porter (1985). The advantage of this approach is that it does not aim at individually comparing the sub-cases

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with each other, but on analyzing their variations, expressions, interactions, and effects (Easton, 1995). By examining the specific variations in a single environment, a more comprehensive overview can be provided, ultimately facilitating an individual high-quality contribution to the overall case. In addition to the role of the value chain members and the individual influence of the start-up on sustainability attributes, the expert interview also addressed the characteristics of the primary business activities as well as the general value creation process. The specific questions for the interview were developed in line with the guidelines of Meuser and Nagel (2009) and are based on issues related to value chain oriented sustainability (Mersiowsky, Bösch, & Feigenbutz, 2019; Schulz, 2012; von Geibler, 2010; Walther, 2010). The individual suppliers were integrated indirectly into the data acquisition phase by adding an additional dimension to the supplier perspective and asking the buying company about the suppliers’ representatives, actions, behaviors, points of view, etc. To ensure high data quality and validity, a detailed record and description of the entire expert interview was sent to the interviewee for re-validation. For the interpretation and evaluation of the accepted record fragments, we utilized the qualitative, summarizing content analysis according to Mayring (2010). Correspondingly, the transcription material was paraphrased, generalized, and reduced in content to solely represent essential statements regarding the given investigation cases. To further increase the internal validity, both transcriptional material and content analysis results were discussed, compared and, where necessary, adapted to the consensus throughout the research team. This dedicated process avoided content inconsistencies and ensured a higher overall data quality. Ultimately, the resulting data sources were used to describe and evaluate the impact of Runamics on the individual sustainability attributes of its value chain members.

5 Case Study Analysis In the subsequent section, the results of the within-case analysis as well as the crosscase analysis concerning the identified sub-cased are presented.

5.1 Descriptive Case Presentation—Within-Case Analysis Founded in 2019, Runamics is a start-up organization that emphasizes sustainable value creation and product development, both in terms of entrepreneurial and strategic focus. Distribution lines exclusively include running-, fitness- and sportswear products featuring a minimal proportion of plastics and microfibers. Although the aim of the start-up is the highest degree of sustainability regarding the ecological, economic, and social dimension, it already faced a major decision problem right after its foundation. Since sportswear needs to satisfy a high range of application requirements, it is currently not possible for some products (e.g., tights or leggings) to

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Fig. 3 Exemplary representation of the primary activities within the value chain of the start-up Runamics (own illustration)

completely dispense artificial raw materials and synthetic fibers such as spandex. For this reason, products with a minimal plastic content are currently sold. Nevertheless, the company’s long-term goal is to intensely support sustainability research in the textile industry in order to be able to sell 100% plastic-free sportswear in the near future. Along the value chain of Runamics, besides of raw material suppliers and spinning mills/yarn suppliers, also fabric manufacturers (embroidery/weaving mills), haberdashery suppliers, sewing companies, fulfillment partners, and courier services play an essential role. The entire value chain as well as the interdependencies of their entities and the respective activity spectrum are exemplary shown in Fig. 3. With regard to inbound logistics, Runamics is essentially confronted with the fact that the entire sustainability topic has a very low priority for many suppliers, which means that requirements for raw and semi-finished materials are often negligently disregarded or that generally no interest in a business relationship is given. Especially the economic dimension (profitability of the business model) is challenged by many suppliers. In addition, due to comparatively low purchase quantities and the size of the start-up, there is merely a slight cooperation pressure. Although an increasing proportion of the society has demanded sustainably produced clothing in the decades prior to 2019, sustainable value creation in the clothing industry is still often underestimated and disregarded. In order to increase its impact on inbound logistics as a small company, Runamics has skipped communication in the first stage of the value creation (raw materials) and directly contacted established, international, textile-fiber processing spinning mills. Subsequently, the networks and connections of the spinning mills were utilized to

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find suitable raw material suppliers and fabric manufacturers. Through this approach, Runamics was able to improve its reach as well as the willingness of suppliers to cooperate, so that the sustainability claim of the company could be enforced, both in terms of ecological raw materials, economic value creation, and social cooperation. The search for and cooperation with a suitable fabric manufacturer has proven to be particularly difficult and tedious, since the latter is required to have its own concept for sustainable production and product development as well as to include plasticfree/low-plastic materials in its portfolio. Due to a lack of expertise in dealing with sustainably produced textiles, many suppliers are unable to meet the requirements of the start-up. For example, plastic-free prototypes are wrapped in poly bags and packed several times, which ultimately is contrary to the idea of sustainability. Accordingly, Runamics is working hard to maximize transparency along the entire value chain to ensure ecologically valuable products and sustainable production cycles, while avoiding cooperation problems due to cultural differences and communication issues. If no agreement can be reached on environmentally friendly inbound logistics, the start-up generally desists from a cooperation with the respective player. Packaging materials are sourced directly from Germany and are both plastic-free and reusable. In addition, the company abstains from multiple packaging for individual products and established a packaging system with minimal amounts of empty space, overall resulting in less packaging materials required. Here, the selection and sustainability awareness of the suppliers are much more pronounced, so that no additional sustainability demands are set up by the start-up. The primary activities Operations, Marketing and Sales as well as Customer Service are completely taken over by Runamics itself and include: ● the development of product designs, quality control of raw materials, semifinished and final products, acceptance of customer orders, packaging, and shipping preparation (Operations) ● the distribution of the products via the own website as well as a dedicated fulfillment partner, network marketing with high-profile personalities (influencers) and event marketing (Marketing & Sales) ● the handling of customer orders, claim and complaint management as well as general customer service (Customer Service) In all sub-areas, sustainable value creation is taken into account in all dimensions. Accordingly, for example partners for network marketing are also selected in line with the company philosophy and only used as distribution partners if they share the sustainability requirements and mindset of the organization. Sales activities are supported by a fulfillment partner, who respects the demands and claims of Runamics and has a major focus on sustainable value creation. The recycling of packaging materials, the use of ecological raw materials and climate-neutral shipping processes are important criteria for choosing a suitable sales partner. While large, established companies such as Amazon are not considered as an additional distribution channel due to their own demands and a lack of sustainability claims, they are occasionally deployed as additional sales platform. However, in this case, shipping and distribution are still handled by Runamics, while the platform is exclusively used as sales point.

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The final customer is ultimately supplied by an external courier service, which is selected on the basis of its compensation programs. Due to the economic pressure on start-ups, currently it is not possible for the organization to base courier choices on more comprehensive selection criteria for sustainably deliveries. Existing compensation programs already meet many of the company’s requirements and are attractive from an economic as well as an ecological point of view. However, in the future, Runamics plans to evaluate individual sustainability components such as empty runs and transport modes and subsequently make a selection based on a sound component analysis. Supportive activities such as recruitment and infrastructure are not yet relevant due to the size of the enterprise. Nevertheless, Runamics’ strategic alignment already envisages sustainability-related requirements for these activities as the company progresses. These requirements include, among other things, aspects such as short journeys and service routes when it comes to recruitment as well as a climate-friendly energy supply of the office facilities.

5.2 Cross-Case Analysis In the previous paragraph, we presented the case study and analyzed the specific subcases, also taking into account individual peculiarities of the respective instances. In addition, information and large amounts of data from the expert interview were clearly summarized and structured by means of the case study report to prepare for cross-case analysis. While the start-up Runamics served as main case object, we defined and analyzed each of its primary business activities as sub-case, which are assessed on an individual basis and compared to each other in terms of a crosscase analysis. The results of the sub-cases are now combined on a higher level of abstraction and generalized. Finally, the outcomes are used to derive hypotheses that generally explain the impact of start-ups on sustainability in global value chains. In the field of inbound logistics, there is an indirect influence on the ecological value creation of the raw material and yarn suppliers by determining product and process requirements using the fabric supplier. However, since there is usually no contact with the suppliers, the transparency along the value chain is limited at this point. In addition, fabric suppliers are generally not willing to disclose their suppliers. Nevertheless, in order to ensure sustainable product development, spinning mills complying with the requirements can be described and analyzed in order to cooperate with their fabric manufacturer network. At first glance, cooperation between suppliers and start-ups may appear to be an economically attractive option, but since there is no pronounced cooperation necessity among suppliers, cooperation capabilities and cooperation quality take a back seat. The suppliers’ cooperative awareness in inbound logistics with regard to sustainability attributes is therefore rather deficient. The cooperation awareness of the spinning mills with regard to the sustainability requirements of the start-up results from a positive development of all its components. In particular, the indirect use of the spinning mills’ relationships

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influences the suppliers’ willingness to cooperate. This increases the quality of cooperation with the spinning mills, which share the sustainability requirements of the start-up. The fabric manufacturer, who acts as a direct business partner and supplies pulp in the form of piece goods, can usually be directly influenced by the company’s sustainability requirements and is guided by the expectations of the customer in terms of product composition, quality, treatment, and refinement. However, due to the lack of transparency, sustainability along early stages of the value chain cannot be guaranteed. Vertically positioned manufacturers also take over the dyeing processes, so that statements about the sustainability of this production step can be controlled and coordinated more easily. Due to the scaling possibilities and the larger selection of specialized dyeing companies, additionally integrating separate dyeing factories into the value chain seems economically valuable when purchase quantities of the company are increasing. Cooperative awareness toward sustainability characteristics in terms of quality and capabilities is directly fostered by the close cooperation as well as the requirements exerted by the start-up. Due to the large selection and strong competitive pressure in the haberdashery industry, there is a great influence on the sustainability attributes of the haberdashery suppliers. The same applies to fulfillment partners and courier services. Sewing plants, which are, besides of the material suppliers, the most important partners in inbound logistics, are difficult to influence in their added-value creation, even in direct partnerships. Especially in an international context, working conditions and linguistic barriers play a major role. Moreover, economic business parameters must be taken into account, since this processing step represents the largest cost factor for the final product. Thus, at this point the impact of the start-up on sustainable value creation is less important than the selection of global partners who meet the respective requirements with regard to the economic, ecological, and social dimensions. Despite its endeavors and investments to emphasize sustainable value creation and raise cooperative awareness regarding sustainability attributes across its global value chain, the start-up is generally confronted with a superordinate decisionconflict. Economic business goals have to be aligned with sustainability requirements when establishing collaborations across the entire supply chain. Hence, cooperation between the start-up and its suppliers is mainly influenced by the individual degree of cooperation (e.g., high, low), the point of contact (e.g., direct, indirect) and the relative bargaining power of the supplier compared to the start-up (e.g., high, low). While the latter is an important element to assess the influence of start-ups on sustainability in value chains, the cooperative awareness concept allows for a deeper level of assessment and facilitates the evaluation of stakeholder relationships across the value chain from an integrated sustainability-oriented rather than an individual profitability-oriented perspective, taking into account the three dimensions of sustainability by means of analyzing cooperative capabilities, cooperation opportunities as well as communication quality for initiating and realizing cooperation. Depending on the respective conditions and constraints in given supply networks and relationships, the influence on the cooperative awareness concerning sustainability

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characteristics can highly differ on an individual basis. In order to close the cooperation gap and act as relevant supporter as well as initiator of sustainability attributes in the global value chain, Runamics follows multiple approaches. 1. By constantly re-evaluating existing as well as alternative supply networks and supplier relationships, the start-up puts continuous pressure on its suppliers to ensure sustainably value and product creation. 2. A high degree of transparency across all production and value creation processes is promoted by establishing uniform and intercorporate communication routines as well as networks. 3. Benefits of sustainable value creation for suppliers within the global network are highlighted in terms of economic (e.g., additional sales potential), social (e.g., increased customer satisfaction), and environmental (e.g., reduced emission outputs by production processes) dimensions. 4. To ensure sustainable value co-creation and foster cooperative awareness concerning sustainability attributes, the start-up incentivizes suppliers by means of additional monetary investments. Consequently, it is willing to adjust economic structures and increase purchasing prices when supply networks and cooperation partners are able and eager to satisfy its sustainability requirements.

6 Conclusion—Sustainability in Value Chains as the Result of Cooperation Awareness Depending on how strong the influence of the start-up is on the individual activities within the value-added chain architecture, their role as relevant carriers, supporters and initiators of sustainability attributes in global value chains increases. However, a sustainable entrepreneurial mindset generally creates a value-added chain that has a high degree of sustainability, since suppliers who do not meet the requirements of the start-up are not integrated into the architecture or stimulated by rigorous demands for sustainable value creation at all levels. The extent to which influence can be exercised by small and young companies depends primarily on the nature of the cooperation partners and their business model. In this contribution, the role of start-ups as relevant supporters of sustainability attributes in global value chains has exclusively been investigated for a representative SME in the clothing industry. Depending on networks set-ups, market conditions and business characteristics, the respective impact of start-ups can highly differ across other industry sectors, which should be investigated in future research. Nevertheless, we have identified and outlined major influencing factors on cooperative awareness as well as sustainable value creation across international supply chains, serving as useful basis for future analyzing the impact of SMEs on sustainability in other industries. The interpretation of the cooperation awareness of the respective partners in the dimensions described above can be performed based on the collected information. In this way, evaluations of the cooperation behavior are possible which, if positive, force the penetration of the value-added chains with sustainability attributes.

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If sustainability-relevant concepts and ideals are shared, there is generally more transparency and readiness to adapt. In addition, the size of the cooperation partners plays an important role. While corporations and large companies generally have little cooperative awareness, willingness to work with start-ups and cannot be influenced by sustainability requirements, SMEs as well as family-owned businesses are, despite missing sustainability concepts within the clothing industry, prepared to adopt sustainability attributes from start-ups and adapt to their added-value creation requirements. For SMEs and family-owned businesses, it can be a viable strategy for the future to deal more intensively with their cooperation awareness and, above all, the development of cooperation skills (cooperative capabilities) in order to generate longterm and sustainable competitive and innovative advantages through cooperation with start-ups.

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Progressive University Technology Transfer of Innovation Capabilities to SMEs: An Active and Modular Educational Partnership Mauricio Camargo, Laure Morel, and Pascal Lhoste

Abstract Regarding SMEs’ relationship with R&D and technology, university technology transfer (UTT) programs have evolved in recent years toward approaches that are more focused on a systemic and continuous exchange between firms, university departments, and R&D centers. Financial support such as innovation vouchers and open initiatives has been applied for a few years, and only recently have research works analyzed the impacts of these programs on the innovative capabilities of SMEs. Existing studies are based on short-term analysis, but there are no studies on the medium- or long-term influence of innovation vouchers on firms’ innovation capabilities. This chapter aims to contribute to this topic through a longitudinal exploratory study of two SMEs in eastern France. It puts forward an original modular program proposed by an engineering school at the University of Lorraine, where groups of students participate throughout the academic year in innovation-related projects. Empirical evidence shows that this type of project has positive impacts on firms’ innovative capabilities, but also fosters the analytical skills and self-directed learning capabilities of students. Keywords UTT · SMEs · Innovation capabilities · Open innovation · Innovation vouchers · Problem-based learning

1 Introduction For several years now, academia and policymakers have been convinced of the need to get political, economic, and academic actors to work together to foster the development of territories by boosting the skills and performances of SMEs (McAdam, M. Camargo (B) · L. Morel · P. Lhoste Université de Lorraine, ERPI-ENSGSI, 8 rue Bastien Lepage, 54000 Nancy, France e-mail: [email protected] L. Morel e-mail: [email protected] P. Lhoste e-mail: [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_11

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Miller, McAdam, &Teague, 2012). For many universities and schools, this third mission in addition to their classical missions of training and research, which makes them an actor of economic development, is often considered as unnatural. In particular, in order to support the implementation of innovation processes within SMEs, most universities rely on technology transfer offices (TTOs) to disseminate their research results toward these companies. In our view, this close, almost inseparable link between research and innovation has not yet been fully understood by politicians, scientists, and the media, and thus represents one of the obstacles to the effective development of innovation in small companies in traditional sectors. Most SMEs (at least in France) that seek to innovate—and not only from a technology point of view—are discouraged when they learn that research is the answer. Indeed, while research can lead to innovation, it is often an uncertain, time-consuming, and expensive process. And these companies need to develop their presence in their markets very rapidly and, if possible, at low cost. Furthermore, the innovation process is usually much more structured (in terms of processes and resources) than that of SMEs. The challenge for public policies is then to provide a simple but effective ecosystem offering SMEs access to innovation pathways. Some of these companies do not have sufficient financial resources to develop their own R&D function or are not ready for the scale and nature of the risks involved. In order to overcome these difficulties, partnerships with universities are an alternative route toward innovation for these firms. However, the real challenge is not to develop one-shot partnerships, but a genuine, long-term, and trusting relationship that allows firms to enhance their capabilities and build toward an adapted process of innovation (Peerbaye & Mangematin, 2005). Although there is a vast conceptual literature on this topic, most SMEs in traditional sectors are still looking for satisfactory answers to this issue. Through a longitudinal case analysis, this chapter seeks to provide empirical evidence of the mechanisms supporting technology appropriation by SMEs, but also to compare these cases with the conceptual models proposed in the literature. Our cases have been collected and analyzed over a period of 10 years within the framework of an innovation engineering school founded in 1993 in France (ENSGSI: Ecole Nationale Supérieure en Génie des Systèmes et de l’Innovation/ National School for Systems and Innovation Engineering) (Castagne, 1987). Within this school, special pedagogical tools have been developed and deployed with firms at the regional level, resulting in a modular process to support local SMEs’ innovation capabilities (Boly, Morel, Assielou, & Camargo, 2014). Some of these tools combining training and industrial partnerships are financially supported by the Regional Council as part of its policy of economic development for SMEs under the innovation voucher scheme. The program under the coordination of the University has been integrated in a modular process to accelerate innovation capabilities and technological appropriation named ATI-Activ’PME. The program itself is composed by a set of tools, called “48hours to bring ideas to life® ” (48heures pour faire vivre des idées® ) and “Transfer and Innovation Workshop®” (ATI: Atelier de Transfert et d’Innovation® ), but also supported by technological platforms. These constitutive elements will be

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further detailed in this chapter and their concrete application explored in terms of the transfer of innovation capabilities at microlevel. As a conclusion, this study will demonstrate through empirical evidence and case studies that it is possible for universities and schools to support the appropriation of innovation capabilities by SMEs through dedicated pedagogical means. It will also show that it is possible and virtuous for universities and schools to make a contribution to the regional economic development of SMEs in tandem with their educational and research missions.

2 Overview of University Technology Transfer-Linked Concepts: Innovation Vouchers and Action Research The broad development and popularity of university technology transfer (UTT) and Open Innovation theories (Chesbrough, 2006) has provided a potentially fertile ground for integrating SMEs from traditional sectors into the university-research ecosystem. However, in our view, as well as high motivation and managerial engagement, these firms have to achieve a minimum level of technological innovation and need sufficient organizational capabilities to become effective receptors of competencies and technologies and to transform them into new products and services. As a consequence, continuous interactions with universities are needed to support this learning process, following a constructivist approach (Boly, Morel, Renaud, & Guidat, 2000). In this sense, as proven by the research of Wynarczyk (2013) on a sample of SMEs in the UK, Open Innovation practices, and in particular external collaborations with universities, have a strong influence on the firm’s competitiveness. Among the different modes of university technology transfer (Fig. 1) (Carayannis & Campbell, 2012; Gibbons et al., 1994), policymakers are searching for more open and co-creational ways to lead the UTT process more efficiently. In Fig. 1, technology transfer “Mode 3” (Carayannis & Campbell, 2009) proposes a “systemic approach to this transfer for the 21st century” alongside the traditional technology transfer office. Through a dynamic knowledge exchange further to continuous joint activities such as training, small-scale development projects, and long-term collaboration, this approach offers a better and more natural mechanism of transfer between universities and firms (Miller, McAdam, & McAdam, 2018). Moreover, as pointed out by Bjerregaard (2010), a long-term collaboration approach may be relevant not only as an opportunity for technological knowledge production, but also for learning within the organization. Furthermore, there is still a need for practical evidences proving that local enterprise partnerships can benefit from a faster learning curve (McAdam et al., 2012). In recent decades, governments have attempted to support this type of technology transfer initiative (Sala, Landoni, & Verganti, 2016). An example of this type of policy is the innovation vouchers, which are defined as small lines of credit/grants

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Fig. 1 Knowledge production modes and university knowledge transfer (own figure, according to Gibbons et al. [1994])

(up to 10,000 euros) provided by governments to enterprises (SMEs) to purchase services from public knowledge providers with a view to introducing innovation (new products, processes, or services) into their business operations (Cornet, Vroomen, & Van der Steeg, 2006). They are traditionally used to solve minor technological problems or scope out larger technological issues. This scheme has been widely used for more than 20 years in Europe and elsewhere because of its simplicity. In Europe alone, by 2009 more than 25 such programs were reported at regional level. By 2018, it is estimated that more than fifty voucher schemes operate at all levels of territorial governance.1 In further research, Bakhshi et al. ( 2015) found that 75.8% of firms receiving an innovation voucher were more likely to engage in future innovations. More recent research on the effects of innovation vouchers on SMEs (Chapman & Hewitt-Dundas, 2018) found evidence on the effects of vouchers on the attitudes of senior managers to innovation, within the framework of a program carried out over the period between 2012 and 2015 in the UK. Specifically, their findings show that these vouchers induce positive changes in managers’ support for innovation, increasing their risk tolerance and openness to external knowledge. These results suggest that the innovation voucher is a particularly effective mechanism to change attitudes to external knowledge. These recent studies, covering more than 2500 firms, have provided significant evidence of the relevance of this type of support tool for long-term learning by SMEs, innovation openness, and risk-seeking attitudes. However, in these studies, the answers collected remain declarative based on individual experience, and the studies 1

http://www.eurada.org/eurada-news-2019-02-europes-innovation-voucher-schemes/.

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offer a short-term analysis. In conclusion, there are no studies on the medium- to long-term influence of innovation vouchers on firms’ innovation capabilities. In this chapter, we intend to show how this type of Open Innovation-based tool can redefine government/enterprise/university relationships by developing new ways to finance innovation. In a period of budget reduction, universities and regional authorities need to develop new tools to provide innovation support services to companies, especially SMEs, in a win-win relationship. The following section will describe how an innovation voucher-type program was designed at the University of Lorraine. Within an action research (AR) approach (Avison, Baskerville, & Myers, 2001; Kaplan, 1998), two firms using the voucher program were then monitored over a period of 10 years. This qualitative research approach, with the emphasis on collaboration between the research team and practitioners, implies the direct involvement of researchers in the project. This approach was selected for this research because it provides a twofold contribution: solving practical issues and contributing to a scientific field. It has been widely used in social sciences, but also in management and entrepreneurship (Schultz, Mietzner, & Hartmann, 2016). A further original contribution of the present work is that it focuses on the application of AR in industrial engineering for a period of about 20 years, within the ENSGSI educational program. More precisely, our discussion, centered on two SMEs from different industrial sectors that used the program over a period of 10 years, will illustrate the observed implications of our work and the evolution of the university-firm relationship.

3 ATI-Activ’PME: A Modular Process to Accelerate Innovation Capabilities and Technological Appropriation The approach used was conceived as a modular program whose constitutive modules aim to fit with firms’ current technological needs and enhance their innovation capabilities over the long term. As shown in Fig. 2, it starts with a formalization of the firm’s need, followed by ideation (48hours to bring ideas to life), and then the ATI projects themselves, supported by the university’s research facilities and available platforms. This implies that the modules are deployed in line with each project’s dynamics. While past experience has shown that not all companies go through the entire process, participation in the program represents a unique opportunity for them to form ties with the regional research and innovation system. Since the first deployment in 2004, the program has been extended to the entire University of Lorraine and more than 70 SMEs have taken part. Today the program is supported by the regional authorities and the European Union through the European Regional Development Fund (ERDF), under the name ATI-Activ’PME. For the current cohort through to 2020, 18 SMEs are participating in the program.

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?

A modular process to accelerate innovation in SMEs !

Research

Research Issue

to make ideas live®

Needs of the SME

(PhD Students, Post-Docs)

Period in Enterprise

…

Innovation Ecosystem Ecosystem of innovation

Development Issue

Projects, ATI®, Prototyping, Usage Tests

Lorraine Fab Living Lab

and Platforms

Nomad’Lab

Fig. 2 Modular process to support the development of the innovation capabilities of SMEs

First, a dedicated team of professionals and related people from the relevant schools seek to identify the SMEs’ needs. Then, if needed and depending on the problem to be addressed, a creative workshop of 48hours to bring ideas to life® may be deployed to meet a specific requirement, or as a part of the launch stage within a TIW-ATI® project. As a result of this one-year period of interactions, certain particular issues may be transferred to the university’s local network of 80 research laboratories. Otherwise, depending on the degree of maturity degree of the project, a link will be established with technology platforms and/or the business support ecosystem. As mentioned in Sect. 1, the pedagogical model at ENSGSI is organized around the student-subject-project triangle (Kleine, Giones, Camargo, & Tegtmeier, 2018). Supporting problem-based learning is the preferred approach, as it aims to foster the analytical skills and self-directed learning capabilities of students (Bary & Rees, 2006). It requires students to work on problems proposed by companies in the region during their studies, which implies a commitment from the company, permanent dialogue, and even the participation of company employees as guest lecturers during regular projects (Dupont, Morel, & Lhoste, 2015; Morel & Guidat, 2005). The main dedicated tools comprising the modular program are detailed below.

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4 48hours to Bring Ideas to Life®2 Nurturing creative competencies allows firms and organizations to explore promising fields to be developed, to ignite the emergence of new concepts and designs, or to propose solutions to specific unsolved problems (Gabriel, Monticolo, Camargo, & Bourgault, 2017). Consequently, for a number of organizations, improving these competencies has become a priority, and methodologies and tools to support creativity have gained in popularity among industrialists, as well as in the academic world. Within the Creative Solving Problems (CSP) paradigm (Osborn, 2012), 48hours to bring ideas to life® is a training workshop module developed by the ENSGSI in the form of a creativity contest to generate original ideas, and focused on the early stages of the innovation process. It was created in 2001 (Boly & Grandgeorges, 2001). Since then once a year, this challenge brings together participants from different disciplines, universities, and countries at a simultaneous and distributed event. In 2019, more than 1700 students from six countries participated (8 centers in France and 5 international universities). Topics for the workshop are proposed by partner companies (e.g., in 2019, 14 topics proposed by 14 enterprises), so people from these companies play an active role during the preparation, implementation, and outcomes analysis of the workshop. The aims of the workshop are twofold. First, it allows participant students to develop entrepreneurial capabilities by giving them a better understanding of the main skills and behaviors used by innovators in the early stages of projects. Second, it provides firms with a real outcome, enabling then to explore potential new innovative projects. At the end of the process, each participant firm receives a detailed report containing a set of idea grids (including detailed information on each idea) and an adapted methodology to evaluate these ideas, as described in (Gabriel, Camargo, Monticolo, Boly, & Bourgault 2016). Figure 3 describes the systematic methodology used during the two-day workshop and its principal steps and associated activities. The workshop is composed of two main processes, divergent and convergent thinking (Bary, 2005). During the divergent phase, creativity techniques are applied to ignite the creative process and generate an original set of potential solutions to the proposed problem. Then, during the convergent phase, analytical techniques to challenge and select promising solutions are used by participants. These processes should be as balanced as possible, as each problem is different (context, definition, participants’ skills), and this balance will depend on the degree of definition of the problem, but it is important to ensure that participants identify novel solutions (Leonard, and Swap, 1999). The workshop ends with a presentation before a jury by the different participants of the selected ideas. The set of ideas considered as having potential to become new products or services may be developed by the company or further developed by another group of students within the framework of a Transfer and Innovation Workshop (TIW). 2

https://www.ensgsi.univ-lorraine.fr/48-heures-pour-faire-vivre-des-idees-edition-2020/.

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Fig. 3 48hours to bring ideas to life® —methodology description

4.1 Transfer and Innovation Workshops® (TIW-ATI) The Transfer and Innovation Workshops® or “ATI® ” (for: Ateliers de Transfert et d’Innovation® ) is a program created in 2004 at the ENSGSI and adopted by the University in 2007. Originating from a collaborative process among engineering, business, architecture, and design schools, this program functions as an “innovation vouchers” scheme. It affords SMEs from the Lorraine region3 access to the university and its research infrastructure and competencies to launch innovative projects, while allowing undergraduate students to learn through real projects in an industrial environment (Morel, Camargo, & Lhoste, 2019). The financing principle is simple: it is a co-financing procedure, that is, an SME is awarded a grant of up to 6000 euros per project by the Regional Council and co-finances this project to a minimum amount of 1000 euros. It allows even start-ups to be part of the program. The main expected outcome is to boost the efficiency of exchanges between Research/Training/Companies/Territories, in order to: ● Trigger the exploration of the early stages of new potential projects within local companies, whereby ill-defined constraints (regulatory, market, or customers) and/or spin-off opportunities are not easily measurable in advance. ● Gradually integrate SMEs into the knowledge economy, facilitate and accelerate their innovation capabilities, develop their teams’ knowledge in terms of steering innovative projects, and develop their collaboration and networking practices by providing them with access to a set of methodological and technological resources devoted to innovation. ● Train students using active pedagogy including concrete and innovative needs and foster a collaboration between the university and a company through a multidisciplinary project. 3

https://www.univ-lorraine.fr/content/atelier-de-transfert-et-dinnovation.

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Fig. 4 Organization of an ATI-ITW

● Verify the relevance of an innovation project (products or processes) and its technical and economic feasibility. ● Integrate teams with different skills from different backgrounds: engineering, management, design, and industry. From an operational point of view, Fig. 4 shows the timeline of the TIW-ATI during the academic year. The originality of this concept lies in its design. Three working environments were developed: ● A multidisciplinary team: Each workshop has at minimum students from one engineering school and one business school, two managers (professors at the same schools) and one member of the SME (chosen by the firm’s management). The team has 8–10 participants. ● A dedicated period: The TIW are held every Friday throughout the year and are organized into 2 semesters, with distinct but complementary objectives and methods. As a result, all the engineering and business schools concerned, and the SMEs, have to homogenize their respective schedules and to book the Friday for the workshops. ● Free access for the SMEs to a university technology platform and various kinds of resources (training, research, transfer).

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4.2 Technology Platforms to Support the Modular Innovation Process Getting access to resources enabling to exchange, prototype, and test the emerging products and services is also a fundamental part of the program. Here, some of these resources will be described.

4.2.1

NOMAD’LAB: Technological Support for Innovation

Increasing the number of pathways to promote and develop innovation is one of the challenges successfully taken up by ENSGSI and ERPI thanks to a Mobile Fab Lab (Fig. 5) (named NOMAD’LAB), the first in France (Morel, Dupont, & Lhoste, 2015). Thanks to the Lorraine Regional Council and the University of Lorraine, which made this investment possible, as well as CPME Lorraine (an employers’ union, which financed the first years of operation), Lorraine was the first region in France to acquire such a tool. This embedded Fab Lab, an original concept, was born from the desire to get as close as possible to companies and citizens in order to stimulate their creation and experimentation capacity. By erasing borders (geographical, temporal, economic, social, cultural), the Mobile Fab Lab reverses the usual relationship of access to technology and brings innovation to companies, cities, schools, high schools, etc. This proactive approach establishes a new proximity with users. The development of specific spaces is not always possible or relevant (too long, technical constraints, unsuitable premises,

Fig. 5 NOMAD’LAB, a mobile innovation laboratory

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Fig. 6 The LF2L® Framework

etc.). Employees of SMEs cannot necessarily travel to the heart of the Nancy urban area, so to the Fab Lab has to meet them where they are.

4.2.2

Lorraine Fab Living Lab®: Technological and Methodological Support for Innovation

The priority challenge to which the Lorraine Fab Living Lab®4 (including NOMAD’LAB) was aiming to respond was to initiate and lead a dynamic of collective reflection on our living environment (Morel et al., 2015). The challenges of society are explored in order to provide shared responses, the development of which involves the creation or renewal of products, processes, and even organizations. To successfully carry out this mission and reach economic actors as close as possible to their place of business, the Lorraine Fab Living Lab® (LF2L®) designs and develops concrete solutions using a roadmap system. The LF2L® is a research platform of the ERPI laboratory dedicated to the prospective assessment of innovative usages. It supports creation and the achievement of results through established processes based on the usage paradigm, bringing together complementary advanced tools and methodologies in the same space (Fig. 6). The originality of the LF2L® is its ability to host, support, and associate different communities (user citizens, entrepreneurs, researchers, students, etc.) using a common conceptual framework. This framework includes 2D (concept), 3D

4

http://lf2l.fr.

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(object), and 4D (evolution scenarios) approaches involving different types of stakeholders in order to obtain a forward-looking assessment of a new concept, technology or project. This approach is particularly useful to support the 48hours to bring ideas to life and ATI operations and, further, to accelerate the deployment of demonstrators in various fields (industrial, urban, institutional, etc.).

4.2.3

Innovation Way®: Methodological Support for Innovation

Created in January 2017, Innovation Way® SAS is a spin-off of the ERPI laboratory. For more than 20 years, the laboratory had been convinced that innovation is no coincidence. After auditing more than 300 companies, ERPI developed a set of methods to facilitate the development of innovation in companies. Among these methods, a repository of good practices associated with an algorithm can be used to evaluate the maturity of the companies’ innovation management system (Camargo, Morel, & Boly, 2015). Indeed, before a company takes the decision to carry out innovative projects, it must first measure its capacity to do so (Galvez, Enjolras, Camargo, Boly, & Claire, 2018). The evaluation of the SME’s capacity to innovate is based on 6 innovation management practices (strategy, generation of new ideas, design, project management, human resource management, knowledge management). The self-assessment tool proposed to SMEs by Innovation Way® allows them to compute an indicator of their capacity to innovate, according to 4 key categories: ● Passive: more oriented toward basic improvements than toward a real spirit of innovation, ● Reactive: under strong market pressure, able to react by proposing mainly incremental innovations, ● Preactive: anticipates changes in the environment and knows how to manage innovation projects, ● Proactive: innovation is at the heart of business processes. The main benefit of identifying the profile of the firm is that it enables managers to define priority actions to enhance the innovation capabilities of their firm. A benchmark report is also drawn up, making it possible for them to compare the results with those of other companies in the same sector. Based on the recommendations made to the assessed SMEs, it is possible to offer them suitable development support, for example 48hours to bring ideas to life, ATI, student internships, research, etc. All the supported companies have emphasized the relevance of the approach and have seen their success rate in innovative projects improve. In constant collaboration with ERPI, Innovation Way® is currently developing two similar approaches to measure the ability of SMEs to export and drive their digital transformation.

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5 Empirical Evidence on a Longitudinal Campaign: Two Case Studies Based on our experience since the program was started, this was a first attempt to exploit long-term partnerships with two firms that initiated a collaboration with the university in innovation management through this program. They continue to participate but enlarged their collaboration to research programs. As the primary objective of this type of voucher-based innovation scheme is to encourage knowledge transfer between the higher education sector and small- and medium-sized enterprises (SMEs), our analysis is focused on the vectors of knowledge transfer and creation.

5.1 TEA Ergo5 TEA Ergo is a company specialized in the development, supply, and implementation of physiological sensors for data measurement and analysis, related to physical activity, movement and eye-tracking, as well as software for processing the data from the sensors. It operates on the ergonomic workstation market. TEA has its own set of sensors and different data processing software, including CAPTIV, designed by the National Institute of Research and Safety (INRS), the main French research center focusing on work safety. CAPTIV is itself the result of a technology transfer. TEA focuses exclusively on niche markets, selling sensors and data processing software for workplace ergonomics, neuromarketing, research, and man–machine interfaces. TEA works in a sector where technologies change continuously and rapidly: The market stimulates the company’s activity and its capacity to innovate. The company has proven expertise in R&D and technology and innovation management. This combination of skills allows the company to propose a wide range of products and to adapt to its customers’ needs. TEA also experiences a high rate of technological change: an average of one new technological system every five years. Each new project requires new competences: the company hires new employees, takes students on internships, or organizes partnerships. As the company is part of a dynamic market, finding the right skills quickly represents a key factor of success, as is change management.

5

http://teaergo.com/wp/?lang=en.

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Fig. 7 Firms’ main industrial activities

5.2 Noremat6 Noremat is a highly innovative company that works with professional customers helping them to achieve the most profitable roadside maintenance solutions. The company offers a broad range of innovative machines, technologies, and associated services. At the heart of its business is the ability to listen and provide a close local service to customers through a network of 9 regional centers in France and specialized importers abroad. Internationally, Noremat supports an exclusive network of importers. Today, Noremat has expertise in R&D and technology and innovation management. This combination of skills allows the company to propose a wide range of products and to adapt to customers’ needs. The company’s competencies have allowed it to develop and commercialize an entire vehicle adapted to its mowing machines. It is the leader in the French national market; it is initiating a dynamic international expansion (Fig. 7).

5.3 Data Gathering As mentioned in Sect. 2, this research has been conducted using the action research (AR) approach and is based on a longitudinal study (10 years) of two companies deploying joint ATIs. In order to formalize these experiences, student reports over the period were gathered and projects were classified according to their main goal, as follows: new product development (NPD), exploring new markets (NME), and innovation management (IM) issues. For each case, the set of already completed projects 6

https://www.noremat.fr/en.

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were then discussed with industrial tutors (in both cases, the CEO of the company) and, through semi-structured interviews, the lessons learned were registered and validated after consensus. Table 1 shows the firms’ ATI project highlights.

6 Discussion and Main Implications This experience shows that the SMEs’ primary motivation to collaborate with the university was to resolve short-term technical issues related to product development. Next are exploratory projects for new products, or even new markets. However, as the relation became more trusting over time, strategic issues emerged through discussions and managers are now more confident of tackling subjects related to the innovation capabilities and mean to long-term strategy of the firm. Both experiences show that a higher level of maturity has been achieved through the firm’s engagement in research projects. However, this process may take several years depending on the initial innovation capabilities of the firm, but also on the CEO’s attitudes. For Firm 1, collaboration in research started after 8 years of undergraduate student projects, through the integration of a master’s student internship. As shown in Table 1, the first projects were related to the integration of new sensors into the firm’s existing system, and subsequently centered on design and technical issues (5 projects). Next, there was a period in which projects were carried out related to the exploration of new markets, in particular mass-market products, supported by the core competencies of the firm. The last projects were related to the firm’s strategy, such as building a technological roadmap or creating an adapted methodology to support export capabilities and the firm’s strategy. During this period, research activities gradually came under discussion and master’s students were also involved in projects. As far as the projects start involving joint competencies between the university and the firm, research subjects came to discussion. Seeking for resources to support these research programs was a shared decision. Since 2018, the research department of the university and the firm have been awarded with a research grant by the government and created a joint research laboratory that includes research facilities but also, engineers, PhD students and postdocs.7 The purpose of this program, founded by the French National Research Agency (ANR), is to encourage research actors to create new structured partnerships through the creation of “Joint Laboratories” between an SME or an intermediate-sized enterprise and a research laboratory. A Joint Laboratory is characterized by the signing of a contract defining its operation, including: common governance, a roadmap for research and innovation activities, resources to operate the roadmap, and a strategy to ensure value creation from research activities.8 Regarding Firm 2, it started its participation to the program by proposing a technical issue to be treated by a group of students. In this case, due to the company 7

https://n-hum-inno.eu. https://anr.fr/en/call-for-proposals-details/call/joint-laboratories-between-research-organizat ions-and-smes-or-intermediate-sized-enterprises-labcom-2/. 8

8

17

5 3

3 5

2

2 3

1

2 3

1

2

1

2

PhD

1

1

–

Post-doc

Related postgraduate students IM

Master

NME

Type of project NPD

7

10

Number of joint projects

NPD: New product development; NME: New market exploration; IM: Innovation management

Total

16

280

Ergonomics instruments

Roadside mowing machines

Firm 1

Firm 2

No. of employees 2017

Sector

Firm

Table 1 Typology of projects and R&D-related activities

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size and formal degree, the students projects have been used as exploratory projects before to transfer it to the internal R&D department. Then, after 5 years of student projects, the firm has decided to finance several master’s students and a postdoctoral research project. Consequently, a more ambitious project is in the preparation phase, encompassing several of the university’s research departments. As in the previous case, it should be underlined that this is because the firm has grown in size. The first industrial tutor was the R&D director. Then after three years, and because the subjects were related to the firm’s future strategy, the CEO became the main interlocutor of the academic team. For both firms, undergraduate student projects remain a valuable contribution. The discussion below aims to underline some positive externalities regarding the main stakeholders of such projects: students, firms, and researchers, in order to get a better understanding of all of these phenomena. From the point of view of the students: These projects are carried out as part of their academic program. A slot of one day per week is dedicated to their ATI project throughout the academic year, from October to June. It allows them to familiarize themselves with the dynamics and constraints of industrial innovation projects, and to develop communication skills and technical competencies. However, perhaps the most important competencies are self-directed learning skills. Indeed, these are of prime importance in the success of innovation processes. This led the teaching staff to set up a project aimed at integrating self-directed learning into the curriculum. The system is based on an alternation between active and reflexive phases. It also provides students with continuous support, thus transforming the role of the teacher into facilitator (Bary & Rees, 2006). From the point of view of the firms: The ATI projects opened up a strategic discussion space that allows SMEs to integrate points of view and resources from outside of the firm. Because the projects are formalized through reports, conference papers or journal papers, the companies have more extensively documented results and so can consider strategic issues. Consequently, it can be noted that innovation management and strategy have started to be considered and integrated within the firms’ day-to-day routines. We observed that in both cases the initial joint projects were managed by midranking engineers, but as the number of projects and their appropriation grew, the firm’s CEO became the project interlocutor. This is because the later projects have direct impacts on the firm’s strategy. Also, in both cases the engagement of the industrial tutor was of capital importance in maintaining the dynamics of the project and insuring that the industrial and pedagogical goals were achieved. During the interviews with the industrialists it appears that, in their understanding of the educational role of the project, they considered that it has been part of the social responsibility mission of their firms. From the point of view of researchers: The ATIs provide an opportunity to strike a compromise between two academic activities: research and teaching. As these projects represent an occasion to apply and validate theories, methodologies, and tools, they can be of interest for both the research and the pedagogy. Moreover, when integrating mixed teams of master’s and PhD students, there is usually a synergistic

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effect that allows each person to be aware of and take the measure of their role as part of an important and applied issue. As a non-negligible outcome, these projects will also contribute to one of the researchers’ goals, namely “publish or perish.” To finish, this type of programs is not implemented without difficulties, when asked for the main difficulties they faced through the program, both CEOs agreed that the administrative tasks and the PI discussions with the juridical department of the university were the most time-consuming activities. They consider that the complexity of the entire process could be discouraging for new entrants. Although been part of the learning process, this process should be optimized. It is worth to say also, that since its inception the program has also evolved, and adjustments have been introduced including the financial scheme, the coordination process and dynamics of the project in order to make it more adaptable to the SMEs’ needs. Although this study is based on only two examples and as a consequence lacks statistical significance, the authors consider that this experience is worth sharing as it contributes to the body of knowledge and offers researchers a better understanding of the evolution of this type of support program. Moreover, a modular pedagogical program that could be adapted to the specific characteristics and sector of each firm has been proposed. This experience shows that the program should be articulated with the research capabilities and infrastructure of the university. Additionally, from our experience it could be concluded that the concept of “innovation voucher” cannot be reduced to its material (purely financial) aspect but may also be considered from a non-material (methodologies and competencies) perspective which today plays a real part in the value creation process. Furthermore, this type of program allows stakeholders to learn from each other, even though success is strongly dependent on human attitudes, engagement, and trust. Acknowledgements We thank the companies Noremat and TEA Ergo for their collaboration and for agreeing to appear in this paper. We would also like to thank the “Métropole du Grand Nancy” which provides us spaces used by the LF2L platform. This work was supported partly by the French PIA project “Lorraine Université d’Excellence” INNO_4_SMES (Reference ANR-15-IDEX-04LUE).

Appendix Overview of 48hours to Bring Ideas to Life® Since 2011

5 Cosmetics, Agricultural machinery, Connected objects, Childcare, Cooking food

7 7 Comfort & well-being at home, Meal and 3D printing, Rear of vehicle, Factory of the future, Cosmetics Connected cars, Scaffolding

Regional (Lorraine) & International

National Special SME edition 5 regional centers in France

Regional National International 15 centers with 9 France + 6 international

Regional National International 14 centers with 8 France + 6 international

2012

2013

2014

2015

3 Multimedia, Housing, Metallurgy

3 Boilermaking, Chemistry, Sport

12 (business consortia involved)

10 (business consortia involved)

3

3 5 Sport (2), Urban furniture

Regional (Lorraine)

Number of firms

2011

Industrial subject

Scope

Year

1000

1100

350

130

100

Students

60

79

24 Engineering Schools Management Schools

13 Engineering Schools Lorraine INP + 3 international universities

10 Engineering Schools Lorraine INP

Academic partners

1200

1800

800

400

400

Ideas

(continued)

No information concerning potential patents Many inter-school collaborative projects continued (>10)

Many (>15) ideas developed Many patents (>5) Many inter-school collaborative projects continued (>10)

10 ideas developed, Many patents (>5) registered Many inter-school collaborative projects continued (>5)

5 ideas developed 2 patents registered 2 inter-school collaborative projects continued

8 ideas developed 3 patents registered 1 inter-school collaborative projects continued

Development

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Regional National International 12 centers 7 France + 5 international

2017

10 10 New services using car data, Flying taxis, New personal services, Banker of tomorrow, Multimodal transport, Preservation of fauna and flora in desert area, Forests and cities, Cosmetics, New fertilizers & circular and durable economy, New devices for interaction between men in medical environment, Robotics & personal services

12 15 Sustainable building/construction, Engineering consulting in innovation, Adhesives and solvents, Organic specialized distribution, Energy networks, Aeronautics, Professional equipment, Sports, Professional training, Health, Sanitary equipment, Cosmetics

Regional National International 17 centers with 10 France + 7 international

Number of firms

Industrial subject

Scope

Year

2016

(continued)

1200

1500

Students

60

60

Academic partners

1350

1650

Ideas

(continued)

No information concerning potential patents Many inter-school collaborative projects continued (>10)

No information concerning potential patents Many inter-school collaborative projects continued (>10)

Development

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14 14 Satellite and agriculture, Foosball, Domestic hot water services, Library of the future, Security and palpation, Interactive objects for visually impaired, Artificial intelligence, Connected parks and gardens, New products around wool, Territories and energy transport, Solar power stations, Medical compression, CV of tomorrow, City’s water network in 2050, Living after climate disasters

Regional National International 12 centers 8 France + 4 international

Number of firms

Industrial subject

Scope

Year

2018

(continued) 1500

Students 60

Academic partners 1800

Ideas

(continued)

No information concerning potential patents Many inter-school collaborative projects continued (>10) Specifically, 4 projects continued by groups of ENSGSI students during the academic year and aiming to develop concepts with high innovation potential selected by companies

Development

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8580

90

71

Total

1700

Students

14 14 Personal real estate space, Email of tomorrow, Design experience in a museum, Sanitary networks in the home, Boosting the QSE culture in companies, Smart home, Men and trees, Street of the future, Cleaning of road shoulders, Feminization of technical and IT skills, Well-being of employees in quality control posts, New drones and new uses, Prefabricated concrete elements, Flows and territories in the city.

Regional National International 13 centers 8 France + 5 international

Number of firms

Industrial subject

Scope

Year

2019

(continued)

436

70

Academic partners

11,400

2000

Ideas Operating results in progress

Development

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Themes of ATI Since 2004 2004–2005: Nature in the city, Intermediate housing 2005–2006: Ecological and economic habitat 2006–2007: Intelligent waste sorting, Well-being in the bathroom, Mobile living base for construction workers, Day care structure for the elderly 2007–2008: Healthy house with modular design, School for all, Street furniture, Energy shelters, Rehabilitation of housing in a sustainable development approach, Smart POS advertising, Smart windows, Design of a product from recycled glass 2008–2009: Eco-habitat, Furniture and energy, Open spaces, Urban lighting system and sustainable development, Innovative racing transport systems, Diversification of activities from the mechanical sector to the medical sector, SME innovation audits 2009–2010: Positive energy gymnasium, Transformation of a hemp field into an eco-district, Eco-district in former military barracks, Communication and sustainable development, Actions to aid the development of Vosges SMEs, Collaborative time-sharing work platform for SMEs 2010–2011: Rehabilitation of the site of the former slaughterhouses of Nancy, Promotion of the Meusian culture, Restaurant Quick in 2050 2011–2012: Restructuring/modernization of a military base, Creation of an Ecocentre, Restaurant Quick in 2050 2012–2013: Auto repair garage of the future 2013–2014: Transformation of an old steel site into a creation workshop for artists, User services for multimodal platform, Classroom of the future 2014–2015: School of the future in rural areas, Restructuring of accommodation for the disabled, University residence of the future 2015–2016: Center for learning the languages of the future 2016–2017: Solidarity and integration restaurant on an industrial site undergoing rehabilitation, Innovative wooden structure for events 2017– 2019: No ATI due to a new geopolitical division of regions 2019–2020: Nomadic medical device for the analysis of musculoskeletal disorders, Autonomous barges, Extension of the uses of anti-intrusion concrete blocks.

References ANR, Joint laboratories between research organizations and SMEs or intermediate-sized enterprises (Labcom). Retrieved in https://anr.fr/en/call-for-proposals-details/call/joint-laboratoriesbetween-research-organizations-and-smes-or-intermediate-sized-enterprises-labcom-2/. ATI Atéliers des Transfert et d’Innovation. Retrieved in https://www.univ-lorraine.fr/content/ate lier-de-transfert-et-dinnovation.

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Avison, D., Baskerville, R., & Myers, M. (2001). Controlling action research projects. Information Technology & People, 14(1), 28–45. Bakhshi, H., Edwards, J. S., Roper, S., Scully, J., Shaw, D., Morley, L., & Rathbone, N. (2015). Assessing an experimental approach to industrial policy evaluation: Applying RCT+ to the case of creative credits. Research Policy, 44(8), 1462–1472. Bary, R. (2005). Everyday creativity for everyday people (pp. 1–25). Unpublished Notes, on Creativity for Engineers. Course notes. Nancy, France: INPL. Bary, R., & Rees, M. (2006). Is (self-directed) learning the key skill for tomorrow’s engineers? European Journal of Engineering Education, 31(1), 73–81. Bjerregaard, T. (2010). Industry and academia in convergence: Micro-institutional dimensions of R&D collaboration. Technovation, 30(2), 100–108. Boly, V., & Grandgeorges,M. (2001). 48 Heures Pour Faire Vivre Des Idées, Tout Savoir Pour Organiser Des Modules (pp. 1–15). Unpublished Manual. Nancy, France: INPL. Boly, V., Morel, L., Assielou, N., & Camargo, M. (2014). Evaluating innovative processes in French firms: methodological proposition for firm innovation capacity evaluation. Research Policy, 43(3), 608–622. Boly, V., Morel, L., Renaud, J., & Guidat, C. (2000). Innovation in low tech SMBs: Evidence of a necessary constructivist approach. Technovation, 20(3), 161–168. Camargo, M., Morel, L., & Boly, V. (2015). Mesurer l’innovation En Entreprise: Un Levier Essentiel Pour La Réussite Des Projets Innovants. Nancy, France: Presses universitaires de Nancy Eds. Carayannis, E. G., & Campbell D. F. J. (2009). ‘Mode 3’ and ‘quadruple helix’: Toward a 21st century fractal innovation ecosystem. International journal of technology management, 46(3–4), 201-234. Carayannis, E. G., & Campbell, D. F. J. (2012). Mode 3 knowledge production in quadruple helix innovation systems. In G. Carayannis & D. F. J. Campbell (Eds.), Mode 3 knowledge production in quadruple helix innovation systems: 21st-century democracy, innovation, and entrepreneurship for development (pp. 1–63). Springer Briefs in Business. New York, NY: Springer. Castagne, M. (1987). Le Génie Des Systèmes Industriels: Une Discipline Nouvelle. European Journal of Engineering Education, 12(3), 271–276. Chapman, G., & Hewitt-Dundas, N. (2018). The effect of public support on senior manager attitudes to innovation. Technovation, 69, 28–39. Chesbrough, H. (2006). Open innovation: A new paradigm for understanding industrial innovation. In Open innovation: Researching a new paradigm (pp. 1–19). Oxford: Oxford University Press. Cornet, M., Vroomen, B., & Van der Steeg, M. (2006). Do innovation vouchers help SMEs to cross the bridge towards science? (Vol. 58, pp. 5–49). CPB Netherlands Bureau for Economic Policy Analysis. Dupont, L., Morel, L., & Lhoste, P. (2015). Le Lorraine Fab Living Lab: La 4ème Dimension de l’innovation. In Actes des sessions du colloque Science & You, France (pp. 230–235). Europe’s Innovation Voucher Schemes. Retrieved in http://www.eurada.org/eurada-news-2019-02europes-innovation-voucher-schemes/. Gabriel, A., Camargo, M., Monticolo, D., Boly, V., & Bourgault, M. (2016). Improving the idea selection process in creative workshops through contextualisation. Journal of Cleaner Production, 135, 1503–1513. Gabriel, A., Monticolo, D., Camargo, M., & Bourgault, M. (2017). Conceptual framework of an intelligent system to support creative workshops. In TRIZ—The theory of inventive problem solving (pp. 261–284). Cham, Switzerland: Springer. Galvez, D., Enjolras, M., Camargo, M., Boly, V., & Claire, J. (2018). Firm readiness level for innovation projects: A new decision-making tool for innovation managers. Administrative Sciences, 8(1), 6. Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. Thousand Oaks, CA: Sage.

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Kaplan, R. S. (1998). Innovation action research: Creating new management theory and practice. Journal of Management Accounting Research, 10, 89. Kleine, K., Giones, F., Camargo, M., & Tegtmeier, S. (2018). Building technology entrepreneurship capabilities, an engineering education perspective. In Entrepreneurial universities (pp. 226–247). Cheltenham: Edward Elgar Publishing. Leonard, D. A., & Swap, W. C. (1999). When sparks fly: Harnessing the power of group creativity . MA: Harvard Business School Press Boston. Lorraine Fab Living Lab. Retrieved in http://lf2l.fr. McAdam, R., Miller, K., McAdam, M., & Teague, S. (2012). The development of university technology transfer stakeholder relationships at a regional level: Lessons for the future. Technovation, 32(1), 57–67. Miller, K., McAdam, R., & McAdam, M. (2018). A systematic literature review of university technology transfer from a quadruple helix perspective: Toward a research agenda. R&D Management, 48(1), 7–24. Morel, L., Camargo, M., & Lhoste, P. (2019). Financing innovation: Benefit of the innovation vouchers to foster the link between SME’s needs and university capabilities . Conference Proceedings, 28th International Conference for the International Association of Management of Technology (pp. 1–10). Mumbai, India. Morel, L., Dupont, L., & Lhoste, P. (2015, June). When innovation supported by Fab Labs becomes a tool for territorial economic development: Example of the first Mobile Fab Lab in France . Conférence Proceedings of the 24th International Conference on Management of Technology (pp. 8–10). Cape Town. Morel, L., & Guidat, C. (2005). Innovation in engineering education: A French sample of design and continuous updating of an engineering school to industrial needs. International Journal of Technology Management, 32(1–2), 57–72. Noremat. Retrieved in https://www.noremat.fr/en. Osborn, A. (2012). Applied imagination-principles and procedures of creative writing. Redditch, England: Read Books Ltd. Peerbaye, A., & Mangematin, V. (2005). Sharing research facilities: Towards a new mode of technology transfer? Innovation, 7(1), 23–38. Sala, A., Landoni, P., & Verganti, R. (2016). Small and medium enterprises collaborations with knowledge intensive services: An explorative analysis of the impact of innovation vouchers. R&D Management, 46(S1), 291–302. Schultz, C., Mietzner, D., & Hartmann, F. (2016). Action research as a viable methodology in entrepreneurship research. In E. S. C. Berger & A. Kuckertz (Eds.), Complexity in entrepreneurship, innovation and technology research: Applications of emergent and neglected methods (pp. 267–283). FGF Studies in Small Business and Entrepreneurship. Cham: Springer International Publishing. TEAergo Tech Ergo Appliqués. Retrieved in http://teaergo.com/wp/?lang=en. The N-Hum-Inno Common Laboratory. Retrieved in https://n-hum-inno.eu. What is the 48h to generate ideas? Retrieved in https://www.ensgsi.univ-lorraine.fr/48-heures-pourfaire-vivre-des-idees-edition-2020/. Wynarczyk, P. (2013). Open innovation in SMEs | Emerald Insight. Journal of Small Business and Enterprise Development, 20(2), 258–278.

Facilitating Knowledge and Technology Transfer via a Technology Radar as an Open and Collaborative Tool Marko Berndt and Dana Mietzner

Abstract Digitalization is a key driver throughout all drivers of change because its emerging and rapidly changing digital technologies lead to new products, services, and/or business models. Actors working in knowledge and technology transfer constantly have to keep up with the latest technological developments and cope with their enormous implications. To face these challenges, a variety of knowledge and technology transfer methods exist. This chapter provides an overview of these methods and illuminates the technology radar method as a potential digital tool for facilitating knowledge and technology transfer. Following the lean approach by Eric Ries, we document the process of the development of the technology radar. We present a web-based technology radar that is accessible to everyone on nearly any device, which encourages collaborative development. User testing with experts in the field of knowledge and technology transfer delivers insights and feedback for the further development of this digital tool. The results are conclusively discussed and then an outlook on the upcoming version of the technology radar, as well as different forms of usage in terms of knowledge and technology transfer, is provided. Keywords Technology scouting · Technology transfer · Technology radar · Lean method · Digital tool · Collaborative tool · Open tool

1 Introduction Drivers of change such as demographics, societal values, and digitalization significantly influence enterprises across the globe. But the strength of future densification in the face of increasingly rapid social, technical, and cultural changes is seen most clearly in the world of digital technology (Pelton, 2019, p. 64). The music industry provides an example of this rapid change. Almost 15 years ago, Apple revolutionized M. Berndt · D. Mietzner (B) Technical University of Applied, Sciences Wildau, Hochschulring 1, 15745 Wildau, Germany e-mail: [email protected] M. Berndt e-mail: [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_12

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the industry with the iTunes Store. However, streaming services such as Spotify or Deezer are now relegating this formerly successful model to obscurity (Châlons & Dufft, 2016, p. 20). Digital technologies create disruption in many industries, like FinTechs in the banking sector or many digital health applications in the healthcare segment. Digital technologies such as cloud computing, artificial intelligence, internet of things, and robotics enable a connected world, new ways of working and collaboration, and automation processes (Urbach & Röglinger, 2018, p. 2). Most of these technologies are not revolutionary by themselves, but they develop their innovative power through increased performance, significantly better networking capabilities, and their widespread availability and use (Urbach et al., 2019, p. 123). Companies such as Twitter make use of these circumstances. Twitter, which has transformed the way information is created, processed, and distributed, has not had to develop its own new technology. It simply uses existing and rapidly developing technology platforms (Downes & Nunes, 2013, p. 48). There are multiple other examples: For instance, Airbnb, one of the largest lodging platforms, does not own any of the real estate on its list of rentals. Uber, one of the largest taxi companies in the world, does not own a single vehicle (Goodwin, 2015). This showcases how utilizing new and emerging technologies in a timely manner can improve on current or even create new business cases. Consequently, existing companies are increasingly under pressure to monitor and evaluate technological developments because entire industries can be fundamentally changed due to the rapidly changing dynamics in the digital technology field (Kiel, Arnold, Collisi, & Voigt, 2016, p. 675). With regard to individual companies, the challenge lies in identifying technological developments as early as possible and evaluating the resulting innovation opportunities and risks for their own business model. Schimpf, Heubach, and Rummel (2016) noted that this observation and assessment takes place in almost every company, but also that, from a certain company size or technological complexity onwards, the challenge is to coordinate and harmonize the identification of technological developments and create transparency about the assessment (p. 32). This is where technology scouting is applied, which Rohrbeck defined as follows: a systematic approach by companies whereby they assign part of their staff or employ external consultants to gather information in the field of science and technology and through which they facilitate or execute technology sourcing. Technology scouting is either directed at a specific technological area or undirected, identifying relevant developments in technological white spaces. Technology scouting relies on formal and informal information sources, including the personal networks of the scouts. (Rohrbeck, 2010, p. 3)

The basic idea is to build up a network of experts, consisting of so-called technology scouts (these can be either internal or external employees), who identify and investigate new technologies and make them accessible (Auth, Meyer, & Porst, 2017, p. 940). Technology scouts face the challenge that complex technological developments require an innovation process combining expertise from various scientific disciplines and interactions between different actors (Runiewicz-Wardyn, 2013, p. 1). On the one hand, technology scouts are in a field of tension between different institutions, for instance between a research group and a medium-sized company. On the other hand, they have to keep up with the latest technological developments

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and potentially enormous disruption. For this reason, Wolff (1992) claimed that technology scouts should meet these characteristics. They must become cross-thinking people who are well versed in science and technology, respected within the company and network, and are interdisciplinary and resourceful (p. 11). Facing reality, technology scouts, especially within small- and medium-sized enterprises and public institutions, are often confronted with the fact that the systematic exploration of new technologies, the evaluation of their potential, and the associated creation of a knowledge base as a basis for technology transfer processes can be an enormous challenge due to the considerable personnel and financial expenditure needs as well as necessary methodological grounding. Consequently, it seems reasonable to look for frameworks, methods, and tools that help to reduce complexity, cope with high disruption, and serve as a communication platform. A view into the relevant literature (see Batistella, De Toni, & Pillon, 2016; Piller & Hilgers, 2013) and transfer practice shows that many different approaches to facilitating technology transfer already exist. Hence, the next section gives practical insights into technology transfer methods.

2 Technology Transfer Methods: An Overview According to Autio and Laamanen (1995, p. 647), technology transfer can be defined “(…) as an active process, during which technology is carried across the border of two entities. These entities can be countries, companies, or even individuals, depending on the viewpoint of the observer.” This section especially builds on the definition of technology transfer, according to Mangelsdorf et al. (2019). It assumes two avenues of transfer. First, transfer agents transmit a technology or idea to another party. The technology thus leaves the research institution through the door. The transfer can take place informally, but it often involves formal property rights such as patents, trademarks, utility models, and licenses. Norms and standards are also included. Second, if new knowledge is implicitly in the minds of researchers, transfer can also take place via heads. This includes the participation of researchers in standardization or contributions to open source software projects (Mangelsdorf et al., 2019, p. 5). Socalled Innovation Camps with students who, in cooperation with companies, create and apply new ideas that can act as impulses for those companies, can also be enlisted (Innovation Hub 13, 2019; Mietzner & Schultz, 2014, p. 5). To obtain key parts and takeaways of existing methods and frameworks for facilitating knowledge and technology transfer, the major elements found in the literature and entrepreneurial practice are clustered and subsequently analyzed using three different aspects: description, strength, and weakness (see Table 1). Table 1 is not complete, but it does provide a condensed overview that serves as a basis for the following analysis. The different clusters showcase a wide variety of methods and frameworks and their strengths and weaknesses. Some of them transfer explicit knowledge such as scientific publications, licenses, or products; others transfer tacit knowledge, which is

Written knowledge such as books, articles, conference posters, dissertations, theses Engaging with the media: publishing tweets, podcasts, vlogs, and blogs (Coxon, 2019, p. 91)

Digital publications

Popular among a broad range of audiences, mostly easy access due to the open science approach. It engages dialogue in the world of social media

Detailed written knowledge

Interdisciplinary teams of New approaches for further students work over several weeks development of products, services, on creative and innovative ideas and business models for and with companies (Innovation Hub 13, 2019; Mietzner & Schultz, 2014, p. 5)

Innovation camp

Classic publications

Education (both basic and applied Creating a qualified workforce and research) and the training and receiving input from research promotion of excellent young researchers (Meissner & Sultanian, 2007, p. 11)

Initial and continuing training

Science Communication

Direct swaps of researchers and Creating insights of both worlds, company representatives (Folkerts development of personal & Schüning, 2005, p. 129) relationships between the exchange partners, and starting point for cooperation

Job rotation

Education

Strength

Description

Method/framework

Cluster

Table 1 Overview of selected technology transfer methods

(continued)

Vast amount of information. It involves variation in quality and processing

Rather traditional approach. It involves a vast amount of information, and partly no easy access due to paywalls

Requires high involvement of the actors. It involves short-term activity, structural, organizational, and/or personal barriers

Requires high involvement of the actors. This is a rather basic and traditional approach, tied to individual people

Requires high involvement of the actors. Also, structural, organizational, and/or personal barriers might occur

Weakness

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Entrepreneurial

Cluster

Table 1 (continued)

Members of the institutions High economic potential themselves transfer an invention they have made and their knowledge into practice by founding a new company that implements the scientific knowledge gained in an economic way (Zinkl, 2005, p. 24)

Spin-offs

Development of a licensing model, a precondition for receiving investments from the industry (Zinkl, 2005, p. 24)

Improves universities and research organizations’ accessibility, stimulates stakeholder interaction, and attracts scientists to meet with their potential partners (Boronowsky et al., p. 25)

Strength

Findings are patented to protect the intellectual property of the inventions

“Showrooms” operated by research organizations as specialized demonstration spaces. They are acting as a communication interface between science and the public (Boronowsky, Woronowicz, Hoffmann, & Boboev, 2015, p. 21)

Physical presentation (“showrooms”)

Patents

Description

Method/framework

(continued)

Possible emergence of conflicts of interest between the founder and institution

Time-consuming process (Kulicke, Meyer, Stahlecker, & Jackwerth-Rice, 2019, p. 5). It involves difficulty of identification of all persons involved in the invention (Zinkl, 2005, p. 24)

Limited access due to fixed location. It requires high actor involvement

Weakness

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Maker spaces, fab labs

Lab facility, open for different groups of stakeholders and the public (fab labs), low-threshold access to digital fabrication technologies (e.g., 3D printers, laser cutters)

Transfer facilities act as intermediaries between professors and entrepreneurs. They should be carriers of “know-who,” that bring together different forms of personal knowledge and thus enable corresponding impulses for innovation (Warnecke & Rohde, 2019, p. 90)

Transfer facilities

Infrastructure

In a more general sense, this is research-related consulting activities, including, for example, expert opinions and expert reports (Kesting, 2013, p. 126), Delphi studies (e.g., Mietzner, Ambacher, Hartmann, & Schmid, 2015), or scenario analysis (e.g., Berndt, Hartmann, & Mietzner, 2019)

Surveys, expert reports, etc.

Services

Description

Method/framework

Cluster

Table 1 (continued)

Infrastructure supports creativity and allows collaborative idea generation and the development of solutions with the help of digital fabrication technologies

Regional and technology-specific transfer, specialized consulting services, and permanent contact point for scientists and companies

Stimulates stakeholder interaction, attracts scientists to meet with their potential partners, and involves cooperative publications as a result

Strength

(continued)

Requires lab infrastructure and additional resources (e.g., trained staff)

Transfer facilities support person-related processes, but cannot replace them. It involves restricted networks and insights

Conflicting goals of the actors. It involves missing incentives, structural organizational, and/or personal barriers

Weakness

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A matrix, weighing technologies visually according to their business impact and feasibility in terms of business integration. It depicts the project size for each technology (Peter et al., 2019, p. 7)

Technology matrix

Digital tool delivering a condensed overview, where different dimensions can be considered. It involves easy self-assessment and has potential for collaborative use

Systematic collection of trends and technologies with all the potential of digital data (collaboration, analysis, predictions, etc.). Trend databases are already available

Database for the continuous monitoring and amendment of trends, serving as a knowledge management tool (Rollwagen, Hofmann, & Schneider, 2008, p. 345)

Trend database

Attracts scientists/companies to meet with their potential partners, engages interdisciplinary dialogue

Decentralized databases that First-hand information, record research work and provide low-threshold access contact information (Kröcher, 2005, p. 149)

Attendance or execution of different short-term formats with industry and university participation and a special topic (Kesting, 2013, p. 33)

Workshops, camps, hackathons, etc.

Attracts scientists/companies to meet with their potential partners, engages interdisciplinary dialogue

Strength

Research database

Attendance or execution of conferences such as Transfer Day(s) with industry and university participation (Kesting, 2013, p. 33)

Meetings and conferences

Interaction

Digital tools

Description

Method/framework

Cluster

Table 1 (continued)

(continued)

Missing use cases due to new approach, lack of user interface, and user experience design. It involves missing software application

Requires high involvement of the actors. It is also time-consuming, and there is partly no easy access due to paywalls

Rather traditional approach, decentralized information

Time-consuming approach, conflicting goals of the actors, short-term activity

Rather traditional and time-consuming approach. It involves conflicting goals of the actors, short-term activity

Weakness

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Cluster

Table 1 (continued) Description A visualization giving an overview of technologies, presenting information on the relevance of the technologies, as well as the development phase and the technological field (Rohrbeck et al., 2006, p. 980)

Method/framework

Technology radar

Weakness

Digital tool delivering a condensed Missing open software application overview, where different dimensions can be considered. It is a well-accepted method through different industries and has potential for collaborative use

Strength

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tied to individuals and difficult to codify (Smith, 2001, p. 314). Reinhard (2001) noted that the transfer of tacit knowledge is often an essential component. Therefore, all those forms of transfer are particularly effective where there is direct contact between the various actors (p. 15). As a result, it seems reasonable to develop a method that enables explicit and tacit knowledge transfer and, at the same time, addresses the challenge of reducing complexity and facilitates coping with the dynamics in the field of technology. Considering these demands, it is logical to develop a method in the field of digital technologies itself. Weighing its strengths and weaknesses, the potential to meet this set of heterogeneous requirements is considered greatest there. Comparing the methods of the digital tools cluster, the technology radar introduced by Rohrbeck, Heuer, and Arnold (2006) seems to prove a promising approach because, on the one hand, it offers a compressed overview in the form of an easily understandable visualization, and, on the other hand, a technology radar has already been implemented by numerous companies (AutoScout24, 2018; Delivery Hero, 2019; Haufe Group, 2019; Zalando, 2019). In the following sections, the development of a technology radar will be presented, with a special focus on digital technologies for facilitating technology transfer. Before that, the term technology radar will be defined and its functions in general will be presented.

3 An Open and Collaborative Technology Radar A technology radar is a graphical visualization that contains a short summary, including current developments, research status, and the economic potential of technologies. Often the eponymous radar (i.e., a subdivision of a circle into quadrants and circular distance lines [rings]), serves as the basis for the visualization. Quadrants usually represent nominal attributes (i.e., a technological field), and the rings represent the metrics that were used for evaluation (i.e., achieving market maturity in one, two, or three years). In practice, a large number of specific visualizations exist for a wide variety of purposes (Rohrbeck, 2007, p. 7). After it was first used at Deutsche Telekom in 2004, the technology radar is now gradually finding its way into the business landscape (Rohrbeck, 2007, p. 6). Technologies are usually classified according to their relevance to the company’s business model and the maturity of the technology (Golovatchev & Budde, 2010 p. 993). The functions of a technology radar include the following: 1. The early warning and clarification of possible threats from the company’s environment from a technological perspective 2. The identification of potential new competitors, who offer the same technology but have an extended functional profile 3. The safeguarding of planning cycles and the ability to react more flexible to technological changes

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4. The identification of strategic windows, in which, for example, one’s own technology can be combined with another to address new markets (Ardilio, 2012, p. 61). In summary, using a technology radar enables the structured observation of technological developments and their early integration into corporate planning. Furthermore, a basis for communication is created through which various options for action can be taken into account (Schimpf et al., 2016, p. 42). The evaluation of a large number of complex technologies can be condensed and presented in a tangible form as a technology radar (Auth et al., 2017, p. 946). As has been stated before, to be a valuable method, a technology radar has to meet many different requirements. Therefore, it appears sensible to approach this process based on an established methodology. The aim is to strongly support the technology scouting process in small- and medium-sized companies and public bodies, like universities or research institutions and to ensure its implementation as a useful method in knowledge and technology transfer. In order to obtain this goal, the method chosen for designing and developing the technology radar for knowledge and technology transfer is the lean approach by Eric Ries (2011, 2013).

4 Methodology: The Lean Approach The lean approach involves the combination of customer development, methods, flexible software development, and their lean implementation. The lean approach is about the optimal use of scarce resources to achieve the best insights about a potential product regarding the customer (in this case, the potential user) in the shortest possible time (Maurya, 2013, p. XIX). The objectives of the lean approach match the ambition of the development of the technology radar to create a collaborative digital tool that facilitates knowledge and technology transfer, which is eagerly used and further developed by its users. To achieve these objectives the Build-Measure-Learn-Loop (Fig. 1) was created. An outline of the loop of the particular solution is summarized in Table 2. With the start of a project “KIW – Mittelstand 4.0 Kompetenzzentrum IT-Wirtschaft” in 2017 (KIW, 2020a), which had the subgoal of sensitizing medium-sized IT companies in Germany for existing and emerging digital technologies and to give impulses for their own innovation activities, desk research, and the analysis shown above, resulted in the decision to develop a technology radar. Within two months, a minimum viable product was developed, which was initially tested in-house in an informal way (see Table 2). The resulting feedback was evaluated and the technology radar was adapted correspondingly. This was followed by continuous loops, mainly with target groups at smaller events and workshops. As soon as the developed technology radar was mature, technically and content-wise, the first version was launched on the project website (KIW, 2020b). After its launch, a major update was completed by

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Fig. 1 Adapted build-measure-learn-loop (based on Ries [2013, p. 73])

executing another loop at a workshop with technology transfer experts at the innoX Futures Conference in 2019. After further iteration and tests, the technology radar was transferred to new fields of application (see Table 2). The procedure and results will be presented in Sections 6 and 7 of this chapter. First, however, the initial design and implementation of the technology radar will be discussed in more detail.

5 Initial Design and Development Before the actual design and development of the digital tool can begin, the requirements for it should be established. It seems reasonable to align them with the product quality model according to ISO/IEC 25010:2011. This model categorizes product quality properties into eight characteristics: functional suitability, reliability, performance efficiency, usability, security, compatibility, maintainability, and portability. Each characteristic consists of a number of related subcharacteristics (ISO, 2011). Usually the ISO/IEC 2025:2011 and its predecessor ISO/IEC 9126 are used to evaluate software (Haslinda et al., 2015; Kadi, Idri, & Ouhbi, 2016; Ngah et al., 2015; Suwawi, Darwiyanto, & Rochmani 2015), but, in this case, the norm was used as a framework to identify requirements. That is why the following table (Table 3) not only lists the characteristics and subcharacteristics of ISO 25010:2011 but also shows the identified requirements for the technology radar. During the development of the technology radar, particular attention was paid to the characteristics of usability, maintainability, and portability to ensure the development of an open and collaborative tool that can be easily operated by many actors. A pioneer in the digital implementation of such an open and collaborative technology radar is the multinational software company ThoughtWorks. ThoughtWorks seeks

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Table 2 Stages of the tech radar development process Stage

Research purpose

Data source

Data description

Type of exploration

October 2018

Gathering feedback of the first minimum viable product

In-house discussion and testing (8 participants)

Mutual discussion, articulation of the current feedback

Qualitative

Launch of Tech Radar V1.0 (KIW) December 2018

Gathering feedback of the V1.0 from the target-group

Two workshops organized by Friedrich Naumann Stiftung (18 participants)

Mutual discussion, articulation of the current feedback

Qualitative

May 2019

Gathering feedback of the V1.1 from the target-group

Webinar: Kompetenzzentrum IT-Wirtschaft (5 participants)

Mutual discussion, articulation of the current feedback

Qualitative

September 2019

Gathering general feedback of V1.2 from technologyand knowledge transfer experts

InnoX 2019 Futures Conference: expert workshop (30 participants)

5 Tech Radar Canvases

Qualitative

December 2019

Gathering general feedback of V1.3 from students (M. A.—Business Management)

Lecture: Technology Management (45 participants)

6 Tech Radar Canvases

Qualitative

December 2019

Acceptance and usage of the V1.0-1.3

Matomo web analytics

942 user statistics (December 2018–December 2019)

Quantitative descriptive

March 2020 Transfer of the In-house workshop digital tool to other with transfer scouts use cases (5 participants)

Mutual discussion, articulation of the current feedback

Qualitative

April 2020

374 user statistics Quantitative (January 2020–April descriptive 2020)

Launch of the Tech Radar V1.4 (KIW)

Acceptance and usage of the V1.3-1.4

Matomo web analytics

Launch of the Tech Radar V2.0 (Life Sciences) and Launch of the Tech Radar (Lightweight Construction)

to improve the software industry and to share the knowledge that is gained. For this reason, ThoughtWorks publishes a semiannual radar with technologies that are relevant for software development (ThoughtWorks, 2020). The software platform used for this purpose is available as open source at GitHub (GitHub, 2018). The original code of ThoughtWorks is often used to develop radars for further projects and companies. One out of many examples is the XT Tech Radar, which has particular

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Table 3 Requirements for the technology radar according to ISO/IEC 25010:2011 Characteristic

Subcharacteristic

Requirements for the Technology Radar (TR)

Functional suitability

Functional completeness

The TR should perform the assigned tasks

Functional correctness

The TR should produce the expected results

Functional appropriateness

The TR should carry out the requirements that are needed for the different objectives that have been specified

Time behavior

The TR should work in a timely manner

Resource utilization

The TR should use scarce resources

Capacity

The TR should meet the requirements of the users concerning capacity

Co-existence

The TR should work with existing software

Interoperability

The TR should be able to exchange data and use exchanged data

Appropriateness recognizability

The users should recognize whether the TR is appropriate for their use

Learnability

The use of the TR should be learned easily

Operability

The TR should be operated with minimal effort

User error protection

The TR should make it hard for the user to commit any errors

User interface aesthetics

The TR should be appealing and match modern design expectation

Accessibility

The TR should be accessible to all people, regardless of disabilities

Maturity

All faults of the TR should be eliminated under normal operation

Availability

The TR should be operational and accessible when required for use

Fault tolerance

The TR should operate as intended despite the presence of hardware and software faults

Performance efficiency

Compatibility

Usability

Reliability

(continued)

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Table 3 (continued) Characteristic

Security

Maintainability

Portability

Subcharacteristic

Requirements for the Technology Radar (TR)

Recoverability

The data of the TR should always be recoverable

Confidentiality

The data of the TR should only be accessible to authorized users

Integrity

The TR should prevent unauthorized access or modification of other computer programs

Non-repudiation

The TR should prove that a certain action took place, so that the action cannot be repudiated later

Accountability

The TR should trace the actions of an entity uniquely to the entity

Authenticity

The TR should identify a subject or a resource to be the one claimed

Modularity

The TR should be composed of discrete components such that a change to one component has minimal impact on other components

Reusability

The TR should also work in other systems

Analyzability

The TR should allow the assessment of the impact, diagnose failures

Modifiability

The TR should be easily modified without introducing defects or degrading existing quality

Testability

The TR should be easy to test and should give the best possible results

Adaptability

The TR should work across different devices and platforms

Installability

The TR should successfully be installed and uninstalled

Replaceability

The TR should be easily replaced while upgrading

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Fig. 2 From data to visualization of the technology radar

strengths in terms of usability and visualization (GitHub, 2019). Therefore, it was decided to use the code from the XT Tech Radar as a basic framework for the development of the technology radar to use existing resources and to contribute to further development of the open source applications. This original code has been significantly enhanced, especially in terms of scalability and usability. Furthermore, the graphical representation has been revised. An Excel-based data provision was added, so that a multiuser system with versioning can be implemented without problems. From this database, Java is used to automate the creation of detailed pages as well as to generate the overview visualization in the form of the actual radar. The following figure (Fig. 2) summarizes the process of data generation and visualization in a brief way. The core of the displayed process is the Excel sheet, in which all data (e.g., technology, technology area, maturity, description, signal, filter) are listed and managed. To show that no coding skills are required to generate the tech radar, an excerpt of an example table (Table 4) is provided. The content of the Excel sheet leads to the automated visualization, which will be outlined in the following section (and is shown in Fig. 3). The four quadrants of the radar represent technology areas that have been identified as relevant for the specified use case. Within these quadrants, relevant technological drivers are located, which in turn contain signals. Technological areas (a): The composition of the technological areas divides the radar into four quadrants. These are not completely selective, which means that some blips could also be assigned to other quadrants. Nevertheless, the quadrants help to create a basic order. The individual technological areas can be viewed more closely by clicking on the blip. Maturity (b): The rings of the radar describe the degree of maturity of the technological drivers. According to the metaphor of the radar, the degree of maturity of the technological drivers increases with the approach to the center. The individual

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Table 4 Excerpt of the excel sheet containing data for the technology radar visualization A 1 Technology

B

C

Area

Maturity Description

2 Digital twin Smart Test manufacturing

D A digital twin is an integrated multi-physical, scalable, and probabilistic simulation of a complex product. Sensor data are collected to mirror the system of the corresponding twin. The idea and concept of the digital twin, which consists of a physical product, a virtual product, and associated data, is to extend the product life cycle and make product design, manufacturing, and service more efficient, intelligent, and sustainable

E

F

Signal

Filter

Electric car thanks to the digital twin: The electric car Solo, from the Canadian startup ElectraMeccanica, has been on the market in North America since 2019. It was designed, simulated, and manufactured using Siemens software programs for the digital twin. The company was able to test and optimize all elements, whether mechanical, electronic, software, or system performance, with the digital twin in advance. Thanks to the digital twin, the small team of young engineers was able to design, simulate, and produce the new electric car in just two years

Connected devices, deep learning

rings already give possible recommendations for action (i.e., to apply the technological drivers, to test them, or to assess them first). The assessment of the maturity should be based on a clearly defined metric, if possible. This may include various dimensions, such as the costs associated with the introduction of a technology, the expected benefits (i.e., time, money, efficiency), the time to market or the risk of introduction or non-introduction (Haag, Schuh, Kreysa, & Schmelter, 2011, p. 315). If the metric is multidimensional, reduction to a one-dimensional form can be helpful for the dissemination phase (Granig & Mussnig, 2007, p. 68). Technological drivers (c): Drivers are large, long-term-oriented forces of change that influence the future. They often have deep roots in history and develop from

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Fig. 3 Components of the technology radar

different areas, such as technology, economics, environment, culture, or politics. They are often combinations of several areas (Jacobi & Landherr, 2013, p. 31). For the tech radar, only the technological drivers were considered. Signals (d): The signals are assigned to the technological drivers and are therefore only visible at a deeper level of the technology radar. Signals are specific current events or innovations, such as a new product, service, initiative, policy, or technology—with the potential to increase in impact and influence other places, people, or markets (IFTF, 2020). Signals help to detect emerging phenomena earlier and are intended to inspire decision makers and companies and provide impulses for their own innovation activities. Clicking on the desired technological driver not only opens a description but also delivers a concrete signal of the technological driver, including the source. Additional signals and a summary of the evaluation, as well as the specific technology tags for the filter function, complete the summary of the technological driver. Technology tags (e): If only individual, specific technologies and signals assigned to them are of interest, a slider for the respective technology can be activated. This enables users to search for technological drivers in a more targeted manner. The described components can be adapted flexibly. In some cases, this is already possible via the Excel sheet, such as the filter function, which can be modified and controlled by simply adding data. Stronger interventions, such as the number of technology areas (quadrants), can only be carried out via adjustments in the code. In the following step, it was examined whether further adjustments were necessary and to what extent the technology radar was accepted by technology transfer experts.

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6 Further Development Based on Expert Feedback To complete another cycle of the Build-Measure-Learn-Loop, an interactive workshop was held as part of the innoX Futures Conference 2019 at the Technical University of Applied Sciences Wildau. The workshop was entitled “Tech Radar – Open and Collaborative Tool for Dealing with Technology Trends” and was carried out with about 30 experts in the field of knowledge and technology transfer. After a short introduction on the subject of the technology radar, six groups of five experts each had the opportunity to test the digital tool on several devices and to exchange their experiences and ideas within 30 minutes. A Tech Radar Canvas, reminiscent of the Business Model Canvas (Osterwalder, Pigneur, & Wegberg, 2011, p. 22), was used as a discussion guide and also for documenting the results for later adjustments. The Tech Radar Canvas consists of the following six building blocks: (a) first impression, (b) relevancy of the tool, (c) positive implementation points, (d) lacking implementation points, (e) possible operation scenarios, and (f) next steps. Figure 4 shows the layout of the Tech Radar Canvas and summarizes the results of the workshop. Overall, the feedback from the five groups was positive. The testers considered the visual presentation, the content, and the usability positive. Nevertheless, there were some functions that the digital tool still lacks. These include, in particular, a tutorial, a search function, more transparency in terms of evaluating the technologies, and an easier way to contribute to the technology radar. The prioritized next steps for further development of the technology radar suggested by the workshop participants were

Fig. 4 Layout and condensed feedback of the tech radar canvas

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therefore (a) to provide the system as an individual tool kit, (b) extend the technology radar with a tutorial, (c) provide a search function, and (d) create easier customization options. To meet the requirements of the next steps, it seems reasonable to make the following adjustments to the system: integration of the technology radar into a content management system (WordPress or similar); setting up a database; development of a user structure; integration of a tutorial as a “first-time walk-through”; and creating a search function. All these steps require larger adaptations, which can only be achieved within the code and are currently under development. The mentioned simple errors or suggestions for improvement (i.e., the adaptation of the sources, an additional naming of missing technologies, and wrong links in the navigation) were adapted directly and without much effort. After that, 45 students active in the module technology management as part of their business management studies assessed the technology radar by using the Tech Radar Canvas. The results of the canvases showed (also in addition to the simple errors mentioned above) primarily technical suggestions for improvement, especially in terms of the responsive design of the radar. In the next loop, and in order to learn more about different possible operation scenarios of the technology radar, it was also applied for other sectors (life sciences and lightweight construction) and will be further tested also in theses contexts.

7 Conclusion and Outlook At the beginning of this chapter, it became apparent that a high potential for disruption and a high degree of complexity in the field of (digital) technology and its developments make the important work in the field of knowledge and technology transfer more challenging. To better cope with the dynamics and complexity, a variety of knowledge and technology transfer methods exist. An overview showed different methods and frameworks, which all have their respective strengths and weaknesses. After careful consideration and weighing of the strengths and weaknesses, it was decided to develop a tool in the field of digital technologies to tackle the challenges mentioned above and facilitate the knowledge and technology transfer and ultimately support the actors. The choice fell on the framework of a technology radar. On the one hand, it offers a compressed overview in the form of a simple visualization, and, on the other hand, it has broad applications in practice. Departing the more traditional methods, the lean approach by Ries (2011, 2013) was applied to develop the technology radar in an iterative, resource-saving way that delivers fast results. The initial technology radar, which was developed with special attention to the ISO 25010:2011 characteristics, thus is continuously refined by repeated passes through the BuildMeasure-Learn-Loop. The result is a fully functional web-based technology radar that is accessible to everyone on nearly any device, which encourages collaborative development. An expert workshop showed that the requirements, which are also laid down in the product quality model of ISO 25010:2011, are met. It also illustrated that some features are missing, which will be implemented in the next step.

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Not only the formal and informal feedback in the course of the development of the technology radar but also user statistics show that further development is required. In a period of 17 months, over 1300 different (human) visitors were recorded for the specific technology radar created for the German IT small- and medium-sized business landscape, with an average stay of 55 s (to compare, on average 55% of web users spent fewer than 15 s actively on a website [Haile, 2014]). To achieve the objectives of improving the tool, so that it will be eagerly used by actors of knowledge and technology transfer, the technology radar will be enhanced with more features. The most promising improvement seems to be the implementation integration of the tech radar into a content management system, where user profiles can be created and thus the way to a crowdsourcing platform is being paved. The overall process of developing the digital tool also had an unintended side benefit. It turned out that the newly developed Tech Radar Canvas method, which was used in the expert workshop, is very well suited for collecting feedback on software and web applications in a short period of time. Further research possibilities include the transition to a crowdsourcing paradigm (maybe with incentives for potential participants), investigation of the quality of the results (in potentially different settings and user groups), and also a comparison with the situation and perception of technologies and corresponding applications in different industries, as well as the design of interfaces to different strategy processes. The early integration of potential users also supports the development of different operational scenarios of the radar in daily technology transfer routines. A radar can also be used for simple visualization of technologies in a certain field (e.g., digital technologies) as an overview for current technological developments, which can be of interest for different stakeholders (e.g., startups, companies, technology transfer offices, intermediaries) and several contexts of use. It has the potential to provoke idea generation, while searching for fields of application for certain technologies or delivering insights into the status of technology readiness in general. In this sense, it supports the knowledge base of technology transfer officers as well as of companies and intermediaries as fundamentals for various technology transfer activities and to support innovation processes. In terms of university–industry interaction, technology radars have the potential to deliver an overview of technologies, developed within the university, for companies searching for respective know-how (examples of technology radars for the life sciences sector and the lightweight construction sector as presented by two universities can be found at www.radar.innohub13.de). In this case, the radar visualizes the available technologies in a compact format and can be a starting point for collaborative projects between transfer partners. Furthermore, technology radars can be combined with other technology transfer methods (e.g., workshops, meetings, spinoffs, physical presentations) and have the potential to enrich the methodological portfolio in knowledge and technology transfer.

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Using Open Innovation Platforms for Technology Transfer Frank Piller, Dennis Hilgers, Christoph Ihl, and Lisa Schmidthuber

Abstract The use of Internet platforms such as open innovation platforms is a quite new strategy in innovation management that marks a rethinking from classical principles of coordination in innovation processes. Instead of relying exclusively on the internal expertise of their own researchers and developers, companies are increasingly integrating external problem-solvers (often supported by so-called innovation intermediaries) into their innovation processes. As an alternative to conducting traditional research or commissioning engineering service providers or academics with thirdparty contracts, a large, undefined network of actors are openly invited to participate in the innovative project (known as the “broadcast search” principle). Participants who know an answer to the problem respond by providing solutions—despite never being commissioned to do so by a manager. This procedure offers completely new potential and opportunities for knowledge and technology transfer and gives access to the knowledge held by third parties in new ways. Keywords Technology transfer · Open innovation · Broadcast search

This chapter draws on an earlier publication by the authors in Piller and Hilgers (2013). F. Piller RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany e-mail: [email protected] D. Hilgers (B) Johannes Kepler University Linz, Altenberger Straße 69, 4040 Linz, Austria e-mail: [email protected] C. Ihl Hamburg University of Technology, Harburger Schloßstraße 6-12, 21079 Hamburg, Germany e-mail: [email protected] L. Schmidthuber Vienna University of Economics and Business, Welthandelsplatz 1, 1020 Vienna, Austria e-mail: [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_13

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1 Introduction In the last decade, the concept of “open innovation” has initiated a lively discussion about the involvement of external actors in the entrepreneurial innovation process (Piller & West, 2014; West & Bogers, 2014). Integrating external knowledge from contributors outside the confines of the company such as customers, suppliers and universities is seen as a decisive competitive factor and a path to more successful products on the market (Dahlander & Gann, 2010; Laursen & Salter, 2006). Open innovation platforms provide a new, web-based way to make technological knowledge usable for companies and hence access widely dispersed, previously unknown solution know-how for specific (technical) problems and projects (Pollok, Lüttgens, & Piller, 2019; Schenk, Guittard, & Pénin, 2019). This mechanism of widely broadcasting problems in exchange for prize money and motivating a community of experts to submit solutions represents an effective, new, and innovative method of technology transfer (e.g., from universities, public research institutions, laboratories, etc.). Previous technology transfer concepts have relied on the documentation and active transfer of existing knowledge as a starting point. This transfer can be realized successfully—albeit with poor scalability and very little flexibility—as a “transfer via heads” of the academics participating in research projects to businesses or other organizations (e.g., public administrations). Another and the most dominant method of transfer is less successful: academics publish research results and companies with an applied problem then search for research findings in databases (Pechmann, Piller, & Schuhmacher, 2010). In both cases, a rather local search restricts the effectiveness of knowledge transfer. The areas searched are often too narrow, which means that any knowledge acquired tends not to be optimal. In addition, technological knowledge in databases is typically coded according to the assessment of its authors, which means that it cannot be found for any other potential areas of application outside of the original domain. To overcome these challenges, we suggest in this chapter to use intermediaryadministered innovation platforms as a means of open innovation to facilitate the transfer of knowledge and technology (Diener & Piller, 2019). One advantage of these platforms is that they draw from new and open principles of coordination and motivation to transfer external knowledge in the innovation process (“open marketplace for the transfer of inventions”).

2 The Concept of “Open Innovation” “Open innovation” is a general term describing a conception of the innovation process as an interactive, distributed, and open innovation system (Laursen & Salter, 2006; Piller, Möslein, Ihl, & Reichwald, 2017). The origin of the term can be traced back to Berkeley professor Henry Chesbrough, who contrasted the open innovation process with the traditional closed process (“closed innovation”), where companies only

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use ideas and technical skills from their own domain or from established partners already integrated into their network (Chesbrough, 2003). In contrast, open innovation defines the innovation process as a multi-layered open searching and problemsolving process that unfolds between various actors across company boundaries. The objective of open innovation is to obtain information about needs and solutions by involving external actors in the process and hence broadening the scope of the ideas and solutions (Thomke, 2003). Open innovation is expected to improve the company’s capacity to reduce market and technological uncertainty at early stages of the innovation process and to identify and integrate knowledge from outside the confines of the company (Bogers et al., 2017). In the context of interorganizational technology transfer, open innovation processes might contribute as a new source of innovation to the development of successful products and, at the same time, constitute the required condition for sustainable competitive advantages. Previous research has shown that not only the manufacturer of a product but also external actors play a vital role within the innovation process (Füller, Matzler, Hutter, & Hautz, 2012). The contribution of users to the innovation process has particularly been postulated by the innovation researcher Eric von Hippel (2005, 2016). Depending on the business sector, between 20 and 80% of all new product developments can be traced back to an idea of the users. In this context, a shift from customer orientation toward customer integration can be observed (Ogawa & Piller, 2006; Piller et al., 2017; Schmidthuber, Piller, Bogers, & Hilgers, 2019). At exactly that point, the concept of open innovation adds to the obtainment of information for solutions to technical/creative problem cases in the development phase. The aim of open innovation is to gain access to information for finding solutions under consideration of external actors, thereby extending the range of obtaining ideas and solutions. In contrast to open innovation, “closed innovation” processes are limited to the creative input and the knowledge of a relatively small group of engineers, product managers, and other members of the product development team. Therefore, ideas, creativity, and knowledge are sourced from a much larger basis and, consequently, input factors are made accessible which beforehand have not been available or have not been taken into consideration due to a local search bias. This overcomes the described problem of “local” search as the key problem of technical problem-solving in businesses: Open innovation platforms disclose the boundaries of local search and enable access to new solution knowledge and previously unknown proprietors of the desired (technical) knowledge, potentially from completely different domains and areas of expertise. The problem of the local search bias is caused by the tendency of enterprises and individuals to ignore external information sources when working on problems and to use exclusively skills and methods in close relation to their existing spectrum of knowledge (Chesbrough, 2003; Jeppesen & Lakhani, 2010). Only existing experience and information known from geographical proximity, established technological views or disciplinary connotations seem to be accessed easily and thus applied for solving a particular task (Katila & Ahuja, 2002; Stuart & Podolny, 1996). Different persons possess different local knowledge and routines of problem-solving (Hayek, 1945; von Hippel, 1994)

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and use that local knowledge even if it seems to be not appropriate for finding solutions in a given context (Simon, 1991). Consequently, skills and methods close to an already existing spectrum of knowledge are used to work on a task. This problem is also referred to as limited rationality (Simon, 1957) or the usage of routines when solving a problem (Nelson & Winter, 1982). The result is the use of a limited space of solutions that have a high proximity to existing knowledge. While this turns out to be advantageous and rational for the continuous improvement of existing processes (use of learning effect and experience knowledge), it usually does not lead to radical innovations within the innovation process. Consequently, the most obvious solution is not always the most efficient solution. Apart from reducing the local search bias as an internal barrier within the development process, companies struggle accepting and including externally acquired knowledge in the innovation process or the product development, respectively. Accordingly, open innovation does not only imply an outsourcing of internal development tasks to the periphery but it also demands an active integration of valuable external knowledge into organizational processes (Piller & West, 2014). In order to succeed in opening organizations, expertise and strategic advice provided by external actors must be implemented into organizational processes (Cohen & Levinthal, 1990; Laursen & Salter, 2006). However, the integration of external knowledge often fails at the individual level, as employees refuse to exploit insights from outside the organization, even where they may be beneficial to performance (Katz & Allen, 1982). This problem in knowledge transfer is well-known and termed the Not-InventedHere (NIH) syndrome (Antons & Piller, 2015; Hannen et al., 2019; Hussinger & Wastyn, 2016). According to Cohen and Levinthal (1990), an organization’s ability to exploit external knowledge, referred to as its absorptive capacity, depends on previously acquired knowledge. If firms have already accumulated absorptive capacity, the assimilation and exploitation of external knowledge are facilitated. In the process of integrating knowledge, potential and realized absorptive capacity are distinguished. Whereas the potential absorptive capacity refers to the organizational capacity to acquire and assimilate the contributions of external actors, the realized absorptive capacity relates to the transformation and exploitation of external knowledge into organizational processes (Cohen & Levinthal, 1990; Zahra & George, 2002). When organizations have not yet invested in building up an absorptive capacity, they may encounter problems in the subsequent acquisition and utilization of outside knowledge.

3 Case Example: “InnoCentive.com” The key element of open innovation is to broaden the search for knowledge in the innovation and development process with a question, problem, or project to enable unknown external actors to contribute. This mechanism is known as “broadcast search” and has acquired a new dimension with the advent of the Internet, websites (platforms), and mailing lists thanks to their extensive reach (Jeppesen & Lakhani,

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2010). In this way, a (technical) problem is advertised as widely as possible through an “open call for participation” to reach a large network of actors that are individually unfamiliar and encourage them to evaluate the problem or possibly contribute a solution by offering suitable incentives (Howe, 2006; Piller et al., 2017). The contributors in the periphery of the company will also face a local search problem when solving the problem; in other words, they will primarily resort to information and methods with which they are already familiar. However, since the contributors’ local search fields may be very different from the company’s own fields of expertise, the original local search problem can be overcome. A well-known example of an open innovation platform is the web platform InnoCentive (accessible under www.innocentive.com), designed for problems in the chemical industry and originally founded by the US pharmaceutical company Eli Lilly to access the knowledge potential of retired employees over the Internet. InnoCentive acts as an intermediary that connects problems and external problem-solvers in exchange for compensation. The name InnoCentive was coined as a portmanteau of “innovation” and “incentive.” The business principle of InnoCentive is straightforward: consider a company looking for a solution to a problem that its development department cannot solve on its own. The company simply writes up its question with a description and any relevant formulas and graphs on the InnoCentive website and advertises a cash prize, typically between $10,000 and $100,000. InnoCentive has been used to search for solutions from a wide range of academic and research fields, including chemistry, mathematics, nutritional science, IT, and mechanical engineering. The problem-solver who provides the best solution within a specified period receives the prize money. More than 250,000 inventors have already registered and regularly browse the platform. The seeker company usually remains anonymous to protect company secrets, although companies such as SAP, the Nature magazine, and NASA have recently established their own sections (“pavilions”) on InnoCentive to call upon the masses to solve their problems collaboratively. The business model of the platform itself as an open innovation intermediary requires seekers to pay a fee to advertise their question online. Since it was founded in 2001, the website has constantly expanded, and the number of advertised problems has increased continuously. Based on findings from InnoCentive, Jeppesen and Lakhani (2010) provided evidence that the principle of open innovation is highly efficient. The authors considered 160 chemistry problems advertised to researchers in the InnoCentive community by large research institutions. The typically large and highly specialized research departments had previously worked on the posted questions for periods ranging from six months to two years without success. After being posted on InnoCentive, more than 30% of all open problems were successfully solved by the community. The time span in which the best solution was posted was even more impressive—it only took them 74 h. The authors found that the gap between the field of expertise of the problem-solver and the field of the original problem was positively correlated with the probability of finding a solution. This shows that “outsiders” have the ability to consider problems from a relative distance without preconceived solution ideas. The InnoCentive contributors who win

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innovation competitions often take well-known solutions from their own academic field and apply them to other questions without fixed preconceptions. Of course, the external problem-solvers themselves are just looking for “local” solutions. But since they typically work in different fields with different prior knowledge, they often take a completely different—and highly innovative—approach. Ultimately, the solution is very inexpensive for companies: they invested an average of $60,000 (half for the winner’s prize money and half for the handling fee charged by the InnoCentive platform). By contrast, according to their own estimates, the value of the revenue generated by the solutions was more than $10 million on average. Thus, InnoCentive represents a departure from classical principles of value creation in innovation. The search for solutions is replaced by an openly advertised problem. InnoCentive is a prime example of open innovation and improved access to solution information. The case example shows that open innovation and the broadcast search mechanism are highly effective for identifying, communicating, and transferring technical knowledge. This opens up completely new possibilities for knowledge and technology transfer as part of interorganizational knowledge transfer from knowledge and (basic) research (e.g., universities, research companies, etc.) to the entrepreneurial innovation process (see Fig. 1). Traditionally, technology transfer involves storing existing knowledge in databases. The seeker (namely the company that requires this knowledge for an application-oriented research question) is tasked with finding this knowledge and transferring it to their own field. But this procedure is fundamentally problematic and inefficient, since the information contained in databases is often poorly structured and evaluated, and traditional search methods (e.g., by keyword) are rarely much help for technology transfer due to the information overload problem. The necessary information needs to be time-consumingly aggregated and selected from a mass of data before it can serve as usable knowledge. Therefore, database storage rarely provides concrete (solution) knowledge for the innovation process. Visiting open innovation platforms or screening for problems allows knowledge carriers (e.g., academics participating in research projects) to become problemsolvers for particular industrial projects. The coordination mechanism of selfselection and self-motivation in a broadcast search is therefore contrasted with the approach of assigning tasks to the “best” identified service provider by the industry. At the level of companies, the focus moves away from problem-solving as such toward formulating and advertising the company’s problems. Problem-solvers and solution seekers are brought together on open innovation platforms or other innovation platforms (open marketplace for the transfer of inventions). Unlike traditional coordination principles such as market (hiring external engineering or research services) or hierarchy (assigning research tasks to specific actors within the organization), the principle of broadcast search is based on self-selection and self-integration. Contributing academics and other experts identify themselves as being qualified to propose a solution and participate in the problem-solving process. In this way, the collection of potential contributors forms a solver community that serves as the backbone of the broadcast search principle. For a company’s innovation process, coordination in communities means leaving behind the principles of

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Fig. 1 The principle of broadcast search for technology transfer

hierarchy and the associated obstacles of local search to design incentives, while escaping the necessity of coordinating over the market and its difficulties.

4 Adapting the Method of Broadcast Search for Technology Transfer Within the framework of open innovation, any researcher is free to offer the knowledge available in their expertise to companies (if they are implementing open innovation) or allow companies to benefit from their problem-solving expertise. Existing

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and well-established platforms for knowledge management and technology transfer can be used to connect academics with companies’ projects. Companies can articulate their problems on these intermediary platforms, providing motivation to search for solutions in the form of prize money to incentivize a technology transfer. Table 1 gives an overview of two existing and successful open innovation intermediaries (for a more extensive comparison and overview of these platforms, refer to Diener and Piller [2019] and the website oia.open-innovation.com). Table 1 Overview of two existing open innovation intermediaries InnoCentive

NineSigma

Network size

390,000+ registered solvers from 190+ countries

>2.5 million

Business model

Publication fee: $6000–15,000 Success fee: 40% of the advertised prize money

Publication fee: $12,000–19,000 Success fee: Percentage of advertised prize money or fixed amount

Industry branch/sector

• • • • • • • • •

• • • • • • • • • • • • •

Engineering sciences Telecommunications IT Design (Consumer) electronics Health Medicine Chemical industry Pharmaceutical

Automotive industry Engineering sciences Telecommunications (Consumer) electronics Health Medicine Chemical industry Pharmaceutical Aerospace Defense biotechnology Food industry Beverage industry Manufacturing industry

Prize amount

$5000–1 million

$5000–50,000

Performance

>2000 completed postings 162,000 submitted solutions Total of currently advertised prize money: >$20 million Average success rate: 46%

>5500 completed postings Average of 10 proposed solutions per posting >24,000 submitted solutions from 100 countries 60% of solutions from the industry

What guarantees does the platform operator give in terms of access to problems/solutions?

The solutions are only seen by the posters and InnoCentive; postings are anonymized

Problems can be anonymized if necessary, problem access can potentially be obtained by any interested problem-solver; access to solutions is reserved solely for the posters, but only access to solutions and contact details, not the full solution, just the ability to view the abstract (continued)

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Table 1 (continued) What does the course of a typical project look like in practice?

InnoCentive

NineSigma

1st phase: The poster answers seven questions about the problem, maximum two pages 2nd phase: Based on this, InnoCentive formulates a problem posting (feedback from the company to ensure that the problem is correctly understood) that is tested for comprehensibility over the course of two weeks 3rd phase: The problem is posted to the solver community (broadcast search) Duration: From 1.5 to 5.5 months

Project selection: Selection of problems suitable for posting Needs translation: Translation of the problem into a generally understandable form, opening up of the problem to support alternative ways of thinking. From this, the request for proposals is derived Connecting: Posting to the community by email to around 6000–10,000 potential solvers selected according to the problem Evaluation and selection: With or without NineSigma Acquisition: Typically, the two (or more) contractual partners negotiate the license terms mostly independently from NineSigma Duration: Min. 10–12 weeks

From the perspective of a technology transfer office at a university or research institution, the following aspects need to be considered when taking advantage of open innovation as a method of technology and knowledge transfer.

4.1 Attracting Attention Table 1 shows that there are various innovation and knowledge transfer intermediaries that are very successful at implementing open innovation between researchers and companies. Note that the platform providers listed in the table are not just limited to the industrial sector (e.g., mechanical engineering) with customers from the automotive, engineering, product design, and aerospace (sub)sectors, but also come from the fields of medicine, biotechnology, chemistry, and mathematics. Problems from operations research in business administration are also being advertised more and more frequently. However, to collect solution knowledge, academics have to be aware of the possibility to participate in solving advertised problems. Many research institutions, especially in India and Asia, increasingly count on participation in open innovation competitions as a cornerstone of their funding.

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4.2 Co-screening of Problems Reading or screening the advertised problems regularly is especially important. TTOs (Technology Transfer Offices) can assist with the registration process on specific platforms, as well as independently aggregating and disseminating relevant problems. For example, problems relevant to the university’s core research area could be communicated by the TTO itself (pre-selection) or over the university network with a link to the posting. Third-party funding agencies currently work in a similar manner by forwarding third-party funding advertisements—classified by keyword—to the relevant chairs and research groups.

4.3 Consulting The TTO can act as an agency to connect potential solution seekers and researchers throughout all phases of the solution search process. This is primarily relevant when formulating and submitting a proposed solution and for any subsequent negotiations regarding the transfer of Intellectual Property Rights (IPR) if the solution is successful. Additional consulting services could include training activities, legal advice, and recommendations on draft proposals.

4.4 Offering Guidelines for Problem-Solvers The TTO can also offer “guidelines for problem-solvers” as an in-house service to provide information about each step of the open innovation process. The NineSigma platform has published various White Papers and webinars on its website that can serve as a guide.

4.5 Initiating a Joint Venture with an Open Innovation Intermediary It is promising to work closely with an open innovation intermediary to benefit from their experience. This means integrating the university’s own research network (its various disciplines) into the community of solution seekers, but it also provides the opportunity to learn from the problem-solving experience of researchers from other universities and companies. Open innovation intermediaries have specialized experience that can benefit the university, especially with copyrights and exploitation rights, licensing, and forming new partnerships. Another possibility is to design an in-house platform/website where companies can post problems to a research pool

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or academic community in exchange for prize money. This type of approach is not yet universally common but is perfectly conceivable for government ministries, research institutions, and universities (e.g., in association with local businesses). However, building up an extensive and heterogeneous network of solution-providers and solution-seekers requires enormous effort to ensure the platform’s efficiency and quality. A so-called white label solution is therefore ideal for this type of project. The white label approach operates the platform under the name of the research institute, association, or university while drawing from an open innovation intermediary with an extensive network in the background.

5 Conclusion In this chapter, we introduced the method of open innovation for technology transfer. This approach works by: ● opening the innovation process to external actors, ● involving external expertise from beyond the borders of the company, and ● building connections with the enormous talent pool of researchers and product developers around the globe through networked collaboration. The broadcast search principle is a voluntary interaction process between various actors, making it both a shared problem-solving process and a social exchange process. Even though innovation platforms simply supplement the traditional business R&D process rather than replace it, from an academic perspective, open innovation platforms represent a serious option for companies to involve external experts and their knowledge into the innovation and development process, especially given the prevailing lack of engineers and skilled workers. In the past, successful calls for participation (e.g., on the InnoCentive or NineSigma platform) have achieved extremely impressive results by applying the broadcast search principle. Applying this mechanism to technology transfer from a research institution to incorporate new knowledge into the innovation and development process is therefore a natural step and offers participating (university) researchers the opportunity to also benefit from innovative entrepreneurial added value financially (via prize money and potential licenses and new partnerships). The universities and research institutions themselves can also participate in problem-solving competitions to win prize money as an alternative form of fundraising that is radically different from traditional third-party funding. In this way, open innovation based on broadcast search adds an entirely new dimension to technology transfer.

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Toward Systemic Strategy Development: A Contextual Innovation Framework Florian Grote and Eva-Maria Lindig

Abstract Society is facing diverse challenges in the areas of digitalization and automation or addressing the causes and impacts of climate change. In this situation, technological innovation has more potential for positive impact than ever. At the same time, the risks and consequences of failing to address these topics are also higher than ever before. Complicating things further, none of the challenges society is facing will likely be solved by single innovations. Rather, they require systemic approaches, aligning actors in various fields and sectors. Such alignment is missing in most fields. This chapter proposes an approach to create alignment via a contextual innovation model that takes the perspectives of the various actors into account and contrasts them with goals derived from an overarching objective. From the tension between these perspectives, ideas for innovation are formulated. Thus, the Contextual Innovation Framework becomes a template for integrated strategy development that addresses the requirements of holistic product development in the context of challenges that go beyond the scope of any single organization or actor in society. Keywords Innovation · Context analysis · Circular economy · Innovative methods · Strategy · Design

1 Introduction The relation between society and technology is as old as society itself. Using elements of the environment to create artifacts that allow us to achieve things our bodies would otherwise not be capable of is normal to us. This capability has allowed us to settle and sustain our livelihood almost anywhere on the planet. However, it has also contributed to a globally changing climate, which now might be threatening our ways to live. Facing such a global challenge, it would seem logical to develop products that F. Grote (B) · E.-M. Lindig CODE University of Applied Sciences, Lohmühlenstraße 65, 12435 Berlin, Germany e-mail: [email protected] E.-M. Lindig e-mail: [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_14

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address both its causes and effects. What we observe is that the development of such products remains a goal that is formulated in many sectors of economic activity, but that is lacking in execution. Our hypothesis is that part of the reason for this is the missing alignment between goals in different areas of action. For example, political goals related to climate might be adopted by companies, but they are not being translated to become effective in their operations. Similarly, knowledge generated in scientific communities is not translated well enough for individual consumers to understand the effects of their actions in their pursuit of individual goals. As a reason, companies often listen to the demands of individual consumers at the expense of global goals. This is because no effort has been made to design new alternatives— innovations—to create alignment between the goals of individual consumers, the companies addressing them with their offers, and the global goal of reaching a state of sustainability for society. For this reason, we set out to create a framework that enables product development to make the tension between the different sets of goals explicit, and to foster the construction of new bridges between them.

1.1 Methodology This study is based on extensive literature review in the field of business and product strategy in the automotive sector, in terms of both theory and its application. We use scientific as well as journalistic sources, publicly available surveys, published interviews by industry executives, and self-descriptions by actors within the sector. In addition, we interviewed three representatives of two major car manufacturers and an analyst at an NGO concerned with the translation of scientific findings in the field of climate change into operational goals for global policy-making. These interviews were semi-structured (Lindlof & Taylor, 2002, p. 170), following an interview guide that was adapted to the interviewees’ respective field of expertise. The interviews focused on goal setting regarding sustainability. The reason for this focus is that in product development, goal setting has the most significant influence on operational outcomes (Doerr, 2018). This includes which products get made at the end of a development process. The findings gained from the interviews are made explicit in the analysis of the individual areas of action in Sect. 5.

2 On Innovation Innovation has become increasingly important over the last decades. Considered as a major growth factor for businesses and societies in developed countries, it is also key for the long-term survival of our world society. Coming from a very general definition, where innovation is the process of making something new or introducing changes in something established by, for example, introducing new methods ideas or products (OED, 2019), Müller-Prothmann and

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Dörr (2014) point out that an implemented idea is only considered as an innovation if it is also adopted by the market (diffusion theory) having the consumer and their needs at the center.

2.1 Types of Innovation According to the Austrian economist Schumpeter (quoted by Betz, 1987), innovation will appear in one of these forms: ● ● ● ● ●

product or service innovation, process innovation, opening doors for new markets, developing new supply sources such as materials, equipment, and other inputs, fundamental changes in industrial and organizational structures.

Activities related to the last point are typically summarized under the term “concept innovation,” referring to innovations on the management or organizational level, as well as changes to the overall business model (Kaschny, Nolden, & Schreuder, 2015). Furthermore, one can also find social innovations referring to new social practices that aim to address social or environmental needs, e.g., better working conditions, improvements in the education or healthcare system (Howaldt & Schwarz, 2010).

2.2 Innovation Strategies Organizations can approach the innovation process in three different ways (Kaschny et al., 2015): ● Closed innovation: The innovators are all within the organization. ● Open (and free) innovation: Integration and usage of external competencies and information. ● Transfer innovation: Usage and application of external knowledge on products and processes. According to a recent study by Bundesverband der Deutschen Industrie (Frietsch, Schubert, Feidenheimer, & Rammer, 2018), which analyzed the innovation capacity of 35 countries, the usage of open innovation strategies tends to be economically much more successful than that of closed innovation strategies. This confirms Henry Chesbrough’s (2003) findings that companies are prospering if they are using external knowledge, talent, and technology to solve known challenges or enhance existing capabilities (outside-in/inward openness) and share internal ideas, talent, and technology to tackle unknown opportunities and create new capabilities (inside-out/outward openness) (Fig. 1).

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Exploit

Intrapreneurship Program e.g. Google's 20% time

Corporate Venture Fund e.g. Intel Capital

Open Innovation Ecosystem e.g. LEGO Ideas

Extract

Innovation Incubator e.g. Porsche Lab

Co-Creation Community e.g. Ford Innovate Mobility

Accelerator Program e.g. APX

Explore

Stage of Innovation

Colleague Crowd e.g. Oxfam Future Shapers

Social Listening e.g. P&G House Proud Crowd

Open Innovation Challenge e.g. UBS FoF Challenge

Open Inside

Outside In

Inside Out

Degree of Openness

Fig. 1 Open innovation matrix (Simoes-Brown, 2016)

100% Open Ltd. (Simoes-Brown, 2016) examined different strategies in relation to the stage in the innovation process and the organization’s openness. The matrix they developed starts with organization-internal innovation efforts, such as Oxfam’s “Future Shapers” participatory organizational development effort (SimoesBrown, 2014). From this exploratory approach, the matrix goes to the extraction of internal potential via an internal incubator, such as Porsche’s Digital Lab (Porsche Digital GmbH, 2020). The internal approaches end with exploitational intrapreneurship approaches such as Google’s famous rule, where employees could use 20% of their time on their own projects, owned by Google (Adams, 2016). Outside-in approaches focus on the transfer of knowledge into the organization. This can go from social listening, such as customer co-creation sessions at Procter & Gamble (Procter & Gamble, 2020) to innovation challenges for customers, e.g., at Ford (Ford Motor Company, 2019) or corporate investment funds such as Intel’s Capital unit (Intel Corporation, 2020). Finally, innovation from the inside out enables participants and creates an open ecosystem at the same time. This goes from exploratory challenges such as UBS’ Future of Finance program (UBS, 2019) to company-run accelerator programs such as Axel Springer’s and Porsche’s APX (Axel Springer Porsche GmbH & Co. KG, 2020). The exploitative approach is an open innovation ecosystem such as LEGO Ideas (LEGO Group, 2020), giving consumers the opportunity to pitch their design ideas and prototypes to LEGO and the fan community. Open innovation ecosystems are relying on reciprocity on all sides. Whereas Chesbrough (2003) considers the protection of each individual’s intellectual property as a prerequisite for a successful collaboration, Eric von Hippel (2016) goes one step further and

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promotes the free transfer and usage of knowledge and data (open-science, opensource, and open-data) without intellectual property rights, to ensure an even higher impact on social welfare.

2.3 Why Innovate? According to a report by Innosight (Anthony, Vigurie, Schwartz, & Van Landeghem, 2018), the 33-year average lifetime of companies on the S&P 500 Index in 1964 decreased to 24 years by 2016 and is forecast to sink to just 12 years by 2027. Companies need to innovate for survival. Those who are not continuously innovating are falling behind because other companies are entering the markets with better solutions to fulfill customers’ needs, or they miss out on the opportunity to grow their business by entering new markets. Innovation is a clear competitive advantage and ensures sustainable economic growth of a business. Besides the quality of human capital and R&D expenditures, the innovation capacity of an economy will also profoundly influence its growth (Pece, Oros Simona, & Salisteanu, 2015). As economic growth creates jobs, makes people less dependent on their current employer, and therefore fosters higher financial stability within society, it also stabilizes the political system (Dorfman, 2017). Along with the aspect of financial and political stability, past innovations had a tremendous impact on the living conditions within society, e.g., a better healthcare system prolonging life expectancy or automation of manual processes allowing for more thought-intensive and creative work. Though every innovation was created with the best intentions to solve a human survival or comfort need, it can have unforeseen consequences for the people and the planet. Therefore, there is also a need to continuously innovate on potential side effects of innovations implemented just recently or even embed a holistic and contextual view already in the innovation process itself.

3 Sustainability Goals in Innovation Innovation does not happen without context. Every innovation is driven by society’s demand either for new products, or by curiosity to explore a new technology. On the other end of the innovation process, the result takes the form of a product or service, which itself will have effects on society, whether intentional or unintentional. Therefore, the question of ethics in innovation arises automatically once the realization takes hold that technology is a function of society and innovation itself an iterative process. We chose the field of mobility and specifically the automotive industry as the empirical case for our study of innovation and the development of our framework. We are investigating how specific actors in the field relate to the goal of reducing harmful (to humanity) climate change and its effects. In 2014, road traffic accounted for 17.5% of global carbon dioxide emissions, which is considered to be the most

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relevant gas for human-made effects on global warming (Deutsche Bank & IEA, 2017). Automotive is a sector traditionally open to innovation and can, therefore, potentially be a significant lever in the effort to contain harmful climate change.

3.1 UN Sustainable Development Goals and Paris Climate Accord An initiative at the United Nations set goals for the sustainable development of the world society. These goals were adopted by the UN General Assembly on September 25, 2015 (United Nations Resolution A/RES/7 0/1, 2015a). The initiative set out to provide a positive outlook in the face of multiple global crises spanning health, economy, and the environment. They formulated a set of 17 goals targeting specific global initiatives, which were considered decisive for future developments. Their time horizon was 15 years, aiming for substantial improvements in sustainability to be in place by the year 2030. It was recognized that most goals require broad collaboration between different actors. This conviction is captured in the inclusion of a dedicated goal for collaboration. Out of the UN Sustainable Development Goals (SDGs), we focus on goal 13, “Climate Action.” In the first report on progress toward the goals, climate change is named the “single biggest threat to development” (United Nations, 2016), and it is here that the scale and efficiency of innovation on the products used by society can have the most significant impact, in both positive and negative directions. Also in 2015, the Paris Climate Accord was adopted by the participants of the UN climate conference, with the goal of limiting the warming of the global climate to two degrees Celsius compared to the pre-industrial age (United Nations Document FCCC/CP/2015/L.9/Rev.1, 2015b). This binding (upon ratification) framework abstractly defines the absolute boundaries in all areas of human activity in the hope of retaining a climatic environment that can support the projected growth of the world’s population. In addition, we focus on UN SDGs 9, “Industry, Innovation and Infrastructure,” and 12, “Responsible Consumption and Production.” These two goals represent the future path of product development, taking the manufacturing of physical goods and the supply chains associated with this process into account, while also including business models that thrive in a circular economy as well as the area of actual usage and consumption.

4 Contextual Innovation As mentioned, open innovation ecosystems exhibit the most promising potential for success. This is especially crucial in the pursuit of the UN SDGs, as they seem unattainable for any one individual, organization, or technology. To attain these goals, fundamental concepts of economic activity may have to be shifted in a direction of

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heightened context-awareness. This becomes clear when considering the concept of circular economy, for example. There, economic activity is regarded as a set of flows, e.g., of materials, value, and money. Products that realize their potential through shared use or the recycling of materials rely on the activities of other actors and can only be conceived taking the context of the broader ecosystem into account. Therefore, regarding sustainability, context-awareness and circularity are closely related.

4.1 On Circularity The circular economy is a concept influencing various parts of the economy. Fundamentally, circular economics refers to a more sustainable value of products by reusing, repurposing, or recycling them (Webster, MacArthur, & Stahel, 2017; World Economic Forum, 2014). It is differentiated from the linear economy, which focuses on building products for individual users who dispose of the product once they no longer need its value. In the linear economy, the perceived value of products is often highest at the point of sale, after which it degrades in value by being used. Later value-adding activities, such as customer service and upgrades, are often seen as secondary, after-sales elements of the business model. They are regularly treated more as a necessary evil than as creative and exciting. In contrast to such linear models, circular models place emphasis on retaining a product’s value by designing it to be reused or repurposed by other users, with the overall goal to keep them in service in their original form for as long as possible. Remanufacturing and recycling are seen as less desirable options in the circular process, as any change in the form of the product typically requires spending energy. One core competence for the circular economy is a strong focus on product lifecycle management, but new perspectives are needed for the engineering roles as well. The question to answer is how products can be created engaging in circular flows of value creation and value maintenance, both in the manufacturing process and during use. For this, new business models and product strategies have to be adopted. Methods to achieve this are being developed, for example, the Circular Innovation Framework (Guzzo, Hofmann, Trevisan, Echeveste, & Costa, 2019) and the Circularity Deck (Konietzko, Hultink, & Bocken, 2020). These methods are aimed at businesses and their immediate ecosystems. These methods are aimed at individual businesses, to help them analyze their immediate ecosystem and develop products with circularity of value in mind. For manufacturing processes, the notion of cradle-to-cradle production is establishing the priority to build products with a focus on recyclability. The goal is to use raw materials that can easily be extracted and reused for the manufacturing of other products (C2C PII, 2020). No matter how efficient this design for recyclability is, there will be energy involved in extracting materials during the recycling process. To avoid having to spend this energy on the recycling of raw materials, circular models are aiming to keep products in use in their original form for as long as possible. To support this goal, business models need to look at the full lifecycle of a product.

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Examples of such models can be found in the areas of subscription and sharing, where single-user and single-owner concepts are out of the picture, and hardwarebased products are marketed as a service. Such models focus on access rather than ownership (Wright, 2019). This approach can be observed in the mobility sector, for example in car-sharing services. Studies have shown that in some geographical regions, car-sharing services have the potential to reduce individual car ownership (e.g., Martin & Shaheen, 2016).

4.2 Context-Awareness of Goals The UN Sustainable Development Goals do not define operational goals for industries, nor does the Paris Climate Accord. Both of these are outcome-based goal sets that have to be translated into operational goals by industry and into regulatory frameworks by governments. This still leaves out how public perception reacts to these goal sets, and how individuals translate them into their own actionable goals and values. Innovation processes that drive business models in a circular economy can help reach a more sustainable society. To be effective in the pursuit of this objective, the goals that inform innovation need to be formulated with context-awareness. Our research shows that goals for innovation processes in the mobility sector are often circular at an abstract strategic level, but linear as they drive operations. One main reason for this is the different focus of goals on these levels: While on a strategic level, an organization can adopt UN sustainability goals and subscribe to commitments from a climate accord, user-centricity typically remains the highest goal for operational innovation processes, such as the design of new components for a car or the conceptualization of a new urban mobility service. This was exemplified in an interview given in 2019 by Volkswagen chairman Herbert Diess (Dörner & Caracciolo, 2019). There, he stressed his conviction that for a sustainable future in the transportation sector, systemic changes need to happen. Yet, in the same interview, he concluded that as a car manufacturer, Volkswagen has to continue to create products that cater to individual buyers and their needs and desires. If the goals and needs of these individuals are not aligned with the abstract sustainability goals at the strategic level, the resulting products are not likely going to contribute to these abstract goals as they reach the market. Similarly, sustainability goals and commitments from climate accords are widely adopted in the political process. However, when it comes to the formulation of actionable frameworks to set boundaries for economic activity, linear goals take priority. Examples of this are the emissions targets implemented by the European Union (EC Regulation 443/2009). While car manufacturers have been able to reduce their emissions over time, increased demand for less efficient car models has made it unlikely that the EU targets will be attainable (Ferraris, 2019). Political action can set abstract goals, but there are too few touchpoints that help translate them into operational practice. Indeed, German chancellor Angela Merkel made a statement in 2019 that political priority for more ambitious and relevant climate-related policy can only be borne out of demand from broad alliances in society (Jauer, 2019).

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Attempts to operationalize sustainability goals, and especially climate-related goals as covered by our research, can affect production, but this rarely becomes visible in the marketed product. One car manufacturer in our research had committed to the CO2 reduction goals of the Paris Climate Accord, but the operationalization of these goals was left to its supply chain and manufacturing process, while product design continued to focus primarily on individual users. The expectation within the company was that user goals would over time align with the Paris goals, but it was acknowledged that this might not happen soon enough to have the desired impact. Where sustainability goals have been operationalized through political frameworks, we see products like electric SUVs emerge (Muoio, 2016). There, technologies that were developed and politically supported with climate goals in mind are used to build products that require the least amount of behavioral changes on the part of individual customers. Their needs and goals continue to dominate product development. Such an approach subscribes to the theory that sustainability goals can only be met if industries manage to fulfill them without customers having to change their behavior. Where this is not possible, the expectation is that customers will change their own goals in time and let the companies know, so they feel authorized to build sustainable products. Research about climate effects of industrial production suggests this theory is not viable in the face of a global climate crisis (Boucher & Loring, 2017). Any effects, if they were to come in this approach, would likely be too late. Indeed, this same concern was stated by an industry executive in our interview. For this reason, we are proposing our model of contextual goal analysis as the fundament of an innovation framework that takes sustainable development into account from the outset. Based on our analysis of innovation strategies, we choose the approach of an open innovation ecosystem, as it has the highest potential to achieve significant progress in the pursuit of the overall goals. However, we are expanding on the idea of an open innovation ecosystem, which is typically centered on a specific actor, such as a company. In our framework, we propose to work toward an open innovation ecosystem on the system level, where all relevant actors are included. This includes a focus on the broad, open transfer of knowledge and technologies. A view on the ecosystem of ideas and goals of these actors requires us to map out the impacts of all relevant stakeholders in society. Then, we can consider what their motivations are in relation to the innovation at hand. Having done this, we can compare the motivations we have observed with the sustainability goals the organization has subscribed to. In addition, we can identify those sustainability goals that would be necessary in this sector of the economy. With all this information gathered, we can start to identify innovative bridges between the two sets of goals.

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5 Applying the Contextual Innovation Framework The proposed Contextual Innovation Framework consists of two main components: an impact map of all relevant stakeholders in a specific domain of product development and a goal board for these stakeholders, where their observed goals and the potential goals relevant to the sustainability goal are compared (Fig. 2).

5.1 Building the Model The impact map is based on the quintuple helix model of innovation developed by Carayannis, Barth, and Campbell (2012) and explained further in their monograph (Carayannis & Campbell, 2019). These authors, in turn, expand the triple helix model of technology transfer (Etzkowitz & Leydesdorff, 2000). The triple helix comprises the strands of political, educational, and economic action. As they are tightly interwoven, information can be exchanged with less friction, thus supporting technological innovation and the transfer of knowledge associated with it. In the quintuple helix, this model is expanded to include the natural environment (extraction of natural capital) and the media- and culture-based public as additional strands. Action in any of these strands can have a direct effect on sustainable development, and it can produce knowledge that may flow into action in any of the other strands. While the

NGOs / watchdogs

political system

impact climate & mobility

mass media

individual consumers

science & education

economic system

Fig. 2 Impact map of the CIF model on automotive mobility and climate action

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perspective of the quintuple helix model of knowledge production and communication between the strands retains its relevance on a generalist level, impact mapping as practiced in system dynamics (Sterman, 2000) provides the necessary level of flexibility to adequately represent more specific goals and their contexts. For our study, we analyzed impactful stakeholders in the mobility sector, especially in the automotive industry. As a result of this analysis, we first include the three areas which are also part of the triple helix model: economic, political, and educational action. Economic action is undoubtedly an important stakeholder, as any innovation in this sector will itself most likely be implemented as part of economic activity. Thus, economic action is connected to all other areas by high mutual impact. Political action includes both regulatory work by government bodies and election-focused party politics, both of which can have significant direct or indirect impacts on the pursuit of climate-related goals. It is also connected to all other areas via mutual impact. Educational action is important in two dimensions: as an area fueling scientific studies of climate phenomena and enabling new thinking to tackle future innovation needs. The field of science studying climate phenomena and climate effects is often referenced in mass media and social media debates about climate-related topics, but it is not directly connected to the public opinion. Instead, its findings are interpreted by non-governmental organizations, sometimes acting as watchdogs for government and politics, as they publish studies demanding effective action, mainly from governments. Even though this area is producing the findings on which knowledge of climate change rests and which enables technological innovation, its impact on the attainability of global sustainability goals appears limited, as it is not fully connected. Mass media fulfill a somewhat similar function to NGOs in that they also interpret scientific findings, but for the general public rather than for government bodies and other political actors. This latter area of mass media is playing host to debates around which reactions to scientific findings would be prudent, and it is here that public opinion on products takes shape. However, the overall impact on the possibility of achieving sustainability goals appears limited, mainly by a lack of drive from within mass media to make this topic a priority and to take a systemic perspective on it. In addition to the areas mentioned above, we include the area of action by individual consumers. We do this because it is this area that gets the most attention in product development, where researching the problems, needs, goals, values, and behaviors of current or potential users of a product plays a major role in its definition and design. This is where product development and innovation processes find their main operational goals. Hence, its impact on sustainability goals is high, and it is directly connected to the other three areas with equally high significance.

5.2 Goal Analysis and Formulation For the analysis of actual goals and the formulation of goals that would be aligned with the UN SDGs we chose for our study, we introduce a simple goal board comprising

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three elements: observed goals, potential goals, and bridges. Observed goals dominate the respective area of action at the time of research, while potential goals are derived from the selected main goals, such as a specific UN SDG. Finally, bridges are where new drivers of innovation can be proposed by way of asking the question: “what would need to happen to reconcile the observed goals with the necessary potential goals?” In our study, observed goals are those that we could make out either in the interviews we conducted, or in studies of the respective areas of action, or in the public domain, such as self-descriptions of relevant institutions. As mentioned, potential goals are our perspective on UN SDGs 9, “Industry, Innovation and Infrastructure,” 12, “Responsible Consumption and Production,” and 13, “Climate Action.” As bridges, we started attempts to formulate aspects of potential innovation in the respective areas of action. Some of the discrepancies we make out between observed goals and potential goals may be resolved over time without specific innovations. However, we aim to focus on trends in our analysis and point out discrepancies that are unlikely on a path to resolution. This is where we see the most critical lever for innovation in the pursuit of sustainable economic activity. In workshops utilizing the Contextual Innovation Framework, these ideas are refined continuously and iterated together with actors from the respective areas.

5.2.1

Economic System

Starting with the area of the economy, we identify obvious core goals such as return on investment (ROI) and growth, but also employment. While the former are main performance indicators in any capitalist environment, the social market capitalism we encounter in many European countries continues to accept some level of responsibility for their workforce as well. The first potential goal for the economy would be achieving absolute decoupled growth. The concept of decoupling has been established by Tim Jackson (2016). It is based on the assumption that economic growth can be decoupled from growing emissions of climate-relevant gases. Relative decoupling is achieved if emissions do not follow economic growth on the same trajectory, while absolute decoupling means that economic growth does not lead to more emissions at all. This goes in line with the notion of absolute sustainability, which we propose as a perspective on sustaining the well-being of society, not just of one’s own organization. So far, initiatives by car manufacturers mainly create effects of relative decoupling. Most car manufacturers have initiatives to reduce carbon emissions in their supply chains (as examples: Daimler AG, 2020; Toyota Motor Corp., 2020; Volkswagen AG, 2019b), yet the overall trend is the development of ever-larger cars (Schmidt, 2019). The Volkswagen Group, for example, has the goal to become CO2 neutral by 2050 (Volkswagen AG, 2019a) and reduce its absolute emissions significantly along the way. Porsche, as part of Volkswagen, sees itself on a good path to achieve its sustainability goals, yet it also sees the lure to fulfill the global demand for large SUVs, as Porsche CEO Oliver Blume pointed out (Vetter & Preuß, 2020). To achieve absolute decoupling, a systemic view needs to be established in economic thinking. From a financial perspective, the motivation connected to the

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potential goals is to reduce future costs that will be incurred in the context of climate change. Indeed, starting around 2017, investors are increasingly demanding companies to report on initiatives countering climate change (McPherson, 2019). The more long term the investment strategy, the more are investors looking into the success relating to sustainability goals (Eccles & Klimenko, 2019). This includes both activities to deal with external climate factors and operational provisions to reduce the companies’ effect on climate change. This ongoing “investor revolution” (ibid.) is of great importance due to the bargaining power of banks and investment funds. As bridges for innovation, we identify measuring the return on responsibility or resilience—instead of immediate monetary return—as a way to change the perspectives of projections and internal operational goal setting. This needs to go beyond models of a “triple bottom line” (Elkington, 1999), where results in the social and environmental domain are added to corporate economic reporting. Instead, return on resilience needs to be seen and accounted for as a direct investment into a company’s future—with economic effects. The reason we believe this is important is that the financial bottom line typically has the highest bargaining power for goals inside of for-profit companies. Connected to this, business models need to be innovated to allow for success in creating products for a resilient society in the face of climate change. To achieve this, the workforce inside an organization needs to be educated, and their focus must be shifted to areas of innovation necessary to achieve sustainability. The connection between the economic system and the area of science and education can become a strong link in this task. On the technological side, scientific research labs can cooperate with companies to find innovative solutions advancing the pursuit of sustainability goals. At the same time, however, the educational aspect within the companies should not be underestimated. The field of professional education has the potential to play a crucial role in the transfer of knowledge about building the next generation of organizations with sustainability in mind. By educating the workforce that is already present inside the companies, change can be effected more directly than by traditional educational institutions (Table 1). Table 1 Goal board for the economic system

Observed goals

Potential goals

• ROI • Growth • Employment

• • • •

Absolute decoupled growth Absolute sustainability Systemic view Reduction of future costs

Bridges • Educate and shift workforce to innovative areas • Business model innovations (e.g., for circularity) • Educate and shift workforce to innovative areas

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Individual Consumers

The area of individual consumers is directly linked to the area of the economy in multiple ways. For innovation in product development, we mainly look at how usercentric design and development act on the needs and goals they identify among their current or potential customers. The goals associated with car ownership in general have been surveyed in several markets worldwide (e.g., Statista, 2018, 2019a, 2019b), and “freedom” is always at the top of the list. This is corroborated in a statement by Volkswagen CEO Herbert Diess (Dörner & Caracciolo, 2019), who also named it a primary motivation for individual car ownership. Besides the emotional aspects of freedom as the theoretical absence of constraints on where to travel, more real-world goals of having the flexibility of routing and the size of things to transport come into play. In addition, safety for oneself and one’s passengers is a goal associated with car ownership. Many of these goals have traditionally been met via the production of larger cars, as they offer more flexibility, convenience, and safety. We encounter this strategy in the market trend toward sport utility vehicles (Schmidt, 2019). This same trend can also be seen in electric vehicles, exacerbated by the fact that larger and more expensive cars make it easier to introduce new technologies into the market, as one of our interviewees from a development unit pointed out. However, this trend appears to be directly opposing the selected UN SDGs. Potential goals for the area of individual consumers would need to include the reduction of energy and resource consumption, which would lead to smaller cars that weigh less and need fewer materials to be produced, including the battery in electric vehicles. Over time, technological advancements may make the construction of high-quality smaller cars easier. Nevertheless, the market success and public attention the large and materialintensive cars enjoy divert the focus away from the necessary pursuit of efficiency. To build bridges for innovation that may serve to align the goals, the experience of freedom and convenience needs to be decoupled from the physical product, which can be possible with more powerful augmented reality (AR) technologies inside cars in the future. Self-driving automated cars may decouple safety from physical size (Marshall, 2018), and distributed car-sharing services can already partly decouple availability of transport capacity from ownership (e.g., the service Miles in Berlin: berlin.de, 2019). While some of these goals rely on technological innovation, raising the awareness levels among individual consumers on the impacts their actions have on the climate might drive necessary shifts. This is where efforts to transfer knowledge from the area of science and education come into play, potentially supported by mass media targeting these purposes specifically. On the other end, car manufacturers need to focus their research on their customers’ values regarding issues of sustainability. One of our interviewees pointed out that his employer has started to embrace this focus just recently, caused by the realization that sustainability aspects are becoming an important part of their customers’ buying decisions (Table 2).

Toward Systemic Strategy Development: A Contextual … Table 2 Goal board for individual consumers

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Observed goals

Potential goals

• • • •

• Less weight • Fewer materials • Reduce energy and resource consumption

Freedom Flexibility Safety Convenience

Bridges • Decoupling the experience • Safety through automation • Awareness of impact on climate change

5.2.3

Mass Media

For action in the area of mass media, reach has long been the primary goal, as it drives the connected goals of the number of subscribers and revenue, as well as opinion leadership. Potential goals for this area would be to adopt a systemic view on topics covered, where relations between different opinions become part of the coverage. For example, as long as absolute decoupling has not been achieved, more growth of the economy immediately causes more climate-relevant emissions. However, this connection is not usually made in mass media, where publications often argue for more efforts both to reduce emissions quickly and to boost domestic consumption for the benefit of the economy. As bridges for these sets of goals, we see an opportunity for mass media institutions to become independent observers of systemic relations and take on an advanced perspective on issues whose effect on their audiences will continue to grow. Coupled with that, we see an already positive development toward mass media exercising its role to be a watchdog of political and economic power, especially concerning climate-relevant activity (e.g., the “Climate & Environment” section in the New York Times). Finally, the innovations we assume will be coming out of the economic and scientific-educational areas of action will also provide ample opportunity to generate excitement for mass media publications (see, for example, Transport & Environment, 2020). If mass media institutions can build alliances with other actors, such as NGOs and scientific institutions, this might significantly increase their power to effect change (Table 3). Table 3 Goal board for mass media

Observed goals

Potential goals

• Reach • Subscribers • Opinion leadership

• Systemic view • Translate scientific findings

Bridges • Become independent observers of systemic relations • Act as mass media watchdogs • Create excitement for innovations

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NGOs/Watchdogs

NGOs and other organizations which primarily function as watchdogs for political and economic action have goals that are influenced by their role as institutions loosely connected with various other actors. As such, they are guided by a purpose, such as the support of science-based policy-making in the case of the NGO taking part in our study. In order to gain influence and advance their cause, NGOs need to build a strong network, and they need to act with credibility. This point was made by our NGO interviewee when he noted that one of the aspects of their work was to build relations with individuals involved in policy-making who are already working to reduce climate change and support these individuals with helpful information. As for potential goals, NGOs could also support development more concretely by taking a systemic view on the integration of economic and political action with the influence of individual consumers. In the same context, they could focus on solutions to offer an independent perspective, for example on globally distributed supply chains in the automotive industry (Table 4). In addition, we also see protest groups in this area of action. Fridays for Future (Fridays for Future, 2020) and connected groups, such as Scientists for Future (Scientists for Future, 2020), are examples. Their activity of advocating for the translation of scientific knowledge into operational goals makes them important actors with significant influence in this sector, as corroborated by one of our interviewees within the industry. To build bridges, we could see NGOs taking part in innovative product development processes by benchmarking solutions with UN SDGs in mind. This would require a new type of collaboration between NGOs and private-sector organizations on an operational level. To an extent, this is happening in other industries, e.g., food or fashion, where brands cooperate with NGOs to create transparency for their production processes (Chakravorti, 2016). To advance the potential goals in public opinion, NGOs could go beyond PR work and engage in partnerships with mass media, preferably to establish a joint systemic view on how the relevant UN SDGs might be achieved. The Panos Climate Change Media Partnership program might serve as a precursor on which future initiatives could be modeled (Panos, 2020). Table 4 Goal board for NGOs/watchdogs

Observed goals

Potential goals

• Influence • Credibility • Network

• Systemic view • Focus on solutions

Bridges • Innovation benchmarking • Mass media partnerships

Toward Systemic Strategy Development: A Contextual … Table 5 Goal board for the political system

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Observed goals

Potential goals

• Public approval • Election results • Balancing of interests

• Climate priority

Bridges • Actionable regulations • Clear priorities

5.2.5

Political System

In political action, we see a strong focus on public approval, which gets tested regularly in elections. It must balance the interests of many groups and institutions within society. However, balancing interests does not lead to prioritized action, and therefore, we see the potential goal of having political action in place that prioritizes climate-related topics. To achieve this, we see as a first bridge the communication of clear priorities so that other actors can adapt. This priority must be grounded in actionable regulations that do not fall back to the mere balancing of interests. The influence of individual consumers—and by extension, mass media—will be crucial in this effort. At the same time, the political system could be more proactive in setting framework regulations and actively communicating their justification to individuals and companies. This is where the transfer of knowledge from the science and education system can come into help make such information relatable for individual consumers. Fleet emission limits put into place by the EU were widely regarded as too unambitious when they were first announced (e.g., Mulvey, 2007). However, car manufacturers are not on a path to meet these goals, mainly due to the trend toward larger, heavier cars (Schweikl, 2020). This is where mass media could make stronger connections between scientific knowledge and actual political action, as well as the industry’s economic activity. Economic success is a major argument in any political debate. Changes in the measurement of economic success as it relates to sustainability versus financial and employment impacts will, therefore, likely have direct effects on the political system. This is where innovations in business models and reporting standards could help drive political action toward aligned goals for sustainability (Table 5).

5.2.6

Science and Education System

The sixth area of action in our framework is that of the science and education system. Here, we see as defining goals the production of knowledge, and doing so in ways that adhere to self-defined standards of exactness. In the context of climate-related knowledge, as in many fields of science, the results are usually not compatible with the goals of mass media publications. Also, they often do not relate directly to experiences made by individual consumers. Hence, it is hard to establish systemic views

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taking scientific knowledge into account. Next to these difficulties, however, the science and education system does not give special priority to the study of climaterelated topics and technological innovations connected to them. In the pursuit of the three UN SDGs we selected for this study, such a priority would be a potential goal for the science and education system. This can be noted especially for the scientific disciplines concerned with economic activity and engineering, where foundations for product decisions are researched and taught. At the time of our study, industry collaborations with the science and education system were mostly focused on specific engineering challenges. Some of these challenges were related to innovations in manufacturing efficiency, as pointed out in one of our development-centered interviews. In addition, an interviewee mentioned that the transfer of knowledge and technology from scientific research often takes place via collaborations or acquisitions of technology-focused startups. This way, larger companies, such as the car manufacturer where our interviewee is employed, can ensure that new ideas have proven some success in the market before they implement them in their own organizations. This is where we see potential for innovation in the development of new accounting systems that integrate sustainability effects directly into operational goal tracking. Most likely, such innovations would need to be developed in collaboration between industry and scientific economists. As further bridges, we see collaboration efforts on innovative business models that take a different economic perspective on initiatives driving absolute sustainability. In addition, we see the potential of a more substantial outreach to mass media and active work on the translation of scientific results to meet the media’s needs (e.g., Climate Communication, 2020). At the same time, finding ways to involve more laypeople in scientific research around climaterelated topics in citizens’ science projects could lead to a better understanding of the effects actions of individuals can have (Citizen Science Association, 2020). In any case, to influence the path of innovation, institutions in the science and education system need to build and maintain a strong network to companies and the government, fostering the transfer of knowledge on the causes and effects of climate change in ways that successfully address their target audiences (Table 6). Table 6 Goal board for the science and education system

Observed goals

Potential goals

• Exactness • Knowledge production

• Climate priority

Bridges • • • •

Mass media outreach Citizens’ climate science Strong network to companies and government Collaborations on business innovations for sustainability

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6 The CIF Analytical Method The Contextual Innovation Framework can be used as a universal method to uncover tensions between goals set by actors and potential goals relating to a selected systemwide goal. We use UN SDGs as system-wide goals, but the method is agnostic to this choice. The framework has been used for innovation initiatives in different sectors, such as food, fashion, travel, and automotive (as exemplified in this chapter). To utilize the Contextual Innovation Framework, seven steps should be taken: 1. 2. 3. 4.

Identify the system-wide goals to pursue (e.g., from UN SDGs). Choose the sector of product development on which to focus. Identify the main areas of action influencing this field of product development. Estimate their impact level on the possibility of achieving the global goals you chose. 5. Draw the connections of influence you can observe between these areas. 6. Create goal boards for each area of action, including observed goals (what they currently pursue in operations), potential goals (what they would ideally be doing to pursue the global goals), and bridges that can potentially connect the two sets of goals. 7. For each area of action, estimate the level of tension between its observed goals and the path to achieving the global goals. These steps can be developed in a series of workshops, in groups that either have knowledge of the operations in the areas of action or have studied them extensively. From there on, new ideas for innovations (the “bridges”) can be evaluated for future product development. Templates for the method are provided for free at: https://kon text.works/methods/cif.

7 Conclusion and Further Research In a world where society is facing challenges of increasing complexity, the task of innovating cannot be a means to an end for single actors in fields of science, technology, or the economy. The disruptive innovative power harbored within these fields can unleash its potential for greater benefits if goals are aligned between different areas of action, and with the higher objective of achieving truly sustainable development for society and future generations. With the Contextual Innovation Framework, we are proposing a method to develop what we see missing in many parts of these fields: a systemic perspective in operations. Often, the systemic view remains at abstract strategic levels and rarely trickles down to influence what happens on the factory floor or in software code. In our case study, the political system, economic system, and individual consumers all have high impact ratings. Moreover, they are directly interconnected, as well. Therefore, innovation in these areas will likely have more of a direct effect on climate action than innovation in other areas. In general, our

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research indicates that alliances for the pursuit of sustainability goals should be as broad as possible. Different actors in the sector have different sets of knowledge, and their perspectives have different time horizons. While companies are dominated by their financial reporting cycles, political action is synchronized to legislative periods. NGOs, protest groups, and the science and education system tend to have perspectives that reach out further into the future, yet they are the actors which proclaim the most urgent need for change. Finally, different actors in the sector also have vastly different bargaining power to effect change. NGOs, protest groups, the science and education system, and the mass media can mainly try to influence other actors. In contrast, the political system can set boundary conditions for the actions taken by the other actors. However, the real, measurable change with regard to the SDGs will have to be implemented in the field of action of the consumers and the economic system. This is why it is essential to integrate them into alliances to be forged. Out of the number of potentials for bridges we identified, two stand out: First, collaborations between scientific researchers, NGOs, and mass media could convey more relatable representations of climate-related action in the public mind. Second, collaborations between economists, industry, and political actors have the potential to create a new way of accounting for the return on investment in climate-relevant industry action as a means to alleviate risk. Further research is needed to investigate the effectiveness of suggested innovations and heightened systemic awareness created by the Contextual Innovation Framework. For this, means to measure the progress made by actors in the sector will need to be developed. In addition, the method itself will be iterated further through ongoing applications in other sectors, such as fashion, food, and travel. The next level of complexity to tackle with a future version of the method lies beyond the perspective on individual sectors, but rather in the development of a holistic frame for even broader societal challenges. As an example, the potential transition to a hydrogen-based energy economy requires all actors to forge alliances based on the systemic integration of goals coming from a wide variety of interests. With the Contextual Innovation Framework as it has been developed out of our study of the automotive sector, we hope to have provided a first step in this direction.

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University Technology Transfer as Control Parameter of Complex Entrepreneurial Ecosystems Andreas Liening, Jan-Martin Geiger, Tim Haarhaus, and Ronald Kriedel

Abstract University technology transfer constitutes an important pillar in challenging the transition of inventions to innovations. Its contribution to the development of entrepreneurial ecosystems lies therefore at the heart of entrepreneurial activity. While specific approaches model such ecosystems as quadruple helix, consisting of the interplay of government, university, enterprises, and society, broader approaches attempt to decompose such ecosystems into more granular single actors such as enterprising individuals, start-ups, established firms, (regional) policy makers, venture capitalists, etc. This paper focuses on the heart of knowledge generation and investigates practices of university-based technology knowledge dissemination by reviewing current approaches and best practices. We merge our conceptual findings into a micro-macro-level model in order to provide theoretical as well as practical implications to support the transition from inventions to innovations. Keywords Complexity and entrepreneurship · Entrepreneurial ecosystems · Synergetics · Technology transfer

1 Introduction University technology transfer (UTT), i.e., the “formal efforts to capitalize upon university research by bringing research outcomes to fruition as commercial ventures” (Dill, 1995, p. 370), plays a crucial role in fostering the economic and social development of regions and countries (Klofsten et al., 2010; Miller et al., 2018). A. Liening · J.-M. Geiger (B) · T. Haarhaus · R. Kriedel TU Dortmund, Friedrich-Wöhler-Weg 6, 44227 Dortmund, Germany e-mail: [email protected] A. Liening e-mail: [email protected] T. Haarhaus e-mail: [email protected] R. Kriedel e-mail: [email protected] © Springer Nature Switzerland AG 2021 D. Mietzner and C. Schultz (eds.), New Perspectives in Technology Transfer, FGF Studies in Small Business and Entrepreneurship, https://doi.org/10.1007/978-3-030-61477-5_15

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For instance, effective UTT has the potential to increase universities’ opportunities for revenue generation and funding, promote an entrepreneurial culture and enhance individual student success as well as overall economic growth (McDevitt et al., 2014). Until recently, the cooperation between academia, government, and industry, which together form the triple helix model of knowledge-based economies (Etzkowitz & Leydesdorff, 2000), has been considered as the essential part for successful UTT. However, the adequacy of this approach has been doubted lately, since UTT based on triple helix structures failed to meet expectations regarding innovation, job, and economic growth (Asheim & Coenen, 2005; McAdam et al., 2012; Miller et al., 2018). In consequence, Carayannis and Campbell (2009) introduced a fourth helix, namely societal-based innovation users in order to allow for more pervasive and co-creational UTT (Miller et al., 2018). Whereas the quadruple helix model of UTT provides an improved representation of the various innovation ecosystem stakeholders (Wilson, 2012), existent UTT literature lacks comprehensive frameworks that are able to grasp the complex and dynamic character of UTT processes (Miller et al., 2018). Furthermore, a deeper understanding of how such quadruple helix structures can be implemented at the different levels of the innovation ecosystem is required. Hence, in this chapter, we provide a profound overview of UTT from a quadruple helix perspective and introduce a micro-macro model to illustrate how various stakeholders can be integrated in the UTT process. We identify and discuss activities and structures on the macro-level of UTT to outline the distinct mechanisms of complex knowledge ecosystems which enable technology genesis. For instance, the availability of infrastructure, such as maker- and coworking spaces, globalization trends, regional networks, and the design of transfer curricula can be exemplary here. With respect to the micro-level, we present specific didactical approaches, including specific methods, concepts, and formats that bring together various UTT actors (e.g., students and researchers), which jointly facilitate interdisciplinary cooperation with regard to technology transfer.

2 Theoretical Background 2.1 UTT from a Quadruple Helix Perspective Based on the different types of knowledge production proposed by Gibbons (1994), UTT can be categorized in three different modes (Carayannis & Campbell, 2009; Miller et al., 2018). The Mode 1 UTT describes universities’ conventional role to produce new knowledge and basic research which then result in societal education and learning, whereas Mode 2 UTT refers to the novel function of universities to engage in UTT tasks that aim to commercialize technology (Miller et al., 2018). Mode 3 UTT represents an enhanced model which focuses on universities’ overarching role to exchange knowledge with a large variety of societal and public actors,

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thereby highlighting a co-evolutionary and co-creational approach to innovation (Carayannis et al., 2018; Miller et al., 2018). Moreover, whereas Mode 1 and Mode 2 UTT relate to the triple helix model of knowledge production that incorporates academia, industry, and government, the Mode 3 UTT integrates a fourth actor, namely the civil society (Wilson, 2012), hence forming a quadruple helix innovation system (Carayannis & Campbell, 2009). The quadruple helix innovation system is “an agglomeration of firms, institutions, and other stakeholders intertwined via a helical, dynamic, complex, non-linear, self similar (fractal), and self -organizing higher-order learning architecture of a knowledge production system” (Carayannis et al., 2018). Generally, the quadruple helix concept emphasizes the importance of the public, i.e., citizens or users, for the innovation and knowledge production processes (Carayannis & Campbell, 2011). Consequently, the quadruple helix concept proposes to involve users and citizens in participatory innovation procedures (such as co-evolution, co-creation, or co-opetition) to generate new products and services (Carayannis et al., 2018). In this context, from a quadruple helix perspective, UTT has recently been conceptualized as an enabler of regional entrepreneurial ecosystems (Carayannis et al., 2018; Carayannis & Rakhmatullin, 2014). In the following section, we outline the crucial role of UTT for the emergence and evolution of entrepreneurial ecosystems at the regional scale.

2.2 Quadruple Helix UTT and Entrepreneurial Ecosystem Development The idea that UTT acts as an important enabler of entrepreneurial ecosystems grounds in the notion that the accessibility and distribution of knowledge are crucial for value creation at the regional level and that technology functions as a driver of endogenous economic development (Romer, 1990). Recent literature, for instance, perceives the availability and applicability of digital technologies as enabler of entrepreneurial activity (Nambisan, 2017) that may lead to emerging (intended as well as unintended) artifacts like products, firms, markets, but also entirely new entrepreneurial and innovation ecosystems. Following the conceptualization by Stam & Spigel (2016, p. 5), an entrepreneurial ecosystem can be defined as “a set of interdependent actors and factors coordinated in such a way that they enable productive entrepreneurship within a particular territory.” Quadruple helix UTT facilitates the development of entrepreneurial ecosystems because it connects the different stakeholders involved in innovation processes, thus fostering interaction and knowledge exchange between individuals and other social entities (Carayannis et al., 2018). Furthermore, Mode 3 UTT strengthens the collaborative creation and application of knowledge, ultimately leading to an open, complex network of knowledge production system stakeholders which together increase the entrepreneurial activity within a region (Carayannis et al., 2018).

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However, despite recent initiatives to establish more integrative, participatory UTT structures, the understanding of the increasingly complex and dynamic university technology commercialization procedures remains limited (McAdam et al., 2018). Additionally, current research lacks practical guidance for educators, policy makers, and practitioners. Hence, in the following paragraph, we present a model to illustrate how various stakeholders can be integrated in the UTT process.

3 Integrating UTT into a Micro-Macro-Model Based on the Theory of Synergetics Acknowledging UTT as one important driver in a complex, self-organizing entrepreneurial ecosystem, the need arises for considering non-lineal approaches. Following several authors who state that entrepreneurial processes, such as knowledge transfer and entrepreneurial ecosystem emergence, are characterized by complexity (Autio et al., 2018; Carayannis et al., 2018; Lichtenstein, 2011; Liening et al., 2016; McKelvey, 2004; Nambisan, 2017), we apply the theory of synergetics to develop a micro-macro-model of UTT.1

3.1 Fundamentals of Synergetics Grounded in complexity sciences, synergetics constitutes a theory of selforganization in order to model complex, non-linear and emergent behavior of a system. A system consists of elements which may be simple, but may also constitute systems by themselves (Haken, 2006). The term self-organization implies that a system evolves its structures by interaction on itself, without any specific external intervention (Haken, 1977). When those structures cannot be anticipated or reduced to the system’s elements, one can also speak in terms of emergence (Liening, 2017). Self-organization has been hypothesized on several levels of entrepreneurial and economic activity. Liening et al. (2016) emphasize self-organization as key characteristic of entrepreneurial mindset genesis, assuming that a mindset results from entrepreneurial learning. A broad body of literature describes the complex, selforganizing character of venture creation and development, assuming that early-stage organizations dispose several degrees of freedom that are transferred into structures 1 Although the theory of synergetics is often displayed as the theory of self-organization, it must be noted that with the theory of dissipative structures (Prigogine, 1955), fractal geometry (Mandelbrot, 1987), catastrophe theory (Liening, 2017) and chaos theory (Prigogine & Stengers, 1984), there exist further theoretical, interrelated approaches to examine complexity in self-organizing phenomena. For instance, the theory of dissipative structures focuses on energy supply to systems being absent from equilibration, which are necessarily constituted as open systems. The latter is a necessary condition for synergetic behavior.

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in a continuous process of interaction between venture and environment (Lichtenstein et al., 2007; Lichtenstein & Stroh, 2017; McKelvey, 2004; McKelvey et al., 2012). Finally, the development of entrepreneurial ecosystems is hypothesized as a highly dynamic, non-linear, and self-organizing process (Autio et al., 2018; Haarhaus et al., 2018; Nambisan, 2017). Self-organization prerequisites openness, i.e., the system must be receptive to energy supply from the external environment and be able to dissipate. Although a specific system behavior cannot be predicted by the type of energy supply, its impulse may cause interaction between the system elements and affect emergent structures. Because of the importance of the energy, it is also referred to as control parameter which is specific to the system, because not every kind of energy supply induces system behavior. The control parameter is also unspecific toward the system behavior, acknowledging that emergence results from the constitution and interaction of the systems’ elements (Manteufel & Schiepek, 1994). Figure 1 illustrates the basic model of synergetics. The system elements on the microscopic level are able to interact with each other. However, in absence of specific energy, they remain in their current status. In his original work, Haken (1977) describes gas molecules in a laser beam as system elements which have no order and function as a normal lamp that emits light in absence of specific energy supply that is above a critical value. Only by the stimulation of specific energy (control parameter) that crosses a critical point, the molecules begin to merge an order. With exceeding this threshold, the system becomes instable and sensitive to external perturbations. This circumstance contributes to that the determination of a specific system behavior becomes nearly impossible, especially

Macroscopic pattern/ Order parameter

Control parameter/ Energetic activation

Emergence

Bottom-upTop-down Circuitcausality

Relative macro level

Enslavement

Relative micro level System Elements Fig. 1 Model of synergetics (Liening et al., 2016 [used with permission from Springer Nature]; Schiepek & Strunk, 1994)

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when a system is susceptible to smallest changes. Within the laser beam, the control parameter facilitates self-organization of the molecules and lets them form an order on a macroscopic level, which vice versa synchronizes the system elements on the micro-level (enslavement). While an order is necessarily not observable, certain artifacts based on the order—such as laser light, for instance—can still be observed externally. The term “relative macro level” refers to the possibility that an order parameter may be part of another system, in which it is located, but here on the micro-level, serving as a system element (Haken, 2006).2 Analogous to this, the term “relative micro level” acknowledges that those elements may also consist of several subsystems on their own. Due to its ability to analyze self-organization processes in systems, we apply a synergetics framework in order to model UTT as a part of the quadruple helix model in the following section.

3.2 UTT as Control Parameter in Entrepreneurial Ecosystems A simple adoption of Haken’s theory of synergetics to UTT may lack information, in particular with respect to the number of different stakeholders in an ecosystem. While order creation in a laser occurs under stable environmental conditions, technology transfer into an entrepreneurial ecosystem touches many uncontrollable variables. For this reason, we apply an extended model of synergetics according to Eckert et al. (2006), which also includes the environment and internal as well as external system constraints. As the environment represents dynamic surroundings of the ecosystem, for example, in terms of legal regulatory, the environment may influence the ecosystem by providing a control parameter or stimulating the system elements and may vice versa be influenced by the system, exemplarily though new technology development that requires new regulation. The coagulated system history reflects the memory capability of a system, i.e., that former order parameters (internal constraints) and mediators (external constraints) form the context in which new order creation takes place (Fig. 2).

3.2.1

Microscopic Level

The microscopic level reflects the elements of a synergetic system, which are disordered without energy input and which, in our view, should be different actors in the UTT ecosystem. Adner & Kapoor (2010) illustrate by the example of innovations in aircraft construction that such actors can consist of different suppliers, producers, 2 While, for example, an entrepreneurial mindset can occur as order parameter within an individual’s

cognitive system (Liening et al., 2016), this mindset may simultaneously serve on a team level (e.g., within a start-up) as one element between many mindsets of other entrepreneurs.

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Market (Dynamical pattern)

Relative macro level

Order parameter

Environment

Forms of value (co-)creation, cooperation and competition

Emergence Control parameter Technology Transfer

Internal system activation

Bottom-upTop-down Circuitcausality

Civil society Universities

External stimulation

Coagulated system history

Enslavement

Policy makers

Incumbents Start-ups

Internal and external system constraints Active constraints

Relative micro level

Fig. 2 Technology transfer as control parameter in an entrepreneurial ecosystem (Liening, 2017)

customers and complementary actors like airports. From this perspective, the relationship of the system elements is characterized by their joint action in a value chain. Firms may also, for instance, differ in branch, size and business relation (b2b vs. b2c). McKelvey et al. (2012) show this in a similar way for the software industry and find indications of a fractal relationship of system elements to each other, which are interpreted as an indication of self-organization. Other approaches extend the frame of reference of an entrepreneurial ecosystem beyond incumbents and start-ups to further organizations and institutions like government, incubators, universities but also social norms (Roundy et al., 2018). These actors simultaneously act as stakeholders within an entrepreneurial ecosystem (Carayannis et al., 2018). These stakeholders are related by specific “rules” of interaction such as forms of knowledge exchange, cooperation, or competition (Bingham & Eisenhardt, 2011; Roundy et al., 2018). Academia is characterized by focal points such as engineering, natural sciences, IT, humanities, and social sciences. Furthermore, policy and civil society are marked by attributes like culture and the economic and political system and therefore influence the interplay between stakeholders.

3.2.2

Control Parameter

For a development of the entrepreneurial ecosystem, the energy supply is necessary through a specific control parameter. Autio et al. (2018) and Haarhaus et al. (2018) illustrate that the development and availability of digital technologies can lead to a

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stimulation of entrepreneurial ecosystems, from which in turn new forms of collaboration and value creation can emerge. Digital resources can be produced and used cost-efficiently and over long distances, opening up new forms of business models. This effect can be transferred to other forms of technological progress, so that the transfer of research results is of particular importance. UTT is therefore now interpreted within the synergetic model as a control parameter that must be designed in such a way that it can exert a specific impulse on entrepreneurial ecosystems. Generally, UTT should be formed in an open, flexible, and integrative way, so that interactions with societal and public actors can be established (Miller et al., 2018). By installing such “soft” infrastructures, which can be based on rather informal networking or knowledge transfer activities, universities can involve various stakeholders of the quadruple helix model along the UTT process (Carayannis et al., 2018). In the following, we illustrate in more detail how the key role of UTT, namely technology transfer offices (TTOs), should be designed in order to develop strong relationships with ecosystem stakeholders and to foster the emergence of novel forms of cooperation. UTT entities are constituted by TTOs, which carry out actions related to the commercial application of previously developed scientific knowledge and technology (McAdam et al., 2017; Van Horne & Dutot, 2017). Such commercialization processes typically involve rather formal activities, including patenting, the formulation of licensing agreements, the establishment of spin-offs as well as the management of equity stakes (Calcagnini & Favaretto, 2016; Van Horne & Dutot, 2017). These formal commercialization processes traditionally rely upon the internal knowledge sources of the TTO (McAdam et al., 2017). In this context, four different approaches can support the commercialization staff to exploit knowledge and technologies: First, the identification of the right starting market (Gruber & Tal, 2018). Second, the business model to list all aspects and areas that need to be reviewed (Osterwalder & Pigneur, 2011). Third, agile engineering for the rapid construction of minimally viable products (Bland & Osterwalder, 2019). Fourth, customer development, a process for testing these hypotheses (Blank & Dorf, 2012). In operational terms, this means that commercialization teams which aim to transfer technologies into innovative products or services first need to find out where the respective technology can best be used (Gruber & Tal, 2018). This approach also equally helps the university or the exploitation agencies to set a price for the license. Furthermore, the business model with the three areas “feasibility” (i.e., the technical feasibility), the “desirability” (i.e., the existing customer needs) and the “viability” (i.e., the financial viability) should be outlined from the beginning. The areas and especially the underlying aspects will change several times during the development, but this way the future DNA of the start-up will always be the focal point. In order to be able to test the various aspects with the customers, concrete prototypes are crucial, which will be developed into a minimal viable product. This is created when the prototype manages to emphasize value (viable) for the customer. The customer development process is particularly challenging for B2B solutions and leap innovations (Blank & Dorf, 2012), since this requires a broad network and industry knowledge in order to identify suitable business partners. As a result,

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scientific institutions could specialize in specific areas and thus be able to build up profound expertise and skills in selected disciplines. The four approaches outlined above must now be transformed into a revolving process. Sprint formats (Knapp et al., 2016) are recommended. Phases of input, consultation, and feedback with experts always alternate with phases of independent implementation. However, an overreliance on formal commercialization processes and internal knowledge capabilities might limit TTOs’ ability to act adequately. In a time where knowledge becomes increasingly complex, technology life cycles get shorter and customer needs change rapidly, it might be useful for TTOs to engage in more informal commercialization activities and involve external stakeholders, such as industry experts or end users, in the commercialization process (McAdam et al., 2017). By integrating their TTOs stronger with the industry and the wider society, universities can establish a wide external knowledge base that can be utilized to evaluate the potential of technologies from a broad range of disciplines (McAdam et al., 2017). Whereas many universities strive to involve external stakeholders within UTT, in practice, the potential to exploit new technologies is often limited by a lack of resources of small to medium size TTOs (Miller et al., 2018). For instance, TTOs often miss the necessary human and financial resources to collaborate with societal and public actors (Miller et al., 2018). In fact, TTOs require deep knowledge of key markets and sophisticated technical and management skills to promote successful innovation between various stakeholders in the entrepreneurial ecosystem (Miller et al., 2018). Hence, several authors suggest that appropriate organizational structures and new support measures are needed to strengthen the interactions between the stakeholders (Miller et al., 2018; Seppo et al., 2014). Ultimately, UTT can take a leading role in initiating open knowledge exchange between the different stakeholders of the quadruple helix model, thus providing the opportunity for a truly cocreational, interactive, and cooperative technology development to emerge (Miller et al., 2018).

3.2.3

Macroscopic Level

With respect to the macroscopic level, the goal of UTT activities is to generate the emergence of co-creational and cooperative forms of interaction between academia, government, industry, and societal-based innovation users. In a first step, the system’s control parameter, i.e., UTT, evokes interactions between the system’s elements on the microscopic level. In case the impulse provided by the control parameter is strong enough, novel patterns of collaboration emerge through a process of selforganization. Various forms of collaboration compete against each other, until one pattern of collaboration between the stakeholders becomes dominant. The newly developed system behavior, e.g., strong cooperation between TTOs and end users to test and improve innovative technologies, represents the order parameter. The new system behavior becomes finally visible on the system’s macroscopic level, namely

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in the form of a dynamical pattern. In the case of UTT, such dynamic patterns could be represented by the emergence of entirely new entrepreneurial ecosystems or markets. Whereas the final form of the dynamical pattern on the macroscopic level cannot be foreseen, the appropriate design of the system’s control parameter (UTT) can increase the probability of the development of effective collaboration between the stakeholders of the quadruple helix model and, ultimately, entrepreneurial ecosystem emergence.

4 Conclusion This contribution entails a number of implications that we want to show for the theoretical level as well as for the practice-oriented level of UTT. On the theoretical level, it can be shown that UTT can be understood as a control parameter for entrepreneurial ecosystems by using the complexity science framework of synergetics. While complex relationships have already been hypothesized several times with respect to a quadruple helix in such ecosystems, the approach presented here is based on a concrete complexity science framework, namely synergetics. Synergetics allows to decompose a system into its elements and their relationships, without reducing its complexity. In this way, a separate consideration of the concrete design of UTT seems to be possible, while at the same time considering it as an impulse generator within the helix structure of the entrepreneurial system. It becomes particularly clear that control parameters cannot simply be arbitrarily assigned to a system, but that the system itself chooses its control parameters. Academia, industry, government, and civil society are—synergetically interpreted—only receptive to a specific type of energy supply, which results from the constitution and relationship of their system elements to each other. Since technology as one major outcome of UTT is an important enabler of innovation and entrepreneurial activity, synergetics allows to depict new knowledge and technologies as control parameters that enable new forms of resource combination and value co-creation. Forth, it acknowledges universities, the industry, government, and civil society as interdependent actors that form ways of co-creation and competition, depending on their specific constitution. The differentiation into microscopic and macroscopic levels also makes it clear that these patterns are generated from the system of actors and can therefore be endogenously justified. Taking these considerations into account, there is an approach that can model emergent structures—such as a quadruple helix—within an entrepreneurial ecosystem. On a practical level, important implications for the design of UTT arise. Starting with modeling UTT as a control parameter, UTT must be open to ecosystem actors so that a transfer of knowledge and innovations is possible. This requires cooperative relationships with established companies, start-ups, political decision-makers and members of society, which can be realized via TTOs and can lead to joint formats such as research projects or round tables. Only through close feedback from UTT with the other actors can its topicality (in the sense of a control parameter)

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be guaranteed. The sensitivity of an entrepreneurial ecosystem to particular societal challenges (e.g., structural change), the type of value creation (factor-driven, innovation-driven, efficiency-driven), industries, political framework conditions and educational systems can differ significantly between systems, so that only specific forms of knowledge creation and commercialization can have an effect on the system. An important requirement is therefore the professionalization of TTOs, whose main focus is on transfer activities and which, in addition to technological knowledge, must first and foremost have expertise in commercialization—expertise that is geared to the specifics of the ecosystem. The methods of technology transfer and management presented in this article can be interpreted as examples of the necessary technical and methodological competence of TTOs.

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